Edward Conard

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What if rising concentration were an indication of more competition, not less?

Geoffrey Manne Truth On The Market
Date Posted:
April 28, 2020
Is Database:
Database

Technology has led to increased productivity as well as greater specialization by large firms, challenging the narrative that concentration harms the economy. @GeoffreyManne

Recent studies suggest that increased concentration in certain industries is driven by technological advancements rather than anticompetitive behavior, leading to enhanced productivity and specialization among large firms. This trend is particularly evident in the services, wholesale, and retail sectors, where top firms have expanded their reach across more local markets, resulting in a 93% growth in concentration due to increased market presence rather than employment per location. Despite rising concentration, the employment share of top firms in the aggregate economy remains unchanged, indicating that these firms are not gaining disproportionate power. Instead, they are becoming more specialized and efficient, fostering competition and productivity growth. This challenges the narrative that concentration harms the economy, as data shows that industries with increased concentration have also seen employment growth and improved productivity.

Excellent review ofThe Industrial Revolution in Services

“…The reason for increased concentration appears to be technological, not anticompetitive. And, as might be expected from that cause, its effects are beneficial. Indeed, the story is both intuitive and positive....This means that competition is increasing, not decreasing, whether it is accompanied by an increase in concentration or not....The net effect is a decrease in the power of top firms relative to the economy as a whole, as the largest firms specialize more, and are dominant in fewer industries...Thus, summing up, technology has led to increased productivity as well as greater specialization by large firms, especially in relatively concentrated industries (exactly the opposite of the pessimistic stories):...”

Geoffrey Manne, "What if rising concentration were an indication of more competition, not less?,"Truth On The Market, December 14, 2019, https://truthonthemarket.com/2019/12/14/what-if-rising-concentration-were-an-indication-of-more-competition-not-less/

What if rising concentration were an indication of more competition, not less?

An oft-repeated claim of conferences, media, and left-wing think tanks is that lax antitrust enforcement has led to a substantial increase in concentration in the US economy of late, strangling the economy, harming workers, and saddling consumers with greater markups in the process. But what if rising concentration (and the current level of antitrust enforcement) were an indication of more competition, not less?

By now the concentration-as-antitrust-bogeyman story is virtually conventional wisdom, echoed, of course, by political candidates such as Elizabeth Warren trying to cash in on the need for a government response to such dire circumstances:

In industry after industry — airlines, banking, health care, agriculture, tech — a handful of corporate giants control more and more. The big guys are locking out smaller, newer competitors. They are crushing innovation. Even if you don’t see the gears turning, this massive concentration means prices go up and quality goes down for everything from air travel to internet service.

But the claim that lax antitrust enforcement has led to increased concentration in the US and that it has caused economic harm has been debunked several times (for some of our own debunking, see Eric Fruits’ posts here, here, and here). Or, more charitably to those who tirelessly repeat the claim as if it is “settled science,” it has been significantly called into question.

Most recently, several working papers looking at the data on concentration in detail and attempting to identify the likely cause for the observed data, show precisely the opposite relationship. The reason for increased concentration appears to be technological, not anticompetitive. And, as might be expected from that cause, its effects are beneficial. Indeed, the story is both intuitive and positive.

What’s more, while national concentration does appear to be increasing in some sectors of the economy, it’s not actually so clear that the same is true for local concentration — which is often the relevant antitrust market.

The most recent — and, I believe, most significant — corrective to the conventional story comes from economists Chang-Tai Hsieh of the University of Chicago and Esteban Rossi-Hansberg of Princeton University. As they write in a recent paper titled, “The Industrial Revolution in Services”:

We show that new technologies have enabled firms that adopt them to scale production over a large number of establishments dispersed across space. Firms that adopt this technology grow by increasing the number of local markets that they serve, but on average are smaller in the markets that they do serve. Unlike Henry Ford’s revolution in manufacturing more than a hundred years ago when manufacturing firms grew by concentrating production in a given location, the new industrial revolution in non-traded sectors takes the form of horizontal expansion across more locations. At the same time, multi-product firms are forced to exit industries where their productivity is low or where the new technology has had no effect. Empirically we see that top firms in the overall economy are more focused and have larger market shares in their chosen sectors, but their size as a share of employment in the overall economy has not changed. (pp. 42-43) (emphasis added).

This makes perfect sense. And it has the benefit of not second-guessing structural changes made in response to technological change. Rather, it points to technological change as doing what it regularly does: improving productivity.

