The power of exceptional companies in international markets

As with any investment, your capital is at risk. Past performance is not a guide to future returns.

Paul Taylor (PT): 
Good morning, everyone. Thank you very much for joining us on the webinar. We'll be talking about the power of exceptional companies in international markets.

I'm Paul Taylor. I'm a Director in our Clients Department, working very closely with our high growth international strategies, many of you which will be familiar with. For those of you less familiar with Baillie Gifford, we are an active growth manager based in Edinburgh, Scotland. We've been around since 1908, and throughout that time, we've always been formed as a multi-generational private partnership structure. This morning we'll be discussing why exceptional companies are important, and how we go about finding them and owning them. We'll also be reflecting on how artificial intelligence is continuing to shape our task as long-term fundamental stock pickers. The comments we're going to make will be around our approach to outlier growth investing, rather than how we implement that at an individual strategy level, because there are some important differences there. I'm joined today by my colleague Lawrence Burns. Lawrence is an investment manager on both International Growth and International Concentrated Growth strategies.
We've structured this session as a conversation between Lawrence and myself initially, but we'd be really pleased to have your engagement so please do use the Q&A function on the Zoom and we'll leave plenty of time at the end for your questions. Lawrence, before we dive in, I think it's interesting to give the audience some context. This is a graphical representation of a 30-year study of where value is created in international markets. It was conducted with our support by Professor Bessembinder at Arizona State University. And what it shows is that from 1990 to 2020, in international markets, 1.4 per cent of companies created all the wealth over that period. So, 116 firms creating $16 trillion of value, the next 535 creating $15 trillion, and the rest netting out to zero. So Lawrence, first question, what does this tell us about markets and investing?

Lawrence Burns (LB):
Well, I think, firstly, thank you for everyone for joining. I think the first thing it tells us is that most companies actually don't matter to returns. Out of that 45,000 or so, over half, if you go through the study, didn't outperform one-month treasury bills. And so, if you think about that for a second, you're having half of equities that are unable to outperform the safest and most lowest risk investment available.  And then you flip to the other side of it. And what it's telling you is that there are some really exceptional companies that make equity investing worthwhile. And without which, when you lose that 116 firms, the entire asset class in terms of returns looks very, very different. And so for us, it's always been about where do returns come from as an active manager and making sure we seek out those returns, and it is the small number of outliers. The reason that these outliers exist is really because of a key characteristic of equities, which is the asymmetry.

You can invest in a billion-dollar company and it has the potential to become a hundred billion dollar company.  With equity investing, you have near unlimited upside with mathematically bounded downside. Or to put that differently, if you invest $100 in a stock, you can potentially make $10,000, but the most you can ever lose is that original $100. And it's because of the asymmetry that you get outliers and that can then positively skew the entire distribution of returns in the way that the Bessembinder study shows.  And what I think is also interesting is this isn't something that is just about the last 30 years. It's not something that's just about international markets. Bessembinder’s original study looked at 1926 to 2016, so about 90 years of data in the US, and it found there that half of the net wealth creation over that 90-year period came from just about 90 firms.  So, this is true over a century. This is true globally. And it's also been true again and again in the portfolios we manage, where we see a small number of companies delivering the vast majority of returns in the long run for clients.

PT:
And so what this is about is a small number of companies that are able to compound their advantage, compound growth, and effectively make equities as an asset class worthwhile.  That's a very powerful insight. I guess our audience will intuitively recognize the asymmetry of market returns far from normally distributed, particularly after the recent experience in the US markets with the Magnificent Seven and so forth. And you point out this is not a new phenomenon. We've observed it around when the seven oil majors in the US back in the 50s and 60s, and maybe even thinking back further to the railroads going back more than 100 years. But what's maybe a more difficult question, if I may, is hindsight's a beautiful thing. How do we go about identifying these companies before the outlier returns occur?

