Speaker unverified: prediction

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…We raised a worry. If you train AI to write clean, readable, maintainable code, do you cap its ceiling? Keep it from the alien move 37 style leaps a machine might find on its own. When you talk to mathematicians about the math solving and the lean theorem generation that some of these models have been creating, They are like too detailed, like no human would actually like write that, and it's, but it's like systematically provable. It is just doesn't contribute to knowledge. It's like, well, okay, like you brute force this thing or something. You know, this is, I don't think, I don't think you brute force the right term, but you know, let's just kind of run with it. And yes, so I do think that at some point you should just actually step away from looking at lines of code and just say, like, does this work as I expected? That's great. except to the point where other agents also need to read your code that is generated by other agents and they need to work together on it. And except to the point where you maybe get into like debuggable, like places where you have like very critical code that you're liable to the SEC for, or you're liable to like whatever the healthcare authorities are. And you're like, I'm sorry, like if I quoted this thing, I don't know what's going on in there. Is that an acceptable excuse? And probably not for the next 30 years, you know, after 30 years, who knows? But like, so, but I think it's very astute point. I do think that for most people, you can get by with like, here's a black box, do whatever you want in the black box, but the inputs of this, the outputs of this, I expect this to match this kind of quality, and I expect this to adhere to some sort of coding standards so that my other agents can sort of maintain this and parallelize this. Another harness idea getting attention is the advisor or router model, a cheaper model that notices when it's stuck and calls up a smarter one. We asked Swix what he makes of it.…

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