The Exchanges, every show

Q What are those robots cost? What's the range of the hardware cost, and then what's your business model with these? People buy them and rent the brain. They rent it by the hour. What do you think of as the CEO will be the business model? And what is the business model with these hundreds or dozens of customers deploying hundreds of these?

A Yeah. So we, um, we, we went with a CapEx model to start with Spot. Um, we'll be doing a, probably a robot as a service model, likely with Atlas. Um, we understand through the humanoid form factor, folks may want to spin up at different times and then, and have the ability to, to do decrease. Um, with Spot, it's been pretty effective in CapEx. Um, it's the way these industrial customers think about industrial tools. So they generally want to spend CapEx for this. Um, it depends on their configuration. You know, it ranges anywhere between, you know, a 100,000 dollars for the base robot, all the way up to 300,000 when we're fully loaded with services, integration deployed.

AI assessment note: “ranges anywhere between, you know, a 100,000 dollars for the base robot, all the way”

Q I have a question here. Some countries have a trade surplus. Some have a deficit. Fewer have a surplus, more have a deficit. What happens if this continues 10 years, 20 years down the line?

A Well, one answer potentially is nothing. You know, it depends. There's nothing inherently sustainable or unsustainable about deficits and surpluses. There are reasons why certain places might want to be spending more than they are producing. The canonical example you'd have is a society where you say you have a lot of younger people, it's relatively under-invested infrastructure, a lot of growth potential, maybe it's poor or less technologically developed. That's a kind of place where you would expect that the needs of the people to invest and grow rapidly And their future productive potential are such that it makes sense for people in the rest of the world to invest there, uh, you know, lend them money, export, you know, advanced machinery, things like that, and they could have persistent trade deficits for a while and grow rapidly, and that'd be beneficial for everyone. Um, the flip side is you can imagine a society sort of in the opposite situation, maybe it's older, uh, already at the technological frontier, there's less growth potential, they have already advanced goods, and maybe they would be exporting.

AI assessment note: “Well, one answer potentially is nothing. You know, it depends.”

Q Why don't you take me back to the start of your career? Walk me through the core steps that lead you to where you are today.

A I did not set out to become an investor. I thought I was gonna go into public policy. My parents moved to Washington, D.C. when I was in high school. I was fascinated by policy and government. My whole college career was internships on Capitol Hill, political science major. The fall of my senior year, I got a call from Goldman Sachs. They had an open slot in their San Francisco office for the following fall. Did I want to come interview? I remember sitting there on the phone thinking, I've never taken a class in finance, but why not? I had a good appreciation for the role that markets play in the global economy. As somebody interested in policy, that was important. I crammed for the interview. I talked to all of my friends who had done banking internships or sales and trading internships the summer before. Long story short, I got the job. I graduated in 2008 into the financial crisis with a job at Goldman Sachs. I don't think anything has shaped how I think about portfolio management or how I manage stakeholders Or how I manage my career quite like starting out in the middle of a financial crisis. I learned a lot in the two years I spent at Goldman.

AI assessment note: “I graduated in 2008 into the financial crisis with a job at Goldman Sachs.”

Q decided advantage. That's why customers call it miraculous, game-changing, and an awakening. If that's not how you would describe your investment management tech, request a demo at Ridgeline.ai. And now, back to the show. In some of the areas you're diving into, venture, private equity, notoriously competitive for the best managers. How do you position NYU, you being new in this seat for a long-standing institution, as a desirable LP?

