Azeem Azhar

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

time compression.

And LLM has, as a result, I think of ChatGPT, that famous paper that now feels so outdated, the stochastic parrot paper, have become that byword for AI.

They've encapsulated the general transformer architecture that underpins large language models, but other things like diffusion models and state space models.

The AI index tracks a bunch of notable AI models.

I'm not quite sure what makes a model notable or not, but it tracks about 61 of them at the end of 2024.

Only 40% were true LLMs.

Others used different architectures.

which are needed across domains like time series, electrical systems, proteins, multiomics, and other medical applications.

So the point is there are more exponentials in this AI wave that are doing their thing than just chat GPT and the large language model.