Azeem Azhar

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.

So we all know about NVIDIA's GPUs that are powering these systems.

Across roughly the past decade, NVIDIA's GPUs have delivered about double the usable AI throughput that Moore's law would predict.

So Moore's law essentially said, look, every couple of years, there'll be a doubling of

of performance capability.

And that has been the clock speed of the technology industry for more than 50 years.

It's in a way a social agreement between the ecosystem of semiconductor companies that we needed to deliver on that.

Well, the GPUs have delivered far faster over quite a long period of time, over a decade.

But it doesn't stop there because these chips are really complex.

They require really cutting edge technologies from, you know, the fabs, the photolithography, chemical washing, the bonding, the packaging.

And yet NVIDIA has pushed up its own clock speed, moving from a two year cycle for new chips to a one year cycle.

Another example of