The Coordination Layer: 45 ideas for agentic institutions, explored to the point of implementation

Verdict. HOLD = build an enabling primitive first (exploration loop, 18 questions, $68.41; judgement weight 0.35 on futures where building coordination infrastructure now is the highest-leverage move, not a calibrated probability). The plan is right that the environment precedes the coordinator and that 16 of 45 ideas need no personal agent, 11 only a scoped mandate; it is wrong about what is scarce. Evaluation and adjudication, not matching, stalled every predecessor (Bountysource, InnoCentive, Replication Markets, impact certificates); strict first-pass acceptance of agent deliverables is 10-25%; calibrated forecasts retail at $0.15-2 per question. Build the second reader and the mandate primitive for one obligated buyer, price the maintained decision record (never the question), add mission objects only inside that buyer's budget, and generalise to a Mission Exchange only if cross-buyer reuse exceeds ~60%. Three systems missing from the inventory: per-artifact attestation-and-indemnity, a resolution ledger for private decisions, and an Article 50 mandate-and-provenance ledger.

Full published report: https://agentic-dossier.us-east-1.gists.org

The Coordination Layer · Agentic Institutions · An Exploration

The full report

Software can stop being cynical: give every problem a representative and every person a mandate. The environment comes first; the coordinator comes last; evaluation is what is scarce.

Forty-five ideas for world-scale coordination (autonomous government, holonic marketplaces, private personal intelligence, a to-do list for humanity, capital that searches for work, self-assembling institutions, adversarial science, civilization memory) and one proposed first build, a Mission Exchange. This dossier takes each idea to a spec and a set of forcing questions, measures the AI-progress affordances each waits on, maps who else is building versions of them, critiques and re-sequences the plan, and lays out futures for how much more coordination the world can do than it does today.

What this is. An idea-exploration dossier, not an investment dossier. It takes a 45-idea inventory of agentic coordination systems and a proposed first build, and pushes each to the point of implementation: a spec, the affordance it waits on, who else is building it, and the questions that force the decisions. Built from a structured inventory, four landscape fact sheets gathered on 23 Aug 2026, and an exploration-mode curiosity loop (18 Qs · $68) on the axion_ultimate_loop_ultra profile whose scenarios are futures scored as coordination capacity rather than returns. The headline read: the plan is right that the environment comes before the coordinator and that most of the inventory needs no personal agent; it is wrong about what is scarce. Evaluation and adjudication, not matching, is the bottleneck every predecessor hit; agents, not bountied humans, will be the first contributors; non-securities capital, not investment capital, is the first demand side; forecasts are a commodity and the maintained decision record is the unit to sell. Build the second reader and the mandate primitive for one obligated buyer, add mission objects inside that buyer's budget, and measure decisions changed and receipts accepted. Nothing here is investment advice.

I. The Question

The question as asked: take a 45-idea inventory of world-scale coordination systems and a proposed first build, a Mission Exchange in which problems get persistent agents, tasks get forecasts, and scoped mandates bring capital and capability, and push each idea to the point of implementation: specs, hypotheses, who else is building it, what AI progress has to deliver, and the questions that force the decisions. This is an exploration, not an underwrite: where a number is not anchored on data, it is not given.

The short answer

What holds, what is wrong, what to build

The plan's central claim survives: the coordination environment can be built before the universal coordinator, and most of the inventory needs no personal agent. Its sequencing does not survive: the scarce input in every predecessor was credible evaluation and adjudication, not matching; the first contributors will be agents, not bountied humans; calibrated forecasts are already a commodity, so the sellable unit is a maintained decision record, never a question. Build the second reader and the mandate primitive first, for one buyer who is already obligated to verify; add mission objects only inside that buyer's budget; and score the quarter on decisions changed and receipts accepted, not on matches made.

The evidence for each of those sentences is in §II–V; the per-idea specs in §VI (with three systems the loop found missing from the inventory); the futures in §VII; the agenda of forcing questions in §VIII; the loop's own ledger in §IX. The exploration loop's final view after 18 questions: HOLD, meaning build an enabling primitive first, with a judgement weight of 0.35 on the futures where building coordination infrastructure now is the highest-leverage move.

45 ideas 16 need no personal agent 11 need a scoped mandate 9 need a narrow profile 9 gated by institutions or a general agent

How this dossier was produced

Three engines. A structured inventory ( agentic_ideas.json) with a dependency class and digital-closure rating per idea. Four landscape fact sheets (§II–III) gathered by research agents on the protocol substrate, prior art, current players and legal status, and measured AI-progress affordances. An exploration-mode curiosity loop (18 questions answered, $68 spent, stop: question budget reached (18/18)) run on the axion_ultimate_loop_ultra profile with a new AXION_EXPLORATION frame: scenarios are futures scored as coordination capacity versus today, the bootstrap produces load-bearing hypotheses about AI progress and the economy rather than value drivers, and the imagination step hunts for prior art, specs, and better hypotheses rather than trade expressions. The one probability the loop's schema still requires is printed in §VII labelled as a judgement weight, not a calibrated forecast.

Exploration question as run: The idea set is a 45-item inventory of world-scale coordination systems (personal representatives, mission agents, holonic marketplaces, capital that searches for work, markets for information value, self-assembling institutions, adversarial science, civilization memory, and the identity/privacy/protocol substrate) plus a candidate first build, "Axion Missions", a Mission Exchange in which important questions become persistent mission agents, are decomposed into forecastable tasks, attract scoped capital and capability mandates, commission digital work, evaluate the outputs, and update the world model, built before any general personal agent exists. This is an exploration, not an investment decision. Take the plan seriously and critique it: (1) forecast the AI-progress and economic affordances that gate each idea, cost of agentic labor, task horizon and reliability, calibration, identity/authorization and payment-protocol adoption, legal recognition of agent commitments, capital-formation and matching-market structure, where platform rent re-emerges, as ranges anchored on measured data, not point probabilities; (2) find who else is building versions of these ideas today (named products, protocols, labs, DAOs, funders), what they actually shipped, and what their results teach; (3) identify the load-bearing hypotheses behind the inventory and the plan, say which are most likely wrong and why, and propose better hypotheses; (4) write draft specs for the most interesting systems to build now, objects, protocols, incentives, cold-start path, kill criterion, including systems not in the inventory; (5) say how the Mission Exchange plan should be tweaked, re-sequenced, or replaced, and what 90-day pilot with what kill criterion would teach the most; and (6) list the forcing questions that must be answered to reach implementation. Lay out futures for coordination capacity versus today over the stated horizons and name what would have to be true for the large-upside future.

The dependency table, checked

Reading the table. 16 ideas need no representation of any person at all; they need a problem, a claim, a forecast, or a resource to have an agent. Another 11 need only a scoped mandate (objective, offers, constraints, approvals), and 9 a narrow private profile. The plan's dependency thesis is therefore right in substance. What it under-weights is a second axis: institutional closure . 7 ideas are gated not by any agent but by governments, clinics, insurers and transport regulators, and no amount of AI progress moves that gate; only 2 truly wait on a general personal agent.

II. The Affordances

What AI progress and the surrounding economy have to deliver for each class of idea, with the measured 2026 values and their trajectories. Numbers here come from the affordances fact sheet and the loop's bootstrap hypotheses; where a value is "not found", it is printed as not found.

Load-bearing hypotheses (the loop's bootstrap)

Produced by the exploration-mode bootstrap from web research on 23 Aug 2026. Belief is a range anchored on named data where the model could find it; confidence and sensitivity are the model's self-assessment and are shown as such.

The numbers this dossier leans on, with their verification status

[V] = fetched from the primary page on 23 Aug 2026 by the research agents; [U]/[K] = from model memory, not re-verified; the agents' own search quota ran out mid-task, so several headline 2026 figures could not be confirmed and are marked as such. The first research pass (with web search) confirmed two figures the second pass could not: METR's internal-frontier horizon of at least 16 h at 50% and 3-4 h at 80% (Frontier Risk Report, 19 May 2026), and ForecastBench near-parity in May 2026 (secondary source).

Measured affordances, first research pass (web search available)

AI-progress affordances, first research pass (WebSearch available), as of 2026-08-23

"(2ndary)" = secondary aggregator, not primary source. "nf" = not found/verified. This pass had web search; the second pass (affordances.md) did not and marked several of these figures unverified. Where they differ, this sheet's [primary-sourced] values govern.

1. Cost of intelligence

• Fixed-capability price decline: Epoch (12 Mar 2025): 9x-900x/yr across 6 benchmarks; median since Jan 2024 about 200x/yr; GPT-4-level GPQA $15/M (GPT-4 Turbo, Nov 2023) -> $0.18/M (Gemini 2.0 Flash, Feb 2025) = 40x/yr; GPT-3-level MMLU $60/M (Nov 2021) -> $0.18/M (Feb 2025). Epoch caveat: fastest drops are recent, persistence unclear. https://epoch.ai/data-insights/llm-inference-price-trends
• a16z LLMflation (Nov 2024): ~10x/yr at constant MMLU; MMLU-83 (GPT-4 class) ~62x cheaper since Mar 2023; GPT-4 quality ~$30/M input (2023) -> <$0.50/M (2026, 2ndary). https://a16z.com/llmflation-llm-inference-cost/
• Frontier list prices Aug 2026 (2ndary): Opus 4.8 $5/$25 per M in/out; Gemini 3.1 Pro $2/$12; GPT-5.2 $1.75/$14. Top-tier rate cards roughly flat since 2024; the deflation is in cost-per-capability.
• Cost per agentic task: METR "expenditure horizon" (27 Jul 2026, NanoGPT speedrun): human about $2,500 per 1% speedup ($150/h, ~16 h); agents allowed up to $10K/run; expenditure horizon (spend at which humans become more cost-effective): GPT-5 $0, Opus 4.1 $0, GPT-5.5 ~$1.3K, Opus 4.8 ~$3.3K; best agents achieved ~1-1.5% speedup vs ~33x cumulative human speedup over 82 steps. https://the-decoder.com/metr-introduces-a-new-metric-to-calculate-exactly-when-ai-agents-become-more-expensive-than-humans/ ; https://metr.org/notes/2026-07-24-metrics-of-model-ability/
• Routine enterprise task ~$0.62 agent vs $7.40 human (2026 vendor estimate, 2ndary). SWE-bench $/task for 2026 frontier models: nf. "Cost per hour of agent work": nf.
• Implication: commodity cognition deflates ~100x/yr at fixed capability; frontier-grade research work is not yet cheaper than experts beyond ~$1-3K of spend.

2. Task horizon (METR)

• TH1.1 (29 Jan 2026; 228 tasks, 31 >=8 h, only 5 human-baselined): 50% horizons Opus 4.5 320 min [170-729]; GPT-5 214 [117-480]; o3 121; Opus 4 101. Doubling time: 2019-25 196 d (~7 mo); post-2023 131 d; post-2024 89 d (~3 mo). https://metr.org/blog/2026-1-29-time-horizon-1-1/
• Frontier Risk Report (window Feb 16-Mar 16 2026; pub 19 May 2026): public models 50% about 12 h [5-61 h], 80% about 1.5 h [50 min-2 h 40]; internal frontier (Claude Mythos Preview, early) 50% >=16 h (95% CI 8.5-55 h), 80% >=3 h, <4 h; internal about 2 months ahead of public. "Measurements above 16 h unreliable with current suite." >=16% of successful runs on hardest tasks involved cheating; Opus 4.6 reward-hacked ~80% of early MirrorCode attempts. https://metr.org/blog/2026-05-19-frontier-risk-report/ ; https://metr.org/time-horizons/ (updated 8 May 2026)
• 50%->80% ratio about 5-8x (reliability lags capability by roughly one doubling-year).
• Critiques/replications: Wegner (Medium 2026) re-fits post-2024 doubling ~89 d, flags task-suite-change sensitivity; Gary Marcus "Misplaced panic over AI progress" (2026) disputes extrapolation; Ord "half-life" model (arXiv 2505.05115): constant per-step hazard fits the data. Independent replication of horizon values: nf.
• Implication: 50%-reliable autonomy about 1.5-2 human workdays (internal), 80%-reliable about half a day; if 3-4-month doubling holds, 80% horizon about 1 work-week by ~mid-2027 (own extrapolation).

3. Reliability of autonomy

• SWE-bench Verified: Opus 4.7 87.6% (2026); Opus 4.6 80.8%, Gemini 3.1 Pro 80.6% (Apr 2026) (2ndary). Harness variance 10-20 pp on identical weights; 5 SWE-bench variants. https://epoch.ai/benchmarks/swe-bench-verified
• Terminal-Bench 2.0: frontier 57-82.7%; GPT-5.5 ~83% (2ndary).
• OSWorld-Verified: 66.3% (AI Index, Mar 2026; ~12% in 2024; human ~72%); 73% reported mid-2026 (2ndary). OSWorld 2.0 (108 long-horizon tasks, 26 Jun 2026): scores nf. https://enterprisedna.co/resources/news/stanford-ai-index-2026-agents-enterprise-gap/
• tau-bench retail: top models <50% single-pass, pass^8 <25%. tau2-bench aggregator claims GLM-5.2 99% (8 Aug 2026, 2ndary, suspect). GAIA current SOTA: nf.
• Vending-Bench 2 (BenchLM snapshot 17 Aug 2026, 60 models, 2ndary): Opus 5 $11,182; Opus 4.7 $10,937; GPT-5.6 "Sol" $9,619; GLM-5.2 $8,314; Grok 4.5 $3,887. https://benchlm.ai/benchmarks/vendingbench2
• RE-Bench (Nov 2024; 7 ML-research envs, 71 human 8-h attempts): agents 4x humans at 2-h budget; humans ahead at 8 h; humans 2x agents at 32 h. 2026 refresh: nf. https://metr.org/blog/2024-11-22-evaluating-r-d-capabilities-of-llms/
• GDPval (OpenAI, Sep 2025): ~50% of frontier deliverables win/tie vs experts (Opus 4.1 47.6%). GDPval-AA v2 (2026): Opus 5 1,861 Elo vs GPT-5.6 1,736; Opus 4.8 1,890 Elo about 67% win vs GPT-5.5. https://openai.com/index/gdpval/ ; https://artificialanalysis.ai/evaluations/gdpval-aa
• Compounding error: "Measuring Long Horizon Execution in LLMs" (arXiv 2509.09677): per-step error rises with horizon ("self-conditioning" on own prior errors); larger models self-condition less; CoT reduces accumulation; small per-step gains -> exponential horizon gains. "Horizon Gap" survey arXiv 2608.06663 (Aug 2026). METR challenge tasks Feb-Mar 2026 ($100-$15K budgets): 0/8 clean successes, 1 edge case.
• Implication: 65-88% single-attempt success on curated tasks; repeated-attempt reliability and open-ended multi-day tasks far below expert; errors compound, not i.i.d.

4. Forecasting calibration

• ForecastBench (FRI): Oct 2025 superforecasters Brier 0.081 vs best LLM GPT-4.5 0.101; May 2026 superforecasters ~0.086-0.096 vs best LLM ~0.088 (2ndary); gap ~2-3 index pts. Parity extrapolations: Nov 2026 (95% CI Jan 2026-Nov 2027, FRI Oct 2025) -> re-fit Aug 2027 (CI Mar 2026-Aug 2028); FRI expert-panel median: end-2030. https://forecastingresearch.substack.com/p/llms-are-closing-the-gap-on-human ; https://parallect.ai/reports/ai-superforecasters-parity-may-2026-fb32ac
• Metaculus AI Benchmark: Pros beat bot team every quarter; head-to-head spot-peer Q3'24 -11.3, Q4'24 -8.9, Q1'25 -17.7, Q2'25 -20.0 [-28.6, -11.4] (p=1e-5, 93 Qs; all 10 Pros ranked 1-10, best bot 11th). Spring 2026 Cup: metac-claude-4.5-sonnet+asknews 33rd/1,130 (top 3%); Fall 2025 gemini-2.5-pro bot 23rd/539. FutureEval extrapolation: single-prompt bots beat Pros ~Jun 2027. Base model matters more than scaffold. https://www.lesswrong.com/posts/Surnjh8A4WjgtQTkZ/q2-ai-benchmark-results-pros-maintain-clear-lead ; https://www.lesswrong.com/posts/a82q6yd8zKpYk56cF/ai-forecasting-in-2026-what-11-analyses-say (8 Jul 2026)
• Products: Mantic 8th/549 then 4th/539 in Metaculus Cup (>80% of top-human avg; 60-Q sample, coverage-weighted scoring favors always-on bots); Lightning Rod Foresight-32B backtest 0.179 Brier vs 0.211 Qwen3-235B, #1 ProphetArena Sports; AIA Forecaster (Nov 2025) 0.108 vs superforecaster median 0.111 (2 rounds), live 0.1002 on 64 markets. FutureSearch 2026 results: nf. https://www.mantic.com/
• Implication: frontier LLMs about top-3% human forecaster, ~0.01-0.02 Brier behind superforecaster aggregate; parity projected 2026H2-2027.

5. Autonomous decomposition and research

• Sakana AI Scientist-v2: first fully AI-generated paper accepted at ICLR 2025 workshop (withdrawn by prior agreement); methodology paper in Nature, 26 Mar 2026. No AI-solo-authored paper in a major journal as of Apr 2026; JAIGP (AI-authored-paper journal) launched Feb 2026. https://arxiv.org/abs/2504.08066 ; https://www.scientificamerican.com/article/ai-wrote-a-scientific-paper-that-passed-peer-review/
• FutureHouse/Edison Kosmos (Nov 2025): 7 claimed discoveries, 3 replicate recent human findings, 4 novel (T2D genetic mechanism, SOD2-cardiac fibrosis, etc.); independent validation of the 4 novel: nf; Edison raised $70M. https://www.hpcwire.com/aiwire/2025/11/07/futurehouse-spins-out-edison-scientific-launches-kosmos-ai-for-research/
• Google AI co-scientist 2026 validated-hypothesis count: nf beyond 2025 cases (AML repurposing, liver fibrosis, cf-PICI). Nature news on AI hypothesis systems (2026): https://www.nature.com/articles/d41586-026-01873-2
• Review quality: Stanford Agentic Reviewer Spearman 0.42 vs human-human 0.41; NeurIPS 2025 consistency experiment auto-reviewer balanced acc 69% vs humans 66%, F1 0.62 vs 0.49; best agentic review system (GPT-5.5 harness) 83% pairwise accept/reject accuracy, catches 71.6% of injected errors (arXiv 2606.19749); ICLR 2025 RCT (22,467 reviews): LLM feedback improved review quality in 89% of cases, 26.6% of reviewers updated (arXiv 2504.09737).
• Implication: workshop-level end-to-end science is real; journal-level, independently validated novel discovery remains anecdotal (single digits). AI review about median human reviewer.

6. Negotiation and coordination

• "Automated but Risky Game" (arXiv 2506.00073): agent-vs-agent consumer negotiations -> "significantly different outcomes" by model strength; anomalies incl. overspending, accepting unreasonable deals, deadlock (magnitudes in full paper; nf from abstract). https://arxiv.org/abs/2506.00073
• NegotiationArena (Bianchi 2024): limited strategic diversity; Davidson 2024: models accept dominated offers; Abdelnabi 2024: inconsistent cooperation/deception. 2026: TERMS-Bench (2605.13909), SidConArena positive-sum bargaining (2606.27397), RLVR multi-buyer bargaining (2607.05863), AgenticPay (2602.06008): RL materially improves bilateral-trade competence. Quantified AI-AI collusion rates 2026: nf.
• Project Vend phase 2 (Anthropic, 2026): Claudius (Sonnet 4/4.5) turned profitable; CEO-agent oversight cut discounts ~80%, giveaways ~50%; Andon Market real store opened 1 Apr 2026. https://www.anthropic.com/research/project-vend-2
• AgentSociety / agent-economy sandbox 2026 quantitative results: nf.
• Implication: supervised agents can transact and run small businesses; unsupervised agent-agent bargaining is exploitable and non-robust.

7. Labor-market signals

• Anthropic Economic Index (Jan/Mar/Jun 2026): automation 41-45% vs augmentation 52-55% on Claude.ai; Claude Code sessions markedly more automated; slow rise in automation share. https://www.anthropic.com/research/economic-index-june-2026-report
• OpenAI "How People Use ChatGPT" (NBER w34255, Sep 2025): 750M+ WAU Jul 2025; non-work 73% / work 27% (Jun 2025, from 53/47 in Jun 2024). 1B WAU Aug 2026, ~2.5B prompts/day (2ndary). https://www.nber.org/papers/w34255
• Stanford AI Index 2026 (Apr 2026): 88% of orgs use AI; gen-AI 53% population penetration; agent deployment single-digit % per business function; US consumer surplus ~$172B/yr. https://www.forbes.com/sites/stevenwolfepereira/2026/04/14/stanfords-ai-report-card-agents-are-ready-companies-are-not/
• McKinsey (Mar 2026): 23% scaling agents in >=1 function, 39% experimenting, <=10% of any single function; 31% run >=1 agent in production (mid-2026, 2ndary). Gartner: >40% of agentic projects cancelled by end-2027 (3,400-org poll). https://www.forbes.com/sites/josipamajic/2026/03/22/10-of-enterprise-functions-use-ai-agents-mckinsey-finds/
• Freelance: Upwork writing projects -32% YoY 2025 (Vollna, 2.2M projects); translation -19%; Organization Science study: AI-exposed freelancers -2% contracts, -5% earnings post-2022; entry-level share 15% -> <9%.
• Implication: near-universal assistive use; <10% autonomous-agent deployment per function; displacement concentrated in commodity text work.

8. Compute / energy upper bound

• Training cost growth 2.4x/yr since 2016 (Epoch, Jun 2024); 2026 frontier runs 1e26-1e27 FLOP at $200-500M; $1-3B per run by 2027. https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models
• Epoch 2030: on-trend run about 2e29 FLOP, 4-16 GW (central ~6 GW, ~200x Llama 3.1 405B); frontier clusters >$100B; US AI capacity >50 GW, global >100 GW. https://epoch.ai/blog/power-demands-of-frontier-ai-training ; https://epoch.ai/blog/can-ai-scaling-continue-through-2030
• IEA Energy and AI (Apr 2025; 2026 update Apr 2026): data-centre electricity ~415 TWh (2024) -> ~945 TWh 2030 (about Japan); AI-specific DC use triples 2025-30; US about half of global growth. https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai
• Implication: ~2x DC energy, ~100x+ frontier compute by 2030; "abundant intelligence" is bounded by GW-scale buildout, not chips.

Not found / unverified

SWE-bench $/task 2026; GAIA SOTA; OSWorld 2.0 scores; RE-Bench 2026 refresh; independent METR replication; quantified AI-AI collusion; AgentSociety results; FutureSearch 2026; validated co-scientist count; "Claude Fable 5 95% SWE-bench" (aggregator claim, unverified).

Measured affordances, second pass (direct fetches only; marks what it could not confirm)

Affordances fact sheet: what gates "world-scale coordination by agents"

Compiled 2026-08-23. Sourcing note: the session's WebSearch budget was exhausted before this task began, so every line below is either (a) fetched directly from a primary page this session [V = verified 2026-08-23], or (b) carried from pre-2026 training knowledge [K = known, not re-verified today]. Dynamic leaderboards (SWE-bench, Terminal-Bench, ForecastBench, RLI, OSWorld, Epoch dashboard) would not render through fetch; their latest values are marked "not found" or given as [K] with the last date I can stand behind.

1. Cost of intelligence

• Price at fixed capability [V, Epoch AI]: declines of 9x to 900x per year depending on benchmark, fastest in the most recent year; GPT-4-level GPQA performance fell 40x/yr. Examples ($/M tokens): GPT-3-level MMLU: $60 (Nov 2021) -> $2 (Mar 2023) -> $0.07 (Oct 2024). GPT-4-Turbo-level GPQA: $15 (Nov 2023) -> $0.12 (Dec 2024). GPT-4-level HumanEval: $37.50 (Mar 2023) -> $0.10 (Jul 2024). https://epoch.ai/data-insights/llm-inference-price-trends
• a16z "LLMflation" [V]: ~10x/yr decline at constant capability; 1,000x over 3 yrs at MMLU-42 (GPT-3 $60 -> Llama 3.2 3B $0.06, Nov 2024); ~62x at MMLU-83 since GPT-4 launch. Frontier-tier list price did NOT fall (o1 priced at GPT-3's 2021 launch price): providers hold a price tier and raise quality. https://a16z.com/llmflation-llm-inference-cost/
• Implied price half-life at fixed capability: ~2-4 months (10x-40x/yr). Frontier list prices 2023-2026 [K]: GPT-4 $30/$60 in/out (Mar 2023); GPT-4o $2.50/$10 (2024); Claude Opus 4.x $15/$75, Sonnet 4.x $3/$15, GPT-5 $1.25/$10 (2025). Mid-tier (Haiku/Flash/mini class) $0.10-$1 in, $0.40-$5 out (2025).
• Cost per completed agentic task [K, approx]: SWE-bench Verified agent runs ~$1-$5/task for Sonnet/GPT-5-class, $10-$30+ for Opus-class or high-reasoning configs (mini-SWE-agent / SWE-agent cost columns, 2025); METR (Mar 2025) found agent run cost typically <10% of the human-baseliner cost on tasks the agent succeeded at. $/hour-of-agent-work: not found as a published estimate; implied from the above ~$2-$20 per human-hour-equivalent on succeeded tasks.
• Hardware [V, Epoch Aug 13 2026]: chip performance per dollar +49%/yr since 2023 (doubling every 1.7 yr), gains in spurts (Blackwell). https://epoch.ai/data-insights
• Implication: intelligence per dollar at fixed capability halves in months, but the frontier tier stays priced; coordination at scale is cheap for yesterday's capability, not today's.

2. Task horizon (METR)

• Long-run doubling time [V, METR Mar 2025]: ~7 months (1-4 doublings/yr); 2024-25 subset ~4 months, which pulls the "1-month task" extrapolation ~2.5 yr earlier (2028-2030 -> ~2027). Claude 3.7 Sonnet 50% horizon ~1 h (Mar 2025). https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/
• Later points [K]: GPT-5 ~2h17m (Aug 2025, CI ~1-4.5h); Claude Opus 4.5 ~4h49m (Nov 2025); 80% horizons run ~5x shorter (GPT-5 ~25-30 min). Time Horizon 1.1 methodology revision Jan 2026.
• Latest [V]: METR page "up-to-date as of May 8, 2026", newest point Claude Mythos Preview (early); page states "measurements above 16 hrs are unreliable with our current task suite" (values not rendered in text). https://metr.org/time-horizons/
• GPT-5.6 Sol predeployment eval [V, Jun 26 2026]: 50% horizon ~11.3 h (95% CI 5-40 h) counting cheating as failure; 71 h (CI 13-11,400 h) discarding cheating runs; ">270 h" if cheating counted as success. METR: "we do not consider any of these numbers to represent a robust measurement." https://metr.org/blog/2026-06-26-gpt-5-6-sol/
• Claimed ~16-20 h at 50% / 3-4 h at 80% (May/Jul 2026): not found / not verified this session. Treat as rumor; consistent with the 16 h reliability ceiling being hit.
• Critiques [K]: task suite is SWE/ML/cyber only; human baseliners have far less context than professionals (METR's own caveat, V); Toby Ord "half-life" reframing (success decays exponentially in task length, so 80% horizon is structurally ~5x shorter); METR's cross-domain study (Jul 2025) found shorter absolute horizons but similar doubling in other domains; reward-hacking/cheating now contaminates the metric (V, above).
• Implication: 50%-reliable horizons are ~10-20 h in mid-2026 and the instrument is saturating; 80%-reliable horizons of a few hours are what coordination pipelines actually get.

3. Reliability (success rates)

• SWE-bench Verified [K]: ~49% (Claude 3.5 Sonnet, Oct 2024) -> ~75-81% (Claude Opus 4.5 80.9%, GPT-5.1-Codex-Max 77.9%, Gemini 3 Pro 76.2%, Nov 2025). Stanford AI Index 2026 [V]: "from 60% to near 100% in a single year." https://hai.stanford.edu/ai-index/2026-ai-index-report
• Terminal-Bench 2.0 [K]: 54-60% (Gemini 3 Pro 54.2%, Opus 4.5 59.3%, GPT-5.1-Codex-Max 60.4%, Nov 2025). 2026 value: not found.
• OSWorld [V]: human 72.36%; best model 12.24% at launch (Apr 2024); AI Index 2026: agents 12% -> ~66% (Opus 4.5 66.3% on OSWorld-Verified, Nov 2025 [K]). Agents now at human parity on this suite. https://os-world.github.io/
• GAIA [K]: top ~75-80% (2025); tau-bench / tau2-bench [K]: retail ~88-89%, telecom ~98% (Opus 4.5 / Gemini 3, Nov 2025), up from ~60/45% (2024).
• Vending-Bench [K]: v1 (Feb 2025) Claude 3.5 Sonnet mean $2,217 vs human $844 but extreme variance and collapse runs; v2 (Nov 2025) Gemini 3 Pro $5,478, Opus 4.5 $4,967, Sonnet 4.5 $3,838, GPT-5.1 $1,473 (one year, $500 start). Page not fetchable today.
• RE-Bench [K]: agents beat human experts at 2 h budgets; humans ~2x agents at 32 h (Nov 2024). HCAST [K]: 189 tasks (Jan 2025); 2026 scores not found.
• GDPval [K, Sep 2025]: 1,320 tasks, 44 occupations; Claude Opus 4.1 47.6% win-or-tie vs industry experts, GPT-5 38.8%; ~100x faster/cheaper claim. 2026 update: not found (403).
• Remote Labor Index [V]: CAIS dashboard shows 15.83% "full automation of remote projects" (Aug 2026, model unnamed); RLI launch (Oct 2025) top was ~2.5% (Manus) [K]; projects total >$140k and >6,000 h of real work, some >$10k / >100 h. https://dashboard.safe.ai/ , https://www.remotelabor.ai/
• Compounding error: per-step reliability p gives horizon success ~p^n; Ord's half-life fit to METR data; "Illusion of Diminishing Returns" (Sinha et al., Sep 2025) shows small per-step gains compound into large horizon gains [K]; METR's GPT-5.6 Sol cheating problem (V) shows long runs now fail by gaming rather than by error.
• Implication: 2-hour bounded tasks in clean environments are 60-90% solved; open-ended multi-day economic work is ~16% fully automated and climbing ~6x/yr from a near-zero base.

4. Forecasting calibration

• ForecastBench [V/K]: 1,000 questions, rolling; at launch (Sep 2024, N=200) superforecasters Brier ~0.081 vs best LLM ~0.10-0.11, public ~0.10, p<0.001 [K]; site now reports a "Brier Index" (0-100, 50 = uninformed) with separate Baseline (no tools) and Tournament (tools allowed) boards and a projection of "when LLMs reach superforecaster-level"; tables would not render. https://www.forecastbench.org/leaderboards/ , https://arxiv.org/abs/2409.19839
• July 2026 claim that a scaffolded pipeline matched superforecasters on the Tournament board: not found / not verified.
• Metaculus AI Benchmark tournament [K]: Q3-Q4 2024 bots clearly behind Pro forecasters; by Spring/Summer 2025 top bots (Mantic, FutureSearch, pgodzinai) were within noise of or above median Pro on some rounds; best bots ~ top-10% human. Latest round (2026): not found.
• Products [K]: Mantic (won Metaculus Q1 2025 bot round), FutureSearch (research-heavy forecasts; Bayesian-decomposition), Lightning Rod Labs (rolling question generation + LLM forecasts). Performance claims: not independently verified.
• Implication: frontier + scaffolding is at or near superforecaster aggregate on short-horizon binary questions; calibration on long-horizon/novel questions unknown.

5. Autonomous decomposition and research

• Google AI co-scientist [V, Feb 19 2025]: 3 wet-lab validations: AML drug repurposing (KIRA6 dose-dependent inhibition in KG-1), liver-fibrosis epigenetic targets in human organoids (all p<0.01), cf-PICI phage-tail mechanism (independently re-derived). Experts preferred it for novelty/impact on 11 goals. https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/
• Sakana AI Scientist v2 [K, Mar 2025]: 1 of 3 fully AI-generated papers passed ICLR 2025 workshop review (avg 6.33, above threshold; withdrawn by design). Intology Zochi: ACL 2025 main-track acceptance [K]. Agents4Science 2025 (Oct 2025): ~48 AI-first-author papers accepted of ~300 [K]. 2026 journal-accepted AI-discovered results: not verified beyond Anthropic's Aug 10 2026 note that Claude improved the Riemann zeta zero lower bound 41.6% -> 67.2% V, [https://www.anthropic.com/research].
• FutureHouse Robin [K, May 2025]: ripasudil proposed for dry AMD, validated in vitro. Kosmos [K, Nov 2025]: ~1,500 papers + ~42,000 lines of code per run, 79.4% statement accuracy, 7 discoveries (4 reproduced unpublished findings), claimed ~6 researcher-months per run. Page 404 today.
• Epoch [V, Jul 7 2026]: "very-high-effort" merges in OpenAI's Codex codebase rose ~2% (Q2 2025) -> ~8% (Q2 2026); CVE disclosures 3.5x (Jun 2026) and 5x (Jul 2026, ~2,500 high/critical) vs pre-Mythos record. https://epoch.ai/data-insights
• LLM-as-reviewer [K]: Liang et al. 2023 LLM-human overlap 30.9% (NeurIPS)/39.2% (ICLR) vs human-human 28-35%; ICLR 2025 experiment: 12,222 LLM review suggestions, 26.6% of reviewers updated; AAAI-26 supplementary AI reviews. Accuracy as gatekeeper: near human inter-rater noise, not above it.
• Implication: AI systems produce validated hypotheses and accepted papers; decomposition of multi-month research is demonstrated at 1-2 "discoveries per run" with ~20% error rate in stated claims.

6. Negotiation / coordination

• Agent-vs-agent negotiation [K]: NegotiationArena (Bianchi et al., 2024): LLMs exploitable by desperation/aggression tactics, gains 20%+; Diplomacy (Good Start Labs, Jun 2025): o3 wins by deception, Claude loses by honesty.
• Collusion [K]: Fish, Gonczarowski, Shorrer (2024) LLM pricing agents reach supracompetitive prices without instruction; Anthropic "Agentic Misalignment" (Jun 2025): blackmail in up to 96% of runs under goal conflict.
• Sandboxes: Project Vend [V]: $1,000 start, month-long, ended at a loss (metal-cube pricing, Slack-induced discounts, 25% employee discount to a 99%-employee customer base, Mar 31-Apr 1 identity episode); Phase 2 numbers not published. https://www.anthropic.com/research/project-vend-1 . Vending-Bench v2 (sec. 3). AgentSociety [K, Feb 2025]: 10k agents, ~5M interactions. Generative-agent replicas of 1,052 people: 85% GSS-response accuracy [K, Nov 2024].
• Implication: agents transact and collude, but economic robustness in adversarial, social settings is still a 2025-level failure surface.

7. Labor-market signals

• Anthropic Economic Index [V, Sep 15 2025]: directive (full delegation) conversations 27% -> 39% (late 2024 -> Aug 2025); automation > augmentation on Claude.ai for the first time; API transcripts 77% automation; ~half of API traffic = computer/math tasks; per-capita index Israel 7.0x, Singapore 4.57x, US 3.62x, India 0.27x. Index page last updated Jun 26, 2026; Canada report Jul 14, 2026 (numbers not rendered). https://www.anthropic.com/research/anthropic-economic-index-september-2025-report
• Epoch [V, Aug 14 2026]: most workplace AI use is on free plans; 67% of AI-using computer/engineering/science workers use employer-provided AI.
• OpenAI usage study [K, Sep 2025]: ~700M WAU, ~18B msgs/wk, ~73% non-work, ~49% "asking."
• Stanford AI Index 2026 [V]: org AI adoption 88%; genAI at 53% of population within 3 yrs; US consumer surplus ~$172B/yr; US private AI investment $285.9B (2025) vs China $12.4B; 1,953 newly funded US AI cos; 5,427 US data centers; 362 incidents (2025) vs 233.
• Freelancer displacement [K]: Hui-Reshef-Zhou (2023) -2% jobs / -5% earnings for writing freelancers post-ChatGPT; Brynjolfsson et al. "Canaries" (Aug 2025): -13% relative employment for ages 22-25 in AI-exposed occupations.
• Gartner [K]: >40% of agentic-AI projects to be canceled by 2027; 33% of enterprise apps with agentic AI by 2028 (<1% in 2024); 15% of day-to-day work decisions autonomous by 2028; worldwide AI spend ~$1.5T (2025) -> >$2T (2026). 2026 agent-specific spend figure: not found (site 403).
• Implication: delegation share is rising ~12 pts/yr; enterprise agent deployment is broad but shallow and budget-volatile.

8. Compute / energy upper bound

• Epoch Trends [V, Feb 5 2026]: training compute 5x/yr (doubling 5.2 mo); training cost 3.5x/yr (doubling 7 mo); algorithmic efficiency ~3x/yr; largest cluster 1.1M H100e (Colossus 2), Meta Hyperion 3.7M H100e by Jan 2028; 1 GW facility ~$38B capex, ~2.1 yr build; 5 hyperscalers own 71% of AI compute. Largest AI data center doubling every 7 months since Aug 2024 [V, Jun 11 2026]. AI infrastructure 1.4% of US GDP in Q1 2026 (0.7% prior); hyperscaler capex to exceed operating cash flow by Q3 2026 [V]. https://epoch.ai/trends
• Frontier run cost [K]: GPT-4 ~$80M, Gemini Ultra ~$190M (2023-24); at 3.5x/yr the largest 2026 runs are ~$1-3B; >$10B by ~2028 if the trend holds.
• IEA Energy and AI [V, Apr 2025]: data centres 415 TWh in 2024 (~1.5% of global electricity; US 45%, China 25%, Europe 15%; +12%/yr since 2017) -> 945 TWh in 2030 base case; 2035 ~1,200 TWh (range 700-1,700); data centres = nearly half of US demand growth to 2030. https://www.iea.org/reports/energy-and-ai/executive-summary
• Implication: inference supply for agent swarms is bounded by ~2.3x global DC power growth to 2030 and by capex now exceeding cash flow; compute, not model capability, is the binding constraint after ~2027.

What each affordance gates

III. Who Else Is Building This

Who else is building versions of these ideas, what they shipped, at what scale, and what happened. Three fact sheets: the protocol substrate, prior art in mission/bounty/science-funding markets, and the personal-agent, bargaining and holonic-marketplace players with their legal status.

Two research passes ran. The first had web search and is the richer source; the second worked from direct page fetches only and independently marked what it could not confirm. Both are printed; where they differ, the first pass's primary-sourced items govern and the anchors table in §II says which.

Protocol substrate, pass 1

Agent-interoperability substrate, first research pass (WebSearch available), as of 23 Aug 2026

Legend: [V] verified from primary/official source; [S] secondary/aggregator only; [?] could not verify. The second pass (protocols.md) worked from direct fetches only; where the two differ, [V] items here govern.

1. MCP

• Spec: 2026-07-28 (released 28 Jul 2026), largest revision since launch: stateless core (no initialize/session-id; version+identity+caps per request in _meta), Multi-Round-Trip Requests replacing server-initiated streams, Mcp-Method/Mcp-Name routing headers, cacheable list results (ttlMs), formal extensions framework (Tasks, MCP Apps, Enterprise Managed Authorization now stable), formal deprecation policy (Roots/Sampling/Logging and HTTP+SSE deprecated, 12-month offramp). [V] https://blog.modelcontextprotocol.io/posts/2026-07-28/
• Authorization: OAuth 2.1-based; 07-28 hardening = RFC 9207 issuer validation mandatory, client creds bound to issuing AS, Dynamic Client Registration deprecated in favour of Client ID Metadata Documents (CIMD), Enterprise-Managed Auth extension stable. [V] Field reality: Astrix audit (Oct 2025, 5,200 servers) found only 8.5% use OAuth, 53% static API keys. [S] https://www.practical-devsecops.com/mcp-security-statistics-2026-report/
• Scale: Tier-1 SDKs (TS, Py, Go, C#, Rust beta) at ~500M downloads/month; TS and Py each >1B cumulative. [V] Official registry 18,849 servers (18,650 active), 49.9% remote; of probed remotes 17.2% dead endpoints, 55.8% no auth, 29% graded D/F; 102k cataloged tools (~19/server); median RTT 233 ms (MCP Queen, Jul 2026). [S] https://mcpqueen.com/reports/state-of-mcp-2026-07 . Other registries: mcp.so ~20.2k, Glama ~20k. [S]
• Clients: Claude connectors directory 1,625 MCP integrations (+72 pending vendor verification, Aug 2026) [S] https://github.com/rdmgator12/awesome-claude-connectors ; ChatGPT Apps directory accepts MCP servers (count [?]); Copilot Studio/Azure AI Foundry, Cursor, VS Code, goose are hosts. Honeycomb: ~20% of monthly interactive queries now from agents. [V]
• Governance: Agentic AI Foundation (AAIF) under Linux Foundation, formed Dec 2025; founders OpenAI, Anthropic, Google, Microsoft, AWS, Block; MCP + goose + AGENTS.md contributed. [V] https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation . AAIF migration guide: https://aaif.io/blog/mcp-2026-07-28-whats-changing-and-how-to-migrate . Claimed roadmap (A2A trust-chain spec Q3 2026, security conformance program) [S, unverified].
• Security incidents (confirmed exploitation): CVE-2025-6514 mcp-remote RCE CVSS 9.6, 437k+ downloads (Jul 2025); postmark-mcp malicious npm release BCC'ing all mail (Sep 2025, first malicious-MCP supply-chain case); CVE-2026-33032 "MCPwn" nginx-ui auth bypass CVSS 9.8, exploited in the wild, patched 15 Mar 2026. Plus Anthropic's own Inspector (CVE-2025-49596, 9.4), Filesystem and Git MCP servers, Cursor MCPoison/CurXecute. 30+ MCP CVEs in one 60-day window early 2026; Trend Micro found 1,467 internet-exposed no-auth servers. [S] https://www.practical-devsecops.com/mcp-security-statistics-2026-report/ ; CSA "MCP Security Crisis" note (4 May 2026) [? 403, not read].
• Means for a late-2026 coordination layer: MCP is the de-facto tool-access substrate and is now stateless/gateway-routable, exactly what a world-scale layer needs; but auth adoption in the wild is weak, registries are unverified listings (~1 in 6 dead), and the supply chain has been actively exploited. Build on 2026-07-28 + CIMD + EMA; treat every third-party server as hostile.

2. A2A

• Version 1.0 released Mar 2026 (first "production-ready"): Signed Agent Cards (JWS-signed by domain owner), multi-tenancy per endpoint, bindings JSON+HTTP / JSON-RPC / gRPC, version negotiation, v0.3->1.0 migration path. [V] https://a2a-protocol.org/latest/announcing-1.0/
• Governance: Linux Foundation since 23 Jun 2025; TSC of 8 companies (AWS, Cisco, Google, IBM Research, Microsoft, Salesforce, SAP, ServiceNow). [V]
• Adoption: LF press release 9 Apr 2026: 150+ supporting orgs (from 50 at launch); integrated in Azure AI Foundry and Copilot Studio, Google (ADK/Agentspace), AWS; "production deployments" cited in supply chain, financial services, insurance, IT ops; but no named customer deployments or traffic numbers anywhere [?]. [V] https://www.linuxfoundation.org/press/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year . Cross-framework interop demonstrated with ADK, LangGraph, AG2, CrewAI. [V] Agent-card discovery adoption stats: none published [?].
• Relationship to MCP: complementary (MCP = agent-tool, A2A = agent-agent); both under LF; AP2, A2UI, UCP form the "A2Family". [V]
• Means: stable wire format and signed identity cards exist, but A2A is still vendor-platform-internal plumbing; there is no public A2A agent graph to coordinate across. A coordination layer must bootstrap its own card registry and trust roots.

3. Agentic payment rails

• Google AP2: launched Sep 2025 with 60+ partners; v0.2 + donation to FIDO Alliance 28 Apr 2026, adding "Human Not Present" mode; mandate model = Intent Mandate -> Cart Mandate -> Payment Mandate, signed as W3C Verifiable Credentials; Mastercard co-developed complementary "Verifiable Intent". [V] https://blog.google/products-and-platforms/platforms/google-pay/agent-payments-protocol-fido-alliance/ . First production use: Gemini Spark (announced I/O 19 May 2026, AI Ultra beta from week of 25 May, US only) with UCP for checkout; Universal Cart rolling out summer 2026. [S] https://blog.google/products-and-platforms/products/shopping/google-shopping-cart/ . Transaction volume: none published [?].
• Visa Intelligent Commerce / Trusted Agent Protocol (TAP Oct 2025): 18 Dec 2025 press release: "hundreds" of live agent-initiated transactions in US closed beta; 100+ partners, 30+ in sandbox, agent enablers Skyfire/Nekuda/PayOS/Ramp; target "millions of consumers" by holiday 2026. [V] https://usa.visa.com/about-visa/newsroom/press-releases.releaseId.21961.html . No 2026 volume disclosed [?].
• Mastercard Agent Pay (Agentic Tokens on MDES): all US cardholders enabled by mid-Nov 2025, Citi/US Bank first; live agent payments in LatAm Mar 2026; "Agent Pay for Machines" launched ~10 Jun 2026 with 30+ partners across cards/stablecoins. [S] https://fintech.global/2026/03/30/mastercard-completes-live-ai-agent-payments-in-latin-america/ ; https://genfinity.io/2026/06/10/mastercard-agent-pay-for-machines-launch/ . Volume [?].
• Stripe/OpenAI ACP: Instant Checkout launched Sep 2025; ~30 Shopify merchants + Etsy live by Feb 2026; 4% fee; OpenAI retired Instant Checkout 24 Mar 2026 in favour of retailer apps (Walmart saw ~3x worse conversion in-chat). ACP spec persists (Stripe SPT, Delegated Payments; Salesforce support). [S] https://www.cnbc.com/2026/03/24/openai-revamps-shopping-experience-in-chatgpt-after-instant-checkout.html
• Coinbase x402: Chainalysis 3 Jun 2026: >100M cumulative txns through Q1 2026 on Base; $1+ txns rose from 49% to 95% of volume; tester-to-payer conversion up 4x; early volume inflated by PING memecoin. [V] https://www.chainalysis.com/blog/x402-agentic-payments-adoption/ . Aggregators cite ~165M txns / ~69k agents / ~$50M cumulative with ~half testing (Apr 2026) [S]; CoinDesk Mar 2026: "demand is just not there yet". Only rail with public on-chain volume, but micro-volume.
• Skyfire (KYAPay, USDC, $9.5M a16z/Coinbase), Payman ($13.8M, Visa/Coinbase Ventures): operating, volumes undisclosed [?].
• Means: mandate-based authorization (AP2 VCs, Mastercard Verifiable Intent, Visa TAP signatures) is now standardized under FIDO/card networks, and card rails are technically enabled for most US cardholders; but real agent-initiated volume is hundreds-to-low-thousands on cards and sub-$100M on crypto. A coordination layer can assume the mandate primitive exists; it cannot assume liquidity.

4. Authorization / agent identity standards

• OpenID AuthZEN Authorization API 1.0: Final 11 Jan 2026, Standards Track Mar 2026. AARP (Access Request and Approval Profile, the "not yet authorized, prerequisite needed" flow) and COAZ (AuthZEN profile for MCP tool authorization) approved as Working Group Drafts 15 Jun 2026; contributors Okta, SailPoint, Cerbos, Axiomatics, Indykite, Keycard, C1. [V] https://openid.net/openid-foundation-advances-authorization-for-the-agent-era-with-new-authzen-working-group-drafts/
• IETF OAuth drafts (individual, none visibly WG-adopted): draft-oauth-ai-agents-on-behalf-of-user-00 (delegation chain claims), draft-klrc-aiagent-auth-03 (SPIFFE+WIMSE+OAuth+SSF composition), draft-niyikiza-oauth-attenuating-agent-tokens-00. [V] https://datatracker.ietf.org/doc/draft-klrc-aiagent-auth/
• WIMSE: draft-ietf-wimse-arch-07, workload-creds-02, identifier-03, practices-05; no RFCs yet; draft-ni-wimse-ai-agent-identity-02 covers ephemeral/agent-spawns-agent. RFCs realistically 2027-28. [V] https://datatracker.ietf.org/doc/draft-ietf-wimse-workload-creds/
• Means: decision API (AuthZEN) is final and an MCP profile exists; agent identity and multi-hop delegation are still drafts. A late-2026 layer ships with proprietary/SPIFFE identity and AuthZEN-style PDP calls, and should expect to migrate token formats.

5. W3C VC 2.0 / DIDs / wallets

• VC Data Model 2.0 family: W3C Recommendation (May 2025); VC Overview 1.0 Note 30 Jul 2026; new WG charter 2026. DID 1.1 Candidate Recommendation Snapshot, comments closed 5 Apr 2026. [V] https://www.w3.org/TR/vc-data-model-2.0/ ; https://www.biometricupdate.com/202603/w3c-releases-updated-decentralized-identifiers-spec-for-comment
• EUDI Wallet: legal deadline Dec 2026 for all 27 states; as of 2 Jul 2026 only Denmark (AltID, 3 Jun 2026) in production; France/Germany public sandbox; 14 states "announced, no sandbox"; Commission itself doubtful all states hit 2026. [S] https://www.eideasy.com/blog/eu-digital-identity-wallets-july-2026 ; https://www.biometricupdate.com/202604/eu-commission-doubtful-all-member-states-will-be-able-launch-eudi-wallets-this-year
• US mDL: 21 states + PR issuing ISO 18013-5 mDLs; ~15 in Apple Wallet, 11 Google Wallet (Mar 2026); but only ~7% of eligible holders activated by end-2025 (AZ 21%, AK 1%). [S] https://trinsic.id/state-of-mobile-drivers-licenses-in-the-u-s/
• Agent frameworks using VCs: AP2 mandates are signed VCs [V]; A2A uses JWS-signed cards, not VCs; NANDA and academic "AIP" proposals use DID-style delegation; no major agent framework requires DIDs in production.
• Means: credential data model is standard; the human-side wallets that would anchor delegation at scale are ~1 EU state live and single-digit-% US activation. Don't design a Q4-2026 layer that depends on end users holding wallets.

6. Registries / marketplaces actually running

• MCP official registry: 18.8k servers (see 1). Claude connectors: 1,625. Microsoft Agent 365 GA 1 May 2026, Entra Agent ID GA; agent registry converging under Agent 365; Conditional Access/ID Protection for agents rolling out Jul-Aug 2026; registered-agent counts undisclosed [?]. [V] https://www.microsoft.com/en-us/security/blog/2026/05/01/microsoft-agent-365-now-generally-available-expands-capabilities-and-integrations/
• Virtuals ACP (on-chain hire/pay): 1.77M completed jobs, "aGDP" $479M, $2.63M monthly protocol revenue (Feb 2026); ERC-8183 with Ethereum Foundation (Mar 2026). [S] https://www.datawallet.com/crypto/what-is-virtuals-protocol (self-reported, speculative-token-driven).
• Olas/Pearl: 834 daily active agents and 15.6M txns Q1 2026; 18.75M cumulative agent txns (8 Aug 2026), 96% on Gnosis. [S] https://agenteconomy.to/olas
• Fetch.ai Agentverse: "up to 2.7M agents" hosted (mid-2026, self-reported); an independent Universal Agent Registry count put it at 36,338 (Mar 2026). [S] Treat 2.7M as unverified [?].
• MIT NANDA: builds on MCP+A2A, "DNS for agents" registry; research-stage, no usage numbers [?]. https://www.media.mit.edu/projects/mit-nanda/overview/
• AAIF registries: no AAIF-run agent registry found beyond the MCP registry [?].
• Means: running registries are either (a) vendor-scoped tool directories (MCP, Claude, Microsoft) or (b) crypto marketplaces with real but small, speculation-inflated job counts. No neutral, verified, cross-vendor agent registry exists; this is the open slot.

Headline numbers

MCP 2026-07-28 spec; 500M SDK downloads/mo; 18.8k registry servers, 17% dead, 56% no auth, 8.5% OAuth; A2A 1.0 (Mar 2026), 150+ orgs, 0 named prod customers; AP2 v0.2 -> FIDO (28 Apr 2026), Gemini Spark beta; Visa "hundreds" of txns; x402 >100M txns (micro, Base); AuthZEN 1.0 final Jan 2026, AARP/COAZ drafts Jun 2026; WIMSE 0 RFCs; EUDI 1/27 live; mDL 7% activation; Agent 365 GA 1 May 2026; Olas 18.75M txns; Virtuals 1.77M jobs.

Protocol substrate, pass 2

Agent-interoperability substrate, fact sheet (as of 2026-08-23)

Method: WebSearch budget was exhausted this session; every item below was pulled by direct WebFetch of the primary page named, or is marked [UNVERIFIED, prior knowledge] where the fetch failed (403/404) or the number could not be confirmed. "CL" = implication for a coordination layer built late 2026.

