Mala Gaonkar: Building SurgoCap, Identifying Great Businesses and Learning from Mistakes

Hi everybody, I'm Nikola Tangen, the CEO of the Norwegian, so I'm well-funded and today I'm really happy because I'm here with Mala Gaon Car. I've known for a long time actually. Mala founded CircleCapped with 1.8 billion dollars and now it's at 6 billion dollars and before that she's spent 23 years as a founding partner of Lone Pine Capital, one of the most successful hedge funds of all times. Great to have you here. Great to be here Nikola, thank you. Tell me about Sergio Capp, partners, you know, you'll come. Yeah, CircleCap tries to do what many investment firms try to do. It tries to beat the market over a three to five year cycle with less risk than the market, a risk defined as loss of capital, a lot of volatility, and the way we try to achieve that is by identifying this very small handful of truly great businesses that exist in the world. And we do that through a, our product is really our process, a very transparent process of looking for very specific factors that really are distillation, as you pointed out earlier, of my lessons I've learned, the many mistakes and lessons I've learned, from investing over 23 years with some of the best people in the business, my former colleagues at Lone Pine. So that distillation has led to CERGO, there are many different factors that lead to identification of a truly brilliant business. But the way I define a great business, a business with very long duration modes, and duration really is our true differentiation. So more being that is difficult to compete with them, is difficult to compete them out. How many great companies are other in the world? As I said, a small handful. I don't think there are that many. We focus on really four verticals where I think there is a bit of an edge from one very specific factor, is every business is a technology business, right? So if you're an aerospace company or a med tech business or a financial data business, you are a technology company in your backbone. If you want to deliver at scale and with quality, you have to be a tech business. And I think understanding the tech stack map of businesses is something we spend a lot of time on, especially in non tech businesses. I think that's one. The second is how old technologies can disrupt in very new ways. So if you think about something like, we've talked about this, but the auto industry, which employs far more people in the tech industry, but it's being disrupted right now by technology that was invented in 1976, the lithium ion battery. So I think that's always interesting to me, or when we started investing career, we were buying GPUs to make our video games look a little more fun. And then fast forward to now, they're actually driving what's probably one of the more tectonic plate shifts in terms of social and demographic and technology shifts of our time, which is AI. So I think how old technologies disrupt in new ways and looking for that in non tech businesses is something we spend a lot of time on as well. And welcome back to this. But when you set up the fund, what kind of self-imposed constraints did you put? Yes. So in order to distill to your question about how we identify these truly great businesses and keeping that disciplined, there are a couple ways of doing that. One is focus. We decided I wanted to keep the team small. If there's a consistent piece of advice I got from CEOs, many have been on this podcast with you, whether they're running huge companies or small businesses or founders, was to keep the team size small. So it's not just about AUM. It's obviously it's easier to multiply a dollar than it is to multiply a billion dollars. But team size and keeping team size small and making sure that collaborative and cross-sporter thinking, which is so fruitful for new idea generation, particularly creative idea generation, that was paramount in my brain. How small is small? We have an investment team, including the data science team, and that we regularly meet table the size so that we can have a round table discussion. Jeff Bezos has talked about sort of two pizza box teams. I think we'd like to stay at one pizza box in terms of our team size. One pizza box? So I think that small portion of pizza per person I have to say. Yeah, exactly. We can keep things lean. I'm saying that as in the region, we are the biggest pizza eating country in the world. 11 kilo per person, supposedly. Is that right? Yeah. That's interesting. I've never predicted that. But when you look at your company relative to other tech investors, what would you say is the difference? We're looking at where technology is intersecting with non-technology businesses, and as I said, old technology is disrupting in new ways. That's one angle. We look at the usual checklist of competitive and customer and other checks. I would say the other big lesson is really around biases, and how you can use data science today in a way we couldn't when we started our investment careers, given how fast and how open source now machine learning is. So you can very cost effectively create very strong third-party data checks, including automated surveys, including automated tracking of various data points around markets and tech adoption. And we could not very early on in the late 90s when we were setting up our careers. Give me an example of the kind of thing you could do now that you couldn't do earlier. So very specifically, if you want to track product adoption when we started out, when we started at Lompine 