Edward Conard

Top Ten New York Times Bestselling Author

  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a 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 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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “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 represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Friday, February 20, 2026

AI Summary. The Budget Lab estimates that with tariffs imposed under the 1977 International Emergency Economic Powers Act struck down, US consumers face an average effective tariff rate of 9.1%, versus 16.9% had those tariffs remained. Remaining tariffs are projected to raise about $1.2tn over 2026-35, or $1tn accounting for slower growth.

John Iselin Yale University
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Without the IEEPA tariffs, the mean effective tariff rate is 9.1%, down from 16.9%. The previous tariff regime was projected to raise ~$2.7T over 2026–35 ($2.3T dynamically). The SCOTUS decision will cut revenue by about 50% to ~$1-1.2T with or without refunds.

How much do remaining tariffs cost consumers after emergency powers struck down?

Core argument: The Budget Lab estimates that without IEEPA tariffs, U.S. consumers face an average effective tariff rate of 9.1%, the highest since 1946 excluding 2025, versus 16.9% had IEEPA tariffs remained in effect.

The Budget Lab estimates the effects of all US tariffs and foreign retaliation implemented in 2025 after the decision by the Supreme Court of the United States that President Trump exceeded his authority to invoke the 1977 International Emergency Economic Powers Act (IEEPA) to impose reciprocal tariffs. Without IEEPA tariffs, consumers will face an overall average effective tariff rate of 9.1%, which remains the highest since 1946 excluding 2025. (If IEEPA tariffs had been allowed to stay in effect, this figure would have been 16.9%.) All tariffs to date as of February 2026 are projected to raise about $1.2 trillion over 2026-35, though slower economic growth reduces revenues and brings the net dynamic revenue to $1 trillion. (With IEEPA, these figures would be more than twice as large.) The economic implications of the SCOTUS decision are complicated by two major factors. First, in the short-term, firms will be aggressively seeking refunds on tariffs paid in 2025, which has large revenue effects and uncertain distributional effects. Second, the current Administration has stated its intent to replace IEEPA tariffs with tariffs using other authorities, but there remain timing and other questions regarding these steps.

Takeaways by Macro Roundup® AI

  1. The Budget Lab estimates that without IEEPA tariffs, U.S. consumers face an average effective tariff rate of 9.1%, the highest since 1946 excluding 2025, versus 16.9% had IEEPA tariffs remained in effect.
  2. All U.S. tariffs in place as of February 2026 are projected to raise about $1.2 trillion over 2026–35, falling to $1 trillion on a net dynamic basis as slower economic growth reduces revenues.
  3. The Budget Lab identifies two complications from the Supreme Court decision that President Trump exceeded his IEEPA authority: firms seeking refunds on tariffs paid in 2025, with uncertain distributional effects, and the Administration's stated intent to replace IEEPA tariffs using other authorities.

Related Articles:

  • US Notches One of Its Biggest Annual Trade Gaps Since 1960 — The US goods and services trade deficit reached $901.5bn for the year, one of the largest in data going back to 1960. US companies increased imports of computers and accessories by nearly $145bn from the prior year, an acceleration in demand that reflects large-scale artificial intelligence investment.
  • China Is Making Trade Impossible — Interviews with Chinese economists, technologists and business leaders yielded no clear answer to what products China would want to buy from the rest of the world in future, beyond commodities, luxury goods and higher education. The author argues China sees nothing it cannot eventually make better and cheaper itself, undermining trade as a two-way exchange.
  • 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.

AI Summary. After the US Supreme Court ruled the president cannot use a 1977 emergency law to impose import taxes, other statutes remain available, including Section 338 of the Smoot-Hawley Tariff Act of 1930, which permits tariffs on countries found to discriminate against US commerce without any agency investigation. Never previously used, invoking it could invite legal challenges.

Isabel Gottlieb Bloomberg
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Trump has at least five tools he can use to offset the IEEPA tariffs; however, none of them offer the latitude that Trump’s interpretation of IEEPA allowed. Section 338 of Smoot-Hawley, which has never been used, likely has the broadest scope.

What other laws allow tariffs after the emergency law ruling?

Core argument: The US Supreme Court ruled that President Donald Trump cannot use a 1977 emergency law to impose import taxes, leaving him to rely on alternative legislation to rebuild his tariff wall.

