“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“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
“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 very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…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
“…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 offers deep and well-argued analyses on almost every issue.” - The New York Times
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
AI Summary.China's credit to the nonfinancial sector—corporate, government and household borrowing—topped 300% of gross domestic product, 60 percentage points above its end-2019 level and larger than in the US, according to the Bank for International Settlements. The International Monetary Fund estimates broad Chinese government debt, including local financing vehicles, reached 127% of GDP, up from 98% in 2020.
Brian SpegeleWall Street Journal
Date Posted:
Credit to the Chinese nonfinancial sector was over 300% of GDP in 2025, 60pp above its 2019 level, driven by borrowing and slower GDP growth. It has risen by more than $43T since late 2012.
How much has China's debt risen relative to GDP?
Core argument: China’s credit to the nonfinancial sector topped 300% of GDP last year, 60 percentage points above end-2019 and far above the U.S. level, according to the Bank for International Settlements.
Last year, China’s credit to the nonfinancial sector—a broad measure of corporate, government and household borrowings—topped 300% of gross domestic product, 60 percentage points higher than at the end of 2019 and now far bigger than in the U.S. It has jumped by more than $43 trillion since Xi took office in late 2012, according to the Bank for International Settlements. China’s government says its debts are under control. Central government borrowings were only around 30% of GDP in recent years, small compared with federal debts in the U.S. A broader gauge of overall Chinese government debt—including official local borrowings as well as the debts of these local government financing vehicles—ballooned to an estimated 127% of GDP last year, according to the International Monetary Fund, up from 98% in 2020.
Takeaways by Macro Roundup® AI
China’s credit to the nonfinancial sector topped 300% of GDP last year, 60 percentage points above end-2019 and far above the U.S. level, according to the Bank for International Settlements.
Chinese nonfinancial-sector credit has risen by more than $43 trillion since Xi Jinping took office in late 2012, according to the Bank for International Settlements.
The International Monetary Fund estimates broad Chinese government debt, including official local borrowings and local government financing vehicles, reached 127% of GDP last year, up from 98% in 2020, while central government borrowings were around 30% of GDP.
Related Articles:
Why Global Imbalances Matter— Countries running persistent trade surpluses, such as China, Germany and Japan, have built their economies around an excess supply of tradeable goods and services, leaving them dependent on deficit countries to absorb it. That dependence gives deficit countries bargaining power and exposes surplus countries to pressure to expand domestic demand.
Using China’s Central Government Balance Sheet to “Clean up” Local Government Debt Is a Bad Idea— The International Monetary Fund attributes more than half of the rise in the global debt-to-GDP ratio since 2008 to China, whose ratio is exceeded only by Japan's among major economies and is unusually high for a middle-income country. The author argues this debt reflects extensive unrecognized losses hidden in collective balance sheets, and that delaying recognition raises the eventual cost.
Is China’s High-Quality Investment Output Economically Viable?— China's technological progress and infrastructure do not refute claims of overinvestment, since overinvestment often produces real physical assets and technological gains yet still counts as overinvestment when it generates less real value than the resources, subsidies, and transfers it consumes. When such losses are capitalized, assets are carried above their real economic value.
AI Summary.An artificial intelligence investment boom that is insensitive to interest rates is drawing capital, electricity and labor away from rate-sensitive sectors such as housing and autos, which the author argues will force the Federal Reserve to hold rates higher for longer. The durable fix is expanding power, chip and infrastructure supply.
Torsten SløkApollo
Date Posted:
Sløk warns the rate-insensitive AI buildout has triggered unbalanced growth in the US economy by crowding out capital, electricity, and labor from vulnerable sectors like housing and autos.
Does a rate-insensitive AI boom force the Fed to keep rates higher?
Core argument: The AI boom’s insensitivity to interest rates is draining capital, power and labor from rate-sensitive sectors such as housing and autos, producing a “Dutch disease” dynamic in the US economy.
AI's rate-insensitive boom is draining capital, power and labor from vulnerable sectors like housing and autos. Trapped between inflation driven by the AI boom and weakness in rate-sensitive industries, the Fed will have to keep interest rates higher for longer. Because monetary policy cannot resolve this sectoral imbalance, the only durable fix is expanding the supply of power, chips and infrastructure.
