Edward Conard

Top Ten New York Times Bestselling Author

  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “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
  • “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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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 13, 2026

AI Summary. 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.

Azeem Azhar and Nathan Warren Exponential View
Date Posted:
Is Database:
Database

The ratio of AI capex to revenue has dropped to 4.7x and is poised to fall under 3, a level at which Azhar/Warren argue revenue would be sufficient to support the buildout: “balance sheets and external financing can stop doing the heavy lifting.”

How fast is AI revenue growing relative to investment?

Core argument: The authors' proprietary model estimates monthly AI revenue rose from $772 million in January 2024 to $13.8 billion in December 2025, roughly an eighteen-fold increase in two years, with hyperscalers as the main engine.

According to our proprietary model, monthly AI revenue grew from $772 million in January 2024 to $13.8 billion by December 2025, roughly an eighteen-fold increase in two years. The hyperscalers are the main engine. Our revenue model estimates that AI now accounts for 23% of Google Cloud’s business, 10% of Azure’s (the biggest of which is from OpenAI) and 5% for AWS’s as of the latest quarter. The ratio of investment to revenue – what we call Industry Strain – has dropped from 6.1x to 4.7x in five months. If Industry Strain remains high for sustained periods of time, it means that companies are not recouping their investments, and they are building speculatively. For context, the telecoms bubble peaked at an Industry Strain of just over 4x. In the case of generative AI, strain is still at historically high levels. On our dashboard, it sits in the amber zone. But the trajectory matters: if it holds, the ratio drops below our 3x threshold by Q2 this year. It would signal that revenues are beginning to “carry” the installed base and that the balance sheets and external financing can stop doing the heavy lifting. The question for us has moved from “Are we in a bubble?” to whether there is enough compute to withstand the agent stampede.

Takeaways by Macro Roundup® AI

  1. The authors' proprietary model estimates monthly AI revenue rose from $772 million in January 2024 to $13.8 billion in December 2025, roughly an eighteen-fold increase in two years, with hyperscalers as the main engine.
  2. The authors' revenue model estimates AI accounts for 23% of Google Cloud's business, 10% of Azure's — the largest share of which comes from OpenAI — and 5% of AWS's as of the latest quarter.
  3. The authors' "Industry Strain" ratio of investment to revenue has fallen from 6.1x to 4.7x in five months, remaining above the telecoms bubble peak of just over 4x, and would drop below their 3x threshold by Q2 if the trajectory holds.

Related Articles:

  • Is AI A Bubble? — Generative AI revenues are estimated to be doubling annually, compared with pre-crash revenue growth of 22% for railways before the 1873 bust and 16% for telecoms, which implied doubling times of three and four years. The authors project generative AI revenue could reach $100bn, covering about 25% of that year's capital spending.
  • Chart of the Day: AI Capex, From Bonanza to Burden — Stock price reactions to large artificial intelligence capital spending announcements have shifted from rewarding to penalizing companies, with the valuation multiple markets assign to such spending collapsing and likely turning negative. Technology companies once valued for low debt and low capital intensity are now among the leaders in both.
  • 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. The authors estimate that researcher efficiency, measured as the responsiveness of patenting to knowledge inputs, has been flat or rising over the last five decades. However, they find a negative time trend in firm growth conditional on patenting, indicating that growth, not ideas, is getting harder to find.

Teresa Fort, Nathan Goldschlag, Jack Liang, Peter Schott, et al. U.S. Census Bureau
Date Posted:
Is Database:
Database
Is Important:
Important

Efficiency in the production of “ideas,” as indicated by the elasticities of patent grants, external citations, and breakthroughs with respect to 6 measures of R&D input, is generally rising, but “growth, conditional on ideas, is getting harder to find.”

Are ideas harder to find, or growth from ideas?

Core argument: The authors estimate that researcher efficiency, measured as the elasticity of patenting to knowledge-input stocks, has been flat or rising over the last five decades.

