20VC: Inside Coatue's $70BN Machine: Why Price Matters Least | Why Mega Markets are the Most Important | How to Assess Durability of Revenue and Margins in AI with Lucas Swisher

Summary

Podcast Summary: The Twenty Minute VC (20VC) with Lucas Swisher (Coatue)

Episode Title: 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
Air Date: February 23, 2026
Host: Harry Stebbings
Guest: Lucas Swisher, Co-lead of Coatue’s Growth Fund

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Episode Overview

This episode dives deep into Coatue’s growth investing strategy, particularly in the age of AI and rapidly evolving software markets. Lucas Swisher discusses how his fund evaluates investments, why market size trumps almost everything, why “price matters least,” the durability of revenue in an AI world, the limitations of traditional SaaS frameworks, and how mega funds can still deliver venture-like returns. There’s also an honest examination of mistakes, misses, the myth of kingmaking, margin analysis in AI companies, fund math, and lessons from top-tier investors.

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Key Discussion Points & Insights

1. AI’s Impact on SaaS and the Public-Private Divide

• The Value of SaaS is Being Questioned: Lucas describes how the AI wave is making investors question the “terminal value” of SaaS companies, previously seen as perpetual money-makers (04:57).
  • “For the first time ever with this AI wave, people are questioning the terminal value of SaaS... These were supposed to be like insurance companies… and for the first time, with a lot of AI... you are starting to question that value.” – Lucas Swisher [04:57]
• Uncertainty in the Market: Fast-moving tech cycles make it nearly impossible to tell which SaaS businesses will remain valuable.
  • “The reality is for the next three months, six months, nine months, we’re not really going to know what’s really happening in the world. Things are happening so fast.” [06:16]
• Owning “The Future” Requires Private Market Access: The most promising AI companies (OpenAI, Anthropic, Harvey, Canva, etc.) are private and growing rapidly—public market investors can’t easily access these (08:52, 09:03).

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2. How Coatue Invests: Market Size First, Price Last

• Mega Markets Are Essential: Lucas stresses that the number one investing criterion is being in enormous, expansive markets.
  • “If you ever compromise on that very first principle and you’re paying high valuations, you’re in trouble. Medium TAM, small TAM—you better believe that this thing can be absolutely gigantic.” [14:03]
• Generational Companies Outperform: The vast majority of value creation in private markets comes from a handful of “platform” companies, stressing a highly selective approach.
  • “20 companies have generated 80% of the enterprise value... 4 companies have generated 65% of the enterprise value. And so what really matters is being in those companies.” [21:33]
• Flexible Mandate: Coatue’s structure allows investment at any stage, letting them “row up and down the river” as opportunities appear, rather than being locked into a fund stage. [15:36]

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3. Price Matters—But Not as Much as You Think

• Price is Considered "Last": In hyper-growth environments, valuation is the last thing they consider, provided the company is in a mega-market and executing well.
  • “When a company is growing exponentially... we think about valuation last, it’s the last question we try to answer.” [12:48]
  • “Price does matter, but I think it matters least.” [19:14]
• Returns from “Double Down” Rounds: Big money is made not at initial investment but in aggressive follow-ons as conviction proves out. Citing Jeff Horing: “The best round is the double down round.” [16:52]

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4. Assessing Durability: Revenue, Margins, and S-Curves

• Durability in an AI World Is Complex: Success depends on talent, willingness to reinvent, and agility, not just revenue persistence.
  • “Can you find companies that have the talent density, that are the most forward thinking, that are willing to reinvent themselves over and over again?” [11:02]
• Example: Databricks: Praised for repeatedly reinventing itself across tech cycles, “riding multiple S-curves.” [12:13]
• Early Margins Can Mislead: Low margin early in AI companies is acceptable because cost curves improve fast—gross margins rise as companies scale, mirroring past hyperscaler experiences.
  • “Margin matters at scale... but early, it can be a misleading indicator, especially when an architecture shift is happening.” [33:38]

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5. Fund Math & Mega Fund Returns

• Mega Funds Can Work: If funds remain concentrated and focused only on true outliers (“platforms”), 5x and even 10x investments are still possible. Private companies stay private longer, letting more capital be deployed over time. [25:11, 25:30]
  • “If I invest a billion dollars and I 10x that billion dollars, that’s a 2x on a $5 billion fund.” [25:30]
  • “Outcomes are bigger now than they used to be.” [27:22]
• Vertical SaaS No Longer Fits Mega Fund Math: Huge funds must focus on generational platforms, not niche vertical software companies.
  • “If you have a big fund, I don’t think that’s where you should be focused.” [27:35]
• 3x is Table Stakes, 5x+ is Fund Making: Lucas needs to see cases for returns that excite above 3x for portfolio sizing.
  • “A 3x is not enough to be exciting... I need to see a steady case where you can get that 3x. But I really need to believe that if the company 3x’s, I want to put more money in because it can 3x again.” [28:24]

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6. The Realities of Competitive Investing & Kingmaking

