“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“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
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“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
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…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
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“A full-throated defense of economic dynamism.” - The Wall Street Journal
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.
AI Summary.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.
Economist StaffThe Economist
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The Economist estimates that so far the AI boom has created ~1mm new jobs in the US, exceeding their estimate of ~200,000 layoffs attributed to AI since mid-2023.
Is the AI build-out adding jobs above trend?
Core argument: Employment in five industries tied to the data-centre build-out, from electrical contracting to equipment manufacturing, has risen by roughly 320,000 more since 2023 than broader construction and manufacturing trends would suggest.
[We] tracked five industries at the heart of the data-centre build-out, from electrical contracting to equipment manufacturing. Since 2023 employment in them has risen by roughly 320,000 more than broader construction and manufacturing trends would suggest. Not all of those jobs owe their existence to AI—grid upgrades and other factory building matters too. [We also] tracked employment in professional occupations closest to the AI boom—engineers, software developers, mathematicians and data scientists—and compared their growth since 2022 with professional employment overall. These roles have added roughly 730,000 jobs above trend in recent years. AI will not have created every single one of them. But it has almost certainly created quite a few.
Takeaways by Macro Roundup® AI
Employment in five industries tied to the data-centre build-out, from electrical contracting to equipment manufacturing, has risen by roughly 320,000 more since 2023 than broader construction and manufacturing trends would suggest.
Professional occupations closest to the AI boom—engineers, software developers, mathematicians and data scientists—have added roughly 730,000 jobs above the trend for professional employment overall since 2022.
Not all of these above-trend gains are attributable to AI, since grid upgrades and other factory building also matter, though AI has almost certainly created quite a few of them.
Related Articles:
The College Wage Premium in the Generative AI Era— The U.S. college wage premium fell from 0.626 to 0.575 after four decades of expansion, implying the first sustained negative growth in relative demand for college-educated labor. The authors estimate that weaker wage growth in occupations highly exposed to artificial intelligence accounts for roughly 28% of that decline, though the patterns are not causal.
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.
Looking for the Ladder— The article argues that the hiring downturn in occupations most exposed to generative AI reflects interest-rate sensitivity rather than AI itself, since roughly 38% of workers in the most AI-exposed quintile work in information, finance and insurance, and professional and technical services, versus under 2% in the least-exposed quintile.
AI Summary.Only 20% of global household wealth growth came from net new investment in real assets such as machinery, housing, infrastructure and intellectual property, down from a 30% average over the prior quarter century. Nearly 60% came from asset prices rising faster than inflation and negative net worth in other sectors.
Rebecca Anderson, Jan Mischke, Arvind Govindarajan, Sylvain Johansson, et al.McKinsey
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A McKinsey analysis finds global wealth is increasingly composed of “paper wealth,” driven by the rise of US equity values to 2.4x the book value of net corporate assets, and the debt of Chinese corporations, which grew to 80% of their real asset value.
Is household wealth growth driven more by asset prices than investment?
Core argument: Only 20% of global household wealth growth in 2025 came from net new investment in real assets such as machinery, homes, infrastructure, and intellectual property less depreciation, versus a 30% average over 2000–2024.
In 2025, global wealth growth was driven to a greater extent by paper wealth, or nominal asset value growth decoupled from the real economy. Only 20% of household wealth growth was based on net new investment (real assets including machinery and equipment, homes and buildings, infrastructure, and intellectual property, less depreciation), compared to 30% on average from 2000 to 2024. Nearly 60% came from asset price growth above and beyond general inflation and negative net worth positions from other sectors. [For example, this includes equity value growth above net assets for corporations as well as government bonds greater than the book value of government assets]. This was a marked increase over the average from 2000 to 2024, when paper gains drove one-third of global wealth growth.
Takeaways by Macro Roundup® AI
Only 20% of global household wealth growth in 2025 came from net new investment in real assets such as machinery, homes, infrastructure, and intellectual property less depreciation, versus a 30% average over 2000–2024.
Nearly 60% of 2025 global household wealth growth came from asset price gains above general inflation plus negative net worth positions in other sectors, compared with about one-third on average over 2000–2024.