98 when I was looking at some of the early adoptions, even say around Adobe products back in the day when they were just transitioning to cloud, that was a very manual process. You went out, you called customers, you did surveys, surveys were usually done by phone, response rates were trickier to track. Now we actually can use survey bots to do these things. We can actually have a combination of both augmented human surveyors as well as looking at some of the deeper dives. We can track through machine learning in a very automated way a larger number of product skews. We can scrape the web, which to be clear, wasn't at the scale and scope of what it is today, to see product adoptions, to see who has open APIs to their suppliers, who doesn't. So there's a whole range of both tech stack mapping, product adoption, customer adoption, even outside the typical consumer adoption data that is tracked pretty religiously now as you know within the consumer world. Do you think investment organizations will be smaller in the future? I suspect they will be and they probably should be because I do think there is something about human collaboration that works best at a smaller scale. I think some of the most interesting data and something you're missing ideas come through looking across borders, across the borders of industry. So how AI is influencing Medtech, how AI is influencing material science innovation in aerospace. I think those are more interesting angles or just as interesting angles as purely looking at AI as AI within technology itself. What are some of the most constitutive benefits of AI that you are seeing in your companies? I think what is happening within medical technologies is not perhaps as well or broadly understood as it should. So to give you a very specific instance, if you look at the imaging space, so MRIs, CT scans and so on, the accuracy and the speed at which these images can be conducted or now improving at a pace of almost 70% versus what we would have even a few years ago, that in an aging demographic globally has been incredibly helpful for not just therapeutic care but preventive care and in a way that makes obviously overall care much more cost effective. So I think that is one big area around imaging and really understanding earlier how some of these chronic diseases are evolving and I think obviously nipping these in the butt earlier has been one big one. The other big area has been around how surgery is conducted. So I think the intersection, people talk about AI and robotics in the manufacturing space, but I think people don't realize sometimes that there are about 300 million surgeries conducted globally and they are just being to be penetrated by what is happening with robotic surgery, specifically companies like Intuitive Surgical. So I think that is another big category where you are being to see real innovation happening where the combination of haptic feedback, mapping software, better technologies around surgical conduct itself are really leading to a lower error, easier training for medical students and a overall better context for how these therapeutic care can be conducted. And as you know, there is a big discussion obviously around how health care costs can be contained and I have some hopes that AI applied intelligently can help with this. So yeah, I think broadly this idea of how technology is intersecting with non-technology businesses and how you can use data to analyze that and de-bias the very human decision making process. Something we spent a lot of time on. Very interesting. Do you believe in concentration you have quite big when you go for something you go pretty big right? Correct. Tell me how you think about concentration. I very intentionally set on these four verticals to happen to be enterprise data, tech broadly, financial services and health care services as well as industrial technologies. Because I felt those were the areas where these themes of emerging technology disruption were most relevant and where you could see market leaders with very durable modes where incremental returns and the capital you could put to work with those incremental returns had a clearer path given all the technology trends we're seeing today. So we purely just focus on those four areas. We also think about thematic exposure as well. And the reason for that I think is very simple. One I think that allows you to play offense when at some point or the other due to the passive nature of the market structure now increasingly, the odd factor rotation here or there could disrupt a portfolio that allows us to play offense during those periods. And we believe there is actually sufficiently interesting set of investment ideas across each of these four sectors where we're not compromising. And we have very different drivers of the long these days of each of these names. Now you grew up in India and I heard you on a podcast once talking about food. So how do you, how do you, how do you spice up the portfolio? I think the spice for us is quality. So I think we have really moded long duration businesses and we have very debice ways of tracking the data where it's not just based on my intuition or the team's intuition, but really based on systemic thinking and not silo thinking. That's the spice. That seems really exciting to me because then that's a really hard thing to think about and do well. And I think thinking, I think it's important for us to talk about this a little bit and your real student of the investment process, Nikolai, but you know, we're both, we both love the