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Excess Returns
The Risk at the End of the Whip | GMO’s Tom Hancock on Finding Conviction Amid the AI Hype
00:58:40
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- [00:01]
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- [00:58]
Tom
I think what investors tend to have trouble with is conflating the idea of here's a big secular trend that's going to grow and it's going to change our lives with exactly which Companies that are riding that wave have the differentiated business models. Their revenues are only as durable as the spend from the person above them who is buying their products. And as you do get further down the layers, you do lose visibility in what's going on above you. Nvidia's revenues are OpenAI's CapEx, and OpenAI has the CapEx to spend because they're getting money from Microsoft and also from just because they're spending in advance of that revenue. Bad times can happen to anyone. Things happen in the world and a lot of being quality is just being able to keep going through those tough patches.
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Interviewer
Tom welcome to Excess Returns.
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Tom
Hi, thanks for having me.
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Interviewer
Your head of Focused Equity and a quality portfolio manager at gmo. You spent most of your career there and your focus is on building concentrated portfolios that consist of high quality businesses. So a lot of the conversation today is going to be around this topic of quality investing. What it means, how you define it at gmo. But I thought where we'd start with you is on a topic that is kind of critical to today's market. It's represented in a lot of investors portfolios and even in market cap weighted indices. And that's how to think about AI as a investment opportunity. You recently published a paper titled Hype vs High Conviction. People can download this on GMOs site, arguing that AI may be one of the most important decisions facing equity investors today. So we want to kind of work through that paper with you, which was excellent. And then I think at the end we'll really try to tie back to how you define quality, what makes for a durable business in your view, and how you construct portfolios that consist of those types of companies.
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Co-Interviewer
So great.
- [03:07]
Interviewer
So to start, let's talk about the paper. So you know, AI is on top of a lot of investors minds. We see it all around us. Many of us are using it, trying to figure out how we're going to benefit from it and really trying to figure out the investment implications of AI today and over the long term. So what do you think, in your opinion, what are investors maybe getting wrong about the narrative of AI? Like what's your general sense here?
- [03:37]
Tom
Well, I think part of it they're getting right. I mean clearly it's a great opportunity. And the fact that investors think that is displayed in the fact that AI related companies are such large market cap and such a big part of the market indices. And so investors clearly appreciate the significance of it, have not just a strong position now, but a strong position far into the future. And that's hard, right? In a growth area, things change. So today's leadership isn't always tomorrow or next year's leadership. So I think that's sometimes what investors can struggle with, is sort of playing things forward as, as best one can realistically and investing for the future, not just for the current state of play.
- [04:22]
Interviewer
Right. So it's not, you can't paint all these companies with the same broad brush and think you're going to get the same great investment outcomes. So to your point, there's, there's opportunities, but there's, you know, possible risks. Okay, so with that, and I think this, this is what we're going to put this image in the, in the presentation in the interview here. But you kind of broke the AI ecosystem down into really four layers and you have applications, LLMs, hyperscalers and suppliers. And what you were really doing is kind of looking at the value chain in these four different core buckets of the AI ecosystem and then looking at what both the risks and opportunities or I guess the risks and advantages of each of those areas of the, you know, the overall market. So just kind of walk us through maybe even each one of these, you know, at as much detail as you want. Because I think this is, this is a very critical part of the article.
- [05:22]
Tom
Right? Yeah. So as you say, we divide the AI ecosystem into these four layers of kinds of businesses and some companies do span more than one of these Layers, but we think these four key layers are important way to think of the opportunity. So at the top is applications. This is how the whole world, I guess, interacts with AI. An obvious example of an application is ChatGPT or Copilot, or you could say, you know, autonomous Driving car is an application or a software generating system like Cursor or cod. Those are all applications. That is, if AI is a big commercial success, it'll be monetized by people paying for applications that do things. If you think back to the previous sort of maybe wave of innovation around smartphones, applications do things like Uber, etc. The next layer that we think of beneath that is the compute that runs those applications. So that is the hyperscalers, largely. So that is your copilot sits within Azure and runs on Azure. Microsoft is providing cloud services in that case, actually for themselves, but also for all the people who would build applications on top of AI. And not to foreshadow where we're going, obviously what's interesting about AI is there aren't so many applications now. But for it to succeed in the long term and deliver on its investment promise, there are going to be a lot more of those applications in the future and they'll probably be running on the hyperscalers we see today. Beneath that compute, what the compute level is sitting on top of is the LLMs themselves. So chat GPT is an application, GPT is the LLM or Gemini is the LLM or anthropic, et cetera. There are relatively few of these. They're often at least currently housed in private companies or Gemini, obviously within Google. Those are, those are kind of where the crux of the innovation is the fact that these things actually work as well as they do. It's the amazing development of the last three or four years. But that is the third level of this. And then the fourth level is sort of the infrastructure enabling it. And that's where Nvidia sits. Nvidia is the kind of poster child, maybe for AI over the last few years, but it's actually fairly far down the AI stack. Copilot sits in Azure, runs GPT, which is trained or does compute and inference using Nvidia's GPUs, and then that actually we're bundling this all together as infrastructure. But of course Nvidia depends on TSMC to make the chips and TSMC depends on various companies to you build the tools to make the chips. There's in addition to the semiconductor ecosystem, there is this sort of parallel ecosystem around data structure Power, it's not as big in market cap, but it's clearly an important aspect of it too. So that infrastructure layer, sort of the ultimate picks and shovels, I guess of the AI revolution is kind of the bottom level of that four level stack we're describing.
- [08:56]
Interviewer
Yeah, I think this is a great way when you're thinking about these AI companies to first ask what layer are they in? And then if you just want to touch on these like you know what the, what the risk or I guess the advantage is within each layer. Like I'm just thinking like in the first one, applications, you know, you have chat, GPT going after more individuals where Anthropic is going after more B2B but you know, so those are kind of different markets obviously, but then, you know, and there's a lot of variation in there and then so you got to pick the market, pick the winners. But then you also have the legacy players to the point. I'm just kind of sorry to step on you here but like they have, you know, the legacy players have like the data and they have like the customer like lock in, maybe more. So just talk to these risks and advantages within the value chain.
