Edward Conard

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Proximity to the frontier, markups, and the response of innovation to foreign competition

Ana Cusolito, Alvaro Garcia Marin and William Maloney Center for Economic and Policy Research
Date Posted:
November 5, 2021
Is Database:
Database

Low-productivity firms in Chile saw decline in innovation across all measures in face of import competition, while frontier firms saw gains. @ACusolito @AlvaroGarciaMar @WilliamMaloney

Evidence from Chile indicates that increased import competition from China negatively impacted innovation across all measures for low-productivity firms, while frontier firms experienced gains. Specifically, research and development (R&D), process innovation, product innovation, and quality declined for laggards, with effects worsening when markups fell. Conversely, product innovation and quality improved for leaders, especially when markups rose. This supports the view that proximity to the technological frontier and higher rents are crucial for innovation. The findings suggest that competition's positive impact is greater when a larger share of firms are near the frontier, highlighting the need for policies that enhance firm capabilities and access to resources.

Using evidence from Chile Ana Cusolito, Alvaro Garcia Marin and William Maloney find Chinese competition had a net negative impact on local innovation, though local frontier firms became more competitive, "...This column analyses the impact of increased import competition from China on innovation by Chilean firms. It finds a negative overall impact of competition on innovation indicators. Low-productivity firms in particular see declines across all innovation measures, while the most productive firms experience improvements in product innovation and product quality...We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing…Overall, we find a negative impact of competition on innovation across all measures. But we also find important heterogeneity....We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing. Thus, Aghion etal.’s(2005) view of the importance of proximity to the frontier, as well as the classical Schumpeterian view of the importance of rents, both receive support..."

Ana Cusolito, Alvaro Garcia Marin and William Maloney, "Proximity to the frontier, markups, and the response of innovation to foreign competition," Center For Economic And Policy Research, November 4, 2021, https://voxeu.org/article/proximity-frontier-markups-and-response-innovation-foreign-competition

Proximity to the frontier, markups, and the response of innovation to foreign competition

The widely accepted relationship between competition and growth sits somewhat uncomfortably with the inconclusive literature on the relationship between competition and innovation (for reviews, see Cohen 2010 or Gilbert 2006), and this is becoming increasingly relevant to the debate over trade openness and growth. Despite an extensive literature suggesting that trade liberalisation increases productivity, recent evidence from the US, Canada, and Europe generally finds negative or unclear impacts of rising import exposure on innovation (e.g. Autor et al. 2017, 2020, Bloom et al. 2016, Campbell and Mau 2021, Kueng et al. 2016).

There are conceptual reasons to think that, in practice, the effects could vary greatly with country context. Aghion et al. (2005) offer a synthesis of the view that competition is necessary to get entrepreneurs out of bed in the morning, and Schumpeter’s argument that higher rents are needed to increase innovation. They accept that firms that are far from the frontier may behave à la Schumpeter and retrench with greater competition, but they argue that firms that are closer to it may see innovation precisely as a way to escape from competition (Akcigit et al. 2018). They find supportive evidence for an inverted U-shaped relationship between industry-level innovation and competition in the UK (Hashmi and Van Biesebroeck 2016), although other evidence (e.g. Hashmi 2013, Gorodnichenko et al. 2010) has been more ambiguous. This raises the question of whether the net impact of competition on innovation might be more detrimental in emerging countries which have fewer globally competitive firms than developed economies.

To shed light on the debate, in a recent paper (Cusolito et al. 2021), we revisit this question exploiting the China shock to study the impact of increased competition on innovation in a prominent upper middle-income country, Chile, which is, perhaps, uniquely suited as a case for two important reasons. First, we are able to draw on a matched firm production-innovation panel dataset that contains information on input and product prices at the firm level and therefore allows us to generate measures of markups and efficiency (physical total factor productivity) that correspond more closely to the concepts of rents and technological leadership envisaged in the Schumpeterian literature. Second, the Chilean data permit us to study the effect of foreign competition not just on patenting, as has been the traditional focus, but on a broader range of plant performance and innovation outcomes than has been possible previously, including quality.

Chile offers a clean experiment to explore the effects mentioned above. It is the iconic ‘textbook’ well-run open economy with few micro distortions that, as elsewhere, saw levels of competition shocked by a major increase in import penetration from China. But, distinct from many trade liberalisation episodes, this shock was not accompanied by other sector-specific reforms. Hence, the effects we see are likely to be purely due to differential exposure to increased foreign competition across sectors.

