#126 - Greg Bond: Alpha at Scale, AI for Collaboration

21:44Greg Bond:I think every few months, one firm is going to run to the lead with a different capability. And so you want to have an organization that can scale and change quickly as these new technologies come about and new evolutions of the models. And do you think about the two different sides helping train those models so they can benefit the entire firm? So there's a couple of ways that that could happen. I think right now where we sit and at these technologies, a lot of the IP is in the skill files, right? What, you know, sort of the harness, if you, they call them harnesses. The technology is the horse, but you need a harness on how to move and direct that horse, right?

22:22Greg Bond:And these harnesses, I call them skill files, other things, whatever the phrase is. where you go in that skill, how you want to think about a problem. How does man group want to think about the problem? How does a really good discretionary portfolio manager want to think about a problem? So that when somebody is interacting or tackling a new problem, they already immediately as they sit down and interact with the large language model, there's all of this great IP from across the firm that the large language model already knows about. It knows that it understands in and out of sample testing. It understands being fooled by different regimes.

22:58Greg Bond:So I think that it's kind of boring in one way because it's not as cool as the actual technology, but just how do you go about researching different things and building things is hugely valuable. So I think that's where we've gone. Obviously, technologies have gotten a lot better in the last six to nine months. A lot of excitement, a lot of what I call dashboard building, helping people automate their day-to-day lives and putting together manager performance other things but now it's in that next phase and can we actually bring it to bear on actual problems but in a way that you don't have to continually relearn so i for example been working you know when i write something or work or review a research proposal by somebody i've created my own i guess Craig Bond person in this large language model that will attack it with things that I always ask.

23:48Greg Bond:And then you can also augment it with things like, hey, I want the best mathematician in the world to review this. I want a high-level, multi-strat PM to review. So you can create these personas, I guess, nine or ten personas of things that you like, and just have them review your work. And this is very basic stuff, and I think anybody can do it. But once you've built that skill, I can then send that out to other people You know, and I think that's very helpful organizationally that you have this concept across because anybody can sit down and build a dashboard. Right. That's great. It's fun. But we don't need 100 ,000 dashboards that all do about the same thing.

24:25Greg Bond:We actually need to bring it to bear on real problems. As CIO, how do you personally think about diversification of ideas, not just diversification of assets or strategies? One of the fears I have in our industry, there's a little bit of, I guess I call it FOMO, right? If you're missing out on various strategies, products, what might be out there, or you see a very successful firm and you want to copy that firm or go in that direction. And I think that's very hard to do. You can never quite observe the entire organization. You're sitting externally. You can talk to people inside, outside, whatever it might be.

24:59Greg Bond:But that's really not the way to set a strategy. It's more about where do you want to be in the marketplace. I like our alpha at scale positioning. And if you take that, that makes some decisions internally very clear. I also know over my career, some people work differently. Some people are very good in meetings and it could be a 20-person meeting and they're willing to pound the table and make their voice heard. Other people don't necessarily like to interact that way. Clearly, when the CIO is in the room or our CEO or whoever it might be, they can also dominate the conversation unknowingly, or maybe they feel like they have to dominate it because they're in the position that they are.

25:34Greg Bond:So what we try to do in certain places is really make it much more collaborative, allow multiple ways for people to have their opinions heard. In a systematic engine, the bottom upside at Numeric, for example, we use something called the expert panel where people will vote on an idea in an anonymous fashion. Everybody sees the comments of what everybody's written, but they don't know who wrote them. And then you can use that, particularly for questions that are sort of 55-45 in the voting, to try to drive that to 90-10 or 10-90, right? And try to build some understanding, seeing other people's comments, but not with the bias that can come across if somebody walks into the room.

26:13Greg Bond:If I said, hey, we got to go do this. Those are some subtle things that you can do. A lot of it comes back to collaboration. and the other thing I think philosophically for folks to have them work in different parts of the organization right so I think that also helps decision making because once you've seen the discretionary side or the systematic side or even within systematic the research or the PM side the analyst side of discretionary that you start to get an appreciation for where other people are coming from in some of these these discussions so I think flexibility movement collaboration so a lot of just talent development, growth focus, where can they go in the organization?

26:50Greg Bond:Sometimes, again, it's going back to my, when you hire people in an organization, it might take three or four years to figure out what the best fit is. So maybe you move into a different part of the organization. So that's really important. That's why talents, HR, all of those things are as important as anything else because the people, whether it's discretionary or a quant side of the business, are super important. And I guess increasingly AI may have a voice at the table as well. That's interesting. Is AI another employee, right? You can think of it that way, or some of these large language models.

