One of the World's Largest Hedge Funds on Its 86x Growth in Token Spending

Summary

Odd Lots — Episode Summary

Podcast: Odd Lots (Bloomberg)
Episode: One of the World's Largest Hedge Funds on Its 86x Growth in Token Spending
Date: July 9, 2026
Hosts: Joe Weisenthal & Tracy Alloway
Guests:

• Gary Collier, CTO at Man Group
• Tushara Fernando, Head of Data and AI at Man Group

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Episode Overview

In this episode, Joe and Tracy interview Gary Collier and Tushara Fernando from Man Group—the world’s largest publicly traded hedge fund—on how the firm’s use of AI is transforming traditional and systematic investing. The conversation dives deep into how generative AI and large language models (LLMs) have led to a remarkable 86-fold increase in token consumption since January, what “token spend” even means for an asset manager, how AI is practically changing research and execution workflows, and the major bottlenecks and opportunities AI brings to modern finance and labor dynamics.

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Key Discussion Points & Insights

A New Era: Generative AI’s Role in Investing

• Historical Context:
  • Joe and Tracy frame the conversation within waves of past investment tech (robo-advisors, quants, high-frequency trading). They ask whether current AI is an incremental improvement or something fundamentally different.
    • “Is it like a minor evolution or a moderate evolution or something big?” (Tracy, 02:47)
  • Gary describes Man Group as a “full spectrum active asset manager,” serving both systematic and discretionary investing areas and requiring highly tailored tech stacks for each (06:01).
• The Shift from Machine Learning to AI:
  • Tushara: The title change from ‘Head of Data and Machine Learning’ to ‘Head of Data and AI’ reflects a transition from using traditional predictive models to unlocking “enablement”—where employees generate and synthesize new ideas and workflows with AI (07:37).

Practical Implementation: What’s Actually Changed?

• AI is Ubiquitous:
  • AI is augmenting “pretty much every role” at the firm—from traders and PMs to operations and finance (Gary, 09:01).
  • “Everywhere you look across the discretionary floor, everybody has got an AI-focused window on their screen.” (Gary, 09:13)
• A Concrete Example
  • Synthesizing Alternative Data:
    • AI enables PMs to extract relevant alpha from previously under-used sources, e.g., podcasts.
    • Tushara details how an AI agent transcribed an interview with a hyperscaler’s head of engineering, flagging a bottleneck in data center capacity—a key insight that would otherwise be missed (10:05).
• Efficiency & Agentic Workflows:
  • The research process is now augmented and more asynchronous—AIs distill out what matters so PMs can focus on decisions rather than data wrangling (10:05–12:41).

Agent-Oriented Investing: Automating the Quant Stack

• Agentic Workflow in Practice:
  • Man Group has been building systems where “agents” handle every stage of quant research—from ideation, academic review, code generation, data acquisition, backtesting, to result evaluation (13:45).
    • “15–20 models… started off as models that were ideated by AI, went all the way through the signal construction, the validation process, were reviewed and validated by a human investment committee and deemed fit and proper to trade our clients’ assets with.” (Gary, 15:16)
• Quotes:
  • “This is very real. It’s not make-believe at this point.” (Gary, 15:15)

Bottlenecks, Oversight, and Explainability

• Biggest Bottleneck:
  • Not compute or data per se, but organizational change—embedding robust, safe, explainable AI systems into a regulated environment (18:03).
    • “There’s no shortage of good ideas... It’s making sure we can move quickly but safely.” (Gary, 18:03)
• Filtering & Knowledge Layers:
  • Tagging and structuring unstructured/alternative/internal data is crucial—Man Group invests heavily in unifying metadata and creating a “knowledge layer” that makes AI outputs meaningful and interconnected (20:14–24:11).
    • “We want to have descriptors to tell it [AI] the nuances...” (Tushara, 22:21)
• Frontier Models vs. Quality Data:
  • “Trying to just use a frontier model will get you nowhere.” (Tushara, 24:11)
  • Best results come from well-labeled, interconnected proprietary data, with top models reserved for particular tasks like coding.

Economics of Token Spend: Managing Explosive Growth

• Token Usage Economics:
  • Rapid 86x increase in token usage since January, clearly outpacing expectations and spreading well beyond tech teams (29:09).
    • “We have seen people in finance, people in operations, people in the people team using agentic coding workflows. And as a technologist, that’s just super exciting.” (Tushara, 29:23)
• Provisioning & Efficiency:
  • Departments are given budgets and autonomy over their AI spend; education and transparency (rather than rigid automated routing) guide efficient model use, with users learning to avoid wasteful prompts (25:30–28:34).

Impact on Labor and Skills

• Changing Talent Requirements:
  • Every new hire—regardless of department—must “up the bar with respect to AI” (Gary, 38:12).
    • “It’s very hard to fulfill all of those criteria … and say, well, you know, AI, I don’t know much about it...” (Gary, 38:50)
  • Looking for people able to automate end-to-end, think strategically, and orchestrate workflows—not just those excited to stay “in the weeds” (Tushara, 39:25).
• Democratization vs. Superstar Effect:
  • AI is both leveling the playing field (enabling juniors) and making “superstars” of those who best orchestrate agents.
  • “We’re seeing both, genuinely both … given the size of the firm though, we’re seeing more of the latter (democratization).” (Gary, 40:57)
  • Planning and connecting multi-team workflows is becoming the biggest challenge (Tushara, 41:22).

Oversight, Regulation & Explainability

• Explainable AI:
  • All Man Group trading decisions must be explainable and defensible to risk committees and regulators:
    • “All of our trading decisions are ultimately explainable. And we never want to be in a position where we don’t know why that trade happened. The AI did it.” (Gary, 33:37)
    • AI systems are explicitly required to write in plain English the rationale for each investment signal.

