Nicolola Tangian: Hi everybody. I'm Nicolola Tangian the CEO of the Norwegian S1 wealth fund and today I'm really happy because I'm here with Mala Gong Carr who I've known for a long time actually. Mala founded Sergo Cap with $1.8 billion and now it's at $6 billion and before that she 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.

Mala Gaonkar: Great to be here Nikolai. Thank you. Tell me about Sergio Cap partners your company.

Mala Gaonkar: Yeah. Uh Serbo Cap tries to do what many investment firms try to do. It tries to beat the market uh over a 3 to 5 year cycle with less risk than the market. Risk defined as loss of capital, not volatility. Uh and the way we try to achieve that is by identifying this very small handful of truly great businesses that exist in the world. uh and we do that through a our product is really our process a very transparent process of uh 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 uh from investing over 23 years with some of the best uh people in the business my former colleagues at at Lumpine and so that distillation has led to sero there are many different factors that lead to identification of a truly brilliant business but The way I define a great business is a business with very long duration modes and duration really is our true differentiation.

Nicolola Tangian: So mode being that it's difficult to compete with them. It's difficult to compete amount. How many great companies are there in the world?

Mala Gaonkar: 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 uh from a one very specific factor. So one back to the experience of what I've seen over 24 now 30 years of investing is techn every business is a technology business right so if you're an aerospace company or a medtech 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 nontech businesses um 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 you know technology that was invented in 1976 you know the uh the the lithium ion battery. So I think that is that's always interesting to me or you know in when you know we started investing career we were buying GPUs to you know make our video games look a little little 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 you know 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 spent a lot of time on as well

Nicolola Tangian: and we'll come back to this but just when you set up the fund what kind of self-imposed constraints do you did you put

Mala Gaonkar: yes so I in order to distill to your question about how we identify these truly great business and keeping that discipline there are a couple ways of of doing that um one is focus so we decided I wanted to keep the team small uh if there's a consistent piece of advice I got from CEOs many have been on this uh 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 crossber thinking which is so fruitful for new idea generation particularly creative idea generation that was paramount in my brain.

Nicolola Tangian: How small is small?

Mala Gaonkar: Um we have an investment team including the data science team and that we regularly meet table this size uh so that we can have a roundt discussion. Um you know Jeff Bezos has talked about sort of two two pizza box teams. I think we'd like to stay at one pizza box uh in terms of our our team size.

Nicolola Tangian: One pizza box pizza per person I have to say.

Mala Gaonkar: Yeah exactly. We keep things lean.

Nicolola Tangian: I'm saying that as a Norwegian we are the biggest pizza eating uh country in the world. 11 kilo per person supposedly.

Mala Gaonkar: Is that right?

Nicolola Tangian: Yeah. Yeah.

Mala Gaonkar: Um Interesting. I've never predicted that.

Nicolola Tangian: But when you look at when you look at your company relative to other tech investors, what would you say is the difference?

Mala Gaonkar: We're looking at where techn is intersecting with non-technology businesses and where, as I said, old technology is disrupting uh in new ways. Uh so that's one angle. We look at the 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. uh given how fast and how open source now machine learning is, you can very cost-effectively create uh very strong uh third-party data checks uh including automated surveys um including you know automated tracking uh of various data points around markets and tech adoption that we could not very early on in the you know the late 90s when we're setting up our careers.

Nicolola Tangian: Give me an example on the kind of thing you can do now that you couldn't do earlier.

Mala Gaonkar: Um so very specifically if you want to track sort of product adoption when we started out you know when we started lumpine 98 when I was looking at uh 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. Um now we actually can use survey bots to do these things. We can actually have a combination of both uh 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 SKs. 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. Um so there's a whole range of both techstack mapping, product adoption, customer adoption even outside the typical consumer adoption data that is tracked uh pretty religiously now as you know within the consumer world.

Nicolola Tangian: Do you think investment organizations will be smaller in the future?

Mala Gaonkar: 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 datas and some of my interesting 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 in or just as interesting angles as purely looking at AI as AI within technology itself.

Nicolola Tangian: What are the most what are some of the most counterintuitive benefits of AI that you are seeing in your companies?

