Edward Conard

Top Ten New York Times Bestselling Author

  • “…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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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 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
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “…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
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
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 885
  • Primary focus 485
Showing 485 database articles primarily about Productivity
Currently filtering by:
  • Remove Productivity
  • Remove "primary topics only" restriction
  • Remove 'Database'
Show all 7,206 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

Bitcoin Miners Are Giving New Life to Old Fossil-Fuel Power Plants

Brian Spegele Wall Street Journal
Date Posted:
May 21, 2021
Is Database:
Database

Bitcoin mining’s energy consumption has surged, surpassing Argentina’s total power consumption, with 130 terawatt-hours of annual usage, driven by increasing complexity of mathematical puzzles.

Bitcoin mining’s energy consumption has surged, surpassing Argentina’s total power consumption, with 130...
Bitcoin mining's energy consumption has surged, with a University of Cambridge index estimating annual usage at 130 terawatt-hours, surpassing Argentina's total power consumption. This rise is driven by the increasing complexity of bitcoin's mathematical puzzles, which require significant computational power. As bitcoin prices climb, more miners enter the market, intensifying competition and energy demand. In response, older fossil-fuel power plants in the U.S. are being repurposed for mining, such as the Hardin Generating Station in Montana and Greenidge in New York. These projects aim to reduce operational costs and increase bitcoin production, but they face environmental backlash due to concerns over emissions and resource use. Despite these challenges, companies like Marathon Digital Holdings and Atlas Holdings are expanding their mining capacities, leveraging low-cost power to enhance profitability. The environmental impact has prompted legislative scrutiny, with proposals for moratoriums on cryptocurrency mining in regions like New York.

Brian Spegele and Caitlin Ostroff, "Bitcoin Miners Are Giving New Life to Old Fossil-Fuel Power Plants,"Wall Street Journal, May 21, 2021, https://www.wsj.com/articles/bitcoin-miners-are-giving-new-life-to-old-fossil-fuel-power-plants-11621594803

Bitcoin Miners Are Giving New Life to Old Fossil-Fuel Power Plants

Across America, older fossil-fuel power plants are shutting down in favor of renewable energy. But some are getting a new lease on life—to mine bitcoin. In upstate New York, an idled coal plant has been restarted, fueled by natural gas, to mine cryptocurrency. A once-struggling Montana coal plant is now scaling up to do the same.

The lofty price of bitcoin and other cryptocurrencies has investors pouring money into power generation—and risking a backlash. Elon Musk tweeted last week that Tesla Inc. would no longer accept bitcoin as payment for vehicles over concerns about fossil-fuel use in bitcoin mining. That rocked the market; bitcoin prices are now down around 25% since last week.

The drive for power has its roots in bitcoin’s intractable mathematics: To operate securely, the cryptocurrency’s network relies on computers solving puzzles; in return the solvers get fresh bitcoin. The higher the bitcoin price, the more of these miners compete to solve the puzzles—a process that chews up electricity. The more competition, the harder the puzzles get and the more electricity is used.

A University of Cambridge index pegs the annual power consumption of bitcoin mining at around 130 terawatt-hours, more than three times higher than at the beginning of 2019. That would be more than the power consumption of Argentina.

The coal-fired Hardin Generating Station in Montana had been struggling for years. Late last year, a Nasdaq-listed miner called Marathon Digital Holdings Inc. MARA -4.29% partnered with Hardin’s owner to transform the power plant into a hub for mining bitcoin.

“It was an idle asset,” Fred Thiel, Marathon Digital’s chief executive, said in an interview. “We were able to get access to a large amount of power at a very attractive price.”

The project is in the process of scaling up, with more than 100 megawatts of power capacity planned. Marathon Digital, whose investors include BlackRock Inc. and the hedge fund Renaissance Technologies LLC, said that by tapping the Montana coal plant, its break-even costs to produce a bitcoin will fall to $4,600, 38% less than previously.

The company is aiming to produce at least 55 bitcoins daily by the first quarter of next year, up from an average of two a day in 2020.

Besides mining bitcoin, Marathon Digital said that as of March it had nearly $300 million worth of bitcoin on its balance sheet, in an effort to signal its confidence in bitcoin’s future and attract institutional investors to the stock who might want exposure to the cryptocurrency but were unable to or unwilling to invest in it directly.

BlackRock and Renaissance declined to comment.

