🔍 Read the full analysis: What Farming’s Decline Can Tell Us About AI on ThorstenMeyerAI.com
Get smart everyday buys delivered free with Prime
- Fast, free delivery on millions of items
- Prime Video, Amazon Music and more included
- Member-only deals all year
TL;DR
A November 3, 2026, essay from ThorstenMeyerAI.com uses agriculture’s long decline as a case study for how technology can reduce a sector’s share of employment without reducing food production. It reports that about 1.5% of U.S. workers are now in agriculture, compared with roughly three-quarters around 1800, while arguing—explicitly as a thesis, not a fact—that AI could eventually automate a much larger share of work. The pace, scale and employment effects of AI remain uncertain.
ThorstenMeyerAI.com published an essay on November 3 comparing agriculture’s long decline as a source of employment with the potential effects of artificial intelligence on work. The article says the U.S. farming workforce share fell from an estimated 73.7% in 1800 to about 1.5% today, while food production rose; its suggestion that AI could eventually automate up to 90% of a sector is the author’s thesis, not an established forecast.
The essay, by Thorsten Meyer, is the second installment in the “Work After Work” series. It presents farming as a historical example of how a sector can produce more with fewer workers after changes in tools, fertilizers, seeds and other technology. The piece stresses that the shift unfolded over roughly two centuries in the United States, rather than through one sudden replacement of workers.
For 1800, the essay draws on economic historian Stanley Lebergott’s estimate, later published by the U.S. Census Bureau: about 1.4 million of a 1.9 million-person labor force worked in agriculture. The underlying series covers workers aged 10 and older and includes enslaved people. Figures before 1840 are estimates, not census counts; the author therefore describes the result as about three-quarters rather than treating the percentage as an exact count.
The account traces productivity changes to successive developments, including John Deere’s steel plough in 1837, industrial ammonia production following Fritz Haber’s work, and higher-yield dwarf wheat varieties introduced in India and Pakistan in the 1960s. The author cites Indian official statistics showing wheat production rose from 12.3 million tonnes in 1964–65 to 26.4 million tonnes in 1971–72. These are examples of agricultural output change, not evidence that any single invention alone caused the workforce shift.
Agriculture’s Productivity Lesson for AI
The comparison matters because it separates two outcomes that are often conflated: the number of people employed in a sector and the amount that sector produces. In the essay’s account, agriculture’s share of work fell sharply while food output grew. That history challenges the assumption that fewer workers necessarily mean less output, but it does not establish that displaced workers will find equally secure or suitable jobs elsewhere.
The essay reports an International Labour Organization estimate that 928 million people worked in agriculture worldwide in 2025, around one in four workers. It gives the agricultural share as 43% in India, about 1.5% in the United States and about 1% in Germany. Those differences show that the shift is uneven: national economies remain at different points in structural change, and the global agricultural workforce is still large.
For readers weighing AI’s labor effects, the useful historical point is about time and adaptation. Agricultural employment did not vanish at once, and productivity gains depended on multiple technologies and institutions. The analogy can inform questions about AI, but cannot determine which jobs will change, how quickly, or whether new work will offset losses.
Products Worth Considering
FLEXISPOT EN1 Electric Standing Desk, 60 x 24 in, Black
LG UltraWide 34U620B-B 34-Inch Curved WQHD 144Hz Monitors, 2-Pack Bundle
Canon PIXMA PRO-200S Wireless Professional Inkjet Photo Printer
As an affiliate, we earn on qualifying purchases.
From Farm Labor to Higher Yields
The article places agriculture within a three-part picture of the economy—field, factory and office—and focuses on the oldest sector. It says that around 1800, farming relied heavily on human and animal muscle, limiting how much a household could grow and making a large agricultural workforce necessary. The historical examples extend beyond the United States: the source cites estimates of a high primary-sector workforce share in Japan in 1872, China in 1952 and India in 1951. These figures refer to different countries, dates and definitions, so they are not a single directly comparable series.
Technological change came in stages. Better ploughs and later machinery altered farm work; synthetic ammonia provided a new source of nitrogen fertilizer; and crop breeding produced varieties capable of carrying more grain under suitable conditions. The essay says global cereal harvests grew 4.2-fold from 1961, while the world population grew 2.7-fold over that period. It also cites U.S. Department of Agriculture figures indicating that farm output in 2023 was nearly three times its 1948 level with only a small rise in total inputs. These figures describe historical production and input trends; they do not by themselves measure how the gains were distributed among workers, farm owners or consumers.
The source also notes that employment shares can fall before worker numbers do. In the United States, it says, the number of farm workers continued rising until about 1910, reaching 11.8 million. For Germany, it cites a decline in agriculture’s employment share from 43% to 25% between 1882 and 1939 while farm-worker numbers remained between 8 million and 10 million. The examples underline why a falling percentage should not be confused with an immediate fall in headcount.
“The share fell long before the headcount.”
— Thorsten Meyer, author of the November 3 essay
Limits of the AI Comparison
The essay does not establish that AI will reduce employment in any sector by 90%, nor does it identify a reliable timeline for such a change. That figure is presented as Meyer’s thesis. Agricultural history offers a precedent for long-term productivity change, but AI systems, work tasks, labor markets and the pace of adoption differ from farming’s technologies and history.
Several historical statistics also have limitations. Some early country figures are reconstructions or estimates, and the U.S. 1800 figure is not a census count. The essay’s production comparisons do not, on their own, show how much of the change came from particular technologies or how workers experienced it. It remains unclear how AI-driven productivity gains, if they occur, will affect wages, job quality, employment levels and the creation of new work.
Further Episodes and Labor Evidence
The essay identifies itself as Episode 2 of a series and says Episode 3 will cover mechanical reapers, threshers and tractors. That installment may add detail to the agricultural timeline, but it will not settle what AI will do to employment. A clearer picture of AI’s labor effects will require evidence from actual adoption: which tasks are automated, how employers reorganize jobs, and whether productivity gains lead to new hiring or lower staffing.
For now, the historical record supports a narrower conclusion: agriculture’s employment share declined over a long period as production methods changed, while output expanded. Whether AI produces a comparable transformation—and at what pace—remains an open question.
Key Questions
What is the news development?
ThorstenMeyerAI.com published an essay on November 3, 2026, using agriculture’s long decline as a case study for thinking about AI and work. It is an analysis, not a report of a new AI system or a measured labor-market forecast.
The essay cites a 73.7% estimate for 1800 and says agriculture accounts for about 1.5% of U.S. workers today. The 1800 figure is an estimate for a labor force aged 10 and older, not a census count.
Does the article say AI will automate 90% of jobs?
No. Meyer says automation could affect up to 90% of a sector within a decade or a few decades, but labels that statement his thesis, not a fact. The essay does not provide evidence establishing that outcome or timeline.
Did fewer farm workers mean less food?
The essay argues that agricultural employment fell while production increased. It cites historical cereal-harvest and U.S. farm-output figures, but those comparisons do not show that employment changes alone caused the production gains.
What remains unknown about AI and employment?
The pace of adoption, the tasks affected, the number and quality of jobs changed, and whether new work offsets job losses remain uncertain. Agriculture’s history offers a comparison, not a precise forecast for AI.
Source: ThorstenMeyerAI.com
