The Exchanges

Q What book is in your queue that you are most excited for?

A There are two coming out next year that I'm excited to tell people about in the fortunate position that I've read both of them ahead of time, and they are amazing. The first, Jonathan Tepper, who's been a guest on the show in the past, who runs a long-only firm called Bravat Capital. He was a best-selling author of The Myth of Capitalism, wrote a memoir called Shooting Up. And it's his story growing up in a heroin addict community. His parents were missionaries in Madrid. It is heartfelt. It is heart-wrenching. It is deeply personal, and it's one of the best memoirs I've ever read. The other, John Kim, who until recently was the head of capital formation at General Catalyst, wrote a book called The Dow of Fundraising. It is the best book I've ever read describing the capital formation processes. I think that'll come out in March. John's going to come on the podcast.

AI assessment note: “There are two coming out next year that I'm excited to tell people about”

Q So when you spent your six years at Fidelity and you were exposed to all these different styles, you knew that you were interested in the growth of tech sector. What did you find was your style of investing and how you wanted to pursue the trade?

A One of the things I was good at understanding was that you really wanted to be behind product cycles and true growth. And in tech, there are areas that don't grow that much. And frankly, the period after the internet crashed and before the mobile revolution happened, there weren't any major mega trends happening, but there were minor trends and it would make sure that I was invested behind those. And then, of course, I started in the internet itself in 9796, and got the early days of Amazon. And even before that, I invested in AOL for my own PA at the time, which was just growing like crazy. I devised this three-part framework, which we're known for at Whale Rock, which is S-curve, competitive advantage, underappreciated earnings power. And the first one is all technologies start slowly. They have a lot of barriers to adoption. It might be too expensive or complicated. There might not be the right ecosystem. There might be a lot of inertia. There were smartphones before the iPhone, but they were big, clunky, hard to use. There was no wireless network, and they were expensive. Steve Jobs fixed all that with a 200 dollar phone, a touch screen monitor that Your grandmother or child could use, and then he connected it to the three G network. And so all those barriers were immediately removed. And then you hit that mainstream takeoff phase, that inflection where you go from one perc…

AI assessment note: “I devised this three-part framework, which we're known for at Whale Rock”

Q As you pull that thread now, a couple of years later, how are you thinking about AI as it relates to your framework?

A I think AI is definitely one of the major mega S-curve trends, and it's great that we have another one to invest in. It's going to be a multi-decade story, like the cloud, like mobile. This is, in a lot of ways, more complicated than the previous ones. With smartphone, you had units and ASP. You could really quantify easily how big the market was with precision. When you have a new computing cycle, you've got the whole stack. This was pioneered by Lou Gerstner. He talked about the new stack in a client-server world versus the mainframe world. In the AI world, there's a new stack. And at the bottom, always first comes the infrastructure layer, because you've got to build the compute out. And when you have a new stack, that's when the inflections happen, creating winners and losers. Then above that is the cloud. Most of the AI is going to take place in the cloud. There's the cloud delivery layer with AWS, Azure. There's some of the new Neo clouds, and then some will be delivered on-prem. And then above that is the foundational model layer. These are the big LLM companies like OpenAI, XAI, Google Gemini, Meta. And then above that are the applications. That can be software applications or internet applications, and they can come from startups or incumbents. Our thesis was invest in the infrastructure layer first, because that's always the first to inflect on the S-curve. It also, n…

AI assessment note: “Our thesis was invest in the infrastructure layer first, because that's always the first”

Q How do you think about layering on shorts?

A The S curve framework is good for shorting as well, and you can find great shorts all across the S curve. The classic would be mature and getting disrupted. So traditional media by Netflix or newspapers or CPUs losing to GPUs. Then you can have great shorts in the best part of the S curve because You might be in the sweet spot of the S curve and selling into it, but if you don't have an airtight competitive advantage, you're a zero. You're going to destroy value. In smartphones, if you were RIM, Palm, Nokia, HTC, Motorola, Lenovo, the list goes on, you're a complete zero. And there are a lot of electric vehicle companies that tried to be Tesla or BYD, and they're all very likely to Either fail or not generate any profits. And then there's too early in the S curve. Again, people get excited about new technology. It's real. It's going to happen. But the barriers to adoption are strong and not removed. So AR VR glasses, for example, has been stuck in too early in the S curve. At one point we were short a Japanese video game company where the CEO was like, I'm moving all to VR games. The problem is there's no VR games. Headsets cost 5000 dollars. There's no killer app. There's no market in place. One of our best shorts was an EV battery company 12 years ago spun out of MIT, supposedly had proprietary technology, and they were building capacity for this coming EV boom, but it was 10…

AI assessment note: “The S curve framework is good for shorting as well, and you can find great shorts”

Q If we're at the apex, where does it go?