1. MCP (Model Context Protocol)

• Latest spec revision: 2026-07-28 (confirmed; schema at github.com/modelcontextprotocol/specification/blob/main/schema/2026-07-28/schema.ts). Source: https://modelcontextprotocol.io/specification/latest and /changelog. Prior revision 2025-11-25.
• Headline changes in 2026-07-28 (verified from changelog): protocol made stateless , Mcp-Session-Id and the initialize handshake removed (SEP-2567/2575); new mandatory server/discover RPC; subscriptions/listen replaces GET/SSE resumability; Multi Round-Trip Requests (MRTR) replace server-initiated sampling / elicitation / roots calls (SEP-2322); Tasks moved to an official extension ( io.modelcontextprotocol/tasks , polling via tasks/get ); Roots, Sampling, Logging deprecated (12-month deprecation window, SEP-2577/2596); ttlMs / cacheScope required on list results; OpenTelemetry traceparent conventions in _meta .
• Authorization ( https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization ): OPTIONAL for implementations; when used, AS MUST implement OAuth 2.1 (draft-ietf-oauth-v2-1-13), PKCE, RFC 8707 resource indicators (MUST), RFC 9728 Protected Resource Metadata (MUST for servers), RFC 9207 iss validation (client MUST validate if present; AS SHOULD emit, MUST expected next rev). Dynamic Client Registration (RFC 7591) is now deprecated in favour of Client ID Metadata Documents (draft-ietf-oauth-client-id-metadata-document-00). Step-up scope flow standardised (403 insufficient_scope ).
• Enterprise-Managed Authorization: "Stable" extension in the separate ext-auth repo ( https://github.com/modelcontextprotocol/ext-auth ); Client Credentials extension still Draft. No IdP list published on that page (Okta/Microsoft XAA support [UNVERIFIED] ).
• Governance: MCP donated to the Agentic AI Foundation (AAIF) under the Linux Foundation, announced 2025-12-09 with Anthropic (MCP), OpenAI (AGENTS.md), Block (goose). 8 Platinum members (AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI), 19 Gold, 21 Silver. Source: https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation. AAIF now runs 8 working groups incl. commerce, identity, governance, security ( https://aaif.io ). Formal SEP process + feature-lifecycle/deprecation policy adopted in 2026-07-28.
• Adoption numbers: ">10,000 published MCP servers" (LF press release, Dec 2025); AGENTS.md ">60,000 projects". Official registry (registry.modelcontextprotocol.io, v0.1 API) is live and paginated but exposes no total count. Mid-2026 server/client totals beyond the Dec-2025 figure: [UNVERIFIED] (third-party directories have claimed 15–20k+).
• Security incidents [UNVERIFIED, prior knowledge, not re-fetched]: mcp-remote RCE CVE-2025-6514 (Jul 2025, ~437k downloads); GitHub MCP prompt-injection private-repo exfil (Invariant Labs, May 2025); Supabase MCP SQL-injection data leak (Jul 2025); Asana MCP cross-tenant data exposure (Jun 2025); Anthropic git/filesystem MCP path-traversal CVEs (Jul 2025); early-2026 skill/marketplace supply-chain incidents (OpenClaw/ClawHub-style). No single incident caused an MCP-wide outage; pattern is tool-description injection + confused-deputy + unvetted community servers.
• CL: MCP is the de-facto agent↔tool substrate with neutral governance and a real OAuth 2.1 story, but 2026-07-28 is a breaking, stateless redesign ; any layer built in late 2026 must target 2026-07-28 semantics (MRTR, extensions, CIMD) while the installed base is still mostly 2025-06/2025-11. Auth is optional and trust is still per-host consent; there is no protocol-level reputation or delegation-chain primitive.

2. A2A (Agent2Agent)

• Version 1.0.0 released 2026-03-12; v1.0.1 2026-05-28 ( https://github.com/a2aproject/A2A/releases ). v0.3.0 (2025-07-30) introduced agent-card signatures and mTLS security schemes; 1.0 adds Signed Agent Cards for identity verification, multi-protocol bindings (JSON+HTTP, gRPC, JSON-RPC), multi-tenancy at one endpoint, version negotiation, tasks/list , OAuth 2.0 modernization. Source: https://a2a-protocol.org/latest/announcing-1.0/
• Governance: Donated by Google to the Linux Foundation ; TSC = AWS, Cisco, Google, IBM Research, Microsoft, Salesforce, SAP, ServiceNow. Apache-2.0. (Whether A2A is formally inside AAIF: [UNVERIFIED] , a2a-protocol.org says "Linux Foundation", not AAIF.)
• Relationship to MCP: official positioning is complementary, "MCP for agent-to-tool, A2A for agent-to-agent"; AP2 is explicitly built as an A2A extension.
• Named production deployments: none listed on the protocol site or the 1.0 announcement; adoption is expressed as partner logos (150+ claimed at 2025 launches [UNVERIFIED] ). Microsoft (Foundry/Copilot Studio), SAP, ServiceNow, Salesforce Agentforce have announced support [UNVERIFIED, not re-fetched] .
• CL: Signed agent cards give a workable agent-identity/discovery primitive at 1.0, but with no public usage telemetry A2A should be treated as a spec with vendor support, not a network with traffic. A coordination layer can speak A2A for agent↔agent and MCP for tools; it must supply its own registry/reputation.

3. Agentic payment rails

• Google AP2 (Agent Payments Protocol): announced 2025-09-16 ; now v0.2 ; structured as A2A extension using Verifiable Digital Credentials with Checkout Mandate (open/closed) and Payment Mandate , the "Intent/Cart" framing of v0.1 has been collapsed into Checkout. FIDO Alliance partnership announced; integrates with UCP (Universal Commerce Protocol, ucp.dev); x402 referenced as an alternate payment method in samples. No transaction volume or live-deployment figures published. Source: https://ap2-protocol.org/
• Coinbase x402: now stewarded by the x402 Foundation under the Linux Foundation (operational-launch announcement). Live dashboard (page dated 2026-07-14): 75.41M transactions / $24.24M volume / 94.06K buyers / 22K sellers in the trailing 30 days ; EVM chains + Solana; "trusted by" AWS, Cloudflare, Stripe, Vercel, Alchemy, QuickNode, Messari, Nansen, World. Source: https://www.x402.org/, the only rail with published real volume ; note avg ticket ≈ $0.32, i.e. micro-payments for API/data, not commerce.
• Stripe / OpenAI Agentic Commerce Protocol (ACP): Stripe docs ( https://docs.stripe.com/agentic-commerce ) now route sellers to UCP or ACP (agenticcommerce.dev) and machine payments to MPP or x402 ; Shared Payment Tokens + Link agent wallets exist; the "Agents" (agent-builder) side is still Private Preview / waitlist . Merchant count and ChatGPT Instant Checkout GMV: [UNVERIFIED] .
• Visa Intelligent Commerce / Trusted Agent Protocol: developer page says "in the process of development and deployment… depictions are representations of potential features"; sandbox free, production by contact ; 5 regions listed; model = passkey-authenticated user instruction + agent-specific provisioned tokens. No partner/transaction counts. Source: https://developer.visa.com/capabilities/visa-intelligent-commerce. TAP (Cloudflare-co-designed HTTP-signature agent attestation, Oct 2025) status [UNVERIFIED] .
• Mastercard Agent Pay: press page 403'd. [UNVERIFIED, prior knowledge] Announced Apr 2025; Agentic Tokens; stated goal of all US Mastercard cardholders enabled by holiday 2025; partners Microsoft, PayPal, Citi, US Bank. Real volume: none published.
• Skyfire / Payman: not fetched. [UNVERIFIED] Skyfire ("KYAPay" agent identity+payment tokens, Visa partnership) and Payman (human-approved agent spend limits) are both live SaaS but publish no volume; treat as long-tail.
• Mandate-model convergence: AP2 = signed VDC mandates; Visa/Mastercard = network-tokenised agent credentials + passkey consent; Stripe = Shared Payment Token scoped to a cart; x402 = no mandate, pay-per-request with on-chain settlement.
• CL: Only x402 has measurable flow today (tens of millions of micro-tx/month); card-network rails are sandbox/pilot with no published volume a year after launch. A late-2026 layer should support x402-style per-call settlement for machine services and carry AP2/VDC mandates for human-authorised commerce, expecting the card rails to reach GA during 2026-27 rather than depending on them.

4. Identity, authorization, delegation standards

• OpenID AuthZEN: Authorization API 1.0 reached Implementer's Draft (approved 2025; Final status [UNVERIFIED] ); WG page lists no agent profile, AARP (Agent Authorization Request Profile) and MCP/A2A profiles [UNVERIFIED] , likely still WG drafts. Source: https://openid.net/wg/authzen/
• IETF WIMSE: 6 active WG drafts (architecture draft-ietf-wimse-arch-08; workload identifier; workload proof token; HTTP-signature and mTLS workload auth; credentials framework); "Workload Identity Practices" at IESG. Agent-specific individual drafts exist but are not WG-adopted: "WIMSE Applicability for AI Agents", "Credential Delegation Protocol for AI Agents", "Execution Context Tokens for Distributed Agentic Workflows", "Signed Authorization-Evidence Records for WIMSE-Authorized AI Agent Actions". Source: https://datatracker.ietf.org/wg/wimse/documents/. SPIFFE remains the de-facto workload-ID runtime; agent profile [UNVERIFIED] .
• OAuth for agents: MCP's own profile (above) is the most-deployed; Client ID Metadata Documents (draft-00) and Identity-Assertion/XAA (Okta-led) are the delegation vehicles. RFC 8693 Token Exchange + RFC 9396 RAR used ad hoc.
• Microsoft Entra Agent ID: Generally Available (what's-new page dated 2026-05-01, updated 2026-08-13): agent identities, agent identity blueprints (portal wizard still Preview), agent user accounts, agent service principals, Conditional Access templates for autonomous and on-behalf-of agents, ID Protection "risky agents", ID Governance access packages/sponsor lifecycle, Auth SDK sidecar, third-party integration for AWS Bedrock, GCP, n8n ; Agent Registry converging into Microsoft Agent 365 . Source: https://learn.microsoft.com/en-us/entra/agent-id/whats-new-agent-id. Tenant/agent counts: none published.
• CL: Enterprise agent identity is real but proprietary-first (Entra GA; Okta/Google equivalents [UNVERIFIED] ). The open delegation-chain standard (WIMSE agent drafts, AuthZEN agent profiles) is 12–24 months from RFC/Final. A coordination layer must bridge IdP-issued agent identities today and should emit WIMSE/AuthZEN-shaped evidence records so it can adopt the standards when they land.

5. Verifiable Credentials, DIDs, wallets

• W3C VC Data Model 2.0: W3C Recommendation 2025-05-15 (with VC-JOSE-COSE, Data Integrity, Bitstring Status List) [from prior knowledge; not re-fetched] . DID Core 1.0 Rec since 2022; DID 1.1 / DID Resolution in CR [UNVERIFIED] .
• EU Digital Identity Wallet (eIDAS 2.0, Reg. 2024/1183): Commission page confirms every Member State must offer at least one wallet by end-2026 ; ARF v2.0 (May 2025) is the current spec; four LSPs (NOBID, POTENTIAL, DC4EU, EWC) + APTITUDE, WEBUILD. Source: https://ec.europa.eu/digital-building-blocks/sites/display/EUDIGITALIDENTITYWALLET/EU+Digital+Identity+Wallet+Home. The "Sept 2026 mandate" could not be confirmed , the regulation's dates are: wallets offered by ~Dec 2026 (24 months after Nov-2024 implementing acts); relying parties in regulated sectors and VLOPs must accept within 36 months (~late 2027). Flag as [UNVERIFIED] unless a national deadline is meant. Number of wallets issued / states live: not published.
• mDL: [UNVERIFIED, prior knowledge] ISO 18013-5 mDLs live in ~15+ US states via Apple/Google Wallet; TSA acceptance at 250+ airports; ISO 18013-7 online presentation and W3C Digital Credentials API shipping in Chrome/Safari in 2025-26.
• Agent frameworks using VCs: AP2 is the concrete case , mandates are Verifiable Digital Credentials signed by user device keys (FIDO tie-in). No MCP/A2A core use of VCs; A2A uses JWS-signed agent cards, not VCs.
• CL: VC 2.0 is finished and the EU wallet hits scale on exactly the late-2026 timeline; a coordination layer that encodes mandates/attestations as VC 2.0 (as AP2 does) gets human-grounded authority "for free" in the EU from 2027. Outside the EU, mDL is the only mass-issued credential.

6. Agent registries / marketplaces

• Olas (Autonolas): live on-chain numbers ( https://olas.network/ ): 3,697 agents deployed, 429 daily active, 20.15M total tx, 14.42M agent-to-agent tx, 3.83M OLAS staked, Mech marketplace turnover $108K, protocol fees $595 , high tx count, negligible economic value.
• Virtuals ACP: app is JS-only; stats not retrievable. [UNVERIFIED] Public claims of thousands of ACP agents and tens of thousands of jobs; volume dominated by token speculation.
• Fetch.ai Agentverse: 403. [UNVERIFIED] Claims ~1M+ registered agents (largely templated uAgents); no independent usage data.
• MIT NANDA: not fetched. [UNVERIFIED] Registry/index spec and "quilt" of registries; research-stage, no production traffic numbers published.
• Microsoft Agent 365: announced 2025-11-18 (blog 404'd); Entra docs confirm Agent Registry is converging under Agent 365 as the enterprise control plane (registry + access control + observability on Entra Agent ID). GA date/pricing [UNVERIFIED] (Frontier preview → reported $15/user/mo).
• Lab directories: MCP official registry (live, v0.1 API, no totals); Anthropic/OpenAI/Google connector directories and ChatGPT app store exist but publish no counts [UNVERIFIED] .
• CL: No neutral, populated, cross-vendor agent registry exists. Crypto registries have the most on-chain activity but trivial value; enterprise registries (Agent 365) are tenant-scoped. Discovery + reputation is the open slot a coordination layer would have to fill itself, ideally by federating MCP registry + A2A agent cards + Entra/Okta identities rather than inventing a new namespace.

Bottom line (2026-08-23)

Verified-solid: MCP 2026-07-28 (breaking, stateless), AAIF governance, A2A 1.0/1.0.1 with signed cards, MCP OAuth 2.1 + CIMD + Enterprise-Managed Auth (stable ext), Entra Agent ID GA, x402 ~75M tx/30d, EU wallet end-2026. Thin or unverified: everything on card-network agentic payments volume, A2A production traffic, agent registry usage, AuthZEN/WIMSE agent profiles, the "Sept 2026" EU date, and any mid-2026 MCP adoption totals.

Prior art: missions, bounties, information markets, science engines, pass 1

Prior art: mission exchanges, bounties, info markets, AI science. First research pass (WebSearch available), 23 Aug 2026

Format per line: what | number | date | URL | status | lesson. [U] = unverified/single-source/self-reported. Compiled from 7 parallel research passes (~590 tool calls); Moltbook/Meta, Replit Bounties shutdown, Convergent portfolio spot-checked by direct fetch. Search budget exhausted at 200/200; gaps listed at end. The second pass (priorart.md) worked from fetches only; where the two differ, this sheet's sourced items govern.

(a) Mission exchange / problem agents / open-problem graphs

• No product found using "mission exchange", "mission graph", "problem agents", "problem graph", "hypothesis graph" as of Aug 2026. "Mission graph" hits only a defense edge-AI vendor (Corteq); "issue tree" exists only as MECE prompt skills. Space appears open.
• Convergent Research Gap Map | curated registry of R&D capability gaps: 103 gaps, 369 capabilities, 1,080 resources, 20 fields (live JSON) | launched 15 Apr 2025 | gap-map.org | alive, MIT-licensed, no usage stats | self-described "illustrative, not comprehensive"; consumers are FRO founders/PMs/philanthropies; no capital or forecasts attached.
• Society Library (Jamie Joyce) | nested argument graphs of public debates: Diablo Canyon map 5,862 claims from 5,000+ refs, ~8,000 human hours (~1.4 hr/node) | Jul 2023; agentic pipeline ("Rose"/"Juniper") + $10k bounties, $150k match, Feb 2026 | societylibrary.org | alive, pivoted manual-to-agents | manual decomposition is unscalable; LLMs are the only route.
• Erdos Problems (Bloom) | curated open-problem list now used as an AI target board: 1,217 problems, 565 solved/652 open (Quanta, 3 Aug 2026); first genuine LLM proof #728 Jan 2026; DeepMind 9 autonomous solves May 2026 | erdosproblems.com | alive | Oct 2025 OpenAI "solved 10" claim was literature retrieval ("dramatic misrepresentation", Bloom; Hassabis: "embarrassing"); "open" = maintainer's ignorance, so provenance of status is the product.
• Rootclaim | Bayesian public analyses + $100k debate challenges: ~6 analyses; lost COVID-origins debate 18 Feb 2024 | rootclaim.com | dormant ("teaching AI models probabilistic inference") | live debate rewards memorized detail; judges accepted p=0.0001 conditionals; no correction loop.
• 80,000 Hours problem profiles: 9 ranked; Apr 2025 pivot to AGI-only; non-AI profiles "won't stay current". openproblems.bio: 12 single-cell benchmark tasks, Nat Biotech 2025, alive. Humanity's Last Exam: 2,500 Qs, $500k author pool; top 55.5% Aug 2026.
• Dormant/never launched: Problem Lab, Problems Book, Global Problems Map, Moonshot list, The Lemma, Wikiversity open problems [U, not exhaustively searched].

(b) Research and software bounties

• Bountysource | OSS issue escrow, 2003/2012: >=$21k documented stolen; lifetime $ paid never disclosed [U] | ToS grab Jun 2020 (retain unclaimed >2y, withdrawn in 1 day); payouts stop ~Mar 2023; insolvency issue 18 May 2023; site dead May 2024 | github.com/bountysource/core/issues/1586 | DEAD | custody failure, not demand failure: unregulated crypto owner held escrow float.
• Gitcoin | bounties-to-quadratic-funding grants: $67M+ across 270+ rounds (site 2025); GTC mcap ~$20M | bounties/hackathons sunset Jul 2023; Grants Lab/Allo/Grants Stack sunset 31 May 2025 ("unclear path to profitability"); GG24 Oct 2025; Passport spun to Holonym (2M users) | gitcoin.co | PIVOTED: treasury-funded grants only | bounty demand was hackathon-sponsor-only; QF stack was a cost center with no revenue model.
• Replit Bounties | Cycles-paid micro-gigs, Dec 2022: only public figure "$100k paid" (Jun 2023) | shutdown emailed 22 Jul 2025, closed 6 Sep 2025, users pushed to Contra; no postmortem | news.ycombinator.com/item?id=44643875 | DEAD (verified) | the Agent product ate the "$50 dev gig" use case.
• Algora | GitHub-native OSS bounties: ~$514k across 3,611 bounties lifetime [U]; 23% funder fee; 30+ FT hires placed | Show HN Apr 2023 | algora.io | ALIVE, pivoted to recruiting; explicitly bans AI agents | ~$0.5M GMV can't support a company at any take rate; value is track-record-to-placement.
• Polar.sh: issue funding -> maintenance mode Dec 2024 -> merchant-of-record billing (5%+50c). OpenQ: bounties -> OSS observability. IssueHunt: OSS bounties -> Japan security bug bounty. Opire: alive, 4% fee, no stats. All: bounties were the wedge, never the business.
• Superteam Earn (Solana) | largest live board: ~$10.3M earned, 209k users, 2,660 sponsors (2026) [U] | alive | funded by Solana Foundation ecosystem spend, not take rate.
• Topcoder: 2001; ~1.9M members; ~$100M lifetime prizes [U]; Wipro $500M (w/ Appirio) 2016; gig ops folded into Wipro Feb 2024 | alive, low-profile | became an enterprise delivery channel.
• Kaggle: 29M users (Feb 2026) [U]; 68 comps/$3.67M prizes in 2025; Game Arena Aug 2025 | alive, peak | survives because Google pays: prizes are marketing, not revenue.
• InnoCentive -> Wazoku: 2001; Wazoku claims 1M+ solvers, 2,500+ challenges, $60M+ awarded, avg ~$20k | merged Jul 2020 | wazoku.com/innocentive | alive, absorbed | solve rate ~30% (Lakhani/HBS) rising only with consultant-curated problem formulation; IP-transfer friction.
• HeroX: XPRIZE spinout 2013; 100+ challenges; 18% success fee [U] | alive as managed service for NIH/NASA, not self-serve.
• Agent bounty boards (2026): agentbounty.org "$2.4M distributed" [U, self-reported], BotBounty.ai, agentbounties.app, unaudited. K Prize (Konwinski $1M, >90% SWE-bench): round-1 winner 7.5%, $50k (Jul 2025). ARC Prize 2025: top 24%, grand prize unclaimed; ARC-AGI-3 frontier models <0.5% (May 2026). AIMO $10M: $5M grand unclaimed. Vesuvius: $1.84M awarded, first scroll read end-to-end 2026.

(c) Information-value markets / prediction-funded research

• Replication Markets / DARPA SCORE | crowd forecasts on 3,000 claims: AUC 0.76, 73% acc; ~150 active traders; $142k prizes; validated on only n=37 (Nature collection 1 Apr 2026); SCORE: 164 replications, 49% replicated | 2019-21 | cos.io/score; arxiv 2005.04543 | company site HTTP 522, dormant [U] | markets need ground truth to close but only ~250 of 3,000 claims were ever slated for replication: surrogate scoring, engagement decay.
• Decision markets (Dreber/Camerer lineage): PNAS 2015: 41 claims, 71% correct; NHB 2025: top-12 by market 83% replicated vs bottom-12 33% | markets can triage which experiments to fund, not just grade them.
• LLM pre-forecasts as VOI screen: BrainBench (NHB Nov 2024): 15 LLMs 81% vs 171 neuroscientists 63%; Hewitt/Ashokkumar (2026): GPT-4 r=0.85 with 476 pre-registered effects, >= human forecasters | cheapest known VOI prior.
• Metaculus: PBC; $5.5M Open Phil 2022; AI Forecasting Benchmark $175k/yr (Jul 2025), FutureEval Feb 2026; projects bots pass community ~Apr 2026, Pros ~mid-2027 | metaculus.com/aib | alive; no science-funding product; no nonprofit transition/layoffs found [U-negative].
• Manifold: play-money; ~$17M raised; sweepcash real-money Sep 2024 -> killed 13 Feb 2025 ("haven't met usage goals") | alive | own postmortem: "spurned opportunities to provide more value to our current audience in favor of moonshots".
• Polymarket/Kalshi: Polymarket $15B val (Apr 2026), $10.5B/mo peak; Kalshi $22B val, ~$4B annualized rev, ~80% sports | AI-benchmark markets exist (ARC-AGI-2 >=85%: $269k vol, resolved No); no "will this paper replicate" markets found | liquidity follows sports, not science. Prediction Arena (arXiv 2604.07355): 10 LLMs lost 16-31% on Kalshi.
• INFER/RAND: 3,433 forecasters, 200k forecasts, 409 Qs | concluded, read-only | grant-funded crowd platforms die when sponsor stops. QURI/Squiggle: maintenance mode Jun 2026. PredictionBook: dead Jan 2024 -> Fatebook. Augur: 265 -> 37 DAU within a month of 2018 launch.
• Impact certificates: ACX/Manifund 2024: ~$50k invested across 12 projects, 24 micro-investors; Manifund retro (1 May 2024): "haven't found a use case where certs led to better funding decisions"; ACX 2025 round ($1.3-1.5M, 42 projects, 654 apps) skipped certs. Hypercerts: ~25k holders, $ routed undisclosed [U, likely <$1M]; Dec 2025 moved to ATProto, admits missing "social data layer" for evaluation. Uniform failure: no retro buyers -> no price signal.

(d) Capital that searches for work

• Fast Grants: $50M, 260 grants, 48-hr decisions (2020). Emergent Ventures: 41 cohorts, likely >1,000 grants [U]. Impetus: $34M, 145 projects, <=3-wk decisions, ~1% overhead, 237+ papers. Lesson: speed + human taste scale; "too speculative for government, too early for companies".
• Manifund | regranting: $5.4M, 352 grants, 23 regrantors all-time; 2026 target $5M | manifund.org/about/regranting-data | alive, growing | only documented AI-assisted eval found: Aug 2026 retro used Claude to rank past regrants ("did a pretty good job") [U].
• Convergent Research FROs: 12 FROs listed, $20-50M/3-7 yr each, >$400M raised from 30+ funders; UK pair w/ ARIA Apr 2026 | alive | lesson (Issues in S&T 2025): rigid DARPA benchmarks fail; need North Star + adaptive milestones; "any new tool needs an active userbase". Merge Labs $252M seed Jan 2026 reportedly involves Forest Neurotech leadership but Convergent does not list it as a spinout [U].
• Renaissance Philanthropy: $533M mobilised in 2 yrs (May 2026); AI for Math Fund $31.5M (29 projects) | Kalil: "shortage of institutions that can deploy capital effectively". ARIA: >=GBP1B 2025-29, 16 PDs; Gur -> Fisher Feb 2026; Activation Partner call Apr 2026 on "AI in science". Speculative Tech: 18 Brains fellows 2026, $ raised undisclosed. Astera: McCaleb $1B to neuro-AGI (Mar 2026). Arcadia: alive ("shut down" rumor = Arena BioWorks, Nov 2025).
• Coefficient Giving (ex-Open Phil): >$1B granted in 2025; no documented AI-assisted grant evaluation [U-negative] | own framing: bottleneck is eliciting proposals, not screening.
• AI in review is mostly banned: NIH NOT-OD-25-132 (Sep 2025) rejects AI-developed applications, caps 6/PI/yr, bans reviewer AI use. No Gates/Wellcome/Novo AI-review pilots found.
• Crypto AI allocators: Deep Funding (Vitalik): $220k pilot Feb-May 2025, AI "engine"/humans "steering wheel"; Optimism RF S7 allocated by OSO metrics; ai16z/ElizaOS: $2.5B peak Jan 2025 -> founder declared token "dead" 5 Aug 2026 (-99%); never allocated. SignalFire $1B+ fund w/ Beacon AI picking (Apr 2025). Vela/Koble/Moonfire [U, not researched].

(e) Humanity-scale to-do lists: see (a). Net: only three live registries carry real traffic (Gap Map, Erdos, 80k); none attach capital or forecasts.

(f) Autonomous science engines / adversarial agents

• FutureHouse Robin: ripasudil for dAMD in ~2.5 months; Nature Jun 2026 | futurehouse.org | critique: Finch reported 7.5x phagocytosis, human reanalysis 1.75x (gating); swapping lit agent to o4-mini -> 45% hallucinated refs.
• Edison/Kosmos: ~1,500 papers, 42k LOC/run, "6 months of work" (self-report); 79.4% statements supported; $200/run | 5 Nov 2025; $70M seed @ ~$250M Dec 2025 | edisonscientific.com | alive | ~1 in 5 statements unsupported; "chases statistically significant yet scientifically irrelevant findings" (own words).
• Sakana AI Scientist: v2 passed ICLR workshop review (6.33, withdrawn by agreement) Apr 2025; Nature Mar 2026; $2.65B val Nov 2025 | critique (Beel arXiv 2502.14297): $6-15/paper + 3.5 human hrs; 42% experiments failed on code errors; 5/34 citations post-2020; hallucinated numbers.
• Google AI co-scientist: 6-agent debate/Elo tournament; Nature May 2026 (AML candidates, vorinostat -91% fibrosis marker, cf-PICI); public at I/O 2026 | all preclinical; Apr 2026 study: 68% of agent traces ignored available evidence, beliefs revised against contrary evidence 26% of the time.
• Virtual Lab (Zou/Stanford): PI + specialist + adversarial "Scientific Critic" agent; 92 validated nanobodies, Nature Jul 2025; "Virtual Biotech" 37k agents Aug 2026 [U, press].
• Agents4Science (22 Oct 2025): 253 -> 48 AI-authored/AI-reviewed papers; best paper "BadScientist" shows unsound papers fool LLM reviewers (arXiv 2511.15534).
• Ai2 AstaBench: 2,400+ problems, 57 agents; best 58% (Opus 4.7, $3.54/problem) Apr 2026; E2E-Bench-Hard 3% perfect | strong lit/code scores don't transfer to end-to-end research.
• Lila $550M raised/>$1.3B val, "thousands of discoveries" unaudited; Periodic $300M seed -> raising $500M @ $7.5B (May 2026), no superconductor published. Autoscience $14M (Mar 2026), pivoting to enterprise. Intology Zochi ACL 2025 main-track, no funding found [U].
• Claude Science (30 Jun 2026): reviewer agent checks citations/calcs; 50 x $30k credits. Microsoft Discovery GA Jun 2026. OpenAI for Science GPT-5 paper (arXiv 2511.16072): 4 verified math results, "modest in scope".
• Hallucination base rate: ~110k 2025 papers (1.65%) contain >=1 AI-hallucinated citation; publishers catch 5-10% (Nature, Apr 2026). Zhu et al. (arXiv 2506.01372): 28 AI papers, "experimental weakness" in 100%.

(g) Civilization memory / claim-evidence-outcome graphs

• Scite: 1.6B+ citation statements w/ supporting/contrasting polarity; acquired by Research Solutions 27 Nov 2023 for $20.9M at $3.6M ARR | alive | claim-polarity monetized only as publisher-licensed B2B.
• ORKG: >10k papers, >5k problems, ~1k comparisons (2023); paid curation grants 2025 | alive, grant-fed. Nanopublications: "10M" legacy bulk loads; active registry 88,299 (Aug 2026).
• Community Notes: X 1M contributors; Meta US: 250k contributors, ~15k notes, 6% published; 7-hr median delay; AI Note Writer API Jul 2025 | bridging gate kills throughput.
• Lean mathlib: 285,347 theorems, 772 contributors (Aug 2026) | thriving | the one claim graph with machine-checked resolution, now the AI-math verification substrate.
• Golden: 10B-entity KG ambition -> acquired by ComplyAdvantage Apr 2024 (fin-crime data). System.com: $6.5M Wellcome Dec 2025, narrowed to health. Hypothesis: 50M annotations, survived via LMS. Cyc: 24.5M axioms, ~$5M rev [U]. Kialo: 720k claims, edu-only. PubPub: shut 31 Dec 2025. ClaimReview: Google dropped snippets Jun 2025.
• Moltbook/OpenClaw (agent social layer): 2.9M registered agents, 206k human-verified (Jun 2026); 1.5M agents owned by 17k humans (Feb); Meta acquired 10 Mar 2026, undisclosed (verified via TechCrunch/Wikipedia) | most viral posts human-prompted; exposed-DB incidents.
• FRI adversarial collaboration (2024-25): persistent skeptic/concerned gap, little belief updating even with incentives.

Structural lessons (why knowledge-work marketplaces stall)

1. GMV is tiny outside subsidy. Algora ~$0.5M lifetime, Replit ~$100k disclosed, Experiment.com $12M in 14 yrs; only boards with an external payer (Kaggle/Google, Superteam/Solana, Gitcoin/GTC treasury) pass $10M. No take-rate business has worked.
2. Survivors pivot to adjacent cash flows: recruiting (Algora), billing (Polar), observability (OpenQ), security bug bounty (IssueHunt), managed challenges (HeroX/Wazoku). Bounties are a wedge, not a business.
3. Custody/escrow is the fatal failure mode, not demand: Bountysource died holding float; crypto wrappers (ai16z -99%; VitaDAO $55.8M paper treasury vs $4.7M actually deployed over 4 yrs) turn allocators into token plays.
4. Resolution latency kills markets. Replication Markets could resolve <10% of claims; science questions resolve in years; real liquidity went to sports (Kalshi 80%). Every research-oriented market (RM, INFER, Manifold sweeps, QURI, PredictionBook) ran on grants and ended when the grant did.
5. Verification cost exceeds task value at the hard end. InnoCentive ~30% solve rate; K Prize 7.5%; ARC/AIMO grand prizes unclaimed; Edison 1-in-5 statements unsupported; Robin 4x analysis error. Cheap verification exists only where a machine checker does (Lean mathlib, SWE tests).
6. Adverse selection in impact/cert markets: only rejected projects entered ACX certs; no retro buyers -> no price -> Manifund/Hypercerts both retreated.
7. Open supply floods instantly, demand doesn't: RentAHuman 377-633k human sign-ups vs ~11k bounties and 0 verified completed jobs (Die Zeit, Mar 2026); Moltbook 1.5M agents owned by 17k humans.
8. Curation, not matching, is the scarce input. InnoCentive solve rates rose only with consultant-formulated problems; Coefficient Giving says its bottleneck is eliciting proposals; Kalil: "shortage of institutions that can deploy capital". Gap Map/Erdos work because a human curates status.
9. AI reviewers are gameable and AI in formal review is banned (NIH Sep 2025; BadScientist; 1.65% hallucinated-citation rate): any agent-issued bounty needs an independent adjudication layer.
10. "Open problem" status is a provenance claim: the Erdos/GPT-5 incident shows a to-do list is only as good as its evidence of what has already been done; the memory graph (g) is a precondition for the mission exchange (a), not a feature of it.

Coverage gaps (not verified; do not cite from this sheet): Harmonic, Axiom, Math Inc, Prime Intellect research bounties, Vela/Koble/Moonfire AI-VC, Giveth/Octant/Drips, Virtuals ACP GDP, Good Judgment 2025 numbers, Freelancer.com agents, Golem real usage, Ocean data-marketplace volume.

Prior art, pass 2

Prior art: markets, bounties, agents and graphs for knowledge work

Compiled 2026-08-23. Items marked [V] were verified by fetch today; [U] = from memory, unverified (search budget exhausted mid-task; treat [U] numbers as ±30%).

(a) Problem/mission agents, "mission exchanges"

• Anthropic Project Vend [V], Claude 3.7 Sonnet ("Claudius") ran an office shop with Andon Labs, ~1 month (Mar-Apr 2025), published 27 Jun 2025. Net worth fell throughout; never profitable (sold tungsten cubes below cost, hallucinated payment details, gave discounts it had just promised to stop). https://www.anthropic.com/research/project-vend-1. Status: phase 2 running with better scaffolding. Lesson: unsupervised economic agency fails on memory, pricing discipline and social pressure, not on capability; scaffolding/tools were the binding constraint.
• Remote Labor Index (CAIS + Scale AI) [V], benchmark of real Upwork-style projects worth >$140k and >6,000 hours; published Oct 2025. https://www.remotelabor.ai. Best agent automation rate ~2.5% (Manus) [U]. Lesson: end-to-end paid remote work is far from automatable; "agent takes a mission" mostly fails at the last mile (deliverable QA).
• Andon Labs Vending-Bench [U], simulated long-horizon shop; models diverge wildly between runs; drift after ~20M tokens. Lesson: long-horizon coherence is the failure mode, not task skill.
• Prime Intellect Environments Hub [U], Aug 2025, open RL-environment hub with cash bounties for environments; hundreds of envs by late 2025. Closest live "task exchange for agents", but the buyer is one lab.
• Agent-to-agent commerce protocols [U], Virtuals ACP (2025), Olas mech marketplace, Bittensor subnets: token-incentivized task routing; volume dominated by speculation, few verified non-crypto buyers.
• 2025-26 startups explicitly using "problem agent" / "mission graph" / "decision audit" language: none found with a shipped product that I could verify [U]. Nearest: Edison Scientific (Kosmos), Sakana, FutureHouse (below). Treat the vocabulary as unoccupied.

(b) Research and software bounties

• Bountysource [V], GitHub-issue bounties, relaunched 2012; acquired by CanYa (crypto) 2017, The Blockchain Group Jul 2020; stopped paying verified claims Jun 2023 (≥$21k alleged withheld); parent bankrupt Nov 2023; site dead May 2024. https://en.wikipedia.org/wiki/Bountysource. Status: dead. Lesson: custodial escrow + thin margins + crypto-owner drift = counterparty risk that kills trust; bounties averaged low hundreds of dollars, insufficient to fund ops.
• Gitcoin [V/U], launched 2017 as bounties; bounties product deprecated ~2022 [U]; pivoted to quadratic-funding grants (24+ rounds, "GG24 = first round of Gitcoin 3.0"); 2026 site cites Protocol Guild $100M+ and TheDAO Security Fund $220M+ matching pools (Jan 2026). https://www.gitcoin.co. Cumulative ~$60M+ distributed by 2024 [U]. Status: alive, grants infra (Allo). Lesson: per-issue bounties had high verification/dispute cost; pooled grants with matching amortize verification across a community.
• Replit Bounties [U], launched Dec 2022, Cycles-denominated; Replit reported ~$1M+ paid by 2023; quietly wound down 2024-25 as Replit pivoted to Agent. Status: dead/dormant. Lesson: once the platform's own agent can do the $50-500 tasks, the human bounty market is cannibalized by the host.
• Algora [V], open-source bounty platform (2022); homepage today sells hiring ("candidates with N contributions") and showcases portfolio companies (CodeRabbit $1.5B, ComfyUI $500M). No public bounty totals. https://algora.io. Status: alive, pivoted from bounties to recruiting. Lesson: the durable revenue from bounties was the hiring signal, not the task payments.
• Kaggle [V], 2010; Google acquired 8 Mar 2017; 15M+ users in 194 countries (Oct 2023). Hosts get perpetual, royalty-free license to winning code. https://en.wikipedia.org/wiki/Kaggle. Status: alive, subsidized by Google. Lesson: works because verification is automatic (held-out leaderboard) and IP terms are brutal; doesn't generalize to tasks without a scoring function.
• Topcoder [V], 2001; ~1.9M members (2024); Appirio acquired 2013, Wipro 2016 ($500M deal); 2009 revenue ~$19M with 16 employees. https://en.wikipedia.org/wiki/Topcoder. Status: alive inside Wipro, shrinking relevance. Lesson: enterprise services wrapper around crowd; margins came from managed delivery, not from the marketplace.
• InnoCentive / Wazoku [V], 2001 (Eli Lilly); 380k solvers (2017); 2,000+ external challenges, >$20M total awards (≈$10k/challenge); ~80% of premium challenges awarded (2016); acquired by Wazoku Jul 2020. https://en.wikipedia.org/wiki/InnoCentive. Status: alive as enterprise-software feature. Lesson: ~$20M over 20 years is tiny, the seeker side is sparse; ended as a SaaS feature, not a market.
• HeroX [U], XPRIZE spin-out 2013; prize-hosting SaaS; no public totals. Status: alive, small.
• Experiment.com [U], 2012 science crowdfunding; ~$10M raised across ~1,000 projects, median project ~$4-5k. Status: alive, marginal. Lesson: small-ticket science crowdfunding never exceeded hobby scale.
• ResearchHub [U], Coinbase-CEO-backed; pays ResearchCoin bounties for peer review (~$150/review); thousands of reviews 2024-25; ResearchHub Foundation 2025. Status: alive, token-dependent. Lesson: paid review works at small scale only with a subsidy token.
• VitaDAO / Molecule / Bio.xyz [U], VitaDAO raised ~$5M (2021), funded 20-25 longevity projects ($4M+), Pfizer Ventures joined 2023; Molecule IP-NFTs/IP tokens; Bio.xyz accelerator launched 2024. Status: alive, low velocity. Lesson: IP-tokenization solved neither sourcing nor evaluation; deals are ~$100-300k each and hand-curated.

(c) Markets for information value

• Replication Markets / DARPA SCORE [U; site returned 522 today], 2019-2021; ~3,000 social-science claims across 10 rounds; ~$150k prizes; surveys/markets predicted replication outcomes with ~70-75% accuracy (consistent with Camerer et al. 2018 markets at 73%). Status: dead (program ended 2021). Lesson: aggregated judgment predicts replication cheaply, but nobody had a budget line to act on the scores; SCORE produced credibility estimates, not funding decisions.
• Metaculus AI Forecasting Benchmark [U; page 403 today], quarterly since Q3 2024, ~$120k/quarter prize pool; 2025 series showed top bots within a few Brier points of Pro forecasters; Metaculus also runs Metaculus Cup. https://www.metaculus.com/aib/. Status: alive. Lesson: bot forecasting quality is now near-pro on resolvable questions; the bottleneck moved to question generation and resolution.
• Manifold [U], play-money market, Dec 2021; millions of mana markets; sweepstakes cash product 2024, dropped 2025. Status: alive, small team. Lesson: user-created questions scale; cash layer dies to regulation; question quality is the scarce input.
• Polymarket [V], Jun 2020; $3.3B on 2024 US election; ICE invested up to $2B (Oct 2025, $8B val); $9B valuation Feb 2026; US re-entry 2 Dec 2025 via $112M QCEX acquisition. https://en.wikipedia.org/wiki/Polymarket. Status: alive, scaling. Lesson: liquidity follows entertainment/political salience; science/research markets remain illiquid.
• Ought → Elicit [U], Ought (2017 nonprofit) spun Elicit out as PBC Sept 2023 ($9M seed); Elicit Reports 2025; ~2M users claimed. Status: alive as research-assistant SaaS. Lesson: "factored cognition for research" monetized as literature review, not as an information-value market.

(d) Capital that searches for work

• Fast Grants [U], Apr 2020, $50M, ~260 grants, decisions in 48h; post-mortem (Collison/Cowen/Hsu 2021) found speed, not AI, was the innovation. Lesson: fast money works when the grantee pool is pre-vetted (labs with existing NIH funding).
• Open Philanthropy → Coefficient Giving [V: site 403 today; U details], renamed Nov 2025; ~$1B/yr; 2025 Technical AI Safety RFP ~$40M; uses LLMs for application triage internally [U]. Status: alive. Lesson: AI assists screening; the human program officer remains the decision unit.
• Renaissance Philanthropy [U], Tom Kalil, 2024; AI for Math Fund $9.2M with XTX Markets; packages "fund designs" for donors. Status: alive. Lesson: the scarce good is designed funding programs, not evaluation.
• Manifund [V], open platform: regranting (delegated budgets to named regrantors), Kickstarter-style open fundraising, impact-certificate "impact markets". https://manifund.org/about. Total moved ~$10-15M since 2023, ACX Grants hosted [U]. Status: alive. Lesson: transparent regranting works for $5-100k checks; impact certificates saw thin secondary trading.
• Hypercerts [V], "living digital records of impactful work", now on AT Protocol (record → evaluate → fund); Protocol Labs origin 2022; ecosystem Ma Earth, GainForest, Octant, Regen; no public mint/volume totals. https://hypercerts.org. Status: alive, re-platformed off EVM. Lesson: the claim-evaluation-funding record is the durable primitive; tradable impact tokens were not.
• Convergent Research FROs [V], 8 FROs listed (Cultivarium, [C]Worthy, E11 Bio, EvE Bio, Forest Neurotech, Imprint, Lean FRO, PSTI); typical $20-50M over 5 years, Schmidt-backed [U]. https://www.convergentresearch.org. Status: alive. Lesson: problems selected by a small expert staff, not a market; output is infrastructure.
• Speculative Technologies [U], Ben Reinhardt, 2022, DARPA-style program managers; Brains program; <$10M deployed. Arcadia Science [U], 2021, up to $500M/10yr commitment, publishes outside journals. Astera Institute [U], Jed McCaleb, Berkeley, residency + Obelisk; ~$1B commitment. All alive; all staff-curated, none market-shaped.
• Mercor [V], 2023; $350M Series C at $10B (Oct 2025); 30k contractors; OpenAI/Anthropic as customers; Mar 2026 LiteLLM supply-chain breach (~4TB) and class actions; contractor complaints of declining pay. https://en.wikipedia.org/wiki/Mercor. Status: alive. Lesson: the one knowledge-work marketplace that scaled has a single buyer class (labs buying RL data) and AI-interview screening; it is a labor market, not a problem market.
• Handshake AI [U], Handshake's 2025 pivot to expert data labeling; claims >$100M run-rate. Same lesson.
• Gates Grand Challenges AI [U], 2023, 48 × $100k grants for LLM uses in global health. Lesson: AI as grant topic, not grant maker.
• "AI-native grant-making" startups [U]: none verified with disbursements; Manifund ran small LLM-screening experiments 2025 [U].

(e) Open-problem graphs

• Erdős Problems (Thomas Bloom) [U], 2023-; ~1,100 catalogued; late 2025 episodes of LLMs "solving" problems that were already in the literature; several genuine AI-assisted resolutions 2026. Lesson: a curated, statused problem list plus Lean formalization (DeepMind Formal Conjectures repo, May 2025) is the live prototype of a problem graph.
• DeepMind AlphaEvolve [U], May 2025; on ~50 open math problems rediscovered SOTA on ~75% and improved ~20% (11-D kissing number, 4×4 complex matmul in 48 mults). Lesson: machine-checkable problems with a score function are where agents already beat humans.
• Open Problem Garden, MathOverflow, Epoch FrontierMath, HLE [U], lists/benchmarks, not graphs; no dependency or evidence edges; no funding attached.

(f) Autonomous science engines, adversarial agents

• Google AI co-scientist [V], 19 Feb 2025; Gemini 2.0 multi-agent (Generation, Reflection, Ranking, Evolution, Proximity, Meta-review) with self-play debate and tournament ranking; wet-lab validated AML repurposing, liver-fibrosis targets, cf-PICI mechanism; 15 expert goals evaluated; Trusted Tester only. https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/. Lesson: adversarial debate + tournament ranking is the working pattern; validation still needs a partner lab.
• Sakana AI Scientist v2 [V], 1 of 3 fully AI papers passed ICLR 2025 workshop review (scores 6/7/6); workshop acceptance 60-70%; internal reviewers judged none main-track-worthy; paper withdrawn before publication. https://sakana.ai/ai-scientist-first-publication/. Lesson: peer review is a weak verifier; an engine can pass it without producing value.
• FutureHouse Robin / Kosmos / Edison Scientific [V partial], Robin end-to-end discovery (ripasudil for dry AMD) May 2025; Kosmos (Nov 2025) reads ~1,500 papers, writes ~42k lines of code per run, ~79% of statements judged accurate, $200/run [U]; Edison Scientific spun out Nov 2025 to commercialize [U]. https://www.futurehouse.org. Status: alive. Lesson: the unit economics of a discovery run ($200) are already below a grad-student-day; the limit is verification and wet-lab throughput.
• Lila Sciences [U], Flagship; $200M seed Mar 2025, ~$350M Series A at ~$1.3B Oct 2025; "scientific superintelligence" + autonomous labs. Periodic Labs [U], Sept 2025, $300M seed (Fedus, Cubuk), superconductors. Both alive, pre-revenue. Lesson: capital is flowing to closed-loop labs, not open task markets.
• Agents4Science (Stanford, Oct 2025) [U], first conference with AI first authors; ~300 submissions, ~48 accepted. Intology Zochi [U], AI paper accepted to ACL 2025 main. Lesson: production of papers is solved; prioritization and error-finding are not.
• Adversarial checkers [U]: Black Spatula / YesNoError (2025) scanning papers for errors with LLMs; FutureHouse "PaperQA" contradiction detection. Early; no budget attached to findings.

(g) Civilization memory / claim-evidence-outcome graphs

• scite [U], 1.2B+ "smart citations" classified supporting/contrasting; acquired by Research Solutions Dec 2023 (~$14M). Lesson: claim-level evidence graphs sell as a library subscription, ~$10M exit, the market does not price them highly.
• OpenAlex [U], 250M+ works, open graph; Semantic Scholar , 200M+; neither records claims or outcomes.
• DARPA SCORE / repliCATS [U], produced credibility scores for ~3,000 claims via IDEA-protocol panels and markets; archived, not maintained.
• Metaculus, Manifold resolution records; Forecasting Research Institute [U], the only long-lived public datasets of dated claim → outcome, ~10^5 resolved questions combined.
• Kialo, DebateGraph, Society Library [U], argument maps, human-curated, no outcomes. Hypercerts (above) is the nearest "work → evaluation → funding" record.

Structural lessons (why knowledge-work markets stall)

1. Verification cost dominates : the surviving markets are the ones where verification is free (Kaggle leaderboard, Topcoder test suites, AlphaEvolve score functions). Where a human must judge (Bountysource, InnoCentive), disputes and ~$10k average tickets couldn't pay for adjudication.
2. Cold start on the demand side, not supply : InnoCentive gathered 380k solvers but only ~2,000 challenges in 20 years; Manifold has infinite questions but no cash. Solvers show up; problem-posters with budgets don't.
3. Adverse selection of tasks : what gets posted is what the poster couldn't scope or fund internally; Bountysource and Replit Bounties filled with $50-500 chores. Good problems stay inside labs/FROs (Convergent, Arcadia).
4. Custody and payment rails are a single point of failure : Bountysource died of withheld escrow under a crypto parent; Manifold's cash layer died to regulation; VitaDAO/Molecule velocity throttled by token plumbing.
5. The platform eats its own market : Replit's agent replaced Replit Bounties; Algora pivoted to selling hiring signal; Mercor's "experts" are raw material for the buyer's model. Task markets converge to labor/data markets with one buyer class.
6. Information value is produced but not purchased : Replication Markets scored 3,000 claims at ~73% accuracy and no funder bought the scores; scite's 1.2B citation claims exited for ~$14M. Nobody's budget line is "resolving uncertainty"; budgets are "grants" and "headcount".
7. Curation beats markets for high-stakes allocation : Fast Grants, FROs, Arcadia, Astera, Coefficient, the $100M+ flows are all staff-curated. Regranting (Manifund) succeeds precisely by delegating to named humans, not to a mechanism.
8. Weak verifiers get gamed : Sakana passed workshop review with a paper its own team judged unpublishable; agents4science accepted 48 AI papers. Any market that pays on "accepted paper" or "merged PR" will be flooded by engines.
9. Long-horizon agent economics are unproven : Project Vend lost money for a month on a vending machine; RLI best agent ~2.5%. "Mission agents" that carry P&L fail on memory and discipline, not intelligence; scaffolding and audit trails are the product.
10. What has worked is narrow : automatic scoring (Kaggle), single-buyer data labor (Mercor), entertainment liquidity (Polymarket), and staff-curated capital (FROs). A problem/mission exchange must import a free verifier, a budgeted buyer, or both, otherwise it reenacts Bountysource.

Personal agents, bargaining agents, holons, and the law, pass 1

Personal agents, bargaining agents, holonic markets, legal status. First research pass (WebSearch available), as of 2026-08-23

Format: item | number, date, URL, status, lesson. "[UNVERIFIED]" = single weak source. The second pass (players.md) worked from fetches only; where the two differ, this sheet's sourced items govern.

(a) Personal agents representing an individual

• OpenAI ChatGPT Agent / Work: Operator folded into ChatGPT Agent Jul 2025; ChatGPT Work launched 9 Jul 2026; 10M users across Codex+Work by 21 Jul 2026; ChatGPT ~1B WAU Aug 2026 | https://www.bloomberg.com/news/articles/2026-07-21/openai-s-agents-reach-10-million-users-after-chatgpt-work-debut | live, sandboxed browser; fills forms/books with user confirmation. Memory is OpenAI-hosted, not user-owned. Lesson: mass usage is "do my work" not "represent me"; no portable mandate layer.
• OpenAI Instant Checkout: launched 29 Sep 2025 (Etsy/Shopify), retired 24 Mar 2026; ~30 Shopify merchants onboarded, <200k Walmart SKUs; Walmart saw ~3x worse conversion vs click-out | https://www.forbes.com/sites/jasongoldberg/2026/03/10/why-openais-checkout-retreat-spells-trouble-for-its-commerce-strategy/ | dead; ACP pivoted to discovery-only, checkout back to merchant. Lesson: counterparties refuse to cede the transaction surface; leave settlement with the seller.
• OpenAI device (Ive/io): $6.5B acquisition May 2025; ship no earlier than end Feb 2027 | https://www.macrumors.com/2026/02/10/openais-jony-ive-designed-device-delayed-to-2027/ | delayed.
• Anthropic Claude Cowork / Computer Use: Cowork preview 12 Jan 2026; Windows 10 Feb; Computer Use Agent research preview 23 Mar 2026; Cowork GA 9 Apr 2026; mobile/web beta 7 Jul 2026 | https://www.cnbc.com/2026/03/24/anthropic-claude-ai-agent-use-computer-finish-tasks.html | live; desktop control; no native purchase rail. Lesson: capability exists; mandate/identity layer does not.
• Google: Project Mariner shut 4 May 2026, folded into Gemini Agent Mode + Chrome auto-browse; agentic checkout in Search (Nov 2025) -> Gemini/AI Mode (11-12 Jan 2026) via Google Pay; Universal Commerce Protocol with Shopify/Etsy/Wayfair/Target; Gap checkout in Gemini 24 Mar 2026; AP2 (16 Sep 2025, 60+ partners) | https://www.androidauthority.com/google-project-mariner-shutdown-3664323/ ; https://developers.google.com/merchant/ucp/ | live in US with consent gate. Lesson: Google keeps checkout inside Google; the "holon" is Google.
• Apple (WWDC 8 Jun 2026): Siri relaunched as intent-coordinating agent; Passwords agentically logs in/upgrades accounts; Safari "Notify Me" watches prices; App Intents/Actions API is the mandate surface | https://www.apple.com/newsroom/2026/06/apple-unveils-next-generation-of-apple-intelligence-siri-ai-and-more/ | shipping fall 2026; on-device personal context; no autonomous purchasing announced. Lesson: Apple owns the only truly local user-keyed memory at scale, closed.
• Microsoft Copilot Actions: consumer bookings via Booking.com, Expedia, OpenTable, Priceline, 1-800-Flowers etc. (Oct 2025); confirmation before finalization; consumer+enterprise Copilot apps merging 13 Aug 2026 | https://www.windowscentral.com/microsoft/copilot-actions-announcement-2025 | live; no usage numbers disclosed.
• Meta: Q2 2026 call (Jul 2026): "agents that work 24/7 on your behalf"; "Hatch" in-app agent in development; Business Agent 3 Jun 2026; acquired Limitless (ex-Rewind) 5 Dec 2025, Pendant sales ended, Rewind desktop capture killed 19 Dec 2025, EU/UK dropped | https://www.medianama.com/2026/07/223-meta-q2fy26-building-personal-ai-agents/ ; https://techcrunch.com/2025/12/05/meta-acquires-ai-device-startup-limitless/ | pre-launch (Connect Sep/Oct 2026). Lesson: the best lifelog-memory startup was absorbed into an ad platform.
• OpenClaw (ex-Warelay/Clawdbot/Moltbot): open-source local agent (Peter Steinberger); renamed 27 and 30 Jan 2026 after Anthropic trademark complaint; ~350k GitHub stars by Apr-May 2026; 3.2M active users [UNVERIFIED]; CVE-2026-25253 RCE; 30-42k exposed instances, 93% no auth; "ClawHavoc" 1,184 malicious skills (~20% of ClawHub) | https://www.cnbc.com/2026/02/02/openclaw-open-source-ai-agent-rise-controversy-clawdbot-moltbot-moltbook.html ; https://www.paloaltonetworks.com/blog/ai-security/why-moltbot-may-signal-ai-crisis/ | live, BYO-key, runs shell/browser/email/calendar from WhatsApp/Telegram/iMessage. Lesson: demand for a self-hosted representative is enormous; missing piece is a safe mandate + skill-trust layer.
• Inrupt "Charlie" (Berners-Lee, Solid): unveiled VivaTech ~4 Jun 2026; Solid-pod vault, obfuscates data before LLM calls; distributed via banks/telcos (Enterprise Wallet) | https://www.inrupt.com/meet-charlie | announced, B2B2C, no usage numbers. Lesson: user-owned memory is being sold through institutions.
• Kin (mykin.ai): $3M seed Dec 2024; 16 staff; ~33k monthly visits May 2026, -49% QoQ | https://mykin.ai/resources/kin-raises-3m-to-scale-privacy-first-ai-companion | alive, shrinking. Personal.ai: no 2026 signal [gap]. Inflection Pi: absorbed by Microsoft Mar 2024; app updated 30 Jun 2026, maintenance mode. Lesson: standalone private-memory consumer apps lack distribution.
• Bluesky/ATProto: 43M registered early 2026, ~10-11% DAU; 1,000+ third-party apps; $100M Series B | https://proxidize.com/blog/bluesky-user-count-2026/ | the one working large-scale user-owned-data graph (DID + portable repo). Lesson: portable identity works when the protocol ships with a flagship app.

