Edward Conard

Top Ten New York Times Bestselling Author

  • “…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
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…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 must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “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
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American Innovation, a Murder Mystery

Mene Ukueberuwa Wall Street Journal
Date Posted:
December 16, 2019
Is Database:
Database

US productivity growth has accelerated relative to other high-wage countries, suggesting innovation may not be dead but evolving in new directions @MeneUkueberuwa @WSJ.

US productivity growth has accelerated relative to other high-wage countries, suggesting innovation may not be dead but...
The decline in American innovation is attributed to several factors, including an unconstrained supply of cheap labor from China and Mexico, a surplus of domestic labor post-recession, and a long-term decline in research productivity. Despite high off-trend productivity gains from the internet, these are unsustainable. A shortage of talent and the difficulty of achieving breakthroughs in general-purpose technologies like AI and quantum computing further hinder progress. The shift from centralized manufacturing to decentralized services has slowed productivity growth, while high real estate prices in growing cities limit medium-skilled migration. Despite these challenges, US productivity growth has accelerated relative to other high-wage countries, suggesting that innovation may not be entirely dead but rather evolving in new directions.

Mene Ukueberuwa, "American Innovation, a Murder Mystery,"Wall Street Journal, December 13, 2019, https://www.wsj.com/articles/american-innovation-a-murder-mystery-11576277193American Innovation, a Murder Mystery Addressing 20,000 rallygoers at Dallas’s American Airlines Center recently, President Trump praised a “beautiful new Louis Vuitton plant” he’d toured that morning. The plant will bring about 1,000 jobs to a small town near Fort Worth, and Mr. Trump was displaying his good habit of celebrating work wherever Americans find it. But despite cheers of approval, one imagines he and his supporters would have preferred to christen an advanced “machine and manpower” plant—the kind that might assemble airplanes for the arena’s namesake. A craft workshop for a French luxury brand doesn’t quite capture the MAGA ideal. The president’s pledge to make America great again has always borne the implicit claim that America no longer makes anything great, whether Fords or farm equipment. There’s some truth to that, and both airplanes and handbags are examples of how U.S. manufacturing has fared since the mid-20th century. The key measure is productivity—how much value each worker produces—which bears heavily on job creation. Highly productive industries like chemicals, weapons systems and aerospace still draw strong domestic investment. But employment has shrunk because better machines and logistics have diminished the need for manpower. Productivity has risen more slowly for simpler types of manufacturing such as textiles and home goods, leading many companies to seek cheaper production overseas. There has also been a notable slowdown in improvements to the quality of goods produced. For about a century, beginning with the Second Industrial Revolution in the 1870s, American growth was driven by tinkerers, trusts and corporate labs, which eked out regular advances in products based on engines, electrical circuits and signals, and synthetic materials. Today, another half-century later, a coast-to-coast flight still takes you as long as it took your father in the 1970s. And with the major exception of computers, nothing in your luggage is likely to be much more useful or valuable than dad’s equivalent. Who killed American innovation? Answering that question could be a vital step toward re-energizing growth. Unfortunately, in their search for a culprit, Mr. Trump and many policy makers and commentators have implicated causes that aren’t responsible for the slowdown and tried solutions that hinder growth: • The free traders. Since the beginning of his 2016 campaign, Mr. Trump has often blamed the decline of heavy industry on his predecessors’ trade policies. “I did disagree with Ronald Reagan very strongly on trade,” he said in his final debate with Hillary Clinton. As president he has blasted the North American Free Trade Agreement—a signature achievement of Mrs. Clinton’s other half—as often as any federal policy. Describing the effect Mexico and other trade partners have had on domestic production, Mr. Trump said last year that “they’ve destroyed the steel industry, they’ve destroyed the aluminum industry and other industries, frankly.” In this view, politicians opened U.S. manufacturing to an assault from foreign competitors that produce and sell more cheaply. That reduces American manufacturers’ incentive to invest in better plant and products, benefiting rivals in protected economies. Yet open trade can’t explain the overall slowdown of U.S. innovation. To be sure, competition from Mexico reduces the incentive for U.S. Steel to invest in new fabrication techniques. But progress has also slowed since the mid-20th century for American companies that are heavily supported and protected, like Boeing. And there are countless industries in which U.S. companies retain a large lead in innovation despite trailing in global market share—most notably, cutting-edge electronics like smartphones and semiconductors. U.S. protectionism couldn’t have prevented other nations from eventually catching up. Georgia Tech economic historian Steven Usselman notes that American firms throughout most of the 20th century expected this “convergence” of international capability to occur decades earlier than it did. Yet even as manufacturing executives lobbied for support in their sectors, they backed general liberalization of trade as a net economic good. • The investors. It’s common to blame stagnant innovation on large companies that reduce productive investment in favor of shorter paths toward profit. A growing number of Republicans have adopted this critique, notably Florida Sen. Marco Rubio. In May Mr. Rubio issued a 40-page report lamenting a drop in research and innovation and blaming “shareholder primacy theory” that focuses on increasing returns to shareholders. Instead of investing in plant and products, he argues, executives focus on cutting costs and optimizing debt to boost quarterly earnings. Critics of shareholder capitalism point to charts showing that plant and equipment have shrunk as a share of corporate assets since the 1950s, and that