Pod Insights Odd Lots One of the World's Largest Hedge Funds on Its 86x Growth in Token Spending About this episode We've gone through a number of a technological revolutions in investing, whether it was the dawn of the high frequency trading era or the introduction of robotraders. When it comes to AI, the big question that remains in the investment context is whether or not the technology will be implemented like those past tech innovations — meaning it will be integrated into the flow of the business without upending everything as we know it — or if AI will transform the very nature of investing. Right now, AI's use in investing is a mixed bag: People are excited about its potential, but several firms are still trying to figure out its value. Today, we speak with Man Group's CTO Gary Collier and Head of Data and AI Tushara Fernando about how one of the largest publicly-traded hedge funds in the world is actually implementing AI into its work. We speak with them about empowering their quants with AI tools, the challenge of integrating AI safely, and the creative ways their staff is thinking about token spending, which is up 86-fold this year.
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0:00 Tracy Alloway? There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.
0:26 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting
1:08 Speaker 01 and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.
1:52 Speaker 01 Bloomberg Audio Studios. Podcasts, radio, news.
2:07 Joe Weisenthal? Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.
2:11 Tracy Alloway? And I'm Tracy Alloway.
2:13 Joe Weisenthal? Tracy, I'm very interested in AI.
2:16 Tracy Alloway? No, really? Really, Joe?
2:19 Joe Weisenthal? I am.
2:19 Tracy Alloway? That's a surprise.
2:20 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 stocks 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?
3:01 Joe Weisenthal? 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.
3:15 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.
3:24 Joe Weisenthal? That's right.
3:24 Tracy Alloway? Remember that? We had systematic investing.
3:27 Joe Weisenthal? Yeah.
3:27 Tracy Alloway? Quant. 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?
3:47 Joe Weisenthal? Well, this came up a little bit in our conversation with Ian from Hudson River Trading. And you, 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 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
4:32 Joe Weisenthal? from AI models that are obviously remarkable.
4:34 Tracy Alloway? You can recognize patterns, yes.
4:36 Joe Weisenthal? But they don't, 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.
4:53 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 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.
5:14 Joe Weisenthal? Totally. There was a, just one more thing. There was a, I don't know if you caught it last week, we're recording this July 7th. Last week, while you were on vacation, there was a very interesting paper out from Bridgewater, talking about their use of proprietary data to fine tune an open source version of Quen and for a specific purpose of being able to identify what is newsworthy financial information and not. They found that the combination of open source models plus proprietary data got them better results than the most frontier US models.
5:47 Tracy Alloway? Yep.
5:48 Joe Weisenthal? That is very interesting to me. And like, these are the types of things that I'm very curious about.
5:52 Tracy Alloway? And where does the secret sauce or the alpha actually come from?
5:55 Joe Weisenthal? Totally. 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 Tushar Fernando, the firm's head of data and AI. So like I said, really the perfect guests. Gary and Tushar, thank you both so much for coming on Outlaws.
6:25 Speaker 03 Right, thank you.
6:27 Joe Weisenthal? Let's 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?
6:37 Speaker 03 Sure, I can start with that. Perhaps useful to give a bit of context of the firm. I describe Mangrove really as this full spectrum of active asset managers. 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 investing, and we've got a solutions business that can bring all that together into custom client mandates. And what that means in terms of the CTO role, I'd 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,
7:22 Speaker 03 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.
7:34 Joe Weisenthal? Tushara?
7:35 Speaker 04 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.
7:59 Tracy Alloway? Yeah, Tushara, 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?
8:14 Speaker 04 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 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.
8:54 Speaker 04 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 methods.
9:04 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?
9:37 Speaker 03 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 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.
10:19 Joe Weisenthal? What are they doing? Again, rather than talk about
10:22 Tracy Alloway? We're gonna have to narrow it down.
10:23 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?
10:42 Speaker 03 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?
11:16 Speaker 04 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 then the 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.
11:54 Speaker 04 So just, I suppose, an example might be recently we had a PM that was covering the AI trade. And really that one of the important things is about where's the bottleneck, where's the bottleneck here? Yeah. Is it in GPUs? 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 increasingly difficult to
12:38 Speaker 04 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 that synthesize that data so that they could get better insight into that investment idea.
13:18 Tracy Alloway? Yeah, I feel like podcasts are an important source of alpha, Joe. Everyone should be listening to podcasts.
13:23 Joe Weisenthal? You know, the non-joke version of that, which is one of the things I people say. We had Alex Demas on the podcast and he's 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?
13:38 Tracy Alloway? Okay, so we're clearly talking our own book.
13:40 Joe Weisenthal? Yes.
13:41 Tracy Alloway? Okay, setting that aside for a second, it sounds like what most of your doing and 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?
