20VC: Sequoia's David Cahn on The Winners and Losers in AI | The $0-$100M Revenue Club: Is Triple, Triple, Double, Double Dead? | The Future of Defence: Who Wins and Who Loses | How to Analyse Margins and Growth Rates in a World of AI

Summary

Podcast Summary: The Twenty Minute VC (20VC) – Sequoia's David Cahn on The Winners and Losers in AI, The $0-$100M Revenue Club, Defining Defense in AI, and Investing in a Changing Market

Podcast: The Twenty Minute VC (20VC)
Host: Harry Stebbings
Guest: David Cahn, Partner at Sequoia Capital
Date: October 27, 2025
Episode Theme: A sweeping, candid insight into the current state of AI, its “bubble” dynamics, implications for venture capital investment, the future of defense tech, margin and growth analysis in AI-deep businesses, and a dive into talent, vertical integration, and what matters most in the next decade.

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Episode Overview

This episode welcomes back David Cahn from Sequoia Capital—one of last year’s most downloaded guests—for a refresh on the AI landscape, defense technology, and how investment frameworks are evolving in a world characterized by hyper-speed, inflated valuations, and tectonic technological change. Cahn explains shifts in physical infrastructure, vertical integration, economic winners and losers, how talent markets have gone wild, the realities behind massive pay packages, and what might actually cause (or deflate) the growing "AI bubble."

Stebbings and Cahn get honest about mistakes, founder journeys, the tricky business of “kingmaking”, and the real, day-to-day decisions that define what will be the generational technology event of our lifetime.

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Key Discussion Points & Insights

1. The “Physicality” of AI: From Bits to Atoms

• Prediction Realized: The AI conversation has shifted from abstract focus (compute, models, data) to the hard reality of data centers, infrastructure, power, and supply chain.

  • “People were thinking very abstractly… but they should be thinking in an atom’s perspective about AI.” (David Cahn, 05:06)
  • Now, there's a construction boom, with generators sold out to 2030, and data centers dominating headlines and GDP impact.
  • “We moved from dollars to gigawatts - Sam Altman’s not talking about dollars anymore.” (Cahn, 05:47)

• AI's Impact on GDP:

  • AI is now a top contributor to U.S. GDP growth, mainly because of the construction and physical investments feeding massive new infrastructure.

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2. The $600B—and Now the $8T—Question

• Compute Buildout vs. Revenue Generation:

  • The original “$600 billion question” speculated on required revenue to justify huge chip (especially Nvidia) and data center spending. This is now up to ~$840B in 2025, with a looming “$8 trillion question” if power build-outs reach 800GW.
  • “Is there actually an end user for this compute?” (Cahn, 06:51)

• Delays:

  • Infrastructure construction delays are starting to surface, which adds risk to the pace of AI market maturation.

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3. Surprises & Misses from 2024–2025

• Insane AI Talent Packages:

  • Unexpected $50–100M—and even $1B—packages for high-flying AI experts.
  • “Sometimes I do think the beauty of AI is like reality is stranger than fiction.” (Cahn, 09:25)
  • Cahn sees this as partly desperation to “eke out” progress: simple math (1% chance to win a $T market = $10B value) is easy to abuse.

• Meta’s Underperformance:

  • Cahn wrongly predicted Meta would be a dominant AI winner, but now sees Zuck’s aggressive moves as a potential long-term positive, highlighting the irreplaceable resolve of founder CEOs.

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4. Vertical Integration: Model Providers Become Infra Providers

• OpenAI and Anthropic are rapidly shifting toward vertical integration: buying/building their own data centers, chips, and power sources.
  • “OpenAI and Anthropic are now steel, servers, and power companies.” (Cahn, 12:59)
  • This trend will likely become required for all major model providers.

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5. Are We in an AI Bubble?

• Yes—But That’s Not the Most Interesting Question

  • “We're in an AI bubble. Last year it was a contrarian view, now it's consensus—even bulls like Altman and Bezos are saying it.” (Cahn, 13:56)
  • The important question: Who survives the bubble? Bubbles create fragility but also opportunity for future Amazons.

• Long vs. Short Timelines:

  • Cahn’s view: over a 50-year horizon, AI’s transformation is inevitable; short-term market cycles create risk, but true winners will persist.

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6. Winners vs. Losers in the Compute Economy

• Consumer vs. Producer of Compute:
  • “Consumers of compute benefit from a bubble because if we overproduce, compute prices go down, your COGS goes down, and your gross margin goes up.” (Cahn, 17:48)
  • Producers of compute are stuck in a commodity business—“like oil”—and face cyclical, lower-margin futures.
• Commodity Logic:
  • Big cloud companies are “an artifact of a monopoly era,” and AI will likely not produce monopolies. Everyone can see the opportunity, unlike when Google/AWS were built.

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7. Market Incentives, Funding, and the AI Bubble's Fate

• Game Theory:
  • There is no hidden coordination slowing AI CapEx—it's just each giant player (Microsoft, Amazon, Oracle, Nvidia) reacting rationally.
• Bubble Unwind:
  • "It's not going to be a credit unwind—it's going to be an equity unwind" (Cahn, 29:29)—most capital is equity, not debt. If prices crash, it hits portfolios, not banks.
• Signs of Fragility:
  • The “risk absorber” role has moved from Microsoft/Amazon to smaller, less robust players (Oracle/CoreWeave), and now even chip companies are fronting risk.

“You can’t predict exactly when the wobbly building falls, but you can notice the fragility.” (Cahn, 24:16)

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8. Margins, Growth Rate, and the New SaaS Playbook

• Margin Skepticism:

  • Gross margins are a "directional indicator," but not an absolute limiter—compute costs fall over time, and product complexity grows margins.
  • “Plenty of companies with low gross margins end up super healthy—look at Snowflake.” (Cahn, 39:09)

• Growth Rates Are On Steroids:

  • The triple-triple-double-double SaaS rubric is replaced by “0-to-100” ($M ARR) in AI; the market's demand and infra mean the best teams see rocketship growth.
  • Product-market-fit is revealed by how fast you go from $1–$50M ARR.
  • "It's about smashing product-market-fit, not about hitting theoretical milestones in set timeframes." (Cahn, 41:15)

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9. On Kingmaking, Founder Journeys, Overcapitalization

• Kingmaking Is Overstated:

  • “You can’t make a company succeed. The company has to already be successful. The lesson that punches you in the stomach in venture is you can’t make a company succeed... Ego gets in the way.” (Cahn, 36:15)
  • Top VC brands may help with talent and momentum, but cannot manufacture greatness.

• Fast, Rich Rounds & Overcapitalization:

  • There's tension as capital chases fast-growing companies. Overcapitalization can drive unhealthy team cultures and overconfidence.
  • The best founders “act like the money isn't in the bank.”

• Founder Narrative, Scar Tissue:

  • “Quick success” is the exception, not the rule. Greats like Clay, JuiceBox, and UiPath took years “in the wilderness” before breaking out.

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10. Talent, Mimetic Career Paths, and Risks

• Young Talent Is Undervalued:

  • The most important unsolved problem: hiring dynamic, AI-native builders in their early 20s—who learned ChatGPT as teens and have “slope” (ability to learn fast).
  • The old engineering playbook is outdated in AI: hire for “slope,” not just skills.

• Visible vs. Hidden Risks:

  • Cahn prefers hiring with known, visible risks (like youth and inexperience), vs. “hidden risks” like complacency in experienced hires.

“The new playbook… is actually going to be much more about hires of the AI generalist, this 23, 24, 25-year-old who's really native in AI.” (Cahn, 49:26)

• Mimetic Job-Choice Algorithms:
  • Most graduates still follow what the previous year’s stars did (consulting, banking), which was rational in the past, but AI fundamentally breaks this pattern.

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11. Defense as the “Next AI”—Sequoia’s Late Realization

• Defense Tech is Early in Its AI Adoption:

  • Sequoia was late, missing out on Helsing and Anduril, but is now investing heavily.
  • The “Ukraine war” was Defense's “Transformer moment”, but its "ChatGPT moment" hasn’t happened—i.e., broad public and government consumerization is nascent.

• National Champions, Not Ecosystems:

  • Defense will create a handful of “national champion” companies (e.g., Anduril in the US, Keller in Israel, Stark in Europe)—not dozens as in SaaS or fintech.

• Customer Concentration and Market Limitations:

  • Selling to governments is tough. Market is limited, winners will be concentrated, and it's not a broad category play.

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Notable Quotes & Highlights

• On Bubbles and Survivors:

  “The thing that is more interesting is who’s going to survive the bubble... You can see the fragility. Everybody can see the fragility.”
  (Cahn, 13:56 and 24:51)

• On Overestimation of AI Timelines:

  “The highest status person is the one who says AGI is 100 days away, but the true godfathers of AI—Sutton, Karpathy, Sutskever—believe the timeline is much longer, 20-30 years.”
  (Cahn, 33:56)

• On Kingmaking:

  “You can’t make a company succeed. The company has to already be successful.”
  (Cahn, 36:15)

• On Talent Strategy:

  “Maybe 10 years ago, a senior software engineer had more experience than an L3... The new playbook is much more about hiring the AI generalist, this 23, 24, 25-year-old who’s really native in AI.”
  (Cahn, 49:26)

• On Margin and Growth:

  “Companies with 0% margin can figure it out... what matters is if people love what you’re building and you can figure out how margins scale over time.”
  (Cahn, 39:09)

• On Defense:

  “Defense is the next AI… if the Transformer moment was the starting gun in AI, the Ukraine war was the starting gun in Defense tech. The ChatGPT moment hasn’t happened yet.”
  (Cahn, 57:44)

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Important Timestamps

• 05:00 Physicality of AI: Steel, power, supply chains now central; AI now drives GDP growth.
• 09:25 Wild AI talent packages, symbolic of desperation in the ecosystem.
• 12:59 Model labs become infrastructure companies.
• 13:56 The consensus and contradictions of the “AI bubble.”
• 17:48 Simple framework: consumers vs. producers of compute.
• 24:16/24:51 On bubble fragility and the unwinding of risk absorbers.
• 29:29 The AI bubble likely unwinds through equities, not debt.
• 39:09 Margin as signal, but not barrier, and the path to healthy AI business models.
• 41:15 Zero-to-100 million club: what hypergrowth looks like now.
• 49:26 The single biggest talent paradigm shift.
• 57:44 Why Sequoia was late to defense, and what the “chatbot moment” will be
• 65:01 Defense not a broad category; will produce only a few winners per country.

