Edward Conard

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Reconnecting Americans to the Benefits of Work

AI Summary. Falling pay does not explain declining labor force participation among prime-age men: median hourly compensation including non-wage benefits rose 12% and median annual compensation rose 14%, with gains also at the 10th percentile. The decline is more pronounced among men without a college degree, who are more likely to live in rural areas.

Scott Winship Joint Economic Committee
Date Posted:
October 27, 2021
Is Database:
Database

Decline in prime age male labor force participation unlikely due to declining compensation, as median hourly compensation rose 12% from 1973-2019.

Does falling pay explain declining labor force participation among prime-age men?

Core argument: Median hourly compensation for prime-age men rose 12% from 1973 to 2019, versus a 5% rise in median hourly wages, suggesting declining compensation is unlikely to explain falling labor force participation.

The decline in Labor Force Participation (LFP) among prime-age males is unlikely due to declining compensation, as data from 1973 to 2019 shows. While median hourly wages for prime-age men rose 5%, median hourly compensation, which includes non-wage benefits, increased by 12%. At the 10th percentile, wages rose 3%, but compensation rose 10%. Median annual earnings increased by 6%, and when non-wage compensation is included, the rise is 14%. Even under counterfactual scenarios accounting for non-working men, annual compensation still rose by 11%. The decline in LFP is more pronounced among men without a college degree, who are more likely to live in rural areas. This suggests that factors other than compensation, such as cultural or educational disparities, may be driving the decline in workforce participation.

Takeaways by Macro Roundup® AI

  1. Median hourly compensation for prime-age men rose 12% from 1973 to 2019, versus a 5% rise in median hourly wages, suggesting declining compensation is unlikely to explain falling labor force participation.
  2. At the 10th percentile, hourly wages for prime-age men rose 3% while hourly compensation including non-wage benefits rose 10%, and median annual earnings rose 6% versus 14% including non-wage compensation, from 1973 to 2019.
  3. The decline in prime-age male labor force participation is more pronounced among men without a college degree, who are more likely to live in rural areas, suggesting cultural or educational factors rather than pay.

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 1


“…Another issue missed by wage trends is that over time, non-wage compensation became a greater share of pay. Non-wage compensation includes employer contributions to employees’ health and other insurance, contributions to retirement savings, and the payroll taxes they pay toward federal and state social insurance programs. These contributions were 13 percent of compensation in 1973 but 19 percent in 2019.51Figure 10 shows the same percentiles as in Figure 9, but this time wages at each percentile are adjusted upward by the same factor to account for non-wage compensation growth.52 This factor adjustment still likely underestimates the growth in compensation, particularly for the lower deciles for whom non-wage compensation often makes up a larger share of total compensation.53 Broader measures of compensation show more growth than a simple accounting of only wages. While median hourly wages among prime-age men rose 5 percent from 1973 to 2019, median hourly compensation rose 12 percent. At the 10thpercentile, wages rose 3 percent and compensation rose 10 percent during the same time period. Instead of falling, the 30th percentile of hourly compensation rose by 4 percent, and the 70th and 90th percentiles rose by 33 percent and 52 percent….”

