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Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley. Today, what American football can teach us about the value of ambiguity.

It's Thursday, September 10th at 2p.m. in London.

I really like this time of year. It's a little cooler outside. There's the excitement in the air of a start of a million new school years. And of course, it's finally American football season. Of the top one hundred US television telecasts in 2025, ninety were football games. In an increasingly divided world with an increasingly fragmented ecosystem for content, this unanimity is stunning. And while many factors explain football's popularity, one that I've come to appreciate more with time is its strategic complexity, especially the value of ambiguity.

Not tipping whether the play is a run or a pass, disguising whether and where you're going to blitz. Coaches work hard to keep their options open until the last possible moment. And as we enter September, this strategy is not just confined to football.

Take the Fed. Markets are pricing a roughly two-thirds chance of a rate hike next week, about the same chance that an NFL team passes on second and seven. Part of that uncertainty comes from exactly how you parse Fed Chair Warsh's comments at Jackson Hole. Chair Warsh said the Fed needs to be confident that underlying inflation is moving towards its objective, “clearly and at sufficient speed.” Otherwise, it has, "work to do." This was generally interpreted as a move closer to raising rates. But was it? What is sufficient speed? What counts as underlying inflation? And what does “work to do” actually mean? After all, if inflation is better in the second half of the year, as our economists expect, this framing could just as easily justify no action. We forecast the Fed to stay on hold next week. It is admittedly a close call.

Then there's ambiguity in AI financing. The numbers here are enormous. Morgan Stanley analysts forecast more than 1.3 trillion dollars of spending among the six largest hyperscalers in 2027, a sixty percent increase from the record-setting levels of this year. But how all this gets financed, that's less certain. There's an increasingly rich menu of options for financing across public and private markets, from investment-grade bonds to asset-backed securities, from direct financing to guarantees. The spending seems likely, but what form it takes and how much it impacts other markets is more ambiguous. My colleagues Matthew Hornbach and Vichy Tirupattur discussed some of these ambiguities and their potential effect on Treasury yields earlier this week.

Finally, ambiguity clouds the energy market. Some analysts are optimistic that oil flows are finally normalizing in the Strait of Hormuz. We are not. Coupled with major disruptions to Russian refining capacity, we've now raised our fourth quarter forecast to one hundred dollars per barrel for Brent oil and eighty-eight euros per megawatt hour for European natural gas.

Across these three themes, some of this ambiguity is intentional. Some simply reflects a wide range of possible outcomes. In football and in markets, keeping your options open can be valuable when you're calling the plays, but it's less attractive when you're being asked to price them. And that, for us, is the issue. There is plenty of uncertainty. We're not sure investors are being paid enough for it. A close call September Fed meeting, adverse seasonality, and very low levels of expected volatility leave us positioned for higher volatility across macro markets and cautious on mortgage-backed securities.

In credit, we think all of this issuance is a question of price, not capacity. We continue to expect record investment-grade supply this year with wider spreads as a release valve and prefer collateral-backed assets over unsecured corporates. And with oil a risk to both stocks and bonds, our US equity strategists think that energy equities offer an attractive hedge.

Ambiguity has value, but when the range of outcomes is wide and the price of uncertainty is low, we think investors should demand more compensation for it.

Thank you, as always, for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.

Brian Nowak: Welcome to Thoughts on the Market. I'm Brian Nowak, Morgan Stanley's U.S. Internet analyst.

Today, can the enormous investment behind Gen AI actually pay off?

It's Wednesday, September 9th, at 9am in New York.

AI has moved quickly into everyday life. It helps people write software, research purchases, automate work, find information, among myriads of other use cases.

But we need an infrastructure build-out of extraordinary scale to support all of this activity and more activity to come.

In all, we estimate that the major cloud providers are going to spend more than $1.4 trillion on this AI build-out next year alone. But compute capacity is potentially going to quadruple from 2025 to 2028, reaching roughly 120 gigawatts.

But all of the spending has raised a lot of questions for investors. One of the most common questions is: What kind of return on invested capital can these companies earn from all of these trillions of dollars of data center infrastructure investment?

Well, our bottom-up work points to encouraging answers to this question.

We see paths to roughly 25 to 50 percent return on invested capital, or ROIC, across three emerging AI business models. Now, ROIC is a useful way of measuring whether investments pay off. Think of it as how much after-tax operating profit can be generated relative to the capital required in the first place.

The first business model we've analyzed is renting compute power. This is the infrastructure layer of the AI economy. Cloud providers build data centers filled with advanced graphics processing units, or GPUs, and rent that compute capacity to customers. In our base case, a large next-generation data center can generate a return on invested capital of roughly 30 percent simply renting AI compute power.

And even if rental prices move around, our scenarios still produce returns ranging from low 20s percent to nearly 40 percent. So, despite the enormous cost of building and capital being deployed for these facilities, we think the economics here are quite attractive.

The second business model we've analyzed is  where an AI lab has their own model, and they also own their own infrastructure. They give access to their model through an API to consumers and enterprises who then build upon it, they utilize the model. In some cases, they build applications using that model that can be future sources of productivity or efficiency for the economy.

In this scenario, we think the economics can be even stronger. When the model developer owns their own underlying infrastructure, our base case generates a roughly 75 percent incremental operating margin and a return on invested capital of 40 percent plus.

These returns on invested capital are impressive, but what determines whether these returns can actually materialize?

Well, two things matter a lot. The first is the price the developers are able to charge for tokens, which are the units of information that AI models process. The second factor that matters considerably is how efficient[ly] can this infrastructure process these tokens.

This is why continued improvements in chips and software to drive higher token throughput or more tokens per GPU per second are critical to the long-term unit economics across this AI ecosystem.

The third model we've analyzed is when the AI developers rent their compute infrastructure rather than owning it. So, effectively, they are paying someone else for the data centers and the GPUs that they need. While this lowers their returns on invested capital because another provider takes a piece of the unit economics, our base case still produces roughly a 30 percent incremental operating margin and 25 percent post-tax return potential.

So, while the AI build-out requires enormous investment, the size of the spending alone doesn't tell the whole story about whether or not there are economic returns to come.

What ultimately matters is the revenue and profit that the infrastructure can generate. And as more of the infrastructure shifts from training AI models to serving customers through emerging products and inference, we think we're going to get a much clearer answer to this question investors are asking today.

Was all this spending worth it? Our research suggests: Yes.

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