• It’s an Advantage, Not a Guarantee: While crowding by top VCs (“kingmaking”) provides advantage, it doesn’t automatically dictate the winner. [39:42]
  • “I don’t think the king making concept is a real thing...” [39:42]
• Mega Funds Can Hurt Seed Fund Math: Large funds entering early deals at huge valuations kill traditional seed economics. [36:42]
  • “It’s harder for a seed fund to buy 20% today... because of this dynamic.” [37:17]

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7. AI Margins, Secondary Liquidity, and Company Longevity

• Early AI Margins Are Low but Improve: AI infra costs are falling rapidly. Operating margins may ultimately be higher than classic SaaS due to more efficient structure despite lower gross margin. [35:16]
• Secondary Markets Are a Key Tool: With companies staying private so long, secondaries are “certainly an option for liquidity now” and will continue to shape outcomes. [30:12]
• Companies May Delay Going Public Indefinitely: Great companies (Canva, Stripe) can remain private, tap private liquidity, and only go public for “real capital at scale” or feedback from public markets. [42:03]

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8. Investing Frameworks & Internal Decision Cues

• Lucas's Litmus Test: “If I invest at this price and the company executes, do I want to put more at a higher price?” [20:40]
• Founders vs. Markets: Market size is still primary, but founder’s ability to expand the company through new product cycles is crucial.
  • “Market size is always first. A great founder in a small market… it’s hard to get to $100 billion.” [18:02]

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Notable Quotes & Memorable Moments

• “The best businesses, in particular infrastructure... have had horrific margins early... Snowflake and Databricks. Very low margin early.” – Lucas Swisher [33:38]
• “It’s not revenue growth that you want to chase, it’s... the most incredible adoption of new trends and moving with the next chapter.” [12:13]
• “You have to find those companies that expand TAMs... those are the companies. It’s not like we think these companies are all bad... It’s just our strategy is to find those.” [31:51]
• “Price does matter, but I think it matters least.” [19:14]
• “If I have a zero, I need a six and a six is really, really hard.” [35:08]
• “Data is a prerequisite. It is not the answer.” [47:17]
• “Who's going to want to help you and who's going to want to hurt you? Because that ultimately matters.” [54:18]

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Timestamps for Key Segments

• AI’s Impact on SaaS Value: 04:57–06:16
• Public vs. Private Growth Opportunity: 07:44–09:57
• Mega Platform Companies & Private Market Access: 09:03–10:34
• Flexible Mandate at Coatue: 15:36
• Durability & Margin Analysis in AI: 11:02–12:48, 33:38–35:45
• Mega Fund and Return Math: 25:03–27:35
• Kingmaking Myth: 39:42–40:42
• Early Stage vs. Later Stage Investing Mindset: 38:19–39:25
• Secondary Liquidity and Delayed IPOs: 30:12, 42:03
• Criteria for Fund “Excitement” (Return Potential): 28:24–29:49
• Frameworks for Valuation and Doubling Down: 16:52, 20:40
• Lessons from Misses and Career Moves: 61:21–59:04

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Lessons & Anecdotes from Notable Figures

• Mary Meeker: Analytical rigor, storytelling with data. [46:08]
• Mamoon Hamid: Spotting inflection points with minimal data (e.g., Figma Series A). [48:34]
• Jeff Horing: Power of “the double down round” for outsized returns. [16:52]

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Closing Reflections

Lucas’s Current Outlook:

• AI and the “token machine” era will yield even bigger outcome sizes than SaaS and mobile did.
• The key investment edge is being in the right company, at the right time, in the right wedge of a massively expanding market, with founder(s) able to continually adapt.
• Traditional rules—margins, price discipline, path to IPO—are in flux due to capital availability, company durability, and tech cycles.
• Mega funds are sustainable if they're highly concentrated; index-like “spray and pray” cannot win at the growth stage any longer.

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Host’s Closing Thoughts:
“Seriously, you look at your fireworks, your ligura, your madness. I mean, the list goes on, Sierra... But again, everyone's like, are Benchmark over?... I’d take any of those companies in my portfolio.” – Harry Stebbings [58:54]

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Summary Takeaways

If you want to understand how the very best growth investors are handling the AI era, shifting tech disruption, and megafund scale, this is an essential listen. Lucas Swisher reveals why entry price is (usually) secondary, why marginal improvements in AI infra matter less than market dynamism, and how old rules (margin, TAM, deal timing) are being stressed by unprecedented company and market evolution.

Transcript

[A]

I think price does matter, but I think it matters least. Margin matters but early it can be a misleading indicator. Data is a prerequisite. It is not the answer. One of the places where we don't spend time these pre revenue companies are really high valuations. I don't think the king making concept is a real thing. Who's going to want to help you and who's going to want to hurt you?

[B]

This is 20 VC with me, Harry Stebbings. Now I am bored. I am bored of recycled guests interviews

[C]

that have been done over and over again.

