Edward Conard

Top Ten New York Times Bestselling Author

  • “…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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard 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
  • “…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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Wednesday, June 17, 2026

AI Summary. 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.

Paul Kedrosky Applied Complexity
Date Posted:

AI spending is on track to hit ~$1.5T annually by 2030, 90% of which will be externally financed. Increasingly, “The cycle’s fate is tied to debt market appetite, not hyperscaler conviction,” writes Kedrosky.

Is AI's buildout becoming dependent on outside credit markets?

Core argument: Hyperscaler and data-center investment, negligible through 2022, is tracking toward $1.5 trillion annually by 2030, exceeding in real terms the 2005–06 residential construction boom, the 1990s fiber build peak year, and the 1882 railroad peak combined.

Hyperscaler and data-center investment, negligible through 2022, is now tracking toward $1.5 trillion annually by 2030. At that level, it will surpass, in real terms, the residential construction boom of 2005-06, the 1990s fiber build peak year, and the 1882 railroad peak combined. Hyperscaler and data-center investment has reached funding escape velocity, with external debt and equity soon financing the majority of the buildout, with that share approaching 90% by 2030. AI infrastructure cycle is dependent on capital markets in a way that no prior technology cycle ever was. Debt-market dependency is now systemic. At these levels of dependency, a credit contraction or spread widening stops the buildout cold. The cycle's fate is tied to debt market appetite, not hyperscaler conviction. Exceeding their real-dollar peaks is not evidence of strength. Demand has to clear at this scale. Infrastructure investment at $1.5 trillion annually requires commensurate revenue generation from AI services to service the soaring debt. No prior technology wave has needed to monetize at this rate, this fast.

Takeaways by Macro Roundup® AI

  1. Hyperscaler and data-center investment, negligible through 2022, is tracking toward $1.5 trillion annually by 2030, exceeding in real terms the 2005–06 residential construction boom, the 1990s fiber build peak year, and the 1882 railroad peak combined.
  2. External debt and equity will soon finance the majority of the AI buildout, with that share approaching 90% by 2030, leaving the cycle dependent on capital markets in a way no prior technology cycle was.
  3. At these levels of debt-market dependency, a credit contraction or spread widening stops the buildout cold, and $1.5 trillion of annual infrastructure investment requires commensurate AI services revenue to service the debt.

Related Articles:

  • 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.
  • The Impact Of The AI Capex Boom On S&P 500 Return On Equity — A macro model estimates that each 1 percentage point change in S&P 500 return on equity is associated with roughly a 1x change in the index's price-to-earnings ratio. Consensus estimates put hyperscaler capital spending at $770bn, equal to 100% of their operating cash flows, funded increasingly by debt and equity issuance rather than buybacks.
  • The Unlikely Corporate Winners Of AI — Alphabet, Microsoft, Amazon, Meta and Oracle are projecting $700bn in capital spending in 2026, with Bain & Company estimating the tech industry must generate $2tn in annual AI revenue to justify current data centre spending trends. Electrical equipment suppliers describe AI data centres as a core growth driver.

AI Summary. Underlying U.S. inflation continues to run faster than the Federal Reserve's target and may be accelerating slightly, while growth, consumer spending, and a healthy job market keep wages rising about 1 percentage point faster than in the years just before the pandemic. On that evidence, the author argues rate increases, not cuts, are the appropriate policy.

Matt Klein The Overshoot
Date Posted:

Even well before the war, wage growth was outrunning productivity, and multiple indicators showed underlying inflation was above target and rising, Klein stresses. Yet talk of rate cuts persisted. Klein argues postwar rate hikes are on the horizon.

Do persistent inflation and strong wage growth justify raising rates?

Core argument: Wages continue to rise about 1 percentage point faster than in the years immediately preceding the pandemic, with a reasonably healthy job market accompanying underlying growth much stronger than worrywarts had feared.