[Our production estimation] demonstrates that researcher efficiency in terms of the elasticity of patenting to knowledge input stocks is flat or rising over the last five decades. It also shows that average patenting by semi-decade across firms, after controlling for knowledge-input use and firm fixed effects, declines for some measures of patenting but is flat or rising for others. Given these results, why has aggregate growth not kept pace with rising researcher efforts? To address this question, we examine the link between ideas and growth. We examine the link between patenting and firm growth, allowing the relationship to vary over time. [Equation 7, referenced in Figure 12, in which the left hand side variable is firm growth]. Figure 12 reports the semi-decade fixed effects fixed for these measures of US patenting activity across five samples of firms and for the same six knowledge inputs. These fixed effects capture average patenting in a particular sample and time period, after controlling for those firms’ average patenting and changes in knowledge inputs. We generally find a negative trend in these fixed effects over time, indicating that growth, conditional on ideas, is getting harder to find. The secular decline in these time effects might reflect an aggregate ‘crowding out’ of ideas as the pool of total ideas grow.

Takeaways by Macro Roundup® AI

  1. The authors estimate that researcher efficiency, measured as the elasticity of patenting to knowledge-input stocks, has been flat or rising over the last five decades.
  2. Average patenting by semi-decade across firms, after controlling for knowledge-input use and firm fixed effects, declines for some patenting measures but is flat or rising for others.
  3. Semi-decade fixed effects linking patenting to firm growth generally trend negative across five firm samples and six knowledge inputs, indicating that growth conditional on ideas is getting harder to find, which the authors suggest might reflect an aggregate crowding out of ideas.

Related Articles:

  • Is It a Bubble — Transformational technologies have historically generated excessive enthusiasm and investment, producing more infrastructure than needed, asset prices that prove too high, and losses for those funding them, while also accelerating adoption. AI enthusiasm is argued to fit this pattern, with debt financing having the potential to magnify the effects.
  • Are Ideas Getting Harder to Find? — The authors estimate U.S. research productivity has fallen by a factor of 41 since the 1930s, averaging declines above 5% per year across sectors. Sustaining constant growth in output per person requires doubling research effort every 13 years, challenging growth models that assume constant research productivity.
  • Is The Great Stagnation Actually Just a ‘So-So’ Stagnation? — Long-running U.S. tests show population average reading scores moved from 255 to 256 out of 500 and math from 266 to 271, despite educational attainment rising more than three years. The author argues that if added schooling has not raised productivity, measured total factor productivity growth has been understated, shrinking the apparent "Great Stagnation" by about a third.

AI Summary. The US administration is reviewing the list of steel and aluminium derivative products subject to tariffs of up to 50%, and plans to exempt some items, halt further expansion of the lists, and instead open more targeted national security investigations into specific goods. Officials viewed the tariff regime as too complicated to enforce.

Aime Williams and Andy Bounds Financial Times
Date Posted:

The administration has signaled that tariffs on steel and aluminium products will be scaled back in an effort to reduce consumer prices.

Are metals tariffs being rolled back over enforcement complexity?

Core argument: The Trump administration plans to exempt some products from tariffs of up to 50% on steel and aluminium imports, halt expansion of the affected product lists, and launch more targeted national security probes into specific goods, according to three people familiar with the matter.

[Trump] hit steel and aluminium imports with tariffs of up to 50% last summer, and has expanded the taxes to a range of goods made from those metals including washing machines and ovens. But his administration is now reviewing the list of products affected by the levies and plans to exempt some items, halt the expansion of the lists and instead launch more targeted national security probes into specific goods, according to three people familiar with the matter. Officials felt the tariff regime was “too complicated to enforce,” one person said, and needed to be simplified. One European business leader, who declined to be named, said they knew of a company that had sent four identical containers of machinery to the US and was charged different rates for each one.

Takeaways by Macro Roundup® AI

  1. The Trump administration plans to exempt some products from tariffs of up to 50% on steel and aluminium imports, halt expansion of the affected product lists, and launch more targeted national security probes into specific goods, according to three people familiar with the matter.
  2. Officials found the metals tariff regime, extended to derivative goods including washing machines and ovens, "too complicated to enforce" and in need of simplification, one person familiar with the matter said.
  3. A European business leader, who declined to be named, said a company sent four identical containers of machinery to the United States and was charged a different rate on each one.

