US move to limit human models turns AI race into geopolitical test

The reported regulations aim to limit the risks posed by powerful AI systems and reflect growing concerns in Washington that cutting-edge models are no longer ordinary software products. They are increasingly treated as strategic assets with implications for national security, scientific research, corporate governance, and cyber warfare. But the move also raises warnings that restricting U.S. companies will not slow the global AI race and could instead give an advantage to competitors operating under fewer constraints.

Shay Michel, managing partner at Merlin Ventures, said the decision reflects the beginning of what he calls the “post-Mythos era,” where the capabilities of the frontier model are no longer theoretical. “We are entering a post-Mythos era,” Michel said. “Once models like Mythos and Fable proved possible, the question shifted from whether the world could achieve these capabilities to how quickly others would replicate it.”

Michel said the Trump administration’s reported decision is a short-term move rather than a long-term strategy. “The Trump administration’s decision to limit anthropic models is tactical, not strategic,” he said. “You can slow down U.S. companies, but you can’t stop global competition. China is already proving with deep-seeking that the gap between ‘impossible’ and ‘done’ can close in a matter of months.” He said if China or other rivals launch comparable models without U.S. restrictions, the U.S. government could be forced to choose between keeping strict restrictions at home or loosening them to keep U.S. companies competitive.

“The train has left the station,” Michelle said. “Current regulations are just an attempt to buy time. But in the post-mythos era, time is the only resource that humans cannot create. This is no longer a debate about the safety of a single model, but about the balance of global technological power over the next decade.”

For researchers, the reported closures were not an abstract policy shift. Some academics who had brief access to the new model said the cancellation halted research that had progressed quickly during the short period the tool was available. Dr. Naomi Unkelos-Shpigel of the Braude College of Engineering’s software engineering department said she used Claude Fable 5 for about a day and a half before her access was revoked.

“I was one of the lucky ones who was able to use Claude Fable 5 before it was shut down,” she said. “This model was surprisingly powerful compared to anything we had used before, both in terms of inference and the quality of what it produced. Within the first day and a half of having access to it, we were able to advance our research in ways that would have otherwise taken much longer. Colleagues shared similar experiences and reported breakthroughs to problems that had stalled for years.”

She said the model was shut down before its June 22 announcement date, with little explanation to users. He said he understood the wariness of powerful AI systems, but argued that the process should have been more transparent. “I understand the idea that ‘with great power comes great responsibility,’ and I believe that Anthropic acted carefully rather than carelessly,” she said. “But withdrawing a tool that was actively accelerating scientific progress without any warning and without a clear timeline for what will happen next is a decision that requires more transparency than we have.”

The episode also highlights broader changes within the company. Advanced AI is increasingly being seen not only as an aid in the workplace, but also as a core layer of enterprise infrastructure. Aviv Nahum, co-founder and CEO of Above Security, said the Anthropic case marks a major shift in how companies should think about AI risks. “The human story is not about one model or one vendor, but about broader change. Advanced AI is starting to be treated as a strategic infrastructure rather than just another productivity tool,” said Nahum.

He said companies are moving beyond the question of whether to use AI to the more difficult question of how to manage it within large organizations. The same action can carry different risks depending on who performs it and under what circumstances, he said. “Engineers debugging code, salespeople summarizing customer notes, and retired employees querying internal knowledge all require different levels of visibility and control,” says Nahum.

This means AI security cannot rely solely on blanket bans or static policies. He said companies need to consider how AI is being used and by whom, and whether its actions are compatible with the employee’s role. “The point is not that organizations should delay AI adoption; quite the contrary,” says Nahum. “To deploy AI securely at scale, companies need governance that is dynamic, contextual, and close to how people actually work.”

Cybersecurity experts say the same logic applies to attackers. More powerful AI models will not invent entirely new forms of cyberattacks, but they will allow existing techniques to be carried out faster, in parallel, and by people with lower-level technical skills. Roy Ackerman, Silverfort’s vice president of identity security strategy, said the advanced model could make sophisticated cyber operations available to a much broader group of users.

“Just as generative AI has turned millions of people into content creators and app developers, the latest generation of AI models is lowering the barrier to advanced cyber capabilities,” Ackerman said. “What was once the domain of highly skilled and even nation-state attackers is becoming accessible to a wider range of users.”

A more immediate danger, he says, is speed. AI can test multiple attack paths at once, making traditional security models that rely on detecting suspicious activity and responding reactively less effective. “The real disruption is not that AI can perform entirely new types of attacks, but that AI can simultaneously perform complex operations along multiple paths at a speed and scale that humans simply cannot match,” Ackerman said.

Akerman said that while restricting access may be understandable, organizations should expect that powerful tools will eventually become more widely available, including to malicious actors. “These capabilities challenge many of the assumptions that cybersecurity has relied on for decades,” he said. “Traditional approaches to detecting and responding to attacks become much less effective when attacks unfold at machine speed.”

In recent testing with large organizations, he said, the most effective approach was to assess risk and apply controls in real time to every access request. “If you don’t have time to react, you need to implement protection at runtime before an attack spreads throughout your organization,” he said.

Reported artificial limitations turned access to one company’s models into extensive testing of AI policies. Governments want more control over frontier systems, researchers want transparency when access is removed, companies want to deploy AI without exposing sensitive data, and security teams are preparing for a world where attacks outpace human defenders.

That tension doesn’t seem to go away. As AI models become more capable, the discussion is no longer just about what AI can do. What matters is who can use them, who can restrict them, and whether regulations imposed in one country can keep up with the global competition already underway.