Anthropic Researcher Resigns, Warns AI Labs Are Recklessly Racing Toward Superintelligence

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Jacob Coxon, a former pre‑training researcher at OpenAI and Anthropic, quit Anthropic on September 9, 2026, citing reckless AI development that could lead to self‑improving superintelligence and pose existential threats. In his departure note, he warned that the race to advanced AI could kill millions, potentially humanity, within the decade. The resignation adds to growing safety concerns surrounding the fast‑track AI race at the two leading labs.

Jacob Coxon, who spent three years conducting pre‑training research at both OpenAI and Anthropic, announced his resignation from Anthropic on Tuesday, September 9, 2026. In a departure note posted to X, Coxon said, "Neither company is acting responsibly. They are racing straight to self‑improving superintelligence and gambling with our lives" .

Coxon's departure underscores mounting internal dissent over AI safety at the sector's biggest players. He warned that the unchecked race to develop advanced artificial general intelligence could result in catastrophic outcomes, even suggesting the technology could kill millions—or potentially all humans—by the end of the decade . These remarks echo broader industry anxieties about the pace of AI progress outstripping safety protocols.

The researcher's claims come at a time when both Anthropic and OpenAI are under heightened scrutiny from regulators, investors, and the public. While Anthropic prepares for a high‑profile IPO, critics argue that financial pressures may be driving a risky acceleration in model development. Coxon's public exit adds a personal testimony to the chorus of safety concerns voiced by external experts and policymakers.

Analysts will be watching for any concrete steps the companies take to address these safety warnings, such as the adoption of more rigorous oversight frameworks or transparent risk assessments. The resignation could also influence investor sentiment ahead of Anthropic's upcoming market debut, potentially prompting deeper questions about how AI firms balance rapid innovation with responsible development.

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