Source hierarchy
For every load-bearing claim, we prefer primary sources in this order:
- Government / standards-body publications (NIST, IEA, EU AI Act, BIS, BLS, EPA, ICO)
- Court filings and government inspector reports (Raine v. OpenAI, Robodebt RC, OHCHR)
- Tax filings (Form 990s) and EU transparency disclosures
- Earnings calls, 10-K filings, official ESG reports
- Peer-reviewed academic work (FAccT, NeurIPS, NBER, Nature Computational Science)
- Investigative journalism with named sources (Bloomberg, +972, Karen Hao's reporting, MIT Tech Review, FT)
- Self-published company documents and lab safety frameworks (treated as primary about the company, not as third-party verification)
Where a claim depends on a single source we say so. Where it depends on a contested estimate (training-run carbon, OpenAI valuation, expected AI productivity) we say that too and link the dispute.
The receipt cards
Every numbered claim on the four act pages is wrapped in a receipt card with a small dot in one of three colors:
- live — re-verified within the last 30 days. The number is the latest official disclosure.
- current — verified within the last 90 days. Probably still right but worth a re-check before citing.
- stale — older than 90 days. Re-check before citing; flag in maintenance pass.
Maintenance cadence
The investigation is treated as a living document, not a paper. Per topic the cadence is roughly:
- Quarterly — hyperscaler capex (after each earnings cycle), NVIDIA revenue, Anthropic / OpenAI valuation, MoneyFlow data, MIT NANDA-style adoption surveys.
- Annual — IEA Energy and AI report, Stanford HAI AI Index, Google AI energy disclosure, Pew + Reuters/Ipsos polling, UK AISI Frontier Trends, Open Philanthropy & SFF grants.
- As-they-happen — court filings (Raine, Character.AI, NYT v. OpenAI), AISI red-team disclosures, EU AI Act enforcement guidance, executive orders, sovereign-AI deals.
Every act page has a footer naming the next earliest expected staleness — this is the single most useful pre-flight check before citing any number.
Editorial stance
The investigation has a thesis (Act IV — discourse displacement) but it is built on documentary primary sources, not on opinion. Two specific commitments:
- Disagreement is shown. Where credible sources contest a number — Strubell vs Patterson on training carbon, Acemoglu vs Goldman bull case on productivity, the “ChatGPT bottle of water” framing — we cite both sides and explain the methodological gap.
- Funding is named. When an org is referenced — METR, Apollo, Redwood, Anthropic, AI Now, DAIR — its primary funder is stated. This is not an attack; it is information you need in order to read the org's output.
What is intentionally not in scope
- Predictions. The capability climb chart in Act III shows extrapolation as extrapolation. We do not bet on AGI timelines.
- Endorsements. No vendor recommendations. Where models or hardware are named (Llama on M-series, Whisper on iPhone NPU), it is to make a measurement intelligible, not to recommend a stack.
- Speculative harms without documentation. Act IV's displaced-harms atlas only lists cases with named victims and primary-source coverage.
- Live-fetched data. All numbers are checkpoint-cited with a date. The page does not call APIs at runtime — that's an editorial choice, not a technical one.
Repository / source notes
The full research notes — every claim with every URL — live in this repository at research-notes/ and research-notes/deep-dives/. Each act page's data files live next to the page itself in _components/. Adding a new pin to the atlas, a new node to a money flow, or a new finding to the bias showcase is a one-file change.
Errors, missing sources, broken receipts: the GitHub link in the top-right is the fastest way to flag them.