From Machlup’s Knowledge Industry to Mathew’s Knowledge Industrial Revolution (Academic Edition)

1. Introduction: The Unfinished Economics of Knowledge

Economic theory traditionally studies the production, distribution and consumption of goods and services. The Industrial Revolution transformed these processes through mechanization, factories, energy and mass production. The subsequent information and digital revolutions transformed communication, computation, storage and information processing. Knowledge occupies a distinctive position within this history. It is simultaneously:

• a factor of production;
• a product of intellectual activity;
• a source of innovation;
• an economic resource;
• a component of human capital;
• an organizational capability;
• a public and private good;
• and a potential object of commercial exchange.

The difficulty is that conventional economic accounting often encounters problems when attempting to distinguish knowledge itself from the activities, services and institutions through which knowledge is produced and transmitted. This problem was confronted directly by Fritz Machlup. His 1962 study did not merely discuss the importance of knowledge philosophically; it attempted to measure knowledge-related production as an economic phenomenon. [1]

The question addressed by this paper is what happens if the analysis is taken one step further. Instead of asking how large is the knowledge-producing sector of an economy?, one may ask: can knowledge itself be systematically produced as an economic product? That second question is the conceptual starting point of Mathew’s Knowledge Industrial Revolution framework.

2. Fritz Machlup: The Knowledge Industry as an Economic Phenomenon

Fritz Machlup’s The Production and Distribution of Knowledge in the United States, published by Princeton University Press in 1962, is one of the foundational works in the economic study of knowledge. Princeton’s retrospective description identifies it as a landmark in the study of the post-industrial information society. [1]

Machlup’s approach was ambitious because he attempted to identify a broad range of activities involved in producing and distributing knowledge, extending beyond conventional research and education to include communication, information processing, media and related services. His calculations produced a striking result:

The precise statistical estimates belong to Machlup’s historical methodology and should not be mechanically transferred to contemporary economies. Their enduring importance lies in the conceptual proposition behind them: knowledge-related activity was sufficiently large and economically significant to warrant treatment as an identifiable component of the economy. Machlup therefore helped move knowledge from the realm of an implicit economic factor toward the realm of an explicit object of economic analysis.

3. The Conceptual Significance of Machlup

Machlup’s contribution can be understood through four propositions.

3.1 Knowledge has economic value

Knowledge is not merely a philosophical or cultural phenomenon. Its production and distribution involve scarce resources and generate economic value.

3.2 Knowledge production is economically measurable

Although measurement is difficult, knowledge-related activities can be classified and incorporated into economic analysis.

3.3 Knowledge production involves specialized industries and occupations

Education, research, communications and other activities constitute organized forms of knowledge production and transmission.

3.4 The structure of advanced economies is changing

The increasing importance of knowledge-related activities signals a structural transformation in the economy. These propositions became foundations for subsequent scholarship. Yet Machlup’s framework primarily examined activities associated with knowledge — a distinction essential for understanding Mathew’s later proposition.

4. From Machlup to Porat: The Information Economy

Marc Uri Porat extended the measurement tradition in The Information Economy: Definition and Measurement, published by the U.S. Department of Commerce in 1977. [2] Porat defined information activities in terms of industries and occupations whose principal functions involved producing, processing or transmitting economically valuable information, constructing national-income and input-output frameworks and distinguishing primary and secondary information activities.

According to the published study, information activities had grown sufficiently large to employ approximately 46 percent of the U.S. labour force in 1970. The conceptual progression was therefore from Machlup’s knowledge production and distribution to Porat’s information activity and information-sector measurement. The distinction between knowledge and information would subsequently become one of the most important conceptual problems in the field.

5. Daniel Bell and the Post-Industrial Society

Daniel Bell’s The Coming of Post-Industrial Society (1973) provided another major perspective. Bell argued that post-industrial society would increasingly be organized around services, theoretical knowledge, professional and technical occupations, and the systematic application of science and knowledge to economic and social decision-making. [4] The significance of Bell’s contribution is that knowledge was no longer merely a sector within the economy; it increasingly became a structuring principle of society itself.

6. Peter Drucker and the Knowledge Worker

In Post-Capitalist Society (1993), Drucker argued that knowledge had become the decisive factor of production and emphasized the emergence of the knowledge worker as a central economic and social actor. [3] Drucker’s formulation is especially relevant because it changes the emphasis from the industry producing knowledge to the individual whose principal economic resource is knowledge. The historical progression can therefore be represented as:

Machlup   : Knowledge-producing industry
    |
Porat     : Information economy
    |
Bell      : Post-industrial society
    |
Drucker   : Knowledge economy and knowledge worker
    |
Mathew    : Knowledge as industrial product

The last step is not a conclusion established by the earlier scholars. It is the new proposition examined in this paper.

