E402: $92B Coatue: Where the Value in AI Will Accrue

20:06Lucas Swisher:It was that explosive moment around ChatGPT where the consumer curve and the consumer S curve started. And maybe we're about halfway through that S curve in terms of penetration of the consumer. The second S curve started toward the end of last year, whenever we saw the explosion in developers and coding. We have no idea where we are in that S curve because one, we're low in terms of the actual penetration percentage of developers. But then two, even within developers, but then also that number that they're spending per month is also going up every month. And so you have this dual S curve that you're writing just in developers.

20:39Lucas Swisher:And now what we're seeing is we're starting to see that bleed out into the other use cases. It's why Anthropic and OpenAI have both launched services arms and integration arms, because then we're not stopping at coding, right? Coding this S-curve that maybe we're in, I don't know, the first inning or the second inning of is we're riding that right now, obviously. But then you think about, okay, well, what happens in legal? What happens in HR? What happens in finance? What happens in pharma? What happens in all these other use cases of the economy that we have barely touched yet? you think about, again, all the other application layer companies, Harvey, for example, it's very early in its adoption curve.

21:12Lucas Swisher:It's incredible, right? They have so much room to run because we're just starting to actually be able to integrate this technology and address the broader pool of spend that we've now demonstrated that we can with developers. Speaking of these three waves of AI adoption, there's also almost these three waves of philosophically what AI will do to the future. The first wave was AI is going to disrupt all jobs. Now in the second wave, you actually see the more developer job listings than there are developers in the United States. So at least within developers, AI has actually increased the need for more people than it has disrupted their jobs.

21:50Where do you see this playing out?

21:51Lucas Swisher:This is another thing that I think a lot of folks really believe is that AI is going to lead to massive job loss and we're going to have a huge issue in the economy, I think certainly there will be displacement. At the beginning of every technology wave, there's technological based job displacement. It's an economic concept, right, that we talk about. But there are new jobs created on the other side of that in every wave of technology that's happened so far. And all the evidence in this wave so far actually just points to people are becoming more productive. I think that's actually a very exciting future that a lot of folks don't talk about, which is actually, I can just do more with less.

22:26Lucas Swisher:Now, there is a question, can people that are highly analytical ultimately make the shift to other things? And I think some folks will be able to, some folks won't be able to. But the reality is the job's still there. We're just all more productive. I think that's what you're seeing with coding today, right? We have more developers at Cotu than we did a year ago, but all of our developers are way, way more productive than they were a year ago. And I think that's what we're going to see across the economy. My model of this is David Deutsch's beginning of infinity, in which he argues that innovation is infinite, meaning as you make innovations and you have other innovations, they start to innovate and integrate with each other, which create new innovations.

23:02So the theoretical maximum of the number of businesses, the number of products, number of innovation as a totality is infinite.

23:08Lucas Swisher:I love this framework. I think we don't live in a zero sum world. We live in a positive sum world. I think this is the biggest misconception around technology is that everybody just moves the pie around. Technology fundamentally grows the pie. It makes the whole world more productive, right? We saw this whenever you move from a plow to a tractor. At the end of the day, it just enables you to do more. And there are new jobs created manufacturing that tractor. You see it in machining, you see it in manufacturing, you see it all over the economy. And what it enables the world to do is become more wealthy.

23:40Lucas Swisher:And wealth is not zero sum. Wealth is not a dollar bill. Wealth is a unit of productivity. And I think that's what AI is starting to enable. And I think you have the early inklings of this. Certainly there will be cost reductions in areas. I think there are really obvious places where that's going to be the case, like customer support that will be replaced by technology. But you're also going to have the creation of incredible jobs around four deployed engineers to be able to get the technology to work in production. I will tell you, being on the board of some of these AI application companies and in the room in a lot of discussions with Anthropic and OpenAI and others, there's insatiable demand for that type of engineer.

24:17Lucas Swisher:And the salaries are much higher than they were for that last generation, certainly much higher than for a customer support rep, right? And so I think that's how I think about the world. It's not zero sum, it's very positive sum. And we all have the opportunity and we all AI is creating the opportunity for the world to just become more wealthy. There's a contradiction to how people think about this, because there's a paradox. On one hand, they want to use AI to disrupt people. On the other hand, they wish they had more capable people to do things. Even business owners themselves, whether enterprise, small businesses, they themselves are constantly limited and subconsciously don't realize how much the lack of smart and capable people in their organization is limiting their growth.