Markets, Alpha & Competitive Dynamics

• AI, Labor and Expense Budgets:
  • While token spend is tracked, it’s not yet considered equivalent to labor cost, but this may change as agents take on more tasks (43:17).
  • The real shift may come as workflow-owning agents, not humans, drive most spend (Tushara, 43:37).
• Sustaining Alpha:
  • Core alpha comes from the interplay of data, market access, proprietary backtesting, and coordinated execution—not just AI or data alone (44:51).
    • “There isn’t one code repository in Man Group that I can point out and say that’s where the alpha is. It’s really this network of different systems that interact with each other.” (Tushara, 45:16)
• Expanding Opportunity Set:
  • AI reduces the barrier to researching new or frontier asset classes by automating initial data wrangling and market understanding (Joe, 34:54; Gary/Tushara, 36:03–37:35).

Organizational and Cultural Change

• Structure & Collaboration:
  • Longstanding cross-disciplinary teams (engineers embedded with researchers and PMs) have been key for platformization (Gary, 47:41).
    • “One of my jokes … is like, right, let’s go walk across the fifth floor … I want you to tell me who the engineers are and who the quants are. And I bet you’re going to get it wrong.” (Gary, 47:41)
• Aligning Incentives:
  • Systematic teams share more; discretionary PMs may keep a “10%” secret sauce, but most workflows are increasingly collaborative and encoded in shared AI playbooks (49:49–50:46).

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Notable Quotes & Moments

• On research augmentation:
  • “AI has allowed us to access all of those different types of data, lots of different modalities … and synthesize them into what’s actually meaningful so that a PM can asynchronously get that insight.”
    — Tushara Fernando (10:05)
• On agentic innovation:
  • “There've been a bunch—I think 15, 20 models at the last count—that had gone all the way through, started off as models ideated by AI, went all the way through the signal construction … and deemed fit and proper to trade our clients’ assets.”
    — Gary Collier (15:16)
• On bottlenecks:
  • “The bigger problem is the world of opportunity that’s afforded by the AI capabilities … keeping up with it from a human perspective can be quite hard … the sheer level of organizational change that we need to get involved in... I think that’s the bottleneck.”
    — Gary Collier (18:03)
• On data vs. model:
  • “For a quant research task, I think that looking at the underlying data is what fuels alpha research. Trying to just use a frontier model will get you nowhere.”
    — Tushara Fernando (24:11)
• On token usage:
  • “Since January, I think token consumption has gone up 86 times.”
    — Tushara Fernando (29:23)
• On hiring:
  • “Everyone we hire now into the firm should up the bar with respect to AI, regardless of the role that they're coming in to do.”
    — Gary Collier (38:12)
• On explainability:
  • “We never want to be in a position where we don't know why that trade happened. The AI did it.”
    — Gary Collier (33:37)
• On democratization vs. superstars:
  • “We’re seeing both… given the size of the firm though, we’re seeing more of the latter [democratization].”
    — Gary Collier (40:57)
• On access to new markets:
  • “AI allows us to think about accessing that market in a systematic way earlier than we could before.”
    — Tushara Fernando (36:37)
• On collaborative knowledge:
  • “These are all workflows that are people's expertise, but they're not necessarily the 10% that produces returns. So those areas are encapsulated in AI playbooks … whereas some of the particulars around how the strategy works … are somewhat held back.”
    — Tushara Fernando (50:46)

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Key Timestamps

• 01:30 – Show introduction and framing the AI-in-investing question
• 06:01 – Man Group’s scope and Gary/Tushara’s organizational roles
• 07:37 – The distinction between traditional ML and modern AI
• 09:01 – Where and how generative AI has changed the hands-on investment process
• 10:05–12:41 – Real-life example: Using AI on podcasts and alternative data to surface insights
• 13:45–16:11 – Building end-to-end agentic workflows; AI-driven model creation and validation
• 18:03 – The big bottleneck: organizational change, oversight, risk
• 20:14–24:11 – How data pipelines, tagging, and a shared semantic layer make AI useful
• 25:30–29:09 – Token access, budgets, spend, and efficiency measures
• 29:09–30:51 – 86x token usage, drivers of recent acceleration, agentic workflows
• 32:48–34:54 – Oversight, explainability, and regulatory responsibilities in decision-making
• 36:03–37:35 – How AI opens up new markets and asset classes
• 38:12–41:22 – How talent requirements and job design are shifting
• 44:51–46:20 – Will AI-driven alpha get arbitraged away? Where does the edge come from?
• 47:41 – Forward-deployed engineers and the platformization of Man Group
• 49:49–50:46 – Alignment, collaboration, and the challenge of knowledge hoarding

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Takeaways

• AI is transforming both the pace and breadth of research, automating tasks across all staff levels and letting teams tap new, previously inaccessible data sources and markets.
• The “86x” surge in token usage is a proxy for how AI is being integrated—rapidly and pervasively—across every part of a modern asset manager.
• Organizational structure, education, and collaboration—not just data or compute—are the new battlegrounds for institutions adopting AI at scale.
• Explainability and oversight are non-negotiable, especially in regulated environments like asset management.
• The edge increasingly comes from: proprietary and well-tagged data, access to markets, integration of AI with legacy strengths, and the cultural capacity to act fast while mitigating risk.
• Talent strategies must focus on those able to orchestrate workflows and think in systems, not just technical minutiae or subject matter expertise.

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This episode is an essential listen for anyone interested in how large-scale finance is actually operationalizing (and wrestling with) the potential and growing pains of enterprise AI.

Transcript

[Joe Weisenthal]

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[Bloomberg Announcer]

Bloomberg audio studios podcasts radio news.

[Joe Weisenthal]

Hello, and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal.

[Tracy Alloway]

And I'm Tracy Alloway.

[Joe Weisenthal]

Tracy, I'm very interested in AI in a. No, no, really. Really, Joe, I am.

[Tracy Alloway]

That's a surprise.

[Joe Weisenthal]

I'm very interested in the investment context 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? Or 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, you know, the sort of the most crude version of quote, using AI for investing is obviously a dead end. Doa.

