Edward Conard

Top Ten New York Times Bestselling Author

  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “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
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “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
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “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 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
Buy the Books
  • Macro Roundup
  • About Roundup
  • About Ed Conard
  • Highlights
  • Topics
  • Subscribe
Edward Conard
  • twitter
  • facebook
  • linkedin
  • youtube
  • Email
  • Text Message (SMS)
  • Twitter/X
  • LinkedIn
  • Facebook
  • WhatsApp Message
Subscribe to Macro Roundup Emails
  • Mentions 903
  • Primary focus 492
For whatever topics you select (currently: "Productivity", "Cronyism", "Incentives/Risk-Taking", "Innovation/Research", "Institutional Capabilities", "Intangibles", "Investment", "Startups", "Workforce Reorganization"):
Choose search scope

Your importance filter 'Database' shows fewer articles.

Remove filters to see full article counts

Rising inequality: Industries and mega firms

AI Summary. The authors estimate that differences between industries account for 61.9% of the rise in labor earnings inequality, with 30 industries employing under 40% of workers contributing 98.1% of that between-industry gap. Nineteen high-paying industries contribute 54% through earnings growth, while 11 low-paying industries contribute 44.1% through employment growth, concentrated in firms with 10,000+ workers.

John Haltiwanger Center for Economic and Policy Research
Date Posted:
June 1, 2022
Is Database:
Database

62% of the rise in labor earnings inequality is driven by growing inequality btw industries as opposed to within industries.

How much of rising earnings inequality comes from differences between industries?

Core argument: Between-industry disparities account for 61.9% of the rise in labor earnings inequality, exceeding the contribution of inequality within industries.

The rise in labor earnings inequality is predominantly driven by disparities between industries, accounting for 61.9% of the increase, as opposed to within industries. Thirty industries, representing less than 40% of the workforce, contribute nearly all (98.1%) of the between-industry inequality. Among these, 19 high-paying industries account for 54% of the increase, primarily due to strong earnings growth, while 11 low-paying industries contribute 44.1%, largely due to employment surges. Mega firms, employing 10,000+ workers, have seen significant employment growth in these industries, further exacerbating inequality. This trend highlights the critical role of industry-level dynamics in shaping economic disparities.

Takeaways by Macro Roundup® AI

  1. Between-industry disparities account for 61.9% of the rise in labor earnings inequality, exceeding the contribution of inequality within industries.
  2. Thirty industries employing under 40% of the workforce generate 98.1% of between-industry inequality, with 19 high-paying industries contributing 54% via strong earnings growth and 11 low-paying industries 44.1% via employment surges.
  3. Mega firms employing 10,000 or more workers have recorded substantial employment growth in the thirty industries driving between-industry inequality.

In a new paper…we document an important determinant of how much an employer pays: the industry. An employer’s industry is determined by its production processes or its products. Pay differentials across industries are large and have widened over time, leading to increases in labour earnings inequality. For example, restaurants tend to be low-paying employers. Our data indicate that in recent decades in the US, about one-tenth of the rise in labour earnings inequality can be attributed to restaurants employing more people and paying them less. We use matched employer-employee data for 18 US states to study labour earnings inequality (hereafter, simply ‘inequality’; note that this excludes self-employment and capital income). Thirty industries (out of a total of 301) can account for nearly all of the rise in inequality from the late 1990s to the late 2010s. In these 30 industries, ‘mega firms’ (which employ 10,000 or more) have seen a massive surge in employment. This column describes some of our main findings. Industries drive increasing inequality. Figure 1 reports our measure of inequality, which is the variance of log (annual) labour earnings. The contributions of different industries to between-industry inequality are shown in Figure 2. Recall from Figure 1 that between-industry inequality (the green region of Figure 1) accounts for 61.9% of the total increase in inequality. The 30 industries that contribute most to increasing inequality explain nearly all (98.1%) of the between-industry contribution to inequality - despite employing less than 40% of the workforce. Of these 30 industries, 19 are high-paying and account for 54.0% of the increase in between-industry inequality; 11 are low-paying and account for 44.1% of this increase. The other 271 industries account for only 1.9% of rising inequality. Is increasing inequality driven by changes in employment or earnings? Figure 3 shows that there are sizeable differences in the answer to this question depending on whether the industry is high- versus low-paying. Among high-paying industries, 83.9% of the contribution to inequality is due to strong increases in earnings, while any employment increases in these industries only account for only 16.1% of this rise. In contrast, the employment among the 11 most important low-paying industries surged, accounting for 68.3% of their contribution to between-industry inequality. A much more modest decline in earnings in these low-paying industries explains the remaining 31.7%. Industries drive recent changes in inequality in the US. Thirty out of the 301 industries in our classification can explain nearly all of the rise in between-industry inequality. Continued study of these industries can help shed light on the mechanisms by which inequality continues to increase.

