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Industries, Mega Firms, and Increasing Inequality

John Haltiwanger Institute of Labor Economics
Date Posted:
June 1, 2022
Is Database:
Database

Industry components account for ~62% of the increase in earnings inequality and 73% of btw firm inequality growth.

Rising earnings inequality is primarily driven by between-industry dispersion, with industry effects accounting for 61.9% of the increase in earnings inequality and 72.8% of between-firm inequality growth. A small subset of industries, about 10% of the 301 detailed 4-digit NAICS industries, accounts for nearly all the rising between-industry dispersion while representing less than 40% of employment. These industries are concentrated at the extremes of the earnings distribution, with high-paying sectors like Software Publishing and low-paying sectors like Restaurants contributing significantly. The increasing role of mega firms, defined as those with over 10,000 employees, is notable in these dominant industries, where they have seen a rise in employment share. This restructuring highlights the importance of industry-specific factors and mega firms in understanding the dynamics of rising earnings inequality.

Rising earnings inequality is dominated by rising between-firm inequality. Our analysis as well as the recent literature emphasizes that this largely reflects how firms are organizing themselves in terms of their workforce. High (low) earnings workers are more likely to work with each other (increased segregation), and high (low) earnings workers are more likely to work at high (low) firm premia firms (sorting). Our contribution is to highlight the dominant role of industry effects in accounting for this structural change of how firms organize their workforces. Most of rising between-firm inequality is accounted for by rising between-industry dispersion in earnings. The between-industry component accounts for 61.9% of total increasing earnings inequality, and 72.8% of between-firm inequality growth. This changes the narrative of the sorting and segregation contributions. High (low) earnings workers are more likely to work with each other in specific industries and high (low) earnings workers are more likely to work in high (low) average firm premia industries. Not only do industry effects dominate but it is a relatively small share of industries that account for virtually all the increasing dispersion in earnings across industries. We find that about ten percent of the 301 detailed 4-digit NAICS industries account for almost 100% of the rising between-industry dispersion, while accounting for less than 40% of employment. The ten percent of industries that account for virtually all of the increase are drawn from the top and bottom of the earnings distribution in terms of industry-level averages. For those industries at the top of the earnings distribution, their contribution is dominated by rising inter-industry earnings differentials. For industries at the bottom of the earnings distribution, their contribution is dominated by shifts in employment to these very low earnings industries. For both sets of industries at the top and the bottom of the earnings distribution, increased sorting and segregation between industries dominates but increased dispersion in between industry firm premia also plays an important supporting role. Increased sorting is relatively more important for the rising between-industry dispersion from the industries at the bottom of the earnings distribution. In contrast, increased segregation is relatively more important for the rising between industry dispersion from the industries at the top of the earnings distribution. The dominance of industry effects is closely linked to the rising importance of mega (10,000+) firms in the U.S. economy. The increasing share of employment accounted for by mega firms is concentrated in the thirty 4-digit industries that account for virtually all of rising between-industry dispersion. This rising share of employment at mega firms is accompanied by a declining size-earnings premium in the eleven low-paying industries. For mega firms in the nineteen high-paying industries in the top 30, earnings premia rise sharply relative to other industries (albeit not as rapidly as other large but not mega firms in these industries). Our findings imply that understanding rising earnings inequality during the last several decades requires understanding the restructuring of how firms organize themselves in a relatively small set of industries. Moreover, since it is the between-industry contribution that dominates, it is the common effects of re-organization across firms in the same industry that matter.Many mechanisms such as changing technology, market structure, and globalization likely underlie rising earnings inequality. The focus of future research on the impact of such changes on rising earnings inequality should be on the uneven and concentrated impact of such mechanisms across industries. The top ten percent of industries that account for virtually all of rising between-industry inequality are not randomly spread across the distribution of industries but concentrated in specific industry clusters in the tails of the earnings distribution. At the high end, dominant industries are drawn from high-tech and STEM intensive industries, finance, mining, and selected industries in health. At the low end, dominant industries are drawn from selected industries in retail and health. Notably absent are the vast majority of industries in manufacturing. The top thirty industries are in industry clusters that have exhibited structural transformations that have been the subject of independent study. Our findings imply that the role of inter-industry earnings differentials and the changing composition of employment across industries is much more important for understanding earnings inequality than suggested by the recent literature.

