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

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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 when Chad G.P.T. came out and people were asking what stock should I buy. We did that prediction market episode recently. And it's like one thing you definitely can't get much value out of this saying which contract should I buy or what's inflation going to be so I can trade this contract. But that doesn't mean that there aren't interesting ways. It just seems like the sort of the most crude version of quote using AI for investing is obviously a dead end deal. Here's the question I have. We've been through technological revolutions and investing before. Notably, we had RoboAdvisor. That's right. Remember that. We had systematic investing. Once 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 machine learning dynamics, let's say, versus something more substantial or more revolutionary. Is it a minor evolution or moderate evolution or something big? Well, this came up a little bit in our conversation with Ian from Hudson River Trading. 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. How do you establish that 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 quant things. And that really makes me think about AI because we can get these outputs from AI models that are obviously remarkable. You can recognize patterns, yes. But 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 ideas and strategies, et cetera, that work, we can't really articulate them in planning. 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 LLM. Yes. So that your data is actually processed, it's 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. Totally. There was just one more thing. There was a very, I don't know if you caught it last week or 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 Quinn. And for a specific purpose of being able to identify what is newsworthy financial information, they found that the combination of open source models, plus proprietary data, got them better results than the most frontier US models. Yeah. That is very interesting to me. And like, these are the types of things that I'm very curious about with the. And what is the secret sauce or the alpha actually come from? Totally. Well, yeah, especially when everyone is going to have access to like, really, really intelligent models. And I've talked 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 out on outlaws. Right. Today, I saw you. I'll start with Gary, or maybe, you know, 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? Sure. I can start with that. And perhaps useful to give a bit of context of the firm. I describe Man Group really as a 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 consumer, consumer fund, the mental discretionary investing and the other solutions business that can bring all that together into custom client mandates. And what that means in terms of CTO role, I described that as like full spectrum too because all of the above were very opinionated in when every part of the text that looks like write down from choice of servers, networking devices, all the way through to the end user facing pieces of software and similarly left the 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 middle and back office operating platform. So very much full spectrum role. Do you sure? 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? Yeah, Tashara, 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? Yeah. That's a really good question. And 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 quantum 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 is more than that. It's really about enablement of people to create things as well as using these traditional techniques. So that's why we've changed the title of the role because it encompasses both the generative AI aspects as well as the more traditional machine learning method. 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 and let's say 2022 or something like that, what exactly has changed with the use of generative AI? What are you doing differently? I think if you were to walk across the floor, you would say different windows, different set tools in use now regardless of role, whether it be trading, quant research or discretionary investing. And so 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 focus like window on their screen. And so it's affecting and proving 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. What are they doing? Again, it rather than talking about the problem. We're going to have to narrow it down. 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 it 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? But if you think about traditionally how 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? What AI is allowed us to do is allow orders 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 the PM can asynchronously get that inside. They could go away for a coffee, they can come in overnight and then the agent has actually distilled a change that's happened on the internet and given them the inside as 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 there, one of the important things is about where's the bottleneck, where's the bottleneck here? Yeah, it's in the bottleneck. 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 hype scale. 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 increasingly 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, one that there's GPUs, guests, 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 the head of engineering. This isn't someone who PM would usually interact with. They don't go to the investicles, 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. Yeah, I feel like podcasts are an important source of alpha, Joe. Everyone should be listening to podcasts. You know, the non-joke version of that, which is one of the things I, people say we had Alex Neymar sound the podcast and is like, what's going to be scarce after AGI? People who are able to get good guests on their podcast, people who are able to get those people, you know, okay, so we're talking our own book. Yes. Okay, 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 agentech 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, you 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? 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 quantum systematic part of the business, what we've been doing now, look, if you take a step back and think, well, technology plus quantum 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 multiply, a big leverage multiply to our quant 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 of 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 the economic hypotheses in there that could be real or at least could be worth the testing out. And then having other agents build the code to encapsulate those ideas, get the right market data, run the right back tests, etc. And then further agents that evaluate the output of the prior agent. So this is really one of the, as was early ideas that we thought would give potentially great bang per book. And we're 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 client assets with. So this is very real, it's not to make believe at this point. Harness the power of Bloomberg Intelligence every business day. Hi, I'm Scarlett Foo. And I'm Paul Sweeney inviting you to join us for the Bloomberg Intelligence Podcast. We bring you deep dives into the company's moving markets from stocks like Apple, Nvidia, Microsoft and Alphabet, to private companies in the news like OpenAI and SpaceX. Listen on your way home from work to catch up on the analysis that keeps you ahead of the competition. Subscribe to the Bloomberg Intelligence Podcast today on Apple Spotify or anywhere you listen. What would it be? You can always use more computer 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 so quickly that's keeping up with it from a human perspective can be 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 mangrove, the steel level of like 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 free duty duty. We need to make sure that the things that we're doing, the things that we're deploying are done with the minimum amount of risk. 