Overall, we find a negative impact of competition on innovation across all measures. But we also find important heterogeneity along the lines of Aghion et al. (2005). We illustrate this in Figure 1, where we report the competition-innovation elasticity dividing the sample by leaders and laggards, and then by whether markups rose or declined with the increase in Chinese imports for four of the innovation variables. Research and development (R&D), process innovation, product innovation, and quality all fall for laggards, and the effect is exacerbated when markups are falling. Product innovation and quality rise for leaders, and more so if markups are increasing. Thus, Aghion et al.’s (2005) view of the importance of proximity to the frontier, as well as the classical Schumpeterian view of the importance of rents, both receive support.

Proximity to the frontier, markups, and the response of innovation to foreign competition: Extended Excerpt Image 1


These results are consistent with other recent findings. In contemporaneous work, also exploiting the canonical China shock, Aghion et al. (2021) find a detrimental effect on French firms’ sales and patenting from Chinese competition in output markets - with the negative impact being concentrated in low-productivity firms, defined as being below the median level of revenue total factor productivity (TFP). In a somewhat different experiment, Aghion et al. (2009) find the entry of greenfield foreign firms raises patenting for sectors close to the technology frontier but has a weak or even negative effect in laggard industries, again, where leaders are defined as the top 50%.

What is striking in the Chilean case is that when leaders are defined similarly, there is no positive impact on innovation. Sequentially running our specification with different cut-offs across the physical total factor productivity distribution (TFPQ), we find positive effects only when leaders are defined as the top 10% of the distribution, which accounts for 25% of industrial value added. Intuitively, this is consistent with countries further from the frontier having few firms near the frontier. But it also means that the impact of competition on incumbent innovation will be lower than in advanced countries and, potentially, net negative, as appears to be the case in Chile.

Our findings return us to our original paradox - how to reconcile that plant productivity often appears to increase with trade liberalisation, even in developing countries, but innovation does not. We propose two possible explanations. The first one may arise from the fact that the revenue TFP (TFPR) measure commonly used in the literature to define leaders and laggards combines efficiency, quality, and rents. Thus, if greater trade exposure actually leads to higher margins, as shown by De Loecker and Goldberg (2014), then this will show up as increased ‘productivity’. Our ability to exploit product price data to work with a cleaner measure of proximity to the efficiency frontier, TFPQ, allows us to abstract from mark-ups. The second explanation may arise from the fact that it is possible that increased competition leads to one-off adjustments - shedding excess workers, for example - but does not lead to dynamic increases arising from innovation.

Clearly, a finding of limited incumbent rises in innovation and perhaps lesser confidence in previous findings of increased productivity with trade liberalisation does not dictate reducing competition. Competition works through other margins, such as the reallocation of resources from low-productivity plants to high-productivity plants and through the entry of more productive plants and the exit of less productive ones (Melitz and Redding 2021). For the same period covered in the present study, Cusolito and Maloney (2018) show that over 60% of the gains in TFPQ in Chile arose precisely from entry and exit. Liu (1993) finds that in the early phases of the Chilean reforms, much productivity growth precisely occurred along the extensive margin, which rings true given the extraordinary levels of protection and distortions being unwound at the time.

But the evidence does suggest that the positive impact of competition is likely to be greater, the larger the share of frontier-proximate firms. Hence, raising the capabilities of firms and their access to resources may be an important complement to pro-competition policies. This might include extending managerial consulting programs, strengthening local innovation systems, supporting standards compliance, fostering technology adoption, and ensuring access to longer term finance, among other firm-level initiatives.

References

Akcigit, U, S T Ates and G Impullitti (2018), “Innovation, trade policy, and globalization”, VoxEU.org, 02 July.

Aghion, P, A Bergeaud, M Lequien, M Melitz and T Zuber (2021), “Opposing firm-level responses to the China shock: horizontal competition versus vertical relationships?”, Unpublished manuscript, Harvard University.

Aghion, P, N Bloom, R Blundell, R Griffith and P Howitt (2005), “Competition and Innovation: An Inverted-U Relationship”, The Quarterly Journal of Economics 120(2): 701-728.

