章节 1：开场介绍与新图像模型的病毒式传播

📝 本节摘要：

本章节包含了播客的开场白与正式访谈的破冰部分。OpenAI 首席产品官 Kevin Weil 以一个发人深省的观点开场：我们今天使用的 AI 模型将是我们余生中用过的“最差”的模型，这暗示了技术迭代的惊人速度。主持人 Lenny 随后介绍了 Kevin 的背景（曾任 Instagram、Twitter 产品负责人及 Libra 联合创始人）。在跳过赞助商口播后，访谈以 OpenAI 刚刚发布的新图像生成模型（ImageGen）为切入点。Kevin 分享了该模型在内部测试时的火爆程度，指出“内部使用的病毒式传播”是产品成功的强有力信号，并讨论了模型在风格模仿（如吉卜力风格）和复杂指令遵循方面的突破。

[原文] [Kevin Weil]: The AI models that you're using today is the worst AI model you will ever use for the rest of your life, and when you actually get that in your head, it's kind of wild.

[译文] [Kevin Weil]: 你今天使用的 AI 模型将是你余生中用过的最差的 AI 模型，当你真正理解这一点时，会觉得这有点疯狂。

[原文] [Kevin Weil]: Everywhere I've ever worked before this, you kind of know what technology you're building on, but that's not true at all with AI.

[译文] [Kevin Weil]: 在我之前工作过的每一个地方，你某种程度上都知道你是基于什么技术在构建产品，但在 AI 领域完全不是这样。

[原文] [Kevin Weil]: Every two months, computers can do something they've never been able to do before and you need to completely think differently about what you're doing.

[译文] [Kevin Weil]: 每隔两个月，计算机就能做一些它们以前从未能做到的事情，你需要完全不同地思考你在做什么。

[原文] [Lenny]: You're chief product officer of maybe the most important company in the world right now. I want to chat about what it's just like to be inside the center of the storm.

[译文] [Lenny]: 你是当今世界上可能最重要的公司的首席产品官。我想聊聊身处风暴中心究竟是什么感觉。

[原文] [Kevin Weil]: Our general mindset is in two months, there's going to be a better model and it's going to blow away whatever the current set of limitations are.

[译文] [Kevin Weil]: 我们的普遍心态是，再过两个月，就会有一个更好的模型出现，它将彻底打破当前的各种限制。

[原文] [Kevin Weil]: And we say this to developers too. If you're building and the product that you're building is kind of right on the edge of the capabilities of the models, keep going because you're doing something right.

[译文] [Kevin Weil]: 我们也对开发者这样说。如果你正在构建产品，而这个产品恰好处于当前模型能力的边缘，请继续坚持，因为你做的事情是对的。

[原文] [Kevin Weil]: Give it another couple months and the models are going to be great, and suddenly the product that you have that just barely worked is really going to sing.

[译文] [Kevin Weil]: 再给它几个月，模型会变得很棒，突然之间，你那个原本勉强能用的产品就会真正大放异彩。

[原文] [Lenny]: Famously, you led this project at Facebook called Libra.

[译文] [Lenny]: 众所周知，你在 Facebook 领导过一个叫 Libra 的项目。

[原文] [Kevin Weil]: Libra is probably the biggest disappointment of my career. It fundamentally disappoints me that this doesn't exist in the world today because the world would be a better place if we'd been able to ship that product.

[译文] [Kevin Weil]: Libra 可能是原本职业生涯中最大的遗憾。它至今未能存在于世让我深感失望，因为如果我们当时能发布那个产品，世界本该变得更美好。

[原文] [Kevin Weil]: We tried to launch a new blockchain. It was a basket of currencies originally. It was integration into WhatsApp and Messenger. I would be able to send you 50 cents in WhatsApp for free.

[译文] [Kevin Weil]: 我们试图推出一个新的区块链。它最初是一篮子货币。它是集成到 WhatsApp 和 Messenger 中的。原本我应该能在 WhatsApp 里免费给你转 50 美分。

[原文] [Kevin Weil]: It should exist. To be honest, the current administration is super friendly to crypto. Facebook's reputation is in a very different place. Maybe they should go build it now.

[译文] [Kevin Weil]: 它应该存在。老实说，这届政府对加密货币非常友好。Facebook 的声誉也处于一个完全不同的境地。也许他们现在应该去把它做出来。

[原文] [Lenny]: Today my guest is Kevin Weil. Kevin is chief product officer at OpenAI, which is maybe the most important and most impactful company in the world right now, being at the forefront of AI and AGI and maybe someday super intelligence.

[译文] [Lenny]: 今天我的嘉宾是 Kevin Weil。Kevin 是 OpenAI 的首席产品官，这可能是当今世界上最重要、最具影响力的公司，处于 AI、AGI（通用人工智能）甚至未来超级智能的最前沿。

[原文] [Lenny]: He was previously head of product at Instagram and Twitter. He was co-creator of the Libra Cryptocurrency at Facebook, which we chat about.

[译文] [Lenny]: 他此前曾担任 Instagram 和 Twitter 的产品负责人。他也是 Facebook 的 Libra 加密货币的联合创始人，我们稍后会聊到这个。

[原文] [Lenny]: He's also on the boards of Planet and Strava and the Black Product Managers Network and the Nature Conservancy. He's also just a really good guy and he has so much wisdom to share.

[译文] [Lenny]: 他同时也是 Planet、Strava、黑人产品经理网络（Black Product Managers Network）和大自然保护协会（The Nature Conservancy）的董事会成员。他也是个非常好的人，有很多智慧可以分享。

[原文] [Lenny]: We chat about how OpenAI operates, implications of AI and how we will all work and build product, which markets within the AI ecosystem, companies like OpenAI won't likely go after and thus are good places for startups to own.

[译文] [Lenny]: 我们聊到了 OpenAI 如何运作、AI 的影响以及我们将如何工作和构建产品，还有 AI 生态系统中的哪些市场是像 OpenAI 这样的公司不太可能涉足的，因此是初创公司占领的好机会。

[原文] [Lenny]: Also, why learning the craft of writing evals is quickly becoming a core skill for product builders, what skills will matter most in an AI era and what he's teaching his kids to focus on and so much more.

[译文] [Lenny]: 此外，还有为什么学习编写 Evals（评估测试）正在迅速成为产品构建者的核心技能，在 AI 时代什么技能最重要，以及他教导孩子们关注什么，等等。

[原文] [Lenny]: This is a very special episode and I am so excited to bring it to you. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

[译文] [Lenny]: 这是一期非常特别的节目，我很高兴能带给大家。如果你喜欢这个播客，别忘了在你喜欢的播客应用或 YouTube 上订阅和关注。

[原文] [Lenny]: If you become an annual subscriber of my newsletter, you get a year free of Perplexity Pro, Linear, Notion Superhuman and Granola. Check it out at lennysnewsletter.com and click bundle. With that, I bring you Kevin Weil.

[译文] [Lenny]: 如果你成为我时事通讯的年度订阅者，你将获得一年的 Perplexity Pro、Linear、Notion、Superhuman 和 Granola 的免费使用权。请访问 lennysnewsletter.com 并点击 bundle 查看。好了，接下来为您带来 Kevin Weil 的访谈。

[原文] [Lenny]: This episode is brought to you by Eppo. Eppo is a next-generation A/B testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams.

[译文] [Lenny]: 本期节目由 Eppo 赞助播出。Eppo 是一个次世代 A/B 测试和功能管理平台，由 Airbnb 和 Snowflake 的校友为现代增长团队打造。

[原文] [Lenny]: Companies like Twitch, Miro, ClickUp and DraftKings rely on Eppo to power their experiments. Experimentation is increasingly essential for driving growth and for understanding the performance of new features and Eppo helps you increase experimentation velocity while unlocking rigorous deep analysis in a way that no other commercial tool does.

[译文] [Lenny]: Twitch、Miro、ClickUp 和 DraftKings 等公司都依赖 Eppo 来支持他们的实验。实验对于推动增长和理解新功能表现越来越重要，而 Eppo 帮助你提高实验速度，同时以其他商业工具无法做到的方式解锁严谨的深度分析。

[原文] [Lenny]: When I was at Airbnb, one of the things that I loved most was our experimentation platform where I could set up experiments easily, troubleshoot issues, and analyze performance all on my own.

[译文] [Lenny]: 当我在 Airbnb 时，我最喜欢的东西之一就是我们的实验平台，我可以轻松地设置实验、排查问题并独自分析表现。

[原文] [Lenny]: Eppo does all that and more with advanced statistical methods that can help you shave weeks off experiment time and accessible UI for diving deeper into performance and out-of-the-box reporting that helps you avoid annoying prolonged analytic cycles.

[译文] [Lenny]: Eppo 不仅能做到这些，还拥有先进的统计方法，可以帮你节省数周的实验时间；其易用的用户界面可深入挖掘性能表现，开箱即用的报告功能可帮你避免那些烦人且漫长的分析周期。

[原文] [Lenny]: Eppo also makes it easy for you to share experiment insight with your team, sparking new ideas for the A/B testing flywheel. Eppo powers experimentation across every use case, including product, growth, machine learning, monetization, and email marketing.

[译文] [Lenny]: Eppo 还让你能轻松与团队分享实验洞察，为 A/B 测试飞轮激发新灵感。Eppo 支持所有用例的实验，包括产品、增长、机器学习、商业化和电子邮件营销。

[原文] [Lenny]: Check out Eppo at geteppo.com/lenny and 10X your experiment velocity. That's geteppo.com/lenny.

[译文] [Lenny]: 请访问 geteppo.com/lenny 查看 Eppo，将你的实验速度提升 10 倍。网址是 geteppo.com/lenny。

[原文] [Lenny]: This episode is brought to you by Persona, the adaptable identity platform that helps businesses fight fraud, meet compliance requirements, and build trust.

[译文] [Lenny]: 本期节目还由 Persona 赞助播出，这是一个适应性强的身份平台，帮助企业打击欺诈、满足合规要求并建立信任。

[原文] [Lenny]: While you're listening to this right now, how do you know that you're really listening to me, Lenny? These days, it's easier than ever for fraudsters to steal PII, faces and identities. That's where Persona comes in.

[译文] [Lenny]: 当你现在听这个节目时，你怎么知道你真的是在听我，Lenny 说话？如今，欺诈者窃取个人身份信息（PII）、面部特征和身份比以往任何时候都容易。这就是 Persona 发挥作用的地方。

[原文] [Lenny]: Persona helps leading companies like LinkedIn, Etsy, and Twilio securely verify individuals and businesses across the world. What sets Persona apart is its configurability.

[译文] [Lenny]: Persona 帮助 LinkedIn、Etsy 和 Twilio 等领先公司安全地验证世界各地的个人和企业。Persona 的独特之处在于其可配置性。

[原文] [Lenny]: Every company has different needs depending on its industry, use cases, risk tolerance and user demographics. That's why Persona offers flexible building blocks that allow you to build tailored collection and verification flows that maximize conversion while minimizing risks.

[译文] [Lenny]: 每个公司根据其行业、用例、风险承受能力和用户群体的不同而有不同的需求。这就是为什么 Persona 提供灵活的构建模块，允许你构建定制的收集和验证流程，在最小化风险的同时最大化转化率。

[原文] [Lenny]: Plus Persona's orchestration tools automate your identity process so that you can fight rapidly shifting fraud and meet new waves of regulation. Whether you're a startup or an enterprise business, Persona has a plan for you.

[译文] [Lenny]: 此外，Persona 的编排工具将你的身份流程自动化，这样你就可以应对迅速变化的欺诈行为并满足新一波的监管要求。无论你是初创公司还是企业级公司，Persona 都有适合你的方案。

[原文] [Lenny]: Learn more at withpersona.com/lenny. Again, that's withpersona.com/lenny.

[译文] [Lenny]: 更多信息请访问 withpersona.com/lenny。再一次，网址是 withpersona.com/lenny。

[原文] [Lenny]: Kevin, thank you so much for being here and welcome to the podcast.

[译文] [Lenny]: Kevin，非常感谢你的到来，欢迎来到这个播客。

[原文] [Kevin Weil]: Thank you so much for having me. We've been talking about doing this forever and we made it happen.

[译文] [Kevin Weil]: 非常感谢邀请我。我们聊这事儿聊了很久了，终于成行了。

[原文] [Lenny]: We did it. I can't imagine how insane your life is, so I really appreciate that you made time for this and we're actually recording this the week that you guys launched your new image model, which is a happy coincidence.

[译文] [Lenny]: 我们做到了。我无法想象你的生活有多疯狂，所以我真的非常感谢你抽出时间，而且我们录制这期节目的这周恰逢你们发布了新的图像模型，这真是一个愉快的巧合。

[原文] [Lenny]: My entire social feed is filled with ghiblifications of everyone's life and family photos and everything, so good job.

[译文] [Lenny]: 我整个社交媒体的信息流里都充满了大家生活照和全家福的“吉卜力化”（ghiblifications）版本，干得漂亮。

[原文] [Kevin Weil]: Yep, mine too. My wife, Elizabeth, sent me one of hers, so I'm right there with you.

[译文] [Kevin Weil]: 是的，我的也是。我妻子 Elizabeth 给我也发了一张她的，所以我深有同感。

[原文] [Lenny]: Let me just ask, did you guys expect this kind of reaction? It feels like this is the most viral thing that's happened in AI, which a high bar since, I don't know, ChatGPT launched. Just like, did you guys expect it to go this well? What does it feel like internally?

[译文] [Lenny]: 我想问一下，你们预料到会有这样的反应吗？感觉这是 AI 领域发生的最具病毒传播性的事情，这是一个很高的门槛，毕竟之前还有 ChatGPT 的发布。你们预期到会这么火吗？内部感觉如何？

[原文] [Kevin Weil]: There have been a handful of times in my career when you're working on a product internally and the internal usage just explodes. This was true by the way when we were building stories at Instagram.

[译文] [Kevin Weil]: 在我的职业生涯中，有少数几次当你正在内部开发一个产品时，内部的使用量就突然爆发了。顺便说一句，当我们当初在 Instagram 开发 Stories（快拍）功能时也是这种情况。

[原文] [Kevin Weil]: More than anything else in my career, we could feel it was going to work because we were all using it internally and we'd go away for a weekend.

[译文] [Kevin Weil]: 这种感觉比我职业生涯中任何其他时候都强烈，我们能感觉到它会成功，因为我们所有人都在内部使用它，即使是在周末离开公司的时候。

[原文] [Kevin Weil]: Before it launched we were all using it and we'd come back after a weekend and we would know what was going on and be like, "Oh, hey, I saw you were at that camping trip, how was that?" You were like, "Man, this thing really works."

[译文] [Kevin Weil]: 在发布之前我们都在用，周末回来后我们都知道发生了什么，会说：“嘿，我看你去露营了，怎么样？”你会觉得：“天哪，这东西真管用。”

[原文] [Kevin Weil]: ImageGen was definitely one of those, so we'd been playing with it for, I don't know, a couple months and when it first went live internally to the company, there was kind of a little gallery where you could generate your own, you could also see what everyone else was generating and it was just nonstop buzz.

[译文] [Kevin Weil]: ImageGen（图像生成）绝对属于这一类，我们大概玩了几个月吧。当它第一次在公司内部上线时，有一个小画廊，你可以生成自己的图，也可以看到其他人在生成什么，整个氛围一直非常热闹。

[原文] [Kevin Weil]: So yeah, we had a sense that this was going to be a lot of fun for people to play with.

[译文] [Kevin Weil]: 所以是的，我们有一种预感，这东西会让大家玩得很开心。

[原文] [Lenny]: That's really cool. That should be a measure of just confidence into something going well that you're launching is internally everyone's going crazy for it.

[译文] [Lenny]: 真酷。这应该成为衡量产品发布能否成功的一个信心指标，那就是内部每个人都为之疯狂。

[原文] [Kevin Weil]: Yeah. Especially social things because you have a very tight network as a company socially, so you know each other and you're experts in your product hopefully.

[译文] [Kevin Weil]: 是的。尤其是社交类产品，因为作为一家公司，你们在社交上有一个非常紧密的网络，你们彼此认识，而且希望你们都是自己产品的专家。

[原文] [Kevin Weil]: And so there's some sense in which if you're doing something social and it's not taking off internally, you might question what you're doing.

[译文] [Kevin Weil]: 所以从某种意义上说，如果你在做一个社交产品，但它在内部都没有火起来，你可能会质疑自己在做什么。

[原文] [Lenny]: Yeah, and by the way, the Ghibli thing, is that something you guys seeded or how did that even start? Was that an intentional example?

[译文] [Lenny]: 是的，顺便问一下，那个吉卜力（Ghibli）风格的事情，是你们预埋的彩蛋吗？还是怎么开始的？是有意的示例吗？

[原文] [Kevin Weil]: I think it's just the style people love and the model is really capable at emulating style or understanding what... It's very good at instruction following.

[译文] [Kevin Weil]: 我觉得那只是人们喜欢的风格，而且这个模型非常擅长模仿风格或者理解……它非常擅长遵循指令。

[原文] [Kevin Weil]: That's actually something that I think people... I'm starting to see people discover with it, but you can do very complex things.

[译文] [Kevin Weil]: 这实际上是我认为人们……我开始看到人们正在发现的一点，你可以用它做非常复杂的事情。

[原文] [Kevin Weil]: You can give it two images, one is your living room and the other is a whole bunch of photos or memorabilia or things you want and you say, "Tell me how you would arrange these things."

[译文] [Kevin Weil]: 你可以给它两张图，一张是你客厅的照片，另一张是一堆照片或纪念品或你想要的东西，然后你说：“告诉我你会怎么摆放这些东西。”

[原文] [Kevin Weil]: Or you can say, "I'd like you to show me what this will look like if you put this over here and this thing to the right of that and this one to the left of this, but under that one."

[译文] [Kevin Weil]: 或者你可以说：“我想让你展示一下，如果你把这个放在这里，那个放在那东西的右边，这个放在那个的左边但在这个的下面，看起来会是什么样。”

[原文] [Kevin Weil]: And the model actually will understand all of that and do it. It's incredibly powerful. So I'm just excited about all the different things people are going to figure out.

[译文] [Kevin Weil]: 模型实际上能理解所有这些要求并执行出来。它极其强大。所以我对人们将会探索出的各种玩法感到非常兴奋。

[原文] [Lenny]: Yeah. All right. Well, good job. Good job team OpenAI. Let's get serious here and let's zoom out a little bit.

[译文] [Lenny]: 是的。好了，干得好。OpenAI 团队干得好。让我们严肃一点，把视角拉远一点。

章节 2：AGI的定义、招聘故事与入职体验

📝 本节摘要：

在本章节中，Kevin 分享了他对 AI 定义的独到见解：“AI 就是那些尚未被实现的技术”，一旦技术成熟（如自动驾驶），人们就会习以为常地称之为算法。随后，话题转向 Kevin 加入 OpenAI 的幕后故事。他讲述了自己离开 Planet 后如何联系 Sam Altman，经历了一场极速的面试流程，却意外陷入了“九天的沉默”，让他一度以为自己搞砸了。这段经历不仅展示了 OpenAI 内部的高速运作与混乱并存的创业氛围，也揭示了即使是高管招聘也难免遇到的人性化插曲。

[原文] [Lenny]: The way I see it is you're chief product officer of maybe the most important company in the world right now. Just not to set the bar too high, but you guys are ushering in AI, AGI at some point, super intelligence at some point. No big deal.

[译文] [Lenny]: 在我看来，你是当今世界上可能最重要的公司的首席产品官。不想把门槛定得太高，但你们正在引领 AI、未来的 AGI（通用人工智能）以及某种程度上的超级智能。这可不是小事。

[原文] [Lenny]: I have more questions for you than I've had for any other guest. Actually put out a call-out on Twitter and LinkedIn and my community just like what would you want to ask Kevin? And I had over 300 well-formed questions and we're going to go through every single one. So let's just get started. I'm just joking.