“…The largest contribution is from Restaurants and Other Eating Places (7225), which alone accounts for 16.9% of between-industry variance growth. The second-largest contribution occurs among Other General Merchandise Stores (4529), which accounts for 6.8%. While the most important two industries to increasing inequality tend to offer low-paying jobs, the other three industries that account for more than 5% of between-industry variance growth are high-paying: Software Publishers (5112), Computer Systems Design (5415), and Management of Companies (5511). What about the other 271 4-digit NAICS industries? The contributions of these industries to between-industry variance growth are summarized in Table 2. There are 145 industries that each contribute approximately 0.0% (to be precise, greater than −0.05% and less than 0.05%) to betweenindustry variance growth. This says that almost half of all 4-digit NAICS industries contribute essentially nothing to inequality growth. There are 71 industries that contribute between 0.05% and 1.0%, accounting for 22.3% of between-industry variance growth. These industries are basically offset by another 55 industries that have a negative contribution (< −0.05%), accounting for −20.3% of between-industry variance growth. As seen in Table 4, the top thirty industries include nineteen high-paying industries that account for 54.1% of between-industry variance growth, and eleven low-paying industries that account for 44.1% of between-industry variance growth. The other 271 industries that have small contributing and offsetting contributions to increasing inequality do not occur systematically among high-paying vs. low-paying industries. 146 high-paying industries account for 1.3% of between-industry variance growth, and 125 low-paying industries account for only 0.6% of between-industry variance growth…”

Characteristics of the top thirty industries

“…The top thirty industries reflect a small number of industry clusters that are notable for undergoing structural transformations that have been the subject of independent analysis. Eleven of the nineteen high-paying industries have been defined as high-tech industries in terms of STEM intensity by Hecker (2005) and Goldschlag and Miranda (2016).13 These innovative industries in combination account for about one-third of the between-industry increase in earnings dispersion. The transformation of the retail sector accounts for another one-third of the increase.Other industry clusters evident in Table 3 include four of the nine 4-digit industries in Finance and Insurance (NAICS sector 52), Management of Companies (NAICS sector 55), two of the eleven 4-digit industries in Administrative and Support Services (NAICS sector 56, e.g., Employment Services (5613)), and two of the five 4-digit industries in Mining (NAICS sector 21, e.g., Oil and Gas Extraction (2111)). Finance and Insurance (NAICS sector 55) industries have undergone tremendous restructuring and consolidation following deregulation (see, e.g., Kroszner and Strahan (2014)). Management occupation differentials have risen dramatically over our sample period (see Haltiwanger and Spletzer (2020a)). The Employment Services industry (5613) is a low-paying industry that has experienced dramatic growth and change with the growth of Professional Employee Organizations (NAICS 561330, see Dey et al. (2006)). Oil and Gas Extraction (2111) has long been a high-paying industry and underwent dramatic expansion and innovation with the shale oil boom starting in 2007 (Decker et al. (2016))….”

John Haltiwanger, Henry R. Hyatt and James R. Spletzer, "Industries, Mega Firms, and Increasing Inequality,"Institute of Labor Economics, March 2022, https://docs.iza.org/dp15197.pdf

“…There are five industries that each contribute more than 5% of between-industry variance growth, accounting for 40.7% of between-industry variance growth. These five industries have 8.8% of total employment. An additional twenty-five industries each contribute between 1% and 5% of between-industry variance growth, accounting for 57.4% of between-industry variance growth. In total, the top thirty industries - about ten percent of all 4-digit NAICS industries - account for 98.1% of between-industry variance growth and 39.3% of employment. As nearly two-thirds of the growth in U.S. earnings dispersion has occurred between industries rather than within them, these thirty industries account for most of increasing inequality. We provide detail about these thirty industries in Table 3 (the industries in Table 3 are sorted by NAICS)….”