A Two pieces. The institution and then the team. On the institution side, NYU positions itself. It's the largest private university in the U.S. We have 60,000 students, 700,000 living alumni. Most of the GPs and managers that we talk to have a family member who went to NYU. We're treated at NYU Langone Medical Center. The reach of the university is massive. That's appealing for a lot of our partners, especially when you combine that with the specific role of the endowment, which is to provide accessibility to that institution through financial aid. That's a compelling motivator for our managers. That's great in theory. Then there's the reality of the day-to-day. That's where the team comes into play. Our team leans in to building active partnerships with our managers. That can look like helping secure a room for a recruiting event that they're doing at NYU, or debating what the appropriate pricing model is for a product that we're not even invested in. We want to be the partner of choice because Managers find it valuable to have conversations with us. One of the best compliments I got from a manager was they said, when something's up, we like to call you first, because we know that you'll answer the phone in a timely manner. By the time we hang up, we'll be well prepared for all of the client calls to come. That's the type of value add that we want to be able to provide to our pa…

AI assessment note: “Two pieces. The institution and then the team.”

Q Which two people have had the biggest impact on your professional life?

A The first one I have to go with is my parents. I'll treat them as one because they act like a unit. When I was 16, right around this time where I decided that I was going to go into policy and politics, they sat me down and they said, great, you also need to understand how a stock works. They bought me books. I didn't have much of an interest in the markets at that time. They were adamant that understanding investing was an important life skill. The second person is Will Fox, who was the managing partner in the US at Partners Capital when I joined after Goldman. Will taught me a lot of what I know about managing portfolios and investing. Also running a business. When I went to Will and said, I want to understand how partners works as a business, he gave me that opportunity to understand how the finances worked. He both gave me a lot of confidence to keep doing what I was doing. Pushed me really hard to be better. Never minced words on what needed work. That shaped both my time at Mellon and then at NYU. To the extent that when I initially started interviewing at NYU, one of my first calls was to Will to get his thoughts and hash through what it might look like.

AI assessment note: “The first one I have to go with is my parents... The second person is Will Fox”

Q What did you find when you got there?

A The endowment has been around for a while, although it's much younger than most of our peers. When I got there, the university leadership gave me a mandate to change how the portfolio was being run. While the pool of capital at six and a half billion dollars was sizable, it's relatively small compared to the scale of NYU. Up until 2010, it was a sub two billion dollar pool of capital. A lot of that growth had come several years before I got there. The university leadership was ready to look to the next level, which required more growth from the endowment. The portfolio as it stood at that point was more conservatively positioned. All of the investment decisions were run through the investment committee. The portfolio was all flavors of Bottom-up, fundamental, mostly U.S.-based corporate securities. Mostly equity, some credit. Because of the conservative mandate that preceded me, there was a big focus on managing volatility. When I came in with this mandate that was growth-oriented, we had to rethink that construct across the board. Everything from the governance structure to how we thought about asset allocation to the types of investment managers that made up the portfolio, the team to underwrite all of that. It was all a blank sheet of paper.

AI assessment note: “The portfolio as it stood at that point was more conservatively positioned.”

Q When it comes to asset allocation and portfolio construction, how have you set up that framework to get at your growth objectives?

A We've walked a little bit of the middle ground between a more traditional asset allocation and a total portfolio approach. At the core of our asset allocation, we've tried to hone in on what are the different types of assets and the different roles that we want our assets to play in the portfolio. Grouping those together, which is where the somewhat of an asset allocation framework comes into play, But then creating a list of criteria for every single investment in the portfolio on how it fits into that bucket. What that's meant for us is we have an equity part of the portfolio, which has a private equity component and a public equity component. We have a liquidity and cash component to the portfolio, and we have absolute return and opportunistic. We have a small allocation to real assets, Although I would say that is a heavily debated topic of whether that deserves its own allocation. The idea stems from my view, my team generally shares this, that the most reliable source of return over the long term is equity market exposure. If we are going to generate a return that is going to fulfill the university's objectives of spend plus preserving purchasing power, Anything we invest in needs to be competing with that long term equity market return. There are lots of reasons to move away from equity market returns. We've got to be really clear on what those reasons are for each part …

AI assessment note: “We've walked a little bit of the middle ground between a more traditional asset allocation”

Q What are some of the areas you've leaned into that are a little different from your peers?

