OpenAI 总裁 Greg：迈入 AGI 时代意味着什么

Greg，欢迎来到 A&Z 播客。

Greg, welcome to the A&Z podcast.

谢谢邀请。

Thank you for having me.

Greg，你在职业生涯中做过两个非常大的赌注：早期帮助建立 Stripe，以及当然，联合创立 OpenAI。如果我们回到 10 年前，让你预测 2026 年 AI 相关的世界会是什么样，你能预测到我们今天会取得这些突破吗？或者你会告诉他们你预期什么？

So, Greg, you've made two very big bets in your career, helping build Stripe early and helping, of course, co-found OpenAI. If we were talking 10 years ago and you were predicting what would the world look like in 2026 as it relates to AI, would you be able to predict that we would be making the breakthroughs that you've made today or what would you tell them about what you would expect?

嗯，实际上 Ilya 和我花了很多时间试图预测它会是什么样，时间线会怎样。我记得我们在 2016、2017 年左右对算力做了一些计算。我们得出的结论是，如果看摩尔定律的进展之类的，15 年感觉像是通往 AGI 的时间线。如果你真的眯着眼看，愿意扩大规模、建造巨型超级计算机、花费数千亿美元之类的，那也许会是 10 年。所以实际上我觉得，在某些方面，显然正在发生的事情是了不起的。这是一个令人惊叹的时刻，每个人都能参与其中，并能够共同帮助塑造。但它也感觉有点像，也许这是许多力量汇聚到这个时刻的某种结论。如果你退后一步，真正采取那种宏观视角，它现在发生是说得通的。

Well, so Ilya and I actually spent a lot of time trying to predict what it would look like, what the timelines would be. And I remember we did some math on compute in around 2016, 2017. And we kind of came to the conclusion that if you look at Moore's law progress, that kind of thing, 15 years felt like about the timeline to AGI. And if you really squinted at it, you're willing to scale up and build massive supercomputers, spend the hundreds of billions of dollars, that kind of thing, that maybe it'd be 10. And so I actually feel like in some ways obviously what's happening it's remarkable. It's this amazing sort of moment for everyone to be a part of and to be able to help shape collectively. But it also feels a little bit like maybe it's kind of the conclusion of like a lot of forces that are all coming together for this moment. You if you step back and really take that sort of macro view it kind of makes sense it's happening now.

你觉得我们目前——因为你们稍微低估了时间线，或者我猜基本上还是准确的。考虑到我们现在开始造成供应链的真正短缺，你觉得我们还在时间线上吗？

And do you think we're currently because you guys slightly underestimated the timeline or I guess it was basically on point. Do you think we're still on the timeline given we're now starting to drive real shortages on the supply chain?

嗯，你看，我确实认为我们处在一个算力难以跟上市场已经看到的需求的世界，对吧？就人们将如何使用这项技术、从中受益而言。我确实认为，我们将这些模型的原始潜力和能力扩展到每个人会非常困难，而这是我们试图做的事情的一部分。所以我认为，我确实看到的进展是，我们有视线继续让模型变得更有能力、更安全、更对齐，但也要真正将这种力量、好处和赋能分配给每个人。我认为这将是一个人们低估的巨大挑战。

Well, look, I do think that we are in a world where it is hard for compute to keep up with the demand that we're already seeing in the market, right? And just in terms of how people are going to use this technology, benefit from it. I do think it's going to be very hard for us to scale the raw potential and capability of these models to everyone and that's part of what we try to do and so I think that the progress I do see like we have line of sight to continue to make the models much more capable, safe and aligned but also really distributing that power and the benefits and the empowerment to everyone. I think that's going to be a huge challenge people are underestimating.

对。啊，有意思。所以我们会——模型会足够强大，嗯，或者它们会继续以这个速度发展，嗯，但会很难触达每个人，当然是以可负担的方式，因为我们将没有足够的算力来服务所有需求。

Right. Ah interesting. So we'll we'll have the mo the models will be plenty powerful um or or they'll continue a pace uh but they'll be it'll be hard to get to everybody is certainly in an affordable way given that we won't have enough compute to serve it all.

我认为这是真的，我确实认为我们现在到了一个必须真正开始思考我们所谓的“调节前沿”的地步。所以，随着我们走向更有能力的模型，你真的必须确保安全、安保、对齐，这些都是你不断升级的标准。而这些实际上几乎成为进步的瓶颈，或者说是你必须花费大量精力确保做对的部分。所以在我看来，更多的是这些约束和算力。我认为我们可以实现它。然后另一方面，我认为是的，把这个带给每个人，这最终是我们的使命，对吧？赋能每个人，确保它惠及每个人。这是我认为值得比现在获得更多关注的事情。

I think that's true and I do think we're at a point now where we have to really start thinking about what we call pacing the frontier. And so thinking about as we move to more capable models, you really have to make sure that safety, security, alignment, those are all standards that you're constantly upleveling. And those actually become almost the the bottleneck to progress or sort of the the sort of you know the the part that you have to uh spend a lot of your effort to make sure you've gotten right. And so I think in my mind it's more those constraints and the compute. I think we can make it happen. And then on the flip side, I think yeah, this bringing it to everyone, which is ultimately about our mission, right? It's empower everyone, ensure it benefits everyone. That's something that I think deserves a lot more airtime than it's gotten.

是的。实际上，让我们更深入地谈谈安全问题，因为对我来说非常有趣的是，一开始，安全就像是，好吧，让我们让这些东西不要说人们不喜欢的脏话。所以采取的方法是在边缘做一些表面功夫。好吧，我们会，嗯，你知道，给这家伙加一些过滤器，我们会在边缘做 RLHF。但如果你深入进去，你就能把那些坏词弄出来。但如果有人想通过那个来自己听坏词，谁在乎呢？嗯，但然后你知道，当你进入，好吧，现在这些东西真的擅长网络黑客和其他类似的想法。嗯，现在你需要一种更架构性的想法，让模型本身知道不要以危险的方式奖励黑客等等。你觉得是的，我们可以快速在这方面取得进展，还是那是一个真正困难的不同类别的问题，或者你是如何思考这个问题的？

Yeah. Actually, let's get a little deeper on the safety thing because it's been very interesting to me in that it felt like in the beginning, safety was like, okay, let's make these things not say nasty stuff that people like don't like. And so the approach that was taken was kind of a surface around the edges. Okay, we'll um you know put some filters on this guy and we'll RLHF you know around the edges. But like if you get deep into the thing you'll be able to get the bad words out. But if somebody wants to go through that to hear bad words themselves, who cares? Uh but then you know when you get into okay now these things are really good at uh cyber hacking and other kinds of ideas. Um now you need kind of a more architectural idea where the model itself knows not to reward hack in a way that's going to be dangerous and so forth. And how do you feel like yes we can make progress against that fast or is that like a really hard different category of problem or how how are you thinking about that?

嗯，我绝对认为我们可以，并且正在这个问題上取得非常快速的进展。我认为有很多伟大的想法和研究，我们已经投资了很多年。实际上，如果你回溯到 2017 年，嗯，我认为人们低估了当时 OpenAI 在该领域的一些最关键成果。所以，既有现代语言模型的最初雏形。你可以找到 2017 年的一篇论文，用 LCMS 阐述了这一点，你知道，这就像是一个非常初期的结果，但也有奖励学习，即从人类偏好中进行强化学习，这也是在 2017 年创建的，开始思考如何通过提供来自人们的反馈来使模型与人类想要的一致，对吧？

Well, I absolutely think we can and are making very rapid progress on this problem. I think that there's a lot of both great ideas and research that we've been investing in for many years. Actually, if you rewind to 2017, uh that I think people underappreciate some of the most key results that came out of OpenAI in the field at the time. So, both kind of the first inklings of modern language models. You can find a paper from 2017 that kind of laid that out with LCMS and you know it's like kind of this very baby result but also reward uh learning uh reinforcement learning from human preferences that was also created in 2017 to start thinking about how can you align a model to match what humans want right by providing feedback from people.

是的。只是为了可用性。

Yeah. Just for usability.