sort of Kanamon Spiritsky, Thaler work, right? And I remember my father once giving me this great quote from Bertrand Russell, which is humans will do anything they can to not have to think. And it's very simple like Kanamon talks about system one and system two thinking. And system one thing is that intuitive got thinking that frankly is positive. It drives very quick fluid reactions from humanity day to day and it works perfectly fine. But system two thinking, which is really getting into the weeds and really thinking methodically and logically is really hard. And so I try to focus on making sure my team and our portfolios driven by system two thinking. Here I need to interject that I have actually interviewed you once before for my master's thesis in. That's right. In decision making, which was exactly about that. When do you use intuition and when do you use analysis? So how has your use of pattern recognition changed during your career? My pattern recognition has I think expanded from being less driven by individual analytical viewpoints and more to thinking about the context. And so I've probably over emphasized the person and the CEO or the leadership or kind of the hero model and under emphasized the very powerful social context in which companies, businesses were operating. And I learned this a hard way. I've had plenty of failures. When my first job working in Russian Mongolia as a generalist with the World Bank, I came in there, this is a particularly poignant example, I came in there thinking, okay great, we're going to take these data and enterprises, we're going to value them, we're going to distribute tickets through the various local bank branches, open up the stock market, boom, capitalism, liberal democracy, it's all going to be awesome, the end of history done. What happened was a little bit different, right? We had instead a rise of very corrupt practices, we had the rise of autocracy across these countries and we know the headlines today still speak of these. We cannot take away or abstract away from 70 years of communist history and simply assume that people are going to just have a new framework of thinking. So I think the same is true today, like you really have to think about the very powerful overall context as you're looking bottoms up at businesses. You have to have almost the right eye and the telescope and the left eye and the microscope to really think systemically and clearly about how businesses will evolve in the context in which they're in today. That's something I think think a lot about, is how do we address these very specific failures of our thinking. You can also invest in private companies. Yes. Why did you choose to be able to do that? I think some of the, we obviously know about the very large market caps that exist, very large businesses exist in the private realm and we know about the fact that there are fewer businesses that are out there in the public markets than have been for a while. So those trends aside, I think another reason to look at the private markets is because they're often the most disruptive. Change always happens at the edges, not at the core, right? That's my fundamental view. And the edges is really the small company, the private company, the unsung founder who's just emerging. That's really where change in the market will happen. So making sure that we have our networks and thought processes out there, not just in the US but globally, within these four areas that we focus on, these four big industry categories we focus on is something that I think is incumbent upon us in talking to private companies as part of that process. It's as simple as that. How do you come up with ideas? So you wake up in the morning and then as I bang, I want to look at that. It's not I wake up in the morning and bang, I look at that. It's really a longer duration. There's a long incubation process, I think, for me, of ideas. So I read a lot, I think, I talk to my team, I talk to the broader networks that we all have access to out there. But then back to the point about change happening at the edge, it's really about going out in the field. It's about going to that obscure industry trade show that happens to feature automation of new robotic systems where you might get a new idea. It might happen to be this panel, this regular ongoing survey we've done since before a launch of AI developers in non-tech industries to see what are they actually working on. What are they actually trying out that's working, that's not working. So it's really, I would say, from more obscure lenses that I get my best ideas versus what you would expect, just talking to the existing power players, although that's obviously important as well. So then you have an idea, what do you do with it? The next step is to your point, your question earlier about intuition system 1 versus system 2 thinking is then to put it through the very fine filter of the overall check investment checklist. I very much believe in a kind of a checklist-driven approach. We go through that and then in addition to my point about biases, make sure we have a way to track the thesis and make sure that we can hold on to that rope. The reason for that is I think we are very subject to some very colorfully named biases, whether it's confirmation bias or availability of bias or some cost bias. When I look across my many investment mistakes, they have all been one or the other of these biases. Very human and they are natural and they have some positive aspects, but for investing may be a bit less so. So I'd like to