Takeaways by Macro Roundup® AI
The AI boom’s insensitivity to interest rates is draining capital, power and labor from rate-sensitive sectors such as housing and autos, producing a “Dutch disease” dynamic in the US economy.
Caught between AI-driven inflation and weakness in rate-sensitive industries, the Federal Reserve will have to hold interest rates higher for longer.
Monetary policy cannot resolve the imbalance between AI and rate-sensitive sectors, leaving expansion of the supply of power, chips and infrastructure as the only durable fix.
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.
The Macro Implications of the AI Capex Boom— Artificial intelligence capital spending is estimated to add roughly 1.4 percentage points to US growth in one year and about 1.5 points the next, comparable to business investment's contribution during the tech bubble. A material part of that estimate reflects profits at Nvidia and a few other chipmakers that are unlikely to be recycled back into the economy.
AI Summary.Inflation-adjusted gross domestic product per person has grown at a 1.9% annual pace over the past 10 years, above the 1.7% of the 2010s and 0.8% of the 2000s and just below the 2.1% of the 1980s and 1990s. Avoiding prolonged recessions has supported household incomes and wages.
Justin FoxBloomberg
Date Posted:
Real median household income and real wages have risen since the mid-2010s, as real GDP per capita grew 1.9% annually over the past decade. Average real weekly earnings have grown since 2010, after falling substantially between 1973 and 1990.
Have incomes and output per person grown faster than in recent decades?
Core argument: U.S. per-capita GDP has grown at a 1.9% annual inflation-adjusted pace over the past 10 years, exceeding the 1.7% of the 2010s and 0.8% of the 2000s.
Some of the increase in both household incomes and wages can be chalked up to steady economic growth. Per-capita gross domestic product has risen at a 1.9% annual, inflation-adjusted pace over the past 10 years, which doesn’t sound like much but is better than in the 2010s (1.7%) and 2000s (0.8%) and only slightly below the 2.1% growth rate of the 1980s and 1990s and 2.2% of the 1970s.Avoiding recessions — and ensuring that the unusual pandemic-caused downturn in 2020 was short-lived — has done a lot for Americans’ incomes.
Takeaways by Macro Roundup® AI
U.S. per-capita GDP has grown at a 1.9% annual inflation-adjusted pace over the past 10 years, exceeding the 1.7% of the 2010s and 0.8% of the 2000s.
The 1.9% per-capita GDP growth rate of the past decade sits only slightly below the 2.1% pace of the 1980s and 1990s and the 2.2% pace of the 1970s.
Avoiding recessions and keeping the pandemic-caused 2020 downturn short-lived has done a lot for Americans’ incomes, with steady economic growth accounting for some of the rise in household incomes and wages.
Related Articles:
What Six Years of Inflation Mean for the Midterms— US consumer prices have risen almost 29% over the past six years, about 3x the pace of the prior six years, and on average prices have risen faster than wages over that period.
Income in the United States: 2025— Median U.S. household income rose 2.6% to $87,460, the highest level on record in data going back to 1967. Median income increased 3% for White households, 2.9% for non-Hispanic White households, and 4.8% for Black households, while changes for Asian and Hispanic households were not statistically significant.
US Focus: The Effect Of Soaring Profits— US nominal pre-tax corporate profits in the national accounts grew 23% over the past year, a pace not seen outside post-recession spikes since the mid-2000s. Wider margins were the key driver, as domestic profit growth far exceeded the 8% rise in corporate value added, pushing margins near all-time highs while the labor share hit new lows.
AI Summary.A basket of 42 AI stocks accounts for 60%-80% of all S&P 500 earnings growth, price returns and capital spending, while 60% of S&P 500 stocks sit 20% or more below their highs and only 30% outperform the index. AI-related construction spending has risen 38%, the only positive trend as manufacturing, commercial real estate and residential construction decline.
Michael CembalestJ.P. Morgan
Date Posted:
Since January 2024, JPM’s 42 AI-related stocks drove 67% of S&P 500 returns, 58% of earnings growth, and 81% of capital spending. Cembalest notes, “the technology share of GDP growth has risen from almost zero in 2022 to ~33%,” and only 20% of states are outpacing national GDP growth, the lowest level since 1979.
How concentrated are US market and economic gains in AI?
Core argument: A basket of 42 AI stocks accounts for 60%–80% of all S&P earnings growth, price returns and capital spending since January 2024, with AI primarily benefitting AI supply chain companies rather than the companies adopting it.
