🔍 Read the full analysis: AI At Work: Practical Ideas For Better Productivity on ThorstenMeyerAI.com
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TL;DR
A November 12, 2026, article from ThorstenMeyerAI.com examines whether workplace AI could lift productivity, drawing comparisons with earlier technology waves. It reports a 15% gain for support agents in one study, while executives estimate AI has added about 0.2 percentage points a year to productivity growth so far; the scale and durability of broader gains remain unclear.
AI is producing measurable gains in some workplace tasks, but the evidence cited in a November 12 article from ThorstenMeyerAI.com does not yet point to a large economy-wide productivity lift. The article reports a 15% improvement for support agents in one study and says executives estimate AI’s contribution to overall productivity growth at about 0.2% a year, underscoring the gap between results inside individual firms and broader economic measures.
The article defines labor productivity as output per hour worked and argues that sustained gains matter because they compound over time. At 2% annual growth, output per hour doubles in roughly 35 years; at 1%, the doubling takes about 70 years. These are illustrative calculations in the source, not forecasts for any particular country or workforce.
For historical comparison, the article cites economist Robert Solow’s 1957 analysis of US output per man-hour from 1909 to 1949. Solow attributed 87.5% of the increase to technical change and 12.5% to increased capital use, such as machinery. The source notes that the category economists call total factor productivity is a residual: it captures output growth not explained by measured increases in labor and capital, and its contents are debated.
The article also traces the uneven record of earlier technologies. Its calculations from a Bureau of Labor Statistics index put annual productivity growth in the US nonfarm business sector at 2.8% from 1948 to 1973 and 1.4% from 1973 to 1995. Growth then reached about 3.1% a year from 1995 to 2004. Citing Federal Reserve economists Stephen Oliner and Daniel Sichel, it says information technology and computer production accounted for about two-thirds of a one-percentage-point acceleration 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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From Task Gains to Economic Growth
The distinction between a task-level result and an economy-wide change is central to assessing AI at work. A tool can help employees complete a defined job faster without immediately changing a company’s total output per hour: firms may need time to reorganize work, train staff, integrate systems, and measure the resulting output. The article’s figures suggest some local gains are being reported, while its estimate of AI’s wider contribution remains modest.
That matters for workers, employers and policymakers because productivity growth can support higher output and living standards over time. But the historical record cautions against treating the spread of a technology as proof of a productivity boom. Computers became common before productivity growth accelerated in the late 1990s. For AI, the relevant test is whether task improvements spread across organizations and appear in consistent, economy-wide data—not only in selected studies or executives’ estimates.
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What Earlier Technology Waves Show
In a 1987 review of a book about manufacturing, Solow remarked that the computer age could be seen everywhere except in productivity statistics. The line became known as the Solow paradox. The later acceleration in US productivity, particularly from 1995 to 2004, showed that an apparent lag between adopting a technology and recording broad gains can complicate early assessments.
The source also uses US agriculture to illustrate how output can rise while employment and total inputs grow much less. It says farm output in 2023 was nearly three times its 1948 level, while total inputs rose by about 9% over the period, citing US Department of Agriculture figures. The shift in the share of US workers employed in farming—from about one in eight in 1950 to an estimated 1.5% in 2025—illustrates how productivity can change where people work, rather than simply eliminating work.
How Broad AI Gains Will Be Measured
The source does not identify the support-agent study behind the 15% result, including its sample, setting, duration or method. It also does not specify how executives’ estimate of about 0.2% annual productivity growth was collected or whether it refers to percentage points, a direct measured change, or a survey-based assessment. Those limits make the figures difficult to compare directly.
It is also too early, on the information provided, to establish how much AI has changed productivity across the full economy, which industries account for any gains, or whether improvements will persist as adoption expands. The source provides no detailed methodology for its AI estimates and no new official productivity release tied specifically to AI. Broader effects remain uncertain.
Watch for Economy-Wide Evidence
The next meaningful test is whether reported workplace improvements are reproduced across more employers and appear in official output-per-hour data over time. Readers should distinguish study-specific task results and executive assessments from national productivity statistics, which cover much wider activity and are published with a lag.
As organizations adopt AI, future evidence will need to show not only whether workers finish particular tasks faster, but whether that time produces more or better-valued output after accounting for implementation costs and changes in staffing or workflow. Until then, the figures in the article offer early indicators, not a settled verdict on AI’s contribution to long-run productivity growth.
Key Questions
What does productivity mean in this article?
It means output per hour worked, also called labor productivity. It measures how much value is produced for each hour of labor.
What AI productivity gains does the source report?
It cites a 15% improvement for support agents in one study and an estimate from executives that AI has contributed about 0.2% a year to productivity growth so far. The source does not provide enough study or survey detail to assess those figures fully.
Does the article show that AI has raised productivity across the economy?
No. It describes reported gains in some tasks and a modest executive estimate, but does not establish a broad, economy-wide effect. Wider changes remain uncertain.
Why compare AI with the spread of computers?
The computer era included a period when productivity growth slowed despite visible technology adoption, followed by an acceleration from 1995 to 2004. The comparison shows that the timing and scale of technology’s economic effects may not be clear at first.
Source: ThorstenMeyerAI.com
