Nikolai: Hi everybody, I'm Nikolai Tangen, CEO of Norges Bank Investment Management. And today I'm very happy because I have Mala Gaonkar with me. We've known each other for quite some time. Mala started SurgoCap Partners with $1.8 billion, and today it has grown to $X billion. Before that, she worked for 23 years as a founding partner at Lone Pine Capital, which is one of the most successful hedge funds ever. Happy to have you here.

Mala Gaonkar: It's great to be here. Thank you. Tell me about SurgoCap and what it does. Yes, SurgoCap tries to do what many investment firms do. It tries to beat the market with less risk than the market over a three to five-year cycle. Risk means loss of capital, not volatility. And we try to achieve this by identifying a very small number of truly great businesses in the world.

Mala Gaonkar: And we, our product is actually our process. A very transparent process that looks at very specific factors. Which are actually, as you mentioned earlier, a distillation of my lessons learned, my many mistakes, and lessons learned from investing with some of the best people in the business, my former colleagues, for 23 years. So Surgo was built from this distillation. There are many different factors to identify a great business. But in my opinion, a great business is one that has very long-lasting moats. And this duration is our real differentiation.

Nikolai: So they're hard to compete with. They're hard to outcompete. How many great companies are there in the world?

Mala Gaonkar: As I said, it's very few. I don't think there are that many. We actually focus on four verticals where I think there's a bit of an edge from a very specific factor. So, coming back to 30 years of investing experience. Every business is a technology business, right? So if you're an aerospace company, or a med-tech business, or a financial data business, then 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.

Mala Gaonkar: And I think understanding the tech stack map of a business is something we spend a lot of time on, especially in non-tech businesses. That's the first point. The second is how old technologies are disrupting in completely new ways today. For example, if you look at the auto industry, which employs far more people than the tech industry, it's being disrupted today by a technology invented in 1976, namely the lithium-ion battery. This always fascinates me a lot. When we started investing in Korea, we were buying GPUs to make video games more fun and engaging. Now, fast forward to today, those same GPUs are driving AI, which is one of the biggest social, demographic, and technological shifts of our time. For me, it's very interesting that old technologies are causing disruption in new ways, and we look for such opportunities even in non-tech businesses.

Nikolai: And we'll come back to that. But when you set up the fund, what kind of self-imposed constraints did you put on yourself?

Mala Gaonkar: Yes. So there are a few ways to do that. One is focus. So we decided, I wanted to keep the team small. There are a few ways to do that. One is focus. So we decided, or rather, I wanted our team to remain small. If I've received one consistent piece of advice from CEOs, and many of them have been on this podcast, whether you're running large companies or founders of small businesses, the main point was this: keep the team size small. It's not just about AUM. Yes, multiplying $1 is easier than multiplying billions of dollars. But keeping the team small and creating a collaborative cross-border thinking environment, which is necessary for generating creative ideas, was most important to me.

Nikolai: How small?

Mala Gaonkar: We have an investment team that also includes a data science team, and we meet on a regular basis, around a table like this, so we can have a proper round table discussion. Hmm, hmm.

Mala Gaonkar: As you may have heard, Jeff Bezos talks about two-pizza box teams. But we prefer to stay at a one-pizza box team size.

Nikolai: Just one box.

Mala Gaonkar: So, I would say that's not much pizza per person.

Mala Gaonkar: Exactly. We keep things simple. We Norwegians are said to be the biggest pizza eaters in the world. It is said that we eat about 11 kg per person.

Nikolai: Really?

Mala Gaonkar: Yes, yes.

Nikolai: That's quite interesting. I hadn't thought of that.

Nikolai: But when you look at your company compared to other tech investors, what do you think is the difference? We are seeing where technology is meeting non-technology businesses, and as I said, how old technology is bringing about change in new ways. So that's one angle. We also look at the common checklist of competitive and customer and other checks. I would say the second big lesson is about bias, and how you can use data science today, as we couldn't at the beginning of our investment careers. Given how fast and open source machine learning has become, you can build very robust third-party data checks at very low cost. This includes automated surveys, automated tracking of different data points around markets and tech adoption, which we couldn't do at the beginning of our careers in the late 90s.