Related Articles:

  • 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.
  • Are Businesses Absorbing the Tariffs or Passing Them on to Their Customers? — About three-quarters of manufacturing and service firms facing tariff-driven cost increases reported passing at least some of those costs to customers through higher prices, while roughly a quarter of each said they absorbed the full increase without raising prices. Nearly a third of manufacturers and about 45% of service firms reported full pass-through.
  • U.S. Manufacturing Is in Retreat and Trump’s Tariffs Aren’t Helping — U.S. manufacturing employment has declined for eight straight months following the announcement of sweeping new tariffs, leaving fewer Americans working in manufacturing than at any point since the end of the pandemic. Federal figures show more than 200,000 manufacturing jobs have disappeared since 2023.

AI Summary. 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.

Mary Amiti, Chris Flanagan, Sebastian Heise, and David Weinstein Federal Reserve Bank of New York
Date Posted:
Is Database:
Database
Is Important:
Important

A FRBNY analysis finds that ~90% of tariffs’ economic burden was borne by American firms and consumers in the first 8 months of 2025. Between January and November, however, that incidence declined 8pp as firms reorganised supply chains.

Are U.S. buyers bearing most of the cost of tariffs?

Core argument: U.S. buyers bore 94% of the 2025 tariff incidence during the first eight months of the year, with a 10% tariff associated with only a 0.6 percentage point decline in foreign export prices.

94% of the tariff incidence was borne by the U.S. in the first eight months of 2025. This result means that a 10% tariff caused only a 0.6 percentage point decline in foreign export prices. The tariff pass-through into import prices has declined in the latter part of the year. That is, a larger share of the tariff incidence was borne by foreign exporters by the end of the year. In November, a 10% tariff was associated with a 1.4 percent decline in foreign export prices, suggesting an 86% pass-through to U.S. import prices. Given that the average tariff in December was 13% our results imply that U.S. import prices for goods subject to the average tariff increased by 11% (13 times 0.86) more than those for goods not subject to tariffs. These higher import prices caused firms to reorganize supply chains.

Takeaways by Macro Roundup® AI

  1. U.S. buyers bore 94% of the 2025 tariff incidence during the first eight months of the year, with a 10% tariff associated with only a 0.6 percentage point decline in foreign export prices.
  2. Tariff pass-through into U.S. import prices declined later in 2025: in November a 10% tariff was associated with a 1.4% decline in foreign export prices, suggesting 86% pass-through.
  3. With the average tariff at 13% in December, the results imply U.S. import prices for tariffed goods rose 11% more than for untariffed goods, prices that led firms to reorganize supply chains.

Related Articles:

  • A Note On Tariffs From The Real World — A large retailer reports that pre-tariff inventory is still being sold, so tariff costs will show up as margin hits later, and that prices must eventually rise roughly 10–15% to cover them. The retailer says its sector's tariff rate went from about 12% to about 33%, and that retailers are holding prices through the holiday season before raising them.
  • U.S. to Cut Tariffs on Bananas, Coffee and Other Goods From Four Countries — The U.S. announced joint statements with four Latin American countries under which it would reduce tariffs on a range of goods not produced domestically, including bananas and coffee from Ecuador and beef from Argentina. Officials said no government-sponsored beef shipments are planned, but tariff cuts on beef are expected.
  • Tariffs Are Generating Meaningful New Revenue — New U.S. tariffs are estimated to generate $1.3tn in net new revenue through the end of the current presidential term and $2.8tn through 2034 before economic effects, about $600bn more than previously enacted tariffs. If a court ruling striking down some tariffs is upheld, the remainder could raise as little as $800bn through 2034.

Tuesday, February 17, 2026

AI Summary. 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.

Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, et al. National Bureau of Economic Research
Date Posted:

A survey of 6,000 Western senior executives finds 70% of firms are using AI, 80% of which report no impact as of yet. Respondents forecast a productivity boost of ~1.4%, reflecting a 0.8% output increase and a 0.7% employment cut over the next 3 years.

How do firms report AI affecting their productivity and employment?

Core argument: Around 70% of surveyed businesses currently use AI technologies, with larger, more productive, and higher-paying firms more likely to be adopters, and adoption expected to increase further.

We outline four key findings. First, AI technologies are currently used by around 70% of businesses, and adoption is expected to increase. Larger, more productive, and higher-paying firms are more likely to be using some AI technology. Second, we show that AI technologies are being actively used by senior survey respondents (typically CEOs, CFOs, and senior finance managers). On average, respondents use AI for around 1.5 hours in a typical working week, and this frequency has increased sharply since the start of 2025. Third, firms estimate that AI has had little impact on their employment so far, and only a modest boost to productivity over the past three years. Finally, firms expect AI to have larger impacts over the medium-term. Over the next three years, firms predict that the adoption of AI will boost productivity by around 1.4%, on average, while reducing employment by around 0.7%. This is in sharp contrast to expectations of employees, who expected higher job creation as a result of AI, along with smaller productivity gains over the next three years.

Takeaways by Macro Roundup® AI

  1. Around 70% of surveyed businesses currently use AI technologies, with larger, more productive, and higher-paying firms more likely to be adopters, and adoption expected to increase further.
  2. Firms predict AI adoption will boost productivity by around 1.4% and reduce employment by around 0.7% over the next three years, while reporting little employment impact and only modest productivity gains so far.
  3. Senior survey respondents, typically CEOs, CFOs, and senior finance managers, use AI for around 1.5 hours in a typical working week, a frequency that has increased sharply since the start of 2025.

Related Articles:

  • Productivity During and Since the Pandemic — Total factor productivity growth since early 2021 has run only slightly above its 2010–19 average, suggesting the 2020 productivity surge reflected cyclical dynamics layered on a continuation of the slow pre-pandemic trend. Productivity has since grown somewhat faster than the authors' analysis predicted, but remains well below the 1995–2004 pace.
  • 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.
  • Concentrated Growth: The Role Of The IT Sector — Since 1988, total factor productivity in the information technology sector has risen about 178%, versus about 12% across all non-IT sectors combined, while aggregate productivity rose about 40%. The IT sector accounts for roughly 45% of cumulative aggregate productivity growth, once sectoral linkages to downstream industries are weighted in.

AI Summary. 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.

Torsten Sløk Apollo
Date Posted:

Noting “no signs of AI in profit margins or earnings expectations” for the S&P 493 as of the start of 2026, Sløk observes that, in terms of AI, value creation will be found in how the technology diffuses across the economy.

Why has AI not yet appeared in macroeconomic data?

Core argument: Three years after ChatGPT's release, AI is absent from employment, productivity and inflation data, and shows no signs in profit margins or earnings expectations for the S&P 493, echoing Robert Solow's 1987 productivity paradox.

The Nobel Prize–winning economist Robert Solow said in 1987, “You can see the computer age everywhere but in the productivity statistics.” This observation is the so‑called Solow productivity paradox. The same can be said today: AI is everywhere except in the incoming macroeconomic data. Today, you don’t see AI in the employment data, productivity data or inflation data. Similarly, for the S&P 493, there are no signs of AI in profit margins or earnings expectations. Maybe there is a J‑curve effect for AI, where it takes time for AI to show up in the macro data. Maybe not. Whether there is a J-curve effect depends on the value creation from AI. There is fierce competition between the builders of large language models (LLMs), which is driving the price of LLMs toward zero for end-users. From a macro perspective, the value creation is not the product, but how generative AI is used and implemented in different sectors in the economy. The academic literature is still inconclusive about the potential macro effects of AI [see table in gallery]. The [disparate and inconclusive results in the table suggest that] after three years with ChatGPT and still no signs of AI in the incoming data, AI will likely be labor-enhancing in some sectors rather than labor-replacing in all sectors.