R&D in manufacturing have declined. But it’s unlikely these trends were caused by a sudden aversion to long-term commitments among investors and managers. Consider that in place of hard goods, companies are investing more in intangible assets, like the patents gained by acquiring a startup, or enterprise software that simplifies communication and accounting. These expenses may not boost employment in the same way as, say, new delivery trucks. But they reflect as much willingness to spend in ways that don’t deliver returns immediately, and in some cases never do. More important, there are entire business models based on risky long-term strategies—and those companies don’t have trouble finding backers. American financiers overinvested in rare-earth mining and hydraulic oil and gas production a decade ago based on rosy projections of global growth. And no one could accuse WeWork’s owners of valuing quarterly earnings over long-term potential. The rise of venture capital means more investors than ever are seeking the riskiest bets, and not only in software. Lavish support is available in biotechnology, robotics and other types of engineering in which backers have faith in an eventual payoff. • The regulators. This is the favorite suspect of innovators themselves, who understandably bristle under the increasing burden of federal rules. A company that makes or moves physical things faces limits on land use, passenger safety, pollution, chemical content and more, nearly all of which have gotten stricter since the 1970s. Peter Thiel, the billionaire tech investor, condemns federal regulators as the greatest obstacle to American innovation. “I would say that we’ve lived in a world in which bits were unregulated and atoms were regulated,” he said in a 2015 interview. His point is that restrictive rules are responsible for the slowdown in the engineering of mechanics (atoms) relative to software (bits). Whatever the benefits, every mandatory trial, emissions cap and maintenance rule reduces manufacturers’ output, decreases profit and restrains the ability to experiment. Yet the international comparison again casts doubt on this explanation. Countries that have developed quickly since the innovation slowdown, such as China, Japan and South Korea, placed less emphasis on consumer and worker protections than the U.S. did. Their bureaucrats attempt to boost industrial output, rather than hinder it. Despite decades of infrastructure marvels and greater shares of the world manufacturing market, none of these countries have caught up to the U.S. in achieving breakthroughs. Unlike 20th-century America, most of their growth has come from technologies discovered elsewhere. If regulation were decisive in hampering technological progress, one would expect zealously permissive regimes to surpass the fussy West. With legislators, investors and regulators largely off the hook, what’s left? The best evidence suggests that the seemingly boundless American progress of last century died a natural death. After 100 years of success, engineers around the 1970s found themselves less able to develop ever more productive designs and applications for the core technologies of the Second Industrial Revolution: combustion, electricity, signals, synthetics. As with previous revolutionary technologies—the steam engine, steel, gunpowder—the returns from each discovery gradually diminished. Productivity growth has slowed as a result. No other explanation accounts for why the slowdown has affected nearly every field of manufacturing. The internationally competitive garment industry and the protected sneaker industry both use similar production methods to those of 50 years ago. Neither high-tech combine harvesters nor simple winter jackets fulfill their functions much better now than two generations ago. No other country, regardless of trade or industrial policy, has demonstrated any more ability to break through. This realization is vital because it reframes every development in the U.S. economy since engineering progress diminished. For manufacturers since the 1970s, with few opportunities to grow by investing in better plant and equipment, offshoring parts of the production process may have become their only way to compete, not merely a way to grow faster. When companies lost much of their ability to improve product quality, lower production costs let them cut prices instead, raising living standards by other means. Critics like Mr. Rubio argue that the “financialization” of the economy has been counterproductive. In truth, the financial sector’s keenness for viable bets has helped sustain the overall growth of U.S. manufacturing. The low rate of nonperforming loans suggests the U.S. isn’t overleveraged compared with peers that boast a higher manufacturing share of output. Most important, the rise of software and high-end electronics looks less like a distraction from productive growth and more like a savior that has kept America moving when no other industry could. From IBM to Snapchat, tech companies have produced countless exportable goods and services and brought massive sums of foreign dollars into the U.S. economy. The software revolution has also propped up manufacturing by improving design capability, supply-chain logistics and marketing. A 2016 Deloitte survey of industry executives world-wide predicts that the U.S. will overtake China as the most competitive destination for manufacturing, as an ever-increasing share of growth occurs at the high end. Recognizing that the 20th century’s innovation streak died naturally should encourage policy makers to resist the allure of a Frankenstein industrial policy: slapping tariffs, subsidies and incentives on industries like electrodes, trying to revive the circumstances of a bygone economy. Protectionism can help companies maintain or increase market share. But manufacturing is shrinking as a share of employment in developed and developing economies alike. There’s little evidence that state support fosters commercially viable breakthroughs better than the market, so it’s an inefficient strategy for an advanced economy. Incentives to redirect capital, like Mr. Rubio’s proposal to punish stock buybacks through the tax code, would cause waste by discouraging companies from saving for better investments later. There’s no telling how innovation in engineering may eventually spring back to life, whether a new style of software next year or a mind-bending new field of physics next decade. Meanwhile, policy makers should take care to preserve the economic freedom that helps businesses find ways to prosper absent revolutionary progress. And Mr. Trump should heed his better angels by continuing to support every type of great thing America makes.