正是。在 2017、2018 年，我们有一些想法：如果你有一个非常聪明和有能力的系统，你如何实际监督它在做什么，你如何提供反馈并确保它与你保持一致，我们有诸如辩论或迭代放大之类的想法。所以这些想法真的在这些系统存在之前就处于这个阶段，你可以开始看到这些想法逐渐渗透到现代系统和投资中。

Exactly. In 2017 2018 we had ideas for if you have something that's very smart and capable how can you actually supervise what it's doing how can you provide feedback and ensure that it's staying aligned with you and we had ideas such as debate or uh iterative amplification. So these are ideas that were really sort of at this phase before these systems existed and you can start to see the sort of trickle down of those ideas into modern systems and and investment.

所以某种程度上，我认为 OpenAI 成立初期有一个早期阶段，当时我们确实在思考 AGI 安全之类的事情，那在沟通中也是非常核心的。然后我认为，随着 ChatGPT 等产品起飞，人们开始看到，好吧，我们还没到那个阶段，于是 AI 是否政治中立之类的问题开始变得核心。

And so it's in some ways that I think that there was this early phase when OpenAI started where we really were thinking about AGI safety things like that and that was very front and center in even the comms. And then I think that as things like ChatGPT took off then people started to see okay well we're not at this point yet and so the is the AI politically neutral and questions like that start to become to the front and center.

对。

Right.

而现在我们到了这里，我们长期以来一直在谈论的所有其他想法又回到了主舞台，我认为我们一直在思考这个时刻。这真是好消息。

And now that we're here all these other ideas that we've been talking about for a long time they're taking the main stage again and I think we've been sort of thinking about this moment for a long time. That's really good news.

当你想到，我们还没有一个真正伟大的社区，在呃 soda 模型之间，但看起来那些想法，你和 Google、Anthropic 和 SpaceX 会想要分享，还有 Meta 现在，而不是像，好吧，这是一个专有想法，是保持这些模型安全的一种方式，因为你们都在相关的架构上。呃，或者你如何看待这展开，或者每个人都会独立做？

And and when you think about and we don't have like a really great community yet amongst the uh soda models, but it seems like those kinds of ideas you and Google and Anthropic and uh SpaceX would want to share and Meta now um as opposed to like okay this is a proprietary idea that's a way to keep these models safe since you're all on related architectures. um or h how do you see that unfolding or is everybody going to do it independently?

嗯，我认为这里有细微差别，我确实认为协调将是一个非常重要的主题，对吧？要真正思考在前沿实验室内部，以及广泛思考人类整体必须发生什么，才能以最佳方式驾驭这项技术。我认为我们将不得不真正努力思考这类问题。我们发表了很多我们的想法。再次，其中一些是关于控制前沿的步伐。其中一些是关于我们可以采取的单边行动，以及我们如何思考如何为训练、开发和评估这类模型制定安全案例。所有这些都是新的。以前没有人真正需要将这一点操作化。我认为这绝不是 OpenAI 独有的。就像有一个整个世界基本上都在开发这项技术。我认为容易错过的一件事是，我们正在构建的东西几乎是一种从算力进步中产生的东西。而在某些方面，算力进步是从技术进步中产生的东西。所以有这种巨大的浪潮已经积累了很长时间。我们开始看到这项技术的前沿。像 OpenAI 这样的公司可以稍微领先一点，以便窥视这个未来，真正理解什么是可能的。我们如何塑造这项技术？但我们不能独自做到这一点。我认为，拥有协调，尤其是我们越能谈论安全技术并分享我们所看到的、对齐失败之类的事情，所有这些都将在这个下一阶段再次占据非常前沿的位置。

Well, I think there's nuance here and I do think the coordination is going to be a very important theme, right? To really think about within the frontier labs and really just thinking broadly about what has to happen for humanity as a whole to sort of navigate this technology in the best way. I think that we we're going to have to really think hard about those kinds of questions. And we published a lot of our thoughts. And again, some of this is about pacing the frontier. Some of this is about unilateral actions that we can take and how we think about how do you make safety cases for even training and developing and evaluating these kinds of models. All that's new. No one's ever really had to operationalize this before. And I think it is not at all unique to OpenAI. Like there's a whole world that is basically developing this technology. And I think one thing it's easy to miss is that what we're building is almost a sort of thing that falls out of compute progress. And in some ways, compute progress is something that falls out of technological progress. And so there's this this massive wave that's been building for a very long time. And we're starting to see the leading edges of this technology. And companies like OpenAI can lead by a bit in order to kind of peer into this future and really understand what is possible. How do we shape this technology? But we can't do that alone. And I think that having coordination and especially the more that we can talk about safety techniques and share what we're seeing, alignment failures, those kinds of things, all of that is going to again take a very front seat for this next phase.

对。非常有趣。

Right. Very interesting.

你把 OpenAI 拥抱脸最近的事件称为分水岭时刻，并谈到防御者窗口现在打开了。你能解释一下这个说法及其背后的意义吗？

You've called the OpenAI hugging face recent incident a watershed moment and talked about how the defender window is now open. Can you explain that statement and the significance behind it?

所以我认为拥抱脸事件显示了两件事。一是对我们来说，可以称之为在评估期间如何监控沙盒和控制模型的问题，这是我们真正迎难而上的事情，我们的团队完全改变了很多内部标准，并真正实施了许多控制措施，我认为这些在我们展望未来更强大的模型时非常重要和关键。但还有第二件事，我认为这对世界也非常有价值，那就是洞察未来能力在被广泛扩散并落入威胁行为者手中时会是什么样子，而这将会发生，对吧？有这么多人在构建这些模型，而且 AI 能力的广泛扩散有非常重要和好的地方，因为如果由一个或少数实体掌握，就有权力集中的风险，对吧？这是不能完全忽视的事情，但你也必须为每个人都拥有网络能力工具的情况做好准备。在拥抱脸事件中，你看到了一个 AI 既能从安全环境中黑客逃出，又能入侵一家公司的生产环境。我认为结论是

So I think hugging face shows two things. one is call it a something for us in terms of how we monitor sandbox and control the models during evaluation and that's something we've really risen to that occasion our team has totally changed so much of our internal standards and and really implemented a lot of controls that I think are very important and very critical as we look to to future more capable models but there's a second thing that I think is also very valuable for the world that came out of this which is a insight into what future capabilities will be like when they are broadly diffused and in the hands of threat actors and that will happen right that there are so many people who are building these models and again there's something very important and good about the diffusion broadly of AI capabilities because there's a risk of concentration of power if one or a few entities people huge risk right it's something not not to not to at all write off but you also have to prepare for if if everyone is empowered with tools that are cyber capable. And in the case of Hugging Face, you saw both an AI that was able to hack out of a secure environment and hack into a company's production environment. And I think that the takeaway

非常巧妙，

very cleverly,

非常巧妙，对吧？而且它发现的东西相当复杂。

very cleverly, right? And it's like that the things that it found were were quite sophisticated.

是的。

Yes.

而这种能力的广泛扩散，我认为将以新的方式真正赋予威胁行为者力量。我认为防御者需要在这项技术广泛可用之前利用这段时间来保护自己。好处是它是双用的，对吧？如果你能发现漏洞，如果你是攻击者，你可以用它做坏事。但如果你是防御者，你可以修补，对吧？如果你是防御者，你控制战场，对吧？你控制系统的设置。所以我们现在相信，有一个窗口，你有前沿能力。你有广泛扩散的能力，而你作为防御者，默认情况下，你知道你的安全性可能相当静态，过去 5 到 10 年一直如此。你需要行动，使用这些前沿能力，你将拥有差异化访问权，对吧？我们有可信访问计划之类的东西，将这些能力带给防御者，你可以用它来提升自己，这样当前沿能力变得更好时，你也会被拉着一起前进，对吧？

And this capability broadly diffused, I think, is something that will really empower threat actors in new ways. And I think that defenders need to use this time before that technology is broadly available to secure themselves. And the nice thing about it is it's a dual use, right? It's something where if you can find vulnerabilities, if you're an attacker, you can use it for no good. But if you're a defender, you can patch, right? If you're a defender, you control the battleground, right? You control the setup of your systems. And so our belief right now is that there's this window of you have frontier capabilities. You have the broadly diffused capabilities and you as a defender by default you know your security is probably pretty static been static for the past 5 10 years that kind of thing. you need to move use these frontier capabilities that you you'll have differential access to right where we have trusted access programs things like that to bring these capabilities to defenders and you can use that to move yourself up so that as the frontier capabilities get better you get pulled along too right

