Epochal change is everywhere.
Have you noticed that? There’s a bull market in tectonic shifts.
That’s tongue-in-cheek, but now that I think about it, it makes intuitive sense. Watershed moments tend to beget more paradigmatic change.
That’s a kind of paradox in that definitionally speaking, watersheds are singular events. At the same time — i.e., despite being in some sense oxymoronic — it’s almost a truism.
Put as a question: What would it even mean for “epochal change” to happen in a vacuum? When a critical levee breaks, there are downstream consequences every bit as dramatic, and in many cases even more so, than the initial event.
Nagging ROI questions notwithstanding, it’s safe to say AI’s an epoch. (There’s a machine learning joke in there. Don’t miss it.) This is not a drill. Maybe it’s not the wheel or the internal combustion engine or the PC, but AI’s not the metaverse.
And while it’s too early to speculate on the full spectrum of downstream consequences, we’ve already witnessed one knock-on event of historic import: An overnight transformation among the most important companies in the history of capitalism from lean, mean cash machines, to debt-laden, asset-heavy businesses.
I realize this is well-worn territory by now, but it’s kinda hard to resist the temptation to harp on it given the rapidity and magnitude of the shift.
The figure above, from Goldman, gives you some context. Among the hyper-scalers, revenue-to-assets exploded from 55% to nearly 80% in the half-dozen years leading up to the launch of ChatGPT. Then, everything changed.
“The physical footprint required to support AI workloads has led to a steady decline in asset turnover, which analysts expect will continue in 2027,” Goldman’s Ben Snider wrote, adding that “the capex boom boosting semiconductor earnings today will weigh on hyper-scaler ROE going forward as depreciation expenses grow.”
Note from the chart that depreciation and amortization as a share of sales is expected to double by 2028 from pre-ChatGPT levels.
You’re reminded that this year’s capex plans call for more than $750 billion in outlays across the hyper-scalers. That figure, Snider remarked, is equivalent to 100% of free cash flow. Hence the need to ramp up borrowing and, more recently, raise equity to avoid taking on too much debt.
The figure on the left, below, shows you the inflection in hyper-scaler leverage beginning, again, with the launch of ChatGPT.
As Snider went on to write, “share counts have begun to rise” too, as illustrated on the right.
The prospect that between AI IPOs and hyper-scaler follow-ons, net equity supply may turn positive next year is a new bogeyman for market participants.
If the overall supply of US public equity does indeed stop contracting as a result of the hyper-scaler arms race, that’ll be yet another example of how watershed moments tend to multiple and propagate.