- [09:44]
Tom
Yeah. And I think more fundamentally at the application level, if we're honest with ourselves, I think we have to say we just don't know what all the applications are going to be. I used the example of Uber before because I think that's an illustrative one of. It was easy to see that smartphones were awesome before. It was easy to see that Uber would be a killer use case for smartphones. It really couldn't exist without them but became incredibly valuable with them. And I think that's for us the caution of investing at the application level and is it's sort of hard to get those right until they're proven. And I think it's a little bit premature to be too specific in what you bet on. I think there, there are some applications to be clear that are, are proven. Now I think probably from a commercial point of view, co generation and chatbots are kind of the two things that you are clearly monetizing today. Right. I'm paying as a subscription to get Gemini. We're paying at, at work, we're paying subscriptions to Anthropic to, to get access to Claude. Those, those are clearly working. But there's a lot that isn't determined yet. It's not even clear yet which kind of companies are going to win. Is it going to be startups like an Uber, is it going to Be existing companies, you know, like anthropics trying to move up the chain. Right. GPT OpenAI is moving up the chain by providing their own applications. Will it be legacy software players like a soft a salesforce with agent force. And we to your point believe those companies like have a lot of advantage because they do have the data, they do have the embedding with their customers. They can layer AI onto what's already there. But one has to realize that sometimes in these big technology inflections, it is new companies doing things we haven't thought of yet that often do get very big market position. K Pop Demon Hunters Saja Boy's Breakfast
- [11:42]
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- [11:45]
Tom
They're calling this a battle for the fans.
- [11:47]
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- [11:49]
Tom
It's not a battle. So glad the Saja boys could take breakfast and give our meal the rest of the day. It is an honor to share. No, it's our honor. It is our larger honor.
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Co-Interviewer
No, really stop.
- [12:01]
Tom
You can really feel the respect fact in this battle.
- [12:05]
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- [12:08]
Tom
participating McDonald's while supplies last.
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Interviewer
One of the points of the paper was, you know, following the cash. So that's trying to look through, you know, how these AI investment dollars are actually flowing from layer to layer. That will give investors, you know, a better way to assess maybe the risks of any one individual company. So can you just talk us through that idea?
- [12:37]
Tom
Yeah, I mean that's one of the things that led us to think about this layer approach is that basically from the top of the layer, a company gets revenues and then its investments or capex or expenses are the revenue of the layer beneath it. So I'm an application. I get revenue from an end user. I, I pay a fee to get my compute and then the compute has to invest in Microsoft. It has to invest in capex that will go well. They have to pay the LLM. They also have to invest either directly or through the LLM. Like OpenAI is spending a lot of CapEx that's going to Nvidia. So Nvidia's revenues are OpenAI's CapEx. And OpenAI has the CapEx to spend because they're getting money from the Microsoft and also from just because they're spending in advance of that revenue, they're getting a lot of money from outside investors, of course. But in equilibrium, this is going to be funded all flowing down from the applications, not from just external investors who are bootstrapping the system today. And then Nvidia'S spending money that goes to tsmc, that goes to Applied Materials, et cetera, all the way down. And I think that's an important way to think about it because for any company, however much they're earning now, their revenues are only as durable as the spend from the person above them who is buying their products. And as you do get further down the layers, you do lose visibility in what's going on above you. So it's a, it's a harder way to manage a business. Perhaps sometimes you touched on something that's
- [14:18]
Co-Interviewer
really unique here of this boom relative to say the.com boom, which is this idea that a lot of this is being funded from cash. I mean, do you think that makes this inherently like more stable because the big players are spending their own free cash flow or something like the dot com that was much more, I think, driven by debt?
- [14:33]
Tom
Yeah, yeah, I think it absolutely does. Although to a limit. It doesn't enslave you from all the risks. But yeah, if you think of companies like Microsoft and Alphabet investing, they're not going to stop investing because the Fed hikes interest rates by 50 basis points. If that were to happen. Right. They're playing a long game that's based on the fundamentals and they're not borrowing money to invest it. So there are a lot of kind of macro shocks like they're not going to stop investing at least immediately because the Strait of Hormuze is closed, for example. So that, that is a real difference that you have long term committed strategic investors. A couple of caveats to that. One is while those have, they're very deep pockets, they're not infinite pockets. And we're seeing some of these companies get down to a point of sort of break even on cash flow. And so the grade of growth that we've seen over the last few years, we are getting to the point where they will either have to take on debt or slow down their growth. I think frankly both are possibilities. There are also some other things to think about. I just mentioned the straight of four moves and right now we're getting outside capital. So Middle Eastern investors are a big supporter of things like Stargate and OpenAI and stuff. So there's still funding risk out there. But I'm saying that just to sort of, I don't know, modulate my answer a little bit to your basic question. Yeah, I absolutely think it's a lot of a safer, safer situation than the 99, 2000 tech bubble in some ways. You know, we talk about that as a bubble of course, in hindsight, but we think about a lot of the business models that sort of were thought of then actually did succeed. Ultimately. They just had a very long period in the wilderness first.
- [16:15]
Co-Interviewer
Yeah, I was thinking about this idea because you mentioned the idea that debt is starting to maybe first, like, get used a little bit more now. Like, I was thinking about the whole existential nature of the way the company's participating in this think about this whole thing, which is like, I'm wondering how much debt they'll be willing to take on because they view this as like an existential battle between each other and that they'll be like the winner that gets to AGI. So I think that's just something interesting to watch going forward. Obviously they're much more stable companies, but just thinking about how much debt they're going to be willing to take on, like in pursuit of that goal.