LB:
Yeah. So, I think the first thing I would say, first two things would be, one, what we've found in doing this is that the hardest thing is not the identification and the analysis. It's more the behavioural of being willing to take a risk on a company of an uncertain set of outcomes, being willing to hold that through periods of difficulty and not overly trimmed from success. Actually, I think it's more the behavioural that is even harder than the identification. The second kind of thing I would say is that sometimes we get this reputation, some of undeserved, of being super early in some of these outliers. And I don't think it's also the case of being super early in identification and advance.  So actually, this strategy, this approach, often works very well when you're backing a company that is already showing signs of success, it is building traction, its product is working, and it's more about extrapolating where that takes you to and using your imagination to think how valuable that could be if it succeeds. So, you know, Baillie Gifford have been long-term investors, particularly heavily in the past in Tesla, but, you know, it'd been public three years before we first invested and we were sending people out there that were test driving the cars, and we were doing research on the company.But actually, before we invested, Tesla actually phoned us up and said, we've gone through Amazon's cap table. And you're one of the relatively few investors that haven't actually invested in both Amazon and Tesla, and we think it really suits your philosophy. So that was one in some ways, we were so late to the point that they were phoning us up about it, yet it still has been one of the most successful investments we've made for clients. So, you don't have to be super early in that identification process. 

But going back to the root of your question, you could say there's a huge haystack. So, in that 30-year study, there's 45,000 stocks and you're looking for a very small number of needles. So how do you then find them? I think that if you actually create your entire process and philosophy around: I am looking for outliers, this is what drives returns and I'm specifically looking for them, that 45,000 starts to become more manageable. Because then you then start to ask yourself a question like, what are the companies out of that 45,000 or that several thousand that can go up fivefold in five years, using that as a hurdle rate.  Quickly, if we went through the list, you'd be able to rule out a very large portion of that 45,000 that are highly unlikely, particularly through growth, to ever exhibit that type of return, and that starts to make that haystack a lot smaller and a lot more manageable. Then I think you go through other processes of thinking about where are the big areas of structural change in the world and focusing your efforts alongside that.  And if you take a step back in some ways, what we're trying to do is think about what the economy of the future might look like in five- and ten-years’ time.  And to do that, I think what we've found really helpful is to effectively rely on people that are much smarter than us to help us. So, building these networks of insight, of talking to industry visionaries, company founders, and academics who aren't going to tell us you should invest in company X or Y, but they are very helpful in saying these are the interesting things that are happening in the world, challenges to our mental model and processes. These are the areas we think you should be looking at and thinking about.  And so that also helps with that narrowing of that large addressable universe. 

And then the other element is you need to be taking a degree of risk. Outliers at the beginning, even at the stage that we invest in, they are uncertain propositions. They're obviously often controversial in some way because they're trying to do something that is very hard or has actually never been done before.  And so, you have to be willing to invest in things that may not work out and accept that you will not always be right. And you have to accept that you may look foolish, because when an outlier doesn't work, it usually doesn't work in quite a material way. And you've gone against convention. And so, there's that looking foolish and handling that volatility. So you need to be risk-seeking as well as you go through that process. You can't just do comfortable investments in that search. And so, if you take a step back then what I think is really powerful though is you don't have to be right every time. If you get one of those outliers, one of those big winners, it can make up for a very large number of those misidentifications and can really drive returns of only a few actually having to succeed to work.

So, I often feel investing in outliers is very rewarding but it's also a very humbling process because a lot of the time you'll also be wrong.  

PT:
Yeah, I think a lot of clients will have heard us discuss that before, so it's really good to revisit that. I've just got the next slide up here, bringing our experience of that to life. So this is taken from our international growth strategy, but the shape of the chart is very similar, if not even more exaggerated than the international concentrated growth strategy. And what it shows is, the trailing 10 years of the top 10 and bottom 10 stocks over that time period. The blue bars are the share price movements over that period. And sorry, the dark blue bars, the light blue bars are the drawdowns that the stocks have experienced over that holding period. So, there's 10 years’ experience in this chart looking at 20 different companies across the international growth portfolio.

How could we describe/try and make sense of this for clients in terms of any sort of commonalities that we see among some of the big winners?  

