🔍 Read the full analysis: The AI Revolution In Knowledge Work on ThorstenMeyerAI.com
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TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com traces the growth of knowledge and information work, now a large share of employment in several wealthy economies, and examines its exposure to generative AI. Exposure does not establish job loss: the article says broad U.S. employment data has not yet shown a clear AI effect, while younger workers in more-exposed jobs may be falling behind peers.
Generative AI is aimed at the office tasks that make up a large share of work in wealthy economies, according to a November 10 analysis by ThorstenMeyerAI.com. The article traces the expansion of knowledge and information work and says that, although some jobs are more exposed to AI than others, U.S. employment data has not yet shown a clear overall AI effect.
The analysis puts today’s AI debate in historical context. In 1962, economist Fritz Machlup estimated that producing and distributing knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. gross national product, in 1958. The figure measures spending on knowledge-related industries, not the economic value of knowledge itself. Education made up the largest portion of Machlup’s total, according to figures cited from historian of statistics Benoît Godin.
A later measure focused on what people did at work. In a 1977 report for the U.S. Department of Commerce, Marc Porat classified workers by their tasks rather than by their industry’s label. His estimates put information workers at 46.4% of the U.S. workforce in 1970, compared with 5.8% in 1860. That classification included workers handling information inside businesses as well as people in information-focused industries.
The article also cites 2025 International Labour Organization figures for managers, professionals and technicians: these groups accounted for 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. The analysis says higher-income and clerical roles are among those most exposed to generative AI, while stressing that exposure is not the same as a job being eliminated.
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 Office Workforce
The issue matters because AI tools are being applied to tasks that knowledge workers perform routinely, including reading, writing, calculating, checking and planning. Those activities are spread across many industries, so the effects may not be confined to technology companies or dedicated information businesses. A clerk in a factory or a bookkeeper in agriculture, for example, can do information work regardless of the employer’s sector.
That broad reach does not by itself establish how many jobs will change or disappear. The article distinguishes technical exposure from employment outcomes: software may be able to assist with a task without replacing the worker who performs it. The effects could vary by occupation, employer and how organizations deploy the tools. For workers and policymakers, the central question is not only which tasks AI can handle, but how its use changes hiring, responsibilities and pay.
The source reports no clear AI footprint in overall U.S. job data so far, but says younger workers in the most-exposed jobs are falling behind their peers. That observation points to a possible early pressure on entry-level work, though the article does not establish that AI caused the gap. Determining cause and scale will require more evidence than a comparison of exposure across occupations.
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From Factories to Information Work
The article describes knowledge work as a shift in what people do for a living, rather than a new sector that replaced every older one. Management writer Peter Drucker used the term “knowledge worker” in 1959 for people who earn their living through learned expertise. He argued that factory management methods would not automatically fit workers whose main contribution was knowledge.
Machlup’s 1962 study, The Production and Distribution of Knowledge in the United States, grouped activity into education, research and development, communications media, information machines and information services. Because knowledge is difficult to count directly, his estimate used the cost of producing and distributing it. This accounting choice matters: the 29% figure is a measure of spending, not a direct estimate of knowledge’s contribution to output or productivity.
Porat’s later four-sector framework divided employment into agriculture, industry, services and information. By measuring workers’ activities across workplaces, it made visible information jobs that conventional industry categories could miss. The analysis uses these historical studies to explain why AI’s focus on office tasks may reach across the modern economy, while the figures themselves describe earlier U.S. labor markets and should not be treated as current job counts.
AI’s Job Effects Remain Unsettled
The source does not provide a precise count of jobs that AI has already displaced, nor does it quantify how many workers are likely to lose work. It says overall U.S. job data shows no clear AI footprint so far, but does not specify the datasets, time period or method behind that assessment. The reported lag among younger workers in more-exposed jobs is also not proof that AI caused it.
Exposure estimates depend on how researchers define an AI-capable task and whether a tool assists, changes or replaces that work. The cited employment shares also use categories such as managers, professionals and technicians, which may not match other definitions of knowledge work. The pace of adoption, its effects on hiring and productivity, and the distribution of any gains remain unclear.
Track Adoption and Hiring Data
The next evidence to watch is whether measured AI use is followed by sustained changes in employment, job postings, entry-level hiring, wages and workplace tasks. Comparisons across occupations and industries will help distinguish changes associated with AI from broader economic shifts. Longer time series will also be needed to assess whether early differences for younger workers persist.
The source article frames this installment as the final episode of its first arc and points to a following discussion of what economic measures capture and miss. For now, its central conclusion is historical and provisional: knowledge work has become a major part of the economy, and AI is directed at many of its tasks, but the scale of labor-market consequences has not been established.
Key Questions
What is the main development described?
ThorstenMeyerAI.com published an analysis on November 10, 2026, examining how generative AI targets office and information tasks in an economy where knowledge work accounts for a large share of employment.
Does the article say AI is already causing widespread job losses?
No. It says overall U.S. employment data has not shown a clear AI footprint so far. It also notes a reported lag among younger workers in more-exposed jobs but does not establish that AI caused it.
What does Machlup’s 29% figure measure?
It is Machlup’s estimate that spending on producing and distributing knowledge amounted to $136.4 billion, or almost 29% of adjusted U.S. GNP in 1958. It measures costs and spending, not the total value of knowledge.
How did Porat define information workers?
Porat classified people by the work they did rather than only by their employer’s industry. Someone handling records or accounts at a farm or factory could count as an information worker under this approach.
Does AI exposure mean a job will disappear?
No. Exposure indicates that some tasks may be affected by AI; it does not show whether a tool will assist a worker, change a role or replace a position. The employment effects remain uncertain.
Source: ThorstenMeyerAI.com
