Odd Lots Podcast: How Hudson River Trading Actually Uses AI

Hosts: Joe Weisenthal & Tracy Alloway (Bloomberg)
Guest: Ian Dunning, Head of AI at Hudson River Trading (HRT)
Date: October 31, 2025

Episode Overview

This episode explores the practical, non-hyped realities of how Hudson River Trading (HRT), a leading quantitative trading firm, actually employs artificial intelligence (AI) and machine learning in their strategies. With guest Ian Dunning (former DeepMind, now Head of AI at HRT), the hosts probe deeply into the evolution from classic quant trading to modern AI-first approaches, the subtleties of market data, tech infrastructure, competitive edges, risk management, and the AI engineering culture on Wall Street. The discussion aims to dispel “AI mystique” and offers rare transparency on one of the most secretive sectors in finance.

Key Discussion Points & Insights

1. HRT Business Model & Role of AI in Modern Trading

[05:22]

• Market Making as a Service:
  • HRT acts as a sophisticated intermediary or “service provider to markets,” akin to Amazon in retail.
  • Provides liquidity across stocks, futures, options, crypto, and bonds—essentially “moving assets through time and space.”
  • “We’re happy because we essentially pick up a penny in front of a steamroller... if we have a really magical device which tells us how everything should be.” — Ian Dunning [06:00]
• How AI Changed the Quant Landscape:
  • The old approach: Handcrafted features based on human intuition, simple math (e.g., linear regression).
  • The AI leap: Since ~2014, HRT moved toward neural networks that “consume all the data” (order books, trade events) at internet scale, moving away from human bias and toward emergent, superhuman predictive capacity.
  • "Now our trading is entirely driven by this magical machine that consumes all the data." — Ian Dunning [08:54]
  • Difference from LLMs: It’s not a language model, but shares the “all data in, emergent behavior out” paradigm.

2. Nature and Usefulness of Data in Trading AI

[09:57]

• Data at Internet Scale:
  • Every market event—not just price history—is the raw material.
  • Modern AI systems run on petabyte-scale, event-level datasets.
• Execution & Pattern Recognition:
  • AI offers both speed (execution) and pattern-finding (signals missed by traditional models).
  • HRT's models are not interpretable, and predict “a little better than random” (accuracy around 50.1%)—but at scale, that small edge is lucrative.
• “The most useful thing is just market data.” — Ian Dunning [17:58]
  • Alternative or “exotic” data (news, Twitter, Reddit) is largely less useful than raw exchange event data—especially intraday.
• On Alternative Data Hype:
  • “People get caught up on the whole, like, do you have a Twitter feed type of thing... But that is a relatively infrequent thing compared to the overall massive markets.” — Ian Dunning [18:28]
  • Most “alternative data” is oversold; markets are mostly driven by the (democratized) core exchange data.

3. AI Predictive Power, Market Efficiency, and Limits

[12:42]

• Short-Term Predictions are Possible:
  • AI models can and do predict short-term price moves above chance.
  • “The predictions are very bad in some sense... the accuracy is like 50.1%... But at scale, that’s huge.” — Ian Dunning [13:16]
• Time Horizon is Everything:
  • Predictive power drops dramatically with longer horizons; models are effective from seconds to intra-day, up to “low single digit days.”
  • “I genuinely don’t know about months-out predictions. That is not a data-rich environment.” — Ian Dunning [16:19]
• AI Survives Regime Breaks?
  • Models performed well even in “pattern breaks” like COVID—because short-term order flow signals persist.
  • “It was more of an engineering crisis... in terms of predictions, they stayed quite good.” — Ian Dunning [15:26]

4. Interpretability and Opacity of Modern AI Models

[22:43]

• Extreme Opacity:
  • Neural networks at HRT and in general are “big blobs of numbers,” learning in ways unlike human intuition.
  • “They might learn things in a way that is nothing at all like how we learn things.” — Ian Dunning [23:47]
  • This lack of interpretability is acceptable, even beneficial, at the speed and scale HRT operates.

5. Infrastructure: Hardware, Latency, and Engineering Challenges

[28:52]

• From Short Wires to Smart Systems:
  • The “latency wars” (shortest wire, co-location) are mostly over—edges from shaving microseconds are almost gone.
  • “Happy to report... all the latency has been arbitraged for the most part.” — Ian Dunning [30:04]
• Computation & Execution:
  • Massive focus on throughput: using GPUs for model training; FPGAs and custom chips for ultra-fast inference at exchanges.
  • “For any given sort of speed of response, we're making the smartest possible decision we can.” — Ian Dunning [31:29]
• Physical Constraints:
  • Data centers for training (on-site & cloud)—electricity is increasingly the main limiting factor, more so than GPUs or talent.
  • “Spinning up new data centers really feels like: is there electricity?” — Ian Dunning [38:10]
  • “That’s like the only way to get electricity promptly... throw gas turbines outside the building and turn them on.” — Ian Dunning [39:48]

6. Where Does Competitive Edge Come From?

[41:04]

• Talent:
  • High premium on engineers who can both research and build, especially at the interface between massive data infrastructure and AI.
• Systems Integration and Scale:
  • The real edge is “putting it all together” at scale: robust engineering to handle petabyte-scale data storage, reliable streaming, model training, and ultra-fast execution.
  • “I can't imagine how you would start a new company like HRT in the year 2025.” — Ian Dunning [42:02]

