Last week, I had the opportunity to give a keynote on “AI and mobility” to Executive MBA participants at ESADE, one of the most prestigious European business schools.

As often happens when these two subjects are mentioned together, the conversation is quickly expectedOK ple to gravitate toward autonomous vehicles, robotaxis, generative AI and the latest technological breakthroughs. These topics are important and we discussed all of them. Yet I deliberately chose to begin elsewhere.

Before discussing technologies, I raised a simple question:

What is the real objective of a mobility system?

The answer may appear obvious, but it is often misunderstood.

The purpose of a mobility system is not mobility itself, just as the purpose of a healthcare system is not hospitals or the purpose of an education system is not schools. Mobility is a means. The objective is to improve people’s lives while respecting environmental constraints and maintaining economic and financial sustainability.

Several years ago, long before AI became a boardroom topic, I studied some of the most advanced cities in the world and analyzed the indicators they used to assess the success of their mobility policies. What struck me was not their differences but their similarities. Whether in Europe, Asia or North America, the objectives were remarkably consistent. Cities wanted cleaner air, safer streets, better access to employment, education and services, stronger economic activity and public finances capable of supporting long-term development.

Companies approach the question differently but often arrive at surprisingly similar conclusions. They are not interested in mobility for its own sake. They care about efficient logistics, access to talent, productive employees, customer accessibility, sustainability and competitiveness.

Once we start from these objectives rather than from vehicles, roads or public transportation systems, mobility suddenly appears in a different light. What initially looked like a transportation issue reveals itself as a systems issue.

And this, I believe, is the best place to start a discussion about AI.

For most of the last century, progress came primarily from optimizing individual components of the mobility system. Better cars. Better roads. Better trains. Better buses. Better traffic lights. Better logistics. Better urban planning. Each innovation created value, sometimes considerable value, but often less than expected because the surrounding system evolved much more slowly than its individual components.

Today, however, something fundamentally different is beginning to happen.

Vehicles increasingly communicate with infrastructure through V2X technologies. Energy networks interact with vehicles through V2G. Logistics platforms exchange information in real time. Autonomous fleets are moving from experimentation to operation. Hybrid work and virtual collaboration are changing commuting patterns. Cities are questioning how much public space should continue to be dedicated to parked vehicles rather than housing, green areas or human activities.

None of these developments is revolutionary by itself. Most already exist either as commercial solutions or as advanced pilots.

What becomes revolutionary is when they are considered together.

If autonomous shared mobility reaches maturity, if infrastructure becomes intelligent, if logistics becomes coordinated and if physical and virtual mobility evolve together, the potential improvements become extraordinary. Personally, I made a rough estimation that as a result mobility-related carbon emissions could approach zero, urban space dedicated to cars could be reduced by half, time lost in transportation dramatically cut and infrastructure investments lowered by roughly thirty percent through better utilization of existing assets.

The exact numbers can of course be debated and reality will undoubtedly differ from today’s projections. What matters, however, is less the precision of the estimate than the direction it points to. The opportunity no longer lies primarily in improving individual components of the mobility system but in improving the performance of the system as a whole.

A recent example I saw and was presented by CATL at the Beijing Motor Show illustrates this point particularly well.

What interested me was not the technology itself. Battery swapping is not new. Renewable energy generation is not new. Energy storage is not new. Charging infrastructure is not new. Even autonomous vehicles are now moving rapidly beyond the experimental phase.

What was remarkable was the result obtained by considering these elements together.

By combining battery swapping, charging infrastructure, renewable energy generation, storage systems and autonomous fleets into a coordinated ecosystem, CATL reported improvements such as 13% lower electricity losses, five times lower charging infrastructure investments, 85% higher charging station utilization and three times greater service capacity.

The lesson goes far beyond mobility. None of these gains came from a breakthrough battery chemistry or a revolutionary new technology. They emerged because the problem was approached at the level of the system rather than at the level of the component. Instead of optimizing a battery, a charging station or an autonomous vehicle separately, CATL optimized the interactions between them.

This distinction may appear technical, but I believe it is profound. For more than a century, industrial progress has largely been achieved by improving individual components. Increasingly, however, the largest opportunities seem to come from improving the way those components interact.