Aghion, P, R Blundell, R Griffith, P Howitt and S Prantl (2009), “The effects of entry on incumbent innovation and productivity”, The Review of Economics and Statistics 91(1): 20-32.

Autor, D, D Dorn, G H Hanson, G Pisano and P Shu (2017), “Competition from China reduced innovation in the US”, VoxEU.org, 20 March.

Autor, D, D Dorn, G H Hanson, G Pisano and P Shu (2020), “Foreign Competition and Domestic Innovation: Evidence from U.S. Patents”, American Economic Review: Insights 2(3): 357-374.

Bloom, N, M Draca and J Van Reenen (2016), “Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT and Productivity”, Review of Economic Studies 83(1): 87-117.

Campbell, D and K Mau (2021), “On ‘Trade Induced Technical Change: The Impact of Chinese Imports on Innovation, IT and Productivity’”, The Review of Economic Studies, forthcoming.

Cohen, W M (2010), “Fifty years of empirical studies of innovative activity and performance”, Handbook of the Economics of Innovation 1: 129-213.

Cusolito, A P, A Garcia-Marin and W F Maloney (2021), “Proximity to the Frontier, Markups, and the Response of Innovation to Foreign Competition: Evidence from Matched Production-Innovation Surveys in Chile”, Policy Research Working Paper Series 9757, The World Bank.

Cusolito, A P and W F Maloney (2018), Productivity revisited: Shifting paradigms in analysis and policy, Washington, DC: World Bank Publications.

De Loecker, J and P K Goldberg (2014), “Firm Performance in a Global Market”, The Annual Review of Economics 6: 201-227.

Gilbert, R (2006), “Looking for Mr. Schumpeter: Where are we in the competition-innovation debate?”, Innovation policy and the economy 6: 159-215.

Gorodnichenko, Y, J Svejnar and K Terrell (2010), “Globalization and innovation in emerging markets”, American Economic Journal: Macroeconomics 2(2): 194-226.

Hashmi, A R (2013), “Competition and Innovation: The Inverted-U Relationship Revisited”, The Review of Economics and Statistics 95(5): 1653-1668.

Hashmi, A R and J Van Biesebroeck (2016), “The Relationship between Market Structure and Innovation in Industry Equilibrium: A Case Study of the Global Automobile Industry”, The Review of Economics and Statistics 98(1): 192-208.

Kueng, L, N Li and M-J Yang (2016), “The Impact of Emerging Market Competition on Innovation and Business Strategy”, NBER Working Papers 22840.

Liu, L (1993), “Entry-Exit, Learning, and Productivity Change Evidence from Chile”, Journal of Development Economics 42(2): 217-242.

Melitz, M J and S J Redding (2021), “Trade and Innovation”, VoxEU.org, 28 July.

  • Productivity
    • Innovation/Research
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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.

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  • Looking for the Ladder — The downtick in hiring in AI-exposed occupations started 6 months prior to the release of ChatGPT, and is “perfectly” aligned with the start of Fed rate hikes…
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  • AI and Young-adult Jobs: The Real Mystery — Since the summer of 2023, the employment rate for Americans 22–25 has declined for both college grads and non-college workers, a phenomenon beyond both…
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Gross and Net US Investment

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

Timothy Taylor Conversable Economist
Date Posted:
September 4, 2026
Is Database:
Database

U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

Does faster asset depreciation explain slowing productivity growth?

Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.

The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.

Takeaways by Macro Roundup® AI

  1. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

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The AI Re-Acceleration That Wasn’t

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

Paul Kedrosky Applied Complexity
Date Posted:
September 3, 2026
Is Database:
Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

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

Core argument: Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup® AI

  1. Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
  2. Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

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  • Why .400 Hitters Disappeared — and What It Means for AI — As AI model performance converges toward a ceiling, relative gains per improvement cycle shrink, transforming frontier capability from a pricing moat into a commodity where price becomes the primary differentiator and margin pressure intensifies across leading providers.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Small AI models are closing the gap with large ones, achieving the same reasoning benchmarks with 142x fewer parameters than required two years ago. This makes on-device AI viable without data centers, compressing the economic case for cloud-based, per-query AI services.
  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.
  • Innovation/Research
  • Productivity
    • Investment

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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  • 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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  • 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…
  • Investment
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  • GDP
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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.

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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    • Financial Markets
  • Productivity
    • Innovation/Research
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