Azeem Azhar

We didn't really like it.

It sort of fell a bit flat.

But what OpenAI did and what the researchers did is they found a new approach.

They found that approach of reasoning, that's thinking at inference time, the point at which you or I might put a query into the chatbot.

And those reasoning models, 01, 03, 04,

performed really, really well.

I think it was a real milestone moment in how an emerging technology starts to improve.

Now, if you've used Gemini Pro, which Google released an update to a few days ago, it really feels like there's something going on beyond either the reasoning model approach or the large language model.

Of course, Gemini Pro is using both of those techniques.

But it feels like there's a new technology sitting behind that because Gemini 3 is really well grounded in the complexities of the real world.

And perhaps that's a hint to the kind of world model that Demis Hassabis has alluded to.

So what we often see with these exponential technologies is that from a distance, they look like, you know, one planet.

single smooth curve, but in fact, there are a series of overlapping curves of different technologies and different approaches that ultimately give you that exponential.

Now, it's not to say that the foundation models companies are not pursuing scale.

And what Google proved with Gemini was that scale still works at each stage of building these models.

It's just that it's not the only thing that is going on.

So the overlapping S-curves, which I explain in the book, starting to be present and be felt within these chatbots that many of us are using every day.

But that's not just...

The end of the story, it's visible elsewhere.

Think about the chips.