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Closing & Tone

The conversation is candid, fast-paced, and dense, filled with both humility and conviction as Cahn and Stebbings dissect headlines, investment frameworks, and the psychology of founders and venture capitalists. Cahn is frank about misses, careful not to overclaim about the power of VCs, and laser-focused on the next decade as AI’s “Physical World” swings into focus. He’s clear that not everything that worked in the last era will work now, and the “visible risks” may offer the greatest opportunities.

"At the end of the day, AI is the most important story of our lifetime. It's going to completely transform the world. It's a once in human history kind of event."
— David Cahn (70:10)

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Useful for:
Anyone investing in or building tech startups, following macro or micro AI trends, evaluating defense tech, analyzing new labor/talent markets, or navigating the frothy and sometimes fragile world of big technology.

Transcript

[David Khanna]

I do think we're in an AI bubble. You can see the fragility. Everybody can see the fragility. The thing that I think is more interesting is who's going to survive the bubble. Consumers of compute benefit from a bubble because if we overproduce compute prices go down, your cogs goes down and your gross margin goes up. The lesson that punches you in the stomach in venture is you can't make a company succeed.

[Harry Stebbings]

How would you respond to Sequoia? Were asleep at the wheel when it came to defence, not being in Helsing and Anduril, the two clear market leaders in the category.

[Podcast Host/Producer]

This is 20 VC with me, Harry Stebbings. And one of the most downloaded episodes of last year was David Khan at Sequoia.

[Harry Stebbings]

So much has changed in the last year.

[Podcast Host/Producer]

I wanted to have David back for a refresh. I wanted to understand how he thought.

[Harry Stebbings]

About where we were today.

[Podcast Host/Producer]

For those that missed the last show.

[Harry Stebbings]

First, it's a must. But David is a partner at Sequoia.

[Podcast Host/Producer]

Capital and one of the world's leading AI investors. And before Sequoia, David was a general partner at CO2.

[Harry Stebbings]

I loved this conversation today.

[Podcast Host/Producer]

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[Harry Stebbings]

You have now arrived at your destination. David, I love your writing. Our episode last year was one of the most downloaded shows I had. Like the CMO of Meta tell me that it is the single show that he has forwarded to more people and cites more often than any other. Not to make you nervous or set the pressure for this episode, but thank you so much for joining me again dude.

[David Khanna]

Thanks for having me Harry. You were always very Kind now, the.

[Harry Stebbings]

Year of the data center. Sounds wonderful. We had an amazing discussion last year.

[Podcast Host/Producer]

What did you predict last year, David?

[Harry Stebbings]

That happened, and we are seeing in action now.

[David Khanna]

So we talked about last year, this concept of steal servers and power. And I think, if you remember, you know, rewind to summer 2024. The big conversation at that time was compute models and data. That's what everybody was talking about. And I sort of had this view that everyone was underestimating the physicality of these data centers. I'm on the front lines, I'm talking to people every day. You know, you talk to people. They're flying electricians to Texas and they're trying to buy out generator capacity, and generators are sold out until 2030. And so how do you get in line and how do you do that? And so I sort of had this sense that people were thinking very abstractly, sort of in a BIT's perspective about AI, but they should be thinking in an atom's perspective about AI. And I think that prediction came true in two ways. The first way is the best trade of 2025 was the AI power trade. A lot of Wall street people made a lot of money betting on the fact that power was going to be the constraint and we're going to move away. You know, you hear Sam Altman now talking about gigawatts every day. He's not talking about dollars anymore, right? So we're moving away from dollars and we're moving toward gigawatts. And I think that transition has fully happened in the last year. The second way I think it was right. And, you know, it's funny now, like a year and a half later, you see this on the COVID of the Economist and the COVID of the Wall Street Journal, on the COVID of the Atlantic. The mainstream media has now really picked up on this narrative of the physicality of AI is what translates to gdp. I mean, GDP is an imperfect metric, and it generally captures physical things more than virtual things. And so GDP now is picking up all of this construction boom that's happening, all this deal that's getting created, all of the physical stuff that's happening in the AI data centers. And you're seeing these stories, which I think are true, which is AI is now one of the biggest contributors to GDP growth in the United States. And so I think that's the second way in which that prediction has played out.

[Harry Stebbings]

Does its contribution to GDP growth go contra your $600 billion question in terms of where the revenue will come from?

[David Khanna]

Well, the $600 billion question and maybe just to remind folks what that is, I mean, it's basically a very simple equation that says if we invest. And this was 2024 when I wrote this. If you invest 150 billion in Nvidia chips, that's about 300 billion of data center investments. And to pay that back, the person using the compute needs to earn a 50% gross margin. So there's about 600 billion of revenue that needs to get generated. If you redo that analysis in the summer of 2025, it's about 840 billion. So it's grown, but it hasn't grown dramatically. And so the question behind the question was, is the customer's customer healthy? We know that the customer is healthy. We know that people are buying all these data centers. We know that people are building these data centers. We know that those stocks have all gone up. We can see that. But is the customer's customer healthy? Is there actually an end user for this compute? I don't think that's been answered. The question last year, which was the valid question was, if everyone's spending all this money, it hasn't showed up yet because people haven't put the shovel in the ground yet. I literally wrote a piece last summer called AI is shovel Ready. You know, the shovel is going to start hitting the ground. And so now the shovel is hitting the ground. We're mid construction on a lot of these projects. One of the predictions I made last year, in addition to saying it was going to be the year of the data center, I said, hey, we're going to have these construction delays. We're going to have issues now in building out these data centers. And the information has done a very good job of reporting on this, but I think we're at the beginning now of seeing some of that play out as well.

[Harry Stebbings]

Are we going to see a mass proliferation of delays on data center construction, do you think?

[David Khanna]

I think we're going to see variability. One thing I'm always interested in as an investor is like, there's winners and there's losers and there's variability. And I'm very skeptical whenever anyone tells me, like, everybody is going to win or everybody is going to lose or everyone is going to do anything. Like there's always variability. Imagine a race, you have a track race. Like, there's somebody in the front and there's somebody behind, and someone's faster than the other person. And so I think with data center construction, one of my core perspectives that I've been developing over the last 18 months of writing about this is that construction itself is going to be a moat. The ability to build things is hard. I think we underestimate that and I think we continue to underestimate that because we sort of say, oh well, it's fine, like everyone's going to do it. The timeline is two years, okay? But like, there's a lot of complexity that goes into that. And by the way, the complexity compounds when everybody is doing the exact same thing at the exact same time and everyone is trying to buy from the same vendors. And I've written a lot about the AI supply chain for that reason, because you really need to care about not only, okay, Meta and Google are both building a data center, but who's the guy that they're calling and who's the guy that he's calling? And you got to follow it all the way down the supply chain to get to the core of really what's going on.

[Harry Stebbings]

There's so many things I want to unpack within those I do want to go to. What did you not predict or foresee that did play out that you were surprised by?

[David Khanna]

I think there were two big misses last year. I think the first big miss was these like big talent acquisitions. If you'd asked me the probability a year ago that, you know, if you're a 25 year old recent grad from an elite university who is perceived to be an AI expert, you can get a fifty hundred million dollars pay package right now, and if you are a brand name that everyone recognizes your name, you can get a billion dollar pay package right now for a single individual. I totally did not see that coming. And I think that you asked me a year ago to predict that I would have said you were crazy. So sometimes I do think the beauty of AI is like reality is stranger than fiction and a lot of crazy things happen.

[Harry Stebbings]

Do you think those scaled pay packages are justified?

[David Khanna]

I think they're symbolic of this sort of desperation in the ecosystem where it's like we need to eke out progress, we need to prove that all these investments are worth it. And I think there's this logic that gets really abused in the venture world and in the tech world, which is like, hey, if I increase the probability of making a trillion dollars by 1%, that's worth ton of money, right? That's worth $10 billion. And sure that's true, but it's very easy to overestimate the 1%. Is it 1%? Is it a hundredth of 1%? Is it a thousandth of 1%? Is it A ten thousandth of 1%. Our brains are very bad at reasoning at that scale of number. I think to the extent that you believe that hiring this very impressive researcher increases the probability you win by 1%, I totally can see why you will justify a billion dollar pay package for an individual. That said, I think we are psychologically biased to overestimate what that percent contribution is. It may be the case that there's these broader macro variables which we'll talk about, I'm sure later in this discussion. There's these broader macro variables that are actually driving progress in AI that are not a single individual can change.

[Harry Stebbings]

I'm very upset looking at these pay packages that my mother didn't push me towards a more engineering heavy.

[David Khanna]

Doesn't everyone feel that way? I think that's like probably the universal reaction to seeing these packages.

[Harry Stebbings]

I'm like, mom, you should have done better. Bad parenting. You encourage me to do English. Really? Come on. War and Peace doesn't quite make it, does it? When you're getting paid three and a half billion by Zuck.

[Podcast Host/Producer]

What was the second?

