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Reconnecting Americans to the Benefits of Work

Scott Winship Joint Economic Committee
Date Posted:
October 27, 2021
Is Database:
Database

Decline in prime age male labor force participation unlikely due to declining compensation, as median hourly compensation rose 12% from 1973-2019.

The decline in Labor Force Participation (LFP) among prime-age males is unlikely due to declining compensation, as data from 1973 to 2019 shows. While median hourly wages for prime-age men rose 5%, median hourly compensation, which includes non-wage benefits, increased by 12%. At the 10th percentile, wages rose 3%, but compensation rose 10%. Median annual earnings increased by 6%, and when non-wage compensation is included, the rise is 14%. Even under counterfactual scenarios accounting for non-working men, annual compensation still rose by 11%. The decline in LFP is more pronounced among men without a college degree, who are more likely to live in rural areas. This suggests that factors other than compensation, such as cultural or educational disparities, may be driving the decline in workforce participation.

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 1


“…Another issue missed by wage trends is that over time, non-wage compensation became a greater share of pay. Non-wage compensation includes employer contributions to employees’ health and other insurance, contributions to retirement savings, and the payroll taxes they pay toward federal and state social insurance programs. These contributions were 13 percent of compensation in 1973 but 19 percent in 2019.51Figure 10 shows the same percentiles as in Figure 9, but this time wages at each percentile are adjusted upward by the same factor to account for non-wage compensation growth.52 This factor adjustment still likely underestimates the growth in compensation, particularly for the lower deciles for whom non-wage compensation often makes up a larger share of total compensation.53 Broader measures of compensation show more growth than a simple accounting of only wages. While median hourly wages among prime-age men rose 5 percent from 1973 to 2019, median hourly compensation rose 12 percent. At the 10thpercentile, wages rose 3 percent and compensation rose 10 percent during the same time period. Instead of falling, the 30th percentile of hourly compensation rose by 4 percent, and the 70th and 90th percentiles rose by 33 percent and 52 percent….”

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 2


“…Rather than showing trends in hourly wages,Figure 11 displays the trend in median annual earnings. Line 1 shows the trend in annual wage and salary income among those who have no self-employment earnings. From 1973 to 2019, the increase in the annual median was 6 percent, compared with a 5 percent rise in the median hourly wage of prime-age men. Adding nonwage compensation (Line 2) leads to a 14 percent increase (compared with 12 percent for hourly compensation).55 It turns out that adding the self-employed (and their earnings) to this sample does not change that conclusion: median annual compensation (Line 3) rises 13 percent.56 At the 10th percentile (not shown), annual compensation (including the earnings of the self-employed) rose 4 percent, compared with 10 percent for the 10th percentile of hourly compensation among employees. In this case, lower annual compensation compared to the hourly measure is likely because more workers at the 10th percentile do not work consistently throughout the year or work fewer hours overall. A final criticism of the trends shown in this section is that we cannot observe the compensation of non-working prime-age males who have dropped out of the labor force. It may be that the only reason compensation seems to have risen is because would-be workers with low compensation are more likely to drop out of the data. In this telling, demand for less-skilled workers may have fallen, but the charts above fail to show it because they only look at men who continue to work. Line 4 of Figure 11 attempts to address this criticism by displaying a counterfactual trend. Respondents in the Current Population Survey data who did not work in the previous year are asked why they did not work. The possible answers include inability to find work, being sick or disabled, taking care of home or family, going to school, retirement, being in the Armed Forces, or “other.” Imagine that nonworking men who were disabled, sick, retired, or said they were nonworking for ‘other’ reasons not listed did not become a larger group between 1973 and 2019 relative to workers. Further, imagine that all these additional men who would have been working would have been below-median workers had they held down jobs. Finally, imagine that in every year all men with no earnings who said they could not find work had worked at below-median compensation. Line 4 in Figure 11 attempts to say what the trend in prime-age male compensation would have been under those counterfactual circumstances. According to this counterfactual trend, the annual compensation of prime-age men still would have risen by 11 percent (instead of 13 percent).57 Note too that annual compensation estimates include men who worked part of the year before leaving the workforce, some with no intention of coming back anytime soon. For such men, their annual compensation is a poor indicator of what they command in the labor market, and if such men grow more common in the data over time, it will tend to pull the compensation trends downward. If pay growth is stronger for men with stable connections to the workforce, then the counterfactual of annual compensation may also conceal the growth in pay among below median workers with consistent labor force connections. Given this, the growth in annual compensation over time is very likely understated for men consistently participating in the workforce….”