Paper wealth, meaning nominal asset value growth decoupled from the real economy, drove a greater share of global wealth growth in 2025, including equity values above corporate net assets and government bonds exceeding the book value of government assets.
Related Articles:
The Next Crash: Why This Time Might Not Be Different— The "excess CAPE yield" — the gap between cyclically adjusted earnings yields on stocks and inflation-adjusted Treasury bond yields — stands at 1.4%, far below its long-run average of 4.7%. Historically, a low excess yield has been associated with poor subsequent 10-year excess returns on stocks, though it does not reliably predict an imminent crash.
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.
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.US after-tax non-financial corporate profit margins remain elevated by historical standards, estimated at 7.6% versus a post-1949 high of 8.2%, while employee pay as a share of non-financial corporate gross value added has fallen from 66% to 56%. One argument holds that subdued demographic growth leads companies to prioritize maximizing margins over expanding capacity.
Tan Kai XianGavekal
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In Q1 2026, US after-tax non-financial margins were estimated at 7.6%, just short of the post-1949 high of 8.2% in Q2 of 2021. Tan Kai Xian argues US corporate profits may not mean revert due to increased automation as the workforce shrinks relative to the economy.
Does slower population growth push companies to maximize profit margins?
Core argument: US after-tax non-financial corporate profit margins were estimated at 7.6% in the first quarter of 2026, just below the post-1949 high of 8.2% set in the second quarter of 2021.
US corporate profit margins remain elevated by historical standards. In the first quarter of 2026, US after-tax non-financial margins were estimated at 7.6%, only just below the post-1949 high of 8.2% reached in the second quarter of 2021. The second-quarter earnings season has also begun strongly, according to FactSet data. Employee remuneration as a share of gross value added for US non-financial corporate businesses declined from 66% in the fourth quarter of 2001 to 56% in the first quarter of 2026. [Will Denyer argued] "When strong demographic growth powers rapid demand growth, companies concentrate on expanding capacity and sales—on growing along with the growing market. That means they tend to focus less on their margins—less on getting the biggest profit they can out of every dollar of revenues. In contrast, when demographic growth is subdued, and hence there is less potential demand growth, companies are not so bent on expanding capacity as fast as they can, and are far more interested in squeezing the maximum possible margin from every dollar of sales."
Takeaways by Macro Roundup® AI
US after-tax non-financial corporate profit margins were estimated at 7.6% in the first quarter of 2026, just below the post-1949 high of 8.2% set in the second quarter of 2021.
Employee remuneration as a share of gross value added for US non-financial corporate businesses fell from 66% in the fourth quarter of 2001 to 56% in the first quarter of 2026.
Will Denyer argued that strong demographic growth leads companies to prioritize expanding capacity and sales, while subdued demographic growth pushes them to squeeze the maximum possible margin from every dollar of sales.
Related Articles:
The Record Divide Between Corporate Profits and Worker Pay— Labor's share of gross domestic income fell to 51%, the lowest since records began in 1947, while profits' share rose to 12.1%, the highest since 1950. Inflation-adjusted hourly wages are up 3% since the end of 2019, compared with a 50% rise in profits.
The Post‑COVID Decline in the Labor Share— The share of US national income paid to workers fell 1.6 percentage points below its pre-pandemic level, reaching a post-war low. The authors find this decline stems mainly from changes within industries rather than shifts in activity across sectors, following cyclical patterns seen in earlier recessions.
End of an Era: The Coming Long-Run Slowdown in Corporate Profit Growth and Stock Returns— The authors estimate that declining interest and corporate tax rates accounted for over 40% of real corporate profit growth from 1989 to 2019, while falling risk-free rates explain the entire expansion in price-to-earnings multiples. Real earnings before interest and taxes grew slightly more slowly than in 1962–1989, at 2.2% versus 2.4% per year.
AI Summary.The authors trace a channel from rising imports to lower domestic sales, employment, plant closures, profits, capacity utilization, and reduced investment in fixed capital and research. They note the computer and electronic products industry, which contributed most to the US manufacturing productivity slowdown, offshored production to Asia, reaching 84% import penetration.