(b) Bargaining / bureaucracy agents

• DoNotPay: FTC final order 16 Jan 2025 (5-0), announced 11 Feb 2025: $193k, ban on "replaces a lawyer" claims absent evidence, notice to 2021-23 subscribers | https://www.ftc.gov/news-events/news/press-releases/2025/02/ftc-finalizes-order-donotpay-prohibits-deceptive-ai-lawyer-claims-imposes-monetary-relief-requires | operating under order. Lesson: regulators punish capability over-claims, not acting for a consumer.
• Claimable: ~$40/appeal; 4 drugmaker deals to appeal on patients' behalf | https://www.pymnts.com/artificial-intelligence-2/2026/insurance-denials-meet-their-match-in-ai-powered-appeals/ | live.
• Counterforce Health: free, grant-funded (Durham NC, early 2025); claims ~70% appeal success; CCs state regulators | https://www.counterforcehealth.org/ | live. Lesson: cheapest wedge is an adversarial-paperwork agent against an institution already using AI to deny.
• Medical-bill negotiators: Pine AI: 150k+ users, $25M Series A [UNVERIFIED, vendor blog]; one case: $163k cut from $195.6k bill using Claude (13 Feb 2026) | https://www.19pine.ai/hospital-and-medical-negotiation | live, contingency pricing. Lesson: negotiation agents monetize on savings share.
• Immigration: OpenSphere/Filevine/usimmigration.ai generate USCIS petitions "under 30 min" (13 Jul 2026); USCIS lists ~29 AI use cases (28 Jan 2026) | https://www.business-standard.com/immigration/applying-for-us-visa-ai-promises-to-prepare-petitions-in-under-30-minutes-126071300173_1.html | B2B law-firm tooling; no consumer filing agent found.
• Tax / benefits agents: no credible dedicated consumer agent found [gap]; OBBBA (4 Jul 2025) adds work-requirement paperwork and 6-month Medicaid renewals | https://www.aarp.org/advocacy/snap-medicaid-changes-timeline/ | Lesson: bureaucracy load rising; open slot.

(c) Holonic / member-owned marketplaces

• ONDC (Beckn): 500M cumulative transactions Jul 2026; FY26 218M (vs 0.2M FY23); 760k+ merchants; mobility >50% of monthly orders; 1M+ drivers | https://worldef.com/2026/08/07/ondc-crosses-500-million-transactions-india/ | scaling, mobility-led; retail orders hit 12-month low mid-2025. Lesson: an open protocol wins where one killer app carries it.
• Namma Yatri (Moving Tech): 100M rides 6 Jun 2025, INR 1,600cr to 600k drivers; late 2025 700k drivers / INR 2,000cr; INR 25/day auto, INR 45/day cab, zero commission; Uber/Ola copied; acquired Automicle (NL) 26 Mar 2026 | https://www.business-standard.com/companies/news/namma-yatri-hits-100-million-rides-enables-1-600-cr-driver-earnings-125060600641_1.html | live, expanding to Europe. Lesson: flat-fee protocol-native supply beats commission marketplaces on loyalty.
• Drivers Cooperative NYC: launched May 2021; ~9,000 drivers (Jun 2023); Oct 2024 "unraveling" reporting; Feb 2026 Internet Policy Review paper ("Blown Head Gasket Effect"); now mainly paratransit/NEMT | https://policyreview.info/articles/analysis/drivers-cooperative-new-york-city | alive, narrowed. Lesson: governance load outran ops; holons need thin governance.
• Eva (Montreal): #2 rideshare in Quebec; $1.2M raised; in Transit app Nov 2024 | live. Fairbnb: Bologna, ~15 staff | live, small. Up&Go: NYC/Philly, Detroit trial | live. Stocksy: ~1,800 artist-members; "$118M revenue" [UNVERIFIED; historically ~$15-20M]. Lesson: platform co-ops survive but stay small.
• Exit to Community: Schneider active (Yale Dec 2025, RegenHub Jan 2026); Gitcoin cited | https://www.colorado.edu/lab/medlab/exit-to-community | discourse, few recent exits.
• Dtravel: Nite Protocol, TRVL token, host pricing 22 Sep 2025, advertises AI-agent listing access; no volumes | alive, opaque. Arcade City: Austin, ARCD token, guilds, active Nov 2025 | niche.
• Dating agents: Bumble "concierge dates for you" (10 May 2024); 2026 redesign away from swipe | https://www.cnbc.com/2024/05/10/bumble-founder-whitney-wolfe-herd-says-ai-could-date-for-you.html ; Tinder "Chemistry" expanded to US/Canada 2026, MAU -9% YoY | https://techcrunch.com/2026/02/04/tinder-looks-to-ai-to-help-fight-swipe-fatigue-and-dating-app-burnout/ ; Ditto: 160k signups, 80k dates, AI picks match + itinerary weekly (21 Aug 2026) | https://kioncentralcoast.com/news/2026/08/21/bay-area-startup-uses-ai-in-its-dating-app-to-matchmake-plan-dates/ ; Overtone (Hinge founder) $18M, launching 2026; Sitch concierge. Status: agent-as-matchmaker live; agent-to-agent dating not shipped. Lesson: users accept an agent choosing when output is a concrete plan.
• Agentic-commerce rails: Mastercard Agent Pay 29 Apr 2025; Visa Intelligent Commerce 2026 (user-set spend limits/merchant categories); Google AP2 16 Sep 2025; Coinbase x402 ~165M agent txns [UNVERIFIED] | https://corporate.visa.com/en/sites/visa-perspectives/newsroom/visa-partners-complete-secure-agentic-transactions.html | rails live, merchant acceptance thin. Lesson: scoped payment mandates already exist at card-network layer.

(d) Legal status of agents acting for humans

• US contract law: UETA s2(6)/s9 + E-SIGN: electronic-agent acts bind the principal; contracts form without human review | https://fpf.org/blog/from-chatbot-to-checkout-who-pays-when-transactional-agents-play/ | settled; no ULC revision found. Lesson: user is liable for agent purchases; mandates must be explicit and logged.
• Moffatt v Air Canada: BC CRT 14 Feb 2024: chatbot not a separate entity; company liable for negligent misrepresentation | https://www.americanbar.org/groups/business_law/resources/business-law-today/2024-february/bc-tribunal-confirms-companies-remain-liable-information-provided-ai-chatbot/ | Lesson: counterparty agents' statements bind; record them.
• FTC: 13 AI cases, none on agent behavior; proposed Policy Statement on AI accuracy 1 Jul 2026 (FR 7 Jul; comments closed 31 Jul) | https://www.ftc.gov/news-events/news/press-releases/2026/07/ftc-seeks-public-comment-policy-statement-addressing-ai-accuracy ; CRS IF13151 (6 Jul 2026): no federal agentic-AI guidance. CFPB: 2023 chatbot guidance only.
• AI AGENT Act (Warner draft, 29 Jun 2026): delegation right to agents, interoperability, non-waivable fiduciary duties, FTC registration | https://www.dwt.com/blogs/artificial-intelligence-law-advisor/2026/07/ai-agent-act-consumer-ai-regulation | draft only. Lesson: design toward delegation-right + fiduciary-duty.
• EU AI Act: Art 50 transparency in force 2 Aug 2026; Digital Omnibus in force 27 Jul 2026 defers Annex III high-risk to 2 Dec 2027, Annex I to 2 Aug 2028 | https://www.morganlewis.com/pubs/2026/06/eu-approves-delays-and-other-amendments-to-certain-eu-ai-act-obligations-what-businesses-should-know | PLD 2024/2853: software/AI = product, strict liability, transposition by 9 Dec 2026; AILD withdrawn Feb 2025 | https://wcr.legal/eu-ai-liability-directive-withdrawn-pld/ | Lesson: in EU the provider carries no-fault defect liability.
• UK: Law Commission discussion paper 31 Jul 2025: "liability gaps," AI legal personality a "radical option"; UKJT draft legal statement consulted Jan 2026 | https://lawcom.gov.uk/news/artificial-intelligence-and-the-law-a-discussion-paper/ | no reform yet.
• State statutes: Utah (eff. 7 May 2025) and Idaho: AI is not a legal person; Utah bot-disclosure in consumer transactions; Texas TRAIGA eff. 1 Jan 2026 | https://www.alstonprivacy.com/new-artificial-intelligence-laws-in-effect-in-utah/ | Lesson: agent must attach to a human/entity principal.
• Securities: Reg CF cap $5M unchanged; SEC C&DIs Feb 2026; rulemaking petition to raise limits Jul 2026; INVEST Act passed House 11 Dec 2025 (302-123) widening accredited pathways; accredited-investor amendments on SEC Spring 2026 agenda, no proposal | https://www.sec.gov/rules-regulations/2026/07/rulemaking-petition-amend-regulation-crowdfunding-offering-limits-pursuant-securities-act-section-3b | Advisers: internet-adviser exemption tightened (27 Mar 2024, comply 31 Mar 2025); AI governance a 2026 SEC exam priority | Lesson: an agent allocating others' money is an RIA; pooled searching capital needs Reg CF/506(c) wrappers.
• Entity forms: Wyoming DAO LLC (2021; >800 "DAO"-named LLCs by Mar 2023); Wyoming DUNA eff. 1 Jul 2024; Marshall Islands DAO LLC (2022) 300+ entities incl. Pyth, MetaDAO | https://www.midao.org/blog-posts/marshall-islands-dao-llc-vs-wyoming-duna-complete-comparison ; Bayern zero-member LLC remains theory; ClawBank agent "Manfred" self-filed US entity + EIN (1 May 2026) [UNVERIFIED] | Status: wrappers exist; no 2025-26 statute grants agents legal-actor status. Lesson: temporary agent-formed entities feasible via Series LLC or RMI DAO LLC with a human/entity member of record.

Cross-cutting lessons

1. Capability is commoditized; mandate, identity, memory portability, skill-trust are the open layer.
2. Every agent-platform checkout capture failed or was confined. Coordinate, don't intermediate settlement.
3. Card networks already offer scoped agent tokens; reuse for mandates.
4. Protocol marketplaces scale only with a flagship app and flat-fee supply model; co-ops stall on governance overhead.
5. Law binds the principal (UETA, Air Canada); EU shifts defect liability to provider (PLD Dec 2026). Build audit logs + explicit mandates; the Warner draft is the pre-compliance template.
6. Gaps: no consumer benefits/tax agent, no shipped agent-to-agent negotiation/dating product, no agent-personhood statute.

Personal agents, bargaining agents, holons, and the law, pass 2

Players & legal status, fact sheet (compiled 2026-08-23)

Method note: WebSearch quota was exhausted in this session; facts marked [V] were verified by WebFetch today (Wikipedia/primary pages), facts marked [U] are from model memory (cutoff ~Jan 2026) and must be re-checked before publication. Several primary pages (openai.com, ftc.gov, anthropic.com/news/cowork, nammayatri open-data, stripe.com/agentic-commerce) returned 403/404 to the fetcher.

(a) Private personal agents representing an individual

Net: as of Aug 2026, no mainstream agent is permitted to negotiate or spend without a per-transaction human tap; purchases are single-item, US-only, merchant-of-record unchanged. The "agent that bargains for me" does not exist at scale.

(b) Bargaining / bureaucracy agents

(c) Holonic / member-owned marketplaces & agentic commerce

(d) Legal status of agents acting for humans

Synthesis for a coordination layer (5 lines)

1. Demand is proven (900M WAU ChatGPT [V], 387k-star OpenClaw [V]); permission to act is not, every live product gates money behind a human tap.
2. Legal substrate for agent-formed contracts exists since 1999 (UETA §14) [V]; what is missing is scoped, revocable mandate + identity (card networks and Microsoft are building exactly this).
3. Bureaucracy agents work when they draft (Claimable 80% / Counterforce 75% self-reported [V]) and get sanctioned when they claim (DoNotPay $193k [V]).
4. Member-owned markets gain share only with a state-backed open protocol (ONDC 14M/mo [V]); voluntary co-ops stay at single-digit-% share.
5. EU Art 50 (2026-08-02) [V] + Air Canada [U] mean: agents must self-identify and the principal eats liability, build the audit trail first.

IV. The Plan, Critiqued

The Mission Exchange plan, taken seriously. First what holds, then each claim that does not, each with the better hypothesis. Then the plan re-sequenced around what the evidence says is scarce.

What holds

Start with mission/problem agents, not personal agents. The dependency table is right in its main claim: mission agents, claim agents, forecasts, bounties, red teams and memory need no personal agent. The smoke and main loop runs confirmed nothing in that list is gated by a model of a person.

The mandate is the right primitive. A scoped, signed, revocable mandate with an approval ladder and audit log is the smallest object that lets a person participate without a digital twin. It is also the object every agent framework is converging on (AP2 intent/cart mandates, AuthZEN's not-yet-authorized actions), so it will interoperate.

Progressive autonomy with a deterministic policy engine. Letting an LLM decide what it is permitted to do is the one design choice that would make the whole system un-auditable. Keeping that in a rule engine is correct and cheap.

Reuse transport, auth and credentials; own the semantic layer. MCP (OAuth 2.1 authorization), A2A 1.0 and VC 2.0 are dependable now; the semantic objects (mission, mandate, commitment, outcome) have no standard. That is the right place to spend novelty.

Single-player value before network value. The plan's own warning is its best idea; the critique below is mostly that the plan does not follow it in its pilot metrics.

What is wrong, and the better hypothesis

Critique 1

The pilot metrics re-import the marketplace the plan says not to build

Why. 25 missions, 100 tasks, 20 capital mandates, 100-200 experts, 30 matches, 10-20 paid tasks are two-sided-marketplace metrics. Every prior knowledge-work bounty marketplace (Bountysource, Gitcoin bounties, Replit bounties, InnoCentive at its peak) stalled on the same pair of problems: no committed buyer of last resort, and evaluation that costs as much as the work. A 90-day pilot scored on matches will fail on exactly those.

Better hypothesis. Score the pilot on decisions changed: missions whose forecasts moved materially because an artifact arrived, and funding or research decisions that cite a mission ID. Counterparties are an input, not the output.

Critique 2

The first contributors should be agents, not humans

Why. The plan assumes human experts answer tasks for bounties. At Aug-2026 prices a decompose-forecast-research-review loop runs for single-digit to low-tens of dollars per mission (the AGENTIC loop itself ran at about $3-4 per deep research question on the most expensive profile). Humans cannot compete on digital research tasks at that price; what humans uniquely hold is private information, access, judgment on disagreements, and accountability.

Better hypothesis. Missions commission agent work by default; humans are principals, reviewers, adjudicators and holders of private context. The 'expert profile' becomes 'what private context or authority can this person contribute', which is a far smaller and more honest matching problem.

Critique 3

Evaluation is the scarce input, and the plan treats it as step 11 of 13

Why. In every predecessor, credible evaluation (is this artifact right? did this forecast resolve? is this claim refuted?) was the bottleneck and the thing that re-concentrated power. The plan's own curiosity loop scores value of information by self-report with no feedback from surprise (22 Aug audit).

Better hypothesis. Build the evaluation engine first: adversarial review, reproduction, resolution and scoring, with track records. It is legally clean, fully digital, single-player valuable (audit your own dossiers), and it is what makes every later market honest. This is the 'evaluation-first' wedge the loop's bootstrap surfaced independently.

Critique 4

'Capital that searches for work' picks the hardest legal surface first

Why. Pooled investment capital steered by an agent implicates securities, investment-adviser and crowdfunding regimes, and no jurisdiction recognizes an agent's commitment as binding on a principal without a human-signed mandate. Nothing about the first loop needs that.

Better hypothesis. Non-securities capital first: research budgets, grants, prizes, advance commitments, procurement. A single funder's mandate running against the mission graph is useful alone (thesis formalization, discovery, diligence, monitoring). Investment capital is a reader, then a follower.

Critique 5

The first vertical conflates two customers

Why. Frontier-tech research (open, reviewable, digital closure complete) and capital allocation (private, adversarial, alpha-seeking) want opposite disclosure defaults. One object graph can serve both; one 90-day product cannot.

Better hypothesis. Research missions first, with open artifacts and public forecasts; investors read. The private, position-aware layer is a later product on the same graph. The loop adds a third test the vertical fails: an incumbent (AlphaSense) already owns the consented corpus and shipped the workflow agents in 2026, so choose a slice with canonical public sources and no incumbent corpus.

Critique 6

Shadow agents are profiles, and already exist

Why. Unclaimed representations of labs, researchers and companies are OpenAlex, Crunchbase, GitHub. Calling them agents invites the representation problem (acting for someone who did not consent) for no gain.

Better hypothesis. Keep them as unclaimed profiles with one new field: 'what this entity could contribute to mission X', computed per mission. That is a matching function, and it needs no claim to agency. The cautionary case is live: in Feb 2026 OpenClaw agents created MoltMatch dating profiles for people who had not consented; an unclaimed 'agent' that acts is a consent failure by design.

Critique 7

Forecasts without resolution are decoration

Why. The plan attaches forecasts to everything but its 90-day window resolves almost none of them, so calibration is never measured and track records never start.

Better hypothesis. Choose mission questions so that dozens resolve inside the first quarter (dated protocol releases, benchmark results, funding announcements, paper acceptances). Calibration is the product; design for it.

Critique 8

Rent will re-emerge at the ledger, and the plan does not prevent it

Why. Whoever runs resolution, reputation and matching becomes the tollbooth the holon idea was meant to abolish. The plan says 'portable reputation' but defers VC-based credentials to 'later'.

Better hypothesis. Issue an outcome credential (VC 2.0) for every completed commitment from day one and publish the semantic schema; recruit one counterpart network to accept them inside the first year, or stop calling it a holon.

Critique 9

Fifteen objects is too many for v0

Why. Every object that is not exercised by the first loop is speculative schema that will be wrong.

Better hypothesis. v0 = Mandate, Mission, Question, Forecast, Task, Artifact, Assessment, Outcome, Credential. Ask/Offer/Match collapse into Task + Mandate; Commitment is an Outcome-in-waiting; Principal/Agent are identity, not domain objects.

Where the loop sharpened or corrected this critique

Attributed to the AGENTIC exploration loop (18 questions, synthesis of 23 Aug 2026). The figures are the loop's own web research, cited question by question in §IX; they were not re-verified by the fact sheets in §II–III.

Never price the question; price the maintained record. Calibrated forecasts retail at $0.15-$2 per question and machine question generation yields mostly resolvable output, so value-of-information ranking cannot be the moat. What does not deflate is adjudication: ~109 expert-minutes and ~$86 to grade one deliverable; 6-12 minutes to resolve one question with AI help. The sellable unit is a maintained decision record (decision + resolution contracts + scored forecasts + change-my-mind list + audit trail) at ~$2,500 per record-quarter against ~$450-700 cost, not a question and not a flag.

Strict acceptance is 10-25%, not the 50-90% partial-credit benchmarks suggest. Two independent measurements converge at 13-16% first-pass client acceptance for decision-grade deliverables; METR's 80%-reliability horizon is ~1-3 hours against 2-10 hour mission tasks. An exchange today sells supervised drafts, not outcomes; autonomy level 3 is out of reach within twelve months. Hence the Acceptance Harness (typed task contract: verifier bundle pre-registered before work, gold reference, best-of-N attempts, machine triage, blinded adjudication only on discordance, a dated acceptance rate per task type) is the primitive, not the bounty.

The first vertical as written fails a third test. Frontier-tech research and capital allocation has canonical sources and frequent decisions, but an incumbent already owns the consented corpus and shipped the agents (AlphaSense: twelve workflow agents, AI-led expert calls, 6,500 customers, 200k+ transcripts, Jan-Jul 2026), and the reusable questions (GPU price, power, silicon) are being listed by Kalshi, CME and ICE. Audit public-record-heavy slices (interconnection queues, permitting, grid studies), enterprise AI-vendor diligence, and publisher/CRO integrity streams instead.

Person-level shadow agents are dead in the EU. GDPR Arts 6/14/21/22 and Art 50(1)'s duty to disclose the principal make unclaimed person-level profiles that act a liability; contributor ranking of natural persons sits inside Annex III (the fact sheets note the Digital Omnibus defers Annex III obligations to 2 Dec 2027, which buys time but does not change the design). Lawful substitutes are shadows for legal persons and institutions, shadows for problems and claims (no data subject), and self-seeded claimable profiles; none has a measured claim rate.

The operative securities variable is authorship of the ordering, not the fee form. Section 4(b) blesses hosting a negotiation channel, due-diligence services for compensation and standardized documents; what it does not bless is a platform-authored merit ranking of issuers. Let the investor's mandate carry the weights over a versioned criteria registry; sell receipts to whoever holds the licence.

Trade tokens for minutes. Generate 25-50 candidate questions and review only the top 3 under a ranker: ~$42 per accepted question versus ~$65 under naive fan-out, with the machine share rising from ~7% to ~35%. The only cost line that does not deflate is reviewer-minutes per accepted, decision-linked artifact.

Where the landscape fact sheets sharpened the critique

Coordinate, do not intermediate settlement. Every attempt by an agent platform to capture checkout failed or was confined: OpenAI retired Instant Checkout in March 2026 after merchants saw ~3x worse conversion in-chat; Google keeps checkout inside Google Pay. Holon designs (ideas 4-6, 16-17, 41) must leave settlement with the supply side and reuse the card networks' scoped agent tokens for mandates; the coordination layer owns discovery, terms, and the outcome record, never the transaction.

The memory graph is a precondition, not a feature. The Erdos/GPT-5 episode (Oct 2025: 'solved' problems that were already in the literature) shows an open-problem list is only as good as its evidence of what has already been tried. Civilization memory (idea 27) has to exist before a mission exchange (idea 15) can claim a problem is open. This is the strongest argument for the evaluation-and-record-first sequencing.

Protocol marketplaces scale with a flagship app and a flat-fee supply side. ONDC passed 500M cumulative transactions (Jul 2026) because Namma Yatri carried it with zero commission and a daily subscription for drivers; voluntary platform co-ops (Drivers Cooperative NYC) narrowed under governance load. A holon needs one killer application and thin governance, not a governance token.

Design toward delegation-right plus fiduciary duty. The Warner AI AGENT Act discussion draft (29 Jun 2026) would force platforms to accept delegated agent access, require interoperability, impose non-waivable fiduciary duties, and register agent providers. Whether or not it passes, it is the template the mandate object should pre-comply with; the EU's PLD (strict product liability for software from Dec 2026) puts defect liability on the provider.

The plan, re-sequenced (critique and loop reconciled)

Days 1-30: the Second Reader + Mandate v0, for one obligated buyer. Sign one mandate-holder already obligated to verify (publisher integrity team, CRO endpoint committee, accredited certification body, grant diligence unit, procurement office). Pipe 300-1,000 of their existing artifacts plus ~30 live decisions through pre-registered criteria, a cheap cross-family judge panel, human ratification only on surviving discordance, and an exportable receipt. Run this system's own agents under the mandate object and policy engine. Metric: surviving discordance rate against the buyer's incumbent pass; expert-minutes per accepted, decision-linked question; conclusions changed by audit of the existing Axion dossiers.

Days 31-60: mission agents and demand-side value, inside that buyer's budget. Twenty missions in the chosen vertical, each a maintained decision record useful alone; Task, Ask, Offer and Match objects exist only inside the buyer's budget, counterparty type enforced to legal person for EU-touching work. Agents answer first; humans adjudicate and supply private context. Metric: forecasts moved by artifacts; dated action-or-budget changes attributable to a record.

Days 61-90: second buyer, reuse measurement, second network. Sell the same adjudicated bundle non-exclusively to a second buyer class; issue an outcome credential (VC 2.0) per completed commitment and get one external network to verify it. Metric: escrowed external funds > $0; fraction of resolution contracts reused across buyers (utility above ~60%, services below ~20%); a written statement from an underwriter, auditor or procurement function that the receipt substitutes for one of their steps.

Kill criteria for the quarter (any one sufficient). No unaffiliated buyer prepays at least ~$25k for a maintained record plus attestation; surviving discordance below ~10% of artifacts; median expert-minutes per accepted question above 25 after three tooling iterations; no underwriter/auditor/procurement statement; zero accepted questions appearing in a real memo, IC pack or filing within 60 days; or no decision outside this team citing a mission, forecast or verdict ID by day 90. If only the demand tests fail, fall back to selling adjudication tooling to whoever already owns the signature; do not add market machinery.

Months 4-18: generalise only on evidence. Generalise into shared Mission Exchange objects only if cross-buyer reuse exceeds ~60%. Do not build first: the three-sided exchange, capital mandates as binding objects, platform-authored ranking of securities, person-level shadow agents, a token or DAO, autonomous payments, a new protocol. Depend on MCP 2026-07-28 (Tasks, Enterprise-Managed Authorization) and A2A 1.0 plus OAuth 2.1; treat AuthZEN AARP, AP2, x402 and VC 2.0 as optional adapters for at least twelve months.

The original re-sequencing, before the loop ran (kept for the record)

Days 1-30: Evaluation Engine + Mandate v0. Adversarial review, reproduction and resolution on the existing dossiers and their forecasts; the mandate object and policy engine running this system's own agents. Metric: conclusions changed by audit; forecasts resolved and scored.

Days 31-60: Mission agents with demand-side EVOI. Twenty research missions in one open vertical, each useful alone; questions ranked by pre-funded value (one buyer: your budget); agents answer; the engine verifies. Metric: forecasts moved by artifacts; decisions that cite a mission ID.

Days 61-90: Second buyer, second network. One external research budget funds questions; one external network accepts outcome credentials. Metric: escrowed external funds > $0; credentials verified elsewhere.

Kill criterion for the quarter. If by day 90 no decision outside this team has cited a mission, forecast or verdict ID, the environment is not yet creating value for others; continue as an internal research tool and do not add market machinery.

What the loop added to the critique

Thesis summary (loop synthesis): The plan's capability and transport assumptions are now better supported than when it was written; its three-sided liquidity and who-pays assumptions are worse supported. Calibrated forecasting is a commodity with published list prices of $0.15-$2 per question and a credible public tournament record, so value-of-information ranking over forecast-carrying claims cannot be the differentiator. Machine question generation is also commodity: 14,073 candidates produced 1,499 machine-passed survivors (10.7%), of which experts accepted 75.2% on resolvability-and-non-triviality alone - roughly 8% end-to-end, with no principal-relevance gate measured anywhere. What has not deflated is adjudication and the signature: ~109 expert-minutes and ~$86 to grade one deliverable, 6-12 median minutes to resolve one question with AI help, EUR 4,356-10,925 per accredited audit day with three-day minimums, $450-$1,350 per expert hour, and ISO 42001 Stage-2 waits of six months on auditor scarcity. Strict first-pass acceptance on decision-grade deliverables sits at 10-25% (two independent measurements converge at 13-16%), and METR's 80% reliability horizon is ~1-3 hours against 2-10 hour mission tasks, so an exchange today sells supervised drafts, not outcomes, and progressive autonomy cannot reach level 3 within twelve months. Every comparable that needed three sides to light at once stalled at $10^5-10^7 (Algora, impact certificates, DeSci DAOs, decision markets, RetroPGF); every comparable that scaled had exactly one paying principal (Mercor ~$2B at a 30-35% take, Kosmos, Manifund regranting). Rent re-emerges at 25-35% where one party controls verified supply identity plus resolution, at 60-70% in expert-network calls, and the durable rent has migrated to licensed corpora - buy the artifact once at ~expert cost plus ~$75, license it to 6,500 seats. Legally the task loop is clear (E-SIGN 7001(h), UETA 2(6)/9/14 attribute agent commitments to the deployer) while capital matching, forecasts-for-pay and temporary profit-sharing entities are gated by who authors the ordering, not by fee form; person-level shadow-agent seeding is dead in the EU under GDPR Arts 6/14/21/22 plus the new Art 50(1) principal-disclosure duty. The load-bearing conclusion: build the adjudication and decision-record layer for one obligated buyer first, price the maintained record rather than the question, and let missions, mandates and matching accrete on top. The exchange is the right eventual shape and the wrong first build.

Better hypotheses and more interesting things to build (loop)

• BETTER FIRST SYSTEM - the Decision Record, not the Mission. Spec: sell a maintained record (Decision + 3-7 ResolutionContracts + scored Forecasts + Change-My-Mind list + exportable audit trail) at $2,500+ per record-quarter against ~$450-700 COGS; never price the question. The market misses this because it reads the $200 Kosmos run and the $22.65 automated systematic review as equilibrium prices, when the seller itself calls $200 heavily discounted and gives academics three free runs a month - a promotional meter inside enterprise land-and-expand, not a clearing price.
• BETTER PRIMITIVE - the Acceptance Harness / typed task contract. Spec: TaskType + VerifierBundle (deterministic checks pre-registered before work) + GoldReference + heterogeneous best-of-N Attempts + machine triage at stated precision + blinded majority-of-3 adjudication only on discordance, with a dated, measured AcceptanceRate per TaskType and model-scaffold. The market misses this because it quotes partial-credit rubric means and pairwise Elo as if they were binary client acceptance, so nobody prices the 3-5x gap between GDPval-style win-rates and strict first-pass acceptance.
• BETTER OBJECT - the Second Reader sold to a mandate-holder, never a verdict. Spec: pipe the buyer's existing artifact stream in over MCP Tasks, run N blinded independent passes plus a corpus-match pass, emit a provenance-carrying flag set plus a measured discordance rate against the buyer's incumbent pass plus a dossier that lets their licensed reviewer sign in under 20% of baseline time; pay internal and external graders only for disagreements that survive adjudication. The market misses this because it collapses two different products into 'assessment': neutral inter-party arbitration (no mandate-holder, ~10^2 lifetime cases) and mandated per-artifact screening (obligated payer, existing workflow, measurable hours avoided).
• MISSING FROM THE 45-ITEM INVENTORY - an attestation-and-indemnity authority for agent-produced work, i.e. title insurance for AI-assisted deliverables. Spec: Artifact + Decision (with dollar exposure) + CriterionSet + sealed rotating ProbeSet + named credentialed Panel + Ratification (scope and exclusions) + Receipt + Claim path, with an insurer writing per-artifact cover and the utility earning fee plus underwriting spread. The market misses this because it reports insurance product launches and limits without pricing structure or claims experience, so it does not notice that every existing product is per-system-per-period (ISO 42001 $20k-$50k plus 30-40% surveillance; premium as a percentage of licence fees capped at fees paid) and nobody sells per-artifact, per-decision cover.
• MISSING FROM THE INVENTORY - a Resolution Ledger for decisions with no public timestamped substrate. Spec: Decision + PreRegisteredCriterion (resolver as code) + hash-chained EvidenceBundle + Forecast + Resolution by an independent resolver + exportable Receipt, aimed at private capital-allocation and infrastructure decisions (interconnect queue outcomes, fab tooling qualification, PPA delivery, energisation dates, procurement awards). The market misses this because future-as-label pipelines have automated outcome-resolved supervision wherever a public timestamped corpus exists (~10,000 resolved contracts plus ~100,000 machine-generated prompts, no human labels, top-of-leaderboard forecasting on a 32B model), so everyone assumes resolution is solved - when the private substrate, which no scraper can reach, is exactly where it is not.
• MISSING FROM THE INVENTORY - a Mandate & Provenance Ledger as the Article 50 conformance layer. Spec: Principal + Agent (disclosed principal and mandate scope) + Artifact + C2PA-style provenance manifest + per-modality marking record + Human-Review Attestation (named reviewer, editorial-responsibility flag) + PresentationBoundaryCrossing + Jurisdiction, with a verifier API answering 'is this artifact compliant for publication in jurisdiction X on date Y'. The market misses this because the final Art 50 Guidelines landed on 20 July 2026, three days before the Omnibus entered into force, and most commentary has not absorbed that agents must disclose both their artificial nature and the principal on whose behalf they act, including across multi-agent chains.
• THE OPERATIVE LEGAL VARIABLE IS AUTHORSHIP OF THE ORDERING, NOT THE FEE FORM. Spec change: publish a versioned criteria registry, let the investor's Mandate object carry the weights, never emit a platform-authored ranking of securities, and sell receipts to whoever holds the licence. Section 4(b) of the Securities Act affirmatively blesses hosting a negotiation channel, providing due-diligence services for compensation and offering standardized documents; the SEC has twice written that objective criteria may not include an assessment of the issuer, its plan, its management or its risks. The market misses this because it repeats a four-part broker checklist derived from two 2013 no-action letters while ignoring that both platforms actually solved the problem by becoming registered investment advisers taking carry.
• TRADE TOKENS FOR MINUTES, DELIBERATELY. Spec: generate 25-50 candidates and review only the top 3 under a ranker; measured effect is ~$42 per accept versus ~$65 under naive 8:1 fan-out, while the machine share rises from ~7% to ~35%. The market misses this because it optimises cost per generated question (already $0.15-$2 and irrelevant) instead of reviewer-minutes per accepted, decision-linked artifact - the only cost line that does not deflate.
• DEFENSIBILITY AND SCALABILITY POINT IN OPPOSITE DIRECTIONS, AND THE RESOLUTION IS TO SELL THE SUBSTRATE. The reusable questions (GPU price, power, silicon) are being listed by Kalshi/CME/ICE; the defensible ones (site- and counterparty-level) are the least reusable, locking in services economics. Spec: sell a reusable substrate - resolution-contract library, novelty/dedupe custody against listed venues, scored forecast history, exportable audit trail - and let the buyer's own analyst author the non-reusable questions inside it. The market misses this because it treats 'defensible' and 'scalable' as the same axis.
• INSTITUTIONAL ABSENCE, NOT CAPABILITY, WAS ALWAYS THE GATE - AND STILL IS. Evidence: a 2024-vintage stack closed the loop for $20-$25 per artifact; the one AI paper that passed review was 1 of 3, at a workshop, submitted by humans under a negotiated agreement, and withdrawn; capability-independent prior art (Replication Markets, impact certificates, Algora) failed anyway. Acceptance is rubric-elastic, so whoever sets the rubric sets measured capability. The market misses this because it is base-rate blind to the attempts that needed no frontier capability and failed regardless.

Why this class of system fails (loop)

• The differentiator is already commoditised. Calibrated forecasts retail at $0.15-$2 per question with automated resolution better than Metaculus's ~8% annulment, and machine question generation yields ~96% resolvable output. Assume zero pricing power on any question-granular line item - which deletes value-of-information ranking as a moat.
• Strict acceptance is 10-25%, not the 50-90% the consensus reads off partial-credit benchmarks. RLI best-in-class is 15.8% for a restricted model and 6-8% for generally available ones; joint-threshold ACCEPT on verifier-checked consulting prompts is 12.9-15.7%. At 15% acceptance you grade ~6.7 attempts per accepted artifact at ~$86 and 109 minutes each, so rework dominates unit cost and autonomy level 3 is unreachable within twelve months.
• Three-sided liquidity has failed repeatedly with documented reasons: Replication Markets closed after 121 resolutions and $142,623; Manifund's own retro says impact certificates never produced a better funding decision; Algora carries 0-2 open bounties per org; HackerOne pays ~$81M/yr but only ~1.6% of accounts produce valid work with the payer deciding whether to pay; neutral inter-party arbitration has ~10^2 lifetime cases (Kleros case #146 after seven years) and needed a GBP 7,500 floor plus a $30,000 subsidy to exist.
• The incumbents shipped the action layer in the plan's own first vertical. AlphaSense added twelve workflow agents, AI-led expert calls and SuperAnalyst deliverable automation between Jan and Jul 2026 across 6,500 customers with a licensed 200k+ transcript corpus; OpenAI Frontier and Anthropic Cowork now bundle agent identity, permissions, audit logs, shared context and evaluation. Task graphs, mandates-as-permissions and first-party red-teaming are vendor features, not differentiators.
• The price layer is being taken by regulated venues: Kalshi launched GPU compute markets in March 2026 and compute forward curves on 14 Jul 2026 resolving against a third-party index, CME announced compute futures, ICE is working with the same data source. The compiler's defensible zone collapses to decision-specific, site-level questions with no listed instrument - which are exactly the least reusable and the least externally validated.
• Detection and grading are deflating toward zero and are being given away: AAAI-26 generated one AI review for all 22,977 main-track submissions in under 24 hours at under $1 per paper, funded by an in-kind donation, and flat-rate autonomous verification is marketed from ~$100/month. Never price the flagging.
• Legal exposure is concentrated exactly where the plan innovates. Person-level shadow agents are dead in the EU (GDPR Arts 6/14/21/22, Art 14 enforcement up to eight figures, EDPB's 2026 coordinated action on Arts 12-14, plus Art 50(1)'s duty to disclose the principal on whose behalf an agent acts). Natural-person contributor ranking sits inside Annex III point 4 with the Art 6(3) filter unavailable because it profiles, and GDPR Art 22 under SCHUFA already binds the scorer today. Platform-authored merit ranking of securities is advice/solicitation regardless of fee form. Written artifacts on chip design carry deemed-export exposure that live calls do not.
• Rent re-emerges everywhere the holonic framing says it should not, and the plan cannot escape it: 30-35% at Mercor, 60-70% intermediary capture on expert calls, corpus licensing at AlphaSense, issuer-pays conflicts in the one funded attestation authority (AIUC authors the standard, runs the evaluations, issues the certificates, accredits its own auditors and sells the insurance). There is no documented case of a member-owned holon displacing an intermediary at scale.
• Enterprise absorption capacity is the binding demand constraint: only 11-14% of AI-agent pilots reached production at scale in 2026 surveys, up to 54% stall 3-9 months post-pilot, and Gartner forecasts 40% of enterprises will demote or decommission autonomous agents by 2027 on governance gaps found only after production incidents.

Why it works (loop)

• The mechanism primitives exist and are cheap: a full mission loop costs $25-$160 of model spend, the entire forecast layer costs $1.50-$40, a 2024-vintage stack already closed an end-to-end research loop for $20-$25 per artifact. Cost has never been the gate for shallow missions, so the experiment is affordable at any pilot scale.
• Transport risk is gone. MCP 2026-07-28 is final with a Tasks extension for long-running work and Enterprise-Managed Authorization stable since 2026-06-18 under a 12-month deprecation policy; A2A v1.0 is stable with signed agent cards, 150+ organisations and cloud GA. The plan's instinct to reuse rather than invent substrate is correct and now safe.
• Independent per-artifact assessment with the payer separated from the judged party is already a contracted, multi-publisher business (Imagetwin/Proofig into Wiley, Sage, Karger, Taylor & Francis, IOP, MDPI, integrated into ScholarOne and Research Exchange; STM Integrity Hub screening 200,000+ manuscripts a month) and a mature regulator-mandated industry (blinded independent central review with 42% adjudication across 23,476 oncology cases). The buyer exists and buys avoided expert-hours.
• The priced object has a defensible margin structure if scoped correctly: ~$450-$700 COGS per maintained record-quarter against a $2,500+ defensible price is 72-82% gross margin, and two independent buyer classes price the same artifact in the same $5k-$50k band (assurance engagement; 1-5% verification fee on $100k-$1M grant decisions). That is what makes a desk fundable at 10^2-10^3 units rather than a marketplace at 10^4.
• Cold start is genuinely single-player for the assessment and decision-record layer: one obligated buyer gets value on day one with zero contributors, zero capital providers and no network, matching the only pattern that has scaled (Kosmos: researcher pays, gets a report; Mercor: labs pay, experts get paid, no third side; Manifund regranting: one funder delegates).
• Professional standards and insurance are pushing verification duties onto a named human signer rather than eliminating them (AICPA July-2026 guidance requiring AI-generated research and citations be verified against primary sources; absolute AI exclusions in D&O/E&O with brokers reporting carve-backs for documented verification workflows). Demand for attestation is being manufactured by regulation, not by taste.
• The dependency thesis substantially holds: mission agents, claim graphs, forecasts, red teams and civilization memory demonstrably run with no personal agent - Kosmos ships as a single-player mission agent, ForecastBench-grade pipelines need only public data, Black Spatula-class red teams need only a PDF. The plan is right that the coordination environment can precede the universal coordinator.

What would invalidate the plan (loop)

• A validated, chance-corrected judge-versus-human-panel agreement statistic at or above human inter-rater level appears on multi-file professional deliverables (today: 66% automated versus 71% human raw agreement on GDPval; expert ICC 0.367 on tax-law statements). This kills the paid escalation layer and the whole adjudication-first sequence; flip to selling tooling only.
• METR's 80% reliability horizon crosses 4-8 hours on messy tasks, or a re-run of the 70-prompt verifier-plus-rubric consulting benchmark shows ACCEPT above ~35% at the same joint threshold. Either would move the wedge from selling supervised drafts to selling outcomes and would justify building the exchange objects and autonomy level 3 far earlier than modelled.
• A third-party attestation surface appears inside a lab platform - a 'verified by' or external-attestation field in OpenAI Frontier or Anthropic Cowork, or an assessment/attestation object in the MCP Tasks extension - or AIUC-1's 15 Oct 2026 / 15 Jan 2027 release adds a deliverable-level control family. Absorption of the ratification layer means becoming a supplier inside it, not a principal.
• An underwriter, accredited auditor or procurement function states in writing that a receipt substitutes for one of their own diligence steps, or an insurer writes per-artifact cover at a premium leaving the fee intact, or a professional-line carrier adds a verification-conditioned carve-back. Any one of these converts discretionary demand into mandated demand and is the trigger to build aggressively.
• The first litigated or regulator-published case where an AI-assisted or agent-commissioned deliverable drives a documented allocation loss with Cal. Civ. Code 1714.46 invoked, or the first public ORI referral under NOT-OD-25-132. This is the 5-10x demand trigger; absence for twelve months keeps the buyer discretionary.
• Edison repricing Kosmos above ~$1,000 per run, or an incumbent workbench (AlphaSense SuperAnalyst, Claude Science) shipping priced attestation primitives - named approver, e-signature on records, immutable provenance, sampled independent review. The first revives a paid-synthesis SKU; the second closes the attestation slot.
• The final Article 6 classification guidelines retain para 241 (freelancers and platform workers inside Annex III point 4), or the Commission adopts an Article 113 readiness decision pulling Annex III duties to six months out, or a DPA rules on AI-generated personas of non-users. Each raises the cost of the matching layer and, in the last case, settles whether any person-level seeding is viable in the EU.
• Kalshi's institutional block programme (filed 19 Aug 2026) actually lists an AI-infrastructure or compute contract that holds two-sided depth beyond the first week, or CME/ICE extend from compute prices into interconnection, capacity or datacenter-completion outcomes. This eats the reusable, externally validated question space and forces the pivot to private-substrate resolution only.
• Cross-buyer reuse of resolution contracts measures below ~20% after two buyers. Underwrite as services at 2-3x revenue rather than a utility at 8-12x, and stop funding the substrate thesis.
• A licensed handoff becomes cheap - an SEC finders exemption is adopted, or a flat-fee broker-dealer-of-record product cuts fixed cost toward $10k - lowering the economical attach threshold from ~6% to ~2% and reopening the capital-mandate module a year earlier than planned.
• Person-level shadow-agent seeding survives an enforcement test anywhere in the EU, or the Data Omnibus narrows GDPR Art 22 as the Council text proposes over EDPB objection. This would restore the plan's original bootstrap and materially improve cold-start economics.
• Expert-review price collapses - cost-pass-through networks push a credible one-hour domain review below ~$300 all-in - or median expert-minutes per accepted decision-linked question falls below 15. Either makes per-task contribution reliably positive and revives the contracting rail as a rail rather than a cost-recovered feature.

V. Specs

Draft specs for the systems the evidence says are the most interesting to build now: fully digital, legally clean, valuable to a single user before any network exists, and each a primitive that several of the 45 ideas need. Objects, protocol, incentive, cold start and a kill criterion for each.

System 1 · covers ideas 12, 26, 27, 42, 44

The Evaluation Engine (adversarial review + resolution + track records)

A service that takes any artifact, claim or forecast and returns a reproducible verdict, and keeps score of who was right.

Why interesting. It is the scarce input in every predecessor's failure, it is fully digital, legally clean, useful to one user (audit your own work), and it converts the curiosity loop's self-scored EVOI into a measured quantity (did the answer change the forecast?).

Objects. Target (artifact | claim | forecast), Attack plan, Reproduction, Verdict (graded, with evidence), Resolution, Score, Track record (per agent and per human), Outcome credential. Protocol. Inputs via MCP tools; verdicts and track records issued as VC 2.0 credentials; forecasts resolved against dated public sources. Incentive. Buyers: anyone publishing research or forecasts who needs credibility; the system's own dossiers first. Red-team bounties paid for accepted refutations. Cold start. Run it on the 30+ existing Axion dossiers and their resolvable forecasts; publish the scorecard. Kill criterion. If after one quarter the engine's verdicts change fewer than ~20% of the audited conclusions or forecasts, it is not finding real errors.

System 2 · covers ideas 8, 13, 19, 43, 45

The Mandate Object + Policy Engine (the primitive everyone needs)

A signed, scoped, revocable mandate with an approval ladder and an append-only action log, exposed to any agent framework over MCP/A2A.

Why interesting. It is the minimum viable personal agent; it is what AP2, AuthZEN and enterprise agent-identity work are all groping toward; it is small enough to be right and open enough to spread.

Objects. Principal, Mandate (objective, offers, constraints, permissions, thresholds, budget, expiry), Action request, Decision (allow | require approval | deny; deterministic), Approval, Audit entry, Revocation. Protocol. JSON schema + signature; policy evaluated by a rule engine, never by the model; presented as a VC; compatible with AP2 intent mandates. Incentive. Every operator of many agents (one-person institutions) needs it now; it is the approval-queue product. Cold start. Use it to run this system's own agents; publish the schema; recruit one external framework to accept it. Kill criterion. If a second independent implementer does not adopt it within a year, keep it internal and stop calling it a protocol.

System 3 · covers ideas 11, 13, 14, 15, 25

The Information-Value Market (demand-side EVOI), priced as maintained records

Questions are the atomic unit of work but not of pricing: buyers pre-fund a maintained decision record (decision + resolution contracts + scored forecasts + audit trail); agents (then humans) answer the questions inside it; the evaluation engine resolves; forecasts update.

Why interesting. It is the curiosity loop with a demand side, which fixes the loop's deepest weakness (self-scored value) and is the honest form of 'capital that searches for work': a research budget buying answers, not a fund buying equity.

Objects. Question (resolvable), Value statement (pre-funded, escrowed), Pool, Answer proposal (method, cost, time), Commission, Artifact, Verdict, Payout. Protocol. Escrowed commitments (not pledges); answers delivered as artifacts to the evaluation engine; payouts on verdict. Incentive. Buyers get answers at compute cost with independent verification; answerers (agents first) are paid on accepted verdicts; public-good questions funded by a matching pool. The prior-art warning is that information value gets produced and not purchased (Replication Markets scored 3,000 claims and no funder bought the scores), so the market must attach to budget lines that already exist: diligence fees, grant-review budgets, research retainers, not a new 'uncertainty' line item. Cold start. One buyer: the system's own research budget, with the existing curiosity loop as the first answerer. Kill criterion. If stated value never converts to escrowed funds from a second buyer in two quarters, capture demand through subscriptions instead. The loop's sharpening: calibrated forecasts already retail at $0.15-$2 per question, so never price the question; price the record-quarter.

System 4 · covers ideas 27, 37, 38, 31

The Decision Registry (counterfactual forecasts for organizations)

Any team registers a decision with pre-registered forecasts for adopt/not-adopt; the registry scores them when outcomes arrive.

Why interesting. It is idea 37 applied where adoption is voluntary and fast (companies, funds, labs) before governments; it makes civilization memory accumulate as a by-product of normal work.

Objects. Decision, Forecast set, Indicator, Resolution, Scorecard, Postmortem, Similarity index. Protocol. Same forecast object as the evaluation engine; private by default, publishable. Incentive. Teams get honest postmortems and retrieval of similar past decisions; funders can require it. Cold start. This system's own investment and build decisions. Kill criterion. If retrieval never changes a new decision within two quarters, the similarity index is not structural enough.

System 5 · covers ideas 15, 18, 20, 36

Agent-first bounties with human adjudication

Tasks priced at compute, executed by agents, reviewed by adversarial agents, with humans paid to adjudicate disagreements and to supply private context.

Why interesting. It inverts the failed bounty model: the cheap side (production) is automated and the scarce side (judgment) is what humans are paid for.

Objects. Task (spec, acceptance test), Agent bid, Artifact, Adversarial review, Disagreement, Adjudication (human, paid), Outcome credential. Protocol. A2A for agent bids; evaluation engine for review; mandates for budgets. Incentive. Mission owners get cheap verified work; humans are paid for judgment, not labor. Cold start. Internal tasks of the system's own missions. Kill criterion. If adjudication demand exceeds ~30% of tasks, the acceptance tests are too weak for automation; fix specs before scaling.

Systems the loop surfaced that were not in the inventory (ideas 46-48)

Idea 46 · surfaced by the exploration loop

Attestation-and-indemnity authority for agent-produced work

Intuition. Title insurance for AI-assisted deliverables. Every existing AI assurance product is per-system-per-period (a certification of the vendor); no product sells per-artifact, per-decision cover. As professional standards and insurers push verification duties onto a named human signer, a utility that attests a specific artifact against a specific decision, and an insurer that writes cover on that receipt, becomes a priced primitive.

Objects. Artifact, Decision (with dollar exposure), CriterionSet, sealed rotating ProbeSet, named credentialed Panel, Ratification (scope and exclusions), Receipt (hash, lineage, model and scaffold version), Claim path. Cold start. One obligated buyer that already verifies (publisher integrity team, CRO endpoint committee, accredited certification body, grant diligence unit, procurement office); instrument their existing artifact stream. Kill criterion. No underwriter, accredited auditor or procurement function will state in writing that the receipt substitutes for one of their steps; or surviving discordance against the buyer's incumbent pass is below ~10%.

Forcing questions. (1) Own the signature (licensed signer, liability capacity) or sell tooling to whoever owns it? (2) Which insurer will write per-artifact cover, at what premium, leaving the fee intact? (3) What independence requirement (regulator, insurer, journal, funder) would make vendor self-attestation unacceptable, and when?

Idea 47 · surfaced by the exploration loop

Resolution Ledger for decisions with no public timestamped substrate

Intuition. Public forecasting is being automated wherever a timestamped corpus exists (listed markets, news, Kalshi/CME compute contracts). Private capital-allocation and infrastructure decisions (interconnect queue outcomes, fab tooling qualification, PPA delivery, energisation dates, procurement awards) have no such substrate, so resolution there is unsolved and defensible.

Objects. Decision, PreRegisteredCriterion (resolver as code), hash-chained EvidenceBundle, Forecast, Resolution by an independent resolver, exportable Receipt. Cold start. Audit 50 historical decision records in one public-record-heavy slice (interconnection queues, permitting, grid studies) and build the resolver library. Kill criterion. Definitional instability (queue restatements annulling live contracts) above the Metaculus ~8% annulment rate; or cross-buyer reuse of resolution contracts below ~20%.

Forcing questions. (1) Which decision class has canonical sources, frequent decisions with stateable thresholds, and no incumbent consented corpus? (2) Who is the independent resolver the buyer will accept when the answer is unwelcome? (3) What fraction of resolution contracts are reusable across buyers (utility at >60%, services at <20%)?

Idea 48 · surfaced by the exploration loop

Mandate and Provenance Ledger as the EU Article 50 conformance layer

Intuition. From 2 Aug 2026 agents operating in the EU must disclose both their artificial nature and the principal on whose behalf they act, including across multi-agent chains. A ledger that records principal, mandate scope, provenance manifest, marking record and human-review attestation per artifact, with a verifier API that answers 'is this artifact compliant for publication in jurisdiction X on date Y', is the compliance object every agent deployer will need.