President Donald Trump can lean on alternative legislation to try to rebuild his tariff wall, after the US Supreme Court ruled that he can’t use a 1977 emergency law to impose import taxes. [Notably], Section 338 of the Smoot-Hawley Tariff Act of 1930. The Depression-era provision empowers the president to introduce tariffs on imports from nations “whenever he shall find as a fact” that these countries impose unreasonable charges or limitations, or engage in discriminatory behavior against US commerce. There’s no prerequisite for a federal agency to conduct an investigation before the president can apply tariffs. Section 338 has never been used before to impose tariffs. If Trump were to lean on this provision, such an unprecedented move may invite legal challenges. The possibility that Trump could tap Section 338 has alarmed some Democrats in the House of Representatives — five lawmakers introduced a resolution in March 2025 to repeal this section of the 1930 law.

Takeaways by Macro Roundup® AI

  1. The US Supreme Court ruled that President Donald Trump cannot use a 1977 emergency law to impose import taxes, leaving him to rely on alternative legislation to rebuild his tariff wall.
  2. Section 338 of the Smoot-Hawley Tariff Act of 1930 lets the president impose tariffs whenever he "shall find as a fact" that a country imposes unreasonable charges or limitations or discriminates against US commerce, with no prior federal agency investigation required.
  3. Section 338 has never been used to impose tariffs, and five House Democrats introduced a resolution in March 2025 to repeal the provision.

Related Articles:

  • Help for the Heartland? The Employment and Electoral Effects of the Trump Tariffs in the United States — The authors find that the combined effect of U.S.-China trade war import tariffs, retaliatory tariffs, and farm subsidies on employment in exposed U.S. locations was at best a wash and possibly mildly negative, while residents of tariff-protected areas became less likely to identify as Democrats and more likely to vote Republican.
  • The Incidence of Tariffs: Rates and Reality — The authors estimate that tariff pass-through into US import prices was 80% in 2018–2019 and 94% more recently, while exchange-rate pass-through was low in both periods. Because imported inputs matter for US manufacturing, they argue much of the tariff burden falls on US producers, roughly a 1–2 percentage-point tax on manufacturing.
  • Who Is Paying for the 2025 U.S. Tariffs? — The authors estimate that 94% of U.S. tariff costs were borne domestically over the first eight months of 2025, with a 10% tariff associated with only a 0.6 percentage point decline in foreign export prices. Pass-through into U.S. import prices later declined to 86%, shifting more of the burden to foreign exporters.

AI Summary. U.S. gross domestic product rose at a 1.4% inflation-adjusted annual rate in the fourth quarter, below the 2.5% economists surveyed expected, weighed down by a record-long government shutdown and slower consumer spending. Full-year growth was 2.2%, down from 2.4% the prior year and the weakest since 2022.

Harriet Torry Wall Street Journal
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The Q4 2025 real GDP numbers surprised to the downside; overall, real GDP growth in 2025 (measured from 2024:Q4) was 2.2%, down from 2.4% in 2024.

Did a government shutdown and slower consumer spending weaken economic growth?

Core argument: U.S. GDP rose at a 1.4% seasonally and inflation-adjusted annual rate in the fourth quarter, below the 2.5% expected by economists surveyed by The Wall Street Journal, the Commerce Department said.

The U.S. economy grew more slowly the fourth quarter of last year, weighed down by last fall’s record-long government shutdown and slower consumer spending. U.S. gross domestic product—the value of all goods and services produced across the economy—rose at a 1.4% seasonally and inflation-adjusted annual rate in the final quarter of last year, the Commerce Department said Friday. Economists surveyed by The Wall Street Journal expected a 2.5% reading. In 2025, the economy grew 2.2%, measured from the fourth quarter of the prior year. That was slower than 2.4% growth in 2024 and the weakest since 2022. Economists surveyed by the Journal last month expected growth of 2.3% for 2025.

Takeaways by Macro Roundup® AI

  1. U.S. GDP rose at a 1.4% seasonally and inflation-adjusted annual rate in the fourth quarter, below the 2.5% expected by economists surveyed by The Wall Street Journal, the Commerce Department said.
  2. The fourth-quarter slowdown reflected last fall's record-long government shutdown and slower consumer spending.
  3. The U.S. economy grew 2.2% in 2025, measured from the fourth quarter of the prior year, down from 2.4% in 2024 and the weakest since 2022.

Related Articles:

  • OECD Economic Outlook, Volume 2025 Issue 2: Resilient Growth but with Increasing Fragilities — U.S. real GDP grew at a 1.1% annualized rate over the first half of 2025, but excluding booming AI-related investment output contracted 0.1%, with non-AI investment weakening. The OECD identifies a correction in equity markets lifted by expectations of high AI returns as a key downside risk to its projections.
  • 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.
  • SF FedViews: Solid Growth, but Small Cracks Have Emerged — Since the second half of 2023, four-quarter US productivity growth has averaged nearly 2.5%, well above the pre-pandemic trend of about 1% to 1.25%, supporting real GDP growth. It remains unclear whether this reflects a higher productivity regime fostered by rising AI use or a temporary post-pandemic reorganization surge.

