AI Revolution Reshapes Production: A New Era of Machine Intelligence
In a groundbreaking analysis published in Yuejiang Academic Journal, Liu Fangxi, a senior researcher and doctoral supervisor at the Institute of Literature, Chinese Academy of Social Sciences, presents a compelling framework for understanding the transformative impact of artificial intelligence on modern production systems. Drawing from Karl Marx’s critique of production technology and Norbert Wiener’s theory of the three forms of existence—matter, energy, and information—Liu articulates a profound shift in the very nature of labor and industrial organization. His work, titled Energy·Information·Motive: A Critique of AI Production Technology, offers a rigorous historical and theoretical lens through which to interpret the ongoing technological revolution, positioning artificial intelligence not merely as a tool, but as an autonomous agent reshaping the material foundations of society.
Liu’s central argument hinges on the concept of the “motive force” (or dongyin in Chinese) in production. He distinguishes between two fundamental types of motive forces: energy and information. In traditional craft production, the human body serves as the primary organ of production, with the worker’s physical strength (energy) and mental faculties (information) acting as the direct driving forces. Tools in this context function as mere conduits, transmitting the worker’s energy and intelligence to the raw material. The Industrial Revolution of the 18th and 19th centuries, what Liu terms the “First Machine Revolution,” fundamentally altered this dynamic. With the advent of the steam engine and the automated machine system, machines began to generate their own motive power. The engine consumed coal to produce kinetic energy, replacing human and animal muscle as the dominant motive force. This marked the maturation of the “energy organ” of what Marx called the “social human’s productive organ”—the machine system as a collective, socially organized entity. The factory, powered by self-sustaining engines, no longer relied on the biological limits of individual workers for its primary power source.
The significance of this first revolution, as Liu emphasizes, was not just technological but deeply social. It shifted the locus of power from the individual artisan to the collective machinery of capital. The worker’s role was reduced from a primary actor to a “tender” or supervisor of the machine, a role requiring less physical exertion but still demanding human intelligence for oversight, maintenance, and control. The machine system, while autonomous in its energy generation, remained dependent on human intelligence for its direction and purpose. The “information organ” of the social productive system was still, in essence, the human brain, externalized through blueprints, operational procedures, and managerial decisions.
Liu’s analysis posits that we are now in the midst of a “Second Machine Revolution,” driven by artificial intelligence, big data, and the Internet of Things (IoT). This revolution is not about automating energy, but about automating intelligence. The core of this new paradigm is the emergence of the machine as an autonomous generator of information—the “information organ” of the social productive system is now developing its own capacity for self-organization and decision-making. Unlike the steam engine, which produced energy, the computer produces and processes information. When a computer runs a program, it is not merely executing a set of human instructions; it is engaging in a complex process of information transformation. In advanced AI systems, particularly those based on machine learning, the machine is not just processing information but is actively generating new information—new patterns, new predictions, and new strategies—by learning from vast datasets.
This is the crux of Liu’s argument: AI is moving beyond being a mere “conductor” of human intelligence to becoming a “substitute” for it. In the same way that the steam engine replaced the human muscle, AI is beginning to replace the human mind in specific domains of production. Consider the example of Computer Numerical Control (CNC) machines. A traditional lathe requires a skilled machinist to manually guide the cutting tool. A CNC machine, however, is driven by software—a set of digital instructions. The software, as Liu points out, is pure information. It is not matter, nor is it energy; it is the directive force that tells the machine exactly how to move, when to cut, and at what speed. The human machinist is no longer the primary source of the motive intelligence; that role has been transferred to the software. The machine now operates on its own “informational motive force.”
This shift is amplified by the convergence of IoT and big data. The IoT creates a network of sensors that continuously collect data from the physical world—temperatures, pressures, vibrations, movements. This data, when aggregated into massive datasets, becomes the raw material for AI. Machine learning algorithms “consume” this data, much like a steam engine consumes coal, to “produce” intelligence. They identify patterns, optimize processes, and make decisions with minimal human intervention. A smart factory equipped with AI and IoT can monitor its own performance, predict equipment failures, adjust production schedules in real time, and even design new products—all driven by an internal, machine-generated informational motive force.
Liu traces the historical lineage of this development. He notes that the seeds of the information revolution were sown with earlier technologies like the telegraph and the printing press. The telegraph was an early form of an “information circulation machine,” enabling the rapid transmission of data over long distances. The printing press was an “information reproduction machine,” capable of mass-producing symbolic content. However, these were still largely tools for dissemination and replication, dependent on human authors and operators. The computer, in contrast, is a true “information production machine.” It can create new content, solve complex problems, and simulate entire systems. The invention of the computer, Liu argues, is as revolutionary for information as the invention of writing was for language. Just as writing allowed humans to store and transmit knowledge outside the biological limits of the brain, the computer allows for the creation and manipulation of knowledge at a scale and speed previously unimaginable.