27:21Greg Bond:I think what's important, I think there is a reinforcing effect because of the new technologies and sort of the older ways of doing things, because it might bring in insights that you just didn't observe. It can help push back. But I think when you, again, going back to design sort of the digital version of your organization, it needs to have a consistent culture with what you're doing on the organic side. So I think of this where one of the short-term applications and near-term applications is just on the research side where historically an organic researcher is kind of dugging away on ideas, but could you augment them with kind of their digital counterparts?

27:59Greg Bond:The digital counterparts have been trained by the organic parts, right? And you can get the scaling effect, whether that's systematic or discretionary. And so that's one way that organizations can scale. That's why I'm not, I don't think we can debate this five to 10 years from now, but I don't think people are looking to do necessarily job reductions. It's more giving more power to people that are in the organization and getting uplift there because ultimately the scaling benefits, I think, could be there if it's done correctly. Would you share some insight about how or what effective collaboration may look like when you're also trying to preserve independent thinking and low correlation across viewpoints?

28:41Greg Bond:Yeah, this is a really interesting debate, right? And I think there are different models. Some people are highly siloed. That's been very successful. I think people that have been on the very end of the very sharp end of collaboration have also been successful. So I'm not sure there's one right answer. It depends on what your organizational philosophy is. I think it's important that if you're going to be in a siloed world, don't hire a bunch of collaborative people. And if you're in a collaborative world, not to hire people that are really into their P &L and the kind of called mercenary side, however you want to describe it.

29:11Greg Bond:Again, all reasonable models, all reasonable human behavior, everything has shown a lot of success. I think we've leaned more on the collaboration side. I think part of that is just the evolution of the firm. I do go back and forth with myself. If I have one idea I want to explore as a firm, is it better to have two teams work on it? And they may come up with a better answer sort of individually that you then bring back together. But the opportunity cost is we could have looked at two ideas. And is that better to have one team per idea, and then you can do two ideas, or do you have two teams on one idea, which means one idea and sort of this concept of scale?

29:51Greg Bond:So that's a question. So you talked about this earlier, but multi-strategy investing, it's become one of the dominant models in hedge funds. From your perspective, what problem does a multi-strategy platform solve that perhaps singles strategy funds may struggle with? It's an interesting thing because you said multi-strategy platform. And I think that's very much how people think about multi-strats today, kind of capital M, capital S. I bring together several hundred portfolio managers, whatever it might be. And I think that is one model and that's been a very successful model, but I think it's not the only way to think about multi-strategies, like move into little M, little S, having multiple strategies in one vehicle, one fund, what have you, has some advantages that investors and allocators can't necessarily do on their own.

30:39Greg Bond:Forget what's inside the multi-strat for a second, because I think there's differentiation there, but just the structure. As an individual allocator, maybe you could allocate to five to 10 hedge funds on your own. People have done that in the past. Fund-to-funds have come in to help manage some of that. But what are the downsides? Well, you have to get to know five to 10 portfolio managers and firms very well. It's a little bit hard maybe to move capital across those five to 10 strategies. Maybe there's lockups. It's also just everybody has investment committees. It's also hard that hopefully these five to 10 hedge funds that you're hiring have low correlation with each other.

31:18Greg Bond:That's great. But if I put them together, maybe I don't have enough volatility to make it worth my while, or at least whatever dollars I'm putting in, maybe not getting as much efficiency. What you'd maybe like to do is lever that, but that can be hard if you've got five to 10 separate fund investments. And so So the theoretical benefits of Multistrat, little M, little s, is that, okay, well, you can hire a firm to go out and internally build those capabilities, internally find a source, additional uncorrelated concepts. And that could be discretionary. It could be systematic. It could be all kinds of different ideas.

31:54Greg Bond:And so the pros are that you can bring that together, that the manager of that fund can then allocate quite quickly across new managers, different risk regimes, other things. They can see the whole risk of the portfolio in one fell swoop rather than sort of relying on monthly reports and then trying to aggregate. So you've got a lot of hyper detail on what's in the portfolio. You can be quite nimble. And then importantly, if it is uncorrelated, that manager can manage the leverage of that fund so that you get your volatility back up to what you may have had on a single fund before, maybe 5 % or 6%, whatever that might be.

