20VC: Inside Coatue's $7BN Growth Fund: Why Price Matters Least | Why Mega Markets are the Most Important | How Mega Funds Can Still Do 5x Returns | How to Assess Durability of Revenue and Margins in AI with Lucas Swisher

20:17Great. Well, we're paying 70 for the A. I can totally see my 3x there. Yeah. Or it's like, well, actually, we're only going from one to four because we're a slow enterprise sales cycle. But we're paying 150 for this incredibly hot A. One to four, I'm not raising at 300 if that's the case. Right. Shit. I'm probably raising a flat round. Correct. Maybe. No, that's how we think about it. Do you have any internal monikers or frameworks for like...

20:40Lucas Swisher:I think the more simplistic way that we think about it, and again, this is not a hard and fast rule and it's more qualitative than anything, is if I invest in this round at this price and the company executes, do I want to put more at a higher price? That's the litmus test. It's to say, all right, say I invest in a company at 5 billion and it does super well this year. Is this a big enough idea? Is this generational enough? Is this transformational enough? Is the founder amazing enough that if in six months they wake up and say they want to raise at 10, that I'm going to want to do that. I think it was Henry Ellenbogen that said once that he wants to invest as much money as possible as the company becomes more expensive, which is one of those kind of counterintuitive statements.

21:18Do you want to kind of spray early, and spray is a derogatory term, and I didn't mean that rudely, but like constrain capital effectively and then double down very aggressively? Or do you want to aggressively get ownership and then focus on constraining as time goes on?

21:33Lucas Swisher:Yeah, we're much more the latter. And I think there are two dynamics around this. One is, our view is there are very few companies that generate the disproportionate value in technology. If you look at the private markets today, take the whole private market ecosystem, 20 companies have generated 80 % of the enterprise value. 20 companies, 80 % of the enterprise value. Of all the private companies that exist in the world. And four companies have generated 65 % of the enterprise value. Four companies. And so what really matters is being in those companies, those 20 platform companies that are generating the disproportionate amount of value.

22:07Lucas Swisher:And then your next question is, all right, well, how? Like this wonderful framework, like we all would love to be in all of these platform companies, but like how? And the answer is from our view, you can't do this brand pray at the early stage or the early growth stage. The reason why is you may be in the wrong horse or you may be in the wrong market and you may be investing your time wrong because there are very few companies. We need to make very few investments, even at the early growth stage or the growth stage. We can't afford to be in the wrong horse. I get you, but actually I'm not asking you about yours, but we see a world of competitive investing.

22:36Andreessen is in competitors consistently. There are many people who are in many companies where they directly compete. You can actually afford to be in the wrong horse today and still do the next horse.

22:47Lucas Swisher:I think you can, but it definitely makes your job harder. It does. Right? You want to make your job as easy as possible and not put up the barriers in ways to being able to win a new investment. But I agree with you at scale, when companies become these platform companies, right, which is our style of investing. Oftentimes, it's almost like buying a pseudo public stock. Sure. In many ways, as a public market investor, I could own Google and Meta. As a private market investor, at the very earliest stages or the early growth stage, should I be investing in like two series Bs that are exactly directly competitive?

23:18Lucas Swisher:That feels really counterintuitive. You probably don't want to do that. One, just because you're making a bet that's directly competing. But at the growth stage, when you get these platform companies, maybe they didn't even start by being competitive, but they grew into it over time. When founders come to you and they're like, how dare you? Like, it started off as a pillow company and now it's doing enterprise payments. How am I to know? And listen, that's part of the game. And as a founder, I completely empathize and understand that, right? I can understand how that would be, you know, a really tricky situation.

23:49Lucas Swisher:From our perspective, it's when you're investing in large markets, Oftentimes, you are going to end up in assets that compete because they naturally expand TAMs. A great example of this is I think we were the only private investor that was invested in Snowflake and Databricks when they were both private. They started off in completely different areas. Databricks didn't have a data warehousing product, and Snowflake didn't really do a lot of ELT. It was mostly built around the ecosystem. They grew together, and we weren't invested when they were starting to compete because Snowflake went public a lot earlier.

24:18Lucas Swisher:But at the same time, that happens in big markets. It's so funny you said that. the enterprise value, I think 65 % is done by four companies or created by four companies. I tweeted not too long ago that basically unless you're an anthropic open AI, cursor lovable, open evidence, Harvey, you name your life, you're irrelevant if you're not in them in venture. And naturally, every irrelevant venture investor came out of the woodwork and said, how dare you, Harry? I'm very irrelevant. I promise. And it just made me laugh. But I did understand the nuance of you don't actually have to be in them if your fund size is constrained.

24:52If you've got a$100 million seed fund and you have a$3 billion outcome, it's still a great business. And so I wanted to ask you, when you think about mega funds, which we see more and more of, do you think they will be able to produce the venture-like returns that we see with early stage funds given the outcome sizes? Or actually, we just have a different LP profile?

25:11Lucas Swisher:Yeah. I would separate the two asset classes almost in some way, right? Venture and growth in many ways, right? We've almost developed completely independently and separately. Obviously, there are firms that do both. There are firms that do both very well. If I was a venture fund staring down the barrel of a$3 billion venture fund, I think that's a tough putt. That's a tough battle to be a part of. What do you mean by that? If you're in one? I mean, if you are a venture fund that is staring down the barrel of having to deploy $3 billion, I think that is hard. Because again, at the early stage, it is hard to capture disproportionate ownership in the few companies that actually generate all of that liquidity.