The challenge for Warsh, and for his dovish supporters in the administration, is that underlying inflation continues to be persistently faster than the Fed’s official target, and may even be accelerating (slightly). Temporary disruptions associated with the closure of the Strait of Hormuz are not the main problem, which explains why rates pricing has barely budged since the announcement of a ceasefire and, more recently, a deal to reopen the Strait. Instead, as I have been arguing for some time, the prewar consensus in favor of lowering rates was mistaken. Underlying growth has been much stronger than worrywarts had feared, with the reasonably healthy job market ensuring that wages continue to rise about 1 percentage point faster than in the years immediately preceding the pandemic. The data over the past four months seem to have convinced traders to come towards my point of view. The obvious reason is that the job market, consumer spending, and inflation all seem to have accelerated. More important is that this acceleration coincided with a massive spike in commodity prices. The easing of constraints on shipping through Hormuz should create an additional boost to growth, and quite possibly to underlying inflation. In those circumstances, rate cuts would not be appropriate—but rate increases would be.

Takeaways by Macro Roundup® AI

  1. Wages continue to rise about 1 percentage point faster than in the years immediately preceding the pandemic, with a reasonably healthy job market accompanying underlying growth much stronger than worrywarts had feared.
  2. Underlying U.S. inflation remains persistently faster than the Fed's official target and may be accelerating slightly, which the author argues makes rate increases, not rate cuts, appropriate.
  3. Rates pricing has barely budged since the ceasefire announcement and the deal to reopen the Strait of Hormuz, and easing shipping constraints there should boost growth and quite possibly underlying inflation.

Related Articles:

  • US Producer Prices Rise at Fastest Pace Since November 2022 — The Bureau of Labor Statistics reported that US producer prices rose 6.5% from a year earlier, the fastest annual pace in more than two years, and 1.1% from the prior month. A core measure excluding food and energy rose 4.9% year over year.
  • US Inflation Picks Up to Three-Year High, Eroding Paychecks — US consumer prices rose 0.5% on the month and 4.2% from a year earlier, according to Bureau of Labor Statistics data, with more than half the monthly advance coming from higher energy costs, while the measure excluding food and energy rose 0.2%. Inflation-adjusted average hourly earnings fell 0.7% from a year earlier.
  • Home Alone: Inflation And The New Fed Chair — Current readings on labor market tightness, manufacturing price pressures, supply chain tightness and the output gap resemble conditions that have historically preceded Federal Reserve rate increases rather than cuts, and futures markets are pricing in hikes. Averaging several monetary policy rules implies a policy rate of 4.00%–4.85% versus the current 3.50%–3.75%.

AI Summary. Bond traders have priced in a full quarter-point interest-rate increase by the end of the first quarter of 2027, with the 10-year US Treasury yield at 4.44%. Most of the rise in yields came from real yields, which strip out inflation expectations, reflecting a stronger growth outlook and expectations of a higher neutral rate.

Georgia Hall, Ruth Carson, and Ye Xie Bloomberg
Date Posted:

Since the start of the Iran War, 10-year real yields have risen ~40bps, reflecting a stronger growth outlook and a perceived rise in the neutral rate.

Are rising bond yields signaling stronger growth and a higher neutral rate?

Core argument: Traders have priced in a full quarter-point rate increase by the end of the first quarter of 2027, even after easing their most intense hike expectations, with US Treasuries unchanged and the 10-year yield steady at 4.44%.

While traders have eased their most intense expectations for hikes, they still have priced in a full quarter-point increase by the end of the first quarter of 2027. US Treasuries were unchanged Wednesday, with the 10-year yield steady at 4.44%. For JPMorgan Chase & Co.’s Jay Barry, the Fed will ultimately have to hike. “The intermediate sector in the US curve is probably trading 20 or 25 basis points too low in yield,” the head of global rates strategy told Bloomberg Television Most of the increase seen across yields was driven by so-called real yields, which strip out inflation expectations, indicating that bond investors’ concerns go beyond price pressures from the Iran war. Those elevated real yields reflect a combination of a stronger growth outlook and expectations that the neutral rate — the level that neither restricts nor stimulates economic activity — will rise over time.

Takeaways by Macro Roundup® AI

  1. Traders have priced in a full quarter-point rate increase by the end of the first quarter of 2027, even after easing their most intense hike expectations, with US Treasuries unchanged and the 10-year yield steady at 4.44%.
  2. JPMorgan Chase & Co. head of global rates strategy Jay Barry told Bloomberg Television that the Federal Reserve will ultimately have to hike and that the intermediate sector of the US curve is probably trading 20 to 25 basis points too low in yield.
  3. Most of the increase across yields was driven by real yields, which strip out inflation expectations, reflecting a combination of a stronger growth outlook and expectations that the neutral rate will rise over time.

