Azeem Azhar

These models will be highly, highly tuned.

And of course, we shouldn't forget that there's an enormous amount of capex that is required and will continue to be required over the coming years, which is going to potentially be a constraint, right?

If the debt providers and credit providers are not willing to back this build-out,

which they are so far, and I think will do for another 18 months or so, that could end up being a constraint.

And the next constraint will be energy, whether the American energy system in particular will be able to cope with ongoing demands.

I mean, it already seems like most

Demand growth for the next few years will get soaked up by AI data centers.

I mean, on this front, of course, OpenAI has built relationships in the Middle East and up in Norway and elsewhere so that it can start to serve customers in those geographies.

A quick note.

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But perhaps one of the big unknowns is what might happen with alternative architectures.

Large language models pushing for scale are doing kind of amazing and interesting things, but they are pretty lumbering.

And there are other approaches.

You know, there are alternative architectures that people are using today.

For example, Liquid AI, which is an MIT spin-out, which has

Models that are about 10 times as efficient as transformers.

You've got Yann LeCun at Meta talking about his framework, that what is needed to move beyond these autoregressive, exponentially decaying large language models are frameworks that have a much better, stronger sense of the world.

Demis Hassabis is a Nobel laureate, runs DeepMind, built some of the greatest AI technologies of the moment.

What we'll say, look,

We don't know whether there aren't going to be really big breakthroughs required to get to the next level of AI capability.