make sure that there's a data point that we can attach our investment thesis to that's unbiased so that the investment team and I can track this and make sure that we're not just assuming issues that might be headwinds to the thesis away. And I spend a lot of time on that. I spend a lot of time also making sure we're talking about errors of omission and commission, but I also want to make sure we don't get into make mistakes. So a lot of my time is also thinking about what are the assumptions that are being made out there that could possibly be wrong? Where could things go astray? Where could market disruptions happen? And so that's something I spend just as much time on and FOMO is probably the biggest, right? So I'd like to tell my team it's not, let's move from FOMO to TOMO, thoughtfully missing out. Making sure that we're looking at whatever happens to be, crypto, work, quantum or whatever the flavor of the day might be. Maybe we can try and expand our circle of confidence, but if we can't, making it clear what our circle of confidence is. Keep pushing it out, but be very clear about what those lines are not overstepping them. So I think I would say my ideas are as much about steering the team as they are about specific stocks. How do you use them? How do they make decisions or they support you in your decision making? I have a very experienced team, I'm very lucky to work with this small handful of truly excellent people have had, you know, decade or so each of long and short stockpaking experience. And I really use them as collaborators. I really think of them as people that are very important to my decision making process. And I could not, this is very much a team sport in my view. And so you do need that strong team. And I view myself as much as coaching and mentoring role. And one of the big drivers' circle cap I hope is that I can attract and mentor the next generation of really great investment talent. So thoughtfully missing out, what are the red flags that will keep you away from something? One is compromise. So if you feel that you are only buying, you know, investing in a business because of pure valuation, or you're investing in business because you think the founder is the next Messiah. But there are other issues, but you're going to kind of smooth those away. So basically essentially being overly emotional in terms of the investment decision making. Whenever I see that, sometimes when I get too excited about something even, I think that's a red flag. To make sure that we're really thinking in a very balanced and thoughtful way about all the aspects of a business as opposed to just focusing on one. And I think that's where the bias is very inherent, you know, as humans, we're very trained to look for that one big, exciting moment or thing. That's how we evolved, right? We were looking for the predator that was about to pounce on us. But in reality, I think in the modern world, it is not that simple. In fact, it's incredibly complex. And there's a multiplicity of factors that could go right and could go wrong. And spending time with that full gamut is something I make sure we do, and I make mistakes when I don't. It's not easy to leave emotions out of it. But I do think you need to have a methodology and a process to bring that into your system to thinking and creating tools so that you and your team can nudge that way. And then remember, back to the nature of the markets themselves, stocks move as much in narrative as they move on numbers. So you have to be respectful of that context and just be appreciative of the context. In fact, my point about context in even the late dying days of the Soviet Union and how powerful that was. So I think that's something we need to balance as well. You mentioned long-term modes as a definition of quality. What are the other type of quality signs you're looking at? Yeah. So when I say mode, what do I mean by that? I mean, a business that has very high incremental ROICs, because ultimately the value of business is going to be the return on incremental invested capital times the dollars you can put to work at those incremental returns, right? So it's a balance of what is driving that ROIC and multiple ways are really the best ways, right? So if it's just by driving a price, I don't love that. But if it's driving a price and there's feature innovation and there's market growth through new geographies or new products, that's great. So multiple levers on driving ROIC is one. The second is the capital you can put to work at those incremental returns. There are many businesses that have, you know, not many, but there's some great business of high incremental returns, but the capital they can put to work at those incremental returns are diminishing. The growth market isn't there. And so trying to understand what category of business that is. And then even if they do have a lot of capital they can put to work and we're seeing this in the technology space today, execution risk is enormous. And so I think we need to make sure that we're getting the level of return for that level of capital being put to work versus the sales and size of the existing business today, because that increases execution risk, which is a risk I need very much to think about. So I think that combination of incremental returns, capital you can put to work in those incremental returns, having multiple levers to both manage the positives in one and the negatives of execution risk in the other is what we think about. So without talking about anything you own in the portfolio just