Mala Gaonkar: I think the uh what is happening within u medical technologies is is not perhaps as well or broadly understood as it should. So to give you a very specific uh instance uh if you look at the imaging space so you know MRIs, CT scans and so on the accuracy and the speed at which these images can be uh conducted are now improving at a pace of almost 70%. Uh 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. Um so I think that is one big area around imaging uh and really understanding earlier how some of these chronic diseases are are evolving and able and I think obviously nipping these in the butt earlier has been one big one. The other big area uh has been uh around how uh surgery is conducted. So I think the intersection people talk about AI and robotics in the manufacturing space. What I think people don't realize sometimes is there about 300 million surgeries conducted globally. Uh and they're just beginning to be penetrated uh by you know what's happening with robotic surgery specifically you know companies like intuitive surgical. Um so I think that is another big category uh where you're beginning to see real innovation happening where the combination of um haptic feedback um mapping software better technologies around uh surgical conduct itself are really leading to uh a lower error easier training for medical students and a overall better context uh for how these uh therapeutic carees can be conducted and as you know there's a big discussion obviously around uh how healthcare costs can be contained and I have some hopes that AI applied intelligently can can can help with this.

Mala Gaonkar: Um, so yeah, I think I think broadly this idea of how technology is intersecting with non-technology businesses and how you can use data to analyze that and debias the very human decision-m process something we spent a lot of time on.

Nicolola Tangian: Very interesting. Now you believe in concentration. You have quite big when you when you go for something you go pretty big, right?

Mala Gaonkar: Correct.

Nicolola Tangian: What um tell me how you think about concentration.

Mala Gaonkar: I very intentionally set on these four verticals just happen to be uh enterprise data so tech broadly um financial services and healthcare services as well as industrial technologies because I felt those were the areas where these themes of um 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 at 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. Um one I think that allows you to play offense when at some point or the other due to you know the passive nature of the market structure now increasingly the odd factor rotation here or there could disrupt uh 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 uh and we have very different drivers of the long thesis of each of these names.

Nicolola Tangian: Now you uh grew up in India and I heard you on a podcast once talking about food. So how do you how how do you spice up the portfolio?

Mala Gaonkar: I think the spice for us is quality. So I think we have really moed long duration businesses and we have very debiased ways of tracking the data where it's not just based on my intuition or the team's intuition um but really based on you know systemic thinking and not siloed 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 thinking I think it's it's important for us to talk about this a little bit and you're a real student of the investment process Nikolai but you know we're both we both love the sort of Kaman Ferriski theor work right and um I remember my father once giving me this great quote from Bertrren Russell which is humans will do anything they can to not have to think and it's very simple like Kaman talks about system one and system two thinking and system one thinking is that intuitive gut thinking that frankly is positive. It drives very quick fluid reactions from humanity dayto-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 portfolio is driven by system two thinking.

Nicolola Tangian: Here I need to interject that I have actually interviewed you once before for my master's thesis in

Mala Gaonkar: Oh, that's right.

Nicolola Tangian: in in decision-m which was exactly about that. you know when do you use uh you know intuition and when do you when do you use analysis? So how has your use of pattern recognition changed during your career?

Mala Gaonkar: My pattern recognition um has I think expanded from being less driven by individual analyt analytical viewpoints and more to thinking about the context. And so I've probably over uh emphasized the person and uh the CEO or the leadership or kind of the hero model and underemphasized the very powerful social context in which companies businesses were operating. Um and uh I learned this the hard way. I've had plenty of failures, but my first job, you know, working in uh in in in Russia and Mongolia as a junior analyst with the World Bank, um you know, I I came in there, this is this is particularly poignant example. I came in there thinking, "Okay, great. We're going to take these stateowned 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. Um we had the rise of autotocracy uh across across these countries and we know the headlines today still speak of these. You cannot take away or abstract away from 70 years of you know communist history and simply assume that people are going to just you know 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 uh to really think uh systemically and clearly about um how these how how businesses will evolve in the context in which they are in today. So that's something I think think a lot about is is is how do we address these very specific failures of our thinking.