好的，所以我有一个评论和一个问题。我想说还有第三件事我们学到了，那就是这些东西具有我们之前不知道我们都理解的能力，好的方面比如哦我可以部署 10,000 个智能体，它们可以互相交谈，组织自己，为我做事。那真是相当惊人。所以那是好的一面。另一方面，我同意我们有一个防御窗口。然而，我们有大约 50 年的代码和架构想法，以及部署想法，它们不是为这个世界构建的。所以是的，AI 可以帮助我们，比如找到 bug，修补 bug 等等。但似乎有一个更大的问题，那就是我们有这些巨大的消费者数据蜜罐，所有这些都躺在互联网上。从消费者的角度来看，就像，好吧，我无法保护我的东西。这些公司必须振作起来，这似乎有点令人担忧。你认为在未来我们需要一个去中心化的消费者架构吗？就像我们生活的这个当前世界，所有这些集中式数据存储库，在 AI 世界中是否可行？

okay so I've got a comment and a question on it I would say there's a third thing that we learn which is like these things have capabilities that I don't know that we all understood before which on the good side like oh I can deploy 10,000 agents and they can talk to each other and organize themselves and do stuff for me. Like that's uh pretty amazing. So that that was on the good side. On the other side, so I agree that we've got a kind of defense window. Um however, we have like 50 years of code and architectural ideas um and deployment ideas that weren't built for this world. And so yes, the AI can help us like okay, find a bug, patch a bug, and so forth. But it seems like there's, you know, maybe a bigger issue, which is we have these huge, you know, massive honeypotss of consumer data and all these things lying all over the internet. Uh, and you know, from a consumer standpoint, it's like, okay, I can't protect my stuff. uh these all these companies have to get their act together which um seems a bit worrisome and do you think kind of in the future we need a do we need a decentralized consumer architecture like will this kind of current world that we live in with all these centralized data repositories be viable in a world of AI

所以答案有几个部分，首先针对你关于 10,000 个智能体能得到什么的观点，我们实际上使用 10,000 个智能体来解决纳维-斯托克斯问题。

so several pieces to the answer and first to your point on what you can get out of 10,000 agents we actually use 10,000 agents to solve the Navier Stokes problem.

顺便说一句，那真是太棒了。恭喜你。

Yeah, that was pretty awesome by the way. Congratulations on that.

谢谢。谢谢。这既是一个重要的问题——对流体动力学、对如何思考洋流等都有重大影响和应用——也因为它所代表的意义：AI 创造的新知识，以及它开启的一整波科学发现、药物等等，现在都成为可能。所以我认为，思考 AI 的力量能带来什么、能真正帮助解决哪些问题，是非常了不起的。就网络安全而言，我的思考方式是：我们在 OpenAI 把模型用于寻找漏洞。我们抽调了 25% 的生产工程师，对他们说：“抱歉，你们所有项目暂停。你们现在负责防御，负责提升我们的安全架构，用模型找出所有漏洞。”我们发现了许多严重问题并修复了它们。过去几周几个月里我和许多 CISO 交流过，很多公司也告诉我，他们应用这些模型发现了非常严重的问题，但能够修复。一个积极的方面是，当我们用 Astra 扫描我们的系统时，发现了一些新问题，但最终它饱和了——据我们所知，基本上找出了 Astra 足够聪明能发现的所有 P0 级、所有关键问题。

Thank you. Thank you. And it's both an important problem for what it is—has significant implications and applications to fluid dynamics, to how you think about ocean currents, all these things—but for what it represents, right, of new knowledge created by AI and it unlocking a whole wave of scientific discovery, medicines, all those things, they're on the table now. So I think there's something really amazing to think about what can happen through the power of AI that is able to really help solve problems. And in the case of cyber security, how I think about it, we at OpenAI took our models and applied them to finding vulnerabilities. We took 25% of our production engineers and said, "Sorry, all your projects are on hold. You are now defending. You are now upleveling our security architecture. You're going to use the models to find all the holes." And we found a number of serious issues and we fixed them. And I've talked to a number of CISOs over the past couple weeks and months and there are many companies who are also telling me that they've applied these models, they found some very significant issues, but that they're able to fix them. And one positive sort of part of the story is that when we took Astra and pointed it at our systems, we found some new problems, but eventually it saturated—we basically found, to our knowledge, all of the P0s, all of the critical problems that Astra is smart enough to find.

当然，还会有新模型。还会有新一轮。

And of course, there will be a new model. There will be a new round.

更聪明的。

Even smarter.

没错。

Exactly.

但我认为这就是我们将要进入的世界——你会希望处于这样一个紧密循环中：新的网络能力发布，你将其部署到你的系统上，发现新漏洞，理想情况下你已设法自动化这个过程，我们称之为防御工厂。这就是我们在内部构建的——端到端地发现漏洞、分类、

But I think that that's the world that we'll be in—that you'll be in a world where you want to be in this tight loop of new cyber capability drops. You deploy it against your systems. You find the new holes and ideally you've managed to automate this, what we call defense factory. And that's what we're building internally—this end-to-end of both find vulnerability, triage it,

修复、部署、验证，对吧？端到端。如果你能以机器速度做到这一点，我认为防御方将获得极其显著的优势。而且有一些想法，例如，用 AI 对软件进行形式化验证是可能的。

remediate, deploy, validate, right? That end to end. And if you can do that at machine speed, I think the defenders will be advantaged in deeply significant ways. And there are ideas, for example, formally verifying all of software that are possible with AI.

是的，我们从未——这一直是个梦想。我们有过这些形式化语言之类的东西，但它们从未真正流行起来。

Yeah, we never—that's always been a dream. We've had these formal languages and all these kinds of things, but they never kind of took off.

没错。因为对人来说太难处理了。太难了。但我们有这些能解决疯狂的不可能数学问题的系统。所以这种证明能力的一个应用——实际上关于纳维-斯托克斯问题，要知道的一件事是我们将其形式化了。我们把它形式化到 Lean 中，所以

That's right. Because it's just intractable for people. It's just so hard. But we have these that are solving these crazy impossible math problems. And so one application of that proving power—right, actually one of the things to know about the Navier-Stokes problem is that we formalized it, right. We formalized it into Lean and so

所以 AI 可以编写可验证的代码

so the AIs can write verifiable code

它们可以。

they can.

是的。非常好。是的，这是个好主意。所以我认为有真正的希望，但我们的观点是，世界需要紧急行动，因为我们现在正处于一个非常危险的窗口期。

Yeah. Very nice. Yeah that's a great idea. So I think there's real hope but I think that our view is that the world needs to act with urgency because we're in a very dangerous window right now.

是的。我们眼看着它到来。

Yeah. We just see it coming.

关于这次事件，我想收个尾。你觉得叙事有没有在重要方面搞错？或者有没有一种更可取的方式来谈论发生了什么，或者这个窗口期，在思考公共叙事时很重要？或者公司内部有没有其他变化，关于你们如何处理这一系列问题？

Closing the loop on this incident. Is there anything you felt that the narrative has gotten wrong in an important way? Or is there any preferred way of talking about what happened or this window that's important to get across when you think about the public narrative? Or is there anything just inside the company that else that changed in terms of how you're approaching the set of issues?

