The White House wants to verify AI models before their release: the major reversal

The White House wants to verify AI models before release: the great reversal

🔎 When AI becomes too dangerous for its own creators

In early May 2026, an unlikely event shook Silicon Valley and Washington: Anthropic refused to publish its own model. Not for commercial reasons, but because it deemed it too dangerous. This model, named Mythos, is capable of identifying cybersecurity flaws in critical systems — and its creator believes the public is not ready to have access to it.

The White House's reaction surprised everyone. The Trump administration, however firmly determined to deregulate AI since taking power, is now considering a mandatory government review process before the release of any new AI model. A 180-degree turnaround, directly triggered by the revelations about Mythos.

The geopolitical context adds to the tension: this internal debate is exploding just days before the Trump-Xi summit on May 14-15, 2026, where cybersecurity and AI were already on the agenda. Washington fears that a model like Mythos could end up in the wrong hands — or that Beijing might develop an equivalent without any constraints.

The key points

• Anthropic refused to publish Mythos, a model capable of finding flaws in critical systems, judging the risk of cyberattack to be unacceptable.
• The White House is considering an executive order imposing government pre-vetting of all new AI models before their public release.
• A working group bringing together tech leaders and government officials is under discussion, modeled on the UK AI Safety Institute.
• Anthropic has an obvious strategic interest: such a mechanism would create a massive barrier to entry for its competitors, notably open-source.
• The major risk concerns open-weight models like DeepSeek V4 Pro and future Llama models, which could find themselves blocked by a validation process designed for large labs.

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Mythos: the model that changed everything

Anthropic built Mythos as a cutting-edge model capable of reasoning about cybersecurity. The problem: it's too good. According to information revealed in late April 2026, Mythos can identify vulnerabilities in critical computer systems — infrastructure, government networks, financial systems.

Anthropic made the decision not to publish it. A rare choice in an industry where the pressure to release the next model is overwhelming. Dario Amodei and his team judged that the risks of misuse far outweighed the benefits of making it publicly available.

The White House was informed. And the reaction was immediate: the administration blocked any attempt to broaden access to Mythos, according to La Presse. J.D. Vance himself was reportedly briefed on the model's capabilities, triggering a series of emergency meetings.

What is striking is the parallel with the debates on free AI models and their quality. The open-source community has been demanding equitable access to advanced models for years. Mythos represents the exact opposite: a model so powerful that even its creator refuses to share it.

The executive order under discussion: what we know

The New York Times revealed on May 4, 2026, that the White House was working on an executive order establishing a formal AI model review process before public release. This is not just a voluntary framework: it would be an obligation for companies developing models above a certain capability threshold.

The exact mechanism is not yet set. But discussions are leaning toward a model inspired by the UK AI Safety Institute, which conducts pre-deployment testing on the most advanced models. The key difference: the American system would potentially be binding, not consultative.

According to Bloomberg, a working group bringing together leaders from Anthropic, Google, and OpenAI with senior officials is planned to define the terms. The stated goal: to prevent another Mythos from becoming accessible without prior evaluation.

The timing is anything but coincidental. This executive order comes as Sean Cairncross, the National Cyber Director, had already announced in April 2026 a series of new executive orders on cybersecurity, according to NextGov. The Mythos affair simply accelerated and reoriented these existing plans.

The key role of Sean Cairncross

Sean Cairncross is the man for the situation. Appointed National Cyber Director by the Trump administration, he led the crisis meetings with tech industry leaders following the revelations about Mythos, according to PYMNTS.

Cairncross is no novice in this area. Even before the Mythos affair, he had publicly announced that new executive orders on cybersecurity were in the works, as reported by Semafor. His credo: national cybersecurity requires a proactive framework, not a reactive one.

His role in the AI dossier has become central. He is the one coordinating exchanges between the White House, security agencies, and the labs. And he is the one championing the idea that a model's ability to find zero-day vulnerabilities should automatically trigger a government review process.