[B]

Today's guest is rarely ever on a podcast. Lucas Swisher. He co leads the growth fund at CO2 and they've backed some of the best companies of the last few years like OpenAI, Harvey Deal, Canva, Anthropic and many more. He also previously worked at Klein and Perkins with the one and only Mamoon Hamid and this is one of his few appearances where we really delve deep into the investment process at CO2 and what they look for in great great companies and found but before we dive into the Show 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. Airtable 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 cells and workflows in real time. Unlock the true scale of your [e-mail] 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 who says hir 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 Elevenlabs, 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, automatically and they help you surface the best candidates in process for 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 out. Hire you Metaview customers close roles 30% faster. Try Metaview today and get a free month of sourcing at Metaview AI20VC. 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. That disconnect between synthetic evaluation and actual system behavior is now a core block off for agentic models. That's why Nvidia, Anthropic, 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. In 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 Agentix systems, Turing provides the structure required to understand why these failures occur. To find out how, visit turing.com 20vc that's t u r-I n g.com 20vc.

[C]

You have now arrived at your destination.

[B]

Lucas, dude, it is so good to

[C]

have you on the show.

[B]

We've walked around High Park. I feel like we bonded in my short shorts.

[C]

I've heard so many things now because

[B]

I stalk the shit out of you

[C]

from David and specifically Jesse at decacagon. So thank you for doing this, man.

[A]

Of course. Thanks for having me. Thanks.

[C]

We're going to dive right in with super Easy question, which is public SaaS companies are getting killed. I'm looking at my book, dude, and I'm like, I thought I was so good at this and now I'm really starting to question it with the amount of red that I'm seeing.

[B]

So why is the public private boundary breaking down?

[C]

And what's the better side to be on?

[A]

For the first time ever with this AI wave, people are questioning the terminal value of SaaS. These were supposed to be like insurance companies, you know, annuity streams that just have Revenue streams and profit pools forever and ever and ever. And for the first time with a lot of AI, and I think in particular in the last six months with a lot of the coding models that have come out of Anthropic OpenAI and others, you are starting to question that value. And when you question that value, a lot of other things happen. The breaks that you got on SBC stock based COMP and Gap versus non GAAP earnings, those all start to go away. So that's the first dynamic. And then I think the second dynamic that's happening is people don't know which SaaS is going to be affected, right? Like you can think of a bull case and a bear for basically every SaaS company in the public markets. And when that happens, people are saying, okay, I'm just going to take my bags and I'm going to walk away and I'm going to do something else. Because why own anything if I'm really not sure which one of these things is going to work? I'm just going to go own consumer Internet or simmies or something else, right? And so I think like that's the real dynamic that's happening is those two things are happening all at once and all of a sudden, you know, that barrier breaks down.

[C]

How do we determine the babies that are being thrown out with the bathwater, so to speak? There are many different profiles of companies that have all been hit relatively to the same extent, but they're very different profiles for sure. How do we determine value in this pool of reductive market caps?

[A]

I think it's really, really hard right now, is the short answer. Right. This is the debate that we have all the time inside of our building. You know, you take a design tool for example, you can make an argument that that design tool is super well positioned in a world of AI because they're going to integrate AI into all the design process and generate so much more value than before. But then you could say, well, I just create all my designs in ChatGPT now, right? So why would I even need this design tool? And I think that's the argument that you're going to have on both sides of this at all times. I think the things that you're going to want to look for, the leading indicators that you're going to want to look for, is the revenue still continuing to grow sequentially, is net new ARR still continuing to climb? What's happening with the retention dynamics of these businesses? The more you can see that, the better you're going to feel. But the reality is for the next three months, six months, nine months, we're not really going to know what's really happening in the world. Things are happening so fast. All of the earnings that happen are retroactive. Right. So you can only see into the past that way. So I think that's why you're seeing people basically walk away from the sector.

[C]

If our job is to make money, which is pretty simple actually, I think we've over romanticized a lot of this job in the last few years.

[A]

Correct.

[C]

Our job is to make money for our investors.

[A]

Correct.

[C]

And we're both fortunate. Most of our investors are amazing institutions. My question is though, opportunity cost adjusted now, surely it has to be better being in the public's where Monday is trading at one and a half, X Wix is trading at two and a half as it's four and a half billion, market cap at 2 billion. Yeah, surely that is better risk adjusted than the $10 billion rounds that we're seeing for private companies.

[A]

Yeah. I think you could make the argument both ways on the public side. One, things may look cheap, but things may look cheap for a reason, Right. When things look really cheap, oftentimes they look really cheap for a reason on the private side, oftentimes the most expensive deals can be the best ones in many ways. And what I would say more on a macro point is if you think about the public markets right now, it's very hard to own the future. You have to like look and really pick and be really careful about the future. You get liquidity, you can trade in and out of things. That's the beautiful part about the public markets. But it's hard to own the future. If you want to own the future, you kind of have to be in the privates, Right. Think about it this way. Say I want to be ultra levered long to the token factory. I think tokens are the next big thing in AI. And I want to be ultra levered long to AI and whatever the next token factory is. You can own some things in the public markets, but you may say I want to own OpenAI and Anthropic in SpaceX, which now owns X AI and this long list of incredible AI application companies that are coming downstream. And so to get growth, something that's growing more than 30%, to get durability of that growth, to get access to the future, you have to own privates.

