“A full-throated defense of economic dynamism.” - The Wall Street Journal
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…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
“…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 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
“…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
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 AzharExponential View
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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
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.
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.
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 GoldschlagEconomic Innovation Group
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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
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.
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.
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.
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 SløkApollo
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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
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.
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.
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.
AI Summary.An expanded unemployment measure for young college graduates — including those outside the labor force who report wanting a job — averaged 10.4% in the most recent summer, the highest of the five summers compared and 0.3 percentage points above the prior high. Estimated associations with occupational AI exposure are positive but not statistically significant.
Robert Fairlie and Jane WuNational Bureau of Economic Research
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For American college graduates aged 22 to 25 and not in school, summer unemployment was 7.3% in 2026, versus 7.1%, 6.3%, 7.8%, and 7.2% in 2022–25. These are ordinary seasonal peaks, with no evidence of an upward trend. A broader measure tells a similar story.
Is AI raising unemployment among recent college graduates?
Core argument: On the expanded unemployment measure including “sidelined” young college graduates, the June–August average was 10.4% in 2026, compared with 9.4% in 2022, 9.3% in 2023, 10.1% in 2024 and 9.4% in 2025.
Figure 2b displays the seasonal comparison for our expanded unemployment measure, which includes young college graduates who are “sidelined,” those not officially defined as being in the labor force but nonetheless report “wanting a job.” Averaging over June, July and August, the expanded measure was 10.4% in 2026, compared with 9.4% in 2022, 9.3% in 2023, 10.1% in 2024 and 9.4% in 2025. Summer 2026 is the highest of the five summers under this definition, although it exceeds the previous high by only 0.3 percentage points. The expanded measure thus indicates a larger increase relative to 2025, while also showing that high rates in the summer are not unique to 2026. The coefficient estimates of interactions between summer 2026 and both measures of occupational AI exposure are positive, but not statistically significant. The point estimate of the interaction with occupational teleworkability is positive but difficult to separate cleanly from AI exposure (Tables 4-6).
Takeaways by Macro Roundup® AI
On the expanded unemployment measure including “sidelined” young college graduates, the June–August average was 10.4% in 2026, compared with 9.4% in 2022, 9.3% in 2023, 10.1% in 2024 and 9.4% in 2025.
Summer 2026 is the highest of the five summers on the expanded measure but exceeds the prior high by only 0.3 percentage points, indicating high summer rates are not unique to 2026.
Coefficient estimates of interactions between summer 2026 and both measures of occupational AI exposure are positive but not statistically significant, and the positive point estimate for occupational teleworkability is difficult to separate cleanly from AI exposure.
Related Articles:
Graduating into Disruption: Labor Market Outcomes for AI-Exposed College Majors— College graduates in the majors most exposed to artificial intelligence show statistically significant employment declines following the arrival of AI chatbots, with the most-exposed 10% of majors down about 5 percentage points and the seventh and eighth deciles down about 2 points. The authors note flat pre-2020 trends and precise timing argue against alternative explanations such as uneven post-pandemic recovery.
The Jobs Apocalypse Is Postponed. An AI Jobs Boom Is Here— Employment in five industries tied to the data-centre build-out, including electrical contracting and equipment manufacturing, has risen roughly 320,000 above what broader construction and manufacturing trends would suggest. Engineers, software developers, mathematicians and data scientists have added about 730,000 jobs above the trend for professional employment overall.
Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence— Employment for workers aged 22–25 in the occupations most exposed to artificial intelligence has declined roughly 18pp relative to less-exposed peers, with no comparable decline in other age groups. The authors find this operates mainly through reduced hiring, concentrated where AI substitutes for human tasks rather than complements them.
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 LustigThe Two Cents
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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
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.
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.
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.
AI Summary.The author argues that a slowdown in artificial-intelligence capital spending would remove a stimulus that has been masking weakness, colliding with overcapacity, falling prices, refinancing needs and weaker labor income. On this view the main risk is deflationary, transmitting through investment, employment, consumption, credit, housing and asset prices at once.
Paul KedroskyApplied Complexity
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Kedrosky argues that the “inevitable transition” from the current inflationary AI-buildout regime to a new regime in which AI-driven productivity gains exert deflationary pressure is “where the real macro risk lies.”
Would an AI spending slowdown create deflationary rather than inflationary risk?
Core argument: The author argues the dangerous phase arrives when the AI capex boom slows, removing stimulus just as overcapacity, falling prices, refinancing needs and weaker labor income hit together, with a refinancing wall waiting in 2028.
