Antoine Buteau

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Lessons from Fernando Flores

Fernando Flores, Chilean engineer, former political prisoner, and management philosopher, applied speech act theory to business. He saw organizations as conversational networks of commitments rather than hierarchies, reframing trust, technology, and the coordination of action.

Lessons from Horace Dediu

Horace Dediu, founder of Asymco who coined “micromobility,” applies disruption theory to hardware cycles and platform shifts. His frameworks explain why timing beats prediction, how technologies take root, and where value moves as markets unbundle.

Lessons from Joelle Pineau

Joelle Pineau, former director of Meta’s Fundamental AI Research lab and current applied AI strategy leader at Cohere, advanced reproducibility in machine learning. Her pragmatic lens weighs open-source weights, reinforcement learning under uncertainty, and enterprise utility.

Lessons from John Tukey

John Tukey, American statistician and mathematician at Princeton and Bell Labs, championed practical data exploration over rigid formulas. By coining “bit” and “software” and inventing the box plot, he made analysis more visual, empirical, and collaborative.

Lessons from Kathy Sierra

Kathy Sierra, co-creator of the Head First series, brought cognitive science into product design and user experience. Her defining principle shifts attention from adding features to upgrading users, asking whether a tool makes people better at their work.

Lessons from Lucy Suchman

Lucy Suchman, an anthropologist at Xerox PARC, showed that people engage machines through context and improvisation, not mental scripts. Her work presses design to accommodate situated behavior and challenges the decision to grant algorithms lethal authority.

Lessons from Marty Cagan

Marty Cagan, founder of Silicon Valley Product Group and author of Inspired, Empowered, and Transformed, studies how strong technology companies decide what to build and ship, centering product discovery and team mechanics as organizational differentiators.

Lessons from Melanie Mitchell

Melanie Mitchell, a Santa Fe Institute computer scientist studying AI, complex systems, and analogy, distinguishes statistical pattern matching from comprehension. Her critique tracks recurring machine-learning fallacies and the unresolved challenge of giving artificial systems common sense.

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