🔍 Read the full analysis: What Happens When AI Changes How We Work? on ThorstenMeyerAI.com
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TL;DR
Thorsten Meyer’s November 3, 2026 article uses the long decline in farm employment to examine how technology may change work. It documents major gains in agricultural productivity and a workforce shift that took generations, while presenting AI’s potential to automate much of a sector as the author’s thesis, not a settled forecast.
Thorsten Meyer’s November 3 article argues that agriculture offers a historical test case for understanding how technology can change work: in the United States, the share of workers in farming fell from about 73.7% in 1800 to roughly 1.5% today, while food production grew. Meyer uses that shift to frame a question about AI, but his suggestion that automation could reach as much as 90% of a sector within a decade or a few decades is his thesis, not an established forecast.
The article draws on an estimate by economic historian Stanley Lebergott, published by the U.S. Census Bureau, that about 1.4 million of a 1.9 million-person U.S. labor force worked in agriculture in 1800. The estimate covers workers aged 10 and older and includes roughly 530,000 enslaved people. Because figures before 1840 are estimates rather than census counts, Meyer describes the result as about three in four workers, rather than treating the percentage as exact.
Meyer traces agricultural change through a sequence of tools, fertilizer and improved seeds, rather than a single breakthrough. Examples include John Deere’s steel plow in 1837, Fritz Haber’s 1908 ammonia synthesis patent and the spread of short-straw wheat in the 1960s. Indian official statistics cited in the article show wheat production rising from 12.3 million tonnes in 1964–65 to 26.4 million tonnes in 1971–72. The comparison uses the year before the 1965–66 drought, which had a lower harvest.
The change in employment did not happen all at once. The article says the number of U.S. farm workers continued to rise until around 1910, reaching 11.8 million, even as agriculture’s share of the workforce began to fall. It also cites International Labour Organization estimates that 928 million people worked in agriculture worldwide in 2025, around one in four workers globally. The figure shows that agricultural employment remains substantial even after steep declines in some high-income countries.
Agriculture’s Long Workforce Shift
The farm example matters because it separates sector-wide productivity gains from the fate of individual workers. As tools, fertilizer and seeds increased output per worker, fewer people were needed to produce food in countries such as the United States. That did not mean farming disappeared: the article estimates that about 1.5% of U.S. workers remain in agriculture, compared with 43% in India and about 1% in Germany.
For readers trying to understand AI’s possible effects, the history offers a caution against assuming either instant mass job loss or a painless adjustment. In the U.S. case, the shift unfolded over roughly two centuries, and the article notes that farm-worker numbers rose for decades before peaking. That history is useful as a comparison, but it cannot establish how quickly AI will change employment, which jobs will be affected, or whether new work will compensate for losses.
The history also challenges the idea that fewer farmers necessarily mean less food. Meyer cites figures showing that global cereal production increased 4.2-fold from 1961, while population grew 2.7-fold during that period. Those measures describe global output and population over the same span; they do not by themselves explain how the gains were distributed, or prove that every technological change benefited all workers equally.
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From Hand Labor to Higher Yields
Meyer’s article is the second episode of a series that divides economic activity into field, factory and office. It starts with farming because agriculture offers a long record of mechanization and productivity change. Around 1800, food production depended heavily on people and animals, with hand tools and weather constraining what a farm could produce. The article argues that limited output per household meant a large share of the population had to work the land.
The sources and dates vary by country, and the measurements are not all directly comparable. Meyer cites historical reconstructions that put agriculture or the broader primary sector at about 85% in Japan in 1872 and 83.5% in China in 1952, alongside India’s 69.7% in the 1951 census. Germany’s first official occupational count cited in the article put agriculture and forestry at 43.4% in 1882; the article notes that women’s work on family farms was undercounted. These figures give a broad sense of the historical scale, not a single standardized international series.
Later agricultural advances combined mechanical equipment with biological and chemical inputs. The article points to ammonia fertilizer, which made nitrogen more available to crops, and dwarf wheat varieties that could support heavier grain heads without falling over as readily. Together with irrigation and other farming practices, these changes helped raise yields. The examples show that automation can involve more than machines: processes and inputs can also reduce the labor required for a given amount of output.
““Automation could become a sector of up to 90% within a decade or a few decades.””
— Thorsten Meyer
AI’s Pace and Employment Effects
The article does not present a new study measuring AI’s effect on employment, nor does it provide evidence that a specific share of jobs or sectors will be automated by a particular date. The 90% figure is explicitly Meyer’s thesis; its meaning, scope and supporting method are not established in the supplied material. It remains unclear which occupations AI may change, how quickly adoption will occur, and how employers or workers will respond.
Agricultural history is a comparison, not a direct forecast for today’s economy. The development of farming technology stretched across generations and took place under conditions unlike those facing office and service workers. The article also does not detail where people who left agricultural work went, or how the transition affected wages, working conditions and communities. Its production comparisons show higher output, but do not settle those questions.
Some historical figures are estimates, and the national statistics refer to different dates and categories. The U.S. figure for 1800 is a reconstruction, while Japan’s cited number covers the primary sector, including forestry and fishing. Current global agricultural employment is an ILO estimate for 2025. These differences limit direct comparisons, and the article does not establish that the same pattern will repeat in AI-exposed occupations.
The Series Turns to Factories
Meyer identifies the next episode as a look at the mechanical side of farming, including reapers, threshers and tractors. That continuation is expected to develop the historical account of how equipment changed farm labor after early improvements such as the steel plow. The series’ broader frame is how the economy is built across the field, factory and office.
For the AI question, the next useful evidence will come from observed changes in workplaces: which tasks employers automate, how staffing and productivity change, and whether workers move into new roles. The article offers historical context but does not supply a timetable or a confirmed employment forecast. Until such evidence is available, its central comparison should be read as a way to frame the debate, not a prediction that AI will reproduce agriculture’s trajectory.
Key Questions
What is the main point of Meyer’s article?
It uses agriculture’s long shift toward higher productivity and a smaller workforce share to examine how technology, including AI, could change work. The agricultural history is documented in the article; its forecast about automation reaching up to 90% of a sector is Meyer’s thesis.
How many U.S. workers farmed around 1800?
The Lebergott estimate cited by Meyer puts about 1.4 million of 1.9 million workers, or 73.7%, in agriculture in 1800. The figures are historical estimates, not census counts, and include enslaved people.
Does the article say AI will eliminate 90% of jobs?
No. Meyer writes that automation could become a sector of up to 90% within a decade or a few decades, and labels this a thesis. The article does not establish that 90% of jobs will disappear or give a verified AI employment forecast.
How many people still work in agriculture worldwide?
The article cites an International Labour Organization estimate of 928 million people in 2025, or about one in four workers worldwide. This is a global estimate for agricultural employment, not a count of workers in any one country.
What remains unknown about AI’s effect on work?
The article does not establish the pace of AI adoption, which occupations will be affected, or whether new roles will offset jobs changed or lost. Agricultural history can inform comparisons, but it does not determine how AI’s effects will unfold.
Source: ThorstenMeyerAI.com