- [16:42]
Tom
Yeah. And I think at some point we may see some of the companies step back and say, hey, you know, we don't really need our own LLM. I mean, Apple kind of didn't get into the fray to begin with. And I think at the time when that first happened, they were kind of widely panned for falling behind and not spending enough. And I think as time passes, it's kind of looking like they took the safe path there. They can license technology from Alphabet or others, as they did with Search, and probably do fine. Of course, they have a lot of other advantages that most companies don't have controlling the platform. And I think in terms of companies that are maybe close to making that decision now, or you could sort of imagine it, I think Meta, for all, you know, all their reputation of being reckless spenders, they did have a year of efficiency, not that long. They're maybe a little closer to the edge than Microsoft or Alphabet is in terms of running out of cash flows. And you could ask, do they really need LLAMA strategically to be there? LLM, it's not so clear. And the other to take the other side of me, the one thing they do have that's really nice is they have their own use case in terms of directing content, ads and so forth. So they're not providing cloud computing to other people who they can't really know what's going on with. They're actually doing it for themselves. So they have probably the best customer visibility of anyone in the conversation.
- [18:10]
Co-Interviewer
Yeah. On Apple, I don't know anything about Apple, but it does seem to me it's interesting, they've kind of taken a step back and let Everybody else do the spending and I have a feeling like at some point they're going to come from behind because they have everyone's personal data through the phone and they're going to have some like breakthrough product probably built on top of other people's technology that's going to come through. But I could be totally wrong about that.
- [18:27]
Tom
Yeah, it's interesting. I don't think it's clear now what the economics would be of a deal where Alphabet partners with Gemini, like who's paying whom kind of when you think about with search. Right, Gemini. I'm sorry, Alphabet was actually paying Apple to get Google search on the iPhone because the iPhone's such a valuable property.
- [18:47]
Co-Interviewer
I'm curious. It seems to me just, and I know you invested through this period, it seems to me like the tech companies of today are, and I know you're a quality investor are like significantly higher quality than the big tech companies of like the late 90s. Do you think that's accurate based on the types of metrics you look at?
- [19:00]
Tom
Yeah, absolutely. Now Microsoft was caught up in the tech bubble in 1999. I think they were a high quality company then as they are now. And actually one difference was that in 1999 Microsoft was at 50 something times earnings and they haven't gotten very much above 30 in this last cycle or considerably less than that. So just this is a different point but the valuations we don't think are quite as extreme. But to your point, yeah, there are a lot of companies that weren't really very high quality there. We talk about sometimes the tech bubble, we also sometimes call it as the TMT bubble. It was telecom companies that were caught up in this just because they were laying fiber, you know, this very capital intensive, relatively undifferentiated business. Whereas now tech is probably the highest quality sector out there. Not to say every company's high quality but they do such differentiated hard things that they really are able to maintain profitability is just kind of our core idea of quality. I used to sort of joke that tech investing was IBM and a bunch of crappy companies and now with all due respect, it's a bunch of great companies. IBM, I mean actually even IBM has come back. So they're not, I don't really imply they're not a good company anymore these days.
- [20:15]
Co-Interviewer
Can you talk about growth versus maintenance CapEx, we'll put this chart up you had in the paper because this is an interesting thing. Obviously initially in this, a lot of this is growth capex building stuff out but it's going to require maintenance capex as time goes by here. So could you just talk about this chart and what you talked about with maintenance versus growth at CapEx in the paper.
- [20:32]
Tom
Yeah. So imagine you're Microsoft. You've been spending all this money and building up of this Azure cloud computing capability and you have GPUs and CPUs and networking chips and all that. And if you were just to keep your present capability of compute going, this stuff depreciates. And you can argue about what the schedule is from accounting point of view or in the real life lifespan of these companies, but say every five years you're replacing it. If you just said oh we have enough compute now, we can actually do everything we need to, your capex would be just kind of a fifth of what your value of the co stuff now just to replace it. But these companies are right now they're growing capex like 60% and virtually all that grow or more and all that growth is adding on new capabilities. So if you look at what the capex of these companies are today, we're splitting between a steady but being diluted level of just keeping things running and this investing for future capability. That's the growth CapEx. Now if you're Nvidia receiving that CapEx actually most of what you're receiving is the growth CapEx. And so if Microsoft were to say inflect a little bit down and say we're not going to grow realistically but we're going to grow a little bit less, we don't need to grow as quickly as we were, we want to keep balanced with our cash flows or whatever, then that would have a much bigger impact on companies down below. Where in the, in this, in these four layers where they are getting more of the growth capex and the maintenance capex from the people above them, which speaks to kind of the volatility as people talk about like the whip of the economic cycle. Right. Who's at the end of the whip and who's sort of close to the handle as you go down these four, four layers you're getting close to the end of the whip and then for better or worse you see a lot more volatility in revenue.
- [22:31]
Co-Interviewer
Yeah, this growth versus maintenance CapEx is so interesting to me because I think it's one of the big questions right now. I mean we don't expect these companies to keep spending at this level probably for a really long period of time but like how much the maintenance capex is going to be once this thing settles down seems Like a very interesting question and a question that's going to impact these companies a lot.
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Tom
Yeah. And I think what it's actually, there's actually been a fair amount of controversy say over the last year about what the right depreciation schedules are and other companies are not depreciating assets. But the fact is the useful life of these chips is fairly long. They just, they do wear out but it takes a while. So that's been one of the things in the industry is generally you can have fully depreciated assets that are still useful, have still useful lives. Like people are still using ampere chips. Right. There's just so much demand that even if they're not as efficient as the new ones, you still get a lot of utility out of them. And, and so that's a good thing from the people who are buying this stuff. They get extra usage out of it. It's a little bit of a, I'd say risk for people lower down in the stack in that maybe the rate at which the level of maintenance capex, the rate at which you just need to renew your current capacity is a little bit less than what you would think if you just looked at depreciation schedules and financial statements.
