五年前,@patrickc 询问 @sama 是否高调融资被低估了

Five years ago, @patrickc asked @sama whether raising colossal amounts of money right out of the gat...

精选理由

@patrickc 和 @sama 的讨论揭示了高调融资在 AI 时代的转变,值得一看。

AI 摘要

五年前,@patrickc 询问 @sama 是否高调融资被低估了。Martin Casado 表示,在 AI 之前,一直存在 Eric Ries 和 Ben Horowitz 的争论,The Lean Startup 与 The Case for the Fat Startup,后者主张融资后全力发展。但现在,我们有了用少量资金高效利用大量资金的方法。这种变化很大,但我们还没有完全理解。在风险投资界,存在一种零和思维……作为风险投资者,你不相信正和思维吗?如果看数据,流向私募市场的资金越多,市场就越大。

图片来源 · a16z
原文 · a16z

Five years ago, @patrickc asked @sama whether raising colossal amounts of money right out of the gat...

Five years ago, @patrickc asked @sama whether raising colossal amounts of money right out of the gate was underrated. Martin Casado says we now know the answer: "Prior to AI, there was always this battle between Eric Ries and Ben Horowitz, The Lean Startup, and then Marc and Ben wrote The Case for the Fat Startup, which basically argued raise the money and go for it." "But there's always been this natural limiter, actually, which is engineering." "Patrick Collison is right. We now have a discipline for taking a lot of money with small teams and using it productively. That's a very, very big change. I don't think we've internalized it." "There's been this view in venture, this zero-sum thinking... You're a venture capitalist, don't you believe in positive-sum stuff? If you look at the numbers, the more capital that flows into private markets, the larger the market gets." @martin_casado @stevesi @eriktorenberg 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 💬 0 🔄 4 ❤️ 24 👀 8896 📊 3 ⚡