High-paying jobs are paying more; low-paying jobs are hiring more

We now address the question of how much of increasing inequality is due to employment versus earnings. Note that changes in either employment or earnings can contribute to earnings inequality. For example, restaurants tend to be among the lowest-paying employers. When (relatively) more workers are employed by restaurants, inequality will increase. A decline in the pay of this industry would also increase inequality.

Is increasing inequality driven by changes in employment or earnings? Figure 3 shows that there are sizeable differences in the answer to this question depending on whether the industry is high- versus low-paying. Among high-paying industries, 83.9% of the contribution to inequality is due to strong increases in earnings, while any employment increases in these industries only account for only 16.1% of this rise. In contrast, the employment among the 11 most important low-paying industries surged, accounting for 68.3% of their contribution to between-industry inequality. A much more modest decline in earnings in these low-paying industries explains the remaining 31.7%.

Rising inequality: Industries and mega firms: Extended Excerpt Image 1


More high-earnings workers are employed by high-paying industries, and among other highly paid workers

Following the pathbreaking work of Abowd et al. (1999), Card et al. (2013), and Song et al. (2019), we consider the question of the extent to which changes in inequality are driven by workers or firms, which is especially important for understanding the contribution of high-paying firms and industries to inequality. Do high-paying firms offer larger premia? Or are they hiring highly paid (i.e. more costly) workers? As Song et al. (2019) demonstrate, there are three channels through which differences among employers contribute to inequality. These are:

1. Pay premia: some firms offer greater earnings to any worker

2. Sorting: high-paying firms employ more highly paid workers

3. Segregation: more highly paid workers concentrate among each other

We explore the relative contributions of these three phenomena to between-industry inequality in Figure 4. Sorting has the greatest role in increasing inequality, and its contribution to the variance of log annual labour earnings increased from 0.078 to 0.112 from 1996-2002 to 2012-2018. In other words, high-paying industries increasingly employ highly paid workers. Highly paid workers tend to be employed in the same industries (to the exclusion of workers with low earnings), and this rises from 0.059 to 0.089. Industry-level pay premia have also widened somewhat over time. This latter channel has had a smaller contribution to the variance of log earnings and rose from 0.033 to 0.044.

‘Mega firm’ employment has surged in the 30 industries that drive increasing inequality

We now explore the role of ‘mega firms’ in increasing inequality. By mega firms we mean those that employ at least 10,000 workers. Mega firms have been increasing as a share of employment, as documented by Bloom et al. (2018) and Autor et al. (2020). In Figure 5, we break this increase down according to our four industry groups.

The rise in mega firm employment is quite dramatic in the 30 industries that drive increasing inequality. It is especially apparent in the low-paying firms that drive inequality through increasing employment. The employment share of mega firms in the 11 low-paying industries that drive between-industry inequality increased by 2.5 percentage points, from 3.1% to 5.6%. Note that this implies that millions of additional workers were employed in mega firms in low-paying industries: the total number of people employed in the US in 2018 was more than 150 million (Bureau of Labor Statistics 2019). The employment share of mega firms in the 19 high-paying industries increased by 1.4 percentage points, from 3.2% to 4.5%. The employment share of mega firms in the other 271 industries fell.

Rising inequality: Industries and mega firms: Extended Excerpt Image 2


Conclusion

Industries drive recent changes in inequality in the US. Thirty out of the 301 industries in our classification can explain nearly all of the rise in between-industry inequality. Continued study of these industries can help shed light on the mechanisms by which inequality continues to increase.

References

Abowd, J, F Kramarz and D Margolis (1999), “High Wage Workers and High Wage Firms”, Econometrica 67(2): 251-333.

Autor, D, D Dorn, L F Katz, C C Patterson, and J Van Reenen (2020), “The Fall of the Labor Share and the Rise of Superstar Firms”, Quarterly Journal of Economics 135(2): 645-709.

