I agree.

To clarify, I don't necessarily agree with the post or their approach. I just thought folks were misreading it. I also think it adds something useful to the conversation.

This is from the first of the caveats that they list:

> Scoped context: Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior"). A real autonomous discovery pipeline starts from a full codebase with no hints. The models' performance here is an upper bound on what they'd achieve in a fully autonomous scan. That said, a well-designed scaffold naturally produces this kind of scoped context through its targeting and iterative prompting stages, which is exactly what both AISLE's and Anthropic's systems do.

That's why their point is what the subheadline says, that the moat is the system, not the model.

Everybody so far here seems to be misunderstanding the point they are making.