Grace Hsiao

So some of these big tech are actually even buying out the contracts that data centers have with some of these labs.

And, you know, they are taking over the compute.

So these labs essentially are now optimizing for data.

the highest quality inference demand, if that makes sense.

They don't actually have enough even supply.

They don't have enough compute power to even like meet the demand that's coming through.

So that's on like how they're servicing clients, how they're changing, I guess, or how they're optimizing their training is that a lot of them are really focused on the post-training.

And this goes back to, so you know how OpenAI has like three buckets where they chuck money at?

There's like the R&D, there's pre-training, there's post-training.

R&D, a lot of times, a lot of money is spent, but say like one out of 10 things stick.

But you need a lot of compute and resource and people to be figuring out where to go.

For a lot of these labs in China, they frankly don't have that luxury.

So they've even given me a metaphor and said, it's kind of like knowing what the answer to the homework is and working backwards.

So they will wait till the frontier labs to come out with where the right direction is for the next frontier model.

And they will work backwards and actually focus all their resources on post-training.

So with post-training, they will optimize a lot of times the data they collect.

For example, if a data provider like Mercore provides a very, very niche set of data set for like an open AI or whatnot, maybe they would charge them 10, $20 million.

The Chinese lab will wait out that exclusivity contract

three to six months time, let's say, and then pay a fraction, if not like a 10th of that price, the same data set.

And that kind of plays into that like six to nine month lag that we hear about as well.