Casado和Sinofsky深入探讨了AI如何改变工程和资本的关系，了解他们的见解可能对你的创业之路有所启发。
Casado和Sinofsky讨论了AI如何将工程问题转变为资本问题，指出20年前10人初创公司获得10亿美元后的困境，现在20人可以用这些资金进行有效投资，AI解决了初创公司的分布问题，使挑战者与微软和Meta站在同一起跑线。他们还讨论了数学家对自动化的喜爱、AI数学的价值、人类无法验证的第一个证明、计算在电力出现之前的情况、IBM在1953年对计算机的解释、孕育网络的失败计算机、哈佛禁止考试中使用计算机、AI是否只是另一个抽象层、20人如何有效使用10亿美元、零和风险投资神话、颠覆是物理学而非商学院、英特尔称为打印机部件的芯片以及200亿美元模型的能力。
Martin Casado and Steven Sinofsky on why AI is turning engineering problems into capital problems: ...
Martin Casado and Steven Sinofsky on why AI is turning engineering problems into capital problems: Martin: "20 years ago, if you're a startup of 10 people and I gave you a billion dollars, what would you do with it?... In software, you hire people and then there's nothing you could do. The Mythical Man-Month is very real." "Right now, if I give 20 people a billion dollars, they can actually use it usefully... We've never been like that before." "This is like a law of physics where our early intuition, which is like all problems are engineering problems, starts to change... It changes the nature of capital versus innovation versus competition versus defensibility." Steven: "Computing was capital-bound for the first 30 or 40 years. If you wanted to do something with a computer, your first step was we have to get one, and then you couldn't. You were capital-bound, and then you were engineering-bound, and now we're capital-bound again." "Mad Men goes through the scenario where the computer shows up at the advertising agency... They couldn't figure out what to do, but they were excited that they had the capital to acquire one, and it made them look like they knew what they were doing." @martin_casado @stevesi 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 💬 2 🔄 3 ❤️ 27 👀 7869 📊 4 ⚡