32:28Greg Bond:In certain cases, maybe you want even more volatility and sort of moving that would depend on some of the content. Hey, maybe you want to take that multi-strat and put it on top of the S &P 500 in like a portable alpha type. So there's a lot of flexibility. So the downside, obviously, relying on this manager to put this together in a very efficient way. It also, you lose some, maybe some transparency. If you're buying into a multistrat, what's actually in there? And then there's this other part around cost and what is the cost of supporting that infrastructure. So there's a lot of pros. There can be cons.

33:03Greg Bond:But I think the important part is there's not just one way to do multistrat. And I think what you're seeing in the place today, in the spectrum, is differentiation of what's there. Some are a bit more quant, maybe some are more discretionary, some are more liquid, some are less liquid. So that profile has changed and augmented. And I think the people that might be getting in trouble or have had difficulty in the space is really going back to that FOMO side of just trying to find a firm that they want to emulate and go out and copy that exactly. I think you have to have some self-reflection about where you want to fit in that spectrum.

33:35I know technology has always been a part of man groups DNA. How do you think about technology as a strategic advantage rather than just a tool?

33:44Greg Bond:So going back to my early days of my career, I wrote cases and did work with Michael Porter at Harvard Business School around strategies in the competition and strategy group at Harvard Business School. And one of his big points was there's a difference between operational effectiveness and strategy. You know, operational effectiveness is doing things better and better. Strategy is actually making choices. You know, which products and services, how do you want to think about the market, pricing, volume, all of those kinds of things because it's hard to do everything. And so if you, the technology in and of itself is not necessarily a competitive advantage, right?

34:19Greg Bond:Everybody's going to continually invest there, get better and better, right? I think if you go way back to the Japanese auto manufacturers in the 80s, right? They were very, very good at operational effectiveness. unless that operational effectiveness was quickly copied around the world, right? So it's hard to maintain that. So the key is how does the technology fit into the strategy? And therefore, you make choices on your tech platform, right? If it's a single strategy fund, the firm has one fund, right? If they have multiple PMs, but it's basically one fund, that leads to one level of complexity, right?

34:57Greg Bond:sort of the narrower complexity in terms of what the product that you're supporting, but maybe you need to have broader capabilities for all the PMs on your platform. If you're offering many different kinds of strategies, different asset classes, and you're doing a lot of bespoke work for clients, that has a different impact and concept on your tech platform. So it's really important that you align those. And particularly now with the development of AI, that you need perfect alignment. And that allows you then to have AI and large language models digested more efficiently through the organization.

35:27Greg Bond:So just be on the lookout for people radically changing their tech stack somehow because of AI. No, no, it's only the tech stack should evolve with the strategy and not the other way around. And I think that's important. I think concepts around being flexible with your technology and let's not forget, even with all of the great AI technology that have been developed, this goes back to your data management. That's the most important thing. And I think eventually you'll see a lot of AI models that'll be open sourced, other things. So it really sits back on how your data estate is managed and protected.

36:01Greg Bond:And that moat, I think, that kind of competitive advantage will sit there for firms that are very, very good with their data. So let's not forget about that kind of very old school block and tackling thing is how do I manage all the data I have organizationally? I guess one way to think about the potential impact of AI is on one hand, it may help you do what you're already doing, producing the same thing more efficiently. And on the other, maybe It helps you create new things to produce and new alpha to generate. And I think that's where we're, you know, in terms of some of the training and as we go through the broader company, how people think about AI not as exactly that productivity piece, but also kind of a partner in the journey along research or creative endeavors.

36:41Greg Bond:So can you create, goes back to my persona concept, right? And that sort of research partner, learning how, I mean, the prompt engineering is really, really critical, how you interact. You can't just say, make me more alpha, right? That doesn't really work very well. It's like a thought partner, right? That has a perspective. That's a great way to describe it. And I think that takes some thinking in how to do that and which task, which model do you want to use for a given task? There's all of that kind of interesting stuff. But even the models that are now, whatever, one or two generations old are still really, really good.

37:17Greg Bond:So yeah, I think we just need to be better users of those technologies, I think. Yeah, it is interesting. Like the way you frame the question, I think is perfect, which is if you just go to an AI and say, help me figure out how to create more alpha. And you go to a person and you say, help me create more alpha. But you put the two together and you say, let's solve this problem together. And they go back and forth with idea sharing and challenging one another. You could see why adding the two together could be more beneficial. And what's really, really important, I find, when I do this, is let the technology know as much about you as possible.