[译文] [Lenny]: 我为你准备的问题比以往任何嘉宾都多。实际上我在 Twitter、LinkedIn 和我的社区里发起了征集，问大家想问 Kevin 什么？我收到了超过 300 个非常完善的问题，我们要把它们一个一个过一遍。所以我们开始吧。开玩笑的。

[原文] [Kevin Weil]: Cool.

[译文] [Kevin Weil]: 酷。

[原文] [Lenny]: I picked out the best and there's a lot of stuff I'm really curious about.

[译文] [Lenny]: 我挑了一些最好的，有很多我真正好奇的内容。

[原文] [Kevin Weil]: Well, it's 1 PM here. It doesn't get dark for a while, so let's do it.

[译文] [Kevin Weil]: 好吧，现在这里是下午 1 点。天还要好一会儿才黑，咱们开始吧。

[原文] [Lenny]: Okay, here we go. Okay, so first of all, I'm just going to take notes here. When is AGI launching? When in December?

[译文] [Lenny]: 好的，我们开始。首先，我准备记个笔记。AGI 什么时候发布？十二月的什么时候？

[原文] [Kevin Weil]: I mean, we just launched a good ImageGen model. Does that count?

[译文] [Kevin Weil]: 我想说，我们刚发布了一个很棒的图像生成模型。那个算吗？

[原文] [Kevin Weil]: It's getting there. It's getting there. There's this quote I love, which is "AI is whatever hasn't been done yet" because once it's been done, when it kind of works, then you call it machine learning, and once it's kind of ubiquitous and it's everywhere, then it's just an algorithm.

[译文] [Kevin Weil]: 快了，快了。有一句我非常喜欢的名言：“AI 就是那些尚未被实现的技术”，因为一旦它做成了，当它某种程度上能工作时，你就叫它机器学习；而一旦它普及了，无处不在了，那就只是一个算法了。

[原文] [Kevin Weil]: So I've always loved that we call things AI when they still don't quite work and then by the time it's like an AI algorithm that's recommending you follow, oh, that's just an algorithm, but this new thing, like self-driving cars, that's it.

[译文] [Kevin Weil]: 所以我一直很喜欢这一点：当东西还不太好用时我们叫它 AI；等到像推荐关注这种 AI 算法出现时，哦，那只是个算法；但这像自动驾驶汽车这样的新东西，那才是 AI。

[原文] [Kevin Weil]: I think to some degree we're always going to be there and the next thing is always going to be AI and the current thing that we use every day and is just a part of our lives, that's an algorithm.

[译文] [Kevin Weil]: 我觉得某种程度上我们永远会处于这种状态，下一个新事物总是 AI，而我们每天使用并成为生活一部分的当前事物，就是算法。

[原文] [Lenny]: It's so interesting because in the Bay Area you see self-driving cars driving around and it's so normal now when four years ago and three years ago, you would've seen this and you'd be like, "Holy shit, what is... We're in the future." And now we're just so take it for granted.

[译文] [Lenny]: 这很有趣，因为在湾区你看到自动驾驶汽车到处跑，现在这太正常了，而在四年前或三年前，你看到这个会说：“我靠，这是什么……我们活在未来。”而现在我们就这么理所当然地接受了。

[原文] [Kevin Weil]: I mean there's something like that with everything. If I showed you... When GPT-3 launched, I wasn't at OpenAI then. I was just a user, but it was mind-blowing.

[译文] [Kevin Weil]: 我觉得所有事情都是这样。如果我展示给你看……当 GPT-3 发布时，我那时还不在 OpenAI。我只是个用户，但它真的让人震撼。

[原文] [Kevin Weil]: And if I gave you GPT-3 now I just plugged that into ChatGPT for you and you started using it, you'd be like, "What is this thing?" It's like mess. Flop, flop.

[译文] [Kevin Weil]: 但如果我现在给你用 GPT-3，把它接入 ChatGPT 让你用，你会说：“这是什么东西？”简直是一团糟。太拉胯了。

[原文] [Kevin Weil]: I had the same experience when I first got into a Waymo, your very first ride, at least my very first ride, my first 10 seconds in a Waymo, it starts driving and you're like, "Oh my God, watch out for that bike." You're holding onto whatever you can.

[译文] [Kevin Weil]: 我第一次坐 Waymo 时也有同样的经历，你第一次坐，至少我第一次坐的时候，前 10 秒钟它开始动，你会想：“天哪，小心那辆自行车。”你会抓住任何能抓的东西。

[原文] [Kevin Weil]: And then five minutes in, you've calmed down and you realize that you're getting driven around the city without a driver and it's working. You're just like, "Oh my God, I am living in the future right now."

[译文] [Kevin Weil]: 然后五分钟后，你平静下来了，意识到你在城市里穿梭却并没有司机，而且它运作良好。你会想：“天哪，我现在就生活在未来。”

[原文] [Kevin Weil]: And then another 10 minutes, you're bored, you're doing email on your phone, answering Slack messages, and suddenly this miracle of human invention is just an expected part of your life from then on.

[译文] [Kevin Weil]: 再过 10 分钟，你无聊了，你在手机上回邮件、回 Slack 消息，突然之间，这个人类发明的奇迹从那时起就变成了你生活中理所当然的一部分。

[原文] [Kevin Weil]: And there is really something in the way that we all are adapting to AI that's kind of like that. These miraculous things happen and computers can do something they've never been able to do before and it blows our mind collectively for a week and then we're like, oh, yeah. Oh, now it's just machine learning on its way to being an algorithm.

[译文] [Kevin Weil]: 我们适应 AI 的方式真的有点像那样。这些奇迹般的事情发生了，计算机能做以前从未能做到的事情，这让我们集体震撼了一个星期，然后我们就觉得，哦，是啊。哦，现在它只是正在变成算法的机器学习罢了。

[原文] [Lenny]: The craziest thing about what you just shared actually is, I don't know, ChatGPT, which is now feels terrible. 3.5 was a couple years ago, and imagine what life will be like in a couple years from now.

[译文] [Lenny]: 你刚才分享的最疯狂的事情其实是，我不知道，ChatGPT 现在感觉（旧版本）很糟糕。3.5 版本也就是几年前的事，想象一下几年后的生活会是什么样。

[原文] [Lenny]: We're going to get to that, where things are going, what you think is going to be the next big leap. But I want to start with the beginning of your journey at OpenAI. So you worked at Twitter, you worked at Facebook, you worked at Planet, Instagram. At some point you got recruited to go and come work at OpenAI. I'm curious just what that story was like of the recruiting process of joining OpenAI as CPO. Is there any fun stories there?

[译文] [Lenny]: 我们稍后会聊到那些，关于未来的方向，你认为下一个大的飞跃是什么。但我想从你在 OpenAI 的旅程开始聊起。你在 Twitter、Facebook、Planet、Instagram 都工作过。在某个时刻你被招募去 OpenAI 工作。我很好奇作为一个 CPO 加入 OpenAI 的招聘过程是怎样的？有什么有趣的故事吗？

[原文] [Kevin Weil]: If I'm remembering the timeline right, we communicated at Planet I was leaving and I was planning to just go take some time. I wasn't going to stop working, but I was also happy to take the summer.

[译文] [Kevin Weil]: 如果我没记错时间线的话，我在 Planet 宣布我要离职，我原本计划休息一段时间。我没打算停止工作，但也乐意休息一个夏天。

[原文] [Kevin Weil]: This was maybe April or something. I was like, cool, I'm going to have the summer with my kids. We're going to go to Tahoe or something and I'll actually get to hang out rather than what I usually do going up and down and all that.

[译文] [Kevin Weil]: 那大概是四月份左右。我想，酷，我要和孩子们过暑假。我们要去太浩湖（Tahoe）之类的地方，我可以真正地陪陪他们，而不是像往常那样忙忙碌碌。

[原文] [Kevin Weil]: And then Sam and I had known each other lightly for a bunch of years and he's always involved in so many interesting things like companies building fusion and all these things. So he'd always been somebody that I would call occasionally if I was starting to think about my next thing because I like working on big tech forward, sort of next wave kind of things.

[译文] [Kevin Weil]: 我和 Sam（Altman）认识好几年了，虽然只是点头之交，但他总是参与很多有趣的事情，比如搞核聚变的公司之类的。所以他一直是我如果开始考虑下一份工作时偶尔会打电话的人，因为我喜欢做那种高科技前沿、属于下一波浪潮的事情。

[原文] [Kevin Weil]: And so I called him and I think Vinod also helped to put us in touch again. And this time it wasn't like, "Oh, you should go talk to these guys working on fusion." He said, "Actually, we're thinking about something, you should come talk to us." I was like, "Okay, that sounds amazing. Let's do it."

[译文] [Kevin Weil]: 所以我给他打了电话，我想 Vinod 可能也帮我们重新牵了线。这次他没有说：“哦，你应该去和那些搞聚变的家伙聊聊。”他说：“其实，我们在考虑一些事情，你应该来和我们聊聊。”我说：“好啊，听起来很棒。来吧。”

[原文] [Kevin Weil]: And it goes really fast, really, really fast. I met most of the management team in a brief period of time, a few days, and they were telling me, 'Look, we're basically going to move as fast as we want to move. And if you talk to everyone, everyone likes you, you're ready to go."

[译文] [Kevin Weil]: 过程真的很快，真的非常非常快。我在很短的时间内，大概几天里，见到了大部分管理团队成员。他们告诉我：“听着，我们基本上想多快就多快。如果你和大家都聊过了，大家都喜欢你，你就准备好上任吧。”

[原文] [Kevin Weil]: Sam came over for dinner and we had a great evening together just talking about OpenAI in the future and getting to know each other better. And at the end I was like, I was going to go in the next day for a bigger round of interviews and Sam was saying, "Hey, it's going really well. We're really excited."

[译文] [Kevin Weil]: Sam 来我家吃了晚饭，我们度过了一个愉快的夜晚，聊 OpenAI 的未来，也加深了彼此的了解。最后我说，我第二天要去参加更大型的一轮面试，Sam 说：“嘿，进展真的很顺利。我们非常兴奋。”

[原文] [Kevin Weil]: And I said, "Cool. So how do I think about tomorrow?" And he said, "Oh, you'll be fine. Don't worry about it. And if it goes well, we're basically there."

[译文] [Kevin Weil]: 我说：“酷。那我该怎么看待明天的面试？”他说：“哦，你会没事的。别担心。如果顺利的话，我们基本上就定下来了。”

[原文] [Kevin Weil]: And so I go in the next day, meet a bunch of people, have a great time. I really enjoyed everybody I met with. In any interview, you can always second guess yourself like, oh, I shouldn't have said that thing or that thing I gave a bad answer on I wish I could redo, but I came away feeling like I think that went pretty well.

[译文] [Kevin Weil]: 于是我第二天去了，见了一堆人，聊得很开心。我真的很喜欢我见到的每一个人。在任何面试中，你总是会事后怀疑自己，比如，哦，我不该说那句话，或者那个问题的回答很糟糕希望能重来，但我离开时感觉应该还不错。

[原文] [Kevin Weil]: And I was expecting to hear that weekend basically because they sort of set expectations as soon as if this goes well, we're ready to go. And I didn't hear anything. And then it was like Monday, Tuesday, Wednesday, I still didn't hear anything and I reached out to folks on the OpenAI side a couple of times, still nothing.

[译文] [Kevin Weil]: 我原本期望那个周末就能收到消息，因为他们设定的期望是如果顺利马上就能定。但我什么都没听到。然后周一、周二、周三过去了，我还是什么都没听到，我联系了 OpenAI 那边的人好几次，还是没消息。

[原文] [Kevin Weil]: And I was like, "Oh my God, I screwed it up. I don't know where I screwed it up, but I totally screwed it up. I can't believe it." And I was going back to Elizabeth, my wife and being like, "What did I do? Where do you think I..." Getting all crazy about it and then it's still nothing.

[译文] [Kevin Weil]: 我当时想：“天哪，我搞砸了。我不知道哪里搞砸了，但我彻底搞砸了。真不敢相信。”我回到我妻子 Elizabeth 身边说：“我做了什么？你觉得我在哪……”整个人都要疯了，结果还是没消息。

[原文] [Kevin Weil]: And finally it was like nine days later, they finally got back to me and it turned out there was a bunch of stuff happening internally and this, that and the other thing, and there's just a million things happening. And they finally were like, "Oh yeah, that went well. Let's do this." And I was like, "Oh, okay, cool, let's do it."

[译文] [Kevin Weil]: 终于大概九天后，他们终于回复我了，原来是因为内部发生了一堆事情，这个那个的，有一百万件事在发生。他们最后说：“哦对了，那次面试很顺利。我们开始吧。”我就说：“哦，好的，酷，那来吧。”

[原文] [Kevin Weil]: But it was nine days of agony and they were just super busy on some internal stuff and there I was fretting every single day and re-going over every line of our interview process.

[译文] [Kevin Weil]: 但那是痛苦的九天，他们只是因为内部事务太忙了，而我却每天都在焦虑，反复回想面试过程中的每一句话。

[原文] [Lenny]: It makes me think about when you're dating someone and you've texted them and you're not hearing anything back, you assume something is wrong.

[译文] [Lenny]: 这让我想起当你和某人约会时，你发了短信却没收到回复，你就会觉得出问题了。

[原文] [Kevin Weil]: Yeah, totally.

[译文] [Kevin Weil]: 是的，完全是这样。

[原文] [Lenny]: They might just be busy.

[译文] [Lenny]: 他们可能只是忙。

[原文] [Kevin Weil]: I have a hard time about it still.

[译文] [Kevin Weil]: 我到现在还是很难释怀。

[原文] [Lenny]: That's wild. I love that it worked out. And I guess the lesson there is don't jump to conclusions.

[译文] [Lenny]: 太疯狂了。很高兴结果是好的。我想那里的教训就是不要妄下结论。

[原文] [Kevin Weil]: Yeah. Have a little bit of chill.

[译文] [Kevin Weil]: 是的。稍微淡定一点。

章节 3：身处风暴中心：AI产品开发的节奏与评估机制

📝 本节摘要：

在本章节中，Kevin 深入剖析了身处 OpenAI 这一“风暴中心”的独特体验。他指出，与传统软件开发基于相对固定的技术栈（如数据库）不同，AI 领域的底层技术每两个月就会发生质变，迫使产品团队必须不断重构思维。

对话的核心随后转向了 Evals（评估测试）。Kevin 将其定义为“模型的单元测试”，并强调这是构建 AI 产品的核心技能。由于 LLM 的输入输出具有模糊性（Fuzzy），开发者必须通过 Evals 来衡量模型在特定场景下的表现（如 60% 准确率与 99% 准确率的产品设计完全不同）。他还以 Deep Research（深度研究） 产品为例，解释了如何通过“爬坡”式的评估优化来打磨产品，并指出未来的趋势是使用企业私有数据微调模型，并通过自定义 Evals 来衡量效果。

[原文] [Lenny]: Speaking of that, I want to chat about what it's just like to be inside the center of the storm. Again, you work at a lot of, let's say traditional companies even though they're not that traditional, Twitter and Instagram and Facebook and Planet, and now you work at OpenAI. I'm curious, what is most different about how things work in your day-to-day life at OpenAI?

[译文] [Lenny]: 说到这，我想聊聊身处风暴中心究竟是什么感觉。同样，你在很多——我们就说是传统公司吧，尽管它们其实没那么传统——像 Twitter、Instagram、Facebook 和 Planet 工作过，现在你在 OpenAI 工作。我很好奇，在 OpenAI 的日常工作中，最不同的地方是什么？

[原文] [Kevin Weil]: I think it's probably the pace. Maybe it's two things. One is it's the pace. The second is everywhere I've ever worked before this, you kind of know what technology you're building on.

[译文] [Kevin Weil]: 我想可能主要是节奏。也许是两件事。第一是节奏。第二是在我之前工作过的每一个地方，你某种程度上都知道你是基于什么技术在构建产品。

[原文] [Kevin Weil]: So you spend your time thinking about what problems are you solving? Who are you building for? How are you going to make their lives better? How are you going to... Is this a big enough problem that you're going to be able to change habits? Do people care about this problem being solved? All those good product things.

[译文] [Kevin Weil]: 所以你会花时间思考你在解决什么问题？你在为谁构建产品？你将如何让他们的生活变得更好？你将如何……这个问题是否大到足以改变人们的习惯？人们是否在乎这个问题被解决？所有这些好的产品思维。

[原文] [Kevin Weil]: But the stuff that you're building on is kind of fixed. You're talking about databases and things and I bet the database you used this year is probably 5% better than the database you used two years ago, but that's not true at all with AI.

[译文] [Kevin Weil]: 但你构建产品所依赖的基础技术某种程度上是固定的。你讨论的是数据库之类的东西，我敢打赌你今年用的数据库可能比两年前用的好 5%，但在 AI 领域完全不是这样。

[原文] [Kevin Weil]: It's like every two months computers can do something they've never been able to do before and you need to completely think differently about what you're doing. There's something fundamentally interesting about that makes life fun here.

[译文] [Kevin Weil]: 就好像每两个月，计算机就能做一些它们以前从未能做到的事情，你需要完全不同地思考你在做什么。这种根本性的变化让这里的生活非常有趣。

[原文] [Kevin Weil]: There's also something we will maybe talk about evals later, but it also really, in this world of... Everything we're used to with computers is about giving a computer very defined inputs.

[译文] [Kevin Weil]: 还有一点，也许我们稍后会讨论 Evals（评估测试），但在这样一个世界里……我们习惯的计算机操作都是关于给计算机非常明确的输入。

[原文] [Kevin Weil]: If you look at Instagram for example, there are buttons that do specific things and you know what they do. And then when you give a computer defined inputs, you get very defined outputs. You're confident that if you do the same thing three times, you're going to get the same output three times.

[译文] [Kevin Weil]: 比如看 Instagram，上面有执行特定功能的按钮，你知道它们是干什么的。当你给计算机明确的输入时，你会得到非常明确的输出。你有信心，如果你做同样的事情三次，你会得到三次同样的输出。

[原文] [Kevin Weil]: LLMs are completely different than that. They're good at fuzzy subtle inputs. Then all the nuances of human language and communication, they're pretty good at. And also they don't really give you the same answer.

[译文] [Kevin Weil]: LLM（大语言模型）则完全不同。它们擅长处理模糊、微妙的输入。对于人类语言和交流的所有细微差别，它们都很擅长。而且它们实际上不会给你相同的答案。

[原文] [Kevin Weil]: You probably get spiritually the same answer for the same question, but it's certainly not the same set of words every time. And so you're much more, it's fuzzier inputs and fuzzier outputs.

[译文] [Kevin Weil]: 对于同一个问题，你得到的答案在精神内核上可能是一样的，但每次用的词肯定不一样。所以这更多的是一种模糊输入和模糊输出的情况。

[原文] [Kevin Weil]: And when you're building products, it really matters whether there's some use case that you're trying to build around. If the model gets it right 60% of the time, you build a very different product than if the model gets it right 95% of the time versus if the model gets it right 99.5% of the time.

[译文] [Kevin Weil]: 当你构建产品时，这一点非常重要，尤其是当你围绕某个用例构建时。如果模型只有 60% 的时候是正确的，你构建的产品将与模型有 95% 或 99.5% 正确率时的产品截然不同。

[原文] [Kevin Weil]: And so there's also something that you have to get really into the weeds on your use case and the evals and things like that in order to understand the right kind of product to build. So that is just fundamentally different. If your database works once, it works every time. And that's not true in this world.

[译文] [Kevin Weil]: 所以你需要深入研究你的用例、Evals 以及类似的东西，以便了解应该构建什么样的产品。这有着根本性的不同。如果你的数据库第一次能用，那它每次都能用。但这在这个（AI）世界里并不成立。

[原文] [Lenny]: Let's actually follow this thread on evals. I definitely wanted to talk about this. We had this legendary panel at the Lenny & Friends Summit. It was you and Mike Krieger and Sarah Guo moderating.

[译文] [Lenny]: 让我们顺着 Evals 这个话题继续聊。我绝对想谈谈这个。我们在“Lenny & Friends 峰会”上有一个传奇的小组讨论，是你、Mike Krieger 和 Sarah Guo 主持的。

[原文] [Kevin Weil]: That was fun.

