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TL;DR
A November 10 article from ThorstenMeyerAI.com argues that AI is aimed at the information and knowledge work that grew into a major part of modern economies. It cites historical estimates that knowledge industries accounted for nearly 29% of adjusted U.S. GNP in 1958, while stressing that AI exposure is not the same as job loss and that broad employment effects remain unclear.
ThorstenMeyerAI.com published an episode on November 10, 2026, examining how information and knowledge work grew into a major part of the economy—and why that matters as generative AI targets office tasks. The article draws on historical estimates by economists Fritz Machlup and Marc Porat, while cautioning that exposure to AI does not establish that jobs will disappear.
The episode’s central historical figure comes from Machlup’s 1962 book, The Production and Distribution of Knowledge in the United States. Using spending as a way to estimate the production and distribution of knowledge, Machlup calculated that the relevant activity amounted to $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” The article says education represented the largest share of that total, followed by media, information services, research and development, and information machines.
The account distinguishes this estimate from a direct measure of knowledge’s economic value: Machlup counted costs, not what knowledge was worth. It also cites Marc Porat’s 1977 study for the U.S. Department of Commerce, which classified workers by the tasks they performed rather than by the industry where they worked. Under that approach, information workers made up 46.4% of the U.S. workforce in 1970, compared with 28.6% in industry, 21.8% in services and 3.1% in agriculture.
The article connects that history to current AI concerns, saying higher-income and clerical jobs appear near the front of the line for generative AI exposure. It also reports that overall U.S. jobs data show no clear AI footprint so far, while young workers in the most exposed jobs are falling behind their peers. Those observations are presented without enough detail in the source to establish that AI caused the reported differences.
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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AI Reaches the Information Economy
The episode’s main point is that AI is being applied to work that has long formed a substantial part of economic activity: writing, calculating, planning, communicating and handling information. That shifts the focus of automation debates beyond factory floors and manual labor toward professional, clerical and administrative tasks.
The distinction between exposure and job loss matters to workers and employers. A task may be affected by AI without an entire occupation disappearing; the technology could change how work is done, which tasks people perform, or how many workers are needed. The source identifies exposure as a concern, not a forecast of specific job cuts. Its historical framing helps explain the scale of the sector at stake, but it does not by itself predict how AI will affect employment.
The article also points to a measurement problem. Machlup estimated knowledge activity by its cost, while Porat classified workers by their work. These approaches describe different things. Readers should not treat the historical figures as a single, directly comparable measure of AI’s present-day economic reach.
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From Knowledge Work to Four Sectors
The episode places the idea of a knowledge economy before Machlup’s attempt to measure it. It credits management writer Peter Drucker with describing the rise of “knowledge work” in his 1959 book, The Landmarks of Tomorrow. The concept refers to workers earning a living through learned expertise and the use of information, rather than primarily through physical labor.
Machlup’s 1962 study grouped knowledge-related activity into education, research and development, communication media, information machines and information services. He also considered white-collar occupations inside other industries. The article reports that his estimate of workers in knowledge occupations rose from about 11% in 1900 to 32% in 1959, using his definitions.
Porat later divided workers into four categories: agriculture, industry, services and information. His classification counted, for example, a clerk working in a steel mill as an information worker. The episode describes this information category as the quaternary, or fourth, sector. It also cites a 2025 International Labour Organization comparison showing managers, professionals and technicians represented 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. These occupational categories are not identical to Porat’s information-worker measure.
„Exposure is not job loss, yet.“
— ThorstenMeyerAI.com, summarizing the episode’s central distinction
AI’s Employment Effects Remain Unclear
The source does not provide the underlying studies, dates, definitions or methods behind its statements about AI exposure and young workers. It says overall U.S. employment data show no clear AI footprint so far, but does not specify which data or time period support that assessment. Its claim that young workers in the most exposed jobs are falling behind their peers is also not accompanied by figures or evidence establishing a cause.
It remains unclear how much of the work identified as exposed will be automated, how quickly businesses will adopt AI, and whether affected tasks will be removed, reorganized or supplemented. The historical estimates also rely on definitions and measurement choices from different studies, so they should not be read as a precise measure of today’s AI-affected workforce.
Further Evidence Will Shape the Debate
The episode is the final installment in the first arc of ThorstenMeyerAI.com’s “Work After Work” series. Its closing note points toward a subsequent episode about what economic numbers measure and what those measures leave out. That topic is relevant to interpreting the historic knowledge-economy estimates described here.
For the AI and employment question, the next useful evidence would be updated employment and wage data, with clear definitions of which jobs and tasks are considered exposed and what comparison period is being used. The source does not identify a specific release date or next milestone for such data. Until more evidence is presented, the article’s account supports a historical comparison and a warning about possible exposure, not a conclusion that AI has already caused broad job losses.
Key Questions
What is the fourth sector in this article?
It is the information or knowledge sector: work centered on producing, processing and sharing information. The article describes it as the quaternary sector, alongside agriculture, industry and services.
What does Machlup’s 29% estimate measure?
Machlup estimated that spending on producing and distributing knowledge in the United States reached $136.4 billion in 1958, almost 29% of adjusted GNP. The estimate measured costs, not the economic value of knowledge itself.
Does AI exposure mean a job will be eliminated?
No. The source explicitly separates exposure to AI from job loss. A technology can affect some tasks in an occupation without eliminating the entire job.
What does the article say about AI’s effect on U.S. jobs so far?
It says overall U.S. job data show no clear AI footprint so far. The source also says young workers in the most exposed jobs are falling behind their peers, but provides no underlying figures or evidence showing that AI caused the gap.
Source: ThorstenMeyerAI.com