[Tracy Alloway]

Here's the question I have. You know, we've been through technological revolutions in investing before. Notably, you know, we had Robo Advisors.

[Joe Weisenthal]

That's right.

[Tracy Alloway]

Remember that we had systematic investing quants, we had high frequency trading. My big question is how much of the current use of AI is basically an iteration or an improvement on some of those kind of, you know, machine learning dynamics, let's say, versus something more substantial or more revolutionary? Is it like a minor evolution or a moderate evolution or something big?

[Joe Weisenthal]

Well, this came up a little bit in our conversation with Ian from Hudson River Trading. And you go, and I'm glad you brought up the quant example because like, all right, big data in some sense has been part of quant since the very beginning. Like how do you establish that quantity, quote value stocks outperform expensive stocks? Well, you just need like a lot of data and computers and running that math, et cetera, to establish that fact. But this idea of, yes, maybe cheap stocks outperform expensive ones, momentum stocks outperform stocks with bad momentum. Things that seem to be true, but we don't really know why. And there's a lot of disagreement as to the source of these things. And that really makes me think about AI because we can get these outputs from AI models that are obviously remarkable.

[Tracy Alloway]

You can recognize patterns, yes, but they don't.

[Joe Weisenthal]

They can't really explain how they arrived at that pattern. And I'm very interested in this sort of where this leads us with investing and whether it's like, okay, do we get these like ideas and strategies, et cetera, that work, but we can't really articulate them in plain English.

[Tracy Alloway]

I'm glad you brought up data as well, because one thing you hear at basically every finance conference nowadays is the importance of data. When it comes to running LLMs and making sure that your data is actually processed, it's collecting clean, that you have a lot of it and that you have hopefully proprietary data. And the people we're going to talk to have a lot of data actually.

[Joe Weisenthal]

Totally. There was just one more thing. There was a very. I don't know if you caught it. Last week, we're recording this July 7th, last week while you're on vacation. There was a very interesting paper out for Bridgewater talking about their use of proprietary data to fine tune an open source version of coin. When and for a specific purpose of being able to identify what is newsworthy financial information than not, they found that the combination of open source models plus proprietary data got them better results than the most frontier US models. That is very interesting to me and like these are the types of things that I'm very curious about.

[Tracy Alloway]

Where does the secret sauce or the Alpha actually come from Totally.

[Joe Weisenthal]

Well, yeah, especially when everyone is going to have access to like really, really intelligent models. Anyway, enough talk from us. I'm really excited we have the perfect guests to talk about this, actually understanding the implementation of AI within the investment or asset management context. We're going to be speaking with Gary Collier, Man Group cto, as well as Tushara Fernando, the firm's head of data and AI. So like I said, really the perfect guests. Gary and Tushara, thank you both so much for coming on Outlives.

[Gary Collier]

Great to be here.

[Tushara Fernando]

Thank you.

[Joe Weisenthal]

Let's start with Gary or maybe we'll go both of you one after the other. Why don't you just give us a very quick description of your roles at Man Group and what specifically you do.

[Gary Collier]

Sure, I can start with that. Perhaps useful to give a bit of context of the firm. I describe Man Group really as this full spectrum active asset manager. So full spectrum being we cover alternatives, we cover long only, we cover public markets, we cover private markets, we cover quant, systematic cover, fundamental discretionary investors, and we've got a solutions business that can bring all that together into custom client mandates. And what that means in terms of CTO role. I describe that as like full spectrum too, because for all of the above, we're very opinionated in what every part of the tech stack looks like, right down from choice of servers, networking devices, all the way through to the end user facing pieces of software. And similarly left to right, we build an awful lot of our own technology right from the custom data feeds all the way through research frameworks, trading systems and the Midland back office operating platform. So very much full spectrum role, Tushara.

[Tushara Fernando]

Yeah, so on my side, I look after data and AI. So that's everything from market data to alternative data to all of the context and knowledge that we have that we plug into our AI models. Then from an AI perspective, it's about how do we give the great capabilities that we now have to our quants to our researchers and to our fundamental portfolio managers.

[Tracy Alloway]

Yeah, Tishara, I wanted to ask you about this because I think your title actually used to be Head of Data and Machine learning and now it's Head of Data and AI. Like what exactly? How do you think about the difference between those two terms, machine learning versus AI?

[Tushara Fernando]

That's a really good question. I think it links to one of the earlier points that you had around whether it was that we were using traditional machine learning techniques or whether we were using generative AI. And I think historically machine learning in a quant firm was really about using these more traditional machine learning techniques that were looking at things like linear regressions, they were looking at neural nets to try to predict some feature. Whereas now with the onset of generative AI, it's more than that, it's really about enablement of people to create things as well as using these traditional techniques. So that's why that we've changed the title of the role because it encompasses both the generative AI aspects as well as the more traditional machine learning methods.

[Tracy Alloway]

Okay, that makes sense. So if I was sitting within a man group office today and I take, you know, Gary laid it out perfectly well, you guys have a lot of different roles. So you have discretionary sort of traditional portfolio managers and then you have more systematic quants and all of that. But if I'm shadowing one of your traders or PMs and I'm watching what they're doing on a screen today versus what they were doing on a screen in let's say 2022 or something like that, what exactly has changed with the use of generative AI? What are you doing differently?

[Gary Collier]

I think if you were to walk across the floor, you would see different windows, different tools in use now, regardless of role, whether it be trading, quant research or discretionary investing. And chatting to a couple of the discretionary analysts last week, and they were commenting to me on how everywhere you look across the discretionary floor, everybody has got an AI focused like window on their screen. And so it's affecting, improving, like augmenting pretty much every role we have in the firm, of course in different ways depending on what the role is. But there's no role that's unaffected by AI.

[Joe Weisenthal]

What are they doing? Again, rather than talk about, we're going

[Tracy Alloway]

to have to narrow it down.

