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.
How would you respond to?
Sequoia were asleep at the wheel when it came to defense, not being in Helsing, and Andril, the two clear market leaders in the category.
This is 20VC with me, Harry Stebbings, and one of the most downloaded episodes of last year was David Khan at Sequoia.
So much has changed in the last year.
I wanted to have David back for a refresh.
I wanted to understand how he thought about where we were today.
For those that missed the last show first, it's a must, but David is a partner at Sequoia Capital and one of the world's leading AI investors.
And before Sequoia, David was a general partner at Co2, I loved this conversation today.
Let me know your thoughts, harry at 20vce.com.
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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 sites more often than any other.
Not to make you nervous or set the pressure for this episode.
Thank you so much for joining me again, dude.
Thanks for having me, Harry.
You were always very kind.
Now, the year of the data center sounds wonderful.
We had an amazing discussion last year.
What did you predict last year, David, that happened and we are seeing in action now?
So we talked about last year this concept of steel servers and power.
And I think, if you remember, 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 bits perspective, about AI, but they should be thinking in an atoms 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 cover of The Economist and the cover of The Wall Street Journal and 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 steel 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.
Does its contribution to GDP growth go contra your 600 billion question in terms of where the revenue will come from?
Well, the 600 billion question, and maybe just to remind folks what that is.
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 is that people haven't put the shovel in the ground yet?
I literally wrote a piece last summer called AI is Shovel Ready.
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.
Are we going to see a mass proliferation of delays on data center construction, do you think?
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.
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.
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?
I think there were two big misses last year.
I think the first big miss was these like big talent acquisitions.
If you had 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 million 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.
Sometimes I do think the beauty of AI is 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 a 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 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.
I'm very upset, looking at these pay packages, that my mother didn't push me towards a more engineering-heavy degree.
Doesn't everyone feel that way?
I think that's probably the universal reaction to seeing these packages.
I'm like, Mom, you should have done better.
Bad parenting.
You encouraged me to do English?
Really?
Come on.
War and peace doesn't quite make it, does it, when you're getting paid $3.5 billion by Zuck?
What was the second?
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.
Have you changed from a buy to a sell on Meta?
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.
I think what Zuck is doing represents that, and so I remain optimistic about meta long-term.
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.
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, 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.
Do you think we'll continue to see that?
We saw Poolside recently announce a two 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?
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.
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?
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 were 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 dot com, a 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 gonna happen in such a short time horizon, with a very specific chip set 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.
How do you balance that being an investor today, David?
Play the game on the field.
Bill Gurley quote.
But then also the awareness of the long-term impact that will come over multi-decades.
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 everyone said, deep learning is a tiny market.
Why would you invest in this company?
And of course, they had a really nice exit to CoreWeave 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.
He had launched this Transformers library.
It's funny, now Transformers is 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 the 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.
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.
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 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, compute 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're 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.
Those are the businesses that are going to do well on the other side of this market cycle.
Are three of the best businesses, not commodity businesses in the form of Google Cloud, AWS and Azure.
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 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 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 going to be big.
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 over-training 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 good for everybody.
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 sort of a monopoly direction.
You said about 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 a 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?
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 at 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 weren't actually wrong.
And so anyways, I think 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.
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.
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.
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?
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 ten 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 pretty uncoordinated and incentive driven.
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?
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, Anti-Fragile 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 it's an 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.
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 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 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.
They 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 CoreWeave 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 Corrive.
And then the second order effect of that is that Oracle and Corrive 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, OK, 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.
Do you think these deals are priming the pump, so to speak?
I 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 mean, 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 Vero Rubin chips.
So let's say it's somewhere between 40 and 60 billion.
100 gigawatts 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 gonna be an important thing that has to play out.
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?
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.
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 B100s and H100s.
And what if it actually takes three years and it's the Reuben chips that get us there, or it's 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'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 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.
Okay, so if that is the case sorry, I just want to stay on this for short, 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?
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 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 lessons of the past.
What I think is going to be interesting 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.
Are you as concerned as I am by the concentration of value in Mag7?
And again, if I'm pushing you on company specifics dude, I mean no really, I'm not a journalist in any way.
