How to Understand the Next Wave of AI Before Everyone Else | Tibo Interview

I can press the button whenever I want, whenever it feels right. I [music] don't tend to look at the competition that much. Like I really look at, you know, what can we do uniquely well and what are our values and like, you know, how do we maximally [music] accelerate towards that? You know, maybe a year or two these speeds will become, you know, maybe if not the default, like very close to the default. And you look at

the cost of Luna, right? It's phenomenal. Technology has a way to become like, you know, very, very efficient over time. [music] We're very focused on like, you know, very broad access and we're optimizing for, you know, the utility that you get out of it directly. [music] I heard there's an actual physical button now.

Yes, Deris.

I will show it to you. It's like [music] very very cool.

Tibo, thank you so much for joining me.

Tibo，非常感谢你来参加我的节目。

Of course. Yeah, glad to be here.

当然，很高兴来到这里。

Yes, really excited to talk to you. I want to actually start with your time at Google. So you were on the DeepMind team and before ChatGPT Google had something called LMChat and you had tweeted uh Google was too nervous to release it. Deep mind was blocked from shipping products that could disrupt Google and I think about that a lot.

我也很高兴能和你交流。我想先谈谈你在谷歌的那段经历。当时你在 DeepMind 团队。早在 ChatGPT 出现之前，谷歌就有一个叫 LMChat 的产品。你当时发推文说，谷歌顾虑太多，不敢发布它；DeepMind 受到限制，无法发布可能颠覆谷歌的产品。我经常想到这件事。

What were you thinking at that time while you were working on these products that was you know well before uh chat GPT really changed the world.

当时你是怎么想的？那时你正在开发这些产品，而那是在 Chat GPT 真正改变世界之前很久。

Yeah it was a very exciting time. So um DeepMind was a very creative place. I was mostly focused on um my specialty was infrastructure and products for to accelerate research. And so there was there was obviously a group working on language models uh and scaling that. And then they had like gotten pretty good results and then it was like a natural thing to think about hey you know can you turn that into something that you

那是一段非常令人兴奋的时期。DeepMind 是一个极具创造力的地方。我当时主要专注于自己的专业领域，也就是用于加速研究的基础设施和产品。那时有一个团队正在研究语言模型及其规模化，后来他们取得了相当不错的成果，于是很自然地开始思考：能否把它变成一个——

know you can chat to and you know can use um you know for various things. And so then naturally like the idea of like something like LMChat sort of emerges um and then it was internal and then there was sort of this ambition to um make it into a publicly available uh tool.

既可以与之聊天，也可以用于各种用途的东西。类似 LMChat 的想法就这样逐渐浮现出来，随后成为内部项目。接着，大家又萌生了一个想法：把它做成一款公开可用的工具。

What year was this?

这是哪一年？

And this was it was like a year before ChatGPT roughly.

大概是在 ChatGPT 推出的前一年。

Okay.

好的。

Yeah.

对。

Um and then but we were also building all sorts of other things that I'm not going to talk about but it was a very creative place. Uh and then just deep mind was not set up to ship product. uh is open AI is a very very different place in that sense. Um we just like research and products just collaborate super closely together. We ideate together. We co-design a lot of things.

当时我们也在开发其他各种各样的东西，我不打算细说，但那里确实是一个非常有创造力的地方。只不过，DeepMind 并不是为了发布产品而建立的。从这个意义上说，OpenAI 是一个截然不同的地方。我们会让研究团队和产品团队非常紧密地合作，一起构思创意，共同设计许多东西。

We have a big bias to ship uh and uh also a big bias towards you know making things available for people which I really love and this is sort of like what uh drove me here the mission the people um the talent density. I mean there's so many great things about open eye really. Did you know at the time where you were involved in LMChat that that was something special or would become something special?

我们强烈倾向于快速发布，也非常重视把这些东西提供给大众。我非常喜欢这一点，这也正是吸引我来到这里的原因，包括这里的使命、这里的人，以及人才密度。OpenAI 确实有太多出色之处了。你当时参与 LMChat 时，是否意识到它与众不同，或者将来会变得非同寻常？

It it felt it felt very special like the models were you know it's sort of like the first time um you know you realized like you could get coherent text and you know something helpful. Initially it was more funny than helpful and then gradually it became more and more helpful.

它给人的感觉确实非常特别。面对那些模型，你第一次意识到，它们能够生成连贯的文本，还能提供一些有用的内容。起初，它们带来的乐趣多于实际帮助，后来才逐渐变得越来越有用。

So you say you think about that often and I I understand that I think you know in a lot of ways Google got in their own way. um what are some of the lessons that you learned there that you took to OpenAI?

你说自己经常想到这件事，我很能理解。从很多方面来看，谷歌是自己绊住了自己。你在那里学到了哪些经验，并把它们带到了 OpenAI？

Yeah, so this is why I think about it often. I I think about it in terms of like the culture that I have on the team uh the culture of OpenAI itself and so like the good parts to preserve and like you know what not to do. Um, OpenAI has a very bottoms up uh culture. Like it's a very empowering culture. Like people can come up with all sorts of ideas and get together and then very very quickly ship something and there's very little

这正是我经常想起这件事的原因。我会从这些方面思考我的团队和 OpenAI 本身所秉持的文化，也就是应该保留哪些优点，又应该避免哪些做法。OpenAI 有着非常自下而上的文化，非常鼓励自主发挥。大家可以提出各种想法，聚在一起，然后迅速发布成果。对于

stop energy uh in general for like new product ideas which is exhilarating and fun and you know it's all about impacting the world in positive ways. Uh and so preserving that is very important to me. The other thing that is also important is like to not make it a mess, right? So you don't want to have like a hodgepodge of like no features and like no overall direction and coherence and so it's counterbalanced with the sense

新的产品创意，整体上几乎不存在阻碍创新的力量。这令人非常振奋，也很有意思，而这一切都是为了给世界带来积极影响。因此，保留这一点对我非常重要。另一件同样重要的事，是不要把产品搞得一团糟。不能让它变成由各种功能东拼西凑而成的大杂烩，既没有整体方向，也缺乏一致性。因此，另一方面也要注重

of um simplicity and you being proud about the quality of the product. Uh I think the ChatGPT iOS app is like you know one of the best apps out there. Uh and so we want we want to keep that. Uh we're investing a lot in you know things like delight performance efficiency simplicity. So there's these overall principles while still empowering everyone everyone to like try new things and ship very quickly.

简洁，并确保产品的品质足以令人自豪。我认为，ChatGPT iOS 应用可以说是市面上最好的应用之一，我们希望继续保持这一点。我们在愉悦体验、性能、效率和简洁性等方面投入了大量精力。因此，我们既遵循这些总体原则，也充分放权给每一个人，让大家尝试新事物并迅速发布。

If you were to give advice to a founder about how to develop that kind of culture uh like what are some of the more tangible elements or practices that occur inside OpenAI that can kind of gives give advice uh to a founder? Yes, I think having having conviction um and finding a way to be to have users and iterate very quickly from feedback and then also uh being willing to disrupt yourself that is not as much relevant

如果请你就如何培养这种文化向创始人提供一些建议，那么 OpenAI 内部有哪些具体的要素或实践可以给他们启发？ 我认为，首先要有坚定的信念，还要设法接触用户，并根据反馈快速迭代。此外，也要愿意颠覆自己。这一点对创始人可能不完全适用，

for founder but is relevant for you know companies like OpenAI like we come up with new research new ideas all the time and being able to identify when is the right moment to go and invest in them even though it means like maybe reallocating resources from you know the main gig uh super important but it's very hard but it's super important to be able to do that.

