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TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com traces the growth of knowledge and information work and examines why it is a central target for generative AI. It cites estimates that higher-income and clerical jobs are more exposed, but says overall U.S. employment data shows no clear AI footprint so far.
The article grounds its account in earlier attempts to define and measure work centered on information. Management writer Peter Drucker described the rise of the “knowledge worker” in 1959. Economist Fritz Machlup then estimated that producing and distributing knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. That figure measured spending, not the value of knowledge itself.
In a 1977 study for the U.S. Department of Commerce, Marc Porat classified workers by the tasks they performed rather than by the industry employing them. His figures put information workers at 46.4% of the U.S. workforce in 1970, up from 5.8% in 1860. The article uses this history to describe information as a fourth economic sector alongside agriculture, industry and services.
For a current comparison, the article cites 2025 International Labour Organization figures showing managers, professionals and technicians represented 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. These categories are not identical to every definition of knowledge work, but they illustrate the scale of occupations that rely heavily on information and expertise.
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 Tasks Face AI Exposure
The development matters because generative AI is being applied to tasks that make up much of professional and clerical work, not only to physical production. If the tools change how organizations handle writing, analysis, checking and routine administration, their effects could reach a substantial share of workers and employers.
But the article draws a clear line between a job being exposed to AI and that job being eliminated. Exposure describes the potential for tasks to be affected; it does not establish how firms will adopt the tools, whether workers will be displaced, or whether new duties will emerge. The article reports no clear overall AI footprint in U.S. job data so far, while noting that young workers in the most exposed jobs are falling behind their peers. It gives no further details in the supplied material about the size or cause of that gap, so it should not be read as proof that AI caused it.
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From Knowledge Workers to AI
The article places today’s debate within a longer shift in the composition of work. Drucker’s 1959 account gave a name to workers whose livelihoods depend on what they know. Machlup’s 1962 estimate tried to quantify industries that produce or distribute knowledge, including education, research, media, information machines and information services.
Porat’s later approach focused on workers’ activities across the economy. A clerk employed by a factory could count as an information worker because of the tasks performed. This distinction helps explain the article’s central point: knowledge work is not confined to technology companies or offices. It can occur inside many industries, making the potential reach of AI difficult to assess by sector alone.
The historical measures also have limits. Machlup counted costs, while Porat classified occupations and calculated economic shares using definitions from his study. Those historical figures show how researchers tried to capture an information economy; they are not direct measurements of today’s AI use or its effect on productivity and employment.
““Exposure is not job loss, yet.””
— ThorstenMeyerAI.com article
AI’s Employment Effects Remain Unsettled
The source article does not provide a specific estimate of how many jobs AI will change or eliminate, nor does it identify the underlying studies behind its statements about higher-income and clerical workers’ exposure. It also does not define the measure used to assess exposure. Those claims should therefore be understood as reported estimates, not confirmed employment outcomes.
The article says U.S. job data shows no clear overall AI footprint so far, but does not specify which datasets, time periods or indicators support that assessment. It also reports that young workers in highly exposed jobs are falling behind peers without establishing that AI is the cause. The timing, scale and distribution of any effects remain uncertain.
Evidence to Watch in Employment Data
The next developments to watch are clearer measures of how employers deploy generative AI and whether changes appear in hiring, job duties, wages or layoffs. The source does not name a forthcoming study, policy decision or release date that would resolve those questions.
For now, the article’s historical account establishes the size and spread of information work, while its discussion of AI describes potential exposure rather than a settled labor-market outcome. Future employment evidence will be needed to distinguish changes caused by AI from broader economic shifts and to determine which workers and tasks are affected.
Key Questions
What is the news development?
ThorstenMeyerAI.com published an article on November 10, 2026, linking the historical growth of knowledge work to present-day generative AI exposure and discussing what employment evidence does—and does not—show.
How large was knowledge production in Machlup’s estimate?
Machlup estimated that U.S. spending on producing and distributing knowledge reached $136.4 billion in 1958, or almost 29% of adjusted GNP. The measure counted costs, not the value of knowledge itself.
Does AI exposure mean a job will disappear?
No. The article distinguishes potential exposure of tasks from confirmed job losses. It says overall U.S. employment data has not shown a clear AI footprint so far.
What did Porat’s workforce figures show?
Porat classified workers by what they did. His study put information workers at 46.4% of the U.S. workforce in 1970, compared with 5.8% in 1860.
What remains unknown about AI’s effects on workers?
The source does not specify how many jobs may change or disappear, provide a clear exposure measure, or establish that AI caused the reported lag among young workers in highly exposed jobs. The scale and timing of any employment effects remain uncertain.
Source: ThorstenMeyerAI.com