[David Khanna]

I think the second one, you know, one thing we talked about on the podcast last year, I predicted that Meta was going to do really well and I think that prediction was clearly false. In a 12 month time horizon. I thought that the vertical integration that Meta had was going to be an advantage. And I think that Meta, these 100 million packages are coming in large part from Meta because they haven't performed as well as they thought they were going to. The reason I thought Meta would do well is that it was vertically integrated and found a run. I sort of continue to believe that in the fullness of time it is possible. And I think the dramatic actions that Zuck is taking represent this. It is possible that I will be proven right in a longer time horizon, which is to say that Zuck's going to fix the problem. It's amazing what founders can do. He's so focused on this, he's spending all of his time on it. But I think if you look back a year ago at the prediction that Meta would do well, I think you would say wrong.

[Harry Stebbings]

Have you changed from a buy to a salon matter?

[David Khanna]

I think the dramatic action that Zuck's taking represents just how deeply invested in this he is. And I think it also shows us what founder CEOs can do and why founder CEOs are different than non founder CEOs. I mean, there's all these studies of like, if you just invest in the basket of founder CEOs, you will outperform the basket of non founder CEOs. And I think what Zuck is doing represents that. And so I remain optimistic about Meta long term.

[Harry Stebbings]

You said about the vertical integration, that being part of your thesis. I totally agree with you and was probably shaped by hearing you, to be quite honest. David, you said to me data center and model teams need to be coupled kind of going to the vertical integration elements. Do you stand by that? How do you think about that when hearing that today? And does OpenAI and Anthropic not having that vertical integration challenge that well?

[David Khanna]

I think the simple version would be OpenAI and Anthropic are now steel servers and power companies. And that's like a big change that's happened in the last 12 months. And so in many ways OpenAI and Anthropic are becoming more and more vertically integrated. Every day you're seeing a lot of announcements around them developing their own chips. Every day you hear Sam Altman talking about gigawatts of power and procuring his own power. And so I think you will continue to see the big labs moving vertically down the supply chain. And that's been one of the biggest trends of the last 12 months.

[Harry Stebbings]

Do you think we'll continue to see that? We saw Poolside recently announced a 2 gigawatt data center that they're building out in conjunction with CoreWeave. Do we think all model providers will need to be vertically integrated in this way?

[David Khanna]

I think that competitive pressures will push all of the model providers to spend more time on this and to have teams focused on this. So I think the answer is yes. I do think that this is a trend that is going to be durable.

[Harry Stebbings]

When we think about where we are today, everyone says bubble. You've heard it, I've heard it. Do you think we're in an AI bubble?

[David Khanna]

I do think we're in an AI bubble. I also think to your point, a year ago when we had our last conversation, it was a very contrarian thing to believe that we're in an AI bubble. Today it's a very consensus thing to believe we're in an AI bubble. I mean, Sam Altman, Vinod Khosla, Jeff Bezos, like some of the biggest AI bulls have now come out and basically said, hey, we're in a bubble of some sort of the other. And each has their own perspective on exactly how that's going to manifest. Right now the bubble conversation has sort of reached kind of full consensus. The thing that I think is more interesting is who's going to survive the bubble, what's going to come next? And so I think there's two components to that. Number one, who are the winners and who are the losers. If you remember from the.com, lot of companies from the 90s still did well. Amazon still became an amazing company after the dot com bubble. So I think there's an opportunity for winners to continue to do well after the bubble. And I think the second thing that's really interesting is just timelines, right? Like I've always said, like my core belief is that in 50 years when you and I are 80 years old, AI is going to have completely changed the world. It's going to dramatically reshape everything about society. And so if you take that time horizon and you say, okay, AI is this tremendous, tremendous technology innovation, it's the most important thing that's going to happen in our lifetimes, probably it's going to be among the most important thing that's ever happened in human history and in the history of this planet, right? So it is this amazing thing and yet the market is implying some probability that all of this is going to happen in such a short time horizon with a very specific chipset and all of this stuff. And so I think unpacking the tension between AI as a long term winning trend and a long term generational change and a short term market cycle that will incinerate capital, I think that's the second kind of area that I think is really interesting.

[Harry Stebbings]

How do you balance that being an investor today? David, play the game on the field. The Bill Gurley quote, but then also the awareness of the long term impact that will come over multi decades.

[David Khanna]

I think it's tricky. I think the one benefit I have is I've been investing in AI for about eight years. For me this is not like, hey, this is like a 12 month thing where you're like running and have this FOMO to get into AI. I started investing in AI in weights and Biases, Series A when everyone said Deep learning was going to be tiny. It was a year after the Transformer paper came out and I said, deep learning is a tiny market, why would you invest in this company? And of course they had a really nice exit to Cory. Recently I invested in Runaway ML when stable diffusion hadn't even been born yet and everyone was saying, oh, Transformers is the only way. And of course stable diffusion introduced a new model architecture and I invested in Hugging face, which I still remember the first meeting I ever had with Clem. You know, he had launched this Transformers library. It's Funny now, Transformers on the tip of everyone's tongue. But that time, nlp, it was nlp, by the way, it wasn't AI at that time. And he had this amazing transformer library. And for folks who are steeped in AI, it was a successor to Bert and this old school of NLP models. So I just say that to say that I think when you take a long enough time horizon in AI over the last eight years, you have more opportunity to find investment opportunities. It's not about finding 10 investment opportunities, at least for me. I don't need to find 10 investment opportunities this year. I'd like to find one or two investment opportunities a year that I really love. This year I've invested in Clay, which I think is an amazing application layer company we can talk about. I invested in Juicebox, which is building an AI recruiter that has tremendous love. And so I think you can find exceptional AI companies that I believe will do really well over the long time horizon and will continue to succeed for decades and decades to come. And one thing I ask myself before I make every investment is, is this company going to succeed in spite of market volatility? If your only way that your company is going to succeed is that it can raise infinite capital in a cheap capital market, that's very difficult. If you have real customer love and you've built something that people absolutely need, you're going to be able to navigate through any market environment. And by the way, we've kind of seen that now with all of these 2021 companies navigating that environment, some of them came out really strong. On the other side, look at Databricks, 60 billion now, 100 billion valuation. So you can come out the other side of market cycles if you have compelling product market fit, a great team, a great founder.

[Harry Stebbings]

So, David, when we play out your question there of the winners and the losers, just so I understand that, who do you think the winners and the losers will be? When we look back on this last 12 to 18 months, I've had a.

[David Khanna]

Very simple framework for this. It's actually, I think, probably the first thing I ever published in AI. An AI's $200 million question way back when in 2023. The framework is this. Consumers of compute benefit from a bubble because if we overproduce, computer prices go down, your cogs goes down, and your gross margin goes up. So I've had the view that you want to invest in consumers of compute. Producers of compute. Imagine you're producing any commodity asset if other people produce a lot of that commodity asset. It doesn't matter. It has nothing to do with you. You might be running the best operation possible, you might be an amazing business person, but if everybody else starts producing the same commodity asset, prices go down. And so it's very hard to control your destiny in commodity businesses. By the way, this is why commodity businesses tend to trade cyclically and tend to trade at lower multiples than non commodity businesses. So I think if you're a producer of compute, you are fundamentally in a commodity business, just like an oil company is in a commodity business. And that is going to trade a different way and that is going to have more cyclicality than if you're in a non commodity business consuming the commodity, consuming the energy and producing intelligence on top of that. And so I think if you're consuming this raw resource, which is power, and you're producing intelligence and doing something that people love with that intelligence, those are the businesses that are going to do well on the other side of this market cycle.

[Harry Stebbings]

All three of the best businesses, not commodity businesses, in the form of Google, Cloud, AWS and Azure.

[David Khanna]

I love this question. So let's talk about it. I think it's really interesting. One thing I've written a lot about and you and I have talked about this is like game theory and these big companies. And one of my core beliefs, or one of the things that I think is underestimated in the market is that we're living in an anomalous monopoly era. And it's funny because there's so many comparisons to industrial revolution and in some ways we're living in this new gilded age. And we have these seven companies and they represent 40% of the S&P 500, which is just mind blowing. And have these amazing monopolistic businesses. And these businesses are cash cows. And I think people extrapolate from that and they say, oh, all businesses are monopolistic. I think people have a mental model that implies too much monopoly and not enough commodity. And what I think people underestimate about the big tech companies is that when the big tech companies were founded, when Google was founded, nobody thought it was going to be a monopoly. Think about YouTube selling for a billion dollars. I mean, that would be crazy if you had known how big all of this was going to be. So nobody knew that Google was going to be monopolistic. And you can build monopolies when they're hiding in plain sight. Nobody can see them. And so you build this monopoly and you don't have that much competition. AWS is the same. You Mentioned aws, nobody knew that the cloud was going to be this tremendous opportunity when I started doing this. And to their credit, that's why they have the biggest market share in the cloud business. And that's been very durable for them. And so I think when nobody sees the monopoly, you can build a monopoly and then you can extract margins on the other side. But AI is so different. Everybody knows that AI is going to be big. Like this is. I think the irony of the AI is that everybody knows AI is going to be massive. But if everybody knows something's going to be massive, then everybody builds companies. And if everyone builds companies, there's tremendous competition. And so I think the difference between the AI era and the big tech era, and it makes sense why everyone is over indexing or overtraining on the big tech era, because that's the era we live in. But the difference is that these monopolies are not hiding in plain sight. We all now know that if you build an amazing tech company, it can be worth a trillion dollars in 2000. If you told people that they could have a trillion dollar tech company, they would have laughed you out the room. And so I think the market environment in which these companies are getting built is dramatically different. And monopoly profits are unlikely to exist. And by the way, that's good for us, that's like good for everybody. Like we shouldn't want monopolies to exist. Monopolies are bad for the consumer. The consumer wants to get things for free and the consumer wants to get things for the cost of capital. And I think that to the extent that there are not monopolies in AI, that's much better for how AI is going to evolve in a healthy way than if it evolved in a sort of a monopolistic direction.

[Harry Stebbings]

You said about kind of consumers of compute will win. I like that. But respectfully, it feels relatively accepted in venture ecosystems for sure, in a way that your bets before weren't weights and biases, weren't Runway, wasn't hugging face, was kind of a kind of weird community play at a point. What do you think is obvious to you that is not obvious to the rest of the community today?