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 2


“…Rather than showing trends in hourly wages,Figure 11 displays the trend in median annual earnings. Line 1 shows the trend in annual wage and salary income among those who have no self-employment earnings. From 1973 to 2019, the increase in the annual median was 6 percent, compared with a 5 percent rise in the median hourly wage of prime-age men. Adding nonwage compensation (Line 2) leads to a 14 percent increase (compared with 12 percent for hourly compensation).55 It turns out that adding the self-employed (and their earnings) to this sample does not change that conclusion: median annual compensation (Line 3) rises 13 percent.56 At the 10th percentile (not shown), annual compensation (including the earnings of the self-employed) rose 4 percent, compared with 10 percent for the 10th percentile of hourly compensation among employees. In this case, lower annual compensation compared to the hourly measure is likely because more workers at the 10th percentile do not work consistently throughout the year or work fewer hours overall. A final criticism of the trends shown in this section is that we cannot observe the compensation of non-working prime-age males who have dropped out of the labor force. It may be that the only reason compensation seems to have risen is because would-be workers with low compensation are more likely to drop out of the data. In this telling, demand for less-skilled workers may have fallen, but the charts above fail to show it because they only look at men who continue to work. Line 4 of Figure 11 attempts to address this criticism by displaying a counterfactual trend. Respondents in the Current Population Survey data who did not work in the previous year are asked why they did not work. The possible answers include inability to find work, being sick or disabled, taking care of home or family, going to school, retirement, being in the Armed Forces, or “other.” Imagine that nonworking men who were disabled, sick, retired, or said they were nonworking for ‘other’ reasons not listed did not become a larger group between 1973 and 2019 relative to workers. Further, imagine that all these additional men who would have been working would have been below-median workers had they held down jobs. Finally, imagine that in every year all men with no earnings who said they could not find work had worked at below-median compensation. Line 4 in Figure 11 attempts to say what the trend in prime-age male compensation would have been under those counterfactual circumstances. According to this counterfactual trend, the annual compensation of prime-age men still would have risen by 11 percent (instead of 13 percent).57 Note too that annual compensation estimates include men who worked part of the year before leaving the workforce, some with no intention of coming back anytime soon. For such men, their annual compensation is a poor indicator of what they command in the labor market, and if such men grow more common in the data over time, it will tend to pull the compensation trends downward. If pay growth is stronger for men with stable connections to the workforce, then the counterfactual of annual compensation may also conceal the growth in pay among below median workers with consistent labor force connections. Given this, the growth in annual compensation over time is very likely understated for men consistently participating in the workforce….”

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 3


"... Figure 12 walks through each of these adjustments to the productivity and compensation data, updating work by labor economist James Sherk.59 The two bolded lines below (light green for adjusted productivity and lightest blue for adjusted compensation) use the same implicit price deflator to show how wages and productivity remain closely associated with one another. The top green line shows growth in net hourly productivity for all workers including the self employed. Proponents of the pay-productivity gap often present just the top line and the bottom dark blue line showing average hourly compensation of production and nonsupervisory workers. By using a broader measure of workers (medium blue line) and using more accurately comparable measures of inflation, the pay-productivity gap all but disappears. The medium blue line is adjusted using the Personal Consumption Expenditures (PCE) index which approximates inflation for the things people regularly purchase. The lightest blue line is adjusted using the implicit price deflator (IPD) a better measure of price changes for the things Americans actually produce and is more directly comparable to measures of the associated changes in productivity. Comparing the lightest blue line to the light green line, more accurately aligns net hourly productivity with average hourly compensation using the same implicit price deflator and a similar universe of workers….”

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 4


Christina King, Scott Winship and Adam Michel, "Reconnecting Americans to the Benefits of Work," Joint Economic Committee, October 2021, https://www.jec.senate.gov/public/_cache/files/5ac0a254-ff00-4a18-baf0-bdfedb9bb154/connections-to-work.pdf

New Winship on declining LFP, finds that pay hasn't declined, nor has pay lagged productivity (though median pay has lagged productivity growth) implies decline in LFP is driven by other factors (cultural, transfers though he doesn’t attempt to quantify those drivers) Key chart, “…Rather than showing trends in hourly wages,Figure 11 displays the trend in median annual earnings. Line 1 shows the trend in annual wage and salary income among those who have no self-employment earnings. From 1973 to 2019, the increase in the annual median was 6 percent, compared with a 5 percent rise in the median hourly wage of prime-age men. Adding nonwage compensation (Line 2) leads to a 14 percent increase (compared with 12 percent for hourly compensation). It turns out that adding the self-employed (and their earnings) to this sample does not change that conclusion: median annual compensation (Line 3) rises 13 percent.56 At the 10th percentile (not shown), annual compensation (including the earnings of the self-employed) rose 4 percent, compared with 10 percent for the 10th percentile of hourly compensation among employees. In this case, lower annual compensation compared to the hourly measure is likely because more workers at the 10th percentile do not work consistently throughout the year or work fewer hours overall. A final criticism of the trends shown in this section is that we cannot observe the compensation of non-working prime-age males who have dropped out of the labor force. It may be that the only reason compensation seems to have risen is because would-be workers with low compensation are more likely to drop out of the data. In this telling, demand for less-skilled workers may have fallen, but the charts above fail to show it because they only look at men who continue to work. Line 4 of Figure 11 attempts to address this criticism by displaying a counterfactual trend. Respondents in the Current Population Survey data who did not work in the previous year are asked why they did not work. The possible answers include inability to find work, being sick or disabled, taking care of home or family, going to school, retirement, being in the Armed Forces, or “other.” Imagine that nonworking men who were disabled, sick, retired, or said they were nonworking for ‘other’ reasons not listed did not become a larger group between 1973 and 2019 relative to workers. Further, imagine that all these additional men who would have been working would have been below-median workers had they held down jobs. Finally, imagine that in every year all men with no earnings who said they could not find work had worked at below-median compensation. Line 4 in Figure 11 attempts to say what the trend in prime-age male compensation would have been under those counterfactual circumstances. According to this counterfactual trend, the annual compensation of prime-age men still would have risen by 11 percent (instead of 13 percent).

