🔍 Read the full analysis: Will AI Bring Another Great Shift In Work? on ThorstenMeyerAI.com
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TL;DR
A November 3, 2026, essay from ThorstenMeyerAI.com uses the long decline in agricultural employment to examine whether AI could reshape work. The figures show that farming’s share of employment fell as tools, fertilizer and higher-yield crops raised output, but the history unfolded over generations and does not establish how quickly AI will change jobs.
ThorstenMeyerAI.com published an essay November 3 examining whether artificial intelligence could bring a major shift in work, using the decline of agricultural employment as a historical comparison. The essay reports that about 73.7% of the U.S. labor force worked in agriculture around 1800, compared with roughly 1.5% today, and argues that this change came through successive innovations over about two centuries—not a single, sudden wave of automation.
The essay’s central historical point is that agriculture became far less labor-intensive while food production rose. It cites International Labour Organization estimates that 928 million people worldwide still work in agriculture, about one in four workers, with substantial differences by country: 43% in India, around 1.5% in the United States and around 1% in Germany. These are employment shares, not measures of the sector’s total output.
In the United States, the essay draws on an estimate by economic historian Stanley Lebergott, published by the Census Bureau: in 1800, 1.4 million of a labor force of 1.9 million people aged 10 and older worked in agriculture. The source notes that figures before 1840 are estimates and that the series includes enslaved people. It also 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.
The account traces productivity gains to multiple changes, including improved ploughs and machinery, synthetic fertilizer, irrigation and higher-yield crop varieties. It cites global cereal production rising 4.2-fold from 1961 while the world population grew 2.7-fold. The author then offers a forecast—not a verified finding—that automation could eventually affect a sector on a scale of up to 90% within a decade or a few decades.
Why the Farm Shift Matters for AI
The agricultural example is relevant to workers and employers because it shows that fewer people in a sector do not automatically mean less output. Mechanization and agricultural science made it possible for a smaller share of workers to produce more food. The essay uses that history to challenge the idea that technology’s effect can be judged only by counting jobs directly replaced.
It also points to a difficult distinction: a sector can shrink as a share of employment without its workforce immediately falling in absolute numbers. The U.S. farm-worker count reportedly kept growing into the early 20th century even as other parts of the economy expanded. For current debates, that is a reminder that job displacement, new work and changes in total employment can occur on different timelines.
However, farming’s history cannot establish what AI will do. Agriculture’s tools, production needs and pace of adoption differ from today’s digital systems. The comparison offers a way to frame questions about productivity and worker movement, not evidence that AI will eliminate a particular share of jobs or repeat the same two-century path.
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The source describes agricultural change as a sequence rather than a single breakthrough. A steel plough associated with John Deere’s 1837 work addressed soil that could stick to earlier ploughs. Later, mechanical reapers, threshers and tractors reduced the need for some manual tasks. Those developments were joined by chemical fertilizer and advances in crop breeding.
One example is the introduction of short-straw wheat varieties in India and Pakistan in the mid-1960s, associated with plant breeders around Norman Borlaug. According to Indian official statistics cited in the essay, the wheat harvest rose from 12.3 million tonnes in 1964–65 to 26.4 million tonnes in 1971–72. The source attributes the gains to varieties that could support more grain without falling over, particularly when combined with fertilizer and irrigation.
The essay also points to the Haber-Bosch process, which made it possible to produce ammonia from nitrogen in the air and supply synthetic fertilizer at industrial scale. These changes took place across generations and alongside broader economic shifts. The article’s historical comparison is therefore about the combined effects of technology and changing production—not a claim that one invention alone moved workers out of farming.
“The share fell long before the headcount.”
— Thorsten Meyer, ThorstenMeyerAI.com
Limits of the AI Comparison
No evidence in the source establishes how many jobs AI will replace, which occupations will be affected most, or how quickly employment may change. The suggested possibility of automation affecting up to 90% of a sector is the author’s forecast, and the essay does not identify a specific sector or a defined measure for that figure.
The historical figures also have limitations. The U.S. estimate for 1800 predates reliable census counts and includes enslaved workers; several country comparisons rely on historical reconstructions or different definitions of agricultural work. The worldwide total of 928 million is an ILO estimate for 2025, while national employment shares refer to different country conditions. Those numbers provide a broad comparison, not a single uniform global time series.
It remains unclear how AI adoption will affect wages, job quality, new occupations and the distribution of productivity gains. The essay raises these issues through history but does not provide an employment forecast or assess the effects of specific AI systems.
The Next Test: Work Beyond Farms
The source presents this as the second episode in a series on how the economy is built, following an earlier installment that divided work into field, factory and office sectors. Its next installment is expected to examine the machinery that reshaped farming, including reapers, threshers and tractors.
For readers tracking AI and employment, the relevant developments will be measured evidence about adoption, productivity and hiring—not historical analogy alone. The essay does not give a publication date for the next episode or a timetable for testing its AI forecast. Until further data are available, the scale and speed of any AI-driven labor shift remain open questions.
Key Questions
What is the main news in the essay?
ThorstenMeyerAI.com published a historical comparison on November 3, 2026, asking whether AI could reshape work on a scale comparable to agriculture’s long transformation. It does not report a new study measuring AI’s effect on jobs.
How many people still work in agriculture worldwide?
The essay cites an ILO estimate of 928 million people in 2025, or about one in four workers worldwide. The figure is an estimate of agricultural employment, not a forecast of future job losses.
Does the essay say AI will automate 90% of jobs?
No. The author describes the possibility of automation affecting a sector of up to 90% within a decade or a few decades as a thesis, not a fact. The source does not establish that this will happen or specify a supported job-loss projection.
What does farming’s history suggest about technological change?
The comparison suggests that employment shares can fall over a long period while production rises, and that the number of workers may not decline immediately. It does not prove that AI will follow the same timeline or produce the same outcomes.
Source: ThorstenMeyerAI.com
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