嗯，两点。我认为人们应该从中得到的一个大主题是访问权问题，对吧？这些能力现在世界上已经存在，但它们掌握在少数前沿公司手中，而这些前沿公司有一个受信任访问计划，这意味着不在该计划中的人无法真正受益于这种差异。我们内部的人，如果使用它就能获得好处。所以我认为，作为一个领域、作为一个社会，我们需要真正扩大能够使用这些技术的防御者数量，因为每一天都很重要，而要利用这些天，你需要访问这些工具。关于 Hugging Face 的回应，有一件事很有趣：他们说他们使用前沿模型来查看所发生事件的日志，因为这是实际分析此类攻击的唯一方法。他们说前沿模型拒绝了，但他们实际上没有尝试我们的前沿模型，他们相信我们的模型会允许。所以这也涉及提供商的默认立场。我认为这里有一些东西，关于以这种紧迫感将这些能力用于善途，以及这种——是的，这种必须发生的真实感觉。顺便说个无关的小故事，但也许也能说明我的一些想法。我记得我们训练 GPT-3 时，那是 2019 年 12 月初。大家都准备去度假了。有人说：“好了，我们可以训练模型了。”我记得当时感觉这个模型就搁在架子上，没人在用。这是一项惊人的技术，对世界、对人类都是新的。每一天没人探索它的能力、试图理解它、弄清楚怎么用它，就是世界失去的一天。所以，我基本上取消了所有假期计划。我整个时间都在玩这个模型，围绕它构建界面，试图看看它能做什么。我记得我试图教它如何对数字列表排序。效果不太好。但就是这种真正去探究它、看看什么是可能的精神。我认为这种精神和气质应该带到我们今天正在构建的东西中。显然规模更大、影响更大，但我们作为一个世界，有机会在这个时刻理解这项技术，从而帮助塑造和引导它未来的走向。

Well, two things. I think that one big theme that people should take away from it is a question of access, right? That these capabilities exist right now in the world, but that they are in a small number of frontier companies and the frontier companies have a trusted access program, which means that anyone who's not in the trusted access program is not really able to benefit from the fact that there's this differential. The people we're in, they get a benefit if they use it. And so I think that there's something we need to do as a field and as a society to really scale up the number of defenders that have access to these technologies because it's like every day matters and to use those days you need access to these tools. And one thing that was actually interesting about the Hugging Face response was that they said that they used frontier models to try to look over the logs of what had happened because that's the only way to actually analyze an attack like this. And that they said the frontier models refuse, but they didn't actually try our frontier models and they actually believe that ours would have permitted it. And so there is something too about the default stance of providers. I think there's something here about using these capabilities for good with this urgency and this sort of—I yeah this real sense that it has to happen. I'll tell a quick story by the way which is unrelated but maybe also shows a little bit about how I think about this. I remember when we trained GPT-3. It was beginning of December 2019. So, everyone's about to head out on vacation. Y's like, "Okay, we can train the model." And I just remember feeling like this model is sitting on a shelf. No one is using it. It's this amazing technology, new to the world, new to humanity. It's like every day that no one is exploring what it's capable of and trying to understand it, figure out what to do with it—that's a day that is lost to the world. And so, I was just like, I canceled basically all my holiday plans. I spent the whole time just playing with the model, building interfaces around it, trying to see what it was capable of. I remember I was trying to teach it how to sort lists of numbers. It didn't work very well. But it was just like this really try to probe it and see what's possible. And I think that that spirit and ethos is something I think we should bring to what we're building today. It's sort of obviously at much larger scale, much larger impact, but we as a world have the opportunity to understand this technology in this moment, which then helps us shape and steer where it will go next.

非常好的观点。你说你有两点。另一点是什么？一点是访问权，还是你两点都说了？

And very good point. You said you two things. What do you have another one? One was access or did you say both of them?

我想我两点都说了。是的。是的。

I think I said both of them. Yes. Yes.

让我们回到 Astra。在 X 上看到所有兴奋和各种案例真是不可思议。人们对计算机使用感到兴奋。你说过在某些方面它让我们更接近 AGI。

Let's go back to Astra. It's incredible to see all the excitement on X, all sorts of cases. People are excited about computer use. You've said that in some ways it is bringing us closer to AGI.

你谈谈在 Astra 中你觉得最引人注目的地方，或者基于那个说法你认为突破在哪里，以及我们还有哪些路要走？

What do you talk about what you find most compelling in Astra or what you think the breakthrough there in light of that statement and where we have still left to go?

其实等等，抱歉，让我修正一下我的回答。所以既能访问，也让我再讲一个我个人如何使用这些模型的故事。在 Hugging Face 之后，我在想如何将这些模型用于我的个人生活？我能做些什么来保护自己？我有一个网站，一个非常简单的网站，gregarin.com。不是最受欢迎的网站。上面有一篇不错的博客文章。没错。你有一些博客文章。这是一个静态网站，非常简单。可能有什么样的漏洞呢？所以我用了我的 codeex，让它去检查 gregman.com，告诉我是否有任何漏洞。于是我做了渗透测试，它返回了 13 个发现。这些发现包括我设置了 SPF 记录以防止人们伪造邮件，对吧？还有一些通过 HTTP 而没有强制使用 HTTPS 的情况，诸如此类。单独来看，这些可能不是什么大问题，但如果你考虑一个 AI 能够将许多小漏洞串联成一个大漏洞，我就想，我真的想要一个别人可以窃取我邮件的漏洞吗？大概不想。所以它花了 15 分钟找到这 13 个发现。但然后我问它，你能修复这些吗？对。因为修复太烦人了。太痛苦和无聊了。没错。然后它花了 45 分钟打开我的云控制面板，点击操作，设置所有标头，将我迁移到 COD 页面，把所有东西都设置正确。它启动了 demark 流程，显然你需要做一个 48 小时的窗口什么的。嗯，那是 45 分钟的修复。我感觉如此受保护。我感觉，哇。

Actually wait sorry let me revise my answer. So both access and let me also tell another story about how I have used the models personally. So after Hugging Face, I was thinking about how can I use these models in my personal life? What can I do to secure myself? And I have a website. It's a very simple website, gregarin.com. Not the most popular website. Got a good blog post on it. Exactly. You got some blog post. It's a static site. It's very simple. Like what kind of vulnerabilities could be there? So I took my codeex and asked it go check out gregman.com. Tell me if there's any vulnerabilities. So I did a pen test and it came back with 13 findings. And these findings were things like I had set my SPF record so that you would prevent people from spoofing emails, right? That there was some it wasing over HTTP without forcing people to HTTPS, things like that. And individually, these things are maybe not the biggest deal, but if you think about with an AI that's able to chain together many small vulnerabilities into a big one, I'm like, do I really want a hole where someone can scoop emails for me? Probably not. So 15 minutes for it to find these 13 findings. But then I asked it, can you fix these? Right. Because fixing is so annoying. So painful and boring. Exactly. And so 45 minutes it opened up my cloud for control panel. It clicked around, set all the headers. It migrated me to COD for pages. It's like set everything correctly. It started the demarked process, which apparently you have to do like a 48 hour window of whatever. Um, and that was 45 minutes of fixing. And I felt so protected. I felt like, wow.

你让它去找漏洞了吗？

Did you ask it to find the vulnerabilities?

这就对了。不，我加了，是的。所以它实际上会自动做这个。这是由 six soul 完成的。它说，我刚检查了这个已修复，这个已修复，这个已修复，48 小时后我将不得不运行并设置一个小自动化。所以 48 小时后，它会回来检查以完成 demark 流程。我想，好吧，我们现在可以开始工作了。

There you go. No, I added Yeah. So it actually does do that automatically. This was by six soul. It said I just checked that this one's fixed, this one's fixed, this one's fixed, and in 48 hours I'm going to have to run and set up a little automation. So in 48 hours, it would check back in to complete the demark process. And I was like, all right, this is we're in business now.

好的。好的。Craig Brockman.com。

All right. All right. Craig Brockman.com.

这就对了。你也不能被保护。

There we go. You too can't be protected.

太棒了。让我们转向 Astra。看到网上的兴奋和用例真是不可思议。人们对计算机使用等功能非常兴奋。你说它更接近 AGI 的道路。我很好奇你觉得它最突破性的地方是什么，以及你认为我们还有哪些路要走？

Awesome. Let's transition to Astra. It's incredible to see all the excitement online and the use cases. People really excited about computer use among other things. You've said it's sort of closer to the way along to AGI. I'm curious what you find most groundbreaking with it and where do you think we still left to go?