Robert Gordon and Kenneth RyuNational Bureau of Economic Research
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Citing complementarities btw production and innovation, Gordon et al trace the origins of the end of productivity growth in U. S. manufacturing back to 2000, when sectoral output growth ceased amid increased import competition.
Do rising imports explain the US manufacturing productivity growth slowdown?
Core argument: The computer and electronic products industry, which contributed most to the U.S. manufacturing productivity growth slowdown, largely offshored production to Asia after 2005, reaching an import penetration ratio of 84% by 2018.
We trace a channel of causation from the arrival of imports to declining domestic sales and employment, plant closures, lower profits and capacity utilization, and less investment in fixed capital and R&D. The effect of imports on domestic innovation is more nuanced. Some authors argue that domestic manufacturing and innovation are complements; when production of components is offshored the growing distance from the production process inhibits further improvements that combine design and process innovation. The computer and electronic products industry, which contributed most to the manufacturing productivity growth slowdown, largely offshored production to Asia after 2005. By 2018 its import penetration ratio had reached 84%. This did not occur primarily because of lower labor costs. Instead the attraction of Asia was its emphasis on process innovation. “Manufacturing optimization” leads to the ability to ramp up production quickly at massive scale. Further explanations include government subsidies, geographically concentrated supplier clusters, and support for worker training.
Takeaways by Macro Roundup® AI
The computer and electronic products industry, which contributed most to the U.S. manufacturing productivity growth slowdown, largely offshored production to Asia after 2005, reaching an import penetration ratio of 84% by 2018.
The authors trace a channel of causation from the arrival of imports to declining domestic sales and employment, plant closures, lower profits and capacity utilization, and less investment in fixed capital and R&D.
Asia's attraction lay less in lower labor costs than in its emphasis on process innovation, with "manufacturing optimization" allowing rapid production ramp-up at massive scale, alongside government subsidies, concentrated supplier clusters, and support for worker training.
Related Articles:
Why Is Manufacturing Productivity Growth So Low?— Manufacturing productivity growth fell behind other sectors after computer and semiconductor manufacturing productivity, which had grown at roughly 15% and 11% a year, decelerated substantially. The authors also present suggestive evidence that quality improvements are missed in price measures, estimating manufacturing productivity growth is understated by 0.8 percentage points, including 1.7 points for durable goods.
What Would It Take to Bring Back US Manufacturing? Part 1: America’s Structural Headwinds— The US imports roughly 40% of the goods it consumes, directly and through imported inputs, with labor-intensive goods like apparel almost entirely imported. The authors argue America's main comparative disadvantage is its cost of production, driven mainly by labor costs, with real estate costs and a strong dollar also contributing.
Speed Can Reindustrialize America— The US has a relative surplus of low-paid hourly workers and a shortage of high-skill workers, so many less productive US manufacturers could buy a robot but could not attract the talent to program it. China faces the opposite conditions, with underemployed STEM graduates, residency rules complicating coastal factory hiring, and robot programmers earning a fraction of US wages.
AI Summary.European productivity growth has trailed the United States for three decades, and measures using current purchasing-power-parity conversions versus national price deflators answer different questions. The authors argue the flat current-purchasing-power-parity series reflects changing the price yardstick each year rather than showing Europe is keeping pace.
Philippe Aghion, Antonin Bergeaud, and Luis GaricanoSilicon Continent
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Aghion et al. note that Krugman does not dispute the claim that Europe’s productivity has lagged the US for 30 years, but sidestep his case for measuring the role of falling prices of US tech goods in cushioning the welfare impact on European consumers.
Does Europe's flat productivity gap reflect a shifting price yardstick?
Core argument: European productivity growth has trailed the United States for three decades, a point on which both Krugman and the authors agree, with Krugman stating explicitly that he is not arguing European productivity is mismeasured.
Krugman and we agree that (1) European productivity growth has trailed the United States for three decades, and Krugman says so clearly (”I am not arguing that European productivity is mismeasured, and never said that.”) (2) Current-PPP chain and a national deflator answer different questions. (3) The weighting mechanism by which an economy that produces goods with falling relative prices sees part of its volume gain valued away, is part of the channel to explain the paradox. We disagree still on one crucial question: Krugman reads the flat current-PPP line as evidence that Europe is not falling behind, while we see it as the product of moving the price measurement stick every year. Hence we do not see the current- PPP line as indicative of anything interpretable.