37:52Greg Bond:Load up as many documents, things you've written, how your firm approaches, whatever the topics are, because that context is very, very important. And I think even in certain cases, some of the newer models, you can actually talk into the large language model. So talking, you get more context than maybe you're just sitting there and typing. So the more information you give it, the more likely you're going to get an answer that's productive. So when you think about AI and large language models, what excites you the most at a conceptual level? Well, again, when it first came out, you know, I was particularly on the systematic side, maybe less excited only in the sense that, you know, we already do a lot of reviews of analyst reports, been using AI for many, many years.

38:33Greg Bond:In some ways, it's letting the competition catch up. Correct. And then I'd say over the last 18 months, this kind of partner or thought partner part has really, really improved, partly because the technology has, also the way we use it has improved and interacting with it. So that's, I think, very exciting. The ability to use it to help you think and go through problems and also try things you may not have been able to do in the past because it would have taken too much time. So just one of the things I like to do and approach problems is this concept of ensemble thinking, which is you bring different ways of doing it together and sort of average across them rather than picking one way or another to do it that could be a portfolio allocation question it could be a model question it could be how do we want to allocate uh you know across discretionary management whatever it might be and so it allows you to kind of open up your mind a bit on how to approach it and i also find it's very good as you go through recording your thoughts in that research process so kind of creating a live document or living document as you attack these things.

39:33Greg Bond:Because then you can go back and look at it. How did I get to this point? That's exciting for folks. And I think the question, and as the models get better and better, at what point do you switch to the outsource? You take an open source model, bring it in-house, and then start to train your own things. I think the smaller models trained over your own work actually might be a very productive thing going forward, which you could see in the next couple of years rather than off the shelf. And I know a lot of the providers out there are building very specific agents, finance agents, other things. But you could imagine a world where you don't necessarily need that.

40:13Greg Bond:The models that are open source are powerful enough that you can actually build something for your own use. Yeah, if you can incorporate some of your proprietary insights into the model that others don't have access to. You could see how that could create an edge. Absolutely. I think we've got a lot of history doing that. I mean, I think a lot of the firms that do traditional kind of machine learning and other things, they often start with the standard open source package or whatever, and then you put your own IP around it. And I think the other nice thing is you do bring in some of these models on the discretionary side in particular, right?

40:50Greg Bond:You can start to train that a bit more with your human data, right? And I think that's going to be very reinforcing for a portfolio manager on the discretionary side. If you think about AI feeds on data, and if what is publicly available all of a sudden is easily accessible by everyone, then it's the differentiated data that could be the source of future alpha. Differentiated data. One of the questions we talk about quite a bit is, will AI let people make better or worse decisions? On the one hand, you should make better decisions, but then you can have more people trying to make decisions in the space because it is so democratized.

41:29Greg Bond:And I don't know how that can affect ultimately policy-making decisions as well, which ultimately have a huge impact on the market. So it's not clear to me that we'll make better or worse decisions. It might create more alpha opportunities because of that. So that's one way to think about it because I do get questions around, is everybody just going to think the same way? And I think, well, to your point, having more data, different data, that's going to help. But also the way you deploy the technology, there's just a lot of different ways to do it. That'll create some dispersion. And there will be bad uses of the technology in terms of bad decision making that could come out of it.

42:00Greg Bond:So it's going to be a very interesting, you know, next decade. I think the interesting part about what you just described is I think it's important to keep in mind that better and worse decisions are relative metrics. And, you know, you can argue better decisions are made today than 50 years ago, but it's relative to the higher level of expectations. decisions so so maybe ai uh allows us to in in absolute terms make better decisions but you have to think of it relative and is it better relative to others is a worse relative to others with a higher bar so society benefits from better decision making in general but the dispersion arena the sort of it makes it harder and harder for the people to add value above and beyond that exactly and that's the nature of what we do which is why it makes it fun so the other thing that i think is interesting is we obviously live in a period of great uncertainty and oftentimes investors feel very uncomfortable in periods like that.

42:58Do you think that can actually be healthy for active management?