25:00Said another way, as soon as you had much more productivity and much more people available that you could hire for these positions, people's minds will become more open towards where they can take their businesses. Of course.

25:12Lucas Swisher:I mean, we see it with many businesses. Folks and businesses are constrained by the amount of people that they can hire at the end of the day. We all want to do more. We all want to build more. We all want our businesses to grow, not shrink. right and at the end of the day a lot of that is constraint for talented folks that can do what we need them to do on a day-to-day basis and so ai is giving us superpowers and so i think that's the key it's not that we're sort of barreling toward a world where of course everyone's going to lose their jobs that feels like a little bit of a misnomer to me and something that's never happened with new technology waves in the history of technology there certainly will be displacement along the way there are always things that are shaken up in the economy at the beginning of every technology wave.

25:53Lucas Swisher:But at the end of the day, we're always better off than we were before. Other VC firms are also starting to invest heavily into AI. Do you see second order effects of this being that there's going to be much more consensus bets if everybody's looking at the same data? I'm not sure. I think we sort of come from different places. I think if you think about someone like us, our heritage is in the public markets in many ways. We think about very big ideas. We tend to be very market structure driven as a firm. Support for today's episode comes from Square. It's all in one way for business owners to take payments, book appointments, man staff, and keep everything running in one place.

26:28Whether you're selling lattes, cutting hair, running a boutique, or managing a service business, Square helps you run your business without running yourself into the ground. I was actually thinking about this the other day when I stopped by a local cafe here. They use Square and everything just works. Check out as fast, receipts are instant, and sometimes I get loyalty rewards automatically. There's something about businesses that use Square. They just feel more put together. The experience is smoother for them and it's smoother for me as a customer. Square makes it easy to sell wherever your customers are, in-store, online, on your phone, or even at pop-ups, and everything stays synced in real time.

27:00You can track sales, manage inventory, book appointments, and see reports instantly, whether you're in your shop or on the go. And when you make a sale, you don't have to wait days to get paid. Square gives you fast access to your earnings through Square checking. They also have built-in tools like loyalty and marketing, so your best customers keep coming back. And right now, you can get up to$200 off Square hardware when you sign up at square.com slash go slash how I invest. That's S-Q-U-A-R-E dot com slash go slash how I invest. With Square, you get all the tools to run your business with none of the contracts or complexity.

27:32Run your business smarter with Square. Get started today. Support for today's episode comes from Square. It's all in one way for business owners to take payments, book appointments, man's staff, and keep everything running in one place. whether you're selling lattes cutting hair running boutique or managing a service business square helps you run your business without running yourself into the ground it's actually thinking about this the other day when i stopped by a local cafe here they use square and everything just works check out as fast receipts are instant and sometimes i get loyalty rewards automatically there's something about businesses that use square they just feel more put together the experience is smoother for them and it's smoother for me as a customer square makes it easy to sell wherever your customers are in store, online, on your phone, or even at pop-ups and everything stays synced in real time.

28:15You could track sales, manage inventory, book appointments, and see reports instantly, whether you're in your shop or on the go. And when you make a sale, you don't have to wait days to get paid. Square gives you fast access to your earnings through Square checking. They also have built-in tools like loyalty and marketing. So your best customers keep coming back. And right now you can get up to$200 off Square hardware when you sign up at square.com slash go slash how I invest. That's sqare.com slash go slash how I invest. With Square, you get all the tools to run your business with none of the contracts or complexity.

28:48Run your business smarter with Square. Get started today. Support for today's episode comes from Square. It's all in one way for business owners to take payments, book appointments, man's staff, and keep everything running in one place. Whether you're selling lattes, cutting hair, running a boutique, or managing a service business, Square helps you run your business without running yourself into the ground. I was actually thinking about this the other day when I stopped by a local cafe here. They use Square and everything just works. Check out as fast, receipts are instant, and sometimes I get loyalty rewards automatically.

29:16There's something about businesses that use Square. They just feel more put together. The experience is smoother for them and it's smoother for me as a customer. Square makes it easy to sell wherever your customers are, in-store, online, on your phone, or even at pop-ups and everything stays synced in real time. You could track sales, manage inventory, book appointments, and see reports instantly, whether you're in your shop or on the go. And when you make a sale, you don't have to wait days to get paid. Square gives you fast access to your earnings through Square checking. They also have built-in tools like loyalty and marketing.