[David Khanna]

When I first started saying this 18 months ago, it was definitely not consensus. And so one thing that is tricky in the business of ideas is that as soon as the idea becomes accepted, it was always obvious. But in the moment where you propose a contrarian idea, everyone kind of criticized it. So I do think it's been interesting to see the change. And then by the way, the people who had the wrong opinion very quickly changed their opinion such that they were. They weren't actually wrong. And so anyways, I think the. The idea game is a tricky one. And the second thing I would say to that is while people say they believe this, and you and I talked about this on the podcast last year, you probably remember this. Everyone says they believe this. And then you look at these pitchbook charts where it's like, where's the dollars going? Probably 80% plus of the dollars in AI are still going to producers of computer, not consumers of compute. So I do think you're right that it's an accepted narrative. But the producers of compute consume so much more capital than consumers of compute that if you are in a capital deployment strategy and you're trying to deploy as much capital as possible, you have to invest in the producers of compute. And I think that's one of the dangerous things in investing. There's this almost like incentive to invest in people who consume more capital because they're calling you every day. And the people who don't consume capital don't want to raise capital. And I think some of the best investments are those companies that don't want to raise capital. When Sequoia invested in Zoom, they didn't want to raise capital, right? They were profitable. They were doing really well. Those are the businesses that I think as an investor, you really have to focus your time on.

[Harry Stebbings]

I spoke to Sonia on your team beforehand, and she gave me a fantastic question. She said, if this is a game theoretic bubble, is there a coordinating mechanism for the spending to stop and the bubble to pop?

[David Khanna]

You know, I love game theory. So I mean, my basic framework on AI and this is actually kind of how I write all these pieces, is there's like 10 players around this big chess board and they're extremely powerful, and each of their moves affects the other people's moves. So it's kind of recursive. And so you sort of have to think first order, second order, third order, how does my move affect other people's moves? And these are very sophisticated players doing this. The simple answer to your question is it's not coordinated. That's the beauty of the invisible hand. That's the beauty of people's incentives. These are big companies that are acting out these incentives. I think until the incentives change, the behavior is not going to change. There is no coordinating mechanism. I do think that's one of the. It's always the surprising fact of capitalism, like, everyone wants to believe that everything is kind of coordinated. It's easier for our brains to grok everything being coordinated. But I actually think it's, it's pretty uncoordinated and incentive driven.

[Harry Stebbings]

You said earlier it is definitely a bubble and we're seeing this consensus across the different visionaries in our ecosystem. If it's a bubble, does it pop or does it deflate? And how do you expect that to play out?

[David Khanna]

I'm a student of Nassim Taleb and I will lean on Nassim Taleb's sort of. He's a hedge fund investor and philosopher and he's written Fooled by Randomness, Antifragile, Black Swan. I think these are books that a lot of folks will be familiar with and really influential books in the investing world and his philosophy. And he says this in antifragile. It's really hard to know if a building is going to fall down, but you can see when it's wobbly. And so you can't really predict when the wobbly building falls. But you can notice the fragility. I think my perspective on AI right now is you can see the fragility. Everybody can see the fragility.

[Harry Stebbings]

Can I ask you what specifically makes you say you can see the fragility?

[David Khanna]

When I think about why did this AI bubble narrative go from contrarian a year ago to consensus today? I think the main thing driving the consensus is these circular deals and the big tech company Dynamics. Let me unpack that. A year ago, hyperscalers were holding up the AI ecosystem and everybody felt very comfortable with that because everyone knew that these were very robust businesses. Microsoft and Amazon specifically were driving the vast majority of the AI CapEx growth. And they were explicitly saying, hey, we're going to buy out your generator capacity for five years, we're going to sign a 20 year lease on this data center and we'll back it up with our credit. So they were basically putting themselves in front of all the risk. And the way I thought about it a year ago and wrote about it a year ago is like they're almost grabbing the hot did demand hot potato and saying like, it's ours, don't worry about it, we got this covered. A year later, Microsoft and Amazon have really stepped back. This started in the beginning of the year. There was this big public announcement or leak or whatever you want to call it, where Microsoft walked away from two data centers and it sent a message to the market like, hey, we're not stepping up. We're not going to take all the risk on everybody else's behalf. We're not going to be this risk absorber in the ecosystem anymore. And then what happened later this year is Oracle obviously stepped up and took on a huge amount of the compute demand, and Core Weave has really stepped up and taken on a huge amount of the compute demand. And so you have this shift from Microsoft and Amazon to Oracle and Core Weave. And then the second order effect of that is that Oracle and Coriv are a lot smaller than Microsoft and Amazon. They simply can't absorb as much risk as Microsoft and Amazon could. And so the chip companies are now stepping up and saying, okay, we'll absorb some of the risk, we'll put in the capital to finance this build out, where the demand on the other side is not so clear because of course, the chip companies also get to book this as revenue. So their cost of capital is very low. One might even say their cost of capital is negative. In some of these deals, it's the cheapest capital available. And so moving from expensive capital from these big tech companies to cheaper capital from the chip companies themselves, who get to benefit from circularity, I think that's probably been the biggest change in the last 12 months in AI. And I think that's something a lot of people have observed. It's fairly obvious. And so that, I think has changed a lot of people's minds.

[Harry Stebbings]

Do you think these deals are priming the pump, so to speak?

[David Khanna]

I think all of these deals now are priming the pump. I mean, you basically announced the deal, they're 10 or 20% funded, and then you have to go raise capital to fund the rest of it. And so, you know, everyone announces these deals in gigawatts, not dollars anymore. I think most people don't know how many dollars a gigawatt is. And so the rough math is, you know, a gigawatt is $40 billion to build out. Jensen says it's 50 or 60 if you use the next generation Vera Rubin chip. So let's say it's somewhere between 40 and 60,800, GW of power build out, which is what people are talking about now. That would be AI's $8 trillion question. 250 gigawatts of power is AI's $20 trillion question. So we've totally upped the ante and the magnitude is just much, much bigger. But of course that's not funded. And so I think the funding for these deals is going to be an important thing that has to play out.

[Harry Stebbings]

How do you read them? When I hear you speak now, I feel very concerned, like I Think is there even the capital supply in the world for these? You know, we've heard about Sam Altman and the trillion dollars that he needs and requiring the same energy as Japan. And you're actually looking at that going well. Not even the sovereigns have enough money for that, actually.

[David Khanna]

Well, we're living through this amazing moment, and I do think it's precarious. We're living through this amazing moment where like the entire capital market is just AI. 40% of the S&P 500 is these big tech companies. They're all basically trading on AI. Private capital is all targeted AI. And so I do think the world's capital machine is directed in a single direction. I think the risk is that it's all focused on a very constrained period of time. I actually think in the fullness of time, it's not that risky. Like, these things are going to play out. AI is going to be amazing. We're going to get these huge technological breakthroughs, tremendous revenue is going to get created. It's going to be a big driver of the economy. The problem is, and the simple way to think about it is it's all B1 hundreds and each one hundreds. And what if it actually takes three years and it's the Ruben chips that get us there or it's the Feynman chips that get us there, which is the 2028 chip. Right. So I think again, it comes back to where we started, which is the physicality of AI. You can't just say like, oh, I'm upgrade my chip. Great, snap my fingers, I've upgraded my chip. No, you have a giant warehouse sitting with these chips and they might be legacy chips and maybe it's going to take us 10 years to get there instead of two years to get there. And I think that is kind of the risk that the financial ecosystem is taking on. Whereas as an AI investor and an AI believer, we actually just need to spread that risk over a longer period of time and a greater number of bets.

[Harry Stebbings]

Okay, so if that's the case, sorry, I just want to stay on this for sure. Because Oracle is one of the biggest players that we've seen enter the market. As you mentioned there, when you look at their debt to equity ratio, traditionally considered very, very high, do you not think they're out over their skis?

[David Khanna]

I think that one narrative that I have been thinking about a lot is this narrative that I think a lot of the media has also been painting of like, hey, debt is going to unwind the AI bubble, which is to say A lot of these AI investments are debt funded and the problem with credit is that credit unwinds and then when you have a credit unwind a lot of bad things happen. I actually think that's not the way it's going to play out, which is maybe surprising. I think that the reason people are so anchored to this sort of debt narrative is that 2008 was a debt credit unwind and people understand how messy credit unwinds are. I actually think that what's interesting about this AI buildout is that for the most part, and let's put Oracle aside, which maybe has some debt, but for the most part the AI build out today has been equity funded and cash funded. So I think it's actually every bubble looks different and every unwind looks different and I think we always sort of over anchor on the lessons of the past. What I think is going to be interesting if to the extent that the bubble unwinds at some point it's going to be an equity unwind. And what that looks like is 40% of the S&P 500 is basically a bet on AI. And so to the extent that the bet unwinds, stock prices go down. What's different this time again versus 2008 is a greater percentage of Americans net worth is equities than I think ever before in history. And so people are going to feel this in the form of their equity portfolio going down more likely than some credit unwind where the banks get affected and all of that stuff.

[Harry Stebbings]

Are you as concerned as I am by the concentration of value in Mag 7? It's not a. And again, if I'm pushing you on company specifics, dude, I mean I know really, I'm not a journalist in any way, like I have zero desire to get a clickbait answer but like I look at the concentration of value in Mag 7 as a class or cohort and I am worried.

[David Khanna]

Yeah, I was sitting down yesterday with Sandy Noren, who's the author of this book the Engines that Move Markets, which is one of the all time great tech investing books. And we were talking about AI and we were talking about markets and he sort of made this comparison to Japan in the 90s where if your portfolio was not levered to Japan in the 90s, then you were like the best performing fund in the 90s. I think he said, and this was like really surprised me. He said that Japan was basically 43% of the equity market and the US was 41%. So it was really, really a huge percentage of the market. Right. And that really unwound. And so I think you have a similar dynamic here where the Mag 7 are just a humongous portion of the market. Now, these companies are great. They have cash machines like they're going to do fine. But I do think we should be concerned that these companies represent such a huge fraction of the market and that any change in the AI narrative really affects them. I want to discuss.

