The Daily Spark

AI-related issuance already accounts for nearly 40% of longer-duration IG corporate bond supply, and the financing needs are only getting larger. We estimate the AI ecosystem could fundamentally support more than $2 trillion of additional IG debt, while public IG markets may be able to absorb less than $1 trillion of that amount through 2030 because of concentration and ratings constraints.

That gap creates a significant opportunity for private IG. We expect more than $1 trillion of financing could migrate toward private placements, infrastructure debt, asset-backed facilities, equipment financings and project-level structures — often with collateral, contractual support and structural protections that are unavailable in unsecured public bonds.

Read more in our 2026 Midyear Credit Outlook.

Spider-Man and The Odyssey have lifted weekly box office grosses to a record high, see chart below.

Consensus earnings expectations still imply remarkably little disruption from AI. Of more than 200 publicly traded software and white-collar services companies we track, only 10 are currently expected to experience both revenue and EBITDA declines over the next two years. That suggests markets may be pricing in the possibility of AI disruption without yet fully incorporating its potential impact on earnings and margins.

We think about that AI pressure through three channels: direct replacement, where AI performs the same task at a lower cost; labor displacement, where AI reduces the number of employees, contractors or users supporting a business model; and execution risk, where AI-native competitors innovate faster and take market share. As adoption accelerates, these are the channels we are watching for signs that AI disruption is beginning to show up in fundamentals.

Read more in our 2026 Midyear Credit Outlook.

Europe and the US face the same AI displacement. Only the US gets what offsets it: the startup formation, the capex and the hiring that comes from building the technology rather than only absorbing it.

The widening unemployment gap between France and the US is starting to look like the price of being on the wrong side of that asymmetry.

Chipmakers and health care sectors have converged to identical forward P/E ratios for the first time in years, see chart below.

The S&P 493 is spending heavily on AI. But it is not showing up in profit margins, see the first chart below.

Looking at the individual sectors of the S&P 500 outside tech shows that the picture is flat, cyclical or worse, see charts 2, 3 and 4.

Health care margins have halved since 2015; consumer staples are stuck near 6% and consumer discretionary near 8%; energy and materials have given back most of their 2022-23 gains; real estate is going sideways; and the only genuine improvements look like an ordinary cyclical recovery rather than a technology-driven step change.

The bottom line is that the AI capex boom is so far only showing up in the sellers' margins, not the buyers'.

This is important because the longer it takes the S&P 493 to generate ROI, the bigger the downside risks to an economy and a market this concentrated in the AI trade.

For more, see here.

Banks provide less than one-fifth of nonfinancial corporate debt, down from half in the 1970s.

What replaced banks is not one thing but many: syndicated and public markets for the largest borrowers, and private capital for everything that needs certainty of execution, customized structures, or longer duration than a bank balance sheet can comfortably hold.

The bottom line is that the financial system has shifted from short-dated deposit funding toward long-dated institutional capital, matched more closely to the assets it finances.

A wider lender base means borrowers have more places to go, which is good for growth and financial stability. Credit risk now sits with long-duration investors who chose the exposure and are funded to hold it through a cycle, rather than on leveraged, deposit-funded bank balance sheets.

In business, profit margins are frequently higher for the owner of the end-customer relationship.

But that is not the case for AI. In AI, profit margins are higher the further you get from the end user, see chart below.

This is important because it means the AI boom's profits are currently being funded by investors rather than earned from customers. The upstream margins are real, but they are paid for out of capital raised by the layer losing money, not out of cash generated by end demand. That makes the 41% contingent on the -59% continuing to be financeable.

The bottom line is that the most profitable part of the AI value chain depends on the least profitable part continuing to grow revenue or raise capital. Capital can bridge the gap for a while, but not indefinitely. And therein lies the risk: will the ROI show up for AI's end customers fast enough to sustain the spending that is generating those upstream margins?

The first chart below shows that the consensus expects hyperscaler capex to run at roughly 3% of GDP every year from 2027 to 2029, up from 0.3% of GDP in 2019 and 1.4% in 2025.

The second chart shows that this is more than twice the peak of the telecom and fiber buildout of the late 1990s, which topped out at 1.2% of GDP in 2000 before collapsing and tipping the economy into the mildest post-war recession.

The third chart shows that the data-center buildout is still less than half the size of the housing boom, which peaked at 6.6% of GDP in 2005.

There are three ways to look at this data:

1. In level, the ongoing data-center buildout sits between the fiber and housing cycles: more than twice the fiber peak, less than half the housing peak.
2. In cumulative change, what matters is not the level of the share but how much it moves, because that is what adds to or subtracts from GDP. On the data in these charts, data-center capex rises 2.5 percentage points, from 0.6% of GDP in 2023 to 3.1% in 2027, against 0.4 percentage points for telecom in the late 1990s and 2.2 percentage points for housing from the mid-1990s to 2005. On this measure, the data-center buildout is the bigger capex cycle.
3. In speed, the contrast is sharper still, and it holds even when each cycle is measured over its own fastest stretch. Data-center capex adds 1.7 percentage points in just two years, from 1.4% of GDP in 2025 to 3.1% in 2027, or roughly 0.85 percentage points a year. Housing's quickest phase, from 5.1% in 2002 to 6.6% in 2005, ran at 0.5 percentage points a year, and telecom's at around 0.15. The AI cycle is building at close to twice the pace of the housing boom at its fastest.

The bottom line is that the data-center buildout is smaller than housing in level but larger in the change in share of GDP, and faster than either previous cycle.

The same arithmetic runs in reverse: housing's unwind, from 6.2% of GDP in early 2006 to 3.0% by the end of 2008, is what made that recession severe, while telecom's much smaller reversal produced the mildest one.

A cycle that builds at 0.85 percentage points a year can unwind at a similar pace, and that, rather than the buildout itself, is the macro risk if AI demand disappoints.

Fed Chair Kevin Warsh has been unfairly criticized. The decision to eliminate forward guidance isn't reckless. It's pragmatic:

1. It stops policy inertia and restores real market signals
   Explicit rate commitments lock the Fed into predetermined paths. This makes it harder to pivot when conditions change.
2. It ends false certainty
   Forward guidance creates a false sense of predictability, which distorts asset pricing and encourages excessive risk-taking.

The bottom line is that Warsh's logic is sound. Cleaner data signals are better than false certainty, and the Fed gains flexibility.

But Warsh could dampen this newly introduced volatility by providing clearer framework guidance. Without it, markets struggle to understand how the Fed will reach 2% inflation. Is it through higher rates, a smaller balance sheet or tighter financial conditions? This uncertainty has steepened the yield curve and pushed up risk premiums, see also here.

By eliminating forward guidance, the Fed gets more flexibility. But higher volatility is the cost, and the cost can be minimized by providing clearer framework guidance.