29:45So your best customers keep coming back. And right now, you can get up to$200 off Square hardware when you sign up at square.com slash go slash how I Invest. That's S-Q-U-A-R-E dot com slash go slash how I invest. With Square, you get all the tools to run your business with none of the contracts or complexity. Run your business smarter with Square. Get started.

30:06Lucas Swisher:We think a lot about data. Data has been at our core for a very long time, and we ingest a lot of signals and try to understand and understand what's sort of happening in the world and create a mental model of the world based on that. We come from a very different place than, say, someone like a founder's fund, right? They're primarily focused on the early stage. They're very founder centric. They think a lot about, I mean, famously, most of what they think about is do we like this founder or not? We come from very different places. And so actually it may mean that we have a little bit more dispersion.

30:35Lucas Swisher:For example, one of the places that we've been spending a lot of time as a firm is at the infrastructure layer, right? Both the data center layer, the chip layer, the data infrastructure layer. That's not a place that a lot of other firms have spent a lot of time over the last five years, right? VCs haven't invested in a lot of new chip companies. My partner Thomas led the Series B in Cerebris, for example, and we made an investment in CoreWeave when many VC firms thought it was not a business at all. And so I think it may actually tend to let us play to our strengths in a certain way and mean that the world becomes a little bit less consensus.

31:08Lucas Swisher:Speaking of data, one of your co-founders, Thomas LaFont, shared some data on how as companies get bigger, there's a higher probability that they could 10x. This is really rattling the venture ecosystem. Tell me about that. This is really interesting. And it's a data point that comes because we analyze a lot of our own data internally. And we look back on, okay, our track record, where have we done well? Where have we not done well? And then the ecosystem, right? The track record of the ecosystem, where folks done well, where folks not done well. And this is a counterintuitive data point. We actually figured this out two years ago, what we found in the data is you're actually, you're much more likely to get a 10x.

31:44Lucas Swisher:in the pool on a percentage basis, if you're above$10 billion in market cap, then below. You're even more likely, if you can get to 100 billion, you're much more likely to get a 10X on that investment than the 10 to 100 or the 1 to 10 range. And so we've found this is really interesting. And I think what it really speaks to is when you can get to scale, your advantage tends to compound. It tends to be durable and it tends to compound over time. You're a better magnet for talent. You have more access to capital. Maybe you have a network effect or some business model that enables you to sustain yourself.

32:16Lucas Swisher:And I think we're seeing this as an example with OpenAI and Anthropics separating themselves, and maybe SpaceX now, separating themselves from the rest of the language model market. Their advantage at scale has grown versus the quote unquote rest of the Neo Labs in some ways. And I think you're really, you're seeing this in revenue. You're seeing this in the talent that's concentrating within those few names. And it just gives them this flywheel and this advantage to be able to continue to get bigger. It's so interesting because there's been this paradigm investing that ideas are cheap, execution is what matters.

32:47And with AI, some are arguing that it's actually the ideas that are the value added and the execution that's more of a commodity. Then you have to think about where do great ideas come from? They come from talent. So really what you should be focusing on hiring the best people that could come up with the great ideas.

33:01Lucas Swisher:That is exactly right. I mean, when you think about just the inputs around what is made, the language models that are successful today, very successful. the OpenA's and the Anthropics of the world. The key input is obviously compute. You have to have the compute. But then outside of that, it really is the talent, right? If you think about Anthropic as an example, very famously, they've lost one of the 17 original employees. It's incredible how many companies have been able to do that at scale. If you think about OpenAI, they're able to attract talent that sometimes has left and even come back.

33:31Lucas Swisher:We've seen some of this over the last three, four months or so. And that's a huge weapon for these companies. Talent really matters. This idea of ideas and execution is super interesting. I'd use another example, which is Databricks. If you think about Ali, the CEO of Databricks, he's been able to drive this company to hop multiple waves in technology. It's so rare to be able to shift from being a company that helped move data around the enterprise to being the core system, the core data warehouse, to now being the central repository where a lot of folks interact using AI with data in the enterprise and being able to hop those waves.

34:02Lucas Swisher:That's talent, right? That's all down to the talent. Snowflake also has the same opportunity, but if you look at their growth rates, it was at least published a couple of days ago, and that Databricks' growth rate's over 80 % now. I think it was published last year that it was kind of the mid-double digits. Snowflakes are on 34%. A lot of this has to do with team and the decisions that they've made. That's where it really starts to matter is this talent in an AI world matters way more frankly than it did in a SaaS world, where all SaaS companies tasted like chicken. They all looked very, very similar.