The implementation of new technology seems to be conferring benefits — it’s just that these benefits are not evenly distributed across all firms and industries. But the assumption that larger firms are causing harm (or even that there is any harm in the first place, whatever the cause) is unmerited.

What the authors find is that the apparent rise in national concentration doesn’t tell the relevant story, and the data certainly aren’t consistent with assumptions that anticompetitive conduct is either a cause or a result of structural changes in the economy.

Hsieh and Rossi-Hansberg point out that increased concentration is not happening everywhere, but is being driven by just three industries:

First, we show that the phenomena of rising concentration... is only seen in three broad sectors - services, wholesale, and retail.... op firms have become more efficient over time, but our evidence indicates that this is only true for top firms in these three sectors. In manufacturing, for example, concentration has fallen.

Second, rising concentration in these sectors is entirely driven by an increase the number of local markets served by the top firms. (p. 4) (emphasis added).

These findings are a gloss on a (then) working paper — The Fall of the Labor Share and the Rise of Superstar Firms — by David Autor, David Dorn, Lawrence F. Katz, Christina Patterson, and John Van Reenan (now forthcoming in the QJE). Autor et al. (2019) finds that concentration is rising, and that it is the result of increased productivity:

If globalization or technological changes push sales towards the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms, which have high markups and a low labor share of value-added.

We empirically assess seven predictions of this hypothesis: (i) industry sales will increasingly concentrate in a small number of firms; (ii) industries where concentration rises most will have the largest declines in the labor share; (iii) the fall in the labor share will be driven largely by reallocation rather than a fall in the unweighted mean labor share across all firms; (iv) the between-firm reallocation component of the fall in the labor share will be greatest in the sectors with the largest increases in market concentration; (v) the industries that are becoming more concentrated will exhibit faster growth of productivity; (vi) the aggregate markup will rise more than the typical firm’s markup; and (vii) these patterns should be observed not only in U.S. firms, but also internationally. We find support for all of these predictions. (emphasis added).

This is alone is quite important (and seemingly often overlooked). Autor et al. (2019) finds that rising concentration is a result of increased productivity that weeds out less-efficient producers. This is a good thing.

But Hsieh & Rossi-Hansberg drill down into the data to find something perhaps even more significant: the rise in concentration itself is limited to just a few sectors, and, where it is observed, it is predominantly a function of more efficient firms competing in more — and more localized — markets. This means that competition is increasing, not decreasing, whether it is accompanied by an increase in concentration or not.

No matter how may times and under how many monikers the antitrust populists try to revive it, the Structure-Conduct-Performance paradigm remains as moribund as ever. Indeed, on this point, as one of the new antitrust agonists’ own, Fiona Scott Morton, has written (along with co-authors Martin Gaynor and Steven Berry):

In short, there is no well-defined “causal effect of concentration on price,” but rather a set of hypotheses that can explain observed correlations of the joint outcomes of price, measured markups, market share, and concentration. As Bresnahan (1989) argued three decades ago, no clear interpretation of the impact of concentration is possible without a clear focus on equilibrium oligopoly demand and “supply,” where supply includes the list of the marginal cost functions of the firms and the nature of oligopoly competition.

Some of the recent literature on concentration, profits, and markups has simply reasserted the relevance of the old-style structure-conduct-performance correlations. For economists trained in subfields outside industrial organization, such correlations can be attractive.

Our own view, based on the well-established mainstream wisdom in the field of industrial organization for several decades, is that regressions of market outcomes on measures of industry structure like the Herfindahl-Hirschman Index should be given little weight in policy debates. Such correlations will not produce information about the causal estimates that policy demands. It is these causal relationships that will help us understand what, if anything, may be causing markups to rise. (emphasis added).

Indeed! And one reason for the enduring irrelevance of market concentration measures is well laid out in Hsieh and Rossi-Hansberg’s paper:

This evidence is consistent with our view that increasing concentration is driven by new ICT-enabled technologies that ultimately raise aggregate industry TFP. It is not consistent with the view that concentration is due to declining competition or entry barriers..., as these forces will result in a decline in industry employment. (pp. 4-5) (emphasis added)

The net effect is that there is essentially no change in concentration by the top firms in the economy as a whole. The “super-star” firms of today’s economy are larger in their chosen sectors and have unleashed productivity growth in these sectors, but they are not any larger as a share of the aggregate economy. (p. 5) (emphasis added)

