🔍 Read the full analysis: AI And The Evolution Of Knowledge Work on ThorstenMeyerAI.com
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TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com traces how knowledge and information work became a major part of the U.S. economy, drawing on estimates by Fritz Machlup and Marc Porat. It argues that generative AI is aimed at many office tasks, while stressing that exposure is not the same as job loss and that broad U.S. employment data has not shown a clear AI effect so far.
ThorstenMeyerAI.com published a report on November 10 tracing the rise of knowledge work from mid-century economic research to today’s generative AI debate. The episode argues that AI is aimed at many tasks performed by office workers, but says exposure does not establish job loss and that U.S. employment data has not yet shown a clear overall AI footprint.
The report revisits economist Fritz Machlup’s estimate for 1958: spending on producing and distributing knowledge in the United States totaled about $136.4 billion, or “almost 29 per cent of adjusted GNP.” Machlup’s 1962 book grouped knowledge-related activity into education, research and development, communications media, information machines and information services. The estimate measured costs and spending, not the economic value of knowledge itself.
The episode also draws on Marc Porat’s 1977 study for the U.S. Department of Commerce, which classified workers by the tasks they did rather than the industry where they worked. Porat estimated that information workers made up 5.8% of the U.S. workforce in 1860, 30.8% in 1950 and 46.4% in 1970. The report says Porat found information work had become the largest employment group by the mid-1950s.
For current comparisons, the source cites 2025 International Labour Organization figures showing that managers, professionals and technicians accounted for 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. It also says higher-income and clerical work is among the employment areas more exposed to generative AI. Those figures describe occupations and exposure, not confirmed job losses caused by AI.
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 Economy
The history matters because it places the AI debate within a long shift in what economies produce. Work based on reading, writing, planning and handling information is not a small niche: the historical estimates cited in the report show it grew into a large share of employment and output. Generative AI tools are designed to assist with some of these same activities, bringing the technology into roles often thought of as less vulnerable than factory work.
That does not mean the technology will remove those jobs. Exposure may lead to task changes, productivity gains, altered hiring or displacement, and the source does not establish which outcome will prevail. The distinction matters to workers and employers weighing training, staffing and investment decisions: task exposure is a signal to watch, not a measure of employment impact.
The report also points to an uneven early picture. It says overall U.S. job data shows no clear AI footprint so far, while young workers in the most exposed occupations are falling behind their peers. That observation is presented without enough detail in the source to determine the size of the gap or whether AI caused it. It nonetheless underlines why effects may differ by age and occupation rather than appearing as a single economy-wide trend.
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From Knowledge Work to Information Jobs
Management writer Peter Drucker used the term “knowledge worker” in 1959, according to the episode. Drucker described people whose livelihoods depend primarily on what they know rather than on manual labor. He argued that the management methods developed for factories would not necessarily suit workers whose output was intellectual and difficult to standardize.
Machlup followed in 1962 with an attempt to quantify the production and distribution of knowledge. His estimate included institutions and industries as well as the costs associated with knowledge occupations. Historian of statistics Benoît Godin’s breakdown, as cited in the source, assigns 44% of Machlup’s total to education, 28% to media, 13% to information services, 8% to research and development, and 7% to information machines.
Porat’s later framework divided the workforce into agriculture, industry, services and information. By classifying a bookkeeper on a farm as an information worker, for example, his method focused on the work performed rather than the employer’s sector. The episode uses this framework to explain why AI’s potential effects extend beyond technology companies to administrative and professional roles across the economy.
““knowledge worker””
— Peter Drucker, as described in the episode
What AI Exposure Shows So Far
The source does not provide the underlying studies, definitions or time periods behind its statements that higher-income and clerical jobs are more exposed to generative AI, or that young workers in highly exposed jobs are falling behind their peers. The size of any employment or earnings effects, and whether AI is responsible for the reported gap among younger workers, are therefore unclear from the material provided.
It is also not yet possible to say how much AI will change total employment rather than the tasks inside existing jobs. Machlup’s and Porat’s historical measures use different methods and describe earlier periods; they offer context, not direct forecasts for current technology. The report says broad U.S. job data shows no clear AI footprint so far, but does not specify which indicators or data window support that assessment.
Tracking Jobs and Changing Tasks
The immediate question is whether future employment and wage data will show consistent changes in occupations that use generative AI heavily, compared with less-exposed work. The episode supplies no date for a next labor-market update or a specific forecast, so no next milestone is confirmed.
For now, readers should distinguish reported exposure estimates from observed job outcomes and watch for evidence that separates AI’s effects from other economic changes. More detailed data on hiring, pay, hours and task composition could clarify whether the technology is changing how knowledge workers do their jobs, how many such workers employers need, or both.
Key Questions
What is the main development in this report?
ThorstenMeyerAI.com published an episode on November 10, 2026, connecting the historical growth of knowledge work with current generative AI exposure in office occupations.
What did Machlup estimate?
Fritz Machlup estimated that U.S. spending on producing and distributing knowledge in 1958 was about $136.4 billion, or almost 29% of adjusted GNP. His measure tracked costs, not the value of knowledge itself.
Does AI exposure mean jobs are being lost?
No. The source explicitly distinguishes exposure to AI from confirmed job loss. It says overall U.S. job data has not yet shown a clear AI footprint.
Which jobs are described as more exposed?
The episode says higher-income and clerical jobs are among those at the front of the line for generative AI exposure. It does not provide detailed methods or occupation-by-occupation estimates in the supplied material.
What remains uncertain about AI’s effect on work?
The scale of job changes, whether AI is causing reported differences among younger workers, and how effects vary across occupations remain unclear. The source names no specific next data release or milestone.
Source: ThorstenMeyerAI.com
