Edward Conard

Top Ten New York Times Bestselling Author

  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
Upside of Inequality Oxford Unintended Consequences

AI Summary. In artificial intelligence, profit margins rise the further a company sits from the end customer, reversing the usual pattern in which owning the customer relationship commands the highest margins. The author argues the upstream profits are funded by capital raised by the loss-making consumer-facing layer rather than by cash generated from end demand.

Torsten Sløk Apollo
Date Posted:

AI profits have so far come only from the buildout itself, while the end-product generates losses. Slok notes that in the absence of revenue from models and applications, raising capital “can bridge the gap for a while, but not indefinitely.”

Are AI profit margins higher farther from the end customer?

Core argument: In the AI value chain, profit margins rise the further a firm sits from the end user, reversing the usual pattern in which the owner of the end-customer relationship earns the higher margin.

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. 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.

Takeaways by Macro Roundup® AI

  1. In the AI value chain, profit margins rise the further a firm sits from the end user, reversing the usual pattern in which the owner of the end-customer relationship earns the higher margin.
  2. The AI boom's upstream 41% margins are paid out of capital raised by the layer losing money at -59%, not out of cash generated by end demand.
  3. The most profitable part of the AI value chain depends on the least profitable part continuing to grow revenue or raise capital.

Related Articles:

  • The State of the AI Economy — An analysis estimates the AI ecosystem generated $110bn in revenue over 12 months excluding double-counting, with the most recent month annualizing to a $175bn run rate. After separating AI-specific capital spending from the roughly $120bn hyperscalers already spent annually and depreciating compute over 6 years, revenues attributable to hyperscalers roughly cover depreciation.
  • AI: The ROI Runway Could Be Long Outside the Tech Sector — AI company valuations rest on the assumption that profit margins across non-tech firms will eventually rise, but outside a handful of sectors adoption is slow, creating a divergence between front-loaded valuations and slower cash flows. If the per-use costs of AI models converge toward zero, revenue may be insufficient for all major cloud providers.
  • A Slower AI Payoff Would Be Everyone’s Problem — If artificial intelligence profits arrive later than expected, committed capital spending and depreciation would still hit on schedule, squeezing margins, stretching balance sheets toward more debt and possible ratings downgrades, and repricing equities. Because a handful of large technology firms dominate the indices, the author argues the damage would spread to chips, power, data centers and the broader market.
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