Thus, to begin with, the claim that increased concentration leads to monopsony in labor markets (and thus unemployment) appears to be false. Hsieh and Rossi-Hansberg again:

e find that total employment rises substantially in industries with rising concentration. This is true even when we look at total employment of the smaller firms in these industries. (p. 4)

ectors with more top firm concentration are the ones where total industry employment (as a share of aggregate employment) has also grown. The employment share of industries with increased top firm concentration grew from 70% in 1977 to 85% in 2013. (p. 9)

Firms throughout the size distribution increase employment in sectors with increasing concentration, not only the top 10% firms in the industry, although by definition the increase is larger among the top firms. (p. 10) (emphasis added)

Again, what actually appears to be happening is that national-level growth in concentration is actually being driven by increased competition in certain industries at the local level:

93% of the growth in concentration comes from growth in the number of cities served by top firms, and only 7% comes from increased employment per city.... verage employment per county and per establishment of top firms falls. So necessarily more than 100% of concentration growth has to come from the increase in the number of counties and establishments served by the top firms. (p.13)

The net effect is a decrease in the power of top firms relative to the economy as a whole, as the largest firms specialize more, and are dominant in fewer industries:

Top firms produce in more industries than the average firm, but less so in 2013 compared to 1977. The number of industries of a top 0.001% firm (relative to the average firm) fell from 35 in 1977 to 17 in 2013. The corresponding number for a top 0.01% firm is 21 industries in 1977 and 9 industries in 2013. (p. 17)

Thus, summing up, technology has led to increased productivity as well as greater specialization by large firms, especially in relatively concentrated industries (exactly the opposite of the pessimistic stories):

op firms are now more specialized, are larger in the chosen industries, and these are precisely the industries that have experienced concentration growth. (p. 18)

Unsurprisingly (except to some…), the increase in concentration in certain industries does not translate into an increase in concentration in the economy as a whole. In other words, workers can shift jobs between industries, and there is enough geographic and firm mobility to prevent monopsony. (Despite rampant assumptions that increased concentration is constraining labor competition everywhere…).

Although the employment share of top firms in an average industry has increased substantially, the employment share of the top firms in the aggregate economy has not. (p. 15)

It is also simply not clearly the case that concentration is causing prices to rise or otherwise causing any harm. As Hsieh and Rossi-Hansberg note:

he magnitude of the overall trend in markups is still controversial... and... the geographic expansion of top firms leads to declines in local concentration... that could enhance competition. (p. 37)

Indeed, recent papers such as Traina (2018), Gutiérrez and Philippon (2017), and the IMF (2019) have found increasing markups over the last few decades but at much more moderate rates than the famous De Loecker and Eeckhout (2017) study. Other parts of the anticompetitive narrative have been challenged as well. Karabarbounis and Neiman (2018) finds that profits have increased, but are still within their historical range. Rinz (2018) shows decreased wages in concentrated markets but also points out that local concentration has been decreasing over the relevant time period.

None of this should be so surprising. Has antitrust enforcement gotten more lax, leading to greater concentration? According to Vita and Osinski (2018), not so much. And how about the stagnant rate of new firms? Are incumbent monopolists killing off new startups? The more likely — albeit mundane — explanation, according to Hopenhayn et al. (2018), is that increased average firm age is due to an aging labor force. Lastly, the paper from Hsieh and Rossi-Hansberg discussed above is only the latest in a series of papers, including Bessen (2017), Van Reenen (2018), and Autor et al. (2019), that shows a rise in fixed costs due to investments in proprietary information technology, which correlates with increased concentration.

So what is the upshot of all this?

First, as noted, employment has not decreased because of increased concentration; quite the opposite. Employment has increased in the industries that have experienced the most concentration at the national level.
Second, this result suggests that the rise in concentrated industries has not led to increased market power over labor.
Third, concentration itself needs to be understood more precisely. It is not explained by a simple narrative that the economy as a whole has experienced a great deal of concentration and this has been detrimental for consumers and workers. Specific industries have experienced national level concentration, but simultaneously those same industries have become more specialized and expanded competition into local markets.

Surprisingly (because their paper has been around for a while and yet this conclusion is rarely recited by advocates for more intervention — although they happily use the paper to support claims of rising concentration), Autor et al. (2019) finds the same thing:

Our formal model, detailed below, generates superstar effects from increases in the toughness of product market competition that raise the market share of the most productive firms in each sector at the expense of less productive competitors.... An alternative perspective on the rise of superstar firms is that they reflect a diminution of competition, due to a weakening of U.S. antitrust enforcement (Dottling, Gutierrez and Philippon, 2018). Our findings on the similarity of trends in the U.S. and Europe, where antitrust authorities have acted more aggressively on large firms (Gutierrez and Philippon, 2018), combined with the fact that the concentrating sectors appear to be growing more productive and innovative, suggests that this is unlikely to be the primary explanation, although it may important in some specific industries (see Cooper et al, 2019, on healthcare for example). (emphasis added).