A On the capital market side, you see hundreds of billions of dollars destroyed of people that play the same game that you did back in 2000 with fiber optic cables. The counter to that, if you listen to some of my friends would say, well, back then you had dark fiber. Today, there's no dark GPUs. I still beg to differ. What the individual company does rationally, collectively is irrational. There's going to be a glut and there's going to be a collapse. Debt has not really entered the system until now. Debt is the thing that causes bubbles to collapse. You've got Facebook issuing twenty-five billion dollars of debt. You have this crazy structure with Grok and XAI from Elon. You've got CoreWeave. These things are messy. Dave Einhorn had a good recent snippet in one of his recent quarterly letters that was talking about how a dollar of OpenAI revenue results in eight dollars of seeming revenue. It's a dollar to OpenAI. Then they pay Microsoft two dollars, so they're losing money, 50% negative margins. Microsoft then pays CoreWeave. CoreWeave then pays NVIDIA. That one dollar that I'm paying translates into eight dollars of revenue and a hundred dollars of equity market. That's a house of cards that's going to collapse. The next wave that I'm bullish about is going from two-dimensional AI in this current wave, which is pretty much saturated. That is everything from voice, video, imag…

AI assessment note: “There's going to be a glut and there's going to be a collapse.”

Q What is the actual process of renovating a unit look like? The dollars you put in, what you're doing to the unit, and what you get on the back end?

A Most of the properties we buy, we typically underwrite somewhere between 12,020 thousand per unit for renovations. We'll do this all at the onset of the investment, so we call all of the capital up front. We have a large contingency fund for anything that can come up given our longer duration hold period. We'll give ourselves sufficient buffer, but we are going to look to renovate the community over the course of four years. We'll look for high ROI items that are going to drive leasing and demand for the community. Think fitness centers, pools, curb appeal from landscaping, signage, anything like that. I would say 70% of the equity allocated for rehab is going to the interiors. Kitchen and bath upgrades, it's flooring, it's things that are going to help us operate it more efficiently over the long haul. We don't want carpet in the units. We want wood plank flooring. We want stone countertops. Anything that's going to help reduce our capex spend down the road And enable us to charge more for a rent premium versus what's in place versus the comps is a good outcome for us. We'll end up renovating any given community, 10 to 20% of the units. We'll increase renewals to create a little bit more vacancy if we don't see that we're getting units back where we can renovate them. We're typically renovating anywhere from four to 10 units a month at most of our communities.

AI assessment note: “typically underwrite somewhere between 12,020 thousand per unit for renovations.”

Q There are a couple aspects of the strategy that you've intimated at the first, obviously multifamily. Why focus on multifamily real estate?

A If you look at the different real estate asset classes, it's the least controversial one. We can debate work from home with regards to office. We can debate on consumption patterns with retail. Data centers are too big for someone like us to address. Multifamily, there's no debate. People need a place to live. That's the most important thing. Second most important thing is it always has access to the lending market. Because of buyers, we can borrow off of Fannie and Freddie financing, we always have access to borrow from the bond market effectively, whereas the other asset classes have more risk in accessing the debt capital market. This is the easier asset class to own for predictability, for safety, sleep at night, investing money. The other thing I would point out is that multifamily is a asset class that Has historically had low capex. It lends itself well to making distribution, whereas things like office or retail are tenant improvement heavy. Therefore, maybe more episodic in its ability to return capital to investors. What we are trying to create is a synthetic fixed income replacement stream for an investor and a tax advantage one. Multifamily lends itself best of all those asset classes to doing that.

AI assessment note: “Multifamily lends itself best of all those asset classes to doing that.”

