The evolution of software and its social impact. What lessons can be learned from the Machine Age?

A Parallel between Alain Touraine's Labor Phases and the Societal Implications for the GenAI Era

Abstract

Alain Touraine's sociological framework on the evolution of industrial labour—spanning Phase A (craft autonomy), Phase B (fragmented Taylorism[1]) and Phase C (automated reintegration)—originated from his groundbreaking empirical research at the Billancourt Renault Factory, detailed in L’Évolution du travail ouvrier aux usines Renault (Evolution of Labor at the Renault Factories, 1955), a model that provides a powerful lens for understanding technological shifts in the computer age. This paper draws a parallel between Touraine's phases and software development, from early mainframes and custom programming (akin to Phase A) to standardized frameworks like Java/J2EE (as Phase B) and the current Generative AI (GenAI) revolution (as Phase C). An exanimation the of the relationship between workers, technology and organizational structures, highlights a paradigm shift toward supervision over execution created by GenAI. The analysis further exposes the societal implications, including class struggles and divisions, along with the actions required to ensure equitable adaptation, such as prioritizing AI literacy and regulation. Ultimately, Touraine's insights, rooted in real-world factory observations, warn that without intervention, GenAI could exacerbate inequalities, but that proactive policies can harness it for broader societal benefit.

Overview of Touraine's Thesis on Labor Evolution

Alain Touraine was one of the most prominent sociologists of the 20^th century. His seminal work in the area of industrial sociology is still regarded today as the foundational work of that discipline. In post-war France, between 1950 and 1955, Touraine conducted protracted field research at Renault Billancourt factory (Renault), where he was exposed to machinery and processes which were in a transition from what he would later call “Phase A” – the era of universal multipurpose machines – to “Phase B” – single-purpose machines, specialized in performing a reduced number of operations – possibly just one – to “Phase C” – the first transfer[2], numerically controlled machines. Touraine, by his own admission, was lucky, in the sense that those three eras (or phases) of technological evolution were coexisting in the same space and therefore “visible” to an attentive external observer.

Touraine’s central thesis, as explored in foundational works like L’Évolution du travail ouvrier aux usines Renault (1955) - and later expanded on in The Post-Industrial Society (1971) - posits that Taylorism is not a timeless model but rather a response to a specific technological stage (Phase B), one that fundamentally reshapes the organization of labor, hierarchies, and human skills, while influencing broader societal dynamics such as class structures and power relations. In Phase A, the era of the universal machine, characterized by flexible, multipurpose tools like electric lathes that demand constant human intervention, the worker emerges as a skilled artisan who acquires knowledge empirically, "listening" to the machine to adjust and ensure precision, thereby fostering a true dialogue where the individual controls both method and execution within organic hierarchies based on expertise and blurred lines between formal and informal authority—a system Touraine observed in early mechanized production lines at Renault.

By contrast, Phase B, the era of the specialized machine, enabling mass production through single-purpose devices that embodied Fordism[3] and scientific management, deskilled workers into repetitive tasks, transforming them into mere extensions of the machine and alienating them from the holistic process understanding as the machine imposed its rhythm. Humans serve the machine under rigid, top-down structures with artificial hierarchies that replace genuine skill, creating alienation as Touraine noted pointedly, due to the repetitive assembly-line tasks at the factory.

Finally, Phase C, the era of automation, where integrated systems reabsorbed fragmented tasks and freed humans from repetition, shifted the worker's role to that of a supervisor who intervenes during anomalies, emphasizing technical and social competence in a reversed dynamic where humans manage the system rather than serve it. This led to a flattening of hierarchies and a focus on integration and problem-solving rather than discipline, as evidenced in Touraine's observations of the emerging automation at Renault.

Detailed Explanation of the Three Phases in the Computer Age

Drawing from Touraine's model, the computer age parallels these industrial phases through a progression of technologies that transform software development and usage, highlighting a shift from ad-hoc craftsmanship to standardized industrialization and, ultimately, AI-driven automation, with each stage building on explicit technologies and examples that illustrate the evolving nature of human interaction with computational tools. It can also be argued that, as Touraine observed at Renault, the three IT phases, equivalent to their machine age counterparts, are all visible at the same time in today’s offices. In other words, in large corporations today we can still sense the presence of a bygone era – the Cobol/Mainframe applications that simply refuse to die (Phase A) coexisting with what is still the majority of software applications and processes based on well-established technologies and frameworks (the IT equivalent of a Phase B) and with the leading-edge generative AI systems (Phase C) being deployed at an increasingly aggressive pace.

