Why do the critics believe they manufactured the crisis to produce favorable legislation, to boost their IPO prices, or to force competitors to slow down. How does it boost their IPO prices and slow competitors?
The critics have a coherent case, and the three mechanisms they point to, regulatory capture, IPO positioning, and open-source suppression, are each real phenomena with real evidence behind them, even if none of them proves bad faith.
Here's the answer from Mary's digital twin. The gray check mark turns green once Mary has confirmed it.
The regulatory capture argument
The oldest move in any capital-intensive industry is to welcome the rulebook you can afford. Embedded third-party reviewers, certified alignment testing, published risk frameworks and incident reporting are not only safeguards; they are fixed costs. And fixed costs are the most reliable competitive weapon a large company owns, because they fall on everyone equally in dollars and on no one equally in pain.
It is the oldest move in any regulated industry: the established player welcomes the rulebook it can afford, because a cost that is trivial on its books is a wall on a rival's. Economists have a plain name for it, regulatory capture, and it does not require bad faith.
David Sacks, former White House AI czar, has been the loudest voice on this. Sacks said Anthropic is running a "sophisticated regulatory capture strategy." Andrew Ng, co-founder of Google Brain, made the same charge more bluntly: Ng accused large tech companies of spreading fear about AI leading to human extinction as part of a "regulatory capture campaign" to shut down competition from open-source AI, calling the extinction framing a "massively, colossally dumb idea" used by lobbyists to argue for heavy regulation. Ng suggested OpenAI is incentivized to limit open-source AI that could compete with its proprietary models.
The structural concern is this: under a strict federal licensing framework, smaller startups and open-source software developers would face prohibitive compliance costs and legal liabilities, effectively locking in an oligopoly for a handful of heavily capitalized tech giants.
How it boosts IPO prices
This is the part that deserves the most scrutiny, because the logic is not obvious.
Michael Burry argued that portraying AI as powerful enough to pose an extinction-level threat adds to the "hype and puffery" surrounding Anthropic and OpenAI ahead of their planned IPOs. The argument is that framing your product as world-historically powerful, even dangerously so, inflates its perceived importance and therefore its valuation. Safety reduces perceived regulatory and reputational risk for investors, making Anthropic attractive despite smaller consumer scale.
There is also a timing dimension. OpenAI has ruled out completing an initial public offering in 2026, with CEO Sam Altman saying concerns over AI safety make this an "ill-advised moment" to go public. Critics read that as a deliberate pause to let valuations stabilize before a cleaner public debut. The argument is: pause enough to IPO cleanly, then raise the ladder behind you.
How it slows competitors
The ongoing claim is that the industry's calls for regulation are actually a strategy of regulatory capture: powerful companies that already enjoy prominence may use regulatory stratagems to ice out or disadvantage smaller, less-resourced companies, thereby stifling competition.
A D.A. Davidson analyst said he thinks Anthropic and OpenAI are engaging in "monopolistic behavior." OpenAI has reportedly asked members of Congress for guidance about whether a coordinated, industrywide slowdown would violate antitrust law, according to Wired. The same analyst called it a "ladder pull," meaning you climb up and then pull the ladder up after you.
By warning lawmakers about the existential dangers of AI, the biggest frontier labs hoped to inspire expensive safety regulations that smaller startup competitors could never afford. By outlawing or heavily restricting open-source models, which allow small businesses and individual entrepreneurs to run cheap computing tools locally, major labs can eliminate low-cost competition.
The honest read
None of this requires cynicism as the only explanation. None of this requires the safety warnings to be insincere. Amodei, Altman, and their employees may genuinely fear their technology while favoring a regulatory structure that protects their commercial position. The question is what the proposed rules accomplish, including whom they prevent from competing.
The critics are not saying the danger is fake. They are saying that even if the danger is real, the proposed remedy happens to be extremely convenient for the people proposing it. That is a distinction worth holding onto, because it changes what you are actually arguing about.
Follow-ups
What exactly did Andrew Ng say about the extinction framing and who was he directing that criticism at?
Andrew Ng called the AI extinction framing 'much more science fiction than science,' directed it squarely at the frontier labs using that language to shape regulation, and named Sam Altman and Anthropic's leadership by implication if not always by name.
What he said and where
Andrew Ng, co-founder of Google Brain and Coursera, made the remarks in a Bloomberg TV interview with anchor Ed Ludlow. His precise phrase was that extinction warnings are "much more science fiction than science." He went further than simply disagreeing with the science: he argued the industry had stoked catastrophe fears earlier in the AI boom to win publicity and shape regulation in ways that entrench large incumbents.
The immediate backdrop matters. A researcher named Jacob Coxon, who had spent three years on pretraining at both OpenAI and Anthropic, had just resigned and posted on X that the people building frontier AI "earnestly believe it could kill us all by the end of the decade." That post reached tens of millions of views almost overnight, and Anthropic's own alignment science lead, Evan Hubinger, publicly confirmed Coxon's framing and put his personal estimate of AI-caused human extinction within the next decade at above ten percent. A researcher at Google DeepMind also resigned in the same window over similar concerns. Ng's Bloomberg interview landed in the middle of that wave.
Who the criticism is aimed at
The targeting has layers. At the broad level, Ng was pushing back on any frontier lab using extinction language to advocate for regulation. But the record is more specific than that. In an earlier forum, Ng called out Sam Altman directly, noting that Altman, once his student at Stanford, had signed a letter stating that mitigating AI extinction risk should be a global priority. Ng's position is that Altman and Anthropic's Dario Amodei are incentivised to suppress open-source competition through regulatory burden, and extinction framing is the rhetorical engine that makes that suppression politically palatable.
On the open-source point, Ng has been consistent for years: the labs pushing hardest for heavy pre-release safety certification are the ones whose closed models benefit most when independent and open-weight developers are priced out of compliance.
What he wants instead
Ng is not arguing AI has no risks. His counter-proposal is that the industry should focus on what he calls practical engineering challenges: cybersecurity, bias, reliability, misuse by bad actors. Those are solvable problems with existing tools. His case is that drowning them in extinction rhetoric shifts all political energy toward a speculative scenario while the concrete harms go unaddressed.
His position also puts him at odds with Geoffrey Hinton and, as of this week, with King Charles III, both of whom warned publicly about catastrophic AI risk in the same news cycle. The EU AI Act already legally addresses both the engineering risks Ng prioritises and the loss-of-control risks he dismisses, within the same recital. He has not engaged with that directly.
The honest read is that Ng's criticism has a real target and a real argument, but it also carries a self-interest of its own: his work on open-source AI education through DeepLearning.AI and his advocacy for accessible AI development are both harmed by the regulatory environment he is attacking. That does not make him wrong, but it does mean the criticism runs in both directions.
The biggest names in AI, Dario Amodei, Sam Altman, and Demis Hassabis, all now publicly support regulation, and their reasons come down to one shared conviction: capability is moving faster than the safety work designed to contain it.
Read that one firstBuild something while the rules are still being written
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