🔍 Read the full analysis: Navigating Knowledge Work In AI Age on ThorstenMeyerAI.com
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TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com traces how economists identified knowledge and information work as a major part of the U.S. economy. It connects that history to generative AI’s exposure of office tasks, while stressing that exposure does not establish job loss and that broad labor data has not yet shown a clear AI effect.
ThorstenMeyerAI.com published a report on November 10 tracing the rise of knowledge work—from economists’ mid-20th-century estimates of its share of the U.S. economy to today’s generative AI systems aimed at office tasks. The article says AI exposure is not the same as job loss and reports that overall U.S. employment data does not yet show a clear AI footprint.
The report centers on 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% of adjusted gross national product. In his 1962 book, Machlup grouped knowledge industries into education, research and development, communication media, information machines and information services. The figure measured spending and costs, not the value of knowledge itself.
It also describes Marc Porat’s 1977 analysis, which classified workers by the tasks they performed rather than the industry where they worked. Porat estimated that information workers made up 46.4% of the U.S. workforce in 1970, compared with 5.8% in 1860. The report says information work had become the largest employment group by the mid-1950s, according to Porat’s framework.
For present-day comparisons, the article cites International Labour Organization figures for 2025: managers, professionals and technicians accounted for 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. These occupational categories are not identical to Machlup’s or Porat’s definitions. The report says higher-income and clerical jobs are among those more exposed to generative AI, and that younger workers in the most exposed jobs are falling behind peers; it does not provide further detail in the supplied material on the underlying studies or measures.
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 in Focus
The historical figures show that office and information tasks are not a small corner of modern economies. Reading, writing, calculating, checking and communicating became central work activities long before generative AI, and they now form a large share of employment in wealthy countries. That scale helps explain why tools that can produce or process information have drawn attention from employers and workers.
For readers, the distinction between task exposure and employment outcomes is key. A technology may be able to assist with parts of a job without eliminating the role or reducing total employment. The report’s account of young workers falling behind in highly exposed occupations could signal a change worth monitoring, but the material does not establish that AI caused the gap. Decisions about hiring, training and job design will depend on evidence beyond technical capability alone.
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From Knowledge to Information Work
Management writer Peter Drucker used the term “knowledge worker” in 1959, describing people whose work depends on what they have learned rather than primarily on physical labor. His argument, as summarized in the source article, was that organizations built around factory methods would need different approaches to manage work based on expertise and judgment.
Machlup then tried to quantify the knowledge economy by counting spending across industries and occupations. Porat later classified jobs according to whether workers produced or handled information, including clerks employed in factories or farms. Together, their approaches helped describe a fourth sector—information or knowledge work—alongside agriculture, industry and services. Their categories and methods differ, so the numbers should be read as historical estimates, not as a single continuous statistical series.
AI Effects Remain Unmeasured
The source says overall U.S. job data shows no clear AI footprint so far, but it does not specify which datasets, dates or employment indicators support that assessment. It also says younger workers in the most AI-exposed jobs are falling behind their peers without identifying the comparison group, the size of the difference or whether researchers linked it causally to AI.
The cited exposure estimates are not a forecast of layoffs. The material does not give a detailed methodology for deciding which jobs are exposed, nor does it establish how much work AI will replace, complement or change. The ILO occupational figures also use categories that may not map directly onto historical definitions of information work.
Evidence to Watch in Employment
The report frames the current moment as a question of how AI affects knowledge work, but the supplied material gives no announced follow-up study, policy decision or employment milestone. The next evidence to watch is more detailed labor-market data tracking hiring, wages and job changes in occupations with different levels of AI exposure, alongside research that separates AI’s effects from broader economic shifts.
Employers’ use of these tools will also matter: adoption, changes in daily tasks and training could produce different outcomes across workplaces. Until those developments are measured, the historical rise of information work provides context for the scale of the issue, not a prediction of how many jobs AI will create or eliminate.
Key Questions
What is the news development?
ThorstenMeyerAI.com published a report on November 10, 2026, connecting the history of knowledge work to generative AI’s reach into office and information tasks.
What did Machlup estimate?
Machlup estimated that U.S. spending on producing and distributing knowledge in 1958 was about $136.4 billion, nearly 29% of adjusted GNP. The estimate measured spending, not the full value of knowledge.
Does AI exposure mean a job will disappear?
No. Exposure describes potential contact with AI capabilities; it does not by itself show that a job will be eliminated. The source says broad U.S. employment data has not yet shown a clear AI footprint.
What remains uncertain about AI and jobs?
The supplied material does not establish how many jobs AI will change or eliminate, or whether AI caused the reported gap affecting younger workers in highly exposed jobs. It also does not identify the datasets and methods behind those claims.
Source: ThorstenMeyerAI.com