Ed Comment:What killed innovation? 1) unconstrained supply of cheap Chinese/Mexican/immigrant labor; 2) surplus of domestic labor and capacity coming out the recession (just now tightening); 3) long-term decline in research productivity interrupted by unusually high off-trend line productivity gains from internet; i.e., probably not sustainable; 4) shortage of talent; rest of world's talent contributing almost nothing 4) J-curve--next breakthrough of general purpose innovation (AI, quantum computing, driverless car, cure for cancer, etc.) have proven very hard to crack, i.e., progress is, in part, exogenously determined; 5) shift from centralized command and control manufacturing with productivity growth largely driven by engineered capital investment to decentralized, hard-to-manage services with much slower productivity growth; 6) low-skilled workforce has been largely left unsupervised as talent pursues more valuable opportunities. 7) sky-high real estate prices in fastest growing cities precludes medium-skilled migration.Like running a marathon, exogenously given hilly terrain has a big effect on the pace. Best measure is productivity gains relative to rest of high-wage world. By that measure, US productivity growth has accelerated.

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Previous articleDecember 16, 2019Whats behind Rising Returns to High-Quality College Education?Higher-quality students are driving rising returns to high-quality college education. Adjusting for student composition, Type 4 graduates earn only 5% more annually than Type 2 peers.Next articleDecember 16, 2019Germans Keep on Saving Their MoneyEven When It HurtsGermany’s elevated savings rate, at 11% of disposable income, has left households poorer due to low investment returns & negative interest rates. German median household wealth is €61,000, lower than Greece’s. @TomFairless
Showing 484 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

The College Wage Premium in the Generative AI Era

AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.

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.

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

Core argument: 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.

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

  1. 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.
  2. Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
  3. the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

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  • Looking for the Ladder — The downtick in hiring in AI-exposed occupations started 6 months prior to the release of ChatGPT, and is “perfectly” aligned with the start of Fed rate hikes…
  • How Students and Recent Grads are Responding to the Rise of AI — Far from shying away from AI, American undergraduates “are flocking towards the most-AI-exposed degrees,” with enrollment in these majors up 8% last year…
  • AI and Young-adult Jobs: The Real Mystery — Since the summer of 2023, the employment rate for Americans 22–25 has declined for both college grads and non-college workers, a phenomenon beyond both…
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Gross and Net US Investment

AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

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.

Does faster asset depreciation explain slowing productivity growth?

Core argument: 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 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

  1. 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.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. 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.

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The AI Re-Acceleration That Wasn’t

AI Summary. 615). Claims of re-acceleration result from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

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.”

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

Core argument: 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.

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

  1. 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.
  2. 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.

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  • 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.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Small AI models are closing the gap with large ones, achieving the same reasoning benchmarks with 142x fewer parameters than required two years ago. This makes on-device AI viable without data centers, compressing the economic case for cloud-based, per-query AI services.
  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.
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Understanding AI and Productivity

AI Summary. U.S. productivity growth has accelerated to ~2.2% annually since mid-2022, above the 2010s baseline, though pandemic-era labor market and business formation dynamics likely contributed alongside AI. Historical general-purpose technology booms sustained labor productivity growth above 2.5% for a decade or more, making the current acceleration substantial but not unprecedented.

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.

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

Core argument: 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.

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

  1. 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.
  2. 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.
  3. 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.

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  • AI and Productivity — Rising US labor productivity is driven by higher capital utilization—factories, servers, and hotel rooms running harder—rather than new investment or efficiency gains at the individual task level.
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US Widens AI-Driven Investment Gap With Europe

AI Summary. US corporate investment in equipment and facilities is projected to grow 40% in real terms by the end of next year, versus 12% in the euro area, widening a productivity gap where output per hour worked rose $14 in the US compared with $2 in Europe since 2018.

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.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Core argument: 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.

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

  1. 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.
  2. 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.

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

AI Summary. Nine major technology companies carry ~$3tn in off-balance-sheet AI commitments — 5x their ~$600bn in reported capital spending — obligations that are growing faster than traditional investment and triple their combined lease and debt liabilities.

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.

Are technology companies hiding the true cost of artificial intelligence?

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.

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  • The Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
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