A SpaceX intern once described eating lunch next to Elon Musk in the company cafeteria.
The intern said Elon sat down at a regular table with a regular tray of food. No entourage. No reserved section. He sat next to two junior engineers who were clearly terrified. One of them was explaining a problem with a valve component to the other.
Elon listened without introducing himself or interrupting. After about three minutes he turned to the engineer and said "have you tried running it at a lower pressure gradient first." The engineer stared at him. Then realized the CEO of the company had just casually solved a problem his team had been stuck on for a week.
The intern said what happened next was the part that surprised him. Elon didn't leave. Didn't check his phone. He spent the next twenty minutes asking the engineers questions about their work. Not CEO questions about timelines and budgets. Engineering questions about fluid dynamics and material stress tolerances. He was genuinely curious. The cafeteria conversation was indistinguishable from a technical review.
The intern said he understood in that moment why people work 80-hour weeks for this man. Not because he's inspiring. Not because he pays well. Because he's the only CEO in the world who sits in the cafeteria and solves your engineering problem between bites of a burrito.
People follow leaders who are above them. People worship leaders who sit next to them and prove they understand the work better than the people doing it.
This has been obvious to anyone who studies history and previous technological revolutions.
We abstract lower level problems away and move up the stack to new problems. That creates new complexity and a wider variety of jobs.
Complexity breeds more complexity.
The work is infinite because the problems are infinite. No machine, human, or man and machine hybrid can solve them all.
AI is not magic and we need to stop thinking about it that way.
The stack of problems is infinite and never ending.
@VictorTaelin Já vi muitos modelos de negócio, cobrar por uma linguagem de programação é novidade. Sugiro olhar o modelo de negócio da Databricks, ferramentas open source, plataforma paga para vc não ter que um trabalhão cuidando de clusters spark.
@RogerLMartin I was reading Pete Bernhardt’s post on your “Strategy & Boards” piece, and I kept thinking:
many people keep trying to reinvent pieces of the SCSP while avoiding the parts that actually make possible for it to create real strategy!
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Correção
Não existe débito técnico mas sim dívida técnica. Débito é cobrado à vista, diretamente. Dívida incorre em juros (mais trabalho).
Em inglês é tech debt e não tech debit.
Espalhe a palavra.
Obrigado.
@Riggs_martin@RogerLMartin@bovolox @greatcreator147 Hi Lethal, hi Gentlemen!
Thank you for sharing the article, Lethal — it was a very very interesting read.
It make me reflect on several things, I'll send you just 3 of them:
@RogerLMartin@bovolox @greatcreator147 @jm_robles03
Hi, I've just seen that Strategy Science has published a couple of articles on the topic of "can AI do strategy?".
I have not read them yet to be honest, just browsed through but they look interesting
https://t.co/GSrik5uxst
This week features an original Playing to Win/Practitioner Insights post. This one is on Sharing Responsibility Productively: With Both Humans and Artificial Intelligence.
https://t.co/24RCFWLnVK
The piece tracks the uptake on a model that I debuted in 2002 in my first book, The Responsibility Virus, through a student’s post in 2017 to an Andreessen Horowitz podcast in 2025.
It puts the diffusion of a new idea within my 25-year rule. And it is fun to see an idea that was created in the pre-AI world be translated into usage in our current AI world.
I describe the nuances that I would apply to utilize the thinking more productively than less.
Hope you enjoy!
"Surprisingly, occupations with higher exposure to AI have grown faster than least-exposed ones, not slower."
Not surprising! Productivity growth -> economic growth -> job growth.