When liability exposure approaches 0—when no one pays the price for unverified failures—verification budgets collapse...Deployers flood the Runaway Risk Zone with unmonitored agents, capturing the gains of automation while socializing catastrophic risks.
LLMs constantly hallucinate and cannot be trusted. I still have to verify and iterate a lot but now I usually focus on architecture and design instead of code style.
but I can tell you with first hand experience nuclear power plants do not care about this stuff. They're actually just fine with with with thorough testing.
I think the skill nowadays is less about prompt engineering and more about figuring out how do you give Claude a hard task that seems a little bit too hard. And then how do you make it possible for Claude to verify its work along the way? And the verification I think is probably the single most important thing that people do not get right
However, these LLM reasoners that generate informal reasoning in natural language are fundamentally limited by the lack of precise, machine-checkable semantics, making their outputs prone to hallucinations [Huang et al., 2025b] and precluding autonomous verification, a prerequisite for tackling open-ended mathematical research.
One is that the LLMs are getting increasingly good at writing these proofs. And if we don't have to write the proof by hand as humans, it just becomes feasible to do them in situations where previously it would have not been economical.
So certainly one thing that a lot of them seem to be bottlenecked on is now having interesting ideas and in particular having interesting design ideas.
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