[译文] [Kevin Weil]: 那很有趣。

[原文] [Lenny]: So fun. The thing that I heard that kind of stuck with people from that panel was a comment you made where you said that writing evals is going to become a core skill for product managers, and I feel like that probably applies further than just product managers.

[译文] [Lenny]: 太有趣了。我听到人们对那个讨论印象最深的一点是你发表的一个评论，你说编写 Evals 将成为产品经理的一项核心技能，而且我觉得这可能不仅仅适用于产品经理。

[原文] [Lenny]: A lot of people know what evals are. A lot of people have no idea what I'm talking about. So could you just briefly explain what is an eval and then just why do you think this is going to be so important for people building products in the future?

[译文] [Lenny]: 很多人知道 Evals 是什么，也有很多人完全不知道我在说什么。所以能不能请你简要解释一下什么是 Eval，以及为什么你认为这对于未来构建产品的人来说如此重要？

[原文] [Kevin Weil]: Yeah, sure. I think the easiest way to think about it is almost like a quiz for a model, a test to gauge how well it knows a certain set of subject material or how good is at responding to a certain set of questions.

[译文] [Kevin Weil]: 当然可以。我认为最简单的理解方式就是把它看作是给模型的一个小测验，一个用来衡量它对某套主题材料掌握得如何，或者回答某套问题的能力有多好的测试。

[原文] [Kevin Weil]: So in the same way you take a calculus class and then you have calculus tests that see if you've learned what you're supposed to learn. You have evals that test how good is the model at creative writing? How good is the model at graduate level science? How good is the model at competitive coding?

[译文] [Kevin Weil]: 就像你上微积分课，然后通过微积分考试来看你是否学到了该学的东西一样。你也需要 Evals 来测试：模型在创意写作方面有多好？在研究生水平的科学方面有多好？在编程竞赛方面有多好？

[原文] [Kevin Weil]: And so you have these set of evals that basically perform as benchmarks for how smart or capable the model is.

[译文] [Kevin Weil]: 所以你有一套 Evals，基本上作为衡量模型有多聪明或能力有多强的基准。

[原文] [Lenny]: Is it a simple way to think about it, like unit tests for model?

[译文] [Lenny]: 把它理解为模型的单元测试（Unit Tests），是不是一种简单的方式？

[原文] [Kevin Weil]: Yeah, unit tests, tests in general for models. Totally.

[译文] [Kevin Weil]: 是的，单元测试，或者通俗地说是模型的测试。完全正确。

[原文] [Lenny]: Great, great. Okay. And then why is this so important for people that don't totally understand what the hell's going on here with evals? Why is this so key to building AI products?

[译文] [Lenny]: 好的，太好了。那么，对于那些完全不明白 Evals 到底是怎么回事的人来说，为什么这如此重要？为什么它是构建 AI 产品的关键？

[原文] [Kevin Weil]: Well, it gets back to what I was saying. You need to know whether your model is going to... There are certain things that models will get right. 99.95% of the time and you can just be confident.

[译文] [Kevin Weil]: 嗯，这回到了我刚才说的。你需要知道你的模型是否会……有些事情模型会在 99.95% 的时间里做对，你可以很有信心。

[原文] [Kevin Weil]: There are things that they're going to be 95% right on and things they're going to be 60% right on. If the model's 60% right on something, you're going to need to build your product totally differently.

[译文] [Kevin Weil]: 也有它们只有 95% 正确率的事情，还有只有 60% 正确率的事情。如果模型在某件事上只有 60% 的正确率，你需要以完全不同的方式来构建你的产品。

[原文] [Kevin Weil]: And by the way, these things aren't static either. So a big part of evals is if you know you're building for some use case. So let's take our deep research product, which is one of my favorite things that we've released maybe ever.

[译文] [Kevin Weil]: 顺便说一句，这些东西也不是静态的。所以 Evals 很重要的一部分是，如果你知道你是为某个用例构建的。以我们的 Deep Research（深度研究）产品为例，这可能是我最喜欢的已发布产品之一。

[原文] [Kevin Weil]: The idea is with deep research for people who haven't used it, you can give ChatGPT now an arbitrarily complex query. It's not about returning you an answer from a search query, which we can also do.

[译文] [Kevin Weil]: Deep Research 的概念是——给没用过的人解释一下——你现在可以给 ChatGPT 一个任意复杂的查询。它不仅仅是给你返回一个搜索结果，那个我们也能做。

[原文] [Kevin Weil]: It's here's a thing that if you were going to answer it yourself, you'd go off and do two hours of reading on the web and then you might need to read some papers and then you would come back and start writing up your thoughts and realize you had some gaps in your thinking.

[译文] [Kevin Weil]: 它是针对这种情况：如果你要自己回答这个问题，你可能需要上网阅读两个小时，然后可能需要阅读一些论文，再回来开始写下你的想法，然后意识到你的思维中有一些漏洞。

[原文] [Kevin Weil]: So you go out and do more research. It might take you a week to write some 20 page answer to this question. You can let ChatGPT just like chug for you for 25, 30 minutes. It's not the immediate answers you're used to, but it might go work for 25, 30 minutes and do work that would've taken you a week.

[译文] [Kevin Weil]: 于是你又出去做更多的研究。要写出一份 20 页的回答可能要花你一周时间。而你可以让 ChatGPT 替你忙活个 25 到 30 分钟。它不是你习惯的那种即时回答，但它可能会工作 25、30 分钟，完成你原本需要一周才能完成的工作。

[原文] [Kevin Weil]: So as we were building that product, we were designing evals at the same time as we were thinking about how this product was going to work and we were trying to go through hero use cases. Here's a question you want to be able to ask. Here's an amazing answer for that question.

[译文] [Kevin Weil]: 所以当我们构建这个产品时，我们在构思产品如何运作的同时也在设计 Evals，我们试图梳理出一些“英雄用例”（核心用例）。这是一个你想要问的问题，这是该问题的一个绝佳答案。

[原文] [Kevin Weil]: And then turning those into evals and then hill climbing on those evals. So it's not just that the model is static and we hope it does okay on a certain set of things, you can teach the model. You can make this a continuous learning process.

[译文] [Kevin Weil]: 然后把这些变成 Evals，并在这些 Evals 上进行“爬坡”（不断优化）。所以模型不是静态的，我们不仅仅是希望它在某些事情上表现尚可，你可以教模型。你可以让这成为一个持续学习的过程。

[原文] [Kevin Weil]: And so as we were fine-tuning our model for deep research to be able to answer these things, we were able to test is it getting better on these evals that we said were important measures of how the product was working? And it's when you start seeing that and you start seeing performance on evals going up, you start saying, "Okay, I think we have a product here."

[译文] [Kevin Weil]: 因此，当我们为了 Deep Research 微调模型以使其能够回答这些问题时，我们可以测试它是否在我们设定的、衡量产品运作情况的重要 Evals 上变得更好了。当你开始看到这一点，看到 Evals 的表现提升时，你就会开始说：“好的，我觉得我们的产品有戏了。”

[原文] [Lenny]: You made a kind of a comment along these same lines around evals that AI is almost capped in how amazing it can be by how good we are at evals. Does that resonate? Any more thoughts along those lines?

[译文] [Lenny]: 你之前发表过类似的评论，大意是 AI 能有多惊艳，几乎取决于我们在 Evals 方面做得有多好。这有共鸣吗？在这个思路上还有什么想法吗？

[原文] [Kevin Weil]: I mean, these models are their intelligences and intelligence is so fundamentally multidimensional so you can talk about a model being amazing at competitive coding, which may not be the same as that model being great at front-end coding...

[译文] [Kevin Weil]: 我的意思是，这些模型是智能体，而智能本质上是多维的。你可以说一个模型在编程竞赛方面很棒，但这并不等同于这个模型擅长前端代码……

[原文] [Kevin Weil]: ...may not be the same as that model being great at front-end coding or back-end coding or taking a whole bunch of code that's written in COBOL and turning it into Python. And that's just within the software engineering world.

[译文] [Kevin Weil]: ……也不等同于它擅长后端代码，或者擅长把一堆用 COBOL 写成的代码转换成 Python。而这还仅仅是在软件工程领域内。

[原文] [Kevin Weil]: So I think there's a sense in which you can think of these models as incredibly smart, very factually aware intelligences, but still most of the world's data, knowledge, process is not public. It's behind the walls of companies or governments or other things.

[译文] [Kevin Weil]: 所以我认为，从某种意义上说，你可以把这些模型看作是极其聪明、对事实非常了解的智能体，但世界上大多数的数据、知识和流程仍然不是公开的。它们被隔离在公司、政府或其他机构的高墙之后。

[原文] [Kevin Weil]: And same way, if you were going to join a company, you would spend your first two weeks onboarding. You'd be learning the company-specific processes. You'd get access to company-specific data. The models are smart enough, you can teach them anything, but they need to have the raw data to learn from.

[译文] [Kevin Weil]: 同样的道理，如果你加入一家公司，你会花前两周进行入职培训（Onboarding）。你会学习公司特定的流程，获得访问公司特定数据的权限。模型足够聪明，你可以教它们任何东西，但它们需要有原始数据来学习。

[原文] [Kevin Weil]: So there's a sense in which I think the future is really going to be incredibly smart, broad-based models that are fine-tuned and tailored with company-specific or use case-specific data so that they perform really well on company-specific, or use case-specific things. And you're going to measure that with custom evals.

[译文] [Kevin Weil]: 所以我认为未来的趋势确实是极其聪明、基础广泛的模型，通过公司特定或用例特定的数据进行微调和定制，以便它们在公司特定或用例特定的事务上表现出色。而你将使用自定义的 Evals 来衡量这一点。

章节 4：初创公司的机会与OpenAI的“迭代部署”哲学

📝 本节摘要：

本章节探讨了初创公司在 AI 巨头阴影下的生存空间。Kevin 引用了 Twitter 前创始人 Ev Williams 的名言——“墙外的聪明人永远比墙内多”，指出 OpenAI 不可能覆盖所有垂直领域，特别是涉及行业私有数据的场景，这正是创业者的护城河。

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随后，话题转向 OpenAI 内部的高效运作机制。Kevin 揭示了其“自下而上（Bottoms-up）”的产品文化与“迭代部署（Iterative Deployment）”的核心哲学：与其闭门造车，不如尽早发布以获取反馈。他特别提出了“模型极大主义（Model Maximalism）”的概念，建议开发者不要为当前模型的缺陷构建过多的“脚手架”，而应通过预判模型能力的提升来构建产品——如果你的产品现在处于模型能力的边缘勉强能用，两个月后它将大放异彩。

[原文] [Lenny]: So you came to a space that I think a lot of AI founders are thinking about is just, where's OpenAI not going to come squash me in the future? Or one of the other foundational models.

[译文] [Lenny]: 你进入了一个我认为很多 AI 创始人都都在思考的领域，就是：未来在哪些地方 OpenAI 不会过来把我“碾压”掉？或者是其他基础模型公司。

[原文] [Lenny]: So it's unclear to a lot of people just like, "Should I build a startup in this space or not?" Is there any advice you have or any guidance for where you think OpenAI, or just foundational models in general likely won't go and where you have an opportunity to build a company?

[译文] [Lenny]: 所以很多人都不清楚，“我到底该不该在这个领域创业？”你有什么建议或指导吗？关于你认为 OpenAI 或一般的基础模型公司不太可能涉足哪些领域，从而哪里有机会建立一家公司？

[原文] [Kevin Weil]: So this is something that Ev Williams used to say back at Twitter that's always stuck with me, which is, "No matter how big your company gets, no matter how incredible the people are, there are way more smart people outside your walls than there are inside your walls."

[译文] [Kevin Weil]: Ev Williams 以前在 Twitter 时常说一句话，让我记忆犹新，那就是：“无论你的公司变得多大，无论你的员工多么了不起，墙外的聪明人永远比墙内的多。”

[原文] [Kevin Weil]: And that's why we are so focused on building a great API. We have 3 million developers using our API. No matter how ambitious we are, how big we grow, by the way, we don't want to grow super big, there are so many use cases, places in the world where AI can fundamentally make our lives better.

[译文] [Kevin Weil]: 这就是为什么我们如此专注于构建一个伟大的 API。我们有 300 万开发者在使用我们的 API。无论我们多么雄心勃勃，无论我们发展得多大——顺便说一句，我们并不想变得超级庞大——世界上有太多的用例和地方，AI 可以从根本上改善我们的生活。

[原文] [Kevin Weil]: We're not going to have the people. We're not going to have the know-how to build most of these things.

[译文] [Kevin Weil]: 我们不会拥有足够的人力，也不会拥有相关的专业知识去构建这其中的大部分东西。

[原文] [Kevin Weil]: And I think, like I was saying, the data is industry-specific, use case-specific, behind certain company walls, things like that. And there are immense opportunities in every industry and every vertical in the world to go build AI-based products that improve upon the state of the art.

[译文] [Kevin Weil]: 而且正如我之前所说，数据是特定于行业、特定于用例的，或者是被隔离在某些公司的墙后的，诸如此类。在世界上的每个行业和每个垂直领域，都有巨大的机会去构建基于 AI 的产品，从而改进现有的技术水平。

[原文] [Kevin Weil]: And there's just no way we could ever do that ourselves. We don't want to. We if we did want to, and we're really excited to power that for 3 million-plus developers and way more in the future.

[译文] [Kevin Weil]: 我们绝不可能自己做完所有这些事。我们也不想做。即使我们想做也做不到，而且我们非常兴奋能为这 300 多万以及未来更多的开发者提供动力。

[原文] [Lenny]: Coming back to your earlier point about the tech changing constantly and getting faster, not exactly knowing what you'll have by the time you launch something in terms of the power, the model. I'm curious what allows you to ship quickly and consistently in such great stuff?

[译文] [Lenny]: 回到你之前的观点，关于技术不断变化且速度越来越快，你在发布产品时甚至无法确切知道到时候会有什么样的模型能力。我很好奇，是什么让你们能够如此快速且持续地发布这么棒的东西？

[原文] [Lenny]: And it sounds like one answer is bottoms-up empowered teams versus a very top-down roadmap that's planned out for a quarter. What are some of those things that allow you to ship such great stuff so often, so quickly?

[译文] [Lenny]: 听起来答案之一似乎是自下而上（Bottoms-up）的授权团队，而不是那种规划好一个季度的自上而下的路线图。还有哪些因素让你们能如此频繁、快速地发布好产品？

[原文] [Kevin Weil]: Yeah. I mean, we try and have a sense of where we're trying to go, point ourselves in a direction so that we have some rough sense of alignment. Thematically, I don't for second, and we do quarterly roadmapping. We laid out a year-long strategy. I don't for a second believe that what we write down in these documents is what we're going to actually ship three months from now, let alone six or nine.

[译文] [Kevin Weil]: 是的。我的意思是，我们会试着明确我们要去哪里，指明一个方向，以便我们有某种大致的一致性。从主题上讲，虽然我们确实做季度路线图规划，也制定了年度战略，但我一秒钟都不相信我们在这些文档里写下的东西就是我们三个月后实际要发布的东西，更别说六个月或九个月后了。

[原文] [Kevin Weil]: But that's okay. I think it's like an Eisenhower quote, "Plans are useless. Planning is helpful," which I totally subscribe to, especially in this world. It's really valuable.

[译文] [Kevin Weil]: 但这没关系。我想这就像艾森豪威尔的一句名言：“计划是无用的，但规划是主要/有用的（Planning is helpful）。”我完全赞同这一点，尤其是在这个领域。这真的很有价值。

[原文] [Kevin Weil]: If you think about quarterly road roadmapping for example, it's really valuable to have a moment where you stop and go, "Okay. What did we do? What worked? What went well? What didn't go well? What did we learn and now what do we think we're going to do next?"

[译文] [Kevin Weil]: 比如你考虑季度路线图规划，有一个时刻让你停下来思考：“好吧，我们做了什么？什么奏效了？什么进展顺利？什么不顺利？我们学到了什么？现在我们认为接下来要做什么？”这真的非常有价值。

[原文] [Kevin Weil]: We try and keep that really lightweight because it's not going to be right. We're going to throw it out halfway because we will have learned new things. So the moment of planning is helpful even if it's only partially.

[译文] [Kevin Weil]: 我们试图保持这种规划非常轻量级，因为它不会完全正确。我们可能会在半途把它抛弃，因为我们会学到新东西。所以规划的那个时刻是有帮助的，哪怕只是部分的。

[原文] [Kevin Weil]: We really do try and go very strongly bottoms up, subject to our overall directional alignment. We have great people. We have engineers and PMs and designers and researchers who are passionate about the products they're building and have strong opinions about them and are also the ones building them.

[译文] [Kevin Weil]: 我们确实试图非常强烈地坚持自下而上，但要服从于整体方向的一致性。我们有很棒的人才。我们有工程师、产品经理、设计师和研究员，他们对正在构建的产品充满热情，有强烈的观点，而且正是他们在亲手构建这些产品。

[原文] [Kevin Weil]: We are happy making mistakes. We make mistakes all the time. It's one of the things I really appreciate about Sam. He pushes us really hard to move fast, but he also understands that with moving fast comes, we didn't quite get this right or that we launched this thing, it didn't work. We'll roll it back.

[译文] [Kevin Weil]: 我们乐于犯错。我们一直在犯错。这是我非常欣赏 Sam 的一点。他非常用力地推动我们快速行动，但他也理解伴随快速行动而来的是：我们可能没完全做对，或者我们发布了这个东西但它没奏效。那我们就回滚。

[原文] [Kevin Weil]: Look at our naming. Our naming is horrible. It's absolutely atrocious and we know it, and we will get around to fixing it at some point, but it's not the most important thing and so we don't spend a lot of time on it.

[译文] [Kevin Weil]: 看看我们的命名。我们的命名糟透了。简直惨不忍睹，我们知道这一点，我们会在某个时候去修复它，但这并不是最重要的事情，所以我们没有在上面花很多时间。

[原文] [Kevin Weil]: So we have this philosophy, we call iterative deployment, and the idea is we're all learning about these models together. So there's a real sense in which it's way better to ship something even when you don't know the full set of capabilities and iterate together in public.

[译文] [Kevin Weil]: 所以我们有一种哲学，我们称之为“迭代部署（Iterative Deployment）”，其核心理念是我们都在共同学习这些模型。所以在某种真正意义上，即使你不知道全部功能，先把东西发布出去并在公众面前共同迭代要好得多。

[原文] [Kevin Weil]: I think the other thing that ends up being a part of our product philosophy is the sense of model maximalism. The models are not perfect. They're going to make mistakes. You could spend a lot of time building all kinds of different scaffolding around them.

[译文] [Kevin Weil]: 我认为构成我们产品哲学的另一部分是“模型极大主义（Model Maximalism）”的意识。模型并不完美。它们会犯错。你可以花很多时间在它们周围构建各种各样的“脚手架”（辅助支撑结构）。

[原文] [Kevin Weil]: And by the way, sometimes we do because sometimes there are kinds of errors that you just don't want to make, but we don't spend that much time building scaffolding around the parts that don't match that because our general mindset is in two months there's going to be a better model and it's going to blow away whatever the current set of limitations are.

[译文] [Kevin Weil]: 顺便说一句，有时我们确实会这样做，因为有些错误是你绝对不想犯的，但我们不会花太多时间为那些不符合这种情况的部分构建脚手架，因为我们的普遍心态是，再过两个月就会有一个更好的模型出现，它将彻底打破当前的各种限制。

[原文] [Kevin Weil]: So if you're building, and we say this to developers too, if you're building and the product that you're building is right on the edge of the capabilities of the models, keep going, because you're doing something right because you give it another couple months and the models are going to be great, and suddenly the product that you have that just barely worked is really going to sing.

[译文] [Kevin Weil]: 所以如果你正在构建产品，我们也对开发者这样说，如果你构建的产品恰好处于当前模型能力的边缘，请继续坚持，因为你做的事情是对的。再给它几个月，模型会变得很棒，突然之间，你那个原本勉强能用的产品就会真正大放异彩。

[原文] [Lenny]: I had the founder of Bolt on the podcast, StackBlitz is the company name, and he shared this story that they've been working on this product for seven years behind the scenes and it was failing. Nothing was happening.

