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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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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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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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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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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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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The Navy’s star lesson: Why fundamental skills matter more than ever in the age of AI

When discussing the rapid advance of technologies such as AI, two opposing reactions emerge: a strong fear of the consequences (“The accounting profession will disappear”) and unbridled enthusiasm (“Everyone can be a Michelangelo now!”). Both reactions assume that professional skills will become less important in the face of machines. However, this is far from certain. An interesting decision made by the US Navy sheds light on this issue.

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Defend science? What if universities started by putting their own house in order?

Under the slogan “Stand up for science,” marches were organized on March 7 to defend science as a pillar of democracy. These demonstrations were aimed at protesting the budget cuts and massive layoffs in American organizations and universities decided by the new Trump administration. No institution, even the most prestigious, seems to have been spared from this wave of repression. But academic institutions are not simply innocent victims. They bear a large part of the responsibility for the crisis of legitimacy they are experiencing, having long since forgotten the ideal of truth in order to serve ideological causes.

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Europe’s competitiveness: the missed opportunity of the Draghi report

Electroshock. Emergency call. Existential threat. The report on Europe’s competitiveness that Mario Draghi, former head of the European Central Bank, presented to the President of the European Commission on September 9 has caused quite a stir, to say the least. The report marks a salutary awakening to Europe’s decline, the symptoms of which it clearly identifies. But the proposed remedies remain conventional – a plan, a loan and an industrial policy. It’s a missed opportunity, or almost.

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