🔍 Read the full analysis: The Farming Age And The AI Age: What Connects Them? on ThorstenMeyerAI.com
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TL;DR
A November 3 essay from ThorstenMeyerAI.com uses agriculture’s long shift from labor-intensive work to mechanized, science-supported production to examine automation in the AI age. Historical data show farming’s share of employment fell sharply as output rose, but the proposed parallel with AI remains an interpretation, not a forecast established by the evidence.
ThorstenMeyerAI.com published an essay on November 3 arguing that agriculture’s long decline as a source of employment offers a useful historical comparison for thinking about AI and work. The piece documents how farming’s share of U.S. employment fell from an estimated 73.7% in 1800 to about 1.5% today, while stressing that this change unfolded over generations and did not mean food production disappeared.
The essay, by Thorsten Meyer, traces agricultural productivity gains to successive changes in tools, crop science and fertilizer, rather than to a single invention. It cites the steel plough developed by John Deere in 1837, the commercial production of synthetic ammonia following Fritz Haber’s 1908 patent, and high-yield, short-straw wheat introduced in the 1960s. These developments allowed fewer workers to produce more food, although their effects varied by place and period.
The U.S. employment figures come from economic historian Stanley Lebergott’s estimates, later published by the Census Bureau. The historical series counts 1.4 million of 1.9 million workers in agriculture in 1800. The essay cautions that figures before 1840 are estimates, not census counts, and that the total includes enslaved people. It also notes that the number of U.S. farm workers continued rising until about 1910, reaching an estimated 11.8 million, even as agriculture’s share of the workforce fell.
Worldwide, agriculture still employed an estimated 928 million people in 2025, about one in four workers, according to the International Labour Organization figure cited in the essay. The share varies sharply: it gives about 43% for India, 1.5% for the United States and 1% for Germany. The article’s central caution is that the farming precedent took roughly two centuries in the United States; its relevance to AI does not establish how quickly, or how fully, other sectors may change.
Why Farm Productivity Matters to AI
Agriculture’s history shows that a smaller workforce in a sector does not automatically mean less output. The essay reports that global cereal production increased 4.2-fold from 1961, while world population grew 2.7-fold over the same period. It also cites U.S. farm output in 2023 as nearly three times its 1948 level, with only a small rise in total inputs. Those figures frame a key distinction for current AI debates: technology can reduce labor needed per unit of production while the sector continues to supply more.
That distinction matters to workers, employers and policymakers considering how AI might affect jobs. Agricultural change involved shifts in tasks, livelihoods and where people worked, not simply the disappearance of food production. Yet the comparison has limits. Farm technologies changed physical production over long periods; AI systems can affect office and service tasks on a different timeline and through different channels. Meyer’s suggestion that automation might reach as much as 90% of a sector within a decade or several decades is explicitly a thesis, not a measured forecast.
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From Farm Labor to Higher Yields
The essay sets the U.S. experience alongside other historical estimates. Japan’s primary sector accounted for about 85% of gainfully occupied people in 1872, according to a reconstruction by economic historians. Germany’s official 1882 occupational census counted 43.4% of workers in agriculture and forestry, though the source notes that women’s work on family farms was undercounted. China’s employment series began in 1952 with 83.5% in the primary sector, while India’s 1951 census counted cultivators and agricultural laborers as 69.7% of workers. These figures use different dates, definitions and sources, so they are not perfectly comparable.
The mechanism behind the long change was cumulative. Better ploughs and later machinery increased the area a worker could cultivate. Synthetic nitrogen fertilizer expanded the supply of plant-available nutrients. Improved crop varieties raised potential yields when paired with inputs such as water and fertilizer. In India, official figures cited by the essay put wheat production at 12.3 million tonnes in 1964–65 and 26.4 million tonnes in 1971–72; the comparison uses the year before the 1965–66 drought rather than the drought low. Together, these examples explain why productivity—not a single machine—is central to the article’s historical comparison.
““Farming shows a whole sector can be emptied of human hands.””
— Thorsten Meyer, essay author
How Far the AI Comparison Holds
The essay does not provide a method or independent evidence for its proposed possibility that automation could affect up to 90% of a sector within a decade or a few decades. It also does not specify which sector, jobs, countries or measure of automation the estimate refers to. That figure should be read as the author’s hypothesis rather than a confirmed outcome.
Historical comparisons also carry limits: employment categories and data quality differ across countries and centuries, and some early figures are reconstructions. The source material does not establish that AI will follow agriculture’s timeline, produce similar employment shifts, or create enough new work to offset jobs affected. The effects will depend on adoption, the tasks systems can perform, business decisions and broader economic conditions.
Further Episodes and Evidence
The essay is identified as Episode 2 of the “Work After Work” series, with the next installment expected to examine machinery such as reapers, threshers and tractors. That episode could add detail on how mechanization changed farm work after early improvements in tools and crop production.
For the AI comparison, the next useful evidence will be observed changes in tasks, hiring, wages and output across industries, rather than a direct assumption that farming’s history will repeat. Meyer’s essay offers a historical lens; its proposed AI timeline remains unverified.
Key Questions
What is the main point of the essay?
It argues that agriculture offers a historical example of technology reducing the share of workers needed in a sector while food output rose. The comparison is offered as a way to think about AI, not proof that AI will produce the same results.
How much of the U.S. workforce farmed in 1800?
The essay cites Stanley Lebergott’s estimate that 73.7% of the U.S. labor force worked in agriculture in 1800. The figure is an estimate, not a census count, and includes enslaved workers.
Does the essay predict that AI will automate 90% of jobs?
No. Meyer describes automation affecting up to 90% of a sector within a decade or a few decades as his thesis, not an established forecast. The essay does not specify a sector or define how that percentage would be measured.
How many people still work in agriculture worldwide?
The essay cites an International Labour Organization estimate of 928 million people in 2025, or about one in four workers worldwide. National shares vary substantially.
Source: ThorstenMeyerAI.com