LB:
So, I think the first thing would be actually they're a pretty heterogeneous bunch. If we look at the ones, both that have worked and haven't worked, but particularly focusing on the ones that have. We've got Wix there, which is a company that's about 18 years old, and you've got Ferrari, which celebrated its 85th birthday last year. I think that's a helpful reminder that in that search for outliers, you have to be open-minded about where growth might come from, what industries, whether they're young companies or old companies and the financial profile that delivers it. So, I think it's important not to be too prescriptive about what the characteristics are that make an outlier and set too many kind of rules effectively. But what I would say is there are some high-level commonalities that we do see again and again. The first one would be obvious; you need a very large market opportunity to be able to grow to multiples of your size. And actually, within that what we see again and again that give us some of our best outliers are companies that start off going after one very large market, and then leverage success there to go and succeed in another very large market, and sometimes even a third.And that therefore really gets you that positive skew. And that is much more common, I think, than people think. So if we give a US example, that's Amazon, even if we don't say starting with a bookseller, we say starting with an online general e-commerce store and leveraging the success there that they had, the giant website and traffic they were dealing with, and using that expertise to build out Amazon Web Services as their second act. And Amazon Web Services today is about 70 per cent of Amazon's profits. The second act has been even more valuable than the first. And we see those multiple acts again and again. It's NVIDIA starting with graphics chips and going to AI. It's Mercado Libre starting with e-commerce in the international markets and going to fintech and potentially advertising next. 

A second would be that outliers are often unconventional. So, if you're wanting to achieve something extraordinary, it's pretty unusual that you achieve that extraordinary thing by using ordinary methods. You're not going to get there by doing the same things as everybody else. And that leads to companies that can be quite unconventional but end up being some of the most successful.  So, in terms of the strategy, the culture and the people can be unorthodox, and that also leads to a bit of risk because when it works, everyone loves them as stories of how they're so different. When they don't work, they're seen as, well, they were red flags - it was obvious that it wasn't going to work because they went against convention. But if you think through Steve Jobs and Apple and his approach, it was very unconventional, very unorthodox. And if you think through more recently Elon Musk with Tesla and SpaceX, you do see a degree of repetition of these examples of unusual, unconventional behaviours leading to outlier outcomes. 

A third is simply that you need the outliers, the companies themselves to be long term focused. You don't get outlier returns in a quarter or even a couple of years, they're built over years of five and 10 years at least. As they seize new market opportunities, as they create new markets, it takes time. And they have to be companies that are able to and focus on the long term. Amazon, if we go back to that as a US example, was reinvesting very heavily in their business. Mercado Libre has done the same in international markets. WiseTech has done the same in international markets. And you can only do that if you're able to think long term and you're not optimizing for the quarter. So we go for the International Concentrated Growth Fund, for example, 80 per cent of companies are founder-owned or family-owned, and that allows them to be long term. So those would be some of the characteristics, not all of them, that we see repeating again and again among outliers.  

PT:
Interesting, thank you.
In the interest of balance, the dark blue bars are obviously much larger than the light blue bars, but we have had our fair share of misadventures over this past 10 years and over our experience of investing in this way. On the other side of the sort of the traits of the big winners, are there any sort of lessons that we would learn or share about some of the paths that we maybe shouldn't have gone down in hindsight?  

LB:
Yeah, so we've made a number of mistakes, and you can see our 10 worst, I think, that are on the screen now. I'd highlight two sort of high-level mistakes that have been particularly impactful. Well, I could sort of depressingly go through each one, but if I focus on that, I would say one of the mistakes that we've made in a couple of different versions that we've tried to learn from has been; I talked earlier about big structural opportunities, and you've got to make sure that yes, there can be a large structural opportunity, but you don't overweight that to the expense of thinking very, very deeply about the quality of the people that are running the business and the quality/potential quality of the underlying business model. And I think there's a couple of cases where we put a disproportionate focus on the big structural opportunity and not enough on those two factors. So, the energy transition would be a good example of that, where there's a huge long-term opportunity driven by the falling cost of wind energy, of solar, of batteries, all sort of following Wright's law, but you still need that underlying business model and quality of people. So, on that chart we've got SolarEdge for example, which would be an example I think we put too much emphasis on the big picture and not enough on the management and the business model. Ones that fall just outside of that top 10 would also be Vestas and Umicore. So, I think that's one type of mistake of making sure that we balance between those different factors appropriately.  