Nicolola Tangian: You can also invest uh in uh private companies.

Mala Gaonkar: Yes.

Nicolola Tangian: Why did you choose to be able to do that?

Mala Gaonkar: I think some of the we obviously know uh 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 uh than have been for a while. Um 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? And that's my fundamental view. And the edge is is really the small company, the private company, the unsung founder who's just emerging. That's really where change at the margin 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 and talking to private companies as part of that process. It's as simple as that.

Nicolola Tangian: How do you come up with ideas? So you wake up in the morning and then it's like bang, I want to look at that.

Mala Gaonkar: It's not a wake up in the morning and bang, I'll look at that. it's really a longer duration. Um, you know, there's there's a long incubation process, I think, for me of ideas. So, I read a lot. I think, you know, uh, I talk to my team. I talk to, uh, the broader networks that, uh, 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, you know, obscure industry trade show that happens to feature automation of, you know, new robotic systems where you might get a new idea. It might happen to be uh this panel, this regular ongoing survey we've done since before our launch of AI developers in non- techch industries to see what are they actually working on, what are they actually trying out that's working, that's not working. Um so it's really uh I would say from more obscure lenses that I get my best ideas versus what you would expect, you know, just just talking to uh the existing power players, although that's obviously important as well.

Nicolola Tangian: So then you have an idea. What do you do with it?

Mala Gaonkar: The next step is to to your point uh your question earlier about intuition system one versus system two thinking is then to put it through a very fine filter of the overall check investment checklist. I very much believe in a kind of a checklist driven approach. um we go through that and then in addition uh to my qu my my point about biases making sure we have a way to track the thesis uh and make sure that we are we can hold on to that rope and the reason for that is uh I think we are very subject to some very colorfully named biases right whether it's confirmation bias or availability bias or some cost bias I've you know when I look across my many investment mistakes uh they've all been you know one or the other of these biases and they're very human and they're natural and they have some positive aspects But for investing maybe 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 you know we're we're we're not uh just assuming uh issues that might be headwinds to the thesis away.

Mala Gaonkar: Uh and uh I spent a lot of time on that. I spent a lot of time also making sure you know 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. Uh, and so that's something I spend just as much time on. And FOMO is probably the biggest, right? So, I 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 it happens to be, crypto or quantum or whatever the flavor of the day might be. Maybe we can try and expand our circle of competence, but if we can't, making it clear what our circle of competence is. Keep pushing it out, but be very clear about what those lines are and not overstepping them. So, I think I would say my ideas are as much about uh steering the team as they are about specific stocks.

Nicolola Tangian: How do you use them? How do you um do they make decisions or they support you in your decision-m?

Mala Gaonkar: I have a very experienced team. I'm very lucky to work with this small handful of of of truly excellent people who have had, you know, decade or so each of of long and short stock making experience. Uh, and I really use them as collaborators. I really think of them as people that are very important to my decision-making process. And uh, I could not this is very much a team sport in my view. And so you do need that strong team. Uh, and I view myself as much a coaching uh, and mentoring role. And one of the big drivers of circle cap I hope is that I can attract and mentor uh the next generation of really great investment talent.

Nicolola Gaonkar: So um thoughtfully missing out what are the red flags that will keep you away from something?

Mala Gaonkar: Uh one is compromise. So if you feel that you are only buying a investing in a business because of pure valuation or you're investing in a 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, you know, basic essentially uh being overly emotional uh in terms of the investment decision-m whenever I see that when I when I 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 uh as opposed to just focusing on one and I think that's where the bias is very inherent by you know as humans we're very trained to look for that one big exciting moment or thing uh that's how we evolved right? We were looking for the predator that was about to pounce on us. Um, but in reality, I think in the modern world, uh, 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 in that full gamut is something I make sure we do and I make mistakes when I don't. It

Mala Gaonkar: It's not easy to leave emotions out of it. I do think you need to have a methodology and a process to bring that into your system to thinking and and creating tools so that you and your team can nudge that way. Um and then remember uh back to the nature of the markets themselves, stocks move as much a narrative as they move on numbers. So you have to be respectful of that context and just be appreciative of the cont. back to my point about context uh in even the the late dying days of the Soviet Union and how powerful that was. Um so I think that's something we need to balance as well.