[Harry Stebbings]

You mentioned earlier in the conversation, and we mentioned that concentration of value of max 7, a lot of that's predicated around the belief that it will impact GDP meaningfully. And we touched on it earlier. Massa said that he thinks that we'll see 5% GDP impact. How do you think about and respond to the magnitude of which we will see AI impact GDP and productivity levels?

[David Khanna]

I think Masa makes an interesting point here and I actually agree with him fundamentally that AI is going to affect 5% of GDP. Probably where I disagree with Masa. So I think he used the $9 trillion. I think that's the number he used. It's going to disrupt 9 trillion of GDP. And then he says his next assumption is there's going to be a 50% profit margin and then it's going to be $4 trillion of economic profit. So I agree with him. It's going to affect 5% of GP, maybe more in the fullness of time. But I think this comes back to the point we were discussing earlier where people overestimate the monopolistic nature of businesses and that we're living in this sort of unique Gilded Age monopolistic era and that that is not the steady state of business. I found this McKinsey report recently which said that if you look at total global GDP, 1% of global GDP is economic profit above the cost of capital, which I think is surprising. And I think again confirms this intuition that I think is important, which is for the most part, GDP accrues to the regular people, working people who get wages and salaries, and it is very hard to sustain an economic profit above your cost of capital. And again, to moralize for a second like that's a good thing. I do think that's really good. And I hope that the economic benefits of AI accrue to everybody and not.

[Harry Stebbings]

Just a few companies in terms of overestimations. You know, I was just chatting with Rory o' Driscoll from Scale and Jason Lamkin, who we have our weekly show and they actually said the biggest problem with today is we're seeing this overestimation of demand. They were specifically Talking about legal, where every law firm is looking for an AI provider today because they've been told, look for an AI provider that will not be the case next year and the year after. And so it is a atypical market cycle where 100% of market is looking for a new provider or a provider where normally it would only have been 5%. Do you think that's a fair description?

[David Khanna]

I think there's a number of things that are being overestimated. I think the most important one is the timeline. You probably seen there's a lot of commentary now in the last few days about this like, AGI timeline getting pushed out and this thing I've been talking about for the last like four months. And because a lot of the leading indicators were there in June, July, but this did change over the summer. So it makes sense why everyone's talking about this right now, which is in June or July. Andrej Karpathy at Y Combinator said, hey, we're in for the Decade of Agents as opposed to AGI in 2027. A few weeks ago, Richard Sutton was on the Door Cash podcast and basically explained why. And Dorkash, I think, has been doing a good job of fleshing out why the current technology paradigm is not enough potentially to get us to AGI. And then Sam Altman came out, I think also in June or July and said, hey, it's going to be a more gentle singularity. I've actually been surprised by how gradual the change has been as opposed to being sort of this crazy change. And so for me, there's this contrast between what I think of as like the lunchroom conversation at these big labs. Like you have these 25 year olds sitting around lunch being like, AGI is 100 days away. No, it's 200 days away. No, it's 300 days away. And like the highest status person is the person who says it's 100 days away because they're the most aggressive. But you contrast that against like the true thought leaders and godfathers of AI, the people who've really invented this category, people like Richard Sutton, people like Andrej Karpathy, people like Ilya Sutskever who said in December that pre training is dead. And those people think, hey, the timeline's actually like 20 years, 30 years, et cetera. I think that contrast is probably the biggest thing that's being underestimated. And I think the irony of that is that it's actually the people who are the forward thinking leaders who sort of led us down this path. Like the Path we're on was invented by these people who are raising the most concern or saying the timeline is longest and it's the people who've been in AI the shortest, who I think are saying like, hey, it's going to come tomorrow. And I think there's sort of this experience curve of these things are just hard and they take time. And by the way, if this happens, it's a cataclysmic event in the history of our species. So it doesn't really matter if it happens in 200 days or 50 years. What matters is that it does happen.

[Harry Stebbings]

I almost feel apologizing because you're so smart and intellectual. And then I'm like, yeah, well, venture baby. But like, king making is a real thing. Making one person the anointed winner with a large amount of capital, distribution and brand a la Harvey is a very real dynamic that we're seeing play out. How do you balance the importance of king making today with the long cycles, the decade plus that we're talking about there?

[David Khanna]

I don't believe in kingmaking, and that's maybe a controversial thing to say. I think one of the lessons, you know, you'd think like, oh, Sequoia should be able to kingmake companies and like, that's so great. And that would be, by the way, if that was true. It would be really economically valuable for our LPs if that was true. And I don't think that we think that's the case. And I think, if anything, some of the hardest learned lessons in this business are like, you think that your capital is going to change the business. It's not. It's not fundamentally. The founder has to be amazing, the idea has to be amazing. Product market fit is to be amazing. Maybe we can help them navigate a few difficult decisions along the way. And we like to think of ourselves as company builders. The lesson that punches you in the stomach in venture is you can't make a company succeed. The company has to already be successful. And then I think the second order effect of that is like, you should be humble because the company succeeded, not because of you. The company succeeded because of the founder. And maybe you helped a little bit, but you can't make companies succeed as a, as a venture capitalist. Ego gets in the way where people think they can, and I just don't think they can.

[Harry Stebbings]

So you don't think in a market like profound, that Sequoia and the subsequent quick round has helped them significantly get great talent, get great customers and get subsequent funding, which has then widened the moat between them and the plethora of other people. I. I'm sorry, I love you, but I respectfully disagree.

[David Khanna]

I think that there are flywheel dynamics for sure in venture. And so I'm not saying that having a brand name, great VC who's going to work really hard on your cap table doesn't change the probabilities. I just think it changes the probabilities less meaningfully than people think, on average. You use profound as an example because I was in the pitch when they came to the ic, the business was ripping. It was an amazing business. They had tons of customers lining up at their door to buy the product. Yeah, we're lucky to be in business with them and we're grateful to be in business with them. And I hope that we can shape the journey in some way. And if there's five engineers that having SCOIA involved to help them join, phenomenal. And by the way, I think that's the number one way that companies do benefit from the cap table is that is talent and recruiting. And we can talk more about that. And I'm fascinated by recruiting and recruiting dynamics. So I do think Sequoia helps with that. It especially helps with folks who are more mimetic, where I think the brand name really helps. That said, I just resist the idea that, like, I think this is just something that you learned the hard way in this business, like, oh, I'm going to put 20 million in this business now it's the Sequoia company in this space and suddenly it's going to succeed. Like, it doesn't work that way. We've learned that the hard way. And I think we, in our investment committee conversations, we really resist that because I think that is how you make mistakes in venture.

[Harry Stebbings]

So funny. I remember when I interviewed Doug and he was like, people think that, like, because we're so queer, everyone just comes and says, here you are, here's my deal. You must have it, take it. And he's like, I wish. I would love that. It's not how it works. I have to fight and fight and fight. And I'm like, your biceps are bulging, Doug. I totally believe that you have to.

[Podcast Host/Producer]

Fight for the fairy deal.

[Harry Stebbings]

It's all good. You mentioned a couple of companies that you work with. The common critique posed to consumers of compute is margins, margin structure, unhealthy margins. Do margins matter today in this entry point of AI or not?

[David Khanna]

I think they matter. And the companies I've invested in typically have reasonably high margins. That said, I think they're a directional indicator of how much product you've built on top of the foundation models, they are not absolutely important. You know, I remember investing in a company many years ago that had the 30% gross margin and now it has a 70% gross margin. And so gross margins go up over time. I think one thing as an investor that I guess you viscerally experience is that plenty of companies that get critiqued for having low gross margins end up being super healthy businesses in the long run. One of the biggest indicts on Snowflake in the early days was that it had a low gross margin. Obviously it's a very good business. So I think if you have a real product that delivers a lot of value and there's reasons why as you get bigger the cost is going to go down. And in AI there's such an obvious reason which is the cost of compute just keeps coming down every year. So the trend line is very clear. I think you can build a healthy business. And so I would even go to the extreme and I haven't invested in any of these companies, but I would go to the extreme to say that even some of these companies 0% gross margins, I can imagine how they're going to work now. The companies I've invested in typically have higher gross margins than that. And I think that's an indicative of the amount of product that they've built. At the end of day, our job is to invest in companies that become really successful, not to be like super smart about analyzing them. And so I think sometimes the instinct to criticize a gross margin can get in the way of money making. And you mentioned Doug. I sort of the thing I've learned from Doug or the thing I admire most about Doug is like the job is to make money at the end of the day for LPs, for founders, for everybody. We all, that's the business that we're in. And so I try to keep that as the, as the goal. At the end of the day I.

[Harry Stebbings]

Have something called wwdd which is what would Doug do? Which is in a tough situation. I'm like wwd we mentioned margins is one, growth rates is another. The companies are just growing so much faster than we've ever seen before. I had him on, on the show from gc. He said trouble, trouble, double, double. I say go like you know, come back when you got something better. Brian Kim said recently it caused a lot of Ferrari if 2 million in ARR. Like in a 10 days like come on. How do you feel about this growth rate on steroids requirement from VCs. And how do you feel is triple, triple, double, double dead?