The idea is therefore not to create a perfect smart city from the top down. Several attempts around the world have shown the limits of that approach. The opportunity is rather to create what could be described as an Internet of mobility, a network of services capable of exchanging information, negotiating priorities and collectively moving toward common objectives.

This observation also provides a useful way to think about AI.

Many discussions jump directly to visions of intelligent cities and fully optimized ecosystems. Reality usually follows a more progressive path. Before redesigning products or coordinating complex systems, AI must first learn how to operate in the real world.

This is precisely the stage we are entering today.

For years, autonomous mobility looked like one of those technologies that always seemed five years away. Demonstrations multiplied, promises accumulated and skeptics became increasingly vocal. Yet history suggests that exponential curves are often invisible until after the turning point has been crossed.

Looking at what is happening today in China and in parts of the United States, I believe that point is now behind us. Autonomous mobility is no longer primarily an R&D topic. It is becoming an operational reality. The first large-scale services are operating commercially, costs are falling, regulatory frameworks are evolving and investment is progressively shifting from experimentation to deployment.

The debate is no longer about technical feasibility. The more interesting questions now concern the speed of adoption, the business models that will prevail and the ecosystems that will emerge around them.

Like many technological revolutions, this evolution follows the pattern described by Roy Amara. We tend to overestimate the impact of a technology in the short term and underestimate it in the long term. After years of expectations that often exceeded reality, autonomous mobility appears to have entered the scaling phase. Depending on the application, operating costs could eventually fall by as much as 90%, while entirely new services and markets become possible.

And this is only the beginning.

Once AI learns how products operate, it starts learning something equally important: how they are actually used.

That naturally leads to the next stage.

After spending much of my career developing vehicles at Renault, Nissan and Toyota, I have often been struck by a paradox. Most privately owned vehicles are designed around an enormous envelope of possible needs. They must accommodate daily commuting, family holidays, shopping trips, occasional cargo transport, severe weather conditions, regulatory requirements and an almost endless list of exceptional situations. They must also cope with human behavior, which can be rational, emotional, distracted, impatient, tired or simply unpredictable.

The result is perfectly understandable. Engineers design for the exceptional because they cannot afford to ignore it. Yet this approach inevitably produces vehicles that are optimized for the complete envelope of possible needs rather than for the mission they perform most of the time.

I remember discussions about braking systems designed to withstand extreme usage scenarios that very few drivers would ever encounter. Yet nobody could exclude the possibility that one driver, on a hot summer day, might repeatedly perform maximum braking ten times in a row. One never knows.

The consequence is that a vehicle capable of doing everything is rarely the best solution for any specific task.

This is where autonomous mobility and AI begin to change the equation.

When I look at many of today’s robotaxis, I am often reminded of the first automobiles. Early cars were essentially horse carriages without horses. Engineers naturally started from what already existed because it was simpler, less risky and because nobody could yet imagine where the technology would ultimately lead.

Today’s robotaxis often follow a similar logic. We remove the driver, but we retain most of the assumptions inherited from a world based on private ownership. Yet if a vehicle only needs to perform a specific task within a broader mobility ecosystem, why should it continue carrying all the constraints associated with satisfying every conceivable need of a private owner?

History suggests that such transitional phases rarely last very long. Once the new technology proves viable, it gradually sheds the assumptions inherited from the previous world. The first automobiles stopped resembling horse carriages. The first smartphones stopped resembling mobile phones with keyboards. I suspect shared autonomous vehicles, starting with robotaxis and last mile delivery pods, will follow the same path

Once autonomous operation becomes widespread and AI-driven design tools mature, vehicles will increasingly evolve toward specific uses. Urban pods, autonomous shuttles, delivery vehicles, mobile offices and long-distance transport solutions will each follow different design logics. Generative design, simulation and digital twins will accelerate this evolution dramatically. Some components are already being designed automatically. Requirements are increasingly linked to vast databases containing information about cost, weight, performance, environmental footprint and even emotional responses to design. It is not difficult to imagine modules being generated automatically tomorrow and eventually, in the 2030’s, complete vehicle concepts being proposed to engineers, product planners, marketers or even customers themselves.

