Will US debt burst the AI bubble? With Ruchir Sharma

Chris Giles (0:00)

Everyone's looking for the pin that could pop the AI bubble. And if you think this is just a spectator sport for Silicon Valley insiders, well, think again. The tech giants driving this AI boom dominate global stock markets, and if they stumble, the shockwaves will rip straight through your pension, your savings and your investments.

But what if the pin that brings this all crashing down has nothing to do with AI at all? What if that pin is actually America's enormous ballooning national debt? The AI boom needs vast amounts of borrowed money. The problem? Well, so does the US government, and increasingly they're competing for the same pool of cash. So could America's debt problem be the thing that finally pops the AI bubble?

Hello, and welcome to The Economics Show. I'm Chris Giles, the FT's economics commentator, standing in for Soumaya Keynes, and to talk us through how the AI boom and US debt are perhaps fatally connected. My guest is Ruchir Sharma, chair of Rockefeller International. His latest book is What Went Wrong With Capitalism. Welcome to the show, Ruchir.

Ruchir Sharma (1:15)

Thanks, Chris. Great to be back with you again.

Chris Giles (1:18)

So we always start the economic show with a question asking you to rate something on a scale of one to 10 On that scale, how close do you think we are to the bursting of the AI bubble, where one is not at all and 10 is it's a highly pressing near-term threat?

Ruchir Sharma (1:35)

Well, I'd say on that scale, we're pretty much at 7 to 8, I would say, Chris.

The first thing, of course, is that we have to first agree that this is a bubble or not, right? Because when it comes to bubbles, there is no scientific definition. By some accounts, a bubble is seen to be something which crashes by 40 percent at least. But how we know that we're the top of that summit before that crash comes, there's no scientific definition of this. So what I had done in the past and also written about is to identify that what are the major signs that you need to first watch for to identify a bubble. In that regard, I came up with something called the four O's. The first being overvaluation, the second being over ownership, the third being over investment, and the fourth being over leverage. Those are the four O's I said. If you look at the history of bubbles, going back hundreds of years, that you typically find that these are the four characteristics which are present when we do see a bubble.

Chris Giles (2:44)

Okay, let's go to the four O's in your AI bubble. Which of them are present?

Ruchir Sharma (2:49)

Right. So first, if you look at overvaluation, that you look at the valuation of AI stocks, or you just look at the broad valuation of the US stock market. What you find is that the US stock market in general, but the AI stocks in particular, are about as expensive as they have ever been in history, right? From a valuation perspective, we are somewhere in what would be defined as the 90th to 100th percentile.

Chris Giles (3:17)

So I'm going to put a tick by that one. How about the other three?

Ruchir Sharma (3:21)

Right. So if we go to the other three, the other is over ownership.

Here I think that in America again, the amount of equity ownership we have seen is quite unprecedented. In fact, this is for the first time that I know of any country in history where its population owns more of its wealth in the stock market than it owns in the property market. So this is quite unprecedented, how exposed the American public is to the stock market, and a large chunk of that is towards AI plays, the hyperscalers and other sort of AI plays. About 40 percent or so are direct AI plays in the US stock market. So from an ownership perspective, you can argue that this is really a new high. Okay.

Chris Giles (4:10)

I'll put a tick by that one as well. If briefly, you could go through the other two.

Ruchir Sharma (4:15)

Yes. So the other one I speak is over investment. Now here again, the acceleration that we have seen in investment on the AI front if you measure it as tech investment as a share of GDP or just the acceleration in overall AI investment in the economy, that is pretty sizable. So those ratios now are again approaching the levels that we have seen in past very big cycles, the housing cycle in 2006, 2007 in the US, the commodity cycle globally that we saw at the end of that decade. So we're pretty much approaching those kind of levels where the spend on one particular thing as a share of GDP is somewhere around 5% of GDP and some aspects even bigger. So I'd say on these three O's, over valuation, over ownership and over investment, I'd put a tick.