The Limits of AI in Trading
Market structure expert Kevin McPartland on AI's dot-com moment
Kevin McPartland has spent more than 20 years studying how technology changes market structure.
He expects AI to have an internet-scale impact on markets:
๐ ๐ข๐ฎ ๐ข ๐ฃ๐ฆ๐ญ๐ช๐ฆ๐ท๐ฆ๐ณ ๐ต๐ฉ๐ข๐ต ๐ต๐ฉ๐ช๐ด ๐ช๐ด ๐ช๐ฏ ๐ด๐ฐ๐ฎ๐ฆ ๐ธ๐ข๐บ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ต๐ฉ๐ฆ ๐ง๐ช๐ณ๐ด๐ต ๐ฅ๐ฐ๐ต-๐ค๐ฐ๐ฎ ๐ฃ๐ฐ๐ฐ๐ฎ. ๐๐ถ๐ณ๐ฆ, ๐ช๐ต ๐ธ๐ช๐ญ๐ญ ๐ค๐ฉ๐ข๐ฏ๐จ๐ฆ ๐ซ๐ฐ๐ฃ๐ด ๐ข๐ฏ๐ฅ ๐ต๐ฉ๐ฆ๐ณ๐ฆ ๐ธ๐ช๐ญ๐ญ ๐ฃ๐ฆ ๐ซ๐ฐ๐ฃ ๐ญ๐ฐ๐ด๐ด๐ฆ๐ด, ๐ธ๐ฉ๐ช๐ค๐ฉ ๐ฏ๐ฐ๐ฃ๐ฐ๐ฅ๐บ ๐ฆ๐ท๐ฆ๐ณ ๐ธ๐ข๐ฏ๐ต๐ด. ๐๐ถ๐ต ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ญ๐ฐ๐ฏ๐จ ๐ณ๐ถ๐ฏ, ๐ต๐ฉ๐ช๐ด ๐ช๐ด ๐ข ๐ต๐ฐ๐ฐ๐ญ ๐ข๐ฏ๐ฅ ๐ข๐ฏ ๐ข๐ช๐ฅ ๐ต๐ฐ ๐ฉ๐ฆ๐ญ๐ฑ ๐ฑ๐ฆ๐ฐ๐ฑ๐ญ๐ฆ ๐ฅ๐ฐ ๐ต๐ฉ๐ฆ๐ช๐ณ ๐ซ๐ฐ๐ฃ๐ด ๐ฃ๐ฆ๐ต๐ต๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ต๐ฐ ๐ค๐ณ๐ฆ๐ข๐ต๐ฆ ๐ฏ๐ฆ๐ธ ๐ซ๐ฐ๐ฃ๐ด ๐ธ๐ฆ ๐ฅ๐ฐ๐ฏโ๐ต ๐ฌ๐ฏ๐ฐ๐ธ ๐ข๐ฃ๐ฐ๐ถ๐ต ๐บ๐ฆ๐ต. ๐ ๐ณ๐ฆ๐ข๐ญ๐ญ๐บ ๐ต๐ณ๐ถ๐ญ๐บ ๐ง๐ฆ๐ฆ๐ญ ๐ญ๐ช๐ฌ๐ฆ ๐ต๐ฉ๐ข๐ตโ๐ด ๐ธ๐ฉ๐ฆ๐ณ๐ฆ ๐ต๐ฉ๐ช๐ด ๐ช๐ด ๐จ๐ฐ๐ช๐ฏ๐จ.
He leads market structure and technology research at Crisil Coalition Greenwich, where he tracks how banks, asset managers, and trading firms deploy new systems. He previously worked at BlackRock and TABB Group.
But AI adoption on trading desks has yet to scale.
A recent report from the firm shows the most common use cases among bond traders are still data analysis and document review, not execution or decision-making.
Trading desks face clear regulatory and reputational risk, where firms need to explain and defend decisions to clients and regulators. As McPartland puts it, you canโt tell regulators: โWell, the AI did it.โ
In this interview, McPartland explains where AI is being deployed today, whatโs holding back trading applications, and why coding and developer productivity may be the most important near-term use case.