The product itself will change because the system around it has changed.

Yet the most powerful contribution of AI may ultimately lie neither in operating products nor in designing them, but in coordinating increasingly complex systems.

For the last two decades, enormous efforts have been dedicated to connecting infrastructure, vehicles, logistics platforms, energy systems and public transportation networks. This connectivity is valuable because it allows information to circulate, but information alone does not optimize outcomes. The next step is therefore not simply to connect systems but to coordinate them around common objectives.

The distinction matters because a connected system can still behave inefficiently, whereas a coordinated system continuously seeks the best compromise between competing objectives such as safety, accessibility, sustainability, productivity and cost.

Imagine a rainy evening during rush hour. A concert has just finished. Ride-hailing operators are trying to dispatch vehicles toward the venue. Traffic management centers are attempting to avoid congestion. Public transportation operators are dealing with a sudden surge of passengers. Logistics companies are still trying to complete deliveries despite deteriorating conditions.

Every actor behaves rationally. Every actor optimizes its own objectives. Yet the sum of these individual optimizations rarely produces the best collective outcome.

Anyone familiar with game theory will recognize a variation of the prisoner’s dilemma.

The difficulty is not a lack of intelligence. Cities, logistics operators, public transportation companies and infrastructure managers are already managed by highly competent people. The difficulty is scale.

A modern mobility ecosystem involves millions of daily decisions, thousands of interacting actors and objectives that frequently conflict with one another. Human organizations are remarkably good at optimizing a limited number of variables. They struggle when the number of variables becomes almost infinite.

This is precisely where AI changes the game.

Not by replacing human judgment, but by helping systems simulate, negotiate, anticipate and coordinate at a scale impossible to achieve otherwise. More importantly, this coordination does not require a centralized authority controlling everything. It can emerge progressively through local interactions, simulations and negotiations between intelligent agents pursuing compatible objectives.

And this is why operating products, redesigning products and coordinating systems should not be viewed as separate developments.

Operating products without redesigning them leaves much of the value untapped. Designing new products without integrating them into a broader system produces only local optimization. Attempting to optimize systems without intelligent products is equally limiting. Each stage enables the next, creating a reinforcing cycle in which better operation improves design, better design improves systems and better systems create new opportunities for operation.

History suggests that major mobility revolutions often follow a similar pattern.

If AI had been invented before Internal combustion engine, nobody would own a car, there would only be autonomous shared electric vehicles, and the cities would be very different.

When railways appeared in the nineteenth century, people understandably focused on trains. Yet their most important consequences appeared elsewhere: tourism, trade, industrial organization and urban development. The automobile followed the same path. Early observers saw a faster alternative to the horse. Few anticipated suburbanization, shopping centers, mass tourism, drive-through services or the profound transformation of urban life.

The most important effects appeared outside the vehicle itself.

I suspect AI-enabled mobility will follow a similar trajectory.

This is why I believe organizations should be careful not to start with the question that dominates many AI discussions today: what can AI do inside an existing process? That is often a silo question.

A more useful starting point is to ask what problem genuinely needs to be solved. For one organization it may be logistics efficiency. For another it may be employee commuting, customer accessibility, talent attraction, sustainability targets or operational resilience. The objective will vary from one situation to another, but the principle remains remarkably consistent: begin with the outcome you are trying to achieve and only then explore how AI can improve operations, redesign products and coordinate systems in support of that objective.

Approaching the problem in the opposite direction often leads to local optimizations inside existing silos while the larger opportunity remains invisible.

For leaders, this requires what I have always considered one of the fundamental responsibilities of strategy: ambidexterity. The current business must continue to perform while the next system is being prepared.

The question for leaders is no longer “Should we use AI?”

The real question: what businesses, ecosystems and society become possible once intelligence is embedded everywhere in the mobility systems?

The best way to begin is not through a massive transformation program but through learning. Identify an operation that can benefit from AI. Identify a product whose design assumptions may no longer hold in a world of intelligent systems. Identify a broader ecosystem where coordination could create more value than local optimization. Run experiments. Build capabilities. Develop partnerships. Learn from failures.

The objective is not to predict the future perfectly. It is to be ready when it arrives.