Takeaways by Macro Roundup® AI

  1. Three years after ChatGPT's release, AI is absent from employment, productivity and inflation data, and shows no signs in profit margins or earnings expectations for the S&P 493, echoing Robert Solow's 1987 productivity paradox.
  2. Competition among builders of large language models is driving the price of LLMs toward zero for end-users, so the macro value creation lies in how generative AI is implemented across sectors rather than in the product itself.
  3. The academic literature on AI's potential macroeconomic effects remains inconclusive, and the disparate results suggest AI will likely be labor-enhancing in some sectors rather than labor-replacing in all sectors.

Related Articles:

  • Four Takeaways from Microsoft’s Satya Nadella at WEF Today — Microsoft's chief executive says the price of AI model usage ("tokens") falls by roughly half every three months. Commentary argues that such rapid deflation pulls demand forward, encourages overbuilding, and commoditizes models, shifting competitive advantage to orchestration and context rather than the model layer.
  • 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.
  • Looking for the Ladder — The article argues that the hiring downturn in occupations most exposed to generative AI reflects interest-rate sensitivity rather than AI itself, since roughly 38% of workers in the most AI-exposed quintile work in information, finance and insurance, and professional and technical services, versus under 2% in the least-exposed quintile.

AI Summary. US payroll growth was revised down by about 403,000 jobs while real GDP stayed strong, including 3.7% growth in the fourth quarter, a combination of high output with lower labor input that the author identifies as productivity growth. The author estimates US productivity rose roughly 2.7%, nearly double the 1.4% annual average of the past decade.

Erik Brynjolfsson Financial Times
Date Posted:

Brynjolfsson argues that downward revisions to job creation while real GDP growth remains steady suggest that AI may be impacting US productivity growth. He estimates labor productivity grew 2.7% last year, compared to a 1.4% mean over the past decade.

Is strong output with weaker job growth a sign of rising productivity?

Core argument: US total payroll growth was revised down by roughly 403,000 jobs even as real GDP stayed strong, including 3.7% growth in the fourth quarter.

Data released this week offers a striking corrective to the narrative that AI has yet to have an impact on the US economy as a whole. While initial reports suggested a year of steady labour expansion in the US, the new figures reveal that total payroll growth was revised downward by approximately 403,000 jobs. Crucially, this downward revision occurred while real GDP remained robust, including a 3.7% growth rate in the fourth quarter. This decoupling — maintaining high output with significantly lower labour input — is the hallmark of productivity growth. My own updated analysis suggests a US productivity increase of roughly 2.7% for 2025, a near doubling from the sluggish 1.4% annual average that characterised the past decade.

Takeaways by Macro Roundup® AI

  1. US total payroll growth was revised down by roughly 403,000 jobs even as real GDP stayed strong, including 3.7% growth in the fourth quarter.
  2. The author estimates US productivity rose roughly 2.7% in 2025, nearly double the 1.4% annual average of the past decade.
  3. The author argues that maintaining high output with lower labour input is the hallmark of productivity growth and a corrective to claims that AI has not affected the US economy.

Related Articles:

  • 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.
  • Claude Code is the Inflection Point — Claude Code authors 4% of public GitHub commits, and the authors project it will reach 20%+ of daily commits by the end of 2026 and drive revenue growth that lets Anthropic outgrow OpenAI. They argue falling costs and rising quality of AI tokens have inverted the coder-tool relationship.
  • Looking for the Ladder — The article argues that the hiring downturn in occupations most exposed to generative AI reflects interest-rate sensitivity rather than AI itself, since roughly 38% of workers in the most AI-exposed quintile work in information, finance and insurance, and professional and technical services, versus under 2% in the least-exposed quintile.

AI Summary. State-level measures of exposure to industrial robots show substantial geographic variation across the US, with the highest concentrations in the Southwest, Midwest, and Northwest and the lowest in the Mountain West, parts of the South, and northern regions. The authors find state robot exposure is positively correlated with state minimum wage increases.