This interview has been edited for clarity and length.
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Why AI Adoption in Trading Is Moving Slowly
Matt: I was checking your reports on AI, and you guys are focusing on how itโs in the back office. That seems like itโs the story across the street. When do you think it goes beyond that to more trading applications?
Kevin: I think weโre starting to get there. I was at FIA Boca earlier this week and there was definitely a lot of talk about AI. I think the industry is excited and interested but really trying to be cautious. Thereโs a reputational risk issue, a regulatory issue. You donโt want to do the wrong thing for your clients from the sell-side perspective. If thereโs an issue and regulators come to you and ask what happened, you canโt just say, โWell, the AI did it, Iโm not sure.โ Thatโs not a good answer. So I think thatโs leaving people cautious.
We actually just got back a study of bond traders and we asked them where they saw the opportunity in AI. Not surprisingly, data analysis was number one, document review number two. So it still really is about pouring through data and unstructured data to help digest it, find insights, find patterns. I think thatโs still the biggest use case now.
My two cents โ I think where really a lot of the impact will be in the short, medium, and long term is on the coding side. Everything from making the most sophisticated quant developers even more efficient than they already are, to letting business users prototype what they want in a way they never could before, and then handing it off to IT. I just think the possibilities are absolutely huge in that regard.
ICYMI
AI Regulation and Governance Are Still Catching Up
Matt: Whatโs your sense of what needs to happen on the regulatory and standardization side? Itโs such a new technology โ there are no best practices yet.
Kevin: It does need to happen, although itโs hard to put a finger on it, because almost by definition itโs not something that is structured. You could ask different LLMs or even the same LLM the same question and it might give you a different answer. So by definition, itโs not structured. But yes, maybe itโs just continuing to learn what the potential risks and pitfalls are. How do you look out for them? How do you catch them? How do you prevent them?
Of course the models themselves will continue to get better, which should limit some of those things, but it could create new ones as well. Just saying โno, itโs not safe, we canโt use itโ โ thatโs not the answer either. This is here to stay. Itโs going to have a big impact on the market. All that work is required, and I think weโre already starting to see more working groups and roundtables and people working through it, talking to their peers, trying to understand what are the best practices. What are you doing? What are you doing? So we can all sort of try to figure out the most effective way forward, because there is just a lot of opportunity.
Coding May Be the Most Underestimated AI Use Case
Matt: What do you think is underappreciated or not talked about enough in this space?
Kevin: The coding agents are talked about broadly โ Claude Code and OpenClaw, thatโs all over the news. But for capital markets and trading specifically, I donโt feel like itโs talked about very much. What does that look like? How is it used on a trading desk? Is it used on a trading desk yet? Are there rules there?
Matt: I spoke to Man Group. Theyโve developed something called AlphaGPT. I think the hedge funds have a little more flexibility โ theyโre regulated, but theyโre not tens of thousands of people usually. Some of the quants are doing this, but I think the technology has moved faster than the humans in terms of how they can actually put this out in a responsible way.
Kevin: Yeah. To me it all feels inevitable. Itโs just figuring out how to test it and how to do it safely.
AIโs Impact on Finance May Look Like the Dot-Com Era
Matt: Is that opinion shared broadly? A year or two ago, AI in finance was not really considered as impactful as some other areas. Is the industry stance now that this is going to have a big impact?
Kevin: This industry doesnโt ever all agree on anything. I am a believer that this is in some ways like the first dot-com boom. Sure, it will change jobs and there will be job losses, which nobody ever wants. But in the long run, this is a tool and an aid to help people do their jobs better and to create new jobs we donโt know about yet. I really truly feel like thatโs where this is going. Not about large-scale job loss, but people in the seats being able to do things they never knew how to, never had time for, or just never could before.
Matt: High frequency trading has gotten so fast that itโs approaching the speed of light, so you canโt really top that. You have to find other ways to make money.
Kevin: Thatโs right. Itโs not just about speed. In equities, maybe it is, but thatโs why thereโs only a few dominant firms left doing it at scale. Somebody said to us a year or two ago, itโs not about being faster anymore โ itโs about being smarter.