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Inside Hudson River Trading's Blistering Token Burn
Brief
Ian Dunning, head of AI at Hudson River Trading, spoke live at Odd Lots’ New York show (City Winery, ~350 people) about how fast‑moving model advances and constrained compute infrastructure are reshaping quant trading. Dunning stressed that HRT has the expertise and growing compute footprint to train large models, but cautioned that matching frontier labs is extremely capital‑intensive. He traced recent leaps — naming Anthropic’s Opus releases as examples — and described internal experiments that treat models as research accelerants (code, idea generation, monitoring) rather than magic boxes. He also noted an emergent behavior window where models clustered meme/crypto/momentum names together in their representation, giving practitioners interpretable slices but leaving other slices inscrutable.
A central theme was scarcity: silicon alone isn’t the full bottleneck. Dunning argued the hard constraint is housing and powering GPU fleets at scale — long lease terms, megawatts of power, networking and idiosyncratic site setups. He used the hypothetical of sourcing thousands of Blackwell GPUs and warned that next‑generation 'Rubin' GPUs (2027) will be hard to secure early. HRT negotiates multi‑year capacity deals with hyperscalers and data centers, weighs counterparty and credit risk, and even keeps a Norway site that is insufficient for demand. On deployment, HRT runs rigorous automated risk checks to safely apply AI to high‑frequency strategies; Dunning voiced skepticism about how to replicate that safety layer for long‑term discretionary trades that carry concentrated, multi‑month exposure. Staffing and productivity were also discussed: Dunning sees AI changing required skill sets (prompting, system composition) and reported token spend of roughly $100–$200/day per engineer with some bursty outliers, raising concerns about a compounding 'haves vs. have‑nots' advantage. He was open to market innovations (compute futures or exchange liquidity) but skeptical about standardizing 'compute' as a tradable, physically deliverable commodity. Overall, the conversation balanced excitement about model-enabled research velocity with pragmatic warnings about infrastructure limits, risk management, and the uneven competitive effects of access to compute and tokens.
Why it matters
Ian Dunning (head of AI, Hudson River Trading) said the firm could train large models — they have the people and compute — but reaching frontier-level labs (e.g., 'DeepSeek') is capital‑intensive and daunting.
Key details
- At a live Odd Lots show in New York (City Winery) with ~350 attendees, Dunning described rapid model progress: Anthropic’s Opus 4.0 felt like a false start, while Opus 4.5 materially closed the gap in months.
- Dunning reported internal token (API/LLM) spend roughly $100–$200 per day per team member (some bursty users in the $1,000/day range) and said one contact claimed a team became ~50% more productive with AI.
- Compute bottlenecks are predominantly data‑center space, power and long‑term leases — not just GPU silicon. Dunning used a hypothetical request for '6,000 Blackwell GPUs' to illustrate that packaging chips with site/power is the scarce problem.
- HRT uses strict automated risk checks to safely deploy AI in high‑frequency/short‑term trading, but Dunning warned the same approach is unclear for long‑horizon discretionary trades where large idiosyncratic exposures persist.
- On supply strategies: HRT sources capacity from hyperscalers/neoclouds, explores custom inference hardware (Broadcom/others) and recognizes next‑gen chips (codenamed 'Rubin' for 2027) will be in high demand and scarce initially.