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 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. Yeah, that makes sense. Let's get into a couple of specifics and I want to get back to this 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 Quinn 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, etc. that you have to like this ingestion pipeline? Like what is it consistent? Yeah, I can say no. And so 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 man group? How do we tell it the right way to run a back test? So those are really the three areas that we focus on? 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 a signal from, you know, yeah, but 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 from tier 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? So we have historically looked at fine tuning for a couple of cases, but where we're seeing the most bang for our book 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 has said semantic, they are 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 that AI can connect different data sets together. Because that's really an important thing in idea generation. It knows that a field in one data set is linked to a field in another data set. It can quickly navigate between tickers and sectors for macro insight. What do you think is most important? Having the latest frontier model or a beautiful set of structured tagged and labeled data? Yeah, 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 one research task, I think that looking at the underlying data is what fuels our research, trying to just use a frontier model will get you nowhere. 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've all these different teams. And I assume everybody wants 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 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 get figure out what is the maximal potential value that they can get from AI? Yeah, the economics questions and interesting one is important one. What we've had 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. I mean, 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. Of course, budget's a fungible if departments want to move money from some other spend and say, right, I think we should buy more tokens than 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 at a number of the different open source open white models. Do you build a model that routes queries to the optimal sort of 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 have and how so how do you solve that problem? We could do that. 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. So what we've leaned on instead is education. So we have a very rich data 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 figure out. Asking Fable what the weather will be tomorrow. Exactly. And that is a well-known problem if you're in the weeds of AI, but we have 17, 1800 people at my group 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've been 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. We'd say more about that. So 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 that instead of calling an LLN to do inference and tool calls, it can just intercept that tool call and do it outside of the agentsic loop. 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? So since January, I think token consumption has gone up 86 times. Wow. So it's really, really quite, quite incredible. We were not expecting usage to be the way there has been. And it's been across the board. 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 tech 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. Well, this actually gets questioned that I've been wondering about. And it definitely feels like December and January, where it's 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 cloud code or a coworker 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? 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 that 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 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. There's resulted in larger token usage. The future of entertainment is happening now. Join Bloomberg Screen Time in Los Angeles, where the leaders of film streaming music sports gaming and technology come together to discuss what's next. Here from the executives, creators, investors and innovators shaping the future of content, culture and creativity. Bloomberg Screen Time, September 30th through October 1st, where creators and capital connect. Request your invitation today. BloombergLive.com/screentime/radio. I want to go back to the oversight question. We all know that finance is highly regulated environment. You touched on this earlier, but you're still approving a lot of these model outputs through humans. So there's some oversight there. 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. 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? How am I translating the model outputs into something that is understandable and also, I guess, defensible? You're right. Explainability is super important to us. 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 multidimensional spaces and the output not being at all insurve-sable. We're not in that space. We're not trading and holding period horizons at specifically days to weeks to months. So all of our trading decisions are ultimately explainable. We never want to be in a position where we don't know why that trade happened. The AI did it. 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. The model will go as far the system. The agent goes 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. I'm curious, obviously, we haven't even gotten to the question of 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 as a way to ask this question differently is AI allowed you to look at markets that you wouldn't have had the bandwidth before. For example, 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's 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? I think it's likely happening incrementally at the margins. If we start to sum up all of the different like 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. Yes, I think it's absolutely happening, but it's just some of a lot of different parts across the firm. 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. For developed markets, that's really easy. You go and look at the order book, but for less developed markets, things like crypto, secure attached 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. Just going back to the labor market side of things, I guess. 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 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 stronger intuition about how these things might play out or some sort of combination of characteristics? What do you look for now? I think very fair to say, and this is something I've been making at Strong Case, 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 today. I think that applies as much in the operation space as it does in the front office space. What does that mean? So, what, like, someone, okay, I'm capable of upping the bar with respect to AI. What does that say more about, okay, in the recruiting process? What does that person look like? 