AI: The ROI Runway Could Be Long Outside the Tech Sector

The first chart below shows that so far there are no signs of profit margins rising outside the tech sector. This is ultimately what we are waiting for, because the value of AI companies today rests entirely on the promise that margins in the S&P 493 will eventually climb.

That promise is the link to current market prices, since implicit in the valuations of AI companies are assumptions about future earnings. That's why the current debate about token costs, model routing and token marketplaces is important. If token costs converge toward zero for most AI use cases, then there is not enough revenue for all hyperscalers even in a situation where compute demand surges higher. For more discussion, see also this great piece from my colleagues in Apollo Thematic Investing.

The key issue is the length of the ROI runway outside the tech sector. In a handful of sectors, software and tech above all, implementation is nearly immediate, since these firms can fold AI into their own products and processes overnight. But that is the exception. Across most of the economy, and especially in capital-intensive, heavily regulated sectors, deep process re-engineering and data governance requirements could delay structural productivity gains well beyond what the market currently projects. The list of slow-moving sectors is long, spanning health care, banking and insurance, energy and utilities, defense and aerospace, pharma and life sciences, manufacturing, transportation and logistics, construction and real estate, education, legal and the public sector.

This creates a dangerous divergence between aggressive, front-loaded valuations today and a much slower cash flow reality, since equity markets priced for instant earnings growth will face a painful repricing if the productivity hockey-stick takes five years rather than five months, see the second chart below. Put differently, companies will slow their AI spending if they don't see ROI quickly, and the current focus on token optimization is an early warning that AI implementation could be a bumpier, slower road than expected.

The bottom line is that a mismatch between current earnings expectations and the actual time firms need to generate ROI on AI investments could have significant implications for many AI company valuations today.