The implications of this second revolution are profound and far-reaching. Economically, it promises unprecedented levels of productivity and efficiency. Entire industries, from manufacturing to logistics to finance, are being restructured around AI-driven automation. However, Liu warns that this technological leap carries significant social risks. The First Machine Revolution displaced manual laborers, leading to the rise of the industrial proletariat and periodic social unrest. The Second Machine Revolution, he argues, is now displacing intellectual laborers—the “white-collar” workers whose jobs involve data analysis, routine decision-making, and administrative oversight. As AI systems become more capable, the demand for human cognitive labor in these domains will inevitably decline.
This leads to a critical concern: the potential for massive unemployment and a widening of the wealth gap. If the primary motive forces of production—both energy and intelligence—are generated by machines owned by a small capitalist class, then the majority of the population, stripped of their traditional roles as producers, will be left without a source of income. The “social motive force” of capitalism, which Liu identifies as the relentless pursuit of profit and the accumulation of ever-greater surplus value, will continue to drive innovation, but it may do so at the expense of social stability. The immense wealth generated by AI-powered production could become concentrated in the hands of a technological elite, while a large segment of the population is rendered economically redundant.
Liu’s analysis, grounded in Marx’s dialectical materialism, suggests that the solution lies not in rejecting technology, but in reorganizing the social relations that govern it. The key, he implies, is to transform the ownership and control of these advanced productive forces. If AI and automated machine systems are to serve humanity as a whole, they must become the “common property of associated workers,” rather than the private property of a capitalist few. This would require a fundamental shift in the “social motive force” of production, moving from the pursuit of private profit to the fulfillment of collective human needs. Only then can the immense potential of the Second Machine Revolution be harnessed for the benefit of all, rather than the enrichment of a privileged few.
The transition from human-driven to machine-driven intelligence is not a simple replacement but a complex process of organic development. Liu uses the metaphor of an “organic body” to describe the evolution of the social productive system. Just as a biological organism develops its physical organs before its cognitive ones, the social productive system first developed its “energy organ” (the automated machine) before beginning to develop its “intelligence organ” (the AI system). The First Machine Revolution was the “first development” of this organic body, maturing its physical capabilities. The current AI revolution is the “second development,” the maturation of its cognitive capabilities. This “second development” is built upon the foundation of the first; the intelligent machine cannot function without a reliable source of energy. Thus, the two revolutions are not separate events but phases in a continuous historical process of technological and social evolution.
Furthermore, Liu highlights the crucial role of “general intellect” in this process. Marx observed that under capitalism, “general social knowledge” increasingly becomes a direct productive force. In the age of AI, this is more evident than ever. The knowledge embedded in software, algorithms, and databases is the very substance of the new motive force. This knowledge is not the product of any single individual but is the cumulative result of centuries of scientific and technological advancement, social collaboration, and collective experience. It is “objectified” or “materialized” in the form of code and data. Therefore, the power of AI is not just a technical achievement; it is a social achievement, a product of the entire history of human civilization. Recognizing this fact underscores the argument that the benefits of this technology should be shared by society as a whole.
The integration of information and material production is another key theme. Historically, information technologies like the telegraph operated primarily in the sphere of circulation, facilitating the movement of goods and capital. Today, with technologies like 3D printing and smart manufacturing, information is directly shaping physical matter. A digital design file (information) is transmitted to a 3D printer, which then constructs a physical object (matter) layer by layer. This blurs the line between the information economy and the manufacturing economy, creating a new paradigm of “informationized manufacturing.” In this new mode, the primary input is no longer raw materials or human labor, but information. The most valuable asset in this system is not a factory or a mine, but a high-quality dataset or a sophisticated algorithm.
Liu’s work provides a powerful framework for understanding these complex dynamics. It moves beyond the typical techno-optimism or techno-pessimism that often characterizes public discourse on AI. Instead, it offers a nuanced, historically grounded analysis that recognizes both the revolutionary potential of the technology and the deep-seated social contradictions it exacerbates. By reviving and reconstructing Marx’s “Critique of Production Technology” through the lens of Wiener’s information theory, Liu provides a unique and indispensable perspective for policymakers, economists, and technologists navigating the challenges of the 21st century. His analysis serves as a timely reminder that the future of work, and indeed the future of society, will be determined not just by the machines we build, but by the social and economic systems we choose to build around them.
Energy·Information·Motive: A Critique of AI Production Technology by Liu Fangxi, Institute of Literature, Chinese Academy of Social Sciences, published in Yuejiang Academic Journal, DOI: 10.1674-7089(2021)02-0005-10