[译文] [Lenny]: 我邀请过 Bolt 的创始人上播客，公司名叫 StackBlitz，他分享了这个故事：他们在幕后开发这个产品七年了，一直很失败，毫无起色。

[原文] [Lenny]: And then all of a sudden it was, sorry to mention a competitor, but Claude came out or a Sonnet 3.5 came out and all of a sudden everything worked and they've been building all this time and finally it worked.

[译文] [Lenny]: 然后突然之间——抱歉提到竞争对手——Claude 发布了，或者是 Sonnet 3.5 发布了，突然之间一切都能用了，他们一直在构建的东西终于成功了。

[原文] [Lenny]: And I hear that a lot with YC, just like things that never were possible now are just becoming possible every few months with the updates to the models.

[译文] [Lenny]: 我在 YC（Y Combinator）也经常听到这样的说法，就像那些以前不可能的事情，现在随着模型每隔几个月的更新，突然变得可能了。

章节 5：交互设计：模仿人类思考过程与对话界面的普适性

📝 本节摘要：

在本章节中，Kevin 分享了在构建 AI 产品时最反直觉的发现：设计 AI 交互的最佳方式是将其视为通过聊天与另一个人进行互动。他以 OpenAI 的推理模型（o1系列）和 Deep Research 为例，探讨了当模型需要“思考”20秒甚至25分钟时，用户界面（UI）应如何设计。他指出，好的设计既不是让 AI 像死机一样沉默，也不是像 DeepSeek 那样展示所有“喋喋不休”的原始思维链，而是像人类一样提供简短的进度摘要。此外，Kevin 强力为 Chat（对话界面） 辩护，认为它是与超级智能交互的“通用接口”，因为它模拟了人类最高带宽的沟通方式——即不受限制的自然语言交流。

[原文] [Lenny]: What would you say is the most counterintuitive thing that you've learned after building AI products or working at OpenAI, something that's just like, "I did not expect that?"

[译文] [Lenny]: 在构建 AI 产品或在 OpenAI 工作之后，你会说你学到的最反直觉的事情是什么？就是那种让你觉得“我真没想到会这样”的事情？

[原文] [Kevin Weil]: I don't know, maybe I should have expected this, but one of the things that's been funny for me is the extent to which you're trying to figure out how some product should work with AI, or even why some AI thing happens to be true, you can often reason about it the way you would reason about another human and it works.

[译文] [Kevin Weil]: 我不知道，也许我本该预料到这一点，但对我来说很有趣的一件事是，当你试图弄清楚某个产品应该如何与 AI 配合工作，甚至为什么某个 AI 现象是真实的，你通常可以像推断另一个人那样去推断它，而且这很管用。

[原文] [Kevin Weil]: So maybe a couple examples. When we were first launching our reasoning model, we were the first to build a model that could reason, that could, instead of giving you just a quick system one answer right away to every question you asked...

[译文] [Kevin Weil]: 举几个例子吧。当我们首次推出推理模型时，我们要构建的是第一个能够推理的模型，而不是对你问的每个问题都立即给出一个快速的“系统一”（直觉式）回答……

[原文] [Kevin Weil]: ...it was the third Emperor of the Holy Roman Empire, here's an answer. You could ask it hard questions and it would reason. The same way that if I asked you to do a crossword puzzle, you couldn't just snap fill in everything.

[译文] [Kevin Weil]: ……比如神圣罗马帝国的第三任皇帝是谁，马上给个答案。你可以问它很难的问题，它会进行推理。就像如果我让你做一个填字游戏，你不可能啪的一下就把所有空都填满。

[原文] [Kevin Weil]: You would be, "Well, okay. On this one across, I think it could be one of these two, but that means there's an A here. So that one has to be this, away, back track, step-by-step build up from where you are." Same way you answer any difficult logistical problem, any scientific problem.

[译文] [Kevin Weil]: 你会说：“好吧。在这个横向词条上，我觉得可能是这两个中的一个，但这意味这里有个 A。所以那个必须是这个，不对，回溯一下，从现在的线索一步步推导。”这和你解决任何困难的后勤问题或科学问题的方式是一样的。

[原文] [Kevin Weil]: So this reasoning breakthrough was big, but it was also the first time that a model needed to sit and think. And that's a weird paradigm for a consumer product. You don't normally have something where you might need to hang out for 25 seconds after you ask a question.

[译文] [Kevin Weil]: 所以这个推理突破是巨大的，但这也就是第一次模型需要坐下来思考。对于消费级产品来说，这是一种奇怪的范式。通常你不会遇到问完问题后还需要干等 25 秒的情况。

[原文] [Kevin Weil]: So we were trying to figure out what's the UI for this? With deep research where the model's going to go and think for 25 minutes sometimes, it's actually not that hard because you're not going to sit and watch it for 25 minutes. You're going to go do something else.

[译文] [Kevin Weil]: 所以我们试图搞清楚这种（思考过程的）UI 应该是什么样的？对于 Deep Research（深度研究）来说，模型有时会思考 25 分钟，这其实不难处理，因为你不会坐那儿盯着它看 25 分钟。你会去干点别的。

[原文] [Kevin Weil]: You're going to go to another tab or go get lunch or whatever, and then you'll come back and it's done when it's like 20, 25 seconds or 10 seconds, it's a long time to wait, but it's not long enough to go to do something else.

[译文] [Kevin Weil]: 你会切换到另一个标签页或者去吃午饭什么的，回来时它就完成了。但如果是 20秒、25 秒或 10 秒，这种等待时间很长，但又没长到足以让你去干别的事。

[原文] [Kevin Weil]: So you can think, if you asked me something that I needed to think for 20 seconds to answer, what would I do? I wouldn't just go mute and not say anything and shut down for 20 seconds and then come back. So we shouldn't do that. We shouldn't just have a slider sitting there. That's annoying.

[译文] [Kevin Weil]: 所以你可以想一下，如果你问我一个需要思考 20 秒才能回答的问题，我会做什么？我不会直接静音，什么也不说，关机 20 秒然后再回来。所以我们不该那样做。我们不该只是在那里放个进度条。那很烦人。

[原文] [Kevin Weil]: But I also wouldn't just start babbling every single thought that I had. So we probably shouldn't just expose the whole chain of thought as the model's thinking, but I might go like, "That's a good question. All right." I might approach it like that and then think.

[译文] [Kevin Weil]: 但我也不会开始喋喋不休地把我的每一个念头都说出来。所以我们大概也不应该把模型思考时的整个思维链（Chain of Thought）都暴露出来，但我可能会说：“这是个好问题。好的。”我可能会这样切入，然后开始思考。

[原文] [Kevin Weil]: You're maybe giving little updates and that's actually what we ended up shipping. You have similar things where you can find situations where you get better thinking sometimes out of a group of models that all try and attack the same problem, and then you have a model that's looking at all their outputs and integrating it and then giving you a single answer at the end.

[译文] [Kevin Weil]: 你可能会给出一些小的进度更新，这实际上就是我们最终发布的产品形态。有些类似的情况是，你会发现如果让一组模型尝试攻克同一个问题，有时会得到更好的思考结果，然后让一个模型查看所有的输出并进行整合，最后给你一个单一的答案。

[原文] [Kevin Weil]: I mean, sounds a little bit like brainstorming. I certainly have better ideas when I get in a room and brainstorm with other people because they think differently than me. So anyways, there's just all these situations where you can actually reason about it like a group of humans or an individual human and it works, which I don't know, maybe I shouldn't have been surprised but I was.

[译文] [Kevin Weil]: 我的意思是，这听起来有点像头脑风暴。当我在房间里和其他人一起头脑风暴时，我肯定会有更好的点子，因为他们的思维方式和我不同。总之，在所有这些情况下，你实际上可以像对待一群人或一个人那样去推断 AI，而且这很管用，我不知道，也许我不该感到惊讶，但我确实惊讶了。

[原文] [Lenny]: That is so interesting because when I see these models operate, I never even thought about you guys designing that experience. To me, it just feels like this is what the LLM does. It just sits there and tells me what it's thinking.

[译文] [Lenny]: 这太有意思了，因为当我看到这些模型运作时，我甚至从未想过是你们设计了这种体验。对我来说，感觉就像这就是 LLM 本身的行为。它只是坐在那里告诉我它在想什么。

[原文] [Lenny]: And I love this point you're making of let's make it feel like a human operating and well, how does a human operate? Well, they just talk aloud. They think, here's the thing I should explore.

[译文] [Lenny]: 我很喜欢你提出的这一点，即让它感觉像是一个人类在操作，那么人类是如何操作的呢？嗯，他们就是把想法说出来。他们思考，这是我应该探索的东西。

[原文] [Lenny]: And I love that deep sequence to the extreme of that where they're just like, "Here's everything I'm doing and thinking." And people actually like that too, I guess. Was that surprising to you, "Maybe that could work too. People seem to like everything?"

[译文] [Lenny]: 我也喜欢那种深层序列（Deep Sequence）做到极致的感觉，它们就像是说：“这是我正在做和思考的所有事情。”我猜人们其实也喜欢那样。那让你感到惊讶吗？比如说，“也许那样也行。人们似乎喜欢看所有细节？”

[原文] [Kevin Weil]: Yeah. We learned from that actually because when we first launched it, we gave you the subheadings of what the model was thinking about, but not much more. And then deep seek launched and it was a lot and we went, I don't know if everyone wants that.

[译文] [Kevin Weil]: 是的。实际上我们也从中吸取了教训，因为当我们第一次发布时，我们只给了你模型正在思考内容的子标题，没有更多了。然后 DeepSeek 发布了，它展示了很多内容，我们当时想，我不确定是不是每个人都想要那样。

[原文] [Kevin Weil]: There's some novelty effect to seeing what the model's really thinking about. We felt that too when we were looking at it internally. It's interesting to see the model's chain of thought, but it's not... I think at the scale of 400 million people, you don't want to see the model babble a bunch of things.

[译文] [Kevin Weil]: 看到模型真正在想什么有一种新奇效应。我们在内部观察时也有这种感觉。看模型的思维链很有趣，但它不是……我觉得在 4 亿用户的规模下，你不会想看到模型胡言乱语地扯一堆东西。

[原文] [Kevin Weil]: So what we ended up doing was summarizing it in interesting ways. So instead of just getting the subheadings, you're getting one or two sentences about how it's thinking about it and you can learn from that. So we tried to find a middle ground that we thought was an experience would be meaningful for most people, but showing everybody three paragraphs is probably not the right answer.

[译文] [Kevin Weil]: 所以我们最终做的是以有趣的方式对其进行总结。因此，你不仅仅是看到子标题，而是会看到一两句关于它如何思考的句子，你可以从中学习。所以我们试图找到一个中间地带，我们认为这对大多数人来说是一种有意义的体验，但向每个人展示三个段落可能不是正确的答案。

[原文] [Lenny]: This reminds me of something else you said at the summit that has really stuck with me, this idea that chat, people always make fun of chat is not the future interface for how we interact with AI, but you made this really interesting point that may argue the other side, which is, as humans we interface by talking and the IQ of a human can span from really low to really high and it all works talking to them and chat is the same thing and it can work on all kinds of intelligence levels.

[译文] [Lenny]: 这让我想起你在峰会上说的另一件事，让我印象深刻。就是关于聊天的观点，人们总是嘲笑说聊天（Chat）不是我们与 AI 互动的未来界面，但你提出了一个非常有趣的相反观点，即作为人类，我们通过交谈进行互动，而人类的智商可能跨度很大，从很低到很高，但通过交谈都能行得通，聊天也是一样，它适用于各种智力水平。

[原文] [Lenny]: Maybe I just shared it, but I guess anything there about just why chat actually ends up being such an interesting interface for LLMs?

[译文] [Lenny]: 也许我已经把话说完了，但我想问还有什么关于为什么聊天最终成为 LLM 如此有趣的界面的看法吗？

[原文] [Kevin Weil]: Yeah. I don't know, maybe this is one of those things I believe that most people don't believe, but I actually think chat is an amazing interface because it's so versatile. People tend to go, "Chat. Yeah. We'll figure out something better."

[译文] [Kevin Weil]: 是的。我不知道，也许这属于那种我相信但大多数人不相信的事情，但我实际上认为聊天是一个了不起的界面，因为它太通用了。人们倾向于说：“聊天啊。是啊。我们会想出更好的东西的。”

[原文] [Kevin Weil]: And I think it's incredibly universal because it is the way we talk. I can talk to you verbally like we're talking now. We can see each other and interact. We can talk on WhatsApp and be texting each other, but all of these things is this unstructured method of communication and that's how we operate.

[译文] [Kevin Weil]: 而我认为它是极其普适的，因为这就是我们交谈的方式。我可以像现在这样口头和你交谈。我们可以看到彼此并互动。我们可以在 WhatsApp 上交谈并互发短信，但所有这些都是这种非结构化的沟通方式，这就是我们的运作方式。

[原文] [Kevin Weil]: If I had some more rigid interface that I was allowed to use when we spoke, I would be able to speak to you about far fewer things and it would actually get in the way of us having maximum communication bandwidth. So there's something magical.

[译文] [Kevin Weil]: 如果我们在交谈时我只能使用某种更僵化的界面，我能和你谈论的事情就会少得多，而且这实际上会阻碍我们拥有最大的沟通带宽。所以这里面有一种魔力。

[原文] [Kevin Weil]: And by the way, in the past it never worked because there wasn't a model that was good at understanding all of the complexity and nuances of human speech, and that's the magic of LLMs. So to me, it's like an interface that's exactly fit to the power of these things.

[译文] [Kevin Weil]: 顺便说一句，过去这行不通是因为没有一个模型擅长理解人类语言的所有复杂性和细微差别，而这正是 LLM 的魔力所在。所以对我来说，这就像是一个恰好契合这些东西能力的界面。

[原文] [Kevin Weil]: And that doesn't mean that it always has to be just like I don't necessarily always want to type, but you do want that very open-ended, flexible communication medium, it may be that we're speaking and the model's speaking back to me, but you still want the very lowest common denominator, no restrictions way of interacting.

[译文] [Kevin Weil]: 这并不意味着它总是必须得是我不想打字的那种情况，但你确实想要那种非常开放、灵活的沟通媒介。也许是我们在说话，模型在回应我，但你仍然想要那种最低公分母（最基础通用）、无限制的互动方式。

[原文] [Lenny]: That is so interesting. That's really changed the way I think about this stuff is that point that chat is just so good for this very specific problem of talking to superintelligence basically.

[译文] [Lenny]: 这太有意思了。这真的改变了我对这些东西的看法，就是那个观点：聊天对于“与超级智能交谈”这个特定问题来说简直太棒了。

[原文] [Kevin Weil]: By the way, I think it's not that it's only chat either. If you have high volume use cases where they're more prescribed and you don't actually need the full generality, there are many use cases where it's better to have something that's less flexible, more prescribed, faster to specific task, and those are great too, and you can build all sorts of those.

[译文] [Kevin Weil]: 顺便说一句，我认为这并不意味着只有聊天。如果你有大量高频的用例，它们更具规定性，你实际上不需要完全的通用性，那么在很多用例中，拥有某种不那么灵活、更具规定性、针对特定任务更快的界面会更好，那些也很棒，你可以构建各种各样的此类界面。

[原文] [Kevin Weil]: But you still want chat as this baseline for anything that falls out of whatever vertical you happen to be building for. It's like a catch-all for every possible thing you'd ever want to express to a model.

[译文] [Kevin Weil]: 但你仍然希望将聊天作为基准，用于处理那些超出你正在构建的任何垂直领域范围的事情。它就像一个万能的兜底方案，能承载你想向模型表达的任何可能的事情。

章节 6：[插播] OneSchema 与企业数据集成

📝 本节摘要：

本章节为播客的中插赞助环节。Lenny 连线了数据导入平台 OneSchema 的创始人 Christina Gilbert。Christina 介绍了他们的新产品 OneSchema FileFeeds，旨在解决产品团队从 ERP 等复杂系统导入 CSV 数据的难题。她指出，通过该工具，企业无需动用工程团队，仅需 15 分钟即可通过 SFTP 完成集成，并利用内置的验证层（Validation Layer）确保数据准确性，从而避免因脏数据导致的系统故障和客户信任流失。

[原文] [Lenny]: I'm excited to chat with Christina Gilbert, the founder of OneSchema, one of our long-time podcast sponsors. Hi, Christina.

[译文] [Lenny]: 我很高兴能与 OneSchema 的创始人 Christina Gilbert 聊天，她是我们要闻已久的播客赞助商之一。嗨，Christina。

[原文] [Christina]: Yes. Thank you for having me on, Lenny.

[译文] [Christina]: 是的。谢谢你邀请我，Lenny。

[原文] [Lenny]: What is the latest with OneSchema? I know you now with some of my favorite companies like Ramp, Vanta, Scale and Watershed. I heard that you just launched a new product to help product teams import CSVs from especially tricky systems like ERPs?

[译文] [Lenny]: OneSchema 最近有什么新动态？我知道你们现在和我最喜欢的一些公司合作，比如 Ramp、Vanta、Scale 和 Watershed。我听说你们刚刚发布了一个新产品，帮助产品团队从 ERP（企业资源计划）等特别棘手的系统中导入 CSV 文件？

[原文] [Christina]: Yes. So we just launched OneSchema FileFeeds, which allows you to build an integration with any system in 15 minutes as long as you can export a CSV to an SFTP folder.

[译文] [Christina]: 是的。我们刚刚发布了 OneSchema FileFeeds，只要你能将 CSV 导出到 SFTP 文件夹，它就允许你在 15 分钟内构建与任何系统的集成。

[原文] [Christina]: We see our customers all the time getting stuck with hacks and workarounds, and the product teams that we work with don't have to turn down prospects because their systems are too hard to integrate with.

[译文] [Christina]: 我们总是看到客户陷入各种临时修补和变通方案的泥潭，而与我们合作的产品团队再也不必因为潜在客户的系统太难集成而拒绝他们了。

[原文] [Christina]: We allow our customers to offer thousands of integrations without involving their engineering team at all.

[译文] [Christina]: 我们允许客户提供数千种集成，而完全无需他们的工程团队介入。

[原文] [Lenny]: I can tell you that if my team had to build integrations like this, how nice would it be to be able to take this off my roadmap and instead, use something like OneSchema and not just to build it, but also to maintain it forever.

[译文] [Lenny]: 我可以告诉你，如果我的团队必须构建这样的集成，能把这个任务从我的路线图中移除，转而使用像 OneSchema 这样的工具，那是多么美好的一件事，而且不仅仅是构建它，还包括永久的维护工作。

[原文] [Christina]: Absolutely, Lenny. We've heard so many horror stories of multi-day outages from even just a handful of ad records. We are laser-focused on integration reliability to help teams end all of those distractions that come up with integrations.

[译文] [Christina]: 绝对是这样，Lenny。我们听过太多恐怖故事，仅仅因为几条广告记录就导致了多天的服务中断。我们专注于集成的可靠性，帮助团队终结所有因集成问题而产生的干扰。

[原文] [Christina]: We have a built-in validation layer that stops any bad data from entering your system, and OneSchema will notify your team immediately of any data that looks incorrect.

[译文] [Christina]: 我们有一个内置的验证层，可以阻止任何坏数据进入你的系统，如果有任何数据看起来不正确，OneSchema 会立即通知你的团队。

[原文] [Lenny]: I know that importing incorrect data can cause all kinds of pain for your customers, and quickly lose their trust. Christina, thank you for joining us. And if you want to learn more, head on over to oneschema.co. That's oneschema.co.

