How to drive word of mouth | Nilan Peiris (CPO of Wise)
Alexander Embiricos: Codex is OpenAI's coding agent.
Alexander Embiricos：Codex 是 OpenAI 的编码代理。
We think of Codex as just the beginning of a software engineering teammate.
我们认为 Codex 只是软件工程队友的开始。
It's a bit like this really smart intern that refuses to read Slack, doesn't check Datadog unless you ask it to.
这有点像这个非常聪明的实习生，他拒绝阅读 Slack，除非你要求，否则不会检查 Datadog。
Lenny Rachitsky: I remember Karpathy tweeted the gnarliest bugs that he runs into that he just spends hours trying to figure out nothing else has solved, he gives it to Codex, lets it run for an hour and it solves it.
Lenny Rachitsky：我记得 Karpathy 在推特上发布了他遇到的最棘手的错误，他只是花了几个小时试图找出其他没有解决的问题，他把它交给 Codex，让它运行一个小时，它解决了它。
Alexander Embiricos: Starting to see glimpses of the future where we're actually starting to have Codex be on call for its own training.
Alexander Embiricos：开始看到未来的一瞥，我们实际上开始让 Codex 随时待命进行自己的培训。
Codex writes a lot of the code that helps manage its training run, the key infrastructure.
Codex 编写了大量有助于管理其训练运行（关键基础设施）的代码。
So we have a Codex code review that's catching a lot of mistakes.
因此，我们进行了 Codex 代码审查，发现了很多错误。
It's actually caught some pretty interesting configuration mistakes.
它实际上发现了一些非常有趣的配置错误。
One of the most mind-blowing examples of acceleration, the Sora Android app, like a fully new app, we built it in 18 days and then 10 days later, so 28 days total, we went to the public.
Sora Android 应用程序是最令人兴奋的加速示例之一，就像一个全新的应用程序一样，我们在 18 天内构建了它，然后在 10 天后构建了它，所以总共 28 天，我们走向了公众。
Lenny Rachitsky: How do you think you win in this space?
Lenny Rachitsky：您认为如何在这个领域获胜？
Alexander Embiricos: One of our major goals with Codex is to get to proactivity.
Alexander Embiricos：我们与 Codex 的主要目标之一是积极主动。
If we're going to build a super system, has to be able to do things.
如果我们要建立一个超级系统，就必须能够做事。
One of the learnings over the past year is that for models to do stuff, they're much more effective when they can use a computer.
过去一年的经验之一是，对于模型来说，当他们可以使用计算机时，他们做事的效率会更高。
It turns out the best way for models to use computers is simply to write code.
事实证明，模型使用计算机的最佳方式就是编写代码。
And so we're kind of getting to this idea where if you want to build any agent, maybe you should be building a coding agent.
所以我们有点接近这个想法，如果你想构建任何代理，也许你应该构建一个编码代理。
Lenny Rachitsky: When you think about progress on Codex, I imagine you have a bunch of evals and there's all these public benchmarks.
Lenny Rachitsky：当您考虑 Codex 的进展时，我想您会进行大量评估，并且有所有这些公共基准。
Alexander Embiricos: A few of us are constantly on Reddit.
Alexander Embiricos：我们中的一些人经常使用 Reddit。
There's praise up there and there's a lot of complaints.
那里有很多赞扬，也有很多抱怨。
What we can do is as a product team just try to always think about how are we building a tool so that it feels like we're maximally accelerating people rather than building a tool that makes it more unclear what you should do as the human?
作为一个产品团队，我们能做的就是始终思考我们如何构建一个工具，这样我们就能最大程度地加速人们的速度，而不是构建一个让你更不清楚作为人类应该做什么的工具？
Lenny Rachitsky: Being at OpenAI, I can't not ask about how far you think we are from AGI.
Lenny Rachitsky：在 OpenAI，我不能不问你认为我们离 AGI 还有多远。
Alexander Embiricos: The current underappreciated limiting factor is literally human typing speed or human multitasking speed.
Alexander Embiricos：当前未被充分认识的限制因素实际上是人类的打字速度或人类的多任务处理速度。
Lenny Rachitsky: Today, my guest is Alexander Embiricos, product lead for Codex, OpenAI's incredibly popular and powerful coding agent.
Lenny Rachitsky：今天，我的嘉宾是 Alexander Embiricos，Codex 的产品负责人，Codex 是 OpenAI 非常受欢迎且功能强大的编码代理。
In the words of Nick Turley, head of ChatGPT and former podcast guest, "Alex is one of my all time favorite humans I've ever worked with, and bringing him and his company into OpenAI ended up being one of the best decisions we've ever made." Similarly, Kevin Weil, OpenAI's CPO, said, "Alex is simply the best."
用 ChatGPT 负责人、前播客嘉宾 Nick Turley 的话来说，“Alex 是我合作过的最喜欢的人之一，将他和他的公司带入 OpenAI 最终成为我们做出的最佳决定之一。”同样，OpenAI 首席产品官 Kevin Weil 表示：“Alex 简直是最棒的。”
Lenny Rachitsky: In our conversation, we chat about what it's truly like to build product at OpenAI, how Codex allowed the Sora team to ship the Sora app, which became the number one app in the app store in under one month.
Lenny Rachitsky：在我们的谈话中，我们讨论了在 OpenAI 构建产品的真实感受，Codex 如何允许 Sora 团队发布 Sora 应用程序，该应用程序在不到一个月的时间内成为应用程序商店中排名第一的应用程序。
Also, the 20x growth Codex is seeing right now and what they did to make it so good at coding, why his team is now focused on making it easier to review code, not just write code, his AGI timelines, his thoughts on when AI agents will actually be really useful, and so much more.
此外，Codex 现在看到了 20 倍的增长，以及他们做了什么使其在编码方面如此出色，为什么他的团队现在专注于让代码审查变得更容易，而不仅仅是编写代码，他的 AGI 时间表，他对人工智能代理何时真正有用的想法等等。
A huge thank you to Ed Bayes, Nick Turley, and Dennis Yang for suggesting topics for this conversation.
非常感谢 Ed Bayes、Nick Turley 和 Dennis Yang 为本次对话提出的主题。
If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.
如果您喜欢这个播客，请不要忘记订阅并在您最喜欢的播客应用程序或 YouTube 中关注它。
And if you become an annual subscriber of my newsletter, you get a year free of 19 incredible products, including a year free of Devin, Lovable, Replit, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, Mobbin, PostHog, and Stripe Atlas.
如果您成为我的时事通讯的年度订阅者，您将获得一年免费的 19 种令人难以置信的产品，包括一年免费的 Devin、Lovable、Replit、Bolt、n8n、Linear、Super human、Descript、Wispr Flow、Gamma、Perplexity、Warp、Granola、Magic Patterns、Raycast、ChatPRD、Mobbin、PostHog 和 Stripe Atlas。
Head on over to lennysnewsletter.com and click Product Pass.
前往 lennysnewsletter.com 并单击 Product Pass。
Lenny Rachitsky: With that, I bring you Alexander Embiricos, after a short word from our sponsors.
Lenny Rachitsky：在我们的赞助商简短发言之后，我为您带来了 Alexander Embiricos。
Lenny Rachitsky: Here's a puzzle for you.
Lenny Rachitsky：这是给你的一个谜题。
What do OpenAI, Cursor, Perplexity, Vercel, Plaid, and hundreds of other winning companies have in common?
OpenAI、Cursor、Perplexity、Vercel、Plaid 以及其他数百家获奖公司有什么共同点？
The answer is they're all powered by today's sponsor, WorkOS.
答案是它们全部由当今的赞助商 WorkOS 提供支持。
If you're building software for enterprises, you've probably felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by big customers.
如果您正在为企业构建软件，您可能已经感受到集成单点登录、SCIM、RBAC、审核日志和大客户所需的其他功能的痛苦。
WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS.
WorkOS 通过专为 B2B SaaS 构建的现代开发者平台将这些交易阻碍因素转变为嵌入式 API。
Whether you're a seed stage startup trying to land your first enterprise customer or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise ready and unlocking growth.
无论您是试图吸引第一个企业客户的种子期初创公司，还是正在全球扩张的独角兽，WorkOS 都是让企业做好准备并释放增长的最快途径。
They're essentially Stripe for enterprise features.
它们本质上是用于企业功能的 Stripe。
Visit workos.com to get started or just hit up their Slack support where they have real engineers in there who answer your questions super fast.
请访问 workos.com 开始使用，或者直接联系他们的 Slack 支持，他们有真正的工程师，可以快速回答您的问题。
WorkOS allows you to build like the best, with delightful APIs, comprehensive docs and a smooth developer experience.
WorkOS 允许您以最佳方式进行构建，提供令人愉悦的 API、全面的文档和流畅的开发人员体验。
Go to workos.com to make your app enterprise ready today.
立即访问 workos.com 让您的应用程序企业做好准备。
Lenny Rachitsky: This episode is brought to you by Fin, the number one AI agent for customer service.
Lenny Rachitsky：本集由 Fin 为您带来，Fin 是第一大客户服务人工智能代理。
If your customer support tickets are piling up, then you need Fin.
如果您的客户支持请求堆积如山，那么您需要 Fin。
Fin is the highest performing AI agent on the market with a 65% average resolution rate.
Fin 是市场上性能最高的 AI 代理，平均分辨率为 65%。
Fin resolves even the most complex customer queries.
Fin 甚至可以解决最复杂的客户查询。
No other AI agent performs better.
没有其他人工智能代理表现得更好。
In head to head bake offs with competitors, Fin wins every time.
在与竞争对手的正面交锋中，芬每次都获胜。
Yes, switching to a new tool can be scary, but Fin works on any help desk with no migration needed, which means you don't have to overhaul your current system or deal with delays in service for your customers.
是的，切换到新工具可能会令人恐惧，但 Fin 可以在任何帮助台上工作，无需迁移，这意味着您不必彻底检修当前系统或处理客户服务延迟的问题。
And Fin is trusted by over 6,000 customer service leaders and top companies like Anthropic, Shutterstock, Synthesia, Clay, Vanta, Lovable, Monday.com and more.
Fin 受到超过 6,000 家客户服务领导者和 Anthropic、Shutterstock、Synthesia、Clay、Vanta、Lovable、Monday.com 等顶级公司的信赖。
And because Fin is powered by the Fin AI Engine, which is a continuously improving system that allows you to analyze, train, test, and deploy with ease, Fin can continuously improve your results too.
由于 Fin 由 Fin AI 引擎提供支持，这是一个不断改进的系统，可让您轻松分析、训练、测试和部署，因此 Fin 也可以不断改进您的结果。
So if you're ready to transform your customer service and scale your support, give Fin a try for only 99 cents per resolution.
因此，如果您准备好转变您的客户服务并扩大您的支持范围，不妨尝试一下 Fin，每个解决方案仅需 99 美分。
Plus Fin comes with a 90-day money back guarantee.
Plus Fin 提供 90 天退款保证。
Find out how Fin can work for your team at fin.ai/Lenny.
请访问 fin.ai/Lenny，了解 Fin 如何为您的团队工作。
That's fin.ai/lenny.
那是 fin.ai/lenny。
Lenny Rachitsky: Alexander, thank you so much for being here and welcome to the podcast.
Lenny Rachitsky：亚历山大，非常感谢您来到这里，欢迎来到播客。
Alexander Embiricos: Thank you so much.
亚历山大·恩比里科斯：非常感谢。
I've been following for ages and I'm excited to be here.
我关注了很多年，很高兴能来到这里。
Lenny Rachitsky: I'm even more excited.
Lenny Rachitsky：我更兴奋。
I really appreciate that.
我真的很感激。
I want to start with your time at OpenAI.
我想从您在 OpenAI 的经历开始。
So you joined OpenAI about a year ago.
您大约一年前加入了 OpenAI。
Before that, you had your own startup for about five years.
在此之前，您已经拥有自己的初创公司大约五年了。
Before that, you were a product manager at Dropbox.
在此之前，您是 Dropbox 的产品经理。
I imagine OpenAI is very different from every other place you've worked.
我认为 OpenAI 与你工作过的其他地方有很大不同。
Let me just ask you this, what is most different about how OpenAI operates and what's something that you've learned there that you think you're going to take with you wherever you go, assuming you ever leave?
让我问你一个问题，OpenAI 的运作方式最不同的地方是什么？你在那里学到的东西是什么，你认为无论你走到哪里，假设你离开了，你都会随身携带？
Alexander Embiricos: By far, I would say the speed and ambition of working at OpenAI are just dramatically more than what I can imagine.
Alexander Embiricos：到目前为止，我想说在 OpenAI 工作的速度和雄心远远超出了我的想象。
And I guess it's kind of an embarrassing thing to say because everyone who's a startup founder thinks like, "Oh yeah, my startup moves super fast and the talent bar is super high and we're super ambitious." But I have to say, working in OpenAI just made me reimagine what that even means.
我想这是一件令人尴尬的事情，因为每个初创公司创始人都会想，“哦，是的，我的初创公司发展得非常快，人才标准非常高，我们非常雄心勃勃。”但我不得不说，在 OpenAI 工作让我重新想象了这意味着什么。
Lenny Rachitsky: We hear this a lot about feels like every AI company is just like, "Oh my God, I can't believe how fast they're moving." Is there an example of just like, "Wow, that wouldn't have happened this quickly anywhere else"?
Lenny Rachitsky：我们经常听到这样的说法，感觉每个人工智能公司都在说，“天哪，我简直不敢相信他们的发展速度有多快。”有没有这样的例子：“哇，这在其他地方不会发生得这么快”？
Alexander Embiricos: The most obvious thing that comes to mind is just the explosive growth of Codex itself.
Alexander Embiricos：我想到的最明显的事情就是 Codex 本身的爆炸性增长。
I think it's a while since we bumped our external number, but it's like the 10x-ing of Codex's scale was just super fast in a matter of months and it's well more since then.
我认为我们已经有一段时间没有增加我们的外部数字了，但 Codex 规模的 10 倍增长在几个月内就变得非常快，而且从那时起它的速度就更快了。
And once you've lived through that, or at least speaking for myself, having lived through that now, I feel like anytime I'm going to spend my time on building tech product, there's that speed and scale that I now need to meet.
一旦你经历过这个，或者至少对我自己来说，现在经历过这个，我觉得无论何时我要花时间构建科技产品，我现在都需要满足这样的速度和规模。
Alexander Embiricos: If I think of what I was doing in my startup, it moved way slower and there's always this balance with startups of how much do you commit to an idea that you have versus find out that it's not working and then pivot.
Alexander Embiricos：如果我想想我在初创公司所做的事情，它的进展速度要慢得多，而且初创公司总是存在这种平衡，即你对自己的想法投入了多少，而不是发现它不起作用然后转向。
But I think one thing I've realized at OpenAI is the amount of impact that we can have and, in fact, need to have to do a good job is so high that I have to be way more ruthless with how I spend my time now.
但我认为我在 OpenAI 意识到的一件事是我们可以产生的影响力，事实上，做好工作的需要是如此之大，以至于我必须更加无情地对待我现在如何度过我的时间。
Lenny Rachitsky: Before we get to Codex, is there a way that they've structured the org or, I don't know, the way that OpenAI operates that allows the team to move this quickly?
Lenny Rachitsky：在我们讨论 Codex 之前，他们有没有一种方式来构建组织，或者我不知道 OpenAI 的运作方式可以让团队快速行动？
Because everyone wants to move super fast.
因为每个人都想移动得超快。
I imagine there's a structural approach to allowing this to happen.
我想有一种结构性方法可以让这种情况发生。
Alexander Embiricos: I mean, so one thing is just the technology that we're building with has just transformed so many things from both how we build, but also what kinds of things we can enable for users.
Alexander Embiricos：我的意思是，我们正在构建的技术刚刚改变了很多东西，无论是我们的构建方式，还是我们可以为用户提供的服务。
And we spend most of our time talking about the sort of improvements within the foundation models, but I believe that even if we had no more progress today with models, which is absolutely not the case, but even if we had no more progress, we are way behind on product.
我们大部分时间都在讨论基础模型中的改进，但我相信，即使我们今天在模型方面没有更多进展（绝对不是事实），但即使我们没有更多进展，我们在产品上也远远落后。
There's so much more product to build.
还有更多的产品需要构建。
So I think just the moment is ripe, if that makes sense.
所以我认为时机已经成熟，如果这有意义的话。
Alexander Embiricos: But I think there's a lot of counterintuitive things that surprised me when I arrived as far as how things are structured.
Alexander Embiricos：但我认为，当我了解事物的结构时，有很多违反直觉的事情让我感到惊讶。
One example that comes to mind is when I was working on my startup and before that, when I was at Dropbox, it was very important, especially as a PM to always rally the ship and it was like make sure you're pointed in the right direction and then you can accelerate in that direction.
我想到的一个例子是，当我正在创业时，在那之前，当我在 Dropbox 时，这一点非常重要，特别是作为一名产品经理，要始终团结一致，就像确保你指向正确的方向，然后你可以朝着那个方向加速。
But here, I think because we don't exactly know what capabilities will even come up soon and we don't know what's going to work technically, and then we also don't know what's going to land even if it works technically, it's much more important for us to be very humble and learn a lot more empirically and just try things quickly.
但在这里，我认为因为我们不知道什么功能很快就会出现，我们不知道什么在技术上会起作用，然后我们也不知道什么会落地，即使它在技术上起作用，所以对我们来说更重要的是要非常谦虚，更多地凭经验学习并快速尝试。
And the org is set up in that way to be incredibly bottoms up.
该组织的建立方式是令人难以置信的自下而上。
Alexander Embiricos: This is, again, one of those things that, as you were saying, everyone wants to move fast.
Alexander Embiricos：正如您所说，这又是每个人都希望快速行动的事情之一。
I think everyone likes to say that they're bottoms up, or at least a lot of people do, but OpenAI is truly, truly bottoms up.
我想每个人都喜欢说他们是自下而上的，或者至少很多人都这么说，但 OpenAI 确实是自下而上的。
And that's been a learning experience for me that now it'll be interesting if I ever work at...
这对我来说是一次学习经历，现在如果我在……工作将会很有趣。
I don't think it'll even make sense to work at a non-AI company in the future.
我认为未来在非人工智能公司工作甚至没有意义。
I don't even know what that means.
我什至不知道这意味着什么。
But if I were to imagine it or go back in time, I think I would run things totally new.
但如果我想象一下或者回到过去，我想我会采取全新的方式。
Lenny Rachitsky: What I'm hearing is this ready, fire, aim is the approach more than ready, aim, fire.
莱尼·拉奇茨基：我听到的是，“准备、开火、瞄准”比“准备、瞄准、开火”更重要的是方法。
And there's something, and as you process that, because that may not come across well, but I actually have heard this a lot at AI companies is because you don't know, and Nick Turley shared I think the same sentiment, because you don't know how people will use it it doesn't make sense to spend a lot of time making it perfect.
有一些东西，当你处理它时，因为这可能不太好理解，但实际上我在人工智能公司经常听到这样的说法是因为你不知道，尼克·特利也有同样的观点，因为你不知道人们将如何使用它，所以花费大量时间使其完美是没有意义的。
It's better to just get it out there in a primordial way, see how people use it, and then go big on that use case.
最好以原始方式将其推出，看看人们如何使用它，然后深入研究该用例。
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
Okay, to use this analogy a little bit, I feel like there is an aim component, but the aim component is much fuzzier.