Nikolai: Can you give an example? What can you do now that you couldn't do before?

Mala Gaonkar: So, very specifically, if you need to track product adoption, when we were starting, like in 1998 at Lone Pine, when I was looking at early adoptions, like about Adobe products when they were transitioning to the cloud, it was a very manual process. You had to go out and contact customers. Surveys mostly had to be done over the phone. Tracking response rates was also quite tricky. But now for these tasks, we can actually use survey bots. We can use a combination of both. Augmented human surveyors and also do deeper dives. We can track more SKUs in a very automated way with machine learning. We can web scrape. And frankly, there wasn't such scale and scope before to look at product adoptions. To find out who keeps open APIs for suppliers and who doesn't. So this includes tech stack mapping, product adoption, customer adoption, even various types of general consumer adoption data, which is now tracked quite strictly in the consumer world.

Nikolai: Do you believe that investment organizations will be smaller in the future?

Mala Gaonkar: I think they will be smaller, and perhaps they should be smaller, because human collaboration works best at a small scale. The most interesting data and those ideas that are often missing come from across industry borders. For example, how AI is influencing med-tech, or how it's affecting material science innovation in aerospace. I think these angles are far more interesting than just seeing AI as AI in technology. What are some of the biggest or some of the most surprising benefits of AI in your companies?

Mala Gaonkar: I think what's happening in medical technology is perhaps not as well and widely understood as it should be. So let me give you a very specific and interesting example. If you look at the imaging space, like MRI, CT scans, PET scans, etc. The accuracy, clarity, and speed with which these medical images can now be taken has been improving at a rate of almost 70% in the last few years, whereas a few years ago, this was not at all the case. For the aging population worldwide, this has been very helpful not only for therapeutic care but also for preventive care, and in this way, obviously, it makes care more affordable. So I think this is a big area around imaging, and understanding how these old diseases are increasing, and you know, I think preventing these diseases early on has been a big thing. The second big area is how surgeries are performed. So I think people talk about AI and robotics in the manufacturing sector. What people sometimes don't understand is that almost 300 million surgeries are performed worldwide, and these are now being influenced by robotic surgery, especially through companies like Intuitive Surgical. So I think this is another big category where you're seeing real innovation happening. Where the combination of haptic feedback mapping, software, and better technology around performing surgery is really leading to fewer errors. Easier training for medical students, and overall a better environment for how these therapeutic treatments can be delivered. And as you know, there's a big discussion about controlling healthcare costs, and I hope that properly implementing AI can help with this. So yes, I think this idea of how technology is intersecting with non-tech businesses, and how we can use data to de-bias the human decision-making process, is something we pay a lot of attention to.

Nikolai: Very interesting. Now you believe in concentration. When you choose something, you take a pretty big step, right?

Mala Gaonkar: Yes.

Nikolai: So tell me, how do you think about concentration?

Mala Gaonkar: I have deliberately focused on these four verticals: enterprise data (broadly tech), financial services, healthcare services, and industrial technologies. Because I felt that these are the areas where these themes of emerging technology disruption are most relevant, and where you can see market leaders with very durable moats that work for incremental returns, and the path to deploy capital on that incremental return is clearer today given current technology trends. So we only focus on these four areas, and we also think about thematic exposure, and the reason for this is very straightforward. Firstly, I think it gives you an opportunity to play offense. Because at some point, due to the passive nature of market structure, a slight odd factor rotation can disrupt a portfolio. This gives us an opportunity to play offense during those periods, and we feel that in each of these four sectors, there are many interesting investment ideas where we are not making any kind of compromise. In addition, for each name, we have different and strong drivers for a long-term thesis. You grew up in India, and I heard you talk about food on a podcast, so how do you spice up your portfolio?

