You know, Greg, this is our fourth time speaking and we've spoken every time Greg，这是我们第四次交流了。我们每次都会讨论
about OpenAI's product direction and I think I'm starting to get it. Um, you OpenAI 的产品方向，我想我开始有点理解了。
know, there was this conversation that a super app was the wrong term for what 比如，过去有人讨论说用“超级 App”来形容
you were doing with um the app that you're building bringing codeex which is 你们正在做的事情并不合适。你们要把 Codex，也就是
coding side of OpenAI's product browser and chat GPT together. And when you use the word super app, people would be OpenAI 的代码端、浏览器端和 ChatGPT 整合起来。
like, "No, a super app is actually something that you can just use every other app within." Um, and now as we've 过去人们觉得：“不对，超级 App 应该是一个能运行所有其他 App 的平台。”
seen these products come together, actually super app might be the correct term. You know, at least for us on the outside, we're starting to see it that 但现在看到这些产品融合，“超级 App”也许真的是最准确的词。
when you need to do anything, um, you it will start with a prompt in chat GPT and 至少在外界看来，当你需要做任何事时，都会先从 ChatGPT 提问开始，
then OpenAI's technology will use either your browser or your computer to get that done for you. Is that the right way 然后 OpenAI 的技术会控制浏览器或电脑帮你搞定。可以这样理解吗？
to think about it? 可以这样理解吗？
I I think that's a pretty good perspective, right? And I think to really zoom out, the thing we're 我认为这是一个挺好的视角，对吧？如果真的把视野拉宽，
actually trying to build is in AGI, right? That if you think about what people have been using since chat GBT, 我们真正试图构建的其实是 AGI。如果你想想大家在 ChatGPT 推出以来的使用，
it's a language model, right? There's a big gap between these. 它实际上只是个语言模型，这与 AGI 之间有着巨大的鸿沟。
It's amazing. You can talk to chat talks back to you. Great. Wonderful. But when we launched in 2022, there was no memory, right? It's not hooked up to any 和 ChatGPT 对话确实很神奇，它能给你回应，这很好。但 2022 年推出时它没有记忆，没有连接
tools. Has no context. And so it really is that this conversational intelligence is only one part of what people really 任何工具，也没有上下文。所以这种对话式智能，只是人们真正需要用来
need to get work done to be able to achieve their goals. And where we're going is to have an AI that's really looking out for you, right? That you can 完成工作、达成目标的一部分。而我们的方向是，拥有一个真正为你着想的 AI，
provide the goals, the directions, that it's constantly thinking about what can I do for Alex today? Uh that it's able to go and solve super hard problems, 提供目标和方向，让它不断思考：“今天我能帮 Alex 做点什么？”它能解决极具挑战性的问题，
very mundane problems. you wake up your inbox is organized but also if there's like a health plan that you are thinking about that it can help you help you 也能解决日常琐碎。比如早晨醒来收件箱已整理好，或者你想做健康计划，它能帮你
achieve that figure out medical treatments uh or you know sort of back and forth provide you with that kind of information at least and I think that 实现，甚至寻找医疗方法，或者至少在来回交流中为你提供这类信息。我认为，
the question of what's the interface you want what is the product that you want is what we spend a lot of time thinking about and the answer is you want almost 大家常花时间思考需要什么交互界面或产品，而答案是：你几乎不需要
no interface you want no product right you want this to be like what's the interface between you and me right just being able to talk to a persistent 任何界面，不需要产品。你只想让它像你我之间的交流一样，只需要和一个持续存在的
entity of some form firm that's able to go and accomplish goals for you. And so building that is hard. It will take 实体对话，让它去帮你达成目标。构建这一点很难，需要
time, but we have a lot of the pieces, right? We're increasingly bringing together the product layer, trying to make the models better, trying to make 时间，但我们已经拥有了很多拼图。我们正逐步整合产品层，努力让模型更强大，
the whole system just so there's less like clicking buttons and toggles and changing modes and all these things. Not 让整个系统减少按钮点击、模式切换这些繁杂操作。这并不是说
to say that there won't be some of those along the way, but the long-term trajectory is towards simplification, unification. Yeah, it's very interesting 过程中不会有妥协，但长远来看是朝向简化和统一。是的，这很有趣，
that you say the interface uh will melt away. And so to go a little bit deeper with my question, um many of us who use 你说交互界面会消失。为了更深入探讨，很多目前使用
products like chatbt today will see that the bot will make a suggestion at the end. You know, you ask it about nutrition and it says should I make a ChatGPT 等产品的人会发现，机器人在结束时会提供建议。比如你问营养问题，
health plan for you or make a diet plan for you? You ask it, sorry Ron John, we just talked about travel, but you ask it about travel and then it will give you 它会问要不要帮你做健康或饮食计划。抱歉，Ranjan，我们刚才谈过旅游，但当你询问旅游时，
an agenda for instance. And so am I hearing you right that what's going to 它会提供行程安排。
happen within chat GPT just to give an example is you talk to it about your health decisions and it might say you 所以在 ChatGPT 中，当你讨论健康决定时，它可能会说
know you probably need to you know go to this specialist let me make an appointment for you and then it will go 你可能需要看某个专家，并帮你预约，
and actually take that action on your behalf. So it goes from simply a conversation interface to actually understanding your intent and then going 然后去执行。它从一个对话界面，变成了理解你的意图并帮你实现它。
out and accomplishing that for you. that that I that's exactly right and I think that if you've used codecs and by the way how many people in the room have 这完全正确。如果你用过 Codex，顺便问一下，现场有多少人
used have used codecs people yeah decent number of people um and that our goal is to really bring the 用过 Codex？人数不少。我们的目标是
power of codecs to everyone right to bring agents to everyone you that technology exists right now right you 把 Codex 的能力带给每个人，把 Agent 带给每个人。这种技术现在已经存在，
can hook up like I hook up my codeex to slack to my Gmail to my calendar and there are many people within openai 比如我把自己的 Codex 连接到 Slack、Gmail 和日记上。在 OpenAI 内部，很多
nontechnical users you know it's got code in the name but it's not really about code. It's really about having this general purpose tool using harness 非技术用户也在用。虽然名字里有“代码”，但它并不只写代码，而是作为一个通用的 Agent 工具。
an agent. And that the kinds of things, for example, someone on our comm's team does is um she was organizing an event 例如我们公关团队的一位同事，她在组织一场活动时，
and it would just ask all of the uh event attendees for their dietary preferences, set up a whole seating 机器会自动询问所有与会者的饮食偏好，并排好座位表，
chart, kind of did all of that work uh so that she could focus on the parts that she wanted to and and really thinking about the vision of what she 搞定了所有这些工作。这样她就能专注于自己真正想做的事情，思考她想达到的活动效果。
wanted to achieve. And I think that we're going to see this across the board. So the it's not sci-fi anymore to think about an AI that's hooked up to 我认为我们会在各行各业看到这种情况。AI 连接工具已经不再是科幻小说了。
these tools. And I remember with you know our very first attempt at tool use 我记得我们在 ChatGPT 中第一次尝试工具使用是在 2023 年。
in chatbt was 2023. I think in like March or April or something we released plugins. Do people remember plugins back 大约在三四月，我们发布了 plugins（插件）。大家还记得早期 ChatGPT 的插件吗？
in early chat days? 那个其实不行。
That didn't work. 是的，完全不行。
It didn't work at all because the models the models weren't ready. Right. The form factor is correct. Obviously you're going to have an AI that's able to like 因为那时候的模型还没准备好。虽然产品形态是对的，AI 显然应该能和你的 Gmail 交互。
talk to your Gmail like no question. But we could only like have three different connectors exposed to the model at a time or start forgetting. You know, we 但当时我们每次只能给模型提供三个连接器，否则它就会开始遗忘。当时
had like 2K maybe 4K token context. Like there's just no memory, right? It's kind of like when you had early computers in 我们只有 2K 或 4K 的 token 上下文，根本没有记忆。这有点像 60 或 70 年代早期的电脑，
the 60s or 70s or something, right? You know, that you had tiny little me memory banks and today you have your phone that's like better than any 内存非常小。而今天你的手机已经比那个时代的任何超级计算机都要强了。
supercomputer from that era. And I think that's where we're going with these models, right? That the rate of improvement has been so steep. So now you can have hundreds of different tools 内存非常小。而今天你的手机已经比那个时代的任何超级计算机都要强了。