Nicolola Tangian: You mentioned uh MOAT uh long-term moat um as a as a definition of quality. What are the other type of quality signs you're looking at?

Mala Gaonkar: Yeah. So uh when I say mode, what do I mean by that? I mean a business that has very high uh incremental ROIC's because ultimately the value of a business is going to be the return on incremental invested capital times the do dollars you can put to work at those incremental returns right

Mala Gaonkar: so it's a balance of what is driving that ROI I see and multiply you know there 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 or new geographies or new products that's great so multip multiple believers 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 have 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 the sales and size of the existing business today because that increases execution risk which is a risk I very much need 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.

Nicolola Tangian: So um without uh talking about anything you own in the portfolio just now but um an example of a great investments that you have had which would kind of tick some of these boxes.

Mala Gaonkar: Yeah. Um I would say the uh broader you know and we've had some of these investments in my prior role as well. This is the great aspect of these businesses. Um 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. Um so you have you know businesses like TSM that are well known that have really kind of

Nicolola Tangian: there's a Taiwan

Mala Gaonkar: corner in Taiwan kind of the process engineering aspects of that. So it's not really just about one thing it's all about multiple things. So I love that sort of systemic modes where there isn't just one little simple uh it's not you just own the gold only gold mine in town. It's that you own the process around uh how how how the gold mine is is extracted. So I think we I I I like businesses that have those kinds of those dynamics to them. I also think there's something really interesting happening now in the in the chip layer in AI where um there's been a lot of talk about training but I think you know training is a little spiky but inference is the is the annuity stream right ultimately this is what we're going to be calling on the models to do day in day out and those are going to be within specific LLMs Google will have its own inference stack um open AAI uh anthropic and others and I think what is happening around Google's TPU is really interesting in terms of reducing that so I I think um AS6 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 of sort of where great businesses uh are are really emerging and forming and have formed. Um so those are probably two big buckets in what's happening around the chip stack layer which have really and and the medtech which have played off the last 20 years. So these are not new these are I think um 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 data analytics, there's some proprietary data providers we we uh 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.

Nicolola Tangian: What type of companies would that be?

Mala Gaonkar: So those would be businesses um that provide for example financial data. So think about um even for um your portfolio uh 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 for a LLM for example to ever disrupt because of the real-time nature of that. But that's embedded in real workflows around trading and compliance that I think are very hard to disrupt as well. Um so I think there's a perception that those can be scraped away uh by the AI uh models but I think that's almost very hard for me to see or understand.

Nicolola Tangian: What kind of time horizon do you have when you invest or when you think about investments

Mala Gaonkar: for as long as possible on the long side obviously um shorts are obviously different. They're more catalyst driven and usually tend to have sort of you know sub ninemonth kind of catalyst periods but on the long side as I said uh 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 3 to 5 year cycle in terms of our valuation horizon but ideally we see levers beyond that.

Nicolola Tangian: How uh does shorting compare with long investing? Can the same person do both?

Mala Gaonkar: It's a it's a great question because it's really the opposite muscle of the long side as you know it's really thinking about uh so sometimes you will see clear the shorts that are the broken mirror images of the longs x is winning y is losing um I find those are very rare uh 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 uh winner loser dynamics will play out usually uh if it plays out through um some combination of uh clear price competitive pressures, clear share competitive pressures leading to those pricing pressures um and then that combined with you know other factors such as just misexecution by management uh you have a pretty good pretty good short. The problem though is that combination of factors occurring within the time frame you need uh is a tricky one. Uh and then add to that all of the sort of um factor rotations and the passive uh nature of the markets today often driven by quant and ETF and other funds. You have other dynamics that are driving trading you need to think about.

Nicolola Tangian: Do you short?

Mala Gaonkar: We do short.

Nicolola Tangian: It's just so so stressful.

Mala Gaonkar: I mean it's because you are you are wrong for such a long period of time and when you're right it's just like in these bursts, you know, and they don't last very long. You may be like super right for one week and then you're kind of wrong again for two years, right? It's hard taking day in day out.