- [23:48]
Co-Interviewer
How do you think about the changing nature of that? We can call the Mag 7 or the Hyperscaler companies or whatever in terms of they seem to be, you know what made them great was they were these free cash flow generating machines, they were capital light and now they've changed. And you know, I always ask this question about like have they changed forever? Have they changed for the worst? And it was interesting. We had Michael Mobason on, we posted it today and one of the points he made is obviously these companies are investing way more than they have in the past but they've always invested, they've just invested in R and D and they've invested in things that got expensed, you know, through the income statement. So maybe my question, the premise of my question is a little bit wrong in terms of maybe they're not as changed in their nature as, as I think they are. Maybe they're spending the money in different ways. So how do you think through that?
- [24:26]
Tom
Yeah, and Amazon is sort of the famous one for investing through the income statement and sort of in an accounting sense really never, at least not consistently earning very much money at all but at the same time being successful, fantastically successful and growing. Of course Amazon's also fairly capital intensive companies from the warehouses and stuff even before you got into AWS and cloud Computing and we have with cloud computing that is a case where we've sort of seen this movie before. Like that was the critique of AWS and of Azure when they first came up is oh, we thought these were software companies and they have super high gross margins and now you don't because you have all this like dirty crimy hardware that collects dust and you have to replace. And I think they've proven that that can be a good business. Like capital light is great, but capital heavy, if you're getting a high return on that capital, that's that's fine too. So I think the question is not so much are they capital intensive and is that therefore bad businesses? Okay, yeah, they're getting these things that are capital intensive and is that a good business? A tsmc, right. Very capital intensive. Been a great business for a long time now. Mainly these are the exceptions rather than the rules. Like most capital intensive businesses aren't as great. But there is something to the moat that great scale provides in these capital intensive industries that mean you can, if the need to invest is really, really big in dollar terms, that means there aren't that many people who can do it. And so if it, there's a real economic value to being at, to it being done, you are able to get high returns out of doing it. So I'd say we're not overly worried about just the fact that capital intensity is high. I think one thing you do see in capital intensive industries is the lead time between when you make that capital investment and when it pays off can be high and that does present risk. But that's also true to your point about R and D spending, whether it's in tech or pharma or any of these areas where they're R and D intensive. You're talking about payoffs decades out. Yeah, a lot of the things you try don't pay off. When you're thinking about the return on capital on that investing, you're sort of kind of playing the odds that on average it'll deliver high return in some ways the capex that they're doing now, it's a little more certain I think that it'll get a return. Maybe it's the time frame isn't so certain, but the eventually I think is pretty certain it'll be used. So again we're not, we don't see that as a huge negative in and of itself. Capital intensity.
- [27:02]
Co-Interviewer
Yeah. To your point, the question I should be asking is maybe not the changing nature of these businesses, but the question I should be Asking is their, their investments in R and D in the past paid off massively. And the question is, are there investments in this tangible assets, are they going to pay off similarly? I mean that's really the question that determines what happens from here, right?
- [27:17]
Tom
Yeah, I think to some extent they won't pay off as much as the initial R and D. And you're sort of seeing kind of survivorship bias here. Like lots of people tried to start Internet companies. A lot of them went nowhere. A few of them achieved escape velocity. And because they're the few that achieve escape velocity to get to where they are today, you know, they were, they were lucky. They were skilled. They were in the right place at the right time. They got these unreproducibly high rates of return. So they're not going to see quite that again. But I think they are in a position where they can get well above average exceptional rates of return on this investment. So not as great as the past, but still pretty darn good I guess would be our forecast.
- [28:06]
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- [28:32]
Tom
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- [28:42]
Tom
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- [28:51]
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- [28:54]
Tom
You can watch the record breaking phenomenon at home.
- [28:57]
Interviewer
You're clearly working at Zootopia 2, now
- [29:01]
Tom
available on Disney plus, rated PG.
- [29:05]
Co-Interviewer
And that would be different in some ways from past tech booms, right? Because in past tech booms the builders have not necessarily been the biggest beneficiaries. You know, a lot of those builders, like in the fiber build out, like ends up going bankrupt. So because of, because, because of this intelligence, I guess because these better companies, you would probably expect the builders to benefit more here relative to the past, right?
- [29:23]
Tom
Yeah.
- [29:23]
Interviewer
Yeah.
- [29:23]
Tom
And that gets to the leverage point you mentioned earlier. Like we haven't gotten to a point where at least for most of the companies we're talking about or all the companies we've talked about so far the debt hasn't gotten to a point where if things go bad and there's a recession or whatever, they won't make it to through to the other side like these companies will. So that's, that's a much different position. You know, we talked about historically, tech wasn't viewed as much as a high quality sector and wasn't because of the, in part at least because of the pace of technological innovation too. You'd have one wave of technology and a company would win and then there'd be a new wave and that new wave would have different companies winning in that and there a lot of companies would fall by the wayside as you went from one generation to the next. And what we've seen that's changed not just with the AI boom, but over the decade or more that's preceded that is it's reached this sort of scale where it is the same winners of the previous generation who are in the position to win in the next generation. I kind of think that's going to be true in a lot of cases in AI. Like obviously there are new companies like OpenAI, but I think a lot of the benefits to AI will actually fall to the incumbent tech companies, not just to new startups.
- [30:38]
Co-Interviewer
How are you thinking about the LLM layer? You seem to be less optimistic on that and that's an interesting layer because a lot of the technological innovation is going on there. But by the same token you could argue eventually we're going to have commoditization there. Some people have even argued eventually like Google, because of their strong power relative to everyone else, might make their best models free to try to take out some of the other companies. So how are you thinking about that LLM layer?
