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Transatlantic Technologies: The Role of ICT in the Evolution of U.S. and European Productivity Growth

Robert Gordon National Bureau of Economic Research
Date Posted:
September 1, 2020
Is Database:
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US productivity gains btw 1995-2005 were largely attributed to ICT advancements, leading to a 17% annual productivity growth in its ICT-hardware industry.

Between 1977-2015, the U.S. experienced a unique productivity boost from 1995-2005, largely attributed to advancements in ICT [Information-Communication Technology], which led to a 17% annual productivity growth in its ICT-hardware industry. This period marked a temporary acceleration in U.S. productivity, contrasting with the EU-10's steady decline. The EU-10's failure to capitalize on the ICT revolution was due to lower ICT investment and inefficiencies in ICT-intensive industries, resulting in a productivity growth slowdown. While the U.S. saw gains primarily in services-producing sectors, the EU-10 lagged, particularly in industries like agriculture and retail, which failed to adopt ICT-driven efficiencies. Consequently, the U.S. productivity narrative was characterized by a slow-fast-slow pattern, whereas the EU-10 experienced a continuous deceleration, highlighting the one-off nature of U.S. gains during this period.

Robert Gordon and Hassan Sayed, "Transatlantic Technologies: The Role of ICT in the Evolution of U.S. and European Productivity Growth," National Bureau Of Economic Research, June 2020, https://www.nber.org/papers/w27425

“…. Stepping back and viewing the postwar growth experience more broadly, the EU-10 started out in 1950-72 with rapid productivity growth of 4.86 percent per year, which we have previously interpreted as reflecting a process of catching up to the benefits of innovations that had buoyed U.S. productivity growth during the interwar and wartime periods…. Then the EU-10 transitioned to a slower productivity growth path of 2.31 percent in 1972-95, mimicking the 2.54 percent growth rate that the U.S. had previously achieved during 1950-72. Skipping over the very different experiences of 1995-2005, the EU-10 wound up in 2005-2015 with a productivity growth rate of a mere 0.63 percent, little different from the U.S. rate for the same interval of 0.87 percent. The remarkable similarity of the U.S. slowdown to 2005-15 from 1950-72 with the EU-10 slowdown to the same late interval from 1972-95 is, we think, more than a coincidence. The same process of the diminishing potency of ongoing innovation was occurring on both sides of the Atlantic. In short the postwar transatlantic productivity experience can be boiled down to three issues - the causes of the overall joint early-to-late slowdown, the sources of the temporary U.S. 1995-2005 acceleration, and the factors that held Europe back from enjoying a similar 1995-2005 revival. Our diagnosis of the first is the diminished impact of innovation over the postwar period that operated on both sides of the Atlantic, of the second is the U.S. success during 1995- 2005 in achieving a one-time boost in the level of efficiency in the production and use of ICT, and of the third is the multi-faceted failure of the EU-10 to mimic the U.S. achievement in producing ICT hardware, in making a similar level of investment in ICT, and in capturing the efficiency gains of the lower level of investment that actually occurred.….”

Looking at the two growth experiences overall Gordon argues they broadly mirror each other and support his thesis of the declining impact of innovation in contrast with previous waves of innovation:

“…Why did the EU-10 fail to benefit from the ICT revolution and instead why did it experience a two-step slowdown in labor productivity and TFP growth? The diagnosis has four components. First, despite all the U.S.-led innovation that drove its ICT-hardware EM industry to register a 17 per cent annual rate of productivity growth in 1995-2005, that same industry in the EU-10 actually experienced a productivity growth slowdown during the same decade. Second, the EU-10 had substantially lower values of the contribution-based ICT-use-intensity indicator variable, indicating less rapid growth of ICT investment. Third, the regressions reveal virtually no difference in the extent of the EU growth slowdown experienced by ICT-intensive versus non-intensive industries, indicating a failure of EU-10 industries to exploit the efficiency opportunities provided by the limited ICT investment that did occur. And fourth, the EU-10 shortfall in productivity growth during 1995-2005 can be traced to particular industries in which performance fell far short of the same industries in the U.S.These outlier industries include not just ICT-producing electric machinery but also agriculture, petroleum refining, and the large wholesale and retail sector where for many reasons EU nations lagged behind the U.S in adopting the big-box retail format which exploited the opportunities provided by the ICT revolution….”