And if the history of mobility teaches us anything, it is that major transformations often seem slow for much longer than expected and then accelerate much faster than anticipated. Looking at what is happening today, I believe autonomous mobility has already passed that inflection point. The race has started, and the organizations that begin learning now will be in a very different position from those still treating AI as a technology project rather than as the foundation of a new generation of mobility systems. Ultimately, mobility was never the objective. Quality of life, economic vitality and sustainability were. AI does not change that. What it changes is our ability to pursue these objectives simultaneously rather than accepting the traditional trade-offs between them. That may turn out to be the most important mobility innovation of all.

In October 1989, I stood on the floor of the Tokyo Motor Show.

I was a young Renault engineer, surrounded by a dizzying display of technological dominance and creativity. I remember Nissan unveiling a remarkable series of concept cars and Honda launching the new Accord with what seemed, to us, almost unimaginable ambition: two brand-new platforms with different architectures, two new 2-liter engines—one four-cylinder and one five-cylinder—and three different body styles, one for each of its sales channels. Same size, same design language, but three distinct products. At Renault, such a program would have taken years.
Japanese automakers were rewriting the rules of engineering, manufacturing, quality control and product development. To those of us watching from Europe, the conclusion seemed almost inevitable: Japan was going to conquer the world.
The superiority appeared overwhelming. Japanese factories were benchmarks. Their products were admired. Their management systems were studied everywhere. Their export machine seemed unstoppable.
One year later, at INSEAD, I chose the elective Japanese Enterprise to better understand the recipe behind such success. It was the era of The Machine That Changed the World, the book that convinced many of us that Japan had discovered a fundamentally superior way to organize industry.
Less than ten years later, Renault signed an alliance to rescue a nearly bankrupt Nissan, which I joined in charge of product planning.
How was such a reversal possible?
The answer was not that Japan suddenly became incompetent. Nor was it that Western manufacturers had discovered a secret formula.

The answer was convergence.

During that decade, Renault and many other Western companies systematically absorbed the lessons of Japanese industry. We adopted Total Quality Control. We implemented lean manufacturing. We redesigned supply chains. We improved reliability, reduced defects, shortened development cycles and fundamentally changed the way we worked. At the same time, we continued building on our own strengths: creativity, innovative concepts, strong designs and a deep understanding of customers’ unmet needs.

The capability gap narrowed.

What had once appeared to be an insurmountable advantage gradually became a competitive benchmark that others learned to replicate.

One of the most important lessons from the early years of the Renault-Nissan Alliance was that neither company had all the answers. Each possessed strengths the other lacked. Renault brought creativity, bold product concepts, marketing innovation, rigorous cost control and procurement management. Nissan brought engineering rigor, manufacturing excellence and operational discipline. The success of the Alliance came not from imitation but from mutual learning. By 2004, it had become a benchmark studied around the world—before later facing challenges of its own. But that is another story.

Recently, while walking through the Beijing Motor Show, I experienced a profound sense of déjà vu.

The breathtaking speed of China’s EV transition, the vertical integration of its industrial champions, the sophistication of its battery technologies, the rapid progress of software-defined vehicles and the sheer scale of production all evoked memories of Japan’s ascent decades ago. Above all, what impressed me most was the feeling of an entire ecosystem moving together: manufacturers, suppliers, technology companies, universities, financial institutions and public authorities all pushing in the same direction.

Today, many Western observers see China as unstoppable. The mood often reminds me of what I experienced with Japan in the late 1980s. Yet behind the headlines, the rest of the world is already doing what it always does when confronted with a superior competitor: learning. Western manufacturers are studying Chinese champions, partnering with them, opening engineering centers in China and benchmarking products, technologies and development processes. This does not guarantee success, but it is exactly how convergence started with Japan.

My experiences at Nissan and later at Toyota taught me that every successful organization has blind spots. When an industry leader appears unbeatable, attention naturally focuses on its strengths. Yet sustainable competitive advantage often comes from identifying the areas that receive less attention. Once competitors learn from the strengths and exploit the weaknesses, the gap can close surprisingly quickly.