Related Articles:

  • Home Alone: Inflation And The New Fed Chair — Current readings on labor market tightness, manufacturing price pressures, supply chain tightness and the output gap resemble conditions that have historically preceded Federal Reserve rate increases rather than cuts, and futures markets are pricing in hikes. Averaging several monetary policy rules implies a policy rate of 4.00%–4.85% versus the current 3.50%–3.75%.
  • Are Government Bonds Safe in Times of War and Pandemic? — Government bonds severely underperform in wartime, in absolute terms and relative to equities, housing, and economic growth, unlike in financial crises when bonds tend to outperform stocks. The U.S. and U.K. reduced war-driven debt-to-GDP ratios without explicit default, using surprise inflation and financial repression that shifted fiscal burdens onto bondholders.
  • Global Debt Report 2026 — Gross central government borrowing across OECD countries rose 7% to a record $17tn, about 23% of GDP, and is projected to reach roughly $18tn, or 24% of GDP. The United States and Japan account for close to 80% of the area's refinancing requirements, with the US share rising from 57% to 70%.

AI Summary. Treasury bills make up almost 85% of gross US government debt issuance, near the highest share in over two decades. Tilting issuance toward short-dated debt ties government borrowing costs more closely to short-term interest rates, making its financing increasingly dependent on Federal Reserve policy.

Torsten Sløk Apollo
Date Posted:
Is Database:
Database

T-bills are ~85% of gross Treasury issuance, meaning Federal borrowing costs are more closely tied to the front end of the curve, the part most directly under the Fed’s control.

Does heavier short-term debt issuance tie government borrowing costs to Fed policy?

Core argument: T-bills account for almost 85% of gross Treasury issuance, near the highest share in over two decades.

T-bills now account for almost 85% of gross Treasury issuance, near the highest share in over two decades. By tilting issuance toward short-dated debt, the government ties its borrowing costs more closely to the front end of the curve, making its financing increasingly dependent on Fed policy.

Takeaways by Macro Roundup® AI

  1. T-bills account for almost 85% of gross Treasury issuance, near the highest share in over two decades.
  2. By tilting issuance toward short-dated debt, the U.S. government ties its borrowing costs more closely to the front end of the yield curve.
  3. The Treasury's shift toward bills makes federal financing increasingly dependent on Federal Reserve policy.

Related Articles:

  • The Budget and Economic Outlook: 2026 to 2036 — The Congressional Budget Office projects the federal deficit will grow from $1.9tn (5.8% of GDP) to $3.1tn (6.7%) over a decade, above the 3.8% averaged over the past 50 years, with rising net interest costs driving much of the increase. Federal debt held by the public is projected to rise from 101% to 120% of GDP.
  • The Robin Hood State Is Coming For The Rich — In the US, increased redistribution has offset rising pre-tax inequality: Congressional Budget Office data show inequality of taxable income near record highs while inequality after taxes and transfers was lower than in several earlier years and only slightly above 1986. The top 1% of earners paid 40% of income tax in 2022, up from 33% in 2001.
  • The United States Capital Structure — Because Social Security and Medicare benefits are politically untouchable, retirees effectively hold the most senior claim on US government resources while Treasury bondholders hold the riskiest one. On this argument, bondholders absorb adverse fiscal shocks through surprise inflation, financial repression, or both.

AI Summary. The U.S. has invoked the Defense Production Act to expand munitions output amid doubts that American weapons makers can meet increased demand. U.S. officials say more than 1,000 long-range Tomahawk missiles and 1,500–2,000 air-defense interceptors have been fired in the war with Iran, and replacing those stockpiles could take up to six years.

Lara Seligman and Marcus Weisgerber Wall Street Journal
Date Posted:

In an effort to accelerate munitions production, already an issue prior to the Iran war, Trump invoked the Defense Production Act. The war further stressed magazine depth; replenishing stockpiles at existing production rates could take six years.

Can American weapons makers replace depleted missile stockpiles quickly?

Core argument: The U.S. has fired more than 1,000 long-range Tomahawk missiles since the war with Iran began Feb. 28, along with 1,500–2,000 air-defense interceptors including Thaad, Patriot and Standard Missile, according to U.S. officials who declined to give exact figures.