A basket of 42 AI stocks accounts for 60%-80% of all S&P earnings growth, price returns and capital spending since Jan 2024. At the same time, 60% of S&P 500 stocks are down 20% or more from their all-time highs despite the market being close to all-time highs. Only 30% of stocks are outperforming the S&P 500, one of the lowest figures since 1991. AI is primarily benefitting AI supply chain companies rather than the companies adopting it. The technology share of GDP growth has risen from almost zero to 2022 to ~33%. Only 20% of US states are growing faster than the national average, almost the lowest figure since 1979. AI accounts for the only positive trend in US construction spending, having risen by 38% since January 2024. Unfortunately the other three main categories are down since then (manufacturing -28%, commercial real estate -13% and residential -6%). That’s why the decline in planned data center projects is something to watch.
Takeaways by Macro Roundup® AI
A basket of 42 AI stocks accounts for 60%–80% of all S&P earnings growth, price returns and capital spending since January 2024, with AI primarily benefitting AI supply chain companies rather than the companies adopting it.
60% of S&P 500 stocks are down 20% or more from their all-time highs even as the index sits near record levels, and only 30% of stocks are outperforming the S&P 500, one of the lowest figures since 1991.
AI accounts for the only positive trend in US construction spending, rising 38% since January 2024, while manufacturing is down 28%, commercial real estate 13% and residential 6%.
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.
How Much Revenue Does The AI Buildout Need In 2030?— 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.
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.Second-quarter real GDP growth was revised up 0.7 percentage points to +2.2% at an annual rate, reflecting upward revisions to consumption (to +3.8%) and investment (to +4.6%). Private fixed investment was revised up to +9.0%, driven by data center spending in nonresidential structures.
Jan Hatzius, David Mericle, Alec Phillips, Ronnie Walker, et al.Goldman Sachs
Date Posted:
GS forecasts that the FOMC will not hike in October, will do one 25bp hike in December and “ultimately conclude that additional rate hikes are unnecessary.”
What drove the upward revision to second-quarter GDP growth?
Core argument: U.S. real GDP growth in Q2 was revised up 0.7pp to +2.2% quarter-over-quarter annualized, led by upward revisions to consumption (+0.4pp to +3.8%) and investment (+1.9pp to +4.6%).
Real GDP was revised up by 0.7pp to +2.2% in Q2 (quarter-over-quarter annualized), largely reflecting upward revisions to consumption (+0.4pp to +3.8%) and investment (+1.9pp to +4.6%) growth. The strength in consumer spending in Q2 likely continued to reflect the boost to consumer cashflow from larger-than-usual tax refunds, but we continue to expect consumer spending growth to slow somewhat in 2026Q4 as the fiscal support fades and the headwind from higher energy prices remains. Stronger investment growth reflected an upward revision to private fixed investment (+0.5pp to +9.0%), driven by data center investment in the nonresidential structures category (+1.9pp to +0.1%), and an upward revision to the contribution from inventories (+0.2pp to -0.5pp).
Takeaways by Macro Roundup® AI
U.S. real GDP growth in Q2 was revised up 0.7pp to +2.2% quarter-over-quarter annualized, led by upward revisions to consumption (+0.4pp to +3.8%) and investment (+1.9pp to +4.6%).
Private fixed investment growth in Q2 was revised up 0.5pp to +9.0%, driven by data center investment in nonresidential structures, revised up 1.9pp to +0.1%, alongside a +0.2pp revision to the inventory contribution, to -0.5pp.
Q2 consumer spending strength likely reflected the cashflow boost from larger-than-usual tax refunds, with spending growth expected to slow somewhat in 2026Q4 as fiscal support fades and higher energy prices remain a headwind.
Related Articles:
The Fed Finally Gets It— Federal Reserve officials' assessed risks to their employment and price stability mandates had tended to move together rather than inversely, suggesting common forces were driving both. That pattern has now shifted, with officials more optimistic on growth and employment and less concerned about downside risks than before the pandemic.
The Diesel Shock Is a Core Inflation Shock— The US Gulf Coast diesel crack spread cleared $100 per barrel for the first time on record, against a normal range of $15 to $30. Because diesel is an intermediate input embedded in the delivered cost of nearly every physical good, the author argues the increase migrates with a lag into core goods and services inflation.