Phase A, the “Craft” era

Phase A, the "Craft" era is equivalent to Touraine's universal machines. Computing begins with early hardware as a blank slate requiring intimate, manual configuration, encompassing technologies such as the Assembly language, first-generation mainframes with limited memory like the 48-kilobyte systems, and early high-level languages including COBOL and Fortran. Every program was created ad hoc and customized for specific business needs—often demanding months or years to deliver outcomes due to the absence of reusable components. Programmers manipulated hardware directly, for instance, by managing memory registers in the Assembly language to craft solutions from scratch for tasks like payroll processing or scientific simulations. This era spanned the 1950s to 1970s and was exemplified by IBM System/360 mainframes that emphasized empirical knowledge passed through "tribal" mentorship. The bespoke nature of each project implied protracted delivery timelines, a process akin to artisanal machining where developers "listen" to the machine's constraints, such as optimizing for limited RAM, or saving bytes to reduce the necessary amount of storage, to ensure functionality.

Phase B, the “Industrial” era

Phase B, the "Industrial" era mirrored Taylorism's fragmentation, with tools created to standardize and optimize development. This reduced the need to "reinvent the wheel" with the advent of key technologies like 4GL (Fourth-Generation Languages) tools, relational databases such as SQL-based systems, including Oracle or DB2, which represented a new paradigm shift in the field of data access/data storage alongside an increase in the use of Java and J2EE (Java 2 Enterprise Edition). These provided standard infrastructural components for database access via JDBC, queue access through JMS for messaging, and web access with servlets and JSP. Developers were no longer required to build those components from scratch at every new assignment. The same standardization occurred with other frameworks such as Spring for dependency injection and inversion of control, microservices architectures using Docker and Kubernetes for modular deployment, and agile methodologies. Scrum, for example, breaks work into sprints – two-week iterations, relying on self-organizing teams to deliver incremental value. Tools like DEC Datatrieve (in 1980s), or early BI software (starting in the 1990s) empowered business users to be able to query data directly, bypassing programmers for routine tasks.

From the 1980s to 2010s, enterprise applications for scalable systems in e-commerce or banking that fragmented skills into specialties like front-end versus back-end, were progressively standardized, avoiding custom low-level coding, and optimized for efficiency in "software factories". Creativity was constrained within rigid patterns, much in the way assembly lines dictated worker pace in the machine era.

Phase C, the “Generative” era

The current paradigm shift culminated in Phase C the "Generative" era, an equivalent to automation. This was brought about by Generative AI (GenAI), where Large Language Models (LLMs such as GPT, Gemini or Claude Sonnet) and tools such as GitHub Copilot allowed for automation of code generation, data analysis, and content creation, incorporating manual tasks into intelligent systems that enable low-code/no-code platforms like Bubble or Microsoft Power Apps. Users were no able to “prompt” for entire applications and shift focus from writing syntax to verifying outputs.

Industry applications such as AI-driven DevOps for auto-generating tests or deployments, and creative tools including DALL-E for visuals or ChatGPT for drafting, have lowered barriers for non-technical users to orchestrate complex outcomes while still requiring human oversight for accuracy, comparable to the supervisors at Renault whose role became limited to intervening when a malfunction occurs.

Parallels with the Computer Age: Comparative Tables

The following tables summarize the parallels between Touraine’s phases and the eras of computerization and GenAI.

Table 1: Touraine's Evolution of Industrial Labour

Touraine's Phase	Technological Context	The Worker	The Relationship	The Organization
Phase A (Universal Machine)	Multipurpose machines (e.g., electric lathes requiring adjustments).	Skilled artisan with empirical craft; intervenes intuitively.	Interactive dialogue; worker shapes method and execution.	Organic hierarchies based on shared expertise and informal.
Phase B (Specialized Machine)	Single-purpose machines for mass output (e.g., assembly lines).	Deskilled into repetitive roles; alienated from process.	Machine-driven; worker as servant to rhythm.	Rigid, bureaucratic; scientific management enforces control.
Phase C (Automation)	Integrated systems (e.g., transfer machines absorbing tasks).	Monitor/supervisor; focuses on anomalies and oversight.	Reversed; human manages automated system.	Flattened; emphasizes integration and collaboration.