但对 OpenAI 这样的公司却很重要。我们始终会涌现出新的研究成果和新想法，必须能够判断何时才是投入资源的正确时机，哪怕这意味着要从核心业务中重新调配资源。这极其重要，也非常困难，但具备这种能力确实至关重要。

Yeah. I mean that's the exact thing that you were describing at Google. They kind of weren't able to do that. Um that's great. I mean does that they have a plan to be fair. It's like you know it was all it was all part of a big plan. Um but to me it wasn't it wasn't uh it wasn't the right place. at at OpenAI or or any company as it matures does that become more difficult to maintain that kind of culture of

这正是你刚才谈到谷歌时所描述的情况。他们似乎没能做到这一点。说得很好。那么，是不是…… 公平地说，他们是有计划的，这一切其实都是一个宏大计划的一部分。但对我而言，那里并不适合我。 那么，当 OpenAI 或任何公司逐渐成熟之后，要维持这种快速发布、愿意自我

shipping and willingness to disrupt yourself especially when you you know if you have a cash cow just printing money and you have this other new thing over here that might be something cool and innovative. We are a very very forward-looking um and the the the future of AI and what it will all look like and how humanity benefits doesn't really wait or doesn't really care for you know whatever you have established

颠覆的文化，会不会变得更加困难？尤其是当公司已经拥有一头源源不断印钞的现金牛，而另一边又出现了一个新事物，它或许会成为某种很酷、很有创新性的东西。 我们非常着眼于未来，关注人工智能的未来、它的最终形态，以及它将如何造福人类。未来不会等待你，也不会在意你现在已经建立起来的任何东西。

here you know over the next month or 3 months and so you know I think it's very important to lean in um and to you know just be like openeyed about where it's all going and then you know figure out like how to position yourself. So that you know you you you do catch that wave. Um you know even even for open it's like we we train models and then we discover their capabilities like we don't benchmarks

无论着眼于未来 1 个月还是 3 个月，我都认为主动拥抱这一切非常重要。你必须睁大眼睛，看清这一切将走向何方，然后设法找准自己的位置，才能赶上这股浪潮。即使对 OpenAI 来说也是如此：我们先训练模型，然后再探索它们的能力。基准测试

don't tell you everything. We have to play quite a bit with the models themselves to sort of like realize uh it's like oh you know maybe we haven't thought about you know benefiting from it in like this specific way or like oh it can do this. Um and then you're you're just like oh that I mean that's a shift in like you know how we think about the product. for example, like right now like you know we we we

无法告诉你一切。我们必须亲自大量试用这些模型，才能逐渐意识到：“也许我们以前没有想到，能够以这种特定方式从中受益”，或者“原来它还能做到这一点”。随后你会发现：“这意味着我们思考产品的方式需要转变。”比如，最近我们

launched the new voice uh voice and it's super delightful to talk to. Uh it's very natural now. It's capable of tool use as well.

上线了新的语音功能。与它交谈非常愉快，现在的体验也非常自然，而且它还具备工具调用能力。

Yeah.

对。

And that changes things like now I spend a lot more time just talking to it. Um another thing I I do all the time is like dictation because the quality of the dictation is like so so good and it's like much more efficient as a as a way instead of like typing the prompt.

这改变了很多事情。现在，我会花更多时间直接与它交谈。我也经常使用语音听写，因为它的听写质量非常高，而且比手动输入提示词高效得多。

And so in the morning I just like I sit there with my phone and I'm like blah blah blah. It's just like you know couple of things to do for charge and um and then it just goes and like does it it has access to all my tools.

早上，我会拿着手机，不停地向它交代几件需要处理的事情。随后，它会直接完成这些任务，因为它能够访问我的所有工具。

Yeah.

是的。

Um and that is like that was not possible before we had like really good voice models and so that completely changes in suddenly how you think about the product.

在真正优秀的语音模型出现之前，这根本无法实现。因此，它彻底改变了你思考产品的方式。

Yeah. Let's continue talking about new models, new harnesses. Um a few weeks ago I'm going to start with another one of your tweets because you know these are bangers. Uh [laughter] codeex will seem primitive in two to three months.

是的。我们继续讨论新的模型和新的运行框架。几周前，我想先从你的另一条推文谈起，因为这些推文都堪称经典。（笑）你说，Codex 会在两到三个月后显得很原始。

We're about to go through another major evolution. The next generation of models need more than your laptop. Um, what areas, let's start with the harness first. What areas of the harness are still ripe for innovation as a model gets better?

我们即将经历又一次重大演进。下一代模型需要的不只是一台笔记本电脑。我们先从运行框架谈起：随着模型能力不断提升，运行框架的哪些方面仍有很大的创新空间？

Yeah, so many so many. Um, so I talked about voice like one thing that um, right now if you're like a sophisticated user of of of Codex and you know any other coding agent is you sort of have gotten used to a little bit of the clunkiness, right? So you know you have to manage skill files and you know this is like a way to sort of like teach it stuff but it's also I think a lot of people have realized it's kind of like

可以创新的方面非常多。以刚才谈到的语音为例。如果你是一位资深的 Codex 用户，或者经常使用其他编程智能体，可能已经在一定程度上习惯了它们的笨拙之处。例如，你需要管理技能文件，以此教会智能体一些东西。但我认为，很多人已经意识到这些文件带来的问题。

hard to maintain over time. Um the memory is sort of like a thing but it doesn't always remember uh everything like if you if you have sub agents you have to care about sub agents and it's like sort of like constructs a little network and the illusion kind of gets broken into v in in in at various parts um when you interact with it. And really what you want is just something that deeply understands you, understands your

这些文件久而久之会变得很难维护。虽然系统具备记忆功能，但它并不总能记住所有事情。如果使用子智能体，你还需要操心它们。整个系统如同搭建出一个小型网络，原本浑然一体的交互体验会在各个环节遭到破坏。你真正需要的，是一个真正了解你的系统。

goals, understands your day-to-day, understands what you know your team is up to as well. And then optimally is sort of like reacts and also is proactive uh and just helps you in your day-to-day and doesn't break that illusion right of like that's this perfect little partner uh that you have and so that's what we're uh working towards. Another thing that you know you realize when you have very very powerful models is that your

它应当了解你的目标、日常生活，以及你的团队正在忙些什么。理想情况下，它既能作出响应，也能主动采取行动，帮助你处理日常事务，同时又不会破坏这种浑然一体的体验。它就像你身边完美的小搭档，而这正是我们努力的方向。此外，当模型变得极其强大时，你会意识到另一个问题。

laptop kind of becomes a constraint in and by itself. um you know the amount of work that you can do on a laptop it was designed for humans right so it's designed roughly to be able to absorb the amount of work that you know you can produce or you know how fast you can type and how fast you could think you know how many applications you need open all these things are human constraints um the model doesn't have the same

笔记本电脑本身会成为瓶颈。笔记本电脑原本就是为人类设计的，因此它所能承载的工作量，大致取决于人类能够产出多少内容、打字和思考的速度，以及需要同时打开多少个应用程序。所有这些限制都以人类为尺度，而模型并不受同样的限制。

constraints the model can you know for example handle you know a 100 applications opened at the same time perfectly fine you know maybe in the future and so in terms of access to resources is it's very clear that you know models of the future will need access to more than the resources of your laptop. Do you I mean I I'm guessing you're talking about cloud agents and and all of a sudden like you know when you have things like ultraast

例如，模型未来或许可以同时处理 100 个打开的应用程序，完全不会遇到问题。因此，从资源获取的角度看，未来的模型显然需要使用超出笔记本电脑本身的资源。你的意思是……我猜你指的是云端智能体。这样一来，当你拥有 ultra-fast 这类技术时，情况就会突然发生变化。

which we're going to talk about in a little bit when you have token speeds that are 10 I think 14 is the the stated number 10 14 times faster than what fast is um the the bandwidth changes or sorry the bandwidth constraint changes uh the CPU now becomes the bandwidth like literally tool calls network tool any kind of overhead in the stack becomes the the limiting factor.

我们稍后会继续讨论这一点。当 token 速度提升到 10 倍——我记得官方数据是 14 倍——也就是比“快速”模式快 10 到 14 倍时，带宽限制也会随之变化。CPU 反而会成为带宽瓶颈，例如工具调用本身。网络、工具，以及整个技术栈中任何形式的额外开销，都会成为限制因素。

But then you know you can compensate by doing multiple things uh concurrently as well. And so you can you can think about you know having like maybe you know exploring on one end writing tests as well compiling uh you know testing a new hypothesis like all at once. And so then you're not then you're you're you're shifting the bottleneck around because you know you're able to do more uh concurrently and then you know the

不过，你也可以通过并发处理多个任务来弥补这一点。例如，在进行探索的同时编写测试，并同步完成编译、测试新假设等工作。这样一来，瓶颈实际上发生了转移，因为你能够并发完成更多任务，而模型也可以同时推进这些工作。

model can like sort of like think very efficiently and very quickly through it.