7. Risk Management & Guardrails – Learning from Knight Capital

[50:21]

• Multi-layer Defenses:
  • AI does not directly send orders to markets; multiple risk, audit, and “sanity check” layers stand between models and real money.
  • “It's not about losing money or making money... it's operational risk. But paranoia is deep.” — Ian Dunning [51:52]
• Regulatory Focus:
  • HRT is more worried about actions that could draw regulatory ire (and banishment from markets) than losing capital on trades:
  • “If you lose that trust of regulators, you lose it for a very long time.” — Ian Dunning [52:56]

8. AI vs. Humans: The Limits of Automation in Trading

[54:31]

• The Human Edge Lingers:
  • Even in high-speed, event-driven trading (e.g., after jobs reports), humans using specialized UIs still play a major role, especially in options and in integrating complex information fast.
  • “It’s really tough, but it’s a learnable skill... In an efficient market sense, this should be AI-able—it is challenging though.” — Ian Dunning [54:54]
• LLMs Not Fast Enough:
  • Latency of mainstream LLMs (like ChatGPT) is far too high for markets.
  • Trusting them to respond to breaking news is also hard given overfitting and knowledge of past events.

9. AI Engineering Culture and Openness

[48:05]

• Balancing Secrecy and Innovation:
  • HRT was once disadvantaged in recruiting compared to Google, where open research publication was possible.
  • Now, big tech labs are equally secretive, and open-source culture is waning in high-stakes AI.
  • “The problem solved itself a little bit for me. And people now recognize that IP should be protected.” — Ian Dunning [48:48]

10. Broader Market Structure and Fairness

[27:53]

• Flow Monsters and Democratization:
  • Data and AI do allow “flow monster” advantages, but the trend toward off-exchange/dark trading resists full democratization.
  • “AI thrives on data—being in the rooms where trading happens remains an advantage.” — Ian Dunning [28:12]
• Market Design Concerns:
  • HRT prefers fair, predictable market access for all, and dislikes designs that introduce undesirable uncertainty or manipulable latency. [36:15]

Notable Quotes & Memorable Moments

• On Prediction vs. Reality:
  • “It seems — if someone’s saying they can predict the price of a stock in an hour, your instinctual reaction is incredulity... but no, these models can predict this... The predictions are very bad in some sense... like 50.1% — but at scale, that’s huge.” — Ian Dunning [12:59]
• On Guardrails:
  • “There is not some neural network directly sending orders to NYSE. It is in some sense providing a plan, and then traditional human, heavily audited, risk-checked layers take the actions.” — Ian Dunning [51:15]
• On Culture Shift:
  • “The golden era of being able to work at a big tech company, be paid for publishing research, is very much over. The papers that do come out of the big AI labs are essentially kind of either very stale or not important.” — Ian Dunning [48:20]
• On Market Making as a Service:
  • "We're a service provider to markets... picking up a penny in front of a steamroller." — Ian Dunning [05:59]
• On Data:
  • “By far the most useful thing is just market data.” — Ian Dunning [17:58]
• On the Practical Edge:
  • “You need to be just optimizing the whole stack... putting it all together.” — Ian Dunning [41:44]
• On Constraints:
  • “Electricity is quite clearly a very binding consideration. Spinning up new GPU-based training data centers, it really feels like: is there electricity?” — Ian Dunning [38:10]

Key Timestamps

• Trading & Market-Making Model Explained: [05:22]
• Old Quant vs. New AI Approaches: [07:27]
• Predictive Power, Signal/Noise: [13:39], [16:43]
• Nature of Useful Data: [17:39]
• Why “Alternative” Data is Overhyped: [17:58]
• Interpretability and Opaqueness: [22:43]
• Hardware, Physical Constraints, End of Latency Race: [28:52]
• Competitive Advantages Now: [41:04]
• Risk Controls Post-Knight Capital: [50:21]
• Human vs. AI in Fast Trading Scenarios: [54:31]
• On Recruiting and Openness: [48:05]

Summary Takeaways

• AI is Real, and It’s Not All Hype: HRT’s experience shows deep learning adds real practical alpha to short-term trading—if not as dramatically as the “AI mystique” would suggest.
• The Edge is in Integration at Scale: Data, compute, and talent matter, but integrating everything—reliably, securely, and at speed—is the core competitive moat.
• Markets Are Not Go or Chess: Slightly-better-than-random predictions are absolutely enough to be profitable, with the caveat that trading is a positive-sum, service-based business at its core.
• Irreducible Risk and Regulatory Scrutiny: Robust, multi-layered paranoia is required to avoid disasters and stay in the good graces of global financial regulators—AI does not remove operational risk.
• Openness vs. Secrecy in AI Is Shifting: As Wall Street and Big Tech converge on similar approaches, both are becoming more guarded and proprietary about breakthroughs.
• Electricity, Not Wires or Even GPUs, Is the New Bottleneck: The AI hardware arms race in trading is now bumping up against power grid constraints.

For newcomers or non-listeners, this episode is a refreshingly candid look behind the “black box” of AI in high-frequency trading, replacing hype with grounded, nuanced technical and strategic insight—relevant equally for finance professionals, AI researchers, and market skeptics.