🔍 Read the full analysis: Will AI Move People Into New Kinds Of Work? on ThorstenMeyerAI.com
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TL;DR
A November 3, 2026, essay uses agriculture’s long shift from a dominant employer to a small share of the U.S. workforce to explore whether AI could move people into new kinds of work. It documents tools, fertilizers and improved seeds as contributors to rising output and changing employment, while presenting AI’s potential to automate most work in a sector as the author’s thesis—not an established forecast.
A November 3, 2026, essay by Thorsten Meyer uses the history of agricultural employment to examine whether artificial intelligence could shift people into new kinds of work. The account traces how farming’s share of the U.S. workforce fell from an estimated 73.7% in 1800 to about 1.5% today, but it does not establish that AI will produce the same outcome or specify what future jobs might be.
Meyer’s central historical point is that agriculture changed through a series of tools and innovations, not one sudden switch. The examples include John Deere’s steel plough in 1837, ammonia synthesis developed at the start of the 20th century, and higher-yield dwarf wheat introduced in parts of South Asia in the 1960s. Together with other changes, these developments helped raise how much food could be produced with a smaller proportion of workers in farming.
The essay distinguishes the share of workers in farming from the number of farm workers. In the United States, the estimated number of agricultural workers continued to rise until about 1910, reaching 11.8 million, even as the economy changed. In Germany, agriculture’s share of workers fell from 43% to 25% between 1882 and 1939 while the number of farm workers remained between 8 million and 10 million. Those examples show that a falling employment share does not, by itself, mean an immediate drop in the number of people doing the work.
Meyer cites an International Labour Organization estimate that 928 million people worked in agriculture worldwide in 2025, around one in four workers. The proportions vary sharply: the essay gives agriculture’s share as 43% of workers in India, about 1.5% in the United States and about 1% in Germany. It also reports that global cereal production rose 4.2-fold from 1961 while the world population grew 2.7-fold. These figures describe farming and food production; they do not directly predict AI’s effect on employment.
What Farming Shows About Work
The agricultural record gives readers a concrete example of technology changing the labor mix over generations. It challenges the simple assumption that automating tasks must make society produce less: the essay reports that food production rose as farming’s workforce share declined. But that history is not proof that workers displaced by AI will find suitable new jobs automatically, or that the adjustment will be painless.
For workers and employers, the distinction between productivity, job numbers and job composition matters. Farming’s transformation involved new equipment, scientific advances and broader changes to the economy. The historical comparison can help frame questions about AI—what tasks are automated, how quickly, who gains access to new roles and whether workers can move into them—but the source provides no measured estimate of AI-related job losses or new occupations.
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How Farming’s Workforce Shrank
The essay’s U.S. estimate for 1800 comes from economic historian Stanley Lebergott’s historical labor-force series, published by the U.S. Census Bureau. It counts 1.4 million agricultural workers out of a 1.9 million labor force, or 73.7%. The series includes enslaved people and covers workers aged 10 and older. Figures before 1840 are estimates rather than census counts; Meyer therefore describes the result as roughly three in four, rather than treating it as an exact headcount.
The comparisons across countries also rely on different records. Japan’s estimate of about 85% in the primary sector in 1872 is a historical reconstruction covering farming, forestry and fishing. Germany’s 1882 occupational census recorded 43.4% in agriculture and forestry, though the source says women working on family farms were undercounted. India’s 1951 census put cultivators and agricultural laborers at 69.7% of workers, while China’s statistical series recorded 83.5% in the primary sector in 1952. The measures and dates are not identical, so they illustrate broad patterns rather than a perfectly comparable ranking.
In the essay’s account, higher output came from multiple developments. Improved tools helped farmers work land more efficiently; industrial ammonia made a major source of plant nitrogen available at scale; and new wheat varieties produced more grain under suitable conditions. India’s wheat harvest, for example, rose from 12.3 million tonnes in 1964–65 to 26.4 million tonnes in 1971–72. These examples support the account of agricultural change, not a forecast of AI’s pace or effects.
“The farm is the best test case this season has for its big question, because here a whole sector of human work has already been almost completely automated.”
— Thorsten Meyer, in the November 3, 2026, essay
What AI’s Labor Impact Cannot Yet Show
The essay does not identify which AI-related jobs will disappear, emerge or change, nor does it provide employment forecasts, a definition of a sector becoming “up to 90%” automated, or evidence for that proposed timeline. Meyer presents the 90% idea as a thesis. It should not be read as a confirmed outcome.
The farming comparison also has limits. Agriculture’s transformation unfolded over roughly two centuries in the United States, and its effects depended on innovations in machinery, chemistry and biology as well as changes beyond the farm. The source does not establish whether AI adoption will be faster, how its effects will differ across occupations and countries, or whether new work will match displaced workers’ skills, pay and locations. Those questions remain open.
The Essay’s Next Historical Case
The article is labeled Episode 2 of “Work After Work”, a series about how the economy is built. Meyer says the next episode will address mechanical reapers, threshers and tractors, continuing the examination of changes in farm work. The essay does not announce a policy proposal or a date for a specific labor-market forecast.
For readers following the broader question about AI, the next evidence to watch is not just whether employers adopt automation, but how employment and job duties change over time. The historical account offers a comparison to test against future data; it does not settle whether AI will move workers into new occupations, how many will make that shift, or how long it will take.
Key Questions
What is the news in this article?
Thorsten Meyer published an essay on November 3, 2026, using agriculture’s long-term workforce decline to discuss whether AI could change the kinds of work people do.
Does the article say AI will eliminate 90% of jobs?
No. Meyer suggests that automation could reach a sector of up to 90% within a decade or a few decades, but presents this as his thesis. The essay does not define the measure or provide a validated forecast of job losses.
How many people still work in agriculture worldwide?
The essay cites an International Labour Organization estimate of 928 million people in 2025, around one in four workers worldwide. The figure is an estimate for agriculture, not a count of people whose jobs are exposed to AI.
What happened to U.S. farm employment over time?
A historical estimate puts agriculture at 73.7% of the U.S. labor force in 1800; the essay gives the current share as about 1.5%. The number of farm workers did not fall immediately: it reportedly kept rising until about 1910.
Does farming prove displaced workers will find new jobs?
No. The history shows a major change in employment share alongside rising food output, but it does not prove that AI-driven displacement will lead to equivalent new work or that workers will move into it easily.
Source: ThorstenMeyerAI.com