accessible that we have the ability to hook them up to whole file system. So you can almost have the full power of the internet and like almost any 我觉得我们的模型也是这个趋势，它的提升速度非常快。现在你可以连接成百上千个工具，
application you want at the model's fingertips. And it's smart, right? It's got 52 million token context, depends 我们有能力把它们连接到整个文件系统。所以模型几乎触手可及地拥有整个互联网和几乎任何应用的能力。
how you squint on it. And the capability level is also getting so so powerful, right? These models are now solving 而且它很聪明，拥有 5200 万 token 上下文，取决于你怎么看。它的能力也变得非常强大，
unsolved math problems and physics problems, right? And really helping people be able to achieve things they couldn't otherwise. like we are on the 这些模型现在正在解决未解的数学和物理问题，真正帮助人们实现以前无法做到的事情。
era on the edge of this era of agents really transforming how we all operate whether it's in software engineering 我们正处于 Agent 时代的前夜，它将彻底改变我们的工作方式，无论是在软件工程、
finance legal sales and in our personal lives too. So just to unpack that example that you were giving there one 金融、法律、销售，还是在个人生活中。为了进一步说明，
of your colleagues is chatting with Chad GPT about an event and then suggest hey you know uh how should we you know 比如你刚才提到，你的同事在和 ChatGPT 聊活动，
contact event attendees about something and instead of like saying okay I have to do that and going into an event 然后 AI 建议如何联系与会者。接着，不用你亲自去做，
program basically what happens is the the interface will take over from there once it says it's a good idea and you 交互界面就会接管，连接你使用的工具，然后帮你搞定。
agree and then hook into whatever tools you're using and then do it for you. 没错。它会通过 Gmail 连接器搜索你的收件箱，找到所有与会者，
Exactly. So it uses its you know Gmail connector searches through your inbox to find all the people who are attending 并查看谁有饮食限制，谁已经提供了，谁还没有，并起草好邮件。
and then if you're on the like what what'sever in dietary restriction sees oh these people I already have their 根据你的设置，它可能会说：“我起草了这些邮件，可以发送吗？”
dietary restrictions these people I do not um drafts an email depending on exactly how you have things set up it 如果连接器不允许它发送邮件，它会提示：“我已经起草好了，你需要自己发送。”
might say hey I drafted these emails can I send them if you have a connector that doesn't even let it send emails said I 但在另一个世界里，如果你对系统建立了足够的信任，它会说：“我
drafted it you need to send them And uh in a different world, you could also imagine that it's you've built enough trust with the system where it says I 起草并发送了邮件。”我认为这指出了 Agent 时代一个非常重要的方面，那就是
drafted the emails and I actually sent them. And I think that this actually points to a really important aspect of 信任，对吧？
the agentic era, which is trust, right? 信任，对吧？
That we need to really learn how to build trust with these systems, where they're good, where they're not. Figure out what you want to delegate to them 我们需要学习如何建立对这些系统的信任，了解它们的优缺点，决定把什么任务和责任委托给它们，
and how you want to entrust them with responsibility. And that's something we view as earned, right? It's not something that that we can we can grant 以及如何向它们委托责任。我们认为信任是赢来的，而不是凭空赋予的。
but by providing lots of tools and control and oversight and supervision to the operator to the person who that this 需要通过提供大量的工具、控制、监督和管理，让操作者能真正掌控
AI is operating on behalf of like we think that that is going to be such an important thing and so that's a key product feature and differentiator. AI 替他们执行的操作。我们认为这非常重要，是关键的产品特色和优势。
Yeah. Yeah. And when you go back to some of the early attempts at this, there was this like move that OpenAI had to let 是的。回想早期的尝试，OpenAI 曾尝试让你在
you call an Uber within Chat GPT. And it it followed a long line of companies that have tried to get you to take ChatGPT 里打 Uber。许多公司都尝试过在聊天中直接进行操作，
action within chat, but it never really took off. And the difference here might 但都没有真正流行起来。这里的区别可能在于，
be that the chatbot can take control of your browser or take control of your computer and then you don't necessarily 聊天机器人能控制你的浏览器或电脑，你不需要担心
have to worry about like is this plug-in going to work? it goes and accomplishes that for you by taking over your 插件是否能用，它直接接管你的机器来帮你完成。
machine. So I wonder you know if you expect a fight from the user interfaces 我想知道，你是否预计会遇到来自现有用户界面的阻力，
that we have today aka like all the other apps all the software where to be 也就是其他 App 和软件。因为要真正发挥作用，
truly useful chat GPT will have to not be blocked to be able to go out and execute these actions on behalf of a ChatGPT 必须不被阻挡，能够代表用户执行这些操作。
user. Well, look f first of all, I'd say that it's this is not theoretical at this point, right? That people have been using codec. So, it's a separate 首先，这在目前已经不是理论了，大家已经在用 Codex 了。它是一个独立的 App，
product, separate app. You have to install it separately. Really starting to focus on software engineering. But the amount of non-software work that has 需要单独安装，目前主要专注于软件工程。但 Codex 上的非软件工作量
been happening codeex has been absolutely exploding, right? It's been this like incredible exponential curve, exactly the thing that you you would 呈现了爆炸式增长，是一条非常惊人的指数曲线，正如你所预料的那样。
expect. And within OpenAI, we basically have the same level of penetration now in usage as Slack, right? It's like everyone and OpenI is like an entirely 在 OpenAI 内部，我们现在的使用渗透率基本上和 Slack 一样高。
slack based company. We do not use email for the most part. It's like really like if you're not on Slack, you're not going to do any work. Um, and it's kind of OpenAI 是一家完全基于 Slack 的公司，我们几乎不用邮件，如果你不上 Slack 就没法工作。
feeling that way now with codeex app as well. And that everyone's codex is hooked up to all of these tools. Um, how 现在的 Codex 也是这种感觉，每个人的 Codex 都连接了这些工具。
the ecos ecosystem evolves. I think it's going to be a very nuanced thing because I think one thing that is very important is that we believe that there should be 至于生态系统如何演变，我认为会非常微妙。因为我们相信，应该有一个充满活力和繁荣的生态系统，
an ecosystem that gets to be vibrant and thriving and that people can really build and see the benefits. And so we've 让人们真正能进行开发并获益。我们已经从合作伙伴公司那里看到了这一点，
actually seen this from uh partner companies where uh you know that we I remember there's a couple different partners where we said hey we really 比如我们曾问几个不同的合作伙伴：“我们希望能训练 AI 去使用你们的软件。”
want to train our AI to be really good at using your software and we didn't know what they would say and actually the response we got is this is the most 这是我们收到过最友好的合作请求，你们想训练 AI 专门使用我们的软件。
partner friendly outreach we've ever had right that the idea that you will make your AI specifically good at using our 因为他们看到了机会，让 AI 专门使用他们的工具，其工具的使用率就会大大增加。
tool and they just see the opportunity because their tool will be used just so much more as a result and that everyone 他们看到了机会，因为他们的工具会因此被更频繁地使用。并且每个人
is trying to think about how do they not just survive as a company into the AI era but thrive? like how do you really 都在思考，如何在 AI 时代不仅生存下来，而且蓬勃发展？
get the advantages of the fact there's going to be so much more activity and if you don't have AI in there if you shut it out then you're actually going to be 如何获得 AI 带来的红利？如果你没有 AI，或者把 AI 拒之门外，你实际上是在衰退，而不是繁荣。
declining not thriving right this this kind of makes open AI puts open AAI so first of all you're going to bring you talk about people 这让 OpenAI 处于独特的地位。首先，你谈到了大家都在使用 Codex，
using codeex so one of your colleagues shared and I think you've talked about this too that you've brought chat GPT 一如你刚才提到的，你的一位同事分享过，你们已经把 ChatGPT 引入了 Codex，
into codeex so you can bring codeex into chat GPT which is basically like if we're users of chat GPT 或者你也可以把 Codex 引入 ChatGPT，这基本上就相当于，作为 ChatGPT 的用户——
this experience that we talked about of chat GPT not only suggesting what you might want to do next but going to do it 我们所讨论的这种体验：ChatGPT 不仅会建议你下一步做什么，还会帮你去执行。
for you that's going to happen and so it makes you effectively an operating system don't you think but not the 这确实会发生，这让你们实际上成为了一个操作系统，你不觉得吗？
operating system like an iOS where you would like go open up the your phone and then tap different apps it's almost as 但不是像 iOS 那样的操作系统，你打开手机然后点击不同的 App。它几乎就像——