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. When I'm hiring people, 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. I think it's very hard to fulfill all of those criteria, particularly nowadays and say, "Well, AI, I don't know much about it. I don't really use it as part of my job." I think that the other piece that u1 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. 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. Somebody who is thinking about things in broader strategic terms is much more valued than they used to be. This leads to the other thing I wanted to ask, which is you could see the arrival of AI tools generating 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 the 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. I think we're seeing both genuinely both. I think given the size of the firm though, we're seeing more of the latter. I mean, as I said at the start of the chat almost, or everybody's using it today-to-day, but examples of a huge genuine right at the cutting edge of thinking. I think they're naturally more rare. There's a fair few of them even said to me. 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. Q 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? It's very difficult sometimes to take people out of the seat and get them to collaborate and plan a workflow rather than just going in, trying to build a proof of concept and the execute on the idea. 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 itself, was 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 talking about the ratio of token spend to employee salaries, et cetera. And that is the tab 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 spend part of your tech budget or is it like something that is a true line item that's distinct that's more on part with labor? And when you think about man group in 2027, 2028, do you talk about expected ratios of salaries to token spend? No, we generally haven't started having that conversation. Yeah, I mean, I would guess at some points that it will come. And the company's set up in such a way that we want direct resources to where they produce the best economic outcomes for us. So I'm not sure that time will come. One of the interesting things in the token budgeting process as opposed is that what we're increasingly seeing is that the spend is actually not by people, is 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. 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 were at in terms of like micrometers. Yeah, in terms of second increments or time increments. But it's the 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? 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 to 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 is this whole network, this whole ecosystem that together I think drives the alpha. There isn't one code repository in my 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. 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. Gary, I want to go back to something you said earlier when we're 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 arguably, this is even like what a company like Palantir is trying to do, which is 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. Yeah, I did smile at the multi-billion dollar forward deployed engineer and division that you just mentioned. It partly because I mean, that's the way we've been like set up internally here for about, I think, 15 years. Same with very big on platforms and we've got a bunch of teams and Tishara one runs one of those that's built 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 in having for a decade and a half forward deployed engineers sitting with our quants, sitting with our discretionary managers. And one of my jokes often, so people I'd be interfering to come join the team, it's not right. Let's go and walk across the fifth floor here in Ribbon 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 tonight as it did to take out to go. 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 better 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 at all kinds of different firms where compensation is linked to someone's like 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 a data and information? I think in some areas that's still a little bit of a work in progress, in some areas because in others, notably the systematic, the quant areas of the business, this highly collaborative approach and shared code basis has 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, you know, quite so free and open. I've talked a bunch of them about their processes and, you know, the open with 90 percent, but there's the 10 percent, well, you know, this is where my personal value had lies. I'm not so comfortable talking about about that. But even that said, there's a very decent and genuine amount of collaboration there as well. It's only when you get perhaps to the very sensitive areas that people might be a little bit more reluctant to talk. Yeah, I think the high level, there's a huge amount of shared workflows. If you think about the way that we back test, the way that you read an investment report, these are all workflows that are people's expertise, but they're not necessarily the 10 percent 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 pieces are somewhat held back in certain cases. That makes sense. All right, Gary and Tosharo, thank you so much for coming on AdLod. Really appreciate your taking your time and talk about where you're at. Thank you. Tosharo, thanks a lot. (upbeat music) - You know what I think was really interesting about that conversation in part is like, there's so much to figure out still, right? - I mean, just the basic token budget and like where stuff that's allocated. - Yeah, and like 86X in since January is pretty crazy. - 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. - No, and you figure like right now, so you have this 86X explosion, but as they say, there's still in the moment where they haven't gotten to, we wanted 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 could 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. - 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 workers to give up their own super sauce that basically is responsible for them having a job in the first place. - Keystrokes or valence solves all of this, right? - Yeah, you know, the way those articles, I have to say. - Yeah, you need them to offer it up. - You just, I have to say, I have to say, there was some story that came out a while back about Omeda is gonna start using like, training its models on it. So, and I was like, they weren't doing this already. Like I was actually like really surprised that this wasn't already. - Especially in finance and investment, which is a highly regulated industry already, and I'm sure is monitoring pretty much everything anyway. - Yeah, I kind of assume that all of these companies are really building models or already using all of the data that their own employees were generating. But yeah, you have to, like, oh, just the person, just the rainmaker suddenly just started writing everything down on pen and paper. It's said, they're not implicitly, uploading all of their knowledge to the AI. I think that's a pretty interesting question in itself. - All right, clearly lots of interesting questions, but shall we leave it there for now? - Let's leave it there. - Okay, this has been another episode of the All Thoughts Podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. - And I'm Joe Wasnthal. You can follow me at the storework. 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