Sequoia Partner, David Cahn on Who Wins in…

1. Key Themes

The Physicality of AI Infrastructure as GDP Driver

The massive physical buildout of AI data centers is becoming one of the biggest contributors to US GDP growth, shifting focus from abstract computing concepts to tangible infrastructure constraints.

David Cahn: "GDP is an imperfect metric and it generally captures physical things more than virtual things. GDP now is picking up all of this construction boom that's happening, all the steel that's getting created, all of the physical stuff that's happening in the AI data centers and you're seeing these stories...AI is now one of the biggest contributors to GDP growth in the United States" [00:02:35]

He added: "I 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 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" [00:01:36]

Consumers vs. Producers of Compute: The Fundamental Investment Thesis

The AI bubble will primarily harm producers of compute (commodity businesses) while benefiting consumers of compute who can build differentiated products on top of cheaper infrastructure.

David Cahn: "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...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" [00:15:06]

He elaborated: "I think 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 consumes 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" [00:19:56]

The Fragility of Circular Deal Structures

The AI ecosystem has shifted from robust hyperscaler backing to more precarious circular financing arrangements involving smaller players, creating visible fragility in the market.

David Cahn: "A year ago, hyper scalers were holding up the AI ecosystem and everybody felt very comfortable with that because everyone knew that these were very robust businesses...A year later, Microsoft and Amazon have really stepped back...And then what happened later this year is Oracle obviously stepped up and took on a huge amount of the compute demand. And Corrieve 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 Corrieve" [00:22:45]

He continued: "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" [00:24:17]

2. Contrarian Perspectives

Monopolies Are Unlikely in the AI Era

Unlike the previous tech era where companies built monopolies "hiding in plain sight," AI's visibility means intense competition will prevent monopolistic profits.

David Cahn: "Everybody knows that AI is going to be big. Like this is, I think, the irony of the AI is that everybody knows AI is going to be massive. But if everybody knows something's going to be massive, then everybody builds companies. And if everyone builds companies, those tremendous competition...the difference between the AI era and the big tech era...is that these monopolies are not hiding in plain sight" [00:17:48]

He added a normative perspective: "Final point on this, that's good for us. That's like good for everybody. Like we shouldn't want monopolies to exist. Monopolis 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" [00:18:27]

AGI Timelines Are Dramatically Overestimated

The most experienced AI researchers are signaling much longer timelines than the "lunchroom conversation" at labs suggests, with fundamental technological limitations becoming apparent.

David Cahn: "There's this contrast between what I think of as like the lunchroom conversation at these big labs, like you have these 25-year-olds sitting around lunch being like, AGI is 100 days away. No, it's 200 days away. No, it's 300 days away. And like the highest status person is the person who says it's 100 days away because they're the most aggressive. And then you contrast that against like the truth thought leaders and God fathers of AI, the people who really invented this category, people like Richard Sutton, people like Andre Karpathy, people like Ilya Satskever who said in December that pre-training is dead. And those people think, hey, the timelines actually like 20 years, 30 years" [00:33:22]

Venture Capital Cannot "Kingmake" Companies

Despite conventional wisdom about brand-name VCs creating winners through capital and connections, Cahn argues the impact is far more limited than believed.

David Cahn: "I don't believe in Kingmaking and that's maybe a controversial thing to say...the lesson that punches you in the stomach, adventure, is you can't make a company succeed. The company has to already be successful...I just think it changes the probabilities less and meaningfully than people think on average" [00:34:59]

He explained: "I think having Sequoia on your cap table makes your company more successful. So I'm not saying that having a brand name great DC who's going to work really hard on your cap table doesn't change the probabilities. I just think it changes the probabilities less and meaningfully than people think on average" [00:36:22]

23-25 Year Olds Are Dramatically Undervalued in AI

Due to AI's newness, young engineers have nearly equal footing with experienced ones, making them better hires than conventional wisdom suggests.

David Cahn: "Chad GV's been around for five years. Nobody has more than five years of experience in AI. The playing field is super level. And I think in a changing and dynamic market environment, dynamism and slope and ability to learn are more valuable than ever...I think the new playbook for these AI startups is actually going to be much more about hired AI generalist, this 23, 24, 25 year old who's really native in AI, really passionate about it" [00:50:35]

Economic Profit Will Remain Concentrated Despite AI's Impact

Even as AI disrupts 5% of GDP, competitive dynamics will prevent the monopolistic profit margins some predict.

3. Companies Identified

Clay

An AI application layer company that spent years finding product-market fit before achieving rapid growth.

David Cahn: "This year, I've invested in Clay, which I think is an amazing application layer company...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 Korea and I think the man is like enlightened from this experience" [00:13:53] and [00:43:46]

Juice Box

An AI recruiting company founded by extremely young entrepreneurs (22 and 19 at founding) that took three years to find product-market fit.

David Cahn: "I invested in Juice Box, which is building an AI recruiter that has tremendous love...this company started three years ago. The founders, the CEO is 25. The CEO is 22. Sorry, he's now 25. The CEO is 22. He had dropped, you know, finished Harvard in three years. The CTO dropped out of Dartmouth. He was 19. They took them three years" [00:13:57] and [00:42:40]

Harvey

A legal AI company on trajectory to reach $100M revenue quickly, representing the "zero to 100 club."