Q What do you see as the biggest risks to the strategy not playing out as you expect it will?

A One is higher interest rates. I tend to think that if they were to have higher interest rates, it would likely come with higher inflation, which we benefit from. Population growth. If the United States population growth doesn't grow at the rate it has historically, that could be problematic. And replacement costs. If replacement costs goes down, if there are cheaper ways to build, particularly some of the markets that may have more land than others, that could potentially be a risk. We have never seen that. It's always possible some form of technology allows you to build a lot cheaper. The last one would be high unemployment at an inopportune time. For example, unemployment is meaningfully higher than it has been historically. Those are the large risks that I would see playing on. If you have a higher unemployment, it likely comes with lower interest rates, means the capitalization rates are lower and the value of the income stream is higher. There are all sorts of offsets to each of the risks that I mentioned.

AI assessment note: “One is higher interest rates. I tend to think that if they were to”

Q If you go back around that time, what was the public real estate investable universe then?

A It was about three hundred billion dollars, so it wasn't huge. It was much more concentrated by sub-sector. They used to call it the four major food groups, office, industrial, retail, and residential, and those companies accounted for over two-thirds of the entire REIT index. The entire space was more correlated. Real estate really moved together. Certainly some subsector had more supply than another, and you had to normalize for that. If you were an analyst covering apartments, you could pretty easily cover the office space as well. From then till now, you've had two things happen. You've had the introduction of secular risk in real estate. We learned that with office and COVID. We learned that positively for industrial, negatively for malls. With e-commerce, I'm sure that's not the last of it. We'll see more of that in real estate. That's been a big factor in the old days. If real estate went down, you could buy it and just wait, and generally it would come back over time, and that's certainly not true anymore. The second thing that's happened is, as the public market has proven to be an efficient place to own and hold assets, more and more sub-sectors have come to the public markets. Data centers, towers, cold storage REITs, single family for rent REITs, gaming REITs. That's led to more dispersion within the space. In short, the absolute space has grown, the correlation is …

AI assessment note: “It was about three hundred billion dollars, so it wasn't huge.”

Q So let's walk through the different aspects of how you go about doing that. Let me just fast forward to today. How do you think about your investable universe, the different companies you're looking at to potentially invest in?

A When people think about real estate, they naturally gravitate to REITs, and REITs is not the only thing we do. There are a lot of companies that are not REITs that are 100% real estate companies. Hotel management companies, home builders, real estate service providers like CB Richard Ellis or Jones Lang LaSalle. I would define everything we do as full, 100% real estate companies, just not solely REITs. Both spaces have evolved. REITs have grown a lot, and there are more options on the menu to exploit a view, but there's been a lot of growth and evolution in the non-REIT side of the business too. When you look at home builders or hotel companies, the way many of these business models have evolved They're not acyclical businesses, but they become much less cyclical than they once were. When you compare a home builder pre-GFC with a lot of land on its balance sheet and a lot of leverage and a business model that forces you to invest your free cash flow while the cycle is getting hot, and then ultimately the cycle stops and you're writing down book value and you're having problems, that's evolved to almost 100% asset light models in some cases. No leverage, real free cashflow generation that's used to shrink the float. And so these have become much better businesses. And when you look at the hotel business, it's not dissimilar. There's been splits of the cash flows that come out of…

AI assessment note: “I would define everything we do as full, 100% real estate companies, just not solely REITs.”

Q I'd love to tease through part of that capital allocation investment process. So as you started to go from a single relationship with GSO to what to become five asset classes, some done directly, some with managers, how did you think about, in a big world of asset managers, who you wanted to partner with?

A When we set out for partnerships, we thought about The individual spaces that we believed had risk reward that was appropriate for the private wealth channel. Varying forms of private credit stood out among them. Middle market private credit, unit tranche lending, real estate, commercial real estate lending. These were areas that we felt strongly about offered a very attractive entry point for individual investors through their advisors. For what they were looking for, which again, going back was very income focused in terms of the objective set. We then came to who do we believe are the best of breed managers across these different areas. GSO and KKR stood out as that. Golden tree in the hybrid between public and private credit. EIG and energy credit. Rialto in commercial real estate lending. A lot of these were personal relationships that we had. These were People we knew very well. We knew their teams understood their orientation towards risk as with all forms of credit. It's not just someone who can source and originate and underwrite credit, but also someone who can deal with problems when they arise and credit problems always arise. And so we wanted strong risk orientation and workout capabilities across those areas. That's how we decided on the varying combination of personal relationships and core competencies.

