🔍 Read the full analysis: AI Redefining Professional Knowledge on ThorstenMeyerAI.com
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TL;DR
A November 10 article from ThorstenMeyerAI.com traces how knowledge and information work grew into a major part of the U.S. economy, citing estimates by Fritz Machlup and Marc Porat. It argues that generative AI is aimed at many of those office tasks, while stressing that exposure does not prove job loss and that broad employment effects remain unclear.
ThorstenMeyerAI.com published a report on November 10 tracing the rise of knowledge work—from a roughly 29% share of adjusted U.S. gross national product in Fritz Machlup’s 1958 estimate to the information sector’s lead in Marc Porat’s later workforce analysis—and connecting that history to generative AI’s exposure of office and professional tasks. The article says exposure is not the same as job loss, and that broad U.S. employment data do not yet show a clear AI effect.
In The Production and Distribution of Knowledge in the United States, published in 1962, economist Fritz Machlup estimated that spending on producing and distributing knowledge reached $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” He grouped activity into education, research and development, communications media, information machines and information services. The figure measured costs and spending, not the economic value of knowledge itself.
Marc Porat’s 1977 report, The Information Economy, classified workers by the tasks they performed rather than the industry where they worked. Its estimates put information workers at 5.8% of the U.S. workforce in 1860 and 46.4% in 1970; Porat said the group had been the largest since the mid-1950s. The source also cites 2025 International Labour Organization figures showing managers, professionals and technicians accounted for 52.5% of jobs in the United States, 62% in Singapore and 47.9% in Germany.
The article links those historical measures to present-day generative AI, which can perform or assist with some reading, writing, calculation and administrative tasks. It reports that estimates place higher-income and clerical jobs nearer the front of the line for AI exposure. It also cautions that task exposure is not a forecast of job elimination: the source says overall U.S. jobs data show no clear AI footprint so far, while young workers in the most exposed jobs are falling behind peers. It does not provide the underlying study, measurement period or comparison details for that employment claim.
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 Jobs Face AI Exposure
The economic shift matters because AI tools are being applied not only to factory production or routine physical work, but also to tasks that make up a large share of professional and administrative jobs. Reading, drafting, summarizing, calculating and checking are common workplace activities, and the historical figures in the article show how much economic activity has come to depend on information and expertise.
For workers and employers, that creates a distinction between changing tasks and eliminating jobs. A tool may speed up part of a role, alter what skills are valued or change how many people an organization needs; those outcomes are not interchangeable. The source’s warning that exposure is not job loss is important for interpreting estimates that rank occupations by their potential contact with generative AI.
The reported lagging outcomes for younger workers could matter for hiring and early-career experience if confirmed by further evidence. But without details of the analysis behind that claim, it cannot establish that AI caused the gap. The practical significance is that workforce effects need to be tracked at the level of specific tasks, occupations and workers, rather than inferred from exposure estimates alone.
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From Knowledge Work to Information Work
The account connects two late-1950s and 1960s attempts to describe a changing economy. Management writer Peter Drucker used the term “knowledge worker” in his 1959 book The Landmarks of Tomorrow, describing people whose work draws on what they have learned. Machlup then tried to quantify knowledge-related activity by counting its costs across industries and occupations.
Porat’s later approach classified people by what they did during the workday. In his framework, an employee handling information inside a farm or factory still counted as an information worker. That method produced a four-part picture of the economy: agriculture, industry, services and information. The source calls this fourth area the quaternary sector, while noting that its historical estimates depend on the definitions and methods used.
These figures are not directly comparable measures of one thing: Machlup estimated spending in 1958, while Porat classified workers and economic output in later years. Together, they provide historical context for the article’s central point: information work expanded well beyond a small group of specialists, creating a broad set of office tasks that modern AI systems may affect.
“Exposure is not job loss, yet.”
— ThorstenMeyerAI.com
What AI Employment Data Show
The source says overall U.S. employment data show no clear AI footprint so far, but it does not identify the data series, analysis or time period supporting that assessment. It also says young workers in the most exposed jobs are falling behind their peers without specifying the size of the gap, its baseline or whether the comparison establishes a link to AI.
It remains unclear how quickly companies will adopt generative AI for the tasks identified, whether adoption will reduce hiring, reshape roles or raise productivity, and how those effects will vary across occupations. Exposure estimates alone do not answer those questions. The cited international job shares describe managers, professionals and technicians, not a direct measure of jobs that AI can automate.
Tracking Adoption and Worker Outcomes
The next evidence to watch is whether studies with clear time windows and comparison groups find changes in hiring, wages, hours or job duties in occupations with high generative-AI exposure. Employers’ adoption patterns and workers’ use of AI tools will also help show whether the technology is replacing tasks, supporting them or changing the mix of skills required.
The source frames this article as the final episode of its first arc and says its next episode will examine what economic measures capture and what they leave out. That discussion may help readers interpret the historical figures, but the article provides no timetable or further details on new employment data. For now, the confirmed development is the publication of a historical account and its connection to an unsettled question about AI and work—not evidence of economy-wide job losses.
Key Questions
What is the news development?
ThorstenMeyerAI.com published an episode on November 10, 2026, linking the historical growth of knowledge work to generative AI’s potential effect on office and professional tasks.
What did Machlup’s 29% estimate measure?
Machlup estimated that producing and distributing knowledge cost $136.4 billion in 1958, equivalent to almost 29% of adjusted U.S. GNP. The measure was spending, not the value or productivity of knowledge.
Does AI exposure mean a job will be eliminated?
No. Exposure indicates that some tasks in a job could be affected by AI; it does not establish that the whole job will disappear or predict how employers will use the technology.
What does the article say about current U.S. job effects?
It says overall U.S. job data show no clear AI footprint so far. It also reports that young workers in the most exposed jobs are falling behind peers, but gives no underlying study or details that would establish AI as the cause.
What evidence would clarify AI’s effect on work?
Studies tracking hiring, pay, hours and job duties over time, with clear comparison groups and measures of AI adoption, could help distinguish task changes from job losses and identify which workers are affected.
Source: ThorstenMeyerAI.com