I have zero desire to get a clickbait answer.
But I look at the concentration of value in Mag7 as a class or cohort, and I am worried.
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 made this comparison to Japan in the 90s, where if your portfolio was not levered to Japan in the 90s, then you were the best performing fund in the 90s.
I think he said and this 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 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 narrative really have I want to discuss.
You mentioned earlier in the conversation and we mentioned the concentration of value of Mag7.
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?
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 GDP, 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 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.
Terms of overestimations, you know I was just chatting with Rory O'Driscoll from scale and Jason Lemkin, 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 and 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?
I think there's a number of things that are being overestimated.
I think the most important one is the timeline.
You've probably seen a lot of commentary now in the last few days about this AGI timeline getting pushed out.
And this is something I've been talking about for the last 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.
Andre 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 Dworkesh podcast and basically explained why.
And Dworkesh, 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.
For me, there's this contrast between what I think of as the lunchroom conversation at these big labs.
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 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 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.
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 kingmaking today with the long cycles, the decade 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 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 venture capitalist.
Ego gets in the way where people think they can, and I just don't think they can.
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'm sorry, I love you, but I respectfully disagree.
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 Skoia involved to help them join, phenomenal.
And, by the way, I think that's the number one way that companies do benefit from having Skoia on the cap table is talent and recruiting.
And we can talk more about that.
And I'm fascinated by recruiting and recruiting dynamic.
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 think this is just something that you learn 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.
So funny.
I remember when I interviewed Doug and he was like, people think that like, because we're Sequoia.
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 fight for the fair every deal.
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?
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.
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 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 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 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.
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 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 the 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 moneymaking.
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.
That's the business that we're in.
And so I try to keep that as the goal at the end of the day.
I have something called WWDD, which is what would Doug do?
In a tough situation, I'm like, hmm, WWDD.
We mentioned.
Margins is one, growth rates is another.
That companies are just growing so much faster than we've ever seen before.
I had Heymont on the show from GC.
He said, treble, treble, double, double.
I say go, like, you know, come back when you've got something better.
Brian Kim said recently it caused a lot of ferrari.
If two million in ARR, like, in 10 days, like, come on.
How do you feel about this situation? growth rate on steroids requirement from VCs.
And how do you feel?
Is triple, triple, double, double dead?
I think of it as the zero to 100 club.
So I think it's a variation on this.
Which is the best?
AI companies right now are going zero 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 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'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 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 don't fight the tape.
You can't ignore the traction on the ground.
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 one to 50.
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.
You know, one of yours is UiPath, and he's a dear friend of mine, Daniel.
And I mean, it took nine years to get to 550K of ARR.
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 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 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 Ishan, 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, 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 all the good companies, they raise the seed and then they raise the A, and then they raise the B and it's all in 12 months.
That's not really how most of these companies work.
It's funny.
We've been talking about Juicebox 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.
So 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 really well.
At least in the case of two of the investments that I'm most excited about, that was definitely not what happened.
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 Juicebox before.
Both said.
I told you I didn't have one person not respond 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 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?
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 fuel, but capital does not create the engine.
This is a tension.
I think this will always be a tension.
And this is definitely a tension for companies right now, where when 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 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 a tension.
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 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 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.
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 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 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 they'll listen to me 10% of the time, and that's fine.
But a partner in 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 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.
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, for me, what I think is astonishing is 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.
Give a nerd billions of dollars, nerd buys five cars and a boat.
Not so productive.
Sorry to be so blunt and direct, but it's the same with 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 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 ChaiGBT 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.
In 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 ChatGP 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.
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 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.
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 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.
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 Takoya.
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.
Probably 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.
I completely agree from the employer side.
On the flip side, if you were advising your younger sibling 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?
Well, this is like the biggest learning.
Because I've met with 200 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 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 decision at that stage.
Before that, in the early 2010s, Google and the big tech companies were the hot place to go work.
And I think 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 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 going to 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 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 the 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 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 is 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.
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 regularly, 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 nomadic chain?
And maybe it's AI and the proliferation of AI in popular culture and media, and But that is the fight that I'm still fighting.
I think it's changing.
I agree with you.