7. The Mathew Research Programme

According to MKIC’s published account, Dr. Raju M. Mathew began foundational research in this field in 1978. The MKIC chronology identifies 1985 as the year in which his theories concerning knowledge consumption and production growth were published through the USSR Academy of Sciences context, followed by the formulation of Knowmatics in 2005 and the 2011 publication of Knowmatics—A New Revolution in Higher Education. [5] Mathew’s own retrospective account describes his research as focusing on the nature of human intellect, thinking patterns, knowledge, knowledge consumption, knowledge production, growth of knowledge and skills, and the possibility of treating knowledge as an economic product or resource. [6]

This is a substantially different starting point from Machlup’s statistical project. Mathew’s concern is not principally how much knowledge-related activity exists? but rather: what is knowledge, how is it consumed and produced, and can its production be systematically organized and industrialized?

8. Mathew’s Theories of Knowledge Consumption and Production

Mathew reports the formulation of two related theories: the Theory of Knowledge Consumption–Production Correlation and the Stage Theory of Knowledge Consumption and Production Growth. The first proposes a relationship between the quality and level of knowledge consumption and the quality and level of knowledge production. The second proposes progressive stages:

These should be understood as Mathew’s proposed theoretical framework, rather than established consensus within mainstream economics. Their significance lies in a methodological move: knowledge is treated not merely as information received by an individual, but as something that can be conceptualized in terms of consumption, production, growth, quality and economic application. Mathew states that these theories were developed in the context of his work on theoretical informatics and were published in 1985 under the editorship of A. I. Mikhailov in Theoretical Problems of Informatics. [6,7] Because the original 1985 material is not presently available in a readily accessible digital scholarly archive, future academic work should ideally reproduce the original bibliographic details before making stronger priority claims.

9. Knowledge as Product

The most consequential conceptual move in Mathew’s framework is the treatment of knowledge as a product. This does not mean knowledge is identical to a physical commodity; knowledge has different economic properties. It can be intangible, replicable, cumulative, transferable, combinable, divisible into modules, customized, continuously updated, quality-sensitive, and capable of generating further knowledge. The proposition is therefore not “knowledge is just another commodity” but:

If knowledge is an economic product, then economic analysis must eventually address knowledge production costs, knowledge productivity, knowledge quality, knowledge standardization, knowledge packaging, knowledge distribution, knowledge consumption, knowledge markets and knowledge-product value. This represents a significant extension of conventional knowledge-economy analysis.

10. From Knowledge Product to Knowledge Industry

The next logical step is industrial organization. A conventional industry requires some combination of inputs → production → processing → quality control → storage → distribution → consumption. If knowledge is a product, an analogous knowledge-production system can be conceived:

Knowledge inputs
    v
Human expertise and experience
    v
Knowledge creation
    v
Knowledge engineering
    v
Quality control and validation
    v
Knowledge storage
    v
Knowledge modules and products
    v
Distribution
    v
Knowledge consumption
    v
Feedback  -->  Further knowledge production

This is the conceptual architecture of a Knowledge Industry. Machlup’s knowledge industry was principally an economic classification of activities associated with knowledge production and distribution. Mathew’s Knowledge Industry is proposed as a production architecture for knowledge itself. This is the central distinction of this paper.

11. Knowmatics

The concept of Knowmatics occupies a central position in Mathew’s proposed architecture. MKIC defines Knowmatics as the mathematics, cybernetics and engineering of knowledge, and describes it as the science and technology underlying the Knowledge Industry. [5] Mathew reports that the concept was formulated in 2005 and subsequently developed through the paper Knowmatics—A New Revolution in Higher Education, published in the Journal of the World Universities Forum in 2011. [5,8]

The significance of Knowmatics is methodological. Economics can examine value, cost, production, consumption and allocation. But an industrial knowledge system also requires methods for representation, classification, structuring, transformation, integration, retrieval, validation and delivery — partly engineering and computational problems. Knowmatics therefore occupies a proposed interdisciplinary position at the intersection of Economics + Informatics + Mathematics + Cybernetics + Knowledge Engineering + Artificial Intelligence + Human Intelligence.