Berlingieri, G, P Blanchenay, C Criscuolo (2017), “Great Divergences: The growing dispersion of wages and productivity in OECD countries”, VoxEU.org, 15 May.

Bloom, N, F Guvenen, B S Smith, J Song and T von Wachter (2018), “The Disappearing Large-Firm Premium”, AEA Papers and Proceedings 108: 317-322.

Bloom, N, T Hassan, A Kalyani, J Lerner and A Tahoun (2021), “How disruptive technologies diffuse”, VoxEU.org, 10 August.

Card, D, A Cardoso and P Kline (2016), “Bargaining, Sorting, and the Gender Wage Gap: Quantifying the Effect of Firms on the Relative Pay of Women”, Quarterly Journal of Economics 131(2): 633-686.

Card, D, J Heining and P Kline (2013), “Workplace Heterogeneity and the Rise of West German Wage Inequality”, Quarterly Journal of Economics 128(3): 967-1015.

Cooper, Z, S Craig, M Gaynor and J Van Reenen (2019), “The Price Ain’t Right? Hospital Prices and Health Spending on the Privately Insured”, Quarterly Journal of Economics 134(1): 51-107.

Decker, R A, A Flaaen and M D Tito (2016), “Unraveling the Oil Conundrum: Productivity Improvements and Cost Declines in the U.S. Shale Oil Industry”, FEDS Notes.

Dey, M, S Houseman and A E Polivka (2006), “Manufacturers’ Outsourcing to Employment Services”, Working paper.

Dey, M, S Houseman and A Polivka (2010), “What Do We Know About Contracting Out in the United States? Evidence from Household and Establishment Surveys” in K G Abraham, J R Spletzer and M Harper (eds), Labor in the New Economy, Chicago: University of Chicago Press.

Dorn, D, J Schmieder and J Spletzer (2018), “Domestic Outsourcing in the United States”, Unpublished.

Fernald, J (2014), “Productivity and Potential Output before, during, and after the Great Recession”, NBER Macroeconomics Annual 29(1): 1-51.

Foster, L, J Haltiwanger and C J Krizan (2006), “Market Selection, Reallocation, and Restructuring in the U.S. Retail Trade Sector in the 1990s”, Review of Economics and Statistics 88(4): 748-758.

Foster, L, J Haltiwanger, S Klimek and C J Krizan (2016), “The Evolution of National Retail Chains: How We Got Here”, in E Basker (ed), Handbook on the Economics of Retailing and Distribution, Northampton, MA: Edward Elgar Publishing.

Fulton, B (2017), “Health care market concentration trends in the United States: Evidence and policy responses”, Health Affairs 36(9): 1530-1538.

Goldschlag, N and J Miranda (2016), “Business Dynamics Statistics of High Tech Industries”, US Census Bureau Center for Economic Studies.

Hecker, D (2005), “High-technology employment: a NAICS-based update”, Monthly Labor Review 128(7).

Kroszner, R and P Strahan (2014), “Regulation and Deregulation of the US Banking Industry: Causes, Consequences, and Implications for the Future”, in N L Rose (ed), Economic Regulation and Its Reform: What Have We Learned?, Chicago: University of Chicago Press.

Luo, T, A Mann and R Holden (2010), “The expanding role of temporary help services from 1990 to 2008”, Monthly Labor Review 133(8): 3-16.

Marin, D (2016), “Inequality in Germany: How it differs from the US”, VoxEU.org, 23 June.

Mion, G, L D Opromolla and G Ottaviano (2020), “Dream jobs: A comparison of career prospects in Portuguese firms”, VoxEU.org, 28 August.

Song, J, D Price, F Guvenen, N Bloom, and T von Wachter (2019), “Firming Up Inequality”, Quarterly Journal of Economics 134(1): 1-50.

John Haltiwanger, Henry Hyatt and Jim Spletzer, "Rising inequality: Industries and mega firms,"Center For Economic and Policy Research, https://voxeu.org/article/rising-inequality-industries-and-mega-firms

Rising inequality: Industries and mega firms

Why has earnings inequality been increasing? Evidence from an increasing number of countries indicates that where people work is of key importance (Card et al. 2013, 2016, Marin 2016, Berlingieri et al. 2017, Song et al. 2019, Mion et al. 2020). Some employers pay more than others, and these pay differentials have widened in recent decades. An important question is why employer-driven inequality has been rising over time.