好吧，稍微用一下这个比喻，我觉得有一个目标组件，但目标组件要模糊得多。
It's kind of like, roughly what do we think can happen?
有点像，我们认为大概会发生什么？
Someone I've learned a ton from working here is a research lead, and he likes to say that at OpenAI, we can have really good conversations about something that's a year plus from now, and there's a lot of ambiguity in what will happen, but that's a right sort of timeline.
我从这里工作中学到了很多东西，他是一位研究负责人，他喜欢说，在 OpenAI，我们可以就一年多后的事情进行非常好的对话，并且对于将会发生的事情存在很多模糊性，但这是一个正确的时间表。
And then we can have really good conversations about what's happening in low months or weeks.
然后我们就可以就几个月或几周内发生的事情进行非常好的对话。
But there's this awkward middle ground, which was as you start approaching a year, but you're not at a year where it's very difficult to reason about, right?
但是有一个尴尬的中间立场，那就是当你开始接近一年时，但你并没有处于一个很难推理的一年，对吗？
Alexander Embiricos: And so as far as aiming, I think we want to know, "Okay, what are some of the futures that we're trying to build towards?" And a lot of the problems we're dealing with in AI, such as alignment are problems you need to be thinking out really far out into the future.
Alexander Embiricos：就目标而言，我想我们想知道，“好吧，我们正在努力打造哪些未来？”我们在人工智能中处理的许多问题，例如对齐，都是你需要在未来考虑的问题。
So we're kind of aiming fuzzily there, but when it comes down to the more tactically like, "Oh yeah, what product will we build and therefore how will people use that product?" That's the place where we're much more like, "Let's find out empirically."
因此，我们的目标有点模糊，但当涉及到更具战术性的问题时，比如“哦，是的，我们将制造什么产品，人们将如何使用该产品？”这就是我们更像是“让我们凭经验找出答案”的地方。
Lenny Rachitsky: That's a good way of putting it.
Lenny Rachitsky：这是一个很好的表达方式。
Something else that when people hear this, people sometimes hear companies like yours saying, "Okay, we're going to be bottoms up.
另外，当人们听到这个时，人们有时会听到像你们这样的公司说：“好吧，我们将自下而上。
We're going to try a bunch of stuff.
我们将尝试很多东西。
We're not going to have exactly a plan of where it's going in the next few months." The key is you all hire the best people in the world.
我们不会制定未来几个月的具体计划。”关键是你们都雇用了世界上最优秀的人才。
And so that feels like a really key ingredient in order to be this successful at bottoms up work.
因此，这感觉像是在自下而上的工作中取得成功的一个非常关键的因素。
Alexander Embiricos: It just super resonates with me.
Alexander Embiricos：这引起了我的强烈共鸣。
I was just, again, surprised or even shocked when I arrived at the level of individual drive and autonomy that everyone here has.
当我达到这里每个人都拥有的个人动力和自主权水平时，我再次感到惊讶甚至震惊。
So I think the way that OpenAI runs, you can't read this or listen to a podcast and be like, "I'm just going to deploy this to my company." Maybe this is a harsh thing to say, but I think very few companies have the talent caliber to be able to do that.
所以我认为 OpenAI 的运行方式，你不能在阅读这篇文章或听播客时说，“我只是要把它部署到我的公司。”也许这句话说得有些刺耳，但我认为很少有公司有人才能够做到这一点。
So it might need to be adjusted if you were going to implement this.
因此，如果您要实施此操作，可能需要进行调整。
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
So let's talk Codex.
那么我们来谈谈法典。
You lead work on Codex.
您领导法典工作。
How's Codex going?
法典进展如何？
What numbers can you share?
您可以分享哪些号码？
Is there anything you can share there?
有什么可以分享的吗？
Also, just not everyone knows exactly what Codex is, explain what Codex is.
另外，并不是每个人都确切知道 Codex 是什么，请解释一下 Codex 是什么。
Alexander Embiricos: Totally, yeah.
亚历山大·恩比里科斯：完全是的。
So I had the very lucky job of living in the future and leading products on Codex.
所以我有一份非常幸运的工作，能够生活在未来并在 Codex 上领先产品。
And Codex is OpenAI's coding agent.
Codex是OpenAI的编码代理。
So super concretely, that means it's an IDE extension, a VS code extension that you can install or a terminal tool that you can install.
非常具体地说，这意味着它是一个 IDE 扩展、一个可以安装的 VS 代码扩展或一个可以安装的终端工具。
And when you do so, you can then basically pair with Codex to answer questions about code, write code, run tests, execute code, and do a bunch of the work in that thick middle section of the software development lifecycle, which is all about writing code that you're going to get into production.
当您这样做时，您基本上可以与 Codex 配对来回答有关代码的问题，编写代码，运行测试，执行代码，并在软件开发生命周期的中间部分完成大量工作，这都是关于编写将要投入生产的代码的。
Alexander Embiricos: More broadly, we think of Codex as what it currently is just the beginning of a software engineering teammate.
Alexander Embiricos：更广泛地说，我们认为 Codex 目前只是软件工程团队的开始。
So when we use a big word like teammate, some of the things we're imagining are that it's not only able to write code, but actually it participates early on in the ideation and planning phases of writing software and then further downstream in terms of validation, deploying and maintaining code.
因此，当我们使用“队友”这样的大词时，我们想象的一些事情是，它不仅能够编写代码，而且实际上它早期参与编写软件的构思和规划阶段，然后进一步参与下游的验证、部署和维护代码。
Alexander Embiricos: To make that a little more fun, one thing I like to imagine is if you think of what Codex is today, it's a bit like this really smart intern that refuses to read Slack and doesn't check Datadog or Century unless you ask it to.
Alexander Embiricos：为了让这更有趣，我喜欢想象的一件事是，如果你想想今天的 Codex，它有点像一个非常聪明的实习生，他拒绝阅读 Slack，并且除非你要求，否则不会检查 Datadog 或 Century。
And so no matter how smart it is, how much are you going to trust it to write code without you also working with it?
因此，无论它有多智能，您在多大程度上相信它能够在您不使用它的情况下编写代码？
So that's how people use it mostly today is they pair with it.
这就是人们今天最常使用它的方式，即与它配对。
But we want to get to the point where it can work just like a new intern that you hire, you don't only ask them to write code, but you ask them to participate across the cycle.
但我们希望能够像你雇用的新实习生一样工作，你不仅要求他们编写代码，而且要求他们参与整个周期。
So you know that even if they don't get something right the first try, they're eventually going to be able to iterate their rate there.
所以你知道，即使他们第一次尝试没有得到正确的结果，他们最终也能够在那里迭代他们的速度。
Lenny Rachitsky: I thought the point about not reading Slack and Datadog was it's just not distracted, it's just constantly focused and is always in flow.
Lenny Rachitsky：我认为不阅读 Slack 和 Datadog 的意义在于它不会分心，它只是不断专注并且始终处于流动状态。
But I get what you're saying there is it doesn't have all the context on everything that's going on.
但我明白你的意思，它没有提供所有正在发生的事情的背景信息。
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
And that's not only true when it's performing a task, but again, if you think of the best team and teammates, you don't tell them what to do.
这不仅在执行任务时如此，而且，如果你想到最好的团队和队友，你不会告诉他们该怎么做。
Maybe when you first hire them, you have a couple meetings and you're like, "Hey," you learn, "Okay, these prompts work for this teammate, these prompts don't.
也许当你第一次雇用他们时，你会召开几次会议，然后你会说，“嘿，”你会了解到，“好吧，这些提示对这位队友有用，但这些提示不起作用。
This is how to communicate with this person." Then eventually you give them some starter tasks, you delegate a few tasks.
这就是与这个人沟通的方式。”然后最终你给他们一些入门任务，你委派了一些任务。
But then eventually you just say like, "Hey, great.
但最终你只会说，“嘿，太棒了。
Okay, you're working with this set of people in this area of the code base.
好的，您正在代码库的这个区域与这群人一起工作。
Feel free to work with other people on other parts of the code base too, even.
甚至也可以随意与其他人一起处理代码库的其他部分。
And yeah, you tell me what you think makes sense to be done." And so we think of this as proactivity and one of our major goals with Codex is to get to proactivity.
是的，你告诉我你认为应该做什么是有意义的。”因此，我们认为这是积极主动的，而我们 Codex 的主要目标之一就是积极主动。
Alexander Embiricos: I think this is critically important to achieve the mission of OpenAI, which is to deliver the benefits of AGI to all humanity.
Alexander Embiricos：我认为这对于实现 OpenAI 的使命至关重要，即为全人类带来 AGI 的好处。
I like to joke today that AI products, and it's a half joke, they're actually really hard to use because you have to be very thoughtful about when it could help you.
我今天喜欢开玩笑说人工智能产品，这是半个笑话，它们实际上很难使用，因为你必须非常仔细地考虑它何时可以帮助你。
And if you're not prompting a model to help you, it's probably not helping you at that time.
如果你没有提示模型来帮助你，那么它当时可能对你没有帮助。
And if you think of how many times the average user is prompting AI today, it's probably tens of times.
如果你想想今天普通用户提示人工智能的次数，可能是几十次。
But if you think of how many times people could actually get benefit from a really intelligent entity, it's thousands of times per day.
但如果你想一想人们实际上可以从一个真正智能的实体中受益多少次，那就是每天数千次。
And so a large part of our goal with Codex is to figure out what is the shape of an actual teammate agent that is helpful by default.
因此，我们 Codex 的很大一部分目标是找出默认情况下有用的实际队友代理的形状。
Lenny Rachitsky: When people think about Cursor and even Cloud Code, it's like a IDE that helps you code and auto completes code and maybe does some agentic work.
Lenny Rachitsky：当人们想到 Cursor 甚至 Cloud Code 时，它就像一个 IDE，可以帮助您编码并自动完成代码，也许还可以执行一些代理工作。
What I'm hearing here is the vision is different, which is it's a teammate.
我在这里听到的是愿景不同，这是一个队友。
It's like a remote teammate, a building code for you that you talk to and ask to do things.
它就像一个远程队友，一个为您制定的构建代码，您可以与之交谈并要求做一些事情。
And that also does IDE, auto complete and things like that.
这也可以实现 IDE、自动完成等功能。
Is that a kind of a differentiator in the way you think about Codex?
这是您对 Codex 的看法的一种差异化因素吗？
Alexander Embiricos: It's basically this idea that if you're a developer and you're trying to get something done, we want you to just feel like you have superpowers and you're able to move much, much faster.
Alexander Embiricos：基本上这个想法是，如果您是一名开发人员并且正在努力完成某件事，我们希望您感觉自己拥有超能力，并且能够更快地行动。
But we don't think that in order for you to reap those benefits, you need to be sitting there constantly thinking about, "How can I invoke AI at this point to do this thing?" We want you to be able to plug it in to the way that you work and have it just start to do stuff without you having to think about it.
但我们认为，为了让您获得这些好处，您不需要坐在那里不断思考“此时我如何调用人工智能来完成这件事？”我们希望您能够将其融入您的工作方式，并让它开始做事情，而无需您考虑。
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
I have a lot of questions along those lines, but just how's it going?
我有很多类似的问题，但进展如何？
Is there any stats, any numbers you can share about how Codex is doing?
您可以分享有关 Codex 进展情况的任何统计数据或数字吗？
Alexander Embiricos: Yeah, Codex has been growing absolutely explosively since the launch of GPT-5 back in August.
Alexander Embiricos：是的，自 8 月份推出 GPT-5 以来，Codex 一直在爆炸性增长。
There's definitely some interesting product insights to talk about as to how we unlock that growth, if you're interested.
如果您感兴趣的话，肯定有一些有趣的产品见解可以讨论我们如何释放这种增长。
But again, the last stat we shared there was we were well over 10x since August.
但我们再次分享的最后一个统计数据是，自 8 月份以来，我们的增长率远远超过 10 倍。
In fact, it's been 20x since then.
事实上，从那时起已经增长了 20 倍。
Also, the Codex models are serving many trillions of tokens a week now, and it's basically our most served coding model.
此外，Codex 模型现在每周服务数万亿个代币，这基本上是我们服务最多的编码模型。
One of the really cool things that we've seen is that the way that we decided to set up the Codex team was to build a really tightly integrated product and research team that are iterating on the model and the harness together.
我们看到的一件非常酷的事情是，我们决定建立 Codex 团队的方式是建立一个真正紧密集成的产品和研究团队，一起迭代模型和工具。
And it turns out that lets you just do a lot more and try many more experiments as to how these things will work together.
事实证明，这可以让你做更多的事情并尝试更多的实验来了解这些东西如何协同工作。
Alexander Embiricos: And so we were just training these models for use in our first party harness that we were very opinionated about.
Alexander Embiricos：所以我们只是训练这些模型，以便在我们非常有意见的第一方安全带中使用。
And then what we've started to see more recently actually is that other major API coding customers are now starting to adopt these models as well.
事实上，我们最近开始看到其他主要 API 编码客户现在也开始采用这些模型。
And so we've reached a point where actually the Codex model is the most served coding model in the API as well.
因此，我们实际上已经达到了这样一个地步：Codex 模型实际上也是 API 中使用最多的编码模型。
Lenny Rachitsky: You hinted at this, what unlocked this growth, I'm extremely interested in hearing that.
Lenny Rachitsky：你暗示了这一点，是什么开启了这种增长，我对此非常感兴趣。
It felt like before, I don't know, maybe this was before you joined the team, it just felt like Cloud Code was killing it.
感觉就像以前一样，我不知道，也许这是在你加入团队之前，感觉就像 Cloud Code 正在杀死它。
Just everyone was sitting on top of Cloud Code.
只是每个人都坐在 Cloud Code 之上。
It was by far the best way to code.
这是迄今为止最好的编码方式。
And then all of a sudden Codex comes around.
然后法典突然出现了。
I remember Karpathy tweeted that he just has never seen a model like this.
我记得卡帕蒂在推特上说他从未见过这样的模特。
I think the tweet was the gnarliest bugs that he runs into that he just spends hours trying to figure out nothing else has solved, he gives it to Codex, lets it run for an hour and it solves it.
我认为这条推文是他遇到的最棘手的错误，他只是花了几个小时试图找出其他没有解决的问题，他把它交给 Codex，让它运行一个小时，它解决了它。
What'd you guys do?
你们做了什么？
Alexander Embiricos: We have this strong sort of mission here at OpenAI basically to build AGI.
Alexander Embiricos：我们 OpenAI 的使命是构建 AGI。
And so we think a lot about how can we shape the product so that it can scale.
因此，我们思考了很多关于如何塑造产品以使其能够扩展的问题。
Earlier I was mentioning like, "Hey, if you're an engineer, you should be getting help from AI thousands of times per day," and so we thought a lot about the primitives for that when we launched our first version of Codex, which was Codex Cloud.
早些时候我提到过，“嘿，如果你是一名工程师，你应该每天从人工智能那里获得数千次帮助”，因此，当我们推出 Codex 的第一个版本（即 Codex Cloud）时，我们对原语进行了很多思考。
And that was basically a product that had its own computer, lived in the cloud, you could delegate to it.
这基本上是一个拥有自己的计算机、存在于云端的产品，你可以委托给它。
And the coolest part about that is you could run many, many tasks in parallel.
最酷的部分是您可以并行运行许多很多任务。
But some of the challenges that we saw are that it's a little bit harder to set that up, both in terms of environment configuration, like giving the model the tools it needs to validate its changes and to learn how to prompt in that way.
但我们看到的一些挑战是，在环境配置方面，设置起来有点困难，比如为模型提供验证其更改所需的工具以及学习如何以这种方式提示。
Alexander Embiricos: My analogy for this is, going back to this teammate analogy, it's like if you hired a teammate, but you're never allowed to get on a call with them and you can only go back and forth asynchronously over time.
Alexander Embiricos：我对此的类比是，回到队友的类比，就像你雇佣了一个队友，但你永远不被允许与他们通话，你只能随着时间的推移异步来回。
That works for some teammates and eventually that's actually how you want to spend most of your time.
这对一些队友来说是有效的，最终这就是你想要度过大部分时间的方式。
So that's still the future, but it's hard to initially adopt.
所以这仍然是未来，但最初很难采用。
And so we still have that vision of like, that's what we're trying to get you to, a teammate that you delegate to and then is proactive, and we're seeing that growing.
所以我们仍然有这样的愿景，那就是我们试图让你成为一个你可以委派的队友，然后是积极主动的，我们看到这种情况正在增长。
But the key unlock is actually first you need to land with users in a way that's much more intuitive and trivial to get value from.
但关键的解锁实际上是首先你需要以一种更直观、更简单的方式与用户接触并从中获取价值。
Alexander Embiricos: So the way that most people discover, the vast majority of users discover Codex today is either they download an IDE extension or they run it in their CLI and the agent works there with you on your computer interactively.
Alexander Embiricos：所以，大多数人发现 Codex 的方式，绝大多数用户今天发现 Codex 的方式是下载 IDE 扩展，或者在 CLI 中运行它，然后代理在您的计算机上与您交互工作。
And it works within a sandbox, which is actually a really cool piece of tech to help that be safe and secure, but it has access to all those dependencies.
它在沙箱中工作，这实际上是一项非常酷的技术，可以帮助确保安全，但它可以访问所有这些依赖项。
So if the agent needs to do something, it needs to run a command, it can do so within the sandbox.
因此，如果代理需要做某事，它需要运行命令，它可以在沙箱内执行此操作。
We don't have to set up any environment.
我们不需要设置任何环境。
And if it's a command that doesn't work in the sandbox, it can just ask you.
如果该命令在沙箱中不起作用，它只会询问您。
And so you can get into this really strong feedback loop using the model.
因此，您可以使用该模型进入这个非常强大的反馈循环。
And then over time, our team's job is to help turn that feedback loop into you as a byproduct of using the product, configuring it so that you can then be delegating to it down the line.
随着时间的推移，我们团队的工作就是帮助您将反馈循环转变为使用该产品的副产品，对其进行配置，以便您可以将其委托给该产品。
Alexander Embiricos: And again, analogy, keep coming back to it, but if you hire a teammate and you ask them to do work, but you just give them a fresh computer from the store, it's going to be hard for them to do their job.
Alexander Embiricos：再次打个比方，继续回到这个话题，但是如果你雇佣了一名队友并要求他们工作，但你只是从商店给他们一台新电脑，那么他们将很难完成他们的工作。
But if as you work with them side by side, you could be like, "Oh, you don't have a password for this service we use, here's the password for this service.
但如果你与他们并肩工作，你可能会说，“哦，你没有我们使用的这项服务的密码，这是这项服务的密码。
Yeah, don't worry, feel free to run this command," then it's much easier for them to then go off and do work for hours without you.
是的，别担心，请随意运行此命令，”然后他们就可以更轻松地在没有您的情况下继续工作几个小时。
Lenny Rachitsky: So what I'm hearing is the initial version of Codex was almost too far in the future.
Lenny Rachitsky：所以我听到的是 Codex 的初始版本几乎是遥远的未来。
It's like a remote in the cloud agent that's coding for you asynchronously.
它就像云代理中的远程代理，为您异步编码。
And what you did is, "Okay, let's actually come back a little bit, let's integrate into the way engineers already integrate into IDs and locally and help them on ramp to this new world,"
你所做的是，“好吧，让我们实际上回过头来，让我们融入工程师已经融入 ID 和本地的方式，并帮助他们进入这个新世界，”
Alexander Embiricos: Totally.
