🔍 Read the full analysis: AI And Productivity: A New Way To Approach Work on ThorstenMeyerAI.com
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TL;DR
A November 12 article from ThorstenMeyerAI.com compares AI with earlier waves of technology, using the history of U.S. productivity growth to explain why workplace gains may take time to appear in economy-wide data. It cites a study reporting a 15% productivity increase among support agents and an executive estimate that AI has added about 0.2 percentage points a year to productivity growth so far; the source does not identify the study or the executive survey in the supplied material.
AI has produced measurable productivity gains in some workplaces, but its effect on overall U.S. productivity growth remains modest so far, according to an article published November 12 by ThorstenMeyerAI.com. The article cites a study reporting a 15% gain among customer-support agents and an estimate from executives that AI is contributing about 0.2 percentage points a year to productivity growth, setting the figures against earlier technology waves that took years to show up in national statistics.
The article defines labor productivity as output per hour worked, a measure of how much value the economy produces for each hour of labor. It argues that even small differences in the annual growth rate accumulate over time: at 2% a year, output per hour doubles in about 35 years; at 1%, it takes about 70 years. These are illustrative calculations, not forecasts of future living standards.
To explain why technology’s contribution is difficult to isolate, the article revisits economist Robert Solow’s 1957 analysis of U.S. output per worker from 1909 to 1949. Solow estimated that 87.5% of the increase came from technical change and 12.5% from more capital per worker. The source describes technical change broadly, including better methods, organization and skills as well as technology.
The article also compares AI with the computer era. U.S. productivity growth slowed after 1973 even as computers spread, then rose to about 3.1% a year from 1995 to 2004, based on the article’s calculations from a Bureau of Labor Statistics index. It cites a 2000 estimate by Federal Reserve economists Stephen Oliner and Daniel Sichel that information technology and computer production accounted for about two-thirds of a one-percentage-point acceleration. The source does not provide comparable detail for the AI estimate or the support-agent study.
Infographic 01 · The sector clock
Productivity moves the sector clock
US farms produced nearly three times their 1948 output in 2023, with only about 9% more inputs in total. Over the same decades, farming's share of US jobs fell from about one in eight to 1.5%.
- Field12.1%
- Factory29.9%
- Services58.0%
Lebergott · own calculation
- Field1.5%
- Factory18.9%
- Services79.6%
ILO estimate
Farm output +210% (1948-2023, 1.52% a year); all inputs +0.12% a year (about +9% in total); total factor productivity +1.40% a year (USDA ERS). 1950: services = remainder, incl. unemployed.
Sources: USDA ERS, Agricultural Productivity in the U.S. (updated 25 Aug 2026); US employment shares: S. Lebergott, in US Census Bureau, Historical Statistics of the United States (1975; 1950, own calculation, see episode 2); ILO modelled estimates via World Bank (2025).
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Why Economy-Wide Gains Take Time
The distinction between a productivity improvement at one company and a change in national productivity statistics matters for workers, employers and policymakers. A tool can help particular employees complete tasks faster without immediately increasing total economic output per hour. Gains may be limited to certain jobs, offset by implementation costs, or take time to spread across businesses.
The historical comparison is relevant but not proof that AI will follow the same path as computers. The article’s examples show that productivity data can lag behind the arrival of a technology, while offering no firm timetable for when AI’s contribution might grow. Its 0.2-percentage-point estimate is presented as an executive assessment, not as a definitive measure established by the supplied evidence.
If productivity growth does rise and persist, it can support higher output without requiring the same increase in hours worked or labor input. But the article does not establish that AI will produce such an economy-wide change, or how any gains would be shared among workers, companies and consumers.
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From Solow’s Paradox to AI
In a 1957 paper, Solow separated growth associated with additional capital from growth not explained by that input. The article reports his finding that technical change accounted for 87.5% of U.S. output-per-hour growth between 1909 and 1949. Economists later used the term total factor productivity for the portion of growth not explained by measured increases in capital and labor; it is a residual, not a direct count of innovation alone.
Solow’s name is also attached to a 1987 observation about computers. Reviewing a book on manufacturing, he wrote that the computer age could be seen everywhere except in productivity statistics. U.S. productivity growth, as calculated in the source from a BLS index, averaged 2.8% a year from 1948 to 1973 and 1.4% from 1973 to 1995. It then averaged about 3.1% from 1995 to 2004, when the cited Federal Reserve research examined information technology’s role.
The article offers farming as another example of productivity’s effect on the structure of work. It says U.S. farm output in 2023 was nearly three times its 1948 level, while total inputs grew by about 9% over the period, drawing on figures attributed to the U.S. Department of Agriculture. It connects such changes to a long shift in employment away from agriculture. The examples provide historical context; they do not measure AI’s current effect.
“You can see the computer age everywhere but in the productivity statistics.”
— Robert Solow, quoted in a 1987 New York Times Book Review review
What the AI Estimates Do Not Show
The supplied article material does not name the study behind the 15% support-agent productivity result, describe its methods, or state when it was conducted. It also does not specify which executives supplied the estimate of AI’s roughly 0.2-percentage-point annual contribution, the survey period, or how that estimate was calculated. Those details are needed to judge how representative the figures are.
It is also unclear from the material whether the 0.2 figure is an estimate of productivity growth already recorded in official statistics, a forecast, or executives’ assessment of AI’s contribution. The article does not provide a broad, causal estimate of AI’s effect across industries, nor does it say how the reported workplace gains compare with the costs of adopting the tools.
Past computer-era patterns are not a guarantee that AI will generate a similar productivity rebound. The timing and scale of any economy-wide effect, and its consequences for employment, wages and working conditions, remain unsettled in the information supplied.
Evidence to Watch in Later Data
The next test is whether productivity gains reported within individual firms spread to more employers and appear in economy-wide measures of output per hour. Readers can follow future Bureau of Labor Statistics productivity releases for changes in measured U.S. productivity, while keeping in mind that those data alone may not identify AI as the cause.
Further reporting would also need to identify the studies and executive survey cited for the current AI estimates, including their dates, methods, industries and comparison groups. Until those details and broader data are available, the figures support a limited conclusion: some reported workplace gains exist, but the scale of AI’s overall effect remains uncertain.
Key Questions
What does productivity mean in this article?
It means output per hour worked: the amount of economic output produced for each hour of labor. It is distinct from working longer hours or employing more people.
How much productivity improvement does the article attribute to AI?
The article cites a study reporting a 15% gain for support agents and says executives estimate AI’s contribution to productivity growth at about 0.2 percentage points a year so far. The supplied material does not give enough methodological detail to assess either figure’s scope.
Why does the article compare AI with computers?
It points to a delay between computers becoming widespread and a later increase in U.S. productivity growth. That history shows why a new technology’s early presence does not necessarily mean its economy-wide effect is already visible; it does not establish that AI will follow the same timeline.
Is AI already raising productivity across the U.S. economy?
The material does not establish a definitive economy-wide causal effect. It presents reported gains in one workplace study and an executive estimate, while leaving the size and timing of broader effects uncertain.
Source: ThorstenMeyerAI.com