if all interaction with all apps will happen through this interface is that the ambition 所有的交互都将通过这个界面进行。这是你们的野心吗？
I think that you could describe it that way but I think of it a little differently. Like the way that I think 我认为可以这样描述，但我看法有些不同。我思考的是，
about this is that what is the ideal interface to an AGI or we call it kind 什么是 AGI 或者我们所说的“个人 AGI”的最理想界面。
of a personal AGI. And I think that it's again the same interface that you and I are using right now. You just want to 我认为它和我们现在的交流界面是一样的。你只想和助手对话，
talk to an assistant, right? You want to talk to something that can go and and and work and and operate on your behalf. 和能代表你工作和操作的实体交流。
And so that yes, like that agent, that AGI, that AI will have its own computer, right? It'll have its own access to 因此，那个 Agent，那个 AGI，那个 AI 会有它自己的电脑，有它自己的访问权限。
things. maybe can you know like ideal co-orker would be they can come over and type things on your computer too. So 也许就像理想的同事能过来在你的电脑上打字一样，
some access some delegated access to your own own system and you know maybe 它也需要对你的系统拥有一些授权，
you delegate access to your inbox sometimes maybe it has its own inbox with some sort of uh some sort of you 比如授权访问收件箱，或者它自己有一个邮箱窗口，
know window into into the things that it needs you forward emails to it. Th these are not actually if you if you think 来处理需要的事务，或者你转发邮件给它。
about this is not unprecedented, right? 其实这并非没有先例。
It's like the way that you work with an assistant who's a person that we've we've actually or any co-orker really, 就像你和人类助理或任何同事合作一样，
we've spent a lot of time really thinking about how do you build these trust boundaries and make sure that you're able to operate together. And so 我们花了很多时间思考如何建立信任边界并协同工作。
I think of it as just a different thing. 所以我认为这是不一样的。
It's not it you could think of it as an operating system, but an operating system is almost something from a different time, right? It's a different 它不是你所想的操作系统，操作系统几乎是上一个时代的概念了，
layer of the stack. This is really more about how do you interface with technology broadly. And I think that the 处于技术栈的不同层级。这更多是关于如何与技术进行交互。
beautiful thing about AI is it's really about bringing the machine closer to the human rather than us having to contort 我认为 AI 最美妙的是让机器更近人类，
ourselves into like files and folders and like all these details that somehow are not natural, right? That are more about how the machine operates rather 而不是让我们去适应文件、文件夹等这些并不自然的机器细节。
than how we operate. 而是人类的运作方式。
Yeah. Talking about a personal intelligence, it sort of um I don't did you watch WWDC last week? 说到个人智能，你看上周 WWDC 了吗？
Uh no, no, I missed it. I was I was banned, but um I watched it on TV. Um 没有，我没去成，我被禁了，但我看电视了。
come on, Apple. Anyway, it does look like you and and and Siri, the new Siri are going to come kind of into 好吧，苹果。不过，看起来你和新的 Siri 之间会产生某种竞争关系，对吧？
competition, right? Because they're an app that's going to sit or an intelligence that will sit on top of all of your apps and let you take action. 因为 Siri 也是一个运行在所有 App 之上、能帮你执行操作的智能体。
And Chacht will be an app on the iPhone. 并且 ChatGPT 将成为 iPhone 上的一个 App。
So then talk a little bit about whether that positioning is going to be difficult for open AI and how you're thinking about that strategically. 那么，这种定位对 OpenAI 来说会有困难吗？你们战略上是怎么考虑的？
Well, I just think again think of it a little differently. Like I think that we're in the beginning of this new 我还是觉得可以换个角度来看。比如我认为我们正处于全新
agentic era and the way that this has always gone in AI is that when you have a new level of capability, it means you Agent 时代的开端，AI 领域向来如此：当你有新一级的能力，
have an opportunity to rethink everything, right? Rethink how people interface, how like what the 就意味着有机会重新思考一切，重新思考人们的交互方式，
techn is capable of. And I think that this is no no different, right? In my mind, like the kinds of things that I 以及技术的可能性。我认为这次也不例外。在我的脑海中，
see on the horizon, for example, AI for solving scientific problems, right? And I think 我看到的愿景，例如用 AI 来解决科学问题。我认为
we're starting to see the inklings of this. Like for example, today we announced 我们正开始看到这方面的端倪。例如我们今天宣布，
we have in uh peer-reviewed literature people doctors who are using 03. 在同行评审的文献中，有医生在使用 o3。
Remember 03? 还记得 o3 吗？
Yep. 是的。
That was like forever ago now, right? 那感觉像是很久以前的事了，对吧？
That was like one of our earliest reasoning models using that to find 那是我们最早的推理模型之一，用它
diagnosis for people who I had no no answers from doctors for many many years. You know, there's an example of 为多年无法确诊的患者找到了诊断结果。
someone who had spent 20 years with a mysterious ailment. Finally, it's been diagnosed through the use of this technology. And if you're like, okay, 比如有个患者忍受了 20 年的怪病，最终通过这项技术确诊了。如果你觉得，好吧，
you've got models that can do that, they can do that. And then it's really about like, you know, the same like distribution and like, you know, can you 如果你有了能做到这些的模型，它们就能做到。然后这其实是关于分发，以及你
get access to an app? You know, to me it's it doesn't type check. It's like we have something fundamentally new. And so that's not to say that there won't be competition. I actually think that there 是否能获得一个 App。对我来说这不太对劲。我们拥有的是全新的东西。
will be and it's going to be great for everyone, but I just think that the ways in which you're going to use this technology, the things it will be capable of and what it'll make you 但这并不是说没有竞争，竞争会很激烈且对大家都好，
capable of doing are just totally different from anything we've seen before. 但我认为你使用这项技术的方式，以及它能带给你的能力，和我们以前见过的完全不同。
You know, I was going to ask you um well, does it mean that you'll have to um you know, create your own device 我本来想问，这是否意味着你们需要制造自己的设备，
assuming that like my concept is is you know that you're going to have to go through Apple to get to the user. Um 毕竟要接触到用户可能必须通过苹果。假设这有一定道理，
assuming that's somewhat valid. But the answer is you already are right. You're so OpenAI is working on a device right now. 但答案是你们已经在做了，媒体也报道了 OpenAI 正在开发设备。
It certainly has been publicly reported. 媒体已经公开报道了。
I I was in your office in December and Sam told me that this is happening. It's it's multiple devices. Um so if you 我十二月去你办公室时，Sam 就告诉我这正在发生，而且是多个设备。
think about the way that again you're going to interface with um with these AIs, how does that device play in or 所以如果你想想我们与 AI 的交互方式，这个设备或系列设备扮演什么角色？
series of devices? Well, look, I I think again I I would just step back and say that I think this is the beginning of 我还是想退一步说，这是全新事物的开始。我认为
something very new and that I think 最大的变革不在于设备本身，
about the way that I think I want to say like I think the biggest shift that has happened in terms 而是从对话智能，也就是对话式问答——
of interface again it's not even about devices and and and things like that. 而是人机交互界面方面的转变。这甚至不是设备之类的事情，对吧？
It's really about the shift from conversational intelligence like kind of the chat paradigm where it's like kind of you have an AI that's personalized 其实是从对话智能，也就是你向 AI 问答的模式——这个 AI
enough to you that it's worth reading its output, right? You ask it a question, you get an answer, it's something that's useful to you to agents 被个性化定制，以至于其输出值得你去阅读，你问一个问题，它给你一个有用的回答——转变成 Agent 模式，
where they're capable enough to actually do things for you. Like that is a big shift and that that 在这里它们足够有能力真正替你去执行任务。这才是巨大的转变，
implies a difference in how you want to interact. And so you kind of are just going to want a single agent that has 而这种转变也意味着人机交互方式的变化。因此你更倾向于只需要一个能
access to your context. And this will be true in personal life. This will be true in a business context, right? You 访问你上下文的单一 Agent。无论在个人生活还是商务场景中，这都适用。对吧？
imagine, for example, having a uh you know, imagine you have a PhD in every 想象一下，你有一个在各个领域都拥有 PhD 水平，
field co-worker, you know, Nobel prizes, multiple of them, and you hire one of these, you hire a hundred of them, and 获得过多次诺贝尔奖的同事，你雇佣了一个，甚至雇佣了一百个这样的人，
you don't invite them to any meetings. 却不让他们参加任何会议。