[C]

So funny, I was asked the other day what are the top three stocks that you're most like to own? And I said it's easy, it's anthropic. It's revolution. It's open evidence and really interesting. You cannot get any of those in the public markets.

[A]

Correct.

[C]

And I thought that was just a really interesting realization of. Huh. It's absolutely right. In a decade ago, I probably could have got them all in the public

[A]

markets at this point, actually, you know, that's absolutely right. Right. We've seen the emergence of, I mean, we call them platform companies right at the top. Call it 20 rough justice, 20 companies in the private markets. 18 of those would probably be public today, a decade ago. But we've seen the emergence of these platform companies. They're huge scale, growing way faster than basically anything you can access in the public markets. They have multiple products. They've shown they can be great public companies, but they're choosing to stay private in those platform companies. You cannot get access to as a public market investor. As a normal person, you can't buy stock in OpenAI or Revolut or open evidence, all CO2 portfolio companies. Right. You can't. You can't get access to those. And one, I think that's a shame for the normal person that they can't go and buy that stock. But two, it is an enduring trend that we've seen over the last five to 10 years.

[C]

It's a shame, but it's the greatest gift of venture capital that we could have ever wished for because it's allowed for us to transition from fidelities in the large, previously public entities working on BIPs to shifting to 2 and 20. And respectfully, your CO twos and your GCs and lightspeeds of the world ballooning fund sizes.

[A]

Our job to make incredible investments and generate real returns. That's what we are 100% focused on, generate real returns for our investors. I think it's one of the benefits of having a somewhat flexible mandate. Right. Is I'm not tied to having to do a Series B this year. If this trend emerges and it continues to persist, some of the best trades for us, some of the best investments for us are in that segment of the market.

[C]

I'm going to move to flexible mandate later because I just want to touch on the durability of revenue. You described it brilliantly as like the insurance annuity that kind of previous software revenues were. And now we have this transience of technology superiority, which sounds really kind of wanky, but like technology cycles has changed so far. So Gemini is better and then Claude's better and then opening eyes better that your durability of revenue seems to be More questionable and transient than ever. Should we ascribe value to revenue in the same way that we used to?

[A]

I think you're absolutely right. I think it's changing and I think it changes during every architecture shift. Right. This is the really critical part of technology. Right. As you moved from on premise technology to SaaS technology, as you moved from the Internet to mobile Internet, you had a potential for all of the companies in the prior generation to completely evaporate. And the big question is, can you find the companies that have the talent density, that are the most forward thinking, that are willing to reinvent themselves over and over and over again, which is so hard. And those are the companies that you want to find out. I think of a great example every time I think of this point, which is databricks. If you talk to Ali from databricks, we've been an investor since 2019. I think one of the things that we've seen from there and even before, I mean I looked at the company when I was at Kleiner Perkins. I've seen this company for a very long time is his ability to reinvent that company over and over and over again and ride multiple S curves. From being basically an ELT data transformation layer to running in training models to being the center of all data in the enterprise. Those are like multiple S curves that he's hopped and multiple times he's reinvented the company. It's not revenue growth that you want to chase, it's that it is the

[C]

most incredible adoption of new trends and moving with the next chapter that I think we've ever seen actually. Because you could look at like a replit and go similar actually how they've co attached to a new cycle. I mean this is another league. You mentioned the growth of revenue there. This is the other hard thing which

[B]

is like when we did Lovables A,

[C]

it was like at 3 million in revenue. By the time the legals were done it was at 20. And so the multiple had gone from 70x to 10x.

[A]

Correct.

[C]

I mean Anton should have been asking for a trade. I want to renegotiate this. How do we value assets that are growing in such disproportionate or previously unseen ways?

[A]

Yeah, this is one of the hardest things. It's why we actually think the framework that we use internally is we think about valuation. Everybody has to think about valuation. But when a company is growing exponentially 10x year on year, 50x year on year. Right. The things that we're seeing now, we think about valuation last it's the last question we try to answer. Is the valuation great? Because you mentioned you may invest in a series C at 20 million of ARR at 3 billion post, and that seems insane, but if the 20 goes to a 200 in one year and then 600 the next and 3 billion the next, all of a sudden that looks extremely cheap. And so our job is, how do you find the things that are on that curve?

[C]

Okay, let's take that. Actually, that's an interesting one. Let's say you are investing in a company that is doing, I don't know, 50 million in revenue and you're paying four and a half billion.

[A]

Yep.

[C]

Specific numbers. And they say we're going to be at 250 million. And then you go at the end of the year and you're like, wow, gosh, you're going to 5x in a year. And then we're going to 3x the next year to 750 million. Wow. Well, you paid four and a half. So even if it triples and then doubles again, you're still not at the 6 or 7x that it will be valued at in a public market. How do you just get your head around the hard dynamics of what it will be in a public market?