25:47Lucas Swisher:If you're a small venture fund, I think it's super possible in today's world. You don't actually have to be in, you don't have to catch the seed of SpaceX. You'd really like to because those are the only, the platform companies generate liquidity. But at the end of the day, like you can get by with not capturing all of the great outcomes. If you have a$3 billion venture fund, the math is really hard. You have to capture a lot of those. The growth funds are a little bit different math. But I think just go back to your question about can a$5 billion growth fund scale and work? The answer is yes.

26:14Lucas Swisher:The reason why is the market's changing in two different ways. Change number one, these companies are staying private longer. They're getting bigger while they're private. There are more opportunities to invest over time. So now, where 10 years ago, you couldn't put a billion dollars in a company. Now you can invest a billion dollars in any given round. If I invest a billion dollars and I 10x that billion dollars, that's a 2x on a$5 billion fund. Now I need to be concentrated to make that happen, right? And I think, again, that's why we go back to our strategy. Few investments, big checks. You have to have that type of discipline to make those fund sizes work.

26:45Lucas Swisher:The spray and pray does not work, but you can absolutely make it work. And then I think the second dynamic that's different, the outcomes are bigger now than they used to be. In the SaaS wave, I think it would have been really hard to make that fund size work because SaaS, you're constrained. The largest SaaS company in the world that's independent outside of Microsoft and the hyperscalers, Salesforce, Salesforce Workday ServiceNow, those are like a couple hundred billion dollars of market cap. So it's going to be hard in that world. But in an AI world, if we actually think that we're augmenting labor, if we think that we can address a lot of these really big markets, if you move from human inputs to tokens, then you're going to have much bigger outcomes and the math works.

27:22Do you think in a world of mega funds with$5 billion plus funds, which are several now, vertical SaaS is no longer an investable category just because the outcome sizes will not be enough to generate the mega outcomes needed?

27:34Lucas Swisher:Listen, vertical SaaS, I think you could talk about it a lot of different ways. Constraint, TAM, AI risk, all kinds of stuff. They're still great businesses today. People have made a lot of money in vertical software over time. Think about Insight, right? They've had incredible exits in vertical software over time. Multi-billion dollar exits. In today's world, if you have a big fund, I don't think that's where you should be focused. I think you should be focused on the absolute mega outcomes, the platform companies that are going to generate that disproportionate return and you're actually going to get liquidity out of.

28:03Don't laugh. What is an attractive enough upside scenario to get you excited? We always hear an early stage in my business, oh, it needs to be a fund returner. Sure. What is attractive enough for you? Like Revolut, I think, is a phenomenal company. Incredible. I'd love to be an ambassador. At$75 billion, I'd love to be. And I think there's a clear pathway to$250 billion.

28:21Lucas Swisher:For sure. Is that 3X enough to be exciting? No. A 3X is not enough to be exciting. And the math is really simple, right? Say I'm a fund and I'm co-to. I want to make a 3X net return for my investors. sort of the baseline for what people would say is like a top quartile return and people get really excited about 3x net return for a fund, you know, 25 % net IRR, something around those bands. I'm going to have some things where I swing and I miss. Say I have a 1x, I need a 5x on the other side of that. Heaven forbid I have a loss and I have a zero, I need a six. We obviously really try to avoid those, right?

28:54Lucas Swisher:If I have a two, I need a four. So for me, I need to see a steady case where you can get that 3x. But I really need to believe that if the company 3x is, I want to put more money in because it can 3x again. And I think this is a really critical thing that a lot of folks end up missing over time is ultimately, I need to imagine a case where after I've made my 3x, somebody else thinks they can make their 3x. Because otherwise, one, I'm not going to get those 6x pluses that I'm going to need in my fund. But two, the company's not going to exit. I have to imagine this is why the big idea of being in big ideas really matters.

29:27Lucas Swisher:Somebody's got to sit on the other side of that stock. I have to be able to walk down the hallway to the folks that operate on our public side and say, do you want to buy this stock? Do you want to buy this stock more than all the other opportunities that you have? And so every investment I make, that is the rigor and the framework that I use is someday is my public counterpart going to want to own this stock over everything else in their book? Or at least is there a chance? With the extension of those private markets and the outcome sizes, and your entry point, as we said, can be flexible. But you know, the 300 to 5 billion is very standard.

29:57I know it can go much higher. Given that delay in public market entry that we've seen from private companies, you have the chance to sell a lot more than you used to. How do you think about taking advantage of secondary markets pre-going public and doing great returns for your investors?

30:12Lucas Swisher:It's certainly an option for liquidity now, right? A lot of folks, especially the early stage funds that have been in companies for a really long time, are taking advantage of this. And I think rightfully so. And again, I think it's why, even if you're an early stage fund, this is a great style of investing. And it's the type of company that you want to be in, because it's the only type of company that can get access to liquidity, whether it's private or public. When you have doubled down and it has been a mistake, what did you not see that you wish you'd seen? And you don't need to name the company.

30:40Lucas Swisher:Yeah, of course. I think, again, it goes back to that very simple principle. It's the big idea and the multiple products. And it's why we're really focused on that and why I harp on it literally nonstop is we've just overestimated TAM and we've overestimated the ability for companies to launch multiple products and expand into new TAMs. We're usually not getting things wrong on the basis of metrics or the team being good, or it wasn't growing fast enough. It's really that question. And it's why we have applied and really raised the bar on the type of investing that we do is because of that, right?