AI assessment note: “who do we believe are the best of breed managers across these different areas.”

Q So in addition to making sure there's a calibration of what people understand, there's the potential very large volume of capital coming into these strategies. We've already seen it in private credit, and maybe it happens in private equity. What does that mean for, say, the institutional owner who's there today who sees a lot more demands than they had in the past?

A They should understand that it's here to stay. This isn't a temporal situation. The demand from private wealth will be the fastest growing source of capital into alternatives for the foreseeable future. If you're an institutional investor or allocator, you can think of it as competing with your capital. You can look at it as displacing your capital. You can think about it as an opportunity to potentially partner with that capital or use it in some advantageous way. If you think about that last category, there are institutions and we work with a number of them who will Seed vehicles that will be primarily offered to private wealth channels. By doing so, they will gain economics and ownership in the revenue stream of those vehicles. There are institutions that are buying GP stakes in managers who for the first time are offering their strategies to private wealth. So they will take advantage of the growth of that channel on those businesses and the value of those businesses. And there are institutions that are using that as an opportunity to avail themselves of liquidity in the secondaries market. If you take endowments that are looking at selling some of their private equity and venture positions, the primary demand for those secondary positions are coming from evergreen private equity vehicles. If you try to continue to carve your assets away from that trend, you can do that. Yo…

AI assessment note: “institutional investors should learn to coexist with it.”

Q What opportunities most excite you both on the investment side and on the business side?

A In terms of opportunities, if you're looking over the long arc, there's still tremendous opportunity in private credit. Despite recent concerns about individual credits, despite the fears that there might be a bubble forming, we are still undercapitalized in private credit to where the opportunity set is. The amount of dry powder in private equity, the number of companies that want access to lending capital. We are just scratching the surface on areas like asset-based finance, which is a multi-trillion dollar market. We're only just beginning to offer those in private credit. There's still significant room for growth there. In the secondaries market, we have a 10 trillion dollar private equity market globally. Only two hundred billion dollars of volume in private equity secondaries this year. It's two percent of the stock in private credit. That number is a hundred, a hundred twenty billion dollars, almost two trillion dollars of stocks of small amounts of secondary markets will grow. We're in the very early innings of secondary markets for both fund level investments, individual GP led single asset secondaries, pre IPO secondaries and things of that nature. That area is another big area for growth. Outside of that, AI gets a lot of the press in terms of what that will do for the economy, for financial services and healthcare and all of these businesses. The area that probably …

AI assessment note: “there's still tremendous opportunity in private credit.”

Q If you go down that next level, if you're on the financing need, good momentum, what might a next question be to determine in that subset which companies are likely to outperform?

A Typical questions would be about volatility. We tend to find momentum works better when it is consistent. When the stock price is rising in a consistent manner, it leads to better outcomes than companies that have one giant price move driving the momentum measurement. Company age also comes into account there. We find that momentum typically is more meaningful when you're looking at newer companies than companies have been around for a long time. They're generally higher growth businesses. They are more often in industries that are evolving. Knowing that the sentiment is strong around those companies is an even more positive Indicator of future returns than knowing that a company that's been around for a hundred years had a good quarter.

AI assessment note: “Typical questions would be about volatility. We tend to find momentum works better when it is consistent.”

Q When you've been working with machine learning models for a long time, what does the introduction of Chad GPT change, if anything, in the way you've approached what you've done?

A Large language models and ChatGPT specifically are not anything that we're presently making use of in our modeling. One of the big challenges for folks who are trying to use those types of models in a stock picking context is the problem of in sample versus out of sample. Especially if you're using a commercial model, you don't have any control over What data that model was trained on. When you're running a back test through the better part of the last decade, ChatGPT knows that Nvidia became a multi-trillion dollar company. ChatGPT knows what the mega trends were in the economy and the market over those timeframes. It's not realistic to trust a back test that ChatGPT generated. That said, there are exciting things going on in the AI space, and we use a lot of proprietary software and tools in our investment process. One area in AI that is really appealing to us is the idea of software development co-pilots. The idea that AI can make and enhance software development at an organizational level, We're a small team with a lot of software, and any ways in which we can improve efficiencies there are valuable to us.

