
Apollo economist warns AI profit gains outside tech could take "well beyond" what Wall Street expects
Quick Answer
Apollo's chief economist, Torsten Slok, warns that AI's profit gains outside tech sectors may lag behind Wall Street's expectations, particularly in regulated industries.
Quick Take
Apollo's chief economist, Torsten Slok, warns that AI's profit gains outside tech sectors may lag behind Wall Street's expectations, particularly in regulated industries. He suggests that productivity improvements could take years to materialize, affecting stock valuations and cash flows significantly.
Key Points
- AI valuations depend on rising margins in S&P 493 companies, excluding top tech firms.
- Productivity gains in regulated sectors like healthcare and banking may take longer than expected.
- Market forecasts for rapid earnings growth could misalign with actual cash flows.
- Measuring productivity gains in knowledge work remains challenging without clear metrics.
- Falling token costs could limit revenue for hyperscalers.
📖 Reader Mode
~1 min readOutside tech, there's no sign AI is boosting profit margins, writes Torsten Slok, chief economist at US financial firm Apollo. AI company valuations rest entirely on the promise of rising margins at S&P 493 companies (the index minus "the magnificent seven"). In regulated industries like healthcare, banking, energy, pharma, and manufacturing, process overhauls and privacy requirements could delay productivity gains "well beyond what the market currently projects." Markets are pricing in fast earnings growth, but real cash flows could trail far behind, Slok says. If the productivity bump takes five years instead of five months, many AI stocks face a painful repricing. Falling token costs could also cap hyperscaler revenue.

There's another problem: Even where individual employees are already more productive, the gains are hard to measure in knowledge work. Without clear metrics, management can't act, and productivity improvements don't show up on the balance sheet. They get absorbed into daily operations. We broke this down in Frontier Radar #3.
— Originally published at the-decoder.com
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from The Decoder
See more →
An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
Epoch AI's MirrorCode benchmark reveals Claude Opus 4.7 as the leader with a 56% solve rate, reconstructing a 16,000-line toolkit in 14 hours. Despite this, all models tested struggle with the most complex tasks, highlighting limitations in current AI capabilities. The single task consumed $2,600 over 19 days, raising questions about cost-effectiveness in AI development.