31:12Lucas Swisher:It's like where we've gone wrong. And the nice thing is we have a, you know, we tend to have a very low loss ratio because of the style of investing that we do. But where we've gone wrong, it's that. When we say raise the bar, the challenge that I have with a lot of companies today is They're like good enterprise companies, okay? But they're doubling and tripling at 10, 20 million in revenue. What happens to that generation of SaaS companies from 2020, 2021? They're good companies. Great companies. But more respectfully, they're not great companies. They're good companies. And in the prior cycle, they would have been funded.

31:44And they've been funded well. But now, are you really going to jump out of bed for a 10 million growing to 25 million?

31:51Lucas Swisher:The short answer is, I don't know. I don't know what's going to happen to those companies. I don't know what the terminal value is. I don't know what the exit pathways are, right, with private equity and the place that it's in with the public markets where it is. All I know is I have a lot of conviction and I see a path in the style of investing that we do. I don't know how to comment on the other part of the markets, right? There's this notion that like the triple, triple, triple, double, double, double is dead and these companies suck and all this stuff. I don't think that's true. There are great companies.

32:19Lucas Swisher:You can drive real margin from them. They make incredible businesses. is just not our strategy, right? And I think in today's world, the reality is in a SaaS world, the triple, triple, double, double, double was a thing. It was an incredible metric. These businesses were incredibly repeatable, very compable. Now we exist in a world where if you have a product that the market likes, it is going to absolutely yank you into that market. It's not gonna triple at the earliest stages. It is going to scream. And I think you really see that, right? And so those are the companies, And again, it's not like we think these companies are all bad and this and that.

32:53Lucas Swisher:It's just our strategy is to find those and to work with those companies. Because that's where we think the disproportionate returns come from. And it's the companies that we have an advantage working with. You mentioned the word margin there. And I think why I think so many people feel really insecure as investors today is because there's so many priors that are being fundamentally questioned. Of course. Whether it's growth rates or rule of 40s. I was always taught that margin mattered. I walk with my mother around London. I'm like, Jules, margin matters. And now I'm looking at it. It's how you wake up every morning?

33:21Pretty much. Margin matters. I put my feet on the ground. I say margin matters. But I start to question whether margin actually does matter in the early days. If your company is rocking, you're spending on inference, and that is a sign of good usage and love. Does margin matter anymore?

33:38Lucas Swisher:I think the same business principles that have applied to businesses for the last three decades in technology are the same business principles that matter today. Margin matters, but it's nuanced. I would add an addendum to that. Margin matters at scale. The best businesses, in particular infrastructure, whenever there's a technology wave happening and an architecture shift, some of the best businesses, not all of them, but some of the best businesses have had horrific margins early. The hyperscalers. The hyperscalers were low margin early. Those are the best software platform businesses in the world.

34:11Lucas Swisher:Snowflake and Databricks. Very low margin early. A lot of people passed on those early rounds because they're, oh, in SaaS, you have to have 80 % gross margin. Look at Snowflake, it's got 20, you know? Margin matters, but early, it can be a misleading indicator, especially when an architecture shift is happening. The reason why in AI, and I'll give you the bull case on this, right, is the reason why margin might not matter early on in a company's life in AI is the cost curve is coming down so fast. Say my inference margin is 10 % today. It may have been negative a quarter ago and super negative two quarters ago, but the token costs are coming down so fast.

34:43Lucas Swisher:If I'm an application AI company, I'll probably be able to develop my own model for some of the workloads. I'll probably want to use frontier models for some of the workloads. I'll probably want to use really small, cheap models for some of the workloads. And over time, I'll be able to optimize my margin. That's what we really believe is going to happen over time. But listen, these companies are structurally lower margin than the last generation because you pay the cloud and you pay the LLM. And so we just get used to actually larger outcome sizes with larger probably revenue pools associated, but a slightly lower margin profile.

35:16Lucas Swisher:Well, from I think gross margin, yes. But what you might say is, hey, I'm actually substituting a lower gross margin for lower OPEX because my engineering team, maybe it's more efficient. My sales team is using AI tools now. Maybe it's more efficient. My legal team, maybe it's smaller and maybe I'm more efficient. So your terminal operating margin may actually be higher in this world than the last world. Your gross margin might be lower, but your operating margin, which is ultimately at the end of the day is really what matters, may end up being higher. What else do you think a lot of investors oscillate or focus on, which is total shares?

35:50Lucas Swisher:One of the places where we don't spend time, these pre-revenue companies are really high valuations. I think this is a lesson that at least we've taken about ourselves from 2021 is that is not our business. The pre-revenue company at a really high valuation with no product is not our business. And I think a lot of investors are focused there right now. Because what ends up happening is if you can't invest in OpenAI and Anthropic and Revolut and SpaceX and Canva and all of the companies that are these great platform companies, and you're locked in to a certain part of the ecosystem, you make decisions that you can make.

36:22Lucas Swisher:And so I think a lot of people are focused on that part of the ecosystem right now. And for us, that doesn't make sense from a risk reward perspective. Our focus is real businesses that are growing really fast that we think are going to be really durable outcomes and actually generate liquidity for our investors. And again, it goes back to this principle around if I have a zero, I need a six. And a six is really, really hard. You mentioned earlier that you wouldn't want to be a seed fund deploying three billion or staring down the gun of three billion or whatever it is. In a way, I would because I can absolutely destroy the economics of all the seed fund players.