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 3


"... Figure 12 walks through each of these adjustments to the productivity and compensation data, updating work by labor economist James Sherk.59 The two bolded lines below (light green for adjusted productivity and lightest blue for adjusted compensation) use the same implicit price deflator to show how wages and productivity remain closely associated with one another. The top green line shows growth in net hourly productivity for all workers including the self employed. Proponents of the pay-productivity gap often present just the top line and the bottom dark blue line showing average hourly compensation of production and nonsupervisory workers. By using a broader measure of workers (medium blue line) and using more accurately comparable measures of inflation, the pay-productivity gap all but disappears. The medium blue line is adjusted using the Personal Consumption Expenditures (PCE) index which approximates inflation for the things people regularly purchase. The lightest blue line is adjusted using the implicit price deflator (IPD) a better measure of price changes for the things Americans actually produce and is more directly comparable to measures of the associated changes in productivity. Comparing the lightest blue line to the light green line, more accurately aligns net hourly productivity with average hourly compensation using the same implicit price deflator and a similar universe of workers….”

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 4


Christina King, Scott Winship and Adam Michel, "Reconnecting Americans to the Benefits of Work," Joint Economic Committee, October 2021, https://www.jec.senate.gov/public/_cache/files/5ac0a254-ff00-4a18-baf0-bdfedb9bb154/connections-to-work.pdf

New Winship on declining LFP, finds that pay hasn't declined, nor has pay lagged productivity (though median pay has lagged productivity growth) implies decline in LFP is driven by other factors (cultural, transfers though he doesn’t attempt to quantify those drivers) Key chart, “…Rather than showing trends in hourly wages,Figure 11 displays the trend in median annual earnings. Line 1 shows the trend in annual wage and salary income among those who have no self-employment earnings. From 1973 to 2019, the increase in the annual median was 6 percent, compared with a 5 percent rise in the median hourly wage of prime-age men. Adding nonwage compensation (Line 2) leads to a 14 percent increase (compared with 12 percent for hourly compensation). It turns out that adding the self-employed (and their earnings) to this sample does not change that conclusion: median annual compensation (Line 3) rises 13 percent.56 At the 10th percentile (not shown), annual compensation (including the earnings of the self-employed) rose 4 percent, compared with 10 percent for the 10th percentile of hourly compensation among employees. In this case, lower annual compensation compared to the hourly measure is likely because more workers at the 10th percentile do not work consistently throughout the year or work fewer hours overall. A final criticism of the trends shown in this section is that we cannot observe the compensation of non-working prime-age males who have dropped out of the labor force. It may be that the only reason compensation seems to have risen is because would-be workers with low compensation are more likely to drop out of the data. In this telling, demand for less-skilled workers may have fallen, but the charts above fail to show it because they only look at men who continue to work. Line 4 of Figure 11 attempts to address this criticism by displaying a counterfactual trend. Respondents in the Current Population Survey data who did not work in the previous year are asked why they did not work. The possible answers include inability to find work, being sick or disabled, taking care of home or family, going to school, retirement, being in the Armed Forces, or “other.” Imagine that nonworking men who were disabled, sick, retired, or said they were nonworking for ‘other’ reasons not listed did not become a larger group between 1973 and 2019 relative to workers. Further, imagine that all these additional men who would have been working would have been below-median workers had they held down jobs. Finally, imagine that in every year all men with no earnings who said they could not find work had worked at below-median compensation. Line 4 in Figure 11 attempts to say what the trend in prime-age male compensation would have been under those counterfactual circumstances. According to this counterfactual trend, the annual compensation of prime-age men still would have risen by 11 percent (instead of 13 percent).