[A]

Yeah, it filters down into every decision that we think about all the time. And I think the key is, first you want to be in giant gantic tams, big ideas only because if you ever compromise on that very first principle and you're paying high valuations, you're in trouble. Medium tam, small tam. You better believe that this thing can be absolutely gigantic. We have this test internally. Right. Where it used to be five years ago. We called it the $10 billion public company test. Right. Can be a $10 billion plus public company. That bar has changed in this new world because we are tackling much larger markets than we used to. And so now that test is, can you be just an enduring public company? And that may mean 50 billion of market cap, it may mean 100 billion in market cap. It really depends based on the stage. But really it's big idea first. And then is the market absolutely yanking you into that giant market? Do you feel that market pull such that that revenue curve and subsequently down the line, that earnings path is really achievable. And so what you need to really believe is like take this 50 million ARR 5 billion post type company. You need to believe that someday you can get to 5 billion of revenue with 30% margin minimum, growing really fast. So what does that mean? I better believe there's 50 billion of revenue to go get.

[C]

And you also then are saying that risk adjusted, that is the best place you believe to put your capital, which is where I get stuck. I'm like in an ecosystem where there's so much opportunity, I understand that you can get there, but is that really the best place to put my money over the 10 other homes where I don't have to double, triple and then do a somersault into Kenya?

[A]

That's really. No, it's really fair. And again it's, it's something we think about a lot. The two things you want to consider when it comes to that. Right. And it's one of the reasons why again we love having a flexible mandate. We are not tied to just being able to do a series B at 300 posts. And like that's all we can do is because we can have almost this like rowboat that rows up and down the river. Anytime we see something opportunistically that we think is the best risk adjusted opportunity at that moment we can invest. And then the second thing we're really looking for, right, take that round as an example. And one of the reasons why we go back to this big idea test is I want to believe that if that company works that its best days are ahead of it and I can continue to invest. One thing that Jeff Horing from, from Insight always says is the best round is the double down round. And so by getting access to that company at a certain stage, if I think it has a shot at being a hundred billion dollar company, that round may not actually be the best round, but it gives me the opportunity to double down and make an even larger investment where more of my capital is going to be deployed over long periods of time. And again it's important why this market structure is changing. Right. I now because companies are staying private longer, these platform companies are staying private longer. You have the opportunity to make those bets. Previously you might not have, but now we have the opportunity to make those kinds of bets.

[C]

It's so interesting you said there about the lesson from Jeff Waring about the value of the double down round being such a good place for like value accretion or like resource deployment in some ways. I always remember Brian Siemann say we drastically underestimate the ease of the next double. And it's like it's much easier for Harvey to go from 6 to 12 billion than it is a company to go from 0 to 6 billion. Yes, that's really freaking hard. I really always remember that actually. And it Impacts a lot of how I think about selling.

[A]

There's actually a stat around this that I love. We have this chart internally which just shows at each market cap band, the percentage of companies that 10x. And the counterintuitive thing is, as you go up those bands, the percentage increases. So from a 10 to $100 billion valuation, I have a better shot at picking a 10x, not like a better return a 10x than I did in the prior band.

[C]

I just, again, I want to go back to the fascinating statement that you said there. Like, number one is market size. We need gigantic markets. Do we need gigantic markets over the best immediately? Incredible founders. I know that's a really shitty question to ask and forgive me for it, but I've actually learned that a good founder in a fucking great market almost trumps a great founder in an average market.

[A]

Yeah, no, I think the founder is incredibly important, right? You go back to the example of databricks. Most founders in that situation, they would have been handed, well, not handed. They would have built an incredible company in that first wave, but maybe they wouldn't have found their way to wave two and three and four. And it's again, it's why we like this type of company that we call a platform company, right? Which is the. Has shown the ability to skip TAMs, to have multiple TAMs over time. And I think that founder is tied to the market, you know, is tied to that market dynamic. And they're, they're equally important. But market size is always first. A great founder in a small market with a wedge that is not easily able to expand. I think we'll build an incredible business, but without having that core market and that core trend, it's hard to get to 100 billion, right? Like, you could be in this niche area that's very hard to expand. It's really easy to get an Act 1, but hard to get the Act 2 and the Act 3 and the Act 4 and to build that enduring company, right? You see it with SaaS today. You have to have the Act 2 and the Act 3 and the Act 4 and those things, that's why they go really in tandem.

[C]

Because of the expansion of TAMs and outcome sizes. Can you be thoroughly elastic on entry price even at the real growth stage? Or does price elasticity constrain significantly with increasing enterprise value?

[A]

Ultimately, price always does matter, right? I think some folks will say price doesn't matter. I think price does matter. But I think it matters least. You, of course, could make the argument of, oh, Lucas, well, you do it 5. Why not 6 or 7 or 8 or 9 or 10 billion? What if it was 20 billion? What if it was 30 billion? There does come a delineation point where you feel like the returns are going to erode, such that you would pass on an opportunity. But I'd say, by and large, if you're the one instigating these rounds and you're the one that's preempting these rounds, you can kind of help figure out what the right price is for a company needing at any given moment. And I do think you want to think about it last, because, again, these generational companies, it's almost never too late for them.