36:53And it's something that we see. We lost a deal recently to a large mega fund and we did three on 15 and they did 10 on 100 with no lick pref. pre-anything, destroyed all the economics. And I told the founders, you should absolutely take that deal and sell tomorrow for like 5 million bucks and you've made money. But they can destroy the economics. Is seed still a business when you have mega fund entry with different economics in

37:16Lucas Swisher:the way that we do? I mean, I think it's gotten harder for two reasons. One is you do have this mega fund dynamic. But the other thing is we're in a different world than we were five years ago, right? Which is in general, people are coming out of the gate with bigger check sizes and bigger valuations. And that just raises the risk dramatically over time, right? So I think those are the two dynamics that are really at play. It's harder for a seed fund to buy 20 % today or 10 % today or 5 % today than it was a few years ago because of this dynamic. And that has to do with a lot of different things.

37:48Lucas Swisher:One of them is in a SaaS world, you didn't need that much capital. You started up, you kind of get going, whatever. In this world, businesses tend to be more capital intensive. They may be actually more durable at scale because of this. Makes it harder for the next entrant to come in. But the reality is they're harder to start. They take more capital. And that has led to some of these like very big ballooning seed rounds. I think that makes it harder to be a seed investor in today's world. And again, why having a flexible mandate where you can row up and down that river and not have to be there is really a nice place to be.

38:19Do you think a good investor at A can be a good investor at D? A lot of LP mindsets are like, no, early stage is different to growth. And that's very different. I think Josh, who's a dear friend at Thrive, has proved that actually that's not the case. But other people still very

38:34Lucas Swisher:much hold that true. I don't think it's impossible, but I do think it is very hard. I think that's because the type of frameworks that you use, the types of things that you see are very different at different scales. Being able to read a balance sheet actually does matter for a pre-IPO company, right? Like that really matters. But seeing thousands of founders, thousands and thousands and thousands really matters for seed because what else do you have, you know, to go off of? And so I do think it really matters. I don't think it's impossible. I think there are some funds that have done it exceptionally well, but I think it's why you see for us, right?

39:07Lucas Swisher:We as a fund, I actually think the public market skillset and the private market skillset is also different. And so having different folks that are focused on different things is really important because there are different parameters, different things that you see all day. And there are other people that you're competing with in all of those different segments that make it really tough to be the best at everything. There seems to be a consensus of excitement around certain companies. And we see the concentration of cash to few players in select industries, which has led to this idea of kingmaking.

39:35When we think about kingmaking, do you think that is a rational or real thing or do you not? I don't think it's a real thing.

39:44Lucas Swisher:You don't? I don't think the king making concept is a real thing. I think some companies attract more capital early. Some companies slingshot from behind. Do you not think if you raise a lot of money from large tier ones who then are very vocal and loud, it dissuades other people from investing in anyone else? It certainly does. And it's an advantage. But it doesn't mean you can't build a great business just because a bunch of tier ones are crowding into a name. I think there is the concept of it gives you an advantage. More capital does give you an advantage. There are some cases where historically it's given you a disadvantage.

40:16Lucas Swisher:If you have so much capital and not a lot of product market fit, I'd say you probably had a disadvantage. If you have a lot of capital and insane product market fit that allows you to go hire a huge sales force, that's a huge advantage, right? Like if you're actively taking a market and you have way more capital, a better, right? Like it's, this is like almost tautological. That is a huge advantage. But do I think that there's this concept of, well, if Kotu and Sequoia and Kleiner all pile into a company that it's over? No. Totally get that. I think it is an advantage, but I don't think it makes it, which is probably where king making goes too far.

40:49Do you think we are foie gris in companies today in the same way we have done before? What do you mean by that? You know foie gris. Yeah. You know, they shove a tube down and then force feed it and then it explodes. Yeah. So we are putting too much money into companies and then they are artificially inflating and then exploding.

41:05Lucas Swisher:I think there are segments of the market where it feels like that is, that feels like a little bit of a problem. I think for these companies, and I'll just focus on what we do, right? For the companies that are explosively growing at the growth stage and have real product market fit, real product, real traction, I don't think so, right? You look at these companies that raise really rapid rounds in succession at the growth stage that actually have something underneath. No, because there's real ROIC on the capital that's being invested, right? There's real ROI for the dollars that are going into these businesses.

41:36Lucas Swisher:Sometimes I think when growth funds in particular chase venture companies, right, we've talked about that delineation point. That's where I think it can get quite dangerous. It can make companies complacent. It can make companies spend too much on things that maybe it's not great. At that early stage, that kind of capital scarcity, I think, can breed actually great things. And so I think there are parts of the market where that's certainly true. These growth stage companies with this insane momentum, I don't think so. Do you worry that there are a generation of companies a la Canva, a la Stripe, which do not need to go public?

42:10Great businesses, great businesses in private markets, ample liquidity for those that want it, very active secondary markets if they need to. Why would we go public, as John said, I don't want some fucking 30-year-old analyst at some big bank telling me that I should increase sales.

42:23Lucas Swisher:Yeah. I mean, I think this is one of the reasons why companies have stayed private longer. I don't think most of those platform companies will stay private forever. I think there are a couple of reasons that are good to go public today. One is real capital at scale. Real capital at scale. Still, say you're a trillion dollar plus company. It's available, but true liquidity. That's not layers and layers and layers of SPVs and all this tricky shit. And like managing your cap table. Like true liquidity at scale. I'm just tweeting about your layered SPVs. I mean, you've seen some of the things around some of these companies where it's like unbelievable.

42:58Lucas Swisher:Like the opacity of this. And the companies don't want that either. They want to know who their investors are. and you get the investors that you deserve as you scale and you go public. So liquidity at scale is certainly one reason. The second reason is, and this second reason I think really does cut both ways, but the public markets are an incredible feedback mechanism for businesses. If you think about Netflix during their transition, the public markets were some of the first folks, like the analysts and the public teams, to really speak about that transition from the disc to streaming, from the CD to streaming.