Mala Gaonkar: We still look for absolute profit, dollar shorts.

Nicolola Tangian: Do you care about valuations?

Mala Gaonkar: I do. I think valuation matters a lot. Uh we look at free cash flow multiples. Uh we expend stockbased comp. We do all the unfashionable things and look at things pretty conservatively. Uh and the reason for that is what I said earlier about wanting to manage risk. So thinking I think about riskadjusted returns and I think valuation and what you pay upfront is very much a part of that and particularly uh thinking about valuation and the information asymmetries in the market even more so.

Nicolola Tangian: Let's go back to your time at uh Lumpine uh and you you know you set it up with Steve Mandal. Um what did you what was the most important learnings from your time at Lomine?

Mala Gaonkar: Yeah. Um I think uh there were several I mean I think uh and I was really lucky to work with the the brilliant group of people there including Steve. Um 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'll give you a couple of examples. Um and you talked about shorts. We can start with shorts. One of one of my biggest mistakes at Lomine was shorting Nokia

Mala Gaonkar: and I shorted Nokia for went down at the end.

Mala Gaonkar: Yeah. Um I think uh there were several I mean I think uh and I was really lucky to work with the the brilliant group of people there including Steve. Um 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'll give you a couple of examples. Um and you talked about shorts. We can start with shorts. One of one of my biggest mistakes at Lomine was shorting Nokia and I shorted Nokia for went down at the end. So this is what's interesting. So, you know, 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 and for my firm. Um, 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 of the short side. So, what was the lesson? The lesson is um 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, you know, strategic value of the business broadly over time. So, thinking about a business not in 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 Noki example is one. The other is avoiding um balance sheet leverage like uh public LBOS's I've learned the hard way are probably not my thing. I've made mistakes where I have invested in decent businesses that were over pretty decent business that were overlevered. Uh but as a result they had much less maneuvering cap capacity when times of inevitable macro tensions arose. So that's another big big lesson and I made mistakes there. um businesses like Altis for example. Um the third is I think maybe the most interesting one going back to discussions of the AI chip stack uh and of how old technologies can disrupt in new ways which is uh Nvidia. So Nvidia um saw it very much as a GPU business what it was. But then 2015 we had the uh deep reinforcement learn 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 par essential for that parallel compute to work to drive this interesting new engine of machine learning. didn't know what generative 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, gener generative AI. Um, and the fact that was back then apart from just uh 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 was going to be this AI thing and that's going to be big. uh turns out they missed ma you know significantly because of a you know concentrate crash in crypto and and uh game uh permitting delays in China which were driving a big portion of demand for the video gaming part of the business. Um so I sold which I 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 this some cost bias I find is a very powerful one and that's why I spend so much time now at SIGRO 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 missed. So areas of emission as well to make sure we don't bump into this issue repeatedly.

Nicolola Tangian: It's so difficult to buy back things you sold lower on. Yeah, but that's in my opinion that's not an excuse.

Mala Gaonkar: And I think I think precisely because that is so hard it's a really important and interesting opportunity set for new ideas uh when when you are wrong which happens a lot. Um and so at least to me so I think that's some those are those are probably three concrete examples of clear lessons. one around systemic not siloed thinking, the other around weariness around financial leverage versus the other drivers of leverage that we all are happy about and the third being around these biases uh that we talked about earlier including the sun cause bias.

Nicolola Tangian: Uh well, I'd love to ask you um a couple of questions just on um slightly more personal nature. Do you think uh I mean you grew up partly in the US, partly in India. Um do you think that m multicultural background is impacting the way you view the world and investments?

