🔍 Read the full analysis: How To Explore AI Without Losing Sight Of Productivity on ThorstenMeyerAI.com
Get business pricing on monitors, keyboards and dev gear
- Business-only prices and quantity discounts
- Tax-exempt purchasing
- Multiple users, one account, clear invoices
TL;DR
A Nov. 12 essay from ThorstenMeyerAI.com uses economist Robert Solow’s work to examine whether AI will raise productivity across the economy. It contrasts reported gains within some firms with executives’ estimate that AI has added about 0.2 percentage points a year to productivity growth so far, while stressing that the broader effect remains uncertain.
ThorstenMeyerAI.com published an essay on Nov. 12 examining whether artificial intelligence will lift productivity across the economy, using economist Robert Solow’s historical research to explain why new technology can take years to register in national statistics. The article says some studies report substantial gains within individual firms, but executives estimate AI’s contribution to overall productivity growth so far at about 0.2 percentage points a year.
The article defines labor productivity as output per hour worked and argues that its growth has long-term consequences because gains compound. At an annual rate of 2%, output per hour doubles in roughly 35 years; at 1%, it takes about 70 years. Those estimates illustrate why a seemingly small difference in the growth rate can matter over generations.
Solow’s 1957 analysis of U.S. output per worker-hour from 1909 to 1949 attributed 87.5% of the increase to technical change and 12.5% to increased capital, such as machinery and buildings. The essay says the broad category of technical change can include better methods, technology, organization and skills; economists often measure its unexplained contribution as total factor productivity.
The article also compares past technology waves. U.S. nonfarm business productivity grew at an annual average of 2.8% from 1948 to 1973, then 1.4% from 1973 to 1995, according to the article’s calculations from a Bureau of Labor Statistics index. From 1995 to 2004, it rose to about 3.1% a year. The piece cites Federal Reserve economists’ estimate that information technology use and computer production accounted for about two-thirds of the one-percentage-point acceleration between the periods they analyzed.
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).
Powered by Thorsten Meyer AI
Why Economy-Wide Gains Take Time
The distinction between productivity improvements at a particular workplace and faster growth across the whole economy is central to the AI debate. A tool can help some workers complete tasks more quickly without immediately changing aggregate output per hour. Its effects may be limited to certain industries, take time to spread, or be offset by the cost of adopting it and reorganizing work.
That matters because productivity growth supports long-run gains in living standards. The article’s historical examples caution against treating early enthusiasm or individual success stories as proof of an economy-wide shift. They also show why a lag in national statistics does not, on its own, establish that a technology has no productive value.
ThorstenMeyerAI.com cites one study reporting a 15% productivity gain for customer-support agents using AI. That is a firm- or task-level result, not an estimate of a 15% increase in national productivity. The essay contrasts it with executives’ estimate of AI’s current contribution to productivity growth, about 0.2 percentage points per year.
Products Worth Considering
Apple Watch Series 8 GPS 41mm Midnight Aluminum Case with Midnight Sport Band
DJI Mini 4 Pro Fly More Combo with DJI RC 2
Kindle Paperwhite Signature Edition 32GB E-Reader, 7-Inch Display, Seaglass Green
As an affiliate, we earn on qualifying purchases.
From Solow’s Paradox to AI
The installment is episode 10 of Thorsten Meyer’s “Work After Work” series, in an arc on economic basics. Its starting point is the productivity paradox associated with Solow. In a 1987 review of a book about manufacturing, Solow observed that productivity growth had slowed even as computers spread through workplaces. His often-cited line was: “You can see the computer age everywhere but in the productivity statistics.”
The apparent mismatch did not last in the same way. U.S. productivity growth accelerated from the mid-1990s to the early 2000s, and Federal Reserve economists Stephen Oliner and Daniel Sichel attributed a substantial part of that pickup to information technology. The essay uses that sequence—adoption, a period of weak measured growth, then an acceleration—as a comparison for AI, not as proof that AI will follow an identical path.
It also points to U.S. agriculture as an example of long-term productivity change. Citing U.S. Department of Agriculture figures, it says farm output in 2023 was nearly three times its 1948 level while total inputs had risen by about 9% over the period. The comparison illustrates how output can grow much faster than the resources used to produce it.
“You can see the computer age everywhere but in the productivity statistics.”
— Robert Solow, quoted in the ThorstenMeyerAI.com essay from his 1987 New York Times Book Review review
What AI Productivity Data Shows
The essay does not identify the study behind its reported 15% customer-support gain, including its sample, methods or time period. It also gives no source or calculation details for executives’ estimate that AI has contributed about 0.2 percentage points a year to productivity growth. Those figures should be read as claims reported by the article, not as a settled measure of AI’s economy-wide impact.
It is not yet clear whether AI will produce a sustained acceleration in national productivity statistics, when any such change might appear, or how benefits and costs will be distributed across occupations and industries. The historical comparison with computers offers context, but does not establish that AI will generate the same timing or scale of gains.
Tracking AI’s Broader Effects
The next test is whether reported task-level improvements spread across companies and industries and lead to a durable rise in output per hour. Readers should distinguish individual workplace studies from national productivity measures, and check the underlying study design, time period and comparison group when interpreting claimed gains.
The source does not announce a specific follow-up study, data release or policy milestone. Broader evidence will depend on future productivity statistics and research that can separate AI’s contribution from other changes in technology, investment, staffing and business organization.
Key Questions
What does productivity mean in the article?
Labor productivity is output per hour worked: the amount of value produced in an hour of work.
Does the article say AI has already raised productivity by 15%?
No. It cites a study reporting a 15% gain for customer-support agents. That task-level result is not an estimate of economy-wide productivity growth, and the source does not provide the study’s methods or time period.
What is the Solow paradox?
It refers to the observation that computers seemed widespread in workplaces while measured productivity growth slowed. The essay notes that U.S. productivity later accelerated, with Federal Reserve economists attributing part of the pickup to information technology.
How much has AI added to overall productivity growth so far?
The essay reports that executives put AI’s contribution at about 0.2 percentage points a year. It does not give details about the estimate’s methodology, so it should not be treated as a definitive measure.
Source: ThorstenMeyerAI.com
