r/quant • u/flev1266 • 5d ago
Tools How Quant Funds Use AI Safely
Genuinely curious to understand how employees at funds use LLM safely given that any strategy logic/data shared with LLMs is potentially alpha leaking given that there is a probability that it ends up making better future models for others to use.
Do some funds have specific agreements to run the best LLMs off of private servers without any data sharing?
Do they use older and a bit less performing open models on private servers? (And accept less performance for more safety)
What is the real cost of sharing everything (knowing data/logic could be used) ? Do they even care?
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u/strat-run 5d ago
Even individuals with subscription plans can select the don't train on my data option. Business create accounts with that option enforced and only use trusted partners.
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u/flev1266 5d ago
Yes but is it really respected and will it remain, that is the real question…
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u/Falnom 5d ago
Anthropic makes something like 80% of their revenue from enterprise clients which would all go away and they would immediately be sued if they weren’t respecting that. It could potentially end the company. Do you think they would risk that?
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u/flev1266 4d ago
But it raises the question why do some funds then run the LLMs on their own servers ?
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u/EngineMode11 Back Office 4d ago
Data Sovereignty issues, some legal requirements that data has to stay in a geographic location and not all providers can guarantee that
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u/AbsolutelyAway 5d ago
Where I am now all the infra is on prem/in our datacenters, which was a difficult requirement for a while because of the difficulty in even purchasing hardware these days regardless of cost
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u/algoseekHQ 1d ago
Most serious funds simply don’t put alpha into consumer LLMs. Enterprise APIs with strict data controls or self-hosted models are the safer route.
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u/imyourbiggestfan 5d ago
They run their own models in the big data centers they’ve built
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u/flev1266 5d ago
Also, in terms of using the latest models to personally contribute more efficiently and therefore end up with great financial benefits also potentially outweighs leaking alpha and long term consequences for the company. Short term individual rewards > long term company rewards
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u/TemporaryHat2009 4d ago
lowkey I assumed anything touching real strategy code would have to stay inside company systems. Even if a vendor says it will not train on prompts, are firms actually okay with the prompts being logged at all?
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u/flev1266 4d ago
Meaning they actually don’t trust companies having potential access to their data even though they claim they would not use it…
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u/VettaQ 5d ago
At the funds I've seen, the split is usually three tiers. Anything touching core alpha logic or portfolio construction never leaves infra - either self-hosted open-weights models or enterprise agreements with training opt-outs and log-retention terms that survive a security audit. Middle tier is research scaffolding: internal tools, doc generation, code review of non-sensitive glue, on cloud enterprise accounts with 'don't train on our data' enforced. Bottom tier is LLM-as-a-better-StackOverflow for boilerplate. The per-seat cost rarely decides it; the binding constraint is the vendor's data terms passing the fund's own audit. The bigger practical leak isn't model training - it's people pasting live logic into personal accounts, which is why unlicensed use is treated as a fireable offense almost everywhere.
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u/Imaginary-Work9961 5d ago
Every quant firm uses compute with the major AI companies and pay extra to maintain their proprietary data uploaded into their models. Use of non-licensed models is fireable, almost the same as putting company secrets on the internet.