The decision to turn to the Defense Production Act comes amid doubts that American weapons makers are able to meet increased demand. The U.S. has fired more than 1,000 long-range Tomahawk missiles since the war with Iran began Feb. 28, as well as 1,500 to 2,000 air-defense missiles, including Thaad, Patriot and Standard Missile interceptors, according to U.S. officials who declined to give exact figures. Completely replacing those stockpiles could take up to six years, officials told The Wall Street Journal at the time.

Takeaways by Macro Roundup® AI

  1. The U.S. has fired more than 1,000 long-range Tomahawk missiles since the war with Iran began Feb. 28, along with 1,500–2,000 air-defense interceptors including Thaad, Patriot and Standard Missile, according to U.S. officials who declined to give exact figures.
  2. President Trump invoked the Cold War-era Defense Production Act to boost munitions production amid doubts that American weapons makers can meet increased demand.
  3. Completely replacing the expended U.S. missile stockpiles could take up to six years, officials told The Wall Street Journal.

Related Articles:

  • Last Rounds? Status of Key Munitions at the Iran War Ceasefire — U.S. forces may have expended more than half of prewar inventory for four of seven heavily used munitions in the 39-day air and missile campaign against Iran, with rebuilding to prewar levels projected to take one to four years. These munitions are also critical for a potential Western Pacific conflict.
  • Pentagon Approaches Automakers, Manufacturers to Boost Weapons Production — The Pentagon has approached U.S. automakers and other manufacturers about using their workers and factory capacity to boost production of munitions and other equipment as wars deplete stocks, asking whether they could rapidly shift to defense work. Most large non-defense manufacturers already hold Pentagon contracts, but these are typically limited in scope and value.
  • US Warns Japan Of Severe Delays In Tomahawk Deliveries Due To Iran War — The US has warned Japan that deliveries of 400 Tomahawk cruise missiles, expected by April 2028, could be delayed by as much as two years. The Center for Strategic and International Studies says the US fired more than 1,000 Tomahawks from an estimated prewar inventory of 3,100 during five weeks of operations against Iran.

Thursday, June 18, 2026

AI Summary. The authors find AI-native U.S. startups are 25% smaller than non-AI startups in the same industry and cohort, with 13% higher engineer shares, roughly 15% lower shares of entry-level workers and managers, flatter hierarchies, and comparable valuations. They argue embedding AI into products lets firms scale knowledge work without large teams.

Hyunjin Kim and Rembrand Koning Social Science Research Network
Date Posted:

“AI native” firms – firms organized around AI founded btw 2020 and 2024 – had headcounts ~25% lower than their industry peers. These firms’ share of engineers is ~13% higher, while their share of entry-level workers and managers is ~15% lower.

How do AI-native startups differ from other startups in workforce structure?

Core argument: AI-native startups are 25% smaller than non-AI startups in the same industry-cohort, yet their valuations are comparable, which the authors say suggests higher value created per employee.

Drawing on Y Combinator batches W20–F24 and U.S. venture-backed startups whose first financing closed between 2020 and 2024, we classify each firm’s AI-native status and link it to workforce microdata on team size, function, seniority, and hierarchy. Relative to non-AI startups in the same industry-cohort, AI-native firms are 25% smaller. Their share of engineers is 13% greater, and the shares of entry-level workers and managers are each roughly 15% lower. Their hierarchies are half a seniority level flatter—yet valuations are comparable, suggesting higher value created per employee. We argue these patterns reflect two channels: a process channel, in which AI changes how people work inside the firm, and a product channel, in which AI capabilities are built into what the firm sells. Using text from product descriptions and job postings, we find that embedding AI into the product, beyond layering on AI tools into existing workflows, is a primary way startups are scaling “knowledge work” without large teams of knowledge workers.

Takeaways by Macro Roundup® AI

  1. AI-native startups are 25% smaller than non-AI startups in the same industry-cohort, yet their valuations are comparable, which the authors say suggests higher value created per employee.
  2. In AI-native firms, the share of engineers is 13% greater, the shares of entry-level workers and managers are each roughly 15% lower, and hierarchies are half a seniority level flatter.
  3. Drawing on text from product descriptions and job postings for Y Combinator batches W20–F24 and U.S. venture-backed startups first financed between 2020 and 2024, the authors find embedding AI into the product is a primary way startups scale knowledge work without large teams.

