Mark Cuban on the AI Bubble: Who Actually Gets Wiped Out?
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Is today’s AI market a bubble like the dot‑com era and who stands to lose the most?
You and I lived through a couple of bubbles. We've seen this movie before. And this wave seems very different than the dot-com wave. So let's talk about that. Are you concerned about a bubble? We're seeing bubbly-like behavior, people.
It's not the traditional dot-com bubble, right? Because back then, there were companies going public, getting crazy valuations, and people are buying them. And the stock would go up 50%, 100% with companies that had no revenue, no traffic, no nothing. And you'd go get a cab back then, and people would be talking about them.
Yeah.
And you don't you don't see that at all today. So it's not a bubble that's going to impact most people in the room. Right. Or most people across the US. But it could just destroy a lot of VCs and a lot of funds and a lot of PE. Right. Because they're going all in.
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It used to be that product managers for brokerages had to outperform their numbers, right? For, you know, the SPX or whatever. But now you got to outperform to keep the money coming in. You got to outperform the fund next door. And they're all in anthropic and getting their outcomes and SpaceX and celebrating. But if shit hits the fan.
Yeah.
Yeah.
It really is. I've only done venture for just over 10 years and it is wild to watch so many people who deployed at the wrong time just out of business. They just invested at the peak and entry price matters. You and I have been in a bunch of deals together and we used to get to investing companies at 5 million, 10 million as angel investors. And then all of a sudden the request was 40, 50, 60 and the product's not launched and you're like, how does this work?
Yeah. And- And what's happening now is the market leaders, Google, et cetera, Meta, they're borrowing hundreds of billions of dollars.
Yeah, that's interesting.
And there's already a private credit problem right now. So you just layer on private credit, like Al Capital getting all the refunds. And then you have these huge companies that have cashflow, but they're spending all their cashflow on CapEx. And then they're borrowing on top of that. 50-year bonds. Right. That's planning for perfection. And that's going to be hard. And we're building these data centers. And if there's a price performance curve on AI that minimizes the power requirements, there's going to be a lot of data centers that are going to be turned into pickleball courts.
Interesting, because they just can't get the power turned on.
No, and then just the power, just everybody thinks that, okay, there's going to be so much more utilization. And there will be, right? It'll just scale like everybody expects. But there's going to be breakthroughs and technological breakthroughs as well. Just like we saw fiber back in the day, it was all about putting in fiber. Then it went from one gigabyte fiber to 10 to 100 gigabyte. And then there wasn't a fiber problem anymore. There wasn't a bandwidth problem anymore.
quite the opposite. We had dark fiber that people bought for pennies on the dollar.
And just sitting there, right? And how is it not going to be the case that we don't get the same price performance improvements on the AI side and on the data center side?
Yeah.
And then with all the hate going against data centers, I think that could be protecting them. And I mean, spending, committing tens of billions of dollars for 10, 20 years going out. Yeah. I mean nobody can predict that well.
I mean, you used the word pricing to perfection, I think, earlier in the conversation, and that's really what's happening.
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Chapters
5 chapters
1
Is today’s AI market a bubble like the dot‑com era and who stands to lose the most?
0:00–7:16
2
How could data center spending and private credit create fragility for AI infrastructure?
7:16–15:42
3
Why should AI startups consider going public now instead of staying private?
15:42–24:14
4
What practical hedges can employees at OpenAI/Anthropic use to protect equity upside and downside?
24:14–28:40
5
What real‑world limits make enterprise AI deployment harder than expected?
28:40–41:38
Speakers
2 identifiedMore from All-In with Chamath, Jason, Sacks & Friedberg
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