34:32Lucas Swisher:In this world, it's less rinse and repeat. It's more reinvention and adaptation. And I think that's making a huge difference. Said another way, Ali from Databricks and other great founders have figured out a way to break innovators dilemma. There's been this innovators dilemma that every new technological wave disrupts the previous technological wave. But if you're able to continue to think from first principles and recruit the very top people, you're able to continue to grow your mode. That's exactly right. It's very hard. And it's actually what we look for a lot is companies that are reinventing themselves.

35:04Lucas Swisher:We found that that's one of the strongest, scale is great, but we found that's actually one of the strongest signals of whether a company is going to be an enduring great public company over time is, can you reinvent yourself? Can you adapt? Can you launch new products that launch into new TAMs that were bigger than your prior wave? Why one of the things, one of our characteristics of a KOTU platform company, right, is that you have multiple products that have worked. What's the best way to predict that you'll be able to create new products in the future? You've done it already. What differentiates a company that continues to innovate and continue to reinvent itself versus one that is not able to do that?

35:38Lucas Swisher:I just use KOTU as an example of this. I think Philippe and Thomas, our co-founders, have been phenomenal at executing this in our business. I think it is all driven from the top. I think it is all driven from the management team that is willing to not rest on their laurels, that works harder than many of their employees, and that has a mentality of adaptation and change. Technology is all about change. Investing, frankly, is all about change. The world changes extraordinarily quickly and being able to adapt is ultra critical. I think that is the number one skill set. It's this paranoia, this necessity to always be reaching for something that you don't have, right, that this like willingness to adapt and change that really drives this.

36:22I previously had Eric Becker, who co-founded Acrescent, which today is$250 billion family office. He was sitting right where you were sitting. And in between, before he started Crescent and he had several successful exits and he spent several years going around and interviewing these family businesses that were around two, 300 years. He wrote a game. He wrote a book called The Long Game. One of the things that he figured out is that if you're truly trying to build an enterprise that survives 100, 200 years, there's really only one source of leverage and that is culture. Because culture is the only thing that could lead to the right talent.

36:57and talent is the only thing that can navigate you through many different cycles.

37:01Lucas Swisher:That makes total sense, right? Again, there are fantastic companies that have very different cultures. I think almost famously at this point, like there's no one good culture. There's no one good culture. And I also think it differs based on the spaces. Would you want the same culture in LVMH as KOTU? Probably not, right? Being a great fashion house is very different from being an investment firm, is very different from being a language model company. I think Anthropic does a fantastic job of this, for example, right? They are a very small team for the size of the company, and they filter aggressively based on culture.

37:33Lucas Swisher:And what do they really care about? They care about people that care about the mission. I think folks know that if you're working in AI, you're probably going to make a lot of money, right? I think they really aggressively filter for folks who care about the research. They care about working in the frontier. They care about the product. They care about their users. They care about the impact they're going to have in the world. A good example of the culture being broadcast into the world, I think, is Dario's essays. the two, the two and now three sort of big essays that he's written, the sort of broadcast, all right, these are the types of folks that we want at Anthropic.

38:03Lucas Swisher:And I think they've held really true to that. It's why their retention is so strong. I think a lot of people, including myself, get a bit confused when they see engineers choosing between open AI, Anthropic, working at Google, and they see these incredible pay packages. And yet these engineers are making decisions based on culture. How do you explain that? There's more to life than money. That's the biggest thing. And And especially at the increment, especially at the incremental level. I mean, yeah, you think about these pay packages like you're going to be compensated very well at any of those three labs.

38:33Lucas Swisher:You're going to be very compensated very well at SpaceX. That sort of ultimately, I think, is not the tradeoff that folks think about. Some people certainly, but I think a lot of folks really do care about the mission. If you spend a lot of time with AI researchers, you spend a lot of time with AI developers. I think they care about something that is more than the incremental$10 million pay package over three years. That's just not something that is in many of their calculuses. And I think that's one of the ways that Anthropic and OpenAI and others have done a very good job of retaining talent is by giving incredible talent, incredible people access and leeway to be able to work on things that they're very interested in.

