“Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
“…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
“Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…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
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“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
Research on business investment, capital formation, equipment, structures, software, and the conditions that turn spending into higher productivity. Coverage connects financing, expected returns, policy, technology, and long-run growth.
AI Summary.Large US cloud and artificial-intelligence companies have raised $48bn in European-currency bonds, more than triple the prior year's total, spread across €27bn, £13bn and CHF7.5bn. The issuance adds highly rated US borrowers to European credit indices and deepens the thin market for longer-dated maturities, with few signs so far of crowding out other issuers.
Huw van SteenisApollo
Date Posted:
Hyperscalers have raised $48B in European-currency bonds this year, more than 3x all of 2025. They now account for 3% of euro IG issuance, 10% of sterling and 22% of Swiss franc, versus 8% in the US.
Are European bond markets funding the US AI buildout?
Core argument: Hyperscalers have raised $48 billion in bonds denominated in European currencies this year — €27 billion, £13 billion and CHF7.5 billion — more than triple the full-year 2025 total.
Hyperscalers have raised $48 billion in bonds in European currencies this year, already more than triple the entire amount raised last year. This breaks down as €27 billion, £13 billion, and CHF 7.5 billion. There are few signs of crowding out: non-hyperscaler IG issuance remains strong and little changed YoY, and corporate spreads remain tight. Rather, hyperscalers are adding highly rated US borrowers (AA- or higher) to European credit indices that have traditionally had more A-to-BBB exposure. Hyperscalers account for 7% of euro issuance with maturities of 10 years or more, versus 3% across all maturities, and 3% of the 10-year-plus euro IG index versus 1% of the overall index. This adds depth at longer-dated maturities—thin outside sovereign issuance—at a time when some governments, including the UK, have been shortening duration.
Takeaways by Macro Roundup® AI
Hyperscalers have raised $48 billion in bonds denominated in European currencies this year — €27 billion, £13 billion and CHF7.5 billion — more than triple the full-year 2025 total.
Hyperscaler issuance shows few signs so far of crowding out European borrowers, with non-hyperscaler investment-grade issuance little changed year-over-year and corporate spreads still tight.
Hyperscalers account for 7% of euro issuance with maturities of 10 years or more versus 3% across all maturities, adding borrowers rated AA- or higher to indices traditionally weighted toward A to BBB.
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.
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 Is Driving Up Treasury Yields: ‘It Just Touches Everything’— Investment-grade companies have sold nearly $1.5tn of bonds, up 36% from a year earlier, and some investors have sold US Treasuries to buy higher-yielding corporate debt. Bank of America economists estimate the surge in corporate-debt sales, along with more mortgage-backed securities issuance, pushed 10-year rates up by about 0.3 percentage point.
AI Summary.Incorporating tariffs into the standard cost-of-capital framework, researchers estimate US tariffs raised the economy-wide marginal effective tax rate on new investment by 4.2 percentage points to 17.0%, with equipment hit hardest at 26.1 percentage points. That increase offsets the 3.5 percentage point reduction from the One Big Beautiful Bill Act.
Kyle Pomerleau, Thomas Brosy, Robert McClelland and John WongAmerican Enterprise Institute
Date Posted:
The IEEPA tariffs through January 2026 raised equipment’s user cost of capital 9%, from 17.7% to 19.3%. With depreciation unchanged, the required pre-tax net return rose 1.6pp, 4.3% to 5.9%, a tax at purchase that expensing cannot eliminate.
Do tariffs raise the effective tax rate on new investment?
Core argument: Tariffs in force before the February 20, 2026 Supreme Court ruling in Learning Resources v. Trump raised the economy-wide marginal effective tax rate on new US investment by 4.2 points, from 12.8 percent to 17.0 percent, and the cost of capital by 2.7 percent, the AEI authors estimate.