AI assessment note: “ChatGPT specifically are not anything that we're presently making use of in our modeling.”

Q And in one individual tree, to get from the top to the bottom, how many different decision points and nodes are there?

A Typically we ask between two and five questions in each tree. The reason we don't ask more questions is we found that as you ask questions deeper and deeper in the tree, you're working on smaller and smaller pools of data because the trees are customized to the branch of the tree that you're working down. If you think about trees breaking up fifty-fifty at the second layer of the tree, each question is motivated on half of your original data. Down another layer, it's a quarter. Down 10 layers, each question is going to be motivated on one 1000th of the data. Down 20 layers, you would be operating on one one millionth of the data. You can quickly see that there's a sharp limit to how deep you want to make these trees. Fortunately, we have another approach to asking more questions about companies, which is rather than relying on a very deep tree, Relying on a forest of relatively shallow trees.

AI assessment note: “Typically we ask between two and five questions in each tree.”

Q Which two people, other than your wife, have had the biggest impact on your professional life?

A I've worked at MDT my entire career, and I was really fortunate to have two mentors from day one, David Goldsmith and Sarah Stahl. David was the founder of the Quant Group and the CIO. Sarah was one of David's first hires who led analytical and portfolio attribution effort here for many years. What was great about the two of them was that they were incredibly different from one another in terms of mentors. David was the mad scientist of our group. He would be thinking about algorithms, twenty-four-seven, come in and tell us about the idea he had while he was in the shower. Sarah was also very brilliant in a less wild and unconstrained way. She was very meticulous, very focused on craftsmanship and Understanding precisely what was driving the returns of our models. They were both great mentors and helped me appreciate that success in investment management. It's not all about being the brightest and having the most genius ideas. There are a lot of geniuses who failed. It's not just about meticulousness and craftsmanship, but both of those things are very important. It's a success in this business. I'm really indebted to David and Sarah.

AI assessment note: “I was really fortunate to have two mentors from day one, David Goldsmith and Sarah Stahl.”

Q Within private credit, with all the explosion that's happened in the activity, the volume of assets to put to work, what are you seeing in the behavior of the participants?

A It's a fine asset class. What you're seeing is if new buyouts are the lifeblood of that business, and new buyout activity has been muted for the last several years, you're seeing a lot of competition in that space, and spreads have come down. You have an interesting environment where the height of direct lending was in 23. Because of the failure of the syndicated loan market, there were a lot of hung deals in 22, and banks lost a lot of money on paper. You had a major participant out And a lot of these direct lenders were financing deals that were getting done at spreads of 700. What that means at the time, S, the base rate was five and a half. Adding seven to five and a half is 12 and a half percent for senior secured paper. If you look at prices that were being paid for the assets, they were paying 18 times, 17 times. So you were a third into the capital structure being paid 13% on levered. That was nirvana. Everyone went all over the world and said, hey, forget equity. I can give you 13%. I can lever it in a diversified pool and make you 15. That caused a lot of capital to flood into the ecosystem. Now what you're seeing is those businesses are attached to a lot of very large alternative asset managers, many of whom trade on FRE. A good way to create FRE is to take several billion dollars of loans and charge one percent on them. That's what you're seeing in the ecosystem, so…

AI assessment note: “you're seeing a lot of competition in that space, and spreads have come down”

Q How's that impacted what you're saying in private equity?

