🔍 Read the full analysis: AI Influence On Professional Knowledge on ThorstenMeyerAI.com
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TL;DR
A November 10, 2026, report from ThorstenMeyerAI.com examines how knowledge and information work became a large part of the economy and why generative AI is aimed at many of those jobs. The report cites historical estimates and recent international employment figures, while noting that AI exposure does not establish job loss and that overall U.S. employment data shows no clear AI effect so far.
ThorstenMeyerAI.com published a report on November 10 tracing the rise of knowledge and information work and explaining why those occupations are among those most exposed to generative AI. The episode uses historical economic research and 2025 International Labour Organization figures to frame a current concern: AI is aimed at many office tasks, though exposure is not the same as job loss, and the report says broad U.S. employment data shows no clear AI footprint so far.
The report, part of the series Work After Work, identifies Peter Drucker’s 1959 description of “knowledge work” as an early account of workers earning a living through what they know. It then turns to economist Fritz Machlup’s attempt to put an economic value on the production and distribution of knowledge in the United States.
Machlup estimated that knowledge-related spending in 1958 was $136.4 billion, or almost 29% of adjusted U.S. gross national product. His estimate covered education, research and development, communications media, information machines and information services. The report stresses that Machlup measured spending costs, not the value or quality of the knowledge produced.
Marc Porat later classified workers by the tasks they performed rather than by their employer’s industry. In his 1977 study, he estimated that information workers made up 46.4% of the U.S. workforce in 1970, compared with 5.8% in 1860. The report also cites ILO figures for 2025 showing managers, professionals and technicians account for 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany.
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 Exposed
The historical figures help explain why AI’s potential effects extend beyond factories and manual occupations. Many knowledge jobs involve activities such as reading, writing, calculating, planning and checking—tasks that generative AI systems can assist with or partly perform. The report says estimates place higher-income and clerical jobs near the front of the line for AI exposure.
That does not establish that these jobs will disappear. Exposure can mean a task is affected, not that an entire occupation is automated or that workers are laid off. The distinction matters to employees and employers deciding how work may change, and to policymakers tracking whether new tools alter hiring, wages or job security. The report’s account of young workers in the most exposed jobs falling behind their peers is a concern it raises, not proof that AI caused the difference.
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From Knowledge Work to Information Sector
Drucker’s 1959 account gave a name to the growing role of work based on learned expertise. Machlup’s 1962 book, The Production and Distribution of Knowledge in the United States, attempted to measure the scale of that activity by counting spending across industries. His estimate placed knowledge-related expenditure at almost 29% of adjusted GNP in 1958; it should not be read as a direct measure of the knowledge sector’s output or quality.
Porat’s later framework classified jobs by what workers did, including information roles within farms and manufacturing companies. His estimates showed information work’s share of U.S. employment rising over time, reaching 46.4% in 1970. The report describes this grouping as a fourth, or quaternary, sector alongside agriculture, industry and services. The historical figures provide context for AI’s focus on office tasks; they do not by themselves predict how many jobs the technology will change.
What AI Exposure Does Not Show
The report says overall U.S. job data has not shown a clear AI footprint so far, while younger workers in the most exposed jobs are falling behind their peers. It does not provide a detailed measure, time window or comparison baseline for that youth employment observation, so its scale and cause cannot be determined from the material presented. The report does not establish that AI is responsible.
It is also unclear how quickly employers will adopt generative AI, which tasks they will change, and whether affected workers will see altered duties, new roles, reduced hiring or job losses. The historical estimates describe changes in the composition of work, not the effects of today’s systems. Exposure estimates alone cannot answer those questions.
Tracking Work as AI Spreads
The published episode is the final installment of the series’ first arc. Its immediate next step is a planned discussion of what economic measures count and what they leave out, a question relevant to interpreting estimates of knowledge work and technological change.
For the broader AI debate, the evidence to watch includes employment and wage trends, hiring among younger workers, and changes in specific tasks as organizations adopt generative systems. The report provides historical context and current exposure concerns, but it does not set out a forecast or a timeline for job losses. Whether AI reshapes roles or reduces employment remains an open question.
Key Questions
What is the main development in the report?
ThorstenMeyerAI.com published a report on November 10, 2026, connecting the historical growth of knowledge and information work to current concerns about generative AI exposure.
What did Machlup estimate?
Machlup estimated that U.S. spending on producing and distributing knowledge reached $136.4 billion in 1958, or almost 29% of adjusted GNP. That was a spending-based estimate, not a direct measure of knowledge’s value.
Does AI exposure mean jobs will be lost?
No. The report distinguishes exposure to AI from confirmed job loss. It says overall U.S. job data shows no clear AI footprint so far.
Which workers does the report identify as exposed?
It says higher-income and clerical jobs are among those at the front of the line for generative AI exposure, because many involve information tasks. It does not identify a confirmed number of jobs that will be eliminated.
Source: ThorstenMeyerAI.com