43:28Lucas Swisher:And I think, especially in an AI world, The public markets, folks in the public markets are really, really smart. The 25-year-old analysts, this and that, right? Like everybody's going to have varying degrees of intelligence or opinion. But the public markets are this like incredible weighing machine that can give founders and teams amazing feedback on their businesses. And then the third thing is when you go public, it's sort of harder to touch you. In some ways, it's easier to touch you from a buying and selling stock. But you're now a public company. You're now levered to 401ks, to indices.

43:59Lucas Swisher:When you're a private company, people can mess with you a little bit more. It just is what it is. They can mess with you. When you're a public company and you are a big, important public company, it's harder to mess with businesses. And so you kind of have this rigor around you. I adore Cliff and Mel, and I think they're amazing. But you said about the platform companies, and you included Canva. If you would be a harsh critic, you'd say, well, Figma is worth$11 billion today. Image generation, graphic generation is right in the pathway of a lot of large AI companies. Is Canva really a platform company?

44:31Lucas Swisher:What I love about Canva is they've shown that same ability that Databricks has, where they're able to hop multiple S-curves and develop multiple products. They started as, I'm sure you know the story, but it's incredible. Mel and Cliff started this business as a yearbook business, making yearbooks. They successfully transitioned that online. They successfully transitioned that to SaaS. And now they've transitioned to many, many, many products, right? Right. Canva is a suite of like a dozen products that are all growing extraordinarily quickly. So you have that dynamic. And then the other thing that I love is they were one of the first companies that really leaned into AI.

45:06Lucas Swisher:I remember Cliff called me about this very early on because we were early investors in stable diffusion. If you remember the image generation company in OpenAI and a few of these other businesses, he called us really early in this wave, like pre-ChatGPT in this wave and was like, hey, we're going to start integrating AI into our business now. And so that type of mentality, both the ability to develop multiple products and hop TAMs and to stay ahead of the curve in AI, I think is going to serve them very well. Again, I love Cliff and I love Mel. And I totally agree with you in terms of that expansion.

45:34You know what I also love about that story? Married couple. Australia. Non-technical. To be fair, the seed investors of that, and I'm not taking anything away from Mel and Cliff, again, I think they're exceptional. But you've got to be quite mentally plastic away from the

45:49Lucas Swisher:traditional investing rules to be like, yep, all in. Credit to those folks. And I mean, credit to the growth investors who took a leap on that one a little early too, right? It was very non-obvious. It's, I mean, I worked for Mary Meeker when I was at Kleiner Perkins and she was one of the folks that took a leap on Canva. What was your biggest lesson from working with Mary? I think the biggest lesson is she has this, and it comes from her background of being at Morgan Stanley for a really long time. She has this incredible analytical bent of being able to see things and see stories and numbers that other folks don't and being willing to lean against the grain whenever she feels things.

46:24Lucas Swisher:And she's able to tell these incredible stories with data and understand what's happening in the world based on data. I'll give you one example is I remember my second week at Kleiner, I didn't know how to model. I came from inside. I could barely model. I was great at talking to founders, but could barely model. And I found myself in the middle of a modeling exercise with Mary and just getting absolutely destroyed. And one of the things she taught me is like that is actually really important. Being able to express a complex company in a few lines in Excel and tell stories with data is like an incredible skill.

46:56Lucas Swisher:And she has this knack of being able to look at like sell in 95 and know there's an error. And so that's what I learned is to be like highly analytical, very detail oriented, and to tell the story with the data. To what extent does that truly matter versus phenomenal founder, big market growing fast? So the way that I phrase data and I phrase this to our team a lot, and this is what I really believe is data is a prerequisite. It is not the answer. The data must be very good. It's not the whole picture. And I remember I was sitting in a very old IC and we were when we were looking at Databricks at KOTU way back when.

47:34Lucas Swisher:And Thomas was like, Lucas, you're missing the force through the trees here. Just because net new ARR didn't accelerate dramatically in any given quarter does not mean this trend is not happening. And so net new ARR or whatever metric you want to use, they're incredible guideposts, but you can't miss the forest through the trees in this. Like the bigger picture, it really matters. But it is helpful, right? I'd say like the thing that I'm looking at the most with a lot of these kind of AI native businesses is if you're low margin, I need you to have high retention. You have to have it. Because you leave no margin for error if that's not true.

48:06Lucas Swisher:If you're going to be a low margin business to start, the customer behavior must be so sticky. It's got to be so sticky. Because otherwise, you're really, really fragile. One move the wrong way and you have no margin for error, right? So like those are the types of places where data can help you. It can hurt you if you live in Excel all day and you are like just missing the forest through the trees. Totally agree with that. You worked with Mamoon too. Yes. I really love Mamoon. Me too. What was your biggest lesson from working with Mamoon? The gift that Mamoon hasn't, I think from the SaaS era, Mamoon, my view is he was the best Series A investor in the SaaS era, period.

48:40Lucas Swisher:Right? I mean, if you look at his track record, it's incredible. Figma, Glean, Rippling, Slack. I mean, it's just this unbelievable, like, hit after hit after hit. What Mamoon is special at and what he pays attention to and what I learned from him is there are distinct inflection points in companies. There are moments where they really kink. They kink up. And he is the master at seeing that around the Series A. With very little data, being able to see it. I remember going back to, I worked on Figma with him when I was an associate at Kleiner. And I cut all the data for Mamoon. This is, it was a very fun time.