AI Summary. An early estimate puts the floor contribution of artificial intelligence capital spending to fourth-quarter US GDP growth at about 64%, and likely closer to 80% including externalities and multipliers, though the calculation is not adjusted for imports. The author also notes that token price deflation persists and that US-China token price differences are immense and growing.

Paul Kedrosky Notes on Complexity
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Kedrosky’s back-of-the-envelope math suggests the AI buildout was responsible for btw 64 and 80% of US GDP growth last year. He notes that, as of December, China’s flagship Zhipu GLM-5’s output is 5.4x cheaper than that of OpenAI’s GPT-5.2.

How much of quarterly US growth comes from AI spending?

Core argument: Kedrosky's early estimate puts the floor contribution of AI capital expenditure to U.S. Q4 GDP growth at roughly 64%, rising to about 80% once externalities and multipliers are included, in a calculation not adjusted for imports.

Playing around with just-released (tepid) Q4 GDP figures, and my early estimate is that the floor contribution of AI capex to fourth quarter growth was on the order of 64%. And it is likely more like 80% with externalities and multipliers [note the calculation isn't adjusted for imports]. [Kedrosky also notes] a) token deflation persists, and b) the U.S/China [token] price differences are immense and growing.

Takeaways by Macro Roundup® AI

  1. Kedrosky's early estimate puts the floor contribution of AI capital expenditure to U.S. Q4 GDP growth at roughly 64%, rising to about 80% once externalities and multipliers are included, in a calculation not adjusted for imports.
  2. Kedrosky characterizes the just-released Q4 GDP figures as tepid.
  3. Kedrosky notes that token deflation persists and that token price differences between the U.S. and China are immense and growing.

Related Articles:

  • The Geopolitics of AI: Decoding the New Global Operating System — The performance gap between the top American and Chinese AI models narrowed from 9.3% to 1.7%, with Chinese firms relying on cheaper open-source models—DeepSeek prices usage 20-40x below OpenAI's ChatGPT. This price advantage could win adoption in developing countries and build dependency on Chinese AI ecosystems.
  • US Notches One of Its Biggest Annual Trade Gaps Since 1960 — The US goods and services trade deficit reached $901.5bn for the year, one of the largest in data going back to 1960. US companies increased imports of computers and accessories by nearly $145bn from the prior year, an acceleration in demand that reflects large-scale artificial intelligence investment.
  • 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. 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.

Martha Gimbel Yale University
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Martha Gimbel notes that it will be hard to untangle potential AI-driven productivity gains from compositional gains as lower-skilled immigrants are removed from the labor market.

Does rising measured productivity reflect workers actually becoming more productive with AI?

Core argument: Measured productivity jumped during Covid and rose in 2009 because job losses fell disproportionately on low-wage workers, whose exit from the data raises average productivity without any worker becoming more productive.

Productivity can rise in response to compositional issues. In the graph, you can see productivity rising dramatically during Covid (and less dramatically in 2009). Did we all become more productive when we were stuck at home and others couldn’t go to work at all? You may be shocked to hear that we did not. Instead, since the people who lost their jobs were disproportionately low-wage workers (who show up as lower productivity in the data), productivity rose due to a compositional effect. (A similar thing happened in 2009). One reason job growth in 2025 was so low was because of changes in immigration policy. If the people being removed from the labor force were lower productivity workers, that will show up as an increase in productivity even though the productivity of the workers who remain behind has not changed. In fact, the same thing skewing job growth down may be skewing productivity growth up. If you look at the productivity data, it appears that much of the boost is coming from capital utilization due to increased productive investment. That would [appear to be] consistent with an increase in productivity due to AI. But at this point it is people investing in AI, not people becoming more productive by using AI.

Takeaways by Macro Roundup® AI

  1. Measured productivity jumped during Covid and rose in 2009 because job losses fell disproportionately on low-wage workers, whose exit from the data raises average productivity without any worker becoming more productive.
  2. Changes in immigration policy were one reason job growth in 2025 was so low, and if the workers removed from the labor force were lower-productivity, measured productivity growth is skewed upward even with no change among remaining workers.
  3. Much of the apparent productivity boost appears to come from capital utilization tied to increased productive investment, consistent with people investing in AI rather than people becoming more productive by using AI.