Table 2: The Computer Age Parallel to Touraine’s Phases

Touraine's Phase	Technological Context (Computer Age)	The Worker (Developer/User)	The Relationship (Human-Computer Interaction)	The Organization (Management & Structure)
Phase A (Craft Era)	The "Craft" Era Mainframes (48KB of memory), Assembly, COBOL, Fortran. Characteristics: Code is written ad hoc from scratch. No libraries. Direct hardware manipulation.	The "Wizard" / Artisan Possesses deep, empirical knowledge of the specific hardware (registers, memory addresses). Like Touraine's skilled worker, he/she does not just use the machine, but intimately understand its inner workings.	Total Control & Dialogue The programmer has full autonomy over the logic. The relationship is manual and intimate. The human dictates every bit and byte. Optimization is manually crafted through a deep "listening" to the machine's constraints.	Competence-Based Hierarchy Teams are small and "tribal." Authority comes from technical prowess, not job titles. Delivery takes months/years because every solution is a bespoke "cathedral" built without standardized parts.
Phase B (Industrial Era)	The "Industrial" Era 4GL, SQL/Relational Databases, Java/J2EE, Frameworks. Characteristics: Standardization. "Don't reinvent the wheel." Use of infrastructure components (JMS, JDBC) to assemble rather than create.	The "Component Integrator" The developer becomes specialized (Frontend vs. Backend, DBA). Skill shifts from understanding the whole machine to mastering specific tools/frameworks. Creativity is constrained by the rigid rules of the platform (e.g., J2EE patterns).	Framework Dependency The "Framework" dictates the workflow (Inversion of Control). The developer feeds parameters into pre-existing structures (Databases, APIs) just as a Phase B worker feeds a machine. The process is optimized for scalability, not individual artistry.	Scientific Management (Taylorism Equivalent) Rise of rigid methodologies (Waterfall, heavy Scrum). Work is fragmented into "tickets" and measured by velocity. A complex hierarchy of Architects, Seniors, and Juniors emerges to manage the complexity of the "Software Factory."
Phase C (Generative Era)	The "Generative" Era Generative AI, LLMs, Copilots, Low-Code/No-Code. Characteristics: The machine generates the syntax. The system "absorbs" the manual labor of coding.	The "Supervisor" / Orchestrator The worker shifts from writing code to prompting and verifying it. Technical barriers lower. Business users can generate applications. The worker is responsible for the intent and quality assurance, not the manual implementation.	Supervision & Correction The dynamic reverses: the machine produces the output, and the human monitors/corrects it. The relationship is conversational. The human acts as an editor or conductor, intervening only when the AI "hallucinates" or deviates from the goal.	Flattened & Integrated Reduction of the "middle-management" of code. The distinction between Business and IT blurs. Teams become smaller and cross-functional again, focused on solving problems rapidly rather than managing complex deployment pipelines.

Touraine's Phase

Technological Context (Computer Age)

The Worker (Developer/User)

The Relationship (Human-Computer Interaction)

The Organization (Management & Structure)

Phase A (Craft Era)

The "Craft" Era

Mainframes (48KB of memory), Assembly, COBOL, Fortran.

Characteristics: Code is written ad hoc from scratch. No libraries. Direct hardware manipulation.

The "Wizard" / Artisan

Possesses deep, empirical knowledge of the specific hardware (registers, memory addresses). Like Touraine's skilled worker, he/she does not just use the machine, but intimately understand its inner workings.

Total Control & Dialogue

The programmer has full autonomy over the logic. The relationship is manual and intimate. The human dictates every bit and byte. Optimization is manually crafted through a deep "listening" to the machine's constraints.

Competence-Based Hierarchy

Teams are small and "tribal." Authority comes from technical prowess, not job titles. Delivery takes months/years because every solution is a bespoke "cathedral" built without standardized parts.

Phase B (Industrial Era)

The "Industrial" Era

4GL, SQL/Relational Databases, Java/J2EE, Frameworks.

Characteristics: Standardization. "Don't reinvent the wheel." Use of infrastructure components (JMS, JDBC) to assemble rather than create.

The "Component Integrator"

The developer becomes specialized (Frontend vs. Backend, DBA). Skill shifts from understanding the whole machine to mastering specific tools/frameworks. Creativity is constrained by the rigid rules of the platform (e.g., J2EE patterns).

Framework Dependency

The "Framework" dictates the workflow (Inversion of Control). The developer feeds parameters into pre-existing structures (Databases, APIs) just as a Phase B worker feeds a machine. The process is optimized for scalability, not individual artistry.

Scientific Management (Taylorism Equivalent)

Rise of rigid methodologies (Waterfall, heavy Scrum). Work is fragmented into "tickets" and measured by velocity. A complex hierarchy of Architects, Seniors, and Juniors emerges to manage the complexity of the "Software Factory."

