Martin Casado谈AI风险认知偏差

Martin Casado spent years dismissing the AI doom scenario and says what he got wrong was how long th...

Martin Casado spent years dismissing the AI doom scenario and says what he got wrong was how long the scaling laws would keep holding: "I was responding to this Bostrom notion of recursive self-improvement, fast takeoff. You create one of these things, you step back, and it takes over the world... That's clearly not what's happening." "But here's what I got wrong. What I got wrong is I did not know that we could effectively just continue to pour money in this. The scaling laws are holding." "I don't know what it means to do a $100 billion training run, to have this thing that you're putting $100 billion in, and then that money comes from this meta-economic machinery that may want to solve whatever." "They may want to solve cancer, but they may also want to create a weapon. Like, who knows?" "This concentration of this many resources in a useful way, I think, is very new. I don't think we understand the implications. I think you could reasonably argue that that's very dangerous if you apply that $100 billion in the wrong way... What does it mean to be able to concentrate resources?" @martin_casado Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z Your startup intuitions were trained on a world that no longer exists. Steven Sinofsky and Martin Casado have watched computing flip from an engineering-bound field to a capital-bound industry. Twenty people can now put a billion dollars to work productively, AI solves the distribution problem that kept startups small, and challengers sit on a level playing field with Microsoft and Meta for the first time. With Erik Torenberg, they get into why some mathematicians are cheering on their own automation, every AI panic that already happened in past eras of computing, and why nobody can predict the capabilities of a model built with $20 billion. 00:55 Why mathematicians love being automated 02:45 Is AI math worth any money? 08:50 The first proof humans couldn't check 14:35 Computing before electricity 19:50 How IBM explained computers in 1953 26:00 The failed computer that birthed the web 27:50 When Harvard banned computers from exams 30:20 Is AI just another abstraction layer? 38:15 When 20 people can spend $1B productively 42:50 The zero-sum VC myth 46:25 Disruption is physics, not business school 53:25 The chip Intel called a printer part 55:05 What a $20B model can do 59:45 What Martin got wrong about AI risk YouTube: youtube.com/watch?v=GHPB1M… @stevesi @martin_casado @eriktorenberg Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 3 🔄 2 ❤️ 15 👀 6588 📊 4 ⚡