Related Articles:

  • Firm Data On AI — Survey data show around 70% of businesses use some artificial intelligence technology, with larger, more productive, and higher-paying firms more likely to adopt it. Firms report little employment effect and a modest productivity boost so far, but predict adoption will raise productivity by about 1.4% and cut employment by about 0.7% over three years.
  • Waiting for the AI J-Curve — Artificial intelligence has yet to appear in employment, productivity, or inflation data, or in profit margins and earnings expectations outside the largest technology firms. With fierce competition pushing large language model prices toward zero for users, value creation depends on sector-level implementation, and the author argues AI will likely enhance labor in some sectors rather than replace it across all.
  • Everyone’s Looking For A Bubble. No One Sees The Stampede — A proprietary model estimates monthly artificial intelligence revenue grew roughly 18-fold in two years, from $772m to $13.8bn, with AI accounting for an estimated 23% of Google Cloud, 10% of Azure and 5% of Amazon Web Services. The model's ratio of investment to revenue fell from 6.1x to 4.7x, still historically high.

AI Summary. Treasury Department data show overseas investors bought a net $1.55tn of long-term US financial assets, up from $1.18tn the prior year, including $720bn in equities and just under $409bn in Treasury notes and bonds. China was a net seller of $208.6bn, with its Treasury holdings ending at $683.5bn.

Chris Anstey and Anya Andrianova Bloomberg
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Foreign investors bought a net $1.55T of American long-term US financial assets in 2025, including $720B of net equity purchases and $409B in Treasury notes and bonds, up from $1.18T in 2024.

Are foreign investors increasing their purchases of US assets?

Core argument: Overseas investors bought a net $1.55tn of long-term US financial assets in 2025, up from $1.18tn the previous year, according to Treasury Department data.

Overseas investors bought a net $1.55 trillion of long-term US financial assets in 2025, data released by the Treasury Department showed. That’s up from a net $1.18 trillion of purchases the previous year. Combining private and official investors, there were $720 billion of net equity purchases, with just under $409 billion flowing into Treasury notes and bonds. Dollar depreciation last year might have even encouraged some overseas managers to load up on US securities. Geoff Yu, senior macro strategist at BNY — one of the world’s largest custodians — said earlier this month that was what happened after the market gyrations spurred by Trump’s “Liberation Day” tariff announcement last April. “Our data show that cross-border investors took full advantage of the adjustment in dollar valuation to add to US equity exposure,” Yu wrote in a note last week. “Although allocations are not as strong as during the ‘US exceptionalism’ period from 2023 to 2024, the cross-border ‘premium’ remains intact.” China was a notable net seller of US long-term financial assets, in the amount of $208.6 billion. Its [official] holdings of Treasuries ended the year at $683.5 billion, the lowest level since 2008.

Takeaways by Macro Roundup® AI

  1. Overseas investors bought a net $1.55tn of long-term US financial assets in 2025, up from $1.18tn the previous year, according to Treasury Department data.
  2. Private and official foreign investors together made $720bn of net equity purchases and just under $409bn of net purchases of Treasury notes and bonds.
  3. China was a net seller of $208.6bn of US long-term financial assets, ending the year with official Treasury holdings of $683.5bn, the lowest level since 2008.

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.
  • American Unexceptionalism — The S&P 500 has outperformed other developed-market equities by a cumulative 150% over the past fifteen years, but the authors attribute about 80% of that gap to a strengthening dollar and expanding relative valuations rather than fundamentals such as dividends, buybacks, and organic growth. U.S. valuations sit around the 90th percentile on price-to-earnings measures.
  • 2026 Outlook: Tech Tonic — A Broadening Bull Market — Most major equity markets have outperformed the US, with Europe, China and Asia generating almost double US total returns in dollar terms and gains of 54% in Italy, 73% in Spain and 71% in Korea. US returns have been driven by strong earnings, particularly in technology, while Europe's have come largely from rising valuations.

Monday, February 23, 2026

AI Summary. 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.

Austin Vernon Austin Vernon Blog
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High US wages for skilled workers constrain robot adoption in manufacturing. “Many less productive US manufacturing firms could buy a robot, but couldn’t attract the talent to program it.” The US can’t “emulate” the low-wage, high-skilled China model.

Does a shortage of high-skill workers limit US factory automation?

Core argument: The bottom 10% of US workers earn roughly as much as their counterparts in other developed countries, while US workers in the upper deciles perform much better than their international peers, making a skilled-for-hourly substitution a poor trade in many cases.