They also examine why the EU10 failed to benefit from the diffusion of ICT:

We find that additionalproductivity growth in ICT intensive industries drove almost all of the post-1995 revival in U.S. productivity growth. However,this change in growth occurred almost entirely in services-producing industries rather than commodities-producing industries except for the electric machinery industry that produces computer hardware. This makes sense because the most intensive users of ICT were industries in the services sector. The EU-10 story is quite different. Productivity growth in producing computer hardware in the EU-10 actually slowed after 1995 in contrast to its explosive growth in the U.S. Further in the EU-10 there was little difference between the productivity growth slowdown after 1995 and after 2005 in industries that were intensive in ICT use versus the non-ICT industries. Europe not only invested less in ICT hardware but failed to reap its benefits even in industries that were heavy ICT users….”

He then looks the drivers of the decline in terms of sectors:

“…A retardation in the growth of labor productivity and of total factor productivity (TFP) has characterized both the United States and western Europe, in the sense that on both sides of the Atlantic growth has been slower since 2005 than it was before 1995. The notable difference in performance occurred in the middle interval of 1995-2005, when a sharp acceleration of growth in the U.S. contrasted to a growth slowdown in western Europe. As a result, the story of U.S. productivity growth since the mid-1970s has been one of slow-fast-slow over the three intervals divided at 1995 and 2005, in contrast to a two-step deceleration in Europe.…”

His research finds the decline in productivity growth was a three step process in the US (with an uptick btw 1995-2005) versus a steady decline in the EU10:

New Gordon argues that the ICT Revolution (Information-Communication Technology) is best understood as a temporary technological shock due to the lag in productivity growth after 1995. The uptick in productivity growth btw 1995-2005 was driven by the electronics manufacturing industry and ICT using service sector. Europe is a disaster, the E10 was unable to reap benefits (in terms of productivity growth) from ICT due to lack of response to ICT investment, as opposed to sheer absence of ICT investment.

Ed Comment 1/3:The thing he misses is what I identify in my chapter. High-skilled productivity seems to have grown faster than low-skilled and the US has a lot more low-skilled relative to Europe (more than the north, same as the south) so 1) our high-skilled seems to have grown faster and 2) our low-skilled productivity grew at least as fast but with substantially less supervision. What at the dual columns in table 5?

Transatlantic Technologies: The Role of ICT in the Evolution of U.S. and European Productivity Growth: Comments Image 1

Steve Comment: Re: The dual columns“….Table 5….with results for the U.S. on the left and for the EU-10 on the right. We repeat the results for labor productivity growth in the total economy from Table 4 for ease of comparison with our new results explaining TFP growth. Thus, in Table 5, columns (A) and (B) repeat the U.S. results shown in Table 4 for the total economy, columns (A) and (D). Likewise columns (E) and (F) repeat the EU-10 results shown in Table 5, columns (E) and (H);these differ only in the choice of the ICT indicator….”

Ed Comment2/3 Ps I wonder how this effect the Gordon’s calculations on contributors productivity. This says intangible investment is mismeasured. Add this note to the Gordon entry with a link to this paper.

“…The first of these is the ICT “share indicator,” which is formulated as in Stiroh (2002). We compute the average ICT share of investment, which is the annual nominal expenditure on computing equipment, communications equipment and computer software and databases, all divided by the annual nominal expenditure on total capital investment.Initially we examine the actual values of this ratio for individual industries and subsequently in the regressions we convert the share indicator into a share dummy variable equal to unity for industries which are ranked above the median value in the 1991-95 period and zero otherwise. The other KLEMS ICT variable is the “contribution indicator,” equal to the contribution of ICT capital to real value-added growth. This is available at the industry level only beginning in 1999….”

Those are the “share indicator” and the “contribution indicator” defined earlier:

Ed Comment 3/3IT might be having a greater impact on productivity than Gordon acknowledges. It’s possible, for example, that as manufacturing productivity growth eliminates jobs, the only jobs left are low productivity jobs afflicted by Baumol’s cost disease. If low-skilled productive is declining (as high-skilled productivity is rising due to IT) IT’s contribution would be understated. It might also be the case that the capability of IT is growing but also growing more complex. With a shortage of properly trained talent, it may be the case that only the largest and most successful companies have the wherewithal to capitalize on the increased capability. That would explain why those companies are investing more in IT, growing their productivity faster, gaining share, and increasing their profitability. So it might be the case that productivity isn’t growing faster, not because IT isn’t delivering results, but for other reasons, namely constraints on the economy’s capacity to capitalize on it and therefore an increase need for economies of scale maximize the value of the constrained resources. Arguably, that can all be lumped in with “IT.” But lumping it might understating our ability to produce valuable innovation. Perhaps we’re still capable of producing it, but we are bumping into other constraints because of it

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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.

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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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.

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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  • 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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  • Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools — Event studies indicate that adoption of AI coding tools raised “commits” (saved code updates) ~180%, but releases by only ~30%. Large upstream…
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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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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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