The standard Hall-Jorgenson user cost of capital framework defines the minimum gross required return on an additional unit of capital. The service price of capital (SS) is the minimum pretax gross return required on a new investment; the minimum pretax net return (ρrho) equals SS minus economic depreciation. Consider imported equipment requiring a 25% gross return: 20% to cover depreciation and 5% shareholders’ demanded return. A 10% tariff raises the cost of capital to 27.5%. [Thus the minimum pretax net return—the hurdle rate—rises from 5% to 7.5%]. For equipment, tariffs raise the cost of capital 9%, from 17.7% to 19.3%: 86% of the burden is direct, 14% indirect. 100% bonus depreciation reduced the equipment METR from 19.1% to 4.1%; tariffs raised it to 30.3%, more than offsetting the tax cut.
Takeaways by Macro Roundup® AI
Tariffs in force before the February 20, 2026 Supreme Court ruling in Learning Resources v. Trump raised the economy-wide marginal effective tax rate on new US investment by 4.2 points, from 12.8 percent to 17.0 percent, and the cost of capital by 2.7 percent, the AEI authors estimate.
Equipment bore the largest estimated burden, with its marginal effective tax rate rising 26.1 points from 4.1 percent to 30.3 percent and its cost of capital up 9.0 percent, while mainframes and PCs saw a 26.6 percent cost-of-capital increase.
The estimated 4.2 point tariff-driven increase in the marginal effective tax rate offset the One Big Beautiful Bill Act’s 3.5 point reduction, leaving a net 0.8 point rise economy-wide and an 11.1 point net increase for equipment.
Related Articles:
Blunt Tariffs Undermine Efforts To Reshore High-Tech Manufacturing— Computer and electronic products manufacturers import 37% of their intermediate inputs and capital equipment, chemical manufacturers 33%, and transportation equipment manufacturers 27%. Broad tariffs may push some sourcing domestically over time, but many inputs will simply cost more, making American manufacturers less competitive globally.
Help for the Heartland? The Employment and Electoral Effects of the Trump Tariffs in the United States— The authors find that the combined effect of U.S.-China trade war import tariffs, retaliatory tariffs, and farm subsidies on employment in exposed U.S. locations was at best a wash and possibly mildly negative, while residents of tariff-protected areas became less likely to identify as Democrats and more likely to vote Republican.
Tariffs are Dumb Enough to (Almost) Work— A universal 15% tariff on imported goods could raise roughly $300–$400bn annually, a fraction of what a value-added tax would generate but real revenue given U.S. imports of about $4tn. The author argues tariffs are a poorly designed form of consumption tax that could build political groundwork for replacing them with a more efficient value-added tax.
AI Summary.Utilization-adjusted total factor productivity from the Federal Reserve Bank of San Francisco sits slightly below zero with no acceleration since the artificial intelligence investment cycle began, even as output per hour runs near 2.5%, above its post-2005 average. That combination is the signature of capital deepening rather than a technology shock.
Torsten SløkApollo
Date Posted:
The FRBSF’s utilization adjusted TFP series shows no sign of acceleration since the start of the AI capex cycle. Slok reiterates that the rise in output per hour “alongside flat TFP is the signature of capital deepening, not of a technology shock.”
Does productivity data show evidence of an AI technology shock?
Core argument: Utilization-adjusted total factor productivity from the San Francisco Fed sits slightly below zero with no acceleration since the AI capex cycle began, while output per hour runs near 2.5%.
The chart shows utilization-adjusted TFP from the San Francisco Fed, and it is currently sitting slightly below zero with no sign of acceleration since the AI capex cycle began. Output per hour, by contrast, is running near 2.5%, comfortably above its post-2005 average, and that strength is exactly what gets cited as evidence that AI is already working. But strong output per hour alongside flat TFP is the signature of capital deepening, not of a technology shock. TFP has swung between roughly -3% and +4% over the past 40 years with no discernible trend, so the current sub-zero reading is not in itself unusual. Electricity and IT both took a decade or more to show up in aggregate numbers.
