Sequoia's David Cahn on The Winners and Losers in AI

So we talked about last year this concept of steel servers and power. And I think if you remember, you know, rewind to summer twenty twenty four, 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 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 in a bits perspective about AI, but they should be thinking in an Adam’s perspective about AI. I And 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 gonna be the constraint and we’re gonna move away.

You 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 cover of The Economist, on the cover of The Wall Street Journal, on the cover 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 centres. 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.

Well, the $600,000,000,000 question, and maybe just to remind folks what 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 a 150,000,000,000 in Nvidia chips, that’s about 300,000,000,000 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 600,000,000,000 of revenue that needs to get generated. If you redo that analysis in the summer of twenty twenty five, it’s about 840,000,000,000.

So it’s 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 gonna 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 gonna be the year of the data center, I said, hey. We’re gonna have these construction delays. We’re gonna 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.

I think we’re gonna 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 gonna win or everybody is gonna lose or everyone is gonna 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 eighteen months of writing about this is that construction itself is gonna 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 gonna 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 gotta follow it all the way down the supply chain to get to the core of really what’s going on.

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 $50,100,000,000 dollar 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 I do think the beauty of AI is, like, reality is stranger than fiction, and a lot of crazy things happen. Do you think those scaled pay packages are justified? 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 $10,000,000,000. 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 were very bad at reasoning about 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 would 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.

Feel that way? I think that’s,

I think the second one you know, one thing we talked about on the podcast last year, I predicted that Meta was gonna do really well. And I think that prediction was clearly false in a twelve month time horizon. I thought that the vertical integration that Meta had was gonna be an advantage, and I think that Meta these 100,000,000 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 to 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 gonna 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.

Have you changed from a buy to a salon matter? I think the dramatic action that Zuck’s taking represents just how deeply invested in 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.

Well, 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 twelve 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 over the last twelve months.

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 gonna be durable. When we think about

Yeah. You’ve heard it. I’ve heard it. Do you think we’re in an AI bubble? 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 gonna manifest.

Right now, the bubble conversation has sort of reached kind of full consensus. The The thing that I think is more interesting is who’s gonna survive the bubble? What’s gonna 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, a lot of companies from the nineties still did well. Amazon still became an amazing company after the .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 fifty years, when you and I are 80 years old, AI is gonna have completely changed the world. It’s gonna 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 gonna happen in our lifetimes. Probably, it’s gonna 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 gonna 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 kinda area that I think is is really interesting.

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 twelve 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 gonna be tiny. It was a year after the transformer paper came out, and everyone said, deep learning is a tiny market. Why would you invest in this company?

And, of course, they had a a really nice exit to Corey recently. I invested in Runway 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 gonna succeed in spite of market volatility? If your the only way that your company’s gonna 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 gonna be able to navigate through any market environment. And by the way, we’ve kind of seen that now with all of these twenty twenty one companies navigating that environment. Some of them came out really strong on the other side. Look at Databricks, 60,000,000,000, now a 100,000,000,000 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.

I’ve had a very simple framework for this. It’s actually, I think, probably the first thing I ever published in AI, in AI’s $200,000,000 question way back when in 2023. The framework is this. 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. So I’ve had the view that you wanna 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’re fundamentally in a commodity business just like an oil company is in a commodity business, and that is gonna trade it different way and that is gonna 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 gonna do well on the other side of this market cycle.

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. 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 and 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 gonna 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 gonna be.

So nobody knew that Google was gonna 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 gonna be this tremendous opportunity when AWS 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 gonna be big. Like this is, I think, the irony of the AI is that everybody knows AI is gonna be massive. But if everybody knows something’s gonna be massive, then everybody builds companies. And if everyone builds companies, there’s tremendous competition. So And 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 gonna evolve in a healthy way than if it evolved in in a sort of a monopolistic direction.

of the community today? When I first started saying this eighteen 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 kinda 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 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 pitch book charts where it’s like, where’s the dollars going? Probably 80% plus of the dollars in AI are still going to producers of compute, 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 wanna raise capital.

And I think some of the best investments are those companies that don’t wanna raise capital. When Sequoia invested in Zoom, they didn’t wanna raise capital. Right? They were profitable. They were doing really well. Those are the businesses where I think as an investor, you really have to focus your time on.