Related Articles:

  • Attention (And Money) Is All You Need: Why Universities Are Struggling to Keep AI Talent — The annual compensation gap between top industry and academic artificial intelligence researchers widened from roughly $294,000 to $1.5m, coinciding with accelerating academic out-migration toward large incumbent technology firms. Researchers who move to industry cut paper production by about 63% while increasing patent filings nearly sixfold, consistent with a shift from open science toward proprietary innovation.
  • 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.
  • Solopreneurs, Solow, and the SaaSpocalypse — For three decades after commercial electricity delivery began in 1882, inflation-adjusted output per worker grew just 0.5% annually, accelerating to more than double that pace only after factories were redesigned around electric motors rather than retrofitted from steam. The author argues a near-term absence of productivity acceleration from AI is not evidence none is coming.

AI Summary. Computing infrastructure's share of US nominal gross domestic product held near 0.5% from 2015 to 2022 before climbing toward about 1.6% by 2026, with the increase coming from AI compute hardware, data center construction, and networking while non-AI compute spending stayed flat. The buildout is a concentrated bet by a small number of hyperscalers, concentrating both upside and systemic risk.

Paul Kedrosky Applied Complexity
Date Posted:

The AI buildout has driven computing infrastructure’s share of GDP to ~1.6%, ~ double that of 2024. Kedrosky notes the buildout is “a narrow, concentrated bet by a small number of hyperscalers. This concentrates both the upside and the systemic risk.”

How much has AI spending raised computing's share of GDP?

Core argument: Computing infrastructure's share of nominal US GDP held near 0.5% through 2015–2022 before breaking upward around 2023, nearly tripling to approach 1.6% of GDP by 2026, according to an Epoch figure.

I have written about the sharply growing role of AI capex in US GDP growth, but it is now becoming material in GDP outright. [An Epoch figure shows] the ongoing AI infrastructure buildout has compressed a decade of capital spending into about two years. Computing infrastructure's share of nominal US GDP sat near 0.5% for most of the 2015–2022 period, with a gentle upward trend. Then it broke sharply upward around 2023, nearly tripling to approach 1.6% of GDP by 2026. This was driven almost entirely by AI-related compute hardware, data center construction, and networking layered on a non-AI compute data center base that never moved. Non-AI compute hardware (~0.66% of GDP) has barely budged. The entire increment is AI-specific. The buildout is not a broad-based tech investment but a narrow, concentrated bet by a small number of hyperscalers. This concentrates both the upside and the systemic risk.

Takeaways by Macro Roundup® AI

  1. Computing infrastructure's share of nominal US GDP held near 0.5% through 2015–2022 before breaking upward around 2023, nearly tripling to approach 1.6% of GDP by 2026, according to an Epoch figure.
  2. The entire increment in computing's GDP share is AI-specific, coming from AI compute hardware, data center construction, and networking, while non-AI compute hardware has barely budged at roughly 0.66% of GDP.
  3. The AI infrastructure buildout has compressed a decade of capital spending into about two years as a narrow bet by a small number of hyperscalers, concentrating both the upside and the systemic risk.

Related Articles:

  • 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 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.
  • How Much Is Donald Trump Costing America’s Economy? — 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.

AI Summary. Workers most exposed to artificial intelligence are substantially more educated than those most exposed to the China trade shock, with exposure to AI rising alongside educational attainment. The most AI-exposed group, under 1% of the workforce, is 3x more likely to hold an advanced degree than to have a high school education or less.

Adam Ozimek, Jason Harrison, and Nathan Goldschlag Agglomerations
Date Posted:

27% of the American workforce has a job where AI could impact at least 50% of their tasks; however, unlike the China Shock, jobs with high AI exposure employ people with higher educational attainment, suggesting these workers can adapt.

Are AI-exposed workers more educated than trade-shock-exposed workers?

Core argument: Workers in the most AI-exposed group, less than 1% of the workforce, are three times more likely to hold an advanced degree than to have a high school diploma or less.

The more concentrated the AI shock, the more educated those affected become. Workers in the most exposed AI shock group, representing less than 1% of the workforce, are three times more likely to have an advanced degree than to have a high school diploma or less. In short, regardless of how broad or narrow we define the AI shock, it will affect a group of workers who are substantially more educated than workers who were most exposed to the China shock. As AI exposure rises, so does educational attainment. One of the reasons for the disparity is simply that average education rates increase over time, with the China shock reflecting an early 1990s workforce. Between the early 1990s and early 2020s, the share of workers with a high school degree or less fell from about 48% to just over 30%. But even if we redo the analysis comparing shocked workers in the same period, the educational gaps persist.