[译文] [Lenny]: 我知道导入错误的数据会给客户带来各种痛苦，并迅速失去他们的信任。Christina，谢谢你加入我们。如果大家想了解更多信息，请访问 oneschema.co。网址是 oneschema.co。

章节 7：组织架构：研究与产品的融合及产品经理的角色

📝 本节摘要：

在本章节中，Kevin 描述了 OpenAI 从纯粹的研究实验室向产品化公司的转型过程。他强调了打破部门壁垒的重要性，指出最好的产品源于研究、工程、产品和设计团队的深度融合（而非仅仅将研究成果通过 API 扔给产品部门）。Kevin 透露，尽管公司规模巨大，但产品经理（PM）团队被有意保持精简（约 25 人），旨在避免官僚主义并赋予“具有产品思维的工程师”更多自主权。他详细定义了 OpenAI 理想 PM 的画像：具备“高度能动性（High Agency）”，能在极端模糊的环境中从零定义问题，并通过决断力和影响力推动项目，而非依赖层级指令。

[原文] [Lenny]: I want to come back to that you talked about researchers and their relationship with product teams. I imagine a lot of innovation comes from researchers just like having an inkling and then building something amazing and then releasing it, and some ideas come from PMs and engineers.

[译文] [Lenny]: 我想回到你之前谈到的关于研究人员及其与产品团队关系的话题。我想象很多创新来自于研究人员，就像他们突然有个预感，然后构建出一些惊人的东西并发布出来，而有些想法则来自产品经理（PM）和工程师。

[原文] [Lenny]: How do those teams collaborate? Does every team have a PM? Is it a lot of research-led stuff? Give us a sense of just where ideas and products come from mostly.

[译文] [Lenny]: 这些团队是如何协作的？每个团队都有 PM 吗？很多东西是由研究主导的吗？给我们讲讲创意和产品主要来自哪里吧。

[原文] [Kevin Weil]: It's an area where we're evolving a lot. I'm really excited about it, frankly. I think if you go back a couple of years when ChatGPT was just getting started, obviously, I wasn't in OpenAI, but... We were more of a pure research company at the time.

[译文] [Kevin Weil]: 这其实是我们正在通过大幅演进的一个领域。坦率地说，我对此非常兴奋。如果你回溯到几年前 ChatGPT 刚起步时——显然那时我还不在 OpenAI——但那时我们更像是一家纯粹的研究公司。

[原文] [Kevin Weil]: Chat GPT, if you remember, was a low-key research preview.

[译文] [Kevin Weil]: 如果你还记得的话，ChatGPT 当时只是一个低调的研究预览版（Research Preview）。

[原文] [Lenny]: For many years.

[译文] [Lenny]: 好多年都是这样。

[原文] [Kevin Weil]: Yeah. It wasn't a thing that the team launched thinking it was going to be this massive product. And it was just a way that we were going to let people play with and iterate on the models.

[译文] [Kevin Weil]: 是的。团队发布它时并没有想着它会成为一个如此巨大的产品。它只是我们要让人们试玩并在模型上进行迭代的一种方式。

[原文] [Kevin Weil]: So we were primarily a research company, a world-class research company, and as ChatGPT has grown and as we've built our B-to-B products and our APIs and other things, now we're more of a product company than we were.

[译文] [Kevin Weil]: 所以我们主要是一家研究公司，一家世界级的研究公司，而随着 ChatGPT 的成长，以及我们要构建 B2B 产品和 API 等其他东西，现在我们比以前更像一家产品公司了。

[原文] [Kevin Weil]: I still think we can't... Open AI should never be a pure product company. We need to be both a world-class research company and a world-class product company, and the two need to really work together, and that's the thing that I think we've been getting much better at over the last six months.

[译文] [Kevin Weil]: 我仍然认为我们不能……OpenAI 永远不应该成为一家纯粹的产品公司。我们需要既是一家世界级的研究公司，又是一家世界级的产品公司，这两者需要真正地协同工作，这就是我认为我们在过去六个月里做得越来越好的地方。

[原文] [Kevin Weil]: If you treat those things separately and the researchers go do amazing things and build models and then they get to some state and then the product and engineering teams go take them and do something with them, we're effectively just an API consumer of our own models.

[译文] [Kevin Weil]: 如果你把这些事情分开处理，研究人员去做了惊人的事情、构建了模型，等到达到某种状态后，产品和工程团队再去接手做点什么，那我们实际上就成了自己模型的 API 消费者。

[原文] [Kevin Weil]: The best products though are going to be, it's like I was talking about with deep research, it's a lot of iterative feedback. It's understanding the products you're trying to sell or the problems you're trying to solve, building evals for them, using those evals to go gather data and fine-tune models to get them to be better at these use cases that you're looking to solve.

[译文] [Kevin Weil]: 然而最好的产品——就像我之前谈到的 Deep Research（深度研究）那样——需要大量的迭代反馈。它需要理解你试图销售的产品或解决的问题，为它们构建 Evals（评估测试），利用这些 Evals 去收集数据并微调模型，让它们在你试图解决的这些用例上表现得更好。

[原文] [Kevin Weil]: It's a huge amount of back and forth to do it well. And I think the best products are going to be ENG product design and research working together as a single team to build novel things.

[译文] [Kevin Weil]: 要做好这一点需要大量的来回打磨。所以我认为最好的产品将是由工程（ENG）、产品、设计和研究作为一个单一团队共同工作，构建出新颖的东西。

[原文] [Kevin Weil]: So that's actually how we're trying to operate with basically anything that we build. It's a new muscle for us because we're kind of new as a product company, but it's one that people are really excited about because we've seen every time we do it, we build something awesome, and so now every product starts like that.

[译文] [Kevin Weil]: 这实际上就是我们要尝试运作基本上所有构建项目的方式。这对我们来说是一种新的能力（Muscle），因为作为一家产品公司我们还算是个新手，但这让大家非常兴奋，因为我们看到每次这么做都能构建出很棒的东西，所以现在每个产品都是这样开始的。

[原文] [Lenny]: How many product managers do you have at Open AI? I don't know if you share that number, but if you do.

[译文] [Lenny]: 你们 OpenAI 有多少产品经理？我不知道你是否分享这个数字，如果可以的话。

[原文] [Kevin Weil]: Not that many, actually. I don't know, 25. Maybe it's a little more than that. My personal belief is that you want to be pretty PM light as an organization just in general.

[译文] [Kevin Weil]: 其实不多。我不确定，大概 25 个吧。也许比这稍微多一点。我个人的信念是，作为一个组织，总体上你应该保持“轻 PM（PM light）”的状态。

[原文] [Kevin Weil]: I say this with love because I am a PM, but too many PMs causes problems. We'll fill the world with decks and ideas versus execution.

[译文] [Kevin Weil]: 我这么说是出于善意，因为我自己就是个 PM，但太多的 PM 会导致问题。我们会用 PPT 和想法填满世界，而不是执行。

[原文] [Kevin Weil]: So I think it's a good thing when you have a PM that is working with maybe slightly too many engineers because it means they're not going to get in and micromanage. You're going to leave a lot of influence and responsibility with the engineers to make decisions.

[译文] [Kevin Weil]: 所以我认为，当一个 PM 配合“稍微偏多”数量的工程师工作时是件好事，因为这意味着他们没空去微观管理（Micromanage）。你会留给工程师很大的影响力和责任去做决策。

[原文] [Kevin Weil]: It means you want to have really product-focused engineers, which we're fortunate to have. We have an amazingly product focused, high agency engineering team.

[译文] [Kevin Weil]: 这意味着你需要真正关注产品的工程师，幸运的是我们拥有这样的团队。我们拥有一支极其关注产品、具有高度能动性的工程团队。

[原文] [Kevin Weil]: But when you have something like that, you have a team that feels super empowered, you have a PM that's trying to really understand the problems and gently guide the team a little bit but has too much going on to get too far into the details, and you end up being able to move really fast.

[译文] [Kevin Weil]: 当你有这样的配置时，你的团队会感到超级受权，你的 PM 会努力真正理解问题并温和地引导团队，但因为事情太多而无法陷入细节，最终结果就是你们能跑得非常快。

[原文] [Lenny]: I imagine being a PM at Open AI is a dream come true for a lot of people. At the same time, I imagine it's not a fit for a lot of people. There's researchers involved, very product minded engineers. What do you look for in the PMs that you hire there for folks that are like, "Maybe I shouldn't go work there. I shouldn't even think about that."

[译文] [Lenny]: 我想象在 OpenAI 做 PM 是很多人梦想成真的事情。但同时，我也觉得这并不适合很多人。这涉及研究人员，还有非常有产品头脑的工程师。你在招聘 PM 时看重什么？对于那些在想“也许我不该去那儿工作，我想都不该想”的人，你有什么说法？

[原文] [Kevin Weil]: I think, I've said this a few times, but high agency is something that we really look for, people that are not going to come in and wait for everyone else to allow them to do something, they're just going to see a problem and go do it. It's just a core part of how we work.

[译文] [Kevin Weil]: 我想，我说过好几次了，但“高度能动性（High Agency）”确实是我们非常看重的特质，那种进来后不会等着别人允许才做事，而是看到问题就去解决的人。这是我们工作方式的核心部分。

[原文] [Kevin Weil]: I think people that are happy with ambiguity, because there is a massive amount of ambiguity here, it is not the kind of place, and we have trouble sometimes with more junior PMs because of this, because it's just not the place where someone is going to come in and say, "Okay, here's the landscape, here's your area, I want you to go do this thing."

[译文] [Kevin Weil]: 我认为还有那些乐于面对模糊性的人，因为这里有大量的模糊性。这里不是那种——我们也因此在较初级的 PM 身上遇到过困难——这里不是那种有人会进来说：“好了，这是版图，这是你的领域，我要你去把这个东西做出来”的地方。

[原文] [Kevin Weil]: And that's what you want as an early career PM. I mean, no one here has time and the problems are too ill-formed and we're figuring them all out as we go. And so high agency, very comfortable with ambiguity, ready to come in and help execute and move really quickly. That's kind of our recipe.

[译文] [Kevin Weil]: 那是你作为职业生涯早期的 PM 才想要的东西。我的意思是，这里没人有时间，问题也都还没成型，我们都在边做边摸索。所以，高度能动性、对模糊性非常适应、准备好进来协助执行并快速行动。这大概就是我们的配方。

[原文] [Kevin Weil]: And I think also happy leading through influence because... I mean it's usual as a PM, people don't report to you, your team doesn't report to you, et cetera, but you also have the complexity of a research function, which is even more sort of self-directed and it's really important to build a good rapport with the research team. I think the EQ side of things is also super important for us.

[译文] [Kevin Weil]: 还有我认为要乐于通过影响力来领导，因为……通常作为 PM，没人向你汇报，你的团队不向你汇报，但在我们这里还增加了研究职能的复杂性，那是更加自我导向的，与研究团队建立良好的融洽关系非常重要。所以我认为情商（EQ）方面对我们来说也超级重要。

[原文] [Lenny]: I know at most companies, a PM comes in and they're just like, "Why do we need you?" And as a PM you have to earn trust and help people see the value, and I feel like at Open AI it's probably a very extreme version of that where they're like, "Why do we need this person? We have researchers, engineers, what are you going to do here?"

[译文] [Lenny]: 我知道在大多数公司，PM 进来时大家会想：“我们为什么需要你？”作为 PM 你必须赢得信任并帮助人们看到价值，我感觉在 OpenAI 这可能是一个极端版本，大家会想：“我们为什么需要这个人？我们有研究员、工程师，你来这儿干嘛？”

[原文] [Kevin Weil]: Yeah, I think people appreciate it done right, but you bring people along. I think one of the most important things a PM can do well is be decisive. So there's a real fine line.

[译文] [Kevin Weil]: 是的，我觉得如果做得好大家会很感激，但你要带着大家一起走。我认为 PM 能做好的最重要的事情之一就是果断。这里有一条非常微妙的界线。

[原文] [Kevin Weil]: It's kind of like, I don't love the PM as the CEO of the product illusion all the time, but just like Sam in his role would be making mistakes if he made every single decision in every meeting that he was in. And he would also be making mistakes if he made no decisions in any meetings that he was in, right?

[译文] [Kevin Weil]: 就像是……我并不总是喜欢“PM 是产品的 CEO”这种错觉，但就像 Sam 在他的角色上，如果他在参加的每个会议上都做每一个决定，那就是在犯错。而如果他在参加的任何会议上都不做决定，那也是在犯错，对吧？

[原文] [Kevin Weil]: It's understanding when to defer to your team and to let people innovate. And when there is a decision to be made that people either don't feel comfortable with or don't feel empowered to make, or a decision that has too many different disparate pros and cons that are spread out across a big group and someone needs to be decisive and make a call, it's a really important trait of a CEO.

[译文] [Kevin Weil]: 关键在于理解什么时候该听从团队的意见，让人去创新。而当有一个决定需要做，但大家对此感到不适或觉得没有权力去做，或者这个决定有太多不同的利弊分散在一个大群体中，需要有人果断拍板时，这就是 CEO 一个非常重要的特质。

[原文] [Kevin Weil]: It's something Sam does well, and it's also a really important trait of a PM kind of at a more microscopic level. So because there's so much ambiguity, it's not obvious what the answer is in a lot of cases, and so having a PM that can come in and...

[译文] [Kevin Weil]: 这是 Sam 做得很好的地方，这也是 PM 在更微观层面上一个非常重要的特质。因为有太多的模糊性，很多情况下答案并不明显，所以需要有一个 PM 能进来并且……

[原文] [Kevin Weil]: And by the way, this doesn't need to be a PM, I'm perfectly happy if it's anybody else, but I kind of look to the PM to say, if there's ambiguity and no one's making a call, you better make sure that we get a call made and we move forward.

[译文] [Kevin Weil]: 顺便说一句，这不一定非得是 PM，如果是其他人我也完全没意见，但我某种程度上指望 PM 能做到：如果存在模糊性且没人做决定，你最好确保我们做出决定并继续前进。

章节 8：未来工作流：Vibe Coding、微调与模型组合

📝 本节摘要：

在本章节中，Kevin 探讨了 AI 如何重塑产品开发流程。他坦言即便在 OpenAI 内部，工作流的变革仍不够彻底，并大力推崇 “Vibe Coding”（凭感觉编程）——即利用 AI 工具（如 Windsurf 或 Cursor）快速构建原型而非依赖传统的 Figma 设计稿。随后，他预测未来的产品团队将普遍配备研究人员以进行模型微调（Fine-tuning）。最后，他详细解释了“模型组合（Model Ensembles）”的概念：将复杂问题拆解，根据成本和能力（如推理能力或响应速度）调用不同的模型协同工作，这正如一家公司是由具有不同技能的员工组成的“集成系统”一样。

[原文] [Lenny]: This touches on a few posts I've done of just, where is AI going to take over work that we do versus help us with various work? So let me come at this question from a different direction of just how AI impacts product teams and hiring, things like that.

[译文] [Lenny]: 这涉及到了我之前写过的几篇文章，关于 AI 究竟是会接管我们的工作，还是会协助我们完成各种工作？所以让我从另一个角度来切入这个问题，也就是 AI 如何影响产品团队和招聘之类的事情。

[原文] [Lenny]: So first of all, there's all this talk of LM's doing our coding for us, and 90% of code is going to be written by AI in a year. Dario at Anthropic said that. At the same time, you guys are all hiring engineers like crazy, PM's like crazy. Every function is dead, but you're still hiring every single one.

[译文] [Lenny]: 首先，大家都说 LLM（大语言模型）会替我们写代码，一年内 90% 的代码都将由 AI 编写。Anthropic 的 Dario 就这么说过。但与此同时，你们却在疯狂地招聘工程师，疯狂地招聘产品经理。明明说每个职能都要“死”了，你们却还在招聘每一个岗位。

[原文] [Lenny]: I guess just, first of all, let me just ask this, how do you and the team, say engineers, PMs, use AI in your work? Is there anything that's really interesting or things that you think people are sleeping on in how you use AI in your day-to-day work?

[译文] [Lenny]: 我想首先问一下，你和团队，比如工程师、PM，在工作中是如何使用 AI 的？有没有什么特别有趣的地方，或者你认为人们忽略了的、关于你们日常使用 AI 的方式？

[原文] [Kevin Weil]: We use it a lot. I mean, every one of us is in Chat GPT all the time summarizing docs, using it to help write docs with GPTs that write product specs and things like that, all the stuff that you would imagine.

[译文] [Kevin Weil]: 我们用得非常多。我的意思是，我们每个人都一直在用 ChatGPT 总结文档，利用那些专门写产品说明书的 GPTs 来辅助编写文档等等，所有你能想象到的用法都有。

[原文] [Kevin Weil]: I mean talk about writing evals, you can actually use models to help you write evals and they're pretty good at it. That all said, I'm still sort of disappointed by us, and I really mean me, in, if I were to just teleport my five-year-old self leading product at some other company into my day job, I would recognize it still.

[译文] [Kevin Weil]: 比如谈到编写 Evals（评估测试），你实际上可以用模型来帮你写 Evals，而且它们写得相当不错。虽说如此，我对自己，真的就是对我自己，还是感到有点失望。如果我把五年前在其他公司领导产品的自己传送到现在的日常工作中，我仍然能认出这就是原来的工作模式。

[原文] [Kevin Weil]: And I think we should be in a world, certainly a year from now, probably even more now, where I almost wouldn't recognize it because the workflows are so different and I'm using AI so heavily, and I'd still recognize it today. So I think in some sense, I'm not doing a good enough job of that.

[译文] [Kevin Weil]: 而我认为我们应该处于这样一个世界——当然一年后肯定是这样，甚至现在也该更多如此——就是我几乎认不出原来的工作模式，因为工作流已经截然不同，而且我极度依赖 AI。但今天我还是能认出来。所以从某种意义上说，我觉得我在这一点上做得还不够好。

[原文] [Kevin Weil]: Just to give an example, why shouldn't we be vibe coding demos right, left and center? Instead of showing stuff in Figma, we should be showing prototypes that people are vibe coding over the course of 30 minutes to illustrate proofs of concept and to explore ideas. That's totally possible today, and we're not doing it enough.

[译文] [Kevin Weil]: 举个例子，为什么我们不应该在大力推广“Vibe Coding（凭感觉编程）”演示呢？与其在 Figma 里展示东西，我们应该展示人们在 30 分钟内通过 Vibe Coding 做出的原型，以此来演示概念验证（PoC）并探索想法。这在今天完全是可能的，但我们做得还不够。

[原文] [Kevin Weil]: Actually, our chief people officer, Julia, was telling me the other day, she vibe coded an internal tool that she had at a previous job that she really wanted to have here at Open AI and she opened, I don't know, Windsurf or something, and vibe coded it. How cool is that?

[译文] [Kevin Weil]: 实际上，我们的首席人力官 Julia 前几天告诉我，她 Vibe Code 了一个内部工具。那是她在前一份工作时用过的，她很想在 OpenAI 也用上，于是她打开了——不知道是 Windsurf 还是什么软件——然后把它 Vibe Code 出来了。这多酷啊？

[原文] [Kevin Weil]: And if our chief people officer is doing it, we have no excuse to not be doing it more.

[译文] [Kevin Weil]: 如果连我们的首席人力官都在做这件事，我们就没有借口不更多地去做了。

[原文] [Lenny]: That's an awesome story. And some people may not have heard this term vibe coding. Can you describe what that means?

[译文] [Lenny]: 这个故事太棒了。有些人可能没听过“Vibe Coding”这个词。你能描述一下这是什么意思吗？

[原文] [Kevin Weil]: Yeah, I think this was Andrej's term.

[译文] [Kevin Weil]: 是的，我想这是 Andrej 的术语。

[原文] [Lenny]: Karpathy. Yeah.

[译文] [Lenny]: Karpathy。是的。

[原文] [Kevin Weil]: Andrej Karpathy. Yeah. So you have these tools like Cursor and Windsurf and GitHub Copilot that are very good at suggesting what code you might want to write. So you can give them a prompt and they'll write code and then as you go to edit it, it's suggesting what you might want to do.