David Cahn: "I think the best AI companies right now are going zero to 100 million of revenue very quickly...companies that are on that trajectory or have crossed that trajectory are companies like Harvey" [00:40:34]

Profound

An AI company with exceptional business metrics and strong customer demand at time of investment.

David Cahn: "I worked on this before joining Sequoia. But I remember, you know, Profound was this amazing company. The numbers were incredible. It was profitable...I remember we lost, and we lost a dragineer. And I never confirmed this with dragineer, but the story behind it really stuck with me, which was that dragineer had this list of 20 companies. And they only worked on this 20 companies" [00:36:04] (Note: He was describing DataDog initially, then referenced Profound as recent win)

Weight and Biases

Early AI infrastructure investment that recently had a successful exit to Cohere.

David Cahn: "I started investing in AI in weight 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 ever said deep learning is a tiny market. Why would you invest in this company? And of course, they had a really nice exit to Corey recently" [00:12:54]

Runway ML

Invested before stable diffusion existed, demonstrating early conviction in alternative model architectures.

David Cahn: "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" [00:13:07]

Hugging Face

Early investment in what became the leading open-source AI model repository and community.

David Cahn: "I invested in a hugging phase, which I still remember the first meeting I ever had with Klem. It was the, 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" [00:13:16]

Kela (Defense)

Israeli defense AI company positioned to become a national champion with exceptional talent consolidation.

David Cahn: "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...they've become a massive talent consolidator in Israel. I think the two big talent consolidators right now in Israel are Kela and Descartes" [00:56:44]

Stark (Defense)

European defense company that Sequoia has backed over two rounds as potential European national champion.

David Cahn: "The second company in Europe is a company called Stark, which Sequoia is 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" [00:56:52]

Sesame (Voice AI)

AI voice conversation company founded by former Oculus CEO with exceptional product-market fit.

David Cahn: "We just, I think today, right before this podcast, we announced our investment in a company called Sesame, which is an AI voice company and AI conversation company...The founder is the CEO of former CEO of Oculus...They launched this product, this AI voice product that you can talk to you. Got a million users in a few weeks, five million minutes, like just tremendous product market fit" [01:11:00]

4. Operating Insights

The "Zero to 100 Club" as Quality Filter

The best AI companies are reaching $100M in revenue extremely quickly because universal demand means truly valuable products get adopted fast.

David Cahn: "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...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" [00:40:29]

He added context: "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...the companies with smashing product market fit are growing faster right now" [00:41:14]

Visible Risk vs. Hidden Risk in Hiring Decisions

Companies should prefer hiring decisions with obvious downsides over those with hidden risks, as visible risks can be managed while hidden risks are ignored.

David Cahn: "I really believe in trade-offs. I think everybody wants the free launch thing. When you don't know the trade you're making, then the negative is hiding from you...When you hire a 23 year old, there's a very visible risk. They're emotionally mature. 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 like less obvious, the risk that you're taking" [00:52:00]

He continued: "People have this tendency to favor the hidden risk by the prices of 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" [00:52:39]

Construction Capability as a Moat

The ability to actually build data centers quickly and effectively will be a differentiating advantage, not just having capital or plans.

David Cahn: "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 mode. The ability to build things is hard. And I think we underestimate that...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" [00:05:11]

Focus on Top 5 Opportunities Only

Learned from losing DataDog: spend 80% of time on top 5 opportunities, 20% on next 15, nothing else.

David Cahn: "The story behind it really stuck with me, which was that dragineer had this list of 20 companies. And they only worked on this 20 companies. And they had been spending years and years in years quoting DataDog. And like, this was their number one priority...it's a principle that has really shaped how I pursue new investments, which is, if it's not, I want to really focus my time" [01:10:10]

5. Overlooked Insights

The Equity Unwind Risk vs. Credit Risk Narrative

While everyone focuses on debt unwinding the AI bubble (like 2008), the actual risk is an equity unwind affecting everyday Americans' portfolios since most AI buildout is equity-funded.

David Cahn: "I actually think what's interesting about this AI buildout is that for the most part...the AI buildout today has been equity-funded and cash-funded...What that looks like is 40% of the SME 500 is basically a bet on AI. And so to the extent that the bet unwinds stock prices go down. And what's different this time, again, versus 2008 is more Americans, you know, 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" [00:28:01]

This is hugely significant because it means the AI bubble's impact could be more democratized and widespread than traditional financial crises, affecting retail investors directly rather than being mediated through banking system failures.

The 1% Global Economic Profit Reality Check

The McKinsey finding that only 1% of global GDP represents economic profit above cost of capital fundamentally challenges assumptions about AI profit margins.

David Cahn: "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...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" [00:31:11]

This was mentioned almost in passing but undermines the entire bull case that assumes 50% profit margins on AI disruption. If the historical norm is 1% economic profit on GDP, the $4 trillion profit projection from AI (based on 50% margins) is likely off by an order of magnitude, even if AI does impact $9 trillion of GDP. This single data point may be the most important valuation reality check in the entire conversation.