Edward Conard

Top Ten New York Times Bestselling Author

  • “…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 very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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 full-throated defense of economic dynamism.” - The Wall Street Journal
  • “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
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “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
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 894
  • Primary focus 490
Showing 490 database articles primarily about Productivity
Currently filtering by:
  • Remove Productivity
  • Remove "primary topics only" restriction
  • Remove 'Database'
Show all 7,315 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

American Innovation, a Murder Mystery

AI Summary. The article attributes slowing American innovation to abundant cheap labor from China and Mexico, a post-recession surplus of domestic workers, declining research productivity, a talent shortage, the shift from centralized manufacturing to decentralized services, and high real estate prices in growing cities that limit medium-skilled migration. US productivity growth has nonetheless accelerated relative to other high-wage countries.

Mene Ukueberuwa Wall Street Journal
Date Posted:
December 16, 2019
Is Database:
Database

US productivity growth has accelerated relative to other high-wage countries, suggesting innovation may not be dead but evolving in new directions @MeneUkueberuwa @WSJ.

US productivity growth has accelerated relative to other high-wage countries, suggesting innovation may not be dead but...

What is slowing American innovation?

Core argument: The decline in American innovation is attributed to an unconstrained supply of cheap labor from China and Mexico, a surplus of domestic labor after the recession, and a long-term decline in research productivity.

The decline in American innovation is attributed to several factors, including an unconstrained supply of cheap labor from China and Mexico, a surplus of domestic labor post-recession, and a long-term decline in research productivity. Despite high off-trend productivity gains from the internet, these are unsustainable. A shortage of talent and the difficulty of achieving breakthroughs in general-purpose technologies like AI and quantum computing further hinder progress. The shift from centralized manufacturing to decentralized services has slowed productivity growth, while high real estate prices in growing cities limit medium-skilled migration. Despite these challenges, US productivity growth has accelerated relative to other high-wage countries, suggesting that innovation may not be entirely dead but rather evolving in new directions.

Takeaways by Macro Roundup® AI

  1. The decline in American innovation is attributed to an unconstrained supply of cheap labor from China and Mexico, a surplus of domestic labor after the recession, and a long-term decline in research productivity.
  2. High off-trend productivity gains from the internet are unsustainable, while a shortage of talent and the difficulty of achieving breakthroughs in general-purpose technologies such as AI and quantum computing further hinder progress.
  3. US productivity growth has accelerated relative to other high-wage countries, suggesting innovation may be evolving in new directions rather than dead, even as the shift from centralized manufacturing to decentralized services has slowed productivity growth.