The US has a relative surplus of low-paid hourly workers and a shortage of high-skill workers, making a skilled-for-hourly replacement a poor trade in many cases. Our bottom 10% of workers earn roughly as much as those in other developed countries, even though US workers in the upper deciles perform much better than their international peers. Many less productive US manufacturing firms could buy a robot, but couldn't attract the talent to program it. China has the opposite problem, making robots more attractive. It has a surplus of STEM graduates that are often underemployed, while "Hukou" residency restrictions complicate hiring hourly labor in coastal factories. Low-level robot programmers earn a fraction of the US wage, even at a much higher supply in absolute and per capita terms. These jobs are boring and require long stints away from home in relative backwaters to support factory retoolings. The alternative offers for those with programming skills in the US are much better. Two key concepts come from this. First, high-end labor costs and availability are a challenge for US manufacturing. Second, technologies and market structure are contextual, where the US can't simply emulate China's manufacturing.

Takeaways by Macro Roundup® AI

  1. The bottom 10% of US workers earn roughly as much as their counterparts in other developed countries, while US workers in the upper deciles perform much better than their international peers, making a skilled-for-hourly substitution a poor trade in many cases.
  2. Many less productive US manufacturing firms could buy a robot but could not attract the talent to program it, since alternative offers for those with programming skills in the US are much better than long stints away from home supporting factory retoolings.
  3. China faces the opposite problem, with a surplus of often underemployed STEM graduates, low-level robot programmers earning a fraction of the US wage, and "Hukou" residency restrictions complicating hourly hiring in coastal factories, making robots more attractive there.

Related Articles:

  • Boeing Battles Brain Drain As Engineers Chase the Allure of Space — The average tenure of a Boeing engineer has fallen from 16.4 years to 12.6 years, according to data from the union representing 12,000 Boeing engineers, with tenure shortening in almost every age bracket. Analysts, recruiters and union officials describe this "brain drain" as a drag on operations that could make launching a new plane harder.
  • What Would It Take to Bring Back US Manufacturing? Part 2: Making American Manufacturing More Productive — The authors argue the United States is a decade away from the scale of automated "lights-out" manufacturing needed to meet its own end-to-end demand at comparable cost. With 98% of US manufacturers being small or medium enterprises, low deployment of mature productivity technologies reflects limited commercial adoption and investment rather than technical readiness.
  • America Is Missing The New Labor Economy – Robotics Part 1 — Building an identical robotic arm in the US costs about 2.2x more than in China, and US-labeled components still depend heavily on Chinese parts and materials with no scalable alternative. The author argues China is the only country positioned to capture full-scale intelligent robotic automation, which would let it outcompete the US across production.

AI Summary. The Budget Lab estimates the average effective US tariff rate faced by consumers reached 16%, the highest since 1936, before court-struck tariffs cut it to 9.1%, with new 15% Section 122 tariffs lifting it to 13.7%. If those tariffs expire after 150 days, the authors project $1.3tn in revenue over 2026-35, or $1.1tn after accounting for slower growth.

John Iselin Yale University
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The new 15% tariffs under Section 122 bring the mean effective tariff rate to 13.7%, down from 16.9% prior to the SCOTUS ruling that the IEEPA tariffs were illegal. Section 122 tariffs will expire in 150 days unless extended by Congress.

How have court rulings and new tariffs changed effective tariff rates?

Core argument: The Budget Lab estimates the US average effective tariff rate facing consumers at 13.7%, down from 16% before the IEEPA-based tariffs were struck down — the highest since 1936 — and up from the 9.1% reached immediately after that ruling.

The Budget Lab (TBL) estimates the effects of all US tariffs and foreign retaliation implemented through February 20, 2026, including the new 15% Section 122 tariffs and the elimination of IEEPA-based tariffs. Under our baseline case, the Section 122 tariffs expire after 150 days. Before the IEEPA tariffs were struck down, consumers faced an overall average effective tariff rate of 16%, the highest since 1936. Immediately following the IEEPA ruling, the rate fell to 9.1%. After the Section 122 tariffs were imposed, the rate rose to its current level of 13.7%. If those tariffs expire in 150 days, the rate will fall again to 9.1%. Assuming Section 122 tariffs expire in 150 days, the administration’s tariffs will raise about $1.3 trillion over 2026-35, though slower economic growth reduces revenues and brings the net dynamic revenue to $1.1 trillion. (If they are instead made permanent, these figures would be $2.2 trillion and $1.9 trillion.)