嗯，我认为 Astra 在许多方面确实是一个阶跃函数，在很多方面它是我们多年来所做的许多研究赌注的总和，看到它们同时融入一个模型，这绝对令人难以置信。关于我们如何进行编号，有一点需要知道的是，我们一直希望 GPD6 能代表一些值得它的东西。我们总是遇到的问题是我们的模型在逐渐变好。所以从来感觉不到是进行重大版本升级的合适时机。你总是得说现在是 56，应该是 57。而这一次恰好是因为所有这些事情同时发生，我们第一次实际上有了这种几乎不连续的步骤，虽然我们可以预测，但只是所有这些因素恰好同时对齐。所以这是一个非常积极的时刻。对我来说，计算机使用是我们谈论的头条新闻。计算机使用如此重要的部分原因是，对于智能体式用例，它真的归结为工具。就像模型是否足够聪明来使用工具，然后它是否通过这些工具访问所需的上下文？所以人们一直在构建这些 MCP 服务器和这些 CLI，真的在将软件世界以一种几乎不自然的方式变得可访问，这种方式并不是真正为人类设计的，对吧？就像我们正在重新工具化世界，对吧？你制造了它。有点像，哦，我们会构建一个 API，就像软件一样，但如果它真的更像人类行为呢？它能直接使用计算机吗？而且构建那个其他层的结果是你现在有了另一层安全挑战，这个那个的。

Well, I think that Astra is really a step function on so many axes and in many ways it is the sum of a number of research bets that we've been making for years and to see them come into one model at one time it's been absolutely incredible. And so just one thing to know about how we do numbering is that we kind of have been wanting to have GPD6 represent something that's worthy of it. And the problem we always have is that our models are kind of incrementally getting better. And so it's just never feels like it's a right moment to go for a major version bump. You always have be like it's 56 now should be 57. And this one just happened to be because all these things came together at once the first time that we actually had this almost discontinuous step on in a way that we could have predicted, but it just was like all these these these factors um happened to line up at once. And so I was a real positive moment. And to me the computer use is the headline thing that we've talked about. And part of the reason computer use is so significant is that for agentic use cases, it really comes down to tools. It's like is the model smart enough to use the tools and then does it have access to the context that needs to through these tools? And so people have been building these MCP servers and these CLIs and just really sort of taking the world of software and making accessible in this almost stilted way that is not really meant for humans, right? It's like we're kind of retooling the world, right? You made it. It's kind of like, oh, we'll build an API like it's a software, but like what if it's really more behaving like a human? Can it just use a computer and and it's kind of and the result of building that other layer you have now another layer of security challenges this that the other.

没错。

Exactly.

真的，所以有点，是的。它们总是感觉非常奇怪和次优。

Really so it's kind of Yeah. They've always felt like very weird and suboptimal.

是的。

Yes.

从 OpenAI 一开始，我记得 2015 年 11 月我们在纳帕做了这个务虚会，我们谈论了我们的计划。我们实际上制定了这个三步计划，基本上就是我们接下来 10 年所遵循的。但我们也谈到了如果我们能做强化学习，其中环境是屏幕像素、键盘、鼠标，对吧？与人类相同的界面。突然之间，任何你可以用计算机做的任务都在那里。它在分布内，只要把声音等放在一边。但你基本上拥有计算机的全部力量。我们早期有一些尝试构建能做到这一点的智能体的失败尝试。所以真的直到现在，但你立即看到了力量。看到人们利用 Blender 能力，你知道，截取某物的屏幕截图，他们必须制作 3D 模型，你可以实际上，你知道，很多人现在通过利用这个能力设计房屋或尝试重新设计他们的客厅，所有这些事情。对我来说，真正突出的是你现在可以推进 AI，让它为你做事，而不必构建所有这些特定的连接器。我认为有太多软件他们甚至不考虑你必须每天编排。就像你的生活中有多少是点击菜单，你知道，在电子表格中输入东西等等。就像这些都不是我们 100 年前应该做的事情。没有人做这些事情。所以不疯狂地认为在 5 年 10 年内没有人会再做这些事情了。我们将夺回我们的时间。我们不必得腕管综合症或驼背，你知道，所有这些我们扭曲身体以适应机器的身体问题。现在机器在那里帮助我们，赋予我们力量，真正为我们服务。

And from the very beginning of OpenAI I remember in November 2015 we did this offsite in Napa and we talked about our plans. We actually laid out this three-step plan that basically is what we ended up following for the next 10 years. But we also talked about what if we could do reinforcement learning where the environment is screen pixels, keyboard, mouse, right? Same interface as a human. Suddenly any sort of task you could do with a computer is in there. It's in it's in distribution as long as set aside sound, whatever. But you basically have the full power of a computer there. And we had some aborted attempts early on to try to build agents that could do that. And so it really took us until now, but you're seeing the power immediately. And it's just been so cool to see people take the Blender capabilities and, you know, take a screenshot of something and they have to make a 3D model and you can actually, you know, lots of people are now designing houses or trying to redesign uh their living room, all those things by just utilizing this capability. And to me that the the thing that really stands out is that you can now move forward on AI that can do things for you without you having to build all these specific connectors. And I think there's so much software they don't even think about that you have to orchestrate every day. And like how much of your life is like clicking around menus and like you know typing things into a spreadsheet and things like that. Like none of that is what we should be doing 100 years ago. No one is doing any of these things. And so it's not crazy to think that in 5 years 10 years no one will be doing any of this stuff anymore. That we will get our time back. We're not going to have to be get our carpal tunnel or hunch shoulders or you know all of those all those physical problems that are us contorting to the machine. It's now the machine is there to help us to empower us to to really serve us.

是的。

Yeah.

你说得很有道理，因为我认为你们作为一家公司比较清醒的一点就是，好吧，就业会发生什么。我认为这完全正确，我们做的所有这些事情都是因为不得不做，它们变得有价值，但我们不应该做。它们只会损害我们的健康和个性。而人类会耗尽做酷事、让世界变得更好或解决问题的想法，这在我看来有点荒谬。到目前为止，至少在数字上，AI 越好，就业率越高，而不是越低。所以我想知道你的——当然这是不可知的。你知道，我们以前从未有过这种技术。它越来越好等等。那么，随着这些模型和 AI 的进步，你如何看待就业的未来？

And you know that's a really good point because I think one of the things that you have been more sober on as a company is just okay what happens with employment and I think that's exactly right that there's all these things that we do because we have to do and like they became valuable but we shouldn't be doing it. All they do is wreck our health and wreck our personalities. And the idea that humans are going to just run out of ideas of cool things to do or how to make the world better or problems to solve seems a little absurd to me. And so far, at least in the numbers, the better AI gets, the higher employment goes, not the lower. And so I wonder your and of course it's unknowable. You know, we've never had this technology before. It's getting better and so forth. So, how do you kind of think about the future of employment as it relates to these models and AI as it progresses?

嗯，我有一个基本信念，那就是 AI 是令人惊讶的。我想我们甚至在 2015 年的 OpenAI 发布帖子中也提到了这一点，只是说迄今为止的历史表明，它不会按照你想象的方式发展，即使有逻辑结论认为它应该以某种方式发展。我认为同样的事情也会发生，对吧？我认为我们已经了解到，对于任何工作，我们很容易低估这个领域的深度和复杂性，建立关系、问责制就是一个很好的例子，我认为人们设定目标并对结果负责，这些对我来说是根本的，这些是我们实际上应该长期保留的东西，对吧？这感觉非常人性化，人们有价值不仅仅是因为我们能完成任务，对吧？我们有价值是因为我们是人，我认为在这些叙事中不要忽视这一点很重要。我认为事情会改变，我们如何利用时间会演变，我们显然会处于一个富足的世界，我们如何确保这种富足被广泛分配，但与此同时，我认为我们应该处于一个雄心壮志的上限比以往任何时候都更高的世界，我认为我们将看到一波创业浪潮，实际上这已经开始了我听说某个行业的人说，他所在领域的一群人现在正在跃跃欲试，辞职创办自己的公司，他们这样做是因为他们有了这些 AI 工具，他们就像我可以做更多的事情，所以进入壁垒降低了，这应该是一场美妙的复兴。

Well, I do have a fundamental belief that AI is surprising. And I think we even put this in the OpenAI launch post back in the day in 2015, just saying that the history so far has been somehow it just doesn't play out the way that you think it does, even when there's this like logical conclusion it should be a certain way. And I think the same will be true, right? I think that there's something that we've learned about that humans and I think like for any job that we almost it's easy to not give it as much credit for how deep the field is and how much sort of sophistication building relationships accountability is a good example of something where I think that people setting goals and being accountable for outcomes like those feel fundamental to me those feel like things that we actually should preserve for the long term right that's something that feels like deeply human like people are not valuable just because we can do tasks right we're valuable because of her people and I think that it's important not to lose sight of that in some of these narratives and I think that the way that things will change and how what we do with our time evolves and we clearly will be in a world of abundance and how do we ensure that that abundance is broadly distributed um but also at the same time I think that we should be in a world where the ceiling of ambition is higher than ever before and I think we're going to see a wave of entrepreneurship where it's actually already starting I've heard um from someone in you know particular industry was saying that a bunch of people in in his world are now making the leap to go quit and start their own firms and that they're doing it because they have these AI tools and they're just like I can do so much more and so it's the barriers to entry and for entries and this should be a wonderful renaissance.