- [30:59]
Tom
Yeah, actually I'm a little bit nervous about the LLM layer for reasons you allude to. I think there are probably more people trying to produce LLMs now than there need to be LLMs out in the world. I think it's an open question. I am a little bit uncomfortable with the AI evolution is how much people are willing to extrapolate the progression of models into the future for any period of time. There isn't real risk that we kind of squeeze all the juice we can out of all the data we have and just throwing more compute at it or slightly cleverer algorithms isn't necessarily going to get anymore. That would be the plateauing and that hasn't happened. And to be fair, people worried about that with Moore's Law, with semiconductors for decades and decades before it kind of finally started to happen. So maybe I'm too cautious about that, but I think that is a real worry. And I think with LLMs what would really differentiate that one company differentiate from the other is not so much we came up with a fancier algorithm where we have more GPUs than you, but more we have differentiated data. And then for an Alphabet like apart from having the best scientists, they also have the best data for a lot of purposes of any of that. So I do think they are in somewhat a privileged position. But for a company that's just sort of trying to come up with an LLM, sort of with me too data and a lot of capital, I think that's going to be hard to differentiate and succeed.
- [32:32]
Co-Interviewer
How about the software companies? They're interesting to me because I would and you can disagree with me on this, but I would have considered them very, very high quality businesses coming into this because of the recurring nature of their revenue. And this kind of came out of left field. Now everybody's, they've been completely derated. Everyone thinks every software company is going to disrupted. Like how are you thinking about that? How are you thinking about the software companies in light of this?
- [32:51]
Tom
Yeah, it's kind of ironic because three or four years ago people would say on podcasts like this, I probably would have said with a straight face like oh of course software is the best business in the world. Right. Recurring revenues, asset light like close to 100% gross margins once you don't stop reinvesting in rev sales. And suddenly this is a case through the early conversation I guess where suddenly not having tangible assets and being very asset light kind of worked against you with at least theoretically with a AI being able to come in and reproduce what you do in at least in people's minds a very quick and efficient way. Now I think, I think those fears are overdone. I think the sell off this year certainly in the breadth of how many different software and software related business models have fallen is clear respect investors are just throwing the baby out with the bathwater and shooting first, asking questions later to, to toss my metaphors around, there is no mercy for any company that one can even imagine being replaced by AI. I think a lot of what existing software companies have though is things like proprietary data, regulatory lock in entrenched position, the workflow of their customers. There even sort of network effects for software that is consistently used across practitioners and industry and becomes an industry standard. There are a lot of things that keep software from being disrupted other than just the code is really good. So I think the AI fears are a little bit high there, but it's clearly had a big effect on markets and was clearly turned upside down what people thought of as high quality two or three years ago.
- [34:35]
Co-Interviewer
And it's interesting because in businesses where trust is important or in businesses where failure is a massive issue, you think people are probably overstating the ability to go vibe code it. You know, if like we're in the investment management business, like we can't fail 0.1% of the time, we've got to fail 0% of the time. And so you think like the software that meets those types of tests, it's going to be very hard, I think for AI to overtake that, I would guess.
- [34:56]
Tom
I mean to push back on that slightly. I have to say with all humility in the investment management business you do fail a lot of the time in the sense of not every stock I pick goes to I guess more in
- [35:06]
Co-Interviewer
terms of like losing client data or something like that.
- [35:08]
Interviewer
Not in terms of your.
- [35:10]
Tom
So client data. You know, financial system generally sort of regulated regulatory data. You can get in a lot of trouble with one slip up. So that's the sort of thing that probably isn't going to be replaced very quickly by AI, you know, logistics in your supply chain. Not, not something you want to mess with. The cost of failure is just too high. And the other thing is software is, yes, it's a huge industry, whatever 1.5 trillion, whatever it is spent on enterprise software, but it's actually a pretty small industry relative to the cost of the people who hire to use the software. So just getting someone a cheaper version of Salesforce doesn't really save a company that much money. What they would really need to do is replace the person who's using Salesforce to save money. The kind of software to be less sanguine about, I think it's more at risk is maybe like data visualization, like you don't have proprietary data. It's more if it's not right, it's sort of immediately obvious it's not right. So sort of easy to check the AI. I think that's a, that's really kind of a key concept of how, how bad is if it's wrong and how easy is it to check if it's right too. So I think there's, there's stuff that's definitely a risk. I don't want to minimize the disruption of a disruptive technology, but I think there's a lot of stuff that's not.
- [36:28]
Co-Interviewer
This is a good time to take a step back because we have been talking about quality and, and quality is like. It's probably one of the hardest factors to define. I mean first of all you've got your discret people who, you know, they do it in their own mind. I mean the types of stocks Warren Buffett might own, people probably consider quality. But the for the quants like this is probably the biggest variation in terms of the quant shops and their definition of any factor is probably quality. People do it in so many different ways. Like how do you think about the definition of quality? How do you define it?
- [36:53]
Tom
Yeah. And you probably don't have too many guests on your show and who say, don't say they buy high quality stocks. When you talk to equity investors, the so GMOs history as an investment firm that goes back to the 70s. It actually started from very deep value groups. Jeremy Grantham and Dick Mayo were at battery March in the 70s and kind of made a name for themselves buying small cap value stocks. So GMO's on train to quality was sort of coming from hey, we buy all these low multiple stocks and we're always missing out on these great companies that consistently have high profitability. They seem to do really well over time despite being at higher multiples because they just stay at higher multiples and, and they grow very well. And oh by the way, during recessions and times of economic downturn, these stocks actually hold up better. So it was not so much a. This is a silver bullet factor that just gives you better performance. It's more, almost more like better risk adjusted performance. You can get, you can get an equity return but without. With lower risk or you could say you can take lower risk than the broad market without giving up return. So that's what kind of got GMO into the idea of quality. In terms of specifically how we define it, coming from that background is what kinds of companies deserve to trade a premium. And yeah, it's growth, but it's really profitable growth. If you can redeploy capital into a business and get a higher return on that redeployed capital than your investor could if you just dividended out to them and they did whatever with it, then you should trade a higher multiple. And that's kind of what we're trying to identify future high, consistently high return on capital. Of course, in a capitalist system with competition that mean you have some mode around your business, you do something that competitors can't equally duplicate. We do have backward looking factors so we could have a. Like if you were to and GMO did in the 80s, come up with a quality factor. We would look at a history of high profitability, high return on equity stability across the economic cycle, strong balance sheets. Those are the kinds of factors. But really what we're trying to get at, where our fundamental work comes in is will that be true in the future?