Takeaways by Macro Roundup® AI

  1. Workers in the most AI-exposed group, less than 1% of the workforce, are three times more likely to hold an advanced degree than to have a high school diploma or less.
  2. Regardless of how broadly or narrowly the AI shock is defined, the affected workers are substantially more educated than those most exposed to the China shock, with educational attainment rising alongside AI exposure.
  3. The share of workers with a high school degree or less fell from about 48% in the early 1990s to just over 30% in the early 2020s, yet educational gaps persist when shocked workers are compared within the same period.

Related Articles:

  • AI Adoption Rises to 19.8% — US Census Bureau survey data show artificial intelligence adoption among US establishments rose 0.9 percentage points to 19.8%, with firms expecting 23.0% within six months, led by information, professional, education, and finance/insurance services. Equity analysts expect global semiconductor revenue to grow 50% from current levels by the end of 2026.
  • 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 and Young-adult Jobs: The Real Mystery — Employment rates have declined for young adults both with and without college degrees, with non-degree holders faring marginally worse, and employment growth for young workers of all education levels has lagged that of other workers. Artificial intelligence can replace some lower-skilled jobs, but those jobs are too few to drive youth labor market weakness.

AI Summary. Ukraine struck Moscow with nearly 200 drones in its largest-ever attack on the city, hitting the capital's largest oil refinery, with Moscow's mayor saying air defences downed at least 194 drones. The strike demonstrates Ukraine's growing capacity to hit deep behind Russian lines with largely homegrown long-range drones.

Max Seddon and Fabrice Deprez Financial Times
Date Posted:

Moscow’s mayor reported that at least 194 Ukrainian drones were shot down on the third consecutive day of attacks. The city’s largest oil refinery was damaged in the raid.

Is Ukraine expanding its ability to strike deep inside Russia?

Core argument: Ukraine struck Moscow with nearly 200 drones in its largest-ever attack on the city, hitting the capital's largest oil refinery and sending plumes of smoke over Moscow's south.

Ukraine hit Moscow with nearly 200 drones in its largest-ever attack on the city on Thursday, striking the Russian capital’s largest oil refinery and sending huge plumes of smoke billowing over the city’s south. Sergei Sobyanin, Moscow’s mayor, said anti-air defences had shot down at least 194 Ukrainian drones on Thursday morning in the third consecutive day of attacks. The apocalyptic images of flame and smoke engulfing the Russian capital were a remarkable demonstration of Ukraine’s increasing capacity to strike deep behind enemy lines with its largely homegrown long-range drones.

Takeaways by Macro Roundup® AI

  1. Ukraine struck Moscow with nearly 200 drones in its largest-ever attack on the city, hitting the capital's largest oil refinery and sending plumes of smoke over Moscow's south.
  2. Moscow mayor Sergei Sobyanin said anti-air defences shot down at least 194 Ukrainian drones on Thursday morning, the third consecutive day of attacks on the Russian capital.
  3. The attack demonstrated Ukraine's increasing capacity to strike deep behind enemy lines using its largely homegrown long-range drones.

Related Articles:

  • The U.S. Wants to Break China’s Drone Dominance. Here’s Where It Will Struggle — Fielding a drone fleet able to compete with China requires the U.S. to break China's monopoly on batteries and motors and to cut the cost of other components, where weak domestic demand is a key obstacle. Chinese producers hold 80% of the U.S. commercial drone market, and experts warn building mass-production infrastructure could take a decade or more.
  • Taiwan’s Bid To Export Drones Free Of Chinese Parts Is Taking Off — Taiwan is marketing drones built without Chinese parts to the US and allies concerned about Chinese espionage and supply-chain control, with production rising from about 10,000 units to more than 12x that level and exports climbing more than 35-fold to roughly 123,000 units. The Czech Republic and Poland were the largest buyers.
  • Ukraine Drone Strikes Hit Nuclear Bombers Deep Inside Russia — Ukraine used drones smuggled into Russia aboard trucks and released remotely from mobile wooden structures to strike strategic airfields as distant as eastern Siberia. An official in Ukraine's Security Service said more than 40 Russian aircraft, including Tu-95 and Tu-22 M3 long-range bombers and A-50 surveillance planes, were damaged, with losses assessed at at least $2bn.