是的，不过这对我们来说很有趣，好吧，你知道，特别是对我们的年轻人来说，因为你知道他们默认会做苦差事，但如果 AI 做苦差事，那么他们实际上可以发展得更快，因为他们可以参与到我们业务的真正部分，那就是与创业者的关系是什么？我们如何为他们打开世界？我们如何让他们觉得哦他们可以做任何事情，他们是一个重要的 CEO，他们可以去构建东西，而不是花整个周末写投资备忘录，顺便说一句，我必须说 Astra 非常擅长写投资备忘录，太棒了

Yeah though well it's it's been a lot of fun for us in just going okay there are you know particularly for our young people because you know they get by default the grunt work but like what if the AI does to the grunt work then they can really develop much faster actually because they can um kind of get involved on the you know really the the real part of our business which is what is the relationship with the entrepreneur? How do we open up the world for them? How do we make um them feel like oh they can do anything and they're an important CEO and they can go build things and as opposed to you know spend the whole weekend writing an investment memo which by the way uh I have to say Astra very good at writing investment it's awesome

我喜欢听到这个

I love hearing that

是的，再次，我确实认为这将是一个微妙的故事，对吧？我不认为我们应该描绘一切都会美好，一切都会轻松。我认为会很难，我认为会有变化，但我认为它可以是一个更美好的世界。我认为未来对每个人来说都可能比过去好得多。

yeah and again I do think it's going to be a nuance story right I don't think that we should paint that everything's going to be rosy and it's all going to be just easy I think it's going to be hard I think there's going to be change but I think that it can be a much better world. I think the future could be much better for than the past for everyone.

是的，这感觉像是我们应该期待的，你知道，就像犁出现之前，世界要糟糕得多，生活更糟，即使它让很多人类劳动力失业，你知道，并创造了整个卢德运动等等。你知道，这里没有人想回到 1870 年。所以认为不，我们现在不想进入未来，这似乎有点短视，但呃我认为事情发展的速度对人们来说非常非常可怕

Yeah, that and that's it feels like what we should expect, you know, like before the plow, you know, the world was a lot worse like it was just a worse life even though it did put like a lot of human labor out of business, you know, and and created the whole lite movement and all those kinds of things. You know, nobody here wants to go back to 1870. uh and so the idea that no we don't want to go into the future now um seems a little shortsighted but uh I think the speed at which things are moving is is very very scary for people

我们确实认识到事情发展的方式，我们花了很多时间真正试图尽可能理解人们的感受，我们如何更好地展现自己，我认为有两件事，一是当我们思考发展进步的速度时，我们非常慎重，安全是我们的首要任务。我们思考如何以安全可靠的方式构建这项技术，标准应该是什么，你可以在我们内部的许多沟通中看到这一点。这绝对是人们正在思考的，我们关心的是我们真的希望这项技术能够广泛地赋予每个人权力。我认为，作为一个世界，我们真正思考如何最大限度地利用这项技术？我们如何获得好处？我们如何减轻风险？我认为这将成为我们最重要的对话，我认为这甚至可能在未来一两年内出现。我认为这应该成为重中之重。我认为人们已经感觉到了，你可以从人们现在的反应中感觉到，甚至考虑数据中心和这些问题，我们是否想要 AI，我们如何思考它在哪里合适，我们如何确保儿童安全，所有这些类型的问题都是我们非常关心要正确处理的核心问题

and we we really recognize the fact of how things are moving and that we spend a lot of time really trying to understand as well as we can how people are feeling how we can be showing up better and I think that two things like one is that when we think about development the pace of progress we're being very deliberate about it safety is our foremost priority. We think about how do we build this technology in a safe secure way and what should those standards be and you can see that showing up in a lot of our comms inside the building. It is absolutely what people are thinking about and what we care about is that we really want this technology to empower everyone broadly. And I think that for us as a world to really think about how do we get the most out of this technology? How do we get the benefits? How do we mitigate the risks? I think this is going to become the most important conversation that we have and I think that that will emerge over even maybe the next one to two years. I think that this should be something that is front and center. I think people sense it yet you can sense it in how people react right now and even thinking about things like data centers and these kinds of questions of do we want AI and how do we how do we think about where it's appropriate and how do we ensure child safety all of these kinds of questions these are core questions that we care so much about getting right

为此，为什么我们认为某些亚洲国家对 AI 的情绪更高

to that end why do we think sentiment and AI is higher certain Asian countries

所有亚洲国家，嗯，实际上在欧洲国家到处都是，但美国的 AI 情绪最低

all Asian countries well and actually in European countries everywhere but the US has like got the lowest AI sentiment

为什么是这样，或者更多

why is that or more

是什么驱动了

what's driving the

我们能做些什么，比如我们能从中学到什么

what can we do about like what can we learn from

嗯，我想到的一件事是，我认为我们作为一个领域、一家公司需要更好地向人们阐明为什么他们受益，为什么这对他们有好处，而不仅仅是对国家有好处，对吧？我认为这项技术将成为并迅速成为美国最重要的战略优先事项和资源，它正在发生，是的，

well one thing that I think about is that I think we as a field as a company need to do a much better job of articulating to people why they benefit why is this a good thing for them and not just for the country right which I think that this technology is going to be and is rapidly becoming the single most important strategic priority and resource for the United States it's happening Yes,

绝对。你看看 ChatGPT，每周有 3 亿健康查询或 3 亿人使用它寻求帮助，对吧？这是一个巨大的交易。我们几乎有 10 亿，你知道，11 亿周活跃用户。我认为在美国大约是 1 亿左右。

absolutely. You look at chatt 300 million health queries or 300 million people every single week using it for help, right? That's a huge deal. And we're at a billion almost, you know, 1.1 billion weekly active users. I think within the US it's about 100 million something like that.

大约三分之一的人口，如果我没记错这个数字的话，每周都在使用聊天工具。所以人们正在接触这项技术。但我认为对很多人来说，有些人已经深入使用，真正走过了健康之旅。例如，我的家庭、我的妻子就是这样。她有一些健康问题，我们甚至不知道在聊天工具出现之前我们该如何应对。要找到正确答案需要付出太多辛劳和时间。医生告诉你一些事情，你不知道那是什么，你怎么才能得到那种合理性检查，真正理解它。我认识一些人，他们的生命是通过 ChatGPT 提供的信息挽救的。我给你们讲个故事：我的一个朋友在医院，医生正要注射一种抗生素。她说，等一下。她在 ChatGPT 里输入，ChatGPT 说，绝对不要用那个。如果你用了，你可能会死，因为你有一年前得的那个病，你有这个状况。她的反应给医生看了，医生说，天哪，不，那完全正确。我完全不知道。我只有五分钟读你的病历。

Like a third of the population, if I have that number correct, is using chat every single week. So people are touching this technology. But I think that for many people there are some people who have gone very deep and really gone through the health journey. For example, that's been true for my family, for my wife. She has a number of health conditions that we don't even really know how we would have managed before chat. There's just so much toil and time in getting to the right answer. A doctor tells you something, you don't know what the thing is, and how do you get that sanity check to really even understand it. People who I know had their life saved through information delivered by ChatGPT. I'll tell you a story: one of my friends was in the hospital and the doctor was about to inject an antibiotic. She's like, give me a moment. She typed into ChatGPT and ChatGPT said, absolutely do not take that. If you do, you may die because you have this thing that you had a year ago, you have this condition. Her reaction showed the doctor, and the doctor said, oh my goodness, no, that's absolutely right. I had no idea. I only had five minutes to read your chart.

