The Trillion-Dollar Question Hanging Over AI

What the capex boom means for the companies building it, and how we think about owning the theme

If you have followed markets at all this year, you’ve heard the number. To justify the money now being poured into artificial intelligence, the companies leading the buildout will eventually need to generate revenue at a scale the technology industry has never produced before. Anthropic CEO Dario Amodei recently put a version of that math into public view, suggesting his company would need to reach $1 Trillion annual revenue within a few years to stay ahead of its own spending. That single statistic has become shorthand for a broader worry: the spending is happening now, and the revenue that pays for it has to show up later. Given that, it’s worth a few minutes to lay out how we think about the capex supercycle and its risks, because the answer shapes how we own one of the most important themes in markets today.

The spending is unlike anything we’ve seen before

The capital flowing into AI is a supercycle, not passing enthusiasm. The largest technology companies are spending on data centers, chips, and power at a pace that now rivals their entire operating cash flow. Capital intensity that used to define oil majors and telecom carriers has arrived at businesses the market spent a decade prizing for being capital-light.

When a company plows close to all of its cash flow back into physical infrastructure, two things follow. Depreciation climbs, which weighs on reported earnings in the years ahead. Simultaneously, the returns the business earns on its capital tend to compress, because there is simply more capital in the denominator. Consensus already expects the return on equity of the largest AI spenders to step down over the coming year as those costs build. Increasingly, the gap between cash flow and ambition is being funded with debt and equity rather than pure profit. This doesn’t necessarily mean the spending is a mistake. However, it does mean the nature of these businesses is changing, and the way investors get paid will change with it.

Returns now come from earnings, not from a higher price tag

For most of the past several years, owning the AI theme was easy. Valuations expanded, and a rising multiple did much of the work. We do not think that is the setup from here.

The prices being paid for the AI leaders now require steadily more optimistic assumptions about the future to make sense. When a stock already embeds a near-perfect outcome, the multiple has little room left to expand, and it can contract quickly if the story wobbles. We’ve seen some of that in recent weeks, with relatively large (albeit low-volume) pullbacks on relatively small news stories. That puts the burden of future returns squarely on earnings growth and on the growing gap between the winners and also-rans. In our view, this is a market where the spread between companies matters far more than the direction of the AI story as a whole.

The bear case deserves a fair hearing

We take AI skeptics seriously, because the questions they raise are the right ones. The first is the funding question that opened this piece. The frontier labs are spending today against revenue they expect tomorrow, and the timeline for that revenue to arrive is uncertain. Even if the end state they expect is ultimately reached, if adoption disappoints along the way or financing gets more expensive, the most aggressive spenders are the most exposed.

There’s also a sharper version of that worry, and we think it’s currently underappreciated. Much of the spending, and the valuations attached to it, rests on an assumption that the leading labs will win on quality alone and command premium pricing because their models are better. That assumption is worth questioning. The performance gap between the top models has been narrowing, and a wave of capable competitors including several of the major (cheaper) Chinese models are pushing hard on price. So far, the benchmark differentials between labs have not been wide enough to justify paying up at almost any price for one provider's tokens. If the contest shifts from who has the best model to who has the cheapest, the economics for the labs change a great deal. Inference turning into a commodity is, in our view, one of the more meaningful risks the market is not yet pricing in.

The second risk is quieter but just as important, and it shows up in credit rather than equities. A wall of software and technology borrowers will need to refinance debt over the next few years, at the same time markets are debating which of them AI strengthens and which it disrupts. That uncertainty is hard to disprove, which means the cloud over the more vulnerable names is likely to linger, and may spill over to some AI names as well.

How we position around it

Our job is not to predict which scenario plays out. In macro, being right a little more than half the time makes you one of the best in the world. The more durable approach is to build exposure that can hold up across a range of outcomes, and that principle drives how we think about AI.

We want to own the theme, but are selective about how. Rather than concentrate in the handful of crowded, richly priced names that everyone already owns, we lean toward the suppliers and enablers of the buildout, including the power and grid infrastructure that the data centers cannot run without. We pair that exposure with downside protection, because hedges that cost relatively little today earn their keep if sentiment turns. Finally, we look for sources of return that don’t depend on the AI trade working at all.

The aim is straightforward. We want to participate if the AI supercycle keeps compounding, stay protected if it stumbles, and avoid being forced to guess which one happens.

The bottom line

The trillion-dollar question is a good one, and it does not have a tidy answer yet. The spending is real, the technology is real, and so is the risk that today's prices have run ahead of tomorrow's profits. Holding both of those ideas at once is uncomfortable, which is exactly why it is the right place to stand. For long-term investors, the opportunity is not in calling the top or the bottom. It is in owning what you believe in, respecting what you cannot know, and building a portfolio that does not need a crystal ball to do its job.

This material is for educational and informational purposes only and is not investment, tax, or legal advice, or a recommendation to buy or sell any security. It reflects views as of Mon, Jun 29, 2026 and is subject to change.