Takeaways by Macro Roundup® AI

  1. The Budget Lab estimates the US average effective tariff rate facing consumers at 13.7%, down from 16% before the IEEPA-based tariffs were struck down — the highest since 1936 — and up from the 9.1% reached immediately after that ruling.
  2. The Budget Lab projects the administration's tariffs will raise about $1.3 trillion over 2026–35 if the new 15% Section 122 tariffs expire after 150 days, with net dynamic revenue of $1.1 trillion after slower economic growth reduces receipts.
  3. Should the Section 122 tariffs be made permanent, The Budget Lab estimates revenue of $2.2 trillion over 2026–35 and $1.9 trillion on a net dynamic basis; if they expire after 150 days, the effective tariff rate falls to 9.1%.

Related Articles:

  • US Notches One of Its Biggest Annual Trade Gaps Since 1960 — The US goods and services trade deficit reached $901.5bn for the year, one of the largest in data going back to 1960. US companies increased imports of computers and accessories by nearly $145bn from the prior year, an acceleration in demand that reflects large-scale artificial intelligence investment.
  • Help for the Heartland? The Employment and Electoral Effects of the Trump Tariffs in the United States — The authors find that the combined effect of U.S.-China trade war import tariffs, retaliatory tariffs, and farm subsidies on employment in exposed U.S. locations was at best a wash and possibly mildly negative, while residents of tariff-protected areas became less likely to identify as Democrats and more likely to vote Republican.
  • Did Tariffs Make American Manufacturing Great? New Evidence from the Gilded Age — Using digitized industry-level US tariff data from 1870–1900 matched to state-level manufacturing census data for over 80 industries, the authors estimate that tariffs reduced labor productivity. They argue the results are consistent with weaker international competition allowing less efficient firms to survive, reduced incentives to innovate, and lobbying for protection misallocating resources.

AI Summary. Unemployment in occupations exposed to artificial intelligence rose to 3.8% from 3%, a larger increase than the 0.3 percentage point rise in the national rate, while unemployment in non-exposed occupations edged lower. The rise is concentrated among workers aged 34 and under, whose jobless rate jumped to 5.3% from 3%.

Bernard Yaros and Matthew Martin Oxford Economics
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Unemployment is rising more rapidly in AI-exposed occupations relative to non-exposed occupations, especially among younger workers. One driver of this is a rising supply of workers due to an uptick in participation.

Is unemployment rising faster in AI-exposed occupations, especially for younger workers?

Core argument: Unemployment in AI-exposed occupations rose to 3.8% in December 2025 from 3% in January, a sharper increase than the 0.3ppt rise in the national rate, while unemployment in non-exposed occupations edged slightly lower, based on Bureau of Labor Statistics microdata.

Using microdata from the Bureau of Labor Statistics, we split employment into two groups – AI-exposed and non-exposed occupations – and calculate separate unemployment rates (Chart 8). The jobless rate for AI-exposed occupations is lower than the overall unemployment rate but has risen to 3.8% in December 2025 from 3% in January, a sharper increase than the 0.3ppt rise in the national rate over the same period. Meanwhile, unemployment in non-exposed occupations has edged slightly lower. For more seasoned professionals in AI-exposed occupations – those 35 years and over – the rise in unemployment has been modest and consistent with the gradual softening in broader labor-market conditions over the past three years, rather than indicative of an AI-driven shock (Chart 9). The increase has been more pronounced among workers aged 34 and lower, where the unemployment rate has jumped to 5.3% in December from 3% at the beginning of 2025. The data are noisy, and younger workers are usually more sensitive to weakening labor market conditions. But we tested this relationship and the recent increase in unemployment for younger workers is larger than we'd expect given the rise in the national unemployment rate.

Takeaways by Macro Roundup® AI

  1. Unemployment in AI-exposed occupations rose to 3.8% in December 2025 from 3% in January, a sharper increase than the 0.3ppt rise in the national rate, while unemployment in non-exposed occupations edged slightly lower, based on Bureau of Labor Statistics microdata.
  2. Among AI-exposed workers aged 34 and under, the unemployment rate jumped to 5.3% in December from 3% at the beginning of 2025, larger than would be expected given the rise in the national unemployment rate.
  3. For AI-exposed workers aged 35 and over, the rise in unemployment has been modest and consistent with the gradual softening in broader labor-market conditions over the past three years, rather than indicative of an AI-driven shock.

Related Articles:

  • 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.
  • Unprecedented ‘Jobless Boom’ Tests Limits of US Economic Expansion — Forecasters expect US gross domestic product to have expanded 2.7% over the year while employment barely grew, a divergence drawing comparisons to the "jobless recovery" that followed the early-2000s technology stock collapse. Unlike that episode, which began with a recession, this "jobless boom" is occurring without one — a first in the postwar era.
  • What Sweeping Revisions and a Blowout Month Tell Us About the U.S. Job Market — Labor Department revisions lowered previously estimated job gains for the past two calendar years, cutting the combined net change in construction, manufacturing, and retail by 115,000 jobs. Healthcare added about 437,000 jobs and social assistance about 321,000 over 12 months, while all other private-sector jobs combined shrank.