What initially looks like an unassailable advantage can sometimes become a source of vulnerability. Success has a tendency to hide imperfections from those who benefit from it.

Whether Western companies will successfully navigate the transition toward software-defined vehicles, connected mobility and autonomous systems is another matter. Some manufacturers never survived the Japanese challenge. Others survived only thanks to mergers, bailouts or protected markets and are now much smaller players and some were able to adapt successfully and continued to grow. This new transformation will probably produce similar winners and losers.

Before going further, a word about perspective.

I am not an economist. I studied economics at École Polytechnique and later at INSEAD, but my career has been spent in industry. Over the past forty years, I have had the privilege of observing from inside some of the most important transformations in the automotive industry. Through roles at Renault, Nissan, Toyota, Booz Allen Hamilton and Dassault Systèmes, with half of my career spent in Japan, working for Japanese companies or leading activities across Asia, I have been fortunate to observe these changes from unusually close positions.

The reflections below are therefore less an economist’s model than an attempt by a practitioner to connect forty years of observations, experiences and lessons learned across three continents. Looking back, I have often found that understanding change is less about predicting the future than recognizing patterns early enough to adapt before everyone else does.

The real challenge facing China may not be technological, industrial or even geopolitical. It may simply be the challenge that every successful society eventually encounters: how to adapt when a generation that fought for prosperity is followed by a generation that wants to enjoy the prosperity its parents created.

The limit of a production-led economy

To understand China’s current crossroads, we need to begin with a simple observation.

China’s economy has been built around production.

Household consumption accounts for only about 40% of China’s GDP, compared with roughly 54% across OECD economies and often more than 60% in mature consumer-led economies.This is largely the result of choices made over several decades. China prioritized investment, industrial development and infrastructure in order to accelerate growth and modernize the country.
The results have been extraordinary. Hundreds of millions of people have been lifted out of poverty. China has become the world’s manufacturing powerhouse. Entire industries have been transformed. High-speed rail networks span the country and supply chains operate at a scale never before seen in human history. Perhaps even more impressive is the way China has managed to synchronize entire ecosystems—industry, technology companies, universities, financial institutions and public authorities—around common objectives.

More importantly, China has moved far beyond its former reputation as a fast follower. Chinese engineers and researchers are now leading in many technological domains. A recent assessment by the Australian Strategic Policy Institute found that China ranks first in 66 of 74 critical technologies.
From an industrial perspective, the achievement is remarkable but every economic model creates trade-offs.
The same system that maximized investment and production also limited the share of national income flowing directly to households. As a consequence, production capacity has grown faster than domestic consumption.
If exports absorbed the difference for many years, that mechanism is becoming more difficult.
As Western countries increasingly respond with tariffs, industrial policies, local-content requirements and strategic reindustrialization initiatives, Beijing faces a dilemma. The traditional response has been to produce more, seek new markets, deepen trade relationships across the Global South and strengthen control over strategic industries, critical minerals, battery materials and industrial supply chains. From an engineering perspective, the logic is understandable. If production capacity is your strength, you leverage that strength.
The question is whether additional supply alone can solve a problem increasingly rooted in demand.

The Two Chinas
The comparison with Japan is useful, but only up to a point.
Japan entered the 1980s as a largely homogeneous developed nation. China remains a far more complex reality.
Shanghai, Shenzhen, Beijing and Guangzhou to name a few increasingly resemble advanced economies in terms of infrastructure, education, technology adoption and living standards. Yet vast inland regions are still in the middle of a development journey, with lower incomes and significant modernization challenges ahead.
China therefore contains, at the same time, elements of both a developed and a developing economy.
This creates a challenge that Japan never had to confront at such scale.
Chinese leaders are still trying to complete one of the largest development projects in human history while simultaneously managing the expectations of an urban middle class that increasingly behaves like its counterparts in Europe, Japan or North America.
Infrastructure, industrial investment and manufacturing capacity remain powerful tools for achieving that objective.
From Beijing’s perspective, the country is not only managing a modern economy. It is still finishing the construction of one.