It's changing too slowly.
And that's why I'm having these conversations.
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 saying the last 24 months, I'm not saying the last 36 months.
It took two years after ChatGPT for this to really start flowing through.
And, by the way, a lot of the 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 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 going to take five or 10 years to become a really meaningful contributor.
That's not really true anymore, right?
You're sort of entering it like much more parity with everybody else.
And so I think there's good reason why people are making this change.
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 defense, not being in Helsing, and Andriel, the two clear market leaders in the category.
I would say, and I think this ties into our conversation so far, that defense is the next AI.
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 chat GPT 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 and we have that humility.
And why do you think defense is the next AI?
Sorry.
So I think that it's funny because I started investing, as we were talking about, a year after the Transformer paper in 2018.
And I think that defense reminds me in some ways of a few years after the Transformer paper, which is to say, people who are really paying attention understand that defense is going to change.
And the transformer moment 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 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, 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 the Ukraine war.
And then I think the Chai TVT moment hasn't happened yet.
And so I think their defense is actually underhyped in some ways or underestimated in some ways.
And that's why I started getting interested in defense two years ago.
When you look forward to the world of AI, you've assumed that everyone will be improved with AI, will use it hundreds of times a day and it'll be a part of everything that we do and think and say in many respects.
Taking that view on defense 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 the defense layer, I would suggest that that is the case.
How do you feel about that?
So I think, by the way, I'll 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 understand 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 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.
The world order is fundamentally changing.
That leads to this interesting opportunity where we have to catch up.
There's a 50 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 transformer paper, we're not even at the chat GPT 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 hundred 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 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 DC.
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 chat GPT moment, what's going to happen is that pre-chat GPT.
If you were paying attention, you noticed.
After chat GPT, 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.
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 defense is actually what Brian Singerman told me about what makes Andrel so special, which is the complementary skill set of the founding team, whether it be GTM into like defense 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?
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.
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.
It reminds me of Wiz, where Wiz really benefited from the rise of cloud.
And you would have said, what do you mean?
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 RD companies that come out and they'll get consolidated into the national champions.
And in my view, Andrewal 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 Kela 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 really well.
But they're earlier.
I spoke to Alon at Cala.
Alon and Hamutal.
Phenomenal people.
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 Kela and Descartes.
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 Andrews and maybe two to three more in the US.
And I think there's Keller and Helsing and Stark.
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?
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 of years.
But you have to go after the right opportunities.
You have to build in the right way.
And the winners are going to keep scaling.
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 freaking idea what you're talking about.
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.
I want to do a quick fire round.
So I say a short statement.
You give me your immediate thoughts.
Does that sound OK?
Perfect.
So what have you changed your mind on in the last 12 months?
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.
Come on.
We were equal on one thing, which is we could...
Why did we do that?
I encourage you, Harry.
Go out and learn.
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.
Tell me, how has being a father changed you?
You know, a lot of people say this and it's true.
It just focuses your priority.
It's so important.
I think 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.
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 wished to emulate it.
And I was like, wow, fuck, I can't.
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 always.
If your wife is smarter than you, David, I'm worried for the conversations you have at dinner.
I'm excited for you to meet her.
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 lot.
What's your biggest miss?
And what did you not see that you should have seen with the benefit of hindsight?
One 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 Dragoneer.
And I never confirmed this with Dragoneer, but the story behind it really stuck with me, which was that Dragoneer had this list of 20 companies and they only worked on those 20 companies.
And they had been spending years and years and years courting Datadog.
And 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.
I missed deal.
I could have done two on ten at deal in an eight million fund.
So painful.
Bugger.
Penultimate one, dude.
What one technology do you think is wildly undervalued and why?
I think that people are underestimating voice as an interface for AI.
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 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, like just tremendous product market fit.
I always had this view that you know we're not always going to 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 kind of sucked like they were not good.
They were boring 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 ten 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.
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?
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, it 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 it's going to change our lives a lot.
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.
Okay, great.
We're going to get a photo for Roloff. of both of us in a car driving.
I like it.
Beautiful.
Dude, you're a star.
Thank you so much for joining me, man.
Thanks for having me, Harry.
This was very fun.
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