Edward Conard

Top Ten New York Times Bestselling Author

  • “Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “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
  • “…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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
Upside of Inequality Oxford Unintended Consequences

AI Summary. AI valuations embed assumptions of rapid profit growth across the broader economy, but outside the tech sector, returns on AI investment are slow to materialize, creating a gap between current market prices and actual cash flows that could force a sharp repricing.

Torsten Sløk Apollo
Date Posted:
Is Database:
Database

Sløk notes there are no signs of rising profit margins outside of the tech sector; AI’s ROI runway outside the tech sector may be long, creating a divergence between “front-loaded valuations today and a much slower cash flow reality.”

Will non-tech companies ever recoup their AI investments?

Core argument: Token cost convergence toward zero across most use cases threatens hyperscaler revenue sufficiency despite surging compute demand, leading to potential.

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. The key issue is the length of the ROI runway outside the tech sector. In a handful of sectors, implementation is nearly immediate. But that is the exception. 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. 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.

Takeaways by Macro Roundup® AI

  1. Token cost convergence toward zero across most use cases threatens hyperscaler revenue sufficiency despite surging compute demand, leading to potential.
  2. That’s why the current debate about token costs, model routing, and token marketplaces is important.
  3. In a handful of sectors, implementation is nearly immediate.

Related Articles:

  • The State of the AI Economy — AI infrastructure revenues across major cloud providers narrowly cover depreciation costs when compute assets are written down over 6 years. Demand for AI compute still exceeds supply, and every 10% price cut drives 12–18% more token usage, meaning total spending rises as prices fall.
  • Why .400 Hitters Disappeared — and What It Means for AI — As AI model performance converges toward a ceiling, relative gains per improvement cycle shrink, transforming frontier capability from a pricing moat into a commodity where price becomes the primary differentiator and margin pressure intensifies across leading providers.
  • Semiquincententacles — Frontier AI labs are shifting to usage-based pricing that reflects total costs, raising token prices and pushing some enterprises toward cheaper open models for routine tasks, while frontier models remain essential for high-performance applications like cybersecurity and scientific discovery.
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