亚历山大·恩比里科斯：完全可以。
And it was quite interesting because we dogfood product a ton at OpenAI.
这很有趣，因为我们在 OpenAI 上测试了大量产品。
So dogfood as in we use our own product.
所以狗粮就像我们使用我们自己的产品一样。
And so Codex has been accelerating OpenAI over the course of the entire year, and the cloud product was a massive accelerant to the company as well.
因此，Codex 在这一年中一直在加速 OpenAI，而云产品也对公司起到了巨大的促进作用。
It just turns out that this was one of those places where the signal we got from dogfooding is a little bit different from the signal you get from the general market because at OpenAI, we train reasoning models all day and so we're very used to this kind of prompting and think upfront, run things massively in parallel and it would take some time and then come back to it later asynchronously.
事实证明，这是我们从测试中获得的信号与从一般市场获得的信号有点不同的地方之一，因为在 OpenAI，我们整天训练推理模型，所以我们非常习惯这种提示和预先思考，大规模并行运行事物，这需要一些时间，然后稍后异步返回。
And so now when we build, we still get a ton of signal from dogfooding internally, but we're also very cognizant of the different ways that different audiences use the product.
因此，现在当我们构建时，我们仍然会从内部测试中获得大量信号，但我们也非常了解不同受众使用该产品的不同方式。
Lenny Rachitsky: That's really funny.
莱尼·拉奇茨基：这真的很有趣。
It's like live in the future, but maybe not too far in the future.
这就像生活在未来，但也许不会太远。
And I could see how everyone at OpenAI is living very far in the future, and sometimes that won't work for everyone.
我可以看到 OpenAI 的每个人都生活在遥远的未来，但有时这并不适合每个人。
Lenny Rachitsky: What about just intelligence training data?
Lenny Rachitsky：情报训练数据怎么样？
I don't know, is there something else that helped Codex accelerate its ability to actually code?
我不知道，是否还有其他东西可以帮助 Codex 加速其实际编码的能力？
Is it better, cleaner data?
是更好、更干净的数据吗？
Is it more just models advancing?
仅仅是模型的进步吗？
Is there anything else that really helped accelerate?
还有其他什么真正有助于加速吗？
Alexander Embiricos: Yeah, so there's a few components here.
Alexander Embiricos：是的，这里有一些组件。
I guess you were mentioning models and the models have improved a ton.
我猜你提到的是模型，模型已经改进了很多。
In fact, just last Wednesday, we shipped GPT-5.1-Codex-Max, a very accurately named model, that is awesome.
事实上，就在上周三，我们发布了 GPT-5.1-Codex-Max，这是一个命名非常准确的模型，非常棒。
It is awesome both because it is for any given task that you were using GPT-5.1-Codex for, it's roughly 30% faster at accomplishing that task.
它非常棒，因为它对于您使用 GPT-5.1-Codex 执行的任何给定任务来说，完成该任务的速度大约提高了 30%。
But also it unlocks a ton of intelligence.
但它也释放了大量的智慧。
So if you use it at our higher reasoning levels, it's just even smarter.
因此，如果您在更高的推理水平上使用它，它就会更加智能。
And that tweet you were saying Karpathy made about, "Hey, give this your gnarliest bugs," obviously there's a ton going on in the market right now, but Codex-Max is definitely carrying that mantle of us tackling the hardest bugs.
你所说的 Karpathy 发布的那条推文，“嘿，给你最棘手的 bug”，显然现在市场上有很多事情正在发生，但 Codex-Max 绝对承担了我们解决最难的 bug 的责任。
So that is super cool.
所以这太酷了。
Alexander Embiricos: But I will say it's like some of how we're thinking about this is evolving a little bit from being like, "Yeah, we're just going to think about the model and let's just train the best model," to really thinking about what is an agent actually overall?
Alexander Embiricos：但我想说的是，我们对此的思考方式正在从“是的，我们只需要考虑模型，然后训练最好的模型”，到真正思考代理实际上是什么？
And I'm not going to try to define agent exactly, but at least the stack that we think of it as having is it's like you have this model, really smart reasoning model that knows how to do a specific kind of task really well, so we can talk about how we make that possible.
我不会尝试准确地定义代理，但至少我们认为它拥有的堆栈就像你有这个模型，非常聪明的推理模型，知道如何很好地完成特定类型的任务，所以我们可以讨论如何使之成为可能。
But then actually we need to serve that model through an API into a harness, and both of those things also have a really big role here.
但实际上我们需要通过 API 将该模型提供给线束，而这两件事在这里也发挥着非常重要的作用。
Alexander Embiricos: So for instance, one of the things that we're really proud of is you can have GPT-5.1-Codex-Max work for really long periods of time.
Alexander Embiricos：举例来说，我们真正感到自豪的一件事是您可以让 GPT-5.1-Codex-Max 工作很长时间。
That's not normal, but you can set it up to do that or that might happen.
这不正常，但您可以对其进行设置，否则可能会发生这种情况。
But now routinely we'll hear about people saying, "Yeah, it ran overnight or it ran for 24 hours." And so for a model to work continuously for that amount of time, it's going to exceed its context window.
但现在我们经常会听到人们说：“是的，它运行了一夜或运行了 24 小时。”因此，对于一个模型来说，要连续工作这么长的时间，它将超出其上下文窗口。
And so we have a solution for that, which we call compaction.
所以我们有一个解决方案，我们称之为压缩。
Alexander Embiricos: But compaction is actually a feature that uses all three layers of that stack.
Alexander Embiricos：但压缩实际上是一个使用该堆栈的所有三层的功能。
So you need to have a model that has a concept of compaction and knows like, "Okay, as I start to approach this context window, I might be asked to prepare to be run in a new context window." And then at the API layer, you need an API that understands this concept and has an endpoint that you can hit to do this change.
因此，您需要一个具有压缩概念的模型，并且知道“好吧，当我开始接近此上下文窗口时，可能会要求我准备在新的上下文窗口中运行。”然后在 API 层，您需要一个能够理解这个概念的 API，并且有一个可以用来执行此更改的端点。
And at the harness layer, you need a harness that can prepare the payload for this to be done.
在线束层，您需要一个线束来准备有效负载以完成此操作。
So shipping this compaction feature that now just made this behavior possible to anyone using Codex actually meant working across all three things.
因此，提供这个压缩功能，现在让任何使用 Codex 的人都可以实现这种行为，实际上意味着要同时处理所有三件事。
And I think that's increasingly going to be true.
我认为这将越来越成为事实。
Alexander Embiricos: Another maybe underappreciated version of this is if you think about all the different coding products out there, they all have very different tool harnesses with very different opinions on how the model should work.
Alexander Embiricos：另一个可能被低估的版本是，如果你考虑一下所有不同的编码产品，它们都有非常不同的工具，对于模型应该如何工作有非常不同的看法。
So if you want to train a model to be good at all the different ways it could work, maybe you have a strong opinion that it should work using semantic search.
因此，如果您想训练一个模型使其擅长所有不同的工作方式，也许您强烈认为它应该使用语义搜索来工作。
Maybe you have a strong opinion that it should call bespoke tools or maybe you have, in our case, a strong opinion that it should just use the shell and work in the terminal, you can move much faster if you're just optimizing for one of those worlds.
也许您强烈认为它应该调用定制工具，或者在我们的例子中，您可能强烈认为它应该只使用 shell 并在终端中工作，如果您只是针对这些世界之一进行优化，那么您可以移动得更快。
So the way that we built Codex is that it just uses the shell, but in order to make that safer and secure, we have a sandbox that the model is used to operating in.
因此，我们构建 Codex 的方式是仅使用 shell，但为了使其更安全，我们有一个模型用于在其中运行的沙箱。
Alexander Embiricos: So I think one of the biggest accelerants, to go all the way back to answer to your question, is just we're building all three things in parallel and tuning each one and constantly experimenting with how those things work with a tightly integrated product and research team.
Alexander Embiricos：因此，我认为最大的促进因素之一，回过头来回答你的问题，就是我们正在并行构建所有三件事并调整每一项，并不断尝试这些事情如何与紧密集成的产品和研究团队一起工作。
Lenny Rachitsky: Do you think you win in this space?
Lenny Rachitsky：你认为你在这个领域获胜吗？
Do you think it'll always be this kind of race with other models constantly leapfrogging each other?
你认为这种比赛会永远是其他车型不断超越的比赛吗？
Do you think there's a world where someone just runs away with it and no one else can ever catch up?
你认为世界上是否存在一个人只能逃跑而其他人无法追上的世界？
Is there a path to just, "We win"?
有没有一条途径可以让“我们赢了”？
Alexander Embiricos: Again, comes back to this idea of building a teammate, and not just a teammate that participates in team planning and prioritization, not just a teammate that really tests its code and helps you maintain and deploy it.
Alexander Embiricos：再次回到构建队友的想法，而不仅仅是一个参与团队规划和优先级排序的队友，而不仅仅是一个真正测试其代码并帮助您维护和部署它的队友。
But even a teammate...
但即使是队友...
If you think, again, an engineering teammate, they can also schedule a calendar invite or move standup or do whatever, right?
如果您再次认为，工程团队成员也可以安排日历邀请或移动站会或做任何其他事情，对吗？
And so in my mind, if we just imagine that every day or every week some crazy new capability is just going to be deployed by a research lab, it's just impossible for us as humans to keep up and use all this technology.
因此，在我看来，如果我们只是想象每天或每周研究实验室都会部署一些疯狂的新功能，那么我们人类就不可能跟上并使用所有这些技术。
So I think we need to get to this world where you kind of just have an AI teammate or super assistant that you just talk to and it just knows how to be helpful on its own.
所以我认为我们需要进入这样一个世界：你只要有一个人工智能队友或超级助手，你只要与之交谈，它就知道如何自己提供帮助。
So you don't have to be reading the latest tips for how to use it, you've plugged it in and it just provides help.
因此，您不必阅读有关如何使用它的最新提示，您已经将其插入，它只是提供帮助。
Alexander Embiricos: So that's kind of the shape of what I think we're building.
Alexander Embiricos：我认为这就是我们正在构建的形状。
And I think that will be a very sticky winning product if we can do so.
我认为，如果我们能做到的话，这将是一个非常具有粘性的获胜产品。
So the shape that in my head, at least I have, is that we build...
因此，在我的脑海中，至少我的想法是，我们构建......
Maybe a fun topic is like, "Is Chat the right interface for AI?" I actually think Chat is a very good interface when you don't know what you're supposed to use it for.
也许一个有趣的话题是“聊天是人工智能的正确界面吗？”实际上，当您不知道应该使用 Chat 做什么时，我认为 Chat 是一个非常好的界面。
In the same way that if I think of I'm on MS Teams or in Slack with a teammate, Chat is pretty good.
同样，如果我认为我在 MS Teams 中或与队友一起在 Slack 中，那么聊天就非常好。
I can ask for whatever I want.
我可以要求任何我想要的东西。
It's kind of the common denominator for everything.
这是一切事物的共同点。
So you can chat with a super assistant about whatever topic you want, whether it be coding or not.
因此，您可以与超级助手讨论您想要的任何主题，无论是否是编码。
And then if you are a functional expert in a specific domain such as coding, there's a GUI that you can pull up to go really deep and look at the code and work with the code.
然后，如果您是特定领域（例如编码）的功能专家，您可以使用一个 GUI 来真正深入地查看代码并使用代码。
Alexander Embiricos: So I think what we need to build as OpenAI is basically this idea of you have Chat, ChatGPT and not as a tool that's ubiquitously available to everyone, you start using it even outside of work to just help you.
Alexander Embiricos：所以我认为我们需要构建的 OpenAI 基本上是这样的想法：你拥有 Chat、ChatGPT，而不是作为每个人都可以使用的工具，你甚至在工作之外就开始使用它来帮助你。
You become very comfortable with the idea of being accelerated with AI.
您对通过人工智能加速的想法感到非常满意。
So then you get to work and you just can naturally just, "Yeah, I'm just going to ask it for this and I don't need to know about all the connectors or all the different features.
然后你开始工作，你就可以自然地说，“是的，我只是要问它，我不需要知道所有的连接器或所有不同的功能。
I'm just going to ask it for help and it'll surface to me the best way that it can help at this point in time and maybe even chime in when I didn't ask it for help." So in my mind, if we can get to that, I think that's how we really build the winning product.
我只是想向它寻求帮助，它会向我展示此时它可以提供帮助的最佳方式，甚至可能在我没有向它寻求帮助时插话。”所以在我看来，如果我们能做到这一点，我认为这就是我们真正打造成功产品的方式。
Lenny Rachitsky: This is so interesting because with my chat with Nick Turley, the head of ChatGPT, I think he shared that the original name for ChatGPT was Super Assistant or something like that.
Lenny Rachitsky：这非常有趣，因为在我与 ChatGPT 负责人 Nick Turley 的聊天中，我认为他分享了 ChatGPT 的原始名称是“超级助手”或类似的名称。
And it's interesting that there's that approach to the super assistant and then there's this Codex approach.
有趣的是，有超级助手的方法，还有 Codex 的方法。
It's almost like the B2C version and the B2B version.
它几乎就像B2C 版本和B2B 版本。
And what I'm hearing is the idea here is, okay, you start with coding and building and then it's doing all this other stuff for you, scheduling meetings, I don't know, probably posting in Slack, I don't know, shipping designs.
我听到的想法是，好吧，你从编码和构建开始，然后它会为你做所有其他事情，安排会议，我不知道，可能在 Slack 上发帖，我不知道，交付设计。
I don't know, is the idea that this is the business version of ChatGPT in a sense, or is there something else there?
我不知道，从某种意义上来说，这是ChatGPT的商业版本，还是还有其他什么东西？
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
So we're getting to the one-year time horizon conversation.
因此，我们将开始一年的时间范围对话。
A lot of this might happen sooner, but in terms of fuzziness, I think we're at the one year.
很多事情可能会更早发生，但就模糊性而言，我认为我们已经到了一年。
So I'll give you a contention and a plausible way we get there, but as for how it happens, who knows?
所以我会给你一个论点和一个我们到达那里的合理方式，但至于它是如何发生的，谁知道呢？
So basically, if we're going to build a super assistant, it has to be able to do things.
所以基本上，如果我们要打造一个超级助手，它必须能够做一些事情。
So we're going to have a model and it's going to be able to do stuff affecting your world.
所以我们将有一个模型，它将能够做一些影响你的世界的事情。
And one of the learnings I think we've seen over the past year or so is that for models to do stuff, they're much more effective when they can use a computer.
我认为我们在过去一年左右的时间里看到的一个教训是，对于模型来说，当他们可以使用计算机时，他们会更有效。
Alexander Embiricos: Right, okay, so now we're like, okay, we need the super assistant that can use a computer, or many computers.
Alexander Embiricos：是的，好吧，所以现在我们就像，好吧，我们需要可以使用一台计算机或多台计算机的超级助手。
And now the question is, okay, well, how should it use the computer?
现在的问题是，好吧，那么它应该如何使用计算机呢？
And there's lots of ways to use a computer.
使用计算机的方法有很多种。
You could try to hack the OS and use accessibility APIs, maybe a bit easier as you could point and click.
您可以尝试破解操作系统并使用辅助功能 API，也许更容易一点，因为您可以点击。
That's a little slow and unpredictable sometimes.
有时这有点慢而且不可预测。
And another way, it turns out the best way for models to use computers is simply to write code.
另一方面，事实证明模型使用计算机的最佳方式就是编写代码。
So we're kind of getting to this idea where, well, if you want to build any agent, maybe you should be building a coding agent and maybe to the user, a non-technical user, they won't even know they're using a coding agent, the same way that no one thinks about are they using the internet or not, which is they're more just like, "Is WiFi on?"
所以我们有点接近这个想法，如果你想构建任何代理，也许你应该构建一个编码代理，也许对于用户来说，一个非技术用户，他们甚至不知道他们正在使用编码代理，就像没有人考虑他们是否使用互联网一样，他们更像是，“WiFi 打开了吗？”
Alexander Embiricos: So I think that what we're doing with Codex is we're building a software engineering teammate, and as part of that, we're kind of building an agent that can use a computer by writing code.
Alexander Embiricos：所以我认为我们正在使用 Codex 做的是我们正在构建一个软件工程团队伙伴，作为其中的一部分，我们正在构建一个可以通过编写代码来使用计算机的代理。
And so we're already seeing some pull for this.
所以我们已经看到了一些这方面的吸引力。
It's quite early, but we're starting to see people who are using Codex for coding adjacent product purposes.
现在还为时过早，但我们已经开始看到有人使用 Codex 来编码相邻的产品。
And so as that develops, I think we'll just naturally see that, oh, it turns out we should just always have the agent write code if there is a coding way to solve a problem instead of...
随着这种情况的发展，我想我们会很自然地看到，哦，事实证明，如果有解决问题的编码方法，我们应该总是让代理编写代码，而不是......
Even if you're doing a financial analysis, maybe write some code for that.
即使您正在进行财务分析，也可以为此编写一些代码。
Alexander Embiricos: So basically like you were like, "Hey, is this the two ends of this product for the super assistant of ChatGPT?" In my mind, just coding is a core competency of any agent including ChatGPT.
Alexander Embiricos：基本上就像你说的那样，“嘿，这是 ChatGPT 超级助手产品的两端吗？”在我看来，编码是包括 ChatGPT 在内的任何代理的核心能力。
And so really what we think we're building is that competency.
所以我们认为我们真正正在建立的是这种能力。
So here's the really cool thing about agents writing code is that you can import code.
因此，代理编写代码的真正酷之处在于您可以导入代码。
Code is composable, interoperable.
代码是可组合的、可互操作的。
Because one very reductive view we could have for an agent is it's just going to be given a computer and it's just going to point and click and go around.
因为我们对代理的一个非常简化的看法是，它只需拥有一台计算机，它只需点击并四处走动即可。
But that is the future.
但这就是未来。
And then how we get there is difficult to chart a path because a lot of the questions around building agents aren't like, "Can the agent do it?" But it's more about, "Well, how can we help the agent understand the context that it's working in?" And the team that's using it probably has a way that they like to do things.
然后我们如何到达那里很难绘制一条路径，因为围绕建筑代理的很多问题都不是“代理能做到吗？”但更重要的是，“那么，我们如何帮助智能体了解它正在工作的环境？”使用它的团队可能有他们喜欢的做事方式。
They have guidelines.
他们有指导方针。
They probably want certain deterministic guarantees about what the agent can or cannot do.
他们可能希望对代理能做什么或不能做什么有某些确定性的保证。
Or they want to know that the agent understands this detail.
或者他们想知道代理是否理解这个细节。
Alexander Embiricos: An example would be if we're looking at a crash reporting tool, hitting a connector for it, every sub-team probably has a different meta prompt for how they want the crashes to be analyzed.
Alexander Embiricos：一个例子是，如果我们正在查看一个崩溃报告工具，点击它的连接器，每个子团队可能都有不同的元提示来说明他们希望如何分析崩溃。
And so we start to get to this thing where, yeah, we have this agent sitting in front of a computer, but we need to make that configurable for the team or for the user and let them...
因此，我们开始着手解决这个问题，是的，我们有一个代理坐在计算机前，但我们需要为团队或用户进行配置，并让他们......
