Edward Conard

Top Ten New York Times Bestselling Author

  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…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
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…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 is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “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
  • “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
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
Upside of Inequality Oxford Unintended Consequences

AI Summary. The author argues that as incremental gains from larger AI training runs shrink, the return on additional computing power falls, weakening the case for the training spending that dominates data center demand. Survivors of model commoditization will be those owning the orchestration layer or the applications where outcomes are priced.

Paul Kedrosky Notes on Complexity
Date Posted:
Is Database:
Database

As incremental model gains shrink, the return on investment in massive training runs weakens. With most capex and data center load tied to training, Kedrosky asserts, “In a slowing intelligence regime, architecture beats raw capability.”

Does shrinking returns from larger AI training runs undermine data center spending?

Core argument: The author argues that capital expenditure tied to model training, which constitutes most AI spending and most data center load, looks increasingly misguided and even stranded as returns on additional compute decline.

Declining progress rate breaks capital dynamics. When incremental gains shrink, justification for massive training runs weakens. The return on additional compute declines unless it unlocks step-function capability, which is masked anyway by these new tools. Investors begin asking whether orchestration and product engineering yield higher ROI than another modest benchmark improvement. The vendors that survive model commoditization will not be those with the marginally highest benchmark score. They will be those who either (a) own the orchestration substrate that defines how models are used, or (b) own the applications where outcomes are priced and embedded. It is even possible, and perhaps likely, I've argued, [that] the winner will be the first frontier model company to abandon spending on models. Current capex related to model training—which is most of spending, and most of data center load—looks increasingly misguided and even stranded. It is pressured at one end by model distillation and open source, and at the other by commoditizing orchestration layers.

Takeaways by Macro Roundup® AI

  1. The author argues that capital expenditure tied to model training, which constitutes most AI spending and most data center load, looks increasingly misguided and even stranded as returns on additional compute decline.
  2. Survivors of model commoditization will be vendors that own the orchestration substrate defining how models are used or the applications where outcomes are priced and embedded, not those with marginally higher benchmark scores.
  3. Model training spending is pressured at one end by distillation and open source and at the other by commoditizing orchestration layers, and the author suggests the winner may be the first frontier model company to abandon spending on models.

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  • Chart of the Day: AI Capex, From Bonanza to Burden — Stock price reactions to large artificial intelligence capital spending announcements have shifted from rewarding to penalizing companies, with the valuation multiple markets assign to such spending collapsing and likely turning negative. Technology companies once valued for low debt and low capital intensity are now among the leaders in both.
  • Four Takeaways from Microsoft’s Satya Nadella at WEF Today — Microsoft's chief executive says the price of AI model usage ("tokens") falls by roughly half every three months. Commentary argues that such rapid deflation pulls demand forward, encourages overbuilding, and commoditizes models, shifting competitive advantage to orchestration and context rather than the model layer.
  • The Geopolitics of AI: Decoding the New Global Operating System — The performance gap between the top American and Chinese AI models narrowed from 9.3% to 1.7%, with Chinese firms relying on cheaper open-source models—DeepSeek prices usage 20-40x below OpenAI's ChatGPT. This price advantage could win adoption in developing countries and build dependency on Chinese AI ecosystems.
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