A One is on the hedge fund side, where when I was at the Mellon Foundation, we were relatively early to underwriting hedge fund strategies that are more trading oriented, that run higher levels of leverage, where you don't have necessarily a single persistent source of return. It's much more down to technology or manager skill or Or some insight into the data. We've leaned in heavily there. That absolute return and opportunistic bucket that I described, almost all of that right now is in those strategies. We're able to do that because we've built a team that has a lot of expertise in those strategies. We can underwrite the different types of risks effectively. The other thing we've done that's different Is a function of the structure of the portfolio at NYU, where we have liquidity. When I got to NYU, less than 15% of the endowment was in private assets. Our team has the view that there is still a lot of value to be had over the long term in private markets. Maybe not every private market. We want to be more discerning there. But we have the ability, and we have been growing that portfolio Substantially over the last few years when many of our peers have been pulling back their allocations.

AI assessment note: “growing that portfolio Substantially over the last few years when many of our peers have been pulling back”

Q Amazing. Where does physical AI show up first in a way that's really economically real? Are you seeing that already?

A We conjectured that, ah, that robotics was going to come along and decided that the first application of robotics that has both a large enough market Um, relatively standardized technology so that we could scale and get the flywheel going, um, and has real economic value was, uh, self-driving cars. And so, uh, inside Waymo, uh, our chips from NVIDIA, uh, at, at Tesla, we were in the car, uh, now we're in the data center. Um, uh, Mercedes, we're in the data center, we're in the car, we're the software stack. Uh, we, uh, worked on Alpamayo, and we open sourced it, and the reason why we open sourced the self-driving car stack is because you need it for agriculture, you need it for mail delivery, you need it for warehouse AMRs. There's so many different ways that you could apply, um, uh, autonomous navigation, uh, and none of those markets are big enough to be a self-driving car market, and we thought it was sufficiently diverse that we would create the whole stack for it. And so we're working with autonomous vehicles in all kinds of different places. Our robotics business, autonomous vehicle business, basically physical AI business is probably almost, it's like ten billion dollars, so it's really, really big already. Um, likely this will be one of the largest industries in the world, and, um, uh, it'll take longer than a couple, two, three years. It'll take less than 10, and so th…

AI assessment note: “first application of robotics that... has real economic value was, uh, self-driving cars.”

Q When you look at that dispersion, what do you think is inaccurate? You said you don't really believe the ninety-ten. What, what do you believe a more accurate representation is?

A Well, in our, uh, apex, uh, benchmarks, we're getting closer to around, uh, 50%, um, of long horizon workflows. Um, top models are scoring around, around, around that much, but I think that, um, the percentage for, there's a, there's a class of workflows that are just sufficiency based where you do it and it's done and you're, you're good. This is something like updating a CRM. Um, you couldn't really get much better at it. And then there's a class of workflows that we shouldn't even be thinking about in terms of, you know, binary, like, can the models do it or not? Um, and these can be things like legal arguments or, Uh, to an extent, medical advice where you could always get better. Um, and in those cases, I think that the percentage framing is, is just totally off, and we need to be thinking more about continuous uncapped rewards.

AI assessment note: “we're getting closer to around, uh, 50%, um, of long horizon workflows.”

Q All right, so we're going to talk V to G. I want to start from the technical perspective. What's actually required for a vehicle to discharge its battery into the grid?

A Yeah, there's three key components. The first is a bi-directional electric vehicle. It has to have the ability to draw power from the battery and send it to buildings or to the grid. The second piece is you have to have a bi-directional charger that can both send power to the vehicle and can also receive power and send it through the charger back to buildings with the grid. And the third piece is you need Uh, some sort of software platform that manages and optimizes that power flow. It responds to constraints that are established by, uh, the customer, the owner of the EV to ensure that the vehicle, uh, is first and foremost providing the mobility services, uh, that, uh, the vehicle was purchased for from the consumer. So those, those three pieces. And, uh, you know, we can take it, uh, another, uh, level, um, particularly around where the conversion Of the power from DC. We know we take DC power from a battery, and we convert it to AC power for use in buildings in the grid, and there's, there's two different flavors of architecture out there, and I'm happy to talk about that if you think that would be of interest.