39:11Let's talk about power and energy, because without energy, none of this comes into fruition. What's the main bottleneck in power today?

39:19Lucas Swisher:There's this sort of famous chart that we have inside of KOTU, which looks at energy production in the US and energy production in China. And I think you can see in China, it's really a linear curve that bends upwards. In the US, it's kind of like this. In the US, it's much more stable. And maybe it rises a little bit, but it's much more stable. And so I do think over the next five years, the next 10 years, certainly, we're going to have to have real innovations here. I think you're already starting to see some really interesting novel ways that folks are dealing with this problem. If you think about metanthropic, two of the folks that are investing heavily in CapEx, they at their data centers are building the power capacity on site.

39:57Lucas Swisher:I think that's super interesting because it's sort of a net zero or they're actually building excess power capacity. So they're feeding it back into the communities around them. And I think that's frankly going to be necessary from a public policy perspective, but also that's one of the ways that they're already solving this. But I think on a five to 10 year basis, we're going to have to use everything that we have at our disposal. It's going to be traditional energies like oil and natural gas, down to all the renewables that have been a big focus for the last decade to, you know, new technologies or technologies that may be being revived like nuclear, right?

40:29Lucas Swisher:And I think you're seeing a lot of actually really interesting, promising innovation. You have companies that are reinventing the battery, or really reinventing storage of energy like base power, for example, which has a huge vision around this. You're seeing new nuclear companies come up like General Matter with Scott Nolan, fantastic early stage business. You're seeing new fusion technologies that may be on the horizon with Commonwealth Fusion. I mean, other businesses that look like that. I think that necessity breeds innovation. There is a necessity here. We will breed innovation. We will build.

41:01Lucas Swisher:I think there are a lot of companies that are both in the CO2 portfolio and not, frankly, that are really focused on this. I think the public policymakers are really focused on this. We all sort of know this is a problem. And I think we will innovate our way to making sure that we're able to have the power. that we need. Pasta energy bottleneck is chips. NVIDIA today has roughly 80 % of market share. Do you see a new chip company disrupting that or do you see the hyperscalers building their own chips? There's almost like three things happening at once. If you were to have asked me three, four years ago, will NVIDIA be disrupted knowing they had this incredible software platform around CUDA and they basically had a monopoly on the chip market, I would have said, no way.

41:42Lucas Swisher:They just have this incredible advantage. But I think what we've seen, and this is really started by, I think, Anthropic a couple years ago is the primary driver of this is actually you can run AI workloads on heterogeneous chips. They demonstrated that with the TPU, which is highly performant. They demonstrated that with Tranium. I think that's a really interesting example. So you have, I think, the cloud providers, right, that now have their custom chips. There's a lot of news over the last week about Amazon selling their Tranium chips externally. Obviously, it required a lot of work for Anthropic to be able to use these chips.

42:14It

42:14Lucas Swisher:it's certainly possible now. So you have the cloud providers as one source. You have custom silicon, right, with TSMC and Broadcom, companies that enable you to build your own chips. I think you will see OpenAI, Anthropik, SpaceX has already spoken about this. You'll see a lot of the companies on the demand side of AI certainly try to build their own chips. And then what's really interesting is we haven't really actually seen new innovation at the CPU or the accelerator in like a decade. Cerebris and Grok, which was acquired obviously, and Cerebris, which just went public, a great KOTU investment, were really the last innovations.

42:52Lucas Swisher:And those were founded nearly 15 years ago. And so if you think about it, we're sort of due for an innovation, especially given we've started a new cycle. And I think you see really interesting companies coming up like NuviCore, which is a fantastic team that came out of Apple that built a lot of the Apple chips, that's building a new CPU from the ground up. I think you're seeing really interesting companies like Maddox and Fractile, which are building new accelerators. Those are really strong teams. The Maddox team, for example, was one of the core operators in and around the TPU, right? And so you're starting to see new companies and you almost wonder, it used to take a really long time for new chips to come up.

43:28Lucas Swisher:In this world with AI, can we get to tape out a lot faster? I think the answer is almost certainly yes, right? Especially folks that have done it before. And so NVIDIA, it will continue to grow. I think the question is, what percentage of the market do they grab when you have all these competing forces now? And a lot of folks in the ecosystems whose incentive is to find something other than the GPU. It's still by far the best today. I think people acknowledge that, but this is something that we think about a lot. How would you explain to a layperson why NVIDIA has such a competitive advantage?

