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TL;DR
A November 12 article from ThorstenMeyerAI.com uses the history of productivity to examine whether AI will raise output across the economy. It cites a study finding a 15% productivity gain among customer-support agents, while executives estimate AI has added about 0.2 percentage points a year to productivity growth so far; the broader effect remains uncertain.
ThorstenMeyerAI.com published a report November 12 examining whether artificial intelligence can lift productivity—the output produced per hour worked—and comparing early AI evidence with past technology shifts. The article cites a study reporting a 15% gain for customer-support agents using AI, but says executives put AI’s contribution to overall productivity growth at about 0.2 percentage points a year so far, leaving the wider economic impact unsettled.
The article, part of the “Work After Work” series, argues that productivity matters because its growth compounds over time. At a sustained annual rate of 2%, output per hour doubles in roughly 35 years; at 1%, the doubling takes about 70 years. The piece says that difference shapes how quickly living standards can rise across generations, although output per hour is not by itself a complete measure of household well-being.
It uses economist Robert Solow’s 1957 analysis of U.S. output per worker-hour from 1909 to 1949 to explain why productivity gains are not simply a matter of adding equipment. Solow attributed 87.5% of the measured increase to technical change and 12.5% to increased capital use. The article notes that the residual category can include improved methods, organisation and skills, and that economists debate what it captures.
Historical figures in the report show that technology’s economic payoff can take time to appear. U.S. nonfarm business productivity grew at an average 2.8% a year from 1948 to 1973, then about 1.4% from 1973 to 1995, according to the article’s calculations using a Bureau of Labor Statistics index. Growth later accelerated to about 3.1% a year from 1995 to 2004, during the expansion of information technology. The report cites Federal Reserve economists’ estimate that IT use and computer production accounted for about two-thirds of a one-percentage-point increase in productivity growth in that period.
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 AI’s Productivity Payoff Matters
The distinction between a gain inside one workplace and faster growth across the economy is central to the report. A tool can help particular workers complete tasks faster without changing national productivity figures much, especially if adoption is limited, implementation takes time, or gains are offset elsewhere. The cited 15% support-agent result is a specific study finding; it does not establish that all workers or industries will see the same result.
Economy-wide productivity growth affects how much an economy can produce from its available labour and resources. If AI improves output per hour across many sectors, the effects could accumulate over time. But the report’s cited estimate of about 0.2 percentage points annually so far suggests executives see a modest contribution to overall growth at present. That figure is an estimate, not a settled measurement of AI’s eventual impact.
The historical comparison also counsels against treating the arrival of a new technology as proof of an immediate productivity surge. Computers spread through workplaces before the late-1990s acceleration appeared in productivity statistics. For workers and businesses, the practical question is not only whether AI can perform tasks, but whether its use changes workflows in ways that raise measured output without reducing quality or shifting costs elsewhere.
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From the Solow Paradox to AI
In a 1987 review, Solow observed that the computer revolution was not yet visible in productivity statistics. His remark became known as the Solow paradox. Productivity growth had slowed after 1973 even as computers became more common in offices and factories. The later acceleration from 1995 to 2004 showed that the gap between adopting a technology and seeing its broad economic effects can close, though the historical sequence does not prove AI will follow the same path.
The article also points to agriculture as an example of productivity reshaping employment. It reports that U.S. farm output in 2023 was nearly three times its 1948 level, while total inputs had risen by about 9% across that period. The USDA figures cited put average farm total-factor productivity growth at 1.40% a year. As output per worker increased, the share of U.S. workers in farming fell substantially; the article cites an International Labour Organization estimate of 1.5% in 2025.
These examples frame productivity as a long-running process of producing more with labour, equipment and other inputs—not simply a count of new machines or software tools. The report’s focus is whether AI moves from visible use within individual firms to measurable changes across the wider economy.
“You can see the computer age everywhere but in the productivity statistics.”
— Robert Solow, quoted in a July 1987 New York Times Book Review review
How Broad AI Gains Will Be Measured
The report does not identify the customer-support study by name or provide its sample size, methods or study period in the supplied material. Readers therefore cannot assess from the article excerpt how broadly its 15% result applies. The estimate that AI contributes about 0.2 percentage points a year to productivity growth is also attributed to executives, but the excerpt does not specify the survey, respondents or calculation behind it.
It remains unclear how quickly AI adoption will affect national productivity statistics, which sectors may benefit most, and whether measured gains will persist after implementation costs and changes in output quality are taken into account. The historical computer comparison offers context, not a forecast. The article does not establish that AI will produce a productivity boom on the timetable or at the scale seen during the late 1990s.
Tracking AI in Productivity Data
The next test is whether early workplace gains become visible in broader productivity measures over time. That will require comparing output with hours worked across sectors, while distinguishing AI’s contribution from other changes in investment, staffing, demand and work practices. The article sets out historical data and current estimates but does not give a specific date for a future assessment or identify a next report in the series.
Readers can watch for updated productivity statistics and more detailed studies that disclose their methods, time periods and comparison groups. Until those data are available, the report’s figures support a limited conclusion: some firms may be seeing task-level gains, while AI’s contribution to overall productivity growth remains comparatively small by the estimate cited.
Key Questions
What does productivity mean in this report?
It means output per hour worked—the value of goods and services produced for each hour of labour. It is one measure of economic performance, not a complete measure of living standards.
What AI productivity gains does the article cite?
The article cites a study reporting a 15% productivity gain among customer-support agents. It also says executives estimate AI’s effect on overall productivity growth at about 0.2 percentage points a year so far. The excerpt does not provide enough methodological detail to verify or generalise either figure.
Does the report show that AI is already driving an economy-wide boom?
No. It describes gains at some firms but cites only a modest estimated contribution to overall productivity growth so far. Whether AI will produce a larger, lasting economy-wide effect remains uncertain.
Why does the article discuss computers and the Solow paradox?
The computer era illustrates that widespread adoption does not necessarily show up immediately in productivity statistics. U.S. productivity accelerated later, from 1995 to 2004, but that history does not guarantee AI will have the same timing or effect.
Source: ThorstenMeyerAI.com
