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TL;DR
ThorstenMeyerAI.com’s Nov. 12 article uses the history of computers to explain why AI’s productivity impact may take time to appear across the economy. It reports large gains in one study of customer-support agents, but says executives estimate AI has added about 0.2 percentage points a year to productivity growth so far.
ThorstenMeyerAI.com published an article on Nov. 12 explaining how workers might bring AI into daily workflows and why the technology’s broader effect on productivity remains difficult to measure. The report points to a study finding a 15% productivity increase among customer-support agents, while saying executives estimate AI has so far added about 0.2 percentage points a year to productivity growth.
The article, part of the series Work After Work, frames AI through a basic economic measure: output per hour worked. It argues that sustained productivity growth is a key driver of rising living standards because the gains compound over time. At 2% annual growth, output per hour doubles in about 35 years; at 1%, the doubling takes about 70 years, according to the article.
Its central historical example is economist Robert Solow’s analysis of U.S. output per hour from 1909 to 1949. In a 1957 paper, Solow attributed 87.5% of the increase to technical change and 12.5% to increased use of capital, such as machinery. The article uses that finding to explain why productivity depends not only on buying tools, but also on changes in methods, skills and organisation.
The article contrasts local improvements with economy-wide results. It says one study found customer-support agents were 15% more productive with AI, but that executives put AI’s contribution to productivity growth at roughly 0.2 percentage points per year so far. Those figures describe different kinds of evidence: a result in a particular work setting and an estimate of the technology’s broader economic effect.
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 Gains May Take Time to Spread
The distinction between a tool helping particular workers and lifting national productivity matters to employees, employers and policymakers. A company may see faster task completion without the change immediately appearing in economic statistics. Wider gains may depend on how organisations redesign work, train staff and apply AI across different tasks.
The article’s comparison with computers cautions against treating early enthusiasm as proof of a broad productivity surge. It says U.S. nonfarm business productivity grew about 2.8% annually from 1948 to 1973, then about 1.4% from 1973 to 1995, even as computers spread. From 1995 to 2004, growth rose to about 3.1% a year. The sequence shows that the effect of a new technology may emerge unevenly and with a lag; it does not establish that AI will follow the same path.
For people bringing AI into daily work, the practical implication is to distinguish measurable task-level changes from claims about overall productivity. Faster drafting or support responses may be useful, but the article does not establish that every use case produces a net gain once review, errors, training and workflow changes are counted.
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What the Computer Era Shows
The article revisits the “Solow paradox,” named for a 1987 observation by Solow: “You can see the computer age everywhere but in the productivity statistics.” The line described a period when computers were increasingly visible in workplaces but productivity growth had slowed. Later, productivity growth accelerated. The article cites Federal Reserve economists Stephen Oliner and Daniel Sichel, who estimated that information technology use and computer production accounted for about two-thirds of the one-percentage-point increase in productivity growth they examined.
It also gives a sector example from U.S. agriculture. According to figures it attributes to the U.S. Department of Agriculture, farm output in 2023 was nearly three times its 1948 level, while total inputs had risen by about 9% over the period. The case illustrates how output can rise substantially without a comparable increase in resources used, though it does not directly forecast AI’s effect on other sectors.
The piece is an explanatory report, not a workplace implementation study or a new measurement of AI’s economy-wide impact. It uses historical productivity data and cited research to provide context for a question that is still being measured.
“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 AI Productivity Figures Miss
The source does not identify the customer-support study by name, provide its sample size or describe the conditions under which its 15% result was measured. It also does not explain which executives supplied the estimate that AI has contributed about 0.2 percentage points a year, or how that estimate was calculated. The figures should therefore be read as the article presents them, not as a complete or independently verified measure of AI’s effect.
It remains unclear how much reported task-level improvement will persist across different jobs and organisations, or whether those gains will translate into higher economy-wide output per hour. The source does not quantify potential costs such as implementation, oversight, errors or changes to staffing. It also does not establish that AI will replicate the computer-era productivity rebound.
Tracking Wider Workplace Effects
The next test is whether measured benefits extend beyond individual tasks and appear in company and national productivity data. Employers adopting AI will need to track comparable measures, such as time spent per completed task, output quality and the work required to check results, rather than relying on adoption rates alone.
For the economy-wide picture, future productivity releases and further research will help show whether today’s AI use is associated with a lasting change in output per hour. ThorstenMeyerAI.com’s episode offers historical context, but it does not provide a timetable for when any broad effect might become visible. The scale and distribution of the gains remain open questions.
Key Questions
What does productivity mean in the article?
Productivity is output per hour worked: how much value workers produce in a given hour. The article focuses on labor productivity as a measure of how efficiently work produces output.
Does the article say AI has already increased productivity across the economy?
No. It reports a study finding a 15% gain among customer-support agents and an estimate that AI has added about 0.2 percentage points a year to productivity growth so far. It does not establish a broad, settled effect across the economy.
Why does the article compare AI with computers?
It uses the computer era to show that widespread use of a technology does not always produce an immediate rise in measured productivity. U.S. productivity growth later accelerated, but the article does not claim AI will follow the same timeline.
What should workers measure when using AI?
The source does not prescribe a specific system. Its discussion suggests separating task-level improvements from wider productivity claims by tracking output and time alongside quality, review work and other costs.
Source: ThorstenMeyerAI.com