AI assessment note: “there's three key components. The first is a bi-directional electric vehicle.”

Q Yeah, let's do that. Talk about how you can configure it. And then, and then we'll talk about sort of like where we are today in terms of those. Do we have those three things out in the wild and where?

A Yeah, absolutely. So there's, um, what we call a DC and an AC architecture, and that really defines where the conversion, uh, in a device called an inverter, which is a grid interactive inverter. It takes the DC power and converts to AC power. As I mentioned before, for the DC architecture, that conversion happens Offboard the vehicle. It's either in the charger or a wall box that's adjacent to, um, to the charger. So it's offboard. So it's drawing DC power from the EV, converting it to AC power offboard and sending it to the building or to the grid. And then, um, the other flavor is V to G AC, and this is where their onboard, uh, conversion occurs. So the vehicle sends Uh, AC power from the vehicle to an AC bidirectional charger. And so just to give you a sense of, uh, where we are commercially, the, the Tesla Cybertruck, uh, V to G product is an AC architecture. So there's an onboard, uh, inverter capabilities and the Ford F- one-fifty and the, um, GM, uh, suite of EVs that have V to H and, uh, increasingly V to G are based on that offboard, uh, V to G DC architecture.

AI assessment note: “just to give you a sense of, uh, where we are commercially, the, the Tesla Cybertruck”

Q Would you be where you are today though, if that round had come together?

A No, no. Cause I don't think we would have pivoted as hard into COVID when COVID hit. Um, I mean, it would have probably been easier cause we had, at that point we actually had a lab license. So You know, in the US you need this thing called a clear license to run these kind of tests. And we had got one of these licenses over like a painstaking two year process. And then in December of 2019, as part of the wind down, I sold that license to a company in San Diego for 150,000 dollars to pay some of the creditors. And then five months later, acquired a company in Southern California to get the same license for twenty seven million. So timing is everything.

AI assessment note: “No, no. Cause I don't think we would have pivoted as hard into COVID”

Q I didn't, I used to run a lot when I was young, and my needs were wonderful, and I used to love how I built this, because they would ask the questions like this, which is like, how do you go from like cows and musculature on cows and milk yield optimization to at-home STD testing? Like, it doesn't feel that natural a jump.

A Yeah, on the back end, it's more natural, right? All of these things have DNA in them, and so if you're, if you're looking to do better DNA testing, you're just looking for markets where people care more about that, and anything human, people obviously care a lot more about, are more willing to pay for, and are much larger markets, and so we sort of did like a market-first approach of, you know, where, where could there be interesting things, and we narrowed in on Uh, antibiotic resistance in STDs as being like a particularly interesting area where they're getting harder and harder to treat because you get more and more antibiotic resistance, and if you're doing the DNA testing, you can predict what the best drug is going to be early, treat with that drug, and then you're not using the most aggressive antibiotics.

AI assessment note: “All of these things have DNA in them, and so if you're looking to do better DNA testing”

Q Would you be where you are today though, if that round had come together?

A No, no. Cause I don't think we would have pivoted as hard into COVID when COVID hit. Um, I mean, it would have probably been easier cause we had, at that point we actually had a lab license. So You know, in the US you need this thing called a clear license to run these kind of tests. And we had got one of these licenses over like a painstaking two year process. And then in December of 2019, as part of the wind down, I sold that license to a company in San Diego for 150,000 dollars to pay some of the creditors. And then five months later, acquired a company in Southern California to get the same license for twenty seven million. So timing is everything.

AI assessment note: “No, no. Cause I don't think we would have pivoted as hard into COVID”

Q It was. I didn't, I used to run a lot when I was young, and my needs were wonderful, and I used to love how I built this, because they would ask the questions like this, which is like, how do you go from, like, cows and musculature on cows and milk yield optimization to at-home STD testing? Like, it doesn't feel that natural a jump.