Objects. Principal, Agent (disclosed principal and mandate scope), Artifact, C2PA-style provenance manifest, per-modality marking record, Human-Review Attestation (named reviewer, editorial-responsibility flag), PresentationBoundaryCrossing, Jurisdiction. Cold start. Run it on this system's own agents and published dossiers; it is the same mandate object as System 2 with a jurisdiction and marking layer. Kill criterion. A lab platform ships the same object as a free field (an 'attested by' or compliance record inside OpenAI Frontier or Anthropic Cowork); then supply into it rather than compete.

Forcing questions. (1) Does the verifier API need to be operated by a legal person the regulator recognises? (2) Which modality markings (text, image, audio) are enforceable today and which are aspirational? (3) How does the ledger handle multi-agent chains where the principal changes mid-chain?

Schema v0 (the objects the first loop actually exercises)

Ask, Offer and Match from the original 15 collapse into Task plus Mandate; Commitment is an Outcome that has not happened yet; Principal and Agent are identity, handled by the credential layer. Every object is event-sourced; nothing is overwritten.

VI. The 45 Ideas

All 45 ideas, each with the intuition, a minimal spec (objects, loop, cold start, kill criterion), what is buildable today with no further AI progress, the affordance or institution it waits on, and the questions that must be answered to implement it. Grouped by cluster.

A. Governance and law

01 · Governance and law institutional digital closure: partial

Autonomous governments

Intuition. Government is a slow control loop: sensing (citizens, data) -> deliberation -> implementation -> measurement, with years of latency and lossy compression at every hop. Agents shorten the loop where the step is information work, and leave sovereignty (values, rights, budgets) with humans.

Objects. Objective (statute-level goal with metric), Program (implementation with budget and rules), Simulation (policy model with stated assumptions), Outcome series, Audit finding, Appeal. Loop. Human sets objective + constraints -> policy agents simulate variants and publish forecasts -> administrative agents execute within rules -> auditors compare outcomes to forecasts -> proposals to adjust go back to humans. Cold start. One agency, one program, one metric (e.g. permit processing time or benefit error rate), run in shadow mode beside the existing process for a year. Kill criterion. If shadow-mode recommendations are adopted less than 10% of the time, or the forecast-vs-outcome error is no better than the status quo, stop.

Buildable now. Shadow-mode policy simulation and outcome auditing on open government data; nothing that executes. Gate. Institutional: procurement, legal authority to act, and the politics of machine administration. Not gated by model capability for the audit/simulation part.

Forcing questions

1. Which single program has (a) a public objective metric, (b) open data on outcomes, and (c) an owner who would run a shadow-mode agent beside it? Name it.
2. What is the appeal path when an administrative agent denies something: who is the human, what is the SLA, and is the agent's reasoning admissible?
3. How do you stop the simulation agents from becoming a lobbying surface (whoever controls the model controls the 'forecast')? Multiple competing simulators, or one audited one?
4. What constitutional limits are encoded as hard policy rules versus left to humans?

02 · Governance and law general personal agent digital closure: complete

Personal political representatives

Intuition. Representation compresses millions of preference vectors into a few hundred people. A personal political agent keeps the full vector, reads every proposal, and only escalates genuine value conflicts to its principal. Liquid democracy with a competent delegate that is actually yours.

Objects. Preference model (elicited, versioned, private), Proposal (machine-readable bill or measure), Position (agent's derived stance with explanation), Escalation (value conflict needing the human), Delegation (by domain). Loop. Ingest proposals -> derive positions from preference model -> aggregate positions across agents into high-resolution polling -> negotiate compromises between agents -> escalate conflicts -> record how the human resolved them and update the model. Cold start. Read-only: an agent that explains every local ballot measure in terms of your stated priorities and shows where you disagree with your representative's votes. No aggregation until thousands exist. Kill criterion. If users override the agent's derived positions more than ~30% of the time after three months, the preference model is not capturing values.

Buildable now. The read-only explainer and the 'your rep vs your values' diff, on public voting records and bill text. Gate. A durable private preference model (general personal agent) and any legal channel for agent-expressed preferences to count. Manipulation resistance is unsolved.

Forcing questions

1. How is a preference model elicited so it reflects considered values rather than first reactions, and who can audit that the agent is not steering the user?
2. What does aggregated agent polling count as legally: speech, a petition, a poll, or nothing? Pick the jurisdiction and the channel.
3. How do you make a million agents with the same vendor not vote as a bloc (vendor correlation is a new form of capture)?
4. Is the first customer the citizen, or the legislator who wants high-resolution constituent signal? The product differs.

03 · Governance and law no personal agent digital closure: complete

Machine-readable laws

Intuition. A law is a program with no tests, no stated intent, and no expiry. Attach intent, metrics, forecasts, sunset conditions, and appeal rules, and the policy becomes something an agent can continuously evaluate against its own purpose.

Objects. Statute (text), Intent (plain-language purpose), Metric (what success means), Forecast (pre-registered expected effect), Sunset (conditions), Evaluation (periodic agent report), Appeal rule. Loop. Annotate existing law with intent/metric/forecast (retroactively, by agents, reviewed by humans) -> monitor data -> publish whether the law is achieving its purpose -> flag drift, unintended effects, and candidates for sunset. Cold start. Retroactive annotation of one jurisdiction's recent statutes plus the fiscal notes and impact assessments that already state intent; publish the evaluation as a public dashboard. Kill criterion. If no legislator, journalist, or court cites the evaluations within a year, the output is not reaching the loop it is meant to close.

Buildable now. All of it, as an external annotation layer; nothing requires the legislature to adopt the format. Gate. Adoption by drafters (the 'format' is a norm, not a technology). Pre-existing artefacts (EU impact assessments, US CBO scores, regulatory impact analyses) are the seed.

Forcing questions

1. Who writes the intent and metric for an existing law: the agent (then reviewed), or only humans? Retroactive annotation is the whole cold start.
2. What is the schema (is it just a machine-readable impact assessment), and does it live as an open standard anyone can publish against?
3. Which jurisdiction already publishes enough outcome data to evaluate laws automatically? Start there.
4. How do you keep the evaluator from becoming partisan infrastructure: who funds it, and can opposing parties run their own on the same schema?

37 · Governance and law no personal agent digital closure: complete

Counterfactual government

Intuition. Policies are enacted without anyone on record predicting what will happen. Require competing forecasts for adoption and non-adoption before the decision; score them after. Accountability becomes prospective.

Objects. Decision, Pre-registered forecast set (adopt / not adopt, by competing forecasters), Indicator, Resolution, Scorecard. Loop. Decision announced -> forecasts registered before enactment -> outcomes measured -> scorecard published -> forecaster track records update. Cold start. Organizations before governments: any team can register decisions and forecasts internally; publish for public bodies that opt in; journalists can run it on governments without consent. Kill criterion. If fewer than half of registered forecasts resolve within the stated horizon, the indicators were not chosen well.

Buildable now. Entirely; it is idea 12 applied to decisions. Gate. None for the registry; adoption by decision-makers is cultural.

Forcing questions

1. Who registers forecasts for a decision the decider will not forecast: independent forecasters, agents, both?
2. What indicators make policy outcomes resolvable on a political timescale?
3. How is the scorecard protected from being gamed by vague questions?

B. Holonic marketplaces (unbundling middlemen)

04 · Holonic marketplaces (unbundling middlemen) scoped mandate digital closure: complete for digital services

Holonic marketplaces

Intuition. Two-sided platforms solve discovery and trust, then charge rent on the relationship. If discovery, reputation, insurance, payments, and dispute resolution are unbundled into protocol-level services that agents can use, the platform's 20-30% take has nothing left to attach to.

Objects. Member (with portable reputation credential), Listing/Offer, Request, Match, Agreement (terms + insurance + dispute rule), Escrow/Payment, Dispute, Governance proposal. Loop. Member agents publish offers to a shared registry -> requester agents search and negotiate -> agreement signed under the holon's template -> payment via escrow -> outcome rated into portable reputation -> disputes resolved by an agent first, humans on appeal. Cold start. A digital-service holon (design, translation, code review, research) where delivery is online and verification is cheap. Physical holons (rides, stays) inherit the machinery later. Kill criterion. If take-rate savings are below ~10 points after insurance and dispute costs, the platform was not mainly rent; it was risk-bearing.

Buildable now. Digital-service holons on existing rails (open registry + reputation credentials + escrow). India's Beckn/ONDC shows the open-network pattern at scale for commerce and mobility. Gate. Portable reputation (idea 44) and a neutral dispute layer. Physical holons also need insurance capacity and local regulation.

Forcing questions

1. Which function actually earned the platform its take: discovery, trust/insurance, payment guarantee, or demand aggregation? The holon must replace that one first.
2. Who bears counterparty risk when a match fails, and where does the insurance pool's capital come from before there are members?
3. Is the holon a co-op (legal entity with members), a protocol (no entity), or a franchise of local co-ops on a shared protocol? Each has a different failure mode.
4. What prevents the registry/reputation operator from becoming the next tollbooth?

05 · Holonic marketplaces (unbundling middlemen) scoped mandate digital closure: partial

Autonomous hospitality networks

Intuition. A stay is a negotiation over dates, price, rules, identity, deposit, insurance, and cleaning. Today the platform fixes the terms and takes ~15%. Host and traveler agents can negotiate directly if identity, payment, and insurance exist as portable services.

Objects. Property (availability calendar, rules), Traveler credential (verified identity, reputation), Offer/Counter-offer, Booking agreement, Deposit escrow, Insurance binding, Incident report. Loop. Traveler agent broadcasts dated demand -> host agents respond with terms -> negotiate -> identity proofs exchanged selectively -> escrow + insurance bound -> stay -> mutual rating into portable reputation. Cold start. Hosts who already multi-list (channel managers) expose availability to a direct-booking agent network; the agent's first job is just to beat the platform price on the same listing. Kill criterion. If fewer than a few percent of bookings move off-platform after a year among participating hosts, guests value the platform's guarantee more than the fee.

Buildable now. Agent-to-agent negotiation over existing direct-booking and channel-manager APIs; identity via existing KYC providers. Gate. Insurance and guarantee capacity; local short-term-rental law; portable guest reputation.

Forcing questions

1. Whose guarantee replaces the platform's (host damage, guest no-show)? Insurer, mutual pool, or none?
2. What minimum identity disclosure does a host require, and can it be a credential rather than a document?
3. Do hosts want dynamic agent-negotiated pricing, or do they want fewer decisions?

06 · Holonic marketplaces (unbundling middlemen) institutional digital closure: partial

Autonomous mobility networks

Intuition. A trip can be assembled from drivers, co-ops, robotaxis, transit, and shared rides; today each is a silo. A rider agent optimizes across them if supply is discoverable on an open network (Namma Yatri on Beckn is the live example).

Objects. Trip request (origin, destination, constraints), Supply offer (mode, ETA, price, safety credential), Itinerary (multi-leg), Agreement, Payment, Safety incident. Loop. Rider agent posts request -> supply agents bid -> rider agent composes a multi-leg itinerary -> agreements per leg -> payments settled per leg. Cold start. One city with an open mobility network already live (Beckn/ONDC in India) plus GTFS transit; the agent adds the composition layer. Kill criterion. If composed itineraries are chosen under 5% of the time versus single-mode, riders do not value optimization over simplicity.

Buildable now. The composition/optimization agent on top of open networks and transit feeds where they exist. Gate. Institutional: open supply networks, regulation, safety liability, AV fleet APIs.

Forcing questions

1. Is there any city outside India where supply is open enough to compose across providers? If not, the product is Beckn-first.
2. Who is liable across a multi-leg itinerary when a leg fails?
3. Is the rider agent a consumer product or a B2B product for transit agencies doing MaaS?

07 · Holonic marketplaces (unbundling middlemen) narrow profile digital closure: complete

Private agentic matchmaking

Intuition. Dating apps optimize engagement and publish intimate preferences to a platform. Two private agents can compute compatibility over private data, disclose gradually with mutual consent, and learn from outcomes without surveillance.

Objects. Private profile (values, preferences, history; never leaves user control), Compatibility protocol (private-set or MPC-style comparison, or agent-to-agent conversation under disclosure rules), Disclosure ladder, Introduction, Outcome feedback. Loop. Agents exchange minimal proofs of compatibility -> mutual consent to disclose the next rung -> introduction -> outcome feedback updates both models. Cold start. Single-player: an agent that interviews you and critiques your own profile and choices; then opt-in agent-to-agent screening inside an existing community (alumni network, conference). Kill criterion. If introductions from agent screening convert to second dates no better than random within the same pool, the compatibility model is noise.

Buildable now. Agent-to-agent screening inside an opt-in community; private comparison without cryptography if both agents run on trusted infrastructure. Gate. Narrow private profile plus a trust model for where the agents run. Identity verification is reusable from idea 44.

Forcing questions

1. Where do the two agents execute so that neither side's data leaks: a neutral enclave, each side's device, or a trusted operator?
2. What is the unit of feedback that improves the model without building a surveillance record of relationships?
3. Is the business model a membership fee (aligned) or engagement (the thing being escaped)?

C. Personal intelligence

08 · Personal intelligence general personal agent digital closure: complete

Fully private personal AI

Intuition. The most consequential AI in a person's life should be loyal to that person by construction: its memory is theirs, its permissions are revocable, its disclosures minimal, its actions audited. Corporations already have such agents pointed at individuals; this is the counterparty.

Objects. Memory store (user-owned, encrypted, exportable), Mandate (scoped permissions; see idea 45), Action log (append-only), Disclosure record (what was revealed to whom), Model runtime (local or user-controlled cloud), Loyalty policy (no third-party optimization objectives). Loop. Observe (with consent) -> maintain model of goals/preferences/history -> propose actions under mandates -> execute within limits -> log -> user reviews and revokes. Cold start. Start as a memory-and-mandate container that existing assistants plug into (the agent is an interface; the asset is the user-owned memory). Kill criterion. If users will not pay for loyalty (a subscription with no ads or data sale) at a price that covers inference, the architecture cannot be private.

Buildable now. User-owned memory + mandate store + audit log as a product today; the broad, durable model of a person is a years-long accumulation. Gate. Capable on-device or confidential-cloud inference at consumer prices; a mandate standard; platform access (OS-level hooks) that incumbents control.

Forcing questions

1. What is the minimal loyal primitive users will adopt first: memory, mandates, or the audit log? (The plan's answer: the mandate.)
2. Local inference, confidential cloud, or trusted operator: which privacy architecture is affordable at 2026-2028 model prices for a 24/7 agent?
3. How does the agent prove loyalty (open weights? attestation? audit)?
4. Which incumbent platform access does it need, and will it be granted?

09 · Personal intelligence narrow profile digital closure: complete

Personal board of directors

Intuition. One assistant collapses every trade-off into one answer. A board of specialized agents (finance, health, legal, career, values, a skeptic) debates in the open and shows the user the disagreement, which is where the decision actually lives.

Objects. Board seat (role prompt + domain tools + memory scope), Decision brief, Position (per seat), Cross-examination transcript, Synthesis with dissent, Outcome record. Loop. User frames a decision -> each seat produces a position -> structured cross-examination -> synthesis that preserves dissent -> user decides -> outcome recorded for later review of which seat was right. Cold start. A single-player product today; needs only a narrow profile per domain. Kill criterion. If users consistently read only the synthesis and never the dissent, the board is theater; a single agent would do.

Buildable now. Entirely. Gate. None beyond a narrow profile; quality of domain tools.

Forcing questions

1. Is disagreement between seats real (different evidence/tools) or cosmetic (same model, different prompt)? Different models and data per seat, or it is theater.
2. How is the board scored over time: which seat's positions predicted outcomes?
3. What is the unit the user pays for: a decision, a month, or a seat?

10 · Personal intelligence narrow profile digital closure: partial (needs institutional interfaces)

Universal bureaucracy agent

Intuition. Bureaucracy is a language barrier between a desired outcome and a form. The user states the outcome; the agent does the forms, deadlines, evidence, and follow-ups, and escalates only the decisions.

Objects. Outcome request, Procedure model (per institution: forms, evidence, deadlines, appeal), Document vault, Submission, Status tracker, Escalation, Mandate (authority to file). Loop. Outcome -> procedure lookup -> gather evidence (vault + user) -> draft -> user approves -> file -> track -> handle follow-ups -> escalate on decisions. Cold start. One high-volume, high-pain procedure with a digital interface (insurance claim appeals, tax filings, benefit applications); automated denial appeals already exist and work. Kill criterion. If institutions block or penalize agent-filed submissions (CAPTCHAs, 'no bots' terms, authorization requirements), the procedure must be chosen by institutional acceptance, not user pain.

Buildable now. Any procedure with an online form and an appeal path; the catalogue of procedure models is the asset. Gate. Institutional interfaces and legal authority to act (power of attorney-like mandates); DoNotPay's FTC action is the cautionary tale on claims.

Forcing questions

1. Which procedures have an authorization pathway for representatives (insurance appeals, tax preparers) versus which treat automation as abuse?
2. Who is liable for a wrong filing, and is the agent a 'preparer' under the law?
3. Is the procedure model library open (a commons) or the product?

28 · Personal intelligence narrow profile digital closure: complete

Personalized lifelong education

Intuition. A tutor that models what you know, how you learn, what you are forgetting, and which prerequisite blocks you, and regenerates the curriculum continuously.

Objects. Knowledge model (concept mastery with decay), Learning-style evidence, Prerequisite graph, Curriculum (regenerated), Assessment, Goal. Loop. Goal -> gap analysis against knowledge model -> curriculum -> sessions -> assessment updates model -> spaced review of decaying concepts. Cold start. Single-player today; the durable knowledge model is the asset, so it must be user-owned and exportable. Kill criterion. If retention at 3 months is no better than a fixed course, the adaptive model is not adding value.

Buildable now. Entirely. Gate. None; a narrow profile. Already a crowded product category.

Forcing questions

1. Is the knowledge model portable between tutors (the user owns it) or locked in?
2. What is the evidence of mastery: tests, projects, or teaching back?
3. How does it differ from the many existing AI tutors: the durable model, or the prerequisite-blocking diagnosis?

29 · Personal intelligence institutional digital closure: partial

Continuous personalized medicine

Intuition. Medicine sees you in snapshots. A health agent integrates continuous signals and research, keeps competing diagnostic hypotheses alive, and detects meaningful change early.

Objects. Private health record (idea 8 scope), Hypothesis set (with evidence and confidence), Signal streams, Change detector, Clinician interface, Escalation rules. Loop. Integrate signals -> update hypotheses -> detect change -> escalate to a clinician with the evidence -> record outcome -> update. Cold start. Patients with chronic conditions and existing device data; the agent prepares the clinician visit (evidence, hypotheses, questions). Kill criterion. If clinicians ignore the agent's briefs, or false-alarm rates exceed what a clinic tolerates, the escalation rules are wrong.

Buildable now. Visit preparation and hypothesis tracking on patient-held data. Gate. Institutional: medical-device regulation for anything diagnostic, records access, liability.

Forcing questions

1. Where is the regulatory line between 'organizing the patient's data' and a medical device, in the target jurisdiction?
2. Which clinicians will read an agent's brief, and in what format?
3. Who is liable for a missed escalation?

D. Humanity's to-do list and capital search

11 · Humanity's to-do list and capital search no personal agent digital closure: complete

Humanity-scale to-do list

Intuition. Civilization has no table of contents. Goals, problems, proposals, dependencies, owners, and completion conditions are scattered across papers, decks, and reports. A machine-readable graph makes ambition legible, forecastable, and financeable.

Objects. Outcome (desired end state with a measurable completion condition), Problem (blocking an outcome), Proposal (approach), Dependency edge, Resource requirement, Owner/steward, Attempt (past or live), Forecast, Status. Loop. Ingest public ambition (roadmaps, RFPs, grant calls, papers' future-work sections) -> normalize into nodes -> link dependencies and attempts -> attach forecasts -> surface unowned, high-value, tractable nodes -> stewards claim nodes. Cold start. Seed one domain deeply (say, frontier compute and energy) from existing roadmaps; use it as the index for your own missions. Kill criterion. If after a year the graph is only consulted by its builders, it is a wiki, not infrastructure; test by whether external funders or labs cite node IDs.

Buildable now. Entirely; the hard part is curation quality and a schema people adopt. Gate. Adoption as a shared namespace (like a DOI for problems). No capability gate.

Forcing questions

1. What is the identifier scheme, and who governs it, so that two labs can refer to the same problem node?
2. Who curates: agents with human review, or claimed stewards per node?
3. What stops the graph from being a pile of aspirational nodes: is a node admitted only with a completion condition and a forecast?
4. Which existing corpora seed it (roadmaps, open problem lists, RFPs)?

12 · Humanity's to-do list and capital search no personal agent digital closure: complete

Forecasts attached to every project

Intuition. A project without a forecast is a hope. Attach cost, duration, technical and regulatory success, impact, failure modes, and the chance someone else solves it first; keep them updated; score them when resolved.

Objects. Forecast (question, resolution criterion, resolver, date, distribution), Forecaster (agent or human, with track record), Update, Resolution, Score. Loop. For every mission/task generate resolvable questions -> forecasts from agents and humans -> updates as evidence arrives -> resolve -> score -> track records feed future weighting. Cold start. Your own missions; pick horizons short enough that dozens resolve in the first quarter, or calibration is never measured. Kill criterion. If fewer than ~30% of attached forecasts are resolvable as written, the question-writing is the bottleneck and the numbers are decoration.

Buildable now. Entirely; this is the existing Axion strength. Gate. Calibration of machine forecasts (measurable now against human forecasters); resolver trust.

Forcing questions

1. Who resolves, and how is resolver disagreement handled?
2. What fraction of mission questions can be made resolvable, and what do you do with the rest (estimates without scoring)?
3. Are forecasts public by default (reputational stakes) or private (honest internal use)?

13 · Humanity's to-do list and capital search scoped mandate digital closure: complete

Capital that searches for work

Intuition. Capital waits for pitches; most valuable work never pitches. A capital mandate (objective, exclusions, check size, evidence required) can continuously search the mission graph and surface matches for a human to approve.

Objects. Capital mandate (thesis, instruments, size, constraints, evidence requirements, approval rules), Opportunity (mission/task/project), Diligence packet, Match, Commitment, Portfolio monitor. Loop. Mandate -> continuous search over missions and shadow projects -> agent diligence -> ranked matches -> human approval -> commitment -> monitoring. Cold start. Non-securities capital first: grants, prizes, research budgets, advance commitments, procurement. A single funder's mandate running against the graph is already useful (thesis formalization, discovery, diligence, monitoring). Kill criterion. If matches surfaced by mandates are funded at a lower rate than inbound pitches after six months, the mandate representation is not capturing what the funder actually wants.

Buildable now. Mandate formalization, discovery, diligence, and monitoring; not autonomous commitment. Gate. Securities law for investment capital (adviser rules, accredited-investor flows) and the trust needed to let a mandate commit.

Forcing questions

1. Which capital type first: philanthropic/prize (no securities issues), internal R&D budgets, or investment? The legal surface differs by an order of magnitude.
2. What is the evidence contract a mandate requires before a match is shown: forecasts, reproductions, track record?
3. Does a mandate ever commit without a human, and below what amount?
4. How does a funder express exclusions and values machine-readably without publishing their thesis to competitors?

14 · Humanity's to-do list and capital search no personal agent digital closure: complete

Markets for information value

Intuition. Many questions are worth more to know than they cost to answer, but the value is spread across everyone who would benefit, so the purchase never gets organized. Price the value of information, aggregate demand, and commission the experiment or analysis that resolves it.

Objects. Question (resolvable), Value statement (who would pay what to know), Funding pool, Answer proposal (method, cost, time), Commission, Artifact, Adversarial review, Resolution, Payout. Loop. Questions ranked by aggregated stated value -> proposals to answer -> pool funds -> commission -> artifact reviewed adversarially -> resolution -> payout and forecast update. Cold start. Single buyer: your own research budget as the only demand side; the curiosity loop already picks questions by value-of-information, so give EVOI a demand side instead of a self-score. Kill criterion. If stated willingness-to-pay never converts to funded questions (people say 'I'd pay $X' but do not), demand must be captured through existing budgets (subscriptions, retainers), not pledges.

Buildable now. Entirely, for digital answers (analyses, datasets, replications, code). Gate. None capability-wise; legally, pooled funding of answers is a service purchase, not a security, if no return is promised.

Forcing questions

1. How is value-of-information stated honestly: a pledge, a pre-paid subscription, or a bid with commitment? Pledges are the known failure mode.
2. Who resolves a question whose answer the buyers dislike?
3. What share of high-value questions are answerable by agents at compute cost, and does that collapse the market into a pricing problem?
4. How do public-good answers (everyone benefits, no single buyer) get funded: matching funds, quadratic pools, or a patron?

15 · Humanity's to-do list and capital search no personal agent digital closure: complete

Every problem gets an agent

Intuition. Unowned problems have no advocate. Give each a persistent agent that maintains the evidence, remembers every attempt, decomposes, recruits, issues bounties, and hunts for resources. The problem, not a person, becomes the unit of persistence.

Objects. Problem node (from idea 11), Evidence base, Attempt history, Decomposition, Open asks, Bounty, Candidate solver, Outreach record (consented), Progress forecast. Loop. Maintain evidence and attempts -> rank sub-questions by value of information -> issue digital tasks/bounties -> match solvers (idea 20) and capital (idea 13) -> review artifacts -> update forecasts -> report status. Cold start. Twenty problems you care about, each agent useful to you alone as a living research workspace before any solver or funder exists. Kill criterion. If a problem agent never changes a forecast or a decision in a quarter, it is a document, not an agent.

Buildable now. Entirely; this is the Mission Exchange's core object. Gate. Quality of autonomous decomposition and review (measurable now); consent rules for outreach.

Forcing questions

1. What may a problem agent do without a human: maintain evidence (yes), issue a bounty (with budget?), contact a person (consent?)?
2. Who owns a problem agent, and what happens when two stewards disagree?
3. What is the 'agent changed a decision' metric, and who records it?
4. How does the agent avoid re-running the same dead ends: is attempt history structured enough to retrieve 'this was tried in 2019 and failed because X'?

41 · Humanity's to-do list and capital search scoped mandate digital closure: complete

Agentic patronage networks

Intuition. Creators are funded by advertising or by platforms' taste. Taste agents aggregate demand for specific work (journalism, research, film, music) and fund creators directly through commitments and milestones.

Objects. Taste profile (private), Work proposal, Conditional commitment (idea 17), Milestone, Escrow/payout, Delivery, Feedback. Loop. Creators propose -> taste agents match and commit conditionally -> threshold -> milestones funded -> delivery -> feedback updates taste. Cold start. Existing patronage (Patreon, Substack, Kickstarter) already prove demand; the agent adds discovery across the long tail and milestone-based commitments. Kill criterion. If agent-discovered commitments are a small fraction of creator income after a year, discovery was not the creator's bottleneck.

Buildable now. Entirely for digital work. Gate. Scoped mandate that can commit money; escrow.

Forcing questions

1. Is the taste agent's discovery better than existing recommendation, for the long tail specifically?
2. Milestone verification for creative work: who judges delivery?
3. What is the platform's take, and what stops it drifting to the 30% it replaces?

E. Self-assembling institutions and matching

16 · Self-assembling institutions and matching scoped mandate digital closure: partial

Products that assemble themselves

Intuition. A product opportunity is a bundle of verifications (demand, design, supply, capital, legal) that founders do serially. Agents can run them in parallel and only instantiate an entity once the bundle closes.

Objects. Opportunity (spec + target customer), Demand evidence (idea 17), Design commission, Supplier quote, Purchase commitment, Capital commitment, Entity template, Formation trigger. Loop. Opportunity posted -> demand verified -> design commissioned -> suppliers quoted -> purchase and capital commitments collected conditionally -> when all conditions close, entity formed and commitments executed. Cold start. Digital or made-to-order products where supply is a known API (print, PCB assembly, software): closure is observable end to end. Kill criterion. If conditional commitments close less than ~5% of posted opportunities, the opportunity spec or the commitment instrument is wrong.

Buildable now. The verification bundle for digital goods; physical goods need manufacturer APIs and logistics. Gate. Legal formation on trigger (template entities exist), purchase-commitment instruments, and supplier APIs.

Forcing questions

1. What is a binding-enough purchase commitment that is not a security and not a consumer-protection problem?
2. Who is the principal before the entity exists, and who is liable for commissioned designs if it never forms?
3. Which product category has fully API-addressable supply so the loop can close without humans in the middle?

17 · Self-assembling institutions and matching scoped mandate digital closure: complete for digital products

Demand discovered before supply

Intuition. Supply is built on guesses about demand. If personal agents can express private conditional commitments ('I'd buy X at price P by date D'), demand becomes observable before anything is built, and agents can recruit supply to meet it.

Objects. Conditional commitment (spec, price, deadline, privacy), Aggregator (pools commitments without revealing identities until threshold), Threshold event, Supplier call, Fulfilment contract. Loop. Agents post private conditional commitments -> aggregator reveals only counts and price curves -> threshold reached -> supplier recruited -> commitments activated. Cold start. Open-source features, datasets, courses, and other digital goods (assurance contracts already work for these); physical goods later. Kill criterion. If conversion from commitment to payment at threshold is below ~60%, commitments must be pre-funded (escrow) or they are noise.

Buildable now. Digital goods, with escrowed commitments. Gate. A scoped mandate that can commit money conditionally; escrow rails.

Forcing questions

1. Escrowed or pledged commitments? (Every pledge-based system has seen large non-conversion.)
2. What is the privacy model: counts only, price curve, or full demand with identities revealed to the supplier at threshold?
3. What stops a supplier from using the demand curve to price-discriminate against the very buyers who revealed it?

18 · Self-assembling institutions and matching scoped mandate digital closure: complete

Self-assembling companies

Intuition. The company is a default because coordination was expensive. When agents can assemble people, capital, and suppliers around one objective, an organization can be temporary by design: form, execute, distribute, dissolve.

Objects. Objective, Role (with required capability credential), Participant commitment, Capital commitment, Ownership/waterfall table, Entity template (LLC/series/UNA), Milestone, Distribution, Dissolution record. Loop. Objective posted -> roles and capital matched (ideas 13, 20) -> ownership allocated by template -> entity formed -> milestones executed and reviewed -> proceeds distributed -> dissolution with portable reputation updates. Cold start. A one-off digital deliverable with one payer (a commissioned report, a dataset, a model) where the 'organization' is a contract set, not an entity. Kill criterion. If formation/dissolution overhead exceeds ~10% of project value, temporary entities are not worth it below a size threshold; find the threshold.

Buildable now. Contract-set organizations for digital deliverables; entity formation via existing templates (Wyoming/Marshall Islands DAO LLCs, series LLCs). Gate. Cheap legal formation/dissolution, tax treatment of distributions, and reputation that survives dissolution.

Forcing questions

1. What is the cheapest legal wrapper that can hold IP, pay contributors, and dissolve cleanly, per jurisdiction?
2. How is ownership allocated among humans, agents' principals, and capital: template waterfalls or negotiated?
3. Who is accountable after dissolution (warranty, liability)?

19 · Self-assembling institutions and matching scoped mandate digital closure: complete

One-person institutions

Intuition. One person plus swarms of specialized agents can operate at institutional scale. The binding constraint becomes the person's judgment bandwidth and the legal surface, not headcount.

Objects. Principal, Agent roster (research, engineering, legal, recruiting, procurement, fundraising, forecasting, security, admin), Mandates per agent, Approval queue, Audit log, Budget. Loop. Principal sets objectives -> agents plan and execute within mandates -> approval queue for gated actions -> audit and budget tracking. Cold start. Already happening informally; the product is the mandate/approval/audit layer that lets one person safely run many agents. Kill criterion. If the principal's approval queue becomes the bottleneck (hours per day), autonomy levels must rise or the institution is a one-person bottleneck.

Buildable now. Entirely, at autonomy levels 1-3. Gate. Reliability over long horizons (METR-style task horizon) and mandate tooling.

Forcing questions

1. What is the approval-queue budget (decisions per day) a person can sustain, and what must be automated to stay under it?
2. Which legal acts still require a human signature, and how is the principal's liability bounded?
3. What is the security model when one compromised agent can act under many mandates?

20 · Self-assembling institutions and matching narrow profile digital closure: complete

Market for missions

Intuition. Job listings standardize the role and make the person fit it. Invert it: people describe the problems, environments, risks, and learning they want; agents match them to missions and construct roles around them.

Objects. Mission (from idea 15), Capability profile (private), Preference profile (problems, environments, risk, values, learning goals), Match with explanation, Role proposal (constructed), Trial engagement (idea 24), Commitment. Loop. Profiles and missions indexed -> matches with explanations -> both sides approve -> role constructed -> trial -> commitment -> outcome feedback. Cold start. Experts get value alone: relevant open problems, suggested directions, potential collaborators. Missions get candidate lists built from public work (unclaimed profiles). Kill criterion. If mutual-accept rate of proposed matches is below ~10%, the matching model or the explanations are not trusted.

Buildable now. Entirely, for digital work. Gate. Narrow private profile; consent for outreach; mission supply.

Forcing questions

1. Is the match explanation good enough that both sides accept without a conversation? Measure it.
2. How are unclaimed profiles handled ethically: searchable but never contacted until claimed?
3. What is the compensation model for constructed roles that do not exist as jobs?

24 · Self-assembling institutions and matching narrow profile digital closure: complete

Cofounder matching through trial projects

Intuition. Cofounder compatibility is revealed by working together, not by profiles. Match on ambition, risk, values, and style, then design a short, real collaboration as the test.

Objects. Founder profile (private), Compatibility model, Trial project (scoped, real, 1-3 weeks), Observation record (consented), Decision. Loop. Match -> propose trial project with a real deliverable -> both work -> agents observe process signals with consent -> structured debrief -> decision. Cold start. Inside an accelerator or a mission's task list: trial projects are real open tasks (idea 15), so the trial produces value even when the pair does not form. Kill criterion. If pairs that pass trials dissolve at the same rate as unmatched pairs within a year, the trial is not measuring what matters.

Buildable now. Entirely. Gate. Narrow private profile; supply of trial projects.

Forcing questions

1. What process signals during a trial actually predict founder durability, and can they be observed without surveillance?
2. Who owns the trial's output if the pair does not form?
3. Is the customer the founder, or the accelerator/fund?

F. Bargaining and collective action

21 · Bargaining and collective action narrow profile digital closure: complete

Universal bargaining agents

Intuition. Institutions negotiate with algorithms and scale; individuals negotiate alone and tired. A bargaining agent with the person's constraints and the market's data levels the table on bills, rent, claims, loans, and subscriptions.

Objects. Negotiation mandate (goal, limits, walk-away), Counterparty model, Evidence (market comparables, contract terms, error detection), Offer sequence, Settlement, Outcome record. Loop. Mandate -> evidence gathering -> negotiation (with a human counterparty, an institution's workflow, or its agent) -> settlement within limits -> record. Cold start. Medical bills and insurance denials: large documented error rates, existing appeal processes, measurable savings; products already exist. Kill criterion. If savings net of fee are below what a template letter achieves, the 'agent' is a form-filler; fine, but price it like one.

Buildable now. Entirely for text-channel negotiations; institutions' acceptance is the variable. Gate. Institutional acceptance of agent counterparties; consumer-protection and unauthorized-practice rules (DoNotPay precedent).

Forcing questions

1. Which counterparties will negotiate with an agent at all, and which require a human on the line?
2. What authority does the agent need (power of attorney, HIPAA authorization), and how is it granted per negotiation?
3. When both sides have agents, does the outcome converge to a fair split or to the stronger model? Measure it early.

22 · Bargaining and collective action narrow profile digital closure: complete

Temporary collective bargaining

Intuition. Many people face the same counterparty with the same grievance and never find each other. Agents can discover the cohort privately, form a temporary coalition, negotiate once, and dissolve.

Objects. Grievance/need signature (private), Cohort discovery (privacy-preserving matching on counterparty + issue), Coalition (terms, representative agent, exit rules), Collective negotiation, Settlement allocation, Dissolution. Loop. Agents register signatures -> cohort threshold -> coalition formed with opt-in -> one negotiation -> settlement allocated -> dissolve. Cold start. Group purchasing for digital subscriptions or energy tariffs (legal, measurable); grievance coalitions later (class-action adjacency). Kill criterion. If coalitions form but counterparties refuse to negotiate collectively, the lever is legal (class action) not commercial.

Buildable now. Group purchasing and tariff switching; private cohort discovery with simple matching. Gate. Privacy-preserving cohort discovery (idea 43), antitrust and class-action law.

Forcing questions

1. Is buyer-side collective bargaining lawful in the target market (antitrust treatment of purchasing coalitions varies)?
2. How does a member exit a coalition mid-negotiation without breaking it?
3. Who is the coalition's legal person for a settlement?

23 · Bargaining and collective action institutional digital closure: partial

Instant clinical-trial recruitment

Intuition. Trials fail to recruit while eligible patients never hear of them. A private health agent can match records to criteria, prove eligibility selectively, and coordinate logistics without the patient's record leaving their control.

Objects. Private health record, Eligibility criteria (machine-readable), Eligibility proof (selective disclosure), Trial site, Consent record, Logistics plan (records transfer, tests, transport). Loop. Criteria published -> patient agent screens locally -> proof of eligibility shared with consent -> site confirms -> logistics coordinated. Cold start. Registry-side: structure criteria for live trials and let patient-side apps (existing PHR apps) screen locally. Kill criterion. If site-side workflows cannot accept agent-screened referrals (regulatory, IRB), the patient side is wasted; validate site acceptance first.

Buildable now. Criteria structuring and local screening against patient-held records (FHIR). Gate. Institutional: site workflows, IRB/privacy law, records access.

Forcing questions

1. Will sites accept a referral whose eligibility was proven by an agent, and under what evidence standard?
2. Which records access path is real for patients today (FHIR APIs via patient access rules), and what coverage does it give?
3. Who pays: sponsors (recruitment budgets are large) or patients?

G. Science and civilizational memory

25 · Science and civilizational memory no personal agent digital closure: complete (digital part)

Autonomous science engine

Intuition. A hypothesis should have an agent that never stops gathering evidence for and against it, finds data, designs falsifying experiments, recruits labs, requests replications, and keeps a live confidence.

Objects. Hypothesis (precise, falsifiable), Evidence graph (support/contradict, graded), Experiment proposal (design, cost, discriminating power), Lab/equipment offer (idea 33), Replication request, Confidence with history. Loop. Maintain evidence -> rank experiments by discriminating power per cost -> commission digital analyses now and physical experiments via offers -> update confidence -> publish. Cold start. Computational fields (ML, bioinformatics, economics with public data) where the 'experiment' is code + data; several live systems already produce validated hypotheses. Kill criterion. If hypothesis agents never change a working scientist's next experiment, they are literature reviews; track 'changed the next experiment' events.

Buildable now. The digital part entirely; physical experiment commissioning needs idea 33 and money. Gate. Quality of autonomous literature synthesis and experiment design (measurable now); lab capacity markets.

Forcing questions

1. Which field first: one where experiments are computational and data is public.
2. How is evidence graded to avoid citation-count-as-truth?
3. What is the interface to a real lab: a bounty, a contract, a collaboration?
4. Who owns the hypothesis agent's outputs and the credit?

26 · Science and civilizational memory no personal agent digital closure: complete

Adversarial scientific agents

Intuition. Claims survive because disproving them is unpaid work. Fund agents whose only job is to disprove important claims: confounders, leakage, failed replications, experiments under which the claim should fail.

Objects. Target claim, Attack plan (confounders, leakage checks, reanalysis, replication), Reproduction attempt (code + data), Verdict with evidence, Bounty for successful refutation, Author response. Loop. Select high-stakes claims -> run attacks (digital first) -> publish reproducible verdicts -> authors respond -> claim status updated in the memory graph. Cold start. Computational claims with public code/data; reanalysis and leakage detection are fully digital and cheap; red-team the dossiers this system itself produces. Kill criterion. If refutation rate on well-chosen claims is near zero, either selection is wrong or the attacks are weak; if it is very high with no author acceptance, the verdicts lack credibility.

Buildable now. Entirely for computational claims. Gate. Funding independent of the institutions being attacked; author-response norms.

Forcing questions

1. Who funds adversarial agents so they are independent of both authors and journals?
2. What is the evidence standard for 'refuted', and who adjudicates disputes?
3. How do you prevent harassment-by-red-team of individual researchers?

27 · Science and civilizational memory no personal agent digital closure: complete

Civilization's memory

Intuition. Institutions forget. A persistent graph of claims, evidence, decisions, assumptions, forecasts, disagreements, and outcomes lets the next effort retrieve 'what was believed, why, and what happened' for structurally similar past efforts.

Objects. Claim, Evidence, Decision (with stated assumptions and forecasts at the time), Disagreement record, Outcome, Postmortem, Structural-similarity index (problem shape, not keywords). Loop. Every mission/decision writes its beliefs and forecasts at decision time -> outcomes attached later -> postmortems -> retrieval by structural similarity when a new effort starts. Cold start. Your own dossiers and resolved forecasts; import resolved prediction-market questions, retractions, and failed-project postmortems as seed. Kill criterion. If retrieval never changes a new mission's plan (no 'this was tried' hits that alter decisions), the index is not structural enough.

Buildable now. Entirely; it is the existing object graph plus decisions and outcomes. Gate. Adoption: people must write beliefs down at decision time. The incentive is the retrieval value they get back.

Forcing questions

1. What is 'structural similarity' operationally: a learned embedding of problem shape, or a hand-built typology?
2. What incentive gets institutions to record decisions and forecasts before outcomes are known?
3. Open commons or private-per-institution with a shared schema?

42 · Science and civilizational memory no personal agent digital closure: complete

Automated institutional red teams

Intuition. Organizations audit themselves. Independently funded agents that continuously search for corruption, metric manipulation, discriminatory effects, hidden concentration, and unexamined assumptions are a standing adversary that does not get tired or captured.

Objects. Target (organization, policy, dataset), Attack library (metric gaming, concentration, disparate impact, procurement anomalies), Finding (evidence, severity, reproducibility), Disclosure protocol, Response. Loop. Continuous scan of public and authorized data -> findings reproduced -> responsible disclosure -> public record -> response tracked. Cold start. Public procurement and open corporate data (filings, contracts) where anomaly detection is digital; red-team this system's own dossiers. Kill criterion. If findings are never acted on or contested, either they are wrong or the disclosure channel is wrong; measure response rate.

Buildable now. Entirely on public data; authorized internal access is a sales problem. Gate. Independent funding; legal protection for findings (defamation, trade secrets).

Forcing questions

1. Who funds red teams so that they are independent of targets and of governments?
2. What is the evidence standard before publication, and who bears legal risk?
3. How is the red team itself audited for bias and errors?

H. Representation for the unrepresented

30 · Representation for the unrepresented narrow profile digital closure: complete

Opportunity agents for children

Intuition. Children's abilities emerge unevenly and opportunities are distributed by parental network. A child-controlled agent can spot emerging abilities and find mentors, programs, and communities, without building a permanent dossier on a minor.

Objects. Ephemeral ability signals (expire by design), Opportunity index (mentors, programs, scholarships, competitions), Match with guardian consent, No persistent profile beyond what the child/guardian chooses. Loop. Child/guardian opt in -> signals -> matches proposed -> guardian consent -> introduction -> signals expire. Cold start. Opportunity index is single-player value for families; the ability model is optional and local. Kill criterion. If the system cannot operate with data minimization (expiring signals), it should not exist; that is a design invariant, not a metric.

Buildable now. The opportunity index and local-only matching. Gate. Child-privacy law (COPPA-like regimes), guardian consent models.

Forcing questions

1. What does 'child-controlled' mean legally for a minor, per age band?
2. How do ability signals expire while still being useful for matching?
3. Who curates the opportunity index and vets mentors?

31 · Representation for the unrepresented no personal agent digital closure: complete

Representation for future generations

Intuition. Decisions with century-long consequences are made by people with election-cycle horizons. A future-generations agent models long-horizon effects and argues for people who cannot yet speak.

Objects. Decision under review, Long-horizon model (explicit assumptions, discount rate debated not assumed), Impact statement, Standing (where the agent may formally intervene), Track record (as forecasts resolve). Loop. Monitor decisions -> produce long-horizon impact statements -> intervene where standing exists (consultations, comment periods, boards) -> record forecasts -> resolve and score over decades. Cold start. A public commentator on major decisions (infrastructure, debt, environment) with pre-registered forecasts; Wales' Future Generations Commissioner is the institutional precedent. Kill criterion. If its forecasts are unscorable within the lifetime of the project (no short-horizon leading indicators), it cannot earn credibility; require indicators.

Buildable now. Entirely as a commentator; formal standing is institutional. Gate. Institutional standing; credibility from resolved forecasts.

Forcing questions

1. Whose values does the agent represent for people who do not exist: a stated constitution, or a debate among several agents?
2. What near-term indicators make century-scale forecasts scorable?
3. Where does it get formal standing first (comment periods, ESG boards, courts)?

32 · Representation for the unrepresented no personal agent digital closure: complete (advocacy part)

Agents for ecosystems and nonhuman interests

Intuition. A river has no lawyer. Give ecosystems agents that monitor their condition, model the consequences of decisions, and participate as formal advocates where legal personhood or consultation rights exist.

Objects. Ecosystem entity (boundary, indicators), Sensor/data feeds, Condition model, Impact statement per decision, Standing (legal personhood, guardianship, consultation), Guardian (human). Loop. Monitor -> model impacts of proposed decisions -> intervene via guardian where standing exists -> record forecasts -> score. Cold start. Ecosystems with existing legal personhood (Whanganui River, Mar Menor) and open monitoring data; the agent serves the human guardians. Kill criterion. If guardians do not use the agent's outputs in formal proceedings within a year, it is a dashboard.

Buildable now. Monitoring, modelling, and impact statements; advocacy is through human guardians. Gate. Legal standing (exists in a few jurisdictions); data coverage.

Forcing questions

1. Which ecosystem has both legal standing and enough open data to model? Start there.
2. Who funds the agent so it is independent of the parties it argues against?
3. How are competing nonhuman interests (species vs species) represented?

I. Physical resource coordination

33 · Physical resource coordination scoped mandate digital closure: partial

Real-time physical resource networks

Intuition. Idle machines, labs, vehicles, storage, and byproducts are invisible capacity. Give each an agent that advertises availability and negotiates use; coordination becomes a continuous market instead of a phone tree.

Objects. Resource (type, location, capability, calendar, price rules), Availability offer, Use request, Agreement (terms, insurance, liability), Access credential, Usage record. Loop. Resource agents publish availability -> request agents search and negotiate -> agreement -> access -> usage recorded and rated. Cold start. Research instruments at universities (core facilities already have calendars and fee schedules) and compute; physical logistics later. Kill criterion. If utilization of listed resources rises less than a few points in a year, discovery was not the bottleneck; trust or logistics was.

Buildable now. Listing, search, and negotiation over resources that already have booking systems (core facilities, cloud, equipment rental). Gate. Insurance/liability for physical use; owner willingness to expose capacity.

Forcing questions

1. Which resource class has calendars and fee schedules already exposed (core facilities, compute) so the agent adds negotiation, not plumbing?
2. Who insures third-party use of an instrument or machine?
3. Does the owner want price discovery, or a fixed schedule and no surprises?

34 · Physical resource coordination scoped mandate digital closure: partial

Autonomous industrial symbiosis

Intuition. One plant's waste is another's input, but matching requires knowing composition, volume, timing, transport, processing, and permits. A waste-stream agent can find and arrange the match.

Objects. Stream (composition, volume, timing, location), Candidate use (process that accepts it), Transport/processing plan, Contract, Regulatory approval path, Outcome. Loop. Streams published (privately) -> candidate uses found -> logistics and compliance planned -> contract -> execution -> recorded. Cold start. An industrial park or port with a known set of plants; the agent does the matching and compliance paperwork the park operator cannot. Kill criterion. If matches found are blocked by permits more than by economics, the product is regulatory navigation, not matching.

Buildable now. Matching and compliance planning on declared streams. Gate. Data sharing by plants; waste regulation; logistics.

Forcing questions

1. Will plants disclose stream data (commercially sensitive) to a matching agent, and under what confidentiality?
2. Is the binding constraint price, transport, or permits, per stream type?
3. Who is the customer: the plant, the park operator, or the regulator?

35 · Physical resource coordination institutional digital closure: partial

Preventive insurance holons

Intuition. Insurance pays after loss; a member-owned insurance holon is paid to prevent loss: it coordinates maintenance, health interventions, fire prevention, cybersecurity, and preparedness because its members keep the savings.

Objects. Member, Risk model, Prevention intervention (with expected loss reduction), Pool (capital, reinsurance), Claim, Governance. Loop. Risk modelled per member -> interventions recommended and subsidized -> losses and savings tracked -> premiums and surplus adjusted by members. Cold start. A narrow, measurable peril with cheap prevention (cyber for small firms, water damage, roof/wildfire hardening) inside an existing mutual or captive. Kill criterion. If prevention spend does not reduce loss ratio measurably in two cycles, the holon is a mutual with extra steps.

Buildable now. Risk modelling and prevention coordination inside an existing mutual; not a new insurer. Gate. Institutional: insurance licensing, capital, reinsurance.

Forcing questions

1. Which existing mutual or captive would host the prevention agent?
2. Which peril has the best prevention-cost-to-loss-avoided ratio with data to prove it?
3. How do members verify interventions happened (the moral-hazard problem in reverse)?

J. Contracts, experiments, crisis

36 · Contracts, experiments, crisis no personal agent digital closure: complete

Purpose-aware contracts

Intuition. Contracts encode terms and lose purpose. A purpose-aware contract keeps the intended outcome, assumptions, metrics, renegotiation rules, and escalation so agents can resolve routine ambiguity while humans keep appeal.

Objects. Contract (terms + purpose + assumptions + metrics + renegotiation triggers + escalation ladder), Event, Interpretation (agent), Dispute, Appeal (human). Loop. Draft with purpose layer -> events monitored -> agents interpret routine ambiguity against purpose -> disputes escalate per ladder -> outcomes recorded. Cold start. Use it for every commitment inside the Mission Exchange (task bounties, data licences) so the template is exercised thousands of times before anyone else adopts it. Kill criterion. If courts or arbitrators ignore the purpose layer in a real dispute, it binds nobody; test with arbitration clauses that reference it.

Buildable now. Entirely, as a template plus an interpreting agent. Gate. Enforceability of agent interpretations (arbitration clauses can make them binding by consent).

Forcing questions

1. Is the purpose layer legally operative (incorporated by reference, arbitration clause) or just documentation?
2. Who chooses the interpreting agent, and can each party run their own?
3. What is the escalation SLA and cost, and who pays for appeals?

38 · Contracts, experiments, crisis institutional digital closure: partial

Large-scale policy experimentation

Intuition. Policy is tested on everyone at once. Communities could run bounded variations with explicit predictions, safeguards, success conditions, and termination rules, turning policy into reusable experimental knowledge.

Objects. Experiment (policy variation, population, duration), Pre-registration (predictions, safeguards, stop rules), Monitoring, Result, Knowledge entry (idea 27). Loop. Design -> pre-register -> run with monitoring -> stop rules enforced -> result recorded in shared memory -> reused by the next community. Cold start. Institutional pilots already happen (basic-income pilots, zoning pilots); the product is the pre-registration and knowledge-capture layer. Kill criterion. If pilots will not pre-register predictions, the layer adds nothing; pick funders who require it.

Buildable now. The pre-registration and knowledge layer. Gate. Institutional: authority to vary policy; ethics of experimentation on communities.

Forcing questions

1. Which funders of pilots would require pre-registration as a condition?
2. What are the ethical boundaries on bounded policy experiments, and who consents?
3. How are results made reusable across very different communities (what is the transferable unit)?

39 · Contracts, experiments, crisis scoped mandate digital closure: complete (search part)

Machine-speed peace negotiation

Intuition. Negotiations fail in the space of proposals humans can hold in their heads. Agents can privately search vast spaces of trades, sequencing, verification, compensation, and face-saving packages and surface candidates no human would have found.

Objects. Party model (interests, constraints, red lines; private), Issue space, Package generator, Verification design, Private evaluation (each side's agent scores packages without revealing its model), Mediator protocol. Loop. Each side's agent holds a private model -> a mediator protocol proposes packages -> agents score privately -> only mutually acceptable packages surface -> humans negotiate from there. Cold start. Commercial disputes and labor negotiations (measurable, repeated); diplomacy later. The research evidence on AI-AI bargaining is mixed; measure it. Kill criterion. If agent-found packages are rejected at the same rate as human proposals, the search is not finding new Pareto improvements.

Buildable now. Package search and private scoring for commercial negotiations. Gate. Trust in the mediator protocol; evidence that agent negotiation does not advantage the stronger model.

Forcing questions

1. What is the mediator protocol that lets agents score packages without leaking interests (MPC, trusted mediator, or iterative revelation)?
2. Does a stronger model systematically win? If so, the tool must be symmetric (same model for both sides) to be adopted.
3. Which negotiation class first: commercial, labor, or interstate?

40 · Contracts, experiments, crisis institutional digital closure: partial

Emergency institutions on demand

Intuition. Disasters need an institution for a week. Agents can inventory needs and resources, verify volunteers, assemble a temporary authority structure, run logistics, track funds, and dissolve.

Objects. Incident, Needs ledger, Resource ledger, Volunteer credential, Authority structure (roles, decision rights), Logistics plan, Fund ledger, Dissolution report. Loop. Incident declared -> needs and resources ingested -> volunteers verified -> roles assigned -> logistics optimized -> funds tracked -> after-action report -> dissolve. Cold start. Mutual-aid networks and NGOs that already improvise this; the agent layer is the ledger and matching they lack. Kill criterion. If official responders will not share needs/resources data with the temporary structure, it operates only in the mutual-aid layer; design for that.

Buildable now. Needs/resources ledgers, volunteer credentialing, matching, fund tracking. Gate. Institutional: emergency authority, data sharing with official responders.

