# A small group of startups drives most AI spending

Markdown copy of https://realalpha.app/s/a-small-group-of-startups-drives-most-ai-spending/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Chamath Palihapitiya](https://realalpha.app/people/chamath-palihapitiya/) (Host) · Founder & CEO, Social Capital · Led Facebook's growth team; his firm backed Slack and AI chipmaker Groq.
- Host: [Jason Calacanis](https://realalpha.app/people/jason-calacanis/) · Founder & CEO, LAUNCH · Early Uber angel; sold Weblogs Inc. to AOL; hosts This Week in Startups.
- Host: [David Friedberg](https://realalpha.app/people/david-friedberg/) · CEO, Ohalo · Founded The Climate Corporation, sold to Monsanto for about $1.1B in 2013.
- Show: All-In
- Episode: [AI Cyber Panic, Ken Griffin’s Bubble Warning, The Fall of France?](https://realalpha.app/v/6czo-C3FU7I/), Oct 23, 2026
- Moment: 1:49 to 7:08 of the episode (5 min), playing on https://realalpha.app/s/a-small-group-of-startups-drives-most-ai-spending/; the episode from there: https://realalpha.app/v/6czo-C3FU7I/?t=109

## Quote

> “1% of companies represent 90% of AI spending. So that's tokens. And of that 90%, 80% of them are from **unprofitable VC backed companies**.”
> — [Chamath Palihapitiya](https://realalpha.app/people/chamath-palihapitiya/) at [2:24](https://realalpha.app/v/6czo-C3FU7I/?t=144) · All-In

## Takeaways

1. Chamath estimates 1% of companies account for 90% of AI spend.
2. He says 80% of that spending comes from unprofitable VC-backed firms.
3. Top spenders are increasingly buying GPUs and using open-source models.

## About this segment

Chamath Palihapitiya says AI spend is concentrated in a small set of companies, much of it funded by VC money. The spending must spread to established businesses for AI to prove its broader economic value.

From “AI Cyber Panic, Ken Griffin’s Bubble Warning, The Fall of France?”, Oct 23, 2026. [Watch the full episode](https://realalpha.app/v/6czo-C3FU7I/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[1:54](https://realalpha.app/v/6czo-C3FU7I/?t=114) with Bram's chief economist, Ara Karazian. The first question that they had was because I was doing my own math. And I tweeted this thing, Nick, maybe you can put it back up there, where I said, okay, my back of the envelope math, knowing everything that I know and everything that I see is about 1% of all of the companies represent 90% of all the revenue and Revenue for AI spend? Yeah. So what I said is 1% of companies represent 90% of AI spending.

[2:32](https://realalpha.app/v/6czo-C3FU7I/?t=152) So that's tokens. And of that 90%, 80% of them are from unprofitable VC backed companies. So the cycle here, just to be very precise is VCs raised from pension funds and LPs and foundations and family offices. They put money into a startup. That startup takes that money and invests in growth. Part of growing is by consuming other people's AIs. And so that company then starts to show revenue traction. Those folks then say, hey, look how well we're doing, raises a bunch of money from the same VCs, and it's just this circularity. Why that's important is what Ken is pointing out is that for AI to really scale as a predictable driver of long-term earnings, it has to diffuse out of the startup ecosystem.

[3:24](https://realalpha.app/v/6czo-C3FU7I/?t=204) And so you can't be in a situation where on the one hand, you're talking about breathtaking valuations, but where 75 cents of every dollar is actually just a startup sending it back and forth so that they can mark themselves up. That's, I think, a structural problem. Now, we've seen that structural problem in the past that happened in the early days of cloud. When AWS initially started to expose EC2 and S3, the consumers of an EC2 instance or an S3 bucket were startups. But the difference there is that AWS was abstracted from Amazon.

[4:00](https://realalpha.app/v/6czo-C3FU7I/?t=240) on retail, which was already then a roughly breakeven profitable business, at least on a free cash flow basis. So what's different here is you have a bunch of money losing entities just spinning a bunch of money around, and it has to diffuse more broadly. So I think that that's what Ken is observing. Now, when we brought that data to ramp, the quote is incredible, because this is what my researcher told me, I prepared a list of factors to see if your thesis, this is him texting me around VC money being 80% to 90% of the top 1% of AI spend. But he said, Chamath's actual estimate is pretty much correct. And then the second thing though that he said, he sent us and Ramp hasn't published his chart. So thanks to Ramp and thanks to Aura, he said we could be the first ones to publish this. And just to be clear for the audience, Ramp provides financial services,

[4:44](https://realalpha.app/v/6czo-C3FU7I/?t=284) neobank, credit cards, primarily to startups. So they know startup data. There's a free commercial. Yeah. I think they know broad-based data from other data sources as well. And I think Aura has access to all of it. Now look at this chart. So what this shows is how are the top 1% of spenders in AI spending their money? And I think that Ramp is going to publish this soon, as I said, so thanks to them for letting us preview this. What's amazing to me is the blue box in this histogram. What this shows is what percentage of money they are spending on GPU compute. Now, Now, why does that matter? Well, the rose-colored box is LLM traffic. That's the typical spend you're going to spend on the big frontier labs, closed models effectively, either through things like bedrock on AWS or what have you. What's really interesting is that the blue box is

[5:35](https://realalpha.app/v/6czo-C3FU7I/?t=335) ticking up. The blue box is people buying their own GPUs because they're moving to open source. And so what I think is really interesting is you have this two-pronged thing that's happening. One is the diffusion that we need within AI is happening a little bit slower than we all would like. I see that at 80-90 as well. I think the large companies that we work with, you can almost pinpoint it on a chart. Ever since Alex Karp's commentary on CNBC about data sovereignty, buying patterns have changed. I think people are being a lot more methodical. They are building out their own data centers or at least moving into VPCs, virtual public clouds. My team yesterday was at the CIO event of, let's say, one of the largest banks in America. You know what they said?

[6:23](https://realalpha.app/v/6czo-C3FU7I/?t=383) I'm not going to say the name of the bank. It was the most incredible thing. They said, we have decreased our spend on AWS and GCP. We now have six completely independent ground-up data centers that we've built from scratch for ourselves. I was like, what is going on here? Wow, we're back to the future. People standing up their own data centers, yeah. It's back to the future. So I think if you look at this, I didn't mean to have any commentary other than just observing what I think, but yeah, AI has a diffusion problem. It is too concentrated and ripping around in a small little ecosystem. We need to diffuse out, but as it diffuses out, people are picking open source is what I would tell you.

[7:00](https://realalpha.app/v/6czo-C3FU7I/?t=420) And we were early on that discussion two years ago here on the All-In Pod. I will say Ken