Jean-Stanislas Denain

If what Anthropic says is true, then the Claude Mythos family is a massive leap forward in AI’s cyber capabilities. When they Mythos Preview, they considered it so dangerous that they had to launch a $100+ million initiative to “secure the world’s most critical software”. Then on Tuesday, they one-upped themselves by releasing Claude Mythos 5, which improves modestly on cyber benchmarks.1 But skeptics have argued that Anthropic was exaggerating — or at least, people should chill out about Mythos.

In the popular picture of how AI companies use compute, there are two big buckets: training and inference. But in reality, the R&D side is more complex. The final training run — the one that produces the model with a name — is only the last step in a long, expensive process of exploration.

What are the top priorities and challenges? Interviewees consistently cited robustness against reward hacking as the most important quality criterion. As one neolab researcher put it: “Reward hacking is a big issue. The model might cheat by searching up a solution, or if you’re not careful with how you script the repo, by checking out future commits9. It needs to be robust.