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 5


"... Prime-age men’s LFPR peaked at over 97 percent in 1955, slowly declined to 90.5 percent in 2008, and then dropped to 88 percent by 2014, as indicated in Figure 1. It inched up from there, but pre-pandemic it was only 89 percent in 2019. With the onset of the pandemic-induced recession in 2020, prime-age male labor force participation fell below 88 percent in April 2020, a record low. The pandemic may have worsened decades-long trends in declining workforce attachment, especially among lower-income prime-age workers....A large majority of the out of work force prime-age male population, 82 percent, does not have a bachelor’s degree. In the last twenty years, inactivity rose the most among men without a college degree and among those who previously earned low wages.30 These men are disproportionately likely to live in rural localities—particularly in the Southeast....

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 6


"...Figure 3 shows that labor force participation rises with education, and the participation trends for men and for women follow the same broad pattern as in Figure 1, regardless of education level. However, the decline in male labor force participation and rise in female participation vary by level of schooling..."

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 7


"... Figure 8 displays median wage trends for prime-age men at five different levels of educational attainment.The chart indicates that wages were lower in 2019 than in 1973 among men who lacked a four-year college degree—down 13 percent among those lacking a high school diploma, down 16 percent among those with a diploma but no other schooling, and down 12 percent among those with some college but no bachelor’s degree. Between 1973 and 2019 wages rose 15 percent among prime-age men with a bachelor’s degree but no graduate degree, and they rose 43 percent among those
with a graduate degree.... However, analyzing wages by educational attainment ignores the fact that, as Figure 2 shows, the workforce is growing more educated over time which changes the composition of the education groupings. For instance, looking at men without a high school diploma means assessing the wages of the least educated 30 percent of men in 1973 but the least-educated 10 percent of men in 2019. The group became much more disadvantaged over time, so all else equal, its pay would have fallen even if the pay of the bottom 30 percent did not. Similarly, in 1973, 19 percent of men were in one of the top two groups, but nearly 38 percent were in 2020. Therefore, the men in those top groups were less “elite” in 2020 than 47 years earlier.A better way to assess wage trends for less- and more-advantaged workers is to look at wages at fixed points of the wage distribution. The median wage earner is the one in the middle of the distribution. The earner at the 10th percentile is the one with higher wages than 10 percent of workers but lower wages than 90 percent of workers. Figure 9 shows wage trends for prime-age men at different percentiles. As indicated above, the median male worker saw a wage increase of 5 percent between 1973 and 2019. Below the median, the 10th percentile of wages rose 3 percent, and the 30th percentile fell 3 percent. Above the median, wages grew by 24 percent at the 70th percentile and by 42 percent at the 90th….”

Ed Comment:The last slide shows what a bunch of liars the left are. They claimed and continue to claim that productivity grew without wage growth.

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Previous articleOctober 27, 2021IT and Urban PolarizationIT investment is higher in high-wage cities, driving job & wage polarization. From 1990 to 2015, a 21.4% increase in local price index correlated with a $107.43 rise in average IT budget per worker. @JanEeckhoutNext articleOctober 27, 2021Telemigration and Development: On the Offshorability of Teleworkable JobsTelemigration impact remains modest: US service imports from India/China/Brazil/Canada under $11bn (2019). Data contradicts offshore service job exodus concerns. Key metric shows limited displacement effect.
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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.
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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.
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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
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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
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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.
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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.
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