We explore the expected size of AI-related debt, which vehicles hold it, what could go wrong, and how to protect yourself.
Topics covered include:
- How AI capex boom compares to other large booms and busts
- What fixed income vehicles hold AI-linked debt
- What mechanisms are being used to transfer AI-debt risk.
- How circular finance is contributing to the AI infrastructure buildout
- What could go wrong with the AI infrastructure buildout that could lead to losses on AI-related debt
- How to tell what exposure you have to AI debt
Show Notes
AI debt slide deck used for episode
Hyperscalers Tap External Financing as AI Capex Outruns Cash Flow—FactSet Insight
What History Tells Us About The AI Investment Boom by Larry Swedroe—Financial Advisor
Progress and peril by Pablo Hernández de Cos—BIS
US Life Insurers Held $807 Billion of Hard-to-Sell Credit by Alexandre Rajbhandari—Bloomberg
US Corporate Bonds Statistics—sifma
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Transcript
Welcome to Money for the Rest of Us. This is a personal finance show on money, how it works, how to invest it, and how to live without worrying about it. I’m your host, David Stein. Today is episode 561. It’s titled “How to Navigate the AI Debt Bubble.”
I recently got an email from a member of Money for the Rest of Us Plus, and he was worried to what extent is all this debt that is being used to finance AI infrastructure, new data centers, all the backbone for this AI bubble we’re in, as hyperscalers build out all this infrastructure, all this compute for frontier models, for non-frontier models, as we are in this grand experiment to implement AI, as individuals and businesses use AI, figure out how this new general technology can help us in our daily lives, can help businesses boost productivity, to innovate more.
Now, I’ve talked a lot about AI and the AI bubble, and the very high valuations of AI-related companies. And as I record this, a few episodes ago we did a episode on SpaceX, its IPO, and the exorbitant valuation of that IPO. Now it’s selling for 20% less than the IPO price, and other AI-connected companies have sold off; we’re getting near almost a 10% type correction. Now, this is a really volatile area. But this member was concerned, “Well, where’s all this debt going that is being used to finance these data centers?”
Because something has happened in the past 18 months. Previously, these companies like Meta and Amazon and others generated enough free cash flow to essentially build out infrastructure, data center infrastructures. But now, the level of capital expenditures is so high that they’re having to borrow. And a lot of these companies are investment grade, and they’re out borrowing trillions of dollars, essentially—or it will be trillions of dollars—to fund this grand AI experiment.
And there’s some mismatches in terms of the longevity of the chips that are used to run the compute in the data centers, versus the length of the maturity of the debt.
So we’re going to take a look at it and figure out, should we be worried? Should we be concerned about the sheer amount of borrowing? Is it like the great financial crisis, with all the borrowing related to the housing bubble, that ended up being in pockets of finance on the balance sheet of many institutions that we wouldn’t have thought had exposure? Is that what we’re seeing here? We’ll take a look. But let’s first get a sense of the scale of the borrowing that is occurring in this AI debt bubble.
The Scale of the Borrowing
Now, the overall CapEx, capital expenditures—estimates by JP Morgan, McKinsey, and Goldman Sachs; and they’re just estimates—anywhere from $6 trillion to $7 trillion invested in data centers and infrastructure, power grids to power these data centers. Now, I mentioned previously it was funded out of free cash flow, but they estimate—and this is JP Morgan specifically—75% of this CapEx will be funded with debt. That’s about $4 trillion of new debt outstanding, linked to the AI infrastructure buildout.
And that’s huge. When we think about the overall size of the corporate bond market, both investment grade and non-investment grade, it’s around $11.7 trillion. So roughly a third, really, of new debt issuance as a percent of the overall U.S. bond market will be AI debt. That’s meaningful.
And one of the big questions is, well, how could this impact interest rates and spreads, the incremental yield that an investor demands to hold corporate debt? And we’ll take a look at what spreads are in a few minutes and the potential risk there. But that gives you the idea of the size of the level of borrowing that’s going to occur.
The Capital Cycle
Now, let’s step back and see kind of where this fits in other cycles that we have. There’s something called the capital cycle, and this is from financial historian Edward Chancellor, and others have sort of discussed this, but there’s a new technology, and it sparks excitement, and potentially high returns, but uncertain returns. We saw this in the internet bubble, in railroads. Now we have it in AI. The potential for very high returns if the technology pays out.
So there’s massive investment and new infrastructure. There’s competition. Who can be the first mover? Who can build the best frontier model? Who will capture more of the spoils? And so there’s a huge amount of capital that’s raised partly due to this competition, and often you get more capital raised, more investment than what’s needed. So then you get to a part of the cycle where, well, the market realizes there’s too much.
We saw this during the internet bubble, where there was so much capital invested in internet infrastructure, laying fibers, lines, and other cables to carry the internet, and way more than was needed. And so we saw spreads, especially for non-investment-grade bonds, investment-grade bonds—the spreads ballooned, and those bonds sold off. And there were a lot of losses, and there were defaults, and bankruptcies, and a lot of companies went out of business.
But the infrastructure was still there, and eventually, the demand grew into the supply of infrastructure. We’re likely to see the same thing with this AI buildout. We’re all benefiting from it. There’ll be technology spillover, and it’s going to benefit all companies, both small and large, U.S., non-U.S., households, businesses. We’re all benefiting from this experiment, and—yes, it’s been raised with equity capital and debt capital, and there will likely be an oversupply of that and a retrenchment.
A lot of this borrowing won’t pay off, and we want to make sure that we have minimized our exposure to it, and that’s why we’re doing this episode.
In preparation for this episode, I read a report by BIS, and they looked at sort of other prior cycles and the AI boom in terms of the multiple of capital being spent in this arms race; it’s over four times what was invested in AI kind of prior to it. And so the boom, the CapEx has been larger than the canal mania in the 19th century, which is another boom and bust, building out infrastructure.
It’s larger than the railway mania, larger than the dot-com and the internet and the roaring ’20s. So the scale of this is definitely larger as a percent of the overall capital. The returns aren’t likely to justify what’s being invested, particularly as we see China and other what are called open-weight models, where someone can take the model and customize it; it’s not a closed model. And there’s more and more of them, because the advances of these frontier models are slowing down a little bit.
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