Edward Conard

Top Ten New York Times Bestselling Author

  • “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 very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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 full-throated defense of economic dynamism.” - The Wall Street Journal
  • “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
  • “…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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
Upside of Inequality Oxford Unintended Consequences

AI Summary. The 2020 scaling-laws paper fostered a belief that mixing compute, data, and parameters yields better AI models in a deterministic way, which the author argues let unusually large capital spending plans pass without investor revolt. The author contends that curve was empirical rather than physical and is now breaking, returning AI capital spending to normal economics.

Paul Kedrosky Applied Complexity
Date Posted:

Kedrosky argues that diminishing returns to AI investment are visible, noting “soaring” frontier training costs and slower model improvement. “If scaling laws no longer guarantee anything, the spending mirage fades. Only the bill will remain.”

Did belief in predictable AI scaling shield huge capex from scrutiny?

Core argument: The author argues that the 2020 scaling-laws paper convinced boards and CFOs that AI capability growth was a deterministic function of compute, data, and parameters, derisking corporate spending more profoundly than in any prior technology cycle.

If Moore had his law, AI has Kaplan’s Mirage: the belief, born from the 2020 scaling-laws paper that pre-training drove intelligence in a deterministic and almost mathematical way. The curve was appealing: smooth, monotonic, mathematically tidy. It said that if you mix the right ratio of compute + data + parameters, you will cook up a better model. That conviction derisked corporate spending more profoundly than in any prior technology cycle. CFOs and boards didn’t see risk; they saw a factory process. Capex plans that would normally trigger investor revolt sailed through because the output has felt predictable. But the “law” was empirical, not physical. And empirical curves break. The break is appearing [marginal improvements are shrinking, training stability is worsening, data is finite and heavily duplicated, inference quality is unstable in a reinforcement learning regime]. If you remove the illusion of deterministic capability growth, the AI capex supercycle reverts to normal economics.

Takeaways by Macro Roundup® AI

  1. The author argues that the 2020 scaling-laws paper convinced boards and CFOs that AI capability growth was a deterministic function of compute, data, and parameters, derisking corporate spending more profoundly than in any prior technology cycle.
  2. The author contends that AI scaling laws were empirical rather than physical, and points to shrinking marginal improvements, worsening training stability, finite and heavily duplicated data, and unstable inference quality under reinforcement learning as signs the curve is breaking.
  3. The author argues that removing the illusion of deterministic capability growth would return the AI capex supercycle to normal economics, since capex plans that would normally trigger investor revolt passed only because output felt predictable.

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  • Is AI Hitting A Wall? — Artificial intelligence gains have come from a formula of more data and more computing power, but companies have exhausted freely available internet training data and are now striking data-sharing deals with publishers and copyright holders. Training and running large models is energy-intensive, requiring vastly more Nvidia chips with each generation.
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