43:01Greg Bond:I think we're in a really good environment for active management. I think you could argue that coming out of the GSC and the lower interest rate environment, there wasn't a lot of dispersion in different things, different classes. Cash was zero and it was really, hey, I should just maybe buy equities and it'll be fine. And what we've seen as we've gone into a different rate regime, we've seen better opportunities for alpha. So I think that that also manifests in better risk management, hopefully. So I think one of the reasons you see some of the data maybe on some of the multi-strats have done quite well is that ability maybe to do both the alpha and the risk management a bit more effectively in the market.

43:42Greg Bond:Of course, that's brought in a lot of extra capital into the space as well. So you have that competitive dynamic. But generally, across many sets of strategies, alpha has been pretty good the last several years. Now, it can change year to year, but I would say the opportunity for active management is very good today. So if you look at today's macro environment, what characteristics tend to create the richest opportunity set for alpha? I think it's always the opportunity, but also the danger in some of this. I think the kind of continual shorter term shifts from risk on to risk off, right? That's dominated, you know, the last several years, obviously.

44:20Greg Bond:You go back 24, 25 about inflation as well, which I think you're ending up in around periods where if your portfolio, if you're up at night worried about the next earnings print from a large cap S &P 500 company or the CPI print or a Federal Reserve decision, then your portfolio is probably not as well diversified as you'd like it. So I kind of use that as my backdrop to make sure that going into these events and other things that you may lose some in some parts of your portfolio, maybe you win in others. It's not going to be what we call a left tail disaster. All of a sudden, you have a very, very tough day or two of performance.

44:58Greg Bond:And so that's one of my guiding principles is about just risk balance, less about trying to pick individual strategies that will be the best in this particular environment. It's more about being a bit more balanced. So that does create opportunities because there is so much information flow in the market on a day-to-day basis. You've got the rise of a lot of retail investing coming into the space. There's just a lot of interesting pieces that are different today than there were 10 years ago. I do worry that people, a lot of investors today were not around in 2008. What's going on back then when credit could sell off and there's distress and the markets are...

45:38Greg Bond:So I think just remembering, or at least if you didn't live through that, that when you're thinking about asset allocation or risk, at least have some of that history in mind and what can happen because those are hard lessons. And I think people generationally, maybe they forget that. Like every 10 to 15 years, You need a reminder of some of that. Yeah, it is interesting how living through it is so different from just reading about it because it has to hit you hard for those lessons to stick. And it kind of enforces a certain risk discipline that unless you've been through that or if it happened too long ago, those memories fade.

46:21Greg Bond:Exactly, exactly. So one observation that I've had is talent competition in the industry is pretty intense. beyond compensation, what do the best investors really want from a platform or a firm? Again, I think it comes up to what does the portfolio manager, sort of their style, what do they want to go towards? And so I think you've got a group that have been very successful, clearly on the pod-based, silo-based approach. Other people are looking for a more collaborative type setup. So I think that's interesting. Now, again, if you're aiming for collaborative, it's hard to have 10 portfolio managers on the platform doing the same thing, right?

46:57Greg Bond:That sort of is against, it's the opposite of the thesis. So maybe if you're more collaborative, maybe you have one or two PMs in each of the buckets, not 10 to 15. So that sets up your model, goes back to strategy, organizational design, how you want to think about it. I think stability of the platform, I think the use of technology, are they getting access to the great baiting capabilities? What are their firm's prime broker relationships like so they get good margin. So all of that package as they sit from the outside and looking at it, I don't think it's always about compensation or expected compensation.

47:31Greg Bond:I think particularly as the multi-strap models evolve over time, people are seasoned with it, people may start to value some of the other stability points, collaborative points a bit differently than the pure pod base. But again, people have different preferences and you can do that. I think it's just, not only do you need a strategy when you go out to your clients about of being differentiated. I think you need to have a similar strategy when trying to recruit talent. What do you stand for as an organization? And that can be tricky, you know, but you try to have everything aligned. And ultimately it comes down to alpha.

48:04So if you're, if you join a firm that gives you the best opportunity to generate alpha, ultimately that leads to your compensation as well.

48:12Greg Bond:Correct. No alpha, no business. Right. So that's right. Which is the way it should be, right? Because the clients are paying the fees, they should earn alpha over time. And that feeds the... It is the net alpha that matters. Yes. That's right. For younger listeners considering a career in finance, why do you believe creativity is becoming as important as technical skill? You touched on this a little bit earlier. The technical barriers have dropped significantly. I think that still means you need some technology background in terms of some basic data science work. And I think you're seeing that whether it's in the more traditional technical fields or even humanities, social sciences, they all have a little bit of a quant track, I'll call it.