与此同时，模型还能以极其高效、快速的方式完成整个思考过程。

With current token speeds I find myself kicking off 10 15 agents in parallel and that becomes a pretty significant cognitive overhead for me to do that context switching and just constantly cuz you're kicking it off and you can expect 30 45 minutes before my task comes back. Now with ultra fast speed that workflow changes significantly and I don't think I would be able to have 10 or 15 agents and that might be a good

按照目前的 token 速度，我发现自己会同时启动 10 到 15 个智能体。这给我带来了相当大的认知负担，因为我需要不断切换上下文。智能体启动后，通常要等待 30 到 45 分钟才能返回任务结果。但随着超高速模式的出现，这种工作流将发生很大变化。我认为，自己不可能再同时运行 10 或 15 个智能体，而这或许是一件好事。

thing. Maybe it's three or four at a time. How do you see the the workflow of a solo developer changing over time?

也许一次只使用 3 或 4 个。你认为独立开发者的工作流未来会发生怎样的变化？

Yeah. So I think managing your attention and being much more, you know, friendly to your attention is something that we care a lot about. Like after all, like we're trying to build for humans. We're trying to be like the build the technology that's the most empowering for humans and that requires building around you know your ability to multitask and you know how do you want to manage your attention and do you want

我认为，妥善管理自己的注意力，使工作方式更符合注意力规律，是我们非常重视的一件事。归根结底，我们是在为人类打造产品。我们的目标是创造最能赋能人类的技术，而这要求产品围绕人的能力进行设计，包括你能否同时处理多项任务、希望如何分配注意力，以及希望在何时收到提醒。

something brought up now or is it better to bring it up in 30 minutes um and then when you have ultra fast speeds combined you know maybe with voice is like suddenly you're like okay you know like this thing can operate at the same speed if not faster than you and so you stay in the flow you get to ideulate you get to see prototypes, you know, like you get to build little reports like in real time and that, you know, sort of like

具体而言，你可能希望立即收到某件事的提醒，也可能认为 30 minutes 后再提醒更合适。当超快的速度与语音等功能结合起来时，你会突然发现，它的运行速度可以与你一样快，甚至比你更快。这样一来，你就能持续保持心流，构思创意、查看原型，并实时生成简短的报告。这种体验确实非常出色。

that just feels really good. Um, suddenly you're like, oh yeah, like what I was doing before, multitasking like 10 agents, it's like I don't want to really go back to that.

你会突然意识到，自己再也不想回到过去同时操作 10 个智能体的工作方式。

Yeah.

是的。

Uh, and so we're trying to bring that sort of experience that is just really natural but also feels built for you, you know, where you don't have to adapt.

因此，我们正在努力打造一种非常自然、如同为你量身定制的体验，让你不必主动适应它。

The technology adapts to you. So there's been a number of I guess agentic coding techniques discussed over the last few months. Loops was popular, still is popular. Now I'm hearing about graphs.

相反，技术会适应你。过去几个月里，人们讨论了许多智能体编程技术。循环一度非常流行，现在依然如此；而最近，我又开始听到关于图结构的讨论。

Are are these all techniques to just allow the solo developer to manage or be friendly to their to their attention as you said? I like that term.

这些技术是否都只是为了帮助独立开发者更好地管理注意力，或者像你刚才所说的那样，减少对注意力的打扰？我很喜欢这个说法。

Yeah. So I I think about two different categories of of problems. It's like the first one is building the very best personal AGI or the personal agent that will be in the flow with you proactive raise important new ideas uh when it can find some be very very efficient at doing exactly what you want. It doesn't matter whether it's a technical problem or you know it's just more like research or advice like it can do it all and it's

我认为，这里存在两类不同的问题。第一类是打造最出色的个人 AGI，也就是一个能够与你保持同一心流、主动提出它所发现的重要新想法，并且极其高效地完成你想做之事的个人智能体。无论面对技术问题，还是更偏向研究或建议的问题，它都能处理。

like super super tailored to you. This is like a very important thing and it's like deeply rooted in like you know the understanding of you as a human, you as like an individual that is unique.

同时，它会根据你的情况进行高度定制。这是一件非常重要的事，而且深深植根于对你的理解：它既将你视为一个人，也将你视为独一无二的个体。

That's one category of problem like we're pushing super hard on that. The other category of problem is like full-on automation. Um you know where you're more building intelligent systems that can take care of like a very complex process. You know maybe something that did require you know does require intelligence and seems like very complex. for example, you know, going and looking at production logs and

这是一类我们正在全力推进的问题。另一类问题则是完全自动化，即构建能够处理极其复杂流程的智能系统。例如，有些任务过去和现在都需要智能处理，而且显得非常复杂，包括查看生产日志，

automatically doing performance optimizations or looking at regressions and automatically patching them. In cyber security, we're seeing this as well where it's like you have, you know, something you have a scanner that comes up with a vulnerability like can you automatically patch it and reduce the window um where you have that open vulnerability to like almost zero with without a human in those loops

自动进行性能优化，或者查找回归问题并自动修复。网络安全领域也存在这种情况。例如，扫描器发现漏洞后，能否自动修复，将漏洞处于暴露状态的时间窗口缩短至几乎为零？整个过程是否不需要人工介入？

without a human in the loop or like you know very very minimal where you know you only need to approve uh a high risk action and it's like mostly an automated system but it's also not that much you it's not as important for you to be in direct control of it.

不需要人工介入，或者只需要极少的人工参与。例如，你可能只需审批一项高风险操作，其余工作基本都由自动化系统完成。在这种情况下，直接控制系统对你而言也没有那么重要。

Okay.

好的。

Um and then so I want to slightly change topics and you know ChatGPT and codeex have been on this merge path over the last few months. So I I guess first I just wanted to ask you how's that been going like how does it feel internally? What's the feedback you've been getting from your customers?

接下来，我想稍微换个话题。过去几个月，ChatGPT 和 Codex 一直在逐步合并。我想先问一下，这个过程进展得怎么样？内部感受如何？你们从客户那里收到了哪些反馈？

Um it's it's really been a boon. Uh so the feedback we had initially was like why why do you merge them? It's like you know do you really have to do it? And it's like well the the future the our future models want us to be merged. So um you know we're we're just going to do it because it is the simple and proper thing to do where we're building this very personal super capable agent that can help you in all sorts of ways. This

这确实带来了很大的好处。一开始，我们收到的反馈是：“为什么要把它们合并？你们真的非这样做不可吗？”但未来的模型要求我们将它们合并。因此，我们会这样做，因为这是最简单、最合理的方式。我们正在打造一个高度个性化、能力极强的智能体，使其能够在方方面面帮助你。它

is the same this is going to be the same technology under the hood. Um it's the same harness. It's the same way that we think about it. It's like highly multimodal, you know, voice first, uh, super efficient and and it doesn't matter if you're trying to code or not.

背后将采用同一套技术和运行框架。我们的思路也完全一致：它具有高度多模态能力，以语音为先，效率极高。无论你是否想编程，都没有关系。

Like this this agent is capable of it all. And it's like the high it's like the most efficient at it. And then the interface that you want is like it should tailor itself to your needs. If you shouldn't decide like you know I'm a coder, I want a coder interface or like I'm not technical, I want a nontechnical interface. It's like there's a spectrum of people like you know we come up with labels of like a software engineer, a

这个智能体能够完成这一切，并以最高效率处理这些事情。你所使用的界面也应该能够根据需求自行调整。你不必先认定自己是程序员，因此需要程序员界面；也不必因为自己不懂技术，就只能使用非技术型界面。人的情况其实呈现为一个连续的光谱。我们会给人贴上软件工程师、

designer like you know these are just human concepts that we have invented to deal with abstractions because the reality is too complex for us to handle.

设计师之类的标签，但这些标签只是人类为了借助抽象概念处理问题而创造的，因为现实过于复杂，我们无法直接应对。

But individuals are like they're individual they have their own they're somewhere on the spectrum and so we're trying to build the perfect interface that adapts for everyone. It doesn't matter if you're technical or not. It's just like it adapts like based on your specific indiv individuality. So that's why we went and we did this.