[译文] [Kevin Weil]: Andrej Karpathy。是的。现在有像 Cursor、Windsurf 和 GitHub Copilot 这样的工具，它们非常擅长建议你可能想写的代码。你可以给它们一个提示词，它们就会写代码，然后当你去编辑时，它会建议你可能想做什么。

[原文] [Kevin Weil]: And the way that everyone started using that stuff was, give it a prompt, have it do stuff, you go edit it, give it a prompt, and you're kind of really going back and forth with the model the whole time. As the models are getting better and as people are getting more used to it, you can kind of just let go of the wheel a little bit.

[译文] [Kevin Weil]: 大家开始使用这些东西的方式通常是：给个提示，让它干活，你去编辑，再给个提示，你基本上全程在和模型来回拉扯。随着模型变得越来越好，以及人们越来越习惯，你可以稍微“松开方向盘”了。

[原文] [Kevin Weil]: And when the model's suggesting stuff, it's just like, tap, tap, tap, tap, tap. Keep going. Yes, yes, yes, yes, yes.

[译文] [Kevin Weil]: 当模型提出建议时，你就只是：点、点、点、点、点。继续。对、对、对、对、对。

[原文] [Kevin Weil]: And of course the model makes mistakes or it does something that doesn't compile, but when it doesn't compile, you paste the error in and you say, go, go, go, go, go. And then you test it out and it does one thing that you don't want it to do, so you enter in an instruction and say, go, go, go, go, go, and you just let the model do its thing.

[译文] [Kevin Weil]: 当然模型会犯错或者写出无法编译的东西，但当它无法编译时，你把错误信息粘贴进去，然后说：继续、继续、继续。然后你测试一下，发现它做了一件你不想要的事，于是你输入一条指令说：继续、继续、继续，你就只是让模型自己去搞定。

[原文] [Kevin Weil]: And it's not that you would do that for production code that needed to be super tight today yet, but for so many things, you're trying to get to a proof of concept, you're getting to a demo and you can really take your hands off the wheel and the model will do an amazing job, and that's vibe coding.

[译文] [Kevin Weil]: 并不是说你今天就会对那种要求非常严谨的生产级代码这样做，但对于很多事情，你只是想做一个概念验证，做一个演示，你真的可以把手从方向盘上拿开，模型会做得非常棒，这就是 Vibe Coding。

[原文] [Lenny]: That's an awesome explanation. I think the pro version of that, which is, I think, the way Andre even described it as you talk, there's a step like whisper or super whisper or something like that where you're talking to the model, not even typing.

[译文] [Lenny]: 解释得太棒了。我认为还有一个专业版，我想 Andre 也是这么描述的，就是你直接说话，有一个类似 Whisper 或 Super Whisper 的步骤，你是在对模型说话，甚至都不用打字。

[原文] [Kevin Weil]: Yeah, totally.

[译文] [Kevin Weil]: 是的，完全是这样。

[原文] [Lenny]: Oh man. So let me just ask, I guess, when you look at product teams in the future, you talked about how you guys should be doing this more, instead of designs, having prototypes, what do you think might be the biggest changes in how product teams are structured or built? Where do you think things are going in the next few years?

[译文] [Lenny]: 天哪。那我问一下，当你展望未来的产品团队时，你谈到你们应该更多地这样做，用原型代替设计稿，你认为产品团队的结构或组建方式最大的变化会是什么？你认为未来几年事情会往哪个方向发展？

[原文] [Kevin Weil]: I think you're definitely going to live in a world where you have researchers built into every product team. And I don't even mean just at foundation model companies because I think the future... Actually, frankly one thing that I'm sort of surprised about about our industry in general is that there's not a greater use of fine-tuned models.

[译文] [Kevin Weil]: 我认为肯定会有这样一个世界：每个产品团队里都嵌入了研究人员。我指的不仅仅是基础模型公司，因为我认为未来……实际上，坦率地说，我对咱们这个行业普遍感到惊讶的一点是，微调模型（Fine-tuned models）的使用并没有那么广泛。

[原文] [Kevin Weil]: A lot of people... These models are very good, so our API does a lot of things really well, but when you have particular use cases, you can always make the model perform better on a particular use case by fine-tuning it. It's probably just a matter of time.

[译文] [Kevin Weil]: 很多人……这些模型已经很好了，我们的 API 能把很多事情做得很好，但当你遇到特定用例时，你总是可以通过微调来让模型在该用例上表现得更好。这可能只是时间问题。

[原文] [Kevin Weil]: Folks aren't quite comfortable yet with doing that in every case. But to me, there's no question that that's the future. Models are going to be everywhere just like transistors are everywhere, AI is going to be just a part of the fabric of everything we do, but I think there are going to be a lot of fine-tuned models because why would you not want to more specifically customize a model against a particular use case?

[译文] [Kevin Weil]: 大家目前还不太习惯在每个案例中都这么做。但在我看来，毫无疑问这就是未来。模型将无处不在，就像晶体管无处不在一样，AI 将成为我们所做一切的基础结构的一部分。但我认为会有大量的微调模型，因为你为什么不想要针对特定用例去更专门地定制一个模型呢？

[原文] [Kevin Weil]: And so I think you're going to want sort of quasi researcher machine learning engineer types as part of pretty much every team because fine-tuning a model is just going to be part of the core workflow for building most products. So that's one change that maybe you're starting to see at foundation model companies that will propagate out to more teams over time.

[译文] [Kevin Weil]: 所以我认为你会需要那种“准研究员”性质的机器学习工程师作为几乎每个团队的一部分，因为微调模型将成为构建大多数产品的核心工作流之一。所以这是一个变化，也许你现在在基础模型公司开始看到了，随着时间的推移，它会扩展到更多的团队。

[原文] [Lenny]: I'm curious if there's a concrete example that makes that real, and I'll share one that comes to mind as you talk, which is, when you look at Cursor and Windsurf, something I learned from those founders is that they use a Sonnet, but then they also have a bunch of custom models that help along the edges that make the specific experience that's not just generating code even better like auto-complete and looking ahead to where things are going. So is that one or any other examples of which you... What is a fine-tuned model? Do you think teams will be building with these researchers on their teams?

[译文] [Lenny]: 我很好奇有没有具体的例子能让这更真实一点？我说一个我想到的例子，当你观察 Cursor 和 Windsurf 时，我从那些创始人那里了解到，他们使用的是 Sonnet，但他们也有一堆自定义模型在边缘辅助，使特定的体验不仅仅是生成代码，而是更好，比如自动补全和预测接下来的内容。这是一个例子吗？或者还有其他例子吗……什么是微调模型？你认为团队会带着这些研究人员一起构建吗？

[原文] [Kevin Weil]: Yeah. I mean, so when you're a model, you're basically giving the model a bunch of examples of the kinds of things you want it to be better at. So it's, "Here's a problem, here's a good answer. Here's a problem, here's a good answer," Or, "Here's a question, here's a good answer times a thousand or 10,000."

[译文] [Kevin Weil]: 是的。我的意思是，微调模型基本上就是给模型一堆示例，告诉它你希望它在哪些方面做得更好。比如，“这是一个问题，这是一个好的回答。这是一个问题，这是一个好的回答。”或者是，“这是一个问题，这是一个好的回答”，重复一千次或一万次。

[原文] [Kevin Weil]: And suddenly you're teaching the model to be much better than it was out of the gate at that particular thing. We use it everywhere internally. We use ensembles of models much more internally than people might think. So it's not, "I have 10 different problems. I'll just ask baseline GPT four oh about a bunch of these things."

[译文] [Kevin Weil]: 突然之间，你就教会了模型在那个特定事情上比它出厂时做得好得多。我们在内部到处都在用这个。我们在内部使用“模型组合（Ensembles of models）”的频率比人们想象的要高得多。所以并不是说“我有 10 个不同的问题，我就直接问基准版的 GPT-4o 这一堆事。”

[原文] [Kevin Weil]: If we have 10 different problems, we might solve them using 20 different model calls, some of which are using specialized fine-tuned models, they're using models of different sizes because maybe you have different latency requirements or cost requirements for different questions. They are probably using custom prompts for each one.

[译文] [Kevin Weil]: 如果我们有 10 个不同的问题，我们可能会用 20 次不同的模型调用去解决它们，其中一些使用的是专门的微调模型，或者使用不同大小的模型，因为不同的问题可能有不同的延迟要求或成本要求。它们可能每一个都使用了自定义的提示词。

[原文] [Kevin Weil]: Basically you want to teach the model to be really good at... You want to break the problem down into more specific tasks versus some broader set of high level tasks. And then you can use models very specifically to get very good at each individual thing. And then you have an ensemble that tackles the whole thing.

[译文] [Kevin Weil]: 基本上你是想教模型真正擅长……你是想把问题拆解成更具体的任务，而不是一堆宽泛的高层级任务。然后你可以非常有针对性地使用模型，让它们在每一件具体事情上都做得非常好。然后你就拥有了一个能解决整体问题的组合。

[原文] [Kevin Weil]: I think a lot of good companies are doing that today. I still see a lot of companies giving the model single, generic, broad problems versus breaking the problem down, and I think there will be more breaking the problem down using specific models for specific things, including fine tuning.

[译文] [Kevin Weil]: 我认为今天很多好的公司都在这么做。但我仍然看到很多公司给模型扔去单一、通用、宽泛的问题，而不是拆解问题。我认为未来会有更多拆解问题的做法，用特定的模型解决特定的事情，包括使用微调。

[原文] [Lenny]: And so in your case, because this is really interesting, is that you're using different levels of Chat GPT, like a 1 0 3 and stuff that's earlier because it's cheaper.

[译文] [Lenny]: 那么在你们的案例中，这很有趣，你们在使用不同级别的 ChatGPT，比如 1、o3 以及一些早期的版本，因为更便宜？

[原文] [Kevin Weil]: There'll be parts of our internal stack. I'll give you an example. Customer support, with 400 plus million weekly active users, we get a lot of inbound tickets. I don't know how many customer support folks we have, but it's not very many, 30, 40, I'm not sure, way smaller than you would have at any comparable company, and it's because we've automated a lot of our flows.

[译文] [Kevin Weil]: 我们内部技术栈的部分环节是这样。我给你举个例子。客户支持（Customer Support），我们有 4 亿多周活跃用户，会收到大量的工单。我不知道我们有多少客服人员，但非常少，大概 30 或 40 人吧，我不确定，但这比任何同等规模的公司都要少得多，这是因为我们自动化了很多流程。

[原文] [Kevin Weil]: We've got most questions using our internal resources, knowledge base, guidelines for how we answer questions, what kind of personality, et cetera. You can teach the model those things and then have it do a lot of its answers automatically, or where it doesn't have the full confidence to answer a particular question, it can still suggest an answer, request a human to look at it and then that human's answer actually is its own sort of fine tuning data for the model. You're telling it the right answer in a particular case.

[译文] [Kevin Weil]: 我们针对大多数问题利用内部资源、知识库、回答准则、个性设定等进行处理。你可以教模型这些东西，然后让它自动回答很多问题；或者当它没有十足把握回答某个特定问题时，它仍然可以建议一个答案，请求人类查看，而那个人类的回答实际上本身就是给模型的一份微调数据。你在告诉它在这个特定情况下的正确答案。

[原文] [Kevin Weil]: We're using... At various places. Some of these places, you want a little bit more reasoning, is not super latency sensitive, so you want a little more reasoning, and we'll use one of our O series models. In other places, you want a quick check on something and so you're fine to use four oh mini, which is super fast and super cheap.

[译文] [Kevin Weil]: 我们在不同地方使用……在其中一些地方，你想要多一点推理能力，而且对延迟不那么敏感，所以我们就会用我们的 O 系列模型之一。在其他地方，你只是想快速检查某些东西，所以用 4o-mini 就很好，它超级快且超级便宜。

[原文] [Kevin Weil]: In general, it's like specific models for specific purposes and then you ensemble them together to solve problems. By the way, again, not unlike how we as humans solve problems, a company is arguably an ensemble of models that have all been fine tuned based on what we studied in college and what we have learned over the course of our careers.

[译文] [Kevin Weil]: 总的来说，就是特定模型用于特定目的，然后将它们组合在一起解决问题。顺便说一句，这依然和我们人类解决问题的方式没什么不同，一家公司可以说就是一个“模型组合”，这些模型（员工）都是基于我们在大学学到的东西以及职业生涯中学到的东西进行微调过的。

[原文] [Kevin Weil]: We've all been fine tuned to have different sets of skills and you group them together in different configurations and the output of the ensemble is much better than the output of any one individual.

[译文] [Kevin Weil]: 我们都被微调成了拥有不同技能集的人，你把大家以不同的配置组合在一起，这个组合的产出要比任何一个个体的产出好得多。

[原文] [Lenny]: Kevin, you're blowing my mind. That sounds exactly correct. And also, different people, you pay them less, they cost less to talk to, some people take a long time to answer, some people hallucinating. This is... I'm telling you. This is a mental model but really does work in thinking...

[译文] [Lenny]: Kevin，你让我大受震撼。这听起来完全正确。而且，不同的人，你付给他们较少的钱，和他们交谈的成本较低；有些人回答问题要花很长时间，有些人会产生幻觉。这……我跟你说，这虽然是一个心智模型，但在思考方面真的管用……

[原文] [Lenny]: Oh, right. Yeah. This is great. Some people are visual, they want to dry out their thinking, some people want to talk word cell. Wow, this is a really good metaphor. So again, coming back to your advice here because I love that we circled back to it, you're finding a really good way to think about how to design great AI experiences and LMs, I guess, specifically is think about how a person would do this.

[译文] [Lenny]: 哦，对了。是的。这太棒了。有些人是视觉型的，他们想把思路画出来；有些人喜欢文字交流。哇，这真是一个绝佳的比喻。所以再次回到你的建议，我很喜欢我们又绕回来了，你找到了一个思考如何设计出色的 AI 体验和 LLM 产品的绝佳方式，我想具体来说就是：思考一个人会怎么做这件事。

[原文] [Kevin Weil]: Well, it's maybe not always the answer is to think about how a person would do it, but sometimes to gain intuition for how you might solve a problem, you think about what an equivalent human would do in those situations and use that to at least gain a different perspective on the problem.

[译文] [Kevin Weil]: 嗯，也许并不总是要思考一个人会怎么做，但有时为了获得如何解决问题的直觉，你可以思考一个对等的人类在那些情况下会做什么，并利用这一点至少获得看待问题的不同视角。

章节 9：面向未来：教育、育儿与个性化辅导

📝 本节摘要：

随着对话转向未来，Lenny 抛出了一个许多家长关心的问题：在 AI 时代，孩子应该学习什么？甚至有听众幽默地担心孩子未来是否需要去竞争“顶级管道工”的职位。Kevin 分享了他的育儿观，强调虽然编程技能依然重要，但培养好奇心（Curiosity）、独立性（Independence）和自信心（Self-confidence）等底层能力才是应对不确定未来的关键。

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随后，话题深入到 AI 在教育领域的应用。Kevin 认为，个性化辅导（Personalized Tutoring）可能是 AI除了加速基础科学研究外最重要的贡献。他指出，研究早已证明个性化辅导能带来“多个标准差”的学习效果提升（即著名的 Bloom's 2 Sigma Problem），并对目前尚未出现一个服务于全球 20 亿儿童的通用 AI 辅导产品感到惊讶，认为这是技术改变世界的巨大机会。

[原文] [Lenny]: Okay, so speaking of humans, I want to chat about the future a little bit. So you have three kids, and a community member asked me this hilarious question that I think it's something a lot of people are thinking about.

[译文] [Lenny]: 好了，说到人类，我想稍微聊聊未来。你有三个孩子，一个社区成员问了我一个很搞笑的问题，我觉得这其实是很多人都在思考的问题。

[原文] [Lenny]: So this is Patrick [inaudible]. I worked with him at Airbnb. He says ask what he's encouraging his kids to learn to prepare for the future. I'm worried my 6-year-old by the year 2036 will face a lot of competition trying to get into the top roofing or plumbing programs and need a backup plan.

[译文] [Lenny]: 这是 Patrick 问的，我曾在 Airbnb 和他共事过。他说：问问 Kevin 他鼓励孩子们学什么来为未来做准备？我担心我 6 岁的孩子到了 2036 年，在申请顶级的屋顶维修或管道工课程时会面临激烈的竞争，所以需要一个备选计划。

[原文] [Kevin Weil]: That's funny. So our kids, we have a 10 year old and eight year old twins, so they're still pretty young. It's amazing how AI native they are.

[译文] [Kevin Weil]: 这很有趣。我们的孩子，我们有一个 10 岁的孩子和一对 8 岁的双胞胎，所以他们还很小。令人惊奇的是，他们是彻底的 AI 原住民。

[原文] [Kevin Weil]: It's completely normal to them that there are self-driving cars. That they can talk to AI all day long. They have full conversations with Chat GPT and Alexa and everything else.

[译文] [Kevin Weil]: 对他们来说，这完全是正常的：有自动驾驶汽车，或者可以整天和 AI 说话。他们会和 ChatGPT、Alexa 以及其他所有东西进行完整的对话。

[原文] [Kevin Weil]: I don't know, who knows what the future holds? I think things like coding skills are going to be relevant for a long time, who knows?

[译文] [Kevin Weil]: 我不知道，谁知道未来会怎样？我认为像编程这样的技能在很长一段时间内仍将是相关的，谁知道呢？

[原文] [Kevin Weil]: But I think if you teach your kids to be curious, to be independent, to be self-confident, you teach them how to think, I don't know what the future holds, but I think that those are going to be skills that are going to be important in any configuration of the future.

[译文] [Kevin Weil]: 但我认为，如果你教你的孩子保持好奇心、独立和自信，你教他们如何思考，虽然我不知道未来会怎样，但我认为这些技能在未来的任何形态中都将是重要的。

[原文] [Kevin Weil]: And so it's not like we have all the answers, but that's how Elizabeth and I think about our kids.

[译文] [Kevin Weil]: 所以并不是说我们要有一切答案，但这正是我和 Elizabeth 对待孩子教育的思路。

[原文] [Lenny]: And do you find that AI... There's a lot of talk about AI tutoring. Is that something you guys are doing?

[译文] [Lenny]: 那么你觉得 AI……现在有很多关于 AI 辅导（AI Tutoring）的讨论。这是你们正在做的事情吗？

[原文] [Lenny]: I know they're using Chat GPT, I love all the photos you post where they're playing with prompts and stuff, but I guess is there anything there you're experimenting with or you think is going to become really important?

[译文] [Lenny]: 我知道他们在用 ChatGPT，我很喜欢你发的那些他们玩提示词之类的照片，但我想问的是，这方面有什么你们正在尝试的，或者你认为将会变得非常重要的东西吗？

[原文] [Kevin Weil]: This is something that... It's maybe the most important thing that AI could do. Maybe that's a grand statement. There are lots of important things that AI can do, including speeding up the pace of fundamental science research and discovery, which maybe is actually the most important thing AI can do.

[译文] [Kevin Weil]: 这是……这可能是 AI 能做的最重要的事情。也许这话有点大。AI 能做很多重要的事情，包括加快基础科学研究和发现的步伐，这可能实际上才是 AI 能做的最重要的事情。

[原文] [Kevin Weil]: But one of the most important things would be personalized tutoring. And it kind of blows my mind that there is still... I know there are a bunch of good products out there. Khan Academy does great things. They're a wonderful partner of ours.

[译文] [Kevin Weil]: 但最重要的事情之一绝对是个性化辅导。让我感到不可思议的是，目前依然……我知道市面上有一堆不错的产品。可汗学院（Khan Academy）做得很好。他们是我们很棒的合作伙伴。

[原文] [Kevin Weil]: Vinod Khosla has a non-profit that's doing some really interesting stuff in this space and is making an impact. But I'm kind of surprised that there isn't a 2 billion kid AI personalized tutoring thing because the models are good enough to do it now.

[译文] [Kevin Weil]: Vinod Khosla 有一个非营利组织正在这个领域做一些非常有趣的事情，并且正在产生影响。但我有点惊讶的是，居然还没有一个面向 20 亿儿童的 AI 个性化辅导产品，因为现在的模型能力已经足够做到了。

[原文] [Kevin Weil]: And every study out there that's ever been done seems to show that when you have... Like, education is still important, but when you combine that with personalized tutoring, you get multiple standard deviation improvements in learning speed.