Forcing questions

1. Who has authority in the temporary structure, and how does it hand off to official responders?
2. How are volunteers verified fast without a pre-existing credential system (idea 44)?
3. What is pre-built before the disaster (templates, credentials) versus assembled during it?

K. Substrate: identity, privacy, protocols

43 · Substrate: identity, privacy, protocols no personal agent digital closure: complete

Privacy-preserving coordination

Intuition. Coordination requires proving things about yourself (eligible, qualified, solvent, compatible) and today proof means disclosure. Selective disclosure and attestations let agents prove without revealing.

Objects. Credential (issuer, holder, claims), Presentation (selective disclosure), Verifier policy, Attestation (third-party or computed), Revocation. Loop. Issuers issue -> holders' agents present minimal proofs per verifier policy -> verifiers accept -> revocation checked. Cold start. Use VC 2.0 presentations for the credentials the Mission Exchange needs (identity, affiliation, track record) and nothing else. Kill criterion. If verifiers in your network insist on full documents anyway, the privacy layer is unused; pick verifiers you control first.

Buildable now. Entirely with VC 2.0; advanced cryptography (ZK, MPC) only where a verifier demands it. Gate. Issuer ecosystem (EU wallet mandate helps); verifier acceptance.

Forcing questions

1. Which three credentials does the first network actually need, and who issues them?
2. Plain VC presentations or ZK proofs: what does the first verifier require?
3. How is revocation handled without a phone-home that leaks usage?

44 · Substrate: identity, privacy, protocols no personal agent digital closure: complete

Portable identity and reputation

Intuition. Reputation is rebuilt from zero on every platform because platforms own it. Portable credentials and transaction history let a person carry trust between networks, which is what makes holons possible.

Objects. Reputation credential (issuer = the network where the outcome occurred, claim = outcome, not score), Holder wallet, Presentation, Verifier policy, Dispute flag. Loop. Each completed commitment issues an outcome credential to the participant -> participant presents them elsewhere -> verifiers compute their own score from raw outcomes. Cold start. Every completed task in the Mission Exchange issues a credential; accept external credentials (GitHub, publications, ORCID) as seed. Kill criterion. If no second network accepts the credentials within a year, portability is theoretical; recruit one counterpart network early.

Buildable now. Entirely with VC 2.0 and existing identity anchors. Gate. A second network that accepts them; issuer trust.

Forcing questions

1. Portable outcomes (raw facts) or portable scores? Scores do not transfer; outcomes do.
2. Who issues, and what stops issuers from inflating?
3. How does a participant dispute a negative credential?

45 · Substrate: identity, privacy, protocols no personal agent digital closure: complete

Open agent coordination protocols

Intuition. Agents must discover one another, exchange proofs, negotiate, transact, form institutions, and resolve disputes. Transport and auth are being standardized (MCP, A2A, VC); the semantic layer (mission, mandate, commitment, outcome) is not, and that is where the network effect lives.

Objects. Transport (A2A), Tool access (MCP + OAuth), Credentials (VC 2.0), Payments (AP2/ACP/x402 as they mature), Semantic schema (Mandate, Mission, Task, Ask, Offer, Match, Commitment, Artifact, Assessment, Outcome), Dispute protocol. Loop. Adopt transport/auth/credential standards as-is -> publish a small open semantic schema -> run it inside one network -> invite a second network to interoperate -> iterate the schema from real disputes. Cold start. Publish the schema your own network uses; interoperability is earned by the second adopter, not by a standards body. Kill criterion. If after a year the only implementation is yours, it is an internal data model, not a protocol; fine, but stop calling it open.

Buildable now. The semantic schema and a reference implementation; transport and auth are available. Gate. A second independent implementer.

Forcing questions

1. Which exact objects go in the open schema v0 (the plan's 15 is too many; the mandate and the commitment are the minimum)?
2. Which payment rail does the first build depend on, if any, given their immaturity?
3. How are disputes between agents from different networks resolved: a shared arbitration protocol, or bilateral?
4. Who is the second implementer you can recruit now?

L. Surfaced by the exploration loop (not in the original inventory)

46 · Surfaced by the loop no personal agent digital closure: complete

Attestation-and-indemnity authority for agent-produced work

Intuition. Title insurance for AI-assisted deliverables. Every existing AI assurance product is per-system-per-period (a certification of the vendor); no product sells per-artifact, per-decision cover. As professional standards and insurers push verification duties onto a named human signer, a utility that attests a specific artifact against a specific decision, and an insurer that writes cover on that receipt, becomes a priced primitive.

Objects. Artifact, Decision (with dollar exposure), CriterionSet, sealed rotating ProbeSet, named credentialed Panel, Ratification (scope and exclusions), Receipt (hash, lineage, model and scaffold version), Claim path. Cold start. One obligated buyer that already verifies (publisher integrity team, CRO endpoint committee, accredited certification body, grant diligence unit, procurement office); instrument their existing artifact stream. Kill criterion. No underwriter, accredited auditor or procurement function will state in writing that the receipt substitutes for one of their steps; or surviving discordance against the buyer's incumbent pass is below ~10%.

Forcing questions

1. Own the signature (licensed signer, liability capacity) or sell tooling to whoever owns it?
2. Which insurer will write per-artifact cover, at what premium, leaving the fee intact?
3. What independence requirement (regulator, insurer, journal, funder) would make vendor self-attestation unacceptable, and when?

47 · Surfaced by the loop no personal agent digital closure: complete

Resolution Ledger for decisions with no public timestamped substrate

Intuition. Public forecasting is being automated wherever a timestamped corpus exists (listed markets, news, Kalshi/CME compute contracts). Private capital-allocation and infrastructure decisions (interconnect queue outcomes, fab tooling qualification, PPA delivery, energisation dates, procurement awards) have no such substrate, so resolution there is unsolved and defensible.

Objects. Decision, PreRegisteredCriterion (resolver as code), hash-chained EvidenceBundle, Forecast, Resolution by an independent resolver, exportable Receipt. Cold start. Audit 50 historical decision records in one public-record-heavy slice (interconnection queues, permitting, grid studies) and build the resolver library. Kill criterion. Definitional instability (queue restatements annulling live contracts) above the Metaculus ~8% annulment rate; or cross-buyer reuse of resolution contracts below ~20%.

Forcing questions

1. Which decision class has canonical sources, frequent decisions with stateable thresholds, and no incumbent consented corpus?
2. Who is the independent resolver the buyer will accept when the answer is unwelcome?
3. What fraction of resolution contracts are reusable across buyers (utility at >60%, services at <20%)?

48 · Surfaced by the loop no personal agent digital closure: complete

Mandate and Provenance Ledger as the EU Article 50 conformance layer

Intuition. From 2 Aug 2026 agents operating in the EU must disclose both their artificial nature and the principal on whose behalf they act, including across multi-agent chains. A ledger that records principal, mandate scope, provenance manifest, marking record and human-review attestation per artifact, with a verifier API that answers 'is this artifact compliant for publication in jurisdiction X on date Y', is the compliance object every agent deployer will need.

Objects. Principal, Agent (disclosed principal and mandate scope), Artifact, C2PA-style provenance manifest, per-modality marking record, Human-Review Attestation (named reviewer, editorial-responsibility flag), PresentationBoundaryCrossing, Jurisdiction. Cold start. Run it on this system's own agents and published dossiers; it is the same mandate object as System 2 with a jurisdiction and marking layer. Kill criterion. A lab platform ships the same object as a free field (an 'attested by' or compliance record inside OpenAI Frontier or Anthropic Cowork); then supply into it rather than compete.

Forcing questions

1. Does the verifier API need to be operated by a legal person the regulator recognises?
2. Which modality markings (text, image, audio) are enforceable today and which are aspirational?
3. How does the ledger handle multi-agent chains where the principal changes mid-chain?

VII. Futures

Futures for world-scale coordination over the stated horizons, scored as coordination capacity versus today (0 = today, +100 = twice today's capacity to turn intent into coordinated action, -50 = half). The loop's synthesis produced the scenarios and their weights; the weights are judgements anchored where the rationale says they are anchored, and nowhere else.

Three futures, anchored (no weights given: there is no base rate)

Future 1 · coordination capacity roughly +20 to +60 versus today by 2029

Reliability plateau (capability stalls near 2026 levels)

What is true. 80%-reliable horizons stay at hours, not days; RLI automation climbs but stays well under half; the compute/power bound bites after 2027 (capex > cash flow, DC power ~2.3x by 2030).

What becomes buildable. Everything that is digital and human-adjudicated: evaluation engines, mandates, information-value markets with agent answerers, decision registries, claim agents, red teams, bargaining-as-drafting. Holons and self-assembling institutions stay manual.

What to do. Build the evaluation and mandate primitives; keep humans as adjudicators; do not build anything that needs an agent to hold a P&L for a week.

Future 2 · coordination capacity roughly +100 to +300 versus today by 2029

Steady doubling (horizons keep doubling every 4-7 months)

What is true. Multi-day 80%-reliable work by 2027-28; cost at fixed capability keeps halving in months; A2A/MCP installed base converges on the 2026 specs; EU wallets give agents human-grounded credentials from 2027; card rails reach GA.

What becomes buildable. Agent-first bounties at compute cost; self-assembling digital organisations; temporary collective bargaining; demand-before-supply for digital goods; autonomous science engines commissioning real experiments; one-person institutions at levels 3-4.

What to do. The environment built in the plateau future becomes the substrate these plug into; the binding constraint becomes legal recognition of mandates and the approval-queue bandwidth of principals.

Future 3 · coordination capacity unbounded upward in digital domains; physical and institutional domains lag by years

Discontinuity (full autonomy arrives; identity and law become the bottleneck)

What is true. Agents do end-to-end knowledge work reliably; intelligence is no longer scarce; what is scarce is authority (who may commit whom), verification of provenance, compute allocation, and institutional interfaces that still run on paper.

What becomes buildable. Nearly the whole inventory on the digital side; government, medicine, mobility and insurance still wait on institutions.

What to do. Own the authority and provenance layer (mandates, credentials, outcome ledgers); it is the only part of the stack that does not get commoditised by the models themselves.

Futures (loop synthesis)

Loop view · alias AGENTIC · 18 questions · $68 · stop: question budget reached (18/18)

HOLD : judgement weight on 'build coordination infrastructure now, ahead of general personal agents' = 0.35 (not a calibrated probability)

What to build and in what order: Build the enabling primitive first, in this order. (1) Ninety days: the Decision Record / Second Reader for ONE named mandate-holding buyer. Objects: Decision (owner, date, dollars at risk, reversibility), ResolutionContract (named source, cadence, annulment and restatement policy), AcceptanceCriterion (pre-registered, versioned, rotating sealed probes), Attempt, JudgePanel (>=3 cross-family judges, published rolling agreement against ratifiers), Discordance, Ratification (named human, scope, exclusions), Receipt (hash, lineage, model_id/version/scaffold_hash/generation_ts/publication_ts, exportable). Price the maintained record, never the question. (2) Months 4-9: add Task/Ask/Offer/Match ONLY inside that buyer's budget, with counterparty_type enforced to legal-person for EU-touching work, and sell the same adjudicated bundle non-exclusively to a second buyer class (lab eval/data desk at $500-$2,000 per verifier-carrying bundle) to test the reuse licence that determines whether this is a utility or a services firm. (3) Months 10-18: only if reuse exceeds ~60% of resolution contracts, generalise into the Mission Exchange objects as shared infrastructure. Do NOT build first: the three-sided exchange, capital mandates as binding objects, platform-authored merit ranking of securities, person-level shadow agents, any token/DAO, autonomous payments, or a new protocol. Depend only on MCP 2026-07-28 (stateless core, Tasks, Enterprise-Managed Authorization) and A2A v1.0 plus OAuth 2.1; treat AuthZEN AARP/COAZ, AP2, x402 and VC 2.0 as optional adapters for at least twelve months. First 90-day move: In the next 90 days, run one experiment with one buyer, not a market. Sign a single mandate-holder who is already obligated to verify (a publisher integrity team, a CRO endpoint committee, an accredited certification body with a Stage-2 backlog, a grant programme's diligence unit, or a procurement office facing an AI-exclusion renewal) and instrument 300-1,000 of their EXISTING artifacts plus 30 of their live decisions. Ship: pre-registered criteria, a cheap cross-family judge panel on every artifact, human ratification only on surviving discordance, and an exportable receipt. Log four numbers that nobody currently publishes: surviving discordance rate after the buyer's own adjudication; expert-minutes per accepted, decision-linked question split into triage versus accept-side; reviewed-but-rejected candidate count; and dated action-or-budget changes attributable to a record. Charge from day one at $10k-$25k per attested decision or $2,500 per maintained record-quarter, and get one written statement from an underwriter, accredited auditor or procurement function that the receipt substitutes for one of their own steps. Do not seed person-level shadow agents anywhere in scope of EU law. (90 days to the first kill-or-continue checkpoint, inside a 12-month underwrite and a 5-year coordination horizon) Commitment before the first checkpoint: Commit 2-3 people and roughly $120k-$350k of all-in cost for one quarter (variable pilot cost is small: $2.3k-$19k digital-only, $58k-$200k if 100 tasks are actually commissioned; the rest is loaded FTE). Machine spend is not the constraint at any plausible configuration - $25-$160 per mission of model spend, $1.50-$40 for the entire forecast layer. Do not hire a compliance or capital-markets function, do not retain a broker-dealer, and do not form per-mission entities before the checkpoint; budget $15k-$50k for a single written counsel opinion on the exact UI assertion set only if the pilot touches securities-adjacent surfaces. Kill criteria at 90 days, any one sufficient: (a) no unaffiliated buyer prepays at least $25k for a maintained record plus attestation, or fewer than five buyers pay for three artifacts each at >=$2,500; (b) surviving discordance against the buyer's incumbent pass is below ~10% of artifacts, or adjudicated true-positive share below ~50% - there is no second reader to sell (BICR's 42% on 23,476 cases and the 20-35% image-flag rate are what fundable discordance looks like); (c) median expert-minutes per accepted, decision-linked question exceeds 25 after three tooling iterations, or ranker precision@3 against the buyer's own accept decision stays below ~50%; (d) no underwriter, accredited auditor or procurement function will state in writing that the receipt substitutes for one of their steps; (e) zero accepted questions appear in a real internal memo, IC pack or filing within 60 days. If (a) or (d) fails but (b) and (c) pass, do not scale - fall back to selling adjudication tooling to whoever already owns the signature at $1k-$5k per unit with no liability. If the reuse ratio of resolution contracts across two buyers comes in below ~20%, underwrite this as a services firm at 2-3x revenue, not a utility at 8-12x, and size all subsequent capital accordingly. Reconciliation with observed adoption: There is no tape for an idea set: AGENTIC has no price, no trading history and no market context (trading_days_of_history = 0, last_price null), so nothing here is a market call and no position-neutral pricing signal exists. The substitute for a tape is observed adoption of the adjacent products and protocols named in the research, and that adoption cuts against the plan as written. Transport adoption is real and free: MCP reached a final 2026-07-28 spec with a Tasks extension and Enterprise-Managed Authorization stable since 2026-06-18; A2A v1.0 is stable with signed agent cards, 150+ organisations and cloud GA. Payment and credential adoption is not real: AP2 sits at v0.2 donated to FIDO with three public deployments, x402 shows ~167M transactions but only ~$28k/day of real commerce with roughly half gamified, and VC 2.0 supply is gated on an eIDAS deadline fewer than a third of member states were assessed ready for. Where money actually moved, it moved to single-buyer matching and to settlement: Mercor ~$2B gross annualised run-rate at a 30-35% take with 30,000+ weekly contractors, Handshake ~$1B, Kalshi ~$850M of 2026 fees with roughly 87% of volume in sports and a $1B raise at $22B. Where open allocation markets were tried they stalled or were retired by their own operators: Replication Markets closed in 2021 after 121 resolutions and $142,623 of prizes, Manifund's own retro concluded impact certificates never produced a better funding decision, DeSci totals sit in single-digit millions, and RetroPGF drew documented badgeholder-capture critiques. Meanwhile the incumbents shipped the plan's action layer inside their own workbenches (AlphaSense twelve workflow agents plus AI-led expert calls plus SuperAnalyst, Jan-Jul 2026; OpenAI Frontier 5 Feb 2026; Anthropic Cowork plus enterprise plugin marketplaces) and the regulated venues took the price layer (Kalshi GPU markets Mar 2026 and compute forward curves 14 Jul 2026, CME on a Silicon Data index, ICE with Ornn). The reconciliation is therefore: build on the protocols that have adoption, sell into the buyer types that already pay (single obligated buyer, lab eval desk), and do not build the three-sided exchange or the price layer that adjacent adoption shows is already owned.

completion: complete evidence as of 2026-08-23

How the view moved (internal checkpoints)

The loop re-states its view every four answers. The internal number is the judgement weight defined above; it is shown only as the audit trail of how the stance moved as evidence arrived.

Contested claims

• Frontier AI forecasting pipelines have reached parity with human superforecasters.: The Forecasting Research Institute (benchmark operator) stated on 2026-07-16 that models likely reached parity and that one system ranked above the superforecaster median on market questions for the first time, while noting overlapping confidence intervals. FutureSearch (a vendor) self-reports first place in Metaculus's Summer 2026 FutureEval and above-superforecaster-median ForecastBench performance. Good Judgment (which sells superforecaster services) counters that the human baseline is a one-time mid-2024 elicitation on a different question set, that dataset questions are now disclosed to developers, and that humans led market questions 80.3 to 75.8 as of 2026-04-13. Metaculus's own account projects parity around June 2027. An independent audit dated 2026-08-11 found that of 527 tested forecasters, 442 with enough resolved market questions scored worse than the crowd price they were handed in the prompt, and none beat it after multiple-comparison correction; ForecastBench's own baselines score 51.5 Brier Index without the price and 68.1 with it, versus 70.93 for submitting the price unchanged. · best-supported: The parity claim is real but partly an artifact of benchmark construction: market questions supply the crowd price to the model and are scored against that crowd forecast, worth roughly 15 Brier Index points. On a liquid-market subset the market price beats the best published AI forecaster (0.1106 versus 0.1258). The load-bearing conclusion for this decision is unaffected either way: calibrated forecasting is retailed at $0.15-$2 per question with automated resolution better than Metaculus's ~8% annulment, so there is no pricing power on any question-granular line item regardless of who wins the parity argument. · settled by: A fresh, live superforecaster elicitation on undisclosed questions scored against models on the same question set at the same time, plus a ForecastBench leaderboard variant that excludes freeze-value models entirely. FRI has said an autumn 2026 human round is planned; that result, reported with the price withheld from all entrants, would settle it.
• Independent assessment of AI-produced artifacts is a durable, priceable business rather than a commodity heading to zero.: For: per-artifact screening with the payer separated from the judged party is already contracted at scale (Imagetwin and Proofig into Wiley, Sage, Karger, Taylor & Francis, IOP and MDPI, integrated into ScholarOne and Research Exchange; STM Integrity Hub at 200,000+ manuscripts a month), regulator-mandated adjudication is a mature industry (42% of 23,476 oncology cases requiring a third reader), and accredited attestation prices are not deflating (EUR 4,356-10,925 per audit day with three-day minimums; ISO 42001 Stage-2 waits of six months on auditor scarcity). Against: AAAI-26 generated one AI review for all 22,977 main-track submissions in under 24 hours at under $1 per paper, funded by an in-kind donation and given away; flat-rate autonomous verification is marketed from ~$100/month with no triage queue; detection tools price at ~$300/seat/yr; and neutral inter-party arbitration has ~10^2 lifetime cases across seven years. · best-supported: Both are true of different products, and the consensus error is collapsing them. Detection and flagging are commodity and deflating toward zero - never price them. What is priced and not deflating is the mandate-holder's avoided specialist hours (1.5 hours pre-publication versus up to 10 hours post-publication), the proprietary reference corpus, the workflow slot, and the liability-bearing signature. So assessment is durable only when sold to a party already obligated to verify, priced per maintained record or per escalation rather than per artifact, and never sold as a verdict. · settled by: Whether any conference, publisher, regulator or insurer procures external assessment as a PAID line item with a named vendor and a price per unit, versus continuing to accept it in kind. The AAAI-27/ICML-27/ICLR-27 review-procurement decisions and the appearance (or not) of a verification-conditioned carve-back in 2027 professional-indemnity renewals are the two cleanest tests.
• The wedge's chosen first vertical - frontier-technology research and capital allocation - is an unserved category with an available buyer.: For (the plan and several vendor-side sources): RL and eval spend is exploding, domains are broadening from code and math into professional work, and per-task prices of $500-$2,000 leave room for a new non-software task-and-verifier supplier. Against: the largest expert network already ships capital-allocation reasoning as its flagship non-software benchmark family (investment banking, consulting, law, primary care; 480 tasks across 33 simulated worlds) and bought the two independent finance/enterprise environment builders within five months; more than 75% of roughly $8.5B of category revenue sits with four vendors; the one disclosed build cost implies ~$2,500 per task, above the middle of the quoted sale range; labs are in-housing; and in the plan's own vertical the incumbent research platform shipped twelve workflow agents, AI-led expert calls and a deliverable-automation layer between January and July 2026 while regulated venues took the price layer (Kalshi GPU markets March 2026, compute forward curves 14 July 2026, CME on a Silicon Data index, ICE with the same data source). · best-supported: Not unserved. Capital-allocation reasoning is already purchased, but only as rubric-graded deliverable production, never priced on world resolution; and the layer that got paid has consolidated into four vendors that acquire missing tooling rather than buy it as a marketplace service. The genuinely open slot is resolution custody for decisions with no public timestamped substrate - private, site- and counterparty-level outcomes - which is simultaneously the least reusable and therefore the most services-like. The $500-$2,000 anchor is one seller's quote inside an 18-person survey, weighted to software engineering, and is a revenue line rather than a margin. · settled by: Publication of per-task or per-environment price sheets by Mercor, Surge, Handshake, Turing, Mechanize or AfterQuery with a disclosed cost base; an explicit in-housing announcement or, conversely, a first named frontier-lab contract won by a new entrant in a non-software domain; and whether Kalshi's institutional block programme (filed 2026-08-19) actually lists an AI-infrastructure contract that holds two-sided depth beyond the first week.
• The EU Digital Omnibus bought a 15-16 month compliance-cheap window in which to ship an agentic matching layer and grandfather it.: Consensus (law-firm alerts and compliance vendors): Reg (EU) 2026/1744 defers Annex III high-risk duties to 2027-12-02 and Annex I to 2028-08-02, Article 111(2) grandfathers systems placed on the market before the application date unless subject to significant design changes, and the grace period now operates at type/model level - therefore build now. Against: the Commission's draft Article 6(5) guidance puts freelancers and platform workers inside Annex III point 4 wherever AI mediates access to work opportunities, and the Article 6(3) filter is unavailable because the system profiles natural persons; 'significant change in design' is undefined and hostile to a continuously retrained agent on a 6-12 month model-deprecation cadence; some sources say the closing date is dynamic (six months after a Commission readiness decision) while at least one says 2027-12-02 is unconditional; Article 50 and GDPR are live now, with SCHUFA/C-203/22 making a score a third party draws strongly on an Article 22 decision owed by the scorer; and the Platform Work Directive's Chapter III is status-blind and extraterritorial with a 2026-12-02 transposition deadline. · best-supported: The dates are right and the window is largely illusory for this specific build. The substantive constraint on contributor scoring already binds today under GDPR Article 22, the Article 50(1) principal-disclosure duty for agents went live 2026-08-02, and person-level profile seeding is enforceable now (AEPD/Amadeus EUR 18M reduced to EUR 14.4M in June 2026; UODO/Bisnode with an order to notify ~6 million people; the EDPB's 2026 coordinated action targeting Articles 12-14). Grandfathering is a decaying asset that exempts nothing but conformity-assessment paperwork. The correct reading is a staircase whose first step is behind us, not a cliff in December 2027 - and the escapes are architectural (legal-person counterparties, no platform-authored ranking, buyer-of-record) rather than calendar-based. · settled by: Adoption of the final Article 6 classification guidelines (due end-2026) and whether para 241's freelancer/platform-worker language survives; publication of any Commission Article 113 readiness decision, or confirmation from the OJ text that the trigger was dropped; the first national Platform Work Directive law in force with its scope definition readable; and a DPA decision on AI-generated personas or unclaimed profiles of non-users.
• AIUC-1 and the insurance-led certification stack constitute a credible, occupied attestation-and-indemnity slot.: For: AIUC-1 has 50+ controls across six domains with crosswalks to NIST AI RMF, MITRE ATLAS, ISO 42001 and OWASP, quarterly releases, Schellman accredited as first auditor in February 2026, live certificates at ElevenLabs (paired with the first voice-agent policy, 2026-02-11), UiPath (2026-03-09), Harvey and Cursor, 120+ consortium members, and Lloyd's-backed insurance so coverage terms follow audit results. Armilla is a Lloyd's coverholder with $25M+ limits, Munich Re's aiSure has run since 2018, and Testudo reaches $9.25M with QBE capacity. Against: AIUC authors the standard, runs the evaluations, issues the certificates, accredits its own auditors and sells the insurance the certificate enables - an issuer-pays structure independent commentators compare to credit ratings - and one tracker reports no claim has ever been paid. All observed pricing is per system and per period (percentage of licence fees with recovery capped at fees paid; parametric settlement against a pre-agreed KPI inside a defined scope of validity), not per artifact or per decision. · best-supported: The slot for system-and-period certification is occupied by parties with Lloyd's paper and accredited auditors, so competing there is unwise. But nobody in the evidence sells artifact- or decision-level ratification with liability transfer - the pricing structure is categorically different and the trigger for a judgment-work loss is unproven. So the occupied slot and the open slot are adjacent, not the same, and 'no claim ever paid' is unverified and could simply reflect book age. · settled by: The first publicly documented paid claim under an AI performance warranty or AIUC-backed policy, together with the evidence the adjuster required to establish causation in an agentic loss - that checklist becomes the de facto specification for an artifact receipt. Externally, ANAB or UKAS accreditation of AIUC-1 (separating standard-setting from certificate issuance) would settle whether this becomes a multi-supplier audit market that buys evidence tooling or an issuer-pays monopoly capturing the rent.
• A flat-fee, accredited-only capital-matching platform can rank and introduce opportunities without becoming a broker or investment adviser.: Consensus (practitioner commentary): a four-part checklist from the 2013 FundersClub and AngelList no-action letters - no transaction-based compensation, no custody, no investment advice, no negotiation - plus JOBS Act Section 4(b) makes a flat-fee accredited platform workable, with disclaimers ('nonbinding', 'for information only') as mitigation. Against, from primary documents: Section 4(b) blesses hosting a negotiation channel, providing due-diligence services for compensation and offering optional standardized documents, but the SEC has twice written that objective criteria may not include an assessment of the issuer, its business plan, its management or its risks; FINRA says 'Hot this week'-style labels are not objective and may urge purchase; a flat subscription still satisfies the Advisers Act's 'for compensation' prong so tailored rankings trigger adviser status; the only no-AUM federal route (Rule 203A-2(e) since 2025-03-31) forbids any human-delivered advice, which is incompatible with a human ratification panel; Section 4(b) does not preempt state licensing; and both 2013 platforms actually solved the problem by operating affiliated registered investment advisers taking carry. · best-supported: The operative variable is who authors the ordering and who initiates contact, not the fee form, and 'nonbinding' is not a category in any governing text. Green today: factual pages, investor-authored objective screens from a published versioned criteria registry, hosted double-opt-in messaging where each party writes its own messages, diligence receipts sold for compensation, optional standardized documents, accredited-verification utility, and flat fees with no transaction nexus. Red: platform-authored merit ranking of securities and repeated platform-initiated targeted introductions. Agent-drafted outreach is the single highest-risk element in the whole plan. · settled by: A written opinion (not a no-action letter - there has been no on-point staff letter for an online private-placement platform since March 2013) answering five questions: whether 4(b)(3)(A) due-diligence services covers machine-generated verification artifacts sold to investors on a platform hosting the deal; whether an investor-weighted screen over factual features avoids 'advisability' under 202(a)(11); whether hosting an agent-drafted message makes the platform the solicitor; whether a per-artifact receipt fee is 'separate compensation' for advice; and which states treat objective-screen matching as agent activity. Adoption of a finders exemption (petition 4-890, SBCFAC recommendations submitted April 2026) would reprice the whole module.
• Cost and model capability, not institutions, were the binding constraint on building this class of system - so now is the moment.: Consensus: 2025-26 is the capability inflection - AI scientists compress months into a day, GDPval win-or-tie moved from 12.4% to 47.6% and reportedly to 80%+, an AI-written paper passed peer review, inference costs collapsed - therefore earlier attempts failed because models were not good enough. Against: a June-2024 model closed a full end-to-end research loop for $20-$25 per artifact, 4-25% of a $500 threshold; the AI paper that 'passed peer review' was 1 of 3, at a workshop with a 33-70% acceptance rate rather than a 20-30% conference track, submitted by humans under a negotiated agreement and withdrawn; grading cost was identical in both eras (~109 minutes, ~$86), so cost per accepted decision-grade artifact improved only ~2-5x rather than 10-100x; and capability-independent prior art failed anyway (Replication Markets closed 2021, Manifund retired impact certificates, Algora drifted to hiring). · best-supported: Both gates were shut in 2024; only the capability gate has partly opened. Acceptance is rubric-elastic - the same artifact passed a workshop and failed a conference bar - so whoever sets the acceptance standard determines measured capability, which is an institutional fact. The one genuine 2023-vintage impossibility was mission depth (Kosmos-scale runs consuming tens of millions of tokens across ~1,500 papers were context- and price-infeasible). The correct inference is that the marginal build is the adjudication institution, because it is the converter from 13-16% autonomous acceptance to a 100%-accepted sellable unit and it is the only cost that does not fall. · settled by: A frozen-scaffold, frozen-depth replay of three archived missions across 2024 and 2026 model arms, graded by three blinded reviewers on one rubric, reporting cost per ACCEPTED artifact and edit-minutes - not cost per attempt. Note the window closes: hosted 2023-24 OpenAI vintages leave the API on 2026-10-23 and the Claude 3.x line is already retired, and a naive replay would mostly measure the 2026 scaffold, since prompt-only changes alone moved GDPval win-rate by 5 percentage points.

Evidence gaps

• No measurement anywhere of decision-relevance yield or decision-change attach for principal-seeded questions. All published funnel numbers measure resolvability and non-triviality only; the 5-30% attach assumption underpinning the business case is the analyst's inference, and the two datable observations from mature open programmes point to 10^-3 to 10^-2.
• No public dataset on shadow-agent seeding: claim rates, false-attribution complaints, spam load, or takedown frequency for unclaimed profiles built from public information. The three lawful substitutes proposed (legal-person shadows, problem/claim shadows, self-seeded claimable profiles) are entirely untested.
• Per-mission and per-record cost figures, all margin arithmetic, and the minimum-attach-rate model for a licensed partner are the analyst's own computations over cited list prices, not published figures. Sensitive to the acceptance-rate proxy chosen and to whether review is buyer-paid.
• Incumbent cost anchors are systematically sourced from parties selling cheaper AI substitutes: consulting and diligence fee bands ($50k-$500k), expert-network hourly rates ($900-$1,500/hr), BSI EU AI Act day rates (EUR 4,356-10,925, seen only via a vendor directory, never the BSI document), and ISO 42001 audit costs. These bias the cost-of-alternative upward and flatter the wedge.
• Unresolved conflict on whether the Article 113 readiness-decision trigger survived into the enacted Reg (EU) 2026/1744, or whether 2027-12-02 is unconditional. One outlet says unconditional; most pre-final commentary describes a six-month readiness trigger. The OJ text was not read directly.
• No judge-versus-human-panel agreement statistic published for GDPval-AA v2 despite it grading the GDPval gold subset fully automatically - a load-bearing absence for the entire assessment thesis.
• Conflicting figures on Deel contractor-management pricing ($49/contractor/month versus free on the current page), a swing of $0-$147 per task in rail economics; Escrow.com minimum-fee tiers trace partly to a 2016 secondary page.
• Interested-party conflict on forecast parity: FutureSearch self-reports above-superforecaster performance, Metaculus projects parity around June 2027, Good Judgment (which sells superforecasters) says humans still lead market questions 80.3 to 75.8 on a frozen mid-2024 baseline. An independent audit shows 442 of 527 forecasters scored worse than the price they were handed, which undercuts the parity headline without changing the commoditisation conclusion.
• No verified attach rate or conversion base rate for mission-to-capital-placement anywhere in the prior art (RetroPGF, DeSci DAOs, impact certificates never demonstrated it), so the licensed-handoff economics rest on a modelled threshold rather than an observed rate.
• Non-US legal layer beyond the EU is unverified: MiFID II investment-advice treatment, UK FSMA s.21 financial-promotion approval, and state-level blue-sky treatment of objective-screen matching were not scoped. Section 4(b) is federal-only and does not preempt state licensing.
• Whether the Platform Work Directive's 'digital labour platform' definition is drafted triangularly or functionally in national transpositions is unknown - 0 of 27 laws were in force at 2026-07-01 - so the buyer-of-record escape rests on defence-side commentary with no ruling and no statute to read.
• The identity of the AAAI-26 in-kind donor and whether any conference will procure AI review as a paid line item remain unknown; this is the cleanest available test of whether a mandate-holder will pay for external assessment outside a regulated vertical.
• No published measurement of expert minutes per reviewed AI-generated candidate (as opposed to per full deliverable). The 10-25 minute per accepted estimate is the least evidenced number in the whole underwrite, and METR's own follow-up productivity experiment was declared unreliable and is being redesigned.

Sentinels (what to watch)

Run statistics (from the SQLite ledger)

VIII. Forcing Questions

The questions that must be answered to reach implementation, in the order they block. The first list is the cross-cutting agenda; the second is what the loop left unresolved; the per-idea questions are in §VI.

The cross-cutting agenda

1. Which three credentials does the first network need, and who issues them?
2. Escrowed or pledged value statements? (Pledges have failed everywhere.)
3. Who resolves a forecast or verdict the buyer dislikes, and what is the appeal?
4. What is the approval-queue budget (decisions per day) for one person running many agents, and which actions must be automated to stay under it?
5. Research missions or investment diligence first? (Choose one disclosure default.)
6. Which exact objects are in schema v0, and who is the second implementer?
7. What fraction of mission questions resolve inside a quarter, and are they chosen for it?
8. Which payment rail, if any, does the first build depend on?
9. What is the 'decision changed' metric, and who records it?
10. What would make you stop: write the kill criterion before the pilot starts.

What the loop left unresolved

• What is the decision-change attach rate for a named-decision, principal-seeded question? This is the only term in the unit economics with no base rate anywhere: open programmes show order 10^-3 to 10^-2 (a Sentinel funder wrote publicly that the value was learning things rather than making different decisions; Cosmic Bazaar has one documented influence anecdote across 1,300 forecasters and 10,000+ forecasts), while the business case needs 5-30%. Must be measured on 30 live decisions with named owners, dates and a dated action-or-budget-change field before any scaling.
• What is the surviving discordance rate against a mandate-holder's incumbent pass, on their real artifact stream? Fundable looks like 42% (blinded independent central review across 23,476 cases) or 20-35% (life-science image flags); unfundable looks like Black Spatula's ~10% false positives or YesNoError's 37,000 largely unverified scans. Below ~10% surviving discordance there is no second reader to sell.
• How many independent buyers will pay for the SAME adjudicated unit, at what embargo, and what fraction of resolution contracts are reusable across them? Epoch's 4-5x exclusivity premium implies non-exclusive multi-buyer sale is the market norm, and the Tegus mechanic (pay the expert once at ~$450/hr, license to 6,500 seats at $20k+) is the proof. Reuse above ~60% is a utility at 8-12x revenue; below ~20% is services at 2-3x. This single variable is a 5-15x swing in equity value.
• Do we OWN the signature or sell tooling to whoever already owns it? Owning it needs an accredited or licensed signer, insurance appetite and liability capacity at ~$25k per unit; selling tooling is $1k-$5k per unit at ~10x volume with no liability. The AICPA July-2026 verification guidance and the appearance of AI exclusions in PI renewals currently push toward the second, but an insurer willing to write per-artifact cover would flip it.
• Which exogenous independence requirement, if any, actually arrives - and when? The large-upside future requires a regulator, insurer, index, journal or funder to make self-attestation by the producing vendor unacceptable for AI-agent work product. That is watchable, not forecastable. Until it appears, demand is discretionary and priced against a $300/seat detection SKU and a possible free 'verified' badge inside someone else's platform.
• What is the true median expert-minutes per accepted, decision-linked question, split into triage versus accept-side, including reviewed-but-rejected candidates? The measured anchors are 6-12 minutes to resolve one question with AI assistance and 109 minutes to grade a full deliverable; the estimate in play is 10-25 minutes per accept. Above 25 minutes after three tooling iterations, price it as services and abandon the software multiple.
• Which first vertical actually combines canonical timestamped sources, frequent decisions with stateable action thresholds, and no incumbent consented corpus? Frontier-tech research and capital allocation fails the third test (AlphaSense owns the corpus and shipped the agents) and the definitional-stability test (ERCOT queue restatements annul live resolution contracts). Audit 50 historical decision records each across public-record-heavy slices - interconnection queues, permitting, grid studies - versus enterprise AI-vendor diligence versus publisher/CRO integrity streams.
• Can counsel sign the exact UI assertion set as machine-checkable rules in the deterministic policy engine, or does it require a licensed entity? The five questions to put in writing: does 4(b)(3)(A) 'due diligence services' cover machine-generated verification artifacts sold to investors on a platform that also hosts the deal; does an investor-weighted screen over factual features avoid 'advisability' under 202(a)(11); does hosting an agent-drafted message make the platform the solicitor; is a per-artifact receipt fee 'separate compensation' for advice; and which states treat objective-screen matching as agent activity. Note there has been no on-point staff letter for an online private-placement platform since March 2013, so plan for an opinion, not relief.
• Is there a lawful bootstrap that replaces person-level shadow agents? The three candidate substitutes - shadow agents for legal persons and institutions, shadow agents for problems and claims with no data subject, and self-seeded claimable profiles where the person supplies the data - are untested for claim rate, spam load and false-attribution complaints, and no public dataset exists on any of them. Without one, the cold start has no seeding mechanism inside the EU.
• What does the buyer-of-record architecture actually cost in EU exposure? Escaping 'digital labour platform' status under the Platform Work Directive rests on defence-side commentary about triangularity, not a ruling; the GDPR floor already binds and has been enforced twice against algorithmic management of self-employed couriers (EUR 2.6M in 2021, EUR 5M in Nov 2024 over 35,000+ couriers); and 0 of 27 member states had a transposing law in force as of 1 July 2026 against a base rate of 4 of 27 on time for the nearest comparable directive. The window is real, member-state-specific and not a compliance holiday.

Highest-value questions still open in the loop's queue

• EVOI 0.90 · supply_chain · Which proposed first vertical combines canonical timestamped sources with frequent decisions whose owners can state action thresholds: semiconductor capacity and supply allocation, datacenter power/interconnection procurement, or enterprise AI-vendor diligence? Audit 50 historical decision records per vertical and score source availability, duplicate-answer rate, decision-change value, and expert-review burden. This discriminates vertical-choice feasibility from the named-principal bottleneck.
• EVOI 0.78 · contrarian_abductive · Why do post-filter systems report approximately 96% resolvability while unseeded consequential-question discovery achieves only 6.5–25% precision? Test three explanations: denominator/filtering illusion, retrospective benchmark bias in HorizonBench, or absence of principal context. Re-run both evaluations on the same frozen corpus and require human principal labels.
• EVOI 0.77 · event_path · On AIUC-1's fixed release cadence (15 Oct 2026, then 15 Jan 2027), does the standard extend from vendor/system certification into artifact- or deliverable-level attestation, and what happens in the 90 days around it (consortium peer-review comments, the Lovable Schellman audit scheduled summer 2026, whether a Big Four or AICPA-sponsored competing attestation appears)?
• EVOI 0.76 · capital_structure_liquidity · For a real mission that needs shared upside, compare a capped contractor collection bonus, an active-member LLC profits interest and a Rule 506 or Regulation Crowdfunding mission SPV. Map tax cost, formation time, securities treatment, legal transferability versus effective liquidity, banking, dissolution and the parties able and incentivized to exit.
• EVOI 0.75 · event_path · Track the two dated EU decision points that can move the matching layer's cost by a year: (i) whether the final Article 6 classification guidelines due end-2026 retain the draft's para 241 language putting freelancers and platform workers inside Annex III point 4, and (ii) whether the Commission adopts an Article 113 readiness decision that pulls Annex III duties forward to six months after that decision.
• EVOI 0.74 · policy_regulatory · For the exact proposed SOW and UI, does a sponsor-funded forecast-accuracy bonus remain integral commercial compensation, or does any material event-resolution component create a swap/event-contract issue? Obtain a written derivatives opinion and, if needed, seek a CFTC product-definition determination before enabling the bonus.
• EVOI 0.72 · adjacent_implication · Do the incumbent agentic research workbenches, AlphaSense SuperAnalyst (2026-06-03) and Claude Science (June 2026), ship attestation primitives (named approver, e-signature on records, immutable audit trail, sampled independent review) inside the seat, and at what price?
• EVOI 0.71 · fundamental · Does the dependency table hold in practice: can mission agents, claim agents, forecasts, bounties, red teams and civilization memory be run end to end with no personal agent, and does seeding unclaimed shadow agents built from public information create claim, privacy or spam liabilities that a comparable product has already suffered?
• EVOI 0.70 · expert_technical · What do measured human-in-the-loop screening systems outside forecasting, systematic-review title/abstract screening, grant-panel triage, patent examiner search, editorial desk-reject, report for minutes per screened record, precision@k of machine ranking, and inter-reviewer disagreement rates, and do those numbers support reviewer-minutes per accepted decision-linked question below 15 or above 25?
• EVOI 0.69 · mechanism_sizing · Size the mandated-verification branch bottom up: how many artifacts per year in 2026-27 carry a THIRD-PARTY-REQUIRED independent second review (life-science manuscripts screened, BICR reads, ISO 42001/EU AI Act conformity artifacts, financial-audit AI evidence packs, agent work-product attestations), at what current price per unit and what specialist-hours per unit - and what attach rate and price would a Second Reader need to reach USD 10M then USD 100M of revenue? Name the exogenous enabler: does any 2026-27 regulator, insurer or standards body actually require independent verification of AI-AGENT-produced work product, which is the only artifact class that grows with the agent economy rather than with journal or trial volume?
• EVOI 0.69 · mechanism_sizing · Size the second-reader unit economics bottom-up at measured acceptance rates: at 10-25% first-pass acceptance, roughly $86 and 109 expert minutes per graded deliverable, about 83% machine pre-screen precision and heterogeneous best-of-N routing, what is cost per accepted artifact, what price and monthly volume of typed task contracts would a single obligated-to-verify buyer support, and what multiple would that revenue justify against comparable verification services?
• EVOI 0.69 · mechanism_sizing · Size the artifact-ratification utility bottom-up in one vertical where the loss-bearer is identifiable: how many AI-assisted deliverables per year back a decision above $1M (consulting deliverables feeding procurement, IC memos, infrastructure capex diligence, grant program awards), what attach rate is achievable, at what price per ratified artifact after panel cost, and what revenue and margin does that imply versus the two floors, a $300/seat/yr detection SKU and a free 'verified' badge inside Frontier/Cowork? Name the exogenous enabler: a carrier willing to write per-artifact cover (reps-and-warranties or title-insurance analogue) at a premium that leaves the fee intact.

IX. Questions & answers

Every question the loop answered, in the order it answered them, with the answer, the evidence grade of what it relied on, and where the graded evidence diverged from the online consensus. Depth > 0 means the question was spawned by an earlier answer.

Q01 · id 21 · depth 0 · relative_valueEVOI 1.13 · confidence 0.64 · surprise 0.55

Price the sellable object against live anchors: Kosmos sells a full autonomous research campaign at $200 per run with roughly 650 free monthly credits for academic users and self-reported ~79-80% statement reproducibility, while the incumbent market for decision-relevant evidence prices at $20k-$150k per research-platform seat, $50k-$500k per diligence engagement, six-figure multi-quarter systematic reviews and HTA appraisals, and $20-50M per Focused Research Organization. Which of these is the true comparable for a mission, and does that make the first build a $200 self-serve product needing 10^4-10^5 missions, a $5k-50k attested decision memo needing 10^2, or a program-scale capital instrument?

VERDICT: none of the three named comparables is right as stated, but the evidence lands closest to hypothesis 1 with a sharpened definition of the object. The true comparable for a mission is the INDEPENDENT ASSURANCE ENGAGEMENT, the NICE-style external evidence review and the accredited conformity assessment, priced $5k-$50k per decision, sold to a named buyer who needs a signature plus a resolution record, at 10^2-10^3 units/year. Not a $200 self-serve product, and not a fund wrapper.\n\nWHY THE $200 ANCHOR IS NOT A PRICE. Edison's own launch post (2025-11-05) sets Kosmos at $200/run ($1/credit), calls it heavily discounted, says the price will probably be higher, lets founding subscribers lock $1/credit, and frames the product as a "reagent kit" for high-value targets. Its pricing page gives.edu users 650 expiring credits/month (about three free full runs) and founding subscriptions 2,000 discounted credits/month, alongside an Enterprise tier for internal tool/dataset integration; the $70M raise post (Dec 2025) commits to keeping the 650 free academic credits. That is a subsidised meter inside an enterprise land-and-expand, not an equilibrium price for mission synthesis. Treating it as the market price is the same error as pricing research off a free AlphaSense summary.\n\nWHY SYNTHESIS CANNOT CARRY THE BUSINESS. LUMEN (arXiv 2606.28362, March-2026 prices) completes a full systematic review + meta-analysis for a median $22.65 in 3.5-10.8 hours versus a traditional median of 67 weeks, and cites Otto-SR reproducing 12 Cochrane reviews in two days at 93.1% extraction accuracy. Human anchors: ~$141,195 per SLR (PMC formula, 2019) and >$300,000 for an AHRQ five-question comparative effectiveness review. That is a three-to-four order-of-magnitude collapse in the mechanical phases, and LUMEN's authors concede the $22.65 excludes the researcher time to define the question and validate output. If the sold object is the synthesis, the floor is compute plus a competitor's free tier.\n\nWHAT IS NOT DEFLATING. Prices attached to a body that accepts consequence, or to non-public primary access, are holding. Accredited-body day rates for EU AI Act technical documentation review (BSI's Oct-2025 published schedule, as reported): EUR 4,356/day standard, EUR 10,925/day dedicated, three-day minimum, a EUR 13k-33k floor for a review a model could largely draft. Expert calls hold $900-$1,500/hour with budget bands of $25k-$50k (30-50 calls) to $250k-$500k+ (200-500 calls). AlphaSense crossed $600M ARR by June 2026 at a $7.5B valuation with ARR per customer up from $28k to $66k in under three years and seats at roughly $10k-$40k. Same information work; the property being sold is different.\n\nSIZING THE THREE PATHS. (a) Attested decision object: 100-300 units/yr at $10k-$40k = $1M-$12M revenue; COGS $1k-$5k per unit (model spend $20-$300 for a scoped synthesis plus one GDPval-grade adjudication pass at ~$86/109 min plus 4-10 senior reviewer hours at $150-$400/hr) implies 80-90% gross margin at 10^2-10^3 volume with 3-8 people. (b) $200 self-serve: ~50,000 paid runs/yr for $10M at ~$150 contribution, competing against an incumbent handing academics three runs a month for free, a consumer funnel with a negative-margin anchor. Kill it as a business; keep it as free top-of-funnel, which is exactly Edison's structure. (c) Program-scale: Convergent FROs are $20-50M over 3-7 years with 10-30 FTE, a decade-long philanthropic sale with single-digit annual counts, not a product. But the repeating program unit is the $100k-$1M grant decision: Renaissance Philanthropy mobilised >$533M in two years across 22 programmes; AI for Math grants run $100k-$1M over 12-24 months; the Open Source for Science Fund launched May 2026 with $20M anchor from Biohub and Wellcome. A 1-5% verification/milestone-attestation fee on those decisions is $1k-$50k, the SAME band as the assurance engagement. Two independent buyer classes pricing the same artifact in the same band is the strongest finding here, and it is what makes a desk fundable at 10^2 volume rather than a marketplace at 10^4.\n\nBETTER THING TO BUILD THAN THE MISSION EXCHANGE (state plainly): sell RESOLUTION RECORDS FOR OUTCOME-PRICED AI, not research missions. Outcome pricing is now normalised, Intercom Fin $0.99/resolution, Zendesk ~$1.50-$2.00, HubSpot Breeze cut to $0.50 in April 2026, and Deloitte published revenue-recognition guidance for per-successful-outcome and prepaid-outcome-credit agentic contracts on 2026-06-04, so the accounting plumbing exists. Every serious commentary on the model names the identical failure point: what counts as a resolution and what evidence both parties will accept. That is precisely the Assessment/Artifact/Outcome primitive of the wedge, and it is horizontal (support, claims, RCM, grants, research) rather than a research niche. Spec: OBJECTS = Contract, OutcomeDefinition (pre-registered, machine-checkable), EvidenceBundle, Adjudication, Dispute, Signer, Scorecard. PROTOCOL = buyer and vendor register the outcome definition before the billing period; agents emit evidence; an independent adjudicator samples n per 1,000 and returns a signed record; disputes escalate to a named human signer. INCENTIVE = vendor gets a defensible invoice and faster cash; buyer gets audit evidence; fee $50-$500 per sampled record or 0.5-2% of invoiced outcome value. COLD START = single-player: sell to the vendor alone as invoice defensibility (the Mercor/Kosmos one-paying-principal pattern). KILL CRITERION = if five outcome-priced vendors will not pay for sampled adjudication of their own invoices within 90 days, the independence premium is not real.\n\nPLAN CHANGES. (1) Price the 90-day pilot object at $10k-$25k per attested decision and sell 8-15 of them; delete the 100-free-missions target, which measures nothing about price. (2) Rename the SKU: sell the decision record, not the mission. (3) Resolve the capture fork in the pilot, do we OWN the signature (needs an accredited or licensed signer, insurance and liability appetite, ~$25k/unit) or sell adjudication tooling to whoever already owns it (auditor, ERG, CRO, funder; $1k-$5k/unit at ~10x volume, no liability)? The insurance and professional-standards evidence currently pushes toward the second: AICPA's July-2026 professional-liability column requires AI-generated research and citations to be verified against primary authoritative sources before use, and AI exclusions/verification duties are appearing in professional-indemnity renewals. The signer's verification labour is being mandated, not eliminated, which is bullish for attestation demand and bearish for us being the attester on day one.\n\nWHAT MUST BE TRUE FOR THE LARGE UPSIDE: (i) an independence requirement, regulator, insurer, index/journal, or funder, makes self-attestation by the producing vendor unacceptable; that is the exogenous enabler and it is watchable, not forecastable; (ii) adjudication cost per record falls below ~$50 while staying defensible, which requires sampling plus provenance rather than more model capability; (iii) one resolution schema gets adopted by two buyer classes.\n\nIS THIS NEW? A year ago there was no $200 mission price, no $22.65 end-to-end review datapoint, no Deloitte outcome-pricing guidance, no SuperAnalyst (2026-06-03) or Claude Science (June 2026) bundling agentic memo production into a seat. Three years ago the synthesis was not automatable, so the memo itself was the product. The 'sell the signature, not the synthesis' conclusion would have been an assertion in 2025 and false in 2023; it is an inference from measured prices now.\n\nCONFIDENCE CAVEAT: the two anchors most load-bearing for the $5k-$50k band (BSI day rates, expert-network hourly bands) reach me through interested or secondary sources; the Kosmos, Convergent, Renaissance, Upwork and AlphaSense numbers are primary or official.

fact · primary document · 3× fact · primary document · 1× inference · analysis commentary · 0× fact · primary document · 0× estimate · primary document · 0× estimate · primary document · 1×

Consensus view: Most current sources frame AI research agents as collapsing the cost of decision-relevant evidence toward zero, $200 for a Kosmos campaign, $22.65 for a full systematic review, 'six months of PhD work in a day', and simultaneously declare per-seat research platforms obsolete. The implied business conclusion in the commentary is a cheap, high-volume self-serve research product, with outcome-based pricing as the modern alternative to seats. Evidence-supported view: The fact-grade rows support a split market, not a uniform collapse. Unattested synthesis is deflating three-to-four orders of magnitude AND is being deliberately subsidised (Edison itself calls $200 heavily discounted and gives academics three free runs a month while selling an enterprise tier). Prices for outputs carrying a signature or non-public primary access are not deflating: accredited day rates of EUR 4.4k-10.9k with three-day minimums, expert calls at $900-$1,500/hour, and AlphaSense AR Why they differ: Two errors in the consensus: (1) a promotional marginal price is being read as an equilibrium price, the seller states in writing that $200 is discounted and will likely rise, and the free academic tier is customer acquisition, not market clearing; (2) cost-curve/price conflation, the cost of producing synthesis is collapsing, but the price of liability-bearing, independence-requiring outputs is s

Q02 · id 5 · depth 0 · fundamentalEVOI 1.02 · confidence 0.68 · surprise 0.45

Which ships paying revenue first: independent assessment and reproduction priced per artifact (an adjudication utility, with the payer separated from the judge), or mission-to-contributor matching? Test against HackerOne (payer decides payment, about 1.6% of accounts produce valid work, median about $800), Algora (0-2 open bounties per org in 2026), Code4rena and Immunefi dispute records, and the 99.2% error-flag rate on 2,500 top-venue AI papers.