A What I would say in private equity is you had a couple things happen. Rates were low for a long time. If you're paying 16 times EBITDA for something, and your capital structure is only six, The incremental pickup and basically spread and rate don't crater your LBO model because it's a small part of your structure. What it does do is hampers your flexibility, your ability to do real aggressive add-on M&A. If your coverage ratios are really tight, you have to have confidence about what you're buying. You can't just say, ah, I'll buy a bunch of stuff and see what happens. You're going to have real issues with solvency if you get these wrong. If you do a bunch of M&A and it doesn't produce the earnings you think again, and you keep levering yourself up on a pro forma number, You got to be careful, especially with rates going from zero. Used to be able to borrow Unitronch back in 21 at six and a quarter percent. Now those numbers are around nine and changed today. They were 13. You can get a sense of the impact of that. That impacted buy and build. The other thing in private equity is funds got raised, bigger and bigger funds. What's happened is that a lot of these assets are huge. A lot of these companies that are good businesses and business services and things with high margins that are Low capital intensity. A lot of them are valued at 1516, 17 times EBITDA. And if it's a 150 or…

AI assessment note: “What it does do is hampers your flexibility, your ability to do real aggressive”

Q So what was your time at Stone Point like?

A It was trial by fire. I joined in August, 2008. Obviously there was a lot going on at that time. You had AIG and then ultimately Lehman a month later. We were looking for the first couple of years at the carnage in financial services markets. I was in particular focused a lot on mortgages. We ultimately bought a business that sold foreclosures at auction. That was one of the more successful investments at Stone Point made during that era. It was an incredible training ground because probably a year into that time period, We'd had a week where a number of very prominent folks in the financial services community had come through looking for capital or looking for advice. Stone Point as a whole had Chuck and Steve and Jim and this incredible group of senior executives. I worked particularly closely with Nick Zerbeev and Aga Khan. They were great mentors and taught me a lot of what I know about investing. And I remember I was sitting in a meeting, there was this other group presenting, and I thought to myself, this is the peak. Sitting in that room with capital behind you and the ability to effectuate a transaction was as good as it gets. When you can be a principal investor, it's an incredibly exciting opportunity, and as a fiduciary, it carries a lot of weight, and so they had a lot of trust in me. I was there for six years. I learned a ton when I was there in terms of how to pre…

AI assessment note: “It was trial by fire. I joined in August, 2008.”

Q Where do you hope this business goes over the next couple of years?

A I really want to put a moat around it because it's so interesting and it gives us a chance to stretch our brains almost daily. There's probably two stages for it. One is the scaling up of the asset management business, putting as much capital as we reasonably can to work without reaching diminishing marginal returns. While there's no competition, eventually competition will come It'll erode our purchase discounts. And at that point, we have a lot of optionality in the business. We will be sitting on one of the best private company data sets that exists given the breadth at which we serve the startup employee base. One of the pet ideas I have for a second stage of the business, which is once the purchase discounts have been competed away and there's lots of competition is that the secondary markets for private shares right now are totally anemic. Maybe a hundred late stage companies Stock trades. The primary reason that's the case is because the buy side on these markets just don't know anything about the other 30,000 venture backed startups. Using our data to help characterize those startups at some minimal level to the buy side of the secondary markets should be a profitable thing to do and should provide a lot of value in the world. There's one world in which we run a proper index business on one hand that's a steady state and then start trying to make the secondary markets w…

AI assessment note: “There's probably two stages for it. One is the scaling up”

Q So once you found these people, or they found you, and you're offering them a deal, what are the important pieces of the deal from their perspective?

A The single most important piece is how much money they need all in to do their full exercise. The pieces of the money are what is your total exercise cost, Which is just a strike price times however many options you have across however many grants you have. Then there is the tax cost, which is the tax that is actually associated with the act of exercising. That is typically applied to the paper gain between the current board approved fair market value of the stock as of the moment you're exercising and whatever your strike price is, which is a disaster. There's no reason that that should be taxed. You can't turn your stock into money when you buy it. It's kind of messed up, but it is what it is, and it's hard to change laws. Let's just assume that that's going to continue forward for a while. If you have incentive stock options, that gain will be applied to the alternative minimum tax regime. If you have non-qualified stock options, it'll be applied to the ordinary income tax regime. And then there is the tax related to actually doing a transaction with us. So our specific transaction is that we're buying some of your shares, which means there's a capital gain event, potentially. Those are the three components. If you've been at a company for a long time, the biggest component is usually the second, which is the tax associated with your exercise. If you've been at a company a s…

AI assessment note: “The single most important piece is how much money they need all in”

Q When you have an approach that came from Such a different insight and different methodology than might think of a secondaries firm that's looking for those larger businesses. What have you heard from the LP community when you've gone out to talk to people about the strategy?