The Sociological Friction
Yet this very success is creating a new challenge.
Recently, a Chinese entrepreneur told me that he estimated roughly a quarter of his younger employees in Shanghai were no longer willing to accept regular overtime.
The exact percentage is less important than the signal.
His feeling was that something is changing. The children of those who moved to Shanghai thirty years ago, worked relentlessly and built a better life are not necessarily willing to make the same sacrifices as their parents.
Every time I visit China, I become more aware of how rapidly attitudes are evolving in the country’s largest cities.
Beneath the headlines about industrial policy and technological competition, a quieter transformation is taking place. Many young people increasingly want a better balance between work and personal life, more time for themselves and more opportunities to enjoy the prosperity their parents helped create.
After decades of intense competition, educational pressure, housing challenges and the famous 996 culture—working from 9 a.m. to 9 p.m., six days a week—many young Chinese appear to be questioning assumptions that shaped their parents’ generation.
The emergence of movements such as Tang Ping (”Lying Flat”) and Bai Lan (”Let It Rot”) may be an early indication of a broader sociological shift. For some young Chinese, the relentless pursuit of economic success no longer appears as attractive as it did to their parents’ generation. It is still too early to speak of a fundamental shift, but the weak signals are becoming harder to ignore.
An increasing number seem to be asking whether the destination justifies the journey.

Lessons from Japan: Prosperity Changes Aspirations
Having lived and worked in Japan for more than twenty years, while holding responsibilities across Asia, I have observed a remarkably similar evolution.
When I first arrived in Japan, long working hours were deeply embedded in corporate culture. At Nissan Technical Center in Atsugi, where thousands of engineers worked on future products, traffic jams at the exit gates often started after 10 p.m. That was considered normal.
We had to actively encourage—and sometimes almost force—middle-aged employees to take annual holidays because they simply would not use them. I still remember receiving reports proudly announcing that a few dozen employees had finally “made the effort” to take some vacation days. At that time, taking holidays was considered an effort.
Work came first and the company occupied a central place in people’s identity.
Today, the atmosphere is noticeably different.
Younger generations increasingly prioritize quality of life. Companies employ more temporary workers. Lifetime employment still exists, but changing employers is no longer unusual. Young professionals want time with friends, travel, hobbies and personal fulfillment outside work.
I regularly encounter young entrepreneurs who consciously choose balance over expansion. In Tokyo, many young bakery owners open only four or five days per week and operate from 11 a.m. to 5 p.m.—not because demand is insufficient, but because they have decided that more free time is worth more than additional revenue.
Recently, while giving a presentation at a large Japanese corporation, the bell announcing lunchtime rang. Half the audience stood up and left the room in the middle of my presentation. Young engineers who were not yet managers were simply not allowed to work through lunch. In more than forty years of professional life, including my early years at Renault when it was still a state-owned company, I had never experienced anything similar.
Economic development does not merely increase incomes; it changes aspirations. Once basic needs are met, people begin optimizing for different things. The pursuit of ever-greater output gradually gives way to the pursuit of a better life.
This is not a uniquely Japanese phenomenon. It is simply human.
And China’s most advanced cities could be beginning to display similar characteristics.

Growing Old Before Becoming Rich
Another major difference with Japan is demographic.
Japan entered its aging phase after reaching a very high level of prosperity and, hopefully, will remain a wealthy country despite the challenges ahead. China faces a more difficult situation. Its population is aging rapidly while large parts of the country are still in the process of catching up economically.
The working-age population has been declining for years and the total population is now shrinking. Like most developed societies, China is also experiencing a sharp decline in birth rates. Interestingly, this is no longer just a developed-world phenomenon. As countries become more urbanized, educated and prosperous, fertility rates tend to fall almost everywhere.
At the same time, China is still trying to modernize large parts of the interior and continue raising living standards for hundreds of millions of people.
This creates a difficult balancing act.
China must simultaneously maintain sufficient growth to preserve social consensus, expand domestic demand and complete its development process.
Few countries have ever attempted such a transition at this scale, and none have done so while occupying such a central role in the global economy.