In a new paper, we document an important determinant of how much an employer pays: the industry (Haltiwanger et al. 2022). An employer’s industry is determined by its production processes or its products. Pay differentials across industries are large and have widened over time, leading to increases in labour earnings inequality. For example, restaurants tend to be low-paying employers. Our data indicate that in recent decades in the US, about one-tenth of the rise in labour earnings inequality can be attributed to restaurants employing more people and paying them less.

We use matched employer-employee data for 18 US states to study labour earnings inequality (hereafter, simply ‘inequality’; note that this excludes self-employment and capital income). Thirty industries (out of a total of 301) can account for nearly all of the rise in inequality from the late 1990s to the late 2010s. In these 30 industries, ‘mega firms’ (which employ 10,000 or more) have seen a massive surge in employment. This column describes some of our main findings.

Industry-level differences drive increasing inequality

Industries drive increasing inequality. Figure 1 reports our measure of inequality, which is the variance of log (annual) labour earnings. We estimate inequality in three seven-year intervals: 1996-2002, 2004-2010, and 2012-2018. This is broken down into the inequality within firms and between firms. The between-firm component of inequality is further broken down into that which occurs between firms in the same industry versus that which occurs at the industry level.

Inequality in the US has risen over time. In Figure 1, we show that the variance of log earnings has increased from 0.794 in 1996-2002 to 0.916 in 2012-2018. The variance of log earnings is the sum of three components. Most inequality occurs within firms, and this dispersion increased from 0.512 to 0.531, accounting for 14.9% of the increase in inequality. Among firms in the same industry, dispersion increased from 0.112 to 0.140, accounting for 23.1% of the rise in inequality. Dispersion between industries increased from 0.170 to 0.245, accounting for 61.9% of the increase in inequality. Therefore, most of the rise in inequality has occurred across industries.

Rising inequality: Industries and mega firms: Extended Excerpt Image 3


A small share of industries dominate the rising between-industry inequality

About 10% of industries account for all of the between-industry rise in inequality. We consider 301 industries defined using the North American Industrial Classification System (NAICS) at the 4-digit level. We classify industries based on whether they tend to pay more or less than the average, as well as their contribution to inequality. Thirty industries account for at least 1% (positive) of the rise in between-industry inequality. Another 271 industries each contribute less than 1% to between-industry inequality.

The contributions of different industries to between-industry inequality are shown in Figure 2. Recall from Figure 1 that between-industry inequality (the green region of Figure 1) accounts for 61.9% of the total increase in inequality. The 30 industries that contribute most to increasing inequality explain nearly all (98.1%) of the between-industry contribution to inequality - despite employing less than 40% of the workforce. Of these 30 industries, 19 are high-paying and account for 54.0% of the increase in between-industry inequality; 11 are low-paying and account for 44.1% of this increase. The other 271 industries account for only 1.9% of rising inequality.

Rising inequality: Industries and mega firms: Extended Excerpt Image 4


What are the industries that drive increasing inequality? Identifying these can yield insights into its causes. For example, changes in technology can lead to increases in inequality (Bloom et al. 2021). We therefore list in Table 1 the 30 industries that contribute at least 1% to the increase in inequality.

We start with the 19 high-paying industries. At least ten of these industries have been defined as high-tech in terms STEM intensity according to the criteria of Hecker (2005) and Goldschlag and Miranda (2016). Two high-paying industries are in mining, including support activities such as drilling oil wells. The contribution of these industries is likely related to the shale oil boom (e.g. Decker et al. 2016) Five high-paying industries involve finance, insurance, or corporate headquarters. The outsized role of these industries may reflect restructuring and consolidation that have followed financial deregulation (e.g. Krozner and Strahan 2014).

Health care and social assistance includes both low-paying and high-paying industries. Two high-paying industries include physician offices and hospitals, in which considerable consolidation has occurred (e.g. Fulton 2017, Cooper et al. 2019). Of the three low-paying industries, two deal primarily with care for the elderly, including both in-home care (e.g. hospice) and retirement homes. The remaining low-paying industry, Individual and Family Services, includes adoption and foster care, services for persons with disabilities, and crisis hotlines.