Mene Ukueberuwa, "American Innovation, a Murder Mystery,"Wall Street Journal, December 13, 2019, https://www.wsj.com/articles/american-innovation-a-murder-mystery-11576277193American Innovation, a Murder Mystery Addressing 20,000 rallygoers at Dallas’s American Airlines Center recently, President Trump praised a “beautiful new Louis Vuitton plant” he’d toured that morning. The plant will bring about 1,000 jobs to a small town near Fort Worth, and Mr. Trump was displaying his good habit of celebrating work wherever Americans find it. But despite cheers of approval, one imagines he and his supporters would have preferred to christen an advanced “machine and manpower” plant—the kind that might assemble airplanes for the arena’s namesake. A craft workshop for a French luxury brand doesn’t quite capture the MAGA ideal. The president’s pledge to make America great again has always borne the implicit claim that America no longer makes anything great, whether Fords or farm equipment. There’s some truth to that, and both airplanes and handbags are examples of how U.S. manufacturing has fared since the mid-20th century. The key measure is productivity—how much value each worker produces—which bears heavily on job creation. Highly productive industries like chemicals, weapons systems and aerospace still draw strong domestic investment. But employment has shrunk because better machines and logistics have diminished the need for manpower. Productivity has risen more slowly for simpler types of manufacturing such as textiles and home goods, leading many companies to seek cheaper production overseas. There has also been a notable slowdown in improvements to the quality of goods produced. For about a century, beginning with the Second Industrial Revolution in the 1870s, American growth was driven by tinkerers, trusts and corporate labs, which eked out regular advances in products based on engines, electrical circuits and signals, and synthetic materials. Today, another half-century later, a coast-to-coast flight still takes you as long as it took your father in the 1970s. And with the major exception of computers, nothing in your luggage is likely to be much more useful or valuable than dad’s equivalent. Who killed American innovation? Answering that question could be a vital step toward re-energizing growth. Unfortunately, in their search for a culprit, Mr. Trump and many policy makers and commentators have implicated causes that aren’t responsible for the slowdown and tried solutions that hinder growth: • The free traders. Since the beginning of his 2016 campaign, Mr. Trump has often blamed the decline of heavy industry on his predecessors’ trade policies. “I did disagree with Ronald Reagan very strongly on trade,” he said in his final debate with Hillary Clinton. As president he has blasted the North American Free Trade Agreement—a signature achievement of Mrs. Clinton’s other half—as often as any federal policy. Describing the effect Mexico and other trade partners have had on domestic production, Mr. Trump said last year that “they’ve destroyed the steel industry, they’ve destroyed the aluminum industry and other industries, frankly.” In this view, politicians opened U.S. manufacturing to an assault from foreign competitors that produce and sell more cheaply. That reduces American manufacturers’ incentive to invest in better plant and products, benefiting rivals in protected economies. Yet open trade can’t explain the overall slowdown of U.S. innovation. To be sure, competition from Mexico reduces the incentive for U.S. Steel to invest in new fabrication techniques. But progress has also slowed since the mid-20th century for American companies that are heavily supported and protected, like Boeing. And there are countless industries in which U.S. companies retain a large lead in innovation despite trailing in global market share—most notably, cutting-edge electronics like smartphones and semiconductors. U.S. protectionism couldn’t have prevented other nations from eventually catching up. Georgia Tech economic historian Steven Usselman notes that American firms throughout most of the 20th century expected this “convergence” of international capability to occur decades earlier than it did. Yet even as manufacturing executives lobbied for support in their sectors, they backed general liberalization of trade as a net economic good. • The investors. It’s common to blame stagnant innovation on large companies that reduce productive investment in favor of shorter paths toward profit. A growing number of Republicans have adopted this critique, notably Florida Sen. Marco Rubio. In May Mr. Rubio issued a 40-page report lamenting a drop in research and innovation and blaming “shareholder primacy theory” that focuses on increasing returns to shareholders. Instead of investing in plant and products, he argues, executives focus on cutting costs and optimizing debt to boost quarterly earnings. Critics of shareholder capitalism point to charts showing that plant and equipment have shrunk as a share of corporate assets since the 1950s, and that R&D in manufacturing have declined. But it’s unlikely these trends were caused by a sudden aversion to long-term commitments among investors and managers. Consider that in place of hard goods, companies are investing more in intangible assets, like the patents gained by acquiring a startup, or enterprise software that simplifies communication and accounting. These expenses may not boost employment in the same way as, say, new delivery trucks. But they reflect as much willingness to spend in ways that don’t deliver returns immediately, and in some cases never do. More important, there are entire business models based on risky long-term strategies—and those companies don’t have trouble finding backers. American financiers overinvested in rare-earth mining and hydraulic oil and gas production a decade ago based on rosy