Warsh’s Fed Soars Like a Hawk Into a Hiking Cycle— The Federal Reserve described its rate increase as having "removed a dose of accommodation" and as a start to showing it is serious, implying the policy rate remains accommodative and further increases are coming. Market-implied expectations have shifted to price in three additional hikes before the tightening cycle ends.
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
Date Posted:
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.China faces three options as external demand weakens: shift a larger share of national income to households so consumption absorbs more output, keep relying on investment despite weak returns, or let production and growth slow. The first requires substantial changes in income distribution and saving incentives; the second risks more debt and excess capacity; the third implies higher unemployment.
Michael PettisChina Financial Markets
Date Posted:
“Beijing cannot choose whether China will adjust; one way or another, it will.” Pettis warns Zhongnanhai against postponing rebalancing with another round of debt-financed investment “despite increasingly weak returns.”
What choices does China face as external demand weakens?
Core argument: China faces three options: shift a larger share of national income to households so consumption absorbs more output, keep relying on investment despite weak returns, or allow production and growth to slow.
What it can choose is how painful that adjustment will be. If weaker external demand is met by encouraging more borrowing and domestic investment, regardless of whether that investment is economically justified, China risks postponing the inevitable. China therefore faces the same basic choice Japan confronted, but with deeper imbalances and less accommodating global conditions. It can shift a larger share of national income toward households and allow consumption to absorb more of what the economy produces; or it can continue relying on investment, despite increasingly weak returns; or it can allow production and growth to slow. The first choice requires a substantial change in the domestic distribution of income and the incentives to save. The second risks still more debt and excess capacity and, ultimately, a more painful adjustment. The third implies higher unemployment and weaker growth.
Takeaways by Macro Roundup® AI
China faces three options: shift a larger share of national income to households so consumption absorbs more output, keep relying on investment despite weak returns, or allow production and growth to slow.
China confronts the same basic choice Japan faced, but with deeper imbalances and less accommodating global conditions, and can choose only how painful the adjustment will be.
Meeting weaker external demand with more borrowing and domestic investment, regardless of whether it is economically justified, risks further debt and excess capacity and a more painful adjustment, while slower growth implies higher unemployment.
Related Articles:
The Year of the Trojan Fire Horse: China’s Imbalanced Economy and Unrelenting Mercantilism— About 30% of Chinese industrial companies are unprofitable "zombies" kept alive by subsidies far larger than those elsewhere, rather than exiting amid falling industrial producer prices. Countries with large increases in manufactured imports from China also saw slowdowns in domestic manufacturing, including declining manufacturing shares of output across most of Southeast Asia.
Sticking with What Works: Raising GDP Forecast on China’s Manufacturing Push— Stronger Chinese growth driven by export-oriented manufacturing may not produce net positive spillovers: for every 1pp export-driven boost to China's GDP, other economies may see a 0.1–0.3pp drag, with high-tech producers such as Europe and Japan facing greater pressure. Cheaper Chinese goods continue to exert a disinflationary impulse.
Record Influx Of University Graduates Pushes China’s Youth Unemployment Rate To 18.9%— China's urban unemployment rate for 16- to 24-year-olds excluding students reached 18.9%, the highest since the statistics agency revised its methodology to exclude students. A record influx of university graduates is entering a job market weighed down by weak domestic demand and growing adoption of artificial intelligence.
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
Date Posted:
Is Database:
Database
Is Important:
Important
Stijn Van Nieuwerburgh projects ~$10.3T will be spent on the AI buildout over 2025–2032, averaging 3.63% of GDP. He estimates 2/3 of that spending reflects IT equipment and one-third real estate and associated power infrastructure.
How much of the planned data center pipeline gets built?
Core argument: Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.
The required investment is enormous. A representative 200 MW AI training campus costs roughly $8.2 billion, about two-thirds of which reflects IT equipment and one-third the real estate and associated power infrastructure. We estimate that 183 GW is completed by 2032 with a further 118 GW completed after 2032. This pipeline will trigger about $10.3 trillion dollars of investment over 2025–2032. At an average of 3.63% of GDP per year, the projected buildout would be larger relative to the economy than the major U.S. canal, railroad, electrification, highway, and telecommunications investment booms. It would double the electricity consumption of the entire U.S. residential sector.
Takeaways by Macro Roundup® AI
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.
