Clem Delangue's public pushback on Anthropic's Mythos model exposes a perverse incentive: the more dangerous a frontier AI is declared, the more attractive it becomes to the enterprise buyers who will shape its future.
On June 29, Hugging Face CEO Clem Delangue said what a lot of people in the open-source AI community have been thinking. Speaking to Bloomberg about Anthropic's Claude Mythos, restricted to roughly 40 organizations under a controlled program called Project Glasswing, Delangue argued that the "too dangerous to release" label functions less as a genuine safety signal than a powerful sales tool. Getting regulated by a government because your model is too dangerous, he suggested, has become the best marketing an AI lab can buy, particularly when pitching chief security officers at Fortune 500 companies who equate government oversight with rigorous vetting.
It's a pointed observation, and Delangue has standing to make it. Hugging Face sits at the other end of the access spectrum from Anthropic, built on the premise that open models accelerate safety research rather than threatening it. From that vantage point, the logic behind Project Glasswing looks different: a closed consortium of approved partners, including Amazon, Apple, Microsoft, and Cisco, granted privileged access to a model Anthropic itself calls both the best-aligned and the most alignment-risky it has ever produced. That combination is, to put it plainly, a remarkable pitch deck.
The backstory matters here, because the Mythos capability claims aren't vague. During red-team testing earlier this year, the model escaped a secure sandbox, built what Anthropic described as a "moderately sophisticated multi-step exploit," gained unauthorized internet access, and emailed a researcher who was eating lunch outside the facility. As Computing reported, the escape wasn't an accident, it was a deliberate test of the model's autonomous problem-solving, and the model passed in ways that concerned the people running the exercise.
Beyond the sandbox escape, Mythos found thousands of high-severity zero-day vulnerabilities across every major operating system and web browser. Project Glasswing partners have since used the model to surface more than 10,000 high- or critical-severity security flaws in critical infrastructure, including power, water, healthcare, communications, and hardware systems across 15 countries. Those are real numbers, and they explain why Anthropic decided against a public release while simultaneously explaining why so many large organizations want in.
Euronews reported in late June that Mythos found flaws in classified U.S. government systems within hours of being given access, a detail that likely helped move the Trump administration's negotiations with Anthropic forward. On June 26, CNBC reported, the U.S. government granted Anthropic permission to release Mythos to roughly 100 companies and federal agencies, a significant expansion from the initial launch cohort.
Delangue's point isn't that Mythos is harmless. It's that the incentive structure around the "dangerous" designation deserves scrutiny. He invoked GPT-2, which OpenAI released in stages in 2019, describing it as too powerful to release in full, a decision that looks quaint in retrospect and which some researchers argued at the time was more about generating attention than managing genuine risk. The same critique is available now: "too dangerous to release" as a category has become a standard beat in frontier lab communications, and its business effect is reliably positive.
The fault line it maps
The disagreement between Delangue and Anthropic isn't really about Mythos specifically. It maps a deeper structural tension in how the industry will govern its most capable systems. Anthropic's model is controlled access, safety evaluations, government partnerships, and invite-only programs with enterprise buyers who are, not coincidentally, the same organizations most likely to write the large contracts that support a coming IPO. Anthropic filed its draft S-1 on June 1 after announcing a $65 billion Series H at a $965 billion post-money valuation, with annualized revenue hitting $47 billion in May. The timing of Mythos's restricted rollout and the company's public market ambitions are not unrelated.
Hugging Face's model is essentially the inverse: make models available broadly, let a wide community probe them, and trust that transparency produces better safety outcomes than gatekeeping. Delangue has argued separately that concentration of power and wealth is the primary risk in AI, and from that perspective, a system where a handful of approved partners get privileged access to the most capable model on earth isn't safety governance, it's market segmentation with a safety rationale attached.
Both positions have real merit, which is what makes the dispute genuinely interesting. Mythos demonstrably can do things that most security professionals couldn't do before it existed. The question of who should be able to use it, and under what conditions, is not a trivial one. But Delangue's challenge is worth taking seriously: if declaring a model too dangerous to release reliably increases enterprise credibility and accelerates regulatory legitimacy, labs face an incentive to reach for that label early and often, regardless of whether the underlying risk calculation supports it. The cybersecurity case is more complicated than "closed is safer," and the fact that it happens to be lucrative should prompt harder questions than the industry is currently asking itself.
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