And then I think the other category of mistake would just be to highlight that actually our biggest mistakes are not our mistakes of commission. It's not the things we invest in that don't work out. It's our mistakes of omission. It's missing that big outlier that becomes more mathematically impactful to clients. And that is one that we quite rightly torture ourselves about a lot of trying to think through how do we avoid missing these? How do we have a bias towards owning the next big thing? Because it's so impactful if you miss an outlier. And I think as an industry, we don't pay enough attention to it because it doesn't get captured in performance data. But it can effectively be a very large detractor to performance, particularly when that's your stated philosophy. So on the plus side, when you get them, they're very powerful. And you can see that of the graph making up for a number of mistakes. But those are two of the types of mistakes that I think we've been most wary of and continue to learn from going forward. 

PT:
Thank you. Changing focus slightly and looking to the future, the current environment that we're living in globally, this seems to be more unpredictable, shall we say, than some papers in the past. Is now the right time to be looking and bravely seeking these sorts of outlier companies as an investor? 

LB:
So, two things, one would be, I think there's a slight element where outlier investing always has its appeal over long periods and has done throughout history. And I think when you talk about macro difficulties, what is the attraction of some outliers is that if the structural growth opportunity and the business model is strong enough, what we have found is that you can often overpower even the most dire of macro circumstances within reason. So actually, at the top of our asymmetry chart there, you've got Mercado Libre, where it looks like it delivered 17/18-fold over the last 10 years. That's a company that operates e-commerce and fintech in Latin America. Its largest market is Brazil. Its second largest is Argentina. It's even been tougher in Argentina than Brazil. The macro picture in LatAm has not been fun. It has not been serene. So, the Brazilian real has depreciated about 50 per cent against the US dollar over that 10-year period. The Brazilian economy in US dollar nominal terms has declined 15/10 per cent. Yet, Mercado Libre in US dollars had been able to grow revenues roughly 30-fold, earnings 20-fold, and the share price has gone up 17/18-fold. And so that is one case where if you put too much attention on macro, you end up losing out on some really powerful outliers. And so some of these outliers have the power to overcome even these inhospitable environments. Similar to Intel in 1970s would be another example. 

Another element is also, I've put emphasis on people quite a few times here, hopefully already, and part of that is outsourcing that unpredictable and volatile environment to some of the best leaders of businesses in the world that are able to navigate that volatile environment. And we've seen that with our holding in Spotify, for example, where Daniel Ek very quickly, coming out of the post-COVID period, pivoted the company to focus on efficiency in a world where capital was no longer incredibly cheap, in a world where markets were not going to reward profitless growth to the same degree they did in the past. And so, there's an element of outsourcing some of that volatility to company management teams that are able to navigate it. 

And then I think the final point would just be, one of the reasons we're probably going to be in a volatile era, yes, it's geopolitics. The other is an increasing pace of technological change.  And so, there's an opportunity on the other side of that to own companies that are going to leverage new technologies like AI and others to disrupt markets and bring change. And so actually, when you see an increasing pace of change, more often than not, particularly when that's technology, I think that's a good thing, not a bad thing from a growth manager, because we hope to invest in the companies that are driving that change rather than the companies that are having to defend that change or defend themselves from that change.

PT:
Good. Taking it a step further, once we've identified a portfolio of companies we think truly have the potential to be acceptable, how do we go about investing in them? And I'd like to just explore the patience required to own some of these long-term structural winners, which I think is a very important element of what we do.  

LB:
Yeah, so I think there's two sides to patience and the behavioural. One is actually another Bessembinder study looks at drawdowns of these big net wealth creators, these outliers. And what he found is that basically, all of them had large drawdowns. It wasn't just a bug of a few of them, it was a feature, almost, to be an outlier. And most of them had a drawdown in their golden decades, the decade that they were delivering the best returns of around 40 per cent, and it lasted one to two years. So if you're investing in outliers at the individual stock level, you have to accept that there are going to be periods that are going to be intensely difficult, where there's going to be questions, and where there's going to be volatility, and you're going to again look at foolish, even if they in the end turn out to be successful.nAnd so you need a behavioural edge there, which we think we benefit enormously from Baillie Gifford's partnership structure and the culture that's enabled. And examples of that would be, yeah, Apple's, sorry, Amazon's largest drawdown was 90 per cent. Apple's had three drawdowns in excess of 70 per cent. Mercado Libre, if we go back to that as the best performing stock in that international growth fund, that company has had more than, five drawdowns, more than 30 per cent over the last 10 years. And I can tell you from the scar tissue that there was at least one of them that was a lot more than 30 per cent. And so you have to be patient over that journey. Progress is rarely a straight line is what we've found. 