[C]

I agree with that, that we have a very kind of clear litmus test which will make us make many mistakes and which is why we should change it immediately. But it's like when we think about our entry price, it's true, we think about our entry price. Do we think that we are able to 3x that entry price within the next fundraising round? And so if the fat company says, hey, are we going to go from 1 to 10 million by the end of this year? And because of that, we're going to be able to raise at 350, great, well, we're paying 70 for the A. I can totally see my 3X there. 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 A1 to 4. I'm not raising it 300 if that's the case.

[A]

Right?

[C]

Shit, I'm probably raising a flat round, correct?

[A]

Maybe.

[C]

No, that's how we think about it. Do you have any internal monikers or frameworks for, like.

[A]

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 a 10, that I'm going to want to do that?

[C]

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. Do you want to kind of spray earlier? 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?

[A]

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. 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 the spray and 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.

[C]

I get you, but actually we. I'm not asking you about yours, but we see a world of competitive investing. Andreessen 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.

[A]

I think you can, but it definitely makes your job harder, Right? You want to make your job as easy as possible and not put up the barriers in ways to being able to win, to win a new investment. But I agree with you at 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? 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.

[C]

When founders come to you and they're

[B]

like, how dare you? Like, it started off as a pillow

[C]

company and now it's doing enterprise payments.

[B]

How?

[A]

How?

[C]

I don't know.

[A]

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. 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. But at the same time, that happens in big markets.

[C]

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 OpenAI cursor lovable open evidence Harvey, you name your like you're irrelevant if you're not in the min 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. If you've got $100 million seed fund and you have a $3 billion outcome, still a business. And so I wanted to ask you, when you think about mega funds, which we see more and more of.

[A]

Yeah.

[C]

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.

[A]

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.

[C]

What do you mean? What do you mean by that?

[A]

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. 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. The reason why is the markets change 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 dollar 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. 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 fun 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 in 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.

[C]

Do 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.

[A]

Listen, vertical SaaS, I think you could talk about it a lot of different ways. Constrained, 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. Multibillion 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.

[C]

Don'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.

[A]

Sure.

[C]

What is attractive enough for you? Like Revolut, I think is a phenomenal company. I'd love to be ambassador at 75 billion. I'd love to be as a clear pathway to 250 billion.

[A]

For sure.

[C]

Is that 3 is enough to be exciting?

[A]

No, a 3x is not enough to be exciting. And the math is really simple. Right. Say I'm a fund and I'm coachu. 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. 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, 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 being in big ideas really matters. 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

[C]

with the extension of those private markets and the outcome sizes? And your entry point, as we said, can be flexible, but the 300 to 5 billion is very standard. I 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?

[A]

It's certainly an option for liquidity now, right? A lot of folks, especially the early stage funds that have been in companies for 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.

[C]

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?

[A]

But 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? 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

[C]

when we say raise the bar. The challenge that I have with a lot of companies today is they're like good enterprise companies again, but they're doubling and tripling at 10, 20 million in revenue. What happens to that generation of SaaS companies from 2020, 2021 that are good companies, great companies, but, but respectfully, they're not great companies. They're good companies and in a prior cycle they would have been funded and they've been funded well. But now are you really going to jump out of bed for a 10 million growing to 25 million?

[A]

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 in 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, 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, you can drive real margin from them. They make incredible businesses. It's 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 comparable. 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 going to 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. 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.

[C]

You mentioned the word margin there.

[B]

And I think why I think so

[C]

many people feel really insecure as investors today is because there's so many priors that are being fundamentally questioned by growth rates or rule of 40s or I was always taught that margin mattered. I walked with my mother around London. I'm like, Jules, margin matters. And now I'm looking at how you

[A]

wake up every morning.

[C]

I put my feet on the ground,

[B]

I say, margin matters.

[C]

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?

[A]

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. Snowflake and Databricks. Very low margin early. A lot of people passed on those early rounds because they're 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 2/4 ago, but the token costs are coming down so fast. Maybe I'll be. 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.

[C]

And so we just get used to actually larger outcome sizes with larger, probably revenue pools associated, but a slightly lower margin profile.

[A]

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, 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.

[C]

What else do you think a lot of investors oscillate or focus on, which is total shares.

[A]

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 you know, 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. 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.

[C]

You mentioned earlier that you wouldn't want to be a seed fund deploying through billion or stowing down the gun of 3 billion or whatever it is. In a way I would because I can absolutely destroy the economics of all the seed fund players. And it's something that we see. We lost a deal recently to a large mega fund and we did 3 on 15 and they did 10 on 100 with no liqpref pre anything destroyed all the economics. And I told the founders, you should absolutely take that deal and sell tomorrow for 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 the way that we do?