A Yeah, on the back end, it's more natural. Right. All of these things have DNA in them, and so if you're, if you're looking to do better DNA testing, you're just looking for markets where people care more about that, and anything human, people obviously care a lot more about, are more willing to pay for, and are much larger markets, and so we sort of did like a market-first approach of, you know, where could there be interesting things, and we narrowed in on antibiotic resistance in STDs as being like a particularly interesting area where They're getting harder and harder to treat because you get more and more antibiotic resistance, and if you're doing the DNA testing, you can predict what the best drug is going to be early, treat with that drug, and then you're not using the most aggressive antibiotics.

AI assessment note: “All of these things have DNA in them, and so if you're looking to do better DNA testing”

Q So how many folks are full-time at the company today, and how many of those are engineers thinking about agentic workflows for dental offices?

A So we have about 80 people, uh, worldwide, and about, uh, 42, 43 of them are in the U.S., and the rest of them are in India, and, uh, uh, engineering-wise, we have about 18, 17, 18 developers, and at least three or four of them are thinking about agent workflows. Uh, the rest of them are still working on a lot of the poise, payments, and other stuff, because that stuff doesn't go away, meaning you still have to Make sure your product is stable later on as well. But I personally spend a lot of time because I'm, I'm very technically involved myself, spend a lot of time looking at agentic workflows and how do we make all our existing stack fully agentic as well.

AI assessment note: “we have about 80 people... three or four of them are thinking about agent workflows”

Q And isn't cooling technology, uh, that is being used as something that's, uh, well understood and it's just getting deployed, or is there like fundamental new things happening in cooling right now?

A I think liquid cooling has been around for some time, ah, but has never been deployed at this scale, and so the innovation is more around how to make it reliable, how to make it, ah, cheaper, ah, more scalable, right? So there's a lot of innovation around that. There's also a lot of new innovation, new kinds of liquids, new kinds of materials that can absorb heat better, ah, because anything that can improve the efficiency of heat transfer Uh, is very important for data centers. So we can then run the chips hotter, right? And there is a direct correlation between running a chip hotter and how powerful the compute is. So the hotter the chip, the more memory bandwidth you get, the more flops you get. And so there's more, there's a strong payoff. If you can cool well, that also means you can produce more intelligence.

AI assessment note: “innovation is more around how to make it reliable, how to make it cheaper”

Q With these two sides of the need starting GFC on the lending side, and this capital that needs to find a home with this type of strategy, how did you think about what you wanted to put together in the asset management business?

A We came from a restructuring background. What we wanted to do was to make sure that we embedded the learnings of what goes wrong in credit, not just around the credit risk of your loans, but also around the structures that you're investing in, the structure risk, some of the other pitfalls that can happen through lending, so you put yourself in a good position if something inevitably does go wrong. The other thing that we saw Through the restructuring business was the importance of empowered process, good team structuring, good portfolio management, not just good credit selection. We spent a lot of time designing the infrastructure around the business as to how we were going to approach the market. The other thing that we thought was important was that if we were right on the thesis that you were going to see a significant amount of capital move off bank balance sheets or in a world where bankruptcy We're going to partner with institutions to provide capital. It's going to be a large space. It's not going to be a couple of trillion dollars. It's going to be tens of trillions of dollars. Your problem was not going to be AUM gathering. Your problem was going to be sourcing good quality loans and the ability to produce those loans through cycles. That's why we went down a path of proprietary origination. Everyone says they have proprietary origination, and what people often mean b…

AI assessment note: “That's why we went down a path of proprietary origination.”

Q use to ultimately fund that work. That's really cool. Um, okay. Next big question. Uh, it's a crazy geopolitical moment. A lot of fertilizer that traditionally comes from global markets, specifically around, you know, a particular Gulf and Strait of Hormuz, um, is now not coming into the world. So can you say a bit about the global picture right now for fertilizer supply and what that means for you?