If AI investment has been masking underlying weakness, a slowdown removes the stimulus just as overcapacity, falling prices, refinancing needs and weaker labor income hit together. A refinancing wall sits waiting in 2028. Failure is over-determined. This moment sits at the intersection of the four forces that have produced the most consequential boom-bust cycles in Western economic history: loose credit, government policy, technology, and real estate. There are, as a result, myriad catalysts, each with a low probability of happening, but which collectively guarantee collapse. That is why the principal risk is deflationary rather than inflationary. An AI bust could transmit through capex, employment, consumption, credit, housing, and asset prices simultaneously.
Takeaways by Macro Roundup® AI
The author argues the dangerous phase arrives when the AI capex boom slows, removing stimulus just as overcapacity, falling prices, refinancing needs and weaker labor income hit together, with a refinancing wall waiting in 2028.
The principal risk from an AI bust is deflationary rather than inflationary, according to the author, because it could transmit simultaneously through capex, employment, consumption, credit, housing, and asset prices.
The author places this moment at the intersection of loose credit, government policy, technology, and real estate — the four forces behind the most consequential boom-bust cycles in Western economic history — and sees a plausible decade of Japan-style balance recession with fiscal stimulus constrained by massive deficits.
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.
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.
AI Summary.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.
Ben Inker and John PeaseGMO
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GMO estimates net US equity supply could increase by ~5% or more of total market capitalization over the next year. Inker and Pease note that in 1999–2000, “IPOs peaked at ~5% of total market cap. It doesn’t take much new supply to overwhelm the market.”
How does rising equity supply from new listings affect stock returns?
Core argument: Controlling for valuations, a 1% increase in the market share of IPOs has historically led to an approximate 4% decrease in prices, a multiplier the author applies to a coming surge in equity supply.
Controlling for valuations, a 1% increase in the market share of IPOs has historically led to an approximate 4% decrease in prices [Exhibit 2 in gallery]. Consider what is happening to equity supply. SpaceX’s non-Elon sales alone exceed 1% of the U.S. stock market’s value. We are likely to see IPOs from both Anthropic and OpenAI—together valued at about 5% of the U.S. stock market’s total investable market cap —over the next 12 months. For the first time in twenty years, with the brief exception of the GFC, we are seeing net dilution in the U.S. stock market (i.e., share issuance exceeding share buybacks), as the hyperscalers continue their capital expenditure spree. In aggregate, we are going from a world where equity supply was shrinking by roughly 1% per annum (with buybacks exceeding primary or secondary issuance) to one in which equity supply will be increasing by close to 5%, if not more. If you believe the 4x multiplier [implied by the regression line in Exhibit 2], this means we should expect returns that are some 20% below average (roughly 6% real) over the next 12–18 months from the headwind of rising equity supply alone.
Takeaways by Macro Roundup® AI
Controlling for valuations, a 1% increase in the market share of IPOs has historically led to an approximate 4% decrease in prices, a multiplier the author applies to a coming surge in equity supply.
U.S. equity supply is shifting from shrinking roughly 1% a year, with buybacks exceeding issuance, to growing close to 5% or more, the first net dilution in twenty years apart from the Global Financial Crisis.
Anthropic and OpenAI, together valued at about 5% of U.S. investable market cap, are likely to go public within 12 months, while SpaceX’s non-Elon sales alone exceed 1% of the U.S. stock market’s value.
Related Articles:
Momentum, Rotation and the Value in Growth— After a fifteen-year stretch in which US equities, technology and growth stocks led, equity gains have broadened geographically, with the US the weakest of the major regions. The five largest US stocks now trade at a price-to-earnings ratio only marginally above the other 495, after holding a consistent premium since 2017.
SpaceX and OpenAI Are Ending Wall Street’s Era of Stock Scarcity— JPMorgan projects that share sales will add roughly $1.5tn of stock to the US equity market over the next two years even after buybacks, which would be the strongest net equity issuance since at least the late 1990s. Equity has grown cheaper relative to debt as a funding source.
One Hundred Years in the U.S. Stock Markets— Over a century, U.S. publicly traded stocks generated roughly $91tn in shareholder wealth enhancement, with returns strongly positively skewed and dominated by a relatively small number of high-performing firms. Because most possible outcomes fall below the mean in a skewed distribution, the authors argue financial planning should focus on median outcomes or the full distribution.
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 NieuwerburghBrookings Papers on Economic Activity
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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
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 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.
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.