14:21 Speaker 03 No, that's not too far away at all. In fact, that's something we've been like 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, no, actually a year and a half,
15:07 Speaker 03 is building a system that can actually take all of those different parts of the quant research process. So the idea of 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.
15:28 Speaker 03 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 back tests, et cetera, and then further agents that evaluate the output of the prior agent. So this is really one of the, as those 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
16:09 Speaker 03 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 to make believe at this point.
16:48 Joe Weisenthal? There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief podcast. Every week, experts from Barclays Markets and research get you up to speed on what matters and what to watch next. About the time it takes to grab a coffee. So search Barclays Brief wherever you get your podcasts.
17:14 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Businessweek's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting. loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs. And hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.
17:57 Speaker 01 But according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company. And Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute, brought to you by ChatGPT. Get started at chatgpt.com.
18:37 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?
19:02 Speaker 03 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 that envelope is expanding like so quickly. That's 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, uh, man group. The sheer like level of like organizational like change that we need to get in involved in to put all of this stuff into effect, I think that's the bottleneck. Making sure because we're a
19:45 Speaker 03 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 amounts 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 the 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. You know, 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
20:25 Speaker 03 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.
20:34 Joe Weisenthal? Yeah, that makes sense. Let's get into a couple of 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 Quen 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 consists of?
21:12 Speaker 04 Yeah, I can say that one. So I think that 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.
21:37 Speaker 04 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.
22:22 Speaker 04 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 mangrove? How do we tell it?
22:32 Joe Weisenthal? Yeah.
22:33 Speaker 04 The right way to run a back test? So those are really the three areas that we focus on.
22:39 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, 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 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?
23:20 Speaker 04 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.
24:02 Speaker 04 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. Yeah. 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 or 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.
24:43 Speaker 04 Because that's really an important thing in idea generation that knows that a field in one data set is linked to a data, to a field in another data set. It can quickly navigate between tickers and sectors for macro insight.
25:01 Tracy Alloway? What do you think is most important? Having the latest frontier model or a beautiful set of structured tagged and labeled data?
25:10 Speaker 04 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.
25:33 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 can take this. We have 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 for, and I assume, okay, yes, for like deep computational tasks or soft 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?
26:15 Joe Weisenthal? 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?
26:29 Speaker 03 Yeah, the economics question is 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. I'm a 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.
27:14 Speaker 03 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.
27:41 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?
28:01 Speaker 03 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.
28:12 Joe Weisenthal? Okay.
28:12 Speaker 03 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
28:29 Speaker 04 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.
28:41 Tracy Alloway? Asking Fable what the weather will be tomorrow.
28:43 Speaker 04 Yeah, exactly. And that is, I suppose, like a well-known problem if you're in the weeds of AI. But we have seventeen, eighteen hundred people at Mind 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'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, we've actually seen people finding
29:22 Speaker 04 quite creative ways to reduce token spend and contributing that back to the platform as a result of it.
29:30 Tracy Alloway? Wait, say more about that.
29:31 Joe Weisenthal? Yeah.
29:32 Speaker 04 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 the, instead of calling an L L M to do inference and tool calls, it can just intercept that tool call and do it outside of the agentic loop.
30:08 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?
30:21 Speaker 04 So since January, I think token consumption has gone up eighty six times.
30:28 Joe Weisenthal? Wow.
30:28 Speaker 04 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. We're, 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
30:58 Joe Weisenthal? Yeah.
30:59 Speaker 04 people who haven't been able to use it before.
31:01 Joe Weisenthal? Well, this actually gets to 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 four point seven or four point five to four point six and beyond, or this sort of discovery of these very high quality harnesses, like a Claude Code or Co-Work or whatever, I think that's what it's called, that really allows someone to do things with AI
31:39 Joe Weisenthal? 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?
31:50 Speaker 04 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 sixteen 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
32:36 Speaker 04 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
32:56 Speaker 04 larger tasks to be completed that has resulted in larger token usage.
33:18 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Businessweek's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices,
34:01 Speaker 01 but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.
34:41 Tracy Alloway? I want to go back to, I guess, the oversight question. And we all know that finance is a 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 approving 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?
35:21 Tracy Alloway? And how am I translating the model outputs into something that is understandable and also, I guess, defensible?
35:30 Speaker 03 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 at all inservable. We're not in that space. And that trading and the 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, ooh, 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
36:14 Speaker 03 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.
36:47 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 wouldn't have had the bandwidth before? So for example, I'm at, 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
37:32 Joe Weisenthal? 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?
37:55 Speaker 03 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. So yes, I think it's absolutely happening, but it's the sum of a lot of different parts across the firm.
38:30 Speaker 04 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're maybe traded.