49:12Lucas Swisher:And I remember he took one look at it and within 30 seconds, he was like, we're doing it. And there was this big company in Vision at the time and it was a great company and everybody thought it was the winner. And he looked at that data and he was like, this is going to happen. And what he saw is the net retention curves, the customer behavior of really big companies. I think, I can't remember exactly, but I think the companies were Google and Square and Amazon, right? Like really insane customers. And this is when Figma had 500K of AR. And he saw the usage curves inside those three companies.

49:40Lucas Swisher:He's like, we're at an inflection point. We're doing this. And that's what he's amazing at. I'm not surprised. And time and time again, I'm amazed by the insight. Again, I meet so many investors and I actually find not that many have the insight that Mamoon has, that Neil Mater has, that Pat Grady have. Super unfair question. Super unfair question. You can invest in Mary Meeker's fund and these solo GPs, Mary Meeker's fund, Mamoon's fund, or Jeff Hoaring's fund. Whoa. I want dollars, absolute dollar return. Absolute dollar return. I think you got to split it in some way. I think what you're looking for, if you're an LP.

50:17You know none of them pay you anymore. Yeah, I know, I know, I know.

50:20Lucas Swisher:But if you're looking, if you're an LP, what you're looking for is the best return across different strategies. I think it's going to depend on what you're looking for, what your time residence. Let me give you the benefits, right? Mamoon, I think is going to have an incredibly high slugging average, really amazing returns. But it's going to be more risk. Mary, I think you are going to get like this incredible growth portfolio of blue chip names. And then whoring is going to provide you very strong, stable core returns. And I think like it really depends on what you're looking for, right? And LPs want different things.

50:54Lucas Swisher:And they probably want exposure to all three in different ways. I'm going to ask you another one because you failed at that one. Okay. You've got Pat Grady at Sequoia. You've got David George. and then you've got the folks at Founders Fund, the Napoleons and the behind the scenes people at Founders Fund. You can only invest in one fund. Oh, you can't do this to me. Yes, I can. You can't do this to me, Harry. And I'm not going to let you out of the room. No, it's incredible. I think Founders Fund's strategy of being ultra concentrated in a few companies has just been this incredible strategy over time.

51:26Lucas Swisher:And I think Pat Grady's ability to pick Series Bs is like pretty unmatched. Pick and win Series Bs. He's very, very good. and I think Sequoia is very good at that. So I think, again, they're good for different reasons, but again, it really depends on what you like. Final one before we do a quick fire. You have one final dollar and you can put it in OpenAI or Anthropic. Which one would you put it in? Right, I'll talk about the merits of both. OpenAI, incredible consumer franchise. The retention curves, the growth, all of this stuff, what they've done, it's insane, the innovation that's coming out of that business.

52:00Lucas Swisher:On the consumer side, their strength, that's emerging in enterprise with codex and other coding use cases in these big transformational enterprise deals. And like a third unknown unknown vector, they have this like almost unknown unknown about it because they acquired Johnny Ives company, right? Who knows what that could look like in five to 10 years? They have this like SpaceX, you know, it's like, how do you value space? Well, how do you value AI? It's like this unknown unknown element of just how big could it get? I think that's the, that's the bull case. Did you see the design work that Johnny Ives team did for Ferrari.

52:32Yeah. Oh my God, I can't drive. I don't have a license. I want a fucking car like this because of Johnny's design. You're going to have to learn. You're going to have to go get it. No, my girlfriend's got a license. It'll be a present. You can ride shotgun. Exactly. I'm very happy to hold the phone with the maps. There you go. But I was like, wow, I've never wanted a car as much as Johnny Ives designed.

52:53Lucas Swisher:Yeah, it's amazing. Yeah. It's incredible. And I think like that's the bull case. The bull case on Anthropica is really simple and really straightforward. Their focus on coding has been an unbelievable advantage for them because coding is the first use case in AI that's really taken off. That coding focus has led them to have a beachhead in all the other analytical tasks in the enterprise, right? Everything is code, right? Like everything in the digital world is code. And by having a great coding model, they've been able to do that. In the last strategic decision that they made that I think is really sort of unappreciated by the market is they built for every cloud and they built for every chip platform.

53:26Lucas Swisher:And that gives them incredible optionality and a lot of people want them to win. And so that is like a real advantage. So they all - I'm sorry, I'm really naive here. And I'm not asking who's better or who's worse. Is that different to the other providers? It is. Because some of the other providers have been sort of, at least until this point, right? Like this is always changing. But they from kind of from day one had architected themselves to be able to be partners with every cloud and to be with Tranium and TPUs and GPUs. And that takes a lot of infrastructure investment. But it means in a capacity constrained world where the demand for compute outstrips supply, their ability to do that makes them more cost effective.

54:05Lucas Swisher:It gives them an advantage on where they can deploy. They can take capacity that other people can't. And that means like, hey, in this world, that's an advantage. I totally get you. And actually having more people support you is a very, very advantageous position. Yeah, it's one of the things that we actually, we always try to think about is like, and it's a question that Philippe asks all the time is like, who's gonna wanna help you and who's gonna wanna hurt you? Because that ultimately matters, right? Like having a lot of people want to help you and benefit from your growth is a very nice position to be in.

54:34Clearly, Philippe agrees with kingmaking then. Well, it certainly helps. It totally helps. Listen, I want to do a quick fire. So I say a short statement, you give me your immediate thoughts. That sound okay? Done. What have you changed your mind on in the last 12 months?