They're not going to be very useful. And so there's something about how do you get context into the AI and not just 那他们不会很有用。所以这里面临的核心挑战是：你如何将上下文输入给 AI，不仅是
statically but dynamically right as context evolves as your business processes evolve how do you have a 静态的，更是动态的，随着上下文和你的业务流程的演进而演进。你如何拥有一个
context layer that is accessible to an AI that lets the AI operate to the extent of that raw intelligence and so 能被 AI 访问的上下文层，让 AI 在其原始智能范围内运转，
finding ways to make that AI be accessible so available in your meetings to make it very ergonomic it's very easy 从而找到方法让 AI 能在你的会议中被轻松访问、非常符合人体工程学、易于获取？
to get access to Um, I think all of that's going to require a rethink, but I think it again it's just the core for me 我认为所有这些都需要重新思考。但我认为，对我来说核心依然是
starts from thinking about the agentic form factor and then working backwards to how do you just make this have the 从思考 Agent 这一产品形态出发，然后反推：你如何让它获得
context it needs and again the trust is going to be such a core part of making this whole equation work. 所需的上下文。而信任依然是让整个公式运转的核心部分。
So kind of like having this this device with you at all times and being like I need to get that done and it goes and does it for you. And I think that that 所以，随时随身携带这个设备，当有事情要办时，它就能帮你完成。我认为这就是未来的一部分。
will be part of it. But I almost even think if you don't have a device like that, it's not like you're going to be 但我也认为，即使没有这样的设备，你也不会出局。
out of the game, right? Because it's this a this AI. It's not because there's one thing there's one version of it where you think of it where it's like the device is the AI and you want your 因为这个 AI 并不等同于设备本身。有些人觉得手机就是 AI，或者你想要你的
phone to be the AI. You want, you know, whatever whatever you know custom device you're you're thinking about to be the AI, but it's not going to be like that. 手机就是 AI，或者你正在构想的某个定制设备就是 AI，但事实不会是那样。
It's going to be more like an interface. 它更像是一个交互界面。
Like no more than your phone is you, right? It's an interface to you. It's a way that I can sort of, you know, call you up whenever I need you. uh whenever I want to ask you a question and there's 就像你的手机并不是你，而是你的交互界面，可以在需要时联系到你。
different ways of accessing right there's like synchronous phone call I can text you I can email you and I think that we're going to be much the same 我们可以通过不同的方式联系，比如打电话、发短信、发邮件。同样的，
with how we interact with our agents uh there's been some reports that open is working on these like birectional voice 我们与 Agent 交互也是如此。有报道称 OpenAI 正在开发双向语音
models I think we've talked about that in the past like the goal is to have like an AI that you can you can speak 模型，我们之前也讨论过，我们的目标是拥有一个你可以与之交谈的 AI，
with and we'll be able to process that and speak back with you in a much more natural way can you share anything about that 它能处理你的话并以更自然的方式回应你。你能分享一些细节吗？
No, but no, more seriously, um I mean, look, I think that the that the general shape of the of the technology, like the way 不能，但认真地说，我认为技术的大致形态，
that like we had we had we've had voice models, um you know, kind of a really cool voice experience for, you know, 比如我们之前就拥有语音模型，体验挺好的，
year and a half, two years now. Um you know, we first demoed it back in March, April of 2024. Um brought it to market, 已经有了一年半到两年的时间。我们最早在 2024 年三四月进行了演示，
um you know, maybe late that late that year. And the way that it works and the way that everyone's models work is that 并在大约年底推向了市场。
you basically chain together um well the original way that these things worked was that you would chain together a text 过去语音模型的工作方式是，你基本上需要把几个不同的模型串联起来：
to or a speechtoext model then you do a texttoext model and then you would do a texttospech model. Horribleness, right? 先用 speech-to-text 模型，再用 text-to-text 模型，然后是 text-to-speech 模型。这很糟糕，对吧？
Like these three things chained together. Um it still has been the case that even if you have one unified model that's able to kind of take in input and 三个模型串联在一起。而即使是现在统一的模型，能直接接收输入并输出响应，
then you know able to output a response, you still have this problem of turn taking, right? Imagine that like we have this like you cannot overlap, you cannot 你仍然面临着轮流发言的问题。比如我们谈话时，不能重叠，不能打断。
interrupt. It's just like once you you you speak to me in a turn and then you got to wait for me to finish my whole response. That is not how human 你必须等我说完这一轮，然后我再等你。但人类的对话不是这样的。
conversation works. And so that we we basically have like a hack where we have these models that determine oh it seems 所以我们目前其实是在“打补丁”，用模型去判断：“哦，这一轮好像结束了，”或者“哦，那一轮好像开始了。”
like the turn has ended and oh it seems like the turn has started and we're like why are we talking about turns, right? 我们为什么还要讨论“发言轮次”呢？这太不自然了。这是人类在迁就机器的限制。
Like turns again are so unnatural. This is the humans contorting ourselves to the machine and its limitations. And so the obvious thing that you want to 因此，你显然想要实现一个工作方式和我们一模一样的 AI，
accomplish is a model in AI that works much more like you and I do, right? 它能同时处理输入和输出。这也是该领域许多人正努力实现的目标。
That's able to process input at the same time it's processing output. And all of that is of course something that many people in this field are trying to to 当我们转向这种自然、流畅的人机对话界面时，会非常令人兴奋。
run towards. Um I think it's going to be very very exciting as you move to these natural very human fluid like 以前没有人见过这样的东西。例如，虽然现在的 ChatGPT 语音功能已经很神奇了，
conversational interfaces. No one's seen anything like it. Like one thing that that I think about is the the current interaction with you know chat GBT 许多人在通勤时使用它提问，但它有时也让人抓狂。
voice. many ways it's magical, right? So many people use it on their commute, able to ask all these questions, but it also is so frustrating, right? Whenever 当它打破魔力时，你会发现：“我想补充一点，”但它却一直在对你说话而没有听。
it breaks the magic because it's like you realize, oh, I want to like add some followup and it keeps talking over you and it didn't it's just like that is 这不合理。我认为 AI 的核心目的就是为了让我们能够流畅、自然地进行交互。
just it doesn't make sense. And so I think that part of what we need part of like the whole point of this AI is to be 而且我认为，这不仅适用于个人生活场景，也适用于工作场景。
something that you can interact with interact with fluidly and naturally. And by the way, I think it's not just going 我在 Codex 中最神奇的一些体验就是通过语音进行操作。很多人使用内置语音，
to be about the sort of use case, like we kind of think about the the personal use case, but it's also really the work 有些人使用第三方应用。当你开始意识到，
use case. And I think some of the most magical experiences that that I've had with codecs have been when operating it through voice. Like many people, we have 打字写反馈虽然简单，但写一整段话来说明你要什么，
a voice built in. Some people use third party apps for it. Um, and that you just get a very different experience when you 却很痛苦。没人想做那件事，对吧？你只想说出来，并获得实时的反馈循环。这会很棒。
start to realize that like typing a quick message to give some feedback, easy, but like writing out a whole paragraph and everything you want, 那么，让我们简单聊聊模型的改进。几年前有讨论说，
horrible. No one wants to do that, right? You just want to be like saying things and you want the real-time feedback loop and all of that is going 大语言模型要撞墙了，也就是遇到瓶颈。但事实证明那是错的。
to happen and it's going to be amazing. 大家在想，模型还能变多好？何时停止？
So, let's talk about model improvement briefly. Um, so there was a discussion a couple years ago that large language 你有什么想法吗？我认为，当你亲自构建这些模型时，
models were about to hit a wall. um that was wrong. And um something you know 会得到一种直觉，这在外界是很难体会到的，因为我们能看到所有的数据点，
that I'm thinking about is I think we're all thinking about it is how much better can these models get and when will the 以及为了实现这些改进所付出的努力。这里有两个部分的答案。
improvement stop? Any thoughts? Well, I think that this is a place where when 第一，我认为基础科学是最神秘、最重要的
you're kind of building these models, you get kind of a sense and an intuition that I think is harder to get from the 科学发现和实证观察之一。我们能够构建这些模型，