projections of global growth. And no one could accuse WeWork’s owners of valuing quarterly earnings over long-term potential. The rise of venture capital means more investors than ever are seeking the riskiest bets, and not only in software. Lavish support is available in biotechnology, robotics and other types of engineering in which backers have faith in an eventual payoff. • The regulators. This is the favorite suspect of innovators themselves, who understandably bristle under the increasing burden of federal rules. A company that makes or moves physical things faces limits on land use, passenger safety, pollution, chemical content and more, nearly all of which have gotten stricter since the 1970s. Peter Thiel, the billionaire tech investor, condemns federal regulators as the greatest obstacle to American innovation. “I would say that we’ve lived in a world in which bits were unregulated and atoms were regulated,” he said in a 2015 interview. His point is that restrictive rules are responsible for the slowdown in the engineering of mechanics (atoms) relative to software (bits). Whatever the benefits, every mandatory trial, emissions cap and maintenance rule reduces manufacturers’ output, decreases profit and restrains the ability to experiment. Yet the international comparison again casts doubt on this explanation. Countries that have developed quickly since the innovation slowdown, such as China, Japan and South Korea, placed less emphasis on consumer and worker protections than the U.S. did. Their bureaucrats attempt to boost industrial output, rather than hinder it. Despite decades of infrastructure marvels and greater shares of the world manufacturing market, none of these countries have caught up to the U.S. in achieving breakthroughs. Unlike 20th-century America, most of their growth has come from technologies discovered elsewhere. If regulation were decisive in hampering technological progress, one would expect zealously permissive regimes to surpass the fussy West. With legislators, investors and regulators largely off the hook, what’s left? The best evidence suggests that the seemingly boundless American progress of last century died a natural death. After 100 years of success, engineers around the 1970s found themselves less able to develop ever more productive designs and applications for the core technologies of the Second Industrial Revolution: combustion, electricity, signals, synthetics. As with previous revolutionary technologies—the steam engine, steel, gunpowder—the returns from each discovery gradually diminished. Productivity growth has slowed as a result. No other explanation accounts for why the slowdown has affected nearly every field of manufacturing. The internationally competitive garment industry and the protected sneaker industry both use similar production methods to those of 50 years ago. Neither high-tech combine harvesters nor simple winter jackets fulfill their functions much better now than two generations ago. No other country, regardless of trade or industrial policy, has demonstrated any more ability to break through. This realization is vital because it reframes every development in the U.S. economy since engineering progress diminished. For manufacturers since the 1970s, with few opportunities to grow by investing in better plant and equipment, offshoring parts of the production process may have become their only way to compete, not merely a way to grow faster. When companies lost much of their ability to improve product quality, lower production costs let them cut prices instead, raising living standards by other means. Critics like Mr. Rubio argue that the “financialization” of the economy has been counterproductive. In truth, the financial sector’s keenness for viable bets has helped sustain the overall growth of U.S. manufacturing. The low rate of nonperforming loans suggests the U.S. isn’t overleveraged compared with peers that boast a higher manufacturing share of output. Most important, the rise of software and high-end electronics looks less like a distraction from productive growth and more like a savior that has kept America moving when no other industry could. From IBM to Snapchat, tech companies have produced countless exportable goods and services and brought massive sums of foreign dollars into the U.S. economy. The software revolution has also propped up manufacturing by improving design capability, supply-chain logistics and marketing. A 2016 Deloitte survey of industry executives world-wide predicts that the U.S. will overtake China as the most competitive destination for manufacturing, as an ever-increasing share of growth occurs at the high end. Recognizing that the 20th century’s innovation streak died naturally should encourage policy makers to resist the allure of a Frankenstein industrial policy: slapping tariffs, subsidies and incentives on industries like electrodes, trying to revive the circumstances of a bygone economy. Protectionism can help companies maintain or increase market share. But manufacturing is shrinking as a share of employment in developed and developing economies alike. There’s little evidence that state support fosters commercially viable breakthroughs better than the market, so it’s an inefficient strategy for an advanced economy. Incentives to redirect capital, like Mr. Rubio’s proposal to punish stock buybacks through the tax code, would cause waste by discouraging companies from saving for better investments later. There’s no telling how innovation in engineering may eventually spring back to life, whether a new style of software next year or a mind-bending new field of physics next decade. Meanwhile, policy makers should take care to preserve the economic freedom that helps businesses find ways to prosper absent revolutionary progress. And Mr. Trump should heed his better angels by continuing to support every type of great thing America makes.