[David Khanna]

I think of it as the 0 to 100 club. So I think it's a variation on this, which is the best AI companies right now are going 0 to 100 million of revenue very quickly. And I don't think you have to be at 100 million in revenue to be clear. But I think that as an investor, you want to believe the company is going to be one of those companies. And I think companies that are on that trajectory or have crossed that trajectory are companies like Harvey and Open Evidence and, and Clay and Juice Box. And I think these are companies that are kind of on this trajectory of growing really, really fast. The reason why it's important is because to your point on how there's so much demand right now for AI, the best companies, it is the best indicator we have that you built something really useful. People are, and we've talked about this actually a number of times in our partner meetings at Sequoia. You know, you sort of look back at the Internet, there weren't that many people on the Internet. And so these companies can only grow so fast. Right now everybody's on the Internet and everybody wants to buy AI. So if you have something really good, it's going to get adopted really fast. I do think to the point of playing the game on the ground and adapting to what you see in the market. The biggest thing that we've seen in the market is that These companies growing 0 to 100 are the companies that have smashing product market fit. And so I'm happy to invest in a company with 2 million ARR, that is smashing product market fit. But I would tell you is the companies are smashing product market fit, are growing faster right now. And by the way, they don't always have to grow faster. Like the goal is to invest in something that in 20 years is this amazing public company with billions of dollars of revenue. And that is still the first order thing. But I think you, you know, don't fight the tape. Like you can't ignore the traction on the ground.

[Harry Stebbings]

I always say I don't care how long you take to get to a million in revenue, but I care desperately about how long it takes for you to go from 1 to 50.

[David Khanna]

There's a lot of data that indicates that that is a very good leading indicator of what it's worth. The data I've looked at suggests that that is a historically good algorithm.

[Harry Stebbings]

You know, one of yours is UiPath and he's a dear friend of mine. Daniel and I mean it took 9 years to get to 550k of ARR.

[David Khanna]

I wish I'd invested in him in the first few years. I got to work on the investment when it was later stage. But I mean, obviously amazing story and I think one that should inspire people. One thing I try to talk about with founders also is like I want to inspire founders that it can take a long time because Silicon Valley sometimes has this such a short term time horizon. And I look at Juice Box, you know this company started three years ago. The CEO was 22. He had finished Harvard in three years. The CTO dropped out of Dartmouth, he was 19. They took them three years. They were always focused on recruiting. They had initial music app in college and they evolved that into the recruiting market. And they spent three years figuring out what the product should be. And now of course it's growing really fast and they're really good founders. And one thing I've learned, and this incentivizes me to invest in companies like this is people like David and Ashaan, the Juice Box founders who've sort of been through the founder journey, they've been through the pain. They understand how hard product market fit is. I think in the fullness of time they are better founders for it. And those scar tissue, even though they're really painful, I do think they pay dividends long term. And I think for founders who are listening, who are like in year one and things are hard, you know, that's painful. And there's nothing that I can say that's going to make that less painful. But I think there is like we would love to invest in you as you figure it out and we're super patient. And there's this false narrative, I think that like all the good companies, you know, they raise the seed and then they raise the A and then they raise the B and it's all in 12 months. And the revenue, that's not really how most of these companies work. It's funny, we've been talking about Juice Box and Clay. Clay spent many years in the wilderness figuring out what their product was going to be. Sequoia invested at the Series A in I think 2019. The company spent three or four years in the wilderness really figuring it out. I look at Kareem, I think the man is like enlightened from this experience. Like a super painful experience. Varun ended up joining as a later co founder. Amazing combination. The company completely changed from the Series A. And then I led Sequoia's investment at a little north of A billion, which we are doubling down in the growth stage of the company. And obviously the company now has continued to rip and has done. Has done really well. So I think the default narrative of like, oh, I'm going to start the company and then 12 months later, I'm going to be successful, at least in the case of two of the investments that I'm most excited about. That was definitely not what happened.

[Harry Stebbings]

The reason you come on the show is because I stalk the shit out of you. I spoke to Varun from Clay and David from Juice Box before both said, I told you I didn't have one person not responding to my calls or messages about you, which is like, very, very rare, dude. Like, that's testament to you. You mentioned that, like, oh, for founders who, you know, it's hard and you know, we don't want to present this false picture of being easy. Completely true. But we are seeing these very quick successive rounds. If we look at, say, a rillet or a profound. Do you worry about them? I remember Pat Grady once saying to me that his biggest challenge is that when he does a deal, everyone else wants to put in money at double or treble the price. And that really stuck with me. Do you worry about these very quick successive rounds?

[David Khanna]

I think we try to find the right balance. And to be honest, this is a conversation I have with a lot of founders, right. So this is like a very active conversation. We're all having these conversations all the time. We're obviously in a market where capital is very abundant and very available. I see the argument for why people want to take the capital. I think one lesson we've learned is more capital does not make a company more successful. Capital is a. Is fuel, but capital does not create the engine. This is a tension. I think this will always be attention, and I think this is definitely a tension for companies right now, where we learned this the hard way in 2021. Getting over capitalized has downsides. The biggest downside, in my opinion, is that it leads to this sort of internal perception of like, we're. We're winners, we're so successful, we're so great. The only thing that makes you a winner is having tremendous product market fit and having customers who love you. And so I think that's attention. Some founders, and I've seen some founders do a great job of this that I've worked with, they really act like the money is not in the bank account and they really behave diligently and the team size doesn't grow too fast. And all of this stuff, but I think that is the exception, not the rule. And I think it's actually not the founders that you should be most worried about, but it's the engineer who joins the company the day after the billion dollar fundraise with very little revenue. That dynamic is tricky and I admire the founders who are navigating it. I don't think there's an easy answer. I wish there was. I don't think there's an easy yes, no answer. But I think it's a tension we should be talking about. And as company builders, it's something that we need to, we really need to think about.

[Harry Stebbings]

Speaking of Pat, quite a lot. Poor guy. It's like an advert for Pat. He taught me something that was really interesting, which was two questions, which are a framework for amazing insight from founders. And he said, number one is what does everyone think they know that actually they get wrong? If we apply that to AI and what we see today, what does everyone think they know that they actually are getting wrong?

[David Khanna]

One lesson that I've learned in this business is that anything multiplied by zero is zero. And I think that's one of the really tricky things in investing, which is just to say that market volatility doesn't matter in the long run. If you have a great business, if you overextend yourself and then some crash happens and you go bankrupt, you're bankrupt, right? Like there's no way out of that. And I think that there's sort of this sense, I heard this phrase recently, momentum has its own reality. And I think there's this sense of everyone is living in this like reality distortion field of momentum. I think of it almost like this boomerang, you know, the slingshot. You like, pull the slingshot back and then you release the thing and then it sort of, it has its own momentum after that. And that's sort of a fundamental law of physics. Things in motion stay in motion. Things at rest stay at rest. I think the thing that kind of people feel so confident in is this like reality distortion field that comes from momentum. When that reality distortion field goes away, you need to survive that. And I think one thing that I hope that I can be to my founders is a partner. And, you know, they'll listen to me 10% of the time and that's fine. But a partner in, you know, making sure that we survive those moments and navigate those moments well and position ourselves well against that. And I think that the most prudent of investors, or like the most sober of investors can actually be really Helpful. Your job as a founder is to be maximally aggressive and you should do that. And then the investor should hopefully be giving some advice, helping think these things through, giving some perspectives, understanding a broader time horizon perspective and a broader data set of companies and then you sort of navigate to the right end destination. So I don't think people are thinking about this sort of concept of anything multiplied by zero is, is zero because the time horizon is so compressed into this shorter period of time. And that's just something that I think.

[Harry Stebbings]

A lot about the final one that Pat taught me. And then we'll move to talent, which I do want to touch on for a quick fire. What is no one thinking about that everyone should be thinking about. So like for me, one I think is astonishing is like no one is thinking that if you foie gras engineers in terms of the capital that you are stuffing down any of the multi billion dollar, you may not get an equivalent level of productivity as when they didn't have multiple billions of dollars given Nerd billions of dollars. Nerd buys five cars in a boat. Not so productive. And sorry to be so blunt and direct, but it's the same with companies.

[David Khanna]

I think that companies underestimate 23 year olds and 24 year olds. I think this is something that people really, really underestimate and I think this is more true than ever. Right now in AI I meet probably 200 or 300 young recent college grads every year. And the reason I meet them is I want to recruit them into my companies. A lot of them are founders. This is the population that I learned the most from because I know that my blind spot is going to be that somebody started using ChatGPT when they were 18 and I didn't. And so they're going to have a different perspective. And that's the perspective I need most in my life. Any case, I introduced some of these people to companies and the company is like, well what's their skill set? Like, why should I hire them? And I think this is something that people are not thinking enough about in AI right now, which is ChatGPT has been around for five years. Nobody has more than five years of experience in AI. The playing field is super level. In a changing and dynamic market environment, dynamism and slope and ability to learn are more valuable than ever. And so the thing that inspires me and the thing I spend a lot of time thinking about is in a juice box. For example, how can we get the very best 23 year olds in the world working at this company. And that's a big part of my job. And I spent a huge amount of time on that. A huge amount of time. And they're one day a week right now at Juice Box just working on this. So how do we get the best people in the world inside of these companies? Maybe 10 years ago, in the era of software, a senior software engineer, a staff software engineer, they had more experience than an L3. You know, architecture is hard, writing code is hard, and they were much better. And so maybe it made sense. There was this old playbook for startups of like, oh, you hire the staff software engineer who kind of knows what they're doing and you don't have to train people. I think that the new playbook for these AI startups is actually going to be much more about hire the AI generalist, this 23, 24, 25 year old who's really native in AI, really passionate about it. And I think those are the sort of the front lines that are going to make great companies.

[Harry Stebbings]

Totally agree and understand that. Do you worry about emotional maturity? A little bit. And I don't mean that patronizingly, but Jesus, I mean, I'm 29 now, but when I was 22, 23, I did some things that I would not do.

