Energy consumption of the human brain, far more efficient than AI systems
From Division of Labor to Integration of Purpose
The industrial revolution was built on Adam Smith’s principle of the division of labor: fragment complex tasks into simple, repetitive motions to maximize efficiency. This created the paradigm of “work for others,” where individuals traded autonomy for wages, producing components of a whole they might never comprehend. The result, as economist E.F. Schumacher critiqued, was that work became “a meaningless drudgery” for many. In contrast, Shuichi Fukuda proposes a “Self-Supporting Society,” founded on “work for myself.” This is not a regression to solitary self-sufficiency, but an evolution toward a networked society where technology enables individuals to pursue meaningful, intrinsically motivating tasks that directly serve their community and their own growth. The goal shifts from mass-producing identical goods to mass-enabling unique human potential.
Energy, Ethics, and the Scale of Intelligence
The urgency of this shift is underscored by a stark thermodynamic reality. IBM’s Watson, when winning Jeopardy!, consumed an estimated 85,000 watts. Google’s AlphaGo used about 200,000 watts during its matches. The human brain operates on roughly 20 watts. Our pursuit of centralized, knowledge-based artificial intelligence consumes energy at a scale that is likely unsustainable for planet-wide deployment. Furthermore, it reinforces the expert-user divide, concentrating power and capability. A “Self-Supporting” model, leveraging human instinctive intelligence augmented by low-power, supportive technology, points toward a radically more efficient future. It suggests that the most sustainable and resilient “AI” is a distributed network of engaged, empowered human minds, each operating at 20 watts, supported by tools that enhance rather than replace their innate capacities.
The Playing Manager on the Pitch
The model for this new society is already visible in elite team sports. Legendary football coach Knute Rockne’s adage “11 Best, Best 11” highlighted that a team of stars is less than a star team. Today, the role of the manager has evolved. In fluid games like soccer, the manager can’t dictate play from the sideline; the situation changes too fast. Instead, a “playing manager”—often a midfielder—orchestrates strategy from within the flow of the game. This person doesn’t issue verbal commands but leads through shared understanding, anticipation, and trust. The team operates on communication (reading intent) rather than conversation (exchanging instructions). This is the prototype for a human-machine mixed team: humans providing strategic, adaptive intelligence, supported by machines that handle data retrieval, simulation, and logistics in real-time.
Strategic Engineering for a New World
This demands a new discipline: Strategic Engineering. For two centuries, engineering has been largely tactical, focused on solving well-defined problems (How do we build this bridge?). Strategic Engineering is concerned with defining problems and finding goals in ill-defined, dynamic environments (What should we build, and why, for a flourishing society?). It employs pragmatic, iterative methods like PDSA and tools like pattern-based feedback (MTF). Its value system prioritizes adaptability, evolution, and human flourishing over mere functionality, reproducibility, and robustness. The performance indicator it seeks is not quarterly profit, but sustained increases in collective agency, capability, and well-being.
The Instinctive Future
The path forward is not to reject technology, but to redirect it toward a profoundly human end. It means designing 3D printers not just for prototyping in factories, but for enabling local “Do-It-Myself” manufacturing and repair. It means building social platforms that facilitate “sharing life”—exchanging skills and experiences—not just “sharing economy” assets. It requires engineers to see themselves not as creators of autonomous systems, but as cultivators of human potential. The final synthesis is this: our greatest engineering resource is not silicon, steel, or software. It is the innate, instinctive drive of the human “Self” to explore, adapt, and grow. The most elegant system we can build is one that removes barriers to that drive, creating a world where technology doesn’t command our attention, but quietly, sustainably, supports our flourishing. That is the ultimate engineering challenge, and it begins by learning from our past failures to see the human in the machine.
References
Fukuda, S. (2019). Self engineering: Learning from failures. SpringerBriefs in Applied Sciences and Technology. Rockne, K. (1930). Coaching: The way of the winner. Football Fundamentals Press. Schumacher, E. F. (1973). Small is beautiful: Economics as if people mattered. Blond & Briggs. Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. W. Strahan and T. Cadell.