The Strength and Limit of the Technocratic Elite
Part of the answer may lie in the nature of China’s governing culture.
Following the Cultural Revolution, China built one of the world’s most capable technocracies. Its leadership ranks have historically been populated by engineers, scientists, planners and systems thinkers.
Engineers are trained to solve problems through rigorous analysis. They seek optimal solutions. In engineering, equations have solutions, problems can be decomposed, processes optimized and outcomes often predicted.
This mindset has served China extraordinarily well.
It helped build high-speed rail networks, industrial clusters, advanced manufacturing ecosystems and world-leading battery supply chains.
Yet human societies do not behave like engineering systems nor are people equations.
You can plan and rationally make a factory increase output by 15%.
You cannot order a burned-out generation to consume more, have more children, buy more apartments or rediscover enthusiasm for a lifestyle it increasingly questions.
An engineering mindset naturally gravitates toward what can be measured, planned and controlled. Human fulfillment is considerably more difficult. Consumption-led growth appears less tangible, less controllable and less predictable than industrial expansion.
This may explain part of Beijing’s hesitation.

What China Will Teach the World
None of this diminishes China’s achievements.
Every major industrial power leaves something behind for the rest of the world to learn.
The United States gave the world Fordism and mass production. Japan transformed manufacturing through lean production, quality management and continuous improvement.
China is teaching something different.
It is demonstrating how to orchestrate industrial ecosystems in a connected economy, how to integrate hardware, software, services, supply chains and public authorities around common objectives, and how to accelerate large-scale transitions such as electrification, digitalization and increasingly artificial intelligence.
Whatever happens next, those lessons will survive.
Just as the world absorbed Fordism and the Japanese Production System, it will absorb many of the lessons emerging from China today.

The Great Convergence
Industrial history teaches a simple lesson: no competitive advantage remains permanent. Others learn. Others adapt. Others catch up.

Just as Western manufacturers absorbed Japanese manufacturing expertise during the 1990s, global industry will eventually absorb many of the lessons emerging from China’s industrial revolution. At the same time, China’s coastal societies continue to converge toward patterns already observed elsewhere in advanced economies. Industrial capabilities are converging internationally while human aspirations are converging domestically.

Having watched Japan’s rise, stagnation and transformation, I have learned to be cautious whenever someone declares a winner in industrial history. The real question is not whether China will remain a major power. It will. The ecosystem it has built over several decades is remarkable and still evolving. China will continue to grow, innovate and teach valuable lessons to the rest of the world, even as other countries progressively absorb and adapt many of the capabilities it pioneered.

The more interesting question is what happens when the rest of the world starts learning from you. That is often the moment when success becomes a trap.

Over forty years, I have repeatedly observed a remarkably similar pattern in companies, industries and even countries. It often starts with ignorance. We pay little attention to what is happening elsewhere because it seems distant, irrelevant or inferior. Then come the first signals of change, but denial takes over. “It is not happening.” “It will never work.” “Our customers are different.”

When the evidence becomes harder to ignore, complacency often follows. The current business is still profitable. The existing model still works. Why change? And if success continues long enough, complacency can evolve into arrogance. Learning stops because we become convinced that our past achievements prove we already have the answers. I have heard versions of the same sentence throughout my career: “I know my job. Nobody is going to teach me how to build cars.”

Looking back, the lesson I take from Japan’s rise, China’s rise and the transformations I have witnessed throughout my career is remarkably simple. Success is never a destination. It is a moving target. The winners are rarely those with the biggest factories, the best technology or the largest domestic market. They are usually the ones that remain curious, continue learning, challenge their assumptions and combine what they learn from others with their own distinctive strengths.

That is how Western manufacturers progressively closed much of the gap with Japan. It is how many industries are now learning from China.

Sustainable success is not about building the perfect system. It is about having the humility to question it before others do, the ability to connect the dots that others fail to see, the intelligence to improve it and the agility to reinvent it when the world changes.

Why I Write

Over four decades, I have participated in and observed major industrial transformations from inside Renault, Nissan, Toyota, Booz Allen Hamilton and Dassault Systèmes, working across Europe and Asia, with much of my career spent in Japan. I also teach strategy at Globis MBA in Tokyo and am the co-author of Humanity in Motion.

The hardest challenges I have encountered were rarely technological. They were usually about recognizing change early enough, making sense of weak signals and helping organizations adapt before success turns into complacency.

This publication is an attempt to share some of those lessons.