AI Summary. Capital spending by the largest cloud computing companies is projected to reach about $646bn, roughly 2% of US GDP. For comparison, annual US consumer spending growth is around $1tn, total US bank loan growth was about $700bn, and US defense spending was $917bn.

Torsten Sløk , Rajvi Shah, and Shruti Galwankar Apollo
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Apollo forecasts that the hyperscalers will spend ~$656B or ~2% of US GDP on capex in 2026, outpacing the telecom companies’ spend during the dot-com bubble.

How large is cloud computing capital spending relative to the economy?

Core argument: Hyperscaler capital expenditure is expected to reach roughly $646 billion in 2026, equal to about 2% of US GDP.

The amount of money being spent on capex by hyperscalers is enormous at around $646 billion in 2026. Hyperscaler capex is expected in 2026 to be at approximately $646 billion, or about 2% of US GDP. Annual growth in consumer spending is currently around $1 trillion. For comparison, total US bank loan growth in 2025 was around $700 billion. [American] defense spending in 2025 was at $917 billion.

Takeaways by Macro Roundup® AI

  1. Hyperscaler capital expenditure is expected to reach roughly $646 billion in 2026, equal to about 2% of US GDP.
  2. Projected 2026 hyperscaler capex of about $646 billion compares with total US bank loan growth of roughly $700 billion in 2025 and US defense spending of $917 billion in 2025.
  3. Annual growth in consumer spending is around $1 trillion, exceeding the approximately $646 billion of hyperscaler capex projected for 2026.

Related Articles:

  • Rough Notes, Feb 20, 2026: AI Capex in Q4 GDP, Anthropic Revenues, Agentic Domains, etc. — An early estimate puts the floor contribution of artificial intelligence capital spending to fourth-quarter US GDP growth at about 64%, and likely closer to 80% including externalities and multipliers, though the calculation is not adjusted for imports. The author also notes that token price deflation persists and that US-China token price differences are immense and growing.
  • 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.
  • Everyone’s Looking For A Bubble. No One Sees The Stampede — A proprietary model estimates monthly artificial intelligence revenue grew roughly 18-fold in two years, from $772m to $13.8bn, with AI accounting for an estimated 23% of Google Cloud, 10% of Azure and 5% of Amazon Web Services. The model's ratio of investment to revenue fell from 6.1x to 4.7x, still historically high.

AI Summary. U.S. power plant developers plan to add 86 gigawatts of new utility-scale generating capacity, which would be a record if realized, with solar at 51% of planned additions, battery storage at 28% and wind at 14%. The prior year saw 53 gigawatts added, the largest single-year installation since 2002.

EIA Staff Energy Information Administration
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The US will add 86GW of new utility-scale electrical generation capacity to the power grid in 2026, a record high. Solar will make up 51% of the planned 2026 capacity additions, followed by battery storage at 28% and wind at 14%.

Is U.S. new generating capacity headed for a record high?

Core argument: U.S. power plant developers and operators plan to add 86 GW of new utility-scale generating capacity in 2026, a record if realized, according to the Preliminary Monthly Electric Generator Inventory report.

U.S. power plant developers and operators plan to add 86 gigawatts (GW) of new utility-scale electric generating capacity to the U.S. power grid in 2026 in our latest Preliminary Monthly Electric Generator Inventory report, a record if realized. Solar power makes up 51% of the planned 2026 capacity additions, followed by battery storage at 28% and wind at 14%. In 2025, 53 GW of new capacity was added to the grid, the largest capacity installation in a single year since 2002.

Takeaways by Macro Roundup® AI

  1. U.S. power plant developers and operators plan to add 86 GW of new utility-scale generating capacity in 2026, a record if realized, according to the Preliminary Monthly Electric Generator Inventory report.
  2. Solar power accounts for 51% of planned 2026 U.S. capacity additions, followed by battery storage at 28% and wind at 14%.
  3. The U.S. grid added 53 GW of new capacity in 2025, the largest single-year installation since 2002.