[Joe Weisenthal]

Let's say I'm like a sort of traditional long only stock selector, like the sort of classic thing that in our minds we often think of as an asset manager and it's like, okay, I have now access to some of the world's most advanced models. What am I doing with them?

[Tushara Fernando]

So if you think about traditionally how a PM like that would work, they want to look across a broad set of names and they want to get access to as much data as possible for those names. So they want to look at earnings reports, they want to look at broker research, they want to look at alternative data, they want to look at podcasts, but there's only so much time in the day, There's a few hours in the day and there's 50 names. How are they going to cover them all? How do they get to the really important insight? And what AI has allowed us to do is allowed us to access all of those different types of data, lots of different modalities, podcasts, alternative data, things like broker research reports and synthesize them into what's actually meaningful so that a PM can asynchronously get that insight. They could go away for a coffee, they can come in overnight, and an agent has actually distilled a change that's happened on the Internet and given them the insight that's meaningful to their portfolio, to their investment thesis. So just I suppose an example might be recently we had a PM that was covering the AI trade. And really they're. One of the important things is about where's the bottleneck? Where's the bottleneck here? And everyone's in the bottleneck trade. Is it? Yeah. And it's hard to know exactly where it is. And there was a podcast cast from one of the heads of engineering from a large hyperscaler, and he was saying that it was increasingly important to have more and more GPUs to train models. But data centers were becoming more scarce and it was becoming difficult to find data centers that were actually big enough to train these models. So what you could see there was a couple of things, like one, that there's GPU scarcity. And the other thing is that it's likely that we're going to need better networking between these large data centers in the future. And that was something that came on a podcast from a head of engineering. This isn't someone who a PM would usually interact with. They don't go to the investor calls. They're not somebody they often have access to. But through AI, the PM was able to have an AI agent transcribe that podcast and synthesize that data so that they could get better insight into that investment idea.

[Tracy Alloway]

Yeah, I feel like podcasts are an important source of alpha Joe. Everyone should be listening to podcasts.

[Joe Weisenthal]

You know, the non joke version of that, which is one of the things I people say. We had Alex Emas on the podcast and he's like, what's going to be scarce after AGI? Yeah, People who are able to get good guests on their podcast, people who are able to get those people, you know.

[Tracy Alloway]

Okay, so we're clearly talking our own book.

[Joe Weisenthal]

Yes. Okay.

[Tracy Alloway]

Setting that aside for a second, it sounds like what most of you're doing when you describe that process is augmenting research or allowing your investors, your managers, to be more efficient in Their research process. There's a lot of talk nowadays about agentic workflows and the actual idea that instead of having humans drive every step of a particular trade or project, you have a system that can, you know, do the research, it can generate ideas, it can test the hypotheses, and then it can even execute on them. Is that something that you're sort of working towards or is it still too far away for you guys to even be contemplating?

[Gary Collier]

No, that's not too far away at all. In fact, that's something we've been working on for quite some time now. And if we shift focus to the quant, the systematic part of the business, what we've been doing now look, if you take a step back and think, well, technology plus quant techniques, that gives you the ability to build systematic strategies. So what do we get if we add AI into that mix? Well, we have the ability to think about systematizing the way that we build systematic strategies to begin with and in effect giving a big force multiplier, big leverage multiplier to our quants, our researchers. So what we've been doing there for well over a year now, actually a year and a half, is building a system that can actually take all of those different parts of the quant research process. So the idea formation part, looking at academic papers, looking at carefully labeled data sets, reasoning about the content of those papers, the content of those data sets, are there economic hypotheses in there that could be real or at least could be worth testing out, and then having other agents build the code to encapsulate those ideas, get the right market data, run the right backtests, et cetera, and then further agents that evaluate the output of the prior agent. So this is really one of the, I suppose early ideas that we thought would give potentially great bang per book. And we've been working on some time and just to make it real, to demonstrate this is not just something that's happening in the lab, but doesn't have any real or practical consequences. There've been a bunch, I think 15, 20 models at the last count that had gone all the way through. They started off as models that were ideated by AI, went all the way through the signal construction, the validation process, were reviewed and validated by a human investment committee and deemed fit and proper to trade our clients assets with. So this is very real. It's not a make believe at this point.

[Tracy Alloway]

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[Joe Weisenthal]

Since you mentioned sort of hoovering up information that might appear on a podcast where someone identifies a particular bottleneck in their business, we are on a podcast. What is your bottleneck? What would you like to have more of right now in order to improve your process? Is it compute? Is it data? If you could snap your fingers and have more of something, what would it be?

[Gary Collier]

Well, you can always use more compute and you can always use more data. But I think what the bigger problem is the world of opportunity that's afforded by the AI capabilities that we're seeing. And of course being delivered all the time, that envelope is expanding quickly. That keeping up with it from a human perspective can be like quite hard. There's no shortage of good ideas. Filtering ideas is important. But of course, whilst we're used to making lots of change man group, the sheer level of organizational change that we need to get involved in to put all of this stuff into effect, I think that's the bottleneck making sure because we're a regulated business, we've got fiduciary duty. We need to make sure that the things that we're doing, the things we're deploying are done with the minimum amount of risk. And there's a lot of work in this field that really I think the industry doesn't have firm answers to yet. I mean, evaluations, testing that the results of AI processes are good and proper. That's a rapidly expanding field. Thinking about how we run agents across the business in a safe and controlled fashion, we all, I'm sure, have seen and smiled at the. Well, the AI deleted my inbox or the AI deleted all my photos and I can't get them back. We can't afford to have that type of thing happen at an enterprise level. So it's making sure that we can move quickly but safely, I think is the bottleneck, if you like.

[Joe Weisenthal]

Yeah, that makes sense. Let's get into a couple specifics and I want to get back to the sort of how you move forward safely. But both of you have now mentioned filtering and that actually is precisely what I mentioned in the intro. There was a paper from Bridgewater about fine tuning a version of Qin on their own data precisely for better filtering so that the PMs could just be, you know, have more efficient use of their time to see what signal, what do you, how do you build that? Is this an off the shelf thing? What is the tech, the model, et cetera, that you have to. Like this ingestion pipeline, what does it consist of?