Ed Comment:What killed innovation? 1) unconstrained supply of cheap Chinese/Mexican/immigrant labor; 2) surplus of domestic labor and capacity coming out the recession (just now tightening); 3) long-term decline in research productivity interrupted by unusually high off-trend line productivity gains from internet; i.e., probably not sustainable; 4) shortage of talent; rest of world's talent contributing almost nothing 4) J-curve--next breakthrough of general purpose innovation (AI, quantum computing, driverless car, cure for cancer, etc.) have proven very hard to crack, i.e., progress is, in part, exogenously determined; 5) shift from centralized command and control manufacturing with productivity growth largely driven by engineered capital investment to decentralized, hard-to-manage services with much slower productivity growth; 6) low-skilled workforce has been largely left unsupervised as talent pursues more valuable opportunities. 7) sky-high real estate prices in fastest growing cities precludes medium-skilled migration.Like running a marathon, exogenously given hilly terrain has a big effect on the pace. Best measure is productivity gains relative to rest of high-wage world. By that measure, US productivity growth has accelerated.

  • Productivity
    • Innovation/Research
    • Investment
Previous articleDecember 16, 2019Whats behind Rising Returns to High-Quality College Education?Higher-quality students are driving rising returns to high-quality college education. Adjusting for student composition, Type 4 graduates earn only 5% more annually than Type 2 peers.Next articleDecember 16, 2019Germans Keep on Saving Their MoneyEven When It HurtsGermany’s elevated savings rate, at 11% of disposable income, has left households poorer due to low investment returns & negative interest rates. German median household wealth is €61,000, lower than Greece’s. @TomFairless
Showing 489 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

How Much Revenue Does The AI Buildout Need In 2030?

AI Summary. The authors estimate AI capital spending of $560bn in 2026 and, in a high-investment case, $3.5tn of net AI capital in service by 2030, requiring roughly $1.3tn in annual infrastructure revenue once depreciation, operating costs and a 15% return are covered. That implies 7.4x growth from current annualized revenues, with downstream revenues of $1.9–2.6tn.

William Gildea and Azeem Azhar Exponential View
Date Posted:
October 1, 2026
Is Database:
Database

Beginning with capex projections, Gildea/Azhar derive for their high-growth case a $1.3T annual AI-infrastructure revenue hurdle for 2030. This covers $771B of depreciation and operating costs, plus a 15% return on $3.5T of net capital in service.

How much annual revenue must AI infrastructure generate to justify investment?

Core argument: In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.

We estimate AI CapEx to be $560B in 2026, 64% of the total declared by the hyperscalers. Unfinished facilities can’t generate revenue, so the investment bill and the productive asset base will grow at different speeds. [In the high investment case], by 2030 we see $3.5T of net AI capital in service. The largest component [of the revenue hurdle] is the $555B in compute depreciation. We model lifespans at the current 5-6 years. Shell depreciation and OpEx combined contribute about a further $216B to the required revenue hurdle. [Adding a 15% return on net capital brings required annual infrastructure revenue to $1.3T]. Reaching the $1.3T hurdle would represent 7.4x growth (59% CAGR) from today’s annualized revenues. For its customers to earn 30-50% gross margins, downstream revenues need to be $1.9-2.6T. [Editor’s note: Van Nieuwerburgh’s far larger figure of $3.7T covers mature revenues from projects completed through 2032, using commissioning capital and a 50% infrastructure operating cash-flow margin. This estimate instead charges depreciation and operating costs plus 15% on net capital in service in 2030. Different horizons and cost conventions prevent a direct comparison. Van Nieuwerburgh also has a much higher capex estimate.]

Takeaways by Macro Roundup® AI

  1. In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.
  2. Compute depreciation of $555bn, modeled on 5-6 year lifespans, is the largest component of the 2030 revenue hurdle, with shell depreciation and operating expenses adding about $216bn and a 15% return on net capital completing the $1.3tn total.

Related Articles:

  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • What Investment Data Implies about the AI Transition — The five largest U.S. technology firms are scaling AI capital spending about 50% a year, from $155bn to a forecast $755bn. Embedding this in a two-sector model with rare productivity booms, the authors estimate AI adds roughly 5, 20, or 58pp to cumulative growth over a seven-year transition, depending on scenario.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

AI and Entrepreneurship: What is Really Happening?

AI Summary. Entrepreneurship by businesses with employees is flat or declining, including in AI-intensive information and professional services, where establishment entry has been flat or falling and young-firm employment in AI-exposed industries is falling. Applications for likely solo businesses with no employees rose steeply in those AI-intensive sectors, but self-employment as a main job is flat, suggesting side hustles.