The nine remaining low-paying industries can be divided into roughly two groups. First, there are two industries that provide support to businesses and facilities. This includes temporary help, where employment has increased (e.g. Luo et al. 2010), as well as Professional Employee Organisations (Dey et al. 2006). It also includes cleaning and other support services in which there has been a substantial amount of outsourcing (e.g. Dorn et al. 2018). The remaining six industries include restaurants, retail trade, and gyms, which have been transformed in recent decades with the rise of national chains (e.g. Foster et al. 2016).
Rising inequality: Industries and mega firms: Extended Excerpt Image 5

  • Productivity
    • Institutional Capabilities
  • Comparisons
    • Sector
  • Workforce
    • Inequality
    • Wages/Income
Previous articleMay 25, 2022Air Pollution and Student Performance in the U.SAir pollution has a significant impact on student performance in the US. Each one-unit increase in particulate pollution reduces test scores by 0.02 standard deviations.Next articleJune 1, 2022Industries, Mega Firms, and Increasing InequalityIndustry components account for ~62% of the increase in earnings inequality and 73% of btw firm inequality growth.
Showing 492 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization
Currently filtering by:
  • Remove 'Database'

US Labor Productivity Increases at Fastest Pace in a Year

AI Summary. US labor productivity, measured as nonfarm business output per hour, rose at a 3.6% annualized rate, up from an upwardly revised 2.1% pace, while unit labor costs fell 0.7%. The acceleration suggests a capital spending boom is translating into greater efficiency, according to Bureau of Labor Statistics data.

Julia Fanzeres Bloomberg
Date Posted:
November 5, 2026
Is Database:
Database
Is Important:
Important

Nonfarm business productivity grew at a 3.6% annualized rate in Q3, the fastest pace in a year. Real hourly compensation rose 1.1%, while the labor share fell to 53%, the lowest in data going back to 1947.

Is a capital spending boom lifting worker productivity?

Core argument: US nonfarm business output per hour rose at a 3.6% annualized rate in the third quarter, the fastest pace in a year, following an upwardly revised 2.1% pace in the prior quarter, according to the Bureau of Labor Statistics.

US labor productivity accelerated in the third quarter at the fastest pace in a year, suggesting a capital spending boom is translating into greater efficiency at a time when input costs have soared. Productivity, or nonfarm business output per hour, increased at a 3.6% annualized rate after an upwardly revised 2.1% pace in the prior quarter, data from the Bureau of Labor Statistics showed Thursday. Unit labor costs — what businesses pay employees to produce one unit of output — fell 0.7%, the first decline since the second quarter of 2025.

Takeaways by Macro Roundup® AI

  1. US nonfarm business output per hour rose at a 3.6% annualized rate in the third quarter, the fastest pace in a year, following an upwardly revised 2.1% pace in the prior quarter, according to the Bureau of Labor Statistics.
  2. Unit labor costs, what businesses pay employees to produce one unit of output, fell 0.7% in the third quarter, the first decline since the second quarter of 2025.
  3. The third-quarter productivity acceleration suggests a capital spending boom is translating into greater efficiency at a time when input costs have soared.

Related Articles:

  • Change Is Accelerating Because It’s All Interconnected — The authors estimate that artificial intelligence equity valuations discount roughly 3.5% annual productivity growth and trend nominal output growth well above the recent past, while consensus forecasts and broader markets imply growth closer to recent experience. On that basis, either AI stocks or broader macro markets are meaningfully mispriced.
  • Higher Utilisation Explains The Recent Surge In Productivity Growth — Measured total factor productivity growth is estimated to come almost entirely from higher input utilisation rather than underlying efficiency gains, based on a method that infers unobserved capital and labour utilisation from movements in hours per worker. On this evidence, recent productivity gains reflect "working harder" rather than "working smarter.
  • US Focus: The Effect Of Soaring Profits — US nominal pre-tax corporate profits in the national accounts grew 23% over the past year, a pace not seen outside post-recession spikes since the mid-2000s. Wider margins were the key driver, as domestic profit growth far exceeded the 8% rise in corporate value added, pushing margins near all-time highs while the labor share hit new lows.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
  • Workforce
    • Wages/Income

Change Is Accelerating Because It’s All Interconnected

AI Summary. The authors estimate that artificial intelligence equity valuations discount roughly 3.5% annual productivity growth and trend nominal output growth well above the recent past, while consensus forecasts and broader markets imply growth closer to recent experience. On that basis, either AI stocks or broader macro markets are meaningfully mispriced.