Takeaways by Macro Roundup® AI
European productivity growth has trailed the United States for three decades, a point on which both Krugman and the authors agree, with Krugman stating explicitly that he is not arguing European productivity is mismeasured.
Current-PPP chain measures and a national deflator answer different questions, and the authors argue the flat current-PPP line reflects moving the price measurement stick every year rather than evidence that Europe is not falling behind.
The authors identify a weighting mechanism, in which an economy producing goods with falling relative prices sees part of its volume gain valued away, as part of the channel explaining the paradox.
Related Articles:
How Should We Measure Economic Power?— The authors argue profits measure economic power because they show a firm can do what others can't, and by that metric U.S. firms dominate globally—with Japan and Europe significant and China second-tier—implying greater U.S. power than GDP or industrial output comparisons suggest. The gap favoring the U.S. is most pronounced in high-tech products.
Ed Conard Debates Furman On “The Expected Value of Risk Taking”— A debate on U.S. fiscal policy argues that spending increases, rather than the tax increases needed to fund them, slow growth, and that higher taxes would reduce the expected value of America's innovative risk-taking. The argument holds that cutting debt below 100% of GDP is critical.
Challenging the Narrative of European Decline, Continued— Euro-area income per person falls behind the US when measured in constant 2021 prices but gains on the US when measured using each year's prices, suggesting Europe is holding its own in applying technology. The author argues Europe's real risk is being cut off from strategically important technologies.
AI Summary.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.
Hamza Abdelrahman and Andrew FoersterFederal Reserve Bank of San Francisco
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US labor productivity is ~8pp above 2022 levels, while TFP has shown little growth since 2024, echoing 1990s pre-surge dynamics. A regime-switching model using labor productivity gives a 57% probability the US is in a high-growth regime vs 21% using TFP.
Do recent productivity gains reflect better tools rather than broader advances?
Core argument: A regime-switching model puts the implied probability that the U.S. economy is in a high-productivity growth regime at about 57% based on labor productivity and 21% based on total factor productivity as of the fourth quarter of 2025.
[While] labor productivity has shown solid gains in recent years, the San Francisco Fed’s Total Factor Productivity (TFP) index- which adjusts for how much equipment, technology, and other resources workers have at their disposal - has shown modest growth. A similar divergence between labor productivity and TFP emerged in the 1990s technology before the full productivity benefits became more definitive. Figure 1 plots how labor productivity and TFP have evolved relative to 2022 values, right before the recent surge in labor productivity. Their paths have since diverged amid a surge in business investment in AI technology and infrastructure, such as data centers, with labor productivity growing at a faster pace in recent years. The increased investment has coincided with more productive workers but has not yet produced the broader gains that TFP is designed to measure, suggesting that productivity improvements so far reflect better tools for workers rather than fundamental economic advancement. Figure 2 shows the implied probability [from a regime-switching model] of the U.S. economy being in a high-productivity growth regime using labor productivity (blue line) and TFP (green line). The probabilities based on these two measures have picked up to varying degrees: around 57% based on labor productivity and 21% based on TFP as of the fourth quarter of 2025.
Takeaways by Macro Roundup® AI
A regime-switching model puts the implied probability that the U.S. economy is in a high-productivity growth regime at about 57% based on labor productivity and 21% based on total factor productivity as of the fourth quarter of 2025.
Labor productivity has posted solid gains in recent years while the San Francisco Fed's Total Factor Productivity index, which adjusts for the equipment, technology, and other resources workers have at their disposal, has grown modestly.
The divergence between labor productivity and TFP has coincided with a surge in business investment in AI technology and infrastructure such as data centers, suggesting productivity improvements so far reflect better tools for workers rather than fundamental economic advancement.
Related Articles:
America Is Experiencing A Productivity Miracle— America's post-2019 productivity acceleration came largely from professional services and management—about 10% of output and heavy users rather than producers of new technology—and from oil and gas. The information industry grew productivity around 6% annually, no faster than its 2000-19 average, with its output share steady at 5.3–5.5%.