Mala Gaonkar: I consider quality to be the spice for us. We focus on businesses that have moats and can last for a long duration. And we have very de-biased ways of tracking data that are not based solely on my or the team's intuition, but are actually based on systematic thinking, not siloed thinking. That's the spice. I think it's really exciting because it's an incredibly difficult thing to think about, and doing it well is very challenging, and I think it's important to talk about it a bit more, and Nikolai, you yourself have been a student of the investment process. But you know, we both love the work of Kahneman, Tversky, and Thaler, right? I remember once my father gave me a wonderful quote from Bertrand Russell, which was, 'Men are capable of anything, except they don't want to think.' And it's very straightforward. Just as Kahneman talks about System One and System Two thinking. System One thinking is that intuitive gut feeling, which is actually positive. It helps humanity to give quick and fluid reactions in day-to-day tasks, and it works very well. But System Two thinking, which is very difficult, which is really thinking deeply and methodically and logically. So I always try to make sure that my team and our portfolio are driven by System Two thinking. Here I have to interject that I once interviewed you for my master's thesis. [laughter] That's right. In decision-making, which was actually about this. So tell me, when do you use intuition and when do you use analysis? So how has your use of pattern recognition changed during your career?

Mala Gaonkar: In my pattern recognition, I think the change that has come is that now I think more about context rather than individual analytical viewpoints. And so perhaps I previously gave more emphasis to the person or the CEO or leadership or the hero model, and did not pay as much attention to the social context in which companies and businesses were operating. And I learned this the hard way. I have seen many failures. My first job at the World Bank was working as a junior analyst in Russia and Mongolia. I went there. This is a very poignant example. I went there thinking, 'Okay, when we take over these state-owned companies, we will value them. We will distribute tickets through different local bank branches. We will open stock markets. Capitalism, liberal democracy, everything will be great. The end of history.' What happened turned out to be a bit different, right? Instead, a lot of corrupt and wrong practices rapidly grew. Autocracy emerged in all these countries, and even today, these same things are in the news headlines. You can't just ignore or separate 70 years of communist history and expect people to adopt a completely new way of thinking overnight. I think the same is true today. When you are looking at businesses from the bottom up, it is very important to understand the entire context. You have to look through binoculars and microscopes so that you can think systematically and clearly about how these businesses will evolve in the context they are in today. So, this is something I think a lot about: how we correct our thinking about these specific failures.

Nikolai: You can also invest in private companies.

Mala Gaonkar: Yes. So why did you decide to do that?

Mala Gaonkar: I think we obviously know that there are very large market caps and large companies present in the private sector, and we also know that there are fewer companies in the public markets now than before; in fact, their number has decreased quite a bit over time. So, in addition to these trends, another reason to look at private markets is that they are often the most disruptive. Change happens at the edges, not at the core, right? This is my fundamental view, and at the edges are small businesses, private companies, unsung founders who are just emerging. That's where change will happen at the margins. So, it's our responsibility to ensure that our networks and thinking are not just in America but globally in these four sectors. These four big industries that we focus on. Talking to private companies is part of that process. It's that simple.

Nikolai: How do you find new ideas? Like, do you wake up in the morning and think, 'Bang, I need to look at this'?

Mala Gaonkar: So it's not like I wake up in the morning and think, 'Bang, I need to look at this.' Actually, it's a long process. For me, it's a long incubation process for ideas. So I read a lot, think a lot, and then talk to my team and discuss with whatever broader networks we have. But then, as I said before, change happens at the edges, so it's actually about going into the field. It's about going to that obscure industry trade show that features automation, new robotic systems, where I might get a new idea. It could be an ongoing survey that we are constantly conducting between AI developers and non-tech industries to see what they are actually working on, what they are trying, what is working, and what is not working. So, I would say it actually comes from a more obscure perspective, where I get my best ideas, rather than just talking to existing big players, although that's obviously important.

Nikolai: So what do you do with an idea once you have it?