outside because we see all the data points and we see also the work that goes into these improvements. And so that there's two parts to the answer. 且 scaling laws（缩放定律）依然适用，对吧？
One is I think that the fundamental science is one of the most mysterious and important 只要继续投入更多数据、更多算力、更好的架构，模型就会继续提升，其中包含了很多改进。
just scientific discoveries and empirical observations that that I can that I that I'm aware of that I can imagine right that we are able to 但是每次我们遇到“这似乎没有按预期扩展”时，其实都是因为遇到了 bug，
actually build these models and that the scaling laws continue right that it just is the case that you can just keep 或者数学公式不对，或者代码实现与数学公式不匹配等等。
training these models more data more compute better architectures and there's a lot of improvements that go 这一点非常重要，需要去理解。如果我们回溯到这个领域的起点，
in. But every time we've kind of run into a like, oh, this isn't quite scaling the way we expect, it's we have 神经网络本身是在 1940 年代设计的，甚至早于计算机诞生，
a problem, we have a bug, that our math wasn't quite right, that oh, our implementation isn't isn't isn't quite matching the math, whatever the thing 还有人要检查技术栈的每一层。图表上任何奇怪的波动，都可能意味着神经网络
is. And that is, I think, a very important thing to to sort of internalize. And actually if you we've 缺乏抽象，任何微小的错误都会产生连锁反应，并在更深层显现出来。
done studies where you go back to the beginning of the field right that neural nets themselves were designed in like 所以你需要人们深入理解这一切。不过，只要把优秀的团队召集起来，设定明确的使命，
1940s right before computers right as a model of maybe this is maybe this how the brain processes information first 大家愿意做这些磨人的工作，结果就是值得的、是可实现的。
hardware implementation was 1959 with the perceptron and if you look at landmark results in the field that the 所以我认为进展会继续。那么，我想听听你的看法，
landmark results follow this incredibly smooth deterministic path of more compute being poured into them and so 70 如果模型能比今天进步得多。
years of people maybe 80 years now of people saying this stuff is never going to work, never going to scale, going to hit the wall. Hasn't hit the wall yet. 假设 OpenAI 构建了最好的模型，相当于拥有 15 个 PhD 水平、情商极高、
There's still no wall in sight. And so I think that the fundamentals allow it. 从不抱怨且能为你执行任务的同事。
Now the practicality is hard, right? 而对手构建了稍逊、但情商高、具 13个 PhD 水平的。
Actually building these massive supercomputers. It's hard. It's expensive. It's not easy, right? That we have teams that just like work so hard 当大家都达到那个智力水平时，竞争的差异化体现在哪里？
to solve these incredibly hard technical problems. We have our own network protocol that we've had to design. um 因为我们看到模型制造者们几乎是并驾齐驱的。
that we have people who look at every single layer of the stack that there's weird wiggles in the graph and you the way to think about these neural nets is 一个取得突破，下一个紧跟着也会取得突破。所以大家都会变得非常聪明。
that there's like there's no abstractions, right? It's almost like any little piece that's wrong can have a ripple effect that only shows up down 那时还能实现差异化吗？我认为这个答案有几个维度。
there. And so you need people to deeply understand all of it. And yet if you get the right team together, put the right 第一，我认为从商业模式的角度来看，每个供应商都会卖光他们所有的算力。
mission in front of people and people do that grind, the outcome, it's worth it, right? And it's achievable and it's 所有的算力。
possible. And so I think that for those reasons the progress will continue. 好的，没错。我认为我们正走向一个算力不足以满足所有需求的世界。
So then I'd love to hear your perspective if if models can basically progress much further from where they 我们将走向算力驱动的经济体系，每个人都将一直使用这些模型来完成任务。
are today. Um let's say let's say open AI builds the best model and it the equivalent of like something with like 我们可以看到，现在虽然在讨论算力限制，但使用这些 Agent 的人数
15 PhDs with excellent emotional intelligence that doesn't complain and goes out and does stuff for you. Um, and 大约只有一两千万，我们还没有达到全球规模。
then the the next model maker will build a a less good, but it has 13 PhDs and ChatGPT 有十亿用户，但我们还没有把 Agent 级别的能力带给所有人。
it's like pretty good uh, you know, EQ and we'll still go and do things for you. So, where does the differentiation 所以，目前的使用深度和未来的发展相比微不足道。
come in when you get to that level of intelligence? Because we've seen the model makers kind of move in lock step. 即使有不同的厂商、不同能力水平的开源模型、新型云服务，
One makes an advance, the next one comes in and makes the advance. So, they all become that smart. Do they is it 算力依然会是稀缺资源，所有的算力都会被派上用场。
possible to differentiate? Well, I think there are several dimensions to the answer. Um, number one is I do think 至于这是否是一门好生意，是否值得新玩家入局，我的答案是肯定的。
there's a bit of an attractor state where just like from a business model perspective, every provider sells out 因为这是一个巨大的、我们无法完全覆盖的市场，我们需要更多的能量和动力。
all their compute. 但第二点是，它忽视了智能并不是一维的。
Okay. Right. I think that is just like the world that we're heading towards where there just is not going to be enough compute to serve all the demand. 如果你仔细观察，即便你很有天赋，如果从未练习过，
Right? That we're heading to this compute powered economy that everyone's going to be using these models all the time to be able to accomplish tasks of 第一次做演讲也是做不好的。如果你从未使用过电子表格，
interest. And we just see it. It's like right now we're talking about compute constraints and like like the number of 你也没法做复杂的建模。所以我们得出的一个结论是，
people using these agents is like order of 10 million 20 million maybe you know it's like we're not at planet scale 面对不同的行业和领域，我们必须有所侧重。我们不可能同时在所有领域都做到极致。
chatbt is like a billion users right but we haven't brought the agentic power there yet so you're just looking at these factors and the depth of usage is 提升通用智能确实能提升很多方面的表现，但要真正成为领域专家，
also tiny compared to where we're going and so I think that we're just going to be in a world where even if you have 真正成为 PhD，去推动一个行业的发展，这是很难的。
different vendors different capability level open source models all these things these neoclouds like I think that 另外，我想说，理解当你成功做到这一点时会发生什么，是很有必要的。
compute is just going to be the scarce resource and I think that it's going to go go to use. Um so to some extent I 比如回想 AlphaGo，大家记得第 37 手棋，
think that the the like is this a good business to be in and for new entrance to come into and things like that. My answer is actually yes. I think that 那一手棋改变了人们对围棋的理解，现在玩围棋的人比以往任何时候都多，
there is like a huge market um that we are just not going to be able to address and we need much more energy momentum 它激发了人们更多的兴趣。我认为我们也会看到同样的现象。所以深度是
there. Um but a second thing is that it also misses the fact that intelligence is not a undimensional thing right that 永无止境的。科学领域的探索能有多深？有人曾觉得：“我们已经发现了所有的物理规律，已经结束了。”
that If you really zoom in, being good at different domains is something where even if you you have a lot of raw 但我不认为那是我们要迎接的未来。我们要在解决一个谜题后，
intelligence, getting good if you've never practiced like you've never actually done a pitch or something like you're not going to be good at it your 顺藤摸瓜解开另外十个谜题。所以，我认为还有很多事情要做，
first time, right? And that there's lots of different you've never operated a spreadsheet, right? You're not going to be able to succeed at doing some complex 不同的公司之间有巨大的差异化空间。
modeling. And so I think that there is something that we have been internalizing which is that we look across different industries and 整个游戏才刚刚开始。
different domains and we have to prioritize. We can't possibly be great at every single area at once. There is 无论是市场规模的极速扩大，还是我们自身营收的攀升，我觉得没有谁能预料到未来的斜率会有多陡峭。
definitely a lot of like, hey, you just get the general intelligence up and it'll experience a lot of these things, but to really become a domain expert, to 这其实非常有趣。
really be that PhD, and to really be something that can help push forward the ambition of a field, like that's hard. 前天我们刚刚宣布了化学领域的新成果，能够合成新的、更好的反应。
And and by the way, one thing I also want to say is that I think understanding what happens when you successfully do that. I think that having a good mental model of that is 我们在化学领域发表了成果，比如合成更高效的新化学反应，且
important, which is you look at something like rewind to AlphaGo, right? 比如我们在化学领域发表了成果，合成了更好的新反应，且