[译文] [Kevin Weil]: 而且每一项已有的研究似乎都表明，当你拥有……比如，教育固然重要，但当你将其与个性化辅导结合时，你会获得多个标准差的学习速度提升。

[原文] [Kevin Weil]: And so it's uncontroversial, it's good for kids, it's free. Chat GPT is free, you don't need to pay, and the models are good enough.

[译文] [Kevin Weil]: 所以这是无可争议的，这对孩子有好处，而且它是免费的。ChatGPT 是免费的，你不需要付费，而且模型已经足够好了。

[原文] [Kevin Weil]: It still just kind of blows my mind that there isn't something amazing out there that our kids are using and your future kids are using, and people in all sorts of places around the world that aren't as lucky as our kids to be able to have this sort of built-in, solid education.

[译文] [Kevin Weil]: 让我感到震撼的是，居然还没有某种惊艳的产品能让我们的孩子、你们未来的孩子以及世界各地那些不像我们孩子这么幸运、能拥有这种现成的优质教育资源的人们使用。

[原文] [Kevin Weil]: Again, Chat GPT is free. People have Android devices everywhere. I really just think this could change the world and I'm surprised it doesn't exist and I want it to exist.

[译文] [Kevin Weil]: 再说一次，ChatGPT 是免费的。人们到处都有 Android 设备。我真的认为这可以改变世界，我很惊讶它还不存在，而且我希望它能存在。

章节 10：技术乐观主义：创造力的爆发与模型演进规律

📝 本节摘要：

本章节聚焦于技术乐观主义与创造力的未来。面对 AI 取代工作的担忧，Kevin 坚定地表达了乐观态度，认为技术长期来看总是推动社会进步的力量。他以视频生成模型 Sora 为例，讲述了一位知名导演如何利用 AI 进行低成本的创意迭代（生成50种分镜变体），而非直接替代最终制作。此外，Kevin 强调了模型演进的惊人速度——智力提升、成本降低（两年下降100倍）与安全性增强正同时发生。最后，他再次重申了本期访谈的核心金句：“你今天使用的 AI 模型将是你余生中用过的最差的模型”，鼓励开发者着眼于未来能力的飞跃。

[原文] [Lenny]: This kind of touches on something I want to spend a little time on, which is a lot of people also worry a lot about AI, where it's going, they worry about jobs it's going to take, they worry about the super intelligence squashing humanity in the future. What's your perspective on that and just the optimistic case that I think people need to hear?

[译文] [Lenny]: 这稍微涉及到了我想花点时间讨论的一点，就是很多人也非常担心 AI，担心它的走向，担心它会抢走工作，担心未来超级智能会碾压人类。你对此有什么看法？特别是我想大家需要听听乐观的一面。

[原文] [Kevin Weil]: I mean, I'm a big technology optimist. I think if you look over the last 200 years, maybe more, technology has driven a lot of the advancements that have made us the world and the society that we are today.

[译文] [Kevin Weil]: 我是一个坚定的技术乐观主义者。我认为如果你回顾过去 200 年，甚至更久，技术推动了许多进步，正是这些进步造就了我们要今的世界和社会。

[原文] [Kevin Weil]: It drives economic advancements, it drives geopolitical advancements, quality of life, longevity advancement. I mean, technology's at the root of just about everything, so I think there are very few examples where this is anything but a great thing over the longer term.

[译文] [Kevin Weil]: 它推动了经济进步、地缘政治进步、生活质量和寿命的提升。我的意思是，技术几乎是一切的根本，所以我认为从长远来看，很少有例子能证明它不是一件好事。

[原文] [Kevin Weil]: That doesn't mean that there aren't temporary dislocations or where there aren't individuals that are impacted, and that matters too. So it can't just be that the average is good. You've got to also think about how you take care of each individual person as best you can.

[译文] [Kevin Weil]: 这并不意味着不存在暂时的错位（Dislocations）或者没有个体受到影响，这同样很重要。所以不能仅仅是平均水平变好了就行。你还必须思考如何尽最大努力照顾到每一个个体。

[原文] [Kevin Weil]: It is something that we think a lot about and as we work with the administration, as we work with policy, we try and help wherever we can. We do a lot with education. One of the benefits here is that ChatGPT is also perhaps the best reskilling app you could possibly want.

[译文] [Kevin Weil]: 这是我们思考很多的问题，当我们与政府合作，制定政策时，我们尽力提供帮助。我们在教育方面做了很多工作。这里的一个好处是，ChatGPT 也可能是你梦寐以求的最好的再技能化（Reskilling）应用。

[原文] [Kevin Weil]: It knows a lot of things. It can teach you a lot of things if you're interested in learning new things. These are very real issues. I'm super optimistic about the long run, and we're going to need to do everything we can as a society to ensure that we make this transition as graceful and as well-supported as we can.

[译文] [Kevin Weil]: 它知道很多事情。如果你有兴趣学习新事物，它可以教你很多。这些都是非常现实的问题。我对长远发展超级乐观，而且作为社会，我们需要尽一切努力确保我们能尽可能优雅且获得充分支持地完成这一过渡。

[原文] [Lenny]: To give people a sense of where things might be going. That's a big question in a lot of people's minds. So someone asked this question that I love, which is, "AI is already changing, creative work in a lot of different ways, writing and design and coding, what do you think is the next big leap? What should we be thinking is the next big leap in AI-assisted creativity specifically, and then just broadly, where do you think things are going to be going in the next few years?"

[译文] [Lenny]: 为了让大家对未来的走向有个概念，这是很多人心中的大问题。有人问了一个我很喜欢的问题：“AI 已经在以很多不同的方式改变创造性工作，写作、设计和编程，你认为下一个大的飞跃是什么？具体来说，我们在 AI 辅助创造力方面应该期待什么下一个大飞跃？从广义上讲，你认为未来几年事情会往哪里发展？”

[原文] [Kevin Weil]: Yeah. This is also an area where I'm a big optimist. If you look at Sora, for example. I mean we talked about ImageGen earlier and the absolute fount of creativity that people are putting across Twitter and Instagram and other places.

[译文] [Kevin Weil]: 是的。这也是我非常乐观的一个领域。以 Sora 为例。我们之前谈到了图像生成（ImageGen），以及人们在 Twitter、Instagram 和其他地方展现出的绝对创造力源泉。

[原文] [Kevin Weil]: I am the world's worst artist like the worst. Maybe the only thing I'm worse at than art is singing. Give me a pencil and a pad of paper and I can't draw better than our eight-year-old. But give me ImageGen and I can think some creative thoughts and put something into the model and suddenly have output that I couldn't have possibly done myself. That's pretty cool.

[译文] [Kevin Weil]: 我是世界上最差的艺术家，最差的那种。也许我唯一比画画更差的就是唱歌。给我一支铅笔和一本画纸，我画得还不如我八岁的孩子好。但如果给我 ImageGen，我可以构思一些创意想法，输入到模型中，突然我就能得到我自己绝对做不出来的产出。这真的很酷。

[原文] [Kevin Weil]: Even you look at folks that are really talented. I was talking to a director recently about Sora, someone who's directed films that we would all know, and he was saying, for a film that he's doing, take the example of some sort of sci-fi-ish, think of Star Wars, and you've got some scene where there's a plane zooming into some Death Star-like thing.

[译文] [Kevin Weil]: 即使你看那些真正有才华的人。我最近和一个导演聊起 Sora，他执导过我们都知道的电影。他说，对于他正在拍的一部电影，举个例子，有点科幻风格的，想想《星球大战》，你有一场戏是一架飞机正冲向某种像死星（Death Star）一样的东西。

[原文] [Kevin Weil]: And so you've got the plane looking at the whole planet, and then you want to cut to a scene where the plane's kind of at the ground level, and all of a sudden you see the city and everything else. How are we going to manage that cut scene? And that transition?

[译文] [Kevin Weil]: 你有飞机俯瞰整个星球的镜头，然后你想切到一个场景，飞机处于地面高度，突然你看到了城市和其他所有东西。我们要如何处理那个剪辑场景？以及那个过渡？

[原文] [Kevin Weil]: And he was saying, "In the world of two years ago, I would have paid a 3D effects company a hundred grand and they would've taken a month, and they would've produced two versions of this cut scene for me. And I would've evaluated them. We would've chosen one, because what are you going to do? Pay another 50 grand and wait another month. And we would've just gone with it. And it would be fine. Movies are great. I love them."

[译文] [Kevin Weil]: 他说：“在两年前的世界里，我会付给一家 3D 特效公司 10 万美元，他们会花一个月时间，给我制作这个剪辑场景的两个版本。我会评估它们。我们会选其中一个，因为你还能怎么办？再付 5 万美元并再等一个月吗？我们就那样定下来了。这也挺好。电影很棒。我喜欢它们。”

[原文] [Kevin Weil]: Obviously, we can do great things with the technology that we've had, but you now look at what you can do with Sora. And his point was, "Now, I can use Sora, our video model, and I can get 50 different variations of this cut scene just me brainstorming into a prompt and the model brainstorming a little bit with me."

[译文] [Kevin Weil]: 显然，我们利用已有的技术也能做出伟大的作品，但现在看看你能用 Sora 做什么。他的观点是：“现在，我可以用 Sora，我们的视频模型，我可以得到这个剪辑场景的 50 种不同变体，仅仅通过我在提示词中进行头脑风暴，以及模型和我一起进行一点头脑风暴。”

[原文] [Kevin Weil]: "I've got 50 different versions. And then of course, I can iterate off of those and refine them and take different ideas. And now I'm still going to go to that 3D effects studio to produce the final one, but I'm going to go having brainstormed and had a much more creative approach with an outcome that's much better. And I did that assisted by AI."

[译文] [Kevin Weil]: “我得到了 50 个不同的版本。然后当然，我可以基于这些进行迭代和完善，采纳不同的想法。现在我仍然会去那家 3D 特效工作室制作最终版本，但我去的时候已经经过了充分的头脑风暴，有了更具创意的方法和更好的结果。而这一切都是在 AI 辅助下完成的。”

[原文] [Kevin Weil]: My personal view on creativity in general is that it's no one's going to... You don't type into Sora like, "Make me a great movie." It requires creativity and ingenuity, and all these things, but it can help you explore more. It can help you get to a better final result. So, again, I tend to be an optimist in most things, but actually, I think there's a very good story here.

[译文] [Kevin Weil]: 我个人对创造力的总体看法是，没人会……你不会在 Sora 里输入“给我做一部伟大的电影”。它需要创造力和独创性，以及所有这些东西，但它可以帮助你探索更多。它可以帮你达成更好的最终结果。所以，再次重申，我在大多数事情上倾向于乐观，但我确实认为这里有一个非常好的前景。

[原文] [Lenny]: I know Sam Altman, I think it was him who tweeted recently, the creative writing piece that you guys are working on where it's... He is very bad at writing creative stuff, and he shared an example where it's actually really good. I imagine that's another area of investment.

[译文] [Lenny]: 我知道 Sam Altman，我想是他最近发了推文，关于你们正在开发的创意写作功能……他不擅长写创意类的东西，但他分享了一个实际上非常好的例子。我想那是另一个投资领域。

[原文] [Kevin Weil]: Yeah, there's some exciting stuff happening internally with some new research techniques. We'll have more to say about that at some point. But yeah, Sam sometimes likes to show off some of the stuff that's coming, which is smart.

[译文] [Kevin Weil]: 是的，内部正在发生一些令人兴奋的事情，涉及一些新的研究技术。我们在某个时候会透露更多。但是是的，Sam 有时喜欢展示一些即将到来的东西，这很明智。

[原文] [Kevin Weil]: By the way, it's very indicative of this iterative deployment philosophy. We don't have some breakthrough and keep it to ourselves forever, and then bestow it upon the world someday. We kind of just talk about the things we're working on and share when we can and launch early and often, and then iterate in public. I really like that philosophy.

[译文] [Kevin Weil]: 顺便说一句，这非常能体现这种迭代部署的哲学。我们不会有了某种突破后就永远私藏着，然后在某一天把它恩赐给世界。我们只是谈论我们在做的事情，尽可能分享，尽早且频繁地发布，然后在公众面前迭代。我真的很喜欢这种哲学。

[原文] [Lenny]: I love all these hints that a few things coming. I know you can't say too much. You talked about how there might be a coding leap coming in the near future maybe by the time this comes out. Is there anything else people should be thinking about, might be coming in the near future? Any things you can tease that are interesting? Exciting?

[译文] [Lenny]: 我喜欢这些暗示，说明有些东西要来了。我知道你不能说太多。你谈到了可能在近期，也许在这个节目播出时，会有一个编程方面的飞跃。还有什么人们应该考虑的、可能在不久的将来会发生的事情吗？有什么有趣、令人兴奋的事情可以剧透一下吗？

[原文] [Kevin Weil]: Man, this hasn't been enough for you? Only everything is getting better every day.

[译文] [Kevin Weil]: 伙计，这还不够你消化的吗？只能说一切每天都在变好。

[原文] [Kevin Weil]: Yeah. I'm like, man, I hope we get some of this stuff out before the episode launches so... I don't piss people off. The amazing thing to me is we were talking earlier about how far models have come in just a couple of years. If you went back to GPT-3, you'd be disgusted by how bad it was, even though Lenny of two years ago was mind-blown by how good these were.

[译文] [Kevin Weil]: 是的。我想，天哪，我希望我们能在这期节目发布前把这些东西发出来，这样……我就不会惹毛大家了。对我来说惊人的是，我们之前谈到模型在短短几年内取得了多大的进步。如果你回过头去看 GPT-3，你会厌恶它有多糟糕，尽管两年前的 Lenny 曾被它的优秀程度震撼过。

[原文] [Kevin Weil]: And for a long time, we were iterating every six to nine months on a new GPT model. It was like GPT-3, GPT-3.5, 4, and now with this o-series of reasoning models, we're moving even faster. Every roughly three months, maybe four months, there's a new o-series model, and each of them is a step up in capability.

[译文] [Kevin Weil]: 很长一段时间里，我们每六到九个月迭代一个新的 GPT 模型。像 GPT-3、GPT-3.5、4，而现在有了 o 系列推理模型，我们通过得更快了。大概每三个月，也许四个月，就会有一个新的 o 系列模型，每一个都在能力上上了一个台阶。

[原文] [Kevin Weil]: And so the capabilities of these models are increasing at a massive pace. They're also getting cheaper as they scale. You look at where we were even a couple of years ago. I think the original, I don't know, what was it, GPT-3.5 or something was like 100 x the cost of GPT-4o mini today in the API. A couple of years, you've gone down two orders of magnitude in cost for much more intelligence.

[译文] [Kevin Weil]: 因此，这些模型的能力正在以巨大的速度增长。随着规模的扩大，它们也变得越来越便宜。看看我们几年前的情况。我觉得最初的，不知道是 GPT-3.5 还是什么，在 API 中的成本是今天 GPT-4o mini 的 100 倍。短短几年，成本下降了两个数量级，而智力却高得多。

[原文] [Kevin Weil]: And so I don't know where there's another series of trends like that in the world. Models are getting smarter, they're getting faster, they're getting cheaper, and they're getting safer too. They hallucinate less every iteration.

[译文] [Kevin Weil]: 我不知道世界上还有哪里有这样一系列的趋势。模型越来越聪明，越来越快，越来越便宜，也越来越安全。它们每次迭代产生的幻觉都在减少。

[原文] [Kevin Weil]: And so the Morse Law and transistors becoming ubiquitous. That was a law around doubling the number of transistors on a chip every 18 months. If you're talking about something where you're getting 10 x every year, that's a massively steeper exponential. And it tells us that the future is going to be very different than today.

[译文] [Kevin Weil]: 再看摩尔定律（Moore's Law）和晶体管的普及。那是一个关于芯片上晶体管数量每 18 个月翻一番的定律。如果你谈论的是每年 10 倍的增长，那是一个陡峭得多的指数曲线。这告诉我们，未来将与今天截然不同。

[原文] [Kevin Weil]: The thing I try and remind myself is, the AI models that you're using today is the worst AI model you will ever use for the rest of your life. And when you actually get that in your head, it's kind of wild.

[译文] [Kevin Weil]: 我试着提醒自己的一点是：你今天使用的 AI 模型将是你余生中用过的最差的 AI 模型。当你真正理解这一点时，会觉得这有点疯狂。

[原文] [Lenny]: I was going to actually say the same thing, and that's the thing that always sticks with me when I watch this thing. You're talking about Sora, and I imagine many people hearing that are like, "No, no. It's not actually ready. It's not good enough. It's not going to be as good as a movie I see in the theater." But the point is what you just made that this is the worst it's going to be. It will only get better.

[译文] [Lenny]: 我其实也想说同样的话，当我关注这些事情时，这一点一直让我印象深刻。你在谈论 Sora，我想很多人听到后会说：“不，不。它实际上还没准备好。它不够好。它比不上我在电影院看的电影。”但重点就是你刚才说的，这是它最差的时候。它只会变得更好。

[原文] [Kevin Weil]: Yeah, model maximalism. Just keep building for the capabilities that are almost there, and the model's going to catch up and be amazing. Escape to where the puck is going to be.

[译文] [Kevin Weil]: 是的，模型极大主义（Model maximalism）。只需针对那些即将实现的能力进行构建，模型很快就会赶上并变得惊人。滑向冰球将要到达的地方（意为预判趋势）。

[原文] [Lenny]: This reminds me, I was just using... I was duplifying everything the other day and I was just like, "What is taking so long."

[译文] [Lenny]: 这提醒了我，我前几天在用……我在把所有东西都“生成副本”（注：原文为 duplifying，可能指生成图片或某种处理），我就想：“怎么这么慢。”

[原文] [Lenny]: I was just like, "It's taking a minute to generate this image of my family in this amazing way." Come on, what's taking so long. You just get so used to magic happening in front of you.

[译文] [Lenny]: 我当时想：“生成这张很棒的全家福居然要花一分钟。”拜托，怎么这么久。你真的太习惯于魔法在你眼前发生了。

[原文] [Kevin Weil]: Yeah, totally.

[译文] [Kevin Weil]: 是的，完全是这样。

章节 11：职业回顾：Libra项目的遗憾与反思

📝 本节摘要：

在本章节中，Kevin 回顾了他职业生涯中最大的遗憾——Facebook 的 Libra 项目（后更名为 Novi）。他坦言，未能推出该产品让他深感失望，因为解决跨国汇款高昂手续费（往往高达20%）的问题本能让世界变得更美好。他反思了当时的战略失误：在 Facebook 声誉处于最低谷时，试图“毕其功于一役”——同时推出全新的区块链、一篮子货币以及与 WhatsApp 的深度集成，导致阻力巨大。尽管该技术最终衍生出了 Aptos 和 Mysten (Sui) 等公链，但他仍认为像“在 WhatsApp 里免费转账”这样即时、普惠的功能本应早已存在，并暗示在当前对加密货币更友好的政策环境下，Meta 或许应该重新尝试。

[原文] [Lenny]: Okay, final question. This is going to go in a completely different direction. A lot of people asked about this. So famously, you led this project at Facebook called Libra, which is now called Novi.

[译文] [Lenny]: 好的，最后一个问题。这将是一个完全不同的方向。很多人都问到了这个。众所周知，你在 Facebook 领导过一个叫 Libra 的项目，后来改名叫 Novi。

[原文] [Lenny]: A lot of people always wondered, "What happened there? That was a really cool idea." I know some people have a sense there's regulation challenges, things like that. I don't know if you've talked about this much. So I guess, could you just give people a brief summary of just what is Libra? This project you working on, and just what happened, and how you feel about it?

[译文] [Lenny]: 很多人一直想知道，“到底发生了什么？那真的是个很酷的想法。”我知道有些人感觉是因为监管挑战之类的原因。我不知道你是否经常谈论这个。所以我想，你能不能简要地给大家总结一下什么是 Libra？你从事的这个项目到底发生了什么，以及你对此有何感受？

[原文] [Kevin Weil]: Yeah. I mean, David Marcus led it, and I happily work for him and with him. I think he's a visionary and also a mentor and a friend. Honestly, Libra is probably the biggest disappointment of my career.