- [39:01]
Co-Interviewer
How much of it is balance sheet versus consistency? Like it seems like a lot of quality investors we talk to have maybe gone more towards the consistency side over time and maybe a little bit away of the balance sheet. And that kind of gets into the big tech companies now being very high quality because they've been very, very consistent performers.
- [39:17]
Tom
And they also have traditionally had very strong balance sheets as well. And we do think the balance. So it's both not or in our mind the balance sheet is important because bad times can happen to anyone. Things happen in the world. And a lot of being quality is just being able to keep going through those tough patches. And if you have a lot of debt, you are kind of at the mercy of strangers, as it were. When the, when you go through those rough patches, you also have to dry powder to invest when there are opportunities. And it is kind of a. If you have a great business or generating a lot of cash, you have to ask why a company would need debt too. So we're pretty suspicious and skeptical of high levels of debt. But there are also cases where hey, a company just did a big deal. This is transformative. They have the cash flows to pay it down. They just took a lot of debt on. Now we can be accepting of, of higher debt levels. But I guess if you were to make me choose between the two, the debt service safety surety thing. But the really sine qua non for us, the thing you have to have is that view that you can deploy capital or at a higher rate of profitability, profitability in the sense of return on investments than the average company. That's the core idea. And the debt is our strong balance sheet is an enabler of that.
- [40:36]
Co-Interviewer
I wanted to stop and ask for that definition because I wanted to ask you about Oracle because you refer to your decision to sell Oracle in the paper and I'm sure that has a lot to do with quality. So can you talk about that? Because Oracle's interesting and they are using way, way more debt. We talked about the other hyperscalers using cash. Oracle's using way, way more debt here. So can you talk about the decision to sell Oracle?
- [40:54]
Tom
Yeah, and Oracle is a stock we'd held for a long time and back when we bought it, it was a very, very strong balance sheet at relatively low growth company, but also lower multiple. So it was kind of a very sort of boring, in a good loving way boring kind of business. And they pivoted very successfully and saw growth opportunities invested into them. But they took that beyond the level at which we are comfortable as quality investors. So as you say, they took on a lot of debt and that debt is only serviced through their customers being willing to and able to pay them revenue. So. And they do have a lot of customer concentration. So Oracle can have whatever ironclad agreement, legal agreements with OpenAI that they might have. But that's still, those are only good if OpenAI is solvent. We don't hit a rough patch or they aren't the LLM that drops out in much potentially competitive world that loses out to others. So that puts Oracle in a sort of riskier position than we feel comfortable with as quality investors. So it's not, when we sold it, it wasn't so much a call like, oh, the stock's not got up a lot and the valuation's expensive. It's more like this is the kind of balance sheet that we can't be comfortable underwriting in the portfolio.
- [42:14]
Co-Interviewer
How much of that is quantitative and how much of that is you as a portfolio manager looking at Oracle and say, I'm not comfortable with where they are right now.
- [42:20]
Tom
Yeah. So we have a lot of quality quantitative metrics. We do quantitative screening on quality for sort of idea generation. And to prompt the question, ultimately the decision is always a fundamental one. So I would say it's very, our fundamental decisions are very data informed is the way I put it.
- [42:39]
Co-Interviewer
If this spending slows down, one of the things you talked about is if spending slows down and looking at this whole value chain you talked about earlier, where the places that might get hurt the most are, how would you think that through? If this AI spending does slow down, who would be most impacted by that?
- [42:53]
Tom
Yeah, I think a simple rule of thumb is whatever's gone up the most is probably what's going to go down the most. So that's, you know, in recent months, that would be the memory stocks where that's emerged as a pinch point and the stocks have done fabulously well and I think if things feel badly, they would unwind that pretty quickly. The more general version of that, getting back to the four layers is probably the further down that stack you are, or that whip metaphor I used earlier. The further down that stack you are, the more volatility there's going to be in your fundamentals and therefore probably your stock price. There are mitigating factors like some companies are sort of 100% AI plays and some have diversified businesses and some have stronger balance sheets than others. So of course the more pure play you are in AI, the more you have debt or anything like that, you're going to be more volatile, but generally down that stack will do worse if you're the hyperscalers. Like they're not going to do well if there's an AI slowdown, but they'll do relatively well.
- [43:56]
Co-Interviewer
So you ended the paper with three principles you would think about in terms of where to invest Durable competitive advantages, the ability to prosper across a wide range of scenarios and via resilience, via less volatile AI revenue and balance sheet strength. Can you talk about those?
- [44:10]
Tom
Yeah. So sustainable advantages. And think about companies that you they may do very well now, but five years from now is their products really going to be that differentiate? I think that's where the LLM companies might come into question for us. Like there's a big first mover advantage for OpenAI but I can't really be convinced that they'll be the best and probably aren't the best LLM today or they'll be even a relevant LLM in five years from now. That's relatively unclear. Conversely, I'm pretty sure that everyone's going to be using ASML tools to build the chips that go into AI 5, 10, 20 years from now. You might have a question about how big the market is, but ASML is going to have their piece of it with with pretty high confidence even. Secondly, however big you think AI is going to be 10 years from now, now I think it would be foolish to think it's going to be a linear path from here to there. There are going to be drawdowns I would expect along the way. They could be very savage ones. And so you want to make sure you have companies that can survive those and the way that the two things that help you survive those really are you don't have a lot of debt and you have sources of revenue that are kind of low beta today I funding investment drying up. So you have your Nvidia. They have, they've been sort of a victim of their own success and they're pretty much all their revenue is the AI data center business they have so this is now vestigial gaming businesses that that is their roof. Whereas Broadcom they're growing their their AI business a lot you they do have 40% of their revenue from very annuity like software. They do have Apple revenue, they have other networking chip revenue. It's a very diversified business and so American actually they do have some debt but they have so many other things that we think that's relatively safe or some of the other companies are very cash rich. That element of safety in the business model is I think critical for not just even if you're an ultimate winner, you need to survive the tough times that come along the way to that victory.
