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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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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In praise of indirection, or how problems aren’t always solved by problem solving

Problem solving is a universal paradigm, and a very dangerous one. We believe that the world is full of problems and that we can solve them if we really try. But this is not true. Many problems are solved indirectly, thanks to a solution that was not imagined by those who faced them. That’s why it’s important to allow free innovation, i.e. solutions without problems, no matter how absurd they may seem.

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When innovators are wrong about the impact of their innovation: the case of AI and employment

The rapid expansion of artificial intelligence is sparking widespread fears, and not just among the general public—some of the very innovators driving AI forward are sounding the alarms too, in particular regarding employment. Mustafa Suleyman, a leading figure in AI, recently declared, “AI is fundamentally a tool to replace human labor.” Is this cause for concern? Not necessarily.

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