Phase C (Generative Era)

The "Generative" Era

Generative AI, LLMs, Copilots, Low-Code/No-Code.

Characteristics: The machine generates the syntax. The system "absorbs" the manual labor of coding.

The "Supervisor" / Orchestrator

The worker shifts from writing code to prompting and verifying it. Technical barriers lower. Business users can generate applications. The worker is responsible for the intent and quality assurance, not the manual implementation.

Supervision & Correction

The dynamic reverses: the machine produces the output, and the human monitors/corrects it. The relationship is conversational. The human acts as an editor or conductor, intervening only when the AI "hallucinates" or deviates from the goal.

Flattened & Integrated

Reduction of the "middle-management" of code. The distinction between Business and IT blurs. Teams become smaller and cross-functional again, focused on solving problems rapidly rather than managing complex deployment pipelines.

The Societal Impact of Phase C in the Computer Age: The GenAI Era

Touraine's analysis, grounded in his research at Renault, linked labor phases to class struggles and divisions. Applying this framework to GenAI suggests that such divisions will be intensified in a digital context. His conclusion on the societal impact of each era, that is, that Phase A promoted meritocracy, Phase B fueled alienation and unrest, and Phase C foreshadowed a concentration of power in a programmed society - serves as a foundation to understand how GenAI can impact the workers, relationships, and organizations.

While GenAI democratizes access for workers by enabling non-coders to build applications, it simultaneously displaces traditional roles. McKinsey forecasts that by 2030, automation will reach 45%, creating a divide between those skilled in AI and other IT professionals. For example, coders are already being replaced by tools like Copilot. This is acutely evident on gig platforms like Upwork, where AI competes directly with freelancers.

Another key potential division relates to transparency and control through access to data and platforms. This is exemplified by the difference between open-source models such as Llama, which allow for greater control of output and proprietary closed or “black-box AI” models such as GPT, whose internal mechanisms are hidden and known only to the owners of the platform.

Lessons from a Touraine-Inspired Analysis

Touraine’s analysis, as described above, reveals a number of interconnected lessons that can be used to help society face of GenAI challenges and seize its opportunities, beginning with the need to prioritize equitable access through AI education in public schools. Efforts are already underway to achieve this, such as Singapore's National AI Strategy that aims to bridge skill gaps and foster inclusion and transparency measures like the EU’s AI Act geared towards curbing monopolies and ensuring ethical oversight.

Fostering adaptive social structures is essential, as is supporting initiatives such as universal basic income or reskilling programs amid widespread displacement.

Finally, anticipating and mitigating polarization, which requires more diverse datasets to reduce biases, thereby fostering a more inclusive digital ecosystem that aligns with Touraine's vision of participatory societies rather than ones dominated by technical elites.

Conclusion

A comparison of Touraine's framework, based on his observations at Renault, to contemporary era GenAI, underscores GenAI's potential to reshape society - toward equity or division. By adopting proactive measures that integrate education, regulation and adaptation, we can ensure inclusive progress that transforms technological reintegration into a force for collective good rather than deepening divisions.

References

· Touraine, A. (1955). L’Évolution du travail ouvrier aux usines Renault. Paris: CNRS Editions.

· Touraine, A. (1971). The Post-Industrial Society. New York: Random House.

· McKinsey Global Institute. (2017). Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation.

· PwC. (2018). Sizing the Prize: What's the Real Value of AI for Your Business and How Can You Capitalise?.

· Singapore's National AI Strategy - AI for the Public Good For Singapore and the World. Ministry of Digital Development and Information (MDDI)(2023)

European Union. (2024). Artificial Intelligence Act.

[1] Taylorism, or scientific management, is an early 20th-century management theory developed by Frederick Taylor that maximizes industrial efficiency by analyzing and streamlining production processes. It breaks tasks into specialized, repetitive, and optimized steps, replacing "rule of thumb" methods with standardized, scientific studies to increase productivity.

[2] Transfer machines represent a cutting-edge solution for the automation of industrial manufacturing processes. These modular systems integrate multiple workstations interconnected through automated transport systems, revolutionizing the traditional approach to manufacturing.

[3] Fordism is a 20th-century industrial system of mass production and consumption, pioneered by Henry Ford, defined by assembly-line techniques, standardized products, and, critically, high wages enabling workers to purchase the goods they produced. It revolutionized manufacturing through rigid, specialized, and repetitive labour to achieve maximum efficiency and economic growth.