- [46:27]
Co-Interviewer
Just last one for me before I hand it back to Justin. Just when you close out the paper, you had a really interesting chart here. You're looking at your, you're looking at the different layers of the stack we talked about earlier, but you're looking at the GML quality, what percentage it owns of them versus the S&P 500. And I thought it was really interesting like the inclusions here. So can you just talk really quickly about how your, your quality portfolio looks different than the S&P 500 from this perspective.
- [46:47]
Tom
So, so really where we are is in two of those four layers. We are in the hyperscaler layer and we are in the infrastructure layer. So the, the hyperscalers, they have the safety, they have the cash flow, they have the other businesses. We also think they have the visibility to deploy capital most effectively of anywhere else. They're closest to the end markets and the applications. So we can. And they also by the way are not trading at very high multiples right now. So then that, that's sort of the safe place to invest and the place where in some ways it's safe because they are sort of the arms dealer type companies is even below Nvidia like the TSMCs and the, the semiconductor equipment companies. The stocks are very volatile but I can't see a world in which AI plays out as successful. And it's different companies are building the chips or building the tools to build the chips. Like actually can't quite say the same thing about Nvidia. Like there is competition Nvidia. There are custom chips, there's amd. Even if Nvidia is still relevant, it may not be able to earn the kind of margins it does today. So it may not be the same scale market dominant it has. It'll still be there but I don't think its position will grow from here. If I look down below that into the semiconductor equipment chain, that I think will at least grow, at least with the market, if not more as it becomes more capital intensive. So it's kind of at the top and the bottom where we're invested. Those are like over the long run I think they'll rise together. Over the short run they do tend to move at different trajectories. Like this year we've seen the semiconductors do much better than the hyperscalers. I think that is likely to rotate over the, over the next year or so. But we think those are both very attractive places to invest.
- [48:42]
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- [49:12]
Interviewer
We've talked a lot about AI and the tech space, but I was looking at the, the ETF that, that you run, the high quality, the GMO high quality etf and clearly tech is you know, I think the largest sector alloc. I don't have this, the sector allocations up, I have the holdings up. It looks like tech is probably the largest sector allocation. But you know, it's not, it's not all tech. I mean you, there's, you know, there's some healthcare in here, there's some financials in here. But I'm curious and I don't mean this to be like, like, like put you on the spot, like I got you but like no, Berkshire Hathaway, like how do you, have you guys ever held Berkshire? Do you ever look at Berkshire Hathaway like, how do you view that as a quality company? Just given the business and the cash and stuff like that?
- [49:54]
Tom
Yeah, we don't hold it. I wouldn't rule out holding it. It's we, and we do hold some of the companies are in Berkshire's public equity portfolio, which I guess is one of the reasons holding us back from investing in it. But it's also true that a lot of Berkshire is private companies that we couldn't otherwise access. So I think we generally think it's a mix of businesses is in aggregate pretty high quality because of the capital allocation that, because there are some high quality businesses in there. It's not. I could give you exact number off the top of my head, but 2/3 high quality, one third not so we haven't today, at least not that aggregate. It quite meets Our standard, but it's not, we're not quibble with a peer who had no portfolio.
- [50:41]
Interviewer
How often do you like, how often would a new name come into a quality portfolio? Sort of talk about the, I guess the new idea generation process, what you go through there and then do you, you know, what would you do? Would you replace a name? Would you. What would be the sell criteria to free up some cash to get that name in the portfolio? Like how do you manage that?
- [51:03]
Tom
Yep. So I mentioned we do fair amount of screening on financial statement characteristics of the signature of high quality companies, like long trajectories of high profitability. That's actually kind of what got us into tech originally is like when you know Berkshire and Berkshire Hathaway. Warren Buffett famously was slow to invest in tech. I think a lot of high quality oriented managers were. If you sort of look at the digital signature, the profitability just increasing and the balance sheets being strong, even though it's looking backwards with a lag, it did kind of force you to look at these companies. So that kind of discipline screening can be really valuable. And that's the first step of our process in both directions too. It can also tell you something's deteriorating and sometimes things happen so slowly. As a human, you maybe don't notice these slow incremental changes. We do. However, it is always a human decision in terms of the new names. Sometimes it's a truly new company or company whose performance over the last few years has just risen up to grab our attention. So like Uber, which we had within the last year, there was a period where it wasn't a high quality company and slowly its cash flow started improving and wow, this is actually a very profitable company now. And hey, we should look at it sometimes it's a company that's been around for a long time and we've thought of as high quantum. He was company we've held in the portfolio and then something happens in the market, it falls out of favor. We're asking ourselves, hey, you know, the stock's gone down a lot. Do we think this is still a great business? You know, five years from now it should be fine. If so, maybe we'll buy it. We've actually bought some of the sort of software related companies that would feel like I've been over punished by this AI disruption narrative within the last year, sort of examples of that. It's not so much our view on the quality change, although we had to reiterate with AI but the valuation opportunity was there that hadn't been in the past it's somewhat similar on the sell side. Like the good problem to have is, hey, we have a great company, it's done really well. You know, it's a quality business. But the stock price is just so expensive that we don't see how we can generate a great return from here. And we have liquidated a couple tech companies over the last couple years for that reason. Or sometimes it's a case where this is a less happy situation. It's a company we've held for a while. Things are deteriorating. We just don't think it's a high quality company anymore. Actually we talked about Oracle before. That was one that we essentially downgraded their quality. That was a nice one for us because the stock had done well. Often that is the case though, where you're selling underperformers and I think that's, that's a hard thing for investors to do. At least a hard thing for us to do is sell underperformers. But sometimes those are your, your best trades. It's not strictly one in, one out in the portfolio. We do like to have around 40 to 50 names as sort of a sweet spot where you can get diversification but have a constant enough portfolio that you really feel like you're on top of every position.