54:48Lucas Swisher:The size of outcomes. This is really simple. Like 12 months ago, I wasn't as convinced that we were really going to be able to address labor. this like token machine concept that, you know, human inputs were going to become machine inputs. I wasn't all the way there. We were still kind of in an assistant world versus an agent world. I've become fully convinced on this. A lot of it is due to using a lot of the tools like CloudCode myself and just really feeling this. But that, my opinion has really changed on that in the last year. I think the outcomes this generation in technology are going to be so much bigger than the outcomes from the last generation.

55:22When you think about that labor displacement, Do you think we are overestimating the adoption of enterprise and labor displacement or actually underestimating it's coming sooner than we think?

55:31Lucas Swisher:This is the hardest question, right? And it's like, if you go back to the last era, people always underestimate how long it takes to do things. And I think it's because they look at the consumer and they see how fast the consumer changes and adopts things and apply the same thing to enterprise. I don't think it's unlikely that we're going to wake up tomorrow and all these SaaS companies have evaporated. Change takes time, right? These things are going to take time. That said, these things are happening much faster than they were before. If you look at Anthropic, right, publicly available numbers, 9 billion of ARR growing 800%.

56:02Lucas Swisher:At the same scale, the three hyperscalers on average, when they were 9 billion of ARR, were growing 60%. So it's happening faster than SAS did. We know that. It just is. Like, it's in the data, right? That's the story. But how long is it going to take for all of this to happen? I think it's going to take a long time because people are slow. They're sticky. Change is hard, right? It's not like I can just throw a clot into an enterprise and all of a sudden it works. There's integration work that has to be done. There's deployment that has to be done. This stuff is complex. One of the most bullshit ones is people talk about in the agricultural revolution and the industrial revolution.

56:33And I'm like, yeah, you had to buy a fucking tractor as a farm in France and then train your 75 people on a tractor that comes in a year's time. And then you have to assemble it and then train them on safety and dock. Here it's like Gemini puts in Nano Banana Pro and you're good to go tomorrow. Yeah, it is faster. It's certainly faster. So much faster. What's the single most memorable first founder meeting you've had? I'm not asking

56:55Lucas Swisher:for the best founder, but it's like the most memorable first one. Winston from Harvey. Why? It's not even close. I think it was because one, I already believed when I came into the meeting. And then two, the founder market fit in the story was so clear so early. What are language models good at? Language. Text in, text out. What is one of the most text heavy professions? Law. What have I seen early on? Document generation, document analysis. And his articulation of that thesis and that story. It was just like so spot on. I met him before the series A that Pat did. I remember being like, this is it.

57:27Lucas Swisher:Like, this is the one. Did you lose the A? We had an early stage practice at the time that we were really involved with and we did lose the A. You know, it goes back again to our, and I don't try to do very many A's. It goes back to our strategy, which is even sometimes if you miss an early round for the great companies in the world, there's always another round. Dude, we are doing a time sheet now for a company and we turned down on the seed and we're doing the A. And I said to the team, like, I will not lose out on a great company because we are too egocentric and arrogant to accept our mistake.

57:59Absolutely. So we're not going to do it. Ridiculous. No, I'm kidding. I just fired the seed team. You can invest in one seed firm and one Series A firm. I mean, you guys, obviously, for the seed. Come on. I love it. I'll take that, actually. Yeah, how about that? Which Series A firm?

58:17Lucas Swisher:I would say Sequoia and Benchmark. I want to split my dollar. You're going to allow me to split my dollar. I'll take that. Rory O'Driscoll, who we do a show at Very Thursday, is brilliant. He always says with Benchmark, reports of my death have been greatly exaggerated. I just find it so entertaining. I think so. And I think it's this firm that, again, it's so good. And they have the ability to reinvent themselves, right? I mean, they hired Ev, who's my old analyst. So good for them. Listen, this is always a rough with the smooth. Poor Peter. He's got to deal with that every day. No, I love that.

58:45He's fantastic. But seriously, you look at like your Firework, your Ligora, your Manus. I mean, the list goes on. Sierra. I mean, unbelievable portfolio from this era. But again, everyone's like, our benchmark are over. You're like, I don't know, take any of those companies in my portfolio.

59:00Lucas Swisher:What's been the hardest decision you've made in your career? Leaving Insight for Kleiner. I know a lot of people. So I was Harvard undergrad, then went to Insight. And I left Insight pretty early. I was the first one to leave. We hired classes of 10 back then. So it was like 10 analysts, all really young kids coming out of school. The junior summer internship at Insight was literally dialing for dollars. I mean, it's incredible training ground. I called 50 CEOs a week, like literally cold call. I mean, this was 10 years ago, almost 15 years ago. Don't laugh. What do you say? Hi, it's Lucas from Insight Park.

59:30Lucas Swisher:Hi, I'm 19 years old. I mean, but it's amazing, right? Because you have this platform where young people are empowered and they're able to grow within the organization and bring other people in as they need. and you learn how to like navigate a process at 19, 20, 21 years old. It's this incredible training ground. But I was the first one at Insight to leave my class. And it was hard because it was like, it was basically like stepping off the linear path. Like most of my life had been like very linear decisions. Not very hard to like take the SAT, do well, not very hard to accept Harvard, and then not very hard to go to Insight, even though it was a little abnormal at the time.

1:00:06Lucas Swisher:Like it was a billion dollar fund when I went. Going from insight and leaving your comfortable class in basically, you know, private equity SaaS and going to be the only associate on the West Coast in a place you didn't know. That was a little bit of a leap. I mean, that's my advice to all the young folks in their careers. Like, you've got to get off the linear path. You have to. Like, it's the only way. Get off the linear path. It's so funny. I always say the safe path is so much less safe than you think. The risky path is actually less risky than you think. Do you have to be in San Francisco if you want to build an amazing AI company?