you remember move 37, this move that like changed people's understanding of the game and then now more people play 几乎未经特定领域优化。深耕一个领域能带来巨变，我们还没开始。
go than ever, right? And that it actually inspired people to do even more. I think we're just going to see that. And so I think that the depth is 现在玩围棋的人比以往任何时候都多，它激发了人们更多的兴趣。我认为我们也会看到同样的现象。所以深度是
never going to stop, right? How deep can you go on science, right? Like I I think that people have thought sometimes that, hey, we found out all the physics, it's 永无止境的。科学领域的探索能有多深？有人曾觉得：“我们已经发现了所有的物理规律，已经
all good. We're all done. And I don't think that that's the future we're signed up for. I think we're signed up for one where we're got to keep find 结束了。”但我不认为那是我们要迎接的未来。我们要在解决一个谜题后，
every time you unlock one mystery, every every time you solve one mystery, it unlocks like 10 more, right? So, I think that there's just going to be so 顺藤摸瓜解开另外十个谜题。所以，我认为还有很多事情要做，
much more to do and tons of room for differentiation across different companies. 不同的公司之间有巨大的差异化空间。
So, I think I'm reading you right and that your belief is um maybe there's a way that everybody can scale up these models, but ultimately the company with 所以，我认为我的理解是对的。你的信念是，也许每个人都有办法扩展这些模型，但最终拥有
the most compute is going to win. And you know, we spoke a couple months ago and you had mentioned that like you were 最多算力的公司才会赢。你记得几个月前我们聊过，你说
asked internally, how much compute should we buy? And you said, "All of it." And they said, "No, really, how much should we buy?" And you said, "No, 公司内部有人问你：“我们该买多少算力？”你回答说：“全部。”他们又问：“说真的，买多少？”你回答：“不，
buy all of it." And and OpenAI is definitely the leader in in buying compute. I mean, we see the the the 全部买下。”而 OpenAI 确实是购买算力的领头羊。我是说，我们看到
money going out. Obviously, a lot of money coming in through investment and and now you you're you've built a business with customers, but there's a 源源不断的资金流出。显然，也有大量的资金通过投资流入，并且你们现在也建立了自己的客户业务，但
lot of money going out. Do you ever wonder, hey, are like, do you ever wonder maybe we're not going to be able to pay all this 你是否曾担心过，作为一个全新的品类，我们可能会无法收回这些巨大的投入？
money back because it's a brand new category? Well, the way that I look at it is on the fundamentals, right? You 我看待这个问题是从基本面出发的。
you need to really look at the fact that the way that compute goes is that it's multiple years out before compute 你需要看到算力的建设需要提前数年规划。
actually arrives, right? Depending on exactly what you're doing. For example, we've been investing in our own chip program now for multiple years. And 比如，我们的自研芯片项目已经投资了好几年，
super exciting progress like you know, we'll we'll we'll have more to announce uh actually pretty soon. Um but the fact 进展非常令人兴奋，很快就会宣布一些消息。
that we're able to do that is something very unique, right? really think about the full vertical integration of the supply chain and I think that the world 但能做到这一点是非常独特的，这需要对供应链进行深度的垂直整合。我们正走向一个
we're heading towards is one where again there's just not going to be enough compute in the world to satisfy all the demand and we see this very concretely 算力依然供不应求的世界，我们能非常具体地看到这一点，无论是
like you look at the exponential I mean rewind to the exponential of chatbt um look at the exponentials we're on now uh you think about the problems that we ChatGPT 的指数级增长，还是我们现在的增长曲线。想想我们能够解决的问题，这其实非常有趣，对吧？
that we are able to solve you know it's it's actually kind of interesting 前天我们刚刚宣布了化学领域的新成果，能够合成新的、更好的反应。
that yeah we just yesterday announced um it was two days ago announced a new 我们在化学领域发表了成果，比如合成更高效的新化学反应，且
result in uh uh basically chemistry and being able to synthesize you know new new improved reaction and all of this is 几乎未经特定领域优化。深耕一个领域能带来巨变，我们还没开始。
without much attention you know the thing I just said of if you go deep in a domain you can really transform it and we're not even scratching the surface 实体经济规模极其庞大。这从我们的增长、人们的付费意愿、以及整个行业的膨胀中可以看得很清楚。
yet and so the way to think about is the economy is so massive right and we see it very concretely in terms of our own growth in terms of what people are 所以我思考最多的是：如何跟上这种需求，以及如何提供
willing to pay and kind of the size and growth of this whole industry and so I think that the thing that I think about 能支持在实体经济中大家想做的所有工作的智能？
the most is how do we meet the demand and how do you actually have something that can help support all of the work that people want to do in the economy? 这是一个非常庞大的命题，我想我们都还没有完全理解它。
And I think that is such a vast thing. I don't think any of us have internalized it yet. Hi everyone, Alex Canitz here. I want to tell you about a documentary 大家好，我是 Alex Kantrowitz。我想告诉大家一部我和 Gravity 合作制作的纪录片，
I've made with Gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous 探讨 AI Agent 安全的未来。为了了解我们是否真正做好了迎接自主 Agent 的准备，
agents, I sat down with MIT professor Romesh Rosker, former White House CIO Terresa Payton, Michelin's group chief 我采访了 MIT 教授 Ramesh Raskar、前白宫 CIO Theresa Payton、
data and AI officer Ambbea Roger Gopal, and Sharon Guy, a former executive at Alibaba. 米其林集团首席数据与 AI 官 Ameenah Gopaul，以及前阿里巴巴高管 Sharon Guy。
They each offer unique insights into this evolving landscape. We conclude with Rory Blendell, CEO of Gravity, to 他们对这个不断演变的领域提出了独特的见解。
discuss the path forward. with gravity leading the way. Join us on this journey. You can watch the full documentary at the link in the show 最后，我们和 Gravity 的 CEO Rory Blundell
notes. 探讨了未来的发展道路，Gravity 将在其中处于领先地位。
Yeah, but if I may, there is a price war brewing. I mean, at least that's according to the reports. It's great to have you here to talk about it. The Wall 欢迎观看这部纪录片，链接在节目介绍中。
Street Journal recently had a report that an upcoming OpenAI model might have 是的，但如果可以的话，目前确实在酝酿价格战。至少报道是这么说的。Greg，很高兴今天能在这里跟你讨论。
significant price cuts. And so again, like how can you know if it requires so 《华尔街日报》最近报道说，即将推出的 OpenAI 模型可能会有
much resources to serve this demand and it is growing demand um in an environment where there might be price 大幅度降价。所以，既然提供这种需求需要如此多的资源，且需求还在增长，
cuts, how do you make that math work? 而在价格可能会下降的环境下，
Well, again, I look at it from a different angle. So if you look at the whole history of what we've done, we 你是怎么把这笔账算清的？
actually have been increasing the intelligence cutting price right for a fixed amount of intelligence and people somehow just like like the Japanese 好吧，我同样会从不同的角度来看待。如果你看我们做过的事情，
paradox just keeps happening. And so I think frontier intelligence will always be something that is going to be you know it's always going to be the priciest thing but I think that a year 实际上我们一直在提升智能水平并降低价格，也就是固定智能水平的成本在下降，
from now that level of intelligence is going to feel pretty mundane and like you know going to be much more available. Um, and I think that that the 所以我认为前沿智能永远是最贵的，但一年之后，今天这种高端的智能就会变得平常，
the world that we're in is one where people are starting to really think about value. And it's actually been a 且变得更加普及。另外，
very interesting shift where over the past, you know, first quarter, maybe up until now, people have just been like 我们所处的世界，大家正开始真正思考其价值。这其实是一个
this AI agent stuff, it's all new. We need to bring into our enterprise. Like, we don't want to be left behind. How do we be part of this this future? And now 非常有趣的转变。在过去的第一季度，甚至在此之前，大家觉得
people are like, okay, like let's make sure this actually delivering ROI and value. And I actually think that's a great place to be, right? because people AI Agent 这一切太新了，我们必须引入企业内部，不想被落下。而现在，
are asking the right questions. And I hear this I I had some customer meetings today where people were saying exactly this. They were like, "How can we have 因为大家在问正确的问题。我今天参加了几个客户会议，他们问的正是这个：
even just like good spend controls? How can we have observability?" And I I think we literally today just released “我们如何实施更好的支出控制？如何实现可观测性？”刚好我们今天
spend controls. So, you know, it's like Okay. 发布了支出控制功能，这很巧。