Stuff that the agent does often, we probably just want to build in as a competency that this agent has that it can do.
代理经常做的事情，我们可能只是想将其作为该代理拥有的能力来构建。
Alexander Embiricos: So I think we end up with this generalizable thing, that you were saying, of an agent that can just write its own scripts for whatever it wants to do.
Alexander Embiricos：所以我认为我们最终得到了你所说的这种普遍化的东西，即一个代理可以为它想做的任何事情编写自己的脚本。
But I think that the really key part here is can we make it so that everything that the agent has to do often or that it does well, we can just remember and store so that the agent doesn't have to write a script for that again?
但我认为这里真正关键的部分是我们能否做到让代理必须经常做或做得很好的所有事情我们都可以记住并存储，这样代理就不必再为此编写脚本？
Or maybe if I just joined a team and you are already on the same team as me, I can just use all those scripts that the agents had written already.
或者，如果我刚刚加入一个团队，而你已经和我在同一个团队中，我就可以使用特工已经编写的所有脚本。
Lenny Rachitsky: Yeah, it's like if this is our teammate, they can share things that it's learned from working with other people at the company.
Lenny Rachitsky：是的，如果这是我们的队友，他们可以分享从与公司其他人合作中学到的东西。
It just makes sense as a metaphor.
它只是作为一个比喻才有意义。
Lenny Rachitsky: It feels like you're in the Karpathy camp of, "Agents today are not that great and mostly slop and maybe in the future they'll be awesome." Does that resonate?
Lenny Rachitsky：感觉就像你属于卡帕蒂阵营，“今天的特工不是那么伟大，而且大部分都是马虎，也许将来他们会很棒。”这引起共鸣吗？
Alexander Embiricos: So I think coding agents are pretty great.
Alexander Embiricos：所以我认为编码代理非常棒。
I think we're seeing a ton of value there.
我认为我们在那里看到了巨大的价值。
Lenny Rachitsky: Yeah, that feels right.
Lenny Rachitsky：是的，感觉不错。
That feels right, yeah.
感觉对了，是的。
Alexander Embiricos: And then I think agents outside of coding, it's still very early.
Alexander Embiricos：然后我认为编码之外的代理，现在还很早。
And this is just my opinion, but I think they're going to get a whole lot better once they can use coding too in a composable way.
这只是我的观点，但我认为一旦他们也能以可组合的方式使用编码，他们就会变得更好。
It's kind of the fun part of when you're building for software engineers, at my startup, we were building for software engineers too for a lot of that journey, and they're just such a fun audience to build for because they also like building for themselves and are often even more creative than we are in thinking about how to use the technology.
当你为软件工程师构建时，这是一个有趣的部分，在我的初创公司，我们也在很多旅程中为软件工程师构建，他们是一个非常有趣的受众，因为他们也喜欢为自己构建，并且在思考如何使用该技术时通常比我们更有创造力。
So by building for software engineers, you get to just observe a ton of emergent behaviors and things that you should do and build into the product.
因此，通过为软件工程师进行构建，您可以观察大量的紧急行为和您应该做的事情并将其构建到产品中。
Lenny Rachitsky: I love how you say that because a lot of people building for engineers get really annoyed because the engineers they're just always complaining about stuff.
Lenny Rachitsky：我喜欢你这么说，因为很多为工程师构建的人真的很恼火，因为工程师总是抱怨东西。
They're like, "Ah, that sucks.
他们就像，“啊，那太糟糕了。
Why'd you build it this way?" I love that you enjoy it, but I think it's probably because you're building such an amazing tool for engineers that can actually solve problems and just code for them.
为什么要这样建造？”我喜欢你喜欢它，但我认为这可能是因为你正在为工程师构建一个如此神奇的工具，它可以真正解决问题并为其编写代码。
Lenny Rachitsky: Kind of along those lines, there's always this talk of what will happen with jobs, engineers, coding, do you have to learn coding?
Lenny Rachitsky：沿着这些思路，总是有人谈论工作、工程师、编码会发生什么，你必须学习编码吗？
All these things.
所有这些事情。
Clearly the way you're describing it is it's a teammate, it's going to work with you, make you more superhuman, it's not going to replace you.
显然，你描述的方式是它是一个队友，它将与你一起工作，让你变得更加超人，它不会取代你。
What's the way you just think about the impact on the field of engineering, having this super intelligent engineering teammate?
您如何看待拥有这位超级聪明的工程队友对工程领域的影响？
Alexander Embiricos: I think there's two sides to it, but the one we were just talking about is this idea that maybe every agent should actually use code and be a coding agent.
Alexander Embiricos：我认为这有两个方面，但我们刚才讨论的一个想法是，也许每个代理实际上都应该使用代码并成为编码代理。
And in my mind, that's just a small part of this broader idea that, hey, as we make code even more ubiquitous...
在我看来，这只是这个更广泛想法的一小部分，嘿，随着我们让代码变得更加普遍......
I mean, you could probably claim it's ubiquitous today, even pre AI, right?
我的意思是，你可能会说它在今天无处不在，甚至在人工智能出现之前，对吧？
But as we make code even more ubiquitous, it's actually just going to be used for many more purposes.
但随着我们让代码变得更加普遍，它实际上将被用于更多目的。
And so there's just going to be a ton more need for humans with this competency.
因此，对具有这种能力的人类的需求将会大大增加。
Alexander Embiricos: So that's my view.
Alexander Embiricos：这就是我的观点。
I think this is quite a complex topic.
我认为这是一个相当复杂的话题。
So it's something we talk about a lot and we have to see how it pans out.
所以这是我们经常谈论的事情，我们必须看看结果如何。
But I think what we can do basically as a product team building in the space is just try to always think about how are we building a tool so that it feels like we're maximally accelerating people rather than building a tool that makes it more unclear what you should do as the human?
但我认为，作为一个在该领域构建的产品团队，我们基本上可以做的就是始终思考我们如何构建一个工具，以便感觉我们正在最大限度地加速人们的发展，而不是构建一个让你更不清楚作为人类应该做什么的工具？
Alexander Embiricos: I think, to give an example right now, nowadays when you work with a coding agent, it writes a ton of code, but it turns out writing code is actually one of the most fun parts of software engineering for many software engineers.
Alexander Embiricos：我想，举个例子，现在当你使用编码代理时，它会编写大量代码，但事实证明，对于许多软件工程师来说，编写代码实际上是软件工程中最有趣的部分之一。
So then you end up reviewing AI code.
所以你最终会审查人工智能代码。
And that's often a less fun part of the job for many software engineers.
对于许多软件工程师来说，这通常是工作中不太有趣的部分。
So I actually think we see that this plays out all the time in a ton of micro decisions.
所以我实际上认为我们看到这种情况一直在大量的微观决策中发挥作用。
So we as a product team, we're always thinking about, "Okay, how do we make this more fun?
因此，作为产品团队，我们总是在思考，“好吧，我们如何让这变得更有趣？
How do we make you feel more empowered?
我们如何让您感到更有力量？
Where is this not working?" And I would argue that reviewing agent written code is a place that today is less fun.
这哪里不行了？”我认为，今天审查代理编写的代码已经不再那么有趣了。
Alexander Embiricos: So then I think, "Okay, what can we do about that?" Well, we can ship a code review feature that helps you build confidence in the AI written code.
Alexander Embiricos：那么我想，“好吧，我们能做些什么呢？”好吧，我们可以提供代码审查功能，帮助您建立对人工智能编写的代码的信心。
Okay, cool.
好吧，酷。
Another thing we could do is we can make it so that the agent's better able to validate its work.
我们可以做的另一件事是我们可以使代理能够更好地验证其工作。
And it gets all the way down into micro decisions.
它一直延伸到微观决策。
If you're going to have an agent capability to validate work, and let's say you have...
如果您要拥有代理能力来验证工作，假设您有......
I'm thinking of Codex Web right now, you have a pain that sort of reflects the work the agent did, what do you see first?
我现在正在考虑 Codex Web，你有一种痛苦，这反映了特工所做的工作，你首先看到什么？
Do you see the diff or do you see the image preview of the code it wrote?
您看到差异还是看到它编写的代码的图像预览？
And I think if you're thinking about this from perspective, "How do I empower the human?
我认为如果你从另一个角度思考这个问题，“我如何赋予人类权力？
How do I make them feel as accelerated as possible?" You obviously see the image first.
我如何让他们感觉尽可能加速？”显然您首先看到的是图像。
You shouldn't be reviewing the code unless first you've seen the image, unless maybe it's been reviewed by an AI and now it's time for you to take a look.
除非您首先看到图像，否则您不应该检查代码，除非它可能已被人工智能检查过，现在是时候让您看一下了。
Lenny Rachitsky: When I had Michael Truell, the CEO of Cursor on the podcast, he had this kind of vision of us moving to something beyond code.
Lenny Rachitsky：当我在播客中邀请 Cursor 的首席执行官 Michael Truell 时，他对我们走向代码之外的领域抱有这样的愿景。
And I've seen this rise of something called spec-driven development where you just write the spec and then the AI writes code for you.
我已经看到了所谓规范驱动开发的兴起，你只需编写规范，然后人工智能为你编写代码。
So you start working at this higher abstraction level.
所以你开始在这个更高的抽象级别上工作。
Is that something you see where we're going, just like engineers not having to actually write code or look at code and there's going to be this higher level of abstraction that we focus on?
您是否看到了我们的发展方向，就像工程师不必实际编写代码或查看代码一样，我们将关注更高级别的抽象？
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
I mean, I think there's constantly these levels of abstraction and they're actually already played out today.
我的意思是，我认为这些抽象层次一直存在，而且它们实际上已经在今天发挥出来了。
Today, coding agents, mostly it's prompt to patch.
如今，编码代理大多会提示修补。
We're starting to see people doing spec-driven development or planned and driven development.
我们开始看到人们进行规范驱动的开发或计划驱动的开发。
That's actually one of the ways when people ask, "Hey, how do you run Codex on a really long task?" Well, it's like often collaborate with it first to write a plan.md, like a markdown file that's your plan.
这实际上是当人们问“嘿，你如何在一个非常长的任务上运行 Codex ？”时的方式之一。嗯，就像经常和它合作一样，先写一个 plan.md，比如一个 markdown 文件，这就是你的计划。
And once you're happy with that, then you ask it to go off and do work.
一旦你对此感到满意，你就可以让它开始工作。
And if that plan has verifiable steps, it'll work for much longer.
如果该计划有可验证的步骤，那么它的作用会持续更长时间。
So we're totally seeing that.
所以我们完全看到了这一点。
Alexander Embiricos: I think spec-driven development is an interesting idea.
Alexander Embiricos：我认为规范驱动的开发是一个有趣的想法。
It's not clear to me that it'll work out that way because a lot of people don't like writing specs either, but it seems plausible that some people will work that way.
我不清楚它是否会以这种方式工作，因为很多人也不喜欢编写规范，但有些人会以这种方式工作似乎是合理的。
A bit of a joke idea though is if you think of the way that many teams work today, they often don't necessarily have specs, but the team is just really self-driven and so stuff just gets done.
一个有点开玩笑的想法是，如果你想想当今许多团队的工作方式，他们通常不一定有规格，但团队只是真正的自我驱动，所以事情就完成了。
And so almost that it's like, I'm coming up with this on the spot, so it's not a good name, but chatter-driven development where it's just like stuff is happening on social media and in your team communications tools.
所以几乎就像是，我当场想出这个，所以这不是一个好名字，而是聊天驱动的开发，就像社交媒体和团队通信工具中发生的事情一样。
And then as a result, code gets written and deployed.
然后，代码就被编写和部署了。
Alexander Embiricos: So yeah, I think I'm a little bit more oriented in that way of I don't even necessarily want to have to write a spec.
Alexander Embiricos：是的，我认为我更倾向于这种方式，我什至不一定要编写规范。
Sometimes I want to, only if I like writing specs.
有时我想这样做，前提是我喜欢编写规范。
Other times I might just want to say like, "Hey, here's the customer service channel and tell me what's interesting to know, but if it's a small bug, just fix it." I don't want to have to write a spec for that, right?
其他时候我可能只想说，“嘿，这是客户服务频道，告诉我有什么有趣的事情，但如果是一个小错误，就修复它。”我不想为此编写规范，对吧？
Alexander Embiricos: I have this sort of hypothetical future that I like to share sometimes with people as a provocation, which is in a world where we have truly amazing agents, what does it look like to be a solopreneur?
Alexander Embiricos：我有一种假设的未来，有时我喜欢与人们分享，作为一种挑衅，在一个我们拥有真正令人惊叹的经纪人的世界里，成为一名个体企业家是什么样子？
And one terrible idea for how it could look is that actually there's a mobile app and every idea that the agent has to do is just vertical video on your phone and then you can swipe left if you think it's a bad idea and you can swipe right if it's a good idea.
一个可怕的想法是，实际上有一个移动应用程序，代理要做的每一个想法都只是在你的手机上播放垂直视频，然后如果你认为这是一个坏主意，你可以向左滑动，如果这是一个好主意，你可以向右滑动。
And you can press and hold and speak to your phone if you want to give feedback on the idea before you swipe.
如果您想在滑动之前提供有关想法的反馈，您可以按住并对手机说话。
And in this world, basically what your job is is just to plug in this app into every single signal system or system of record, and then you just sit back and swipe.
在这个世界上，基本上你的工作就是将该应用程序插入每个信号系统或记录系统，然后你只需坐下来滑动即可。
I don't know.
我不知道。
Lenny Rachitsky: I love this.
莱尼·拉奇茨基：我喜欢这个。
So this is like Tinder meets TikTok meets Codex.
这就像 Tinder 遇上 TikTok 遇上 Codex。
Lenny Rachitsky: No, this is great.
莱尼·拉奇茨基：不，这太棒了。
So the idea here is this agent is watching and listening to you, paying attention to the market, your users, and it's like, "Cool, here's something I should do." It's like a proactive engineer just like, "Here, we should build this feature, fix this thing."
所以这里的想法是这个代理正在观察和倾听你，关注市场，你的用户，就像，“酷，这是我应该做的事情。”这就像一个积极主动的工程师，“在这里，我们应该构建这个功能，修复这个问题。”
Lenny Rachitsky: I think it's a really good idea.
Lenny Rachitsky：我认为这是一个非常好的主意。
Alexander Embiricos: Communicating with you in the lowest effort way for your consumers.
Alexander Embiricos：为您的消费者以最省力的方式与您沟通。
Lenny Rachitsky: Yeah, yeah, the modern way we communicate, swipe left to right and vertical feed.
Lenny Rachitsky：是的，是的，我们的现代沟通方式是从左向右滑动和垂直进给。
And then the Sora video, okay, so I see how this all connects now.
然后是 Sora 视频，好吧，我现在明白这一切是如何联系起来的了。
I see.
我懂了。
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
To be clear, we're not building that, but it's a fun idea.
需要明确的是，我们并不是在构建它，但这是一个有趣的想法。
I mean, in this example though, one of the things that it's doing is it's consuming external signals, right?
我的意思是，在这个例子中，它所做的事情之一就是消耗外部信号，对吧？
I think the other really interesting thing is if we think about what is the most successful AI product to date, I would argue, it's funny actually not to confuse things at all, but the first time we used the brand Codex at OpenAI was actually the model powering GitHub Copilot.
我认为另一件真正有趣的事情是，如果我们思考迄今为止最成功的人工智能产品是什么，我认为，实际上根本不混淆事物是很有趣的，但我们第一次在 OpenAI 使用 Codex 品牌实际上是为 GitHub Copilot 提供动力的模型。
This is way back in the day, years ago.
这已经是很多年前的事了。
And so we decided to reuse that brand recently because it's just so good, Codex, code execution.
因此，我们最近决定重用该品牌，因为它非常好，Codex，代码执行。
Alexander Embiricos: But I think actually auto completion and IDEs is one of the most successful AI products today.
Alexander Embiricos：但我认为实际上自动完成和 IDE 是当今最成功的人工智能产品之一。
And part of what's so magical about it is that when it can surface ideas for helping you really rapidly, when it's right, you're accelerated.
它如此神奇的部分原因在于，当它能够真正快速地提出可以帮助你的想法时，当它是正确的时候，你就会加速。
When it's wrong, it's not that annoying.
当它出错时，也不是那么烦人。
It can be annoying, but it's not that annoying.
这可能很烦人，但也不是那么烦人。
So you can create this mixed initiative system that's contextually responding to what you're attempting to do.So in my mind, this is a really interesting thing for us as OpenAI as we're building.
因此，您可以创建这个混合主动系统，根据上下文响应您尝试做的事情。因此，在我看来，这对于我们正在构建的 OpenAI 来说是一件非常有趣的事情。
Alexander Embiricos: So for instance, when I think about launching a browser, which we did with Atlas, in my mind, one of the really interesting things we can then do is we can then contextually surface ways that we can help you as you're going about your day.
Alexander Embiricos：举例来说，当我考虑推出一款浏览器（我们与 Atlas 合作）时，在我看来，我们可以做的真正有趣的事情之一就是我们可以根据上下文提出可以在您日常生活中为您提供帮助的方法。
And so we break out of this, we're just looking at code or we're just in your terminal into this idea that, "Hey, a real teammate is dealing with a lot more than just code.
所以我们打破了这一点，我们只是在看代码，或者我们只是在你的终端中进入这个想法，“嘿，一个真正的队友处理的不仅仅是代码。
They're dealing with a lot of things that are web content.
他们正在处理很多网络内容。
So how can we help you with that?"
那么我们可以怎样帮助你呢？”
Lenny Rachitsky: Man, there's so much there.
Lenny Rachitsky：伙计，那里有很多东西。
I love this.
我喜欢这个。
Okay, so auto complete on web with the browser.
好的，使用浏览器在网络上自动完成。
That's so interesting.
那太有趣了。
Just like, "Here's all the things that we can help you with as you're browsing and going about your day."
就像，“这是我们在您浏览和生活时可以帮助您的所有事情。”
Lenny Rachitsky: I want to talk about Atlas.
Lenny Rachitsky：我想谈谈阿特拉斯。
I'll come back to that.
我会回到这个话题。
Codex, code execution, did not know that.
Codex，代码执行，不知道。
That's really clever.
这真是太聪明了。
I get it now.
我现在明白了。
Okay, and then this chatter, what is a chatter-driven development?
好吧，然后这个喋喋不休，什么是喋喋不休驱动开发？
No, this is a really good idea, but it reminds me, I had Dhanji on the podcast, CTO of Block, and they have this product called Goose, which is their own internal agent thing.
不，这是一个非常好的主意，但它提醒我，我在播客上有 Dhanji，Block 的首席技术官，他们有一个名为 Goose 的产品，这是他们自己的内部代理。
And he talked about an engineer at Block just has Goose watch him with his screen and listens to every meeting and proactively does work that he should probably want to do.
他谈到 Block 的一名工程师只是让 Goose 用屏幕观察他，听取每次会议，并主动做他可能想做的工作。
So ships to PR, sends an email, drafts a Slack message.
因此，发送给 PR，发送电子邮件，起草 Slack 消息。
So he's doing exactly what you're describing in kind of a very early way.
所以他很早就在做你所描述的事情。
Alexander Embiricos: Yeah, that's super interesting.