A Yes. The Persian Gulf, through the Strait of Hormuz, supplies 38% of global urea, which is the most common form of nitrogen fertilizer used in the world. And because of the closure of the Strait of Hormuz, it is estimated that over the next year, an additional forty-five million more people around the world are going to go into food insecurity. And so that, that highlights that the current market structure Ah, which is hypercentralized and reliant on coal and natural gas, that that current market structure is just unacceptable. And so, um, I, I think the thesis that Nitricity has been, um, working on and sharing with the market is that we need to move towards a more, um, uh, local, uh, production model where we use, uh, recycled ag waste and renewable power and air and water to make fertilizer instead. And we're seeing a lot more, and since the, um, uh, the closure of the Strait of Hormuz, we have seen a lot more interest in, in our company. Um, we've quantified it across, Um, like HubSpot or other, like, channels where we measure the number of inquiries that we get across different forms, including, like, number of people that apply to our open jobs. And, uh, and compared to a baseline from before, we've seen about a 500% increase in the amount of inbound interest in the business.

AI assessment note: “The Persian Gulf, through the Strait of Hormuz, supplies 38% of global urea”

Q So at some point, you did enough work that you had a working prototype, right? And it was time to start thinking about building a factory. Can you talk a little bit about that transition?

A Yes, so we had a working prototype, and then we brought it around with us, and we gave a presentation in Fresno, and a farmer stood up in the back of the room and said, you know, if I buy it, will you install it on my farm? And then we got a contract with that farmer, we went down, we installed it on his farm. Um, and then we got another contract on another farm. So, so those, those opportunities came, uh, first, and then second came, uh, funding, and then third actually came building a, a factory, you know, a couple years later. But we, we were really, um, um, you know, building the product directly on farmers' fields.

AI assessment note: “first, and then second came, uh, funding, and then third actually came building a, a factory”

Q dynamic in the market is that because there is so much of a supply constraint in providing power or generation or whatever, um, you know, the, the supplier can demand more out of the customer. So yeah, is that becoming a challenge for the NeoClaus? Is it putting them at a competitive disadvantage in being able to build capacity relative to like the hyperscalers who obviously have big balance sheets?

A Yeah, and a pretty massive one. Um, you can just look at the numbers, you know, CoreWeave, they have, you know, three and a half gigawatts of contracted power. Contracted for that means signed leases for the most part, some self-built, mostly signed leases with third parties. And what you saw was that this number was about, if I remember correctly, 1.3 gigawatts in Q four, 20, 24. Uh, so they've scaled that up pretty fast. Uh, but since Q three, 25, they haven't really been able to secure more. And that sort of coincided with the overall tightening of financial conditions, where you saw a pretty massive bond sell-off, which impacted the likes of CoreWeave, Oracle, and many of these guys. And suddenly, sort of, the high-yield market froze to some extent, right? And that's also, you know, obviously related to the fact that this market is not that big, and basically, you know, they massively increased the supply on that market. Anyways, we get, we sort of get to where we are today, which is that it's getting pretty tough for these companies to get the financing Um, for all of these parts. And they're all, as you said, more and more capital intensive. Utilities now are asking multi-billion dollar commitments for gigawatts, uh, gigawatts of power. Turbines and so on and so forth is the same thing. So yes, pretty massive disadvantage. And again, like I go back to what I said earlier,…

AI assessment note: “Yeah, and a pretty massive one. Um, you can just look at the numbers”

Q Okay. And that was gonna be my next question is, um, is it, you mentioned earlier software, is it exclusively, uh, software or will you include hardware as well?

