Sam Altman x Cory Levy at Internapalooza从 YC 的三个月到不外包判断 · 主题式共读笔记
整理边界:这不是完整访谈摘要,只收录本轮共读中真正停留、反复确认并联系到实际工作的部分
- AI 降低的是实验成本
- 验证之前不要急着规模化
- 答案可以寻找,判断不能外包
- 不确定时探索,确信后集中
- 进入高碰撞环境
- Scale、Emergence 与 Generalization
- 识别人是一场 Practice Game
阅读入口|一场轻松对谈留下了什么
原访谈并不是一堂结构严密的创业课程,但零散回答让我们开始对 Sam、他的两位导师与 YC 经历产生兴趣,也由此提炼出一组能迁移到实际工作的判断
Sam 从 YC 第一届创业者走到 YC 的领导者,又与 Paul Graham、Peter Thiel 两位风格差异极大的导师长期相处,这些人物背景不是本笔记要完整讲述的传记,而是理解他如何看待反馈、非共识、人才与下注时机的入口
本轮共读真正关心的也不是 Sam 给出了哪些创业答案,而是他如何让判断接触更多现实,如何在信息不足时保留探索空间,又如何在确信形成后承担取舍
一、从 YC 的三个月,到 Codex 的 17 分钟
Cory 问,过去在 YC 给创业者的建议中,有哪些已经因 AI 而改变;Sam 用一个极端对比回答了生产周期的变化,但真正值得追问的是反馈循环如何随之改变
Sam Altman: The big thing that has changed is what we expected people to do in three months at YC with great effort and five people is now like 17 minutes and Codex.
If you can make a whole startup in 17 minutes, the rate at which you can test ideas and make small incremental progress and get feedback is just very different.
There’s a very different feedback loop people should get going.
过去,YC 期待一个五人团队用三个月高强度工作做出的成果,如今可能通过 Codex 在极短时间内完成;真正发生变化的不只是开发更快,而是测试想法、小步修改和获得反馈的频率都可以大幅提高,创业公司需要主动建立一种适应新生产速度的反馈循环
AI 同时降低了创业与创作的生产门槛,一个人可以更快写代码、做页面、生成内容和发布版本;但生产数量本身不是学习,只有新版本接触真实世界、返回新信息,并改变下一步行动,反馈循环才真正闭合
二、先找到值得规模化的东西,再去规模化
当观众追问今天怎样推广一家新公司时,Sam 没有先讲增长系统,而是从最初几百名用户、亲自销售与不可规模化的早期工作讲起
Sam Altman: The easiest way to market something is to make something that’s so good or so new that people spontaneously tell all their friends and it becomes a ChatGPT moment.
I don’t think you can make that a strategy you depend on.
What worked the most often is a very hands-on, almost all sales-driven, very little marketing-driven approach to getting the first few hundred users, and then listening to them about what they like about it, how they talk about it, and building a marketing thing around that.
This is the YC advice: do things that don’t scale.
产品好到或新到让用户自发告诉朋友,是最轻松的传播方式,但这种 ChatGPT moment 极少出现,不能成为稳定依赖的策略;更可靠的方法是亲自获得和服务最初几百名用户,听他们喜欢什么、怎样描述产品,再从这些真实语言与行为中形成后续营销方式,这就是 YC 所说的做那些暂时无法规模化的事情
这部分与你的工作形成了直接连接:一旦看见一个可能有效的方法,你很容易迅速建流程、写 SOP、做模板、自动化并扩大内容量;这些能力本身没有问题,真正的风险是触发得早于现实验证
三、答案可以寻找,判断不能外包
观众 Sakina 问,如果 Sam 今天只有 18、19 或 20 岁,会把时间花在哪里、解决什么问题;Sam 没有提供行业答案,而是先解释为什么真正的创业方向无法从一场公开对谈中领取
Sam Altman: Whatever the right answer to that question is, it’s not going to be told to you by anyone at a chat at an event like this.
By the time it is obvious enough to say with some degree of confidence to a room of hundreds of people, you’re not going to have a unique lens on it.
When we were doing OpenAI, no one would have told us to start OpenAI. There’s a 0% chance whoever was speaking would have said you should start an AGI effort.
Don’t take startup ideas from other people.
当一个方向已经明确到某位嘉宾可以在公开活动中自信地告诉几百个人时,听众很难再对它拥有独特视角;当年也没有人会在活动上建议 Sam 创办 OpenAI、启动一项 AGI 事业,因此创业者不能把别人公开给出的方向直接当成自己的创业答案
Sam Altman: You can have a descendant of ChatGPT watch your computer screen, watch every meeting, record every call, and have perfect context of your whole life.
You choose what information you want it to have, but you can connect it to your texts or email or docs or Slack or whatever.
And then you kind of have this thing that is not making decisions for you.
It’ll say, “Maybe here’s another idea,” or “I think you’re making a mistake here; you should consider this,” or “I can do this thing for you to help.”