[Tushara Fernando]

Yeah, I can take that one. I think for us it really depends on the type of data. There's broadly three types of data that we look at. The first one is market data that's often very structured tick data, things like order books. We ingest every tick from most exchanges almost a terabyte of data just from ticks per day. And then we have alternative and unstructured data that is much more messy, it's much more malformed. And there it's really about how we structure the data, how we tag it, how we connect it with each other. What's the knowledge layer on top of it? How do you think about the connectivity between tickers and sectors and companies? How does AI actually interrogate that data in a way that is uniform? What's the common language between all of those data sets? And then the last piece is really something that's quite new. It's our institutional knowledge, it's our context. And this is becoming a new layer in our data architecture. How do we tell AI about our processes? How do we make it speak Mind group? How do we tell it? Yeah, the right way to run a backtest. So those are really the three areas that we focus on.

[Joe Weisenthal]

Sorry, just pushing on this point further, like all of that makes a ton of sense to me. And especially, you know, the second two are like big problems. And we know that generative AI in particular solves a lot of the unstructured data problem. You can actually get a lot of signal from, you know. Yeah, a bunch of text dumped in a file. But what is, what are you building? Like, how does it work? And like, do you have to can off the shelf frontier models? Are they the best for the job? Do you do your own in house fine tuning of open source models? Like right today? What is the best tech stack for that part of the process?

[Tushara Fernando]

So we have historically looked at fine tuning for a couple of cases, but where we're seeing the most bang for our buck at the moment is proper tagging and structuring of the data. So pre processing, what we found is that if you take a data set, like credit card data, for example, AI can look at that, it can see the columns, it can see the rows, but it doesn't really understand the nuances of it. It doesn't really understand what it is. So what we're having to do is invest in trying to add extra color, extra metadata to that information. We want to have descriptors to tell it the nuances. We will say things like, when you look at this data set, each row really means that a person has gone into a shop and bought something. And you tell AI that in plain English, you give it those descriptors and you do that for all of your data sets. And then the second piece is, how do you connect lots of data sets together? They use very different language, they use very different semantics. So investing in a shared language, a said semantic layer, a shared way of doing things, is something that we've had to do so that we tag all of the columns with this unified language. So the AI can connect different data sets together, because that's really an important thing in idea generation that knows that a field in one dataset is linked to a field in another data set. It can quickly navigate between tickers and sectors for macro insight.

[Tracy Alloway]

What do you think is most important? Having the latest frontier model or a beautiful set of structured, tagged and labeled data?

[Tushara Fernando]

Yeah, I'd say it depends on the task that you're trying to do. If you're looking at a coding task, you really want the latest frontier models. But for a quant research task, I think that looking at the underlying data is what fuels alpha research. Trying to just use a frontier model will get you nowhere.

[Joe Weisenthal]

So this is something that comes up in a lot of our AI deployment questions. And I don't know, maybe both of you could take this, but you have all These different teams, and I assume everybody want, really, including people who don't know much about AI, intuitively think, oh, I want the strongest version of the model, I want Opus 4.8, I want Fable, whatever. And for. And I assume, okay, yes, for like deep computational tasks or engineering problems, coding problems, yes, probably those are the best. But there are probably a lot of people who do not need anything like that at all. How do you think about the question of internal provision of token consumption and not wasting money by having people use the most advanced models, but also giving people enough green space to explore and figure out what is the maximal potential value that they can get from AI?

[Gary Collier]

Yeah, the economics question's an interesting one. It's an important one. What we've done there is. Well, towards the end of last year, when we knew growth was likely to accelerate rapidly, we modeled what we thought the company would start to look like in terms of different classifications of users with different use cases, came up with a certain budget. And then what we've done this year is federated that out to all of the business units within the firm. Strong believer in pushing decision making down to the lowest possible level that it makes sense to do so. It allows people to be agile and use the economics that work for them and their departments. And of course, budgets are fungible. If departments want to move money from some other spend and say, right, I think we should buy more tokens, then they are free to do that. And the platform that we've built, the AI platform, has a very rich, not 100% complete, but a very, very rich set of models that can be chosen, including all of the main frontier models and a number of the different open source, open weight models.

[Joe Weisenthal]

Do you build a model that routes queries to the optimal, sort of like cost efficient model? I know there's a lot of interest in this, the sort of classifiers and the big AI labs have them themselves, but the classifiers that route queries, is that something that you built that you have in house or how do you solve that problem?

[Gary Collier]

We could do that.

[Tushara Fernando]

We've chosen not to. I think that the reason is that we want people to understand the dynamics of how best to use AI and which models to use. Okay, so what we've leaned on instead is education. So we have a very rich data set of how people are using AI, so we have great insight and some of the things that we saw were just quite basic mistakes. So often people would be doing multiple tasks in the same context window. They'd be trying to figure out the best Way to write an investment thesis. And then they were trying to figure out where to go to lunch. And then they were trying to asking

[Tracy Alloway]

Fable what the weather will be tomorrow.

[Tushara Fernando]

Yeah, exactly. And that is I suppose, like a well known problem if you're in the weeds of AI. But we have 17, 1800 people at mangroup and the technical understanding of how things work at a fundamental level is varied. So we've been really focusing on education. We're very transparent about the budgets that people have and how they're being spent. And we talk to them about the different classes of models. And it's through that that we have seen better results and we've actually seen people finding quite creative ways to reduce token spend and contributing that back to the platform as a result of it.

[Tracy Alloway]

Wait, say more about that.

[Tushara Fernando]

For example, if you are using a coding agent and you want to do some basic git commands to interact with the version control system, often the coding agent will send the command, it will process the entire result that comes back from that command as tokens. Instead, there's some really simple tools and simple basic steps that you could use. Instead of calling an LLM to do inference and tool calls, it can just intercept that tool call and do it outside of the agentic loop.