A Uh, you know, earlier when we started the, the fund in 2011, we did, uh, invest in a few hardware plays, but over the years we realized to invest in a hardware play, A, you need a longer term perspective, B, you need, uh, a lot of capital, and C, uh, the sales cycle and adoption takes a lot longer. Uh, so we are a early stage, uh, micro fund, Uh, it is not a right strategy for us to be part of, so we've sort of walked away from that, and we exclusively now invest in software. They could be some hardware component where it is a data gathering tool or something of that sort, but the idea is the power is around, um, you know, the data that we are collecting or, uh, collecting, or it could be the decision layer That it's enabling the physical environment it is in.

AI assessment note: “so we've sort of walked away from that, and we exclusively now invest in software.”

Q How do you think about investing in energy, given the capex intense nature of it? You know, we're investing in a company called Fuse Energy. Which I think is incredible. I'm so happy to be, but it, it, it's a CapEx intensive business in terms of like the energy space.

A It is, but I think there's also a lot of innovation happening, and where there's innovation, you can, you can find early teams that are doing science experiments before anyone else is thinking about them. USV a few years ago, uh, invested in a great company called Radiant, which is building small nuclear reactors that literally come off of a factory line. You know, and it's going to be one of the first companies in the world to test, um, in the dome in the United States, um, for, for nuclear energy. And so I think there are a lot of interesting models and ideas happening on the edge of, of innovation. And I think those are the best places to bet as a venture capitalist, because in the earliest days, they're actually not that, um, capital intensive.

AI assessment note: “in the earliest days, they're actually not that, um, capital intensive.”

Q Where did that academic path take you through school?

A My undergrad was in political science, sub-discipline called comparative politics, where you're studying how different political systems evolve and trying to draw conclusions from that. Look at a dozen countries that have transitioned from dictatorship to democracy and say, hey, can we observe any patterns? Which turned out to be unknowingly good training for what we do as investors, because you're looking at different businesses, understanding them in a deep manner, developing pattern recognition. I didn't know that at the time. After undergrad, I had a couple crappy jobs in finance just to pay the bills. One was with a newsletter, one in a brokerage firm, both parts of our industry that are focused on making money off of clients and making money for clients. It didn't really sit well with me, so I went back to graduate school to get a PhD in poli-sci. I discovered there are no jobs for PhDs in poli-sci. Which they don't tell you if you apply. At that point, I was in Chicago. I'd met the woman who is now my wife and didn't want to leave. Morningstar was here. They had a reputation for being on the investor's side, which sat well with me, being willing to take a chance on liberal arts dudes like me. I wound up in Morningstar around the time when they were starting up coverage of individual equities.

AI assessment note: “My undergrad was in political science... I went back to graduate school to get a PhD”

Q How have you thought about managerial style as a lens at looking in the success of someone running one of these businesses?

A There's a subset of managers who are in the trust me category. As an investor, you're betting on the person as much as you are betting on the business. You're betting this person's second act. They've had a successful business, they've sold it, now they're starting another one. Because this person is, quote unquote, a moneymaker, you're willing to maybe overlook some related party stuff or some excessive compensation, and that's totally reasonable. That is a style of manager that History has shown can create a lot of value for shareholders. It is not a style of manager we tend to gravitate towards. That's just personal choice. It's a chocolate versus vanilla thing. I don't think they're good or bad. I do think they have greater risk of left-tail outcomes, of not listening or taking the company down a path that destroys value and not changing course when things are observably not going well. Especially in a concentrated portfolio, we have to think about that left tail risk differently than if we ran 50 stocks. If you run 50 stocks, okay, fine. I'll take a three percent bet on somebody who might be the most amazing manager of all time, but there's a little, some of that left tail risk. 12 stocks, it's a little harder. It hurts.

AI assessment note: “It is not a style of manager we tend to gravitate towards.”

Q When you had a universe of 1700 companies you're following to get to 12, what filters have you used to narrow that lens?