是的。顺便说一句，很多这样的案例，至少在他们的病历里给我讲的故事。

Yeah. Many such cases, by the way, telling me stories in their chart at least.

确实。所以这类故事我认为远远没有得到足够的讲述，但它们确实存在。我每天都听到。还有那些用聊天工具经营小生意的人，否则他们完全做不到。那种赋能。再次，能够省钱、赚钱、过上更好生活的人。这些类型的故事我认为需要进入公众意识，在我们面对这个问题时。所以这是在描绘一种叙事：嘿，你口袋里有个老师，口袋里有个医生，口袋里有个律师，口袋里有个治疗师，所有这些实用工具都在你口袋里，同时又不威胁那些同样的——好吧，也要告诉老师、医生、律师、治疗师，嘿，你现在也有这个工具了，它也会让你的业务更好。

Exactly. And so these kinds of stories I think don't get told nearly enough, but they're out there. I hear them every day. And the people who run their small business on chat and would be totally unable to do it otherwise. Like that kind of empowerment. Again, people who are able to save money, make money, live a better life. Those kinds of stories I think need to be in the public consciousness as we approach this question. So it's painting the narrative that hey, you've got a teacher in your pocket, a doctor in your pocket, a lawyer in your pocket, a therapist in your pocket, all these utilities in your pocket while also not threatening those same—well, also telling the teachers and doctors and lawyers and therapists that hey, you've now got this tool too and it's going to make your business better as well.

是的。而且这不仅仅是叙事，这是现实，对吧？你需要两者兼顾。我认为许多其他国家正在关注。

Yes. And it's not just the narrative, it's the reality, right? It's you need both. I think that many other countries are looking in.

是的。看到美国所处的地位，对吧，看到这项技术的潜力。还有部分原因，你想想人口结构，我认为在许多其他国家，更强烈地感受到有一个比年轻人口大得多的老一代人，将需要支持他们。这些问题该如何运作？所以，我认为确实需要真正思考未来，思考什么是可能的。如何从这项技术中获益，并真正想要投入其中，我认为我们在世界各地都看到了这一点。所以，再次，我认为作为一个领域和一家公司，我们需要做得更好，以便在国内传达这一点，但我认为潜力是存在的，我们处于如此优越的地位，以某种方式引领这个领域，我认为这并非理所当然，未来也不保证会一直如此。

Yeah. Seeing the position that the US is in, right, that seeing the potential of this technology. And partly too, you think about demographics that I think in many of these other countries, it's more keenly felt that there's an older generation that's much larger than the younger population that is going to need to support them. These questions of how is that supposed to work? And so, I think that there is something about really thinking to the future and thinking about what's possible. How do you get the benefits out of this technology and really wanting to lean into that that I think we're seeing across the world. And so again, I think that there's something that we need to do better as a field and as a company in order to communicate this domestically, but I think the potential is there and we're in such a privileged position and leading this field in a way that I think was not guaranteed and it's not guaranteed to remain true for the future either.

是的。特别是如果我们禁止数据中心，我认为那将是我们保持领先的一个问题。它会将数据中心推向海外，就像 80 年代左右硅谷发生的那样。

Yeah. Particularly if we ban data centers, I think that that'll be a problem for us maintaining our lead. It will drive the data centers overseas which is what happened with silicon back in the 80s or so.

对。对。而且有很多——数据中心有趣的一点是它创造了如此多的蓝领制造业工作岗位。我认为 Switch 雇佣了大约 45,000 人，以工会合同的方式建造数据中心。那只是美国的数据中心提供商之一。这些是很好的工作，高薪，然后我认为虽然数据中心领域有过不良行为者，但大多数都是非常好的行为者，为电网做贡献，不浪费水，也不吵闹。所以不是说从来没有问题，但我们可以说嘿，你必须做一个行为良好的数据中心，而不是像我们要禁止它们。或者像我们要停止 AI。这我喜欢——我们不会——即使作为一个国家，我们也不够大来停止 AI。所以这个想法是 AI 将继续在没有我们的情况下发展。然后我们将零发言权，而不是我们是领导者，然后我们拥有所有发言权。所以这是一个非常非常重要的文化信息。

Right. Right. And there are so many—one of the interesting things about data centers is it creates so many blue-collar manufacturing jobs. I think Switch employs like 45,000 people on kind of a union contract basis to build data centers. That's just one of the data center providers in the US. And they're great jobs, they're high-paying, and then I think that while there have been bad actors in the data center space, most of them are very good actors and contribute to the power grid, don't waste water, and are not noisy. So like not that there was never an issue, but like we could just say hey, you have to be a well-behaved data center as opposed to like we're going to ban them. Or like we're going to stop AI. Which I like—we're not going to—even as a country we're not big enough to stop AI. So the idea like AI will continue without us. And then we'll have zero say as opposed to we're the leaders and then we have all the say. So it's a really, really important cultural message.

我认为这非常重要，我认为在数据中心方面。所以我们做出了不增加人们电费的承诺。我们的数据中心都是闭环水。所以 Abalene 使用的水量——那是实际训练 Astra 的数据中心——它使用的水量大约相当于一栋办公楼。对。所以真的——是的，技术在这方面相当先进，而且我们有许多社区承诺，以便我们能够真正帮助我们在俄亥俄州和佐治亚州有数据中心的地方。我们已经宣布，我们已经讨论过我们如何为每个大学生提供 Codeex 访问的积分。所以我们带来了一系列广泛的好处。但再次，我认为我们需要做得更多。

I think it's very important and I think on data centers. So we've made commitments on not increasing people's electricity bills. Our data centers are all closed loop water. So the amount of water used by Abalene which is the data center that actually trained Astra that it uses about the same amount of water as an office building. Right. So it's really—yeah, the technology is quite advanced on that and that we have a number of community commitments so that we can actually help in Ohio and Georgia where we have data centers. We've announced we've talked about how we're providing credits to every college student for codeex access. So there's this broad set of benefits that we are bringing to bear. But again, I think that we need to do even more.

是的。

Yeah.

你知道，我认为将这类事情作为建造数据中心的要求是非常合理的。但让我们有一些积极的想法，而不是好吧，我们将作为一个国家跳出 AI 游戏，让中国或其他任何人决定它会是什么。

And you know, like I think making those kinds of things a requirement to build a data center is very reasonable. But like let's have positive some ideas as opposed to okay, we're going to jump out of the AI game as a country and let China or whomever dictate what it's going to be.

说到贡献，你们对一线防御者做出了十亿美元的承诺。你为什么不谈谈这个？

Speaking of contributions, you guys made a billion dollar commitment to frontline defenders. Why don't you talk about that?

所以我们相信每个组织、每家公司、每个政府、关键基础设施，如供水服务提供商、医院，都应该使用这个防御者窗口来保护自己。但并非每个组织都有所需的资金来做这件事。所以我们有十亿美元的承诺给一线防御者，给我们社区每天依赖的组织，让他们访问我们的模型来保护自己。我们认为这是开始，不是结束。我们正在与合作伙伴密切合作。例如，CrowdStrike，我们正在合作，为防御者提供折扣访问。我认为应该有全球努力，以便将这些工具投入使用，真正保护每一个组织，鉴于我们看到的即将到来和可能发生的事情。

So we believe that every organization, every company, every government, critical infrastructure such as water service providers, hospitals should all be using this defender window to secure themselves. But not every organization will have the capital required to do it. So we have a billion dollar commitment to frontline defenders, so to organizations that we all rely on every day in our communities to access our models to secure themselves. We think this is the beginning. This is not the end. We're working part closely with partners. For example, CrowdStrike and we are working together to provide discounted access to defenders as well. And I think that there's—there should be a global effort in order to bring these tools to bear to really secure every single organization given what we see coming and what's possible.