Mala Gaonkar: The next step, as you asked earlier about intuition, System One, and System Two thinking, is to put it through a very fine filter, which is the overall investment checklist. I strongly believe in a checklist-driven approach. We go through this process, and then, as I said earlier about biases, we ensure that we have a way to track the thesis and also that we can hold onto that rope. And the reason for this is that I think we are susceptible to many biases with colorful names, right? Like confirmation bias, availability bias, or sunk cost bias. When I look at many of my investment mistakes, they were all due to one or another of these biases. This is very human. It's natural, and some of them have positive aspects. But for investing, perhaps a little less so. So that's why I want to make sure that we have data points that allow us to link the investment thesis in an unbiased way so that the investment team and I can track it and see that we are not ignoring issues that could become headwinds for the thesis. So I spend a lot of time on this. I also spend a lot of time making sure that, you know, we talk about errors of omission and commission. But I also want us to avoid making mistakes. So a lot of my time is also spent thinking about what assumptions are being made that might be wrong. Where things can go wrong. Where market disruptions can happen? And this is also something I pay attention to. FOMO is perhaps the biggest problem, right? So I tell my team, 'Let's move from FOMO to TOMO: Thoughtfully Missing Out,' ensuring that we are looking at whatever new flavor there is. Perhaps we can try to expand our circle of competence. But if we can't, then we need to be clear about what our circle of competence is, keep expanding it, but the boundaries should be clear. So I would say that my ideas are as much about steering the team as they are about specific stocks.

Nikolai: How do you use them? Do they make decisions, or do they help you make decisions?

Mala Gaonkar: My team is very experienced, and I'm lucky to work with this small group who have a decade or more of stock-picking experience, and I use them as my collaborators. I truly consider them as people who are very important to my decision-making process. I think this is very much a team sport, so a strong team is needed. I see myself as a coach and mentor. A big driver of SurgoCap is to attract and mentor the next round of brilliant investment talent.

Nikolai: So, thoughtfully missing out. What are the red flags that would keep you away from something?

Mala Gaonkar: Yes. So, in my view, the first is compromise. If you are investing in a business solely because of valuation, or if you are investing in a founder believing them to be the next Messiah, but there are other problems you are ignoring, then that's a red flag. Actually, being too emotional in making investment decisions. Whenever I see that, or when I myself get too excited about something, I consider it a red flag. We should ensure that we are considering all aspects of the business in a balanced and thoughtful way, rather than focusing on just one. And I think that's where bias naturally comes in. Because we humans are naturally wired to find that big, exciting moment. That was our evolution, right? We were looking for that predator that was about to attack us. But actually, I think in today's world, it's not that simple. In fact, it's extremely complex, and there are many factors that can be right and can also be wrong, and spending time on all these aspects is what I look for us to do. If we don't, I make mistakes. It's not easy to let go of emotions.

Mala Gaonkar: I think you need to have a methodology and process that brings it into your System Two thinking and creates tools that allow you and your team to move in that direction. Remember, as soon as we talk about the nature of markets, stocks are driven as much by narrative as they are by numbers. So you should respect that context and appreciate understanding that context. Even in the last days of the Soviet Union, and how powerful it was, and I think we need to balance this.

Nikolai: You mentioned moats, long-term moats, as a definitional quality. So what other kinds of quality signs are you looking for?

Mala Gaonkar: So when I talk about moats, I mean a business that operates on high incremental ROIC, because ultimately the value of a business depends on the return it gets from incremental invested capital, and you will deploy that capital on the returns you can get, right? So it's a balance of what drives ROIC, and then multiplying it. There are many ways, but what are actually the best ways? If it's just by raising prices, I don't like that. But if it's through feature innovation, market growth, new geographies, or new products along with price increases, then that's great. So having multiple levers to drive ROIC is a big deal. Secondly, how much capital can you deploy on those incremental returns? Some businesses, especially the excellent ones, have high incremental returns, but their capacity to deploy that capital is diminishing. That kind of growth market is no longer there. That's why trying to understand what kind of business it is, and if they have a lot of capital they can deploy, and we are seeing this in the technology sector today, then the execution risk is very high. So I think we need to see that we are getting returns commensurate with the level of capital deployed relative to the sales and size of the existing business, because that increases execution risk, which I need to think a lot about. I think the combination of incremental return, the capital you can deploy into those returns, having ways to manage the positive in one and the negative of execution risk in the other. These are all the things we think about.

Nikolai: So without talking about anything you currently have in the portfolio, but give an example of a great investment you've made that fits some of these points.