Takeaways by Macro Roundup® AI
Utilization-adjusted total factor productivity from the San Francisco Fed sits slightly below zero with no acceleration since the AI capex cycle began, while output per hour runs near 2.5%.
Output per hour above its post-2005 average alongside flat total factor productivity is the signature of capital deepening rather than a technology shock, despite that strength being cited as evidence that AI is already working.
Total factor productivity has swung between roughly -3% and +4% over the past 40 years with no discernible trend, and both electricity and IT took a decade or more to show up in aggregate numbers.
Related Articles:
AI and Productivity— The recent pickup in US labor productivity appears driven by higher utilization of existing capital—running factories, server racks, and hotel rooms harder—rather than by firm-level efficiency gains from AI. Adoption remains too thin and the cross-sectional evidence too weak to conclude that AI-driven productivity gains are already widespread.
Have We Entered an Era of High Productivity Growth?— Labor productivity has grown solidly while the San Francisco Federal Reserve's total factor productivity index, which adjusts for the equipment and technology workers use, has grown modestly, suggesting gains so far reflect better tools rather than broader advances. A regime-switching model puts the probability of a high-productivity regime at 57% using labor productivity and 21% using total factor productivity.
Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools— The authors estimate that AI coding tools raise developers' task-level productivity by about 40% with autocomplete, 140% including synchronous agents, and 180% with asynchronous agents, with larger effects for less active developers. Gains attenuate up the production chain: synchronous agents raise lines of code 741% and pull requests 65% but releases only 20%.
AI Summary.Canada's core inflation is around 2% and its budget deficit about 2.4%, versus roughly 6% in the US, yet Canadian bond yields move with US yields. The author argues global AI-related debt issuance, including multibillion-dollar Canadian-dollar bonds from Google and Amazon, helps transmit higher US borrowing costs into Canada.
Paul PodolskyA Letter From Paul
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Canada’s 10-year yield rose from ~3% to ~4% despite a modest 2.4% budget deficit, slow domestic growth and 2% core inflation. Podolsky sees the US bond selloff as raising yields globally. “It can seem odd that bond markets trade in sync, but they do.”
Are US borrowing costs being exported to Canada through global AI debt?
Core argument: Canada’s core CPI runs near 2% and its budget deficit near 2.4%, versus roughly 6% in the US, which the author attributes to Canada not being a meaningful part of the AI boom.
Core CPI in Canada is around 2%. This is because Canada is not a meaningful part of the AI boom and is also not running a big budget deficit. Canada’s budget deficit is around 2.4%, as opposed to about 6% in the US. Canada’s bond-market reward for the US’s truculent, guns-and-butter policy is higher borrowing costs and slower growth. In part this is because the AI debt issuance is global. In May, Google raised C$8.5B and in June Amazon raised C$14B, or about one-third of net new Canadian corporate issuance this year. Over time, however, markets often revert to pricing that accurately reflects local conditions. [The chart shows] yields in the US, Canada, and Canadian unemployment. As you can see, as US yields sell off, Canada sells off, even if Canadian employment conditions are tepid. Obviously, the US (orange line) has sold off more.
Takeaways by Macro Roundup® AI
Canada’s core CPI runs near 2% and its budget deficit near 2.4%, versus roughly 6% in the US, which the author attributes to Canada not being a meaningful part of the AI boom.
Canadian bond yields sell off alongside US yields even when Canadian employment conditions are tepid, leaving Canada with higher borrowing costs and slower growth, though US yields have sold off more.
Google raised C$8.5B in May and Amazon C$14B in June, together about one-third of net new Canadian corporate issuance this year, reflecting the global nature of AI-related debt issuance.