Greg Jensen Bridgewater Associates
Date Posted:
October 30, 2026
Is Database:
Database

Bridgewater estimates that AI equities are pricing in ~3.5% annual productivity growth, while real growth and productivity implied by breakeven earnings in the broader stock market are slightly below current levels. They conclude: “Either AI stocks or broader macro markets are very meaningfully mispriced.”

Do AI stock valuations and broader markets imply incompatible growth expectations?

Core argument: AI equities are priced for roughly 3.5% annual productivity growth, more than is estimated as needed to make US debts sustainable, and across a reasonable range of assumptions imply trend nominal GDP growth well above the recent past.

Different markets are pricing very different futures; most are far from pricing an unrecognizable world. The AI boom has contributed tens of trillions of dollars of market cap in AI companies. As in any gold rush, trillions of dollars of “picks and shovels” only make sense if a great deal of gold (profits) is found. The pricing of a broader universe of AI companies tells us something about the scale of transformation that is discounted. If we calculate the incremental revenue required to sustain the valuation of AI companies and estimate how much of that can be earned by cannibalizing the revenue of existing companies, we can back out a productivity boost required to generate the rest of the incremental revenues. Today, we estimate that AI equities are discounting something like 3.5% annual productivity growth. That’s more than we estimate is needed for the US to make its debts sustainable. Across a reasonable range of assumptions, we assess AI stocks to be pricing trend nominal GDP growth well above the recent past. By contrast, consensus estimates and broader markets imply a world that looks more like the recent past. Making sense of the pricing in AI stocks requires a boom in productivity and growth. Consensus estimates for growth and productivity (from economist and Fed estimates) are slightly lower than current measures, as are rates of real growth and productivity implied by breakeven earnings in the broader stock market.

Takeaways by Macro Roundup® AI

  1. AI equities are priced for roughly 3.5% annual productivity growth, more than is estimated as needed to make US debts sustainable, and across a reasonable range of assumptions imply trend nominal GDP growth well above the recent past.
  2. Consensus growth and productivity estimates from economists and the Fed, along with real growth and productivity implied by breakeven earnings in the broader stock market, sit slightly below current measures, describing a world resembling the recent past.
  3. Either AI stocks or broader macro markets are very meaningfully mispriced, since the AI boom has added tens of trillions of dollars of market cap that only makes sense if a great deal of profit is found.

Related Articles:

  • No Signs of AI Payoff Outside the Tech Sector — Revenue per worker and profit margins are rising for the seven largest technology companies but flat for the rest of the S&P 500, with no signs of artificial intelligence boosting productivity growth. The author argues that as long as spending on artificial intelligence stays confined to a small part of the economy, its macroeconomic effects will remain very limited.
  • Califoreboding — A basket of 42 AI stocks accounts for 60%-80% of all S&P 500 earnings growth, price returns and capital spending, while 60% of S&P 500 stocks sit 20% or more below their highs and only 30% outperform the index. AI-related construction spending has risen 38%, the only positive trend as manufacturing, commercial real estate and residential construction decline.
  • AI and Productivity — The recent pickup in US labor productivity appears driven by higher utilization of existing capital—running factories, server racks, and hotel rooms harder—rather than by firm-level efficiency gains from AI. Adoption remains too thin and the cross-sectional evidence too weak to conclude that AI-driven productivity gains are already widespread.
  • Investment
  • GDP
    • Financial Markets
    • Growth
  • Productivity
    • Innovation/Research

No Signs of AI Payoff Outside the Tech Sector

AI Summary. Revenue per worker and profit margins are rising for the seven largest technology companies but flat for the rest of the S&P 500, with no signs of artificial intelligence boosting productivity growth. The author argues that as long as spending on artificial intelligence stays confined to a small part of the economy, its macroeconomic effects will remain very limited.

Torsten Slok Apollo
Date Posted:
October 26, 2026
Is Database:
Database

Citing flat revenue per worker, and profit margin growth for the S&P 493, Slok argues that so far, AI’s diffusion through the economy has delivered “zero financial payoff to corporate America outside of tech.”

Is artificial intelligence boosting revenue and margins outside big tech?

Core argument: Revenue per worker and profit margins are rising for the Magnificent 7 but flat for the S&P 493, with no signs of AI boosting productivity growth or delivering financial payoff outside the tech sector.