A To be clear, we had to cover the waterfront at Morningstar. That 1700 included utilities and oil and gas and life insurance and auto parts and auto OEMs, which are all not good businesses. Those are easy ones that just throw out. Step A is, is the industry structurally attractive or not? This is one of the hard truths that early investors have to learn is that some industries are tough. You got to respect the managers who are in them and got to respect the CEOs who try to make money there. Making money as an airline is hard. Making money as an auto parts company or a life insurance company or an oil and gas, you're a price taker. Huge parts of your future are not under your control. You can invest in these businesses and do well with them if you develop pattern recognition and understand that world. They're not conducive to creating moats. Those are areas that we largely ignore. The second is, can we understand it? We are global. Historically, about 30, 40% of our portfolio has been outside the U.S., but we're a bunch of folks raised in the U.S. and in Chicago. There's smart investors in Sao Paulo who are going to understand a local drugstore chain a lot better than we are. You have to not get over your skis in thinking you can understand things on the ground better than somebody who's lived in that culture all their lives. We definitely avoid stories where the moat is based on…

AI assessment note: “Step A is, is the industry structurally attractive or not?”

Q What was that initial rubric that you built?

A We had data back to the sixties and looked at every company that had done more than 15% returns on capital for more than 15 years. Totally arbitrary numbers. The idea was, instead of theorizing, let's just get the data. Let's get the companies that have done this and generated sustainably high returns on capital and see if we can observe patterns. Most of the companies that had done that could be sourced to some kind of a intangible asset, like a brand or a patent or a government approval. High customer switching costs, like you see with databases, network effects, or scale advantages, cost advantages. Most of them fit in one of those buckets, and it was like, well, that's what the data says. Let's use that framework going forward.

AI assessment note: “looked at every company that had done more than 15% returns on capital”

Q Are there other important quantitative metrics beyond the original ROIC that you look at when you're trying to figure out if a company has a good moat?

A No, I find it's best to stay away from that because you go down the route of, oh, it should have high margins. What about a distributor? Distributors are often beautiful businesses. They have relatively low margins, but they don't have a lot of capital employed, but they're great companies. So, okay, we can't use profit margins. It really comes down to free cashflow. Okay, fine. But what if they're reinvesting? What if they're putting capital back into high return projects that have a high NPV? Free cashflow may not be the best metric. Obviously, if a company has gone a decade with cruddy financial metrics, there's probably nothing much there. But the point is that the bulk of it is qualitative. Understanding what kind of price the company can take, if it has pricing power, or whether, in some examples, there's what the nomad guys called scale economies shared, where the benefit is not taking price, but passing scale benefits along to customers. Costco is One of the canonical examples, Medline, which recently came public as another great example of passing on scale benefits to customers. They're even saying, oh gee, moats are all about pricing power. Well, no, they often are. It's squishy, and that's why the qualitative angle is more useful than a quantitative metric.

AI assessment note: “No, I find it's best to stay away from that”

Q How do you think about the power of brands when it comes to moats?

A That's a lot of early investors, certainly mine, initial introduction to what is a moat from Buffett's letters a long time ago. I'm talking about the inevitables with Gillette and Coke and whatnot. I've historically invested less in consumer-facing businesses because I don't have a good feel for what a good brand is, but it's useful to kind of distinguish brands in terms of the classic Coke or Gillette, lowering your search costs. You go to the shelf and you see the label, it's what you want, and you don't have to spend a whole bunch of time thinking, what do I want? You just grab it. The consumer can decide to defect with no cost to you. If you say, I would rather try President's Choice Cola than Coke, You can do that, and if you don't like it, you go back. Big deal. If you think about a luxury brand, that's more consensual. I'm not wearing a Rolex because it tells time better. It's because I want people to know I have money. I'm signaling something, but that signal value is only useful if everybody else agrees that a Rolex has signal value. If I decide one day to say I'm going to wear some no-name watch that nobody's ever heard of that cost a 100,000 dollars because I want to signal my wealth, If nobody's ever heard of it, I don't achieve that. We all have to agree. If I defect, there's a cost to me. If I defect out of that luxury positioning ecosystem, the cost is nobody get…

AI assessment note: “useful to kind of distinguish brands in terms of the classic Coke or Gillette”