Erik Brynjolfsson, J. Frank Li, Javier Miranda, Robert Seamans, et al. Stanford University
Date Posted:
Is Database:
Database
Is Important:
Important

Across US states, a 10pp higher growth rate of the minimum wage over 1992–2021 was associated with an ~8% higher-than-expected installation of industrial robots.

Do higher state minimum wages increase adoption of industrial robots?

Core argument: A 0.0056 increase in robot exposure amounts to roughly 0.0056 more excess robots per 100 baseline workers, equal to 8% of the 0.07 mean state excess robot accumulation over the period.

We construct state-level long-difference measures of exposure to industrial robots by combining robot installation information from [several agencies]. Figure 1a displays our robot exposure measure, [revealing] significant geographic variation in robot exposure across U.S. states, with the color gradient ranging from light blue (minimal change) to dark navy (maximum change). States in the Southwest, Midwest, and Northwest show the highest concentrations of robot exposure, reflecting strong manufacturing sectors and states in the Mountain West, parts of the South, and Northern regions, show minimal robot exposure consistent with less dependence on manufacturing. Figure 1b displays state-level minimum wage changes covering the period 2003–2015. The map reveals substantial geographic variation in minimum wage policy exposure across U.S. states, with the gradient ranging from light blue (minimal change) to dark navy (maximum change). Finally, Figure 1c displays a strong positive correlation in the relationship between the state-level robot exposure and the minimum wage measures. [The .0056 increase in robot exposure is 8% of the 0.07 mean — meaning roughly 0.0056 more excess robots per 100 baseline workers, which is 8% of the average state's excess robot accumulation over the period].

Takeaways by Macro Roundup® AI

  1. A 0.0056 increase in robot exposure amounts to roughly 0.0056 more excess robots per 100 baseline workers, equal to 8% of the 0.07 mean state excess robot accumulation over the period.
  2. State-level robot exposure and state-level minimum wage changes over 2003–2015 show a strong positive correlation across U.S. states in the authors' long-difference measures.
  3. Robot exposure is concentrated in Southwest, Midwest, and Northwest states with strong manufacturing sectors, while the Mountain West, parts of the South, and Northern regions show minimal exposure.

Related Articles:

  • The Mysterious Slowdown in U.S. Manufacturing Productivity — US manufacturing labor productivity growth slowed broadly after the Great Recession, with the four largest firms in each industry averaging –0.5% growth from 2010 to 2022 versus 3.7% from 1987 to 2007, and smaller follower firms showing an identical 4.2 percentage-point decline. The authors find the slowdown spans all industries and firm sizes.
  • 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.
  • Did California’s Fast Food Minimum Wage Reduce Employment? — The authors estimate that California's $20 fast food minimum wage, up from $16, raised fast food sector wages roughly 8% relative to the rest of the country while employment fell about 2.7% relative to fast food employment elsewhere in the US. Their median estimate implies 18,000 fewer California fast food jobs than otherwise.

AI Summary. Corporate profit margins have trended upward across roughly three decades of company data. The author attributes the increase largely to a shift in market composition, as technology companies with superior unit economics and economies of scale supplant manufacturing and service businesses.

Aswath Damodaran Musings on Markets
Date Posted:
Is Database:
Database

The S&P 500 had a net profit margin of 13.55% in 2025. American software firms had operating margins of 25–30%, in contrast to industrial firms’ single-digit margins.

Are rising corporate profit margins driven by more technology companies?

Core argument: Corporate profit margins tracked across roughly three decades of Damodaran's datasets show a clear upward trend over the period.

I have tracked profit margins for companies for a long time (about three decades) in my datasets, and there is clear evidence that they have trended upwards during the period. The most salient rationale for margin increases, is that the composition of the market has changed, as technology companies supplant old-economy companies, bringing superior unit economics and economies of scale to play. Put simple, a market that gets the largest portion of its value from tech companies will deliver much higher margins than one that gets much of its value from manufacturing and service businesses.