Exactly. We're we are really investing hard in enterprise readiness and the tools that our customers are telling us 没错。我们在企业级功能上投入了巨大精力，提供客户告诉我们他们所需的工具。
that they need. And I think that that for me is the shift that we've also been going through as a company is really not 这种转变也反映了我们作为一家公司的变化。我们不再只是
just thinking about hey we're just going to release models and you know have a model really thinking about the endto end of the business. How do we bring 发布模型，而是从端到端的业务角度去思考：如何把这些
this into solving real problems for real customers and that is happening so 模型带入客户的实际场景，解决真实问题。这正在每一个行业
quickly across every single industry and the number of different companies that still feel like they're wrapping their 迅速发生。许多公司还在摸索如何最好地利用
mind around how to best make use of these models we're learning at the same time. I think it's just so early in this whole game to me the the like the 这些模型，我们也在一同学习。现在还处于非常早期。
absolute size of the market growing so quickly, our own revenue ramp growing so quickly. I think it's still just like none of us are anticipating how steep 这其实是一个非常早期的阶段，
that's all going to go. 看未来形势如何。
Are you going to cut prices? 那么，你们会降价吗？
So again, the answer is always yes, right? But it's about like I think that what's going to keep happening is that we're going to have frontier models. Um 答案自然是会。但正如我说的，前沿模型会不断推出。
I don't think there's going to be like a massive shift in in the short term. I don't think that that is the kind of thing that's going to happen. But I think the thing you should anticipate is 短期内前沿模型不会有巨幅降价，但我认为大家可以预期的是，
that over a year-long time horizon to get to today's level of intelligence that feels very premier, it's going to 一年之内，今天看起来非常顶尖的智能水平，会变得无比廉价。
be much cheaper. But there's going to be a new thing that is going to be so much better and you're going to be like, why would I ever use this other one, right? 但到那时，又会有更强的新模型，你会想：“为什么用旧的？”
It's just it's just how it's always going to be. 这其实一直都是这样发展的。
So Sadia Nadell has had some interesting tweets and interviews recently. He recently said, "The model is becoming a 微软 CEO Satya Nadella 最近有一些很有意思的推文和采访。
commodity and the valuable asset is company," or this might be a paraphrase, "The valuable asset is a company specific AI system that continually 他说：“模型正在成为商品，最有价值的资产是企业专属的 AI 系统，它能不断基于你的数据进行学习。”
learns from your data." Um, what do you think about that? And is it weird to be competing with Microsoft now? 你怎么看这个观点？现在和微软竞争会感觉很奇怪吗？
Well, look, I don't think that there's any layer of the stack here that is going to just kind of be removed from 好吧，我不认为技术栈中的任何一层会直接从
the value chain. I think that these things multiplied together. And if you think about the most the base layer of 价值链中消失。我认为它们是相辅相成的。如果你看最底层的
compute right that is something where it's just like no compute no AI and to some extent you could say oh 算力，也就是没有算力就没有 AI。在某种程度上，你可以说
comput is commoditized it's just flops who cares about it but in reality like you look at you look at today's chip stocks you look at the uh you know 算力已经被商品化了，不过是算力服务，谁在乎呢？但实际上，看看今天的芯片股，看看
people who are selling compute kind of what what is what the market is valuing people at and they see that there's a fundamental asset here that is just so 算力服务商，看看市场对它们的估值，就会发现这其实是一个至关重要的
critical and I think that is because it is a revenue center it is something that anyone who's building AI has to rely on 基本资产。我认为这是因为它是一个核心收入源，任何构建 AI 的公司都必须依赖它。
and that there's a bunch of very interesting dynamics in terms of the efficiencies that you can squeeze out and the margins, all these things. But 而且这里面涉及很多关于提升效率和毛利的有趣动态。
fundamentally, even though it's like you can kind of squint it and say it's commoditized, it's not. It's it's it's not that that the value goes away. It's 本质上，哪怕你可以把它看作是被“商品化”的东西，它其实并不是。它的价值不会凭空消失，
not that the margins go away. It's like something that the market will reward because it has fundamental value and that the importance of it's going to go 毛利也不会不见。市场会给予它回报，因为它具有底层价值，且重要性会随时间增加。
up over time. You can see that with some of the prices that people are paying for H100s, right? Hoppers are, you know, kind of, you know, not not obsolete, 看看人们为 H100 支付的价格就知道了。虽然 Hopper 已经是上一代芯片，
right? They're they're they're a a previous gen chip and uh nor in any normal situation where you're not totally supply constrained, no one would 如果供求平衡，在正常情况下可能没人会去买，
be buying them. But instead, the market prices are up relative to to where they were before. So there's this inversion that's happening and again I think it's 但现在它的价格不降反升。所以市场发生了一种倒挂，而且我认为这
going to keep happening where because everyone has this avalanche of demand that you're going to see prices and 会继续发生。因为每个人都有排山倒海的需求，你会看到价格和
margins and all of these things AC continuing to increase at various levels of the stack. And I think the same kind of applies for 毛利在技术栈的各个层级继续上涨。我认为这同样适用于
models where the models themselves are also again they're not there's there's a lot of competition there and I think 大模型本身。虽然大模型面临非常多的竞争，而且
that's very good. I think it's good for the enterprise. I think it's good for customers, consumers. Um but I think that there's a lot of areas where for 这很好，对企业、对客户、对消费者都大有裨益。但也有很多领域，比如
example our models have always been the sort of smartest ones, right? The ones that are able to solve these incredibly hard problems. I think we're just 我们的模型始终是最聪明的，最能解决极其困难的任务。我们才刚刚
starting to reach a phase where you're going to see the transformative impact from that, right? It's like if we're 开始迈入一个能看到大模型带来颠覆性影响的阶段。比如，如果我们
really able to speed up science through models, the smarter the model, the faster it's going to go. And it's very 真能通过模型加速科学研究，模型越聪明，研究进展就越快。这
very different from a model that has a conversational interface that you're able to, you know, is able to book your travel, right? Or organize your 和用一个对话模型帮你订机票、管理一下日历，是完全不同的维度。
calendar. So that's also a dimension I think we're going to do a very good job in. But I'm just saying it's it's a different area. Um and then I think that 因此在这个维度上，我们也能做得非常好。但这属于不同的领域。另外，我认为
the question of well how do you actually connect the intelligence to your own customers right to real value to you 如何把智能转化为客户的实际价值，
have all these enterprises that have built incredible businesses in different domains and it's a huge thing and it's not something where if you don't have 各个企业在不同领域积累了宝贵的业务经验，而没有这些经验是做不好的。
domain expertise that you're just going to be able to do right and part of it is that you need you think about regulated industries you think about any area 比如在受规管行业，或者任何
where there's like you know think about education where you have a parent you have a teacher you have student you have these different parties that need to 需要家长、老师、学生多方深度协同的教育领域。
interact in very thoughtful ways for all of these areas, all of these domains that there's a lot of value to be built 在所有场景下，打通工作流并合理编排模型，具有巨大的价值。
by being in that area and thinking about how the workflow should work, how these models should be orchestrated. And so I 所以，我认为我们需要作为一个完整的生态系统共同努力，
I really think that there's more than enough to go around and I think that we have to work together as a whole ecosystem in order to deliver the kind 才能释放出这些系统的全部潜能。
of value that I think is possible from these systems. 我真的认为大家有足够的成长空间，
Okay, just to go back to the SA point one more time, uh he's called models a commodity. He's trying to build his own 好的，回到 Satya 的观点。他把模型称为“商品”，并努力构建微软的前沿智能。
frontier intelligence. He's telling potentially your customers, hey, you got to come work with us because we're going to help build these loops that will 他告诉你们的客户说，要来跟微软合作，因为他们能构建基于数据的学习闭环。
learn from your data. Um, he's got access to your IP, I think, till 2032. 此外，据说微软到 2032 年前都可以访问 OpenAI 的知识产权。