[David Khanna]

Now I think that hiring always has trade offs. I think one thing I believe, more generally speaking, because it's worth saying, is I really believe in trade offs. I think everybody wants the free lunch thing. When you don't know the trade you're making, then the negative is hiding from you. There's no such thing as a trade without negatives. There's no such thing as a decision where it's all positive and no negatives. So I always talk, and I talk about this a lot at Sequoia, actually. It's like hidden risk versus visible risk. When you hire a 23 year old, there's a very visible risk. They're emotionally immature, they don't have any work experience. It's very obvious the negatives that you're taking. When you hire someone who's more experienced, it's less obvious the risk that you're taking, it seems to be lower risk. And every decision is a risk, right? And so maybe the risk that you're taking is that they're not going to work as hard. Maybe the risk that you're taking is that they're less AI native. You know, there's always a risk and I think people have this tendency to favor the hidden risk. By the way, price is a hidden risk. You don't perceive it as a risk, but it is a risk. And so people prefer hidden risk over visible risk. And I prefer visible risk. I want to know exactly what risk I'm taking. And then, by the way, I'm a huge risk taker. I started investigating eight years ago. Right. Like, I love risk, so I think it's important to calibrate that. Like, I love risk taking, but I want to take visible risks. I know the risk I'm taking. And I think herd behavior and consensus mentality is about hidden risks. The risk is just beneath the surface and you're not paying attention to it. Whereas I want to take risks that I can see. And I think there's a lot of areas. The point I'm trying to make is in the hiring dynamic, when you hire a 23 year old, it's like super obvious why you shouldn't hire them. And yet sometimes that's okay because the reason you should hire them makes up for that more completely.

[Harry Stebbings]

Agree from the employer side. On the flip side, if you were advising your younger siblings on choosing their first job, I saw on LinkedIn you said, follow the smartest people a year ahead of you. That moniker of advice may not be relevant anymore. What advice would you give to them?

[David Khanna]

Well, this is like the biggest learning because I've met with two or 300 young people a year. I have a very big data set and I think I've probably spent more time than anybody at Sequoia on this specific, you know, thing. And my biggest lesson is that the way that young people choose their career is this what I call the medic algorithm and the Mitoch algorithm is. Yeah. What did the people one year ahead of me in school that I thought were the best? What did they go do? And it's a recursive algorithm. Right. So it's like, what did the people a year ahead of me do? But those people chose based on the people a year ahead of them did. And those people chose based on the year ahead of them did. Now, one reaction to that would be negative of like, oh, that's so mimetic. They should think for themselves. I actually don't have that perspective. I'm fine with it. I think it's like a reasonably good algorithm. When I graduated from college, Palantir was the hottest company to go work for. All the really smart people went to go to work for Palantir. Going to work behind here would have been a great life decision at that stage. Before that, you know, in the early 2010s, Google and the big tech Companies were the hot place to go work. And I think, you know, those companies were all 10xs over the 2010s, some of them even, I think 25xs. So it was a good decision to go work at Google in 2010. And so I don't think the memetic algorithm is inherently broken and I respect it. And I think that people, to your point of maturity, people are going to go through a maturity curve. They're not going to use this algorithm when they're 30s. They're going to evolve, they're going to change. And so I sort of have disrespect for it. That said, I do think that recursive algorithms break down in the face of dramatic new data. And the dramatic new data is the AI cataclysm. AI has totally changed how the world is going to work and it should change your forecasts on the future. And so the recursive algorithm of like, what are the guy you're above me and the person you're above me do is actually breaking because most people didn't have this information. They didn't know that AI was going to change the world. They didn't understand gen. I think the advice that I try to give young people is just factor that into your algorithm. Like you do you, it's like, join the company that you want to join, go to the place that's going to make you the happiest. But, you know, factor that in and then it's worth at least giving a shout out to this group of people that I call builders. In this substack post that I did, most people, 90 plus percent of people, their question they're asking when they're choosing a job is like, what can I get from this job? What is it going to enable me to do? Who am I going to surround myself with? How am I going to become better? It's a very like, what do I get out of it? I think there's like a 10% group of people, maybe it's 5%, maybe it's 1%. I don't know exactly what the percentage is, but there's this group of people that they're asking the question, what can I contribute? And by the way, if you contribute a lot, you generally get to extract a lot. And so I think contribution, this is again a beautiful thing about capitalism is like when you contribute a lot, I do think you get rewarded for that. Those are the people driving Silicon Valley. Like when you go into a company and you're like, why is this company succeeding? It's those type of people and those are the type of people who like they go from one great startup to another great startup to another great startup. Anyways, that distinction between these two groups of people both valid, no problem with either of them. Like you got to respect career as a very personal decision anyways, depending on what you're trying to solve for what's going to grow my career? Option one, where can I contribute the most and therefore extract the most? Option two, I think there's a bunch of great opportunities ahead of you. Just factor in the AI variable.

[Harry Stebbings]

I think one thing that just frustrates me on this topic is like the mimeticism that continues despite market changes in the uk. And what do I mean by that? Goldman Sachs investment banking consulting is still whatever people tell you. If you go and speak at universities, which I do once or twice a week now, everyone still wants to be an investment banker. And so when you were talking I was thinking what does it take to break the mimetic chain? And maybe it's AI and the proliferation of AI and popular culture and media. But that is the fight that I'm still fighting.

[David Khanna]

I think it's changing. I agree with you. Like it's changing too slowly and that's why I'm having these conversations and I'm trying to help and I'm sure you are as well in these talks that you're doing. One positive that I would say is I've seen a material change in the last 12 months which is sort of interesting because it's not like I'm not seeing the last 24 months, I'm not seeing the last 36 months. Like it took two years after ChatGPT for this to really start flowing through. And by the way, a lot of people I'm talking to are currently investment bankers who want to get into AI companies. So it is sort of funny that way. I think there's more and more of these high performing people want to be inside of AI companies and that's why I think it's sort of a, it is a two way match. Like these companies need these people more than ever but I think these young people can benefit more than ever from being in an AI company. And again maybe to make the value prop clear for like the young person, right? Like the value prop is hey, maybe 10 years ago if you joined a startup and people didn't join startups that often 10 years ago, maybe 10 years ago if you joined a startup, like there's this whole experience curve, you're the junior engineer There's a lot of people who are smarter than you, and you're gonna have to learn, and it's like gonna take five or 10 years to become a really meaningful contributor. That's not really true anymore. Right. You're sort of entering at, like, much more parity with everybody else. And so I think there's good reason why people are making this change.

[Harry Stebbings]

Dude, I'm throwing in a curveball here, but I was told that you're the man who does defense at Sequoia. You know, I say this with love, but I'm going in hardball on this one. How would you respond to Sequoia? Were asleep at the wheel when it came to defence not being in Hellsing and Andrill, the two clear market leaders in the category.

[David Khanna]

I would say, and I think this ties into our conversation so far, that defense is the next AI. And, like, that's how I started getting involved in AI. I think that defense is. If the Transformer moment was sort of the starting gun in AI, I think that the chatbot moment hasn't happened yet. So I do think, look, there's no way around it. SCOIA was late to defense, but I think SCOIA is working really hard to catch up. And that's part of business. You don't always get things right, but you keep trying. And I think we have that ethos and we have that humility.

[Harry Stebbings]

And why do you think defense is the nice AI? Sorry.

[David Khanna]

So I think that, you know, it's funny because I started investing AI as we were talking about a year after the transformer paper in 2018. And, you know, I think that it's sort of defense reminds me in some ways of like, a few years after the Transformer paper, which is to say people who are really paying attention understand that defense is. Is going to change. And the Transformer moment was the. Was the Ukraine war. It was a very odd, you know, before that you had to be a visionary, and to Palmer's credit and Peter Thiel's credit, and people like this, like they were visionaries before the Transformer paper. You're a visionary and, you know, Ilya Andrej Karpathy, these people are visionaries after the Transformer paper. You're an early adopter, right? And I think our job as investors is to be early adopters for the most part, especially in the growth business, to be early adopters. And so you see the change that happened in Ukraine, and I think it was very obvious that, like, you know, warfare, you see these pictures of these tanks, you know, and these like long chains of tanks from Russia. And it's like, wow, like defense technology is 50 years old. And technology has moved so much in 50 years. And yet, like the way that we do war just hasn't changed. And that's because, you know, we've been in this period of golden era for the world of dramatic peace and prosperity and all this stuff. I think that the transform paper moment was, was the Ukraine war. And then I think the chatgpt moment hasn't happened yet. And so I think that defense is actually, you know, underhyped in some ways or like underestimated in some ways. And that's why I started getting interested in defense two years ago.

[Harry Stebbings]

When you look forward to the world of AI, you assume that everyone will be improved with AI, will use it hundreds of times a day and it'll be a of everything that we do and think and say. In many respects, taking that view on defence then assumes this continuing conflict increases, not even decreases from where we are today. That would go against human cycles. There are periods of intense conflict, periods of not. But suggesting that defense AI would suggest that that is the case. How do you feel about that?

[David Khanna]

So I think, by the way, I share a little bit of how I got interested in defense. And you and I know each other now. So like before I got in AI, I was reading all this stuff and I'm trying to learn from people and I think my sort of investing style is like you spend two years learning about the thing and then you kind of start investing in the thing. And so I sort of take my time to sort of build a foundation. And my foundation defense is like reading Napoleon and Churchill and like all of the, you know, the history of wars, the history of defense, like geopolitics really like getting educated. And I probably spent two years really educating myself and meeting founders. And you learn a lot from founders on this space before I got involved. And the thing that I learned, and I think the thing that a lot of people who are deeper in the space than I am already understanding is deterrence is the first thing. You only go to war because you have to. The whole point of defense is to prevent wars. And geopolitics is a real thing and there's real competition between nation states and that will continue. And so as the world gets reshaped and we are living through a reshaping of the world order, I think that's something that a lot of people have seen, have written about. There's a lot of variables about that that we can unpack. I think Ray Dalio's Principles of the Changing World Order is a really good book on this topic. So the world order is sort of fundamentally changing. That leads to this interesting opportunity where we have to sort of catch up. There's a 50 years of catch up that has to happen. That's how I see the current defense moment. And this is why I say we're, you know, two years after the transform paper. We're not Even at the ChatGPT moment yet is we're like 1% there on catching up. Like we're actually so, so early in this defense cycle because now we have a few dozen companies, maybe 100 companies that have sort of new innovations. They're not integrated into the four structure meaningfully yet. There's so much more that has to happen. And I think that we have our, you know, the clear market leader now in the United States with Anduril and I think there's more companies internationally that are going to do really well as well. We've sort of crossed the chasm of like this is a thing that matters. We've crossed the chasm of the government knows this matters. We've crossed the chasm of, you talk to people in Washington D.C. they now understand Palantir and Anduril, they know those businesses. But in terms of the force structure changing, in terms of the way that we actually protect ourselves changing, in terms of US deterrence changing, I don't think it's changed that meaningfully. And I think after the ChatGPT moment, what's going to happen is that pre ChatGPT, if you were paying attention, you noticed after ChatGPT everyone knew this was important. Every American, every single person. And I do think we're going to get to a place in defense where everybody knows that this is really, really important and that we need these companies to succeed.