[Tracy Alloway]

If I was looking at a chart of your overall token consumption, what would I mean? I assume it's upward sloping, despite some of these efficiency efforts. But like, how steep is the slope at the moment?

[Tushara Fernando]

So since January, I think token consumption has gone up 86 times.

[Tracy Alloway]

Wow.

[Tushara Fernando]

So it's really, really quite, quite incredible. We were not expecting usage to be the way that it has been. And it's been across the board where it's not just been in the tech and tech adjacent departments. We have seen people in finance, people in operations, people in the people team using agentic coding workflows. And as a technologist, that's just super exciting to give this new capability, this new power to people who haven't been able to use it before.

[Joe Weisenthal]

Well, this actually gets a question that I've been wondering about and it definitely feels like December and January were just like a very pivotal period. And from your perspective, that sharp inflection point up, how much was it driven by the capability of the models themselves? Whether going from an opus 4.7 or 4.5 to 4.6 and beyond, or this sort of discovery of these very high quality harnesses like a Claude code or cowork or whatever, I think that's what it's called that really allows someone to do things with AI that are beyond asking questions and actually manipulate real work. The model or the harness, which would you describe as the key driver of that huge acceleration?

[Tushara Fernando]

I think the two are coupled. I think one of the interesting benchmarks to look at for this is a benchmark called meter. And what that tries to do is it looks at tasks that humans would do for different time periods from a couple of minutes to many hours. And what we're seeing is that every seven months or so, the amount of time that an agentic workflow can go away and do a task is doubling. So now you can ask an agent to do a task that would take a human 16 hours. And that changes the way that you think about teams. It changes the way that you think about interacting with these agents. You go from a place where you're in the loop, you're asking an agent to solve a problem like writing a unit test, to a problem like building an entire feature of an application or an entire application in itself. So I think it's the scalability that has allowed larger tasks to be completed that has resulted in larger token usage.

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[Tracy Alloway]

to go back to I guess the oversight question and we all know that finance is highly regulated environment and you've touched on this earlier but you're still approving a lot of these model outputs through humans. So there's some oversight there. And I would assume that when you're approving if a human is improving a new model or an output that they have to understand what's actually coming out of it, right? There has to be some explainability there. If I'm a PM or I don't know, a quant sitting in front of a risk management committee or a regulator. What does explainability actually look like and how am I translating the model outputs into something that is understandable and also I guess, defensible?

[Gary Collier]

Yeah, you're right. Explainability is super important to us. And to be clear, the sort of business that we're in is not the high frequency trading business where people are constructing huge neural nets and looking through like multi dimensional spaces and the output not being like at all insurmitable. We're not in that space, that trading and holding period horizons are typically days to weeks to months. So all of our trading decisions are ultimately explainable. And we never want to be in a position where we don't know why that trade happened. The AI did it. And going back to some of the things that we talked about earlier, take the example of the AI coming up with brand new trading hypotheses based on what it's seen in terms of content of a data set, what it's seen in the content of an academic paper. And the model will go as far the system, the agent will go as far as naming and writing the investment hypothesis. It's one of the first things it does before it moves on to writing codes. It's giving us a real English description of what it thinks. The rationale for the trading signal is.

[Joe Weisenthal]

I'm curious, you know, obviously we haven't even gotten to the question of like what is the future of labor and the humans in the loop and how many humans in the loop will we need in the future? But one thing I'm curious about is a way to ask this question differently. Has AI allowed you to look at markets that you, you wouldn't have had the bandwidth before. So for example, I met, you know, let's take the Ethiopian stock exchange or something like that. There's a certain amount of human labor that would be required to gain any familiarity with it whatsoever. Setting aside everything else, and no matter how much potential profit there is in a say, frontier market, there is a minimum amount that's going to cost and that might take some investment opportunities off the board because the potential profit isn't big enough to justify the spend to getting up to speed. This strikes me as something that AI could potentially help with or solve for of creating a new opportunity set by reducing some of the upfront human labor costs. Is that something that you think about or have seen specifically so far in terms of man group?

[Gary Collier]

I think it's likely happening incrementally at the margins. We start to sum up all of the different micro augmentations that we see across the firm. For example, and here's a related one, someone had built a relatively simple AI system to take data from complex instruments PDFs specifications and automatically populate our reference data store with that. So yes, I think it's absolutely happening, but it's the sum of a lot of different parts across the firm.

[Tushara Fernando]

I think what we're seeing as well in the systematic space is that you need a few prerequisites to build a systematic strategy. You need to have some connectivity to trade the instrument. And you need to be able to understand what the price of a market is. Those are two real fundamental things. And for developed markets, that's really easy. You go and look at the order book. But for less developed markets, things like crypto, securitized credit, they're often harder to connect to. They may be traded more verbally or the contracts are complicated and it's difficult to understand the price where there's these unstructured data nuances in the derivation of that price. We've seen that AI allows us to think about accessing that market in a systematic way earlier than we could before

[Tracy Alloway]

just going back to the labor market side of things. I guess, you know, if I'm a PM at Man Group and more and more of my job is using AI for research or even basically supervising agents that, you know, I maybe help develop, what does that mean for what you're looking for in terms of talent? Are you looking for engineers who can tweak these models? Are you looking for more traditional investors who I guess have, have a stronger intuition about how these things might play out or some sort of combination of characteristics? What do you look for now?

[Gary Collier]

I think very fair to say, and this is something I've been making a strong case for, that everyone we hire now into the firm should up the bar with respect to AI, regardless of the role that they're coming in to do. And I think that applies as much in the operation space as it does in the front office space.

[Joe Weisenthal]

What does that mean? So what, like someone like, okay, I'm capable of upping the bar with respect to AI, like what does that say more about, okay, in the recruiting process? What does that person look like?