但每个人都是独一无二的，都有自己的特点，处在这个光谱上的某个位置。因此，我们正在努力打造一种能够适应每个人的完美界面。无论你懂不懂技术，都没有关系。它会根据你独特的个性进行调整。这就是我们决定这样做的原因。

But uh does that mean inevitably it's going to end up with a singular interface? No drop down selecting between products and it it's kind of wild to think that my mom might use the same exact interface as me and then obviously it'll customize to my needs.

但这是否意味着，最终必然只会有一个统一的界面，不再通过下拉菜单选择不同产品？想到我妈妈可能会使用与我完全相同的界面，感觉很不可思议。当然，之后它会根据我的需求进行定制。

Maybe I'll need more information if I'm doing more sophisticated work. Uh but like what is the end state for you?

如果我要完成更复杂的工作，也许会需要更多信息。但你设想的最终形态是什么？

That's right. It's it's the same thing.

没错，就是同一个产品。

Um so you and your mom will you know use the same thing. Uh it will be your personal AGI. You will have very different kinds of tasks and utility that you get from it. You will connect it to different tools in your life. You will bring different ideas, different needs. Uh and then it will continue to tailor itself to maximally benefit you.

你和你妈妈都会使用同一个产品。它会成为你们各自的个人 AGI。你们交给它的任务会非常不同，从中获得的实用价值也会非常不同。你会把它连接到生活中的不同工具，也会带来不同的想法和需求。它会不断进行自我调整，尽可能为你提供最大的帮助。

Okay.

好的。

Uh and it will, you know, do so with your friends and with everyone else.

它也会以同样的方式服务于你的朋友和其他所有人。

So I I want to go back to something you said. You used the word illusion a couple times in that kind of end state.

我想回到你刚才提到的一点。在描述那种最终形态时，你几次使用了“幻觉”这个词。

What is the that perfect illusion for the the typical user? Like what what like if you can envision us a few years from now, what does the interaction between AI and a human look like?

对普通用户而言，那种完美的幻觉究竟是什么样的？如果设想几年后的情形，我们会处于怎样的状态？从现在开始，人工智能与人类之间将以何种方式互动？

Yeah, it's um to me it's something that is very very tailored to to to humans.

对我而言，它应该非常贴合人类。

Um and this this is why large language models are also a success. It's like it's it's it's natural language. Natural language. It's like it's a human concept, right? Uh so, you know, we're used to speaking to each other. Like, you know, if you write me a letter tomorrow, I'll be able to read it.

这也是大型语言模型能够取得成功的原因，因为它使用的是自然语言。自然语言本身就是人类的概念。我们早已习惯彼此交谈。例如，如果你明天给我写一封信，我肯定能够读懂。

Um you know, it's like we we know each other quite a bit now. So, uh you know, it's like I will be able to sort of decipher like a little bit of the emotion or, you know, maybe a little bit of the nuance behind the letter if you wrote me a letter. Um and all of that is is deeply human. So the technology that we're building is, you know, rooted in in humanity and rooted in, you know, the way that humans communicate and get

我们现在已经相当了解彼此，因此我应该能够从信中解读出一些情绪，或许还能体会到字里行间的微妙含义。这一切都充满了人性。因此，我们打造的技术植根于人性，也植根于人类交流和

things done. Um, and there shouldn't really be a thing where, you know, you're like, "Oh, you misunderstood me because, you know, you didn't quite decipher the nuance in, you know, my tone or you didn't quite understand the text, you know, how I meant it." It's like that's that's um that's something that we're trying to avoid. And so we're trying to very much to not have you adapt, but have the technology just like

做事的方式。不应该出现这样的情况：你说，“你误解了我，因为你没有完全领会我语气中的微妙之处，或者没有完全理解这段文字，不明白我真正的意思。”这正是我们努力避免的情况。因此，我们希望不是让你去适应技术，而是让技术本身

be perfectly sort of um created to um to be like a natural extension of how humans already act in the world.

从设计之初就能够完美地成为人类现有行为方式的自然延伸。

When I think about communication between humans, so much of it is non-verbal.

说到人与人之间的交流，其中很大一部分都是非语言交流。

just the way I move my hands, the facial movements and like h how much of that do you see in the future being sensed by artificial intelligence or or read by artificial intelligence maybe through vision. Is that even important? Because what you're describing now is text only.

例如我的手势、面部表情等。你认为未来人工智能能够感知其中的多少内容，或者能够通过视觉等方式解读多少内容？这真的重要吗？因为你刚才描述的只有文本。

And for those of us who grew up online, we're very used to communicating over text and you know adding subtleties to that text to convey what we really mean tone. Um, but like h is it still important to have AI be able to read our facial expressions, our hand gestures, and so on?

对于我们这些在网上长大的人来说，通过文字交流已经成为习惯。我们也懂得如何为文字增添细微含义，从而传达自己真正想表达的语气。不过，让人工智能能够读懂我们的面部表情和手势等信息，是否仍然重要？

I think so. Um, so when when when I think about the future of what we're building, it's it's very ambient. It's very natural. Mhm.

我认为仍然重要。设想我们正在构建的产品未来会是什么样子，我认为它将无处不在，交互方式也会非常自然。

Um if you know tomorrow uh or like you know later I go I go to my office and I write something on the whiteboard and I have an idea it's like it it should be capable of you know being there as well and like you know understanding or you know maybe I tell it like you know hey it's just like you know what about this thing and you know and then we just have a natural conversation just over voice like since we we shipped the new chat

例如，明天或未来某一天，当我走进办公室，在白板上写下一些内容并突然产生一个想法时，它也应该陪在我身边，理解我正在做什么。我也可以问它：“嘿，这个想法怎么样？”然后直接通过语音与它自然交谈。自从我们上线新版聊天功能以来，

voice like the it's it's really taken off. So it's like the amount of users that interact with LGBT just through voice is growing very fast right now.

语音功能迅速流行起来。目前，完全通过语音与 ChatGPT 互动的用户数量增长得非常快。

And uh this is this is I think the lesson is like every time you sort of like lean into something that is more natural like humans just choose the the path of least resistance. You know as you said it's like typing on a little box like you know it's just like it's natural maybe for some of us but not for for everyone. And it's like definitely when you get something that is just like a little bit easier, a little bit

我认为，我们从中得到的启示是：每当更自然的交互方式出现时，人们就会选择阻力最小的路径。正如你所说，在一个小框里打字对我们中的一些人而言可能很自然，但并非人人如此。关键是让每个人都能使用它。即使某样东西只是使用起来更方便，人们也会更愿意采用。

better. like you know you tend to just go and use that and stuff.

如果它的表现也好了一点，人们往往就会直接使用它。

Yeah. Okay. I first of all congratulations. I saw that you posted this morning. Codex reached 20 million users.

对。首先恭喜你。我看到你今天早上发帖称，Codex 的用户数已经达到 20 million。

I've seen the graph and and you know for a while it was like this and then all of a sudden it's vertical. So congratulations. I want to talk a little bit about that competition with anthropic because of course you know a lot of people think OpenAI Anthropic these are the two major competitors in the industry right now. There was a period of time in which Anthropic was kind of sucking all the oxygen out of

我看了那张图：有一段时间，曲线始终保持原来的走势，随后突然垂直上升。因此，恭喜你。我想谈谈你们与 Anthropic 之间的竞争。许多人都认为，OpenAI 和 Anthropic 是目前行业内两大主要竞争对手。曾有一段时间，Anthropic 几乎抢尽了所有风头，当时确实表现得非常强势。

the room, right? They were really dominating and then all of a sudden something changed. Uh so first of all, what's your read on the market today?

