How The AI Bet Pays Off + AI Lab Strategy Game — With David Cahn

**Alex Kantrowitz** (0:00)
How much money does the AI industry need to generate to pay back its investments, in which company is employing the winning strategy? That's coming up with Sequoia partner David Cahn right after this.

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**Alex Kantrowitz** (1:02)
Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation of the tech world and beyond. We have a great show for you today. David Cahn, who's writing we've read on the show week after week, is here with us today to talk about what it's going to take to pay back all the investment going into AI and then we'll go company by company and decide which ones have the winning strategy and which ones might not. So David, it's great to see you. Finally, welcome to the show.

**David Cahn** (1:28)
Thanks for having me, Alex.

**Alex Kantrowitz** (1:30)
So your writing came on to our radar first when you were basically talking in 2024 about the ROI needed to make the AI bets pay off. This was long before the term AI ROI was in vogue and there was a sort of determination that there was some promise in this technology. And so of course you'd want to spend a lot of money in it because you don't want to be left out, right? But you actually very early on started to say, hey, let's at least put some numbers around this to see what it will take to pay off what was coming in. And it started in 2024 where you wrote this AI's $200 billion question. And for us it caught our attention because, wow, like basically you demonstrated that AI would have to make lifetime $200 billion to pay off its investments and that was starting to look like real money. So let me read a quick selection from that and we'll kind of talk about how it might apply today. So you said for every $1 spent on a GPU, roughly $1 needs to be spent on energy cost to run the GPU in a data center. So if Nvidia sells $50 billion in run rate GPU revenue by the end of the year, that implies an approximately $100 billion in cumulative data center expenditures. Let's assume those building them need to earn a 50% margin. That implies for each year of current GPU capex, 200 billion of lifetime revenue would need to be generated by these GPUs to pay back the upfront capital investment. So the total number in 2024, just a mere two years ago, was $200 billion lifetime. Let me talk about where we are today. So conservatively right now, we're looking at $2 trillion in capex, in Big Tech capex this year and next, between cumulative together, 2026, 2027 projected. It's more than $2 trillion, but I'll just say $2 trillion.
To be conservative about it, so by your math, that would be $4 trillion in lifetime revenue on this AI infrastructure needed to make that money back.
So in terms of tech revenue, give us a scale, like a sense of scale of how much is $4 trillion really? Is this something that tech companies make regularly? They don't make often, and how feasible is it for this current level of investment to earn that money to pay back lifetime and make those investors whole?

**David Cahn** (3:53)
Well, maybe first a couple of comments on that. First, the $200 billion was 2023 $600 billion was 2024
Anyways, regardless, these numbers have gotten really big. It's funny to almost hear the numbers from then because they feel quaint. Things have gotten obviously mega-sized since then, and had a big re-inflection in 2026, which is why you had $200 billion question and 600, so 3x growth. 2025, it was 850, so it had slowed down, and it about doubled in 2026 to 1.5 trillion. Then as you say, if you look at 2027 and the forecast there, it's obviously going to scale past 1.5 trillion at this point. Then the last thing to say there is these numbers are cumulative. So if you really want to ask yourself, hey, what's the total CapEx burden? You say for every dollar CapEx, we eventually need to get an ROI.