Chamath Palihapitiya

or probably Google in the lead, actually, if you look at all the Vertex use, Google claims that 75% of GCP customers are active users of Vertex.

So there's probably a pretty sizable market share that Google's captured on the enterprise side as well.

This is also probably why Google stock has absolutely ripped over the last couple of months is they're literally in first place or fighting for first place in enterprise and consumer.

But I still think that there's a lot of opportunity to Chamath's point about the compute and energy capacity constraints in improving how we actually scale and deploy models in both the enterprise and the consumer setting.

And it is such early days.

And I just want to highlight this paper that came out.

from MIT, from these two scientists.

And these guys published a paper on pruning techniques and neural networks.

This paper showed that you could actually reduce the size of these networks by 90% and get the same accuracy out by pruning very large models down to smaller models.

And then you can make a selection on which model to run for inference.

And by doing this, you can actually reduce inference costs by 10x, you can get 10x the output

per energy unit that goes into the data center with no loss of accuracy.

And so it's a really interesting call it algorithmic technique that can be applied to the existing large models to actually make them much lower energy use.

So if you think about it, you're firing up a very large model to answer a very simple question, you can actually prune away that model.

Now,

This is probably going to be the case in AI applications as it is in traditional Google search.

There's a long tail of searches, but there's a few searches that account for a large percentage of search volume.

It's like, what is the weather?

What are the movies, times?

What's the stock price?