Hannah Petrovic

And it seems to me that this part is infrastructure, and they're giving up big time to break into the infrastructure space.

They have famously said like, oh, we want to get to a position where we are building gigawatts of power at a time, which is really ambitious, a very ambitious goal.

Well, it makes sense from my perspective.

If you think that the software part is rapidly depreciating, you might want to get in on this part of building and serving infrastructure at a scale.

It's just a lot here, Matt.

And I just see that there are two primary constraints here.

If you want to scale up the infrastructure, you need enough GPUs and you need enough energy.

It seems to me that energy right now is the thing that everyone talks about.

It's something that we know how to solve.

We know how to build energy.

Like, you don't need that much energy, all things considered, if we need to build

10 gigawatts, 100 gigawatts of extra power, that's only a 10% increase over all installed capacity in the US.

This has happened in the past, in the 2000s, they built enough gas infrastructure to match that level of expansion.

The GPU part though, that's something very unique.

That's something that right now is being chalked hold on production in a few factories in Taiwan, and they have been trying really hard to expand on it with pretty limited success.

So it feels to me that that's probably where the bottleneck to scaling is going to end up being in the long term.

Yeah, well, it's actually very interesting if you let me to build on top of this, because if you look at a fixed level of capabilities, you see this rapid growth where in order to achieve what models could do nine months ago, you already have pretty much an open model that's

I mean, it's going to be kind of there, right?

If you look at the Kimi 2.5 model, like it's arguably at the O3 level, the O3 being the model that OpenAI launched in April last year.

If you look at that, then you see this rapid decrease, this rapid decrease in the amount of resources that you need in order to train and to deploy a model at a fixed level of capabilities.