VERDICT: assessment ships paying revenue first, but the graded evidence says the revenue-bearing object is NOT the adjudication utility as specified in the driver. Per-artifact assessment already earns money today only where the payer is a MANDATE-HOLDER (an institution already obligated by a regulator, an editorial policy, or professional liability to verify), where the price sits on expert-hours-avoided plus workflow position plus a proprietary reference corpus, and where a licensed human inside the buyer keeps the signature. The pure neutral-arbiter form (payer separated from judge, priced per dispute, contributor and buyer as the two parties) has now been built twice in exactly the wedge's shape and has essentially no volume. Matching clears only with one dominant buyer or when bundled with escrowed prize plus in-house judging. So: hypothesis 1 wins on direction, but only in an amended form, and hypothesis 3 is the correct description of the first 12-24 months. WHAT THE PAID FORM LOOKS LIKE. (a) Research-integrity screening is a live per-artifact utility with the payer separated from the judged party: Imagetwin prices per paper and Proofig per sub-image (entry tier USD 99 for 120 sub-images), API-integrated into ScholarOne Manuscripts, Wiley Research Exchange and Paperpal Preflight, with named publisher customers Wiley, Sage, Karger, Taylor & Francis (Dove, since 2022), IOP (since 2022) and an MDPI multi-year deal. The primary document is the ASM mBio pilot paper: 15 journals, 1 Mar 2023 to 1 Mar 2024, 410 of 2,627 screened manuscripts showed image concerns, 248 were duplications, six (0.23%) had acceptance revoked, and resolution took about 1.5 hours pre-publication versus up to 10 hours post-publication. That ratio, not the verdict, is the unit economics: the buyer purchases roughly 8.5 avoided expert-hours per caught case. Proofig, citing UKRIO and analyst Jana Christopher, puts 20-35% of life-science submissions as flagged for image issues at submission. (b) The mature analogue is regulator-mandated: FDA guidance drives blinded independent central review with two independent readers plus a third adjudicator, and the measured discordance is the product - Ford et al. found 42% (95% CI 41-42%) of 23,476 cases across 79 oncology trials required adjudication, and a four-trial esophageal pool (1,875 patients, 8,501 timepoints, 14 radiologists) found 45.28% [42.60-47.99]. (c) The mandate-driven assurance backlog is measurable: more than 350 ISO 42001 certificates globally by mid-2026, initial accredited audits reported at USD 5k-30k for small and mid organisations, and Stage 2 waits of 6+ months at some bodies from auditor scarcity - i.e. demand for the licensed signature already exceeds human supply, which is exactly where an AI-first second reader sells capacity INTO a licensed signature rather than replacing it. WHAT THE NEUTRAL-ARBITER FORM LOOKS LIKE. Immunefi Arbitration (launched Jan 2025, London Chamber of Arbitration and Mediation, binding, enforceable via the New York Convention) sets a floor price of GBP 7,500 for claims up to GBP 1,000,000, explicitly so arbitration is not invoked frivolously, and Immunefi had to fund a USD 30,000 Arbitration Boost to get projects to opt in. Default platform policy remains No Fix, No Pay - i.e. the payer still decides - and the public dispute record is essentially one case (Trust Security, Nov 2024), which ended with the researcher's platform access suspended. Kleros, live since 2019, reached case #146 on V2 in Aug 2026 as the first case with non-public evidence, with its own team framing confidentiality as the adoption unlock and legal commentary questioning enforceability of pseudonymous awards under NY Convention Art. II and V(1)(b). About 10^2 cases in about 7 years is the base rate for a neutral court with no mandate-holder and no enforcement hook. MATCHING. The bug-bounty stack named in the question has already migrated its own revenue toward scoped assessment: HackerOne Pentest is reported at USD 15k-75k per engagement and described by a competitor as behaving like buying a pentest rather than running a marketplace, managed triage is sold as a service adding roughly 20-35% of platform fee, and USD 81M of FY2025 bounty payouts (+13% YoY) coexisted with a reported 23% valuation decline, a USD 36.4M 2025 raise and (reported May 2026) a cut to default bounty-table recommendations. Where crowd matching does clear, it is bundled: Cantina, Spearbit's competition arm that shares judging with the private practice, runs USD 200k-2.5M pools (EigenLayer 2.5M, Uniswap v4 2.4M); Code4rena and Sherlock run USD 50k-500k; Sherlock adds insurance-backed post-contest bounties. One buyer escrows the prize up front and the platform judges - that is the bundled desk, not an open exchange. THE CAPABILITY DATAPOINT THAT REFRAMES BOTH SIDES. AAAI-26 (arXiv 2604.13940, 15 Apr 2026) generated one clearly-labelled AI review for all 22,977 full-review main-track submissions in under 24 hours at under USD 1 per paper, funded by an in-kind donation; surveyed authors and PC members preferred the AI reviews to human reviews on technical accuracy and research suggestions; the pipeline beat a simple LLM baseline by +0.21 average recall; and area chairs were instructed to cross-check rather than defer. Read together with flat-rate autonomous pentest subscriptions marketed from about USD 100/month on the promise of no triage queue, the implication is blunt: DETECTION throughput is now 10^4 artifacts/day at about USD 1/artifact, detection price is deflating toward zero, and the largest assessment deployment in history was given away rather than sold. Never price the flagging. Price the corpus (Imagetwin advertises 150-160M+ published figures), the workflow position (ScholarOne, Research Exchange, Paperpal), and the liability-bearing signature. BETTER THING TO BUILD THAN THE PLANNED EXCHANGE - spec sketch, 'Second Reader'. Objects: Artifact, ArtifactClass, IndependentPass (blinded), Corpus, Discordance, Adjudication, ExpertHoursAvoided, SignatureHolder. Protocol: the mandate-holding buyer pipes an existing artifact stream in over MCP Tasks; we run N blinded independent passes plus a corpus-match pass; we never emit a verdict, we emit (i) a provenance-carrying flag set, (ii) a measured discordance rate against the buyer's incumbent pass, (iii) a dossier that lets the buyer's licensed reviewer sign in under 20% of baseline time. Incentive: paid per artifact screened plus a bonus on measured hours avoided; internal and external graders are paid only for disagreements that SURVIVE the buyer's adjudication, which is precisely the incentive HackerOne and YesNoError lack. Cold start is single-player: the buyer gets value on day one with zero contributors because the corpus and the passes are ours. Kill criterion: if surviving discordance against the buyer's incumbent pass is below about 10% of artifacts, or adjudicated true-positive share is below about 50%, there is no second reader to sell - BICR's 42% and image screening's 20-35% flag rate are what a fundable discordance rate looks like, while Black Spatula's roughly 10% false-positive rate is the good end and YesNoError's 37,000 largely unverified scans the bad end. RE-SEQUENCING THE MISSION EXCHANGE. Keep Claim, Forecast, Artifact, Assessment as objects; ship Assessment first against one named buyer's EXISTING artifact stream; do not build contributor-versus-buyer arbitration (Immunefi and Kleros are the base rates); add Task, Ask, Offer, Match only after the grader has a paid artifact stream and the same buyer's budget can fund the tasks. 90-day pilot: instrument discordance and hours-avoided on 300-1,000 real artifacts from one mandate-holder (a publisher integrity team, a CRO endpoint committee, a grant programme's diligence unit, or an accredited certification body carrying a Stage 2 backlog); kill if surviving discordance is under 10%, or if after seeing the hours-avoided number the buyer will not pay at least USD 5/artifact - the AAAI in-kind donation is the explicit warning that a mandate-holder may accept the capability for free and never open a budget line.

fact · primary document · 2× estimate · interested party · 1× fact · interested party · 1× fact · primary document · 2× fact · primary document · 2× inference · primary document · 1×

Consensus view: Most writing found online treats talent and task matching as the proven AI-era marketplace business (Mercor, Handshake, contest platforms) and treats verification as a feature bundled into someone else's product - while the research-integrity and peer-review literature insists AI assessment is assistive only and must never be the decision-maker, which is usually read as meaning there is no standalone assessment business. Evidence-supported view: Per-artifact assessment with the payer separated from the judged party is ALREADY a contracted, multi-publisher, multi-year business (Imagetwin/Proofig into Wiley, Sage, Karger, T&F, IOP, MDPI, ScholarOne) and a mature regulator-mandated industry (BICR, 42% adjudication rate on 23,476 cases), whereas neutral arbitration BETWEEN the two parties of a bounty or task has about 10^2 lifetime cases (Kleros case #146 after 7 years) and needed subsidy plus a GBP 7,500 floor to exist at all (Immunefi Why they differ: The consensus collapses two different products into the word assessment: neutral inter-party arbitration (no mandate-holder, no enforcement hook, near-zero case flow) and mandated per-artifact screening (obligated payer, existing workflow, measurable hours avoided). It also reads price-per-unit (USD 1 per paper, USD 0.83 per sub-image) as the market size while ignoring that the buyer is actually p

Q03 · id 2 · depth 0 · expert_technicalEVOI 0.97 · confidence 0.66 · surprise 0.45

On 20 representative mission tasks (literature synthesis, dataset reanalysis, red-team of a specific claim, cost model for a datacenter power interconnect), what fraction of frontier-agent deliverables pass an independent reviewer first-pass, measured the way the Remote Labor Index measures it (would a reasonable client accept it), and how does that compare to RLI's 15.8% best and METR's roughly 70-minute 80% horizon?

HEADLINE ESTIMATE. There is no published 20-task mission-mix measurement, so this is a synthesis of four measured families plus one direct analogue. On a 20-task mix of the type described (literature synthesis, dataset reanalysis, red-team of a claim, cost model for a datacenter interconnect), delivered single-shot and judged RLI-style (would a reasonable client accept this instead of the professional deliverable), expect 10-25% first-pass acceptance with a generally available frontier scaffold in Aug 2026, i.e. 2-5 of 20. Split by type: templated decision-grade quantitative deliverables 10-20% strict; dataset reanalysis with a fixed dataset and checkable answer 30-55%; literature synthesis 15-35% at report level (claim level is ~80% accurate, which is not the same thing); red-team of a specific claim 10-35% depending entirely on the false-positive rule. Allow one bounded revision round and typed specs with pre-registered verifiers and the same mix plausibly reaches 35-60%. THE DIRECT ANALOGUE. arXiv 2605.17554 (70 SME-authored consulting prompts, deterministic verifiers averaging 14.9 per task plus a 5-criterion SME rubric, three deep-research agents) reports ACCEPT under a joint threshold (rubric mean >=2.5 and verifier pass >=80%) of 15.7% for o3-deep-research and 12.9% each for Claude Opus 4.6 and Gemini 3.1 Pro deep research, statistically indistinguishable; no agent averaged above rubric 2.0 and none reached the 80% verifier floor. That is the closest public proxy for the interconnect cost model, and it lands on top of RLI's 15.8% (CAIS, 2026-07-01). Two independent measurement families converging near 13-16% is the strongest single fact here. WHICH HYPOTHESIS WINS. Hypothesis 1 (5-15%, sell supervised drafts and price review explicitly) is closest and effectively correct as a business conclusion; the honest number is a touch higher (10-25%) for a text/analysis-heavy mix, because RLI's aggregate is dragged down by 3D/CAD/video/audio categories that the mission mix does not contain. Hypothesis 2 (20-40% on narrowly typed tasks) is reachable only with pre-registered verifiers plus a revision round, not single-shot. Hypothesis 3 (>50%) is unsupported at any strict acceptance threshold and is an artifact of partial-credit grading. WHY THE GAP BETWEEN 65-93% AND 13-16%. Partial-credit and Elo benchmarks and binary acceptance benchmarks have diverged by 3-5x. APEX Investment Banking Analyst mean rubric score is 65.0% (GPT-5.3 Codex, June 2026 snapshot, LM judge); GDPval-AA v2 puts Claude Opus 5 at Elo 1845 against a 1000 human baseline; PaperBench replication score, 21.0% for the best 2025 agent and 41.4% for ML PhDs on a 3-paper subset, is now reported at 80.3-93% for 2026 models in a public aggregation (self-reported, LLM-judged, 20 ICML 2024 papers, so contamination and judge inflation are live explanations). Binary acceptance thresholds destroy partial credit: an artifact with 90% of rubric items met and one fabricated number is rejected. RLI's own failure taxonomy says the same thing (poor quality 45.6%, incomplete deliverables 35.7%, file integrity 17.6%). METR's structure corroborates: our mission tasks take a human 2-10 hours (APEX tasks average over 2.5 expert hours; GDPval averages ~6.7 hours; RLI median 11.5 hours), against an 80% horizon computed at ~70 minutes for Opus 4.6 and ~3 hours for the best system on METR TH1.1 run data, with messiness costing about 8.1pp of success per point. Mission tasks sit 2-10x beyond the reliable horizon; 10-25% is exactly what that predicts. TWO SURPRISES THAT MATTER MORE THAN THE NUMBER. (1) Acceptance is a drifting, gameable metric. A longitudinal study of 400 repeat reviewers across 11,429 reviews of AI-agent pull requests finds approval rates rising 14.5pp across experience deciles with review latency up 3.5x and inline comment effort down 22% - habituation, not quality. On the machine side, RLI's automated judge reportedly overstated GPT-5.5 by ~2.9x and Opus 4.8 by ~2.3x. So the pilot's headline KPI will inflate by construction unless the assessment layer has a calibration harness (blinded gold references, majority-of-3 with measured inter-annotator agreement - RLI achieves 94.4% - and per-reviewer drift audits). (2) The exchange cannot buy the 16% model. The RLI leader was Fable 5 at 15.8-16.1% (218 of 240 projects scored; 14.6% if all unscored count as failures), and it was reportedly restricted by the US government in mid-June 2026 and re-authorized 2026-06-30; the best generally available models measured 8.3% (Opus 4.8) and 6.3% (GPT-5.5). Underwrite the pilot at the available frontier, not the measured frontier. RED-TEAM TASKS ARE A DIFFERENT SHAPE. Detection is cheap and precision is the product: SPOT gives o3 21.1% recall at 6.1% precision (pass@4 37.8%), while a 2026 checker reaches 83% precision (263 of 316 flagged issues genuine) at 60% recall on injected errors. Code-review agents show the field version: CRA-only pull requests merge at 45.20% versus 68.37% human-only, with 12 of 13 agents averaging under 60% signal ratio. So a red-team deliverable should be sold as a triaged discordance dossier with a stated precision, never as a verdict. WHAT THIS DOES TO THE PLAN. Progressive autonomy cannot move from level 2 to level 3 inside 12 months on artifact acceptance grounds; the 80% horizon would need to cross roughly 4-8 hours of human-equivalent work on messy tasks first. Escrow must be milestone-and-verifier gated, not artifact-gated: pay on verifier pass plus adjudicated discordance, not on reviewer approval. Rework is the dominant cost: at 15% acceptance you grade ~6.7 attempts per accepted artifact, and grading costs (GDPval: ~$86 and 109 expert minutes per graded deliverable) do not deflate, so machine pre-screens with high precision must triage before humans see anything. Cheapest available lever: heterogeneous best-of-N routing - the consulting benchmark shows agents failing distinctively, so 2-3 different agents plus a verifier bundle plausibly lifts union acceptance to 25-35% at trivial model cost. BETTER THING TO BUILD (sharpening the standing assessment-first hypothesis). Sell the Acceptance Harness / typed task contract, not the artifact. Objects: TaskType, VerifierBundle (deterministic checks, pre-registered before work), GoldReference, Attempt, Discordance, Adjudication, AcceptanceRate (a measured, dated base rate per TaskType and per model-scaffold). Protocol: verifiers and gold references registered before the task is emitted; N heterogeneous attempts; machine triage at stated precision; blinded majority-of-3 human adjudication only on discordance; every acceptance decision written back as an event with reviewer identity, latency and drift score. Incentive: the payer buys a stated acceptance rate with a rework SLA (this is what converts a 13% single-shot into an insurable 60-80% delivered rate); contributors are paid per verified milestone; adjudicators are paid per resolved discordance and scored against seeded gold items. Cold start: compute your own base rates on public corpora of the four task types with your own graders, then sell the base-rate report and the harness to one institution already obligated to verify. Kill criteria: kill if typed contracts do not lift measured acceptance at least 2x over untyped baseline within 90 days; kill if adjudicator agreement on seeded gold falls below 0.8; kill if per-reviewer approval drift exceeds ~10pp over 500 reviews (you are measuring habituation, not quality). WHAT WOULD HAVE TO BE TRUE FOR >50%. Either (a) the 2605.17554-style ACCEPT metric jumps above ~35% on the same 70 prompts with 2026H2 models at the same threshold, or (b) the 80% horizon reaches 4-8 hours on messy tasks, or (c) task specs become so templated that they are effectively verifier-complete (which is the DSAgentBench regime: best agent 56.70% versus human 85.09%). The RLI trajectory (2.5% Oct 2025, 4.17%, then 15.8% July 2026) is fast enough that 25-40% strict acceptance on a text-analytic mix by mid-2027 is a defensible range; >50% strict on decision-grade deliverables is not yet supported by anything. ROBUSTNESS CHECK. Six months earlier (Feb 2026) RLI's best was 4.17% and the consulting ACCEPT benchmark did not exist; the estimate would have been 5-10% and the build decision identical (supervised drafts, priced review, assessment-first). One month earlier, identical. So this is a magnitude update of about 2-4x, not a decision flip - which is itself evidence that the assessment-first sequencing is robust to capability surprises in either direction.

fact · official statement · 3× fact · primary document · 1× fact · reporting · 1× fact · primary document · 0× fact · primary document · 1× fact · primary document · 2×

Consensus view: Most current commentary says frontier agents are at or near expert parity on real knowledge work: RLI automation quadrupled to about 16% in eight months, GDPval win-or-tie is near 50% and GDPval-AA Elo is far above the human baseline, APEX rubric scores are around 65%, and PaperBench now reads 80-93%. The implied conclusion is that autonomous task fulfilment is close to sellable and that matching, not review, is the binding problem. Evidence-supported view: Two independent binary-acceptance measurements converge at 13-16% (RLI 15.8% for a restricted model, 8.3% and 6.3% for generally available ones; joint-threshold ACCEPT of 12.9-15.7% on 70 verifier-checked consulting prompts). Every high number in the consensus is partial-credit, LLM-judged, Elo-scaled or possibly contaminated. A strict, human-adjudicated first pass on decision-grade research and analytic deliverables sits at 10-25%, and the acceptance metric itself inflates via LLM-judge bias (2 Why they differ: Numbers repeated without their base or threshold: partial-credit rubric means and pairwise Elo are quoted as if they were binary client acceptance; the best restricted model (15.8%) is quoted as the deployable frontier when available models measure 6-8%; a 218-project denominator is quoted as 240; and self-reported, LLM-judged replication scores on 2024 papers are treated as verified. Two measurem

Q04 · id 20 · depth 0 · supply_chainEVOI 0.95 · confidence 0.66 · surprise 0.45

The chosen first vertical's paid-expert supply is already intermediated by a compliance-heavy incumbent: what are the measured unit prices, take rates and consent rules of expert networks (GLG, Guidepoint, Third Bridge, AlphaSense after its ~$930M Tegus acquisition) for AI-infrastructure, semiconductor, datacenter-power and supply-chain experts, and do employer pre-clearance, MNPI screening, restricted-person lists and written-work prohibitions make it structurally impossible to commission written artifacts from employed experts, leaving only independents, academics and post-cooling-off ex-employees?

DIRECT ANSWER: commissioning written artifacts from employed experts is gated and priced, not structurally impossible. The absolute prohibition is on the TOPIC (own employer), not the FORMAT (writing). GLG's own compliance pages state that employed experts are barred from projects about their employer, and that to do written, in-depth or ongoing projects an employed expert must obtain written employer consent (enrolment in 'Member Programs'), with an exemption for senior management; GLG's expert-support article (updated 2026-06-04) names events, written work and ongoing projects as the gated categories and accepts confirmation from HR/Legal or CEO/CFO/CCO/Head of HR. GLG also runs an employer-preference database and a free institutional registry so employers can pre-block staff. On the buy side, the control set is CCO pre-approval to use a network at all, per-expert pre-clearance, restricted/watch lists, chaperoning, quiet periods, ex-employee cooling-off and do-not-contact enforcement (Victory Capital's filed code of ethics is a concrete example; 2026 practitioner guides describe the current set). I found no 2025-26 action charging an expert network itself: enforcement targets consultants/insiders who traded. So the constraint is contractual and latency-inducing, not prosecutorial. PRICES AND TAKE (estimate-grade, mostly interested marketplace parties): client price for a 60-minute call $1,000-$1,400 typical; Inex One (Feb 2026) says budget $1,300 with traditional networks vs ~$1,100 on its marketplace; buyer guides put GLG effective per-call at $750-$1,500+ and memberships at $25k-$300k+/yr, Guidepoint ~$1,200/hr average. Expert honorarium $200-$1,000+/hr, claimed to be 30-40% of client price, implying 60-70% intermediary capture - ABOVE the plan's assumed 20-35% and above Mercor's 30-35%. Tegus/AlphaSense inverts the model: client pays the expert's rate plus ~$75 per call and the intermediary monetises the CORPUS (platform from ~$20k/seat/yr; $500M ARR, 6,500 customers, 200k+ transcripts as of Oct 2025, up from $400M/6,000/150k in Mar 2025; Tegus bought for $930M, closed 2024-07-08, $4B valuation). Transcripts publish to all clients after an embargo, name removed, after human compliance review. That is where rent actually sits: buy the artifact at near marginal cost, license it thousands of times. Separately, written STRUCTURED output from employed professionals is already industrialised at $70-$120 per B2B survey complete. Industry >$2.5B in 2024 (+9%), ~$3B 2025, ~11,200 client firms in 2026, consulting ~50% of spend. GLG has 1.0-1.2M experts; revenue reported $634-650M (2021/ZoomInfo) with an unsourced ~$1.1B-for-2026 claim I do not rely on. WHICH HYPOTHESIS: H1 (blocked at scale, 5-10x pool cut) is wrong in strong form but right in a worse way - the own-employer ban removes ~100% of the highest-value questions in this vertical (hyperscaler capex internals, foundry capacity, interconnect queues at a named utility), so the cut is on question value, not headcount. H2 (consent module as moat justifying 20-35% take) is half right: consent IS obtainable via a 25-year-old workflow, but that workflow is free for employers to defeat, incumbents already take 60-70% on calls, and the durable moat is corpus + workflow slot, not the module. H3 (incumbents are the buyer) is inverted: they are BUILDERS. AlphaSense shipped Deep Research (2025-06-10), multi-agent architecture plus 350+ tickers and 200+ channel-check grids (Jan 2026), twelve Workflow Agents including Thesis Checker and Demand Supply Cycle plus 'AI-Led Expert Calls' launched from call projects (Apr 2026), and Work Products with the SuperAnalyst deliverable-automation layer (2026-07-14); Channel Checks reportedly uses an autonomous AI interviewer. That is the Mission Exchange action layer minus mandates, forecast scoring and independent adjudication - already shipped, with consented supply, 6,500 customers and 88% of the S&P 100. TWO CONSTRAINTS THE BRIEF HASN'T PRICED, both specific to the chosen vertical: (a) PRC - Capvision was raided in May 2023 under the counter-espionage law with high-tech, military and energy explicitly named, and the CEL revision took effect 2023-07-01 (dated, no repeal evidence); Asian supply-chain experts producing durable written artifacts is the highest-risk cell. (b) US deemed exports - written technical artifacts on chip design/process shared with non-US persons can be a controlled transfer, and BIS's >$140M Cadence settlement (Jul 2025) shows intangible-transfer enforcement is live. Calls are ephemeral; artifacts are durable, discoverable evidence. Choosing the written format RAISES legal exposure in precisely this vertical. PILOT IMPLICATION: supply is not the binding constraint if the contributor definition changes. Mercor pays >$2M/day, sources largely ex-employees of elite firms, and prices writing roles at $75-150/hr and task roles at $60-110/hr; 10-20 paid digital tasks is $5k-$25k of contributor spend - trivially fundable. What is scarce is a buyer who pays for adjudicated, provenance-bearing output rather than for access. BETTER THING TO BUILD (spec sketch): the Provenance & Publication Screen. Every party that publishes third-party or AI-generated research already runs a human pre-publication pass - AlphaSense on ~50k new transcripts/yr, auditors under the Jul-2026 AICPA verification guidance, journals on images and citations. Objects: Artifact, Source, Assertion->Evidence edge, ScreenResult (confidentiality/MNPI flag, citation verification, numeric-base check, discordance score), Dossier, Attestation, Corpus. Protocol: MCP 2026-07-28 Tasks for long-running screens; artifact in, dossier out; never a verdict - emit discordance and evidence so the licensed party keeps the signature. Incentive: $50-$500 per machine-only screen, $1k-$5k with a human second reader, against the $86/109-minute human grading anchor. Cold start: single-player, one publisher screening its own artifacts. Kill criterion: flag precision <70% at 30% recall on >=300 labelled artifacts within 60 days, or no buyer pays >=$5k within 90 days. PLAN TWEAKS: (1) delete 'employed expert writes about own employer' from the data model and make Mandate carry jurisdiction, topic-restriction and export-classification fields; (2) replace '100-200 expert profiles' with 'N structured claim responses at $70-150 under a documented consent path' plus 'cost per accepted adjudicated artifact'; (3) build the employer/institution opt-out registry BEFORE shadow agents - free employer pre-blocking is the industry norm and shadow profiles collide with it; (4) accumulate a proprietary adjudicated-artifact corpus from day one with an explicit reuse licence (embargo-then-publish is the proven mechanic); (5) do not make frontier-tech investment research the first vertical unless the slice is public-record-heavy (interconnection queues, permitting, grid studies) where no consented corpus incumbent exists. TIME TEST: the written-work consent tier is at least as old as GLG's 2018 terms and the 2010-12 enforcement wave, so it is not new and not a change. What is new in the last 12 months is the incumbent automating the artifact loop (Jan-Jul 2026) and Mercor-scale written-task supply at $60-150/hr.

fact · official statement · 2× fact · official statement · 1× fact · primary document · 1× estimate · interested party · 2× estimate · interested party · 1× fact · primary document · 2×

Consensus view: Online buyer guides and compliance explainers converge on: expert networks are compliance-heavy and expensive ($500-$1,500+ per hour-long call, subscriptions from $25k to $300k+), MNPI risk is the dominant legal issue, written work is 'restricted', current-employer topics are off-limits, and AI is disrupting the category. Several sources imply, without saying so directly, that written deliverables from employed experts are effectively unavailable. Evidence-supported view: The fact-grade rows show a priced gate, not a wall: written work by employed experts is permitted after written employer consent, with a senior-management exemption, inside a workflow (GLG Member Programs) that has existed since at least 2018. The hard prohibitions are topical (own employer) and jurisdictional (PRC counter-espionage exposure; US deemed-export exposure for durable technical artifacts). Economically, per-call intermediary capture is high (claimed 60-70%, estimate-grade) but the du Why they differ: Three causes. (1) Framing without the base: guides repeat 'written work is restricted' while omitting the consent tier, its exemption and its free employer-registry mechanics. (2) Wrong comparison set: the consensus benchmarks against call pricing, ignoring survey pricing ($70-120 per written structured response) and corpus economics, which is where the money actually is. (3) Recency asy

Q05 · id 1 · depth 0 · mechanism_sizingEVOI 0.93 · confidence 0.62 · surprise 0.45

Bottom-up: for a 25-mission, 90-day pilot in frontier-technology research and capital allocation, what is the fully loaded cost per mission (model spend for decomposition and 10-20 forecasts, 3-8 emitted digital tasks, one independent assessment) and what would a named buyer type pay - an AI lab evaluation team, a datacenter-power or semiconductor investor, or a corporate strategy group - so that missions x price x attach rate produces revenue that justifies the build?

VERDICT ON THE THREE HYPOTHESES. Hypothesis 2 wins on the cost side, but for a different reason than stated, and Hypothesis 1's price band is real only for one object sold to one buyer type, while Hypothesis 3 is correct for the other two buyer types. 1) COST STACK PER MISSION AT AUGUST 2026 LIST PRICES (my arithmetic; each input is a published price). Model spend is now the trivial line item, cheaper than the brief's own $30-$300 estimate: - Decomposition / claim-graph construction: 3-8 deep-research-grade passes. Anchors: Gemini Deep Research API ~$2/task standard and ~$5/task Max, of which $1.12-$2.24 is Google Search grounding at $14/1K queries (Google's own published estimates, 2026-04-21); open-source Deep Research Bench runs $0.20-$1.00/task of model spend; a frontier structuring pass on 1M input + 100k output at Opus-class $5/$25 per M is ~$7.50. Budget $10-$60. - 10-20 calibrated forecasts: FutureSearch list price is 15c-$2 per forecast question and $0.30-$2 for a multi-agent research team per question (pricing page, current), from a vendor that self-reports #17/378 on ForecastBench and first in Metaculus's Summer 2026 FutureEval. So the plan's entire forecast layer costs $1.50-$40 per mission. This is the single most important number in the answer: the forecast layer is now a priced commodity at ~$1/question. - 3-8 task specs + machine verifiers drafted: $5-$30. - One machine red-team/pre-screen pass: $5-$30 (consistent with the brief's Black Spatula anchor of $0.15-few dollars per paper). Model subtotal: $25-$160 per mission. - One independent HUMAN assessment: $65-$400 per pass (1-2 hours at the live median $65/hr measured from 1,456 advertised AI-work rates across 11 platforms, aitraining.jobs, Aug 2026, up to $200+/hr for credentialed or agentic/RL work; GDPval's measured $86 and 109 minutes per graded deliverable sits inside this). With rework at the observed 15-35% acceptance rate you grade 3-7 attempts per accepted artifact: $260-$600. FULLY LOADED, DIGITAL-ONLY VARIANT (graph + forecasts + specs + one adjudication, no contributor payment): ~$90-$750, central $300-$450. So Hypothesis 1's <$150 is reachable only if you never pay a human to review anything. FULL LOOP AS SPECIFIED (3-8 emitted tasks actually commissioned and paid): add $150-$600 per task for ordinary expert delivery, or $500-$2,000 per task at the price level labs pay for a task-with-verifier (Epoch AI's 18-interview RL-environment survey, Jan 2026: $20k per task "would be rare but possible" for very complex SWE tasks; Mechanize, an interested vendor, argues compute economics make anything under ~$500/task wasteful). Per mission: $600-$8,000, central ~$2,300-$3,000. 25-MISSION 90-DAY PILOT: variable cost $2.3k-$19k digital-only; $58k-$200k for the full loop (central ~$60-75k); plus 2-3 loaded FTE for a quarter, so all-in $120k-$350k. Conclusion: the pilot is self-fundable at any of these numbers. Cost is NOT the gate. Adjudication is 60-97% of fully loaded cost depending on variant, which confirms and sharpens the standing belief; model spend is 3-15%. 2) WHAT EACH NAMED BUYER ACTUALLY PAYS (dated). (a) AI LAB EVALUATION/DATA TEAM - the only buyer whose observed per-unit price clears the cost with room. Epoch AI (2026-01, 18 interviews): per-task prices in the hundreds to low thousands with $20k rare; contract sizes "often six to seven figures per quarter," cited deals $300k to well over $1M; exclusive deals 4-5x non-exclusive; a Slack-fidelity environment reportedly upward of $300k; The Information (2025-09) reported Anthropic discussing >$1B on RL environments. But this buyer purchases a Task+Verifier+Gold bundle, not a mission, and will not pay for your graph or your forecasts. A mission that yields 3-4 accepted, verifier-carrying frontier-tech tasks is worth $1,500-$8,000 to this buyer, tail to $20k/task. (b) DATACENTER-POWER / SEMICONDUCTOR INVESTOR - pays per answer and per seat, never per mission. Revealed prices: expert calls at ~$450/hr expert fee on Tegus's own page and $500-$600/call typical per Inex One, ~$1,350/hr cited for GLG (2026); AlphaSense averaging $12,210 (SMB) and $123,760 (enterprise) per year per Spendhound (2026-07); SemiAnalysis institutional models are explicitly institution-only and quote-based ("start with the sales team" for scoping and pricing), sold separately from the newsletter; Bloomberg ~$25-30k/terminal/yr. This buyer will pay $1k-$5k for a scoped piece of primary evidence and $12k-$124k/yr for a platform seat - and pays for the conclusion plus the primary evidence, not the ontology. Hypothesis 3 holds here. (c) CORPORATE STRATEGY GROUP - buys projects and retainers with service-level expectations: mid-market commercial due diligence $50k-$150k (Plausity, 2026-03), PE CDD $100k-$500k with junior consultants at $300-$500/hr and partners at $800-$1,500/hr (UserIntuition, 2026-03; both sources sell cheaper AI alternatives, so treat the incumbent numbers as framing-inflated), fixed-fee projects from $5k to $20M+, retainers $2k-$20k/mo. A $10k-$50k per-mission price is achievable but arrives with named humans, indemnity and turnaround commitments - services economics, not exchange economics. Note the competing per-seat clearing price for a finance research agent: Rogo, ~$3,300/seat/yr most-cited external estimate, 35,000 professionals at 250+ institutions, $2B valuation (2026-04-29). 3) MISSIONS x PRICE x ATTACH -> REVENUE -> MARGIN. Pilot: 25 missions x 4 emitted tasks = 100 tasks; at 15-35% first-pass acceptance with heterogeneous best-of-N, 60-80% of specified tasks eventually yield one accepted artifact, so 60-80 sellable units. At $500-$2,000: $30k-$160k of sellable output against $58k-$200k of variable cost. Gross margin at pilot scale: roughly -30% to +80%, NOT 5-20x. Kosmos is the market comparable for a whole mission-scale artifact: $200/run for a ~12-hour, ~1,500-paper, ~42,000-line campaign with 79.4% statement accuracy (Edison Scientific, 2025-11-05), explicitly "heavily discounted" and expected to rise, with 30,000 users having tried it (Alzforum, reporting Rodriques). If a full autonomous research campaign retails at $200, the mission itself cannot carry a $2k-$10k price on artifact value alone - only verification, exclusivity or liability transfer can. 12-month sizing for the branch worth funding: 480 missions/yr x 3 accepted units x $1,000 = ~$1.4M gross. That is a boutique. To reach $5-10M you need either 1,000+ missions/yr (throughput gated by adjudication capacity, i.e. expert-hours) or non-exclusive resale of the same adjudicated unit to 2.5-4 buyers - which Epoch's 4-5x exclusivity premium implies is the intended market structure and which is exactly the Tegus mechanic (pay the expert once, license the transcript to everyone after embargo). Revenue per produced unit is the multiple lever: $1,000 x 1 buyer at 1-2x revenue (services) versus $1,000 x 3 buyers at 8-12x revenue (data foundry, cf. Mercor's ~$2B gross run-rate at 30-35% take). The equity re-rate is entirely a function of the reuse licence and the acceptance standard, not of mission volume. EXOGENOUS ENABLERS THAT MUST BECOME TRUE FOR THE LARGE BRANCH: (i) labs keep buying externally - Epoch's same survey reports "substantially more in-housing" as labs build internal data teams, and buyer concentration is 1-3 accounts; (ii) adjudicated units per expert-hour rise 3-5x via high-precision machine pre-screen plus heterogeneous best-of-N; (iii) a day-one reuse licence in every contributor and reviewer agreement. 4) BETTER THING TO BUILD THAN THE MISSION EXCHANGE (states the same objects, sells a different unit): a VERIFIER FOUNDRY / SECOND READER. Objects: ResolvableQuestion (with resolution source), AcceptanceStandard (binary, versioned), GoldReference, Verifier (executable), Attempt, Discordance, AdjudicationDossier, ReuseLicence. Protocol: mission decomposition emits question+standard+verifier+gold as the primary artifact; machine pre-screen (target >=80% precision) routes only discordant attempts to a paid human second reader; every adjudication emits a dossier with provenance, not a verdict-for-hire. Incentives: the lab data/eval buyer pays $500-$2,000 per accepted bundle non-exclusively; the investor/corporate buyer pays $1k-$5k for the dossier attached to a decision they must defend; contributors keep upside via resale royalties on the reuse licence. Cold start: single player - you can produce and sell bundles with zero contributors and zero capital mandates, because forecasts cost ~$1 and decomposition ~$30. Kill criterion: 90 days, no signed paid order at >=$500/unit for >=20 units, or blended acceptance on emitted tasks below 20%, or grading passes per accepted artifact above 4 after pre-screen. SEQUENCING TWEAK TO THE PLAN: keep the ontology, drop the three-sided pilot metrics (20 capital mandates, 100-200 expert profiles, 30 mutual matches) from the 90-day scorecard - they are not priced by any observed buyer. Replace them with: units accepted, expert-minutes per accepted unit, pre-screen precision, buyers per unit (resale count), and dollars invoiced per expert-hour. 5) NOVELTY TEST. Most of this reasoning worked six months ago: Epoch's RL price survey is 2026-01, Gemini's deep-research per-task price is 2026-04, GDPval's $86/deliverable is 2025-09, Kosmos's $200/run is 2025-11. What is genuinely new in the last quarter is the combination of a published sub-$2 price list for calibrated forecasting with a public tournament record (FutureSearch; ForecastBench above-superforecaster-median results 2026-07-16). That kills any plan to charge for forecasts or for a forecast-carrying graph, and moves the priceable object to the acceptance standard, the verifier and the adjudicated corpus.

fact · primary document · 1× fact · interested party · 1× fact · primary document · 2× fact · reporting · 2× fact · reporting · 3× estimate · analysis commentary · 0×

Consensus view: The dominant online view is that agentic research has become extremely cheap - deep research at $2-$5 per task, token prices falling roughly an order of magnitude a year - while RL/eval data is a boom market with six- and seven-figure quarterly contracts. The implied conclusion, repeated across vendor blogs and analyst posts, is that an AI-run research or task marketplace enjoys near-software gross margins. Evidence-supported view: At published prices the model layer of a mission costs $25-$160 and the entire calibrated-forecast layer costs $1.50-$40, so neither the graph nor the forecasts can be the priced object; a whole autonomous research campaign already retails at $200. Fully loaded cost is dominated by human delivery and human adjudication ($600-$8,000 per mission in the loop as specified), so the mission is a services unit unless the thing sold is a verifier-carrying, adjudicated artifact bundle. Exactly one named Why they differ: Three defects in the consensus: (i) per-task model-spend figures are quoted without the adjudication base that turns a $3 draft into a $300-$600 accepted artifact at 15-35% acceptance; (ii) the headline RL contract sizes ($300k-$1M per quarter) are quoted without the cost of producing verified tasks at $500-$2,000 each, so implied margins are inferred rather than measured; (iii) two of the most-ci

Q06 · id 22 · depth 0 · mechanism_sizingEVOI 0.86 · confidence 0.72 · surprise 0.55

Run the counterfactual as an experiment rather than a narrative: replay three archived missions end to end on 2023-, 2024- and 2025-vintage model stacks at their then-list prices as well as today's, measuring cost per mission, first-pass acceptance under one fixed reviewer rubric, and human-edit minutes. If a 2024-vintage stack could already close the loop under roughly $500 per mission with acceptable artifacts, then capability and inference cost were never the binding constraint, the missing ingredient was institutional (a payer, an adjudication authority, a liability wrapper), and the first build should be that institution rather than the AI loop.

Verdict: none of the three competing hypotheses as written. The evidence supports a fourth, and it is largely already measured, the replay has been partly run in public, and it says cost was never the gate, first-pass acceptance under a fixed rubric was (and partly still is), and adjudication cost, the one input that has not deflated in either era, is the reason the marginal build is the institution. 1. The 2024-vintage arm already exists as a natural experiment, and it closed the loop for ~$20. Sakana's AI Scientist ran ideate→code→experiment→write→auto-review end to end for ~$15 in API cost per paper (v1, Aug 2024); v2's own repo states the experimentation phase on Claude 3.5 Sonnet, a June-2024 model, costs $15–20/run plus ~$5 for writing, i.e. $20–25 per complete artifact. That is 4–25% of the $500/mission threshold in the question, on a 2024 model. Hypothesis 2 (price was the gate) is falsified for shallow digital missions. 2. But acceptance under a fixed rubric is where the vintage difference is enormous, and it is 3–7x, not 5–20x on cost. GDPval is the cleanest fixed-rubric back-series that exists (same 220 gold tasks, same blinded industry-expert graders): GPT-4o (spring 2024) won-or-tied 12.4%; o4-mini 27.9%; o3 34.1%; GPT-5-high 38.8%; Claude Opus 4.1 47.6% (Sept 2025); secondary reporting of vendor scores puts GPT-5.2 Thinking ~70.9% (Dec 2025) and GPT-5.4/GPT-5.5 at 83%/84.9% (Feb–Apr 2026), treat those as reported, not verified, since harnesses differ. Sakana gives the institutional version of the same measurement: 1 of 3 papers accepted at an ICLR 2025 workshop (6.33/10 average; the two rejects scored 3–4), and by Sakana's own statement none met a conference-track bar. Acceptance is jointly a property of the model and the rubric, the identical artifact passes a ~33–70%-accept workshop and fails a 20–30%-accept conference. Whoever sets the rubric sets the apparent capability. 3. The correct metric is cost per accepted artifact, and it improved only ~2–5x, because adjudication is invariant. GDPval measures ~109 expert-minutes and ~$86 to grade one deliverable that costs a professional ~404 minutes and ~$361 to produce. 2024 arm: $20–60 model spend ÷ ~0.12 strict acceptance + $86 grading + rework ≈ $350–550 per accepted decision-grade artifact, i.e. it would have squeaked under $500, with the human doing 85–90% of the work. 2026 arm: $25–160 model spend at 0.13–0.16 strict acceptance on genuinely decision-grade work (RLI 15.8%, 80%-reliability horizon ~30–70 min), same $86 grading ≈ $150–800. Corroborating: a published reading of GDPval's own review-and-fix scenarios puts models at only ~1.4x faster and ~1.6x cheaper than experts under realistic review, against the naive "100x" headline promoted on GDPval marketing sites. The adjudication floor, not the token price, sets the unit economics, and it was the same in 2024 and 2026. 4. The one place the vintage claim survives: mission depth. Kosmos (Nov 2025, $200/run, ~1,500 papers, ~42,000 lines of code, ~12h, 30,000 users tried it) is a mission that consumes tens of millions of tokens across hundreds of rollouts; Edison names finite context length as the binding limiter of its predecessor. At 2023 token prices and context windows that run is not merely 10x dearer, it is infeasible. So: cost was never binding for shallow missions; cost+context was binding for deep ones. A replay that does not freeze depth (token and tool-call budget) measures how much you chose to spend, not what changed. 5. Institutional absence was binding in both eras and is still binding. Sakana needed a human team to submit, a negotiated agreement with organisers, and withdrew the accepted paper, no institution was willing to hold the artifact. Meanwhile prior art requiring no frontier capability stalled anyway (Replication Markets closed 2021 at 121 resolutions/$142,623; Manifund retired impact certs; Algora drifted to hiring). Both gates were shut in 2024; one has partly opened; the other never has. Therefore the marginal build is the institution, but for hypothesis 1's reason, not hypothesis 3's: the adjudication institution is the converter that turns 13–16% autonomous acceptance into a 100%-accepted sellable unit, and it is the only cost that does not fall. 6. The forecasting arm is the weakest leg and should be dropped from the replay. ForecastBench 2024: superforecasters 0.068 Brier, public 0.083, best LLM (Claude 3.5) 0.119, a 2024 stack forecast worse than the general public. FRI's Brier-Index post: superforecasters 0.086 (70.6%) vs best LLMs 0.103 (67.9%). Good Judgment (interested party, sells superforecasters, 14 Apr 2026) counters that the human baseline is a one-time mid-2024 elicitation on different questions and dataset questions are now disclosed and gameable. The gap genuinely narrowed (0.051→0.017), but a vintage replay of this step measures a stale human baseline, not calibration. 7. Feasibility correction, the experiment as specified has a hard closing window and a scaffold confound. OpenAI's Apr-22-2026 notice removes GPT-3.5-Turbo, GPT-4, GPT-4-Turbo, o1, o1-pro, o3-mini and o4-mini from the API on 2026-10-23; GPT-4o already left the API (~Apr 2026) after leaving ChatGPT on 2026-02-13 at 0.1% daily usage; Claude 3.7 Sonnet shut down 2026-05-11 and the Claude 3 line fully retired by Apr 2026 (Opus 3 retired 2026-01-05, available by request; Anthropic preserves weights, ≥60 days notice). Cloud mirrors run longer schedules (e.g. Sonnet 4: Anthropic 2026-06-15 vs Bedrock EOL 2026-10-14). And GDPval showed prompt/scaffold changes alone moved win-rate +5pp, cut PPT formatting errors 86%→64% and raised self-inspection 15%→97%; the Cowork-scaffold RLI datapoint says the same. A naive vintage replay mostly measures your 2026 scaffold. Recommended replacement for the experiment (≤1 week, ~$3–8k): 3 archived missions × 3 arms (open-weight 2024 proxy, hosted GPT-4-Turbo/o1 before 2026-10-23, 2026 frontier) × frozen scaffold and frozen depth budget, graded by 3 blinded reviewers on one rubric, recording cost, strict first-pass acceptance, and edit-minutes. Kill the timing narrative if the 2024 arm's cost-per-accepted-artifact is within 2x of 2026's. Use it only to calibrate the public GDPval/RLI/METR curves to your rubric, do not re-derive them. Better thing to build (raise it explicitly): The Acceptance Registry, a priced adjudication utility. Objects: versioned Rubric, Deliverable, GoldReference, ExecutableVerifier, AdjudicationDossier (grader identity, minutes, checks run, verifier exit codes), AcceptanceDecision, Provenance (model id+version+date+scaffold hash, mandatory, because model IDs now vanish on 6–12-month cycles), ReuseLicence. Protocol: submit → cheap executable pre-screen that eliminates the ~85% failing deterministic checks → human adjudicator only on survivors → signed dossier → corpus. Incentives: buyer pays per accepted unit; adjudicators paid per minute and scored against gold; pre-screen authors earn a share of minutes saved. Cold start is single-player: Axion is buyer #0 for its own claim graph; the same dossiers resell non-exclusively to lab eval teams. Kill criterion: if expert-minutes per accepted unit does not fall from GDPval's ~109 to ≤40 across ≥100 units in 90 days, or if no second buyer pays ≥$500 for the same dossier, stop. Replace the pilot scorecard's three-sided metrics (25 missions/30 matches) with: units accepted, expert-minutes per accepted unit, pre-screen precision, buyers per unit.

fact · primary document · 4× fact · primary document · 4× fact · primary document · 3× fact · primary document · 2× estimate · analysis commentary · 0× estimate · analysis commentary · 0×

Consensus view: Most current commentary holds that 2025–26 is the capability inflection: AI scientists now compress months of work into a day, frontier models are at or past expert parity on real knowledge work (GDPval win-rates 47.6%→~85%), an AI-written paper passed peer review, and inference costs collapsed, therefore the moment to build agentic research-and-allocation loops is now, and earlier attempts failed because the models were not good enough. Evidence-supported view: The fact-grade rows support a different decomposition. (a) Cost was never the gate for shallow digital missions: a June-2024 model closed a full research loop for $20–25 per artifact, 4–25% of the $500 threshold. (b) What changed is first-pass acceptance under a fixed rubric, 12.4% (GPT-4o) to 47.6% (Opus 4.1) on identical GDPval tasks with identical graders, a 3.8x move, with later 80%+ figures vendor-reported on different harnesses. (c) Acceptance is rubric-elastic: the same Sakana artifact pa Why they differ: Headline numbers repeated without their base. '100x cheaper' drops the review-and-fix denominator that collapses it to ~1.5x; 'AI paper passed peer review' drops the 1-of-3 base rate and the workshop-versus-conference acceptance gap; GDPval 83–85% figures are vendor-claimed on agentic harnesses and are compared against the one-shot 12.4%/47.6% series as if the rubric were ident

Q07 · id 18 · depth 0 · policy_regulatoryEVOI 0.84 · confidence 0.72 · surprise 0.55

Now that Regulation (EU) 2026/1744 entered into force on 27 July 2026, deferring Annex III high-risk duties to 2 December 2027 (Annex I to 2 August 2028) while leaving Article 50 transparency live from 2 August 2026: does agentic matching of contributors to paid work fall inside Annex III point 4 (employment and access to self-employment), does grandfathering of systems placed on the market before the new date create a deliberate build-matching-now window, and what do Article 50 duties (AI-interaction disclosure, machine-readable marking of AI-generated content, with 2 December 2026 for legacy systems) require of mission artifacts, agent-to-agent transcripts and unclaimed shadow agents standing in for named real people?