And then the second element of that patience is kind of a different side it's when they're doing really well, actually there's a huge temptation to take large amounts of money off the table too early and then never actually benefit from an outlier return, that ability to go up 10 or 15-fold. And both of those require a very particular set of behaviours and philosophy and process, but it's both avoiding giving up when they have a difficult time and avoiding prematurely selling down your big winners so that you never actually capture those outlier returns.

PT:
I've got some good questions coming into the Q&A box. So, I would encourage viewers to use that function, we'll come to those after we've covered off the second topic that we'd like to talk through. So shifting gears, it'd be interesting to look at the impact of artificial intelligence and how we think that's going to play out particularly for our industry, rather than in the broader context of our portfolios, you know, the age where information is pretty much ubiquitous is either with us or not far away. And just thinking about our sources of insight and how we go about our task as long-term fundamental stock pickers. Are there any elements of information or sources of insight you think will be harder for AI to capture or will forever be uncapturable by artificial intelligence?

LB:
Forever is a long time, even for us. I talked earlier about those network of insights and I think there's three layers to it, right? And I think each layer provides insight that is differentiated and is unlikely to be captured within AI models because the data, it's not recorded down, it's not available for the large language models (LLMs) to be ingested into them. And so even as you move towards AI being very, very powerful and getting better at a lot of things, it's not clear to me that those networks of insight are disrupted or undermined. In fact, we might go further and say, if every other source of knowledge gets somewhat commoditized, they become more valuable, not less. And if you look at the first layer of them, I think we have differentiated access to companies. And that differentiated access comes in part scale of assets, but also because we have a reputation of being long term holders of companies and supportive investors. And so, if I was to take one of the examples, I won't name it, but from the asymmetry chart, I remember going and visiting them and I spent about eight hours meeting all the different management members and at the end had a meeting with the founder. And afterwards, the head of the investor relations (IR) at the time turned to me and said, “wow, that was a really interesting meeting for me”. And I said, well, surely you must hear a lot of this all the time. And he said, “no, because the founder doesn't meet investors one-on-one, any other investors one-on-one regularly. So actually, this time is quite precious for me to actually understand the company myself as an IR”. And that level of access is really helpful. It's not about what happens in the next quarter or anything close to an MPI, but understanding that long-term vision of the company.  understanding the culture, understanding how they think the world is changing. The time before last, when I was in New York, we had Jensen Huang, the founder of NVIDIA, come and visit us in New York. Really helpful to understand NVIDIA, but very few of any people even better to talk about of understanding the long-term implications of AI. So that access is really helpful to us in understanding and trying to navigate what's happening. 

Another element around it is private companies. Baillie Gifford back in 2012 started investing in private companies and what that means is we have access to talk to the founders of companies that are private. And that's really helpful because these are the companies that are scaling and also building the future. They're companies that might be disrupting our public holdings and they allow us to know our opportunity set or addressable market in the future, four or five years before they're ever public. So, I first met Spotify's co-founder Martin Lorenzton in London in 2014, four years before their direct listing in New York, and we invested privately. So, when they came to the market, we had four or five years of talking to them and understanding their business. And a lot of the time, I think if our view is trying to understand how the future might play out, if you're just doing that through the lens of public equity investing, it’s a bit like trying to construct a puzzle, a very complicated puzzle with half the pieces missing. You need the lens of what private companies are doing as well. 

And then the third is academia, where because we're thinking about the next five to 10 years, not the next 18 months, it makes it a lot more useful for us to spend time with academics about how they think about change and new areas and where we should spend our time.