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 5


"... Prime-age men’s LFPR peaked at over 97 percent in 1955, slowly declined to 90.5 percent in 2008, and then dropped to 88 percent by 2014, as indicated in Figure 1. It inched up from there, but pre-pandemic it was only 89 percent in 2019. With the onset of the pandemic-induced recession in 2020, prime-age male labor force participation fell below 88 percent in April 2020, a record low. The pandemic may have worsened decades-long trends in declining workforce attachment, especially among lower-income prime-age workers....A large majority of the out of work force prime-age male population, 82 percent, does not have a bachelor’s degree. In the last twenty years, inactivity rose the most among men without a college degree and among those who previously earned low wages.30 These men are disproportionately likely to live in rural localities—particularly in the Southeast....

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 6


"...Figure 3 shows that labor force participation rises with education, and the participation trends for men and for women follow the same broad pattern as in Figure 1, regardless of education level. However, the decline in male labor force participation and rise in female participation vary by level of schooling..."

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 7


"... Figure 8 displays median wage trends for prime-age men at five different levels of educational attainment.The chart indicates that wages were lower in 2019 than in 1973 among men who lacked a four-year college degree—down 13 percent among those lacking a high school diploma, down 16 percent among those with a diploma but no other schooling, and down 12 percent among those with some college but no bachelor’s degree. Between 1973 and 2019 wages rose 15 percent among prime-age men with a bachelor’s degree but no graduate degree, and they rose 43 percent among those
with a graduate degree.... However, analyzing wages by educational attainment ignores the fact that, as Figure 2 shows, the workforce is growing more educated over time which changes the composition of the education groupings. For instance, looking at men without a high school diploma means assessing the wages of the least educated 30 percent of men in 1973 but the least-educated 10 percent of men in 2019. The group became much more disadvantaged over time, so all else equal, its pay would have fallen even if the pay of the bottom 30 percent did not. Similarly, in 1973, 19 percent of men were in one of the top two groups, but nearly 38 percent were in 2020. Therefore, the men in those top groups were less “elite” in 2020 than 47 years earlier.A better way to assess wage trends for less- and more-advantaged workers is to look at wages at fixed points of the wage distribution. The median wage earner is the one in the middle of the distribution. The earner at the 10th percentile is the one with higher wages than 10 percent of workers but lower wages than 90 percent of workers. Figure 9 shows wage trends for prime-age men at different percentiles. As indicated above, the median male worker saw a wage increase of 5 percent between 1973 and 2019. Below the median, the 10th percentile of wages rose 3 percent, and the 30th percentile fell 3 percent. Above the median, wages grew by 24 percent at the 70th percentile and by 42 percent at the 90th….”

Ed Comment:The last slide shows what a bunch of liars the left are. They claimed and continue to claim that productivity grew without wage growth.

  • Productivity
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    • Gender
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Previous articleOctober 27, 2021IT and Urban PolarizationIT investment is higher in high-wage cities, driving job & wage polarization. From 1990 to 2015, a 21.4% increase in local price index correlated with a $107.43 rise in average IT budget per worker. @JanEeckhoutNext articleOctober 27, 2021Telemigration and Development: On the Offshorability of Teleworkable JobsTelemigration impact remains modest: US service imports from India/China/Brazil/Canada under $11bn (2019). Data contradicts offshore service job exodus concerns. Key metric shows limited displacement effect.
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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  • 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…
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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.
  • Innovation/Research
  • Productivity
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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.

Related Articles:

  • 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.
  • Google’s AI & Economy ATLAS v1.0: Mapping Gemini Usage in the Economy — Google’s new AI & Economy ATLAS maps 15M AI interactions to occupations, tasks, and activities, showing AI use is pervasive but not intensive…
  • 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…
  • Investment
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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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  • Ed Conard Debates Furman On “The Expected Value of Risk Taking” — I debate @JasonFurman—Pres. Obama’s Chair of the Council of Economic Advisors—at Harvard over the effect of tax increases on the expected value of innovative…
  • 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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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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