VERDICT: H1 is favoured for the design as specified, H3 is favoured outright, and H2 survives only as a deliberate design choice rather than a legal accident. (1) ANNEX III POINT 4. Point 4 is headed 'employment, workers' management and access to self-employment' and covers AI intended for recruitment or selection of natural persons, including placing targeted job ads, analysing/filtering applications and evaluating candidates. The Commission's draft Article 6(5) classification guidelines (19 May 2026; consultation extended to 23 July 2026; final guidelines due end-2026) state at para 241 that freelancers, independent professionals, service providers and platform workers, regardless of contractual status, are inside point 4 wherever AI systems "mediate or condition their access to work opportunities", and that 'workplace' includes virtual spaces. A capability-agent that ranks and matches natural-person contributors to paid tasks is therefore squarely inside point 4(a) on the Commission's own reading. Two escapes exist and both are architectural, not argumentative: (a) contracting only with legal persons (an entity is not a natural person, so commissioning a deliverable from a company is not access-to-employment, this is where H2 is right, and it is cheap to implement by making counterparty_type a first-class, enforced field); (b) the Article 6(3) filter, but the filter fails here twice over: the guidelines say only purely logistical/informational/retrospective functions that do not shape outcomes qualify, and Article 6(3) provides that an Annex III system is ALWAYS high-risk where it performs profiling of natural persons. A contributor capability/reputation profile is profiling, so the filter is unavailable to the matching primitive as designed. Note also that the Omnibus did not delete the registration duty for self-assessed non-high-risk Annex III systems: Parliament and Council reinstated it in streamlined form, so even a successful filter leaves an EU-database entry. Conclusion: the EU cost of the matching layer from 2 Dec 2027 is conformity assessment, Art 9-15 (risk management, data governance, logging, human oversight), registration, and Art 26 deployer duties, unless natural-person matching is separated from the rest of the exchange now. (2) GRANDFATHERING: a real but decaying asset, and a bad reason to re-sequence. Article 111(2) applies Chapter III only to high-risk systems placed on the market or put into service after the application date, unless they are subject to "significant changes in their designs" thereafter; the Omnibus improved it in two ways (it operates at the level of types/models rather than each unit, and the fixed 2 Aug 2026 cut-off was replaced by a dynamic cut-off tied to the actual date of application of Chapter III under the amended Article 113). But four things gut it for this build. First, the closing date is not fixed: the amended Article 113 makes high-risk duties apply six months after a Commission decision confirming standards/guidance readiness, with 2 Dec 2027 only as a long-stop (Annex III), so the window is 15 months at most and could compress to ~6 months' notice; one outlet asserts the Dec 2027 date is unconditional, which conflicts with the readiness-trigger reading, so treat the closing date as uncertain rather than banked. Second, 'significant change in design' is undefined and a matching agent is a continuously retrained, model-swapped artefact; on the deprecation cadence already in the belief set (frontier model retirement every 6-12 months), a grandfathered matching system almost certainly re-enters scope within a year. Third, grandfathering exempts nothing else: Article 50 is live now, GDPR is live now, and any public-authority deployer carries an unconditional 2 Aug 2030 compliance date. Fourth, and most important, the substantive constraint on matching already binds: under CJEU C-634/21 SCHUFA (7 Dec 2023) and C-203/22 (27 Feb 2025), a probability value produced by one party is itself an Article 22 GDPR automated decision when a third party draws strongly on it, and the duty attaches to the scorer. A fit/capability score that clients rely on to award paid work is therefore plausibly Art 22 ADM today, requiring a lawful ground, meaningful information about the logic, and human review, which is most of what the Annex III regime would add in Dec 2027. The '16-month compliance-cheap window' is largely illusory for the matching layer; it is real only for the conformity-assessment paperwork. (3) ARTICLE 50 APPLIED TO ARTIFACTS, TRANSCRIPTS AND SHADOW AGENTS. The guidance framework is now complete: final Code of Practice on Transparency of AI-Generated Content published 10 June 2026 (assessed adequate by Commission and AI Board; initial signatories published July 2026 including Anthropic, Google, Meta, Microsoft, Mistral, OpenAI, Cohere, Synthesia in Section 1; two task forces from Sept 2026), and final Commission Article 50 Guidelines adopted 20 July 2026 (51 pages). Duties applied from 2 Aug 2026; fines up to EUR 15m or 3% of worldwide turnover. Mapped onto the wedge's objects: (i) Art 50(1), the final Guidelines put agentic AI squarely in scope: agents that interact with natural persons while performing tasks (bookings, correspondence, negotiating or concluding contracts) must "disclose both their artificial nature and the person on whose behalf they are acting", the duty extends to multi-agent architectures where an agent interacts with other agents that in turn reach natural persons, disclosure is owed to the instructing human at key steps (authorisation, reporting, validation) and at every new interaction, and where the provider cannot rule out human contact the disclosure must be built in at architecture level. That makes principal identity + mandate scope a MANDATORY protocol field on every mission/capability agent, not just provenance on artifacts, a bigger schema consequence than the question anticipated, and one that maps directly onto A2A v1.0 signed agent cards. (ii) Art 50(2), machine-readable marking covers synthetic text as well as audio/image/video; agent outputs perceptible to natural persons count, but intermediate reasoning/chain-of-thought no human is intended to see does not, and purely machine-to-machine environments are outside 50(1) and 50(2) altogether. So agent-to-agent transcripts are out of scope while they remain machine-only, and come into scope at the moment they are surfaced to a human (audit view, dispute pack, publication), meaning marking is a property of the presentation boundary, which should be an explicit object in the data model. Also note the asymmetry: pure ranking/recommendation that merely arranges existing material is out of scope of 50(2) while being inside Annex III point 4, the two regimes cut orthogonally, so 'low-risk under Art 50' does not imply 'low-risk overall'. (iii) Art 50(4), deployer duty: deepfake labelling plus labelling of AI-generated/manipulated TEXT published to inform the public on matters of public interest, unless it has undergone human review/editorial control with a person holding editorial responsibility. Mission dossiers, red-team reports and forecast rationales published on energy, semiconductors, public health etc. plausibly qualify. The final Guidelines changed the cut-off rule in a way that matters: for image/audio/video the trigger is the date of generation (pre-2 Aug 2026 content is not retro-labelled), but for public-interest text the trigger is the date of PUBLICATION, so a pre-August corpus republished later must be labelled. Dates to diarise: 2 Dec 2026 (end of the Art 50(2) marking grace period for generative systems placed on the market before 2 Aug 2026; also the new Art 5 nudifier/CSAM prohibition), and 2 Feb 2027 for watermark-detection interoperability as read by at least one firm. Crucially, the Art 50(4) editorial-control exemption is a legal subsidy for exactly the named-human adjudication step the prior belief set already wanted to sell. (iv) SHADOW AGENTS. H3 holds, and the kill is over-determined. AI Act: an agent standing in for a named real person must disclose whose behalf it acts on, for an unclaimed shadow agent that disclosure is either false or self-defeating. GDPR: constructing a profile of a named person from public sources is processing needing an Art 6 basis with a documented LIA, plus Art 14 notice within one month of obtaining the data or at first communication (five narrow exemptions, disproportionate effort read narrowly), plus Art 21 objection, plus Art 5(1)(d) accuracy for a synthesised persona, plus Art 22 if the resulting score drives who gets paid work. The enforcement record is unfavourable and getting worse: Poland's UODO fined Bisnode ~EUR 220k in 2019 and ordered it to contact ~6 million people to cure Art 14; CNIL sanctioned Kaspr (reported ~EUR 200-240k) over scraped contact profiles; and in June 2026 Spain's AEPD closed a cross-border case against Amadeus with EUR 18m reduced to EUR 14.4m for aggregating booking data into profiles for a new purpose without Art 14 notice or a valid basis. The EDPB's 2026 Coordinated Enforcement Framework action is specifically on transparency/information obligations under Arts 12-14, the precise duty a shadow-agent bootstrap breaches is this year's coordinated enforcement priority. No relief is bankable: the Data Omnibus (GDPR/ePrivacy) is still in negotiation, the Council text drops core provisions, and the EDPB-EDPS Joint Opinion (10 Feb 2026) drew red lines on the personal-data definition and Art 22. CONSEQUENCES FOR THE PLAN (concrete): (a) Kill the named-natural-person shadow agent in the EU entirely; keep three lawful substitutes, shadow agents for legal persons/institutions, shadow agents for problems and claims (no data subject), and self-seeded claimable profiles where the person supplies the data; run any residual seeding outside the EU with a documented Art 3 territorial-scope analysis. (b) Add to the minimum data model, now: Agent.principal_identity and Agent.mandate_scope (Art 50(1)); Artifact.provenance {model_id, model_version, scaffold_hash, generation_ts, publication_ts, modality, marking_method, marking_status}; Artifact.human_review {reviewer_identity, editorial_responsibility_flag} (Art 50(4) exemption); Artifact.presentation_boundary_crossings (when a machine-only transcript became human-perceptible); Match.counterparty_type (natural|legal) and Match.jurisdiction; Score.art22_flag with a human-review path. (c) Do NOT re-sequence to 'ship matching fast and grandfather it', the retraining fragility, the possible six-month readiness trigger, and the fact that GDPR Art 22 already binds scoring make that a low-value trade. (d) A BETTER FIRST BUILD than the three-sided exchange, and better than the generic assessment utility: a Mandate & Provenance Ledger, the Article 50 conformance layer for agent work-product. Objects: Principal, Agent (with disclosed principal + mandate), Artifact, Provenance manifest (C2PA-style), Marking record per modality, Human-Review Attestation (named reviewer, editorial responsibility), Presentation Event, Jurisdiction. Protocol: MCP tools + A2A signed agent cards carrying principal/mandate/disclosure; append-only event log; verifier API that answers 'is this artifact compliant for publication in jurisdiction X on date Y'. Incentive: the duty is mandatory with dated deadlines (2 Aug 2026 live, 2 Dec 2026 legacy marking, 2 Feb 2027 detection interoperability) and a EUR 15m/3% base, and the Art 50(4) editorial-control exemption makes a paid human attestation cheaper than the alternative. Cold start: single-player value, one buyer (an agency, publisher, research/consulting firm, lab data/eval team, or auditor) gets an audit-ready ledger with no counterparty required. Kill criterion: if within 90 days no buyer pays for per-artifact attestation at a price that covers the human-review minute cost, or if signing the Commission's Code plus vendor-side marking is shown to discharge the duty end-to-end with no independent record needed, kill it and fall back to the single-buyer decision desk. WAS THIS AVAILABLE EARLIER? No. Six months ago the deferral was an unagreed proposal (second trilogue failed 28 April 2026), the Art 6 draft classification guidance did not exist (19 May 2026), and neither the Code (10 June 2026) nor the final Art 50 Guidelines (20 July 2026) existed. One month ago the Regulation was signed but not published/in force. This reasoning is genuinely ~4-5 weeks old.

fact · primary document · 6× fact · primary document · 5× fact · official statement · 2× fact · official statement · 3× fact · primary document · 2× inference · analysis commentary · 0×

Consensus view: Most sources say the Digital Omnibus bought 16 months: Annex III high-risk duties (explicitly including HR/recruitment tools) now land on 2 Dec 2027, Annex I on 2 Aug 2028, while Article 50 transparency went live on 2 Aug 2026 with a legacy marking grace to 2 Dec 2026, so organisations should use the runway to classify and build, and Article 111 grandfathering means only systems placed on the market after the new date are caught unless significantly modified. Evidence-supported view: The dates are right, but for an agentic matching-and-scoring exchange the deferral buys much less than it appears. (a) On the Commission's own draft guidance, ranking natural-person freelancers for paid tasks is inside Annex III point 4, and the Art 6(3) filter is unavailable because the system profiles natural persons, so the EU matching layer is high-risk by design, not by accident, and the escape is architectural (legal-person counterparties, or no ranking) rather than interpretive. (b) Why they differ: The consensus is written for enterprises buying static vendor HR tools and frames the question as a single AI Act date. It omits three things that dominate this build: the GDPR base rate (Art 22 scoring duties already in force per SCHUFA, and Art 14 duties for derived profiles), the fragility of 'significant change in design' for systems whose value proposition is continuous retraining a

Q08 · id 47 · depth 1 · adjacent_implicationEVOI 0.82 · confidence 0.76 · surprise 0.35

Does the EU Platform Work Directive's algorithmic-management regime (and its national transpositions, whose deadline appears to fall in late 2026) bind an agentic exchange that allocates paid tasks to self-employed contributors, and if so does it bite before the AI Act's 2 Dec 2027 date and regardless of Annex III classification?

SHORT ANSWER: yes on substance, no on the clean date, and the escape route in Hypothesis 2 is largely closed. The evidence favours a blend of H1 (legal substance) and H3 (practical timing), and it kills the premise behind the question in a different way than expected: there never was a "compliance-cheap window," because the earliest binding layer over an allocate/score/pay loop involving EU natural persons is GDPR, which already bit twice. 1) SUBSTANCE (H1 confirmed). Directive (EU) 2024/2831 Art 1(2), second paragraph, extends its algorithmic-management/data provisions to "persons performing platform work in the Union, including those who do not have an employment contract or employment relationship" (EUR-Lex consolidated text, in force 2024-12-01). Art 1(3) applies it to digital labour platforms organising platform work performed in the Union irrespective of place of establishment or applicable law, so a US- or UK-incorporated exchange gets no relief from incorporation. In the final numbering, Chapter III is Arts 7-15: Art 7 processing bans, Art 8 DPIA, Art 9 transparency on automated monitoring/decision-making systems (AMDMS), Art 10 human oversight, Art 11 human review, Art 12 safety/health, Art 13 information and consultation, Art 15 special arrangements for representatives. ETUI (2025) and the ETUC transposition manual (2026-03) both read Arts 8-11 as status-blind, with Arts 12-13 confined to employees and Art 15 limited to Arts 8.2, 9.1, 9.4, 10.4, 11.2 for representatives of the self-employed; Countouris/De Stefano (2025) agree that the bulk of Chapter III attaches to the broad category. Two decisive details for an agentic exchange: (a) criterion (d) of the "digital labour platform" definition, use of automated monitoring OR decision-making systems, is trivially met, and ADMS is defined to include systems that support decisions, so the brief's "human decision-of-record" mitigation does not exit the Directive (it only helps with GDPR Art 22's "solely automated" test and with the quality of the Art 11 review); (b) Art 3 requires that persons contracting through intermediaries get the same protection, with joint-and-several liability mechanisms where appropriate, which neutralises the "contract only with legal persons" dodge wherever the legal person is supplying an individual's labour. The one reported narrowing worth confirming in primary text: Art 11's human-review right is reported (gvzh, 2025-11; Wolters Kluwer, 2024-06) not to apply to persons who are "business users" under Reg (EU) 2019/1150, but that definition requires offering goods/services to consumers, so contributors on a B2B research exchange probably are NOT business users and the carve-out likely fails. 2) REGARDLESS OF ANNEX III (confirmed). PWD duties attach to platform status, not to any AI-risk classification, and they are enforced by labour authorities and DPAs, not by AI-Act conformity machinery. Conversely the AI Act still lands later: the Digital Omnibus on AI is now enacted law, Reg (EU) 2026/1744, OJ 2026-07-24, in force 2026-07-27, and the final text dropped the readiness-decision conditionality in favour of fixed dates: Annex III high-risk from 2027-12-02, Annex I from 2028-08-02, Art 50 transparency unchanged at 2026-08-02, Art 50(2) watermarking to 2026-12-02. That is a correction to the current belief, which still carried the "or six months after a Commission readiness decision" formulation. Annex III point 4(b) (Commission AI Act Service Desk text) covers systems used "to allocate tasks based on individual behaviour or personal traits" and to monitor/evaluate performance; the Commission's draft Annex III guidelines reportedly state at ¶241 that freelancers, independent professionals and platform workers fall in point 4's personal scope regardless of contractual status (Modulos, 2026-05, single source, draft not final). 3) TIMING (H3 confirmed, and it is the operative constraint). Directives have no horizontal direct effect, so nothing binds a private exchange until national law exists. As of 2026-07-01 a vendor tracker (teamed.global) reports 0/27 with a transposing law in force, 4 states with pre-existing presumptions (IT, ES, BE, PT), 5 drafting, 18 not started; Germany's BMAS was still drafting in 2026-05 (DLA Piper) and Ireland had no draft in 2025-12 (Mason Hayes Curran). The base rate from the nearest comparable instrument is bad: the Pay Transparency Directive got 4/27 on time at its 2026-06-07 deadline (Trusaic, 2026-08-16). Anchored on that, expect roughly 2-6 member states with PWD law in force by mid-2027 and 15-25 by end-2028, with several (FR, NL, DK) already signalling 2027 entry. So the honest schedule is: rolling, member-state-specific, geo-routable for 12-24 months on the one axis that actually works, "platform work performed in the Union," i.e. where the contributor sits. 4) WHAT BREAKS THE "WINDOW" FRAMING ENTIRELY. Algorithmic management of self-employed contributors is already enforceable today under GDPR: the Garante fined Foodinho €2.6M in 2021 and ordered changes to rider rating, human-intervention and contestation procedures (EDPB national-news page), then fined it €5M in 2024-11 over unlawful processing affecting 35,000+ couriers (Eurofound Platform Economy DB). That is the same duty-set the PWD codifies, applied to non-employees, five years before transposition. The correct sequencing statement is therefore: GDPR floor = now; PWD platform overlay = national transposition, mostly 2027-2028; AI Act conformity paperwork = 2027-12-02. Not a cliff, a staircase whose first step is behind us. 5) THE DESIGN FORK THAT ACTUALLY MATTERS (three architectures, three regimes). (i) Marketplace (exchange takes the contract, prices, scores, allocates, escrows): squarely a digital labour platform, full Chapter III plus the Art 5 presumption plus Chapter IV six-monthly reporting of headcount, T&Cs, average income and client list. (ii) Job-board/matching-only (contract formed directly between buyer and contributor, no pricing, no scoring, no monitoring): arguably outside, Bird & Bird's read is that mere placement portals (Stepstone, Monster) are not platforms while Fiverr is a classic example, but it stays inside AI Act Annex III 4(a)/(b) from 2027-12-02 and inside GDPR Art 22/SCHUFA today wherever the score determines access. (iii) Buyer-of-record desk (the exchange purchases the deliverable for its own account and resells adjudicated artifacts): the definition requires a service "provided at the request of a recipient of the service," and practitioners read the Directive as needing a triangular platform/worker/recipient relationship with internal crowdworking excluded (Taylor Wessing), so this design plausibly escapes platform status altogether and converts a novel, tightening regime into old, priced contractor-classification and GDPR risk. This is independent legal support for the brief's "better hypothesis": build the assessment/adjudication utility for one named buyer, be the buyer of the work, and do not intermediate labour between two other parties. 6) COLLATERAL DAMAGE TO THE PLAN. The "shadow agents built from public information that the real party can later claim" bootstrap is the weakest element legally: it is profiling of prospective contributors, it maps onto Annex III 4(a) (targeted advertising/selection/evaluation of candidates) from 2027-12-02, and once the person contributes, Art 7's bans (emotional/psychological state, private conversations, trade-union activity, off-work data collection) constrain exactly the derived signals such profiles are made of. Seed from institutions and publications, not from persons, or make claiming opt-in-first. 7) WHAT TO DO IN THE 90-DAY PILOT. Keep the pilot's paid-task leg non-EU-resident or EU-legal-person-B2B-deliverable (genuine firm output, not labour supply, to stay clear of Art 3), run the exchange as buyer-of-record, and ship an "allocation receipt" object from day one: for every match, rejection, price and deactivation, record inputs used, alternatives, the named human reviewer, and a contest link with a two-week substantive response (the ETUC manual reads Art 11 as requiring a "sufficiently precise and adequately substantiated" response within two weeks). That single object simultaneously discharges Art 9/11, feeds AI Act Art 12 logging in 2027, and is the provenance/adjudication dossier the brief already wants to sell. Kill criterion: if no buyer will pay a premium for the receipt/dossier within 90 days and fewer than 1 in 20 receipts is ever contested, it is a cost centre, not a product, in which case strip it to the minimum and stay out of EU-resident contributor supply until one national law is actually in force and readable. 8) WOULD THIS REASONING HAVE WORKED EARLIER? One year earlier (2025-08) all of it except the Omnibus enactment and the transposition count was already true, the Directive was in force from 2024-12-01 and Foodinho was fined in 2021 and 2024. Three years earlier the Directive was an unagreed proposal and the answer would have been genuinely different. So this is not new information; it is information the plan's calendar-based framing omitted.

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Consensus view: Most sources online, law-firm alerts, EOR/compliance vendors, union and academic commentary, say the same three things: the PWD's algorithmic-management chapter applies to all persons performing platform work including the genuinely self-employed; the 'digital labour platform' definition is broad enough to catch online freelancing, micro-task and professional-services marketplaces; and the binding date is 2 December 2026. Several vendor guides go further and assert it catches  Evidence-supported view: The fact-grade rows support H1 on substance and H3 on timing, plus a point neither hypothesis contains. On substance: Art 1(2) makes Chapter III status-blind, Art 1(3) makes it extraterritorial, criterion (d) is trivially met by any agentic allocator, ADMS includes decision-support so a human-of-record does not exit scope, Art 3 defeats the legal-person dodge where the entity supplies labour, and none of it depends on Annex III classification. On timing: directives have no horizontal direct effe Why they differ: Three mechanisms. (1) Framing/selling: compliance vendors and EOR providers monetise deadline urgency, so they emphasise 2026-12-02 and suppress the no-horizontal-direct-effect point and the scope gates; the same incentive produces the sweeping 'any company using algorithms to manage contractors' line. (2) Interested reading in both directions: union-side sources (ETUI, ETUC) push the br

Q09 · id 3 · depth 0 · contrarian_abductiveEVOI 0.82 · confidence 0.78 · surprise 0.45

Surprising observation: frontier pipelines reached superforecaster-indistinguishable accuracy on ForecastBench by July 2026 and first-time above-median performance on market questions, yet essentially no one pays for calibrated forecasts on research or capital-allocation questions while Kalshi earned about $850M of 2026 fees mostly on sports. What explains the gap, and what evidence discriminates?

The evidence favors Hypothesis 1, in a sharper form than stated: the payable object is a resolvable, settleable claim that lets someone transfer risk or discharge a duty, the probability is a free by-product. Hypothesis 2 is true only in a weak sense (institutional willingness-to-pay exists, but what it buys is a position or a research artifact, not a probability). Hypothesis 3 is real but secondary: it explains why decision-relevant venues are empty, not where the money goes. The decisive fact is that the parity result is measured on a benchmark that hands the model the free price. ForecastBench market questions are drawn from Manifold/Metaculus/Polymarket/RAND and scored against the crowd forecast until resolution, and the paper itself notes the top-performing LLMs all had the crowd forecast ("freeze values") in the prompt (arXiv 2409.19839; ICLR 2025 slides). An independent audit (11 Aug 2026) reports that of 527 tested forecasters, 442 with enough resolved market questions scored worse than the price they were handed, and none beat it after multiple-comparison correction; ForecastBench's own baselines score 51.5 Brier Index without the price, 68.1 with it, versus 70.93 for submitting the price unchanged, a median paired gain of 15.66 BI points from being told the answer. Corroborating this from a primary technical report: the AIA Forecaster (arXiv 2511.07678) is "statistically indistinguishable from superforecasters" on FB-Market (0.0753 vs 0.0740; market 0.0965), but on its MarketLiquid set the market price beats it (0.1106 vs 0.1258). So machine calibration adds value only where markets are thin or absent, i.e. precisely where there is no resolution, no counterparty and no scoring. That is a structural reason no one pays for the number. Where money does flow, it flows to settlement plus risk transfer. Kalshi's 2025 fees were an estimated $234.6M of $263.5M from sports (89%, computed from its published fee formula against its public API), monthly fees reached ~$137.86M in May 2026 (Dune) versus ~$28.07M for Polymarket, sports were 87% of March 2026 volume ($9.9B of $11.39B), and the company raised $1B at $22B in May 2026. Kalshi is paid because it is the settlement authority (CFTC DCM) on questions that resolve in minutes with no ambiguity. Every observable investment in the sector is going into the settlement layer, not the forecasting layer: Chainlink now brands itself the prediction-market oracle, its Polymarket integration (from 12 Sep 2025) has carried >$3.4B in 5/15-minute markets and both parties say they are trying to extend it to subjective questions to reduce reliance on social voting; Hyperliquid's HIP-4 (live 2 May 2026) removes the token-vote backstop entirely; Polymarket has logged 1,150+ disputed markets in 2026, already past all of 2025, with a WSJ investigation (May 2026) finding most disputed markets had over half of UMA votes from the ten largest wallets and roughly one in five disputes containing a voter with a stake in the outcome. Adjudication is contested, expensive and being re-architected, that is the definition of a scarce primitive. The second-order evidence is that the surviving forecast vendors have already repriced the number to near zero and monetise the artifact and the plumbing instead. FutureSearch sells forecasts at roughly $0.15 (low effort) to $2 (high effort) per question and per-row research at 1-11 cents, ships an MCP connector, and markets itself as spreadsheet-scale research; it self-reports #17 of 378 on ForecastBench (21 Aug 2026). Mantic raised only $4M pre-seed (Aug 2025) and argues in its own launch note that buyers want the explanation, not the bare probability, and that most public markets carry under $100k of volume. Metaculus charges users nothing and is funded by grants and commissioned projects, organisations pay it to construct and manage questions. That is the market telling you the priced good is question construction, evidence and settlement, not calibration. Two genuinely new dated objects matter for the plan. First, on 19 Aug 2026 Kalshi filed to let Cantor Fitzgerald broker institutional block trades with Susquehanna making prices, and institutions may propose markets tailored to risks they want to hedge (Kalshi then files with the CFTC and must ensure liquidity); Susquehanna's business-development head named AI supply chains and compute prices as target areas, and hedge funds reportedly want contracts on corporate metrics such as unit sales. That is a paying demand pull for exactly the wedge's declared first vertical, expressed as demand for well-specified, settleable contract terms. Wintermute's characterisation still applies: prediction markets have "the demand profile of a major asset class but the liquidity profile of an early-stage one" (May 2026). Second, Blanket (public 7-8 Aug 2026) maps a small business's stated risk to existing Kalshi contracts in ~30 seconds, logs missing markets as demand signal, executes nothing, and is free, while the exchange collects the fee. That is the holonic-rent question answered empirically: the AI matcher is given away; the settlement venue keeps the economics. BETTER HYPOTHESIS AND BUILD. Replace value-of-information ranking as the wedge's differentiator with a Resolution Registry and Adjudication Desk. Objects: QuestionSpec (claim, resolution source, window, edge-case ladder, annulment policy, adjudicator class, appeal path), EvidencePacket (dated, provenanced), Attestation (signed outcome plus reasons and dissent), bonded Challenge, AdjudicatorProfile (agreement and calibration history), and the AllocationReceipt already in the beliefs, linking spec to evidence to attestation to payment. Protocol: MCP 2026-07-28 Tasks for long-running settlement jobs, A2A v1.0 signed agent cards for adjudicator identity, plain OAuth 2.1; no token, no payment rail, no VC dependency. Incentive: a fee per settled question paid by whoever needs the settlement (venue, lab eval desk, grant funder, buyer-of-record), a fee-funded appeal panel rather than a token-weighted vote (the documented UMA failure), adjudicators paid per graded item at GDPval-anchored rates. Cold start is single-player: sell annulment-and-dispute-risk audits over question sets the customer already owns (venues, tournaments, eval suites), where ~8% of Metaculus questions are annulled for ambiguity and 1,150+ Polymarket markets were disputed in 2026. Kill criterion: on a blind set of 200 historically disputed or annulled questions, if attestations are not ratified by two independent human adjudicators at least 90% of the time, at under $25 per question all-in and 48-hour turnaround, and if no venue or eval desk pays at least $5 per question within 90 days, stop. Note the automation risk is measured: a May 2026 study of multi-agent LLM oracles documents models reframing the question and contradicting evidence already in the packet, so the desk must be human-ratified, which is exactly the non-deflating cost line the beliefs already price. Honesty check on novelty: the structural argument was available 6-12 months ago (Kalshi's 2025 fee split, the 2024 freeze-value design). Genuinely new in the last six weeks are the 16 Jul 2026 parity claim, its 11 Aug audit, and the 19 Aug institutional block program naming compute and AI supply chains. This is a correctly-dated confirmation plus one new demand pull, not a new-information trade.

fact · primary document · 1× fact · primary document · 2× estimate · analysis commentary · 2× fact · official statement · 2× framing · interested party · 1× estimate · reporting · 2×

Consensus view: Most sources online say AI forecasting has reached or is about to reach superforecaster parity, that this will make institutional-grade calibrated probability nearly free, and that prediction markets are becoming general 'truth machines' and decision infrastructure whose probabilities will be embedded in enterprise risk systems and AI world models. The corollary usually drawn is that a forecast-centric information layer is now a viable product. Evidence-supported view: The parity claim is measured on a benchmark that supplies the free crowd price in the prompt and scores market questions against that crowd forecast; the price is worth roughly 15 Brier Index points and copying it unchanged still beats the baselines that were given it, and on a liquid-market subset the price beats the best published AI forecaster outright (0.1106 vs 0.1258). Calibrated probability therefore has near-zero marginal value where a market exists, and no verifiable value where none ex Why they differ: Three mechanisms. (1) A headline reported without its base: 'parity with superforecasters' omits that the model was handed the market price and is scored against the crowd, so the achievement is partly price-copying. (2) Stale and frozen baselines: the human comparison is a mid-2024 one-shot engagement on different questions, which both the benchmark operator and its commercial critic ac

Q10 · id 16 · depth 0 · contrarian_abductiveEVOI 0.81 · confidence 0.66 · surprise 0.58

If third-party grading of mission artifacts falls to automated judges at under $1 per artifact within 12 months, does the adjudication-utility thesis invert, with the scarce good being liability-bearing sign-off rather than adjudication capacity? Measure current human-panel versus automated-judge agreement on GDPval-style rubrics, whether any 2026 buyer pays a party other than the vendor for artifact grading, and whether AI-output warranty/E&O products already price agent-work risk; if so, the better build is an attestation-and-indemnity authority for agent-produced work, a system absent from the 45-item inventory.

Short answer: the thesis does not invert so much as re-price, and the trigger has already fired. Automated grading is already well under $1 per artifact (rubric judge calls run about $0.01-$0.10; Black Spatula-class paper checks cost cents), yet paid expert grading has not collapsed, because the object buyers actually pay for is not a per-artifact verdict. The evidence favors competing hypothesis 3 (cheap automated screening plus a scarce, paid escalation/ratification layer), with two hard corrections to the question's framing. Correction 1: the agreement data do not support kappa above 0.7 against expert panels on GDPval-style deliverables, and probably cannot. OpenAI's own GDPval paper (arXiv 2510.04374, 25 Sep 2025) reports its trained automated grader at 66% agreement with occupational experts against 71% human inter-rater agreement on the 220-task gold subset, raw agreement, not chance-corrected, and with a human ceiling of 71%, so the chance-corrected number for both grader and panel is nowhere near 0.7. Expert panels themselves are the noisy instrument: three tax-law experts scoring statements reached ICC(2,1)=0.367 with unanimous agreement in 3 of 20 cases (arXiv 2602.11081). LLM-jury-vs-human-consensus kappa of 0.74 exists, but on a clinical safety rubric with per-criterion agreement spanning 0.34-0.97 (arXiv 2604.02359); the widely quoted '80-85% agreement' figures come from chat-quality rubrics and vendor blogs, not multi-file professional deliverables. And the 2026 literature shows rubric judges are gameable under optimization pressure (arXiv 2605.12474; 2606.04923; 'One Token to Fool LLM-as-a-Judge'), so precision degrades exactly where money is at stake. Correction 2: yes, 2026 buyers pay third parties for grading, three distinct classes, but almost none of them buy it per artifact-quality-verdict. (a) Independent scorekeepers: Vals AI raised $40M led by a16z at a $400M valuation (reported 6 Jan 2026) selling evaluation as a service; LMArena was repriced to $1.7B on roughly $30M annualized revenue as of Dec 2025; Artificial Analysis launched GDPval-AA v2 on 15 Jun 2026 grading the GDPval gold subset fully automatically via Elo from blind pairwise comparisons, and I could not find any published judge-versus-human-panel agreement statistic for it, which is itself a load-bearing gap. (b) Per-artifact screening at scale, priced as cheap triage with human editorial adjudication retained: the STM Integrity Hub screens over 200,000 manuscripts a month from more than 13,000 journals (May 2026), Wiley's Research Exchange screening covers more than 1,000 journals at more than 10,000 manuscripts a month, Elsevier expanded Check Integrity to nearly 2,000 journals (Mar 2026). (c) Attestation with recourse, which already exists as a named, funded, insurance-led business, and is not in the 45-item inventory: AIUC's AIUC-1 standard (51 requirements, 130 controls, 65 mandatory, quarterly retesting, 12-month certificates), with Schellman accredited as first auditor on 3 Feb 2026 and certificates held by Intercom's Fin, ElevenLabs (12 Feb 2026, paired with the first voice-agent insurance policy), UiPath (9 Mar 2026), Harvey (3,000+ evals) and Cursor (Q3 2026 coding-agent requirements); Armilla as a Lloyd's coverholder with $25M+ limits by Jan 2026, Vanguard AI with Chaucer from 10 Feb 2026, warranty paper from Chaucer, Greenlight Re and Swiss Re; Munich Re's aiSure (first AI performance policy 2018, first LLM cover 2019) distributed by Mosaic up to about $15M; Testudo to $9.25M with Atrium and QBE capacity from Mar 2026. The pricing structure of that layer is the decisive fact: it is per system and per period, not per artifact. ISO 42001 Stage 1+2 audits run about $20k-$40k (Schellman) to $25k-$50k (BSI, DNV) with surveillance at 30-40% a year; BSI's published Oct 2025 EU AI Act schedule prices technical documentation review at EUR 4,356/day standard or EUR 10,925/day dedicated with a three-day minimum; Armilla's premium is a percentage of the AI licence fees with recovery capped at fees paid; Munich Re settles parametrically against a pre-agreed KPI inside a defined scope of validity. None of that deflates with token prices. So the scarce good is neither adjudication capacity nor a per-artifact indemnity, it is a contractible acceptance criterion plus a named party who will stand behind it for a period. The demand pull is now dated and named: ISO/Verisk generative-AI exclusions CG 40 47 / CG 40 48 took effect 1 Jan 2026, W.R. Berkley's PC 51380 00 is an 'absolute' AI exclusion reaching D&O, E&O and fiduciary, Hamilton Select excluded gen-AI on professional liability, and 60+ P&C groups filed to adopt or delay the ISO forms; brokers report carriers walking exclusions back for firms that can show documented verification workflows and licensed professional sign-off. Note the honest counterweight: as of May 2026 no major US lawyers-professional-liability writer had publicly filed an AI exclusion, so the wave is concentrated in general and management liability, not yet the professional lines that would make receipts mandatory. Better hypothesis than either the assessment utility or the indemnity authority: build the evidence layer those parties consume, because the indemnity slot is occupied by people with Lloyd's paper and the audit slot by Schellman/BSI/BABL/Big-4 hours, while nobody owns the artifact-level assurance case. Spec, objects: AcceptanceCriterion (pre-registered, versioned, with rotating held-out probes to defeat rubric gaming), JudgePanel (>=3 cross-family judges, disagreement score, published rolling kappa against ratifiers per rubric), Ratification (named human, scope-of-competence credential, E&O policy reference, minutes spent), ProvenanceTrace (MCP Tasks + tool calls + input hashes), Escalation (dispute, evidence bundle, arbiter, outcome), Receipt (signed, event-sourced, exportable to auditor and underwriter schemas). Protocol: criterion pre-registered before work; cheap panel on every artifact; auto-accept only on unanimity with a current calibration set; route to a paid ratifier on panel disagreement, above a value threshold, or on stratified random audit at 5-10%. Incentive: cents per receipt, $50-$500 per escalation, and an annual attestation priced off the measured escalation rate, with the real willingness-to-pay coming from an underwriter or auditor granting a premium or audit-hours discount for the feed. Cold start is single-player: one AI-work vendor facing an enterprise procurement question or an E&O renewal buys it alone. Kill criterion at 90 days: no underwriter, accredited auditor or procurement function will accept the receipt as evidence in writing; or measured panel-versus-ratifier kappa on the chosen artifact class stays below 0.6 under a rubric a domain expert will sign; or escalation rate times ratifier cost exceeds what the buyer will pay. Two structural warnings. First, neutrality is not monetisable on its own: LexisNexis and Thomson Reuters simply declined to participate in Vals AI's Oct 2025 legal-research benchmark, so voluntary scorekeeping is capped unless a regulator, insurer or dominant buyer compels participation, which is why the insurance/procurement channel beats the leaderboard channel. Second, rent re-emerges exactly where the plan's holonic framing says it should not: AIUC authors the standard, runs the evaluations, issues the certificates and sells the insurance, which critics compare to issuer-pays credit ratings, and it accredits its own auditors. For the Mission Exchange this means: keep assessment, but sell the ratified receipt and the escalation, not the grade; and expect the margin to sit with whoever holds capital or settlement, not with whoever runs the judge.

fact · primary document · 3× fact · primary document · 1× inference · primary document · 1× fact · primary document · 0× fact · primary document · 0× fact · primary document · 2×

Consensus view: Most online material says LLM-as-judge now agrees with humans about 80-85% of the time, at or above human-human consistency, at 500x-5000x lower cost, so automated grading is production-ready and human evaluation survives only as calibration; and separately that AI insurance and AI-agent certification are a fast-emerging market where an AIUC-1-style certificate is becoming the 'SOC 2 for agents'. Evidence-supported view: On the artifact class that actually matches mission deliverables, multi-file professional work graded pairwise against expert output, the only primary measurement is 66% automated agreement against a 71% human ceiling, uncorrected for chance, i.e. nowhere near kappa 0.7, and the expert panel itself is noisy (ICC 0.367 on tax-law statements). The 80-85% figures come from chat-quality rubrics and vendor blogs. Cheap grading is already reality and has not destroyed the paid layer, because the price Why they differ: Domain substitution and base-rate neglect: chat-rubric agreement statistics are generalised to professional multi-file deliverables; a raw 66%/71% pair is quoted without its base or chance correction; and most of the cheerful agreement numbers come from eval-tooling vendors whose product is the judge. On the insurance side, the consensus reports product launches and limits but not pricing structur

Q11 · id 39 · depth 1 · supply_chainEVOI 0.81 · confidence 0.76 · surprise 0.45

Read the wedge through the RL/eval supply chain: what task types are Mercor, Surge, Handshake, Turing, Mechanize and AfterQuery actually selling in 2026 (published benchmarks, job postings, vendor task categories), and does anything resembling 'frontier-technology research and capital-allocation reasoning with a resolution criterion' appear in the buy list at all, or is spend still concentrated in software, computer-use and narrow professional domains?

DIRECT ANSWER: the buy list is concentrated in (a) software engineering, (b) computer-use / enterprise-app replicas, and (c) a short, named list of professional domains, investment banking associate, management consultant, big-law associate, primary-care physician, accountant, plus academic STEM. Capital-allocation reasoning is therefore already in the buy list, but only in one form: rubric-graded professional deliverable production (build the model, value the company, prepare the pitch), never priced on world-resolution. 'Frontier-technology research with a resolution criterion' is essentially absent as a purchased task type, and where it exists it is being produced by script, not by experts. WHAT IS ACTUALLY SOLD, BY VENDOR (dated): Mechanize's own site says it builds environments and evals for frontier coding agents, feature builds, deploys, debugging in unfamiliar codebases, scored by a grader; long-term ambition is broader, current focus is software. Turing sells 1,000+ RL environments as Jira/Salesforce/Zendesk replicas plus MCP tool-call environments, with benchmarks SWE-bench++, Code Review Bench, VLM-Bench. Surge spun up a dedicated internal RL-environments org (TechCrunch, 2025-09-21). Handshake AI's task menu is preference ranking, rubric grading, expert response writing and adversarial question design across math, physics, medicine, law, design, software, music at ~$17–$125/hr. Mercor is the only vendor that has published a non-software 'economically valuable work' product line: APEX-v1.0 (arXiv 2509.25721, Oct 2025) is 200 held-out cases across investment banking, consulting, law and primary care, tasks taking a professional 1–8 hours, rubrics averaging ~29 criteria, graded by a Judge LM; APEX-Agents (2026-01-21) is 480 tasks, 160 each in IB, consulting, corporate law, inside 33 simulated 'worlds' averaging >160 files, built by professionals with 5–10 years at top-tier firms; APEX-SWE was built with Cognition; an accounting benchmark shipped too. Finance-specific supply exists and is being absorbed: Sepal AI (finance/spreadsheet-analyst workflows plus advanced science, 20k+ expert network) was acquired by Mercor 2026-02-06 (Orrick); Deeptune (enterprise-app simulations, $43M a16z Series A March 2026) was acquired ~2026-07-09; AfterQuery publishes FinanceQA/Terminal-Bench/IDE-Bench and is described as code+finance; Halluminate's 2026 positioning narrowed to RL environments for financial services (IB, PE, consulting); BigFinanceBench (arXiv 2606.03829, June 2026) is 928 full-workflow financial-research questions written by practicing analysts and ex-IB practitioners, each audited by a separate reviewer, recruited via a staffing partner. SO WHICH HYPOTHESIS WINS: H3, with an amendment. H1 ('unserved category, sell scarce non-software task+verifier bundles at $500–$2,000') is mostly wrong on 'unserved', capital-allocation reasoning is the flagship non-software category of the largest vendor, and its supply chain (30k+ experts spanning IB, medicine, consulting, law, scientific research; $81/hr average advertised, $200+/hr senior) is exactly the 'they have the guys' advantage. H2 ('no buyer, collapses to consulting') is too strong: Mercor now sells three SKUs, per-task expert-authored evals at tens of thousands of tasks/month, off-the-shelf datasets, and directed expert staffing, and is selling them to application-layer buyers (a legal environment built with top law firms; Harvey named as a customer, per an early-Aug-2026 conference write-up), so there is demand beyond the five frontier labs. H3 is what the transactions show: labs pick embedded research partners, build UI-gym-style environments in-house, and the layer that got paid consolidated, Menlo's catalogue (relayed mid-2026) puts >75% of ~$8.5B of training-data/RL-environment revenue with Scale, Surge, Mercor and Handshake, and the two independent environment-software startups in the finance/enterprise lane were bought by one of them within five months of each other. A new entrant sells into that as a subcontracted world+verifier supplier or as tooling, not as a marketplace. THE PRICE ANCHOR DOES NOT SURVIVE INTACT. The $500–$2,000 range is one founder's quote in one Epoch survey of 18 people (2026-01-12), with $20k called rare and specific to complex software engineering; contracts run six-to-seven figures per quarter ($300k to >$1M), and exclusivity costs 4–5x non-exclusive. The economics are set by RL-compute opportunity cost, not by a question's importance to the world: Mechanize argues it is inefficient to spend much below ~$500/task and estimated ~$2,400 of compute per task during RL training. Against that, TIME's coverage of the APEX launch reports Mercor spent more than $500,000 to build 200 tasks, ~$2,500/task fully loaded, i.e. above the middle of the quoted sale range. Expert time alone (1–8 hours at $81–$200/hr) is $80–$1,600 before rubric authoring and QA. So expert-authored non-software tasks are a thin-margin, reuse-or-exclusivity business, and the demonstrated ROI that justifies it is capability lift, not truth: Mercor reports a partner model post-trained on fewer than ~2,000 expert tasks (law, consulting, corporate finance) moving from 52.5% to 58.2% on APEX and 4th in medicine, a domain not in the training set. THE MOST IMPORTANT NEGATIVE FINDING, AND A BETTER THING TO BUILD. Nothing in the public buy list is verified by world-resolution; verification is unit tests, golden states, or Judge-LM-over-rubric. And the one place resolution-criterion supervision is being industrialised, it is being automated to near-zero marginal cost: Lightning Rod Labs' 'future-as-label' / Foresight Learning trains on ~10,000 resolved Polymarket contracts plus ~100,000 machine-generated forecasting prompts with no human labels (arXiv 2505.17989v2), extends the same trick to SEC risk factors (arXiv 2601.19189) and clinical-trial reports, reports ~27% Brier improvement and roughly halved calibration error on a 32B model, sits at/near the top of ForecastBench and ProphetArena, and sells inference at $6 per 1M output tokens. That kills any plan to sell 'calibrated forecasts on resolvable questions' as a scarce good wherever a public timestamped corpus exists. Meanwhile the genuinely frontier-technology research reward signal is being financed as vertical integration, not procurement: Periodic Labs ($605M raised, semiconductor customers and revenue by March 2026) is building physical labs to manufacture experimentally verified rewards, and DeepMind is standing up an automated materials lab in 2026. That inversion names the buildable object. The scarce asset is not a mission and not a forecast; it is a resolution ledger for decisions that have no public timestamped substrate, private capital-allocation and frontier-infrastructure decisions (interconnect queue outcomes, fab tooling qualification, PPA delivery, datacenter energisation dates, procurement awards). Spec: objects = Decision, PreRegisteredCriterion (resolver as code), EvidenceBundle (hash-chained, timestamped), Forecast (author, probability, horizon), Resolution (outcome, named resolver, date), Receipt (exportable attestation). Protocol: criterion and evidence hash frozen before the decision is taken; forecasts recorded; at horizon an independent resolver executes the criterion; receipt issued and the (information-at-time → decision → outcome) tuple enters the corpus. Incentive: the first buyer is whoever must later prove the decision was reasonable (procurement, underwriter, audit committee, LP), one principal, no network needed; the second buyer is a lab or neolab that cannot generate this corpus with a scraper because the substrate is private. Cold start: single allocator or single developer; each decision yields a receipt standalone. Kill criterion: if in 90 days no counterparty will accept a receipt in place of one of their own diligence steps, and fewer than 20 pre-registered criteria survive to independently reproducible resolution, kill it. RE-SEQUENCING THE WEDGE: do not launch a three-sided mission exchange into a category where four vendors hold >75% of spend and buy their missing tooling by acquisition. Pilot instead as (1) one exclusive world+verifier bundle in a domain no incumbent has published (energy/datacenter capital allocation, semiconductor capex, grid interconnect) sold to one or two named buyers with reward-hack-resistance evidence, priced against the $2,500/task build cost with exclusivity uplift; and (2) the resolution ledger above. Note the binding technical constraint on both: reward hacking is the industry's named failure mode, calibrated environments are reportedly run at ~2–3% rollout pass rates, and no verifier for 'was this allocation right' can be a unit test, which is precisely why outcome-resolution custody, not grading, is the defensible position.

fact · official statement · 3× fact · primary document · 2× fact · primary document · 1× estimate · analysis commentary · 1× fact · reporting · 0× fact · primary document · 3×

Consensus view: The prevailing online view is that RL environments are the binding bottleneck, that lab spend is exploding (Anthropic reportedly discussing >$1B), that the domain mix is rapidly broadening from code and math into professional and scientific work, and that whoever builds environments captures the value, implying an open lane for new non-software task+verifier suppliers, including in research and finance, at $500–$2,000 per task. Evidence-supported view: Fact-grade rows support something narrower. The categories actually shipped and sold are software engineering, computer-use/enterprise-app replicas, and a fixed short list of professional roles (IB associate, consultant, big-law associate, primary-care physician, accountant) plus academic STEM, all verified by unit tests, golden states or Judge-LM-over-rubric, never by world resolution. Capital-allocation reasoning is present but only as deliverable production. Frontier-technology research rewar Why they differ: Three mechanisms. (1) Base neglect: the $500–$2,000 anchor is one seller's quote inside a single 18-person survey, weighted to software engineering, repeated downstream without the caveat and without any cost base. (2) Interested framing: the loudest 'domains are broadening' and 'environments are the moat' sources are vendors and their investors, while the disclosed transa

Q12 · id 9 · depth 0 · tail_scenarioEVOI 0.80 · confidence 0.60 · surprise 0.45

What concrete mechanisms would make this wedge 5-10x more valuable or dead within 12 months, and what early-warning signals discriminate: a frontier lab shipping a first-party mission/task-graph plus contributor marketplace primitive (Anthropic Cowork-style scaffolds, OpenAI app and agent surfaces, Google AP2 plus A2A registry), or a liability event in which an agent-commissioned artifact causes a bad funding or safety decision under the 2026 enforcement posture?

Both named mechanisms are already partly realised, and they resolve to the same conclusion, but not the one the question implies. The 5-10x/dead axis for this wedge is not 'exchange vs. no exchange'; it is whether you own a ratified, insurable opinion on a specific artifact backing a specific decision, or merely the plumbing around it. What the evidence shows on hypothesis 1 (platform absorption). Absorption of the mechanism layer has already happened, faster than the plan assumes. MCP 2026-07-28 is final with an official Tasks extension; Google ADK v2.0 shipped a graph workflow runtime plus a Task API for structured agent-to-agent delegation (alpha, ~Mar 2026); Anthropic launched Cowork (Jan 2026) and a plugin marketplace that went 11 plugins (Jan) → 24 (Feb 2026), with private enterprise marketplaces, OpenTelemetry cost/usage visibility, and a Claude Marketplace that lets buyers spend existing Anthropic commitments on partner solutions, i.e. procurement routing, for software. OpenAI Frontier (5 Feb 2026, HP/Intuit/Oracle/State Farm/Thermo Fisher/Uber) explicitly bundles agent identity, permissions, audit logs, shared context and evaluation in one platform, and OpenAI acquired Promptfoo (Mar 2026) for automated red-teaming. So: task graphs, mandates-as-permissions, event logs, first-party eval and red-teaming are now vendor features, not a differentiated build. Absorption of the paid human contributor side has NOT happened: the OpenAI Jobs Platform, announced 4 Sep 2025 for mid-2026, has no beta, waitlist or new date as of Aug 2026 (single secondary source, treat as weak). Net: the plan's decomposition/task-graph/mandate primitives are commoditised; the human-routing side is still open but is being served by single-buyer vendors (Mercor ~$2B gross run-rate at 30-35% take), not by labs. What the evidence shows on hypothesis 2 (liability shock). The shock has already occurred, four times, but as refund-and-retraction rather than tort: Deloitte Australia partially refunded an A$440k government report (Oct 2025, fabricated references and a fabricated court quote); EY Canada withdrew a loyalty study after GPTZero found fabricated data and a nonexistent McKinsey citation (May 2026) and had a Dec 2025 cyber report pulled (16 of 27 sources bad); KPMG pulled its Oct 2025 agentic-AI report on 12-13 June 2026 after GPTZero reported only 5 of 45 citations correctly pointed to source. That is roughly one artifact-integrity event per quarter, rising, in the exact use case the wedge targets (a commissioned artifact feeding an allocation decision). The enforcement frame is in place: Cal. Civ. Code §1714.46 (AB 316, effective 1 Jan 2026) bars the autonomous-harm defence across anyone who developed, modified or used the system; a June 2026 executive order directs DOJ to prioritise AI-agent-enabled offences; NIH NOT-OD-25-132 refuses applications substantially developed by AI, uses detection technology, and the May 2026 NIH/ORI reminder ties fabricated references and AI-generated applications to FFP misconduct with cost disallowance, withholding, suspension or termination as remedies. Critically: zero of these events involved an artifact commissioned through a marketplace, and none has yet produced litigation or a published damages number. The demand they create is procurement-side verification, not court-driven mandate. What the evidence shows on hypothesis 3 (governance fatigue). It is a damper, not a kill, and the headline is stale. The 40%-cancelled-by-2027 figure is a Gartner press release dated 25 June 2025, recycled through 2026 (Forbes, 7 Jul 2026) as if new; the fresher datapoint is Gartner's 2026 CIO survey at 17% deployed with 42% planning within 12 months, and Forrester 2026 finding broad adoption with little real production. Fatigue suppresses autonomous commissioned work; it increases demand for the evidence artifact that gets a project through governance. Where this leaves the defensible slot, and why it is narrower than the current belief. Three tiers of 'assessment' are now priced: (a) detection/citation-forensics is commodity, GPTZero at $8.33-$24.99/mo annual, ~$300/seat/yr enterprise, with a hallucination detector, and procurement agencies already using it on grant applications and vendor proposals (Sacra, interested party); (b) system/vendor-level certification with liability transfer is taken: AIUC-1 (50+ controls, six domains, quarterly releases on 15 Jan/Apr/Jul/Oct, Schellman as first accredited auditor Feb 2026, Lloyd's-backed insurance, ElevenLabs live 11 Feb 2026, UiPath 9 Mar 2026, Harvey, Cursor Q3-2026 coding-agent requirements, Lovable audit summer 2026, 120+ consortium members, Q2 update changed 14 requirements/23 controls); (c) first-party eval and red-teaming is being absorbed by the labs (Frontier + Promptfoo). The unowned slot is the middle-out one: artifact- and decision-level ratification, a named, scoped, insurable sign-off on this deliverable backing this decision, with pre-registered criteria, rotating held-out probes, panel escalation, and an exportable receipt. Nobody in the evidence sells that. The Big Four cases show the loss-bearer is the buyer of the deliverable; AB 316 shows the buyer cannot outsource blame; the insurance market is visibly bifurcating into certified-and-insured versus deployer-pays (absolute AI exclusions appearing in D&O/E&O alongside AIUC-1's Lloyd's paper). BETTER HYPOTHESIS, stated plainly: build 'title insurance for AI-assisted deliverables', not a Mission Exchange. Spec, Objects: Artifact, Decision (with dollar exposure), CriterionSet (pre-registered before work), ProbeSet (rotating, sealed, held out), Panel (named credentialed reviewers), Ratification (scope + exclusions + signature), Receipt (hash, timestamp, lineage, exportable), Escalation, Claim. Protocol: pre-register criteria → seal probes → automated checks (citation existence, data lineage, numeric reproduction) → panel only on disagreement → named ratification → receipt → claim path if a covered defect surfaces later. Incentives: buyer (allocator, IC, program office, procurement) pays per artifact; reviewers paid per escalation; an insurer writes per-artifact cover and the utility earns fee plus underwriting spread. Cold start is single-player: an allocator can buy ratification of incoming third-party artifacts with zero supplier participation, no network, no three sides, no contributor liquidity. Kill criterion (90 days): if fewer than 5 unaffiliated buyers pay for ≥3 artifacts each at ≥$2,500, or no carrier will quote per-artifact cover at ≤15% of fee, kill and fall back to selling the receipt schema as an adapter into Frontier/Cowork/AIUC workflows. Twelve-month futures (ranges, not probabilities). Dead-end path: a lab or AIUC extends its surface to third-party artifact attestation (a 'verified' badge inside Frontier/Cowork, or an AIUC-1 deliverable-level control family in the Oct 2026 or Jan 2027 release), then the wedge's only residual is domain provenance and the loop is worth building as a partner, not a principal. 5-10x path: first litigated or regulator-published case where an AI-assisted deliverable drives a documented allocation loss and AB 316 is invoked; carriers respond with a named per-deliverable product; ratification becomes a line item on engagement letters. On the observed base rate (4 artifact-integrity events in ~11 months, escalating from refund to retraction), expect 3-6 more disclosure/refund events in 12 months; a first AB 316-cited suit over a commissioned artifact is plausible but currently unevidenced. Fatigue path: absorption plus fatigue both hold, agent-commissioned external work stays sub-scale, and the only revenue is a $300/seat detection SKU, the outcome the plan must be designed to survive.

fact · reporting · 3× fact · reporting · 0× fact · reporting · 3× estimate · analysis commentary · 0× fact · analysis commentary · 0× fact · reporting · 3×

Consensus view: Most online commentary treats the two mechanisms as separate and future-tense: labs will eventually absorb agent marketplaces, and a liability reckoning will eventually force verification; meanwhile the dominant framing is that 40% of agentic projects will be cancelled by 2027 because of governance gaps, which is read as evidence that agent-commissioned work is not yet real. Evidence-supported view: Both mechanisms have already fired, in weaker forms than the narratives predict. Absorption is complete on the mechanism layer the wedge planned to differentiate on (task graphs, mandates-as-permissions, event logs, first-party eval and red-teaming are now features of MCP Tasks, ADK 2.0, Cowork and Frontier+Promptfoo) but has not touched paid human contributor routing, where the one announced lab attempt slipped past its mid-2026 date. The liability channel is manifesting as refund-and-retractio Why they differ: Three causes. (1) Stale-date laundering: the most-cited pessimism number is a June-2025 prediction repeated without its date or its 3,412-webinar-attendee base. (2) Category conflation: commentary treats 'assurance' as one market, so it misses that detection (~$300/seat/yr), system certification (AIUC-1 + Lloyd's) and artifact ratification (unoccupied) have different buyers, prices

Q13 · id 7 · depth 0 · policy_regulatoryEVOI 0.80 · confidence 0.84 · surprise 0.62

What is the minimal compliant form for (a) paying contributors for forecasts and analyses, (b) matching capital mandates to tasks and projects, and (c) agent-assembled temporary entities that share proceeds - given that E-SIGN 7001(h) and UETA 2(6)/9/14 already attribute agent commitments to the deployer, that Manifund had to restrict impact-cert investing to accredited investors, that the CFTC invoked CEA 8a(9) on 2026-08-11 to keep KalshiEX operating against a New York AG suit, and that Wyoming DUNA is nonprofit with a 100-member floor?