[译文] [Kevin Weil]: 是的。我想说是 David Marcus 领导了这个项目，我很高兴能为他工作并与他共事。我认为他是一位有远见的人，也是我的导师和朋友。老实说，Libra 可能是原本职业生涯中最大的遗憾。

[原文] [Kevin Weil]: When I think about the problems we were solving, which are very real problems. If you look at, for example, the remittance space, people sending money to family members in other countries, it is maybe... I mean it's incredibly regressive, right?

[译文] [Kevin Weil]: 当我想到我们当时试图解决的问题时，那些是非常现实的问题。如果你看看，比如汇款领域，人们把钱寄给国外的家人，这可能是……我的意思是这极具累退性（Regressive，指对低收入者剥削更重），对吧？

[原文] [Kevin Weil]: People that don't have the money to spend are having to pay 20% to send money home to their family. So outrageous fees, it takes multiple days, you have to go then pick up cash from... It's all bad.

[译文] [Kevin Weil]: 那些本来就没钱可花的人，还要支付 20% 的手续费才能把钱寄回家。如此离谱的费用，还要花好几天，你还得去取现金……这一切都很糟糕。

[原文] [Kevin Weil]: And here we are with 3 billion people using WhatsApp all over the world, talking to each other every day, especially friends and family, and exactly the kind of people who'd send money to each other. Why can't you send money as immediately, as cheaply, as simply as you send a text message?

[译文] [Kevin Weil]: 而我们有 30 亿人在世界各地使用 WhatsApp，每天彼此交谈，尤其是朋友和家人，正是那种会互相汇款的人。为什么你不能像发短信一样即时、便宜、简单地汇款呢？

[原文] [Kevin Weil]: It is one of those things when you sit back and think about it, that should just exist. And that was what we set out to try and do. Now, I don't think we played all of our cards perfectly. If I could go back and do things, there are a bunch of things I would do differently.

[译文] [Kevin Weil]: 这就是那种当你坐下来思考时，会觉得理应存在的事情之一。这就是我们要去尝试做的事情。现在看来，我认为我们并没有把牌打得完美。如果我能回到过去，有很多事情我会采取不同的做法。

[原文] [Kevin Weil]: We tried to get it all at once. We tried to launch a new blockchain. It was a basket of currencies originally. It was integration into WhatsApp and Messenger, and I think the whole world kind of went like, "Oh my God, that's a lot of change at once."

[译文] [Kevin Weil]: 我们试图毕其功于一役。我们试图推出一个新的区块链。它最初是一篮子货币。它要集成到 WhatsApp 和 Messenger 中，我想全世界的反应都有点像：“天哪，这也一下子改变太多了吧。”

[原文] [Kevin Weil]: And it happened also to be at the time that Facebook was at the absolute nadir of its reputation. And so that didn't help. It was also not the Messenger that people wanted for this kind of change. We knew all that going in, but we went for it.

[译文] [Kevin Weil]: 而且这恰好发生在 Facebook 声誉处于绝对谷底（Nadir）的时候。所以这并没有帮助。它也不是人们想要用来进行这种变革的信使。我们进去之前就知道这一切，但我们还是去做了。

[原文] [Kevin Weil]: I think there are a bunch of ways that we could do that that would've introduced the change a little bit more gently, maybe still gotten to that same outcome, but fewer new things at once and introduced the new things one at a time. Who knows? Those were decisions we made together. So we all own them. Certainly, I own them.

[译文] [Kevin Weil]: 我认为有很多方法可以让我们稍微温和一点地引入变革，也许仍然能达到同样的结果，但不要一次性推出那么多新东西，而是一个接一个地引入。谁知道呢？那些是我们共同做出的决定。所以我们都要为此负责。当然，我也要负责。

[原文] [Kevin Weil]: But it fundamentally disappoints me that this doesn't exist in the world today because the world would be a better place if we'd been able to ship that product. I would be able to send you 50 cents in WhatsApp for free. It would settle instantly. Everybody would have a balance in their WhatsApp account. We'd be transact... I mean, it should exist.

[译文] [Kevin Weil]: 但它至今未能存在于世让我从根本上感到失望，因为如果我们当时能发布那个产品，世界本该变得更美好。原本我应该能在 WhatsApp 里免费给你转 50 美分。它会即时结算。每个人的 WhatsApp 账户里都会有余额。我们可以交易……我的意思是，它应该存在。

[原文] [Kevin Weil]: I don't know. To be honest, the current administration is super friendly to crypto. Facebook's reputation, Meta's reputation is in a very different place. Maybe they should go build it now.

[译文] [Kevin Weil]: 我不知道。老实说，这届政府对加密货币非常友好。Facebook 的声誉，或者说 Meta 的声誉也处于一个完全不同的境地。也许他们现在应该去把它做出来。

[原文] [Lenny]: I was looking at the history of it, and apparently, they sold the tech to some private equity company for 200 million bucks.

[译文] [Lenny]: 我看了一下它的历史，显然，他们把技术卖给了一家私募股权公司，卖了 2 亿美元。

[原文] [Kevin Weil]: Yeah, yeah, and... There are a couple of current blockchains that are built on the tech because the tech was open-sourced from the beginning. Aptos and Mistin are two companies that are built off of this tech.

[译文] [Kevin Weil]: 是的，是的，而且……目前有几个区块链是基于该技术构建的，因为该技术从一开始就是开源的。Aptos 和 Mistin（注：指 Mysten Labs，Sui 公链的开发方）就是基于这项技术建立的两家公司。

[原文] [Kevin Weil]: So at least all of the work that we did, did not die and lives on in these two companies, and they're both doing really well. But still, we should be able to send each other money in WhatsApp, and we can't today.

[译文] [Kevin Weil]: 所以至少我们所做的所有工作并没有消亡，而是延续在这两家公司中，而且它们都做得很好。但尽管如此，我们本应该能在 WhatsApp 里互相转账，而今天我们却做不到。

[原文] [Lenny]: Hear, hear. Well, thanks for sharing that story, Kevin. Is there anything else you want to share or maybe a last negative advice or insight before we get to our very exciting lightning round?

[译文] [Lenny]: 说得好。好吧，感谢分享这个故事，Kevin。在进入我们非常激动人心的快问快答环节之前，你还有什么想分享的，或者最后的一条建议或见解吗？

[原文] [Kevin Weil]: Ooh, the lightning round. Let's just go do that.

[译文] [Kevin Weil]: 噢，快问快答。我们就直接开始吧。

章节 12：快问快答：推荐资源、提示词技巧与结语

📝 本节摘要：

访谈进入最后的快问快答环节。Kevin 分享了他的推荐书单，涵盖 AI 协作（Ethan Mollick）、地缘政治（Peter Zeihan）以及商业传记（《Cable Cowboy》）。在谈及人生格言时，他引用了扎克伯格的一句话：“长期持续地做好工作”，强调这比寻找“银弹”更为重要。

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针对听众关心的提示词（Prompting）技巧，Kevin 提出了反直觉的观点：未来的 AI 应该让“提示词工程”消失。但在当下，他建议通过提供示例（Examples）来进行“穷人版微调”，或者通过设定角色（Personas，如“你是爱因斯坦”）来调整模型的心智模式。最后，他呼吁用户直接通过 Twitter 向他反馈 ChatGPT 的优缺点，并以此结束了本次深度对话。

[原文] [Lenny]: Let's do it. With that, Kevin, we reached our very exciting lightning round. Are you ready?

[译文] [Lenny]: 来吧。那么，Kevin，我们要进入非常激动人心的快问快答环节了。准备好了吗？

[原文] [Kevin Weil]: Yeah. Let's do it.

[译文] [Kevin Weil]: 好的。来吧。

[原文] [Lenny]: Okay. What are two or three books that you find yourself recommending most to other people?

[译文] [Lenny]: 好的。你最常向别人推荐的两三本书是什么？

[原文] [Kevin Weil]: Co-Intelligence by Ethan Mollick, a really good book about AI and how to use it in your daily life as a student, as a teacher. He's super thoughtful. Also, by the way, a very good follow on Twitter.

[译文] [Kevin Weil]: Ethan Mollick 的《Co-Intelligence》（共生智能），这是一本关于 AI 以及作为学生或教师如何在日常生活中使用它的好书。他非常有思想。顺便说一句，他在 Twitter 上也很值得关注。

[原文] [Kevin Weil]: The Accidental Superpower by Peter Zion. Very good if you're interested in geopolitics and the forces that sort of shape the dynamics happening.

[译文] [Kevin Weil]: Peter Zeihan 的《The Accidental Superpower》（意外的超级大国）。如果你对地缘政治以及塑造当前动态的力量感兴趣，这本书非常好。

[原文] [Kevin Weil]: And then I really enjoyed Cable Cowboy, I don't know who the author is, but the biography of John Malone. Just fascinating. If you like business, especially if you want to get into... I mean the man was an incredible dealmaker and shaped a lot of the modern cable industry. So that was a good biography.

[译文] [Kevin Weil]: 还有我非常喜欢《Cable Cowboy》（有线电视牛仔），我不记得作者是谁了，是关于 John Malone 的传记。非常迷人。如果你喜欢商业，特别是如果你想了解……我是说这个人是个不可思议的交易撮合者，塑造了现代有线电视行业的很多方面。那是一本很好的传记。-

[原文] [Lenny]: Next question. Do you have a favorite recent movie or TV show that you really enjoyed?

[译文] [Lenny]: 下一个问题。你最近有什么特别喜欢的电影或电视剧吗？

[原文] [Kevin Weil]: I wish I had time to watch a TV show, so I'm-

[译文] [Kevin Weil]: 我希望我有时间看电视剧，所以我——

[原文] [Lenny]: Just Sora videos.

[译文] [Lenny]: 只看 Sora 生成的视频是吧。

[原文] [Kevin Weil]: Yeah, right. I don't know. When I was a kid, I read the Wheel of Time series and now Amazon has it as they're in the third season of it, so I want to watch that. I haven't yet.

[译文] [Kevin Weil]: 是啊，没错。我不知道。我小时候读过《时光之轮》（Wheel of Time）系列小说，现在亚马逊拍了电视剧，正在播第三季，我想看那个。我还没看。-

[原文] [Kevin Weil]: Top Gun 2 was an awesome movie. I think that's no longer new. But I like the idea. I want more Americana. I want more being proud of being strong. And I thought Top Gun 2 did a really good job of that. Pride and patriotism, I think the US could use more of that.

[译文] [Kevin Weil]: 《壮志凌云 2》（Top Gun 2）是一部很棒的电影。我想那已经不算新片了。但我喜欢它的理念。我想要更多的美式风格（Americana）。我想要更多那种为强大而自豪的感觉。我觉得《壮志凌云 2》在这方面做得很好。自豪感和爱国主义，我认为美国需要更多这样的东西。

[原文] [Lenny]: Is there a favorite product that you've recently discovered that you really love, other than your super intelligence internal tool that you all have access to?

[译文] [Lenny]: 除了你们都能用的那个内部超级智能工具之外，最近有没有发现什么你真正喜爱的产品？-

[原文] [Kevin Weil]: Well, I think vibe coding with products like Windsurf is just super fun. I'm having a great time doing that. I still just love that our chief people officer vibe coded some tools.

[译文] [Kevin Weil]: 嗯，我觉得用像 Windsurf 这样的产品进行“Vibe Coding（凭感觉编程）”超级有趣。我玩得很开心。我还是很喜欢我们的首席人力官竟然也 Vibe Code 了一些工具这件事。

[原文] [Kevin Weil]: Maybe the other one is Waymo. Every chance I get, I'll take a Waymo. It's just a better way of riding, and it still feels like the future. So they've done an amazing job.

[译文] [Kevin Weil]: 也许另一个是 Waymo。只要有机会，我就会坐 Waymo。这是一种更好的乘车方式，而且它仍然让人感觉像是未来。他们做得非常棒。

[原文] [Lenny]: A couple more questions. Do you have a favorite life motto that you often repeat yourself, find really useful in work or in life?

[译文] [Lenny]: 还有几个问题。你有没有什么经常对自己重复的、在工作或生活中觉得非常有用的座右铭？

[原文] [Kevin Weil]: Yeah. So actually, this is interestingly enough, it is more of a philosophy, but then I thought Zuck encapsulated it one time on a Facebook earnings call. So I actually had this made into a poster. It sits in my room.

[译文] [Kevin Weil]: 有的。实际上这很有趣，这更像是一种哲学，但我记得 Zuck（扎克伯格）有一次在 Facebook 的财报电话会议上概括了这一点。所以我实际上把它做成了一张海报，放在我的房间里。-

[原文] [Kevin Weil]: But somebody was asking Mark... "So what did you do? What was it that you launched? What was the one thing that drove all this growth for you?" And he said something to the effect of, "Sometimes it's not any one thing, it's just good work consistently over a long period of time." And that's always stuck with me.

[译文] [Kevin Weil]: 当时有人问 Mark……“你们做了什么？你们发布了什么？是什么单一因素推动了所有的增长？”他说的大意是：“有时并不是任何单一的事情，它只是长期持续地做好工作。”这句话一直留在我心中。

[原文] [Kevin Weil]: And I think it is. I mean I run ultra marathons. It's like it's just about grinding. I think people too often look for the silver bullet when a lot of life and a lot of excellence is actually showing up day in and day out, doing good work, getting a little bit better every single day...

[译文] [Kevin Weil]: 我认为就是这样。我跑超级马拉松。这就像是在磨练。我认为人们太常寻找“银弹”（一劳永逸的办法），而生活和卓越往往在于日复一日地出现，做好工作，每天进步一点点……

[原文] [Kevin Weil]: ...and you may not notice it over a week or even a month. And a lot of people then kind of get dismayed and stop. But actually, you keep doing it. The gains keep compounding. And over the course of a year, two years, five years, it adds up like crazy. So good work consistently over a long period of time.

[译文] [Kevin Weil]: ……你可能在一周甚至一个月内注意不到变化。很多人就会因此灰心并停下来。但实际上，如果你坚持下去，收益会不断复利。在一年、两年、五年的过程中，它会疯狂地累积。所以，要长期持续地做好工作。

[原文] [Lenny]: I love that. I got to make a poster of this now. Okay, final question. I'm going to ask if you have any prompting tricks. You're constantly prompting ChatGPT. What's one tip, one trick that you found to be helpful in helping you get what you want?

[译文] [Lenny]: 我喜欢这句话。我也要把这个做成海报了。好的，最后一个问题。我想问问你有没有什么提示词（Prompting）技巧。你经常给 ChatGPT 写提示词。有什么你发现特别有帮助的技巧或窍门能帮你得到想要的结果吗？-

[原文] [Kevin Weil]: Well, I'll say, first of all, I want to kill the idea that you have to be a good prompt engineer. I think if we do our jobs, that stops being true. It's just one of those sharp edges of models that experts can learn.

[译文] [Kevin Weil]: 嗯，我首先想说，我想扼杀“你必须成为一名优秀的提示词工程师”这种想法。我认为如果我们工作做到位了，这就不再是事实了。这只是模型目前还比较粗糙的边缘之一，专家们可以去学习。-

[原文] [Kevin Weil]: But today, we're not totally there. I think by the way, we are making progress there. I think there is less prompt engineering than there had to be before. But in line with some of the fine-tuning stuff I was talking about and the importance of giving examples, you can do effectively poor man's fine-tuning by including examples in your prompt of the kinds of things that you might want and a good answer.

[译文] [Kevin Weil]: 但今天，我们还没完全达到那个状态。顺便说一句，我认为我们正在取得进展。现在的提示词工程需求已经比以前少了。但结合我之前谈到的微调和提供示例的重要性，你可以通过在提示词中包含你想要的类型和好答案的示例，来有效地进行“穷人版微调”。

[原文] [Kevin Weil]: So like, "Here's an example and here's a good answer. Here's an example, and here's a good answer. Now, go solve this problem for me." And the model really will listen and learn from that. Not as well as if you do a full fine-tune, but much more than if you don't provide any examples.

[译文] [Kevin Weil]: 就像这样：“这是一个例子，这是一个好的回答。这是另一个例子，这是另一个好的回答。现在，去帮我解决这个问题。”模型真的会听取并从中学习。虽然不如完全微调那么好，但这比不提供任何示例要好得多。-

[原文] [Lenny]: That's awesome. One tip that I heard, I'm curious if this works is you tell it, "This is very, very important to my career." Make it really understand like, "Someone will die if you don't answer me correctly." Does that work?

[译文] [Lenny]: 太棒了。我听说过一个技巧，很好奇这是否管用，就是你告诉它：“这对我的职业生涯非常非常重要。”让它真正理解，就像“如果你不正确回答我，有人会死”一样。这管用吗？

[原文] [Kevin Weil]: It's really weird. There's probably a good explanation for this. But you can also say things. So, yes, I think there is some validity to that. You can also say things like, "I want you to be Einstein. Now, answer this physics problem for me," or, "You are the world's greatest marketer, the world's greatest brand marketer. Now here's a naming question."

[译文] [Kevin Weil]: 这真的很奇怪。这背后可能有很好的解释。但你也可以说其他的话。所以，是的，我认为这确实有些道理。你也可以说这样的话：“我要你是爱因斯坦。现在回答这个物理问题。”或者，“你是世界上最伟大的营销人员，最伟大的品牌营销人员。现在这是一个命名问题。”-

[原文] [Kevin Weil]: And there is something where it sort of shifts the model into a certain mindset that can actually be really positive.

[译文] [Kevin Weil]: 这有点像是把模型切换到了某种心智模式，实际上能带来非常积极的效果。

[原文] [Lenny]: Kevin, this was incredible. It's a real honor to have you on here and to talk to you and to hear where you think things are going and what we need to be thinking about, so thank you for being here, Kevin.

[译文] [Lenny]: Kevin，这太棒了。真的很荣幸能邀请你来这里，和你交谈，听听你对未来走向的看法以及我们需要思考什么，所以谢谢你的到来，Kevin。-

[原文] [Kevin Weil]: Oh, thank you so much for having me. I get to work with the world's best team, and all credit to them, but really appreciate you having me on. It's been super fun.

[译文] [Kevin Weil]: 哦，非常感谢邀请我。我有机会与世界上最好的团队共事，所有功劳都归功于他们，但真的很感谢你邀请我。这超级有趣。

[原文] [Lenny]: I forgot to ask you the two final questions. Where can folks find you if they want to reach out, and how can listeners be useful to you?

[译文] [Lenny]: 我忘了问最后两个问题。如果人们想联系你，去哪里找你？听众能为你做些什么？

[原文] [Kevin Weil]: I am @kevinweil, K-E-V-I-N-W-E-I-L on pretty much every platform. I'm still a Twitter DAU after all these years. I guess an X DAU, LinkedIn, wherever.

[译文] [Kevin Weil]: 我在几乎所有平台上的 ID 都是 @kevinweil，K-E-V-I-N-W-E-I-L。这么多年了，我仍然是 Twitter 的日活跃用户（DAU）。或者说是 X 的 DAU，还有 LinkedIn，哪里都行。

[原文] [Kevin Weil]: And I think the thing I would love from people, give me feedback. People are using ChatGPT. Tell me where it's working really well for you and where you want us to double down. Tell me where it's failing. I'm very active and engaged on Twitter. I love hearing from people, what's working and what's not, so don't be shy.

[译文] [Kevin Weil]: 我想我最希望大家做的是给我反馈。大家都在用 ChatGPT。告诉我它在哪里对你很有用，你希望我们在哪里加倍投入。告诉我它在哪里失败了。我在 Twitter 上非常活跃且乐于互动。我喜欢听到人们说什么是管用的，什么是不管用的，所以别害羞。

[原文] [Lenny]: And by the way, 400 million weekly active users all emailing you feedback. Here we go.

[译文] [Lenny]: 顺便说一句，4 亿周活跃用户都给你发邮件反馈。来吧。-

[原文] [Kevin Weil]: Yes, let's do it.

[译文] [Kevin Weil]: 是的，来吧。