Mala Gaonkar: Very much so. Uh I would say uh growing up in India, growing up in a Bangalore that was not the Bangalore it is today. A very sleepy town. It was actually considered a retirement town at that point. My parents are both um my mother's a doctor, my father's an academic. Uh and they you know worked uh there in in the in that capacity. uh and it was a very quiet, very academic uh very cerebral up upbringing in many ways. But it was not without exposure to the very uh stark realities of India at the time. Uh an India that was uh suffering in many ways from the u from the walls that were put up. It was it was a closed off economy at the time, the license raj as it was called. Uh and it was very apparent that that was leading to real you know issues for the 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 in trust in institutions the resulting corruption that that often results in uh was something I grew up with and was I just was all around me and I think it's something that led in me at least to a very fierce sense of a need to give back and be of service. uh and I think that's partly also my my family. Um my great-grandmother was in jail working because of her work uh with the independence movement under Gandhi. Uh 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 barrier. a very strong local culture and local dynamic and a very rich um literature and uh sense of history and self that came about uh as well from some of the the fact that you know India was more more closed off at that point in time and so I think as a result of all of that I also came away with this sense of great pride in in in what India stands for and what it has become today and what will become in the future.

Nicolola Tangian: you are a role model for many women and um and I think when you when you launched your fund it was the biggest launch biggest hedge fund launch of any woman ever right that's is pretty pretty amazing any reflections around being a woman in the investment world

Mala Gaonkar: I would say look I first of all I hope that's a record that's broken very quickly uh and I and I'm sure it will be uh there's some really amazing talented women out there um look I hope I'm not just a role model for women but you know the broader investment community and hopefully over time the philanthropic community as well. That's a very important part of my my my work and my ethos and my identity. Um I would say um I'm a little I really think the investment business broadly uh whether you're uh whether whether you're an outsider in any way, shape, or form um 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, there'll be a line out the door uh you know, wanting to get my my product, right? So I think it's a very meritocratic industry and I just hope that places like Serggo, places like uh you know the platform you're running here are just ways for more people to add value in this industry because I really think it's a it's a terrific one for people who are intellectually curious of all stripes and shapes and sizes to come in and and and be of be of use to the world at large.

Nicolola Tangian: Well, you have indeed many stripes because you are interested in so many different things. you I mean you've written you've written a book, you've been involved with a play. Um how does uh all this um play into your kind of creativity and curiosity?

Mala Gaonkar: I think uh

Nicolola Tangian: but tell me about some of the stuff you do first because you do a lot of funky stuff.

Mala Gaonkar: Uh I do two main things apart from my professional works. One is the philanthropy uh which we can talk about and you asked about the creative works. I can talk about that first. Um the the well let's talk about the philanthropy. So on the philan phil philanthropic front um I started very early. So from year one at Lomi I felt it was very important to start giving back. Uh and just as I do as investor it was really about backing really great social entrepreneurs and making sure we could see them and see them early. Um so I was very lucky to meet uh Paul Farmer who was early in building partners in health at the time. Um uh he sadly passed away uh but was a great mentor to me. uh and uh he said he always said you know public health is more like you know armed robbery than it is you know helping the old lady cross the road. You have to break things to build things. And it it truly is and and there's a lot that needs to be done in terms of breaking siloed thinking to really get solutions to people uh in a rural maternity clinic in India for example um that I learned through people like him through people like Otul Gowande uh and others that I've worked with and that all led to uh uh Sema Guyire who was uh working at Gates Foundation at the time and I to set up Sergo Health uh and this this really going back now over a decade and 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. So I think one of the most interesting things happening potentially in AI is adding the why to AI. Not I AI is very good at solving what questions. What ad will Nikolai click on next but not why. And I think adding um this large scale behavioral data uh which you can do with you know sort of some pretty basic basian uh networking math to say this is why a woman is not going to the maternity clinic to to deliver. this is why uh XYZ is not using contraception in this community is something that we've been spending a lot of time on and and data as a public good and data science as a public good is something we we we spend a lot of time on. So that's that's a big part of the philanthropic effort and we work with groups like the Beninda Gates Foundation and others as well as local governments to help. So for example and one thing that's 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 and then we're getting calls from people in the US saying maternal mortality is rising here it's a real problem how can we use those same techniques here in the US and then that then led to a really program with Uber you know with DAR actually I reached out to I think you've interviewed here um to do rides for moms so the problem was not you The problem was having a maternity plan and making sure women actually went to the clinic in which case transportation was actually an interesting thing. It was transport not hospital care. So understanding the why is really important. So that 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. Um look I think we're happiest as people when not when we're you know obsessing over ourselves but when we forget ourselves whether it's our professional work or philanthropic work or creative work. And so I very much do it out of joy have since college. um writing short stories that is and um I think at the end of the day everything is a narrative right and we had dinner recently with a group of very diverse range of very successful people and

