🔍 Read the full analysis: How AI Shapes The Fourth Sector 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 Nov. 10, 2026, article from ThorstenMeyerAI.com traces the rise of the information economy, or fourth sector, from mid-century research to current employment data. It says generative AI is especially exposed to office and professional tasks, but exposure is not proof of job loss, and broad U.S. employment data has not yet shown a clear AI effect.
ThorstenMeyerAI.com published an account on Nov. 10 of how knowledge and information work grew into a major part of the economy, arguing that generative AI is now directed at many of the tasks that define that work. The article draws on economic studies by Peter Drucker, Fritz Machlup and Marc Porat, while stressing that exposure to AI does not by itself mean workers will lose their jobs.
The report revisits economist Fritz Machlup’s estimate that the production and distribution of knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. Published in 1962, Machlup’s study grouped knowledge-related activity into education, research and development, communications media, information machines and information services. The figure measures spending associated with producing and distributing knowledge, not the economic value of knowledge itself.
Marc Porat’s 1977 study for the U.S. Department of Commerce classified workers by what they did rather than the industry that employed them. It estimated that information workers made up 46.4% of the U.S. workforce in 1970, up from 5.8% in 1860, and said this group had become the largest by the mid-1950s. These historical categories are not identical to today’s job classifications, but they illustrate the long-term expansion of work centered on information.
The article also cites 2025 International Labour Organization figures showing managers, professionals and technicians held 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. It reports that higher-income and clerical roles are among those more exposed to generative AI. That is a measure of potential task exposure, not a forecast that those jobs will disappear.
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.
Powered by Thorsten Meyer AI
AI Reaches Information-Rich Work
The shift matters because AI’s potential reach is not limited to factory equipment or routine manual tasks. Generative systems can be used for activities such as writing, reading, calculating, checking and organizing information—the kinds of tasks that account for much knowledge work. That places office and professional roles in the debate about automation, including jobs that have often been viewed as less vulnerable than factory work.
The report’s historical figures also put current concerns in perspective: information work became a large share of the economy over decades, as technology and organizations changed what workers did. AI could alter tasks within existing jobs, change demand for some skills or affect hiring. The source does not establish which of those outcomes will dominate. For workers and employers, the distinction between task exposure and actual job losses is central.
The article says there is no clear AI footprint in overall U.S. job data so far, while citing evidence that young workers in the most exposed jobs are falling behind peers. Those observations do not establish that AI caused the difference. They do, however, make it relevant to monitor employment and hiring patterns by age, occupation and exposure rather than relying on economy-wide totals alone.
Products Worth Considering
Dell UltraSharp U2724DE 27-Inch WQHD IPS Black Monitor
Logitech MX Ergo S Advanced Wireless Trackball Mouse USB-C – Graphite
FLEXISPOT Electric Adjustable Workbench, 55 x 28 Inches, White
As an affiliate, we earn on qualifying purchases.
From Knowledge Work to AI
Management writer Peter Drucker used the term “knowledge worker” in 1959, describing people whose work depends primarily on what they know. His argument, as summarized in the source article, was that the rules developed for factory production would not be enough to manage workers whose output was intellectual rather than physical.
Machlup attempted to quantify the emerging economy by counting expenditure on knowledge-related industries. Porat later added an occupational approach, sorting people according to whether their work involved producing or handling information—even when they worked in sectors such as farming or manufacturing. Together, those approaches helped describe what is often called the quaternary, or information, sector, alongside agriculture, industry and services.
The source places today’s AI debate against this longer growth in information work. Its central comparison is about the kind of work machines may affect, not a claim that today’s AI is equivalent to earlier technologies or that the historical estimates predict a particular employment outcome.
““almost 29 per cent of adjusted GNP””
— Fritz Machlup, as quoted in his 1962 study
Exposure Is Not an Employment Count
The article does not provide a detailed method or underlying dataset for its statement that higher-income and clerical jobs are at the front of the line for generative AI exposure. It also does not specify which occupations, tasks or time period are included in that assessment. Exposure estimates do not show how many jobs will be eliminated, how quickly employers may adopt AI, or whether new tasks and roles will offset reductions elsewhere.
The source says overall U.S. employment data show no clear AI footprint so far, but does not identify the specific measures or studies behind that conclusion in the supplied material. Its observation about younger workers in highly exposed jobs falling behind peers is not, by itself, evidence that AI caused the difference. The scale, duration and causes of that pattern remain unclear.
Historical estimates also need careful interpretation. Machlup counted costs, while Porat used his own occupational categories; neither figure is a direct, like-for-like measure of today’s knowledge economy. The source does not establish a single current share for the fourth sector across countries.
Watch Hiring and Task Changes
The immediate question is whether AI exposure begins to appear in measurable changes to hiring, wages, working hours or job duties. Following those indicators across particular occupations—and comparing highly exposed roles with less-exposed ones—could help distinguish changes associated with AI from broader economic shifts. The supplied article names no specific upcoming study, policy decision or release date.
Further reporting would need to identify the methods behind current exposure estimates and track how employers use the tools in practice. Until then, the confirmed development is the publication’s historical account and its warning that knowledge work is a target area for AI—not a demonstrated economy-wide wave of job losses.
Key Questions
What does “the fourth sector” mean?
It refers to the information or knowledge economy: work focused on producing, processing or distributing information, alongside agriculture, industry and services.
What did Machlup estimate?
Machlup estimated that spending on knowledge production and distribution reached $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. The estimate measured associated costs, not the value of knowledge itself.
Does AI exposure mean a job will be eliminated?
No. Exposure indicates that some tasks may be affected; it does not establish that an employer will remove a role or how employment will change.
Has AI already caused a clear decline in U.S. jobs?
The source article says overall U.S. job data show no clear AI footprint so far. It does not provide the underlying measures in the supplied material, and it notes a separate concern about younger workers in highly exposed jobs without establishing AI as the cause.
Source: ThorstenMeyerAI.com