[A]

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, that are really at play is 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. 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.

[C]

Do 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 much hold that true.

[A]

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 of 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, we as a fund, we. I actually think the public market skill set and the private market skill set 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.

[C]

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 when we think about king making, do you think that is a rational or real thing or do you not?

[A]

I don't think it's a real thing.

[C]

You don't?

[A]

I don't think the key. The king making concept is a real thing. I think some companies attract more capital early, some companies slingshot from behind. Do you make somewhat less capital?

[C]

You can 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.

[A]

It certainly does. And it's an advantage. But it doesn't mean that you can't build a great business if 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 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 Coatu and Sequoia Incliner all pile into a company that it's over?

[C]

No. Totally get that. I think it is an advantage, but I don't think it makes it. Which is probably where kingmaking goes too far. Do you think we are foie gras companies today in the same way we have done before?

[A]

What do you mean by that?

[C]

You know, foie gras. Yeah, yeah. You know, they shove a tube down it and then force feed it and then it explodes.

[A]

Yeah.

[C]

So we are putting too much money into companies and then they are artificially inflating and then exploding.

[A]

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. 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, that maybe it's not great. Like 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.

[C]

Do you worry that there are a generation of companies a la Canva, a la Stripe, which do not need to go public? Great 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 says, I don't want some fucking 30 year old analyst at some big bank telling me that I should increase sales.

[A]

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 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

[C]

truly tweeting about your layered spv.

[A]

I mean you've seen some of the things around some of these companies where it's like unbelievable, 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 know, you scale and you go public. So liquidity at scale is, is certainly one reason. The second reason is, and this can cut 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. 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. 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.

[C]

I adore Cliff and Mel and I think they're amazing. But you said about like the platform companies and you included Canva. If you were to be a harsh critic and you'd say, well, Figma's 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?

[A]

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 this 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. 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. I remember Cliff called me about this very early on because we were early investors in stable diffusion, if you remember the image, 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.

[C]

Again, I love Cliff and I love Mel and I totally agree with you in terms of that expansion. You know what I also love about that story? Married couple, amazing Australia, non technical yearbooks. Like, to be fair, the seed investors of that. I'm not taking anything away from Melancholf. Again, I think they're exceptional, but you've got to be quite mentally plastic away from the traditional investing rules to be

[A]

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.

[C]

What's your biggest lesson from working with Mary?

[A]

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. 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. I could barely model. I was great at talking to founders, but could barely model. And I found myself, you know, 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 company in a few, a complex company in a few lines in Excel and tell stories with data is like an incredible skill. 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.

[C]

To what extent does that truly matter versus phenomenal founder, big market growing fast.

[A]

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 when we were looking at databricks at COTU way back when, 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, 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 if you leave no margin for error, if that's not true, if you're 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.

[C]

Totally agree with that. You worked with Mamoon too? Yes, I really love Mamoon.

[A]

Me too.

[C]

What's your biggest lesson from working with Mamoon?

[A]

The gift that Mamoon has and I think from the SaaS era, Mamoon, my view is he was the best series investor in the SaaS era, period. 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 we 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 a very fun time. 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, this is gonna 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. He's like, we're at an inflection point, we're doing this. And that's what he's amazing at.

[C]

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, 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 Horing's fund.

[A]

Whoa.

[C]

I want dollars.

[A]

Absolute dollar return, absolute dollar return. I think you gotta split it in some way. I think what you're looking for, if

[C]

you're none of them pay you anymore.

[A]

Yeah, I know, I know, I know. But if you're looking, if you're an elite nlp, what you're looking for is the best return across different strategies. I think it's going to depend on what, on what you're looking for, what your time resonates. 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 Hoaring is going to provide you very strong, stable core returns. And I think like it really depends on what you're, what you're looking for. Right. And LPs want different things and they probably want exposure to all three in different ways.

[C]

I'm going to ask you another one because you failed at that one.

[A]

Okay.

[C]

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.

[A]

Oh, you can't do this to me. You can't do this to me.

[C]

And I'm not going to let you out of the room.

[A]

No, it's incredible. I think Founders Fund strategy of being ultra concentrated in a few companies has just been this incredible strategy over time. And I think Pat Grady's ability to pick Series B's is like pretty unmatched. Pick and win Series B's. He's very, very good and I think Sequoia is very good at that. So I think again they're, they're, they're good for different reasons but it, again, it really depends on what you like.

[C]

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?

[A]

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 just, it's insane. The innovation that's coming out of that business on the consumer side, their strength that's emerging in enterprise with Codex and other coding use cases and 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 I's 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.

[C]

Did you see the design work that Jony I've's team did for Ferrari?

[A]

Yeah.

[C]

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, I was going

[A]

to have to learn. You have to go get it.

[C]

No, I got a license. It'll be a present.

[A]

But I was right. Shotgun.

[B]

Exactly.

[C]

I'm very happy to hold the phone with the map.

[A]

There you go.

[C]

But I was like, wow, I've never wanted a car as much as Johnny Life's design.