他们一度占据主导地位，随后形势突然发生了变化。首先，你如何看待当前的市场？目前，我们真正专注的是以下几件事：

Yeah, really right now we're focused on building the most capable models, building models that are highly highly efficient and then taking a lot of pride in building products for everyone. Um and I this is something that I think open AI does uh really well is caring about the world and caring how about you know how we are taking this very very powerful technology and like putting in the hands of as many people as possible

打造能力最强且非常高效的模型，并为所有人开发值得自豪的产品。我认为，OpenAI 在这方面做得非常好：我们关心这个世界，也关心如何将这项极其强大的技术交到尽可能多的人手中。

and this is what you know we did as well like with merging codeex and ChatGPT. It was like this desire of like we have this we have this technology we we we can make it safer we can make it easier to to use uh for everyone whether you know you're like uh a product manager a designer in sales marketing coms all of that like you know you should be able to use all of it and then uh just very very quickly you know distributed through

这也是我们将 Codex 与 ChatGPT 合并的初衷。我们希望让自己拥有的这项技术变得更安全，也更容易供所有人使用。无论你是产品经理、设计师，还是从事销售、营销、公关或其他任何工作，都应该能够充分使用它的全部功能，并迅速扩大其使用范围。

charge like where we have a ton of users already and so that's been that's been really driving you know this growth Earth as well that you mentioned and I don't tend to look at the competition that much like I really look at you know what can we do uniquely well and what are our values and like you know how do we maximally accelerate towards that.

通过 ChatGPT 进行分发，可以利用那里已有的大量用户。这也确实推动了你刚才提到的增长。我不太关注竞争对手，真正关注的是哪些事情我们能够做得格外出色、我们的价值观是什么，以及如何最大限度地加速实现这一目标。

Okay. Um I want to maybe just dig a tiny bit more into that because I I know you're not thinking about anthropic all that much but a lot of other people do and they're they're thinking about okay which product do I believe in? which product do I want to give my $2200 to? Um, when you look at the market position and the branding and the tone from OpenAI and just the way that it interacts with developers, with the

好的。我想再深入探讨一下。虽然你并没有过多考虑 Anthropic，但其他许多人会思考：我相信哪款产品？我愿意把自己的 $2200 花在哪款产品上？当你审视市场定位、品牌形象和调性，以及 OpenAI 与开发者和

broader audience, how do you see that comparing to the way that Anthropic does?

广大用户互动的方式时，你认为这些方面与 Anthropic 的做法相比如何？

Yeah, I think maybe again like what I care a lot about is like the community building for the world, like bringing everyone along. Um I think you know you can feel that in the way that we we we are super transparent about things like we take a lot of ideas from the community. It's just like it's also so much fun to be honest. Um you know because we get so much energy from it as well. Um and then this technology that

这仍然要回到刚才那一点。我非常重视社区，也重视为全世界打造产品，让每个人都能参与进来。我想，你可以从我们的做法中感受到这一点。我们对许多事情都极其透明，也会从社区中汲取大量创意。坦率地说，这个过程非常有趣，也为我们带来了大量能量。我们正在打造的这项技术也是如此。

we're building we're not building it just for ourselves like we're not just building it to accelerate um just to open AI. It's like it's super important.

这项技术并不只是为我们自己而打造。我们开发它，并不只是为了自身，也不只是为了加速 OpenAI 的发展。这一点极其重要。

the the mission is super important and therefore it's like you know this is where we also get our energy from um and so it just feels to me it feels like very grounded uh it feels fun and then good things happen as a result of that well let's talk about some of those good things I want to talk about the resets for a second to that's kind of like I know it's like what everybody is you know kind of following your every tweet

我们的使命极其重要，也是我们汲取动力的源泉。因此，对我而言，这一切让人感到非常踏实，也很有趣，随后自然会有好事发生。那我们就来谈谈这些好事。我还想谈谈重置，因为这件事受到了广泛关注。几乎每当你发布一条推文，大家都会密切关注，

because of this um specifically like again looking at that growth curve of codeex how maybe this is a silly question. How much do those resets how much of it is a boon towards marketing and growth or is it was it just like goodwill for the developer community?

原因就在于此。具体来说，再看看 Codex 的增长曲线，这个问题或许有些傻：这些重置究竟在多大程度上促进了营销和增长？还是说，这只是为了向开发者社区表达善意？

I think maybe it it's counterintuitive, but OpenAI is a very it's a place where you can just do things. Um and so it just felt right initially to compensate when we were iterating and breaking things or you know maybe we had misconfigured something and it wasn't quite as good as we wanted and so it's like hey you know thank you for trying this product like we know like we're trying very hard to build it. it's like

这或许有些反直觉，但 OpenAI 是一个可以放手做事的地方。最初，当我们在迭代产品的过程中导致某些功能出现问题时，给予用户补偿是很自然的做法。有时可能是我们错误配置了某项内容，导致实际体验没有达到预期。于是我们会说：“感谢你试用这款产品。我们正在非常努力地打造它，但

it's early days. Um you know here's some extra usage because you know we happen to break it you know for like 30 minutes and you know we understand this is like really important and rely on it and you know thank you for being a user and so this is how it started um and you know this is how I still treat it. It's like if we break it um or if the experience is suboptimal and we don't fully understand why it's like you know we

产品目前仍处于早期阶段。由于我们碰巧让它出现了大约 30 minutes 的问题，因此将补偿你一些额外使用额度。我们知道这款产品对你非常重要，你也依赖它。感谢你成为我们的用户。”这件事最初就是这样开始的，直到现在我仍然采用相同的处理方式。如果我们使产品出现了问题，或者体验不够理想，而我们又没有完全弄清原因，

will we will compensate for that. we will reset um the usage limits and then you know it turned into like quite the thing obviously there's a whole reset button now and like but there isn't really a whole a lot of scrutiny behind it. It's like there it's not done in partnership with like marketing or or finance. It's just like I can press the button whenever I want um whenever it feels right. Um and we have these

就会作出补偿，重置使用限额。后来，这显然演变成了一件颇受关注的事，现在甚至有了一个专门的重置按钮。不过，这件事背后其实没有太多审核流程，也不是与营销或财务部门协商后才作出的决定。只要我认为合适，随时都可以按下这个按钮。我们确实有一些

principles that you know we're we're trying to build something amazing and when it is not it's like you know we will make up for it.

始终遵循的原则：努力打造令人惊叹的产品；如果没有做到，就设法作出弥补。

Yeah. I I still think there's a piece of it that really has built so much goodwill in the community and maybe has contributed to the growth at least in a small part. I I think caring for your users does does a lot, right? So, um I think you can pay lip service and say that you care or you know you can be like we actually care and like you know if we break it like you know hey really sorry about it you

是的。我仍然认为，这确实帮助我们在社区中积累了大量好感，或许也在一定程度上推动了增长。我认为，真正关心用户确实很有帮助。你可以只在口头上声称自己关心用户，也可以真正地关心他们。如果我们把事情搞砸了，就会说：“真的很抱歉，

know it's like here's here's like how we make up for it.

我们会以这种方式作出弥补。”

It kind of reminds me of Amazon's return policy. It's like if you're not happy in any sense, go ahead and send it back.

这让我想起 Amazon 的退货政策。只要有任何不满意，都可以直接退货。

And and it you're kind of building that same culture or that same perception of open AI. It's like, hey, if we make a mistake, go ahead, use those tokens again or or you know, have here's a here's a fresh batch of tokens for you.

你们也在营造同样的文化，让大家对 OpenAI 形成同样的印象：如果我们犯了错，你可以重新使用那些 token，或者我们会再送你一批新的 token。

I Yeah, I really appreciate it. So, and then there's also, you know, good moments where we just want to celebrate and mark the moment. And it's just there'sn't really something that we can give that is more meaningful um you know, at times like we always ship new features. is we will ship them as broadly as we can. Um but something to share with the entire community. It's like you know hey go explore this new

是的，我非常欣赏这一点。另外，在一些值得高兴的时刻，我们也希望与大家共同庆祝，纪念那个瞬间。确实没有什么礼物比这更有意义。每当上线新功能时，我们都会尽可能广泛地向用户开放，同时也希望与整个社区分享。这相当于邀请大家探索这个新

thing like you know just like you haven't used ultra yet you know here's some extra usage like you know try it and and I heard there's an actual physical button now.

功能。例如，如果你还没有用过 Ultra，我们就多送你一些额度，让你试试看。我还听说，现在真的有一个实体按钮了。

Yes there is.

是的，确实有。

Yeah. Okay. You'll have to show me that after. [laughter] I will show it to you. It's like very very cool.

“好，那之后你得给我看看。”“我会给你看的。它真的非常酷。”

But with all of these resets like you can really only do that if you've done significant compute capacity planning.