With no impact on the bottom line, mainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins. VC-backed companies are spending more and more on tokens, but mainstream corporate America does not need the latest model and is unwilling to pay for it. As long as agentic AI and token spending are confined to a small part of the economy, the macroeconomic effects of AI will be very limited. The question looking ahead is whether, even as models become commoditized, mainstream corporate America can turn increasingly cheap intelligence into higher revenue growth and higher profit margins. That may be harder than it sounds. The AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector.

Takeaways by Macro Roundup® AI

  1. Revenue per worker and profit margins are rising for the Magnificent 7 but flat for the S&P 493, with no signs of AI boosting productivity growth or delivering financial payoff outside the tech sector.
  2. Mainstream corporate America is pulling back from AI and treating token spending as a cost center that weighs on margins, while VC-backed companies spend increasingly more on tokens.
  3. The macroeconomic effects of AI will remain very limited as long as agentic AI and token spending stay confined to a small part of the economy.

Related Articles:

  • Circularity in AI Shows Up in Token Spending Too — Payment-data analysis finds 80% of the largest corporate AI spenders are venture-backed, versus 13% of the median spender, indicating that much token spending is funded by venture capital rather than earnings. If AI payoffs are slow, venture returns could suffer and a funding pullback could weaken demand for computing power.
  • Califoreboding — A basket of 42 AI stocks accounts for 60%-80% of all S&P 500 earnings growth, price returns and capital spending, while 60% of S&P 500 stocks sit 20% or more below their highs and only 30% outperform the index. AI-related construction spending has risen 38%, the only positive trend as manufacturing, commercial real estate and residential construction decline.
  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • Innovation/Research
  • Productivity

How Much Revenue Does The AI Buildout Need In 2030?

AI Summary. The authors estimate AI capital spending of $560bn in 2026 and, in a high-investment case, $3.5tn of net AI capital in service by 2030, requiring roughly $1.3tn in annual infrastructure revenue once depreciation, operating costs and a 15% return are covered. That implies 7.4x growth from current annualized revenues, with downstream revenues of $1.9–2.6tn.

William Gildea and Azeem Azhar Exponential View
Date Posted:
October 1, 2026
Is Database:
Database

Beginning with capex projections, Gildea/Azhar derive for their high-growth case a $1.3T annual AI-infrastructure revenue hurdle for 2030. This covers $771B of depreciation and operating costs, plus a 15% return on $3.5T of net capital in service.

How much annual revenue must AI infrastructure generate to justify investment?

Core argument: In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.

We estimate AI CapEx to be $560B in 2026, 64% of the total declared by the hyperscalers. Unfinished facilities can’t generate revenue, so the investment bill and the productive asset base will grow at different speeds. [In the high investment case], by 2030 we see $3.5T of net AI capital in service. The largest component [of the revenue hurdle] is the $555B in compute depreciation. We model lifespans at the current 5-6 years. Shell depreciation and OpEx combined contribute about a further $216B to the required revenue hurdle. [Adding a 15% return on net capital brings required annual infrastructure revenue to $1.3T]. Reaching the $1.3T hurdle would represent 7.4x growth (59% CAGR) from today’s annualized revenues. For its customers to earn 30-50% gross margins, downstream revenues need to be $1.9-2.6T. [Editor’s note: Van Nieuwerburgh’s far larger figure of $3.7T covers mature revenues from projects completed through 2032, using commissioning capital and a 50% infrastructure operating cash-flow margin. This estimate instead charges depreciation and operating costs plus 15% on net capital in service in 2030. Different horizons and cost conventions prevent a direct comparison. Van Nieuwerburgh also has a much higher capex estimate.]

Takeaways by Macro Roundup® AI

  1. In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.
  2. Compute depreciation of $555bn, modeled on 5-6 year lifespans, is the largest component of the 2030 revenue hurdle, with shell depreciation and operating expenses adding about $216bn and a 15% return on net capital completing the $1.3tn total.

Related Articles:

  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • What Investment Data Implies about the AI Transition — The five largest U.S. technology firms are scaling AI capital spending about 50% a year, from $155bn to a forecast $755bn. Embedding this in a two-sector model with rare productivity booms, the authors estimate AI adds roughly 5, 20, or 58pp to cumulative growth over a seven-year transition, depending on scenario.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

AI and Entrepreneurship: What is Really Happening?

AI Summary. Entrepreneurship by businesses with employees is flat or declining, including in AI-intensive information and professional services, where establishment entry has been flat or falling and young-firm employment in AI-exposed industries is falling. Applications for likely solo businesses with no employees rose steeply in those AI-intensive sectors, but self-employment as a main job is flat, suggesting side hustles.