Takeaways by Macro Roundup® AI

  1. Corporate profit margins tracked across roughly three decades of Damodaran's datasets show a clear upward trend over the period.
  2. Damodaran attributes rising margins primarily to a change in market composition, as technology companies supplant old-economy firms and bring superior unit economics and economies of scale.
  3. Damodaran argues that a market deriving the largest portion of its value from technology companies delivers much higher margins than one drawing much of its value from manufacturing and service businesses.

Related Articles:

  • S&P 500 Margin Expansion All Coming from Tech — All of the increase in the S&P 500's operating margin over the past 20 years has come from tech-related sectors — information technology, communication services and consumer discretionary — while operating margins for non-tech companies have stayed near 9%.
  • Bayes and Base Rates — Investment in artificial intelligence infrastructure has reached a scale comparable to some of the largest investment booms in U.S. history, with firms issuing historically high growth forecasts. Among U.S. public companies with $2–5bn in starting sales from 1950 to 2024, none sustained the roughly 108% five-year annual growth rate implied by OpenAI's revenue projection.
  • Chart of the Day: AI Capex, From Bonanza to Burden — Stock price reactions to large artificial intelligence capital spending announcements have shifted from rewarding to penalizing companies, with the valuation multiple markets assign to such spending collapsing and likely turning negative. Technology companies once valued for low debt and low capital intensity are now among the leaders in both.

AI Summary. The share of Americans aged 25-54 with a job has held at multi-decade highs, reaching its highest level since 2001, even as the unemployment rate has risen. This prime-age employment rate has been the best indicator of cyclical conditions since women entered the workforce en masse in the 1980s.

Matt Klein The Overshoot
Date Posted:

Since 2023, the share of Americans aged 25–54 with a job has been at its highest level since 1998–2000. Matt Klein warns against rate cuts, noting many CPI components are rising 2–3 standard deviations faster than pre-pandemic.

Is prime-age employment strength at odds with rising unemployment?

Core argument: The employment rate for Americans aged 25–54 has held at multi-decade highs since the start of 2023 and rose slightly in January to its highest level since April 2001.

The share of Americans aged 25-54 with a job has been stable at multi-decade highs ever since the beginning of 2023, and rose slightly in January to its highest level since April 2001. The so-called “prime age” employment rate has been the single best indicator of cyclical conditions since women entered the workforce en masse in the 1980s. It is therefore extremely odd to have had the unemployment rate rise as much as it has during a period when employment has been so strong. Either way, it is hard to say that things have changed much since mid-2024.

Takeaways by Macro Roundup® AI

  1. The employment rate for Americans aged 25–54 has held at multi-decade highs since the start of 2023 and rose slightly in January to its highest level since April 2001.
  2. The prime-age employment rate has, in the author's assessment, been the single best indicator of cyclical conditions since women entered the workforce en masse in the 1980s.
  3. The rise in the U.S. unemployment rate alongside such strong prime-age employment is odd, and on either reading conditions have changed little since mid-2024.

Related Articles:

  • Trump Wants to Run the Economy Hot. There’s a Good Chance He’ll Succeed — A US tax law expanded deductions for state and local taxes, overtime, tips and seniors retroactively, but withholding tables adjusted only later, so workers get both higher take-home pay and refunds. One analyst estimates this injects nearly $200bn, lifting first-half annualized growth by up to half a percentage point.
  • Is the Market Underpricing the Risk of Fed Hikes? — Futures prices imply essentially no chance the Federal Reserve raises rates over two years and over 90% odds of a cut within 12 months. The author argues this understates hike risk, citing prime-age employment near its multi-decade high and stable pay growth, and warns lower rates could unlock inflationary spending.
  • Data Update 5 for 2026: Risk and Hurdle Rates — A histogram of costs of capital across all publicly traded firms shows what typical, high, and low hurdle rates look like, with the cost of capital best understood as an opportunity cost—the return available elsewhere on equally risky investments. US dollar risk-free rates have remained near 4% for a fourth year.