不过，要进行这么多次额度重置，前提是你们已经做了大量的算力容量规划。

like you have to have enough compute to give all of these resets. And I I want to start to talk a little bit about self-improvement because um like speaking of capacity, a few weeks ago, I think it was a few weeks ago, there was this uh article you put out and it stated Soul had optimized Luna efficiency. You dropped the price of Luna by 80%. There was also a price drop for Terra as well. How much of a an uh

你们必须拥有足够的算力，才能提供这么多次重置。我想开始谈谈自我改进。说到容量，几周前，我记得应该就是几周前，你们发布了一篇文章，其中提到 Soul 优化了 Luna 的效率。你们将 Luna 的价格降低了80%，Terra 也降价了。这其中有多少是因为

efficiency gain were you able to eke out of Luna versus how much of it is like we just did really great compute capacity planning and and we can just drop the price like our margins are great and we can still we want people to use it. So like how much of it were algorithmic gains versus um strategic planning? We planned uh compute like way ahead. You know, I think if you look back two years, I think OpenAI was um kind of

你们成功提升了 Luna 的效率，又有多少是因为你们在算力容量规划方面做得非常出色，因此可以直接降价？例如，你们的利润率很高，又希望大家使用它，所以仍然可以降价。换言之，其中有多少来自算法层面的提升，又有多少来自战略规划？你们很早以前就开始规划算力了。回顾两年前，我记得当时 OpenAI

questioned for why, you know, there was like so much investment in compute. Um one of those crazy good bets.

还曾因在算力上投入过多而受到质疑。事实证明，这是一个回报高得惊人的赌注。

Yes. uh and then now we're very happy to have it like a very large fraction of the computers used for research where we invest in our future and you know ever ever better models and then also like the the efficiency of the models that we have and then the amazing thing that's happening is like when we push the frontier of capability for like the most advanced models that we have then we can use these models in order to figure out

是的。现在我们非常庆幸自己拥有这些算力。其中很大一部分用于研究，以投资我们的未来，打造越来越好的模型；与此同时，我们现有模型的效率也在不断提升。更令人惊叹的是，当我们不断拓展最先进模型的能力边界时，便可以利用这些模型，

very very quickly how to serve or how to restructure uh or re-engineer our stack in order to gain very significant efficiency or performance gains. So we haven't just improved this is something that we will publish on as well. We haven't just improved the the cost efficiency, but we have also improved the speed efficiency. You know, outside of ultraast, things have gotten significantly faster over time. They

迅速找出更好的服务提供方式，或者重构、重新设计技术栈，从而显著提升效率或性能。我们不仅提高了成本效率——这方面也会发布相关内容——还提升了速度。随着时间推移，除了ultraast之外，其他模型的速度也有了大幅提升。

have like if you plot it, it's like, you know, the amount of um just the amount of speed that you get now is like, you know, roughly 60% faster than, you know, what it used to be like 3 months ago. And this is just like we're we're just going after every part of the stack and just really making sure that we design it and and engineering it optimally for the kind of workloads that we have. Uh and so you

确实如此。如果将其绘制成图，就会发现目前的速度大约比 3 个月前快了 60%。这是因为我们正在逐一优化技术栈的每一个部分，确保其设计和工程实现都能针对我们的工作负载达到最优。

know and the most powerful models that that we have are the ones like you know just really that make it capable for us to do it you know with a very small team. And so the majority of like what when when whenever we come up with like very significant efficiency gains and cost efficiency like our commitment is to just really to keep things at the frontier of performance cost um and to just also like you know just not just

我们拥有的最强大模型，正是让一支规模很小的团队有能力做到这一点的工具。因此，每当我们在效率和成本效益方面取得非常显著的改善时，我们都会致力于让产品始终处于性能与成本的前沿，也不会只是

pocket you know that uh interesting gain and just make it something that we know we share with our customers we share with our users and that's what we did with with Luna. How how do you what do the discussions look like internally where you're trying to decide compute allocation towards uh researching new models, efficiency gains on existing models, inference like what does that tension look like internally?

将这些可观的收益收入囊中，而是会把它们转化为能够与客户和用户共享的成果，Luna 就是一个例子。你们内部是如何讨论这一问题的？当你们决定如何分配算力，选择将其投入新模型研究、现有模型的效率提升或推理时，会进行怎样的内部权衡？

Um the we we we we usually look at things um from from first principles and we have like an allocation for research, we have an allocation for um for product and then within product we make different kinds of trade-offs but this one was u almost not even a trade-off because uh the the efficiency gains were there. Um so you know we were pretty much like able to use like the same compute envelope in order to you know

我们通常会从第一性原理出发看待这个问题，为研究分配一部分算力，也为产品分配一部分算力，然后在产品内部进行不同的取舍。但这一次几乎不存在取舍，因为效率提升已经实现。因此，我们基本上能够用相同的算力预算提供支持，

serve this like very very significant increase in throughput.

从而实现吞吐量如此显著的增长。

Yeah. So when I mean when I saw the blog post a few months ago prior to the price drop blog post where you guys were talking about one model training the next model or helping kind of optimize the next model. Um then you see these efficiency gains that were achieved by soul looking at how Luna was running. Uh I you know it seems to me like recursive self-improvement in the very early innings. What what are your thoughts

几个月前，在那篇宣布降价的博客文章发布之前，我看到你们谈到使用一个模型训练下一个模型，或帮助优化下一个模型。随后，我们又看到通过深入分析 Luna 的运行方式而实现的效率提升。在我看来，这似乎是递归式自我改进的极早期阶段。你对此怎么看？

there? Is that what is happening? Um yeah, I think recursive self-improvement is uh it's obviously a huge topic right now and it's most often uh I think applied to to research um and you know models developing other models but what we are seeing a ton of success with is you know using those models to develop the infrastructure that is on the critical path of using those models you know which is also a form of recursive

现在发生的正是这种情况吗？是的。我认为，递归式自我改进显然是当前的一个重大话题。这个概念最常用于研究领域，也就是使用模型开发其他模型。但我们已经看到大量成功案例，包括利用这些模型开发相关基础设施，以及优化使用这些模型的关键路径。这也是一种递归式

self-improvement. It's all one big system.

自我改进，因为这是一个庞大的整体系统。

Inference stack, you know, the the the harder like the opt the the the the kernels, uh CUDA kernels that we use. Um developing new products and new ways to interact with those models that are more efficient. You know, you talked about cloud agents. It's like if we if we really crack uh cloud agents, it's like suddenly you become much more productive as well. It's like is that a form of like recursive self-improvement because

其中包括推理技术栈中较难处理的部分，例如优化和内核，也就是我们使用的 CUDA 内核；还包括开发新产品，以及寻找更高效的模型交互方式。你刚才提到了云端智能体。假如我们真的攻克了云端智能体，你的工作效率也会突然大幅提高。那么，这是否也算是一种递归式自我改进？因为

then you know you have a better ability to get the utility from them. I I think it is in some sense, but it's much more um you know infrastructure and then you know being able to then take that and then point it back at itself.

这样一来，你就能更好地从中获得实用价值。我认为，从某种意义上说确实如此，但这更多涉及基础设施。你可以利用这种基础设施，让它反过来改进自身。

Yeah.

对。

And so of course like we're doing that like if we were not doing that um I think that would be pretty silly.

当然，我们确实在这样做。如果不这样做，那就太愚蠢了。

Can you talk a little bit about So as we're on the topic of recursive self-improvement uh OpenAI Sam Altman talked about pausing the absolute frontier of RL right now I believe. Um can you talk a little bit about that?

能详细谈谈吗？既然谈到了递归式自我改进，OpenAI 的 Sam Altman 曾提到暂停推进目前 RL 的绝对前沿，我记得是这样。你能详细谈谈这件事吗？

Like what was that decision like? And I know we talked about the hugging face incident briefly, but like what went into that decision? What does that look like? How did those discussions go?