12. Dynamic Knowledge Banks

A second major component is the Dynamic Knowledge Bank. The 2011 Knowmatics paper, according to MKIC’s account, introduced the Dynamic Knowledge Bank as a core component of the proposed Knowledge Industry. [5] A conventional library primarily preserves and provides access to existing knowledge; a conventional database primarily stores structured information. A Dynamic Knowledge Bank is intended to perform a broader function: collect, organize, classify, authenticate and update knowledge; integrate human expertise; transform knowledge into modules; support retrieval; and deliver knowledge for specific applications. The Knowledge Industrial Revolution requires not merely knowledge storage, but knowledge infrastructure.

13. From Static Repositories to Dynamic Knowledge Systems

Library               -> preservation and access
Database              -> structured information storage
Information system    -> information processing and retrieval
Knowledge management  -> organizational knowledge sharing
Dynamic Knowledge Bank-> continuous organization, transformation,
                         updating and delivery

The Dynamic Knowledge Bank is therefore proposed as an infrastructural unit of the emerging Knowledge Industry. It should be noted that this remains a research and development proposition, not an established category in mainstream information science — a distinction important for academic integrity.

14. The Knowledge Industrial Revolution

The ultimate extension of Mathew’s framework is the concept of the Knowledge Industrial Revolution (KIR). MKIC describes KIR as a transformation in which knowledge itself becomes an object of structured production, engineering, distribution and consumption. [5,9] The concept should not be confused with the established terminology of the Fourth Industrial Revolution, which in the influential formulation of Klaus Schwab refers to the convergence of physical, digital and biological technologies. The KIR proposed by Mathew is conceptually different: its primary object is not technology but knowledge itself. Technology — including AI — is an enabling infrastructure. The proposed transformation is therefore technology industrialization versus knowledge industrialization.

15. Artificial Intelligence and the Knowledge Industrial Revolution

The emergence of generative AI makes the question of knowledge production particularly timely. Modern AI systems can generate text, images, code and other outputs at unprecedented scale. However, information generation is not automatically equivalent to validated knowledge production. A knowledge-industrial system requires mechanisms for provenance, verification, domain expertise, contextual interpretation, quality assurance, updating, integration and human judgement. This is where Mathew’s distinction between Artificial Intelligence and Natural/Human Intelligence becomes relevant. The proposed architecture does not require AI to replace human intelligence; rather, it proposes integrating AI + Natural Intelligence + Knowledge Industry to increase the capacity of individuals and institutions to produce, transform and consume sophisticated knowledge.

16. Comparative Framework: Machlup and Mathew

Dimension	Fritz Machlup	Raju M. Mathew
Principal period	1950s–1960s	1970s–present
Major contribution	Economics of knowledge production and distribution	Knowledge production, consumption, engineering and industrialization
Principal question	How large is the knowledge industry?	How can knowledge itself become an industrial product?
Unit of analysis	Knowledge-producing economic activities	Knowledge as product / resource / system
Method	Economics and statistical measurement	Economics + informatics + mathematics + cybernetics + engineering
Knowledge industry	Sector of the economy	Proposed production ecosystem
Knowledge product	Implicit within knowledge-producing activities	Explicit conceptual category
Production	Measurement of knowledge-producing activities	Systematic production and engineering of knowledge
Consumption	Part of economic activity	Explicit variable in Mathew’s theories
Quality	Economic/statistical classification problem	Proposed quality-control and standardization problem
Storage	Knowledge distribution infrastructure	Dynamic Knowledge Banks
Technology	Supporting knowledge production/distribution	Knowmatics, AI and knowledge-processing systems
Labour	Knowledge transmitters / receivers	Knowledge producers, engineers, entrepreneurs and consumers
Industrial objective	Measure economic significance	Create industrial-scale knowledge production
Historical implication	Knowledge economy	Proposed Knowledge Industrial Economy
Ultimate concept	Knowledge Industry	Knowledge Industrial Revolution

17. The Intellectual Continuum

The relationship between these thinkers should not be presented as a competition. It is more useful to see a historical sequence:

• Fritz Machlup — 1962, Knowledge Industry. Knowledge-producing activities become measurable economic activities. [1]
• Marc Porat — 1977, Information Economy. Information production, processing and transmission are systematically measured. [2]
• Daniel Bell — 1973, Post-Industrial Society. Theoretical knowledge becomes a central organizing principle of advanced society. [4]
• Peter Drucker — 1993, Knowledge Economy / Knowledge Worker. Knowledge becomes a decisive factor of production. [3]
• Raju M. Mathew — 1980s onward, Knowledge as Product. Knowledge is explicitly conceptualized in terms of consumption, production, growth and economic application. [6,7]
• Raju M. Mathew — 2005 onward, Knowmatics. Knowledge is proposed as an object of mathematical, cybernetic and engineering analysis. [5,8]
• Raju M. Mathew — 2020s, Knowledge Industrial Revolution. Knowledge is proposed as the central object of a new industrial production system. [5,9]

This sequence represents an interpretive intellectual genealogy, not a claim that each scholar directly influenced the next.