Mala Gaonkar: Yes, I would say that on a large scale, we have made some of these investments in our previous roles as well, and that's the best thing about these businesses. I think what's really interesting right now is what's happening with chips, so we're spending a lot of time on that. So you have businesses like TSMC, which are well-known, which have pretty much cornered the market, especially in aspects of process engineering. So it's actually not just about one thing, but a combination of many things. That's why I like such systematic moats where there isn't one small, easy thing. It's not just that there's a gold mine in the city, but owning the process of how gold is extracted from the mine. I like businesses that have these kinds of dynamics. I also think there's something very interesting happening in the chip layer of AI right now. Where there's been a lot of talk about training. But I think training is a bit difficult, but inference is the real money-maker, right? So ultimately, we're going to make models do this work every day, and these will be inside specific LLMs. Google will have its own inference stack, OpenAI, Anthropic, and others. And I think what's happening around Google's TPUs is really interesting in terms of reducing this. So ASICs, or ASIC chips, application-specific chips that are designed for specific workloads, and because of this, they create very long-lasting advantages, and that's why we are paying a lot of attention to this kind of theme. We talked about medical technology earlier. But I think the combination of go-to-market moats and continuously increasing process innovation, and it involves a large group of doctors, nurse practitioners around the world who are actually executing it. This also creates a lot of engagement. So I think this is another area where big businesses are really emerging and have already been built. So in what's happening around the chip stack layer, these are perhaps the two big buckets, and the med-tech layer that has been going on for the last 20 years, so it's not new. I think there's another interesting category too, where there's a view that AI is perhaps actually disrupting a little more than it is, and so I think there are some proprietary data providers within real-time data and data analytics. I think they are really interesting and truly great businesses. Where they have both systems of record and systems of workflow together, and these three together, with a very basic innovation engine, often create very strong moats.

Nikolai: What kind of companies would those be?

Mala Gaonkar: So these would be businesses that, for example, provide financial data. So think about all the real-time pricing needed for FX clearance or fixed income or equity markets, even for your portfolio. I think these are real-time data that would be very difficult for something like an LLM to ever change, because of their real-time nature. But they are embedded in the real workflows around trading and compliance, which are also very difficult to change. So people have this idea that AI models can remove them. But I think it's almost very difficult for me to see or understand that.

Nikolai: When you invest or think, what is your time horizon?

Mala Gaonkar: As long as possible, obviously shorts are different. They are more catalyst-driven and usually have catalyst periods of less than 9 months. But on the long side, as I said, I rely on my process to look for duration. So we are really looking for a combination of factors that drive incremental returns and capital that can be deployed on that incremental return, which are actually visible in a 3 to 5-year cycle according to our valuation horizon. But ideally, we look for levers beyond that. So how does shorting compare to long investing? Can the same person do both?

Mala Gaonkar: That's a very good question because it's actually the other muscle of the long side, as you know. It's actually about thinking about it. So sometimes you'll see clear shorts that are broken mirror images of longs. X is winning, Y is losing. I think these are very few and rarely seen, and usually, that's not enough. I think in addition to that, you need to get the timing right as to when those winning or losing dynamics will actually play out. Usually, it happens through some combination. Like clear price competitive pressure, clear share competitive pressure that leads to those pricing pressures, and then those combined with other factors like poor execution by management, you have a pretty good, pretty good short. But the problem is that having that combination of factors within the time frame you need is a difficult task. And then add to that all kinds of factor rotations and the passive nature of today's market, which is often driven by quants and ETFs and other funds. You have many dynamics that are driving trading that you need to think about.

Nikolai: Do you short?

Mala Gaonkar: We do short.

Nikolai: It's very, very stressful. [laughter] Honestly, it means I like it.

Mala Gaonkar: Because you, because you're wrong for so long, and when you're right, it's just like bursts, right? And they don't last very long. You might be very right for a week, and then you're wrong again for two years, right?

Nikolai: It's difficult, and you suffer the pain. You look for absolute profit dollar shorts.

Nikolai: Do you care about valuation?

Mala Gaonkar: Yes, I think valuation matters a lot. We look at free cash flow multiples. We expense stock-based comp. We use all the old methods and look at things quite conservatively. And the reason for that is what I said earlier about the desire to manage risk. So thinking about this, I think about risk-adjusted returns, and I think valuation and how much you pay upfront is a very big part of that, and especially thinking about valuation and information asymmetries in the market is even more important.