Alexander Embiricos：是的，这非常有趣。
And I bet you, so if we went and asked them what the bottleneck to that productivity is, did they share what it is?
我敢打赌，如果我们去问他们生产力的瓶颈是什么，他们会分享是什么吗？
Lenny Rachitsky: Probably looking at it and just making sure this is the right thing to do, yeah.
Lenny Rachitsky：可能会考虑一下并确保这是正确的做法，是的。
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
So we see this now.
所以我们现在看到了这一点。
We have a Slack integration for Codex.
我们为 Codex 提供了 Slack 集成。
People love if there's something that you need to do quickly, people will just @ mention Codex, "Why do you think this bug is happening?" It doesn't have to be an engineer.
人们喜欢如果你需要快速完成某件事，人们只会@提及 Codex，“你认为为什么会发生这个错误？”它不一定是工程师。
Even maybe data scientists often here are using Codex a ton to just answer questions like, "Why do you think this metric moved?
甚至可能数据科学家经常使用 Codex 来回答这样的问题：“你认为这个指标为什么会发生变化？
What happened?" So questions, you get the answer right back in Slack.
发生什么事了？”如果有问题，您可以立即在 Slack 中得到答案。
It's amazing, super useful.
太神奇了，超级有用。
But as for when it's writing code, then you have to go back and look at the code.
但至于到了写代码的时候，那就得回去看代码了。
Alexander Embiricos: So the real, I think, bottleneck right now is validating that the code worked and writing code review.
Alexander Embiricos：我认为，现在真正的瓶颈是验证代码是否有效并编写代码审查。
So in my mind, if we wanted to get to something like the friend you were talking about's world, I think we really need to figure out how to get people to configure their coding agents to be much more autonomous on those later stages of the work.
所以在我看来，如果我们想要进入像你所说的朋友的世界那样的世界，我认为我们真的需要弄清楚如何让人们配置他们的编码代理，以便在工作的后期阶段更加自主。
Lenny Rachitsky: It makes sense.
莱尼·拉奇茨基：这是有道理的。
Like you said, writing code, I used to be an engineer, I was an engineer for 10 years, really fun to write code, really fun to just get in the flow, build architect, test.
就像你说的，写代码，我曾经是一名工程师，我当了 10 年的工程师，写代码真的很有趣，进入流程、构建架构师、测试真的很有趣。
Not so fun to look at everyone else's code and just have to go through and be on the hook if it's doing something dumb that's going to take down production.
查看其他人的代码并不是那么有趣，如果它做了一些愚蠢的事情会导致生产下降，那么就必须仔细检查并陷入困境。
And now that building has become easier, what I've always heard from companies that are really at the cutting edge of this is the bottleneck is now figuring out what to build.
现在构建变得更加容易，我总是从那些真正处于这方面最前沿的公司那里听到的是，现在的瓶颈是弄清楚要构建什么。
And then it's at the end of like, "Okay, we have all this, all 100 PRs to review.
最后，“好吧，我们有所有这些，所有 100 个 PR 需要审查。
Who's going to go through all that?"
谁会经历这一切？”
Yeah.: This episode is brought to you by Jira Product Discovery.
是的。：本集由 Jira Product Discovery 为您带来。
The hardest part of building products isn't actually building products.
构建产品最困难的部分实际上并不是构建产品。
It's everything else.
这是其他一切。
It's proving that the work matters, managing stakeholders, trying to plan ahead.
它证明工作很重要，管理利益相关者，努力提前计划。
Most teams spend more time reacting than learning, chasing updates, justifying roadmaps, and constantly unblocking work to keep things moving.
大多数团队花费更多的时间来做出反应，而不是学习、追求更新、证明路线图的合理性，以及不断地排除工作障碍以保持事情的进展。
Jira Product Discovery puts you back in control.
Jira Product Discovery 让您重新掌控一切。
With Jira Product Discovery, you can capture insights and prioritize high impact ideas.
借助 Jira Product Discovery，您可以捕获见解并优先考虑高影响力的想法。
It's flexible so it adapts to the way your team works and helps you build a roadmap that drives alignment, not questions.
它很灵活，因此可以适应您团队的工作方式，并帮助您构建一个推动一致性而不是问题的路线图。
And because it's built on Jira, you can track ideas from strategy to delivery all in one place.
由于它基于 Jira 构建，因此您可以在一个地方跟踪从策略到交付的所有想法。
Less chasing, more time to think, learn, and build the right thing.
更少的追逐，更多的时间去思考、学习和构建正确的东西。
Get Jira Product Discovery for free at atlassian.com/lenny.
请访问 atlassian.com/lenny 免费获取 Jira Product Discovery。
That's atlassian.com/lenny.
这是 atlassian.com/lenny。
Yeah.: What has the impact of Codex been on the way you operate as a product person as a PM?
是的。：Codex 对您作为产品人员和 PM 的运作方式有何影响？
It's clear how engineering is impacted, code is written for you.
工程受到的影响一目了然，代码是为您编写的。
What has it done to the way you operate and the way PMs operate at OpenAI?
它对您和项目经理在 OpenAI 的运作方式有何影响？
Alexander Embiricos: Yeah, I mean, I think mostly I just feel much more empowered.
Alexander Embiricos：是的，我的意思是，我认为主要是我感觉自己更有力量了。
I've always been sort of more technical leaning PM, and especially when I'm working on products for engineers, I feel like it's necessary to dogfood the product.
我一直是更倾向于技术的 PM，尤其是当我为工程师开发产品时，我觉得有必要对产品进行测试。
But even beyond that, I just feel like I can do much, much more as a PM.
但除此之外，我觉得作为一名产品经理我可以做很多很多事情。
And Scott Belsky talks about this idea of compressing the talent stack.
斯科特·贝尔斯基 (Scott Belsky) 谈到了压缩人才库的想法。
I'm not sure if I've phrased that right.
我不确定我的措辞是否正确。
But it's basically this idea that maybe the boundaries between these roles are a little bit less needed than before because people can just do much more.
但基本上这个想法是，这些角色之间的界限可能比以前更不需要了，因为人们可以做更多的事情。
And every time someone can do more, you can skip one communication boundary and make the team that much more efficient.
每当有人可以做更多事情时，您就可以跳过一个沟通边界并使团队更加高效。
Alexander Embiricos: So I think we see it in a bunch of functions now, but I guess since you asked about products specifically, now answering questions much, much easier.
Alexander Embiricos：所以我认为我们现在在一堆功能中看到了它，但我想既然你具体询问了产品，现在回答问题就容易多了。
You can just ask Codex for thoughts on that.
你可以向 Codex 询问对此的想法。
A lot of PM type work, understanding what's changing.
很多 PM 类型的工作，了解正在发生的变化。
Again, just ask Codex for help with that.
再次强调，请向 Codex 寻求帮助。
Prototyping is often faster than writing specs.
原型设计通常比编写规范更快。
This is something that a lot of people have talked about.
这是很多人都在谈论的事情。
Alexander Embiricos: I think something that, I don't think it's super surprising, but something that's slightly surprising is we see...
Alexander Embiricos：我认为这并不是非常令人惊讶，但令人有点惊讶的是我们看到......
We're mostly building Codex to write code that's going to be deployed to production, but actually we see a lot of throwaway code written with Codex now.
我们主要构建 Codex 来编写将部署到生产的代码，但实际上我们现在看到很多用 Codex 编写的一次性代码。
It's kind of going back to this idea of ubiquitous code.
这有点回到了无处不在的代码的想法。
So you'll see someone wants to do an analysis.
所以你会看到有人想做分析。
If I want to understand something, it's like, okay, just give Codex a bunch of data, but then ask it to build an interactive data viewer for this data.
如果我想理解一些东西，那就好，只需给 Codex 一堆数据，然后要求它为这些数据构建一个交互式数据查看器。
That's just too annoying to do in the past, but now it's just totally worth the time of just getting an agent to go do something.
这在过去太烦人了，但现在完全值得花时间让代理人去做一些事情。
Alexander Embiricos: Similarly, I've seen some pretty cool prototypes on our design team about if you want to...
Alexander Embiricos：同样，我在我们的设计团队中看到了一些非常酷的原型，如果你想……
Well, a designer basically wanted to build an animation, and this is the Coin Animation Codex, and it was like normally it'd be too annoying to program this animation.
好吧，设计师基本上想要构建一个动画，这就是硬币动画代码库，通常情况下，编写这个动画会很烦人。
So they just vibe coded a animation editor and then they use the animation editor to build the animation, which they then check into their repo.
因此，他们只是对动画编辑器进行了编码，然后使用动画编辑器来构建动画，然后将其签入到他们的存储库中。
Alexander Embiricos: Actually, our designers, there's a ton of acceleration there.
Alexander Embiricos：实际上，我们的设计师，那里有大量的加速。
And speaking of compressing the talent stack, I think our designers are very PME.
说到压缩人才堆栈，我认为我们的设计师非常PME。
So they do a ton of product work and they actually have an entire vibe coded side prototype of the Codex app.
因此，他们做了大量的产品工作，并且实际上拥有 Codex 应用程序的完整振动编码侧面原型。
And so a lot of how we talk about things is we'll have a really quick jam because there's 10,000 things going on, and then the designer will go think about how this should work.
所以我们谈论事情的很多方式都是我们会很快陷入困境，因为有 10,000 件事正在发生，然后设计师会去思考这应该如何运作。
But instead of talking about it again, they'll just vibe code a prototype of that in their standalone prototype.
但他们不会再次谈论它，而是只是在他们的独立原型中编写一个原型。
We'll play with it.
我们会玩它。
If we like it, they'll vibe engineer that prototype into an actual PR to land.
如果我们喜欢，他们会将该原型设计成实际的公关以落地。
And then depending on their comfort with the code base, like Codex utilizing Rust is a little harder, maybe they'll land it themselves or they'll get close and then an engineer can help them land the PR.
然后，取决于他们对代码库的舒适度，比如使用 Rust 的 Codex 有点困难，也许他们会自己实现它，或者他们会接近它，然后工程师可以帮助他们实现 PR。
Alexander Embiricos: We recently shipped the Sora Android app, and that was one of the most mind-blowing examples of acceleration, actually, because usage of Codex internally at OpenAI is obviously really, really high, but it's been growing over the course of the year, both in terms of now it's basically all technical staff use it, but even the intensity and know how of how to make the most of coding agents has gone up by a ton.
Alexander Embiricos：我们最近发布了 Sora Android 应用程序，实际上，这是最令人兴奋的加速示例之一，因为 OpenAI 内部 Codex 的使用率显然非常非常高，而且在这一年中一直在增长，无论是现在基本上所有技术人员都在使用它，但即使是如何充分利用编码代理的强度和知识也大幅上升。
And so the Sora Android app, a fully new app, we built it in 18 days.
因此，Sora Android 应用程序是一个全新的应用程序，我们在 18 天内构建了它。
It went from zero to launch to employees, and then 10 days later, so 28 days total, we went to just GA, to the public, and that was done just with the help of Codex.
它从零到向员工发布，然后 10 天后，总共 28 天，我们进入了 GA，向公众发布，而这只是在 Codex 的帮助下完成的。
So pretty insane velocity.
如此疯狂的速度。
Alexander Embiricos: I would say it was a little bit...
Alexander Embiricos：我想说有点......
I don't want to say easy mode, but there is one thing that Codex is really good at if you're a company that's building software on multiple platforms, so you've already figured out some of the underlying APIs or systems, asking Codex to port things over is really effective because it has something you can go look at.
我不想说简单模式，但是 Codex 确实擅长一件事，如果你是一家在多个平台上构建软件的公司，那么你已经弄清楚了一些底层 API 或系统，要求 Codex 移植东西确实很有效，因为它有你可以去看的东西。
And so the engineers on that team were basically having Codex go look at the iOS app, produce plans of work that needed to be done, and then go implement those.
因此，该团队的工程师基本上是让 Codex 查看 iOS 应用程序，制定需要完成的工作计划，然后去实施这些计划。
And it was looking at iOS and Android at the same time.
它同时关注 iOS 和 Android。
And so basically it was two weeks to launch to employees, four weeks total.
基本上，向员工推出产品需要两周的时间，总共四个星期。
Insanely fast.
快得离谱。
Lenny Rachitsky: What makes that even more insane is it became the number one app in the app store.
Lenny Rachitsky：更疯狂的是它成为了应用程序商店中排名第一的应用程序。
This just boggles the mind.
这简直令人难以置信。
Okay, so 28 days?
好吧，那么28天？
Alexander Embiricos: Yeah, so imagine number one app in the app store with a handful of engineers.
Alexander Embiricos：是的，想象一下应用商店中排名第一的应用程序有少数工程师。
I think it was two or three possibly in a handful of weeks.
我认为几周内可能会发生两到三个。
Alexander Embiricos: Yeah, so that's a really fun example of acceleration.
Alexander Embiricos：是的，这是一个非常有趣的加速示例。
And then Atlas was the other one that I think Ben did a podcast, the engine lead on Atlas, sharing a little bit about how we built there.
然后，Atlas 是 Ben 制作的另一个播客，Atlas 的引擎负责人，分享了一些关于我们如何在那里构建的内容。
Atlas is actually...
阿特拉斯其实是...
I mean, it's a browser, and building a browser is really hard.
我的意思是，它是一个浏览器，而构建一个浏览器确实很难。
So we had to build a lot of difficult systems in order to do that.
因此，我们必须构建许多困难的系统才能做到这一点。
And basically we got to the point where that team has a ton of power users of Codex right now, and it got to the point they where basically...
基本上我们已经到了这个团队现在拥有大量 Codex 高级用户的地步，而且他们基本上已经到了......
We were talking to them about it, because a lot of those engineers are people I used to work with before at my startup.
我们正在和他们讨论这个问题，因为很多工程师都是我之前在初创公司时共事过的人。
And so they'd say, "Before this would've taken us two to three weeks for two to three engineers, and now it's like one engineer, one week." So massive acceleration there as well.
所以他们会说，“以前，两到三名工程师需要两到三周的时间，而现在只需要一名工程师一周的时间。”那里也有如此巨大的加速度。
Alexander Embiricos: And what's quite cool is that we shipped Atlas on Mac first, but now we're working on the Windows version.
Alexander Embiricos：非常酷的是，我们首先在 Mac 上发布了 Atlas，但现在我们正在开发 Windows 版本。
So the team now is ramping up on Windows and they're helping us make Codex better on Windows too, which is admittedly earlier, just the model we shipped last week is the first model that natively understands PowerShell.
因此，团队现在正在 Windows 上加大力度，他们也在帮助我们在 Windows 上改进 Codex，这无疑是更早的事情，只是我们上周发布的模型是第一个原生理解 PowerShell 的模型。
So PowerShell being the native Shell language on Windows.
因此 PowerShell 是 Windows 上的本机 Shell 语言。
So yeah, it's been really awesome to see the whole company getting accelerated by Codex from...
是的，看到整个公司通过 Codex 加速发展真是太棒了……
And most obviously, also research and improving how quickly we train models and how well we do it.
最明显的是，还要研究和改进我们训练模型的速度以及训练的效果。
And then even design, as we talked about, and marketing.
然后甚至是我们谈到的设计和营销。
Actually, we're at this point now where my product marketer is often also making string changes just directly from Slack or updating docs directly from Slack.
实际上，现在我的产品营销人员也经常直接从 Slack 进行字符串更改或直接从 Slack 更新文档。
Lenny Rachitsky: These are amazing examples.
Lenny Rachitsky：这些都是令人惊奇的例子。
You guys are living at the bleeding edge of what is possible, and this is how other companies are going to work.
你们生活在可能性的最前沿，这就是其他公司的运作方式。
Just shipping, again, what became the number one app in the app store and just beloved all over the...
再次发布，这款应用程序成为了应用程序商店中排名第一的应用程序，并且受到了全世界的喜爱......
It just took over, I don't know, the world for at least a week.
我不知道，它刚刚接管了世界至少一周。
Built, you said, in 28 days and I don't know, 10 days, 18 days just to get the core of it working.
你说，28 天就建成了，我不知道，10 天，18 天，只是为了让它的核心工作起来。
Alexander Embiricos: Yeah, so it was like 18 days we had a thing that employees were playing with, and then 10 days later we were out.
Alexander Embiricos：是的，就像 18 天我们有一个员工在玩的东西，然后 10 天后我们就出去了。
Lenny Rachitsky: And you said just a couple engineers.
Lenny Rachitsky：你说只有几个工程师。
Lenny Rachitsky: Two or three.
莱尼·拉奇茨基：两三个。
Okay.
好的。
And then Atlas you said took a week to build?
然后你说 Atlas 花了一周的时间来构建？
Alexander Embiricos: No, no, no. So Atlas, not the whole week, but Atlas was a really meaty project.
亚历山大·恩比里科斯：不，不，不。所以 Atlas，不是整个星期，但 Atlas 是一个非常丰富的项目。
And so I was talking to one of the engineers on Atlas about just what they use Codex for.
因此，我与 Atlas 的一位工程师讨论了他们使用 Codex 的用途。
And it's basically like, "We use Codex for absolutely everything." And I was like, "Okay, well, how would you measure the acceleration?" And so basically the answer I got back was, "Previously would've taken two to three weeks for two to three engineers, and now it's like one engineer, one week."
基本上就像是，“我们几乎所有事情都使用 Codex。”我当时想，“好吧，你会如何测量加速度？”所以基本上我得到的答案是，“以前需要两到三名工程师需要两到三周的时间，而现在就像一名工程师需要一周的时间。”
Lenny Rachitsky: Do you think this eventually moves to non-engineers doing this sort of thing?
Lenny Rachitsky：您认为这最终会转移到非工程师来做这类事情吗？
Does it have to be an engineer building this thing?
建造这个东西必须是工程师吗？
Could Sora have been built by, I don't know, a PM or designer?
我不知道，Sora 是否是由 PM 或设计师建造的？
Alexander Embiricos: I think we will very much get to the point, well, basically where the boundaries are a little bit blurred.
Alexander Embiricos：我认为我们将非常明确地指出，基本上，界限有点模糊。
I think you're going to want someone who understands the details of what they're building, but what details those are will evolve.
我认为你会想要一个了解他们正在构建的细节的人，但这些细节将会不断发展。
Kind of like how now if you're writing Swift, you don't have to speak assembly.
有点像现在如果你正在编写 Swift，你就不必讲汇编了。
There's a handful of people in the world, and it's really important that they exist and speak assembly, maybe more than a handful, but that's a specialized function that most companies don't need to have.
世界上只有少数人，他们的存在和讲汇编确实很重要，也许不止少数，但这是大多数公司不需要的专门职能。
Alexander Embiricos: So I think we're just going to naturally see an increase in layers of abstraction.
Alexander Embiricos：所以我认为我们会自然地看到抽象层的增加。
And then the cool thing is now we're entering the language layer of abstraction, like natural language, and then natural language itself is really flexible.
然后很酷的事情是现在我们正在进入抽象的语言层，就像自然语言一样，然后自然语言本身非常灵活。
You could have engineers talking about a plan and then you could have engineers talking about a spec, and then you could have engineers talking about just a product or an idea.
你可以让工程师谈论计划，然后你可以让工程师谈论规格，然后你可以让工程师只谈论产品或想法。
So I think we can also start moving up those layers of abstraction as well.
所以我认为我们也可以开始提升这些抽象层。
Alexander Embiricos: But I do think this is going to be gradual.