18. What Is Original in the Mathew Framework?

For academic purposes, the strongest claims should be formulated precisely. Based on the sources currently available, the following can reasonably be identified as central to Mathew’s own framework:

• Knowledge Consumption–Production Correlation — the proposed relationship between knowledge consumption and knowledge production.
• Stage Theory of Knowledge Consumption and Production Growth — the proposed five-stage progression from Elementary through Affluent.
• Knowledge as Product — the explicit treatment of knowledge as an intangible economic product capable of production, delivery and consumption.
• Knowmatics — the proposed field combining mathematics, cybernetics and engineering in the systematic processing of knowledge.
• Dynamic Knowledge Banks — the proposed knowledge infrastructure for continuously organizing, storing, updating and delivering knowledge.
• Knowledge Industrialization — the proposition that knowledge production can move beyond “cottage-industry” production toward systematic industrial production.
• Knowledge Industrial Revolution — the larger historical proposition that the industrialization of knowledge could constitute a new stage in economic and civilizational development.

MKIC itself identifies these concepts as components of Mathew’s intellectual contribution. [5,9]

19. A Necessary Academic Qualification

The intellectual ambition of this framework should be accompanied by methodological caution. The concepts of Knowledge Economy, Knowledge Industry, Knowledge Management, Information Economy, Knowledge Society and Knowledge Engineering already have extensive scholarly literatures. Consequently, claims of absolute originality should be made only after a systematic literature review. The stronger academic position is not “nobody before Mathew studied knowledge as an economic phenomenon” — that would be historically indefensible. Rather, the defensible proposition is:

20. From Knowledge Economy to Knowledge Industrial Economy

The distinction can be formulated formally. In a Knowledge Economy, knowledge is a major factor of production. In a Knowledge Industry, knowledge-related activities constitute a major economic sector. In Knowledge Engineering, knowledge becomes an object of systematic computational and technological processing. In a Knowledge Industrial Economy, knowledge becomes a major object of production itself. In the Knowledge Industrial Revolution, the industrialization of knowledge produces a structural transformation of the economy and society. This proposed distinction is the conceptual core of the Mathew framework.

21. The New Knowledge Value Chain

A mature Knowledge Industry could theoretically contain the following value chain:

1. Knowledge Generation — human intelligence, research, experience and discovery.
2. Knowledge Capture — documentation of explicit and tacit knowledge.
3. Knowledge Structuring — conceptual organization and classification.
4. Knowledge Engineering — Knowmatics-based processing and transformation.
5. Knowledge Validation — expert review, evidence, authentication and quality control.
6. Knowledge Storage — Dynamic Knowledge Banks.
7. Knowledge Packaging — modules, products, services and solutions.
8. Knowledge Distribution — digital and physical delivery systems.
9. Knowledge Consumption — application by individuals, organizations and societies.
10. Knowledge Feedback — evaluation and creation of new knowledge.

This creates a potentially circular industrial process: Production → Consumption → Feedback → New Production.

22. Economic Implications

If the Knowledge Industrial Revolution develops as proposed, conventional economic categories may require modification. Economists may need to investigate knowledge productivity (how much economically useful knowledge is produced per unit of resources), knowledge capital (how accumulated knowledge should be represented as an asset), knowledge-product markets (how products are priced when reproduction costs are extremely low), knowledge quality, knowledge supply chains, knowledge entrepreneurship, knowledge inequality, and knowledge sovereignty. These constitute a potentially significant research agenda for economists and policymakers.

23. Implications for India

The proposition has particular relevance for India because India’s knowledge resources are highly distributed: scientific and technical knowledge; universities and research institutions; professional expertise; agricultural knowledge; traditional skills; indigenous practices; local histories; cultural knowledge; entrepreneurial experience; engineering capabilities. A Knowledge Industrial Revolution could provide a framework for transforming such distributed knowledge into structured and economically useful knowledge products. The possible development pathway is:

Local Knowledge -> Documentation -> Validation ->
Knowmatics Processing -> Knowledge Product ->
Knowledge Enterprise -> Global Knowledge Market

This could complement India’s existing strengths in information technology, software services, education and scientific research.