now, but an example of a great investment that you have had, which would kind of take some of these boxes. Yeah, I would say the broader, and we've had some of these investments in my prior role as well. This is the great aspect of these businesses. I think what's really interesting right now is what's happening with the chip stack. So we've been spending a lot of time on that. So you have business like TSM that are well known, that are really kind of. There's a Taiwan in Taiwan called the process engineering aspects of that. So it's not really just about one thing, it's all multiple things. So I love that sort of systemic modes where there isn't just one little simple, it's not you just own the goal, only goal mine in town, is that you own the process around how the goal mine is extracted. So I think we, I like businesses that have those kinds of, those dynamics to them. I also think there's something really interesting happening now in the chip layer in AI where there's been a lot of talk about training, but I think you know, training is a little spiky, but inference is the new industry, right, ultimately this is what we're going to be calling on the models to do day and day out. And those are going to be within specific LLMs. Google will have its own inference stack, open AI, and throttic and others. And I think what is happening around Google's TPU is really interesting in terms of reducing that. So I think ASICs or ASIC chips, application specific chips that are designed for specific types of workloads and what's happening around that creates very long duration modes. And so we've been focusing a lot in that kind of thematic. We talked about medical technologies earlier, but I do think the combination of go-to-market modes combined with, you know, process innovation that just keeps compounding embedded in a large pool of, you know, global doctors, nurse practitioners out there actually executing, creates a lot of, you know, stickiness as well. So I think that's another area of sort of where great businesses are really emerging and forming and have formed. So those are probably two big buckets in what's happening around the chip stack layer, which should fully, and the med stack layer, which should play off last 20 years. So these are not new, these are. I think there's another interesting category as well where there's a view that AI is maybe disrupting a little bit more than it actually is. And so I think within sort of real-time data and analytics, there's some proprietary data providers. We, I think are really interesting and are really terrific businesses where they have both the system of record and the system of workflow from combined. And that combined with a very basic innovation engine, those three combined often create very durable. What type of companies will that be? So those are businesses that provide, for example, financial data. So think about even for your portfolio, all of the real-time pricing that's needed for FX clearance or for fixed income or for the equity markets. I think those are real-time data, very hard-free, and a limb, for example, to ever disrupt because of the real-time nature of that. But that's embedded in real workflows around trading and compliance. So I think they're very hard to disrupt as well. So I think there's a perception that those can be scraped away by the AI models. But I think that's almost very hard for me to see or understand. What's kind of time horizon, do you have when you invest or when you think about investments? For as long as possible, on the long side, obviously, shorts are obviously different. They're more catalyst-driven and usually tend to have sub-nine months kind of catalyst periods. But as I said, I trust our process to look at duration. So we're really looking, as I said, for that combination of factors driving incremental returns and capital that can be put to work at those incremental returns that really are playing out certainly over three to five year cycle in terms of our evaluation horizon. But ideally, we see levers beyond that. How does shorting compare with long investing? Can do the same person do both? It's a great question because it's really the opposite muscle of the long side, as you know. It's really thinking about, so sometimes you will see clear the shorts that are the broken mirror images of the longs. X is winning, Y is losing. I find those are very rare and few and far between, and that's not enough usually. I think in addition to that, you have to have the timing correct as to when exactly those winner loser dynamics will play out. Basically, if it plays out through some combination of clear price competitive pressures, clear share competitive pressures leading to those pricing pressures, and then that combined with other factors such as just misexecution by management, you have a pretty good short. The problem though is that combination of factors occurring within the timeframe you need is a tricky one. Then add to that all of the factor rotations in the passive nature of the markets today, often driven by quant and ETF and other funds. You have other dynamics that are driving trading and you need to think about. Do you short? We do short. It's just so, so stressful, because you are wrong for such a low period of time, and when you're right, it's just like in these bursts, and they don't last that long and maybe like super right for one week, and then you're kind of wrong again for two years, right? It's hard. Take the pay and day in the house. We still look for absolute profit dollar shorts. Do you