Ex-Anthropic Researcher Says Mythos Retraining Needs Far Less Compute
Former Anthropic researcher Jacob Coxon says the company can already retrain its Mythos model with "a fraction of the compute originally needed," and cites a steep compute-growth chart from Anthropic's IPO roadshow as a key reason he left. The claim hints that algorithmic efficiency may sharply lower frontier training costs, though it rests on a personal account without official figures.
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Anthropic can already retrain Mythos with "a fraction of the compute originally needed," says former researcher Coxon
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Jacob Coxon, former OpenAI and Anthropic researcher who brought AI fears into the mainstream, says a slide from Anthropic's IPO roadshow was a major reason he quit.
Coxon says a slide Anthropic released for its IPO roadshow was a key reason he left the company after only about four months. The chart shows how much compute Anthropic needs over time to train its models, and the axis is compressed so that a 10x and a 100x increase barely look different.
Yet the curve still climbs steeply, which Coxon says shows that algorithmic efficiency is improving not just quickly but at an accelerating rate. "It's an insane plot and I'm surprised they made it public," he writes.
Coxon argues that Anthropic's Mythos model, a major capability jump in the spring, was part of this accelerating trend. "Mythos was not a one-time deviation, but a point on a superexponential. They could already retrain Mythos with a fraction of the compute originally needed," he writes.
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This slide from Anthropic's IPO roadshow shows the "Pre-training compute multiplier" for code over time. The y-axis is logarithmic. Former Anthropic researcher Jacob Coxon says the steep climb from July 2024 to October 2026 points to superexponential algorithmic progress. Anthropic notes that the data points are based on internal estimates and are for illustration only. | Image: Anthropic
He says algorithmic improvements are "on the verge of being automated," which is why he wants frontier model development paused next year so competitors can catch up. A pause, he writes, would "commoditize human-level intelligence. It would only harm OpenAI and Anthropic."
Coxon previously trained AI models at OpenAI and, most recently, Anthropic. He drew wide attention in September with public warnings about the risks of advanced AI systems, echoed by other protagonists in the industry. One of their fears is that advanced, self-improving AI could get out of control.