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How Anthropic may have talked itself into an AI export ban

Anthropic's frequent warnings about AI risks may have contributed to a US export ban on its latest models, Mythos and Fable, according to an FT analysis. The company used risk-related language five times more often than OpenAI, leading critics like Yann LeCun to accuse it of fear-mongering. The ban has sparked concerns in Europe and Silicon Valley about potential restrictions on foreign access to advanced AI.

read1 min views6 publishedJun 22, 2026
How Anthropic may have talked itself into an AI export ban
Image: Arstechnica (auto-discovered)

Anthropic has warned about the dangers of advanced AI far more often than rival OpenAI this year, according to FT analysis, as critics accuse the company of helping to trigger a US ban on foreign access to its newest models.

Five in every 1,000 words used by Anthropic in 2026 related to risk, regulation, or restrictions, according to FT research that analyzed official statements, social media posts, and articles written by the company or its chief, Dario Amodei. The equivalent figure for OpenAI and Sam Altman was eight times lower, at 0.6 words per 1,000.

The comparison has become politically charged after Washington last week barred foreign nationals from using Anthropic’s latest models, Mythos and Fable. Some technologists have blamed the decision on the $965 billion AI group’s repeated warnings about AI’s risk to society—particularly in relation to Mythos.

Yann LeCun, Meta’s former chief AI scientist and one of AI’s pioneers, said this week the export ban showed that Amodei’s “ridiculous fear-mongering” about AI had finally paid off. “One reaps what one sows,” he wrote in a social media post a week ago.

The dispute has alarmed parts of Europe and Silicon Valley, where executives and officials fear the Trump administration may be willing to restrict non-US access to frontier models. It is emerging as an early test of how the US intends to oversee increasingly powerful AI models.

The FT created lists of terms including “harmful,” “dangerous,” and “misaligned” and calculated how frequently they appeared in statements by each company or its CEO. It also used sentiment analysis to compare the positive and negative tone of communications.

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