care about valuations? I do. I think valuation matters a lot. We look at free cash flow multiples. We expense stock-based comp. We do all the unfashionable things and look at things pretty conservatively. The reason for that is what I said earlier about wanting to manage risk, so thinking I think about risk-adjusted returns, and I think valuation and what you pay up front is very much a part of that, and particularly thinking about valuation and information asymmetries in the market even more so. Let's go back to your time at Lombain, and you set it up with Steve Mundell. What did you, what was the most important learnings from your time at Lombain? Yeah, I think there were several. I think, and I was really lucky to work with the really group of people there, including Steve. I would say one of the big lessons I distilled was really what we discussed around biases. Let me give you a very specific example. I'm going to give you a couple of examples, and you talked about shorts, so we can start with shorts. One of my biggest mistakes at Lombain was shorting Nokia. I shorted Nokia for-- But it went down at the end. So this is what's interesting, so I thought I was absolutely right, and then obviously 2014 Microsoft comes out and buys the thing for $7 billion, and it was a very painful day for me and for my firm. And then 18 months later, Microsoft writes it off, it's gone, and so you can be right, and you can be completely and utterly wrong to your earlier point on the short side. So what was the lesson? The lesson is, if you think about the business as a standalone business, yes, you were right, great. However, you didn't really think about all of the other strategic value of the business broadly over time. So thinking about a business not an isolation, but thinking more systemically, but not in silos, is something I have to learn over and over again, and something I teach my team. So systemic, not silo thinking, that's number one, and the Nokia example is one. The other is avoiding balance sheet leverage. Like, public LBOs, I've learned the hard way, or probably not my thing. I've made mistakes where I have invested in decent businesses that were overlit, pretty decent business, they're overlevered. But as a result, they had much less maneuvering capacity when times in inevitable macro tensions arose. So that's another big, big lesson, and I made mistakes there. Businesses like altese, for example. The third is, I think, maybe the most interesting one, going back to discussions of the AI chip stack and of how old technologies came to disrupt in new ways, which is NVIDIA. So NVIDIA saw very much as a GPU business what it was, but then 2015, we had the deep reinforcement learned the deep mind papers come out, and it was incredibly interesting and exciting. And you began to see how parallel compute would be necessary, how GPUs would be helpful for that. In fact, essential for that parallel compute to work, to drive this interesting new engine of machine learning. I didn't know what gender to AI was going to happen, but you definitely saw what was happening with machine learning and early stages of what would then become, you know, gender to AI. And the fact that was back then, apart from just GPUs for video games, there was a big crypto component as well. So it was gaming and crypto that was driving NVIDIA at the time. And I said, "Well, yeah, but there's going to be this AI thing and this is going to be big." Turns out they missed significantly because of a concomit crash in crypto and game permitting delays in China, which we're driving, a big portion of demand for the video gaming part of the business. So I sold, just shouldn't have, but here's the bigger, that was a mistake, but the bigger mistake was not that. The bigger mistake was then not revisiting NVIDIA later. So there's some cost bias I find is a very powerful one, and this wise man so much time now with SIRGO to make sure that we keep a strong kind of survey of the available idea sets out there and make sure we continually revise, including the names that we've missed, errors of a mission as well, to make sure we don't bump into this issue repeatedly. It's so difficult to buy back things you sold lower down. Yes, but that's, in my opinion, that's not an excuse, and I think precisely because that is so hard, it's a really important and interesting opportunity set for new ideas when you are wrong, which happens a lot. And so, at least to me, so I think that's some, those are probably three concrete examples with clear lessons. One, around systemic knot silo thinking, the other around wearing us around financial leverage versus the other drivers of leverage that we all are happy about, and the third being around these biases that we talked about earlier, including the sunk cost bias. Man, I'd love to ask you a couple of questions just on slightly more personal nature. Do you think, I mean, you grew partly in the US, partly in India? Do you think that multi-cultural background is impacting the way you view the world and investments? Very much so. I would say growing up in India, growing up in a Bangalore that was not the Bangalore it is today. A very sleepy town who's actually considered a retirement town at that point. My parents are both my mother's a doctor, my father's an academic, and they've worked there in that capacity, and it was a very quiet, very academic, very cerebral upbringing in many ways. But it was not without exposure to the very stark realities of India at the time, and India that was suffering in many ways from