Nicolola Tangian: well you you predictions

Mala Gaonkar: you do you do fun dinners

Mala Gaonkar: and we asked them to do predictions they all came up with pretty pessimistic ones so there's this conflict between the narrative self and what I see how I see myself uh versus the social self social context and so I think that conflict is something I I like to to write about and think about and It's it's been really fun.

Nicolola Tangian: What about the theater project?

Mala Gaonkar: Yeah, so the theater project, the theater of the mind, uh it's a uh it was based actually in a series of uh neuroscience experiments. I uh had was very interested in what was happening around some of these economic games. We talked about Kaman Thaylor and Ferriski's work. Uh there's one called the dictators game for example where even though uh an individual in a group playing this game could take all of the coins on the table, they do not. They want to share. So that's hopeful. Uh, and I was interested in that and I wanted to take that to the basement of the science museum in London, one of the great museums of the world. And my friend Brian Eno said, "No, no, no. There's something more to this introduced me to to David Burn who was thinking about an experiment in a very different context. Uh, which was more sensory playing with propriception and how you can inhabit the body of a doll is actually a Swedish lab called Erson Labs that did this work. Uh, and 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. Uh it played very successfully in Denver and it's now moving to the Goodman Theater in Chicago. So that's been really fun as well. So it just it's it's you know it's really about thinking in new and fresh ways and making sure that that feeds into a more creative uh less less siloed thinking.

Nicolola Tangian: How do you bring all this back into investing?

Mala Gaonkar: I think all of this goes back to this idea of how everything is a narrative in some ways and really thinking about um 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. Uh the other is curiosity. I think curiosity is key to pretty much everything we do in each of these, whether it's solving a problem philanthropically, finding a great investment and understanding it as well as you can. Um, or producing a good a good piece of creative work. I think the other is openness. Um, so I think with the writing it's a great exercise in humility. Unlike the other two spheres of 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 and what is revision to make it as good as possible. And what is revision is just openness to possibility and openness to something better. And I think be aware of certainty. I think that's probably another commonality across all of these. Uh the the the the person who's 100% sure that X or Y is happening is something that always raises a red flag with me. It never is that certain.

Nicolola Tangian: What's the key to staying curious and humble?

Mala Gaonkar: Uh I think it is very it's a virtuous circle right because if you're curious and humble to begin with uh you learn and you see how much more there is to learn and that keeps you humble and then you but you also are excited by the possibilities out there. So I think it's just very much a virtuous circle of uh getting on that wheel to begin with and I think plus more fun right what's more fun than than learning.

Nicolola Tangian: When do you wake up?

Mala Gaonkar: Uh I wake up about 6:00 in the morning every day.

Nicolola Tangian: What do you read?

Mala Gaonkar: I typically read the papers. I usually just spend time thinking and then I, you know, I like sometimes go for a bit of a walk and then I then I read all the papers, the usual stuff and then dive right into it.

Nicolola Tangian: How do you think? Do you just sit in a chair and think?

Mala Gaonkar: You just walk, go for a walk, you know, pace around.

Nicolola Tangian: Do you structure your thinking or are you just like looking at dogs and trees and

Mala Gaonkar: I don't believe in I have plenty of structured thinking in the rest of my day. So that point of the day I just let my mind roam and uh see see what comes up.

Nicolola Tangian: How do you relax?

Mala Gaonkar: I relax with spending time with the people I love, my friends, my family, uh reading, you know, going for long hikes, you know, so the so the the usual ways of I think many of us have of disconnecting, being with nature and being being with friends and family.

Nicolola Tangian: Fantastic. Well, it's been great talking to you. You are talking to curious, humble, and an incredible professional. It's great.

Mala Gaonkar: Thank you, Nichollet.

Nicolola Tangian: Thank you.