10 hours ago · Culture · hide · 0 comments

I’ve heard it a hundred times before, and I bet you have too: “the way AI systems are trained means that they can only provide the average answer to any question.” And yet, anyone who’s recently used an LLM would tell you that this obviously isn’t true. Over the last few years I’ve shared many reasons why that assumption is incorrect. I’ve tried to explain how post-training works, why agents are more than just LLMs, and how these systems develop emergent capabilities, but I found a far better explanation in chapter one of Ethan Mollick’s excellent book Co-Existence. Instead of writing a blog post that summarizes his argument, I’d rather share his excerpt (emphasis mine) — and suggest that you buy Ethan’s book! A common misconception about AIs is that they give the “average answer” to a question they are asked. This intuition makes sense if you understand that AIs are trained on all the written data AI companies could get their hands on. But at the same time, this is obviously not…

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