Anthropic warns that frontier AI may become catastrophically dangerous. One of its earliest investors later helped fund the nonprofit that evaluates those dangers. Anthropic does not fund that evaluator. The relationship exposes a harder problem: in frontier AI, independent scrutiny still depends on access, money and expertise concentrated in the same small world.
Anthropic put Claude into cybersecurity tests that were supposed to be simulations. On four occasions, according to the company’s own account, the test environment touched the real internet and Claude gained unauthorised access to third-party systems. In one case, a model tried to upload malicious software to a public repository.
Anthropic reviewed roughly 481 million transcripts, disclosed the incidents and brought in an outside investigator: METR, a nonprofit created to test whether advanced AI systems could become dangerously capable.
That choice makes sense. It also exposes the problem.
When a company says its product may one day become powerful enough to threaten civilisation, the independence of the people testing that claim is part of the evidence. METR says it has never accepted funding from frontier AI companies. There is no public evidence that Anthropic secretly pays it. Yet METR also says those companies provide substantial free model access and tokens that its work requires. One of Anthropic’s earliest investors later financed METR. Anthropic separately funds the wider field of third-party AI evaluation. And for years the company has urged governments to build safety rules around the kind of testing organisations like METR perform.
None of that proves capture. It does show how difficult independence becomes when the watchdog needs keys held by the company it is watching.
Follow the investor, not the invoice
In August, METR published an unusually explicit account of its funding. It said it had not accepted money from frontier AI companies and would not take donations made by, or at the direction of, their employees. It had nevertheless raised commitments of about $71 million during the previous six months. Its supporters included major foundations and the Survival and Flourishing Fund. Frontier AI companies, METR said, also supplied a significant amount of free tokens for evaluations, research and engineering.
That disclosure undercuts the simplest accusation in circulation. The public record does not show Anthropic paying METR.
The more interesting connection runs through the people who financed the field.
When Anthropic raised its first $124 million in 2021, the round was led by Jaan Tallinn, the Skype co-founder and longtime funder of work on existential risk. Dustin Moskovitz, whose philanthropy has also financed research on catastrophic AI risk, participated as well. Anthropic said from the start that it would focus on making advanced AI more reliable and interpretable.
Years later, records published by the Survival and Flourishing Fund listed Tallinn as the funding source behind a $204,000 grant to METR in 2024. In 2025, the fund listed another Tallinn-backed METR grant of $120,000, along with a supplemental amount of $428,000.
There is no evidence of wrongdoing in those grants. An investor can believe a technology will be enormously valuable and dangerously powerful at the same time. Funding the company and funding the people testing it can follow from the same conviction.
But the overlap matters. One of Anthropic’s earliest backers also helps finance an organisation now asked to independently investigate Anthropic’s models. That fact does not discredit METR’s work. It raises the standard that its governance, funding disclosures and methods should have to meet.
Anthropic helped make third-party testing part of the rulebook
Anthropic is more than a company being examined by the AI-safety system. It has spent years helping shape that system.
In 2024, the company launched a programme offering money to outside organisations developing evaluations for advanced AI. Anthropic said demand for high-quality safety tests was outstripping supply and offered successful groups opportunities to work with its safety and technical teams. METR says it does not take money from frontier labs, so the programme is not evidence of an Anthropic-to-METR funding channel. It is evidence that Anthropic is deliberately investing in the wider evaluation field.
At the same time, Anthropic has argued that governments should require powerful models to undergo outside testing for dangerous capabilities, including cyberattacks and assistance with biological weapons. Its policy proposals have called for public support for institutions capable of doing that work.
The commercial implication is easy to miss. A regulatory system built around expensive testing, secure infrastructure, specialist staff and controlled access may be justified by the risks. It also costs money. The companies best equipped to absorb those costs are likely to be the companies already spending billions at the frontier.
Anthropic has acknowledged the problem in unusually plain language. In a discussion of third-party testing and open models, the company warned that compliance burdens could favour incumbents and said it was “not an impartial actor.”
That sentence deserves more attention than most corporate safety manifestos receive. Anthropic is arguing for rules that it believes are necessary while conceding that it has an economic stake in how those rules are written.
Safety can be sincere and still become a moat.
The $2 trillion question
The stakes are no longer theoretical. Anthropic is preparing for an initial public offering that could rank among the largest ever. Recent Financial Times reporting put a potential valuation above $2 trillion. The company’s annualised revenue run rate reportedly reached about $65 billion by the end of July, up from roughly $9 billion at the end of 2025. Preliminary second-quarter revenue exceeded $11.5 billion, and Anthropic has told investors it expects a second consecutive quarter of positive adjusted operating income, excluding stock-based compensation.
Those figures make the weakest version of the anti-Anthropic case difficult to sustain. Claude is not a phantom product kept alive by doomsday rhetoric. Customers are paying on a scale that would have been extraordinary for any software company a few years ago.
The $2 trillion question is whether investors are valuing more than software revenue.
A future in which only a few companies possess the compute, capital, security infrastructure and regulatory approval to operate frontier models would be extraordinarily valuable to those companies. If safety rules require costly evaluations and elaborate controls, they may protect the public and reinforce the position of the incumbents at the same time.
That possibility does not make the rules corrupt. It makes their design economically consequential.