- [54:10]
Interviewer
So I think I probably recognize, you know, 95 of the names in the portfolio. You know, they're mostly large cap, you know, strong big brand names that I think most people would recognize. But is that, how do you think of. And there's not really small caps represented in here. So maybe it's a large cap mandate that you're really trying, you know, that's where you want to kind of play. But is there anything to be said for the different, the differentiation between looking for quality companies, you know, across different market caps and different sizes. And do you ever find that you come could come downstream a little bit from like maybe the 250 or what's in the S&P 500 into other areas, smaller companies.
- [55:00]
Tom
Yeah. And there absolutely are high quality companies that are in smaller cap. And size isn't part of our definition of quality. I would say if you're a great company in a big industry, you're likely to get pretty large. But there are some niche industries where even the strong player in the industry is never going to be that large. In fact, GMO does have a small cap quality strategy, which is it is a more cyclical group. There's not obviously mega cap tech. It's a little bit of a different kind of universe. But there are definitely companies that meet that criteria. And there are a couple of smaller cap companies in the what you might call our large cap or all cap strategy, but smaller. I'm talking about that sort of 10 to 15 billion type market cap range, not real pure small cap companies.
- [55:45]
Interviewer
Do you think that, you know, international stocks typically have traded at multiples lower than here in the US and you know, some people argue that, you know, the US market has better quality, more diversification and therefore that's why you get sort of the premium, you know, in the US market and particularly. But do you think if you were to be looking at international stocks or building international portfolio, would you look at defining quality any differently or would you kind of stay in the same lane?
- [56:19]
Tom
Oh, and we do do some global investing in broader portfolios. We do not define it differently in different markets. I'd say the prevalence of quality is less outside the U.S. one of the reasons why your national markets are cheaper is that they just have a different mix of companies. They have more of the kinds of businesses that are naturally low multiple businesses. But that doesn't mean that I mentioned ASML earlier with lithography. That's a great company, right? That's a high quality company. I have no qualms about that. I also observe that ASML has traded it premium to its closest peers in the US So just because it's non US doesn't mean it's cheaper. In fact, often I would say there's maybe even a scarcity premium for the highest quality companies outside the US so they aren't cheaper. There hasn't been this great valuation driven reason to invest globally. There are certain industries where the best companies are outside the U.S. i think investors are benefit from investing globally sometimes we do actually manage a international quality ETF as well as the US quality etf and it is, I'd say similar kinds of companies or at least companies you meet a similar, the same threshold for quality.
- [57:35]
Interviewer
One of the things that I know Jack and I have always valued in talking to people from GMO is you guys have a good ability to kind of get down to just, you know, like first principles, type of things important in investing in terms of how you're building portfolios, how you're looking at companies. And so, you know, I think this discussion has been a valuable one today largely because of that. And so we, we really appreciate it. We have two standard closing questions we like to ask all of our guests. And you can go anywhere you want with these. But the first is what is the one thing you believe about investing that most of your peers would disagree with you with.
- [58:15]
Tom
Well, this is kind of a small one that came up recently for me is share repurchases. You go to an average investor meeting and the stock's down and you say, should you do a repurchase now? Or the management teams will talk about, oh, we're opportunistic in our repurchase program. To me, like repurchases is sort of a blanket way to return cash is fine and tax deficient. If you're a company that's trying to cherry pick your stock price, you're basically trading on insider information because you know more than everyone else does about your company. And while you're benefiting the investors who hold your stock, if you're right and are buying low, you're doing the opposite. You're disadvantaging your investors who are selling to you. And so I don't really feel that that's appropriate for companies to have share repurchase programs. It's based on the valuation of the stock. So it's a little bit of a pet peeve of mine where I think I'm on a island of pretty close to 1 in the investment world I haven't heard of.
- [59:12]
Interviewer
So what would you do? Would you just have it? Like if you would have like an automatic, like if a company approved $10 billion buyback, you would just automatically set it and have it?
- [59:21]
Tom
Yeah. Do you like if you're the CEO of a company. Right. You can't just go out and buy stocks anytime you want. You have these programs where you buy on a certain cadence or sell more often on a certain cadence that's sort of set in advance. So you know, you are trying to game the stock price and sometimes you get lucky, sometimes not. But you can'. Just be opportunistically trading your own stock.
- [59:42]
Interviewer
And then the last question is, based on your experience in the markets, what's the one lesson you would teach your average investor?
- [59:49]
Tom
Well, I'd say the average, if you're thinking about by average you mean sort of not professional investor. Not professional investors have a big advantage, which is by definition are not paid by someone else means they don't have a boss. So you don't have to be conventional. Right. See, think you can't get fired, so take advantage of that. You can be a lot less constrained by benchmarks. I'm not saying you should concentrate overly and do necessarily risky things, but I feel like people too often just fall into what's conventional or what a professional who's paid and has career risk of being wrong tells you what to do. And I think people can be a little bit more creative without necessarily taking more risk. So that's, I guess my sort of big picture advice, maybe my more micro advice is don't pay as much attention to what happened today. Like, think a year from now. Is what's happening today going to matter that much a year from now? If the answer is no, you shouldn't be trading on it.
- [60:46]
Co-Interviewer
Great.
- [60:47]
Interviewer
Good discussion, Tom. Thank you very much for joining us. We appreciate it.
- [60:50]
Tom
Well, thanks for your time. I enjoyed this very much.
- [60:52]
Interviewer
Thank you for tuning in to this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess returns network@excessreturnspod.com. if you have any feedback or questions, you can contact us@xsreturnspodmail.com no information on
- [61:12]
Co-Interviewer
this podcast should be construed as investment advice.
- [61:15]
Sponsor Announcer
Securities discussed in the podcast may be
- [61:17]
Tom
holdings of the firms of the hosts or their clients.