PT:
The private companies conversation is probably a whole other webinar in terms of why very successful companies stay private for much longer. But just in the interest of sticking on topic, are there any of those interactions that we've had with say academics that you'd point to as having a material significant impact on portfolio returns or how we view the world?

LB:
Yeah, so the first thing I would say is again how we use it, it's not they've told us to go and invest in company X or Y, it's they've told us this area is interesting, they've challenged our mental model. So one of the things we do for our International Concentrated Growth strategy once a year is we have an external risk review, and so we get someone that we think has a track record of being right about the world in some way and we present them our portfolio, our thoughts on it, our process, our philosophy, and we basically say, tell us where we think we're wrong. Because that's a great form to us of risk oversight, of having someone external coming from a different perspective at it. We have our internal risk team that does the same, but we combine the two over the course of the year. And in 2018, we got Brian Arthur, who is an academic professor of complexity science at the Santa Fe Institute, which, yeah, I think it's on the chart there. And he had been rewriting about the knowledge economy several decades ago and was very well thought of in Silicon Valley and elsewhere. And the big thing he pushed us on was, we kind of went, yeah, we know AI is an important technology in 2018. We've talked to Robin Lee, who's the founder of Baidu, and he sort of pushed us about how this is the next big thing after the smartphone and the mobile internet that we should be thinking about. And he really sort of looked almost disappointed at me when saying that and said, no, no, I think you're underestimating this. This isn't the next thing after the smartphone. He said, this is the biggest thing to happen to the world since Gutenberg's printing press in the 14th or 15th century, where that externalized information and made it available and led to the scientific revolution and it was a political change and had this huge but difficult to measure impact on the world because suddenly information was easily available. So, AI is the same kind of thing, but perhaps even bigger because it's about intelligence. And so that really focused our mind back in 2018 on just how big AI could be. And so, for some of our clients, even in the international space, where they've allocated us to own up to 15 per cent of US companies, we ended up holding the video because of that. We also invested in having parts of the semiconductor supply chain, TSMC and ASML, because of the need to produce the chips for AI. So that was a really, really helpful meeting for us back in 2018.

PT:
Yeah, thank you very much for sharing that. I'm going to put you on the spot a little bit here. As a long-term active growth investor, will you be disrupted by AI?

LB:
I think I become less certain, the further out the time horizon is, because I am wary that both to believe but also to say there's a massive self-interest to go no, I couldn't, you know, you couldn't possibly disrupt what we do by AI but I think if you stretch out the time horizon, a lot of things become possible. What I would say is that I think the initial threat is not that we get disrupted by AI on its own, it's that we get disrupted by investors that use AI as a tool better than we do. So, I think the first thing is to try and make sure that we're not a disadvantage, that we're using AI as a tool to make sure that we're on par with others. And I think there is a race to make sure no one gets left behind on that. And then the second element is perhaps the more interesting one of, does it make us obsolete? And I think where my own thoughts have got to this so far is maybe, but I think other forms and time horizons of investigation possibly get disrupted first to give us a warning signal on that. So, if you're trying to invest along much shorter timescales, like quarterly results, for example, even the next six to 12 months, I can see how in a more reasonable period of time, you get artificial intelligence that can adjust to that task.  Because say you've got a retail company, it's got a product, but the AI is able to ingest all the information that's going on social media, everything that everyone is saying about it, it's able to look at satellite data to see how many cars are in the car park of the retailer or anywhere else, it's able to use credit card data, weather, all of these other things, and there's a really strong feedback loop with that quarterly, because every quarter you get to re-improve your model and learn from it. I think when you're doing five to ten years out your feedback loops are a lot longer and so it's harder to improve from a statistical basis. And we're also looking for outliers and companies that have done things that haven't been done before, and so that's again a slightly different shift where we're looking almost at the historical data will tell you hasn't happened in the past. And as you stretch out the time horizon, the number of variables increase exponentially that you'd need to feed into a model to do this. So, I wouldn't want to say it's impossible or certainly that it can't be done alongside a human to make the process a lot better. But I would, in a self-interested way, hope that it's a bit further along the journey of what AI disrupts in investing.

