One of the World's Largest Hedge Funds on Its 86x Growth in Token Spending
Episode
52 min
Read time
2 min
Topics
Personal Finance, Relationships, Investing
AI-Generated Summary
Key Takeaways
- ✓Data quality over model quality: For quant research tasks, structured and tagged proprietary data outperforms access to frontier models. Man Group adds plain-English descriptors to datasets like credit card data, builds a unified semantic layer connecting disparate datasets, and invests in institutional knowledge capture — making data architecture the primary driver of alpha, not model selection.
- ✓Agentic signal generation: Man Group's AI system autonomously generates trading hypotheses by reading academic papers and labeled datasets, writes investment rationale in plain English, builds backtesting code, and runs validation — all before human review. Fifteen to twenty AI-ideated models have already passed human investment committee review and are actively trading client assets.
- ✓Token consumption management via education, not routing: Rather than building automated query-routing classifiers to assign tasks to cheaper models, Man Group federates token budgets to individual business units and runs internal education programs. This approach caught basic inefficiencies — like agents processing entire Git command outputs as tokens — and produced employee-driven optimization solutions organically.
- ✓Agentic task horizon as the key capability benchmark: The METR benchmark, which measures how long an AI agent can autonomously complete tasks humans would perform, doubles every seven months. Agents can now handle tasks requiring sixteen human hours, shifting the relevant skill from prompt-level interaction to end-to-end workflow design and multi-team orchestration across full application builds.
- ✓Alpha durability through ecosystem depth, not single repositories: AI lowers barriers to dataset analysis, but trading alpha at Man Group derives from the combination of broker relationships, proprietary market data, decades of backtesting infrastructure, and market access — not any single code repository. Some dataset features will become risk factors as they commoditize, but the interconnected system sustains differentiated returns.
What It Covers
Man Group CTO Gary Collier and Head of Data & AI Tushara Fernando explain how one of the world's largest hedge funds deploys AI across discretionary and systematic investing, covering agentic workflows, data architecture, token budgeting, and the 86x growth in token consumption since January 2025.
Key Questions Answered
- •Data quality over model quality: For quant research tasks, structured and tagged proprietary data outperforms access to frontier models. Man Group adds plain-English descriptors to datasets like credit card data, builds a unified semantic layer connecting disparate datasets, and invests in institutional knowledge capture — making data architecture the primary driver of alpha, not model selection.
- •Agentic signal generation: Man Group's AI system autonomously generates trading hypotheses by reading academic papers and labeled datasets, writes investment rationale in plain English, builds backtesting code, and runs validation — all before human review. Fifteen to twenty AI-ideated models have already passed human investment committee review and are actively trading client assets.
- •Token consumption management via education, not routing: Rather than building automated query-routing classifiers to assign tasks to cheaper models, Man Group federates token budgets to individual business units and runs internal education programs. This approach caught basic inefficiencies — like agents processing entire Git command outputs as tokens — and produced employee-driven optimization solutions organically.
- •Agentic task horizon as the key capability benchmark: The METR benchmark, which measures how long an AI agent can autonomously complete tasks humans would perform, doubles every seven months. Agents can now handle tasks requiring sixteen human hours, shifting the relevant skill from prompt-level interaction to end-to-end workflow design and multi-team orchestration across full application builds.
- •Alpha durability through ecosystem depth, not single repositories: AI lowers barriers to dataset analysis, but trading alpha at Man Group derives from the combination of broker relationships, proprietary market data, decades of backtesting infrastructure, and market access — not any single code repository. Some dataset features will become risk factors as they commoditize, but the interconnected system sustains differentiated returns.
Notable Moment
Man Group's token consumption grew 86 times between January and July 2025 — a figure the firm did not anticipate. Notably, this growth occurred before implementing any automated cost-control routing, meaning the firm is still in an open experimentation phase despite the scale of consumption already reached.
Episode Transcript
Now a message from Meta. Meta is launching America's Workforce Academy. The program offers paid training, a job, and a path to America's future because the future is for everyone. Learn more at meta.com/america's workforce academy. When you're running a business, the best days are the ones where priorities stay on track. For midsize and large companies, that isn't always easy. Risk can touch multiple parts of an organization at the same time, often in ways that aren't immediately obvious. It might involve property, liability, or cyber. It could stem from regulatory requirements or challenges tied to a specific industry or the scale of an operation. At that level, managing risk becomes an ongoing discipline, not a one time decision. At The Hartford, the focus is on helping businesses manage risk before it turns into something more disruptive. That means working with companies to identify where they're exposed, decide what matters most, and put practical standards in place so risk is managed as part of day to day operations. And when losses do happen, The Hartford Compare that risk control work with insurance coverage grounded in underwriting, risk engineering, and claims experience developed over time. Learn more at the hartford.com/riskmitigation. The thing about AI for business, it may not automatically fit the way your business works. At IBM, we've seen this firsthand. But by embedding AI across HR, IT, and procurement processes, we've reduced cost by millions, slashed repetitive tasks, and freed thousands of hours for strategic work. Now we're helping companies get smarter by putting AI where it actually pays off, deep in the work that moves the business. Let's create smarter business, IBM. Bloomberg Audio Studios. Podcasts, radio, news. Hello, and welcome to another episode of the Odd Lots podcast. I'm Joe Wiesenthal. And I'm Tracy Alloway. Tracy, I'm very interested in AI. In a no. No. I Oh, really? Really, Joe? I am. That's a surprise. I'm very interested in the investment context Mhmm. Specifically. I mean, the actual implementation of, like, how do investors use it? Because I think, obviously, just sort of substantively, incredibly important question for reasons that need no explaining. But I also think it, like, raises very interesting sort of, like, puzzles about what the technology is used for. And I remember, like, when Chad GPT came out and people were, like, asking it, like, what stock should I buy? But we did that prediction market episode recently, and it's like, one thing you definitely can't get much value out of is saying, like, which contract should I buy, or what's inflation going to be so I can trade this contract. So, like but that doesn't mean that there aren't interesting ways. It just seems like in the you know, the sort of the most crude version of, quote, using AI for investing is obviously a dead end DOA. Here's the question I have. You know, we've been through technological revolutions in investing before. Notably, you know, we had robo advisors. That's …
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“The METR benchmark, which measures how long an AI agent can autonomously complete tasks humans would perform, doubles every seven months.”
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