Alexander Embiricos：但我确实认为这将是渐进的。
I don't think it's going to go off to all of a sudden nobody ever writes anything, any code and it's just specs.
我不认为它会突然变成没有人写任何东西、任何代码，而这只是规范。
I think it's going to be much more like, "Okay, we've set up our coding agent to be really good at previewing the build or at running tests," maybe that's the first part that most people have set up.
我认为这更像是，“好吧，我们已经将我们的编码代理设置为非常擅长预览构建或运行测试”，也许这是大多数人设置的第一部分。
And it's like, "Okay, now we've set it up so they can execute the build and it can see the results of its own changes, but we haven't yet built a good integration harness so that it can," in the case of Atlas...
就像，“好吧，现在我们已经设置好它，以便他们可以执行构建，并且它可以看到自己更改的结果，但我们还没有构建一个良好的集成工具，以便它可以，”在 Atlas 的情况下......
By the way, I don't know if they've done any of this or not.
顺便说一句，我不知道他们是否做过这些。
I think they've done a lot of this.
我认为他们已经做了很多这样的事情。
But maybe the next stage is enable it to load a few sample pages to see how well those work.
但也许下一阶段是让它加载一些示例页面，看看它们的工作效果如何。
So then, okay, now we're going to set it up to do that.
那么，好吧，现在我们要对其进行设置。
Alexander Embiricos: And I think for some time at least, we're going to have humans curating which of these connectors or systems or components that the agent needs to be good at talking to.
Alexander Embiricos：我认为至少在一段时间内，我们将让人类来策划代理需要擅长与哪些连接器、系统或组件进行对话。
And then in the future, there will be an even greater unlock where Codex tells you how to set it up or maybe sets itself up in a repo.
将来，将会有一个更大的解锁，Codex 会告诉你如何设置它，或者可能在存储库中设置它自己。
Lenny Rachitsky: What a wild time to be alive.
莱尼·拉奇茨基：活着真是太疯狂了。
Wow.
哇。
I'm curious just the second order effects of this sort of thing, just how quickly it is to build stuff.
我很好奇这类事情的二阶效应，即构建东西的速度有多快。
What does that do?
那有什么作用？
Does that mean distribution becomes much, much more important?
这是否意味着分发变得更加重要？
Does it mean ideas are just worth a lot more?
这是否意味着想法更有价值？
It's interesting to think about how quick how that changes.
想想这种变化有多快就很有趣。
Alexander Embiricos: I'm curious what you think.
Alexander Embiricos：我很好奇你的想法。
I still don't think ideas are worth as much as maybe a lot of people think.
我仍然认为想法并不像很多人想象的那么有价值。
I still think execution is really hard.
我仍然认为执行力真的很难。
You can build something fast, but you still need to execute well on it, still needs to make sense and be a coherent thing overall, yeah, and distribution is massive.
你可以快速构建一些东西，但你仍然需要很好地执行它，仍然需要有意义并且总体上是一个连贯的东西，是的，而且分发是巨大的。
Lenny Rachitsky: Yeah.
莱尼·拉奇茨基：是的。
Just feels like everything else is now more important.
只是感觉现在其他一切都更重要了。
Everything that isn't the building piece, which is coming up with an idea, getting to market, profit, all that kind of stuff.
一切不是构建部分的东西，即提出一个想法、进入市场、利润等等。
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
I think we might've been in this weird temporary phase where, for a while, it was so hard to build product that you mostly just had to be really good at building product and it maybe didn't matter if you had an intimate understanding of a specific customer.
我认为我们可能已经处于这个奇怪的临时阶段，有一段时间，构建产品是如此困难，以至于你必须真正擅长构建产品，并且如果你对特定客户有深入的了解可能并不重要。
But now I think we're getting to this point where actually if I could only choose one thing to understand, it would be really meaningful understanding of the problems that a certain customer has.
但现在我认为我们已经到了这样的地步：实际上，如果我只能选择一件事来理解，那么对某个客户所遇到的问题的理解将是真正有意义的。
If I could only go in with one core competency.
如果我只能拥有一项核心能力就好了。
Alexander Embiricos: So I think that's ultimately still what's going to matter most.
Alexander Embiricos：所以我认为这最终仍然是最重要的。
If you're starting a new company today and you have a really good understanding and network of customers that are currently underserved by AI tools, I think you're set.
如果你今天要创办一家新公司，并且你对目前人工智能工具服务不足的客户有很好的了解和网络，那么我认为你已经准备好了。
Whereas if you're good at building websites, but you don't have any specific customer to build for, I think you're in for a much harder time.
然而，如果您擅长构建网站，但没有任何特定的客户可供构建，那么我认为您的处境就会困难得多。
Lenny Rachitsky: Bullish on vertical AI startups is what I'm hearing.
Lenny Rachitsky：我听到的是对垂直人工智能初创公司的看好。
Yeah, I completely agree.
是的，我完全同意。
There's the general thing that can solve a lot of problems and then there's like, "We're going to solve presentations incredibly well and we're going to understand the presentation problem better than anyone and we're going to plug into your workflows and all these other things that matter for a very specific problem." Okay, incredible.
有一些通用的东西可以解决很多问题，然后就是“我们将非常好地解决演示，我们将比任何人都更好地理解演示问题，我们将插入您的工作流程以及所有其他对特定问题至关重要的东西。”好吧，难以置信。
Lenny Rachitsky: When you think about progress on Codex, I imagine you have a bunch of evals and there's all these public benchmarks.
Lenny Rachitsky：当您考虑 Codex 的进展时，我想您会进行大量评估，并且有所有这些公共基准。
What's something you look at to tell you, "Okay, we're making really good progress," I imagine it's not going to be the one thing, but what do you focus on?
你看什么东西会告诉你，“好吧，我们正在取得非常好的进展”，我想这不会是一件事，但你关注的是什么？
What's something you're trying to push?
你想推动什么？
What's a KPI or two?
一两个 KPI 是什么？
Alexander Embiricos: One of the things that I'm constantly reminding myself of is that a tool like Codex naturally is a tool that you would become a power user of.
Alexander Embiricos：我不断提醒自己的一件事是，像 Codex 这样的工具自然是您会成为高级用户的工具。
So we can accidentally spend a lot of our time thinking about features that are very deep in the user adoption journey.
因此，我们可能会无意中花费大量时间思考用户采用过程中非常深入的功能。
And so we can kind of end up oversolving for that.
所以我们最终可能会解决这个问题。
And so I think it's just critically important to go look at your D7 retention.
因此，我认为查看 D7 保留率至关重要。
Just go try the product, sign up from scratch again.
只需去尝试该产品，然后再次从头开始注册即可。
I have a few too many ChatGPT Pro accounts that I've, in order to maximally correctly dogfood, signed up for on my Gmail and they charge me 200 bucks a month.
我有太多的 ChatGPT Pro 帐户，为了最大限度地正确进行测试，我在 Gmail 上注册了这些帐户，他们每月向我收取 200 美元的费用。
I need to expense those.
我需要花费这些。
But I think just the feeling of being end user and the early retention stats are still super important for us because as much as this category is taking off, I think we're still in the very early days of people using them.
但我认为作为最终用户的感觉和早期保留统计数据对我们来说仍然非常重要，因为尽管这个类别正在起飞，但我认为我们仍处于人们使用它们的早期阶段。
Alexander Embiricos: Another thing that we do that I think we might be the most user feedback/social media pilled team out there in this space is like a few of us are constantly on Reddit and Twitter, and there's praise up there and there's a lot of complaints, but we take the complaints very seriously and look at them.
Alexander Embiricos：我们做的另一件事是，我认为我们可能是这个领域中用户反馈/社交媒体最多的团队，就像我们中的一些人经常在 Reddit 和 Twitter 上一样，那里有赞扬，也有很多抱怨，但我们非常认真地对待这些抱怨并关注它们。
And I think that, again, because you can use a coding agent for so many different things, it often is kind of broken in many sort of ways for specific behaviors.
我再次认为，因为您可以将编码代理用于许多不同的事情，所以对于特定行为，它通常会以多种方式被破坏。
So we actually monitor a lot just what the vibes are on social media pretty often, especially I think for Twitter/X, it's a little bit more hypey and then Reddit is a little more negative but real actually.
因此，我们实际上经常监控社交媒体上的氛围，尤其是 Twitter/X，它有点炒作，然后 Reddit 有点消极，但实际上是真实的。
So I've started increasingly paying attention to how people are talking about using Codex on Reddit actually.
因此，我开始越来越关注人们实际上是如何谈论在 Reddit 上使用 Codex 的。
Lenny Rachitsky: This is important for people to know.
Lenny Rachitsky：人们知道这一点很重要。
Which of the subreddits do you check most?
您最常查看哪个 Reddit 子版块？
Is there like an r/Codex or?
有类似 r/Codex 的吗？
Alexander Embiricos: I mean, the algorithm's pretty good at surfacing stuff, but r/Codex is there.
Alexander Embiricos：我的意思是，该算法非常擅长呈现内容，但 r/Codex 就在那里。
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
I'll take.
我会接受。
Very interesting.
非常有趣。
And then if people tag you on Twitter, you still see that, but maybe not as powerful as seeing it on Reddit.
然后，如果人们在 Twitter 上标记你，你仍然会看到它，但可能不如在 Reddit 上看到的那么强大。
Alexander Embiricos: Well, yeah.
亚历山大·恩比里科斯：嗯，是的。
Well, the thing with Twitter is it's a little bit more one-to-one, even if it's in public.
嗯，Twitter 的特点是它更加一对一，即使是在公开场合。
Whereas with Reddit, those are really good upvoting mechanics and maybe most people are still not bots, unclear.
而在 Reddit 上，这些都是非常好的投票机制，也许大多数人仍然不是机器人，这一点还不清楚。
So you get good signal on what matters and what other people think.
因此，您可以获得有关重要事项和其他人想法的良好信号。
Lenny Rachitsky: So interestingly, Atlas, I want to talk about that briefly.
Lenny Rachitsky：很有趣，Atlas，我想简单地谈谈这个问题。
You guys launched Atlas.
你们推出了 Atlas。
I tweeted actually that I tried Atlas and then I don't love the AI only search experience.
事实上，我在推特上说，我尝试过 Atlas，但我不喜欢纯人工智能的搜索体验。
I was just like, "I just want Google sometimes," or whatever.
我当时想，“我有时只是想要谷歌”，或者其他什么。
Just waiting for AI to give me an answer, I'm like, "I don't want to...
只是在等待人工智能给我答案，我就像，“我不想......
" And there was no way to switch.
“而且没有办法切换。
I just tweeted, "Hey, I'm switching back.
我刚刚发推文说：“嘿，我要换回来了。
It's not great." And I feel like I made some PMs at OpenAI sad.
这不太好。”我觉得我让 OpenAI 的一些产品经理感到难过。
And I saw someone tweet, "Okay, we have Atlas now," which I imagine was always part of the plan.
我看到有人发推文，“好吧，我们现在有了 Atlas”，我想这一直是计划的一部分。
It's probably an example of just, "We got to ship stuff, see how people use it and then we figure it out." So I guess one is that, I don't know, is there anything there?
这可能只是一个例子，“我们必须运送东西，看看人们如何使用它，然后我们弄清楚它。”所以我想一是，我不知道，那里有什么吗？
And two, I'm just curious, why are you guys building a web browser?
第二，我只是很好奇，你们为什么要构建一个网络浏览器？
Alexander Embiricos: So I worked on Atlas for a bit.
Alexander Embiricos：所以我在 Atlas 上工作了一段时间。
I don't work on it now.
我现在不做这件事了。
But a bit of the narrative here for me just to tell my story a bit was I was working on this screen sharing, pair programming startup, and then we joined OpenAI.
但这里我只是为了讲述一下我的故事，我当时正在从事屏幕共享、结对编程初创公司的工作，然后我们加入了 OpenAI。
And so the idea was really to build a contextual desktop assistant.
所以我们的想法实际上是构建一个上下文桌面助手。
And the reason I believe that's so important is because I think that it's really annoying to have to give all your context to an assistant and then to figure out how it can help you.
我之所以认为这一点如此重要，是因为我认为必须向助手提供所有背景信息，然后弄清楚它如何帮助你，这真的很烦人。
So if it could just understand what you are trying to do, then it could maximally accelerate you.
因此，如果它能够理解您想要做什么，那么它可以最大限度地加速您的速度。
So I still think of Codex actually as a contextual assistant from a little bit of a different angle, starting with coding tasks.
因此，我仍然认为 Codex 实际上是一个从不同角度的上下文助手，从编码任务开始。
Alexander Embiricos: But some of the thinking, at least for me personally, I can't speak for the whole project, was that a lot of work is done in the web.
Alexander Embiricos：但是，至少对我个人来说，我不能代表整个项目，一些想法是，很多工作都是在网络上完成的。
And if we could build a browser, then we could be contextual for you, but in a much more first class way.
如果我们可以构建一个浏览器，那么我们就可以为您提供上下文，但以更一流的方式。
We weren't hacking other desktop software which have very varied support for what content they're rendering to the accessibility tree.
我们并没有攻击其他桌面软件，这些软件对渲染到可访问性树的内容有非常不同的支持。
We wouldn't be relying on screenshots, which are a little bit slower and unreliable.
我们不会依赖屏幕截图，因为屏幕截图有点慢而且不可靠。
Instead, we could be In the rendering engine and extract whatever we needed to help you.
相反，我们可以在渲染引擎中提取我们需要的任何内容来帮助您。
And also I like to think of video games, I don't know if you've played, I don't know, say Halo, you walk up to an object, I mean, this is true for many games, you press...
而且我还喜欢思考电子游戏，我不知道你是否玩过，我不知道，比如说光环，你走到一个物体前，我的意思是，对于很多游戏来说都是如此，你按...
Man, it's been a long time, this is embarrassing.
哥们儿，好久不见了，真是尴尬啊。
Press X and it just does the right thing.
按 X，它就会做正确的事情。
And I was one of those guys who always read the instruction manual for every video game that I bought.
我是那些总是阅读我购买的每个视频游戏的说明手册的人之一。
Alexander Embiricos: And I remember the first time I read about a contextual action and I just thought it was this really cool idea.
Alexander Embiricos：我记得第一次读到上下文动作时，我觉得这是一个非常酷的想法。
And the thing about a contextual action is we need to know what you are attempting to do.
关于上下文操作的问题是我们需要知道您正在尝试做什么。
We have a little bit of context and then we can help.
我们有一些背景信息，然后我们可以提供帮助。
And I think this is critically important because imagine this world that we reach where we have agents that are helping you thousands of times per day.
我认为这一点至关重要，因为想象一下我们所到达的这个世界，我们有每天为您提供数千次帮助的代理。
Alexander Embiricos: Imagine if the only way we could tell you that we helped you was if we could push notify you.
Alexander Embiricos：想象一下，如果我们告诉您我们帮助您的唯一方式是我们可以推送通知您。
So you get a thousand push notifications a day of an AI saying like, "Hey, I did this thing.
所以你每天都会收到一千条推送通知，人工智能说，“嘿，我做了这件事。
Do you like it?
你喜欢吗？
" It'd be super annoying, right?
“这会非常烦人，对吧？
Whereas imagine, going back to software engineering, I was looking at a dashboard and I noticed some key metric had gone down.
想象一下，回到软件工程，我正在查看仪表板，发现一些关键指标下降了。
And at that point in time, an AI could maybe go take a look and then surface the fact that it has an opinion on why this metric went down and maybe a fix right there right when I'm looking at the dashboard.
到那时，人工智能可能会去看一下，然后浮出水面，它对这个指标下降的原因有自己的看法，也许当我查看仪表板时就可以解决这个问题。
That would much more keep me in flow and enable the agent to take action on many more things.
这将使我更加保持流畅，并使代理能够对更多事情采取行动。
Alexander Embiricos: So in my mind, part of why I'm excited for us to have a browser is that I think we have then much more context around what we should help with.
Alexander Embiricos：所以在我看来，我对我们拥有浏览器感到兴奋的部分原因是我认为我们对我们应该提供帮助的内容有更多的背景。
Users have much more control over what they want us to look at.
用户可以更好地控制他们希望我们查看的内容。
It's like, "Hey, if you want us to take action on something, you can open it in your AI browser.
就像，“嘿，如果你想让我们对某件事采取行动，你可以在人工智能浏览器中打开它。
If you don't, then you can open it in your other browser." So really clear control and boundaries.
如果没有，那么您可以在其他浏览器中打开它。”非常清晰的控制和界限。
And then we have the ability to build UX that's mixed initiatives so that we can surface contextual actions to you at the time that they're helpful as opposed to just randomly notifying you.
然后，我们有能力构建混合举措的用户体验，以便我们可以在有帮助时向您展示上下文操作，而不是只是随机通知您。
Lenny Rachitsky: Hearing the vision for Codex being the super assistant, it's not just there to code for you.
Lenny Rachitsky：听说 Codex 成为超级助手的愿景，它不仅仅是为您编码。
It's trying to do a lot for you as a teammate, as this kind of super teammate, and that makes you awesome at work.
作为一名队友，作为这种超级队友，它会尝试为你做很多事情，这会让你在工作中表现出色。
So I get this.
所以我明白了。
Speaking of that, are there other non-engineering common use cases for Codex?
说到这里，Codex 是否还有其他非工程常见用例？
Just ways that non-engineers...
只是非工程师的方法...
We talked about designers prototyping and building stuff, are there any fun or unexpected ways people are using Codex that aren't engineers?
我们讨论了设计师原型设计和构建东西，除了工程师之外，人们使用 Codex 的方式是否有任何有趣或意想不到的方式？
Alexander Embiricos: I mean, there's a load of unexpected ways, but I think most of where we're seeing real traction with people using things are still for now very, I would say, coding adjacent or tech-oriented, places where there's a mature ecosystem or maybe you're doing data analysis or something like that.
Alexander Embiricos：我的意思是，有很多意想不到的方式，但我认为我们看到人们使用东西的真正吸引力的大多数地方目前仍然非常，我想说的是，编码相邻或以技术为导向，有成熟的生态系统的地方，或者也许你正在做数据分析或类似的事情。
I personally am expecting that we're going to see a lot more of that over time.
我个人预计随着时间的推移我们会看到更多这样的情况。
But for now, we're keeping the team very focused on just coding for now because there's so much more work to do.
但目前，我们让团队非常专注于编码，因为还有很多工作要做。
Lenny Rachitsky: For people that are thinking about trying out Codex, does it work for all kinds of code bases?
Lenny Rachitsky：对于那些正在考虑尝试 Codex 的人来说，它适用于所有类型的代码库吗？
What code does it support?
它支持什么代码？
If you're like, I don't know, SAP, can you add Codex and start building things?
如果你想，我不知道，SAP，你能添加 Codex 并开始构建东西吗？
What's the sweet spot?
最甜蜜的地方是什么？
Where does it start to not be amazing yet?
从哪里开始还不够令人惊奇？
Alexander Embiricos: I'm really glad you asked this question actually because the best way to try Codex is to give it your hardest tasks, which is a little different than some of the other coding agents.
Alexander Embiricos：我真的很高兴你问了这个问题，因为尝试 Codex 的最佳方法就是给它你最困难的任务，这与其他一些编码代理有点不同。
Some tools you might think, "Okay, let me start easy or just vibe code something random and decide if I like the tool." Whereas we're really building Codex to be the professional tool that you can give your hardest problems to.