Nathan Goldschlag Economic Innovation Group
Date Posted:
September 30, 2026
Is Database:
Database

Nathan Goldschlag finds that available federal data do not yet establish that AI is boosting entrepreneurship. “Across different cuts of the data, there is no clear evidence of AI affecting entrepreneurship, either for likely employers or nonemployers.”

Is AI driving a boom in new employer businesses?

Core argument: Business applications likely to become employer businesses were flat in 2025 and began rising in 2026, with fewer applications of the kind having the highest chance of becoming an employer business.

Employer entrepreneurship is at best rising modestly, and at worst has been falling for several years already. The number of business applications likely to become an employer business was flat in 2025 and began rising in 2026. Fewer of those applications have been the kind that have the highest chance of becoming an employer. Nonemployer entrepreneurship, or “solopreneurship,” is likely rising, but not as the main job of the solopreneurs. These are probably side hustles. Likely nonemployer business applications rose significantly in 2025 and early 2026. But the share of workers claiming to be self-employed nonemployers in their main job — in other words, business owners with no employees — is flat (though noisy). AI-intensive sectors and industries are no exception. Establishment entry in AI-intensive sectors (information and professional services) has been flat or falling in recent years.

Takeaways by Macro Roundup® AI

  1. Business applications likely to become employer businesses were flat in 2025 and began rising in 2026, with fewer applications of the kind having the highest chance of becoming an employer business.
  2. Likely nonemployer business applications rose significantly in 2025 and early 2026, going vertical in the most AI-intensive sectors of information and professional services, while other sectors kept pace until early 2026 and then flattened.
  3. The share of workers reporting self-employment as nonemployers in their main job is flat, though noisy, including among workers in AI-exposed occupations in 2025 and early 2026.

Related Articles:

  • The Age Of The Solopreneur — A surge in US business formation is being driven by solo entrepreneurs, with growth in AI tool usage and in solopreneur activity in high-AI-adoption sectors suggesting artificial intelligence accounts for a meaningful portion of the increase. The authors argue AI is lowering barriers to starting and growing a business by expanding what one-person firms can do.
  • America Has Become An Entrepreneur’s Paradise — US growth in new-business applications is concentrated in professional services (up 25% over four years), retail (18%), and health care and social assistance (18%) rather than artificial-intelligence startups. The share of applications the Census Bureau deems likely to hire at least one employee has fallen to 30% from 38%.
  • Which Entrepreneurs Boost Productivity? — The authors find that a minority of entrepreneurs are "transformative" — generating disproportionate productivity gains, scoring high on IQ tests, being well-educated, and hiring research and development workers. They argue that economies with more such entrepreneurs and R&D workers sustain higher long-run productivity growth.
  • Startups
  • Productivity
    • Innovation/Research
    • Investment

Tech's Trillion-Dollar Internal Inconsistency

AI Summary. Wall Street analysts covering technology project the sector's operating cash flow will more than double to roughly $2.4tn by 2028, a gain of over $1.2tn, while analysts covering the rest of the S&P 500 — technology's customers — project those firms will add far less cash flow. These sector-by-sector forecasts are internally inconsistent.

Torsten Sløk Apollo
Date Posted:
September 29, 2026
Is Database:
Database

Tech analysts expect sector operating cash flow to more than double to ~$2.4T by 2028. Analysts covering other S&P 500 sectors (tech’s customers) see far less cash growth in their sectors. Slok “raises the question of who exactly will be writing all those checks to buy AI services.”

Are tech cash flow forecasts inconsistent with customers' projected growth?

Core argument: Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.

Wall Street equity analysts work in sector silos, and when you add up their forecasts, the numbers are internally inconsistent. The analysts covering tech expect the sector's operating cash flow to more than double to roughly $2.4 trillion by 2028, an increase of over $1.2 trillion. Meanwhile, the analysts covering the other sectors in the S&P 500, which are tech's customers, expect those companies to add much less operating cash flow. In other words, the tech silo is betting on a future in which demand for AI and tech services explodes, while the silos covering the companies that would pay for those services see a much more modest outlook. Both cannot be right at the same time. The bottom line is that either tech's customers will generate a lot more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who exactly will be writing all those checks to buy AI services.

