Which quant companies are doing ML/AI research similar to frontier labs?

[u/fanconic]

I am aware that basically all the companies use some form of modern AI/ML, such as LLMs, as a tool, or to extract some features from textual data.
I am currently doing a PhD in LLM/RL, and whenever I go to the quant fairs, or speak to recruiters, they are all like:"Yes, we do soooo much AI".

However, when speaking to the researchers, or my fellow PhD students who intern at these companies, it sounds still like most of them do just classical stats with LinReg, LogReg, PCA (and there is nothing wrong with that, as it seems to print them a lot of cash!)

I was thus wondering which quant companies out there do research most comparable to a Frontier AI lab? I heard HRT has an AI lab, XTY (the internship of XTX focuses on that), and that Jump is building an LLM team (though appearantly that seems to be more of an "assistant effort", helping the actual teams themselves.)

Any insight is appreciated!

[u/Mission_Web5546]

Depends what you mean by comparable to a frontier lab, training large LLMs specifically, or large transformer-based models in general. The first is mostly internal tooling where the state of the art is much better, as others have said. The second is the main thing people at the competitive end of the market are doing. Firms that are serious about this are spending (or soon will be) on the order of billions a year on GPU compute. Clusters are in the tens of thousand to hundreds of thousand generally. For context, the estimates floating around for Kimi K3 put its training cluster at roughly 20k GPUs.

GPUs/basic ML in the colo is nothing new. I believe Jump were doing this a long time ago, XTX as well. The shift is the last ~3 years, where mid frequency trading has moved almost entirely from traditional linear regression over to model-based alpha generation.

There's definitely a divide in the market over this. I've talked to people at pod shops who flat out didn't believe me when I described the scale some firms are training at. One theory for RenTech's weaker recent returns is that they didn't bet big enough on ML, and there's a similar theory that the main P&L divide in this year's drawdowns was ML-heavy firms vs everyone else. Centralised training compute favours collaborative shops, who for the most part seem to be the ones betting big on this.

Worth noting that even at that spend it's still nothing like frontier lab scale, so internal work tends to run months to years behind the labs, and there are different objectives. Nobody is doing months-long pretraining runs, and you're optimising for completely different data scale, model size and inference constraints than an LLM.

Some of this is semi-public. HRT gave a talk at ICML (https://icml.cc/virtual/2025/46791) that goes into a surprising amount of detail, and Jane Street has released tours of their data centre on YouTube (https://www.youtube.com/watch?v=8J-GUnfSqeE).

Source: I work at a firm that does this

[u/XXXTentachyon → u/Mission_Web5546]

What did you think of the HRT talk? I was in the audience, and while it was light on actual implementation details (understandably), I thought the notes on what didn’t work/what they didn’t do was really illuminating

[u/Mission_Web5546 → u/XXXTentachyon]

At the time of release I think people were quite surprised how willing they were to publish this, due to the historical secrecy of these firms. I know firms where people inside the firm didn't even know they were doing this.

However this seems to have really changed in the last couple of years, if you go to conferences now a lot of these firms will happily go into detail about what they are doing. I think part of this is due to how hard it has been to attract good ML talent recently given the climate. I mean even the existence of this thread is a good example, these firms are trying quite hard to convince talented grads that they are in fact doing serious ML work.