The popular narrative among Neo-Brandeisian antitrust scholars that lax antitrust enforcement has led to concentration detrimental to society is at base an empirical one. The findings of these empirical papers severely undermine the persuasiveness of that story.

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Showing 485 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

Moonshot Capitalism: AI Rewrites The Venture Capital Playbook

AI Summary. Deep-tech investment outside AI has exceeded $150bn since early 2024, surpassing the $133bn invested across the entire prior decade. Falling valuations for traditional software companies and outsized returns from early bets on capital-intensive ventures are pushing investors toward riskier, science-driven deals.

Tim Bradshaw Financial Times
Date Posted:
September 10, 2026
Is Database:
Database

Since the start of 2024, more than $150B of venture capital has been invested into non-AI “deep tech” firms whose products are rooted in significant engineering advances, exceeding the $133B invested in such firms btw 2010 and 2019.

Are investors abandoning software for capital-intensive science bets?

Core argument: Deep-tech investment excluding AI exceeded $150bn since early 2024, surpassing the entire $133bn deployed across the prior decade (through end-2019), as falling valuations for traditional software push venture capital toward capital-intensive scientific bets.

The AI boom is fuelling a resurgence in ambitious “moonshot” bets, as early SpaceX backers’ huge returns and falling valuations for traditional software companies force tech investors to embrace riskier and more capital-intensive dealmaking. Excluding the giant sums ploughed into AI start-ups, global investment in “deep tech” — companies whose products are rooted in big scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn in the entire decade to the end of 2019, according to Dealroom. This year’s deep-tech investments have not yet surpassed 2021’s peak, which was propelled by battery and electric vehicle deals for the likes of Rivian and Northvolt — many of which turned sour, highlighting the risks involved in moonshot dealmaking.

Takeaways by Macro Roundup® AI

  1. Deep-tech investment excluding AI exceeded $150bn since early 2024, surpassing the entire $133bn deployed across the prior decade (through end-2019), as falling valuations for traditional software push venture capital toward capital-intensive scientific bets.
  2. The 2021 deep-tech peak — driven by battery and electric vehicle deals including Rivian and Northvolt — has not yet been surpassed, and the subsequent losses from those deals underscore the capital destruction risk inherent in moonshot dealmaking.

Related Articles:

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  • Public to Private Equity in the United States: A Long-Term Look — Global venture capital returns are highly skewed: 62% of deals lose money, more than half lose 50–100% of invested capital, but fat-tailed outliers drive overall returns. This pattern mirrors historical whaling voyages, where payoffs were similarly variable and driven by rare outsized outcomes.
  • Gross and Net US Investment — 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.
  • Investment
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The College Wage Premium in the Generative AI Era

AI Summary. 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 in four decades. AI exposure in white-collar occupations accounts for ~28% of this drop, as moving from zero to full occupational AI exposure reduced wages by 0.086.

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 in 2022 to 0.575 in 2026—the first sustained negative relative demand growth for college labor in four decades, per Current Population Survey data.

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 in 2022 to 0.575 in 2026—the first sustained negative relative demand growth for college labor in four decades, per Current Population Survey data.
  2. Moving from zero to full occupational AI exposure reduced wages by 8.6 percentage points by 2026; because college graduates concentrate in high-exposure white-collar roles, this mechanism accounts for roughly 28% of the premium’s compression.

Related Articles:

  • 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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      • College
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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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  • Capital Is Making a Comeback — Btw 1985-2021 the capital intensity of the American economy was relatively flat as a rise in intangible investment was offset by a decline in tangible…
  • The Transition to a Higher Cost of Capital — Bridgewater Associates co-CIO Karen Karniol-Tambour expects 10-year Treasury yields to rise from the current ~4.5% to compensate for structurally higher fiscal…
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The AI Re-Acceleration That Wasn’t

AI Summary. AI capability growth follows a linear trend with no statistically significant acceleration; apparent re-acceleration results from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate trend 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.”

Does AI capability growth actually accelerate or just appear to?

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.

Related Articles:

  • 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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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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  • The Future of European Competitiveness – A Competitiveness Strategy for Europe — An EC study of European competitiveness finds that EU gross value-added per hour worked increased by 0.7%/year from 2000-19, vs. 1.2%/year in the US. “Europe…
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