“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
“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 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 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
“…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
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…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
“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
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute

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The Knowledge Machine' Review: Sciences Stubborn Majesty

Christoph Irmscher The Wall Street Journal

Date Posted:: December 21, 2020
Is Database:: Database

Science functions as a “knowledge machine” that operates slowly & indifferently, producing facts about the world over time. @ChristophIrmscher

The scientific process, as described by Mr. Strevens, functions as a "knowledge machine" that operates slowly and indifferently, producing facts about the world over time. Unlike Popper's hypothesis-driven approach or Kuhn's paradigm shifts, this machine is self-sufficient and immune to external critiques, including those from radical subjectivists who question science's objectivity. This methodical progression, while seemingly irrational to outsiders, ensures that science continues to advance. In the context of macroeconomic insights, this slow but steady accumulation of knowledge can inform policy decisions and economic strategies, providing a robust foundation for long-term planning and innovation. As science advances, it holds the potential to address complex global challenges, ultimately contributing to economic stability and growth.

Christoph Irmscher, "‘The Knowledge Machine’ Review: Science’s Stubborn Majesty,"The Wall Street Journal, December 18, 2020, https://www.wsj.com/articles/the-knowledge-machine-review-sciences-stubborn-majesty-11608333769

Description of the scientific process, “…Where Popper envisioned scientific truth rising from a “mass extermination of hypotheses,” Kuhn saw it happening through paradigm changes by which a new way of scientific thinking creeps up on and eventually obliterates another, older, exhausted one. Mr. Strevens’s machine, by contrast, isn’t driven by revolutions or falsification efforts. Like the deadly apparatus in Kafka’s famous story “In the Penal Colony,”it works slowly, indifferently, oblivious to the world around it. Supremely self-sufficient, it is immune to the potshots of the “radical subjectivists,” those humanists or sociologists, disdained by Mr. Strevens, who believe that science has no special claim to objectivity. While the results of the machine’s work are open to debate, the machine itself is not; it is that stubborn imperviousness that, to the outside observer, seems “irrational.”…When left to its own devices, science will advance and eventually save us from ourselves. Or so Mr. Strevens assures us in his final, pandemic-inflected chapter….”

Ed Comment: "... While I think science and innovation largely unfolds like mindless evolution through random trial and error, gradually uncovering the undiscovered structure of reality, (after all, if we knew what came next, it would already be discovered,) I also think that we can explore the (logical/mathematical) structure of reality, with thought experiments (akin to running CAD simulations rather than building the physical prototype, which takes much longer) both though mathematical trial and error and by asking: if I ran this experiment, what problems would I have to solve to do it? Or: if I ended up with this result what would it prove and/or leave unproven? Trial and error is like solving a jigsaw puzzle. There are strategies that increase the chance of success BECAUSE the picture in the jigsaw puzzle has a logic structure. This is the part the author seems to overlook.BTW I took Gordon's book on vaca. Have lots of thoughts both good and bad. I think he misses a lot about the exogenously given structure of the opportunity landscape, ie how manufacturing is inherently different than IT or globalization, and how military-related problem solving has powerful general purpose applications that other types of research/investment doesn't have. Anyway, I'm loving the book….”

The College Wage Premium in the Generative AI Era

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network

Date Posted:: September 4, 2026
Is Database:: Database

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

Takeaways by Macro Roundup^® AI

The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

Gross and Net US Investment

Timothy Taylor Conversable Economist

Date Posted:: September 4, 2026
Is Database:: Database

U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.

Takeaways by Macro Roundup^® AI

Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

The AI Re-Acceleration That Wasn’t

Paul Kedrosky Applied Complexity

Date Posted:: September 3, 2026
Is Database:: Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup^® AI

Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

Understanding AI and Productivity

Chad Syverson Economic Innovation Group

Date Posted:: August 28, 2026
Is Database:: Database

Syverson is skeptical that AI initiated the rise in productivity growth that began in 2023. The acceleration began while AI investment was small, and pandemic-era labor market churn and business dynamism match the acceleration’s start.

Productivity from mid-2022 on has maintained a faster-than-2010s trajectory involving annual growth of about 2.2%. Could this acceleration be due to AI? Perhaps. The timing leans against AI being the sole initial cause. Additionally, there were well-documented increases in economic dynamism (labor market churn and business formation) during the pandemic emergence whose timing matches the acceleration’s start. Regardless of AI’s current effect, the longer the aggregate productivity acceleration continues, the more plausible it is that AI is an important driver. As for the magnitude, a sustained increase from 1.5 to 2.2% annual productivity growth would be substantial (after a decade, GDP per capita would be 7% higher than otherwise), but hardly unprecedented. The 1995–2004 productivity boom saw annual productivity growth of nearly 3% per year, and other past general-purpose-technology-related productivity boosts saw labor productivity growth in excess of 2.5% for a decade or longer.

Takeaways by Macro Roundup^® AI

U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.
The 1995–2004 productivity boom averaged nearly 3.0% annual growth, establishing that a durable AI-driven acceleration to 2.2% would be meaningful but well within historical precedent for general-purpose-technology cycles.
Pandemic-era surges in labor market churn and business formation align more precisely with the productivity acceleration’s start date than AI adoption does, complicating AI-as-sole-cause narratives.

US Widens AI-Driven Investment Gap With Europe

Sam Fleming, Amy Borrett and Olaf Storbeck Financial Times

Date Posted:: August 24, 2026
Is Database:: Database

Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Corporate spending on new equipment and facilities in the US is projected to increase 40% in real terms between 2021 and the end of next year, according to forecasts from Oxford Economics. The US surge compared with a real-terms increase of just 12% in the euro area, while German business investment is expected to have all but stagnated over the same period. Europe also faces a large and growing productivity gap with the US. “The United States has recently pulled further ahead of Europe,” Bart van Ark, a professor at the University of Manchester, told policymakers at the ECB Forum in Sintra. GDP per hour worked increased $14 in the US between 2018 and 2025, compared with just $2 in Europe. “The gap is not only a digital sector story,” added van Ark, stressing that the US outperformance extended to other sectors, including wholesale and retail as well as professional services.

Takeaways by Macro Roundup^® AI

U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.
U.S. labor productivity rose $14 per hour worked between 2018 and 2025, versus $2 in Europe, with outperformance spanning wholesale, retail, and professional services—not solely the digital sector.

Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

Peter Rudegeair and Peter Santilli Wall Street Journal

Date Posted:: August 17, 2026
Is Database:: Database

A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

Science functions as a “knowledge machine” that operates slowly & indifferently, producing facts about the world over time. @ChristophIrmscher

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is the college degree losing its economic value to artificial intelligence?

Takeaways by Macro Roundup® AI

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U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

Does faster asset depreciation explain slowing productivity growth?

Takeaways by Macro Roundup® AI

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Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Are AI performance gains accelerating or just appearing to through selective measurement?

Takeaways by Macro Roundup® AI

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Syverson is skeptical that AI initiated the rise in productivity growth that began in 2023. The acceleration began while AI investment was small, and pandemic-era labor market churn and business dynamism match the acceleration’s start.

Is AI-driven productivity growth sustainable at historical technology boom levels?

Takeaways by Macro Roundup® AI

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Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Takeaways by Macro Roundup® AI

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A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Are technology companies hiding the true cost of artificial intelligence?

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Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI