entrepreneurship, innovation

The Entrepreneur and the Engineers, or How 1+1 Equals 1,000

In twenty years, SpaceX has revolutionized the space industry. Yet when the company was founded in 2002, its founder, Elon Musk, had neither the best technology nor the most experienced engineers in the industry. Those engineers were working at Boeing and Lockheed Martin, heirs to sixty years of expertise dating back to Mercury and Apollo. But that expertise operated within a mindset so ingrained that it had become invisible: a rocket is single-use, a launch costs hundreds of millions, and that’s just the way it is. Musk, however, asked a seemingly naive question: why couldn’t a rocket be reusable, like an airplane? His resounding success shows that in disruptive innovation, the factor that makes the difference is not technical resources, but the mental model. This touches on the very essence of entrepreneurship.

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innovation

AI, Disruption, and Automation: Is the Academic World the Next Kodak?

In public discourse, the emergence of large language models is typically discussed in terms of its implications for for knowledge workers such as programmers, lawyers, and accountants. However, little is said about the effects of this technology on the producers of knowledge themselves—those whose profession has consisted of reading, synthesizing, conceptualizing, and transmitting for centuries. Yet, the disruption is profound. The academic world is currently experiencing its own “Kodak” crisis but does not seem to be aware of it.

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innovation, Management

The activist innovator’s failure is primarily social.

It is very difficult for an innovator to bring about change within their organization. There are many reasons for this, but the main one is that many believe that to succeed, you need to have good ideas and see them through to completion. They are convinced that it is the objective quality of their work that will earn them the group’s acceptance. In reality, the opposite is true. The failure of the innovator tasked with bringing about change is primarily social.

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Economy and society, entrepreneurship, innovation, Society

Disruptive Innovation: Ignore the Elite, Bet on the Underdogs

History is full of unfortunate predictions. However, the New York Times’s claim in 1903 that human flight would not be possible for another one to ten million years is one of the most striking examples. Is this a classic case of pessimism from an era unable to anticipate technological progress? Not quite. The story is far more interesting. It’s about an elite that uses its own failure as proof of impossibility while underdogs persist in trying and ultimately succeed.

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Economy and society, innovation, Management

AI and Productivity: The Key Lesson from the Textile Industry

The rapid advances in artificial intelligence should lead to significant productivity gains. Yet, this is not always the case. Why? Technology alone is not enough. There is no direct, linear relationship between technological use and performance gains. In some cases, technology can hinder productivity. It all depends on how technology is integrated and used. To better understand the challenges of AI, it is helpful to look back at the introduction of mechanical looms in the 19th century.

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decision making, Management, uncertainty

Turning Uncertainty into Performance: the role of Processes

Processes, uncertainty, performance: three words that are used constantly, rarely together, and almost never with precision. Yet their relationship is at the heart of what distinguishes an organization that masters its activity from one that improvises. Because a process is not just a tool for standardization—it is the way in which an organization gradually transforms novelty into something manageable. Understanding this mechanism changes the way we manage an organization, diagnose its weaknesses, and evaluate what really works—and why.

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innovation

Are mental models the key to the next stage of AI?

Despite its spectacular results, particularly since ChatGPT’s release in 2022, AI faces a significant structural limitation today: it relies on superficial statistical correlations rather than a profound comprehension of the laws of reality. AI is incapable of true causal reasoning, resulting in logical hallucinations and an inability to plan complex tasks over the long term. This lack of internal structure renders learning extremely inefficient, necessitating vast amounts of data when a human would require only a few examples to comprehend and predict a new situation. It is precisely this idea of “internal structure” that could enable the next big step in AI: the use of mental models.

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strategy

The real lesson from Ukraine: in uncertainty, strategy is a matter of models.

Sticking to an outdated worldview is a universal risk, especially in times of rapid change, and one of the most dangerous. The war in Ukraine is a striking example of this. In just a few years, the battlefield has undergone a complete reinvention: drones are ubiquitous, information is available in real time, and responses occur within minutes. Military certainties that had been solid for decades became obsolete overnight. In a rapidly and unpredictably changing world, the ability to question one’s models is not just a competitive advantage; it’s a matter of survival. This places new demands on strategic thinking for military staff and organizations alike.

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Uncategorized

Is ambition possible in uncertainty?

We live in uncertain times. The future seems unclear due to climate change, geopolitics, and technological change. Faced with this uncertainty, many people give up on making plans. Any ambition seems impossible. Why aim high when everything could change tomorrow? This resignation is based on two misunderstandings: one about what uncertainty is, and the other about what ambition is.

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decision making, Strategic surprise

Resolving uncertainty with AI, or the scientist illusion of management

“With AI, it’s now easier to resolve uncertainty,” a business leader recently told me with confidence, arguing that with the mass of data now available and the almost infinite capacity to analyze it, the subject was more or less closed. This is a widespread, long-held belief… and a very false one at that, a scientistic illusion of management that refuses to die. The link between data and uncertainty is much more complex. Without a thorough understanding of this link, decision-making in uncertainty is based on flawed models, with catastrophic consequences.

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