PT:
Thank you, thank you for those comments. That brings us to the end of the conversation that Lawrence and I were prepared to have. So, I'd like to now bring in the audience. We've got some questions here. I'm going to paraphrase the first one, because it's actually something I recognize that we've discussed about internally, Lawrence. So, you have to invest in the world you are in, not the world you would like to be in. And we invest in a number of trends, although I would reiterate that we are stock pickers, so we are looking for individual businesses rather than investing in broad trends. But how do we guard against a bias of believing the world we prefer to be in versus the world we have - thinking around backlash against climate action and increasing geopolitical conflict? Those might not be as attractive ideas to try and invest behind, but how do we deal with how the world is changing and making sure that we're open-minded?

LB:
Yeah, so I think there's a few different strands to that. One would be that it does create an additional analytical and portfolio construction overlay that makes the task harder. So, if you go to China, for example, thinking through both more interventionalist domestic regulatory environment and the left tail of geopolitics means that there's another layer of things that might happen that might not fit with the pure economic arguments. And that makes it harder. And you have to push yourself about what level of insight do I have to be able to navigate that. And so I do see and think that an element of that becomes more challenging, and you just have to include that into your scenario analysis and include it into how you construct a portfolio and how you think about your overall weighting towards different possibilities in a way that I think is necessary but more challenging perhaps in the last decade. But I think the other element would be, you know, actually if you take a step back,  I'm not sure that the pace of technological change is particularly slowing with what we've seen with AI and I think that's what gives probably the most conviction that the world that we are in is still capable of delivering outliers. You may have that geopolitical overlay that you need to keep thinking through and it may alter some of the probabilities and what's successful, but ultimately you still have technology driving large amounts of change in the world. And that type of change is something I think we have a track record of being able to invest in and understand.  And I think what would scare me most about the type of world that we would be in, where I think it would challenge us, would be a type of world where technological progress was slowing down, where change wasn't happening, because ultimately as a growth investor, we're an investor in change. And so as long as some of those structural changes are happening, I think a lot of the other overlays, we've just got to work our way through it in different ways. And we've just got to think of different ways of analysing into our scenario analysis. But the key thing is, if there is a lot of change, particularly technological change, I think that is a really good backdrop for us as investors.

PT:
Thank you. So next question up. And I think you touched on some of this earlier, but I think it'd be interesting to draw it out as it's coming as a question. How do you evaluate business models in emerging industries, particularly when there's limited historical data? So different business models for a different time question.

LB:
Yeah, so first I would make the comment I made earlier about you can afford to be surprisingly late in an outlier and still get an outlier return. And I think a lot of the time we're investing in a business that is showing signs of success rather than betting on a particular technology before it's proven.  Should we go back to the automotive industry and Tesla? I forget when, but somewhere probably in the middle of the last decade, we had an auto expert that came in and he'd spent decades consulting for all the big auto companies, knew more than I probably know about any other subject about the automotive industry. And it was interesting because he was a really strong critic of Tesla. He didn't think Tesla would work. I think it was also EVs as well as Tesla, or it was both or just Tesla, but he was a critic of it. And I think that's the thing about emerging industries is that, yes, we don't have a lot of data about an industry that is new.  But we have a lot of data about change. And so, what that person saw was an industry that hadn't changed very much in many, many decades. There hadn't been a new mass auto market, automatic manufacturer in the US. And therefore, all of his experience in a way was telling him, this doesn't happen. This isn't disrupted. Whereas I think what we bring to looking at different industries is that we're not experts on any one industry, but we do develop a degree of insight and context around change; how it happens, what it looks like when it happens, and what are the signs for it. So even though an industry might be quite new, there's an element that we see a degree of rhyming of other industries that have been disrupted that helps us make some of those decisions and weight them appropriately. It’s not about industry depth and history, as much as it is being people that are just interested in big change across industries.

PT:
Thank you. I've got about five minutes left, so I'm going to try and get through as many questions as we can. To what degree do the outliers we're finding have more capital-intensive business models relative to, say, not necessarily the strategy's history, but our experience of investing in this way?  