43:58Lucas Swisher:One is they've been around for a long time, right? They've sort of become the default. They are the default install. Two is the software platform that enables you to develop on the GPU is incredibly strong. And that is that where the value is on the software layer, not the hardware. I think it's the combination of both. I think this is the mistake that a lot of folks make when they're investing is investing either in the software or the hardware. You want to invest in the systems. That's what's made NVIDIA so strong for a long period of time. It's CUDA plus the underlying chip itself. And I think that's what you're seeing now is.

44:29Lucas Swisher:Anthropic, whenever they went to Google and they went to Amazon to try to use these chips, they had to spend a lot of time developing that system with them. It was a lot of co-development over a couple of years that enabled it to work. And I think that's what you're going to start to see, right? Those systems start to come online for all of these other competitors. And that ultimately may be the thing that sort of cracks the monopoly to a certain extent. I wonder how much of this is mimetic. We were also investors in Cerebris. And once you see these exits like Cerebris and Grok, the talent starts to amalgamate over these different theses.

45:02And then suddenly, magically, these new chips are created. But it's always downstream of the most talented people focusing on a problem.

45:10Lucas Swisher:Yeah. I think people are spending a lot of time thinking about chips now. That's an example of a lot of investors are now woken up to chips. This is something that we have co-to spend a lot of time thinking about for a long time, a long time, in particular, because the public markets, you have to pay attention to what's happening at the simulator. And even in Maddox's case, the fact that the market is now focused on this means, what does that mean? That means they could raise more money. What does that mean? They could hire more and more talent. So even the ones, even the true believers, even the early investors are still benefiting from this.

45:41Absolutely. Everything that we've been talking about, all these different parts of the AI stack, all assumes that the application companies can execute. What mistakes are top founders making today at the application layer?

45:53Lucas Swisher:The primary mistake that I think folks are making the application layer is just trying to drive to revenue too fast, right? Not thinking about the fundamental innovation that a company has, the fundamental thing that they are working on, and instead just trying to chase customers and chase revenue before there's like some real durable advantage. One of the ways that I think companies that have been successful at the application layer have been so is they've really focused on this. Take Winston and Harvey, for example. They have access to very unique data. They've made very unique data deals. They focused both on the corporates and on the law firms.

46:24Lucas Swisher:And there's a little bit of a network effect between them. And they've pursued a very specific strategy that entrenches themselves. So when one of the language models decides they want to come after the space, one, they're already embedded and they're embedded in this network, but they have really unique access to data. And then three, they've built a harness that is able to use all of these tools plus the open source. If you think about some of the things that are happening now, it's people are just, because they know you can scale really fast because we know you can scale really fast. Folks are just coming out of the gate trying to get to 100 million of ARR as fast as humanly possible.

46:54Lucas Swisher:Without thinking about those pieces, you want to take the lessons from the Harveys, which have been incredibly successful at the application layer to date, which is being really intentional about the innovation that you're having and the durable advantages that you can create. How do you square that with what we previously talked about, which is you want to hit the milestones so that you could raise these large capital pools so that you could get the talent to help build the business? It's all a byproduct of the decisions that you make, right? Obviously, you want to grow fast. We all want to grow fast.

47:20Lucas Swisher:All of these application companies want to grow fast. But the fact that Harvey has grown so quickly is a byproduct of those hard decisions that they made very early on to architect their products in a really specific way to go make those deals that they had to with the data providers to get access to that unique data. Everything else is just downstream. Of course, you want to grow. Of course, you want to grow your sales team. Of course, you want to do all these things. But if you do that before you have that sort of competitive, durable advantage, you end up with a product that has bad retention or a leaky bucket, or you're one of 50 and you're not the number one in your category.

47:52Lucas Swisher:And I think the one thing we know is that being number one in the category really, really matters. The disproportionate share of the gains in technology come from being the number one. And having that firm foundation is the most critical part to that. It's almost like you want to crawl and then you want to sprint. Yes. And a lot of people are just trying to walk from day one. Over the next two years, what's the most likely way that investors in AI lose money? I certainly think in today's market, you have echoes of 2021. right it's almost hard today to lose money yeah which is what people thought in 2021 i remember investing during that period trees were growing to the sky every company was working everyone was able to raise money no matter the idea if you were growing you could raise money we certainly see and feel echoes of that today i'll just give you two examples of things that we're sort of avoiding in this market one is pre-revenue companies that come out of the gate at very high valuations i think that's one place where people may lose a lot of money in the cycle.