Concentrated Growth: The Role Of The IT Sector— Since 1988, total factor productivity in the information technology sector has risen about 178%, versus about 12% across all non-IT sectors combined, while aggregate productivity rose about 40%. The IT sector accounts for roughly 45% of cumulative aggregate productivity growth, once sectoral linkages to downstream industries are weighted in.
An AI Productivity Boom? Don’t Count Your (Productivity Data) Chickens— Measured productivity can rise from compositional effects when lower-wage, lower-productivity workers leave the labor force, as happened during Covid and 2009, and immigration policy changes could produce a similar effect. Much of the recent productivity boost appears to come from capital utilization tied to increased investment in AI rather than workers becoming more productive using AI.
AI Summary.Excluding artificial-intelligence-related categories, US non-residential fixed investment has contracted at roughly a 3% annual rate over the past year, versus average growth above 5% in the prior decade, running about $130bn below trend and cutting GDP growth by roughly 0.4 percentage points. Credit remains plentiful, with tight corporate bond spreads.
Economist StaffThe Economist
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The AI buildout drove ~ half of US GDP growth in 2025, according to an Economist analysis. Non-residential non-AI related fixed investment contracted ~3% over the past year.
Is business investment outside artificial intelligence shrinking?
Core argument: Excluding AI-related information-processing equipment and software, U.S. non-residential fixed investment has contracted at an annualised rate of roughly 3% over the past four quarters, against average growth above 5% in the previous decade.
Strip out the splurge on information-processing equipment and software—the categories most closely tied to AI—and the picture looks grim. Over the past four quarters non-residential fixed investment excluding ai-related categories has contracted at an annualised rate of roughly 3%, compared with average growth of more than 5% in the previous decade. Investment in industrial and transport equipment has fallen by more than 2% over the past year. Manufacturing construction is down by 20%. In total, non-AI investment is running about $130bn below its trend from the last decade. This capex recession is reducing gdp growth by roughly 0.4 percentage points. [A] potential explanation, in which strong demand or heavy government borrowing pushes up interest rates and crowds out other private investment, also looks unpersuasive. Credit remains plentiful. The spreads between investment-grade corporate bond and Treasuries have seldom been this tight since the 1990s.
Takeaways by Macro Roundup® AI
Excluding AI-related information-processing equipment and software, U.S. non-residential fixed investment has contracted at an annualised rate of roughly 3% over the past four quarters, against average growth above 5% in the previous decade.
Non-AI investment is running about $130bn below its trend from the last decade, with industrial and transport equipment investment down more than 2% over the past year and manufacturing construction down 20%.
The capex recession in non-AI investment is reducing U.S. GDP growth by roughly 0.4 percentage points, and crowding out by higher interest rates looks unpersuasive as an explanation, since credit remains plentiful and investment-grade corporate spreads over Treasuries have seldom been this tight since the 1990s.
Related Articles:
$14 Trillion in IG Supply Coming to the Market— About $14tn of investment-grade fixed income supply is coming to market, combining roughly $10tn of US government debt to be refinanced over the next 12 months, a budget deficit near $2tn, and about $2tn of gross corporate bond issuance boosted by data-center builders. The author argues this growing supply is putting upward pressure on interest rates and credit spreads.
Let Them Eat Compute: Data Centers’ Implications for Mortgage Finance— The author argues the housing affordability crisis stems from the sharp decline in 30-year fixed mortgage rates rather than their subsequent rise, since fixed rates let rate dips boost purchasing power, lifting existing-home sales 25% and prices 32% relative to rents. A costly data center buildout is crowding out for-sale housing.
Larry Summers and Bob Rubin Join Our CIOs to Discuss the Surprising Economic Cycle— Adopting the Simpson-Bowles fiscal consolidation when interest rates were at zero would have risked excessive disinflation, since long rates had no room to fall to provide an offsetting expansionary impulse. After the large run-up in debt and rise in real interest rates, the developing fiscal situation risks inflationary pressure, Treasury market disruption, or crowding out of private investment.