The question many are asking: does Cairncross have the means to carry out his policy? The National Cyber Office does not have the NSA's budget or the FTC's regulatory power. An executive order would give him leverage, but its actual implementation will depend on the resources allocated and the cooperation of the labs.

Anthropic: the hidden winner of this story

This is the angle the tech community immediately spotted. According to Tom's Hardware, Anthropic has an obvious strategic interest in this political reversal.

Anthropic already has extremely rigorous internal security testing procedures in place. It is precisely thanks to these procedures that Mythos was identified as too dangerous to be published. If the government imposes mandatory pre-vetting, Anthropic is already prepared. Its competitors, who are less advanced in terms of security, will be much less so.

The potential result: a massive barrier to entry. Startups that cannot afford to implement equivalent security processes would find themselves excluded from the advanced model market. The large labs — Anthropic, OpenAI, Google — would dominate all the more.

It is all the more ironic since Anthropic positions itself as the "AI safety" company. Refusing to publish Mythos reinforces this image. But if this refusal leads to a regulatory framework that eliminates competition, one can legitimately question the sincerity of the gesture. The configuration of models in systems like Hermes Agent could also become more complex if the number of available models decreases.

The danger for open-source: Llama, DeepSeek in the crosshairs

This is the most critical point for the AI ecosystem as a whole. A government pre-vetting process poses a fundamental problem for open-weight models.

Take DeepSeek V4 Pro (Max), which scores 88 as a generalist and rivals the best proprietary models. DeepSeek is a Chinese company. How could the US government impose pre-vetting on a model developed in Beijing and distributed via decentralized channels?

The honest answer: it can't. Which means the mechanism would de facto apply only to American companies — and precisely penalize those that choose to publish in open-weight. Meta with Llama, American startups contributing to the open-source ecosystem, would all find themselves disadvantaged compared to actors beyond regulatory reach.

The discussion on Slashdot immediately highlighted this risk of market lock-in. Commentators point out a paradox: in trying to protect itself against dangerous models, Washington could weaken the exact ecosystem that ensures American technological supremacy — open-source.

The Trump-Xi summit on May 14-15 adds a geopolitical layer. If the United States imposes constraints on its own companies while China continues to distribute models like DeepSeek without restriction, the gap could narrow quickly.

What this changes for developers and companies

Beyond geopolitics, pre-vetting would have concrete consequences for those building with AI today.

A slowdown in releases. Models already take months to develop. Adding a government review phase could lengthen this cycle by several weeks, or even months. For companies that plan their roadmaps around model releases, it's a logistical nightmare.

Uncertainty regarding access. Today, when a new model is released, you can integrate it via API almost immediately. With pre-vetting, some models could be delayed, modified, or simply banned from public release. Architectures that rely on free AI models via platforms like OpenRouter could see their catalog shrink.

An advantage for already deployed models. Current models like GPT-5.5, Claude Opus 4.7 (Adaptive), or Gemini 3 Pro Deep Think would not be affected retroactively. Pre-vetting would apply to new models. This creates a competitive advantage for models already in production — and a disadvantage for challengers that have yet to release.

Implications for multilingual use cases. Companies that deploy multilingual AI avatars to speak to their customers in their language rely on access to the most powerful models. If pre-vetting restricts this access, entire use cases will slow down.

Comparison of potentially impacted models

This table illustrates the core problem: the most impacted models would be those that the US government precisely cannot control.

The context of the Trump-Xi summit and the AI race

The summit between Donald Trump and Xi Jinping on May 14-15, 2026, was not initially focused on AI. But the Mythos affair changed the game. Cybersecurity is now a major point of tension between the two powers, and Anthropic's model made a previously theoretical threat concrete.

The nightmare scenario for Washington: a nation-state or criminal group obtains a model capable of finding zero-day vulnerabilities in critical American infrastructure. Mythos proves that this capability exists. The question is no longer whether it's possible, but when a malicious actor will succeed.