Nikolai: Let's go back to your time at Lone Pine, and as I mean, you started it with Steve Mandel. What important lessons did you learn from your time at Lone Pine?

Mala Gaonkar: Yes, I think there were many things. I mean, and I was very fortunate to have the opportunity to work with Steve and a group of many brilliant people there. I would say one of the biggest lessons I learned was the one we talked about regarding bias. Let me give you a very specific example, and you talked about shorts. We can start with shorts. One of my biggest mistakes at Lone Pine was shorting Nokia. And I shorted Nokia.

Nikolai: It eventually went down.

Mala Gaonkar: So that's the interesting thing. So I thought I was absolutely right, and then obviously in 2014, Microsoft came and bought it for $7 billion, and that was a very painful day for me and my firm, and then 18 months later, Microsoft wrote it off. It was over, and so you can be right and you can also be completely wrong, as you said earlier on the short side. So what was the lesson in all this? The lesson is this.

Mala Gaonkar: If you think of a business as a standalone business. Yes, you were right. Great. However, you didn't actually think about all the other strategic value of the business over time. So thinking about a business not in isolation but in a more systematic way, but not in silos, this is something I have to learn again and again, and something I teach my team. So systematic, not siloed thinking. That's number one, and the Nokia example is one. And the second is avoiding balance sheet leverage, like public LBOs, and I've learned the hard way that perhaps it's not for me, and I've made mistakes where I invested in good businesses that were overleveraged, quite good businesses, they were overleveraged, but as a result, when difficult times and necessary maintenance came, they had very little capacity to operate. So that's another big lesson, and I made mistakes there, like in businesses like Altice. As for the third, I think perhaps the most interesting, going back to our discussion on AI chips and how old technology can cause disruption in new ways, which is Nvidia. So Nvidia completely saw itself as a GPU business, which it was, but then in 2015, we had the Deep Reinforcement Learning, DeepMind papers, and it was very interesting and exciting, and you started to see how important parallel compute would be. How GPUs would be helpful for that parallel compute. Actually, they would be necessary for making that new engine of machine learning, that parallel compute, work. We didn't know that generative AI was going to happen. But you definitely saw what was happening in the early stages of machine learning, which later became generative AI. And the truth is, at that time, besides GPUs for video games, there was also a big crypto component. At that time, gaming and crypto were driving Nvidia. And I said, 'Yes, but this AI thing will happen, and it will be very big.' It turned out that they missed it, and look, to a large extent, crypto crashed simultaneously, and gaming was affected due to permit delays in China, which was a big driver of demand for the video gaming part of the business at that time. So I sold it, which I shouldn't have done. But the big thing is, that was a mistake. But the big mistake wasn't that. The big mistake was not looking at Nvidia again later. So I think this sunk cost bias is very powerful, and that's why I now spend so much time at Surgo to ensure that we keep doing a robust survey of the idea sets out there and ensure that we continuously revise them, including those names we missed, to make sure we don't fall into this problem again and again.

Nikolai: It's very difficult to buy back things you've sold at a lower price. No one agrees. I think, I think because it's so difficult, it's a very important and interesting opportunity for new ideas. When you're wrong, which often happens, and at least for me, it does, then I think these are perhaps three solid examples with clear lessons. One about systematic, not isolated thinking. Second about being cautious about financial leverage versus other drivers of leverage that we are all happy about. And third about these biases we talked about earlier, including sunk cost bias.

Nikolai: So Mala, I'd like to ask you some questions of a slightly personal nature. Do you think, I mean, you grew up partly in the US and partly in India. Do you think this multicultural background influences the way you see the world and investing?