[Gary Collier]

That means being as familiar with the technology as you could reasonably expect a person to be, given the wealth of information that's out there. That when I'm, when I'm hiring people, you know, what sort of people do I want? I want bright people and I want people who are motivated, they get things done and are passionate about the subject matter, their field of expertise. And I think it's very hard to fulfill all of those criteria, particularly nowadays and say, well, you know, AI, I don't know much about it. I don't really use it as part of my.

[Tushara Fernando]

Yeah, I think that the other piece that you want is somebody who's a bit more of a long term thinker, somebody who really wants to automate a process from end to end. They are happy not to be in the weeds, in the loop and when you think about technologists, that's actually quite difficult. People love being in the weeds, they love debugging issues, getting into the nitty gritty. But actually fundamentally, you want to level up. You want to be a kind of a conductor of these agents. You want to be in charge of the end to end process rather than necessarily being in the weeds. Almost like a manager who has a lot of technical expertise. So somebody who is thinking about things in broader strategic terms is much more value than they used to be.

[Tracy Alloway]

So this leads to the other thing I wanted to ask, which is you could see the arrival of AI tools generating like two different outcomes here, where you have some people who are just really, really good at using AI and they become superstars and orchestrators of a bunch of different agents, as you put it. Or you could have this sort of democratizing effect where maybe you're a junior employee with not as much experience, but now you can automate a bunch of tasks, you can learn from AI, you can use it for research and things like that. What are you seeing more of at the moment? The superstar dynamic or the democratization of skill sets throughout man group?

[Gary Collier]

I think we're seeing both, genuinely both. I think given the size of the firm though, we're seeing more of the latter. And as I said at the start of the chat, almost, or everybody's using it day to day, but examples of huge, genuine, right at the cutting edge of thinking. I think they're naturally more rare, but there's a fair few of them even

[Tushara Fernando]

saying that they often cut across multiple teams as well. And that is hard because you need to move from a space where you spend most of your time executing to a space where you spend most of your time planning. The time to execute is just going down and down. It's becoming cheaper and cheaper to do that. You can build code, you can build features very quickly. So the focus really needs to be on what should we build, how does it connect together, and what is the process that we want to develop across multiple teams. And it's very difficult sometimes to take people out of their seat and get them to collaborate and plan a workflow, rather than just going in, trying to build a proof of concept and execute on the idea.

[Joe Weisenthal]

Let's talk more about the 86x increase in token spend. If we were having this conversation back in February or January, a lot of the chat would have been very much about like, what does this mean for legacy software companies? Because that's when the big software company software sell off was, you know, really was Quite intense. But I feel like this conversation in July is becoming like, no, these companies are not going after the world of software, they're going after the world of labor. And you see a lot of these conversations like people talk about the ratio of token spend to employee salaries, etc. And that that is the TAM, that it's like all human labor and maybe we're not going to get there for a while. I kind of hope not. But is token spending part of your tech budget or is it like something that is a true line item, that's distinct, that's more on par with labor? And when you think about man Group in 2027, 2028, do you talk about expected ratios of salaries to token spend?

[Gary Collier]

No, we generally haven't started having that conversation yet. I mean, I would guess at some point it will come and the company's set up in, in such a way that we want to direct resources to where they produce the best economic outcomes for us. So I'm sure that time will come.

[Tushara Fernando]

One of the interesting things in the token budgeting process, I suppose, is that what we're increasingly seeing is that the spend is actually not by people, it's by agents. And the agents relate to workflows. And who owns the workflows? Is it this department? Is it that department? And that's a new problem for us, one that we haven't solved yet. But it's a great problem to have.

[Tracy Alloway]

I brought up earlier the sort of machine learning parallel, I guess, and I know you guys don't do a lot of high frequency trading, but we've certainly seen a dynamic in HFT where everyone's competing and it's sort of a race to the bottom where I can't even remember where we're at.

[Joe Weisenthal]

In terms of like micrometers. Yeah.

[Tracy Alloway]

In terms of second increments or time increments. But it's a sort of race to the bottom dynamic. Would you expect AI driven alpha to get sort of competed or arbitraged away relatively quickly as everyone seems to be hopping on the same bandwagon? Or are there certain advantages, you know, we talked about data, for instance, scale perhaps, that you would expect to stick around for some time?

[Tushara Fernando]

Yeah, I'd say it goes back to your question around where is the alpha? And what is true is that it is easier for people to onboard data sets, analyze them and build features, but that doesn't mean that they can trade them. We've been doing this for decades, so what we have are capabilities to actually access these markets. We have the relationships with the brokers. We have access to data that isn't just available off the shelf. So it's putting all of those things together, putting those expertise together, the access to markets, all of the data that we have, the rich market data, and then giving AI access to the capabilities such as back testing compute. It's this whole network, this whole ecosystem that together I think drives alpha. There isn't one code repository in Mangroup that I can point out and say that's where the alpha is. It's really this network of different systems that interact with each other. So I think that definitely some features of data sets will become table stakes. They go from being alpha to being a risk factor, but because everybody has them and it moves the market, but it isn't the only way that we make money. We are able to connect different data sets, different ways of doing things together and then actually trade on those signals.

[Joe Weisenthal]

Gary, I want to go back to something you said earlier when we were talking about bottlenecks and it's like a sure, everybody wants more data, everyone wants more compute. No one would complain about these things. But where the rubber meets the road is like does the institution have the capacity to actually maybe from a cultural standpoint, a sort of hierarchy standpoint to actually get the most out of these tools. And this is clearly a very hot area. And so for example, just last week Microsoft announced a new thing which they're calling the Frontier company, which is basically a new like sort of subdivision that seems to want to specifically solve this problem, go into an organization and figure out this optimal structure. And you know, arguably this is even like what a company like Palantir is trying to do, which is you know, the forward deployed engineers. We know about Claude sending or anthropic sending engineers inside Goldman Sachs to really leverage, I hate using that word because it's so cliche but yes, leverage these tools. What specifically are you seeing happening on that front? Do you have third party companies who are coming to you and saying look, we can work with you to find what is the org structure of the future for man group such that it's getting the most out of these tools for a long time?

