Edward Conard

Top Ten New York Times Bestselling Author

  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
Upside of Inequality Oxford Unintended Consequences
Buy the Books
  • Macro Roundup
  • About Roundup
  • About Ed Conard
  • Highlights
  • Topics
  • Subscribe
Edward Conard
  • twitter
  • facebook
  • linkedin
  • youtube
  • Email
  • Text Message (SMS)
  • Twitter/X
  • LinkedIn
  • Facebook
  • WhatsApp Message
Subscribe to Macro Roundup Emails
  • Mentions 884
  • Primary focus 485
Showing 485 database articles primarily about Productivity
Currently filtering by:
  • Remove Productivity
  • Remove "primary topics only" restriction
  • Remove 'Database'
Show all 7,192 articles
For whatever topics you select (currently: "Productivity", "Cronyism", "Incentives/Risk-Taking", "Innovation/Research", "Institutional Capabilities", "Intangibles", "Investment", "Startups", "Workforce Reorganization"):
Choose search scope

Your importance filter 'Database' shows fewer articles.

Remove filters to see full article counts

How Destructive Is Innovation

Daniel Garcia-Macia, Chang-Tai Hsieh and Peter Klenow Econometrica
Date Posted:
May 26, 2021
Is Database:
Database
Is Important:
Important

Innovation drove 70% of TFP growth from 2003-2013, while creative destruction contributed 22% and new varieties added ~5%. @PeteKlenow

From 2003 to 2013, innovation was the primary driver of Total Factor Productivity (TFP) growth, accounting for 70% of the increase, while creative destruction contributed 22% and new varieties added approximately 5%. The data suggests that most productivity growth stems from incumbents improving their own products rather than from new entrants or the creation of entirely new product varieties. This highlights the significant role of incumbents in driving economic growth through quality improvements. Creative destruction, while vital for understanding job dynamics, plays a smaller role in overall productivity gains. The findings imply that policies promoting research and development should consider the balance between fostering innovation within existing firms and encouraging new market entrants.

Daniel Garcia-Macia, Chang-Tai Hsieh and Peter Klenow, "How Destructive Is Innovation," Econometrica, http://klenow.com/DestructiveInnovation_GHK.pdf

Bottomline, “..How much innovation takes the form of creative destruction versus new varieties versus firms improving their own products? How much occurs through entrants versus incumbents? We try to infer the sources of innovation from the employment dynamics of U.S. firms in the nonfarm private sector from 1983 to 2013. We conclude that creative destruction is vital for understanding job destruction and accounts for around one-fourth of growth. Own-product quality improvements by incumbents appear to be the biggest source of growth. Net variety growth contributes much less than quality improvements doOur findings are relevant for innovation policy. According to Atkeson and Burstein (forthcoming), the consumption-equivalent welfare gain from devoting about 1% more of GDP to research every year is between 0.17 and 0.73 percent in a model calibrated similarly to ours (but with endogenous research investments). They estimated the gains would be larger—0.26 to 2.01 percent—if creative destruction played no role in U.S. growth. Creative destruction is a force raising the private return relative to the social return to research, diminishing the gains from promoting research….”

“…. Table IV presents TFP growth due to creative destruction (row 1), new varieties (row 2), and own innovation (row 3). TFP growth due to each source of innovation is the product of arrival rate of innovation and the quality improvement conditional on innovation. We use equation (2) for this calculation.18 The first column shows that TFP growth due to own innovation was about 1% per year in 1983-1993. Growth due to creative destruction was about half that, at 0.44% per year. And new varieties generate growth of 0.23% per year. The rows in Table V show the contribution of each source of innovation to aggregate TFP growth. About 27% of the 1.66% growth rate in the 1983-1993 period comes from creative destruction. Own-variety improvements by incumbents account for 60%. New varieties à la Romer (1990) are the remainder at around 14%.The columns in Table V also decompose aggregate TFP growth into the percentage contribution of entrants versus incumbents using equation (3). In the ten years between 1983 and 1993, incumbents account for 68% of aggregate TFP growth, with entrants contributing the remaining 32%. Aghion, Akcigit, and Howitt (2014) provided complementary evidence for the importance of incumbents based on their share of R&D spending and patents…”

TFP driver estimates, “Table III presents the eight parameter values inferred from the data using the procedure described above. Based on the data moments from 1983 to 1993, we infer a 70% arrival rate of own-variety quality improvements per 5-year period. Conditional on no own innovation, quality improvements through creative destruction occur 30% of the time by other incumbents. Conditional on no own innovation and creative destruction by another incumbent, quality improvement through creative destruction by entrants occur with probability 1. The unconditional probability that a given product improves due to creative destruction by an incumbent is thus 8.9%, and the unconditional probability of creative destruction by an entrant is 21.0%.16 The unconditional probability that a product is improved upon in a 5-year period is thus 100%, of which 69% is from own innovation and 31% is from creative destruction (the latter from entrants or incumbents). The employment-weighted average step size for quality improvements on existing varieties is given by sq = (θ/(θ − (σ − 1)))1/(σ−1). Given that θ = 154 and σ = 4, the average improvement in quality (conditional on innovation) is 7.5%.17 New varieties are only created by entrants, arrive with 12.3% probability per existing variety, and have an average quality that is 31% of the average quality of existing varieties. Overhead costs imply that the average quality of exiting products ψ is 2% of the average quality of existing varieties, and that the probability a variety exits due to overhead cost δo is essentially zero. The net number of varieties thus grows by 12.3% every five years, which matches the growth of total employment and number of firms from 1983 to 1993.