Related Articles:
Foreign Capital Flows Into US Stocks Hit Record As Appetite For Debt Fades— Foreign investors made a record $942bn in net purchases of US stocks and investment fund shares over 12 months, the highest rolling 12-month total in Treasury data going back to 1985. Foreign demand for US debt securities moved in the opposite direction, falling to a net $188bn in the second quarter from $314bn in the first.
AI Is Driving Up Treasury Yields: ‘It Just Touches Everything’— Investment-grade companies have sold nearly $1.5tn of bonds, up 36% from a year earlier, and some investors have sold US Treasuries to buy higher-yielding corporate debt. Bank of America economists estimate the surge in corporate-debt sales, along with more mortgage-backed securities issuance, pushed 10-year rates up by about 0.3 percentage point.
Is the AI Buildout Pushing Up Yields?— US saving-investment data show that non-financial corporations remain net savers, unlike the early-2000s information technology boom when they flipped to net borrowers and their capital spending accounted for the entire current account deficit. Government dissaving through the budget deficit, not the artificial intelligence buildout, is absorbing national resources.
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
Date Posted:
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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.US defense contractors face a coming requirement to remove certain metals sourced from China and other adversary countries from their supply chains, which many contractors say isn't feasible, while some Pentagon officials target a "closed loop" of US and allied supply for some minerals by 2030.
Heather Somerville and Andrew MollicaWall Street Journal
Date Posted:
CRU, a UK commodities-data provider, projects China will lose market share in many of the elements and minerals it has most tightly controlled by 2030, with the exception of cobalt.
Can defense contractors remove Chinese-sourced critical minerals from supply chains?
Core argument: U.S. defense contractors will be required by next year to eliminate certain metals sourced from adversary countries including China, a requirement many contractors say isn’t feasible despite the progress being made.
By next year, U.S. defense contractors will be required to eliminate from their supply chain certain metals sourced from adversary countries including China, a requirement that many contractors say isn’t feasible despite the progress being made. Some Pentagon officials say they are aiming for a “closed loop” of U.S. and allied supply chains for some minerals by 2030. In April, Deputy U.S. Trade Representative Rick Switzer said that with help from Japan and Europe, the U.S. has the capacity to develop an end-to-end critical-minerals supply chain free of China within six to 12 months, if the country were pressed to do so. Recent projections from CRU, a London-based commodities-data provider, show China losing market share by the end of the decade in many of the elements and minerals it had most tightly controlled.
Takeaways by Macro Roundup® AI
U.S. defense contractors will be required by next year to eliminate certain metals sourced from adversary countries including China, a requirement many contractors say isn’t feasible despite the progress being made.
Deputy U.S. Trade Representative Rick Switzer said in April that, with help from Japan and Europe, the U.S. has the capacity to develop an end-to-end critical-minerals supply chain free of China within six to 12 months if pressed to do so.
Projections from CRU, a London-based commodities-data provider, show China losing market share by the end of the decade in many of the elements and minerals it had most tightly controlled, while some Pentagon officials aim for a “closed loop” of U.S. and allied supply chains for some minerals by 2030.
Related Articles:
China Restricts Trading With Some US Rare Earth Companies— China has added 10 American companies to its "entities list," restricting trading with firms central to US efforts to build a rare earth supply chain rivaling Beijing's, including USA Rare Earth, MP Materials, and motor manufacturer Aveox.
How to Fight an Economic War— China earned about $3.4bn from exporting rare-earth elements and magnets in a year, while the U.S. Geological Survey estimates a 30% disruption to U.S. supply of the rare-earth element neodymium alone would cut U.S. GDP by 2.2%, over $600bn. This asymmetry gives Chinese export controls their leverage.
How Japan Built a Rare-Earth Supply Chain Without China— Japan has reduced its reliance on Chinese rare earths, with industry estimates putting China's share of Japanese rare-earth imports at roughly 60% to 70%, down from 90% or more. Japan's alternative chain runs from an Australian open-pit mine through a separation plant in Malaysia to Japanese magnet makers supplying automakers.
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.