You know I love game theory. So, I mean, my my basic framework on AI, and this is actually kinda how I write all these pieces, is there’s, like, 10 players around this big chessboard, 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 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 gonna change. There is no coordinating mechanism. I I do think that’s one of the it’s always the surprising fact of capitalism. Like, everyone wants to believe that everything is kinda 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. You said

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 and really influential books in the investing world. And his philosophy and and he says this in antifragile. It’s really hard to know if a building is gonna 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. Can I ask you what specifically makes you say you can see the fragility? 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 let me unpack that. A year ago, hyperscalers were holding up the AI ecosystem, everybody and 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 gonna buy out your generator capacity for five years. We’re gonna sign a twenty year lease on this data center, and we’ll back it up with our credit. So they’re 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 demand hot potato and saying, it’s it’s ours.

Don’t worry about it. We got this covered. A year later, Microsoft and Amazon have really stepped back. They started in the beginning of the year. There was this big public announcement or or leak or whatever you wanna 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 gonna take all the risk on everybody else’s behalf. We’re not gonna 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 Quariv 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 Coreave. And then the second order effect of that is that Oracle and Coreave are a lot smaller than Microsoft and Amazon. They simply can’t absorb as much risk as Microsoft and Amazon could. 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, think I that’s probably been the biggest change in the last twelve months in AI. And I think that’s something a lot of people have observed. It’s it’s fairly obvious. And so that, I think, has changed a lot of people’s minds.

Think all of these deals now are priming the pump. I mean, you basically announce 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 $40,000,000,000 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 60,000,000,000. A 100 gigawatts of power build out, which is what people are talking about now, that would be AI’s $8,000,000,000,000 question. Two fifty gigawatts of power is AI’s $20,000,000,000,000 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 is gonna be an important thing that has to play out.

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 and 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 gonna play out. AI is gonna be amazing. We’re gonna get these huge technological breakthroughs. Tremendous revenue is gonna get created. It’s gonna be a big driver of the economy. The problem is and and the simple way to think about it is it’s all b one hundreds and h one hundreds. And what if it actually takes three years and it’s the Rubin chips that get us there, or it’s the Feynman chips that get us there, which is the twenty twenty eight 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’ve upgraded my chip. Great. It’s not 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 gonna take us ten 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 of bets.

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 gonna 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. Actually, that’s not the way it’s gonna 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 build out 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 the lessons of the past. What I think is gonna be interesting if to the extent that the the bubble unwinds at some point, it’s gonna be an equity unwind.

And what that looks like is 40% of the S and 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 gonna 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.

Yeah. I was sitting down yesterday with Sandy Norn, 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 nineties where if your portfolio was not levered to Japan in the nineties, then you were, like, the performing fund in the nineties. 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 seven are just a humongous portion of the market. Now these companies are great. They have cash machines. Like, they’re gonna 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 wanna discuss

I think Masa makes an interesting point here, and I actually agree with him fundamentally that AI is gonna affect 5% of GDP. Probably where I disagree with Masa so I think he he used the $9,000,000,000,000. I think that’s the number he used. It’s gonna disrupt 9,000,000,000,000 of GDP. And then he says his next assumption is this is gonna be a 50% profit margin, and then it’s gonna be $4,000,000,000,000 of economic profit. So I agree with him. It’s gonna affect 5% of GP, maybe more in the fullness of time.

But I think this comes back to the point we’re 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 just a few companies.

I think there’s a number of things that are over being overestimated. I think the most important one is the timeline. You’ve probably seen there’s a lot of commentary now in the last few days about this, like, AGI timeline getting pushed out. And this is something 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, Andreessen 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 DoorDash podcast and basically explained why and and DoorDash, 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 gonna 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, AJ, it’s a hundred days away. No. It’s two hundred days away. No. It’s three hundred days away. And, like, the highest status person is the person who says it’s a hundred days away because they’re the most aggressive. But you contrast that against, like, the true thought leaders and and godfathers of AI, the people who’ve really invented this category, people like Richard Sutton, people like Andrei Karpathy, people like Ilya Sutzkever who said in December that pretraining is dead.

And those people think, hey. The timeline’s actually, like, twenty years, thirty years, etcetera. 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 gonna 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 two hundred days or fifty years. What matters is that it does happen.

plus that we’re talking about there? 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 gonna 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 has 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 and I just don’t think they can. So you

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 gonna 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 have Muskoia involved to help them join, phenomenal. And by the way, I think that’s the number one way that companies do benefit from Muskoia on the cap table is that is talent and recruiting, and we can talk more about that. And I’m I’m fascinated by recruiting and recruiting dynamics. So I do think Sequoia helps with that.