Nathan Goldschlag Economic Innovation Group
Date Posted:
September 30, 2026
Is Database:
Database

Nathan Goldschlag finds that available federal data do not yet establish that AI is boosting entrepreneurship. “Across different cuts of the data, there is no clear evidence of AI affecting entrepreneurship, either for likely employers or nonemployers.”

Is AI driving a boom in new employer businesses?

Core argument: Business applications likely to become employer businesses were flat in 2025 and began rising in 2026, with fewer applications of the kind having the highest chance of becoming an employer business.

Employer entrepreneurship is at best rising modestly, and at worst has been falling for several years already. The number of business applications likely to become an employer business was flat in 2025 and began rising in 2026. Fewer of those applications have been the kind that have the highest chance of becoming an employer. Nonemployer entrepreneurship, or “solopreneurship,” is likely rising, but not as the main job of the solopreneurs. These are probably side hustles. Likely nonemployer business applications rose significantly in 2025 and early 2026. But the share of workers claiming to be self-employed nonemployers in their main job — in other words, business owners with no employees — is flat (though noisy). AI-intensive sectors and industries are no exception. Establishment entry in AI-intensive sectors (information and professional services) has been flat or falling in recent years.

Takeaways by Macro Roundup® AI

  1. Business applications likely to become employer businesses were flat in 2025 and began rising in 2026, with fewer applications of the kind having the highest chance of becoming an employer business.
  2. Likely nonemployer business applications rose significantly in 2025 and early 2026, going vertical in the most AI-intensive sectors of information and professional services, while other sectors kept pace until early 2026 and then flattened.
  3. The share of workers reporting self-employment as nonemployers in their main job is flat, though noisy, including among workers in AI-exposed occupations in 2025 and early 2026.

Related Articles:

  • The Age Of The Solopreneur — A surge in US business formation is being driven by solo entrepreneurs, with growth in AI tool usage and in solopreneur activity in high-AI-adoption sectors suggesting artificial intelligence accounts for a meaningful portion of the increase. The authors argue AI is lowering barriers to starting and growing a business by expanding what one-person firms can do.
  • America Has Become An Entrepreneur’s Paradise — US growth in new-business applications is concentrated in professional services (up 25% over four years), retail (18%), and health care and social assistance (18%) rather than artificial-intelligence startups. The share of applications the Census Bureau deems likely to hire at least one employee has fallen to 30% from 38%.
  • 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.
  • Startups
  • Productivity
    • Innovation/Research
    • Investment

Tech's Trillion-Dollar Internal Inconsistency

AI Summary. Wall Street analysts covering technology project the sector's operating cash flow will more than double to roughly $2.4tn by 2028, a gain of over $1.2tn, while analysts covering the rest of the S&P 500 — technology's customers — project those firms will add far less cash flow. These sector-by-sector forecasts are internally inconsistent.

Torsten Sløk Apollo
Date Posted:
September 29, 2026
Is Database:
Database

Tech analysts expect sector operating cash flow to more than double to ~$2.4T by 2028. Analysts covering other S&P 500 sectors (tech’s customers) see far less cash growth in their sectors. Slok “raises the question of who exactly will be writing all those checks to buy AI services.”

Are tech cash flow forecasts inconsistent with customers' projected growth?

Core argument: Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.

Wall Street equity analysts work in sector silos, and when you add up their forecasts, the numbers are internally inconsistent. The analysts covering tech expect the sector's operating cash flow to more than double to roughly $2.4 trillion by 2028, an increase of over $1.2 trillion. Meanwhile, the analysts covering the other sectors in the S&P 500, which are tech's customers, expect those companies to add much less operating cash flow. In other words, the tech silo is betting on a future in which demand for AI and tech services explodes, while the silos covering the companies that would pay for those services see a much more modest outlook. Both cannot be right at the same time. The bottom line is that either tech's customers will generate a lot more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who exactly will be writing all those checks to buy AI services.

Takeaways by Macro Roundup® AI

  1. Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.
  2. Analysts covering the other S&P 500 sectors, which are tech's customers, expect those companies to add much less operating cash flow, leaving the combined forecasts internally inconsistent.
  3. Either tech's customers will generate much more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who will pay for AI services.

Related Articles:

  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
© Copyright 2026 Coherent Research Institute · All Rights Reserved · Privacy · Terms