🔍 Read the full analysis: How AI Might Change The Job Landscape on ThorstenMeyerAI.com
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TL;DR
A November 3, 2026 essay argues that agriculture offers a historical example of technology shrinking a sector’s share of employment while food production rises. Its author suggests AI could eventually automate much of a sector, but that is a thesis, not a measured forecast; the farming transition took generations, and 928 million people still work in agriculture worldwide.
In a November 3, 2026 essay, Thorsten Meyer uses agriculture’s centuries-long decline as a share of employment to frame how artificial intelligence could change work. The historical figures show that a sector can produce more while employing a smaller share of workers; they do not establish how quickly AI will affect jobs or whether displaced workers will find comparable work.
Meyer cites estimates that about 73.7% of the U.S. labor force worked in agriculture in 1800, compared with about 1.5% today. The 1800 figure comes from economic historian Stanley Lebergott’s estimates, later published by the U.S. Census Bureau. It covers a labor force of about 1.9 million people aged 10 and older, including roughly 530,000 enslaved people. Because early figures are estimates rather than census counts, Meyer describes the historical share as roughly three in four.
The essay presents the decline as the result of successive changes in tools, chemistry and crop breeding, rather than one sudden invention. Examples include John Deere’s steel plough, developed in 1837, synthetic ammonia production after Fritz Haber’s 1908 patent, and short-straw wheat varieties associated with the Green Revolution in the 1960s. In India, official figures cited in the essay show wheat harvests rising from 12.3 million tonnes in 1964–65 to 26.4 million tonnes in 1971–72.
The employment shift was gradual and uneven. In the United States, the number of farm workers continued rising until about 1910, reaching an estimated 11.8 million, even as agriculture’s share of the workforce changed over time. The essay also notes that an estimated 928 million people worked in agriculture worldwide in 2025, about one in four workers, according to the International Labour Organization. That figure underscores that agricultural employment remains substantial in many countries.
What Farm Automation Shows About Work
The agricultural record matters to the AI debate because it separates two outcomes often treated as if they were the same: less employment in a sector and less production. Meyer says global cereal harvests grew 4.2-fold from 1961 while the world population grew 2.7-fold. He also cites U.S. farm output in 2023 as nearly three times its 1948 level, with only a small rise in total inputs. These comparisons suggest that productivity gains can allow fewer workers, relative to the economy, to produce more.
But the history does not show that automation is painless or that workers move smoothly into equally secure jobs. Farming’s transformation unfolded over roughly two centuries in the United States, amid changes in industry, services, migration and public policy. AI’s effects may differ by occupation, employer and country. For workers, businesses and governments, the key questions include which tasks can be automated, how quickly employers adopt the tools, and whether new work offers comparable pay and conditions.
Meyer’s forecast that automation could reach as much as 90% of a sector within a decade or a few decades is explicitly his thesis, not an established finding. The essay offers no AI-specific employment data or forecast method to verify that estimate. Readers should treat the farming comparison as historical perspective, not a timetable for AI-driven job losses.
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A Long Shift in Farm Employment
The essay’s historical comparisons show that agriculture’s high employment share was not unique to the United States. It cites economic historians’ estimate that about 85% of Japan’s gainfully occupied population worked in the primary sector in 1872, a category that included farming, forestry and fishing. For India, the 1951 census counted cultivators and agricultural laborers as 69.7% of workers; China’s statistical series put the primary-sector share at 83.5% in 1952.
The measures are not perfectly comparable. They come from different periods and sources, and some are reconstructions rather than official census counts. The essay notes, for example, that Germany’s 1882 occupational census recorded 43.4% of workers in agriculture and forestry but may have undercounted women helping on family farms. These figures establish a broad historical pattern, not a single uniform path.
For the AI comparison, the central point is that farm work changed through a combination of machinery, scientific advances and improved crop yields. The source’s historical evidence is about agriculture; it does not measure AI’s current effect on employment. Meyer frames the account as the second episode of a series examining how economies shift among field, factory and office work.
“The share fell long before the headcount.”
— Thorsten Meyer, in the November 3, 2026 essay
How Far the Farm Analogy Goes
The source does not provide a forecast of how many jobs AI will eliminate or create, which occupations will be most affected, or how the impact will vary across countries. It also does not define how to measure a sector becoming “automated” or specify the evidence behind the author’s 90% projection. Those questions remain open in the material provided.
Agriculture’s history cannot settle those issues on its own. Farm mechanization unfolded over generations and accompanied major economic and social changes; AI tools may be adopted on a different timetable and affect tasks across many sectors. The essay establishes neither that AI will follow the same path nor that workers displaced from particular jobs will have the same options as workers in earlier periods.
The employment figures also have limits: some early national estimates are reconstructions, and definitions of workers and sectors differ. The 928 million figure is an ILO estimate for 2025, but the source does not provide a country-by-country breakdown beyond selected employment shares. The effects of AI on wages, job quality and total employment are not addressed by the agricultural comparisons.
Evidence Needed on AI’s Job Effects
The essay is part of a planned series: its next installment is described as covering the machinery involved in farming’s transformation. For the broader AI question, readers will need dated, occupation-level evidence on adoption, changes in tasks and employment, and the creation of new roles. Comparisons should identify their time period, population and baseline rather than treating a technology’s capabilities as proof of workforce effects.
Until such evidence is available, the agricultural record is best used as a bounded comparison: it shows that technology can change how many workers a sector needs, while leaving the timing and consequences of AI adoption unresolved. The source does not announce a specific AI employment study or policy decision as the next milestone.
Key Questions
What is the news in this article?
Thorsten Meyer published an essay on November 3, 2026, using agriculture’s long shift toward mechanized production to discuss how AI might affect employment. It is an analysis, not a report of a newly measured AI jobs impact.
Does the essay prove that AI will eliminate most jobs in a sector?
No. Its suggestion that automation could reach up to 90% of a sector is Meyer’s thesis. The essay provides historical evidence about agriculture, not an AI employment forecast.
How many people work in agriculture worldwide?
The essay cites an International Labour Organization estimate of 928 million workers in 2025, or about one in four workers worldwide.
Did fewer farm workers mean less food was produced?
The figures cited in the essay point in the other direction: it says global cereal harvests grew 4.2-fold from 1961 while population grew 2.7-fold. That comparison concerns agricultural output and population; it does not establish what AI will do to food production or other sectors.
What is still unknown about AI and jobs?
The source does not quantify AI-related job losses or gains, identify a timeline, or establish what will happen to wages and working conditions. Those outcomes will require evidence about adoption and employment changes across specific occupations and places.
Source: ThorstenMeyerAI.com