It especially helps with folks who are more memetic where I think the branding really helps. That said, I just resist the idea that, like, I this is just something that you learn the hard way in this business. Like, oh, I’m gonna put 20,000,000 in this business. Now it’s the Sequoia company in this space, and suddenly it’s gonna 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.

So funny. I remember when I interviewed

of AI or not? 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 a 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 experienced 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 big 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 gonna 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 with 0% gross margins, I can imagine how they’re gonna work. Now the companies I’ve invested in typically have higher gross margins than that, and and I think that’s an indicative of the amount of product that they’ve built. At the end of the 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 I I sort of well, 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 I have something called

I think of it as the zero to a 100 club, so I think it’s a variation on this, which is the best AI companies right now are going 0 to 100,000,000 of revenue very quickly. And I don’t think you have be at a 100,000,000 in revenue, to be clear. But I think that as an investor, you wanna believe the company is gonna be one of those companies. And I think companies that are on that trajectory or have crossed that trajectory are companies like Harvey and OpenEvidence and and Clay and Juicebox.

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 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 could 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 zero to a 100 are the companies that have smashing product market fit. And so I’m happy to invest in a company with 2,000,000 ARR that is smashing product market fit.

But I would tell you is the companies that 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 twenty 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.

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

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 wanna 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 Juicebox. 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 him 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 Ashon, the Juicebox 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, are, like, in year one and things are hard, you know, that’s painful, and there’s nothing that I can say that’s gonna 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, then they raise the a, and then they raise the b, and it’s all in twelve months.

And the revenue that’s not really how most of these companies work. It’s funny because we’ve been talking about Juicebox and Clay. Clay spent many years in the wilderness figuring out what their product was gonna 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 Karim. I think the man is, like, enlightened from this experience. Like, a super painful experience. Varun ended up joining as a later cofounder. Amazing combination.

So the company completely changed from the Series A, and then I led Sequoia’s investment at a little north of 1,000,000,000, 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, oh, I’m gonna start the company, and then twelve months later, I’m gonna be successful. At least in the case of two of the investments that I’m most excited about, that was definitely not what happened.

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 wanna 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 a tension, and I think this is definitely a tension for companies right now where and we learned this the hard way in 2021, getting overcapitalized 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 that 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. I And 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. Speaking of Pat quite

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 like, 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 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 kinda 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 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 a lot about.

companies. 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 wanna 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 gonna be that somebody started using ChildGBT when they were 18, and and I didn’t.

And so they’re gonna have a different perspective, and that’s the perspective I need most in my life. Any case, I introduced some 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 Chagibi’s 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 Juicebox, for example, how can we get the very best 23 year olds in the world working at this company? That’s And a big part of my job, and I spend a huge amount of time on that, a huge amount of time. I’m there one day a week right now at Juicebox just working on this. So how do we get the best people in the world inside of these companies?

Maybe ten years ago in the era of software, a senior software engineer, a staff software engineer, they had more experience than an l three. You know, architecture is hard, writing code is hard, and they 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 kinda 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 gonna 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 the sort of the front lines that are gonna make great companies. Totally agree

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 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 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 gonna work as hard. Maybe the risk that you’re taking is that they’re less AI native.

There 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 wanna know exactly what risk I’m taking. And then by the way, I’m a huge risk taker. I started investigating that eight years ago. Right? Like, I love risk. So I think it’s important to calibrate that, like, I love risk taking, but I wanna 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 wanna 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 agree from

Well, this is, like, the biggest learning. I because I’ve met with two or 300 young people a year, I have a very big dataset, 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 medic 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 memetic. 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 for Palantir would have been a great life decision at that stage. Before that, you know, in the early two thousand tens, Google and the big tech companies were the hot place to go work. And I think, you know, those companies were all 10 x’s over the twenty ten’s. Some of them even, I think, 25 x’s. 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, on maturity, people are gonna go through a maturity curve. They’re not gonna use this algorithm when they’re thirties. They’re gonna evolve. They’re gonna change. And so I 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 gonna work, and it should change your forecasts on the future. And so the recursive algorithm of like, what did the guy a year above me and the person a year above me do is actually breaking because those people didn’t have this information.

They didn’t know that AI was gonna change the world. They didn’t understand gen AI. 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 wanna join. Go to the place that’s gonna 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 called builders in this, sub sec post that I did. Most people, 90 plus percent of people, the question they’re asking when they’re choosing a job is like, what can I get from this job?