39:06 Speaker 04 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.
39:28 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 stronger intuition about how these things might play out or some sort of combination of characteristics? What do you look for now?
40:05 Speaker 03 I think very fair to say, and this is like 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 operations space as it does in the front office space.
40:29 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 is that? Say more about, okay, in the recruiting process, what does that person look like?
40:42 Speaker 03 That 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, when I'm hiring people, you know, what sort of people do I want? I want bright people. I don't 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 job.
41:18 Speaker 04 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
41:33 Speaker 04 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 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 valued than they used to be.
42:09 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 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 skillsets throughout Man Group?
42:50 Speaker 03 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 all 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. There's a fair few of them even saying that.
43:16 Speaker 04 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,
43:58 Speaker 04 rather than just going in, trying to build a proof of concept and execute on the idea.
44:05 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, et cetera, 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.
44:51 Joe Weisenthal? 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 par with labor? And when you think about Man Group in 2027, 2028, do you talk about expected ratios of salaries to token spend?
45:10 Speaker 03 No, we genuinely haven't started having that conversation yet. I mean, I would guess at some point it will come. And the company is set up 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.
45:30 Speaker 04 One of the interesting things in the token budgeting process, I suppose, is that what we're increasingly seeing is the
45:39 Speaker 04 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.
45:57 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 where we're at in terms of like Micrometers? Yeah, 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 arbitrage away relatively quickly? As everyone seems to be hopping on the same bandwagon? Or are there certain advantages?
46:37 Tracy Alloway? You know, we talked about data, for instance, scale perhaps, that you would expect to stick around for some time?
46:44 Speaker 04 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 backtesting, compute.
47:28 Speaker 04 It's this whole network, this whole ecosystem that together, I think, drives alpha. 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. 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.
48:04 Speaker 04 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.
48:13 Joe Weisenthal? Gary, I want to go back to something you said earlier when we were talking about bottlenecks. And it's like, 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,
48:56 Joe Weisenthal? 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?
49:34 Speaker 03 Yeah, I did smile at the multi-billion dollar forward deployed engineer and 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, we're very big on platforms and we've got a bunch of teams and Tashara runs one of those that have 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 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,
50:18 Speaker 03 right, let's go walk across the fifth floor here in Red Bank House. I don't 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.
50:36 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 at all kinds of different firms where compensation is linked to someone's
51:16 Joe Weisenthal? 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 data and information?
51:42 Speaker 03 I think in some areas, that's still a little bit of a work in progress. I'm saying in some areas because in others, notably the systematic, the quant areas of the business, this highly collaborative approach and shared code bases 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 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 add lies. I'm not so comfortable talking
52:21 Speaker 03 about about that. But even that said, there's a very decent and genuine amounts 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.
52:39 Speaker 04 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% 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.
53:18 Joe Weisenthal? That makes sense. All right, Gary and Tashara, thank you so much for coming on Odd Lots. Really appreciate you taking your time and talk about where you're at.
53:26 Speaker 03 Thank you. My pleasure.
53:27 Speaker 04 Thanks a lot.
53:40 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?
53:47 Tracy Alloway? I mean, just the basic token budget and like where stuff gets allocated.
53:50 Joe Weisenthal? And like 86X since January is pretty crazy.
53:54 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.
54:11 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 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.
54:54 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?
55:12 Joe Weisenthal? Yeah, you know, the one of those articles, I have to say.
55:16 Tracy Alloway? You don't need them to offer it up. You just take all the data that they're producing.
55:20 Joe Weisenthal? I have to say, there was some story that came out a while back about Meta 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.
55:34 Tracy Alloway? Especially in finance and investment, which is a highly regulated industry already. And I'm sure is monitoring pretty much everything anyway.
55:42 Joe Weisenthal? Yeah, I kind of assumed 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, does the person, does the rainmaker suddenly just start writing everything down on pen and paper, et cetera?
55:58 Tracy Alloway? Cocktail napkins.
55:58 Joe Weisenthal? They're not implicitly uploading all of their knowledge to the AI. I think that's a pretty interesting question in itself.
56:06 Tracy Alloway? All right, clearly lots of interesting questions, but shall we leave it there for now?
56:09 Joe Weisenthal? Let's leave it there.
56:10 Tracy Alloway? Okay, this has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.
56:15 Joe Weisenthal? And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our producers, Carmen Rodriguez at Carmen Armin, Dashal Bennett at Dashbot, Cale Brooks at Cale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to Bloomberg.com/oddlots or have a daily newsletter and all of our episodes. And you can chat about all of these topics 24/7 in our Discord, discord.gg/oddlots.
56:40 Tracy Alloway? And if you enjoy Odd Lots, if you like it when we talk about how finance firms are actually 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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