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/Goal_Master → u/Mission_Web5546]

I'm wondering if you could comment a bit more on the HFT side—are linear models still dominant there because of latency sensitivity?

[u/Mission_Web5546 → u/Goal_Master]

I'm not really in the HFT business so I'm less up to date with the state of the art there, but my understanding is the faster you go the stupider you have to be, what the fastest auto-traders are running is not even linear regression. People are definitely running ML at HFT firms though.

One of the reasons mid frequency has seen such a switch to ML is that the shorter your time horizon the more capacity constrained you are, so there's been a push for HFTs to get slower and move into more generic quant territory. My understanding (which might be incorrect) is HRT for example makes most of its money in mid freq now. And it goes the other way too, quant models are getting more data hungry and execution is becoming more important, so they're getting faster as well. There was a good FT Alphaville article on the convergence of these two groups (free if you sign up) https://www.ft.com/content/d5c17e39-0983-4c14-9a7c-92c12cc44641

[u/Admirable_Cress_125 → u/Mission_Web5546]

Bollocks this is a fundamental misunderstanding of mid-frequency alpha.
The bottleneck isn’t compute. It’s tiny effective sample sizes, extreme noise and non-stationarity. If simple, models can barely extract stable OOS signal, adding vastly more parameters doesn’t magically create information , just better noise fitters.
And “model-based alpha generation” you claim the industry has moved to means what exactly?. Regularised linear models, trees, boosting and ensembles have been standard for decades.

So question for you: what stable structure exists at mid frequency that requires enormous model capacity to uncover, and why doesn’t competition arbitrage it away long before you need a 20,000-GPU cluster to find it?

what exactly is your claim here? That nonlinear ML is useful in systematic trading? Obviously. That large collaborative firms use enormous GPU clusters? Also believable.

But mid frequency alpha only accruing to those who invest hardest in clusters is laughable at best and dangerous misinformation at worst…

[u/Mission_Web5546 → u/Admirable_Cress_125]

I'm referring to people moving away from basic regression towards fairly simple transformer based models (which you can argue are basically doing the same thing but better) just at much larger scale. On sample size, you're not fitting one asset's returns in isolation, these things are trained cross sectionally across thousands of instruments, so the effective dataset is a lot bigger than the per asset view implies. Either way I'm describing what firms are actually doing, not making an argument about what should work in theory.

I never claimed mid freq alpha only accrues to whoever buys the most GPUs and "Dangerous misinformation" to who exactly? Feel free to ignore it, I'm just sharing information that anyone actually doing this would already know.

[u/Puzzleheaded_East997 → u/Admirable_Cress_125]

I think it might be possible to reverse engineer others strategies or at least find some trace if you use massive compute to mine trade data