当时是如何做出这个决定的？我们刚才简要谈到了 Hugging Face 事件，但做出这个决定时考虑了哪些因素？具体情况是什么？相关讨论是如何进行的？

Yeah, this is this is something very much within within research where there's um OpenAI has always uh been able to invest uh its resources where it matters most. Um and as we increase the capabilities of our models, it is very obvious that you know the the alignment and the safety aspect of it is you know ever more important. And so having uh having tremendous um amount of investment there uh is is a very natural

这基本上属于研究领域，而 OpenAI 一直能够将资源投入最重要的领域。随着模型能力不断提升，模型的对齐与安全问题显然也变得越来越重要。因此，在这方面进行大规模投入，对 OpenAI 而言是非常自然的

thing for OpenAI and like something that OpenAI is very committed to. And so we're seeing um a huge surge uh in investment um on this and also the uh the pause was sort of like necessary to uh allow like the teams and the individuals like just really understand and harden all parts of the system uh to then you know ensure that you know we could we could restart training uh with you know like full full full command and

选择，也是 OpenAI 坚定致力于推进的事项。因此，我们看到这方面的投入正在大幅增加。此外，这次暂停在某种程度上也是必要的，可以让各个团队和个人真正理解并强化系统的各个部分，确保我们能够重新开始训练，并完全掌控整个过程。

this is something that you know I believe OpenAI will always continue to do like when when necessary. I've I've not seen I've not seen us internally not able to make such decisions like very efficiently.

我相信，OpenAI 会始终坚持在必要时这样做。我从未见过我们内部无法高效做出此类决定的情况。

Was there like some set goal in place where it was very clear you needed to reach this point before unpausing or was it hey we'll know it when we see it?

当时是否设定了明确目标，规定必须达到某个节点才能恢复？还是在看到相关结果时，就会自然知道时机已经成熟？

Yeah, this is this is something that sits um within within the the safety team and uh they they very much this is like very much a a debate and sort of like a discovery process as you go. Um but then they did reach uh a fairly clear set of principles uh that you know when reached like you know we we would be in a good position. I want to go back to uh ultra fast mode that I think people don't appreciate what that kind

这件事由安全团队负责。对他们而言，这在很大程度上是一个持续讨论、边实践边探索的过程。不过，他们后来确实总结出了一套相当明确的原则；一旦满足这些原则，我们就会处于比较稳妥的位置。我想回过头来谈谈超高速模式，因为我认为大家并未意识到这种

of speed unlocks and so let's start with what use cases are you doing are you using internally that were not possible prior to having those kind of tokens per second.

速度能够解锁哪些可能性。先谈谈你们内部的使用场景：哪些事情以前无法实现，却在达到这样的每秒 token 数后成为了可能？

We see it used a lot when the stakes are high. Um so for example when you have um when we have an outage um the incident commander and the response team gets access to ultra fast um because every every second is you know matters. Um so high stake um high stake scenarios like just really weren't you know using ultra fast. Also um it's it's kind of like a fun thing where uh teams which are like either working on something very

我们看到，超高速模式经常用于事关重大的场景。例如，当服务中断时，事件负责人和响应团队会获得超高速模式的使用权限，因为每一秒都至关重要。在这种场景中，使用超高速模式确实非常值得。另一个有趣的现象是，有些团队正在处理非常

critical or believe they are working on something very critical will always request ultra fast as well. Um does pets fall under that?

关键的事情，或者自认为正在处理非常关键的事情，也会申请使用超高速模式。宠物也属于这种情况吗？

Uh pets.

呃，宠物。

Yeah, pets. [laughter] Pets is not quite hyperritical. But I love I love my pet. It's always on my screen. uh like when you walk around uh you see like you know people's pets on their screen and like also when they they dial in into the the video call it's just like it always like I think it's very delightful and it brings me joy every time I see it but uh pet is not quite critical right now [laughter]

对，宠物。[笑声] 宠物倒没有那么关键，不过我很爱我的宠物。它总是出现在我的屏幕上。在办公室里走动时，你会看到大家屏幕上的宠物；当他们加入视频通话时，你也总能看到它。我觉得这特别令人愉悦，每次看到它都会很开心。不过，宠物项目目前并不是特别关键。[笑声]

um we we do maintain it um and we take good care of our pets but uh say you know someone is working uh on like a a new idea they have and they're like you know hey it's just like you know I really think this could be like something special and we have to try it but like you know we have to make a decision on Monday on like know whether we include this in dev day or not and it's like okay just like you know of

我们确实会维护并悉心照顾宠物项目。不过，有时有人正在研究一个新想法，并表示：“我真的认为这可能会成为一个非常特别的东西，我们必须试一试。”如果我们必须在周一决定是否将它纳入开发者日，就会回答：“好，那当然要试。”

course you know use ultra fast um people have different kinds of preferences on uh whether whether they like to be you know mono threaded as we talked about or multitask a lot for folks who like to multitask a lot you don't benefit as much from ultraast but some people it's like don't like to change context all the time. Um, where do you fall on that spectrum?

对于超快模式，大家有不同的使用偏好：有些人喜欢像我们刚才谈到的那样进行单线程工作，另一些人则喜欢同时处理许多任务。对于喜欢同时处理许多任务的人来说，超快模式带来的收益没有那么大。不过，有些人不喜欢不断切换上下文。你属于这个光谱上的哪一端？

I I have ADHD, so I like I context switch like all the time.

我有 ADHD，所以会不断切换上下文。

It's funny cuz I also have ADHD and I actually don't want to context switch all the time. That's really hard for me.

有意思的是，我也有 ADHD，但我其实不想不断切换上下文，因为那对我来说很困难。

I want to focus on two to three and and that's why I was so excited about ultra fast. That's fascinating that you're the opposite there.

我希望专注于两三件事，因此才会对超快模式如此兴奋。很有意思，你恰好与我相反。

You know, I thrive in context switching and making lots of little decisions. Um but you know sometimes I do want to just stay focused on like one thing and then ultra fast is just delightful because it just keeps you just right there in the flow.

我很适应切换上下文，也喜欢做许多小决定。但有时我确实只想专注于一件事，这时超快模式就特别令人愉悦，因为它能让我始终保持心流状态。

The thing with ultra fast uh that you know we we it it works amazingly well when there's not that many tool calls involved or it's like a lot of generation of context. So for example, if you're trying to prototype um a website or a video game and you know you just need to it to write like a lot of code um then it will do it so so quickly right you know 10 times more quickly but if it's a lot of tool calls like the

超快模式的特点是：当任务不涉及太多工具调用，或者需要生成大量内容时，它的表现非常出色。例如，如果你想制作一个网站或电子游戏的原型，只需要它编写大量代码，那么它的速度会非常快，可以达到 10 倍。但如果任务涉及大量工具调用，

overhead is in like somewhere else in the network or you know somewhere else in the agent trajectory then you know you'll only feel like a 3x or 4x speed up.

开销可能出现在网络或智能体轨迹的其他环节，因此你只能感受到 3x 或 4x 的速度提升。

Yeah.

对。

Um and you'll not get that four like full 14x. So I I know OpenAI employees get unlimited tokens and I can imagine if I had unlimited tokens I would always set it to max thinking 5.6 solar whatever the latest model is it and I would think kind of similarly I would always want ultra fast on it's like when when cost isn't on my mind I'm like okay max it out.

你无法感受到完整的 14x。我知道 OpenAI 员工可以无限使用 token。可以想象，如果我拥有无限的 token，肯定总会将思考力度设为最大，使用 5.6、solar，或者当时最新的模型。我的想法也差不多，总会想开启超高速模式。只要不需要考虑成本，我就会想：“好，直接拉满。”

Is that how it is internally?

内部也是这样吗？

We don't we don't give ultra fast to everyone like we reserve a lot of our capacity for external users and customers.

我们不会向所有人提供超高速模式，因为我们需要将大量算力留给外部用户和客户。

Yeah. Um, so O OpenAI employees have the ability and the capacity to gobble up all of it, right? So gobble up like all of our production GPUs, all of ultra fast is like uh you know we would use all of it but you know we don't like we sort of um we restrict it in a way uh where you know like we we we look at you know how much is reasonable for us.

OpenAI 员工确实有能力，也具备相应条件，可以消耗掉所有算力，包括全部用于生产的 GPU 和超高速模式的算力。我们确实能够用掉全部算力，但不会这样做。我们会通过某种方式加以限制，评估使用多少算力才算合理。

Yeah. So that you know we use it so that we understand the product as well so that we keep improving it so that we benefit from you know recursive self-improvement but um the the vast majority is like reserved for customers.