Core of paper, “…Entrants and incumbents can create new products and displace the products of competitors. Incumbents can also improve their existing products. How much of aggregate productivity growth occurs through each of these channels? Using data from the U.S. Longitudinal Business Database on all nonfarm private businesses from 1983 to 2013, we arrive at three main conclusions:First, most growth appears to come from incumbents. We infer this from the modest employment share of entering firms (defined as those less than 5 years old). Second, most growth seems to occur through improvements of existing varieties rather than creation of brand new varieties. Third, own-product improvements by incumbents appear to be more important than creative destruction. We infer this because the distribution of job creation and destruction has thinner tails than implied by a model with a dominant role for creative destruction…”

Ben Comment:Pete Klenow got back to me re breaking TFP growth down into its components (improved processes, improved products, new products). He has a paper called Missing Growth from Creative Destruction (linked in his email below) that argues that TFP (and therefore TFP growth) is mismeasured because it doesn't properly account for new products replacing old (creatively destroyed) products. The idea is that if a product replaces a product it is a *bigger* gain than the gains achieved by product survival. Their empirical section says this effect is about.5 percentage points per year (pretty big, that takes TFP growth from 2 to 2.5 in a given year) but is mostly in hotels and restaurants and doesn't explain the slow down since 2005. I have to say - I don't really buy it, honestly. Because these results are concentrated in Hotel and Restaurants, it just leaves me a little flat. That's not really the kind of innovation (I think) we want to think about. Sure, it's better if there are more better restaurants around so from a standard of living aspect that's important but it's not really innovation. A second paper he referred me to (and linked below) is HOW DESTRUCTIVE IS INNOVATION? This paper is much closer to what we want. From the abstract: "First, most growth appears to come from incumbents. We infer this from the modest employment share of entering firms (defined as those less than 5 years old). Second, most growth seems to occur through improvements of existing varieties rather than creation of brand new varieties. Third, own-product improvements by incumbents appear to be more important than creative destruction."

Above is the table that is analogous to Ufuk's paper. Looking 2003-2013, it looks like CD accounts for 22% (.29/1.32) of TFP, NV accounts for a bout 5% and own innovation accounts for the rest ~ 70% of TFP growth. Compare that to Ufuk's table (pasted below).

Creative destruction in Klenow is equivalent to New Entry in Ufuk. So the big difference is that Klenow and Ufuk estimate very different growth processes within firms, but they estimate very similar amounts of TFP growth coming from new entrants. I find that reassuring. It also looks like 2003-2013, firms were really focused on improving their current stable of products and less interested in expanding into other areas. I doubt anyone has updated these numbers to see if 2014-today looks any different than what we're seeing here.The final paper that Klenow sent looks specifically at the package goods sector and finds most productivity gains come from product innovation. I don't find this particularly insightful since. My assumption is that packaged goods is pretty competitive so there isn't tons of room for better processes or for new start ups: Heinz is Heinz and Heinz knows how to pack and ship their stuff efficiently; the way for this sector to improve is for Heinz to launch a new product. That's pretty intuitive to me but the smaller scope leaves me cold. Anyway - long story short: the two papers that most closely do the calculation we're interested in both estimate that new entrants only contribute ~25% of growth and that about 75% within firm innovations account for the rest. What's not covered is exactly how changes in sectors' competitive structure would affect incentives to drive that 75% of growth. Clearly it's very important. Let me know what you guys think.
Pete Klenow Comment:And in this paper we argue in passingthat all innovation is pretty much product innovation, though we do not confront process innovations head

  • Productivity
    • Innovation/Research
    • Institutional Capabilities
    • Investment
  • GDP
    • Growth
Previous articleMay 25, 2021Growth through Heterogeneous InnovationsAccording to @WilliamRKerr @nberpubs 55% of productivity growth stems from expansion into new niches, while 20% comes from shop floor improvements & 20% from new firm creation.Next articleMay 26, 2021Quantifying the Sources of Firm HeterogeneityNew and improved products account for 64% of growth in high-turnover sectors, with product upgrading alone contributing 15% to firm growth. @ColinHottman @StephenRedding @DavidWeinstein
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
  • Productivity
  • Workforce
    • Education
      • College
    • Unemployment/Participation

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.

Related Articles:

  • US Stock Market To Stop Shrinking For First Time In 23 Years — US equity supply is turning positive for the first time in over two decades, as a surge in IPOs and large share sales by major technology companies outweighs the buybacks and privatizations that have shrunk the stock market since 2003.
  • Capital Is Making a Comeback — Btw 1985-2021 the capital intensity of the American economy was relatively flat as a rise in intangible investment was offset by a decline in tangible…
  • The Transition to a Higher Cost of Capital — Bridgewater Associates co-CIO Karen Karniol-Tambour expects 10-year Treasury yields to rise from the current ~4.5% to compensate for structurally higher fiscal…
  • Investment
  • GDP
    • Financial Markets
    • Growth
  • Productivity
    • Innovation/Research

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
    • 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.

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.

Related Articles:

  • The Two Europes — The European Union contains two divergent economies: a reforming frontier energized by security threats, and a stagnant interior where structural reform pressure remains absent.
  • 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…
  • Investment
  • Comparisons
    • Europe USA Relative Performance
  • GDP
    • Growth
  • Productivity
    • 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:

  • The Hyperscalers’ Exploding ‘Purchase Commitments’ Reach $1.5tn — Major technology companies have accumulated $1.5tn in lease commitments and $982bn in purchase obligations for chips, computing power, and energy, totaling roughly $2.5tn in future spending. Much of this debt does not appear on standard financial statements, understating true leverage and future cash demands.
  • 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.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
© Copyright 2026 Coherent Research Institute · All Rights Reserved · Privacy · Terms