A Oh, you get all kinds of reactions. The folks who think that you need to be in the winner picking business if you are in VC, which almost definitionally means you need to be a primary VC doing rounds for companies. They were never gonna like the strategy, and one day the cash on cash returns will change their mind, and until then, probably not. For the folks that can't even get access to the asset class, this is a godsend. And then there's probably a middle layer of LPs where the most interesting thing is less the access and the return profile, and more that they know they're supposed to be in VC, but they're not sufficiently staffed to properly run a VC fund manager Sourcing diligence relationship maintenance program. So we see all kinds of reactions both substantively to the strategy and also to the practicalities of the strategy and what it means for them as an investor.

AI assessment note: “Oh, you get all kinds of reactions. The folks who think that you need”

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

A One is Randy Wynn, and the other is Emilio Cejo. Randy was one of the founders and longtime CEO of Capital IQ, but relevant to me, he was my first backer and angel investor and mentor in the first business, and he's been with me all the way through, and he's taught me what it actually means to be somebody's backer. I've lived also in the venture world, and I've seen a lot of venture firms say what they were going to do for you, and not develop any relationship with the people that they invested in, and also frequently fall down in terms of Delivering any value after their capital. I hope I have the opportunity one day to do for somebody else what Randy's done for me. Emilio is the other person. Emilio and Randy and I all met each other around the same time. Emilio was my first partner in that business in which we were building the machine learning models. He is one of the smartest people that I've ever met, but in a way that really opened my eyes about what kind of things you should look for in a person that you work with. He is a mathematical statistics PhD from Columbia. It's easy to put him in a bucket on the basis of that, but when he was rebuilding our liquid asset pricing model inside of the brand name bank, he put on every hat possible and knocked down roadblocks until that thing was algo trading. He got it through legal and compliance, which you have to imagine is a nea…

AI assessment note: “One is Randy Wynn, and the other is Emilio Cejo.”

Q I'd love to turn to where we are today. What do you mean by late cycle?

A Having seen past cycles, they're all different, but there are certain behaviors which always come to the fold. Cycles of tension between fear and greed, and we're very much in greed mode now. There's tremendous complacency. You see it in spreads. Spreads are super tight. They're tighter now than they were before the GFC in the investment grade world, which is mind blowing. Spreads tightened the tightest level since pre-long-term capital management in the late nineties. You see investors willing to accept opacity rather than transparency. You hear the phrase, got to put the money to work, deployment, deployment, deployment, far more than risk and safety. You see a spate of frauds. All of these are indications of late cycle behavior. It doesn't mean that things are going to turn tomorrow, or when they turn, it's going to go kaboom. Who knows? But you just have to be really careful, and everyone has done well for a long time. It's ironic, but when you're on a hot streak like that, and you think you can do no wrong, risk is rising, even though mentally you're thinking, oh, I must be good. I got this. I got to cover it.

AI assessment note: “All of these are indications of late cycle behavior.”

Q And how about something that you exited at that time?

A So one of the challenging things in investing is to sell great companies where there's nothing presumably wrong aside from maybe evaluation and things slightly eroding on the edge. Costco was probably one of the most difficult positions to move on from. We'd done quite well, as did everyone who had owned the stock for any point in time. It got to a point where you used to say, hey, Costco, I'll buy it at 20 times earnings, trim it at 30. But then it went to 35, to 40, to 45, into the fifties, and you began to ask, can you underwrite solid double-digit IRR with Costco? So you ask that question in the vacuum, but then we're also benchmarking every position relative to our focus list, and we start to do work on this company called Three Eye Group, which is based in London. It's a private equity company, but it's basically a holding company, and they made this prolific investment in a retailer called Action. Which, it's basically Costco early days. Huge store runway, an incredible culture. They appeal to scarcity, they rotate two-thirds of the items, and it's worked in every geography they've gone in. Even the areas like Germany, where no global retailer has had success, they've been able to crack that market. They'll probably end up in the U.S. eventually as well. So very early in the store runway, trading low 20 times earnings with higher growth than Costco. So you put those two …

AI assessment note: “Costco was probably one of the most difficult positions to move on from.”

Q How did that lead you to getting involved in investing?