1:00:36Lucas Swisher:No, but it helps. It's like the king making. It certainly helps. I think if you look at some of the advantages that you have being in San Francisco, right, just the incredible amount of talent density, there are not very many people in the world that know how to work with these systems right now. That's just the reality. And many of them are stuck inside of two, three, four companies. But the rest, most of them are in a very small radius in the Bay Area. It's not impossible, but it's kind of like, why would you make your life harder? So with that, do you think the 100 million to 500 million pay packets are actually justified?

1:01:07Yes. I think they should give them to podcasters too. Just putting it out there. You got this. I believe in you. Thank you so much. There's very few people who know how to do this very difficult job. Penultimate one. What's the biggest miss that you reflect on most across your career? Like mine is deal.

1:01:21Lucas Swisher:It's not easy because, you know, if you've done this long enough, you have a lot of misses. A lot. I do remember very distinctly going and visiting Anderrill for the billion dollar round down in LA. I was a SaaS investor at the time. So why I was the one who went to visit Anderrill, I won't know. but it was a classic case of back then, I think my perspective was slightly more myopic, right? I was mostly focused on SaaS, very focused on metrics. And if you looked at that P &L, there's no way, you know, you're a P &L investor, there's no way you invest. Just is what it is. It was an ugly P &L. It was an example of me missing the forest through the trees, not seeing just how special the founding team was there, just how important that trend was, where the world was going, right?

1:02:02Lucas Swisher:And that's an example of where, you know, Founder Fund got that right. A lot of people got that right. We got that wrong. Final one. What most excites you for the next 10 years? I'm excited about the products. This is one of the things that it's ingrained in everyone that joins KOTU. At the end of the day is we do love technology, right? Like we're technology only firm. We love technology. We love these products. And the ability to just like change our lives over the next decade and use so many new things, I think is what has me most excited. Like I cannot wait for OpenAI's new device. like we've it's going to be one of the first exciting new devices in some time like those types of things i think is what i'm the most excited about is the products like using cloud code this year oh my god it's incredible i have to say you're especially right on the open ai device it's like what latest consumer device have you been like i would actually go and wait outside the store for when i was a kid i'm camping out for this thing the ipod nanos and the i was like wow thousands like now with the new iphones let's be honest like no one's like yeah i'm gonna like run to the store.

1:03:01It's like, ah, whatever.

1:03:03Lucas Swisher:This - I've forgotten to trade mine in for four years. I use like four generations old. A hundred percent. I completely agree. So I'm so with you. Lucas, thank you so much for doing this, dude. I've loved having you on. This has been fantastic. Awesome. Thank you. But before we leave you today, over 80 % of Fortune 100 companies are running their businesses with Airtable. Airtable combines AI with the scale of an award-winning, infinitely flexible no-code system, a platform where you can see all of your data in one place and use it to make really big picture decisions. Think of it like mission control for your company.

1:03:38Airtable goes beyond organization and automating repetitive tasks. It lets you use your data to inform strategy, monitor progress, and take action. Every cell is capable of performing hundreds of AI-powered tasks like web research or localization and using those results to inform and update hundreds or thousands of other sales and workflows in real time. Unlock the true scale of your workflows at www.airtable.com forward slash 20vc. Airtable, the infrastructure of innovation. And just like Airtable organizes your workflow data, MetaView organizes your conversation insights. This episode is brought to you by MetaView.

1:04:19Who says hiring has to be fair? Every founder, VC, and exec I speak with knows this. Your ability to hire is the biggest constraint on your company's growth. But recruiting is slow, it's subjective, and only getting more competitive. And that's why teams like Eleven Labs, Brex, Replit, Deal, and 5 ,000 other organizations use MetaView, the AI company giving high-performance teams a real unfair advantage in hiring. MetaView's built a suite of AI agents that behave like recruiting co-workers. They proactively find candidates, they take interview notes automatically, and they help you surface the best candidates in process.

1:04:56For the first time, AI handles the recruiting toil and gives you a single source of truth. That means hours saved per hire and a team focused on what matters most, winning the right candidates as fast as possible. Don't let your competitors outhire you. MetaView customers close roles 30 % faster. Try MetaView today and get a free month of sourcing at metaview.ai forward slash 20VC. After MetaView captures what was said, Turing helps you build with the people who can deliver after it. Frontier Labs keep facing the same limitation. Models perform well on benchmarks, but they fall short once they enter real coding tasks, real tools, and real workflows.

1:05:36That disconnect between synthetic evaluation and actual system behavior is now a core blocker for agentic models. That's why Nvidia, Anthropix, Salesforce, Gemini, and other leading lab partners partner with Turing. Turing is the research accelerator focused on post-training reliability. They build realistic RL environments, next-generation data quality systems built from real-world operational traces, and coding datasets that stress models under conditions where failures matter. State changes, workflow branching, brittle tool calls, and the coding errors that break RL agents but never appear in benchmark reports.

1:06:12In reality, a model may demonstrate correct reasoning in your evaluation setup, yet still select the wrong parameter or mishandle a code update in a realistic interface. Turing makes that failure visible and gives teams the signal they need to fix it. For labs advancing agentic systems, Turing provides the structure required to understand why these failures occur. To find out how, visit turing.com forward slash 20VC. That's T-U-R-I-N-G.com forward slash 20VC.