有些工具您可能会想，“好吧，让我从简单的开始，或者只是随机编码一些东西，然后决定我是否喜欢这个工具。”而我们真正将 Codex 打造为专业工具，帮助您解决最棘手的问题。
And that writes high quality code in your enormous code base that is in fact not perfect right now.
这会在您庞大的代码库中编写高质量的代码，而该代码库实际上目前并不完美。
So yeah, I think if you're going to try Codex, you want to try it on a real task that you have and not necessarily dumb that task down to something that's trivial, but actually a good one would be you have a hard bug and you don't know what's causing that bug and you ask Codex to help figure that out or to implement that the fix.
所以，是的，我认为如果你要尝试 Codex，你想在你拥有的实际任务上尝试它，而不一定把该任务简化为微不足道的事情，但实际上一个好的任务是你遇到了一个硬错误，并且你不知道是什么导致了该错误，然后你要求 Codex 帮助解决这个问题或实施修复。
Lenny Rachitsky: I love that answer.
Lenny Rachitsky：我喜欢这个答案。
Just give it to your hardest problem.
只需解决您最难的问题即可。
Alexander Embiricos: I will say if you're like, "Hey, okay, well, the hardest problem I have is that I need to build a new unicorn business," obviously that's not going to work.
Alexander Embiricos：我会说，如果你想，“嘿，好吧，好吧，我遇到的最困难的问题是我需要建立一个新的独角兽业务”，显然这是行不通的。
Not yet.
还没有。
So I think it's like give it the hardest problem, but something that is still one question or one task to start.
所以我认为这就像给它一个最难的问题，但仍然是一个问题或一项任务的开始。
That's if you're testing and then over time you can learn how to use it for bigger things.
也就是说，如果您正在测试，然后随着时间的推移，您可以学习如何将它用于更大的事情。
Lenny Rachitsky: Yeah.
莱尼·拉奇茨基：是的。
What languages does it support?
它支持哪些语言？
Alexander Embiricos: Basically, the way we've trained Codex is there's a distribution of languages that we support and it's fairly aligned with the frequency of these languages in the world.
Alexander Embiricos：基本上，我们训练 Codex 的方式是我们支持的语言分布，并且它与世界上这些语言的频率相当一致。
So unless you're writing some very esoteric language or some private language, it should do fine in your language.
因此，除非您正在编写一些非常深奥的语言或一些私人语言，否则它应该可以用您的语言做得很好。
Lenny Rachitsky: If someone was just getting started, is there a tip you could share to help them be successful?
Lenny Rachitsky：如果有人刚刚起步，您是否可以分享一些技巧来帮助他们取得成功？
If you could just whisper a little tip into someone just setting up Codex for the first time to help them have a really good time, what's something you would whisper?
如果您可以向第一次设置 Codex 的人低声说一些小建议，以帮助他们度过一段美好时光，您会低声说些什么？
Alexander Embiricos: I might say try a few things in parallel.
Alexander Embiricos：我可能会说同时尝试一些事情。
So you could try giving it a hard task, maybe ask it to understand the code base, formulate a plan with it around an idea that you have and kind of build your way up from there.
因此，您可以尝试给它一个艰巨的任务，也许要求它理解代码库，围绕您已有的想法制定计划，并从那里开始构建您的方式。
And the meta idea here is, again, it's like you're building trust with a new teammate.
这里的元想法再次是，就像你正在与新队友建立信任。
And so you wouldn't go to a new teammate and just give them like, "Hey, do this thing.
所以你不会去找新队友并告诉他们，“嘿，做这件事。
Here's zero context." You would start by first making sure they understand the code base and then you would maybe align on an approach and then you would have them go off and do bit by bit.
这是零背景。”你首先要确保他们理解代码库，然后你可能会调整一种方法，然后你会让他们开始一点一点地做。
And I think if you use Codex in that way, you'll just naturally start to understand the different ways of prompting it because it's a super powerful agent and model, but it is a little bit different to prompt Codex than other models.
我认为如果你以这种方式使用 Codex，你会很自然地开始理解提示它的不同方式，因为它是一个超级强大的代理和模型，但提示 Codex 与其他模型有点不同。
Lenny Rachitsky: Just a couple more questions.
Lenny Rachitsky：还有几个问题。
One, we touched on this a little bit, as AI does more and more coding, there's always this question of, "Should I learn to code and why should I spend time doing this sort of thing?" For people that are trying to figure out what to do with their career, especially if they're into software engineering computer science, do you think there's specific elements of computer science that are more and more important to lean into, maybe things they don't need to worry about?
第一，我们稍微谈到了这一点，随着人工智能进行越来越多的编码，总是存在这样的问题：“我应该学习编码吗？我为什么要花时间做这种事情？”对于那些试图弄清楚自己的职业生涯该做什么的人，特别是如果他们从事软件工程计算机科学，你认为计算机科学的某些特定元素越来越重要，也许他们不需要担心？
What do you think people should be leaning into skill-wise as this becomes more and more of a thing in our workplace?
随着这在我们的工作场所变得越来越普遍，你认为人们应该倾向于什么技能？
Alexander Embiricos: I think there's a couple angles you could go at this from.
Alexander Embiricos：我认为你可以从几个角度来看待这个问题。
Well, the easiest one to think of at least is just be a doer of things.
好吧，至少最容易想到的就是成为一名行动者。
I think that with coding agents getting better and better over time, it's just what you can do as even someone in college or a new grad is just so much more than what that was before.
我认为，随着时间的推移，编码代理变得越来越好，这正是你可以做的，因为即使是大学里的人或应届毕业生也比以前要多得多。
And so I think you just want to be taking advantage of that.
所以我认为你只是想利用这一点。
And definitely when I'm looking at hiring folks who are earlier career, it's definitely something that I think about is how productive are they using the latest tools?
当然，当我考虑招聘职业生涯早期的人时，我肯定会考虑的是他们使用最新工具的效率如何？
They should be super productive.
他们应该非常高效。
And if you think of it in that way, they actually have less of a handicap than before versus a more senior career person because the divide is actually getting smaller because they've got these amazing coding agents now.
如果你这样想的话，与更资深的职业人士相比，他们实际上比以前有更少的障碍，因为差距实际上越来越小，因为他们现在有了这些令人惊叹的编码代理。
So that's one thing, which is, I guess the advice is just learn about whatever you want, but just make sure you spend time doing things, not just fulfilling homework assignments, I guess.
所以这是一件事，我想建议就是学习你想要的任何东西，但要确保你花时间做事，而不仅仅是完成家庭作业，我想。
Alexander Embiricos: I think the other side of it though is that it's still deeply worth understanding what makes a good overall software system.
Alexander Embiricos：我认为另一方面是，仍然值得深入了解什么构成了良好的整体软件系统。
So I still think that skills, like really strong systems engineering skills, or even really effective communication and collaboration with your team, skills like that I think are important or are going to continue to matter for quite some time.
因此，我仍然认为技能，例如真正强大的系统工程技能，甚至与团队的真正有效的沟通和协作，我认为这些技能很重要，或者将在相当长的一段时间内继续发挥作用。
I don't think it's going to be all of a sudden the AI coding agents are just able to build perfect systems without your help.
我不认为人工智能编码代理会突然间在没有你帮助的情况下构建出完美的系统。
I think it's going to look much more gradual where it's like, okay, we have these AI coding agents, they're able to validate their work.
我认为它看起来会更加渐进，就像，好吧，我们有这些人工智能编码代理，他们能够验证他们的工作。
It's still important.
这仍然很重要。
Alexander Embiricos: For example, I'm thinking of an engineer who was working on Atlas, since we were talking about it, he set up Codex so that it can verify its own work, which is a little bit non-trivial because of the nature of the Atlas project.
Alexander Embiricos：例如，我想到了一位正在开发 Atlas 的工程师，因为我们正在谈论它，所以他建立了 Codex，以便它可以验证自己的工作，由于 Atlas 项目的性质，这有点不简单。
So the way that he did that was he actually prompted Codex like, "Hey, why can't you verify your work?
所以他这样做的方式实际上是提示 Codex，“嘿，为什么你不能验证你的工作？
Fix it," and did that on a loop.
修复它，”并循环执行此操作。
And so you still, at various phases, are going to want a human in the loop to help configure the coding agent to be effective.
因此，在各个阶段，您仍然需要有人参与循环来帮助配置编码代理以使其有效。
So I think you still want to be able to reason about that.
所以我认为你仍然希望能够对此进行推理。
So maybe it's less important that you can type really fast and you understand exactly how to write...
所以也许你能快速打字并且你准确地理解如何写作并不那么重要......
Not that anyone writes a 4H loop or something, or you don't need to know how to implement a specific algorithm.
并不是有人写4H循环什么的，或者你不需要知道如何实现特定的算法。
But I think you need to be able to reason about the different systems and what makes a software engineering team effective.
但我认为你需要能够推理不同的系统以及软件工程团队高效的因素。
So I think that's the other really important thing.
所以我认为这是另一件非常重要的事情。
Alexander Embiricos: Then maybe the last angle that you could take is, I think if you're on the frontier of knowledge for a given thing, I still think that's deeply interesting to go down, partially because that knowledge is still going to be...
Alexander Embiricos：那么也许你可以采取的最后一个角度是，我认为如果你处于某一特定事物的知识前沿，我仍然认为继续下去是非常有趣的，部分原因是这些知识仍然会......
Agents aren't going to be as good at that, but also partially because I think that by trying to advance the frontier of a specific thing, you'll actually end up being forced to take advantage of coding agents and using them to accelerate your own workflow as you go.
代理不会那么擅长，但部分原因是我认为，通过尝试推进特定事物的前沿，您实际上最终将被迫利用编码代理并使用它们来加速您自己的工作流程。
Lenny Rachitsky: What's an example that when you talk about being at the frontier of something?
Lenny Rachitsky：当您谈论处于某事物的前沿时，可以举个什么例子吗？
Alexander Embiricos: Codex writes a lot of the code that helps manage its training runs, the key infrastructure.
Alexander Embiricos：Codex 编写了大量有助于管理其训练运行（关键基础设施）的代码。
We move pretty fast and so we have a Codex code review is catching a lot of mistakes.
我们的进展非常快，因此我们进行了 Codex 代码审查，发现了很多错误。
It's actually cause some pretty interesting configuration mistakes.
它实际上会导致一些非常有趣的配置错误。
And we're starting to see glimpses of the future where we're actually starting to have Codex even be on call for its own training, which is pretty interesting.
我们开始看到未来的一瞥，我们实际上开始让 Codex 随时待命进行自己的培训，这非常有趣。
So there's lots there.
所以那里有很多。
Lenny Rachitsky: Wait, what does that mean to be on call for its own training?
Lenny Rachitsky：等等，待命参加自己的培训意味着什么？
So it's running, it's training and it's like, "Oh, something broke, someone needs..." And does it alert people or it's like, "Here, I'm going to fix the problem and restart"?
所以它正在运行，它正在训练，就像，“哦，有些东西坏了，有人需要......”它会提醒人们还是像“在这里，我要解决问题并重新启动”？
Alexander Embiricos: This is an early idea that we're figuring out.
Alexander Embiricos：这是我们正在研究的一个早期想法。
But the basic idea is that during a training run, there's a bunch of graphs that today humans are looking at and it's really important to look at those.
但基本思想是，在训练过程中，今天人们正在查看一堆图表，查看这些图表非常重要。
We call this babysitting.
我们称之为保姆。
Lenny Rachitsky: Because it's very expensive to train, I imagine, and very important to move fast and-
Lenny Rachitsky：我想，因为训练非常昂贵，而且快速行动非常重要，而且——
Alexander Embiricos: Exactly.
亚历山大·恩比里科斯：没错。
And there's a lot of systems underlying the training run.
训练运行背后有很多系统。
And so a system could go down or there could be an error somewhere that gets introduced.
因此，系统可能会崩溃，或者可能会在某个地方出现错误。
And so we might need to fix it or pause things or, I don't know, there's lots of actions we might need to take.
因此，我们可能需要修复它或暂停一些事情，或者，我不知道，我们可能需要采取很多行动。
And so basically having Codex run on a loop to evaluate how those charts are moving over time is this idea that we have to how to enable us to train way more efficiently.
因此，基本上让 Codex 在循环上运行来评估这些图表如何随时间变化，这就是我们必须如何使我们能够更有效地进行训练的想法。
Lenny Rachitsky: I love that.
莱尼·拉奇茨基：我喜欢这个。
And this is very much along the lines of this is the future of agents.
这与代理的未来非常相似。
Codex isn't just for building code, it's a lot more than that.
Codex 不仅仅用于构建代码，它的用途远不止于此。
Lenny Rachitsky: Okay, last question.
莱尼·拉奇茨基：好的，最后一个问题。
Being at OpenAI, I can't not ask about your AGI timeline and how far you think we are from AGI.
在 OpenAI，我不能不问你的 AGI 时间表，以及你认为我们离 AGI 还有多远。
I know this isn't what you work on, but there's a lot of opinions, a lot of, I don't know, timelines.
我知道这不是你的工作，但是有很多意见，很多我不知道的时间表。
How far do you think we are from a humanly human version of AI, whatever that means to you?
你认为我们离人类版本的人工智能还有多远，无论这对你意味着什么？
Alexander Embiricos: For me, I think that it's a little bit about when do we see the acceleration curves go like this?
Alexander Embiricos：对我来说，我认为问题在于我们什么时候会看到加速度曲线像这样？
Or I don't know which way I'm mirrored here.
或者我不知道我以哪种方式镜像在这里。
When do we see the hockey stick?
我们什么时候看到曲棍球棒？
And I think that the current limiting factor, I mean, there's many, but I think a current underappreciated limiting factor is literally human typing speed or human multitasking speed on writing prompts.
我认为当前的限制因素有很多，但我认为当前被低估的限制因素实际上是人类打字速度或人类在书写提示上的多任务处理速度。
And like you were talking about, it's like you can have an agent watch all the work you're doing, but if you don't have the agent also validating its work, then you're still bottlenecked on can you go review all that code?
就像你所说的，就像你可以让代理监视你正在做的所有工作，但如果你没有代理也验证其工作，那么你仍然遇到瓶颈，你可以去审查所有代码吗？
Alexander Embiricos: So my view is that we need to unblock those productivity loops from humans having to prompt and humans having to manually validate all the work.
Alexander Embiricos：所以我的观点是，我们需要消除人类必须提示和人类必须手动验证所有工作的生产力循环。
So if we can rebuild systems to let the agent be default useful, we'll start unlocking hockey sticks.
因此，如果我们可以重建系统以使代理默认有用，我们将开始解锁曲棍球棒。
Unfortunately, I don't think that's going to be binary.
不幸的是，我认为这不会是二元的。
I think it's going to be very dependent on what you're building.
我认为这将非常取决于您正在构建的内容。
So I would imagine that next year, if you're a startup and you're building new pieces, like some new app or something, it'll be possible for you to set it up on a stack where agents are much more self-sufficient than not.
所以我想象明年，如果你是一家初创公司并且你正在构建新的作品，比如一些新的应用程序或其他东西，你将有可能将其设置在一个堆栈上，其中代理比不自给自足得多。
But now let's say, I don't know, you mentioned SAP, let's say you work in SAP, they have many complex systems and they're not going to be able to just get the agent to be self-sufficient overnight in those systems.
但现在让我们说，我不知道，你提到了 SAP，假设你在 SAP 工作，他们有很多复杂的系统，他们不可能让代理在一夜之间在这些系统中自给自足。
So they're going to have to slowly maybe replace systems or update systems to allow the agent to handle more of the work end to end.
因此，他们将不得不慢慢地更换系统或更新系统，以允许代理能够端到端地处理更多工作。
Alexander Embiricos: So basically my long answer to your question, maybe boring answer is that I think starting next year, we're going to see early adopters starting to hockey stick their productivity.
Alexander Embiricos：所以基本上我对你的问题的长答案，也许无聊的答案是，我认为从明年开始，我们将看到早期采用者开始提高他们的生产力。
And then over the years that follow, we're going to see larger and larger companies like hockey stick that productivity.
在接下来的几年里，我们将看到越来越大的公司（例如曲棍球棒）的生产力不断提高。
And then somewhere in that fuzzy middle is when that hockey sticking will be flowing back into the AI labs and that's when we'll basically be at the AGI tier.
然后在那个模糊的中间的某个地方，曲棍球棒将流回人工智能实验室，那时我们基本上将处于 AGI 层。
Lenny Rachitsky: I love this answer.
莱尼·拉奇茨基：我喜欢这个答案。
It's very practical and it's something that comes up a lot on this podcast.
它非常实用，并且在这个播客中经常出现。
Just like the time to reveal all the things AI is doing is really annoying and a big bottleneck.
就像揭示人工智能正在做的所有事情的时间确实很烦人，也是一个很大的瓶颈。
I love that you're working on this because it's one thing to just make coding much more efficient and do that for people.
我很高兴您正在从事这方面的工作，因为提高编码效率并为人们做到这一点是一回事。
It's another to take care of that final step of, "Okay, is this actually great?" And that's so interesting that your sense is that's the limiting factor.
处理最后一步是另一回事，“好吧，这真的很棒吗？”这很有趣，你的感觉是这是限制因素。
It comes back to your earlier point of even if AI did not advance anymore, we have so much more potential to unlock as we learn to use it more effectively.
回到你之前的观点，即使人工智能不再进步，当我们学会更有效地使用它时，我们也有更多的潜力可以释放。
So that is a really unique answer.
所以这是一个非常独特的答案。
I haven't heard that perspective on what is the big unlock.
我还没有听说过关于什么是大解锁的观点。
Human typing speed to review basically what AI is doing for us.
人类的打字速度基本上可以回顾人工智能为我们做了什么。
So good.
超好的。
Lenny Rachitsky: Okay, Alexander, we covered a lot of ground.
Lenny Rachitsky：好的，Alexander，我们讨论了很多内容。
Is there anything that we haven't covered?
还有什么我们没有涉及到的吗？
Is there anything you wanted to share, maybe double down on before we get to our very exciting lightning round?
在我们进入令人兴奋的闪电回合之前，您有什么想分享的吗？
Alexander Embiricos: I think one thing is that the Codex team is growing.
Alexander Embiricos：我认为有一件事是 Codex 团队正在成长。
And as I was just saying, we're still somewhat limited by human thinking speed and human typing speed.
正如我刚才所说，我们仍然在一定程度上受到人类思维速度和人类打字速度的限制。
We're working on it.
我们正在努力。
So if you're an engineer or a salesperson, or I'm hiring a product person, please hit us up.
因此，如果您是工程师或销售人员，或者我正在招聘产品人员，请联系我们。
I'm not sure the best way to give contact info, but I guess you can go to our jobs page, or do they have contact for you actually?
我不确定提供联系信息的最佳方式，但我想您可以访问我们的职位页面，或者他们实际上有您的联系方式吗？
Do listeners have contact for you?
听众有联系方式吗？
Lenny Rachitsky: Where they send me like, "Hey, I want to apply to Codex"?
Lenny Rachitsky：他们向我发送“嘿，我想申请 Codex”之类的信息？
I do have a contact form at lennyrachitsky.com.
我在 lennyrachitsky.com 上有一份联系表。
I'm afraid of all the amazing people that are going to ping me.