Related Articles:

  • Electricity 2026 — Global electricity demand is projected to grow faster than the world economy throughout the forecast period, driven by electricity-intensive industries, air conditioning, data centres and continued electrification. US electricity demand is forecast to rise about 2% a year over 2026-2030, more than twice the past decade's rate, with data centres accounting for almost half that growth.
  • Power Politics: Energy Self-Sufficiency in a Modern Mercantilist World — China leads in adding power to the grid, with more than 30,000 miles of ultra-high-voltage transmission lines versus zero in the US and lead times for large transformers of 48 weeks against a US average of 143 weeks. This positions China to accommodate new demand sources such as artificial intelligence.
  • China’s AI Power Play: Cheap Electricity From World’s Biggest Grid — China generates more than twice as much electricity as the US, and Goldman Sachs projects it will have roughly 400 gigawatts of spare capacity by 2030 — about 3x expected global data-center power demand. Morgan Stanley forecasts US data centers could face a 44-gigawatt electricity shortfall within three years.

AI Summary. Correcting for a mismatch in survey years and for comparing workers' place of work to natives' place of residence cuts the estimated H-1B wage gap to roughly one-third of the original claim. The remaining average masks that H-1B workers aged 21–22 earn about a 20% premium over comparable native-born workers, while a shortfall appears only after age 34.

Jiaxin He and Adam Ozimek Economic Innovation Group
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He/Ozimek dispute Borjas’ finding that H1-B workers are underpaid. A “temporal mismatch” between the native sample and the H-1B sample means “effectively comparing 2020 wages with 2023 prices.” The ⅔ of H-1B winners aged <34 actually earn a wage premium.

Are H-1B workers underpaid relative to comparable native-born workers?

Core argument: Correcting for errors leaves an average H-1B wage gap only one-third as large as the Borjas analysis claimed, with a consistent geographic definition for both groups reducing the gap to 5.3%.

[Whether] H-1Bs are underpaid depends on which workers we examine. [Older] H-1B workers earn less than otherwise similar native-born workers, while [younger] H-1B workers earn more. [Overall, there is a] small average wage gap, [but it is] only one-third as large as the [Borjas] analysis claimed. [The above table] provides a step-by-step breakdown of how these errors manufactured the appearance of a large wage gap. The [most significant mistake] is a temporal mismatch between his H-1B and native-born samples: the H-1B applications span 2020–2023, while the ACS data [covers] just 2023. [Given] 15.1 percent inflation and [an] 18.7% wage increase for software occupations [from 2020 to 2023], comparing [wages of] H-1B workers [2020 to 2023 to] native-born [wages from 2023] overstates the wage gap, effectively comparing 2020 wages with 2023 prices. [Second], Borjas compares H-1B workers’ place of work to natives’ place of residence. [Using] a consistent definition of geography for both groups further reduces the gap to 5.3%. [Even after corrections], the aggregate average hides that younger H-1B workers are paid [more than Americans], while older H-1B workers are paid [less]. [H-1B workers ages 21–22] earn a 20% wage premium [over natives]. [After age 34], a negative wage gap emerges. Two-thirds of H-1B lottery winners are 33 or younger…the very group that earns a significant wage premium over natives.

Takeaways by Macro Roundup® AI

  1. Correcting for errors leaves an average H-1B wage gap only one-third as large as the Borjas analysis claimed, with a consistent geographic definition for both groups reducing the gap to 5.3%.
  2. The most significant error identified is a temporal mismatch: Borjas compared H-1B applications spanning 2020–2023 with 2023 ACS data, despite 15.1% inflation and an 18.7% wage increase in software occupations from 2020 to 2023.
  3. H-1B workers ages 21–22 earn a 20% wage premium over natives and a negative wage gap emerges after age 34, while two-thirds of H-1B lottery winners are 33 or younger.

Related Articles:

  • The H-1B Wage Gap, Visa Fees, and Employer Demand — An analysis merging Labor Department, Homeland Security, and American Community Survey data estimates the average H-1B visa worker earns about 16% less than a comparable US-born worker, implying payroll savings near $100,000 over a six-year visa term. Simulations suggest a $150,000–$200,000 visa fee would barely reduce hiring while raising $10bn–$20bn annually.
  • The Extreme Shortage of High IQ Workers — Semiconductor manufacturing requires workers of above-average intelligence, with the most demanding roles needing IQs in the top 1 in 1,000 — a pool of about 164,000 people in the US, of whom perhaps 100,000 can be placed in such professions. The author argues this scarcity explains reliance on high-IQ workers abroad and on smarter tools and non-human intelligence.
  • Third-Country Effects of U.S. Immigration Policy — U.S. H-1B visa denial rates rose from about 6% to 16%, and the authors estimate a 10 percentage point rise in denials increases Canadian applications by 30%, roughly one Canadian application per four forgone visas. More exposed Canadian firms increased sales and hired about 0.5 native workers per immigrant.