OpenAI’s Deep Research is built for me, and I can’t use it. It’s another amazing demo, until it breaks. But it breaks in really interesting ways.

Every week there’s a better AI model that gives better answers. But a lot of questions don’t have better answers, only ‘right’ answers, and these models can’t do that. So what does ‘better’ mean, how do we manage these things, and should we change what we expect from computers?

Hundreds of millions of people have tried ChatGPT, but most of them haven’t been back. Every big company has done a pilot, but far fewer are in deployment. Some of this is just a matter of time. But LLMs might also be a trap: they look like products and they look magic, but they aren’t. Maybe we have to go through the slow, boring hunt for product-market fit after all.

How do we build mass-market products that change the world around a technology that gets things ‘wrong’? What does wrong mean, and how is that useful?

How do we think about a fundamentally unknown and unknowable risk, when the experts agree only that they have no idea?