TL;DR
Get business pricing on tech for your team
- Business-only prices and quantity discounts
- Tax-exempt purchasing
- Multiple users, one account, clear invoices
A November 10, 2026, essay by Thorsten Meyer places generative AI in the long expansion of knowledge work, drawing on estimates by Fritz Machlup and Marc Porat. The figures show the scale of information work, while recent labor data cited in the essay does not establish that AI has caused broad job losses.
Thorsten Meyer’s November 10, 2026, essay argues that generative AI is aimed at a long-established part of the economy: knowledge work, including office, professional and information tasks. Drawing on historical estimates and recent job data, the piece describes the sector’s scale while stressing that exposure to AI does not establish that workers have lost jobs because of it.
Economist Fritz Machlup estimated that producing and distributing knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. His 1962 book grouped the activity into education, research and development, communications media, information machines and information services. The spending figure measures the cost of these activities, not the value or quality of the knowledge produced.
The essay also cites Marc Porat’s estimate that information workers rose from less than 6% of the U.S. workforce in 1860 to 46% in 1970, becoming the largest occupational group by the mid-1950s. For a contemporary comparison, it reports International Labour Organization figures for 2025 showing managers, professionals and technicians made up 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany.
On AI, the article says estimates place higher-income and clerical jobs among those most exposed to generative AI. It also reports that U.S. employment data has not yet shown a clear overall AI effect, while young workers in the most exposed roles are falling behind their peers. The material does not specify the data series, comparison periods or causal evidence behind those observations.
Where AI fits in fourth-sector work
Generative AI reaches office, professional and information tasks in an economy already shaped by knowledge work. Exposure shows potential overlap with tasks, not proof of job losses.
What counts as knowledge work
The measures capture different parts of work centered on producing, using and communicating information.
Machlup’s categories
Education, research, media, information machines and information services.
Office and professional work
Writing, calculating, checking and communicating overlap with tasks associated with generative AI.
A broad modern workforce
Managers, professionals and technicians are a substantial share of jobs in several wealthy economies.
Different measures, different snapshots
The figures describe a long shift toward information-focused work, but the categories and accounting methods are not identical.
| Measure | Finding | What it captures |
|---|---|---|
| Machlup, 1958 | $136.4 billion; almost 29% | Spending on knowledge industries as a share of adjusted U.S. GNP |
| Porat, 1860–1970 | Less than 6% to 46% | Information workers’ share of the U.S. workforce |
| ILO, 2025 | Singapore 62%; U.S. 52.5%; Germany 47.9% | Jobs held by managers, professionals and technicians |
What AI exposure tells us
Higher-income and clerical jobs are among those described as more exposed to generative AI.
- Tasks that may overlap with AI capabilities
- Where employers may change work
- Occupations to watch over time
- That a job will disappear
- That workers have already been displaced
- That AI caused broad job losses
Evidence still needs context
The essay reports no clear overall AI effect in U.S. employment data so far and says young workers in highly exposed roles are falling behind peers.
What to track next
Clear comparisons can help distinguish broad labor-market changes from effects linked to AI.
Track whether employers introduce AI in occupations identified as exposed.
Watch hiring, tasks, wages and productivity over time.
Compare defined periods to assess what changed and when.
AI Reaches the Office Economy
The historical figures frame AI as a technology entering work that already occupies a large share of modern economies. Tasks such as writing, calculating, checking and communicating appear in Machlup’s account of knowledge production, and they overlap with capabilities now associated with generative AI. That overlap helps explain why the technology debate extends beyond factory automation to office and professional work.
Exposure estimates can identify tasks that might be changed, but they do not tell readers whether jobs will disappear, be redesigned or become more productive. For workers and employers, the distinction matters: the scale of knowledge work makes any shift consequential, but the timing and direction of the effects cannot be inferred from exposure alone.
Products Worth Considering
Logitech MX Master 4 Ergonomic Wireless Mouse with Haptic Feedback, Graphite
Bose Lifestyle Ultra Compact Wireless Smart Home Speaker Bundle, Black
Logitech MX Master 4 for Mac Wireless Ergonomic Mouse, White Silver
As an affiliate, we earn on qualifying purchases.
How Knowledge Work Was Counted
Management writer Peter Drucker used the term “knowledge worker” in 1959, describing people who earn a living through what they know. As summarized in the essay, Drucker argued that organizing such work would require rules different from those developed for factory production. The term gave a name to a change in how many people create economic value.
Machlup’s 1962 study then sought to measure the emerging sector. Because knowledge itself cannot be counted like a physical commodity, he measured spending on industries that produced or circulated it and examined relevant occupations. His five-industry classification included schools and training, research, media, information machines and information services. The resulting 29% figure is a historical estimate based on his definitions and accounting approach, not a direct measure of knowledge’s worth.
The essay’s arc moves from that mid-century accounting to the contemporary workplace. It says managers, professionals and technicians now account for a substantial share of jobs in several wealthy economies, but those categories are not identical to Machlup’s or Porat’s definitions. The numbers show a broad trend toward information-focused work, with different measures and time periods.
“almost 29 per cent of adjusted GNP”
— Fritz Machlup, as quoted in the essay
What AI Exposure Shows So Far
The evidence cited does not establish that AI has caused an overall change in U.S. employment. The essay says there is no clear AI footprint in aggregate job data so far, but provides no specific dataset or analysis in the supplied material. Its observation about young workers in highly exposed roles also lacks a stated time window and comparison baseline here, making the size and cause of the reported gap unclear.
It is also not clear which exposure estimates the article relies on, how they define exposure or how quickly employers will adopt AI in affected roles. Exposure describes potential overlap between technology and tasks; it is not a forecast of layoffs or a count of jobs that will be automated.
Tracking Changes in Office Work
The next step is to watch employment and workplace evidence over time, including whether AI adoption changes hiring, tasks, wages or productivity in the occupations identified as exposed. Such comparisons will need clear time periods and baselines to distinguish broad labor-market shifts from changes linked to AI.
Meyer’s essay also points readers toward a follow-up episode on what economic statistics measure and leave out. That question bears directly on the AI debate: spending, job categories and exposure estimates each describe different aspects of work, and none alone establishes the technology’s eventual effect.
Key Questions
What is knowledge work?
Knowledge work is work centered on using, producing or communicating information. The essay gives examples including planning, writing, calculating and checking.
What did Machlup estimate about knowledge industries?
Machlup estimated that knowledge industries accounted for $136.4 billion, almost 29% of adjusted U.S. GNP, in 1958. He measured spending, not the value of knowledge itself.
Does AI exposure mean a job will be lost?
No. Exposure indicates that some tasks may overlap with generative AI capabilities; it does not establish that an occupation will disappear or that workers have already been displaced.
Has AI caused broad U.S. job losses so far?
The essay says U.S. job data shows no clear overall AI footprint so far. The supplied material does not provide the underlying dataset or prove a causal link between AI and employment changes.
Source: Thorsten Meyer AI