[A]

Yeah, it's amazing. Yeah, it's incredible. And I think like that's the bull case. The bull case on Anthropic 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 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.

[C]

I'm sorry, I'm really naive here and I'm not asking like who's better or who's worse. Is that different to the other providers?

[A]

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 Trainium and GPUs 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. 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.

[C]

I totally get you. And actually having more people support you is a very, very advantageous position.

[A]

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 going to want to help you and who's going to want to 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.

[C]

Clearly, Philippe agrees with king making then.

[A]

Well, it certainly helps.

[B]

It totally helps.

[C]

Listen, I want to do a quick fire. So I say a short statement. You give me your immediate thoughts. That sound okay?

[A]

Done.

[C]

What have you changed your mind on in the last 12 months?

[A]

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 Claude Code 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.

[C]

When you think about that labor displacement, do you think we are overestimating the adoption of enterprise and labor displacement or actually underestimating? And it's coming sooner than we think.

[A]

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% 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 Claude 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. Like, this stuff is complex.

[C]

One of the most bullshit ones is people talk about like in the agricultural revolution, industrial revolution. And 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 onto safety document here. It's like Gemini puts in Nano Banana Pro and you're good to go tomorrow.

[A]

Yeah, it is faster. It's certainly faster.

[C]

So much faster. What's the single most memorable first founder meeting you've had? I'm not asking for the best founder, but it's like the most memorable first founder meeting.

[A]

Winston from Harvey.

[C]

Why?

[A]

It's not even close. I think it was because, one, I already believed when I. 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. Like, this is the one.

[C]

Did you lose the A?

[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. 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.

[C]

Dude. We are doing a term sheet now for a company and we turn down 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.

[A]

Absolutely.

[C]

So we're not going to do it.

[B]

Ridiculous.

[C]

No, I'm kidding.

[A]

Got it.

[C]

I just fired the seed team. You can invest in one seed firm and one series A firm.

[A]

I Mean, you guys, obviously, for the seed. Come on.

[C]

I love it.

[B]

I'll take that, actually.

[A]

Yeah. How about that?

[C]

Which series? A firm.

[A]

I would say Sequoia and Benchmark. I want to split my dollar. You're gonna allow me to split my dollar?

[C]

Yeah, yeah, I'll take that.

[A]

Rory o'.

[C]

Driscoll. Who? We do a show every Thursday. He's brilliant. He always says with Benchmark, you know, reports of my death have been greatly exaggerated. I just find it so entertaining.

[A]

And I think it's this firm that,

[C]

again, portfolio is so good on this.

[A]

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.

[C]

Listen, this is always a rough. With the smooth. Poor Peter, he's got to deal with that every day. No, I love that. He thinks he's fantastic. But seriously, you look at, like, your fireworks, your ligura, your madness. I mean, the list goes on, Sierra.

[A]

I mean, unbelievable portfolio from this era.

[C]

But again, everyone's like, our benchmark are over. You're like, I don't take any of those companies in my portfolio. What's been the hardest decision you've made in your career?

[A]

Leaving Insight for Kleiner. I know a lot of people. So I. 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, you know, almost 15 years ago.

[C]

Don't laugh. What do you say? Hi, It's Lucas from Insight.

[A]

Hi. I'm 19 years old. I mean, but it's. 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 night, you know, 19, 20, 21 years old. It's this incredible training ground. But I was the first. 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's a little abnormal at the time. 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, all the young folks in their careers. Like, you got to get off the linear path. You have to, like, it's the only way. Get off the linear path.

[C]

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?

[A]

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?

[C]

So with that, do you think the 100 million to 500 million pay packets are actually justified?

[A]

Yes.

[C]

I think they should give them to podcasters too. Just putting it out there.

[A]

You got this. I believe in you.

[C]

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.

[A]

It's not easy because, you know, if you've done this long enough, you have a lot of misses.

[C]

A lot.

[A]

I do remember very distinctly going and visiting Anduril 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 Anderil, I won't know, but it was one of the. 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 and L, there's no way, you know, you're a PNL investor, there's no way you invest just is what it is. It was an ugly piano. It was an example of me missing the force through the trees, not seeing just how special the founder, the founding team was there, just how important that trend was, where the world was going Right. And that's an example of where, you know, Founders fund got that right. A lot of people got that right. We got that wrong.

[C]

Final one. What most excites you for the next 10 years?

[A]

I'm excited about the products. This is one of the things that it's ingrained in everyone that joins CO2 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 the 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 Claude code this year. Oh my God, it's incredible.

[C]

I have to say, you especially right on the OpenAI 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.

[A]

I'm camping out for this thing.

[C]

But the ipod nanos and the ipod,

[B]

I was like, wow, thousands.

[C]

Now with the new iPhones, let's be honest, no one's like, yeah, I'm going to like, like run to the store. It's like, ah, whatever this, I've forgotten

[A]

to trade mine in for 4 years. I use like 4 generations old.

[C]

100. 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.

[A]

Awesome. Thank you.

[B]

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