48:53Greg Bond:Again, sort of rudimentary programming, using data to answer questions. So I participate, whatever your field is, and I'm a big fan of multidisciplinary, doesn't have to be finance, doesn't have to be economics. It can be a lot of different fields, but even whatever fields you choose having, there is a little bit of a, in all of those fields, a track that's a bit more quantitative. I think exploring that world is quite helpful. It gives you some skills that allows you to at least, when you're working in this new environment of large language models, that you're able to kind of understand what the code is doing and not doing.

49:27Greg Bond:So you kind of push back on it. I think that's important. But I also think broad experience is really, really helpful. So again, multidisciplinary, multiple majors, multiple minors, all of those kinds of things are really, really good for folks to help stimulate ideas. And if you tie that in with a little bit of technical, then it really opens up what you can do. And again, that's where you're going to go in the discretionary world or the systematic world. I think even on the discretionary side, you still have to run a spreadsheet or however you want to run it, model companies. It's just very specific.

49:58Greg Bond:It's very narrow in terms of the companies you're following versus the broader set on quant side. But I think that's really helpful for folks on whatever career path they pick. So when you reflect on your own career path, what non-obvious experiences ended up shaping how you think as an investor and leader? When I came out of college, I wasn't exactly sure what I wanted to do, but I did know that I wanted to do different things and just see. So part of the career path went from investment banking. I worked at Walt Disney in their strat planning group. I worked for Michael Porter in the Competition and Strategy Group.

50:34Greg Bond:I joined right out of business school, a startup quant shop, which I was doing everything from coding to trading to talking to clients. And then ultimately joining Numeric all the way back in 2003. So I think having different experiences is really useful, different contexts. I took a I had a two or three month break from Numeric and did some work with the Boston Red Sox for a little bit. And my role here has evolved. I was a researcher on the quant side, then ran the hedge funds, was the director of research, did a lot more on the discretionary side as part of our development of a lot of these cross-firm capabilities, and then moving into the broader main group CIO role last summer.

51:17Greg Bond:So part of it is getting different experiences at different parts of the organization is really helpful because that allows you to do more things. But at the same time, it's okay to be an expert in one particular area. So there's a couple of different models for folks. If they love being a portfolio manager covering US financial stocks and they really get it, that's great. If you want to be on the managerial side, organizational design, all that, there's other paths. So I think kind of find what you like to do if you can. And if it went in doubt, then try different things. Are you able to share your experience with the Red Sox?

51:47I think that would be interesting. Oh, yeah.

51:51Greg Bond:On the Alumni Day base, I had a head quant there at the time. This is quite a while ago, so I think it's okay to talk about it. I was brought in to work a bit on their draft strategy. In baseball, you have amateur draft every year and sort of looking at that. So I spent a few weeks. I was an intern. I was punching, taking my card and punching in every day. So it was a lot of fun. you realize you know in well sports is fun sports analytics is fun there's only about there's only 30 baseball teams right and there's one per city it's a very rewarding career but you're also very the alpha there is definitely if you're not winning you might you know not be there as much so i think that was fun for me because i was a bit of an outsider doing it and having fun whereas if you're living it day to day it could be it's just like running running money in the real world right same thing and it's probably more popular today than it was back then yeah i mean quite frankly, if there had been more Moneyball type stuff, maybe that's what I would have ended up doing coming out of college.

52:44Greg Bond:Who knows? It's very interesting. Maybe we've gone too far that way, though. I think, again, back to discretionary and systematic, and I think maybe the systematic parts dominate a little too much. We need to go back to good old fundamental scouting. But that, you know, what do I know? The pendulum swings back and forth. Yes. I think maybe it's gone way too, because it did the other way. So hopefully bring it back a little bit. Well, Greg, this has been a fun conversation, a lot of great insights. I appreciate you joining us. Thank you so much. Oh yeah. Thanks, Alex. It was a lot of fun. Thank you.

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55:15Guests may have current or past relationships with Evoke and MAI, its affiliates, or the host, including as clients, service providers, or business partners. Participation does not constitute an endorsement or testimonial. No compensation has been paid or received for guest participation unless disclosed. MAI and its affiliates may have business relationships with entities mentioned in this podcast, which could create potential conflicts of interest. These relationships may include advisory services, investment management, or other arrangements. MAI seeks to manage such conflicts consistent with its fiduciary obligations and policies.