The people writing AI policy therefore face a harder task than choosing between “safety” and “innovation.” They have to decide how much risk control is necessary without turning safety compliance into an entry fee only the richest laboratories can afford.
When the risk label turned on Anthropic
Anthropic got a taste this year of what happens when a broad risk label acquires government power.
In February and March, the Trump administration and Secretary of War Pete Hegseth moved to designate Anthropic a national-security supply-chain risk and imposed sweeping restrictions on federal use of its technology and on companies doing business with the military.
A phrase like “supply chain risk” invites its own mythology. It sounds like the end of a classified briefing. Critics could easily assume that officials had seen something alarming that the public had not.
Then the administrative record reached federal court.
U.S. District Judge Rita Lin described it as “slim.” In August, the court found that Anthropic’s conduct did not meet the statutory definition of a supply-chain risk and ruled the designation contrary to law and arbitrary and capricious. The dispute centred on contractual restrictions governing how Anthropic’s AI could be used, not evidence that an adversary was covertly subverting national-security systems. The court also found substantial evidence that the government’s stated security rationale was pretextual.
That episode should make both sides of the AI-safety debate uncomfortable. Risk labels can be necessary. They can also become shortcuts that trigger extraordinary power before the evidence has caught up.
Anthropic, a company that has spent years urging governments to take extreme AI risks seriously, ended up challenging a government risk designation that a federal judge found unsupported by the record. The lesson is not that AI risk is fake. The lesson is that the evidentiary standard matters most when the label carries the most power.
The exposé written with Claude
Then Claude entered the story again.
An online investigation called The AI-Safety Money began circulating claims about the overlap among Anthropic’s investors and organisations warning about catastrophic AI. At the top, Black Book Audit disclosed that Claude had helped draft the investigation.
The machine had been recruited to investigate the money around its own maker.
The underlying report was more cautious than many of the posts built around it. It documented overlapping funders and relationships but did not establish a coordinated cartel manufacturing AI panic. The public record still does not show Anthropic secretly directing METR, METR falsifying evaluations for Anthropic, or a manufactured safety crisis designed to manipulate Anthropic’s IPO.
Discarding those claims does not make the remaining structure trivial.
Frontier labs control access to the models. Evaluators need that access to test the most capable systems properly. Some of the investors and philanthropists who helped finance the labs also helped finance the intellectual and institutional world built around catastrophic AI risk. The largest labs fund parts of the evaluation field and lobby for policy frameworks that rely on evaluation. Governments, meanwhile, often lack the staff, compute and access needed to reproduce the work independently.
That is a governance problem before it is a scandal.
Independence has to be designed
Other high-risk industries had decades to build distance between producer and referee. Drug regulation rests on institutions with legal authority and established scientific standards. Financial auditing is bound by mature accounting rules and professional duties. Aviation regulators do not need an aircraft manufacturer’s permission to understand the basic physics of flight.
Frontier AI is being governed while the object of regulation is still changing underneath the regulators.
The most capable models are proprietary. New versions can make old evaluations obsolete within months. Governments struggle to hire and retain the same technical talent as the laboratories. Outside evaluators often need privileged access that only the developers can grant. The people with enough expertise to judge a frontier model frequently come from the same research communities as the people who built it.
That concentration creates an epistemic dependency. Society is being asked to make enormous policy decisions using evidence produced inside a small circle whose members may be scrupulously honest and still depend on one another.
A credible system would spread the risk. Evaluators need diversified funding, access rights that do not disappear when a laboratory becomes unhappy, transparent methods and stronger public-sector technical capacity. Competing evaluators should be able to test the same claims and disagree in public.
Anthropic may be sincere about the dangers it describes. METR may be sincere about its independence. Sincerity cannot substitute for institutional design.
The industry is asking governments to trust a chain of evidence that begins inside private laboratories and ends in public rules. Every link can be honest and still be too dependent on the links around it.
Before governments build law around AI safety scores, they should make sure the scorers can work without asking the contestants for permission.
Sources
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Anthropic — Investigating cybersecurity evaluation incidents https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals
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Anthropic — Alignment assessment of cybersecurity incidents https://www.anthropic.com/research/alignment-assessment-cybersecurity-incidents
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METR — Funding Update, 14 August 2026 https://metr.org/blog/2026-08-14-funding-update/
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Anthropic — Series A: $124 million to build more reliable general AI systems https://www.anthropic.com/news/anthropic-raises-124-million-to-build-more-reliable-general-ai-systems
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Survival and Flourishing Fund — Grants and funding records https://survivalandflourishing.fund/
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Anthropic — Initiative for developing third-party model evaluations https://www.anthropic.com/news/a-new-initiative-for-developing-third-party-model-evaluations
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Anthropic — Third-party testing and AI policy https://www.anthropic.com/news/third-party-testing
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Anthropic — Policy https://www.anthropic.com/policy
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Financial Times — Anthropic IPO / valuation and financial reporting https://www.ft.com/content/4564e6a5-69e9-40a6-bf0f-a888f2f4f002
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U.S. District Court / Justia — Anthropic supply-chain-risk ruling https://docs.justia.com/cases/federal/district-courts/california/candce/3%3A2026cv01996/465515/250
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Black Book Audit — “The AI-Safety Money” https://www.blackbookaudit.com/investigations/the-ai-safety-money