[Harry Stebbings]

Do not worry about the concentration of buyers in that world. Again, when you compare it to defense, you have every business in the world or every consumer in the world. What I really don't like with defence is actually what Brian Singerman told me about what makes Anduril so special, which is the complementary skill set of the founding team, whether it be GTM into defence and government, whether it be product, whether it be intense ops with their CEO Brian Schimpf. And I just don't like the concentration of buyers and the selling to governments and the lack of incentive for them. Do you not worry about that?

[David Khanna]

I think I definitely think about that and I guess my framework and this is the thesis that I've been Investing behind now for the last couple of years is my framework is there are going to be fewer companies that succeed in defense for this reason. Defense is consolidated for good reason. There's a single customer. And so you need to serve that customer really well. What makes a great defense company is to be a national champion. Fundamentally, what makes a great defense company is to understand the customer and to be able to serve the customer and to be able to drive what is fundamentally a nationwide transformation that needs to happen. We are going. You know, people talk about digital transformation. This is a digital transformation for the defense space. That's what it is. It's funny that it's a very old phrase, right? But defense actually hasn't gone through it yet. You know, it reminds me of Wiz, where like Wiz really benefited from the rise of cloud. And you would have said, what do you mean? Like cloud was already a thing in 2017. But of course these things take time. And so I think we're finally going through the digital transformation for defense and I think there's going to be a few concentrated winners in each country and we'll have a venture funded, equity funded, sort of R and D companies that come out and they'll get consolidated into the national champions. And in my view, Anduril is clearly the national champion in the US and credit to that team, just really phenomenal company, phenomenal visionaries. So the other two national champions that I've invested in, one is a company called Keller, which we think is going to be a national champion. It's based in Israel. The thesis is that Israel has the best people in the world for this and they can help defend the United States and they can help defend Europe. And the second company in Europe is a company called Stark, which Sequoia has now invested in over two rounds and that we believe can be the European national champion. And both companies have done, have done really well, but they're earlier I spoke.

[Harry Stebbings]

To Alon at Keller. Alon and Hamutal, phenomenal people.

[David Khanna]

Hamutal was the GM for Palantir Israel. To our conversation on talent, they've become a massive talent consolidator in Israel. I think the two big talent consolidators right now in Israel are Keller and Descartes.

[Harry Stebbings]

I get in trouble for this. I don't think defense is a category. And you're like, what? A category is enough that can support an ecosystem with its breadth and depth. I don't think defense is. I think there is your Andurils and maybe two to three more in the US And I think there's Keller and Helsing and stock. But I don't think it's like SAS where there is 30 to 50. Fintech where there is 20 to 30. Do you agree with me?

[David Khanna]

I do agree with you. I mean, my objective, I've probably invested in a dozen AI companies in my career. I hope to invest in 20 more. My objective is not to invest in 20 more defense companies throughout my career. I think it's going to be a very small handful of companies. Maybe we'll do one every couple years. But you have to go after the right opportunities, you have to build them the right way. And the winners are going to keep scaling.

[Harry Stebbings]

I think so many of the dollars going into it today will be lost. I see so many McKinsey consultants who are now VCs being like, oh my cost per kill. And I'm like, you have no fricking idea what you're talking about.

[David Khanna]

Yeah, we don't think that way. I think we think in terms of defending the country, in terms of having people feel safe and in terms of deterrence. So I agree with you. I don't love that type of language.

[Harry Stebbings]

I want to do a quick fire round. So I say a short statement, you give me your immediate thoughts. Does that sound okay?

[David Khanna]

Perfect.

[Harry Stebbings]

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

[David Khanna]

We talked about this a bit last time, but I can close the loop for people, which is I finally decided to learn how to drive and I got my driver's license in January, which I think is funny because it's kind of like capitulating right before the trade is in the money. Like I was waiting for self driving cars for all these years and then I finally got a license. And now of course self driving cars are on the streets every day.

[Harry Stebbings]

So come on, we were equal on one thing, which is we could.

[David Khanna]

Why did we encourage you, Harry, go out and learn. It was. It's a good experience. Well, Roloff told me that I had to because I was having a baby. And I think that was pretty reasonable to help my wife and drive around the baby.

[Harry Stebbings]

Tell me, how has being a father changed you?

[David Khanna]

You know, people, a lot of people say this and it's true. It just focuses your priority. It's so important. I think it, it makes you less abstract. Like you can think about things in abstract. Your child is not abstract. Like your child has needs and they need them right now. And so I think there's something that in terms of just like bringing you into the present is really Valuable about that.

[Harry Stebbings]

What would be your biggest advice to me on partner selection? So many people told me you had a great, wonderful marriage and that they wish to emulate it. And I was like, wow, fuck. Okay.

[David Khanna]

That is very, very kind. I mean, I would say, I guess pick, right? I mean, my wife is smarter than me and better than me and always.

[Harry Stebbings]

If your wife is smarter than you, David, I'm worried for the conversations you have at dinner.

[David Khanna]

I'm excited for you to meet her. No, I think that. Look, one thing that I've really shaped me over the last few years, especially like after getting married and having a kid, is people talk about the importance of shared values. And I think there's, like, every year that becomes more clear to me that that is true. I met my wife very young. I didn't understand that fully when we first met, and I'm very grateful for that every day.

[Harry Stebbings]

I like that a lot. What's your biggest miss? And what did you not see that you should have seen? With the benefit of hindsight, one big.

[David Khanna]

Financial miss is Datadog. And I worked on this before joining Sequoia. But I remember, you know, Datadog was this amazing company. The numbers were incredible. It was profitable. Like, it was one of these businesses where you just like your mouth waters looking at the financials of that business. And we lost a Dragon Year. And I never confirmed this with Dragonier, but the story behind it really stuck with me, which was that Dragon Year had this list of 20 companies, and they only worked on those 20 companies. And they had been spending years and years and years recording Datadog, and like, this was their number one priority. And they knew it was their number one priority for a few years. And this was probably six years ago now that this happened. But it's a principle that has really shaped how I pursue new investments, which is I want to really focus my time. And I've actually adapted this to if it's not one of the top five opportunities, that's where I really want to be spending 80% of my time. And then I want to spend the next 20% of my time on the next 15. And then after that, just like really trying to focus your time. And so that actually shaped who I became as an investor. And I learned a lot from that.

[Harry Stebbings]

I missed deal. I could have done 2 on 10 deal in an 8 million.

[David Khanna]

So painful.

[Harry Stebbings]

Bugger. Penultimate one, dude. What one technology do you think is wildly undervalued and why?

[David Khanna]

I think that people are underestimating voice as an interface for AI. You know, we just I think today, right before this podcast, we announced our investment in a company called Sesame, which is an AI voice company, an AI conversation company. I got to work on this with Roloff. The founder is the former CEO of Oculus, and it's Roelof, Marc Andreessen, and Santo, who's the founder of Spark on the board. So it's a really good company. And the company, you might have seen their launch a few months ago. They launched this AI voice product that you can talk to. Got a million users in a few weeks, 5 million minutes. Just tremendous product, market fit. I always had this view that we're not always going to be staring at our phones, and that's not like the terminal interface to technology and to AI. And I think that, you know, I always had this view, but you tried all the AI voice products and they all kind of sucked. Like, they were not good. They were born to talk to. They didn't remember anything about you. You couldn't interrupt them. You couldn't really have a dynamic conversation. Your brain just said, like, this is a robot. When Sesame came out, it was just a radically better experience. Within 10 minutes of seeing this technology, I knew that we were going to invest, and we ultimately did. And so I think the idea that we're going to be sitting here in 10 years talking to our AI, having a relationship with our AI, I think that's very likely. And I think it's a little bit sci fi right now, and I think it's going to get less so in the coming years.

[Harry Stebbings]

Well, listen, Sam Altman's opened the door to erotica, so, I mean, you never know what's coming. We're not going to end on that because that would be a weird ending to end on. The thing I want to end on. I like positivity. What are you most excited for when you look forward 10 years? What is like, this is what gets me out of bed.

[David Khanna]

I think this is a good place to end the conversation because my answer is AI. It's sort of funny because we've been talking this whole time about the ups and downs of AI and the risks and the challenges and all the complexity, but at the end of the day, AI is the most important story of our lifetime. It's going to completely transform the world. You know, is this event that is sort of a once in human history kind of event. And I think it's going to be a really, really epic ride to be on. And I'm excited to be on it with you and with everybody else, because I think we're all going to. It's going to change our lives a lot.

[Harry Stebbings]

Do you know what's going to happen, David? I'm going to come to the Valley and if it's okay with you, you're going to take me on a drive and.

[David Khanna]

Okay, great. We're going to get, we're going to.

[Harry Stebbings]

Get a photo for roll off of both of us in a car driving.

[David Khanna]

I like it. Beautiful.

[Harry Stebbings]

Dude, you're a star. Thank you so much for joining me, man.

[David Khanna]

Thanks for having me, Harry. This is very fun.

[Podcast Host/Producer]

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