Mala Gaonkar: To a great extent, I would say. Growing up in India, growing up in a place that wasn't what it is today, a very quiet city. At that time, it was actually considered a retirement city. Both my parents, my mother is a doctor, my father is an academic, and they worked there in that capacity. And it was in many ways a very quiet, very academic, very intellectual upbringing. But I was not unaware of the very harsh realities of India at that time. An India that was in many ways troubled by the walls that had been erected. It was a closed economy at that time, called the License Raj, and it was very clear that this was causing real problems for the economy overall, even as a young girl growing up. But I think seeing the level of income inequality is also something that is shocking, not just in India but around the world, and its dangers became very clear to me, and the lack of trust in institutions, which often leads to corruption, was something I grew up with, and it was all around me. And I think this is something that has at least instilled in me a very strong sense of giving back and service, and I think this is partly my family as well. My great-grandmother was in jail because she was working in the freedom movement under Gandhi, and I think the idea of social service and how you give back was further strengthened by the social realities of growing up in India. Now, having said all that, it was also wonderful in some ways. Partly because the local culture and local dynamics were very strong, and there was a very rich literature and history and self-understanding that also came somewhat from the fact that India was very, very closed at that time. And so I think as a result of all this, I felt very proud of what India is known for, what it has become today, and what it will become in the future.

Nikolai: 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 by any woman ever, right? That's really, really amazing. Any thoughts on being a woman in the investment world?

Mala Gaonkar: I would say, first of all, I hope this record will be broken very soon, and I'm sure it will be. There are some truly talented women out there. I hope I will be a role model not just for women, but for the broader investment community and perhaps later the philanthropic community as well. This is a very important part of my work and my principles and my identity. I really feel that the investment business, broadly speaking, is a very good business. No matter what kind of outsider you are. It's as much a meritocracy as I can imagine. I mean, I could be a green Martian with two horns on my head. But if I deliver investment returns, there will be a line of people outside the door who want to buy my product, right? So I think it's a very meritocratic industry, and I just hope that places like Surgo and the platforms you're running here are ways to add value for more people in this industry, because I really think it's a fantastic place for people who are intellectually curious, all kinds of people who can come and be useful to the world.

Nikolai: Well, you really have many qualities because you're interested in so many different things. You've written. You've written a book. You've been involved in a play. How does all this work into your creativity and curiosity?

Mala Gaonkar: I think... well, first tell me about some of your things, because you do a lot of funky things.

Mala Gaonkar: I do two main things besides professional work. One is philanthropy, and the other is creative work, which I can talk about with you first.

Mala Gaonkar: So on the philanthropy front, I started very early. So from the first year at Lone Pine, I felt that giving back was very important, and as I do as an investor, it was really about supporting very good social entrepreneurs and seeing if we could see them and see them early. So I was very lucky to meet Paul Farmer, who was in the early stages of building Partners in Health at the time. Sadly, he passed away. But he was a very good mentor to me, and he said, he always said, 'Public health is like armed robbery, rather than helping an old lady cross the street.' You have to break things to build things, and it really is like that, and there's a lot that needs to be done in terms of breaking siloed thinking to really get solutions to people. For example, in a rural maternity clinic in India. I learned this from people like him, people like Atul Gawande, and others I've worked with, and this all happened because of Samaj Gear, who was working at the Gates Foundation at the time, and I started Surgo Health, and this was more than a decade ago now, and our perspective was very much about how we can improve healthcare service delivery and really help with targeted data sets. Not just data and data sets, but behavioral data. So I think one of the most interesting things potentially happening in AI is adding 'why' to AI. AI is very good at solving the question of which ad Nikolai will click next, but not why? And I think adding this large-scale behavioral data, which you can do using some fairly basic Bayesian networking math, to explain why a woman is not going to a maternity clinic for delivery, or why XYZ in this community is not using contraception. This is something we spend a lot of time on. And seeing data as a public good and data science as a public good is something we spend a lot of time on. So this is a big part of the philanthropic effort, and we work with groups like the Bill & Melinda Gates Foundation and others, and local governments to help. And one interesting thing is we did a lot of work on maternal mortality in India to help target the state of Uttar Pradesh, which is spending a billion dollars every year just on maternity clinic adoption and increasing it. And then we started getting calls from people in the US. Maternal mortality is increasing here. This is a real problem. How can we use the same techniques in the US? And then a real program started with Uber, with Dara, who reached out. I think you interviewed him, to provide rides for moms. The problem wasn't creating maternity plans and making sure women actually went to clinics. In that case, transportation was actually the interesting thing. It was transport, not hospital care. So understanding 'why' is very important. So I'll stop here. But I think this is a very important side where technology and philanthropy both meet. The really interesting thing about creative work is that I think we humans are happiest when we don't think about ourselves. Whether it's professional work, philanthropic work, or creative work, and that's why I do all this happily, since college, I mean, writing short stories, and I think ultimately everything is a story, right? And we recently had dinner with a group of very successful people of very different kinds, and...