24. Employment and the Knowledge Entrepreneur

The industrial economy created the factory worker. The information economy created the information worker. The knowledge economy created the knowledge worker. The Knowledge Industrial Revolution could create a new category: the Knowledge Entrepreneur — who may transform personal or community expertise into knowledge modules, professional services, educational products, specialized databases, advisory systems, digital knowledge products, intellectual-property assets and AI-assisted knowledge solutions. Instead of asking only what job can I obtain?, the individual may increasingly ask: what knowledge can I produce, package and exchange?

25. Education and the Industrialization of Knowledge

The traditional educational model largely separates learning from production: students consume knowledge while researchers produce new knowledge. The Knowledge Industrial Revolution suggests a more integrated model — Knowledge Consumption → Knowledge Creation → Knowledge Production → Knowledge Product → Knowledge Entrepreneurship. Universities could consequently evolve into Knowledge Production and Innovation Systems, requiring changes in curriculum, research methodology, assessment, intellectual-property policy, knowledge-product development, entrepreneurship, AI-assisted research and interdisciplinary collaboration. Mathew’s 2011 paper explicitly connected Knowmatics with a proposed transformation of higher education. [8]

26. Knowledge Industrial Revolution and AI

It is tempting to equate the Knowledge Industrial Revolution with Artificial Intelligence; this paper argues they should be distinguished. The AI revolution primarily increases the ability of machines to process data, recognize patterns, generate outputs, predict and automate cognitive tasks. The Knowledge Industrial Revolution seeks to create systems for producing, structuring, validating, engineering, storing, distributing and consuming knowledge, and continuously producing new knowledge. AI can therefore become an engine of the Knowledge Industrial Revolution without being identical to it:

27. Research Questions for the Next Generation

The Knowledge Industrial Revolution should not be treated merely as a slogan; it should become a research programme. Important questions include:

• Can knowledge be operationally defined for industrial production?
• Can knowledge products be classified?
• Can knowledge quality be quantitatively or systematically evaluated?
• What constitutes a unit of knowledge production?
• What are the economics of knowledge production, and can knowledge productivity be measured?
• What are the appropriate knowledge-production functions?
• Can Dynamic Knowledge Banks become viable industrial infrastructure?
• How should human expertise and AI-generated outputs be integrated?
• What institutions should certify knowledge products, and how should they be priced?
• How can knowledge markets be developed, and what are the intellectual-property implications?
• Can distributed local knowledge become globally tradable?
• How will knowledge industrialization affect employment, and can it accelerate development in emerging economies?

28. A Proposed Paradigm Shift

The fundamental paradigm shift may be expressed as follows. In the old paradigm, knowledge is an input into production. In the emerging paradigm, knowledge is also an object of production. And ultimately, in the knowledge industrial paradigm, knowledge is simultaneously input, process, product, capital and output. This circularity is what distinguishes knowledge from most traditional industrial commodities — knowledge consumed by one generation can become the input for new knowledge production by another:

This proposition provides the conceptual foundation for Mathew’s theories of knowledge consumption and production growth.

29. Conclusion: From Measuring the Knowledge Industry to Building It

Machlup’s 1962 work represents a historic turning point because it demonstrated that knowledge-producing and knowledge-distributing activities constituted a substantial component of a modern economy. [1] Porat strengthened the measurement of information activity. [2] Bell interpreted the structural transformation toward a post-industrial society. [4] Drucker emphasized knowledge as a decisive economic resource and the knowledge worker as a central economic actor. [3] These developments established the intellectual foundation of the modern knowledge economy.

The Mathew framework proposes a further step: it asks whether the object of economic analysis can move from knowledge-producing activity to knowledge production itself. The answer proposed is an emerging architecture consisting of Knowledge as Product → Knowledge Consumption and Production Theories → Knowmatics → Dynamic Knowledge Banks → Knowledge Products and Services → Knowledge Industry → Knowledge Industrial Economy → Knowledge Industrial Revolution.

The significance of this proposition should not be judged merely by the novelty of its terminology. Its real test will be whether its concepts can be developed into operational definitions, measurable variables, reproducible methodologies, technological systems, viable enterprises and empirically testable economic models. That is the next stage of research.

If this transition can be demonstrated theoretically and empirically, the Knowledge Industrial Revolution would represent not simply another phase of the Information Age, but a potentially distinct transformation in the organization of human production: from producing things with knowledge to producing knowledge itself as an industrial product.