🔍 Read the full analysis: The New Era Of Knowledge Work: AI on ThorstenMeyerAI.com
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TL;DR
A November 10 essay from ThorstenMeyerAI.com argues that knowledge work has become a major part of modern economies and that generative AI is aimed at many of its office tasks. It cites historical estimates and recent occupational figures, while stressing that exposure to AI does not by itself establish job losses; broad employment effects remain unclear.
ThorstenMeyerAI.com published an essay November 10 examining how knowledge work grew into a major part of the economy and why it is now a central target for generative AI. The piece draws on historical economic research and recent occupational data, but says AI exposure is not the same as job loss and that a clear economy-wide employment effect has not yet appeared in U.S. data.
The essay revisits economist Fritz Machlup’s 1962 estimate that producing and distributing knowledge accounted for $136.4 billion, or “almost 29 per cent of adjusted GNP,” in the United States in 1958. Machlup measured the costs associated with knowledge industries rather than the economic value of knowledge itself. His categories included education, research and development, media, information machines and information services.
It also draws on Marc Porat’s 1977 study, which classified workers according to their tasks rather than the industry employing them. Porat’s estimate put information workers at 5.8% of the U.S. workforce in 1860 and 46.4% in 1970. The essay says information work had become the largest of four broad employment groups by the mid-1950s, ahead of agriculture, industry and services.
For present-day comparisons, the article cites 2025 International Labour Organization figures showing that managers, professionals and technicians held 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. It says higher-income and clerical occupations are among those most exposed to generative AI, while noting that the figures describe exposure, not confirmed displacement.
Infographic 01 · The number to remember
Fritz Machlup's first count of the knowledge industries, by branch.
US spending on knowledge by branch, 1958 · share of $136.4 bn
Machlup measured what knowledge cost (spending, the way national accounts count), not what it was worth. “Almost 29 per cent of adjusted GNP” is his figure for 1958. The branch values are as tabulated by Godin (2008); the labour shares are Machlup’s estimate as summarised by his publisher.
Sources: F. Machlup, The Production and Distribution of Knowledge in the United States (Princeton University Press, 1962), as tabulated by B. Godin, “The Knowledge Economy: Fritz Machlup's Construction of a Synthetic Concept” (2008); Princeton University Press, book description.
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Why Office Work Is in Focus
The development matters because AI is often discussed as a technology for automating physical or routine tasks, while generative systems can also assist with writing, reading, planning, calculation and information handling. Those activities are central to the jobs and sectors the essay calls knowledge work. The scale of that workforce means changes to office tasks could affect a broad range of employers and workers, even if individual jobs are not eliminated.
The article’s historical frame also helps distinguish task change from job loss. A technology may alter how work is done, which tasks are assigned to people, or how much output a team can produce. The source does not establish which of those outcomes will dominate. Its account instead points to a large group of workers whose duties may be affected and to the need for evidence on actual employment outcomes.
The essay further cites signs of uneven effects: it says young workers in the most AI-exposed jobs are falling behind their peers, while broad U.S. employment data shows no clear AI footprint so far. Those observations are presented without detailed methods or figures in the supplied material, so they should be treated as indicators to investigate, not proof that AI caused a change in hiring or employment.
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From Factories to Information Work
The essay is the final episode of the first arc of Thorsten Meyer’s “Work After Work” series, which examines how economies are structured. Its prior episode discussed the expansion of service work; this installment argues that information and knowledge activities form a distinct fourth sector, often called the quaternary sector.
Peter Drucker used the term “knowledge worker” in his 1959 book The Landmarks of Tomorrow, according to the article. Drucker’s broader point, as summarized in the source, was that organizations built around factory production might not be suited to managing people whose value comes primarily from learned expertise. Machlup then attempted to quantify knowledge-related activity, and Porat later classified workers by the information tasks they performed.
The measures are not interchangeable. Machlup estimated spending across knowledge industries, while Porat estimated occupational shares and economic contributions using his own categories. They provide historical ways to describe the growth of information work; they do not directly measure today’s AI productivity or forecast how many jobs it will change.
AI’s Employment Effects Remain Unclear
The source does not specify the methods behind its statement that higher-income and clerical jobs are at the front of the line for generative AI, nor does it give a measure of exposure by occupation. It also does not identify the U.S. employment data used to conclude that no clear AI footprint is visible. Those claims cannot, from the supplied material alone, show how many jobs have been eliminated, created or materially changed because of AI.
The reported gap affecting younger workers in highly exposed jobs is also not quantified or causally linked to AI in the source. It remains unclear how much reflects AI adoption, other economic conditions, changes in hiring, or differences among occupations. The historical figures describe the growth of knowledge and information work, but do not predict the scale or timing of future employment changes.
Evidence Needed From Workplaces
The essay’s next installment is described as a discussion of what economic statistics measure and what they miss. That question is relevant to AI: existing national accounts and employment counts may not immediately capture changes in task mix, working conditions or productivity. The supplied source does not announce a policy proposal, research release or timetable for measuring those effects.
For now, the key developments to watch are clearer occupation-level evidence, changes in hiring and employment over time, and research that separates AI’s effects from broader economic shifts. Until that evidence is available, claims about widespread job losses or guaranteed productivity gains go beyond what this source establishes.
Key Questions
What is the news development?
ThorstenMeyerAI.com published an essay on November 10, 2026, tracing the growth of knowledge work and explaining why generative AI may affect office occupations.
What did Machlup estimate?
Economist Fritz Machlup estimated that knowledge production and distribution represented $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. The figure measures spending under his categories, not the total value of knowledge.
Does AI exposure mean jobs will disappear?
No. The article explicitly distinguishes occupational exposure from job loss. It says broad U.S. employment data has not shown a clear AI footprint so far, while leaving the longer-term effects unresolved.
Which workers are described as more exposed?
The essay says higher-income and clerical occupations are among those at the front of the line for generative AI. The supplied material does not provide an occupation-by-occupation exposure measure or quantify resulting job changes.
Source: ThorstenMeyerAI.com