The American response — pre-vetting — is understandable from a security standpoint. But it is questionable on a strategic level. Restricting the release of American models will not prevent China or other actors from developing equivalents. On the contrary, it could accelerate their own programs by justifying the absence of regulatory constraints on their side.

The risk is real: a framework that is too strict in Washington could create a black market for advanced models, where developers would turn to unregulated sources precisely because the regulated sources are too slow or too restrictive.

Reactions from the tech community

The reaction on Slashdot was particularly virulent. Developers see this reversal as a fatal blow to open innovation. The main argument: government pre-vetting does not protect against real risks; it protects existing monopolies.

One commentator perfectly sums up the prevailing sentiment: "Anthropic refuses to publish Mythos, then the White House decides that nobody should publish anything without its approval. It's a hold-up disguised as national security."

Defenders of the measure retort that the status quo is untenable. When a private company can develop a model capable of compromising national infrastructure, the government cannot remain a spectator. The question is not whether to regulate, but how to do so without killing innovation.

Positions are hardening. On one side, the proponents of maximum security, led by Anthropic and supported by Cairncross. On the other, the defenders of open-source and free dissemination, who see pre-vetting as the beginning of the end for an open ecosystem.

❌ Common mistakes

Mistake 1: Confusing pre-vetting with European regulation

The European AI Act classifies models by risk level and imposes a posteriori obligations. American pre-vetting would be radically different: it would block the release of a model before its publication. This is not compliance; it is preventive censorship — with legitimate justifications, but the mechanism is significantly more intrusive.

Mistake 2: Thinking that Mythos will ever be public

Anthropic made a clear decision: Mythos will not be released as is. Even if pre-vetting does not materialize, even if the executive order is not signed, Mythos will remain internal. What is at stake is what happens to the next models — those that are almost as capable as Mythos but whose creators would be willing to publish.

Mistake 3: Believing that pre-vetting concerns all models

The system would target models exceeding a certain capability threshold — likely measured by cybersecurity and reasoning benchmarks. A model like Claude Sonnet 4.6 (agentic score 81.4) would probably not be affected. GPT-5.5 (98.2) almost certainly would be. The threshold line will be the real issue in the working group negotiations.

Mistake 4: Ignoring the time factor

An executive order can be drafted in a few weeks. Setting up a functional working group, defining the threshold criteria, recruiting competent evaluators: all of this takes months, if not years. The risk is an ambitious executive order followed by catastrophically slow implementation.

❓ Frequently asked questions

What exactly is Mythos?

Mythos is an Anthropic model specialized in identifying cybersecurity vulnerabilities. Its capabilities are so advanced that Anthropic deemed it too dangerous to publish, triggering a political crisis in Washington.

Who would be subject to pre-vetting?

Companies developing models above a capability threshold that remains to be defined. In practice, the major American labs (OpenAI, Anthropic, Google) would be the first affected. Foreign open-weight models like DeepSeek would be out of reach.

Is this reversal definitive?

Nothing is signed. The executive order is under discussion. The Trump administration could backpedal, especially if the pressure from the tech industry becomes too strong or if the Trump-Xi summit results in a different bilateral agreement.

What is Anthropic's interest in this affair?

Anthropic already has robust safety procedures. Mandatory pre-vetting would create a barrier to entry for less prepared competitors, strengthening Anthropic's position in the market.

Should developers be worried?

In the short term, no — current models remain accessible. In the medium term, yes: the catalog of available models could shrink, and release cycles could slow down significantly.

✅ Conclusion

The Mythos affair has forced the most pro-business administration in American tech history to consider pre-publication control of AI models — a historic reversal whose consequences could redefine the entire ecosystem, to the benefit of major labs and to the detriment of open-source. If you are building with AI, now is the time to diversify your model providers and reduce your dependence on uncertain future releases.