Takeaways by Macro Roundup® AI

  1. Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.
  2. Analysts covering the other S&P 500 sectors, which are tech's customers, expect those companies to add much less operating cash flow, leaving the combined forecasts internally inconsistent.
  3. Either tech's customers will generate much more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who will pay for AI services.

Related Articles:

  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

Nine Percent of GDP

AI Summary. An analysis estimates that for US data center owners to break even on AI investments, the sector would need about $3.725tn in annual compute revenue by 2032 — roughly 9.2% of projected 2032 GDP. The estimate assumes 183 GW of US capacity completed, a $9.61tn capital base, a 10% discount rate, and a 50% operating cash flow margin.

Hanno Lustig The Two Cents
Date Posted:
September 28, 2026
Is Database:
Database

The US AI-buildout is projected to average ~3.6% of GDP over 2025–32. Lustig notes data-center operators will need ~$3.725T in annual compute revenue by 2032, 9.2% of projected GDP, to earn a 10% unlevered return on the buildout.

How much annual revenue must AI data centers generate to break even?

Core argument: Stijn’s baseline estimate holds that data center owners would need $3.725 trillion of annual revenue by 2032 — roughly 9.2% of projected 2032 GDP — to break even on AI investment at a 10% discount rate.

What do you have to believe about future revenue in order for all data center owners to to break even on their AI investment? Using a 10% discount rate, Stijn’s baseline number is 3.725 trillion of annual revenue by 2032, which would amount to roughly 9.2% of projected 2032 GDP. This number is the top line earned from selling compute in aggregate by whoever operates the 182.7 GW of US capacity completed by 2032.2. The assumptions behind that number: 183 GW of U.S. data-center capacity completed between 2025 and 2032. $8.54 trillion of construction cost, grossed up to a $9.61 trillion capital base to account for the return on capital tied up during construction. A 10% discount rate. A 6-year life for IT equipment (68% of cost) and a 20-year life for everything else (32%). A 50% operating cash flow margin.

Takeaways by Macro Roundup® AI

  1. Stijn’s baseline estimate holds that data center owners would need $3.725 trillion of annual revenue by 2032 — roughly 9.2% of projected 2032 GDP — to break even on AI investment at a 10% discount rate.
  2. The break-even calculation assumes 183 GW of U.S. data-center capacity completed between 2025 and 2032 at $8.54 trillion of construction cost, grossed up to a $9.61 trillion capital base for return on capital tied up during construction.
  3. The estimate assumes a 6-year life for IT equipment, which accounts for 68% of cost, a 20-year life for the remaining 32%, and a 50% operating cash flow margin.

Related Articles:

  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

Financing the AI Buildout

AI Summary. The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.

Stijn Van Nieuwerburgh Brookings Papers on Economic Activity
Date Posted:
September 24, 2026
Is Database:
Database
Is Important:
Important

Stijn Van Nieuwerburgh projects ~$10.3T will be spent on the AI buildout over 2025–2032, averaging 3.63% of GDP. He estimates 2/3 of that spending reflects IT equipment and one-third real estate and associated power infrastructure.

How much of the planned data center pipeline gets built?

Core argument: Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.

The required investment is enormous. A representative 200 MW AI training campus costs roughly $8.2 billion, about two-thirds of which reflects IT equipment and one-third the real estate and associated power infrastructure. We estimate that 183 GW is completed by 2032 with a further 118 GW completed after 2032. This pipeline will trigger about $10.3 trillion dollars of investment over 2025–2032. At an average of 3.63% of GDP per year, the projected buildout would be larger relative to the economy than the major U.S. canal, railroad, electrification, highway, and telecommunications investment booms. It would double the electricity consumption of the entire U.S. residential sector.