What is it gonna enable me to do? Who am I gonna surround myself with? How am I gonna become better? It’s a very, like, what do I get out of it? I think there’s, 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 that you get rewarded for that.

Those are the people driving Silicon Valley. Like, when you enter 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 start to another great startup. Anyways, that distinction between these two groups of people, both valid, no problem with either of them. Like, you gotta respect career as a very personal decision. Anyways, depending on what you’re what you’re trying to solve for, what’s gonna grow my career, option one.

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.

that I’m

I think it’s changing I agree with you. Like, it’s changing too slowly, and that’s why I’m having these conversations. I I’m trying to help, and I’m sure you are as well in in these talks that you’re doing. One positive that I would say is I’ve seen a material change in the last twelve months, which sort of interesting because it’s not like I’m not seeing the last twenty four months. I’m not seeing the last thirty six months. Like, it took two years after ChatGPT for this to really start flowing through.

And by the way, lot of the people I’m talking to are currently investment bankers who wanna 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 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 ten years ago if you joined a startup and people didn’t join startups that often ten years ago. Maybe ten 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 gonna take five or ten 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. Dude, I’m I’m throwing

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 ChattypuT moment hasn’t happened yet. So I do think look. There’s no way around it. Sequoia was late to defense. But I think Sequoia 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, we have that humility.

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 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 gonna 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 and to Palmer’s credit and 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, Andrei, 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 fifty 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 of golden era for the for the world of 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 CHIJTBT moment hasn’t happened yet. And so I think their defense is actually, you know, is underhyped in some ways or, like, underestimated in some ways.

And that’s why I started getting interested in defense two years ago.

So I think by the way, 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 kinda start investing in the thing. And so I 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 understand, is deterrence is is the is the first thing. You 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, like, 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 world order is is sort of fundamentally changing. That leads to this interesting opportunity where we have to sort of catch up. There’s a fifty years of catch up that has to happen.

That’s where I see the current defense moment. And this is why I say we’re you know, two years after the transfer paper, we’re not even at the CHADDPT 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 a 100 companies that have sort of new innovations. They’re not integrated into the force 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 Andoril.

And I think there’s more companies internationally that are gonna 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 DC. They now understand Palantir and Andor Roll. 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 ChatGBT moment, what’s gonna happen is that pre ChatGBT, if you were paying attention, you noticed. After ChatGBT, everyone knew this was important, every American, every single person. And I do think we’re gonna get to a place in defense where everybody knows that this is really, really important and that we need these companies to succeed.

I think I 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 years is, my framework is there are gonna 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 through 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 gonna 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, Andrewell is clearly the national champion in The US, and credit to that team, just a really phenomenal company, phenomenal visionaries. So the other two national champions that I’ve invested in, one is a company called Kela, which we think is gonna 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 I spoke to Alan at Kela. Alan and Hamutal? Phenomenal people. Hamuttal 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 Kela and Descartes. I

Do you agree with me? 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 gonna 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 in the right way, and the winners are are gonna keep scaling.

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 I don’t love that type of language.

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. So Come on. We were we were equal on one thing, which is we could Why do we need that?

Harry, go out and learn. It was it it’s a good 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. Tell me, how has being a father changed you? 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.

very, very kind. I mean, I would say, guess, pick right. I mean, my wife is smarter than me and better than me always. If your wife is smarter than you, David, I’m worried for the conversations you have at dinner. I’m excited for 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. I like that a

big 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 to Dragon Ear. And I never confirmed this with Dragon Ear, but the story behind it really stuck with me, which was that Dragon Ear had this list of 20 companies, and they only worked on those 20 companies.

And they had been spending years and years and years quoting 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 wanna 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 wanna be spending 80% of my time.

And then I wanna 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 I learned a lot from that.

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 Roloff Marc Andreessen, and 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, five million minutes, like, just tremendous product market fit. I always had this view that, you know, we’re not always gonna be, like, 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 kinda 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 ten minutes of seeing this technology, I knew that we were gonna invest, and and we ultimately did. And so I think the idea that we’re gonna be sitting here in ten years talking to our AI, having a relationship with our AI, think I that’s very likely. And I think it’s it’s a little bit sci fi right now, and I think it’s gonna get less so in the coming years.

Well,

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 gonna completely transform the world. You know, it is this event that is sort of a once in human history kind of event. And I think it’s gonna 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 gonna it’s gonna change our lives a lot.

Do you know