by Ioannis Sidiropoulos, Lawyer, LL.M LSE, UvA and Lecturer in Commercial Law and Consumer Protection at CIM-Cyprus Business School
Corporate and commercial law have never been insulated from economic change. Each industrial shift has forced the law to rethink the structures and assumptions of business life. Steam power pushed courts and legislatures to recognise new forms of enterprise. Mass production revolutionised employment relations and product liability. In our teaching today, we face a transformation of comparable scale: an AI-driven economy where algorithms design, negotiate, and optimise far faster than humans can follow.
Rather than treating this moment as unprecedented, legal education can draw on the long history of industrial and economic thought; especially lessons from heavy and motor industries that previously redefined the boundaries of corporate power, labour, accountability, and market structure. Using history in our teaching does not turn law into a museum exhibit. It equips students to spot recurring patterns in the governance of technology and commerce.
Corporations as industrial technologies: From railroads to AI-Driven firms
The corporation itself once counted as cutting-edge technology. In the nineteenth century, railroads demanded business structures capable of raising unprecedented capital and coordinating operations across national distances. Joint-stock companies evolved to meet that demand, bringing with them innovations such as limited liability and modern directors’ duties. Courts struggled with the question: who bears responsibility when vast industrial systems fail?
AI is producing a similar leap in scale and complexity. Consider automotive manufacturing. Modern plants increasingly rely on AI to manage supply chains, schedule production, and reduce downtime through predictive maintenance. Autonomous logistics systems already negotiate with suppliers and coordinate deliveries. When these systems err (say, a procurement algorithm inadvertently violates sanctions or competition law) the challenge resembles early litigation against railroad companies: attributing liability within machine-like corporate structures.
Historical examples show students that the corporation is adaptable. It also shows them that legal doctrines always evolve in tension with industrial needs. The AI-enabled firm is not a rupture with the past but a new chapter in a continuing narrative about organisational technology.
Legal Lag: factories, motorways, and self-optimising markets
Economic historians chronicle a familiar pattern: technology sprints, law jogs behind. Factory safety legislation, diesel emissions standards, and road traffic regulation all emerged only after risk became undeniable. Motor industries, for instance, matured rapidly before product liability law fully understood manufacturing defects or consumer expectations. The birth of strict liability in mass-produced automobiles taught judges that traditional fault-based reasoning could not cope with complex industrial systems.
Today’s version of this dynamic is algorithmic contracting and autonomous commercial decision-making. Smart systems now refine pricing strategies in real time; an echo of the price-fixing concerns that followed the rise of vertically integrated auto giants in the twentieth century. But while the old disputes involved boardroom conspiracy, algorithmic “collusion” may occur without any human intent at all.
History provides both caution and comfort for future practitioners: legal lag is normal, yet law reliably adapts when commercial risk expands faster than rules can manage.
Labour, robots, and the shifting Value-of-Work
Every industrial revolution disrupts labour markets. When combustion engines replaced horses, entire trades dissolved and new ones emerged. The legal system responded with frameworks for collective bargaining, minimum standards, and welfare protection.
Heavy industry today relies increasingly on automation and AI. Car manufacturers routinely deploy collaborative robots (“cobots”) to work alongside humans. Ports now use automated cranes guided by machine-vision systems. The question facing corporate and commercial lawyers: when an AI tool performs economically valuable work, how should the value be allocated, and who deserves protection?
Historical memory helps students identify what is genuinely novel and what repeats familiar patterns. The debate over whether platform gig-workers are employees echoes earlier disputes over industrial homeworkers in textile mills. The legal category of “employee” has never been static. AI will prompt fresh re-drawing of these boundaries, including:
• duties to retrain displaced workers
• rights of oversight for humans supervising automated systems
• profit-sharing where machines drive productivity gains
Industrial history counters the temptation to treat AI-driven workplace change as unstoppable fate. Past disruptions were politically and legally contested; and so will this one be.
Concentration and power: From steel monopolies to data empires
Heavy industry teaches a hard lesson: technological advantages can become entrenched power. Steel barons and automotive giants leveraged economies of scale to dominate markets. Antitrust responses, from breaking up Standard Oil to regulating automotive supply chains, emerged only after harms became socially visible.
Today, AI development is concentrated in a small cluster of firms controlling vast datasets, computing capacity, and distribution platforms. This resembles motor industry consolidation in the 1950s and 60s, but at a global digital scale. Just as vehicle type-approval regimes once shaped who could enter the market, access to foundational AI models and data governance norms will determine future competition.
Teaching this history shows students that market structure is not natural or inevitable. Law has the capacity to re-balance economic power; it has done so before in sectors from steel to shipping.
Industrial risk and corporate responsibility
Legal responses to industrial risk often arise from tragedy. Rail collisions shaped carrier liability. Mining accidents forged occupational safety rules. Automotive crashes gave rise to modern product recall regimes. Commercial law expanded to internalise risk that was once externalised to workers and consumers.
AI introduces risks that are distributed and difficult to diagnose. An algorithmic supply chain malfunction can shut down entire manufacturing ecosystems. Autonomous vehicles raise questions reminiscent of early motor liability cases: is the driver responsible, or the machine?
Students benefit when they see continuity. The assignment of responsibility in complex systems has always been a challenge. Directors’ duties evolved to address foreseeable industrial risk. Today those duties arguably include governance of AI deployment and algorithmic harms.
Why history should be in our law classrooms
Economic and industrial history enhance corporate and commercial legal education in three powerful ways.
It encourages analytical humility. Even when change feels unprecedented, earlier generations wrestled with remarkably similar problems: accountability in new organisational forms, labour displaced by machinery, dominant firms reshaping markets.
It strengthens doctrinal imagination. By seeing how laws adapted to railroads, internal combustion, mass production, and global trade, students develop the confidence to engage with AI regulation not as spectators but as future architects.
It promotes a richer understanding of justice. Industrial progress has always brought winners and losers. Legal systems serve society best when they confront inequality rather than merely manage efficiency.
AI may be the latest engine of transformation, but its core dilemmas (who benefits, who bears risk, and who makes the rules) are centuries old. By integrating history of economic and industrial thought into our teaching, we can help a new generation of lawyers recognise the patterns, anticipate the challenges, and build corporate and commercial law capable of guiding this next industrial era toward equitable prosperity.