🔍 Read the full analysis: How To Make Sense Of AI Through The History Of Farming on ThorstenMeyerAI.com
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TL;DR
A Nov. 3 essay on ThorstenMeyerAI.com uses agriculture’s centuries-long productivity shift to offer a historical analogy for AI and employment. Its figures show farming’s workforce share fell dramatically as output rose, but the author’s forecast that AI could automate up to 90% of a sector is a thesis, not an established finding.
Thorsten Meyer argues in a Nov. 3 essay on ThorstenMeyerAI.com that the history of farming offers a useful, if imperfect, analogy for understanding how AI could change work. The analysis traces agriculture’s decline from a dominant source of employment to a small share of the workforce in some countries, while stressing that the shift unfolded over roughly two centuries in the United States—not in a single technological leap.
Meyer’s central historical point is that agricultural employment fell as productivity and food output increased. He cites an estimate published by the U.S. Census Bureau that about 73.7% of the American labor force worked in agriculture in 1800. The underlying figures are estimates for that period, rather than census counts, and include enslaved people. By 2025, an International Labour Organization estimate put the number of people working in agriculture worldwide at 928 million, or about one in four workers; the share varies sharply by country.
The essay describes change as a sequence of tools and scientific advances, rather than a single event: the steel plough, improved machinery, synthetic fertilizer and higher-yield crops. It cites India’s wheat harvest rising from 12.3 million tonnes in 1964–65 to 26.4 million tonnes in 1971–72, according to official statistics, alongside the introduction of short-straw wheat varieties and other inputs. Meyer also notes that the U.S. farm workforce continued growing until about 1910, illustrating that a falling employment share does not necessarily mean an immediate fall in the number of workers.
The article is the second installment of Meyer’s “Work After Work” series, which examines how the economy is organized across fields, factories and offices. Its comparison between agriculture and AI is an interpretation of historical evidence, not a measured forecast of AI’s effects. Meyer’s suggestion that automation could affect as much as 90% of a sector within a decade or a few decades is explicitly presented as his thesis.
Agriculture’s Long Productivity Shift
The farming comparison matters because it complicates a common assumption about technology and jobs: fewer workers in a sector do not necessarily mean less output. The essay cites global cereal production rising 4.2-fold from 1961 while population grew 2.7-fold, as well as U.S. farm output in 2023 reaching nearly three times its 1948 level with only a small increase in total inputs. These figures suggest that productivity gains can reshape employment while expanding production.
For readers weighing AI’s economic effects, the historical record offers a warning against treating automation as either an instant job wipeout or a guaranteed smooth adjustment. Agriculture’s transformation took generations, and workers’ movement into other kinds of employment depended on changes beyond farm technology. The analogy can help frame questions about pace, output and labor, but it cannot by itself predict which occupations AI will affect, how quickly, or whether new work will offset displaced tasks.
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From Muscle to Modern Inputs
Before modern machinery and agricultural science, food production depended heavily on human and animal labor. Meyer’s account describes households using hand tools and draft animals, with yields constrained by soil, weather and the capacity to return nutrients to the land. In such conditions, a large share of the population had to produce food directly.
The essay identifies several stages in the change. Deere & Company dates the development of its steel plough to 1837; the source says its polished surface shed sticky Midwestern soil more effectively than earlier designs. Later came reapers, threshers and tractors. In 1908, chemist Fritz Haber filed a patent for synthesizing ammonia, which helped make industrial nitrogen fertilizer possible. In the 1960s, dwarf wheat varieties associated with Norman Borlaug’s work were introduced in India and Pakistan. These developments worked alongside irrigation and other inputs, so the yield gains should not be attributed to seeds or machinery alone.
The pace and scale differed across countries. The essay cites historical estimates placing agriculture and related primary-sector work at roughly 85% of Japan’s employed population in 1872 and agriculture at 69.7% of Indian workers in 1951. Such comparisons draw on different definitions and sources; they provide historical context, not perfectly comparable measurements.
“Farming changed in steps, not in one leap.”
— Thorsten Meyer, ThorstenMeyerAI.com
Limits of the AI Comparison
The essay does not establish how many jobs AI will replace, which sectors will change most, or the timeline for those changes. Its estimate that automation could affect up to 90% of a sector is not accompanied in the supplied material by a model, definition of “automation,” or independent evidence supporting that range.
Historical comparisons also have limits. Agricultural employment figures come from different periods, countries and statistical methods; some early counts are reconstructions, and the source notes that Germany’s 1882 census undercounted women working on family farms. The farm transition unfolded over centuries amid policy, demographic and economic changes. The essay does not show that AI adoption will follow the same path or produce the same distribution of benefits.
Further Installments on Work
Meyer says the series will continue with an installment on the machinery that changed farming, including reapers, threshers and tractors. That account may add detail to the agricultural timeline behind his comparison with AI. For the broader question of AI and employment, the next evidence to watch is not a historical analogy alone but measured changes in adoption, productivity, staffing and the creation of new roles across industries.
Key Questions
What is the essay’s main argument?
Meyer argues that farming’s shift from labor-intensive production to higher productivity can help readers think about AI and work. He presents it as an analogy, not a prediction that AI will produce the same outcome.
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.
Did agricultural employment fall as soon as new technology appeared?
No. Meyer says the U.S. farm workforce kept rising until about 1910, even as agriculture’s share of employment was declining. The figures illustrate that workforce share and headcount can move differently.
Is the claim that AI could automate 90% of a sector confirmed?
No. Meyer explicitly calls that figure his thesis. The supplied essay does not provide a method or independent evidence that establishes it as a forecast.
What developments does Meyer identify in farming’s transformation?
He points to improved ploughs and machinery, synthetic ammonia fertilizer, and higher-yield crop varieties, including short-straw wheat. The essay describes these as a series of changes working alongside other inputs such as irrigation.
Source: ThorstenMeyerAI.com
