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Trump administration Okays Anthropic’s powerful Mythos-class AI models
After placing export restrictions on Anthropic’s Claude Fable 5 and Mythos 5 models on June 12—effectively forcing their removal from the market—the Trump Commerce Department has now reversed course. But in the end, the sudden regulatory pivot may play to China’s advantage.
Administration officials removed the controls Tuesday evening, after working with Anthropic for two weeks on a refined set of misuse protections. Anthropic promptly announced that it was turning Fable 5 back on for all customers, and turning on Mythos 5 for a select set of approved enterprise customers.
The concern was that foreign actors might use Mythos or Fable to detect and exploit software vulnerabilities in U.S. government or enterprise systems, tasks for which Anthropic had said the models demonstrated surprising capacity. The Commerce Department also asked OpenAI to “stagger” the release of its latest frontier model, GPT-5.6.
The government’s intervention rattled investors and tech industry folks because the Trump administration, after pledging to stay away from AI regulation, effectively granted itself a “kill switch” over newly released frontier models.
The government has a legitimate interest in the national security implications of frontier models, but the administration had no ready framework for evaluating the national security risk of new models, or a minimum set of guardrails that AI providers must build in to prevent misuse of the models. Anthropic says it hopes the work it did with the Commerce Department over the past two weeks will lay the groundwork for a set of standards that could apply to all U.S. frontier model providers.
The Trump administration’s sudden swerve into AI safety regulation may end up benefiting Chinese model makers. The administration’s export restrictions on the Mythos models imposed a pause on the distribution of the U.S.’s best models at a time when Chinese AI labs are rapidly catching up with their U.S. counterparts—and offering an ever-more compelling alternative to expensive models from Anthropic, OpenAI, and the like.
Many enterprises are already adopting open-weight Chinese models from DeepSeek, Alibaba, and others, which they can download for free, refine, and host in their own private clouds. To justify their high per-token costs, the U.S. frontier models must be demonstrably superior to alternative, open-weight models. And they need to be available.
Some enterprises might hesitate to build their AI stack on top of models that are subject to sudden, unscripted restrictions imposed by a regulator. A real-world example: When the U.S. government restricted access to Anthropic’s Mythos-class models, the Chinese company Z.ai’s (open-weight) GLM-5.2 model quickly climbed leaderboards and gained Silicon Valley users because it was immediately available and less expensive.
U.S. AI companies and their investors are putting hundreds of billions of dollars behind the development of general-purpose frontier models. They’re betting that corporations around the world will use their frontier models as the intelligence engines that power major business functions. With U.S. AI policy in flux, and the threat of Chinese models growing, the business case for monolithic, closed AI models seems less viable.
Central bankers are sounding the AI alarm
The AI boom has been framed as a possible solution to weak productivity. At the European Central Bank’s annual forum in Sintra, Portugal, this week, central bankers and economists focused on a different question: How much financial risk is building around the technology.
If AI delivers large productivity gains, companies may need fewer workers, which could raise unemployment and weaken consumer spending. If AI disappoints, the capital now flowing into data centers, chips, cloud infrastructure, and the like may fail to produce expected returns. Either outcome could create stress in financial markets and the broader economy.
“If AI overdelivers, it will impact financial stability. If AI underdelivers, it will impact financial stability,” Torsten Slok, chief economist at Apollo Global Management, said at the forum, according to Reuters.
The concern is no longer confined to policymakers or investors. A Pew Research Center survey released last year found that 52% of U.S. workers are worried about the future impact of AI in the workplace, while only 36% are hopeful; nearly a third say workplace AI will mean fewer job opportunities for them over the long run. Those fears are landing in an economy where many households have limited room for disruption: The Federal Reserve’s latest household survey found that only 63% of adults could cover a $400 emergency expense with cash or its equivalent.
For investors, the question is whether AI companies can justify the valuations and capital spending already attached to them. The worry for workers, though, is whether a technology that raises productivity for firms will also create a labor-market shock.
AI is a difficult problem for central banks because the risks are spread across several parts of the economy. Market corrections, hiring slowdowns, and infrastructure strains can each create their own unique set of pressures. And traditional tools like interest rates and bank supervision may not be well suited to risks that move through technology systems as well as financial markets.
“The leverage on both sides is very worrisome for financial stability,” said the International Monetary Fund’s Tobias Adrian, according to The Wall Street Journal.
There is precedent for this kind of anxiety. Bank of Canada Governor Tiff Macklem pointed to the dot-com era, when the internet ultimately exceeded expectations while still producing a market bubble. That analogy matters because the dot-com crash was not a verdict against the internet. It was a verdict against timing, valuation, and the assumption that a transformative technology automatically makes every bet on that technology a good one.
The AI cycle may be especially hard to monitor because of its speed and opacity. In markets, AI systems could crowd into similar trades or accelerate bubbles and crashes. In lending, agentic AI could expand credit access while making decisions harder for supervisors to explain. In cybersecurity, advanced models could help companies find vulnerabilities while also giving attackers new tools.
More AI coverage from Fast Company:
- Nvidia says it can cut data center water use. The AI boom has a bigger problem
- How to avoid AI in as many places as possible
- Publishers can’t control AI answers. They can’t ignore them either
- This San Diego school bought $500,000 worth of humanoid robots for the classroom
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