Edward Conard

Top Ten New York Times Bestselling Author

  • “…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 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 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
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “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 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
Upside of Inequality Oxford Unintended Consequences

AI Summary. The five largest U.S. technology firms are scaling AI capital spending about 50% a year, from $155bn to a forecast $755bn. Embedding this in a two-sector model with rare productivity booms, the authors estimate AI adds roughly 5, 20, or 58pp to cumulative growth over a seven-year transition, depending on scenario.

Jessica Wachter and Jonathan Wachter National Bureau of Economic Research
Date Posted:
Is Database:
Database

The AI build‑out projected through 2027 is roughly PV‑neutral if there has already been one rare 2.7× productivity boom in the AI sector. In the absence of a further boom, long‑run valuations and the more aggressive 2028–30 capex plans start to look too high.

What does AI capital spending imply about future economic growth?

Core argument: The five largest U.S. technology firms are scaling AI capital expenditure at roughly 50% per year, from $155 billion in 2022 to a forecast $755 billion in 2026.

The five largest U.S. technology firms are scaling AI capital expenditure at roughly 50% per year, from $155 billion in 2022 to a forecast $755 billion in 2026. This paper embeds this observation in a two-sector model with rare productivity booms to ask what the investment path implies for growth and asset prices. Calibrating the boom size to match the observed investment ramp implies that each boom raises AI-sector productivity by a factor of roughly 2.7. A two-year window of elevated boom probability generates three scenarios: a moderate outcome (one boom only), a transformative outcome (one further boom), and a singularity scenario (two additional booms). Even in the moderate scenario, AI adds ~ 5pp to cumulative GDP growth over the seven-year transition (Figure 2), on top of the ~19% normal-times growth that would otherwise prevail. The transformative and singularity scenarios push the AI contribution to 20% and over 58%, respectively. The non-AI sector continues to grow at its historical rate, [while the] AI sector’s share of the economy rises from ~3% today to btw 8% and 39%, depending on the scenario. As this share grows, the AI sector’s rapid productivity gains increasingly dominate aggregate GDP growth.

Takeaways by Macro Roundup® AI

  1. The five largest U.S. technology firms are scaling AI capital expenditure at roughly 50% per year, from $155 billion in 2022 to a forecast $755 billion in 2026.
  2. Calibrating boom size to the observed investment ramp in a two-sector model with rare productivity booms implies that each boom raises AI-sector productivity by a factor of roughly 2.7.
  3. In the moderate scenario, AI adds about 5 percentage points to cumulative GDP growth over the seven-year transition, on top of the ~19% normal-times growth, rising to 20% in the transformative scenario and over 58% in the singularity scenario.

Related Articles:

  • Have We Entered an Era of High Productivity Growth? — Labor productivity has grown solidly while the San Francisco Federal Reserve's total factor productivity index, which adjusts for the equipment and technology workers use, has grown modestly, suggesting gains so far reflect better tools rather than broader advances. A regime-switching model puts the probability of a high-productivity regime at 57% using labor productivity and 21% using total factor productivity.
  • Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools — The authors estimate that AI coding tools raise developers' task-level productivity by about 40% with autocomplete, 140% including synchronous agents, and 180% with asynchronous agents, with larger effects for less active developers. Gains attenuate up the production chain: synchronous agents raise lines of code 741% and pull requests 65% but releases only 20%.
  • Measurements Matter: The Return of SLOC Slop — Measuring developer productivity by software lines of code captures output rather than value, and AI inflates the metric by generating boilerplate, wrappers, tests, and speculative code at near-zero cost. More code also creates liabilities through added maintenance, complexity, attack surface, and debugging load.
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