Sam 想象中的下一代 ChatGPT 可以在用户授权下长期理解屏幕、会议、电话、邮件、文档和 Slack 等上下文,在人写销售邮件或战略文档时主动补充相关信息、指出可能的错误或接手任务,但它不替人做最终决定
四、不确定时同时赛几匹马,确信形成后集中下注
Cory 追问 Sam 何时把全部时间投入 OpenAI;答案是 2018 年底,而不是 2016 年刚启动时,这让对话进入一个反常识问题:年轻人是否必须从一开始只做一件事
Sam Altman: A lot of young people try to work on 17 projects at the same time, and then other people say, “You can’t do that. You’ve got to focus. You’ve got to go all in on one thing.”
I think that’s bad advice. I think it’s fine to work on multiple projects for a little while because you don’t really know what’s going to work.
You don’t really know what you’re going to be interested in. You don’t really know what’s going to be a good fit.
As soon as you figure out what your highest conviction thing is, that is when you’re supposed to do the painful work of getting free from the other stuff and going all in.
在还不知道什么会成功、自己会对什么保持兴趣、什么真正适合自己时,年轻人可以短期并行探索多个项目;但一旦某个方向成为最高确信的事情,就必须完成痛苦的取舍,从其他项目中抽身并全力投入
五、进入能够持续产生意外机会的环境
Cory 把“应该做什么”改问成“怎样获得 exposure”;Sam 不确定搬去 Bay Area 今天是否仍是最佳答案,但保留了一个更稳定的环境原则
Sam Altman: For the last decade and a half, the highest confidence piece of advice for that question is: move to the Bay Area. I’m not sure if that’s still the case.
I always have thought the internet should decentralize that somehow, but it has definitely been the case that you just want to put yourself in a high collision environment and meet a lot of people.
互联网理论上应该让人才、信息和机会不再集中于少数地区,但过去的现实仍然证明,把自己放进一个人才密度高、相遇频繁、能够认识大量不同人并共同做事的环境,会显著增加意外连接发生的机会
六、Scale 的价值可能不是更多,而是不同
解释 OpenAI 早期为什么能够聚集人才时,Sam 先谈有依据的非共识,再谈不同系统随着规模扩大而出现的涌现性质;这两条共同构成他当时愿意下注的理由
Sam Altman: Doing things that are unpopular, but you have some reason to understand that they’re correct.
It’s not just dumb belief. These are the highest leverage moments, and you have to take them when they come.
We understood in 2016 that deep learning was working and getting better with scale.
真正高杠杆的机会,是一件事暂时不受欢迎,但你掌握了具体理由相信它可能正确;这不是因为别人反对就盲目坚持,而是 OpenAI 在 2016 年已经观察到 deep learning 确实有效,并且随着规模扩大持续变好
Sam Altman: Surprisingly often across many different kinds of systems in the world, there are unusually interesting emergent properties with scale.
YC is an example of this. You scale up the YC batch and there’s a strong network effect. Weird stuff happens, like good stuff.
Companies themselves have very strange returns to scale that people often underestimate. And it turns out that neural networks also have surprising properties at scale.
许多不同类型的系统扩大到一定规模后,不只是原有能力按比例增加,还会出现小规模时不存在的新性质;YC batch 扩大后形成强网络效应,公司可能获得反常的规模回报,神经网络也会在规模扩大时表现出令人意外的能力
共读过程中,我们为了理解 Scale 又引入了 Generalization;这不是 Sam 在原文中主动展开的分支,而是从“规模扩大是否就是泛化”这一疑问出发,进一步区分投入规模、迁移能力与涌现性质
七、识别人也是一场需要反馈的 Practice Game
Sam 先谈为什么愿意把巨大责任交给年轻人,随后又解释 YC 合伙人为何越来越擅长选择创始人;这不是一份静态人才标准,而是一种通过大量判断与结果回看训练出来的能力
Sam Altman: I am a huge believer that if the single thing you do in your career is identify and bet on young, unproven talent, you’ll be wildly successful.
This was true at YC. This is a big part of how we succeed at OpenAI.
如果职业生涯只做好一件事,能够识别年轻、尚未被市场证明的人,并在别人还不确定时给予机会、资源、信任和重要责任,也可能获得极大的长期回报;这既是 YC 的经验,也是 OpenAI 成功的一部分
Sam Altman: One of the surprising things to me about running YC was just how much of a numbers game that is.
The reason that YC partners get so good at picking founders is we just see more data points than anybody else, and it is like a practice game.
I look for high energy, quick thinkers, quick doers, very smart, strongly held opinions, and a high degree of determination. But there’s a lot of people like that.
There’s something else about, “This person has a very unique perspective on the world, and I’m going to bet on that,” and that feels easier to learn with practice.
YC 合伙人之所以越来越擅长选择创始人,是因为他们见过更多样本,并能把当初的判断与创业者之后的真实表现反复对照;精力、思考速度、行动速度、聪明、坚定观点和决心都可以列成显性条件,但真正困难的是判断一个人的独特世界视角只是古怪,还是看到了别人尚未看到的东西,这种模式识别更依赖长期实践