我们会使用这些算力，以便了解并持续改进产品，同时从递归式自我改进中受益。但绝大部分算力仍会留给客户。

Okay. Yeah that's good. Thanks. Um uh [laughter] what latency sensitive use cases outside of open AI are you most excited about that gets unlocked by that kind of that kind of speed?

很好，谢谢。[笑声] 在 OpenAI 之外，你最期待这样的速度解锁哪些对延迟敏感的应用场景？

It's interesting. It's just like really one one thing that I'm very excited about in general is um nontext interactions. So um can you can you like sort of operate on a shared canvas? Can you create things? Can you do can you generate you know ideas and different uh can you generate different images and then select one and like so like know choose your adventure uh and and and then you know have a very quick mockup

这个问题很有意思。总体而言，最让我期待的是非文本交互。例如，你能否在共享画布上操作并创作内容？能否生成创意或不同的图像，再从中选择一张，像“选择你自己的冒险”一样，迅速制作出一个原型模型，

of a prototype that then you can steer um you know like in real time either through voice or through text and then you sort of like just see it right there. Um it's like this very creative process which I think these speeds allow. um where you know like as as an engineer sometimes you know you're just like sort of like you sit back and you're like oh I need to design this whole system I need to think about it

随后通过语音或文字引导它实时调整，并立即看到结果。这是一个极具创造力的过程，我认为这样的速度能够让创意迅速实现。作为一名工程师，有时你可能会向后一靠，想到自己必须设计一套完整的系统，并仔细考虑

the trade-offs the requirements but like you know maybe you know you can just create it in one minute and see like how it actually does um and then sort of like be more like in the flow and like you know in it things better and I think these speeds a lot yeah and so I'm assuming the ultra fast price is going to be significantly higher than than kind of normal speeds do Do you think ultra fast speeds are

各种权衡和需求。但也许你完全可以直接用一分钟把它做出来，看看实际效果。这样一来，你就能更深入地参与整个流程，更好地进行迭代。我认为这能大幅提升速度。因此，我猜超高速档位的价格会远高于普通速度档位。你认为超高速

going to become the standard or are they always going to have a premium price point?

会成为标准配置，还是会一直保持溢价？

Um, that's interesting. So, I think the the the same way as technology usually goes, I think it will become like more broadly and you know broader and broader accessibility over time. Um, the speeds at which like agents get things done like you know will continue to improve like we're seeing massive improvements like month after month. Um this is not just the inference speed. This is also the just how token efficient the models

这个问题很有意思。按照技术通常的发展规律，它会逐渐普及，随着时间推移，能够使用它的人会越来越多。智能体完成任务的速度也会继续提升，我们看到它每个月都在大幅进步。这不仅体现在推理速度上，也包括模型的 token 效率。

are. Like soul is like significantly more token efficient than terra. Next model will be significantly more efficient uh token efficient than than soul as you might expect. And we always pushing on that. And so things just get faster over time. Inference hardware like you know everything you know just like we continue to uh innovate there and it gets faster. So I do think in you know maybe a year or two these speeds

例如，Soul 的 token 效率明显高于 Terra。下一个模型的效率也会显著提升，其 token 效率将高于 Soul，这也在意料之中。我们一直在努力推动这方面的进步，因此速度会不断加快。我们也在推理硬件等各个方面持续创新，速度还会越来越快。因此，我确实认为，也许一两年后，这样的速度

will become you know maybe if not the default like very close to the default but then I do also think you know you will always have like the one tier up um where you know you can always use more hardware you can always do different trade-offs that are like more costly um but that just kind of give you something something extra.

即使不会成为默认标准，也会非常接近默认标准。不过，我也认为永远都会有更高一个档次的选择。你始终可以使用更多硬件，也可以采用成本更高的不同权衡方案，从而获得一些额外的优势。

So TBO the the last question I usually like to end on uh is is for a broader audience. There are a lot of people out there who are quite nervous about AI, whether it's uh job automation, environmental impact, um or just kind of this this thing that's happening. It's and it feels quite foreign. What words of encouragement would you give to the the broader audience?

蒂博，我通常喜欢用最后这个问题收尾，它面向更广泛的受众。现在有许多人对人工智能感到相当担忧，无论是工作自动化、环境影响，还是正在发生的这一切所带来的陌生感。你会对广大受众说些什么来鼓励他们？

Yeah. So we we we really build for the world with with ChatGPT and we are very very much um investing in you know how efficient it is and you know this is directly aligned with like you know broad access and broad utility that we provide. So the cheaper it is you know to serve like you know the more the more you can do with it um the more you get out of it in your daily life and it has gotten very very

我们打造 ChatGPT，真正面向的是全世界。我们非常重视提升它的效率，因为这与我们所追求的广泛可及性和广泛实用价值完全一致。服务成本越低，你能用它做的事情就越多，在日常生活中获得的价值也越大。如今，它已经变得非常

efficient like if you look at uh you know Luna for example like it's it's a much smaller model it is u it is incredibly efficient but like if you rewind six six months ago it would have sat at the frontier.

高效。以 Luna 为例，它是一个小得多的模型，效率高得惊人。但如果回到六个月前，它本可以处于前沿水平。

Yeah.

是的。

Um and you look at the cost of Luna right it's like you know it's like it's it's it's crazy cheap.

再看看 Luna 的成本，确实便宜得离谱。

It's it's um it's phenomenal. Right.

这确实令人惊叹。

It's like a kind of You just did that thing with Reply.

这有点像——你们刚刚也对 Reply 做了同样的事。

You're giving it away for free now.

现在已经免费提供了。

They're giving Yeah. They're It's just like on on on on um in this free mode, right? Um which is like wow. You know, it's like this access to incredible intelligence will become like ubiquitous. Um and it's only possible when you push, you know, you push the efficiency like you know like month after month after month, year after year. And so I think you know whatever was like you know is a frontier now is

是的，他们现在已经将它纳入免费模式。这确实令人震撼。使用这种强大智能的机会将变得无处不在，而这只有通过持续推动并提升效率，月复一月、年复一年地进步，才有可能实现。因此，我认为任何目前处于前沿水平的技术，

like you know will become like way way cheaper to run in six months. And so this is this is like my uh sort of you know this is how I would answer this question is just uh technology has a way to become like you know very very efficient over time.

六个月后的运行成本都会低得多。简而言之，我的回答是：技术总会随着时间推移变得越来越高效。

Um and we're very focused on like you know very broad access and we're optimizing for you know the utility that you get out of it directly. How about for people who are apprehensive to even try AI for the first time? Like what what are you telling them and and how how can you paint them picture a vision of the future in which AI is is helping the world?

我们非常专注于实现广泛可及，同时也在直接优化用户能够从中获得的实用价值。那么，对于那些甚至不敢第一次尝试人工智能的人，你会说些什么？你会如何为他们描绘未来的图景，让他们看到人工智能将如何帮助这个世界？

Yes. Um I think it's you don't have to look very far like ChatGPT helps um people in very personal and deep ways like a lot of um a lot of our users use it for help in writing but also like you know for personal advice or you know medical advice like we we launched um health and finance and you know I I I I use them super regularly and uh I I feel like I get like a lot of uh support that I otherwise like you know it would be

我认为不必舍近求远，ChatGPT 正在以非常切身且深入的方式帮助人们。我们的许多用户会用它辅助写作，也会用它寻求个人建议或医疗建议。例如，我们上线了健康和金融功能，我自己也经常使用。我认为自己从中获得了很多支持，而这类支持通过其他方式

hard for me to get and it allows me for example to be more informed when I go see my doctor. And so you don't you don't need to go very far um you know to kind of see the utility that it can provide. Just I think you know talking to others and then you know getting inspired by you know how others use it and benefit from it is like a great way to you know just maybe um like start considering how you could benefit from

往往很难获得。例如，它让我在就医时能够掌握更多信息。因此，不必舍近求远就能看到它带来的实用价值。与其他人交流，从他们使用它的方式以及受益的经历中获得启发，是一个很好的途径，可以让你开始思考自己能如何从中

it.

受益。

Well Tibo, thank you so much. Thank you appreciate your time.

好的，蒂博，非常感谢你。感谢你抽出时间。