48:47Lucas Swisher:Some of them will certainly work. And the trade off there is these companies have exceptional talent. Of course. But it's also a way and in venture, I think that's one thing. But because you are always swinging for the fences and what we do, which is growth investing, you have to be a little bit more measured. It's going to be a part of the market where people do lose a fantastic amount of money. Is it in these pre revenue companies at big valuations, particularly you think about like these Neo lab oriented companies where two folks in a PowerPoint will raise a billion on 5 billion out of the gate.

49:17Lucas Swisher:That's a really challenging place to play. And then the second challenging place I think is in between the layers that we've talked about, right? You've noted that you've got land, you've got power, you've got the chips, you got the data infrastructure, you got the language model, and you got the apps. There are a lot of companies that play at the intersection of those, that their entire business model is in between the chip and the model. That's a very, very, very scary place. It's a very fragile place. The world changes on you on a dime, it changes on you extremely quickly. And I think that's a scary place to be that if you're in a world where in the biggest compute shortage of all time, you're a 20 % gross margin business, what happens when the tide goes out?

49:55You could become the victim of your own success. If you're really successful, one of these two layers will disrupt you.

50:00Lucas Swisher:Yeah, they certainly have an incentive to. AI is evolving so quickly that instead of asking what you've changed your mind off in the last year, I almost have to ask you over the last six months, what have you changed your mind on? I think the biggest unlock for me personally has been just how big I think this can be. I think I was at the view last year, I'm a growth investor, not a venture investor. And so we're, as growth investors, you're somewhat born of a little bit the West Coast and you want to dream, but a little bit the East Coast and some reality in the public markets. And we think a lot about that.

50:33Lucas Swisher:And I think last year, we really believed that this would change the world. But the question was sort of how much. And we were in this incremental land where the models were certainly getting better, but the revenue was kind of growing incrementally or linearly. And it was really used, like we've spoken about as kind of a co-pilot. It was even these tools were even named co-pilots. And now we're in an agentic world. I think what that really unlocked for me is this true belief that the models can actually do work. They can do white collar work. And so that's been the biggest unlock for me. And I think us as a firm and as a community, frankly, is understanding just how big the opportunity set is here is so much bigger than it felt nine months ago.

51:14Lucas Swisher:And what's kind of ironic is the folks inside the labs were telling us this, right? They've been telling us this for years. And sometimes as investors, it takes us a little bit longer to see it than the folks who are actually building the technology. And before we started recording, you said that you are tethered in the public markets versus most venture capitalists are tethered in the private markets. How does that change how you approach investing. One of the benefits of a platform like Cotu, and we are somewhat unique in this respect, is we do live in both worlds, West Coast and East Coast.

51:42Lucas Swisher:We live in between the intersection of the financial markets and the dreamers on the West Coast. And I think in an AI world, this gives us just an incredible advantage. It gives us way more of an advantage, frankly, than it did in SaaS and than it did in the mobile internet era. Because in this world, you have to understand not just software, you have to understand what's happening with memory, what's happening with the tightness in and around memory, what's happening with the hyperscalers and their development of chips, what's happening inside of TSMC, what's happening with Google. And we're able to stitch all of those dots together and understand, or we try to understand what's happening in the world.

52:16Lucas Swisher:And I think that's a huge advantage at this moment in time more than it ever was before. In the SaaS world, it sort of didn't matter. All the companies looked exactly the same. In an AI world, this really matters because the world is changing so fast. So being able to adapt and have all those data points is just this incredible competitive advantage that we have as a firm. You've become one of the top growth investors in the world, you, Lucas, specifically, but that was not always the case. If you could go back to right before you started as a growth investor, what would be one piece of timeless advice?

52:44I wish I would have dreamed bigger from the first inning.

52:49Lucas Swisher:I started my career at Insight Partners in New York. I moved to Kleiner Perkins. Dream bigger is actually the slogan or the mantra of Kleiner Perkins, ironically. But I think it's to dream bigger about the idea size of the companies that I was chasing. I think for a portion of my career, I sort of was chasing after some, call it more mid-sized ideas that were somewhat less transformational. And so I think it really would be to don't compromise on idea size on TAM, dream bigger. Lucas, thanks so much for giving me a glimpse of the future. Looking forward to doing this again soon. Thanks for having me.