“…Our contribution is to explore and emphasize that rising between-firm dispersion mostly occurs at the industry level. Rising between-industry dispersion accounts for most of the overall increase in earnings inequality, and is driven by a relatively small number of industries. About ten percent of 4-digit NAICS industries account for virtually all of the increase in between-industry dispersion, while accounting for less than 40% of employment. These industries are in the tails of the earnings distribution including high-paying industries such as Software Publishing (5112) and lowpaying industries such as Restaurants and Other Eating Places (7225). Remarkably, the remaining ninety percent of 4-digit industries individually contribute little to rising between-industry earnings inequality. We provide further insights about rising between-industry inequality using an Abowd, Kramarz, and Margolis (1999, hereafter AKM) decomposition of earnings. Changing composition of workers across industries through sorting (high wage workers are more likely to work in industries with high average firm effects) and segregation (high wage workers are more likely to work together in the same industry) account for most but not all of the industry effects - industry-specific pay premia play a smaller but nontrivial role. Importantly, it is increased sorting and segregation between industries, rather than between firms within industries, that primarily matters for rising earnings dispersion. We find differences in the roles of sorting, segregation, and pay premia based on whether the industries tend to be low-paying vs. high-paying. The top ten percent of industries that contribute to rising inequality include nineteen that are high-paying. These industries account for 54.1% of the increase in between-industry inequality. The top three of these are high-paying, high-tech service industries - Software Publishers (5112), Computer Systems Design (5415), and Other Information Services (5191) - and, in total, eleven of these nineteen high-paying industries are high-tech.As discussed in Oliner, Sichel, and Stiroh (2007) and Fernald (2014), these industries are characterized as the source of rapid technological advances. These industries play an outsized role in the tendency for high-paid workers to work both for high-paying firms (sorting) and with each other (segregation). More generally, we find a dominant role for segregation - employees with high worker effects concentrated among each other - in the contribution of these nineteen high-paying industries to increasing inequality. Eleven low-paying industries are in the top ten percent of industries that dominate rising earnings inequality. These industries in combination account for 44.1% of the increase in between-industry inequality. More than one-fourth of the increase is accounted for by just three of these eleven: Restaurants and Other Eating Places (7225), Other General Merchandise Stores (4529), and Grocery Stores (4451). These industries have gone through substantial changes in recent decades, moving away from single establishment firms to large, national chains, see Foster, Haltiwanger and Krizan (2006), Foster et al. (2016), and Autor et al. (2020). In all three of these industries, sorting provides the largest contribution to rising inequality. The dominant role of sorting holds more generally among the eleven low-paying industries that have contributed to rising inequality. A distinctive feature of the dominant ten percent of industries is that they exhibit a sharp increase in the share of employment at mega firms, which we define as firms with more than 10,000 employees. Strikingly, the remaining ninety percent of industries exhibit small declines in the share of employment at mega firms. For the low-paying dominant industries, there is a sharp decline in the earnings of mega firms relative to earnings of the average industry (averaging over all 301 industries). This sharp decline is accompanied by a decline in the size-earnings premium within these low-paying industries. For the high-paying dominant industries, the mega firms experience a substantial increase in earnings relative to both small firms in the same industry and to earnings of the average industry. Thus, we find that the rise in “superstar” firms (see, e.g., Autor et al. (2020)) is concentrated in these dominant industries with accompanying systematic changes in the size-earnings premia…”

“…There are a total of 301 4-digit NAICS industries in our LEHD data. A natural starting point is to group industries by their contributions to increasing inequality, which we explore in Table 2…”

Evidence

The industries that drive increasing inequality

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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…
  • Innovation/Research
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    • Education
      • 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…
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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.

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
  • GDP
    • Growth
  • Productivity
    • Innovation/Research

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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    • Europe USA Relative Performance
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    • Growth
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    • Innovation/Research

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.

Related Articles:

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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.
  • Big Tech Credit Risks Rise Sharply As AI Spending Soars — The cost of insuring major technology companies' debt against default has reached record highs, driven by surging AI infrastructure spending that is straining balance sheets and pushing credit ratings toward junk status.
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  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
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