the walls that were put up as they was a closed-off economy at the time, the license roger, as it was called, and it was very apparent that that was leading to real issues for the economy overall, even as a young girl growing up. But I think also seeing the level of income inequality, something that is striking not just India, but the world at large, and the perils of that became very clear to me, and the resulting loss and trust in institutions, the resulting corruption that that often results in, was something I grew up with, and it was all around me, and I think it's something that led in me at least to a very fierce sense of I need to give back and be of service. And I think that's partly also my family, my great grandmother was in jail working because of her work with the independence movement under Gandhi, and I think that idea of social service and how you give back was very much that became all the more reinforced by the social realities of growing up in India. Now all of that said, it was also wonderful in some ways, partly also because of the very strong local culture and local dynamic, and a very rich literature and sense of history and self that came about as well, from some of the fact that India was more closed off at that point in time. And so I think as a result of all of that, I also came away with a sense of great pride in what India stands for and what it has become today and what will become in the future. You are a role model for many women, and I think when you launched your fund, it was the biggest launch, the biggest hedge fund launch of any woman ever, right? That's pretty amazing. Any reflections around being a woman in the investment world? I would say, look, first of all, I hope that's a record that's broken very quickly. And I'm sure it will be. There's some really amazing talented woman out there. Look, I hope I'm not just a role model for women, but the broader investment community and hopefully over time, the philanthropic community as well. That's a very important part of my work and my ethos and my identity. I would say, I really think the investment business broadly, whether you're an outsider in any way, shape or form, is a great one. It's about as meritocratic a business as I can think of. I mean, I could be a green Martian with two horns in my head, but if I produce investment returns, they'll be a line out the door wanting to get my product, right? So I think it's a very meritocratic industry, and I just hope that places like CERGO, places like the platform you're running here are just ways for more people to add value in this industry, because I'd really think it's a terrific one for people who are intellectually curious of all stripes and shapes and sizes to come in and be of use to the world at large. Well, you have indeed many stripes because you are interested in so many different things. You have been written a book, you've been involved with a play. How does all this play into your kind of creativity and curiosity? I think. Tell me about some of the stuff you do first, because you do a lot of funky stuff. I do two main things apart from my professional works. One is the philanthropy, which we can talk about, and you asked about the creative works. I can talk about that first. Well, let's talk about the philanthropy. On the philanthropic front, I started very early, so from your one at Lompine, I felt it was very important to start giving back. Just as I do as an investor, it was really about backing really great social entrepreneurs and making sure we could seed them early. I was very lucky to meet Paul Farmer, who was early in Building Partners in Health at the time. He sadly passed away, but was a great mentor to me. He always said, "Public health is more like armed robbery than it is, helping the old lady cross the road. You have to break things to build things." It really is. There's a lot that needs to be done in terms of breaking siloed thinking to really get solutions to people in a rural, maternity clinic in India, for example, that I learn through people like him, through people like Otool Gohmande and others that I've worked with. That all led to Samas Gaire, who was working at Gates Foundation at the time and I to set up Surgo Health, and this is really going back now over a decade. Our view was very much how do we take healthcare service delivery and really help with data sets that are targeted, not just data and the set of data, but behavioral data. I think one of the most interesting things happening potentially in AI is adding the Y to AI. AI is very good at solving what questions. What ad will Nikolai click on next, but not Y. I think adding this large-scale behavioral data, which you can do with some pretty basic Bayesian networking math, to say this is why a woman is not going to the maternity clinic to deliver. This is why XYZ is not using contraception in this community. It's something that we've been spending a lot of time on, and data as a public good and data science as a public good is something we spend a lot of time on. That's a big part of the philanthropic effort, and we work with groups like the Belinda Gates Foundation and others and it was local governments to help. For example, one thing that was interesting is the maternal mortality work where we did a lot of this work in India to help target the state of Uttar Pradesh, spending a billion bucks a year on just maternity clinic adoption and driving that up. Then we're getting calls from people in the U.S. saying, "Metral mortality is rising here. It's a real problem. How