So, how does it make you feel to hear this coming from Saudia? 听到 Satya 这么说，你有什么感受？
Look, I think that the most important thing that is happening right now is the usage of AI in the economy to really 我觉得现在最重要的事情，是将 AI 真正带入实体经济并完成转型，造福每个人。
transform the economy and to uplift everyone. And so, I think that that is something that I'm really focused on. 这才是我们关注的焦点。
And the more that people are trying to make that happen, I think that that's better for everyone. 越多的人为此努力，对每个人就越好。
Um, GPT 5.6 is rumored to be on its way. 网上有传闻说 GPT-5.6 即将推出，
Supposed to be this just a Twitter rumor, but I'm going to read it to you. Um, always the best 虽然这只是 Twitter 上的小道消息，但我还是想读给你听。毕竟小道消息总是最精彩的。
rumors. Three times cheaper than Fable up to 1.5 million token context. Uh, 比如“价格比前代便宜三倍，多达 150 万 token 上下文，
stronger agentic coding workflows. Um, how much of that is true? What should we 拥有更强大的 Agent 编程工作流”。
expect for GPT 5.6? 这里面有多少是真的？我们对 GPT-5.6 可以抱有什么样的期待？
I mean, look, you should always expect better, faster, smarter, the whole thing. 我只能说，你可以一直期待更好、更快、更聪明的产品，仅此而已。
So, everything confirmed. 所以你是全部默认了。
Definitely believe everything you read on Twitter. Yeah, maybe not. 千万别相信你在 Twitter 上看到的所有东西，大部分都是假的。
That has actually been a source of problems in my personal life. Um, okay. 确实，这在我的现实生活中也造成过困扰。
So, uh, I want to end on health. Um, you brought it up a couple times. You actually had a question in the audience about it earlier. Um, you know, 好的，我们以健康话题结束。你刚才提到，有时我们读到一些媒体报道，
sometimes there's a story and you read it and you say to yourself, I know this person is speaking to the media and I 听起来似乎是真的，但总觉得里面漏掉了一些细节，不确定这是否代表了未来。
know that what they're saying sounds like maybe it's true. Um, but there's something wrong with the story and we're 我最近读到了几篇这样的报道。其中一篇是关于你的朋友，GitLab 首席执行官 Sid Sijbrandij。
not going to see more of it. Um, and I've read a couple of those recently. 他患了癌症，于是做了所有能做的诊断测试，收集了大量数据，
Um, one is I think is it your friend the GitLab CEO um, Sid Sberage? He had um he 并在一些专门开发的工具辅助下，把这些数据输入 ChatGPT。
he got cancer and used uh he got all the diagnostic testing uh he could have so 虽然不知道“治愈”这个词是否妥帖，但在一定程度上击退了癌症。
just went out and tested like crazy and fed that data into chatt with the assistance of some people who had built 还有个故事是关于澳大利亚一只叫 Rosie 的狗，大家听说过 Rosie 吗？这是一个非常神奇的故事。
purpose-built application for it and was able I don't know if cure is the right word but to beat back the cancer to a 有主人对患癌的 Rosie 进行了活检，把基因突变数据输入 AlphaFold，
degree. There was also this dog Rosie the dog in Australia. You guys heard of Rosie? like the craziest story where 并在 AI 协助下设计了一款 mRNA 疫苗接种给它，
this guy I'm gonna get some detail wrong but a guy um biopsied his dog which had 最终肿瘤缩小了，狗狗又能重新跳过桌子。当我们思考
cancer um ran the mutations across of across Alphafold and then was able to AI 医疗的未来时，这到底是极少数特例，
design an mRNA vaccine that he injected into the dog um with the assistance of chatbots to build this thing which ended 还是在未来会成为常态？
up being able to jump over tables again and the tumor shrunk. Um, when we think 这会成为未来的常态吗？
about the future of AI and health, um, help us sort out the truth with this 绝对会成为常态。
question. Are these a couple of outliers that made good headlines, but there was something about the story we weren't hearing, or is this going to become 绝对会。
standard in the future? 这会成为未来的常态吗？
Absolutely going to become standard. 绝对会成为常态。
Absolutely. And it's I I personally have a number of friends who have done very 绝对会。我个人身边就有很多朋友在做类似的事情，
similar things of get the data right your health diagnostics 也就是获取自己的健康诊断数据，
and use codecs right use these models to get insights from them and I think that there are many people like I think that 并使用 Codex 或大模型来获取洞察。
there's about 230 million people each week who use chat GBT for health queries 事实上，每周大约有 2.3 亿人在使用 ChatGPT 进行健康咨询，
right and that's been that's this is like a staggering scale Right. And this is pe these are people sometimes you 这是一个非常惊人的规模。他们有时是上传扫描件，
upload a scan. Sometimes you have doctors who are telling you conflicting information. And I think that we've been in a world where patients are not 有时是在面临医生给出冲突结论时进行排查。过去患者一直处于相对被动的状态。
empowered, right? Patients have to be the doctor, right? You're the decider. 你必须成为最终的决定者，自己承担责任。
You are accountable, right? You know, doctor makes a mistake and you're going to be paying the price for the rest of your life. Like it's just it's a very 如果医生犯了错，你将用余生买单。这是很不一样的决策机制。
different kind of incentive. And this is very personal for me. you know, my wife has a number of health conditions and I think that we've just been we've not 对我来说，这也是非常切身的事情，
like I don't even know how we'd be able to manage many of her conditions right now without the use of of chat. And I think we're just at the beginning of 我的妻子身患多种健康状况，如果不借助 ChatGPT，
this journey, right? That I think that the degree to which even if you have the best medical team, 我甚至不知道该如何管理和应对她的日常状况。而我们在这个旅程中才刚刚开始。
the best access, the best best experts, there's only so much that can be done, right? That you think about the things 因为哪怕你拥有最优秀的医疗团队、最顶级的专家，人类的能力目前也依然是有局限的。
that are just outside of the reach of humanity or even just sometimes it's like someone didn't even read the chart, right? and kind of missed a detail. All 有许多病症是目前无法治愈的，或者有时仅仅是因为医生太忙，漏掉了病历上的某处细节。
of that we should be able to improve massively through these tools. And so I think that the personalized medicine and 所有这些问题，我们都应该可以通过这些工具来实现巨大的改善。因此，我认为个性化医疗以及
sometimes it's going to be about drugs and drug discovery that are of you know for for mass market but sometimes it'll 针对大众市场的新药研发，但有时甚至也包括刚才提到的像罕见病
be even for the kind of NF1 things like the the disease um diagnoses that I mentioned earlier today. Sometimes it 诊断。
will be for just like trying to understand conditions and trying to come up with with new potential therapeutics. 有时它也适用于
All of that we're seeing it happening right now in front of our eyes. It's not theoretical. It's really happening. And so one of the most I think it's like one 仅仅去理解病症并寻找新的潜在疗法。
of the most astounding possibilities of AI is how much it can improve our health. And you think about the ripple 这一切都已经发生在我们眼前。这并不是理论，它确实正在成为现实。我认为 AI
effects of the system, right? Where so much spending on the health care system happens right now. that's a massive part 最令人激动的可能性之一，就是它能多大程度地改善我们的健康。想想
of the economy and that if you're actually able to help people prevent issues, right, to to get ahead of 医疗系统的支出非常庞大，占了经济的巨大比重。
potential, you know, health health problems, that's something that actually then alleviates a lot of burden, a lot of strain. And 如果能帮人们预防疾病、提早发现潜在健康隐患，就能减轻社会巨大的医疗负担。
we're in a world where doctors are burned out, nurses are burned out, like there's like a real crisis that's happening in front of us. And I think AI 我们知道现在的医生在超负荷工作，护士也在承受巨大的压力，这是一个实实在在的危机。而我认为 AI
will be able to help with all of that. 能在这些方面提供很大的帮助。
Like we have that potential if we deploy it and use it wisely and well. And so I 只要我们明智、妥善地部署和使用它。
think that applying AI to medicine, like that's something that I is really a personal motivation for me in in 将 AI 应用于医疗，是我投身这个行业、以及我们创办 OpenAI 的核心动力之一。
thinking about this whole journey of of what we're building, what we're trying to do with Open AI. And I'm hopeful that we as a as a world and a community can make the most of that. 我希望全世界和我们社区能共同把握住这个机遇。
Let's hope. I think we will. I'm very very confident. 让我们共同期待，我相信一定可以。我非常有信心。
Greg, thank you so much. Greg，非常感谢你。
Thank you. 谢谢。
Great. Thank you. Thank you so much. 太好了，非常感谢。
Oh my god. Thank you everyone. You have a good time today. 天啊，谢谢大家，希望大家今天玩得开心。
Thank you. Should we do it again next year? 谢谢，我们明年还要再办吗？
You going to come? Yeah. 你们会来吗？会？太好了。
All 好的。