A I started pre.com in science working in the, I guess the technology world and did love it and still have a real affinity for it. But company obviously went through some tough times post.com and some of it was idiosyncratic to the company itself. The company sent me to get my MBA and I started to work in strategy and alliances, which was involved more at how do you prioritize the technology portfolio? How do you allocate capital across the portfolio? How do you look at internal versus external? When do you license? When do you buy? When do you develop? So some really interesting challenges on how to allocate capital. I got a call one day from a headhunter saying, would you be interested in coming and talking about a role at a pension plan? And my first reaction was, John Graham's a really common name. Are you sure you have the right John Graham? Yeah, we're pretty sure. We're pretty sure we got the right John Graham. So I went and I met with the team at CPPIB or CPP Investments. It was only a couple hundred people. Still had a reasonably large asset base of probably around eighty billion dollars, but they were one office in Toronto and just starting out. And I remember meeting them and being blown away. And blown away by the organization and what it could be, the governance model it had, the ambition it had. And this organization had the capability to be great and not just great…

AI assessment note: “Made the jump over to CPP Investments and started out as a mid-thirties associate.”

Q What are other examples of, you could call it compounding knowledge that you gained from one investment led to another?

A There are countless examples. We're always talking internally and externally about the through lines between different investments. There's a whole host of investments that are about the transition from analog to digital. You could put Microsoft in that category, selling physical PCs with Windows and Office licenses. You could put Adobe in that category, selling Creative Suite as shrink wrap software. On a CD-ROM transitioning to subscription, Xbox was our first exposure to the video game industry, and that went from being a very difficult industry with lots of boom-bust cycles, huge inventories required to distribute the software to a much higher quality business because of digital distribution and subscription. Probably the place we applied that insight the most, again, in the analog to digital example was at Nintendo. Historically, very closed off Japanese company with, we saw the best IP library in the video game industry. Because of a variety of different factors, they were behind the times in terms of adopting digital distribution, subscriptions, in-game monetization. We called on a lot of the things that we learned at Microsoft with the Xbox team. To develop insights on Nintendo and to try to provide some insights to them on what the potential would be for their business. If they could go from selling 20% of their games through their own eShop to 60 or 70% like Microsoft…

AI assessment note: “We called on a lot of the things that we learned at Microsoft with the Xbox team”

Q Rob, what was your first paid job, and what'd you learn from it?

A I worked for a house painting crew when I was 15 years old and growing up in Massachusetts. My parents were very focused on that I have summer jobs and actually do manual labor. I thought that as a house painter that on the spectrum of manual labor, that was going to be a little bit easier, but I didn't understand that when you're the most junior member of the paint crew, your job isn't to put the paint on, it's to take it off. Up on the ladder, 95 degree heat, scraping paint all day, inhaling paint chips despite the mask I had on. It was the first lesson in hard work. Also, we had a foreman who was an excellent leader of the crew and patient with me and made it fun. He kind of set the tone, got everybody working hard in some very hot summer Massachusetts days, so seeing that benefit of leadership was also key.

AI assessment note: “I worked for a house painting crew when I was 15 years old”

Q When you dive into how you go about implementing this, that first layer of data sources, what's the core set of information that you've wanted to train? And then where are there alternative data sources you've accumulated over the years?

A We started from the information that was hiding in plain sight, hiding because there was so much of it that was hard to get to the insights, even though they were available to every professional in the market. So all the SEC filings, global filings from every country with a stock exchange, earnings call, transcripts, conference presentations at every investment banks, and then of course, press releases, news, and then broker research, getting Wall Street research on a platform where you could now compare what is the company saying, what is an analyst saying about any topic, any company. And then that was still information that people could get access to on other platforms. One big step for us was acquiring a company called Stream, where they had built an expert transcript library. That's allowed us to start scaling and generating high value proprietary content that you couldn't get anywhere else. And we could really point that system to generate information on specific companies. What are their customers saying? What are their suppliers, partners, former employees, executives saying about things that really matter? Before this, you had to rely on what is the company saying? What are they putting out in the press releases or saying in public forums and filings? But you really had to go talk to management to question that or get alternative points of view. And the expert intervie…

AI assessment note: “all the SEC filings, global filings... One big step for us was acquiring a company called Stream”