can we use those same techniques here in the U.S." Then that then led to a really program with Uber, with Dara, actually, every shout-out. I think you've interviewed here to do rides for moms. The problem was having a maternity plan and making sure women actually went to the clinic, in which case transportation was actually the interesting thing. It was transport, not hospital care. Understanding the why is really important. I'll wrap up there, but I think that's a really important side where both the technology side and the philanthropic side overlap really interestingly. On the creative work, look, I think we're happiest as people when not when we're obsessing over ourselves, but when we forget ourselves whether it's a professional work or philanthropic work or creative work. I very much do it out of joy, absence, college, writing short stories that is. I think at the end of the day, everything is an narrative, right? We had dinner recently with a group of very diverse grades of very successful people. You asked them to do predictions, and they were. You do fun dinners. We asked them to do predictions. They all came up with pretty pessimistic ones. This is conflict between the narrative self and how I see myself versus the social self and the social context. I think that conflict is something I like to write about and think about, and it's been really fun. What about the theatre project? Yeah. The theatre project, the theatre of the mind, it was based actually in a series of neuroscience experiments. I was very interested in what was happening around some of these economic games. We talked about Connemon, Taylor and Ferriske's work. There's one called the Dictators Game, for example, where even though an individual in a group playing this game could take all of the coins on the table, they do not. They want to share. That's hopeful. I was interested in that. I wanted to take that to the Basement of the Science Museum in London, one of the great museums of the world. My friend, Brian Eno, said, "No, no, no. There's something more to this. Introduce me to David Byrne, who's thinking about an experiment in a very different context, which was more sensory playing with proprioception and how you can inhabit the body of a doll. It's actually a Swedish lab called Erson Labs that did this work. When we met, we thought, "Okay, this is actually something else completely different," which is a theater piece. It has a narrative. It's a story of a life of a man living his life backwards, dealing with memories with these experimental aspects woven in. It played very successfully in Denver, and it's now moving to the Goodman Theatre in Chicago, so that's been really fun as well. It's really about thinking in new and fresh ways and making sure that that feeds into a more creative, less silent thing. How do you bring all these back into investing? I think all of this goes back to this idea of how everything is narrative in some ways, and really thinking about the context and how powerful that is versus the individual characters of the play and how they all work together to really thinking about a systemic whole. I think that is the common point across each of these. The other is curiosity. I think curiosity is key to pretty much everything we do in each of these, whether solving a problem philanthropically, finding a great investment, and understanding it as well as you can, or producing a good piece of creative work. I think the other is openness, so I think with the writing, it's a great exercise in humility unlike the other two spheres in my work. No one really cares if I write another short story or not. The world is plenty of those. But to really make sure that that people do care, you have to keep revising and revising. What is revision to make it as good as possible? What is revision is just openness to possibility and openness to something better. And I think beware of certainty. I think that's probably another commonality across all of these. The person who is 100% sure that X or Y or Z is happening is something that always raises a red flag with me. It never is that certain. What's the key to staying curious and humble? I think it is very, it's a virtuous circle, right? Because if you're curious and humble to begin with, you learn and you see how much more there is to learn, and that keeps you humble. But you also are excited by the possibilities out there. So I think it's just very much a virtuous circle of getting on that wheel to begin with. And I think plus and more fun, right? What's more fun than learning? When do you wake up? Wake up about six in the morning every day. What do you read? I typically read the papers. I usually spend time thinking, and then I sometimes go for a bit of a walk. And then I read all the papers, the usual stuff, and then dive right into it. What do you think? Do you sit in the chair and think? You just walk, go for a walk, you know, pace around. You structure your thinking, or are you just like looking at dogs and trees? I don't believe in, I have plenty of structures thinking in the rest of my day. So that point of the day, I just let my mind roam and see what comes up. How do you relax? I relax with spending time with the people. I love my friends, my family, reading, going for long hikes, so the usual ways, I think many of us have of disconnecting being with nature and being with friends and family. Fantastic. Well, it's been great to meet you. You are. Are you talking to you? Sure is. Hubble. And an incredible professional. It's great. Thank you. Thank you.