LB:
I think that there is an argument that you may be able to find more outliers in the next 10 years where capital intensity is higher than the last 10 years, simply because a lot of the outliers of the 2010s were driven by businesses that were wholly digital. So I think that there's an element to that, but I think what we're seeing is you're still able to find outliers that are capital-light - that don't require huge amounts of capital, and certainly not to the point where you're really undermining your return profile because of the sheer quantum of capital needed. And there's also an offset for that, that if you take e-commerce, where it's getting a bit more capital intensive as people put down their own logistics and their fulfilment centres, there’s an offset to that increased amount of capital, which is you're building an infrastructure advantage that becomes more durable. So I think you're improving your duration of your competitive advantage period and your odds of success by that placing of capital. But we continue to see companies that are capital light as well within that. But I think it is more mixed in this decade than it was in the last.

PT:
Thank you. Next one. Discuss the tension or contradiction, and I think this is getting at a time horizon type question. Focusing internationally, particularly around emerging market companies, some of the best companies don't necessarily trade on fundamentals, instead more sort of a macro swing that's driving share prices. And I think that's a sort of a time horizon question.

LB:
Yeah, so we try and be quite careful of where we think we have insight, and I think we very rarely have insight on macro swings in economics. So we were discussing a company this morning where we went, well, there might be some macro elements to it, but what we really wanted to get comfortable with is, can we underwrite this, that it may be volatile, there may be swings, but we believe in the long run opportunity and the competitive advantage that we're happy to own this through cycle, rather than do a buy and hold. And I think that’s often where our edge comes in to it by trying to find companies where actually our approach becomes distinctive because we are not going to think tactically around it but we're going to think over the long term and not over complicate the issue and be willing to hold it through. And I think that's again a little bit like MercadoLibre one of you could be trying to be particularly clever and try and do it by political impeachments and macro and other things. But ultimately what we've found is if you find a great company, invest in it, stick with it, back the management team to do incredible things, and only really give up on it if it's very clear that that macro, that change is so sweeping that it undermines your core tenets.  I mean, there's a great Charlie Munger quote of, inaction is much more valuable than action. And that's what we've tended to find, that it's very easy to take action and trade around events. It's much harder to remain solid to what you believe over long periods of time. But we've found that to be the real source of reward in the long run.

PT:
Great. Right. Going to go for a quick fire now. So a couple of last ones. How do you get comfortable investing in China?

LB:
You don't. I don't mean that in the sense that you don't ever invest in China, but I just mean it's not a comfortable process, and if you think it's comfortable, that's a bit strange to me. The way we do it is we think of what our overall China exposure is. We're cognizant that there's a left tail that is correlated, not just China, but Hong Kong listed and Taiwan, because most likely it would be a geopolitical issue over the Taiwan Strait. And there are some incredible Chinese companies that trade also particularly now in quite low valuations that have huge growth opportunities, but you try and get bang for buck in the companies you own within that context, and you make sure that it's a proportion of the portfolio that is able to sort of cope with, you know, that left tail possibility. So, you don't ignore it completely, but you control your overall exposure, and you use it in the way that we've got companies that are really sort of swinging for the fences and you never get comfortable about it.

PT:
And last one, what does a durable moat or competitive advantage look like during the 2030s for a company?

LB:
So I'll try and be pithy and avoid sort of repeating actually a lot of the things that underline competitive advantage now will continue to be. And I'd say, I suspect in the mid-2030s, there's an element to which a durable moat is a company that is not disruptable by AI – One [example]. So, a Hermes or a Ferrari, where it's about an iconic brand that's in some way timeless. Or two, it is a company that is using AI as part of its moat and is using the data to do something special with that. So, either a company moat created by the fact that you're not disruptable by AI or one that is using AI as a tool set as a key part of its moat.

PT:
Great. Well, I think we're up against time now. So, apologies if we haven't got to your question. If we haven't, we will follow up individually with you after the webinar. So that just leaves me to say thank you for joining us today. There's one takeaway I'd leave you with. It's a small set of exceptional, often unconventional companies that drive the lion's share of returns. It's our job to find them for you and hold them with patience. And we look forward to continuing our conversation with you. So, thank you very much and have a great day.

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This communication was produced and approved in September 2025 and has not been updated subsequently. It represents views held at the time of writing and may not reflect current thinking.

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