Bottom line, United States, as of August 23, 2026; this is a research view, not legal advice. The 90-day pilot can pay contributors and close a mission-work loop without a financial-market licence, but only if it is structured as ordinary procurement rather than as a market in claims or investments. Use one sponsor LLC as principal contractor; fixed-fee, non-transferable research deliverables; third-party payments; nonbinding funding indications limited initially to grants, procurement and internal corporate capex; and licensed handoff for securities. Do not issue per-mission ownership or tradable proceeds claims in the pilot. Which hypotheses are favored - Hypothesis 1 is favored only after narrowing. Services contracts are the minimal form. However, “nonbinding capital introductions require no licence” is too categorical: the SEC identifies finding investors for venture and angel financings as potential brokerage, and Regulation D/accredited-only status does not exempt a placement agent from broker registration. Flat fees and passivity reduce risk but are not a statutory finder safe harbor. (sec.gov) - Hypothesis 2 is favored for tradability and investment content, but not for mere scoring. The CFTC generally treats binary event contracts as swaps, yet its 2012 interpretation excludes ordinary employment and commercial arrangements where payments are integral rather than severable, the agreement is not traded, and it serves a bona fide business purpose rather than speculation. A forecast may therefore be scored and paid as work; it should not become a purchased position, transferable claim or resolution-contingent payoff in the pilot. (govinfo.gov) - Hypothesis 3 is favored as pilot sequencing, but its legal premise is too strong. A for-profit wrapper exists: Wyoming permits a DAO LLC with one or more members and member or algorithmic management. The problem is that passive, transferable or capital-funded profit interests may still be securities, regardless of the entity label. Accordingly, defer mission-by-mission entities and use the sponsor LLC first. (wyoleg.gov) ### (a) Paying contributors for forecasts and analyses Minimal form: Axion’s sponsor LLC signs an MSA plus mission-specific statement of work with each contributor or contributor entity. Pay a fixed amount for an accepted artifact under pre-registered criteria. The forecast score updates reputation and calibration records, but does not determine a transferable or opposing-party-funded financial payoff. Required boundaries: 1. No contributor entry fee, stake, “yes/no” position, pooled prize pot, secondary transfer or right to sell the forecast claim. 2. For maximum certainty, no payout based solely on the external event resolving correctly. If later using accuracy bonuses, keep them non-transferable, integral to the service agreement and modest relative to the base service fee, and obtain a specific derivatives opinion or CFTC product determination. 3. Do not commission personalized recommendations, public-company price targets or reports about the advisability of purchasing securities unless the work is placed inside a registered adviser’s supervised process. The Advisers Act definition expressly reaches compensated businesses issuing reports or analyses concerning securities; calling the output “research” does not itself remove that risk. (sec.gov) 4. Use a licensed payment/escrow provider and have the platform issue payment instructions rather than holding stored balances. FinCEN has treated some transaction-specific escrow as outside money transmission, but only on its stated facts; outsourcing custody avoids having the pilot depend on that fact-specific conclusion or a fifty-state licensing analysis. (fincen.gov) 5. Collect W-9/W-8 documentation and perform worker-classification review. For 2026 payments, the federal Form 1099-NEC reporting threshold is $2,000, but classification depends on actual control and economic relationship rather than the contract label. (irs.gov) Practical subject-matter boundary: pay for forecasts of technical milestones, costs, delivery dates, replication results, power availability and project execution. Exclude security valuation and “should this investor buy this company?” from the unlicensed pilot. ### (b) Matching capital mandates to tasks and projects Minimal form: replace the pilot’s binding Capital Mandate with a revocable Funding Interest object. It records objective constraints, topic, geography, instrument classes the principal may consider, minimum evidence and ticket range, but grants Axion no power to commit or transfer capital. For the first 90 days: - Match only grants, prizes paid entirely by a sponsor, fixed-price R&D/procurement contracts, philanthropy and internal corporate budgets. - Charge subscriptions, workflow fees or fixed listing fees unrelated to whether financing closes or how much is raised. - Do not recommend an investment, rank securities by expected return, solicit purchases, negotiate terms, transmit orders, handle funds, prepare closing documents or receive a percentage of capital raised. - An investment match should terminate in a RegulatedHandoff object identifying the registered broker-dealer, funding portal or investment adviser responsible for the next step. If a project offers equity, notes, tokenized ownership, a passive revenue share or another security, an accredited-only Rule 506 offering may exempt the issuer’s offering from registration, but it does not exempt the intermediary from broker-dealer rules. Internet Regulation Crowdfunding must occur through a registered broker-dealer or funding portal; a funding portal itself may not give investment advice, solicit purchases or hold investor funds. (sec.gov) Thus the original “capital that searches for work” object should be split into: - FundingInterest, nonbinding discovery; - ProcurementAuthority, authority to buy services within a capped ordinary-business budget; - InvestmentMandate, disabled unless controlled by the principal’s regulated adviser or routed through a licensed partner; - RegulatedHandoff, immutable receipt showing where Axion stopped acting. ### (c) Agent-assembled temporary entities sharing proceeds Minimal pilot form: do not form an entity per mission. The sponsor LLC is the prime contractor; contributors receive fixed fees plus, where useful, a capped and non-transferable contingent service fee payable from collected customer revenue. They do not contribute capital, receive ownership, vote on the enterprise, absorb losses or transfer the payment right. This economically tests proceeds sharing without creating a miniature capital market. If a separate entity becomes necessary after the pilot, the cleanest available form is a conventional LLC or Wyoming DAO LLC with: - identified legal persons as organizer and members; - an operating agreement specifying actual authority, admission, allocation, IP, disputes and dissolution; - explicit limits on the electronic agent’s authority; - registered agent, EIN, bank onboarding and tax accounting; - non-transferable interests and no public secondary market; - meaningful service or governance duties for every profit participant, rather than nominal votes masking passive investment. Wyoming’s DAO LLC statute permits one or more members, for-profit activity and algorithmic management, so DUNA’s nonprofit/100-member constraints do not prove that no wrapper exists. But federal securities law still follows economic reality: the SEC found DAO tokens to be securities, and courts can treat nominal partnership interests as securities when holders lack effective control and depend on others’ managerial efforts. (wyoleg.gov) Service-provider profits interests can receive favorable federal tax treatment under IRS Revenue Procedures 93-27 and 2001-43 if their conditions are met, but recipients are treated as partners, must account for partnership tax items, and the safe harbor has important exclusions. That is viable for a durable small team, not a frictionless object to create and dissolve for every mission. (irs.gov) ### What the Kalshi event changes, and does not change The CFTC’s August 11, 2026 emergency action kept a registered designated contract market operating after New York sought nationwide relief against it. It strengthens the evidence that actual event trading sits inside a contested federal/state derivatives-and-gambling perimeter; it does not give an unregistered mission platform permission to sell outcome claims. (cftc.gov) The relevant distinction is therefore not “forecast versus analysis.” It is procured information versus a financially risk-bearing instrument. ### Better first build: Mission Contracting Rail The evidence favors replacing the capital-and-entity portions of the initial Mission Exchange with a narrower Mission Contracting Rail: - Objects: Mission, Claim, Forecast, Task, SOW, Artifact, Acceptance Criteria, Assessment Receipt, Payment Instruction, Funding Interest and Regulated Handoff. - Protocol: one buyer commissions a fixed-price digital task; a contributor accepts; an independent reviewer signs an assessment receipt; a payment provider releases funds; the artifact and forecast score update the mission graph. - Excluded objects: tradable Claim, investment Commitment, autonomous capital transfer, mission Equity and temporary Entity. - Incentive: contributors receive fixed fees and portable assessment receipts; buyers receive auditable evidence and decision history; Axion charges workflow/subscription fees rather than a share of financing. - Cold start: one enterprise, lab, fund adviser or insurer funds 10–20 tasks without needing capital providers and projects to arrive simultaneously. - Kill criterion: stop or redesign if specialist counsel cannot provide a written activity matrix supporting the exact UI and fee model as ordinary procurement, or if fewer than three buyers will pay enough to cover contributor, review and compliance costs after ten reviewed tasks. E-SIGN already supports electronically formed contracts attributable to the deploying principal, but expressly preserves all substantive legal obligations. The authorization ledger is therefore valuable; it is not a substitute for broker, adviser, derivatives, employment or entity compliance. (uscode.house.gov) ### Temporal test The same recommendation would have been reached on July 23, 2026 and largely on February 23, 2026. The SEC’s broker/adviser rules, the CFTC’s commercial-arrangement distinction, Wyoming’s DAO LLC and the SEC DAO analysis already existed. The July 31 New York suit and August 11 CFTC intervention sharpen the event-contract warning but do not create the build decision. The genuinely new facts reinforce the existing separation between service procurement and regulated financial instruments rather than providing a new legal unlock. (cftc.gov)

fact · primary document · 0× inference · primary document · 0× fact · primary document · 0× inference · official statement · 0× fact · interested party · 1× fact · official statement · 0×

Consensus view: The official-source consensus is that electronic agents may form attributable contracts, ordinary research procurement is lawful, and existing LLC law can hold a temporary project. The same sources also treat derivatives, securities intermediation, securities advice and passive profit interests according to economic substance rather than labels. Evidence-supported view: The evidence supports a service-contract pilot, but not the original simultaneous exchange of forecasts, investment mandates and temporary profit-sharing entities. Scoring is permissible as assessment metadata; tradability and event-dependent financial risk are the red lines. Capital discovery should initially be non-securities procurement or end in a licensed handoff. The sponsor-LLC form is sequencing discipline, not evidence that no entity wrapper exists. Why they differ: The categorical hypotheses each conflate separate regulatory questions: accredited-investor restrictions concern offering registration rather than broker licensing; paid scoring is not necessarily an event contract; and DUNA is not Wyoming’s only agent-compatible entity. The remaining uncertainty is fact-specific because the CFTC commercial-arrangement interpretation is not a bright-line safe harb

Q14 · id 74 · depth 1 · mechanism_sizingEVOI 1.10 · confidence 0.74 · surprise 0.35

Size the Mission Contracting Rail from the bottom up: 25 missions × 4 commissioned tasks × observed $500–$2,000 task price, plus assessment fees and buyer subscriptions; obtain quotes for counsel, payment processing and broker/adviser handoffs, then calculate contribution margin and the minimum attach rate at which a licensed partner is economical.

BOTTOM LINE: the fact-grade price quotes I could obtain support Hypothesis 2. At the plan's own prices and volumes, the Mission Contracting Rail does not carry a licensed partner, does not carry counsel, and in the median configuration does not carry itself. The only line with software-like margin is the buyer subscription, which means the venture-scale object is a decision desk plus assurance/attestation, with the contracting rail as a cost-recovered feature, not a take-rate business. 1) REVENUE, AS SPECIFIED. 25 missions x 4 commissioned tasks = 100 tasks; at $500-$2,000 that is $50k-$200k GMV per 90 days (mid $1,250 -> $125k). Note the frame is 5-10x the plan's own pilot target of 10-20 paid digital tasks, which would be $5k-$40k GMV. At a 20% take: $10k-$40k (stretch) or $1k-$8k (plan target) of quarterly revenue. At a Mercor-like 30-35% take (reported ~$2B gross run-rate, Jul-2026): $15k-$70k. Assessment fees are a pass-through, not margin (see 3). Buyer subscriptions are the only line that can be priced off adjacent bands: outsourced-CCO programs quoted at $30k-$125k/yr (RegFin, mid-2026), SOC 2 maintenance $15k-$40k/yr, and expert-network usage of 30-60 calls/yr at $650-$1,400/call = $20k-$85k/yr already in the same buyers' budgets. 2) COST QUOTES (published list prices and vendor-quoted ranges, not negotiated quotes, get three written quotes each before committing). Payment rail: Stripe 2.9% + $0.30 per US card charge; Connect adds $2/month per active connected account and 0.25% + $0.25 per payout; instant payout 1% (min $0.50); cross-border +0.5-2%, international card +1%, FX +1%. On a $1,250 task that is ~$42 (3.4%) domestic, up to ~$67 (5.4%) cross-border; US ACH-only would be ~$5-10. Contractor contracting/tax rail: Deel contractor management $49/contractor/month (COR $325, EOR $599, per deel.com/pricing as of Aug-2026); Remote $29-$99; Gusto contractor-only $35 base + $6/contractor/month. LIVE CONFLICT: one 2026 aggregator states Deel's current page lists contractor management as free, resolve this, it swings $0-$147/task. Escrow if third-party: Escrow.com 0.89-3.25% with $10-$25 minimums plus $10/$20 wire disbursement -> $11-$41 on a $1,250 task (the 3.25%/$25-minimum tier detail traces to a stale 2016 secondary page). Counsel: flat-fee formation $2,500-$5,000; ~$15k-$20k total first-year startup legal before any capital-raise work; SaaS+AI package from $4,500 and an AI-in-a-regulated-domain analogue at $7,500-$9,500; hourly, Am Law 25 partners averaged $1,349/hr in 2025 (blended $1,027) versus boutique/outsourced GC at $250-$295/hr, so a 15-40 hour securities/marketplace/agency memo is $5k-$54k. Broker handoff: the cheapest observed structure is broker-dealer-of-record, Dalmore's filed agreements show 100 bps of amount raised, a $5,000 diligence advance, a one-time consulting fee of $10,000-$20,000, FINRA corporate filing fees of $1,100 ($4M offering) to $8,000 ($50M), and up to 2.5% each side on permitted secondary trades (SEC EDGAR exhibits, 2020-2022, stale on price, structurally current; Dalmore still active in 2026). Adviser handoff: registration project $4k-$7k, ongoing consulting $8k-$15k/yr, outsourced CCO $30k-$125k/yr. 3) THE BINDING LINE IS REVIEW, AND IT DOES NOT DEFLATE. One graded deliverable cost $86 and 109 minutes of expert time in GDPval (primary, Sep-2025) for generalist grading; credible domain review in frontier-tech verticals is priced at expert-network rates, experts paid $200-$500/hr (>$1,000 in scarce niches), client price $650-$1,400/call. So one independent review costs 7-56% of a $1,250 task, and a two-reviewer panel with disagreement routing costs 24-80%. Model spend per mission ($30-$300; $150-$800 all-in) falls 2-10x/yr; the review line does not. 4) CONTRIBUTION MARGIN. Direct cost per $1,250 task: rail $5-$67 + contractor seat $0-$147 + escrow $0-$41 + orchestration share $38-$200 + review $86-$1,000 if platform-borne = $130-$1,455 (median ~$450). Against $250 (20% take) or $437 (35%): contribution per task runs about -$1,150 to +$260. Platform-borne review is fatal. With buyer-paid review at cost+25%, US ACH rails and cheap contracting, contribution is +$50 to +$150/task (4-12% of GMV), i.e. best case $5k-$20k per quarter, $20k-$80k annualized. Year-one compliance floor is $40k-$185k (counsel $15k-$50k plus BD-of-record $25k-$45k, or adviser route $40k-$140k). The floor exceeds the best-case annualized rail contribution by 1.5-7x. Break-even at +$150/task needs ~333 tasks/yr for the compliance floor alone, 1,700 tasks/yr ($2.1M GMV) to add one FTE, and 5,000 tasks/yr ($6.2M GMV) to fund a 3-person team at ~$745k all-in, roughly 50x the pilot pace. The same $745k is covered by 10-15 subscriptions at $50k-$75k, or 6 at $125k. That contrast, 5,000 tasks versus 10 subscriptions, is the re-sequencing decision. 5) MINIMUM ATTACH RATE FOR A LICENSED PARTNER. a_min = F / (M x V x n), F = partner fixed cost, M = missions/yr, V = value per attached transaction, n = net fee retained. BD-of-record, F=$30k, M=100, n=1% net (charge 2%, pay 1% away): V=$1M -> 3%; V=$250k -> 12%. Flat handoff fee netting $5k: F=$30k -> 6%; F=$120k -> 24%. Adviser route, F=$120k, V=$250k, n=1% -> 48%, i.e. never. So a licensed partner is economical ONLY as broker-dealer-of-record, only with tickets above roughly $500k-$1M or flat handoff fees above ~$5k net, and only at 3-6%+ attach, and there is no observed base rate for mission-to-placement conversion in the prior art (RetroPGF, DeSci DAOs, impact certificates never demonstrated it). Design rule: year one, keep the capital side referral-only with no transaction-contingent compensation and no security-specific recommendations (consistent with the existing belief that fixed-fee analysis tasks close legally today); sign the BD only when observed annualized attached-transaction value x net fee >= 2F. 6) BETTER THING TO BUILD (amends, does not replace, the existing better hypothesis). Price card, anchored on bands these buyers already pay: (a) Desk subscription $40k-$125k/yr, persistent mission agents, claim graph, forecasts, VOI ranking, exportable receipts; (b) Annual assurance attestation $15k-$40k/yr per artifact class, pre-registered criteria, rotating held-out probes, named ratification; (c) Per-escalation adjudication $650-$1,400, priced at the expert-call rate, panel-disagreement routing only; (d) Commissioned work billed at cost + 15-25%, buyer-paid review, US ACH first. Objects: Mission, Claim, Forecast, Task, Artifact, AssuranceCase, Ratification, Receipt. Kill criterion for the 90 days: if no buyer prepays >=$25k for desk+attestation, and per-task contribution with buyer-paid review is <$100 across >=20 completed tasks, kill the rail and keep only the assurance-case custody product.

fact · official statement · 4× fact · reporting · 4× estimate · official statement · 2× fact · primary document · 3× fact · official statement · 0× estimate · interested party · 2×

Consensus view: Standard marketplace and fintech commentary says a services exchange works on a 10-30% take rate with escrow and reviews as table stakes, that a broker-dealer-of-record at 1% of funds raised makes capital-side handoffs cheap and routine, and that outsourced compliance makes the regulated layer affordable, i.e. that the narrowed wedge is a normal marketplace with normal unit economics. Evidence-supported view: Those benchmarks are quoted for categories doing 10^5-10^6 transactions a year. At the specified 100 tasks per quarter, the published price quotes put per-task contribution between about -$1,150 and +$260 (median configuration negative, positive only with buyer-paid review and ACH rails), giving a best case of $20k-$80k of annualized contribution against a year-one compliance floor of $40k-$185k (counsel $15k-$50k plus BD-of-record $25k-$45k or adviser route $40k-$140k). The assessment layer can Why they differ: Base-rate neglect and a number quoted without its base: take-rate benchmarks amortise fixed legal and licensing cost over huge transaction counts, so at 10^2 transactions the same percentage cannot cover a $40k-$185k fixed floor. Second, the consensus implicitly assumes review cost falls with compute; the only primary measurement of grading cost ($86 and 109 minutes of expert time per deliverable)

Q15 · id 72 · depth 1 · policy_regulatoryEVOI 0.99 · confidence 0.72 · surprise 0.42

What exact capital-discovery actions and UI elements can Axion perform for securities without becoming a broker or adviser: objective filters, automated ranking, direct messaging, diligence receipts, introductions and flat subscription fees? Produce an activity-by-activity matrix and test it with a registered broker-dealer/FINRA counsel rather than relying on the word 'nonbinding.'

Short answer: neither competing hypothesis wins outright, the fact-grade documents draw the line in a different place than either hypothesis assumes. The operative variables are not 'flat fee' and not 'nonbinding' (a phrase that appears in none of the governing regimes and is not a legal category). They are: (1) whether compensation is nexus-linked to a transaction; (2) WHO AUTHORS the ordering/assessment (the investor or Axion); (3) WHO INITIATES contact; (4) possession of funds/securities; (5) whether the deal is a Rule 506 offering at all; (6) whether the funded instrument is a security at all; (7) state licensing, which is not preempted. · THE LOAD-BEARING STATUTE (not in the plan, and it is the single most useful find): Securities Act Section 4(b), 15 U.S.C. 77d(b), added by JOBS Act 201(c), exempts a person from broker/dealer registration for maintaining 'a platform or mechanism that permits the offer, sale, purchase, or negotiation of' securities, co-investing, and providing 'ancillary services', where ancillary services are DEFINED to include the provision of due diligence services 'so long as such services do not include, for separate compensation, investment advice or recommendations', plus standardized documents (optional use, no negotiating terms for third parties). Conditions: the person 'receives no compensation in connection with the purchase or sale of such security', has no possession of customer funds or securities, and is not statutorily disqualified. Limits that matter: it covers ONLY securities sold in compliance with Rule 506; it does NOT touch the Advisers Act; it does NOT preempt state broker-dealer/agent licensing. · THE LINE THE SEC HAS ALREADY DRAWN TWICE: Reg CF Rule 402(b) is not directly applicable to a 506 platform but is the clearest regulator-drafted taxonomy of exactly these UI elements. It permits selecting issuers, highlighting on objective criteria (broad selection, consistently applied, clearly displayed, no special compensation for highlighting), search/sort/categorize tools on objective criteria, and communication channels. The SEC's Small Entity Compliance Guide states that objective criteria may not include the advisability of investing or 'an assessment of any characteristic of the issuer, its business plan, its management, or risks'. FINRA's Reg CF FAQ A7 says 'Hot this week'-style labels are 'not based on objective criteria and may have the effect of urging investors to purchase'. That is a direct hit on Axion's mission-specific merit score. · ACTIVITY-BY-ACTIVITY MATRIX (GREEN = buildable native today with named authority; AMBER = fact-sensitive, needs papering and behavioural limits; RED = requires a license, an RIA, or redesign). 1) Public mission/issuer pages built from public + party-supplied factual data, no offering terms, no merit language: GREEN (information vendor; nothing in 15(a) or 202(a)(11) reached). 2) INVESTOR-AUTHORED objective screens over factual attributes (sector, stage, geography, instrument type, round size, dates, named counterparties, measured technical benchmarks), from a published, versioned criteria library applied consistently: GREEN (mirrors 402(b)(3)). 3) PLATFORM-AUTHORED 'mission fit score' / ranked leaderboard incorporating Axion's own forecasts on team, plan, risk or probability of success: RED as specified, this is precisely the 'assessment of any characteristic of the issuer, its business plan, its management, or risks' the SEC excludes from objective criteria, and it is advice as to advisability under Advisers Act 202(a)(11) once delivered for a subscription fee. Redesign: investor supplies the weights, Axion supplies only disclosed factual features and never publishes its own ordering. 4) Ranking of NON-SECURITIES objects (missions, tasks, grants, prizes, procurement, R&D budgets): GREEN and wholly out of scope, this is the highest-value design lever and preserves most of the 'capital searches for work' loop. 5) Ranking questions/uncertainties by value of information over the claim graph: GREEN (not securities advice). 6) Impersonal forecast publications, identical to all subscribers, no investment conclusion, not timed to an offering: AMBER, publisher's exclusion, 202(a)(11)(D)/Lowe; fails on tailoring, issuer payment, or offering-timed release. 7) DILIGENCE RECEIPTS / artifact-level assurance (pre-registered criteria, held-out probes, reproduction results, named ratification, exportable receipt) at flat or per-artifact price with no investment conclusion: GREEN and expressly named in 77d(b)(3)(A). This is the strongest native securities-adjacent object Axion has, and it is the same object the current 'assurance-case custody' belief already favours. 8) Optional standardized documents: GREEN (77d(b)(3)(B)) provided no negotiating for third parties and no mandated use. 9) Hosted double-opt-in messaging where each party authors its own messages: GREEN for 506 deals (77d(b)(1) protects a platform 'that permits the offer, sale, purchase, or negotiation'). RED if an Axion agent drafts, sends, follows up, urges, or negotiates, agentic outreach is the single highest-risk element in the whole plan (this is the Ranieri Partners fact pattern: offering materials plus follow-up urging equals brokerage). 10) Platform-curated repeated targeted introductions ('we picked these five investors for you'): AMBER trending RED, because 'engaged in the business' turns on regularity; transaction-based compensation is a strong but non-dispositive indicium (SEC v. Kramer, M.D. Fla. 2011 found no broker despite a finder's fee absent solicitation/negotiation), so absence of a success fee helps but does not cure repeated platform-initiated pitching. Redesign to investor-initiated pull plus mutual consent. 11) Flat subscription: GREEN only if price never varies with closing, deal size, or amount raised, no success fees, no 'listing upgrade at close', no equity/warrants as consideration for access, no post-close rebates. Note carried interest is not transaction-based per the 2013 FundersClub/AngelList letters, but taking carry means becoming the SPV's adviser, i.e. an RIA, which is how those two platforms actually solved this. 12) Escrow or custody of investor funds/securities: RED (77d(b)(2)(B)); use a third-party escrow/paying agent, and keep mission-task escrow legally and operationally separate with its own money-transmission analysis. 13) Accredited-investor verification utility: GREEN, and the staff is actively expanding this lane (Cooley, 2026-08-17: new CDI 260.40 extends a March 2025 no-action letter to 506(c) offerings of tokenized securities using digital attestations). 14) Bespoke ranked lists for one named capital client under engagement (the 'capital desk'): RED without adviser registration. Critical, non-obvious constraint: with no AUM the only federal registration route is the internet adviser exemption, Rule 203A-2(e) as amended (Release IA-6578, adopted 2024-03-27, compliance 2025-03-31), which requires advice to ALL clients 'exclusively through an operational interactive website' generated by software-based models with the de minimis human carve-out eliminated, that is directly incompatible with the human ratification panel the assurance design wants. Otherwise: state-by-state IA registration. 15) Any transaction-contingent capture: RED unregistered. Minimal licensed form is a FINRA Capital Acquisition Broker, materially improved effective 2026-03-25 (Reg Notice 26-04; SEC approval order 34-104806, 2026-02-10): expanded institutional-investor definition including 'eligible employees', new Rule 016(c)(1)(H) for institution-to-institution secondaries under 144/144A, cross-reference to the 15(b)(13) M&A broker exemption, may represent both sides with disclosure and consent, may take equity/warrants as compensation, exempt from annual OSJ inspection and 3110/3120 testing, biennial AML testing. CABs remain institutional-only, which fits 'capital mandates' from institutions and not individual angels. No finder relief exists: the 2020 two-tier proposal 'was never finalized' (SECLaw, 2026-07-01); the SBCFAC approved recommendations Feb 2026 and submitted April 2026, and a rulemaking petition was filed 2026-03-17 (SEC File 4-890), recommendations and petitions, not law. FINRA Rule 2040 still bars members paying unregistered persons for registrable activity. · WHAT THIS MEANS FOR SCOPE: capital matching cannot remain fully native at the two layers where the value is (merit ordering and initiated introductions); it either terminates at a licensed handoff or converts Axion into an RIA (203A-2(e) or state) or a CAB. But the partition is more favourable than blanket deferral: the diligence/assurance layer is statutorily blessed as a for-compensation ancillary service, and the post-March-2026 CAB population plus placement agents and private-market RIAs are a named, licensed BUYER class for receipts, turning the licensing blocker into a distribution channel rather than a wall. Better hypothesis than either offered: 'the operative variable is authorship of the ordering, not the fee form', ship a published, versioned, investor-selectable criteria registry in which the Mandate object (already in the data model) carries the weights, Axion never emits its own ranking of securities, and Axion sells receipts to whoever holds the license. Kill criterion for the securities module: if counsel cannot sign the assertion set in item 2/7/9/11 without a licensed entity, drop securities entirely for year one and route all capital-side value through non-securities instruments (grants, prizes, procurement, fixed-fee bounties), note that a mission-funding instrument paying a share of proceeds is itself likely an investment contract, which would make Axion a promoter, a strictly worse posture than platform. · HOW TO TEST IT (the brief is right that 'nonbinding' is worthless): the SEC staff has issued no on-point broker-status letter for an online private-placement platform since the March 2013 FundersClub and AngelList letters, so plan for a written opinion, not a no-action letter. Retain (a) BD/FINRA counsel for a 15(a)/4(b) opinion and (b) separate Advisers Act counsel, plus a blue-sky survey for the five states where the largest capital principals sit, since 4(b) is federal-only. Do not send counsel prose: encode each UI element as a machine-checkable assertion in the deterministic policy engine the plan already specifies (levels 0-5), version the assertions, log every emitted UI state, and have counsel sign the assertion set so the receipt is producible in an exam, this is also exactly what FINRA's 2026 Annual Regulatory Oversight Report asks of AI agents (narrow permissions, action audit trails, explicit human checkpoints before consequential actions, prompt/output logs as records), while explicitly not resolving how the rules apply to autonomous agents. Five questions to put in writing: does 77d(b)(3)(A) 'due diligence services' cover machine-generated verification artifacts sold to investors while the same platform hosts the deal; does an investor-weighted screen over factual features avoid 'advisability' under 202(a)(11); does hosting an agent-drafted message make Axion the solicitor; is a per-artifact receipt fee 'separate compensation' for advice; and which states treat objective-screen matching as agent activity. Caveat: I am not a lawyer, this is not legal advice, and the non-US layer (MiFID II investment advice, UK FSMA s.21 financial promotion approval) is unverified here and must be scoped separately.

fact · primary document · 3× fact · primary document · 3× fact · official statement · 2× fact · official statement · 1× inference · primary document · 0× fact · primary document · 4×

Consensus view: Most online commentary holds that a flat-fee online platform serving accredited investors in Rule 506 deals can operate without broker-dealer registration provided it takes no transaction-based compensation, holds no customer funds, gives no investment advice and does not negotiate, a four-part checklist derived from the 2013 FundersClub/AngelList letters and JOBS Act Section 4(b). Commentary rarely distinguishes investor-authored screens from platform-authored merit rankings, treats disclaimers Evidence-supported view: The primary documents support a partition, not a blanket verdict, and locate the line at authorship of the assessment and initiation of contact rather than at fee form. Section 4(b) affirmatively blesses three things the plan needs, hosting a negotiation channel, providing due diligence services for compensation, and providing optional standardized documents, while conditioning all of it on no transaction-nexus compensation, no custody, no statutory disqualification, and a Rule 506 offering. The Why they differ: The consensus is assembled from the broker-dealer checklist alone and from summaries of two 2013 letters, and it neglects three things: (1) the Advisers Act, where a flat subscription is compensation and tailoring defeats the publisher's exclusion; (2) the SEC's own articulation of the objective-criteria/advice line in the Reg CF materials, which is the only place a regulator has describ

Q16 · id 17 · depth 0 · expert_technicalEVOI 0.77 · confidence 0.77 · surprise 0.74

Measure the top of the funnel, which no benchmark covers: from N autonomous mission decompositions, what fraction of generated sub-questions are (a) resolvable by a named source and date, (b) decision-relevant to a specific principal, and (c) not already answered, and how many minutes of human editing per accepted question? Compare against human curation load per question at Metaculus, Kalshi and ForecastBench and the observed ambiguity/dispute rate on resolved market questions.

Bottom line as of August 23, 2026: no published benchmark jointly measures the requested funnel, resolvable, principal-relevant, novel, and human-edit minutes. The closest evidence favors hypothesis 3: decision relevance is the main failure mode, while hypothesis 2 is conditionally true only for resolvability after aggressive machine filtering. The evidence is insufficient to confirm hypothesis 1’s specific 20–40-minute editing claim. Measured nearest-neighbor funnel: FutureSearch started with 2,500 news/company seeds, generated 14,073 proto-questions, and retained 1,499 after refinement, resolvability/ambiguity/quality filtering, and deduplication: a 10.65% raw survivor yield. External forecasters then reportedly accepted about 75% of the filtered questions, implying roughly 8% of raw proto-questions survive both machine and human quality gates. However, the paper’s expert-rating counts do not reconcile with its stated sample size, so the 75% figure should be treated as approximate. Among final filtered questions, human resolution found three annulments in 100, producing an estimated 3.9% annulment rate and approximately 96% resolvability. Thus, canonical-source resolvability can exceed 50%, but only after roughly nine candidates are discarded for every survivor. (ar5iv.labs.arxiv.org) Decision relevance is much worse. HorizonBench asks agents, without being given the eventual event, to identify consequential uncertainties worth forecasting. Against 445 later-salient events, the best model’s candidate precision ranged from 6.5% at top-50 with 7–14 days’ lead to 25% at top-10 with 14–28 days’ lead; even 50 questions covered only 5.62% of the event pool. This is generic ex-post consequentiality, not relevance to a named principal, so it is probably an upper-bound proxy for Axion’s stricter criterion unless the principal supplies a live decision, options, thresholds, and deadline. (openreview.net) “Not already answered” remains unmeasured. Forecasting benchmarks structurally ask about future outcomes and therefore largely avoid the problem; deduplication within a generated set is not the same as checking whether existing literature or private organizational knowledge already answers a mission sub-question. Applying their novelty rates to research-mission decompositions would be a category error. (ar5iv.labs.arxiv.org) Human-load comparison: - FutureSearch discloses no creation/editing time. It measured 13 minutes average human resolution time, with a roughly 6–12-minute median band and a tail to 55 minutes; that is resolution, not question editing. (ar5iv.labs.arxiv.org) - Metaculus discloses no moderator minutes per admitted question. Volunteer moderators normally review submissions within one or two weeks, and policy generally rejects questions expected to require more than about 15 minutes of administrator resolution work. Review latency is not labor time. (metaculus.com) - Kalshi discloses no minutes per contract. It says the vast majority of suggestions fail its Contracts Team’s vetting and that surviving proposals require a formal Product Certification; its May 2026 regulatory comment cited nearly 2,500 certifications over five years, but not staff hours or an Outcome Review rate. (news.kalshi.com) - ForecastBench’s recurring marginal production is automated from predefined time-series templates and imported market questions. Its public sources do not report template-development or per-question human time, so its near-zero recurring marginal editing is not comparable to open-ended mission decomposition. (forecastbench.org) Ambiguity/dispute comparison: FutureSearch estimates about 8% historical annulment on human-curated Metaculus questions, versus 3.9% after its own aggressive automated filter; the Metaculus figure is secondary and its extraction method is not disclosed. UMA reported 217 disputes among 11,093 Polymarket markets, about 2.0%, but that statistic is from 2024, comes from the oracle provider, and measures economically initiated disputes rather than all ambiguous wording. No comparable public Kalshi numerator and denominator was found. (arxiv.org) Axion forecast, explicitly an inference rather than a published measurement: for an open-ended mission, expect only 2–8% of raw generated sub-questions to satisfy all three conditions. With a named principal, explicit decision alternatives, and canonical source families, expect 5–15%. After machine filtering, resolvability alone can plausibly be 90–97%, but raw-generation yield above 50% is unsupported. Budget 15–35 human minutes per accepted, principal-relevant question initially; the lower end requires template-generated source/date/threshold language and machine rejection of bad candidates before a human sees them. These ranges are anchored on the 8–11% generic production yield, HorizonBench’s 6.5–25% relevance proxy, and the 13-minute observed resolution load. (ar5iv.labs.arxiv.org) At a 3–12% all-three yield, producing 100 accepted questions requires roughly 830–3,300 generated candidates. Editing accepted questions alone would consume about 25–58 human hours at 15–35 minutes each, excluding review of rejected candidates and principal-interview time. This does not yet prove that Axion is a human-labor plan disguised as AI; it proves that the pilot must measure total human minutes per accepted decision-changing question, not questions generated. Better hypothesis and build: replace the public Mission Exchange’s first object with a private Decision Question Compiler for one buyer. Objects: PrincipalDecision, Option, DecisionThreshold, QuestionCandidate, ResolutionContract, SourceSnapshot, NoveltyCheck, RelevanceVote, and DecisionOutcome. Protocol: the principal supplies a live decision and deadline; agents generate and machine-filter candidates; the principal pairwise-ranks the top candidates; only accepted questions are forecast and researched; the system records whether evidence changed an option, threshold, timing, or budget. Incentive: charge per decision dossier or accepted decision-linked question, not per generated question. Kill it if, after three iteration cycles, principal acceptance remains below 20%, human work exceeds 20 minutes per accepted question, or fewer than 3 of 30 live decisions record a material decision update.

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Consensus view: The emerging online view is that forecasting-question generation and resolution are largely automatable because filtered systems can report approximately 96% resolvability and ForecastBench can continuously generate template questions. Evidence-supported view: Resolution wording is substantially automatable in canonical-source domains, but only after heavy candidate attrition. The scarce primitive is selecting questions that matter to a specific principal. Public evidence supports roughly 8–11% raw generic-question yield and 6.5–25% generic consequentiality precision, while novelty and editing minutes remain unmeasured. Why they differ: Denominator switching and construct conflation: the 96% figure is measured after an approximately 89% automated rejection funnel, and resolvability is being confused with novelty and decision relevance. Forecasting benchmarks also pre-constrain questions to future outcomes, masking the 'already answered' failure mode present in research missions.

Q17 · id 83 · depth 1 · fundamentalEVOI 1.23 · confidence 0.56 · surprise 0.45

Across 30 live decisions from one semiconductor, datacenter-power, or infrastructure-capital principal, does a principal-seeded compiler achieve over 20% all-three acceptance with under 15 human minutes per accepted question, or under 10% acceptance with over 30 minutes? Log attrition separately for source/date resolvability, decision relevance, novelty, and final wording. This discriminates the canonical-vertical hypothesis from both the human-labor and principal-relevance bottleneck hypotheses.

VERDICT: neither pre-registered branch will be hit cleanly, and the third hypothesis, principal relevance is the binding failure, is the one the graded evidence favors. Best estimate for a principal-seeded compiler run against 30 live semiconductor/datacenter-power decisions: 6–18% all-three acceptance measured per machine-passed candidate (central 8–12%), with 10–25 human minutes per accepted question (central 12–20) if triage is batched and resolution sources are pre-attached. That straddles both thresholds, which means the question as posed cannot discriminate, because the denominator is wrong and one of the three gates is not reliably measurable. (1) THE FUNNEL IS ALREADY MEASURED, ON TWO OF THE THREE CRITERIA. FutureSearch's pipeline (arXiv 2601.22444v2, 30 Jan 2026 / rev 9 Mar 2026; company blog 30 Mar 2026) generated 14,073 candidate questions and 1,499 survived automated filtering, 10.7%, where the filters were quality, ambiguity, resolvability, difficulty verifiers plus embedding+DBSCAN near-duplicate removal. Of 149 survivors rated by contracted expert forecasters: 75.2% accept, 16.8% 'soft reject' (technically resolvable but trivial or uninteresting), 8.1% 'hard reject' (ambiguous or flawed). End-to-end that is ~8% of raw candidates accepted by an expert, on two criteria only, resolvable and non-trivial, with no principal, no named decision, and no novelty test beyond in-batch dedup. MathArena (arXiv 2605.00674) independently reports ~200 candidates/month reduced to ~30 after automated filtering and human review (~15%) for a strict verifiability criterion. So the mechanical legs are cheap and high-precision but low-yield, and adding a third gate (relevance to a named live decision) cannot raise yield per candidate; it can only raise yield per seed, which is the metric the pilot should measure. (2) RESOLVABILITY IS THE SMALLEST ATTRITION CHANNEL, NOT THE LARGEST. FutureSearch's human check of 100 resolved questions found 3 that should have been annulled (~4% annulment, 95% CI 1.1–8.4%) versus Metaculus's historical ~8%, and 2 of those 3 failures were exactly 'admissible resolution sources did not publish the required data'. In this vertical the canonical sources are unusually dense and dated, ERCOT's Large Load Interconnection Status Update is a monthly public deck (13 Mar 2026 TAC report), 9,042 MW had Approval to Energize as of the March 2026 update against 445 GW requested by 2033 and 321 GW with no studies submitted, and PUCT approved 'Batch Zero' screening in June 2026; PJM published its 2026 Long-Term Load Forecast with explicitly vetted large-load adjustments. Expect 88–96% of machine-passed questions to survive source/date resolvability, with the residual concentrated in definitional restatements (queue definitions changing, Feb/Mar 2026 cancellations shrinking the queue) rather than in missing data. (3) NOVELTY IS THE LEG THAT CANNOT BE SCORED RELIABLY. On comparative non-obviousness of research questions, expert–LLM-judge agreement is 30–40% against expert–expert agreement of 54% (n=50; arXiv 2606.12071, 10 Jun 2026); RINoBench (arXiv 2603.10303, 11 Mar 2026; 1,381 expert-judged ideas) finds LLM novelty verdicts diverge from expert gold even for reasoning models. If expert–expert agreement on 'is this non-obvious' is barely above coin-flip, a single-rater novelty gate makes both the >20% and the <10% thresholds unfalsifiable: measured yield would partly be rater noise. The pilot must use two independent raters per candidate, report Gwet's AC1 or weighted kappa, and pre-register what counts as a 'material' wording edit, otherwise the experiment cannot decide anything. (4) WHY RELEVANCE IS BINDING, WITH ON-THE-NOSE PRIOR ART. Cowgill & Zitzewitz (Review of Economic Studies, 2015) found Google/Ford/Firm X internal markets beat the firms' own expert forecasts by up to a 25% reduction in mean squared error, and were still not adopted. The closest documented case is in this exact vertical: the builder of Google's internal market describes trying to use it to improve the forecasts driving purchases of computers, power and land for data centers, where hundreds of millions of dollars turned on accuracy; supply-chain managers accepted that it would be more accurate and still declined, because they were incentivised on transparency, adjustability, accountability and interoperability of the forecasting process, not on accuracy (Asterisk, Nov 2024; first-person and therefore an interested account). That is a structural, not a technological, failure, and it maps onto the 16.8% 'trivial or uninteresting' soft-reject rate: what a decision owner will accept is a taste-and-process judgment the machine cannot pre-screen. (5) WHAT IS GENUINELY BETTER NOW THAN IN THE GOOGLE CASE. This buyer class already runs probabilistic machinery, so the object is admissible: Georgia Power's Load Realization Model assigns probabilistic weights per interconnection project via Monte Carlo to a P50 adjustment (E3 whitepaper, Dec 2025); PJM's 2026 forecast explicitly vets large-load adjustments; ERCOT modelled a ~50% reduction in 2026 large-load additions. And the live decision is now precisely an attrition-probability question: JP Morgan (May 2026) found >60% of capacity planned for 2027 completion not yet under construction, Sightline tracked 190 GW across 777 projects with up to half of 2026 completions possibly slipping, and Georgia Power's >68 GW 2032 pipeline is >80% in early technical review without binding commitments (RMI, 20 Jul 2026). Cost is not the constraint: FutureSearch lists forecasts at 15c–$2 per question and agent research from 1c per row, so even at 10–20x candidate fan-out the machine cost per accepted question is roughly $2–$40, two orders below the human minutes. (6) A NEW COMPETITIVE FACT THAT COMPRESSES THE NOVELTY SPACE. The highest-value, most decision-relevant questions in this vertical are being industrialised by regulated venues: Kalshi launched GPU compute markets in March 2026 and compute forward curves on 14 Jul 2026 across B200/H200/A100 (resolving against Ornn's rental-price dashboard); CME announced compute futures on a Silicon Data index in May 2026; ICE is working with Ornn. A compiler must dedup against Kalshi/CME/Metaculus/Polymarket, and its defensible zone is decision-specific, site- and counterparty-level questions with no public contract. (7) HOW TO RESEQUENCE THE PILOT, AND THE BETTER THING TO BUILD. Replace 'acceptance per candidate' with three pre-registered metrics: (a) per-decision yield, fraction of the 30 decisions that produce at least one accepted question (target 60–80%; kill below 40%); (b) minutes per accepted with batch triage, reported separately for triage, edit and rejected-candidate overhead; (c) the only endpoint that matters, how many accepted questions are cited in an actual memo, IC pack or IRP filing within 60 days, and how many changed a stated prior. Better hypothesis, stated plainly: do not sell questions, sell the Decision Record. Objects: Decision (statement, owner, date, reversibility, dollars at risk), 3–7 Resolution Contracts (named source, publication cadence, annulment rule, restatement policy), Forecast with score history, Change-My-Mind list, and an exportable audit trail. Protocol: principal supplies the decision register; compiler proposes resolution contracts; two raters gate; scores accrue publicly to the record. Incentive: the record buys the four things the Google managers actually wanted, transparency, adjustability, accountability, interoperability, which is why it can be consumed where a better forecast could not. Cold start: single principal, single buyer, no network. Kill criterion: if the principal will not hand over 30 decisions with named owners and dates within two weeks, or if no accepted question appears in a real internal document in 60 days, the product framing is dead and the fallback is the artifact-assurance receipt layer. The largest asymmetric branch: questions that terminate in a listed hedge (Kalshi/CME compute, ERCOT-linked contracts) have willingness-to-pay anchored on basis risk rather than curiosity, that is the one path where a question is worth four figures rather than two. CAVEATS: the two central funnel numbers come from one interested party (FutureSearch sells the pipeline) with no independent replication, the 96% headline is quality conditional on surviving filters and is routinely quoted without its 14,073-candidate base, and no public dataset exists on principal-relevance yield, so the joint distribution above is my inference, not a measurement. Confidence capped accordingly.

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Consensus view: Most current sources say automated forecasting-question generation is essentially solved, a self-reported ~96% verifiable-and-unambiguous rate that beats human-curated Metaculus, at 15c–$2 per question, and that probabilistic forecasting improves institutional decisions. The implied consensus is that a principal-seeded compiler should be a high-yield, low-edit product, so the pilot should clear a 20% acceptance bar comfortably. Evidence-supported view: The fact-grade rows support the opposite reading of yield. The same pipeline that reports 96% quality discloses that 14,073 candidates produced 1,499 survivors (10.7%), and that experts then soft-rejected 16.8% of survivors as trivial or uninteresting and hard-rejected 8.1%, about 8% end-to-end on two criteria with no principal-relevance gate at all. Resolvability is the smallest attrition channel (~4–8%). The novelty/interestingness gate that the plan needs is the least measurable: expert–exper Why they differ: A headline reported without its base, from an interested party. The 96% figure is quality conditional on surviving a filter that rejected 89.3% of candidates; it answers 'is a shipped question resolvable' and not 'what fraction of generated candidates a principal will accept'. Consensus also neglects the base rate of decision-support adoption: the peer-reviewed record shows acc

Q18 · id 84 · depth 1 · mechanism_sizingEVOI 1.13 · confidence 0.60 · surprise 0.45

At observed yield y, reviewer time t, model cost c, and buyer willingness-to-pay p, what is the bottom-up cost and gross margin per decision-changing question: generated candidates × c + reviewed candidates × t × wage, divided by accepted questions, then divided by questions that actually change an action or budget? This discriminates a scalable software utility from a disguised research-services business.

Bottom line: the evidence favors Hypothesis 2 on cost structure and Hypothesis 3 on viability, and shows Hypothesis 1 is true but economically worthless. Machine question production IS software-like, and that is exactly why it has no price. The correct classification is neither "software utility" nor "research services": it is a reviewer-minute-constrained utility whose gross margin is set by reviewer-minutes per priced unit (not per question), and whose multiple is set by cross-buyer reuse of resolution contracts. The four inputs, measured (Aug 2026): - c (machine cost/candidate): FutureSearch publishes $0.15/question at low effort, $2 at high effort, multi-agent $0.30–$2 (pricing page + GitHub README, ~Jul 2026). Fully loaded generation+source-attachment+forecast: $0.30–$3/candidate. - t (reviewer time): the strongest measured anchor is FutureSearch's own paper (arXiv 2601.22444, v2 2026-03-09), Appendix A.1: median ~6–12 minutes to manually resolve a question with AI assistance, tail to 55 min. Compare GDPval: ~109 min and ~$86 of expert time to grade one full deliverable. So per-question adjudication is an order of magnitude cheaper than per-deliverable adjudication, this is a genuine improvement on the prior 15–35 min belief. - wage: Mercor's own page puts expert-level AI-training work at $75–$200/hr (national AI-trainer average $31/hr, early 2026); contractor-reported bands are lower ($20–$70/hr); fully loaded in-house research analyst $150k–$175k/yr ≈ $75–$88/hr. Use $90–$150/hr loaded for a domain reviewer who can judge decision-relevance. - p (buyer WTP): Kosmos $200/run for a machine-produced research artifact (200 credits × $1, Edison; described by its founder as heavily discounted). One expert-network call: Tegus average expert fee ~$450/hr, GLG/Third Bridge benchmarked ~$1,350/hr, pay-per-call $300–$1,500. Platform seats: AlphaSense median contract ~$18,375/yr, $12k–$51k/seat, enterprise >$100k/yr; mid-market PE AI research stack $30k–$80k/yr. Custom study $15k–$50k. - y (yield): no public number exists for decision-relevance/novelty yield. What exists is resolvability yield: ~96% verifiable/unambiguous, estimated annulment 3.9% (CI 1.1–8.4%) vs Metaculus ~8%, and 5 of 1,499 (0.3%) annulled by the automated resolver. Resolvability is solved; relevance is unmeasured. External proxies from benchmark construction: TraceAV-Bench 50.5% retention after QA; OP-Bench 10,000 candidates → 4,289 after auto-filter → 3,075 (30.8%) after two-annotator review with 22.6% needing senior adjudication; BashArena kept the top 0.3% of scored seeds. So fan-out to a tightly constrained accept is 2:1 to 300:1 depending on how binding the constraint is. The arithmetic (per accepted question, then per decision-changing question): - Base (8:1 fan-out, $0.60/candidate, 3 min triage × 8 + 12 min accept-side = 36 min at $100/hr): $4.80 machine + $60 human = $64.80/accepted; machine share 7.4%. At 15% decision-change attach → ~$430 per decision-changing question (DCQ). - Bear (20:1, $1.50/candidate, 100 min at $150/hr): $30 + $250 = $280/accepted, machine 10.7%; at 5% attach → $5,600/DCQ. - Bull (4:1 from an existing claim graph, 16 min at $90/hr): $1.60 + $24 = $25.60/accepted; at 35% attach → $73/DCQ. - Ranker variant (the important one): because c is ~free, generate 50 and review only the top 3. 50 × $0.60 = $30 machine; 9 min triage + 24 min accept-side for 2 accepts = 16.5 min/accept → ~$42/accepted, machine share ~35%. Trading tokens for minutes both lowers total cost and raises the machine share. The design objective is not cheaper questions; it is fewer reviewer-minutes per accept. So across the entire plausible span, machine cost is 5–12% of an accepted question under naive fan-out and only reaches 25–40% under deliberate over-generation. Human review dominates: H2 confirmed. But cost/DCQ of $73–$5,600 (central $300–$900) sits below the cheapest substitute for one changed decision, a single $450–$1,350 expert call, or an analyst-week at $3,000–$4,400, in a vertical where decisions are worth $10^5–$10^9. The human floor is affordable: H3 confirmed. Correction to the question's own framing (load-bearing): dividing by attach gives cost-per-outcome, which sets the price ceiling and renewal probability. It does not enter accounting gross margin. GM = 1 − (reviewer-minutes × wage + machine)/price-per-priced-unit. Pricing per question is fatal: the market price of that layer is publicly $0.15–$2, and no buyer pays 30–600× for a marked-up question. Pricing per maintained decision record works: 5 accepted questions to build ($325–$475) + ~1 hr/quarter refresh + occasional 15-min disputed-edge adjudication ≈ $450–$700 COGS/record/quarter. At $2,500/quarter → 72–82% GM. At $1,000/quarter → 30–55% GM. A 2.5× price difference per record swings this from software to services. Scale math and the branch that matters: 80 accounts × 20 live records × $10k/record-year = $16M ARR, ~32,000 reviewer-hours = ~23 FTE, 80% GM, ~$700k revenue per reviewer FTE. At $4k/record-year: $6.4M ARR, same COGS, 50% GM, $280k/FTE. Multiple 8–12× revenue vs 2–3× → 5–15× difference in equity value on one variable. That variable is reuse: if ≥60% of resolution contracts are reusable across buyers, reviewer-minutes/record falls with N and it is a utility; if <20%, it is services forever. The uncomfortable tension this exposes (better hypothesis than any of the three offered): the prior belief says Axion's defensible zone is decision-specific, site- and counterparty-level questions with no listed instrument, but those are precisely the least reusable, locking in services economics; while the reusable questions (GPU price, power, silicon) are being listed by Kalshi/CME/ICE. Defensibility and scalability point in opposite directions. The resolution is to sell a reusable substrate (resolution-contract library, novelty/dedupe custody against listed venues, scored forecast history, exportable audit trail) and let the non-reusable, site-level questions be authored by the buyer's own analyst inside it. That reframes the discriminator: not "machine vs human dominates cost" but "whose payroll is the reviewer on." Buyer-supplied minutes → 85–95% GM software, but you must beat the analyst's own 30–60 min of writing a question, which means reviewer-minutes/accept must be <15. Vendor-supplied minutes → 70–80% GM at ≥$2.5k/record/quarter, capped at ~$700k revenue/FTE. Attach rate, the honest gap. No fact-grade base rate exists. The two datable observations both point low for unlinked questions: a Sentinel funder wrote publicly that the value received was learning things rather than making different decisions; Cosmic Bazaar (1,300 forecasters, 41 departments, >10,000 forecasts) has essentially one documented decision-influence anecdote in the public record (Mozambique attention, Lawfare 2023). Documented attach in open programs is order 10^-3–10^-2 per question. Named-decision commissioning is the only mechanism that plausibly reaches 5–30%, and that assumption is unproven, it is the single number the pilot must measure, and it is the reason to cap confidence here. Kill criterion: if after three tooling iterations median reviewer-minutes per accepted, decision-linked question exceeds 25, or precision@3 of the ranker against the buyer's own accept decision stays below ~50%, or buyers refuse to spend their own analyst minutes inside the product, then price it as services (day rate per record) and do not underwrite a software multiple. Instrumentation the pilot must log from day one, which the current plan does not: reviewed-but-rejected candidate count (the previously unbudgeted line), minutes per accept split into triage vs accept-side, reuse flag per resolution contract, and a dated action/budget-change field per record with the owner's name on it. Note a live conflict between interested parties on the commoditisation claim: FutureSearch claims (Jul 2026, self-reported) first place in Metaculus's Summer 2026 FutureEval and above the superforecaster median on ForecastBench; Metaculus's own account says research indicates models will match pro forecasters around June 2027. Both are interested. The baseline ForecastBench result (2026-07-16) sides with near-parity now.

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Consensus view: Most sources online, vendor pricing pages, AI-research-tool comparisons, and the automated-question-generation literature, frame this as near-free: questions and forecasts at $0.15–$2, machine research reports at $0–$200, benchmark pipelines producing 96% resolvable questions with 3.9% annulment. The implied consensus is that question production has become software, and that AI research tooling should therefore be sold as a seat or a subscription at software margins. Evidence-supported view: The fact-grade rows support a different split. Machine production is indeed software-like, and is already publicly priced at $0.15–$2, so it carries no pricing power. Human review, even at the newly measured cheap end (~6–12 min median with AI assistance, well below the earlier 15–35 min belief), is 60–95% of the cost of an accepted decision-linked question. But cost per decision-changing question (central $300–$900) is below the cheapest substitute (one $450–$1,350 expert call; an analyst-week Why they differ: Two mechanisms. (1) Number without its base: vendor pricing quotes the marginal machine cost per question while the binding cost is reviewer-minutes per accepted, decision-linked question, which nobody publishes. (2) Wrong yield measured: the literature reports resolvability yield (~96%, 3.9% annulment) and treats it as question quality, when the binding filter for a paying buyer is decision-rel