Takeaways by Macro Roundup® AI

  1. Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.
  2. The authors’ central scenario allocates the announced pipeline so that 182.8 GW of additional U.S. capacity becomes operational during 2025–32, including 19.0 GW tied to projects already operational in 2025 or 2026.
  3. The authors’ scenario has 117.2 GW of planned data center capacity completed after 2032 and 226.9 GW never completed, applying lower realization rates to more distant and less fully documented projects.

Related Articles:

  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • AI Set to be Largest CapEx Cycle Ever … and Soon Majority Externally Financed — Hyperscaler and data-center investment is tracking toward $1.5tn annually by 2030, which in real terms would exceed the 2005-06 housing boom, the 1990s fiber peak, and the 1882 railroad peak combined. External debt and equity are projected to fund close to 90% of the buildout, making it dependent on credit markets.
  • The Summer I Turned Pretty — A warning sign in a boom cycle is when companies closest to final demand weaken while capital spending beneficiaries keep thriving, as when telecom operators' shares rolled over while communications equipment stocks kept rising. Semiconductor stock prices and free cash flow are soaring while large cloud providers' shares stagnate and their free cash flow plummets.
  • Investment
  • GDP
    • Financial Markets
    • Growth
  • Productivity
    • Innovation/Research

Moonshot Capitalism: AI Rewrites The Venture Capital Playbook

AI Summary. Global investment in "deep tech" — companies whose products rest on major scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn invested in the decade through 2019, excluding sums poured into AI start-ups. Failed battery and electric-vehicle bets from an earlier peak illustrate the risks of such moonshot dealmaking.

Tim Bradshaw Financial Times
Date Posted:
September 10, 2026
Is Database:
Database

Since the start of 2024, more than $150B of venture capital has been invested into non-AI “deep tech” firms whose products are rooted in significant engineering advances, exceeding the $133B invested in such firms btw 2010 and 2019.

Is the AI boom pushing venture capital toward riskier moonshot bets?

Core argument: Global "deep tech" investment excluding AI start-ups has exceeded $150bn since the start of 2024, more than the $133bn invested in the entire decade through 2019, according to Dealroom.

The AI boom is fuelling a resurgence in ambitious “moonshot” bets, as early SpaceX backers’ huge returns and falling valuations for traditional software companies force tech investors to embrace riskier and more capital-intensive dealmaking. Excluding the giant sums ploughed into AI start-ups, global investment in “deep tech” — companies whose products are rooted in big scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn in the entire decade to the end of 2019, according to Dealroom. This year’s deep-tech investments have not yet surpassed 2021’s peak, which was propelled by battery and electric vehicle deals for the likes of Rivian and Northvolt — many of which turned sour, highlighting the risks involved in moonshot dealmaking.

Takeaways by Macro Roundup® AI

  1. Global "deep tech" investment excluding AI start-ups has exceeded $150bn since the start of 2024, more than the $133bn invested in the entire decade through 2019, according to Dealroom.
  2. Huge returns for early SpaceX backers and falling valuations for traditional software companies are pushing tech investors toward riskier, more capital-intensive "moonshot" dealmaking.
  3. Deep-tech investment has not yet surpassed the 2021 peak, which was propelled by battery and electric vehicle deals for companies such as Rivian and Northvolt, many of which turned sour.

Related Articles:

  • Capital Is Making a Comeback — Tangible investment in property, factories and equipment fell from 12.5% to 8.5% of private gross domestic product, while intangible investment in intellectual property, software and process knowledge rose from about 11.5% to 16.75%. The author argues demand for tangible capital investment will now have to rise.
  • Public to Private Equity in the United States: A Long-Term Look — An analysis of more than 31,000 global venture capital deals finds 62% lost money and over half lost 50–100% of invested capital, though the distribution of returns has much fatter tails than buyouts or public equities. Venture funds averaged a public market equivalent ratio of about 1.4, indicating returns above public markets.
  • Gross and Net US Investment — Net US investment has fallen from roughly 40% of gross investment in the 1970s to about 25% in the past decade, meaning about three-quarters of gross investment now just replaces depreciating capital. The author attributes this gap to modern investment being more information-technology-related, which depreciates faster and requires more frequent replacement.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
© Copyright 2026 Coherent Research Institute · All Rights Reserved · Privacy · Terms