我害怕所有那些了不起的人会来找我。
But there we go, we could try that.
但我们可以尝试一下。
Let's see how that goes.
让我们看看情况如何。
Alexander Embiricos: Okay.
亚历山大·恩比里科斯：好的。
Or maybe an easier version, we can edit all that out or up to you.
或者也许是一个更简单的版本，我们可以编辑所有内容或由您决定。
Yeah, or I would just say you can drop us a DM.
是的，或者我只想说你可以给我们发私信。
For example, I'm Embirico on Twitter, and hit me up if you're interested in joining the team.
例如，我在 Twitter 上是 Embirico，如果您有兴趣加入该团队，请联系我。
Lenny Rachitsky: What a dream job for so many people.
Lenny Rachitsky：对于很多人来说这是多么梦想的工作啊。
What's a sign they...
有什么迹象表明他们...
I don't know, what's a way to filter people a little bit so they're not flooding your inbox?
我不知道，有什么方法可以稍微过滤一下人员，这样他们就不会淹没您的收件箱？
Alexander Embiricos: So specifically if you want to join the Codex team, then you need to be a technical person who uses these tools.
Alexander Embiricos：所以具体来说，如果您想加入 Codex 团队，那么您需要成为使用这些工具的技术人员。
And I think I would just ask yourself the question, "Hey, let's say I were to join OpenAI and work on Codex over the next six months and crush it, what does the life of a software engineer look like then?" And I think if you have an opinion on that, you should apply.
我想我只会问自己一个问题：“嘿，假设我要加入 OpenAI，并在接下来的六个月内从事 Codex 工作，然后粉碎它，那么软件工程师的生活会是什么样子？”我认为如果你对此有意见，你应该提出申请。
And if you don't have an opinion on that and have to think about it first, depending on how long you have to think about it, I guess that would be the filter.
如果你对此没有意见并且必须首先考虑它，这取决于你需要考虑多长时间，我想这将是过滤器。
I think there's a lot of people thinking about this space.
我认为有很多人在思考这个领域。
So we're very interested in folks who have already been thinking about what the future should look like with agents.
因此，我们对那些已经在思考代理的未来应该是什么样子的人们非常感兴趣。
And we don't have to agree on where we're going, but I think we want people who are very passionate about the topic, I guess.
我们不必就我们的发展方向达成一致，但我想我们需要对这个话题充满热情的人，我想。
Lenny Rachitsky: It's very rare to be working on a product that has this much impact and is at such a bleeding edge of where it's possible.
Lenny Rachitsky：开发具有如此大影响力并且处于如此前沿的产品是非常罕见的。
What a cool role for the right person.
对于合适的人来说，这是一个多么酷的角色啊。
So it's awesome that you have an opening and this audience is a really good fit potentially for that role.
所以很高兴你有一个空缺，而且这些观众非常适合这个角色。
So I hope we find someone, that would be incredible.
所以我希望我们能找到一个人，那将是令人难以置信的。
With that, we've reached our very exciting lightning round.
至此，我们进入了非常激动人心的闪电回合。
I've got five questions for you, Alexander.
我有五个问题要问你，亚历山大。
Are you ready?
你准备好了吗？
Alexander Embiricos: I don't know what these are, but I'm excited.
Alexander Embiricos：我不知道这些是什么，但我很兴奋。
Let's do it.
我们开始做吧。
Lenny Rachitsky: They're the same questions I ask everyone except for the last one.
Lenny Rachitsky：除了最后一个问题之外，我也问过每个人同样的问题。
So probably not a surprise.
所以可能并不奇怪。
I should probably make them more often a surprise.
我也许应该更多地让他们感到惊讶。
Okay, first question, what are a couple of books that you recommend most to other people, two or three books that come to mind?
好的，第一个问题，您最向其他人推荐的几本书是什么，您想到的两三本书是什么？
Alexander Embiricos: I have been reading a lot of science fiction recently, and I'm sure this has been recommended before, but The Culture, I think it's Ian Banks is the name of the author.
Alexander Embiricos：我最近读了很多科幻小说，我确信之前已经推荐过这个，但是The Culture，我认为作者的名字是Ian Banks。
Part of why I love it is because it's basically relatively recent writing about a future with AI, but it's an optimistic future with AI.
我喜欢它的部分原因是它基本上是相对较新的关于人工智能未来的文章，但它是人工智能的乐观未来。
And I think a lot of sci-fi is fairly dystopian.
我认为很多科幻小说都是相当反乌托邦的。
But the joke, at least on The Culture subreddit is that, let me see if I can get this right, it is a space communist utopia, or I think it's a gay space communist utopia.
但至少在reddit文化子版块上有一个笑话，让我看看我是否能把这个说对，这是一个太空共产主义乌托邦，或者我认为这是一个同性恋太空共产主义乌托邦。
And I just think it's really fun to think about, to use The Culture as a way to think about what kind of world can we usher in and what decisions can we make today to help usher in that world.
我只是认为思考这一点真的很有趣，用文化来思考我们可以迎来一个什么样的世界，以及我们今天可以做出哪些决定来帮助迎来这个世界。
Lenny Rachitsky: Wow.
莱尼·拉奇茨基：哇。
I don't think anyone's recommended that.
我认为没有人推荐这样做。
I know you're reading, you've mentioned before I start recording, Lord of the Rings right now.
我知道你正在读，你在我开始录音之前就提到过，现在正在读《指环王》。
If you want another AI-ish sci-fi book, have you read Fire Upon the Deep?
如果你想要另一本人工智能科幻小说，你读过《Fire On the Deep》吗？
Lenny Rachitsky: Okay, it's incredibly good.
Lenny Rachitsky：好的，这非常好。
It's like a sci-fi space opera sort of epic tale with super intelligence.
这就像一部科幻太空歌剧，具有超级智慧的史诗故事。
Lenny Rachitsky: Yeah.
莱尼·拉奇茨基：是的。
Mostly not optimistic, but somewhat optimistic.
大多不乐观，但有些乐观。
Lenny Rachitsky: Okay, next question.
莱尼·拉奇茨基：好的，下一个问题。
Is there a favorite recent movie or TV show that you've really enjoyed?
最近有没有您真正喜欢的最喜欢的电影或电视节目？
Alexander Embiricos: Yeah, there's an anime called Jujutsu Kaisen, which I really like.
Alexander Embiricos：是的，有一部动画叫《柔术会战》，我非常喜欢。
Again, it's got a slightly dark topic of demons.
再次，它有一个稍微黑暗的恶魔主题。
But what I love about it is that the hero is really nice.
但我最喜欢的是这个英雄真的很好。
And I think there's this new wave of anime and cartoons where the protagonists are really friendly and people who care about the world rather than being sort of, if you look at some older anime that started the genre, there's Evangelion or Akita and those characters, the protagonists are deeply flawed, quite unhappy.
我认为现在有新一波的动漫和卡通，主角们非常友好，人们关心世界，而不是被人看不起，如果你看看一些开创这一流派的老动画，比如《新世纪福音战士》或《秋田》，这些角色的主角都有严重的缺陷，非常不开心。
They didn't start the genre, but it was a trend for a while to poke fun at the idea that in these cartoons the protagonist was very young, but being given a ridiculous amount of responsibility to save the world.
他们并没有开创这种类型，但有一段时间，人们嘲笑这样的想法：在这些漫画中，主角非常年轻，却被赋予了拯救世界的荒谬责任。
So there was kind of a wave of content that was critiquing this by making the character basically go through serious mental issues in the middle of the show.
因此，有一波内容批评了这一点，让这个角色在节目中间经历了严重的精神问题。
And I'm not saying this is better, but at least it's quite fun to have these really positive protagonists are just trying to help everyone around them.
我并不是说这更好，但至少让这些真正积极的主角试图帮助周围的每个人是很有趣的。
Lenny Rachitsky: I love how much we're learning about your personality hearing these recommendations.
Lenny Rachitsky：我很高兴我们通过这些建议来了解您的个性。
Nice protagonists, optimistic futures.
主角不错，未来乐观。
I like the .
我喜欢 .
Alexander Embiricos: I think if you don't believe it, you can't will it into existence.
Alexander Embiricos：我认为如果你不相信它，你就无法通过意志让它存在。
So you need a balance.
所以你需要一个平衡。
This is your training data.: Is there a product you recently discovered you really love?
这是您的训练数据。：您最近发现您真正喜欢的产品吗？
Could be an app, could be some clothing, could be some kitchen gadget, tech gadget, a hat.
可能是一个应用程序，可能是一些衣服，可能是一些厨房小工具、科技小工具、一顶帽子。
Alexander Embiricos: Yeah, so I have been quite into combustion engines and cars.
Alexander Embiricos：是的，所以我非常喜欢内燃机和汽车。
Actually, the reason I came to America initially was because I wanted to work on US aircraft, but now I work in software.
其实我最初来美国是因为想在美国飞机上工作，但现在我从事软件工作。
And so for the longest time, I've basically only had quite old sports cars, old just because they were more affordable.
所以很长一段时间以来，我基本上只拥有相当旧的跑车，旧只是因为它们更便宜。
And then recently we got a Tesla instead.
最近我们又买了一辆特斯拉。
And I have to say that I find the Tesla software quite inspiring.
我不得不说，我发现特斯拉的软件非常鼓舞人心。
In particular, it has the self-driving feature.
尤其是它具有自动驾驶功能。
And I've mentioned a few times today, I think it's really interesting to think about how to build mixed initiative software that makes you feel maximally empowered as a human, maximally in control, but yet you're getting a lot of help.
我今天已经提到过几次，我认为思考如何构建混合主动软件真的很有趣，它让你感觉作为一个人得到了最大限度的授权，最大限度的控制，但你却得到了很多帮助。
And I think they did a really good job with enabling the car to drive itself, but all these different ways that you can adjust what it's doing without turning off the self-driving.
我认为他们在使汽车能够自动驾驶方面做得非常好，但是通过所有这些不同的方式，你可以在不关闭自动驾驶的情况下调整它正在做什么。
So you can accelerate, it'll listen to that.
所以你可以加速，它会听你的。
You can turn a knob to change its speed.
您可以转动旋钮来改变其速度。
You can steer slightly.
您可以稍微转向。
I think it's actually a masterclass in building an agent that still leaves the human in control.
我认为这实际上是构建一个仍然由人类控制的代理的大师班。
Lenny Rachitsky: This reminds me Nick Turley's whole mantra is, "Are we maximally accelerated?"
Lenny Rachitsky：这让我想起了 Nick Turley 的整个口头禅：“我们的加速度是最大的吗？”
Lenny Rachitsky: Feels like it's completely infiltrated everything at OpenAI, which makes sense, that tracks.
Lenny Rachitsky：感觉它完全渗透到了 OpenAI 的一切，这是有道理的，跟踪的。
Lenny Rachitsky: Two more questions.
莱尼·拉奇茨基：还有两个问题。
Do you have a life motto that you often think about and come back to in work or in life that's been helpful?
您是否有一条在工作或生活中经常思考并回想起来对您有帮助的人生座右铭？
Alexander Embiricos: I don't know if I have a life motto, but maybe I can tell you about the number one company value from my startup.
Alexander Embiricos：我不知道我是否有人生座右铭，但也许我可以告诉你我的初创公司的第一大公司价值。
Alexander Embiricos: Which is still something that sticks with me, which is to be kind and candid.
Alexander Embiricos：我仍然坚持善良和坦诚。
Lenny Rachitsky: That tracks.
Lenny Rachitsky：那是轨道。
Kind and candid.
善良、坦诚。
Wow, that's a great combo.
哇，这是一个很棒的组合。
Alexander Embiricos: Yeah.
亚历山大·恩比里科斯：是的。
And we had to put them together because we, as founders, realized that we often would be nice and it wasn't actually the right thing to do.
我们必须将它们放在一起，因为作为创始人，我们意识到我们经常表现得很友善，但这实际上并不是正确的做法。
We would delay the difficult conversations and we were not candid.
我们会推迟困难的谈话，而且我们并不坦诚。
And so every time we would remind ourselves of this motto and then we would become more candid.
所以每次我们都会提醒自己这句座右铭，然后我们就会变得更加坦诚。
And then six months later, we would realize that we were in fact not candid six months ago and we needed to be even more candid.
六个月后，我们会意识到六个月前我们实际上并不坦诚，我们需要更加坦诚。
So then the question is like, "Okay, how should we be candid?" It's like, "Okay, well, let's think of being candid as an act of kindness," but also think of that both in terms of doing it and willing ourselves to do it, but also in terms of how we frame it as people.
那么问题是，“好吧，我们应该如何坦诚呢？”就像，“好吧，好吧，让我们把坦诚视为一种善意的行为”，但也要从做这件事和我们愿意这样做的角度来思考，而且从我们如何将其视为人的角度来思考。
Lenny Rachitsky: That is a beautiful way of summarizing how to lead well.
Lenny Rachitsky：这是一个很好的总结如何领导的方式。
What's the book about challenge directly but care deeply?
有什么书既直接挑战又深切关怀？
Radical Candor.
激进的坦诚。
Lenny Rachitsky: So it's like another way of thinking about Radical Candor.
Lenny Rachitsky：所以这就像思考激进坦率的另一种方式。
Lenny Rachitsky: Okay, last question.
莱尼·拉奇茨基：好的，最后一个问题。
I was looking up your last name just like, "Hey, what's the story here?" So your last name is Embiricos, and I was talking at ChatGPT and it told me the most famous individuals with the surname are the influential Greek poet and psychoanalyst Andreas Embiricos and his relative, the wealthy shipping magnate and art collector, George Embiricos.
我在查找你的姓氏时就想，“嘿，这里有什么故事吗？”所以你的姓氏是 Embiricos，我在 ChatGPT 上聊天，它告诉我这个姓氏中最著名的人是有影响力的希腊诗人和精神分析学家安德烈亚斯·恩比里科斯 (Andreas Embiricos) 和他的亲戚、富有的航运巨头和艺术收藏家乔治·恩比里科斯 (George Embiricos)。
So the question is, which of these two do you most identify with, the Greek poet and psychoanalyst or the wealthy shipping magnate and art collector?
所以问题是，您最认同这两个人中的哪一个：希腊诗人和精神分析学家，还是富有的航运巨头和艺术收藏家？
Alexander Embiricos: I think it's going to have to be the poet because he loved the island that our family's from.
Alexander Embiricos：我认为一定是诗人，因为他喜欢我们一家人所在的岛屿。
Lenny Rachitsky: Wait, you know those people?
Lenny Rachitsky：等等，你认识那些人吗？
Okay, this is not news to you.
好吧，这对你来说不是什么新闻。
Okay.
好的。
Alexander Embiricos: Well, I mean, it's an enormous family.
Alexander Embiricos：嗯，我的意思是，这是一个庞大的家庭。
But it's like Greek, so these big families, everyone's your uncle.
但这就像希腊语，所以这些大家庭，每个人都是你的叔叔。
Alexander Embiricos: You know what I mean?
亚历山大·恩比里科斯：你明白我的意思吗？
My mother's Malaysian and also everyone is my uncle or aunt in Malaysia too, if that makes sense.
我母亲是马来西亚人，每个人也是我在马来西亚的叔叔或阿姨，如果这是有道理的话。
Alexander Embiricos: But yeah, he loved this island that the family initiated from.
Alexander Embiricos：但是，是的，他喜欢这个家庭起源的岛屿。
I believe, I don't actually know where that's shipping magnate lived, I think it was New York or something.
我相信，我实际上不知道那个航运巨头住在哪里，我想是纽约什么的。
But anyway, we all came from this island called Andros, which is a really beautiful place.
但无论如何，我们都来自这个叫安德罗斯的岛屿，那是一个非常美丽的地方。
And it's like there's more livestock there than humans.
好像那里的牲畜比人类还多。
Not too many tourists go there.
去那里的游客不多。
But I think part of what I think is really cool is he published a lot and a lot of his writing is about the beauty of that island, which I think is super cool.
但我认为我认为真正酷的部分原因是他发表了很多文章，他的很多作品都是关于那个岛屿的美丽，我认为这非常酷。
Wow, that was an amazing answer.: Two more questions, where can folks find you if they want to follow you online and maybe reach out?
哇，这是一个惊人的答案。：还有两个问题，如果人们想在网上关注您并可能与您联系，他们可以在哪里找到您？
And then how can listeners be useful to you?
那么听众如何对你有用呢？
Alexander Embiricos: I'm one of those people who has social media only for the purposes of having work.
Alexander Embiricos：我是那些仅为了工作而使用社交媒体的人之一。
My phone turns black and white at 9:00 PM at night.
我的手机在晚上 9:00 变成黑屏和白屏。
But yeah, so Twitter or X, @Embirico.
但是，是的，所以 Twitter 或 X，@Embirico。
And yeah, if you post in r/Codex, I'll probably see it.
是的，如果你在 r/Codex 上发帖，我可能会看到它。
So you can go there.
所以你可以去那里。
Alexander Embiricos: How can listeners be useful?
Alexander Embiricos：听众如何发挥作用？
I would say please try Codex, please share feedback, let us know what to improve.
我想说，请尝试 Codex，请分享反馈，让我们知道需要改进的地方。
We pay a ton of attention to feedback.
我们非常重视反馈。
I think, honestly, the growth has been amazing, but it's still very early times, so we still pay a lot of attention and hope to do so forever.
我认为，老实说，增长是惊人的，但现在还处于早期阶段，所以我们仍然给予很多关注，并希望永远这样做。
And also, I would say if you're interested in working on the future of coding agents and then agents generally, then please apply to our job site and/or message me in those social media places.
另外，我想说，如果您对编码代理以及一般代理的未来感兴趣，那么请向我们的工作网站申请和/或在这些社交媒体上给我发消息。
Lenny Rachitsky: Alexander, this was awesome.
莱尼·拉奇茨基：亚历山大，这太棒了。
I always love meeting people working on AI because it always feels like this very, I don't know, sterile, scary, mysterious thing.
我总是喜欢见到从事人工智能工作的人，因为它总是给人一种非常、我不知道、枯燥、可怕、神秘的感觉。
And then you meet the people building these tools and they're always just so awesome, and you especially, just so nice.
然后你会遇到构建这些工具的人，他们总是那么棒，尤其是你，真是太好了。
And like the examples you shared, optimism and kindness, this is what we want to be.
就像您分享的例子一样，乐观和善良，这就是我们想要成为的。
These are the kinds of people we want to be building these tools that are going to drive the future.
我们希望培养这些人来构建驱动未来的工具。
So I'm really thankful that you did this.
所以我真的很感谢你这么做了。
I'm grateful to have met you, and thank you so much for being here.
我很高兴认识你，也非常感谢你来到这里。
Alexander Embiricos: Yeah, thanks so much for having me.
Alexander Embiricos：是的，非常感谢您邀请我。
This was fun.
这很有趣。
Lenny Rachitsky: Thank you so much for listening.
莱尼·拉奇茨基：非常感谢您的聆听。
If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.
如果您发现这很有价值，您可以在 Apple Podcasts、Spotify 或您最喜欢的播客应用程序上订阅该节目。
Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast.
另外，请考虑给我们评分或留下评论，因为这确实有助于其他听众找到播客。
You can find all past episodes or learn more about the show at lennyspodcast.com.
您可以在 lennyspodcast.com 上找到所有过去的剧集或了解有关该节目的更多信息。
See you in the next episode.
下一集见。