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Nikesh Arora: AI Cyber Panic, Ken Griffin’s Bubble Warning on All-In Podcast (Transcript) 

On this episode of the All-In Podcast, Jason Calacanis, Chamath Palihapitiya and David Friedberg are joined by Palo Alto Networks CEO Nikesh Arora to unpack the PHP2Shell cyber panic, AI agents logging into bank accounts, and who is liable when an agent goes rogue. The besties also debate Ken Griffin’s warning that AI is almost certainly a bubble, the riots spreading from France across Europe, runaway government spending, immigration, and the minimum wage. Read the full transcript of this episode which was premiered on October 24, 2026 below:

Ken Griffin’s AI Bubble Warning

JASON CALACANIS: (00:00:00 – 00:01:50) All right, everybody, welcome to the literal number one podcast in the world. 3 weeks running, we got a lot to get to. Sacks is out this week. We’ve got a special guest coming up. Let’s get to our first story. Ken Griffin says AI is almost certainly a bubble, and Chamath has responded. Chamath responding and moving markets almost every week here on the pod. Look out, France.

The Citadel founder made the comments this week at the company’s annual Future of Global Markets conference. But he says generally, hey, bubbles are normal. Any radical transformation of our economy is generally intertwined with a bubble. He said he’s bullish on AI’s long-term potential, but his concern is that companies are struggling to turn AI spending into productivity, something we’ve talked about here, I guess, last year. And so companies are, quote, “looking at their bills from Anthropic and OpenAI and asking where the productivity is.”

We had this discussion over and over again on Thursday. Tech continued to get crushed on the data center and CapEx front. So people who are either making data centers like CoreWeave down 10% or having a huge AI spend, Meta, which is an extraordinary one there, it’s down 6% after earnings, AMD down 5%, SpaceX was down 5%. But these things have been swinging up and down a bit and generally trending up.

Chamath, what’s your take here on the market sentiment October 22nd, 2026? What are your thoughts here on how markets are viewing the AI quote-unquote “bubble”? And do you have concerns? Because Jensen from Nvidia thinks money is available and money is going to want this opportunity to finance it. And I think we all think companies are getting a ton of value out of AI technologies.

Ramp Data: Who Is Really Spending on AI

CHAMATH PALIHAPITIYA: (00:01:51 – 00:02:25) My Social Capital research team had a really great call with Ramp’s chief economist, Ara Kharazian. 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.

JASON CALACANIS: (00:02:26 – 00:02:27) Revenue for AI spend?

CHAMATH PALIHAPITIYA: (00:02:28 – 00:02:31) Yeah. So what I said was 1% of companies represent 90% of AI spending.

JASON CALACANIS: (00:02:31 – 00:02:32) Got it.

CHAMATH PALIHAPITIYA: (00:02:32 – 00:04:41) 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 raise 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. 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 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’d 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 this chart, so thanks to Ramp and thanks to Ara. He said we could be the first ones to publish this.

JASON CALACANIS: (00:04:41 – 00:04:50) And just to be clear for the audience, Ramp provides financial services, neobank, credit cards, primarily to startups. So they know startup data. There’s a free commercial. Yeah.

CHAMATH PALIHAPITIYA: (00:04:50 – 00:04:54) I think they know broad-based data from other data sources as well, and I think Ara has access to all of it.

JASON CALACANIS: (00:04:54 – 00:04:55) But what he said is—

Open Source, Data Sovereignty and Enterprise AI

CHAMATH PALIHAPITIYA: (00:04:56 – 00:06:39) Now look at this chart. So what this shows is how are the top 1% of spenders in AI spending their money?