One of the most ambitious—and controversial—projects comes from private-equity firm Atlas Holdings. Based in Greenwich, Conn., the firm specializes in turnarounds of troubled companies. It bought the Greenidge coal-fired power station in 2014 after the plant in Dresden, N.Y. had been shut a few years earlier because it was economically unattractive to operate.

Atlas first converted the plant to natural gas from coal. Then, last year, it launched a data center for mining bitcoin using power the plant generated. The company said it currently has 19 megawatts of mining capacity and plans to raise it to 85 megawatts by the end of 2022.

Yvonne Taylor, vice president of the environmental nonprofit Seneca Lake Guardian, said air pollution and water runoff will damage a small community whose fresh air and clean water enables tourism, agriculture and fishing in the Finger Lakes.

Last month, local campaigners led a march to the gates of the power plant, and some groups have written letters to New York’s Department of Environmental Conservation and Gov. Andrew Cuomo urging them to revoke the plant’s permits.

The state has declined to do so. Last month, however, the Department of Environmental Conservation said it was closely monitoring Greenidge’s planned expansion. It said it also would consult the U.S. Environmental Protection Agency about the facility’s greenhouse-gas implications.

Greenidge said in March it was going public through a merger with Nasdaq-listed Support.com, which provides outsourced customer-support services. Under the deal, Support.com shareholders would get 8% of the combined company’s shares.

In exchange, Greenidge said it would use the cash on Support.com’s balance sheet to fund its expansion. There’s another potential benefit as well: Support.com has more than $145 million in federal net operating loss carryforwards, which could significantly lower the combined company’s taxes if the bitcoin operations prove to be profitable going forward.

Greenidge didn’t respond to a question on the potential tax advantages. It said last week it would begin purchasing voluntary carbon offsets and invest a portion of its mining profits in renewable-energy projects. Besides mining bitcoin, Greenidge said the power plant continues to send electricity to the grid.

“Greenidge has transformed an old coal-fired power plant into a clean, reliable source of power for thousands and an integrated data processing center mining bitcoin,” the company said in written response to questions. “We are grateful to enjoy great support from the local community.”

Support.com declined to comment.

The project has drawn the attention of state lawmakers in Albany, where a bill under review would place a three-year moratorium on crytocurrency mining amid emissions concerns.

“New York is literally the world’s headquarters for finance,” said state Sen. Kevin Parker, a Democrat who sponsored the bill. “But we also want that to be done in a way that comports with our values.”

The proposal is a problem for Michel Amar, the CEO of Digihost Technology Inc. In 2015, Mr. Amar and his son began building out mining capacity in northwest New York state, hoping to take advantage of cheap, clean power that comes from hydro generation around Niagara Falls.

Their company produces more than 30 bitcoins each month, and gets more than 90% of its electricity from hydro power.

This year, amid the bitcoin price surge, the company announced it would also buy a 60-megawatt natural-gas plant north of Buffalo, N.Y. It plans to initially direct 35 megawatts toward bitcoin mining while also sending power to the grid when it’s needed.

Mr. Amar said the company would partly fuel the plant with natural gas derived from animal manure and other sources.

At the same time, he said the company is considering leaving New York if the moratorium is imposed, potentially setting up shop in other states or Canada.

“What is the difference between a data center processing for Amazon and a data center for bitcoin?” he said. “Our goal and commitment is to be green as much as we can.”

Ed Comment: Who says Qs don't drive investment!

"....Across America, older fossil-fuel power plants are shutting down in favor of renewable energy. But some are getting a new lease on life—to mine bitcoin. The lofty price of bitcoin and other cryptocurrencies has investors pouring money into power generation.... A University of Cambridge index pegs the annual power consumption of bitcoin mining at around 130 terawatt-hours, more than three times higher than at the beginning of 2019. That would be more than the power consumption of Argentina...."

  • Productivity
    • Investment
Previous articleMay 21, 2021Common Good Conservatisms Catholic RootsEconomic independence, defined as control over productive resources, has historically led to political liberty, as seen in the transition from serfs to free yeomen.Next articleMay 24, 2021World-Dominating Superstar Firms Get Bigger, Techier, and More ChineseTop 50 firms by market cap now account for 28% of global GDP, up from less than 5% in 1990, @TomOrlik reports, highlighting the growing dominance of tech companies & Chinese firms.
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