[Gary Collier]

Yeah, I did smile at the multi billion dollar forward deployed engineer division that you just mentioned. Partly because I mean that's the way we've been like set up internally here for about I think 15 years. We're very big on platforms and we've got a bunch of teams and Tishara runs one of those that build out these big cross cutting elements of platform Technology, in his case, the data and AI platform. But a big part of the tech team are already, and have been for a decade and a half forward, deployed engineers, sitting with our quants, sitting with our discretionary managers. And one of my jokes, often to people I'd be interviewing to come join the team is like, right, let's go walk across the fifth floor here in Ribbank House. And I want you to tell me who the engineers are and who the quants are. And I bet you're going to get it wrong, because what you'll see is very similar stuff on that screen. And that holds just as true today as it did a decade ago.

[Joe Weisenthal]

One last question, but another thing I'm curious about in the investment context. So we know that AI works when there's a big pool of data and that it can pull in the unstructured data and the structured data, et cetera, and communicate across them. In a entity such as Man Group, are there any alignment issues in which, you know, if I have a subject matter expertise that generates alpha, this might be why I have a seat at an organization or I might be a rainmaker. You know, you hear this in all kinds of different firms where compensation is linked to someone's ligament, deep expertise. In some area, do you think about alignment so that the firm, the franchise Man Group, is actually capturing some of the expertise and data of the superstar? How do you get them to sort of, I guess, contribute as much information as possible to this thing that requires a lot of data and information.

[Gary Collier]

I think in some areas that's still a little bit of a work in progress. And saying in some areas, because in others, notably the systematic, the quant areas of the business, this highly collaborative approach and shared code bases, it's just been the way that those areas have worked for a long time now. I'm not saying that you walk across the discretionary part of the floor and all of the fundamental investors are going to be quite so free and open. I've talked to a bunch of them about their processes and you know, the Open with 90%, but there's the 10%. Well, you know, this is where my personal value ad lies. I'm not so comfortable talking about, about that. But even that said, there's a very decent and genuine amount of like, collaboration there as well. And it's only when you get perhaps to the very sensitive areas that people might be a little bit more reluctant to talk.

[Tushara Fernando]

Yeah, I think the high level, there's a huge amount of shared workflows. If you think about the way that we backtest the way that you read an investment report. These are all workflows that are people's expertise, but they're not necessarily the 10% that produces the returns. So those areas are encapsulated in AI playbooks, AI skills that we put in our knowledge platform and they can be used across the floor. Whereas some of the particulars around how the strategy works and the investment thesis are somewhat held back in certain cases.

[Joe Weisenthal]

That makes sense. All right, Gary and Tishara, thank you so much for coming on. Odd lot. Really appreciate your taking your time and talk about where you're at.

[Gary Collier]

Thank you. My pleasure.

[Tushara Fernando]

Thanks a

[Gary Collier]

lot.

[Joe Weisenthal]

You know what I think was really interesting about that conversation in part is like, there is so much to figure out still, right?

[Tracy Alloway]

I mean, just the basic token budget and like where stuff gets allocated like

[Joe Weisenthal]

86x in since January is pretty crazy.

[Tracy Alloway]

Well, this is the other thing. I was thinking like 86 times growth in token usage, like at some point that needs to show up in another concrete number, whether it's expense reduction or revenue generation, income generation. And I don't know how much like leeway there is for that.

[Joe Weisenthal]

No. And you figure like right now, so you have this 86x explosion. But as they say, they're still in the moment where they haven't gotten to. We want to build like an internal router to minimize this. So even with the 86, they're still in the phase where it's okay, you know, figure out what model you want to use and experiment, et cetera, which to my mind is actually kind of bullish if you think about it, for token spend that you can grow 86x and it still doesn't get you to the point where like, oh, we got to really like clamp down on this, like maybe, you know, who knows, like what the point is for a lot of firms that are just starting out where they actually have to start imposing some token austerity.

[Tracy Alloway]

I guess we'll find out at some point. But the other thing that stood out to me, you know, you asked the question about how do you get. How do you get workers to give up their own secret sauce that basically is responsible for them having a job in the first place? Keystroke surveillance solves all of this, right?

[Bank of America Announcer]

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[Joe Weisenthal]

You know those articles, I have to

[Tracy Alloway]

say, you need them to offer it up. You just take all the data that they're producing.

[Joe Weisenthal]

I have to say there was some story that came out a while back about Ometa is going to start using like training its models on its own. And I was like, they weren't doing this already. Like, I was actually like really surprised that this wasn't already.

[Tracy Alloway]

Especially in finance and investment, which is a highly regulated industry already and I'm sure is monitoring pretty much everything anyway.

[Joe Weisenthal]

Yeah, I kind of assumed that all of these companies are really building models, were already using all of the data that their own employees were generating. But yeah, you have to. It's like, oh, does the person, does the rainmaker suddenly just start writing everything down on pen and paper, et cetera, et cetera? E They're not implicitly uploading all of their knowledge to the AI? I think that's a pretty interesting question in itself.

[Tracy Alloway]

All right, clearly lots of interesting questions, but shall we leave it there for now?

[Joe Weisenthal]

Let's leave it there.

[Tracy Alloway]

Okay. This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracee Alloway

[Joe Weisenthal]

and I'm Joe Weisenthal. You can follow me at thestarw. Follow our producers Carmen Rodriguez at Carmen Armand, Dashiell Bennett at dashbot, Kalebrooks at Kalebrooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to bloomberg.com oddlots we have a daily newsletter and all of our episodes and you can chat about all these topics 247 in our discord, discord, GG oddlaws

[Tracy Alloway]

and if you enjoy odd lots, if you like it when we talk about how finance firms are actually implemented, implementing AI, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg Channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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