Nikolai: Well, you asked them to make predictions. You have fun dinners. [laughter]

Mala Gaonkar: And we asked them to make predictions. All of them made quite pessimistic predictions. So this is a conflict between the narrative self and how I see myself versus the social self and social context, and that's why I think conflict is something I like to write and think about, and it's been really fun.

Nikolai: Tell me about the theater project.

Mala Gaonkar: Yes, so the theater project, 'Theater of the Mind,' was actually based on a series of neuroscience experiments. I was very interested in what was happening around some of those economic games we talk about, in the work of Kahneman, Thaler, and Tversky. For example, there's a game called the Dictator Game, where even if a person in the playing group can take all the coins on the table, people still don't do that. They want to share, and that's the hopeful thing, and I was interested in this, and I wanted to take it to the basement of the Science Museum in London, which is one of the great museums of the world. But my friend Brian Eno said, 'No, there's something more to this.' He introduced me to David Byrne, who was thinking about an experiment in a different context, playing more with sensory proprioception and understanding how you can inhabit the body of a doll. This work was done by a Swedish lab, Ersson Labs. And when we met, we thought, 'Okay, this is actually something else, which is a theater piece.' It has a story. It's the story of a man living his life backward and dealing with memories with these experimental aspects. It was very successful in Denver and is now going to the Goodman Theatre in Chicago. So this has also been really fun. So it's really about thinking in new and fresh ways and making sure that it turns into more creative, less siloed thinking. How do you bring all this back to investing?

Mala Gaonkar: I think it all goes back to this idea of how everything is just narrative in some ways, and actually thinking about context and how powerful it is compared to the different characters in a play, and how they all work together. Actually thinking about this one entire systemic process. I think that's the common point in all of them. The second is curiosity. I think curiosity is the most important thing in each of these endeavors. Whether it's solving a problem for social service, finding a great investment and understanding it as much as possible, or doing good, very good creative work. I think the second quality is openness. Writing is a good exercise in humility. Unlike my other work, no one really cares if I write another short story or not. There are many such stories in the world. But to really make people care, you have to revise again and again. What is revision? It's making it as good as possible. It means openness to possibilities and openness to better. And I think, beware of certainty. I think that's perhaps another common thread in all of this. The person who is 100% sure that X or Y or Z is happening, that's always a red flag for me. It's never that certain.

Nikolai: What's the secret to staying curious and humble?

Mala Gaonkar: I think it's a very, it's a good cycle, right? Because if you're curious and humble from the beginning, you learn, and you see how much more there is to learn, and that keeps you humble. And then you, but you're also excited by the possibilities out there. So I think it's just a good cycle. Getting on that wheel at the beginning, and I think it's more fun too, right? What's more fun than learning?

Nikolai: When do you wake up?

Mala Gaonkar: I wake up around 6:00 AM every day.

Nikolai: What do you read?

Mala Gaonkar: I usually read newspapers. I usually just spend time thinking. And then sometimes I like to take a short walk, and then I read all the newspapers. Normal things, and then I get straight to work.

Nikolai: How do you think? Do you just sit and think?

Mala Gaonkar: Usually while walking, moving around.

Nikolai: Do you structure your thoughts, or do you just look at dogs and trees and things?

Mala Gaonkar: No. I have a lot of structured thinking in the rest of my day. So at that time of day, I just let my mind wander and see what comes up.

Nikolai: How do you relax?

Mala Gaonkar: I relax by spending time with the people I love. My friends, my family, reading, going for long hikes. So these are common ways. I think many of us use them to disconnect, to be with nature, and to be with friends and family.

Nikolai: Fantastic. [music] Well, it was great talking to you.

Mala Gaonkar: It was great talking to you too.

Nikolai: Curious, humble, and an incredible professional. This is great. Thank you so much.

Mala Gaonkar: Thank you, Nikolai.

Nikolai: Thank you.