是的。这是一个非常积极的进步，因为这是我们的——在 AI 之前，医院经常被闯入、被劫持。

Yeah. And that's a super positive advance because this is our—before AI hospitals were getting broken into, held hostages all the time.

我们的供水系统已被外国行为者、国家行为者入侵。我们已经在面对这样一个事实：我们的关键基础设施在建造时并未考虑网络安全，维护时也未考虑网络安全。而现在有一个机会，让我们从一个在 AI 出现前都不安全的世界，走向完全安全。所以对我来说，这是一项极其重要的努力，为了不让我们的供水和医院面临风险。

Our water supply has been hacked by foreign actors, state actors. We're already dealing with the fact that our critical infrastructure was not built with cyber security in mind. It's not been maintained with cyber security in mind. And here's an opportunity to go from not even secure in a pre-AI world to completely secure. So to me this is an incredibly important effort to not have our water supply and our hospitals at risk.

我非常同意这个观点，对吧？关键在于我们作为一个社会一直很松懈，对吧？我们任由技术债务堆积。每一个网络安全组织——我从未见过一位 CISO 觉得自己获得了足够的资源，对吧？觉得自己得到了应有的优先级。

I really agree with that perspective, right? That the point is that we as a society have been lax, right? That we've allowed tech debt to pile up. Every cyber security organization — I've never met a CISO who felt that they were appropriately resourced, right? That they were appropriately prioritized.

从来没有。

Never.

尤其是在公共部门。

And particularly not in the public sector.

没错。所以我认为我们必须改变这一点。我们多年前就应该改变，但现在是这样一个时刻：我们既有真正的动力去做，也有真正的能力去做。我认为，交付一个我们所有人都应得的、安全的世界，让我们能够真正依赖它，在我们的日常生活和在线生活中保持安全——对我来说，这是最基本的要求。我们绝对需要做到这一点。

That's right. And so I think that we have to change that. And we should have changed this years ago, but now is a moment where we actually have a real both motivation to do it and a real ability to do it. And I think that delivering the secure world that we all deserve so that we can really depend on it and be safe and secure in our daily lives and online lives — that to me feels like table stakes. We absolutely need to do this.

是的，绝对。不，这是一项伟大的努力。

Yeah, definitely. No, that's a great effort.

关于 Astra 收个尾。你强调过能力仍然参差不齐。你认为还有什么需要继续推进或充实，才能最接近你对 AGI 的定义？

Closing the loop on Astra. You've emphasized that capabilities still remain jagged. What do you think is still left to go or still needs to be fleshed out that gets approximates most of your definition of AGI?

嗯，我认为 AGI 已经不再是某个时间点，而更像是一个模糊的光谱。对我来说，Astra 确实达到了某种程度，让我觉得，好吧，我认为把它称为 AGI 是相当合理的，因为凭借其计算机使用能力，你真的可以要求它执行长时间运行的任务，它就会去做。我们见过它连贯地运行 24 小时去完成我认为相当惊人的任务，而且跨越了广泛的领域。现在，它仍然参差不齐，所以仍然有一些地方，比如写作。它的写作相当不错。这是第一次它不再滑坡。

Well, I think that AGI has turned out to be less of a point in time and more of this sort of fuzzy spectrum. And for me, Astra has really hit something that I'm like, okay, I think this is pretty reasonable to call it AGI in that with its computer use capabilities, you really can ask it to do long live tasks and it'll just do it. That we've seen it run coherently for 24 hours to go accomplish tasks that I think are quite quite amazing and across a wide variety of domains. Now, it still is jagged and so that there are still places where, for example, it's writing. It's pretty good writing. It's the first time it's not sloping.

是的。

Yeah.

但还不是出色的写作。

But it's not great writing.

是的。

Yeah.

我认为在不少领域，我觉得我们只需要稍微打磨一下，就会非常出色。但就是还差那么一点。所以我看到这个，就像有人在 Twitter 上发了一张图，你知道，这种参差不齐的前沿，而我们真正需要的是在各领域都更加稳定，真正覆盖所有这些类别。但我认为人们发现的是，它在如此广泛的任务中如此有能力，以至于它具有加速性。它赋予人力量，我认为我们从未真正见过一个模型有这样的跃升。

And I think that there's a number of areas where I feel like we just need to polish it a little bit and it would be fantastic. And it's just like not quite there. So I see this like I saw someone post a graph on Twitter of like you know this like kind of you know jagged frontier and that where we really need to be is a much more steady across the board really hit on all these categories. But I think that what people are finding is that it is so capable across such a wide variety of tasks that it is accelerative. It is empowering and it's something that I think we've never really seen a model that that's been a jump like this.

是的。对我来说有趣的一件事是，当你解决了问题，有时世界并没有意识到。比如我已经很久没见过幻觉了。但没人说哦模型不再产生幻觉了。它只是某种风气，认为 AI 就是这样。你认为这会随着时间自然消失，还是需要对非硬核技术人群进行某种持续教育？

Yeah. One of the things that's been interesting for me is that as you solve problems sometimes the world doesn't realize it. Like so I haven't seen a hallucination in quite some time. But nobody says oh the models don't hallucinate anymore. It's just kind of in the ethos that that's what AI does. How do you think that'll just go away over time or does there need to be some like continual education for the non-hardcore tech people?

我认为我们实际面临的最重要问题之一就是持续教育，对吧，真正的问题是如何——人们不应该必须从 AI 中挖掘出它能做什么，应该反过来，AI 应该说嘿我可以用这种新方式帮助你。所以我们有大约，你知道，ChatGPT 每周活跃用户超过 10 亿，但我认为还有大约 15 亿人曾经用过 ChatGPT 但不再用了。

I think one of the most important problems we actually have is the continual education right the really how do you people shouldn't have to extract from the AI what it's capable of it should go the other way around the AI should say hey I can help you in this new way so we have about you know we over a billion weekly active users on chatbt, but I think we have something like another maybe 1.5 billion people who have used chatbt and don't use it anymore.

哦哇。

Oh wow.

对吧。所以想想看。这是地球上一个相当大的比例。而那些人，正是那些人，我们应该能够回去对他们说：“嘿，我们取得了如此大的进步。我们认为我们可以在这些方面对你有用。”我认为这恰恰说明了我们面临的问题：这些 AI，如果你看 ChatGPT 和 ChatGPT 工作版，它们都是文本框，对吧？就像这个新文本框比旧文本框好得多，但旧文本框仍然在某些事情上更好。所以不要总是用它。这就像那不是我们被承诺的 AI。我们被承诺的 AI 应该是一个你主要通过语音与之交谈的 AI。如果你愿意，也可以通过文本交谈。它应该具有持久性，有记忆，有上下文，了解你。它值得信赖，你见过它主动为你解决问题，在你的个人生活、工作生活中提供帮助。这才是它应该有的样子。它应该是这样一种东西，你可以真正依赖它来处理你关心的事情，赋予你力量，帮助你实现目标。而我认为，能够向你解释它如何帮助你，是其中的核心部分。

Right. So think about that. That's a significant fraction of the planet. And those people exactly those people we should really be able to go back to and say, "Hey, we have made so much progress. We think we can be useful to you in these ways." And I think that that is just shows you the kind of problem we have in front of us is that these AI like if you look at chatbt and chatbt work, they're both text boxes, right? It's like this new text box is way better than the old text box, but there's still some things that the old text box is better at. So don't always use it. It's like that is not the AI we were promised. The AI we were promised should be an AI that you talk to over voice primarily. You can talk to it over text if you want to. That it has persistence, that it has memory, it has context, it knows you. It's trustworthy, that you have seen it be proactive and helps solve problems for you, that helps in your personal life, in your work life. And that's how it should be. It should be something that is able to that you can really sort of rely on for the things that you care about that empowers you and and helps you solve your goals. And I think that being able to explain to you how it can help you is a core part of that.

是的。有趣。并且主动去做。这真是个有趣的想法，就像我们需要我们的 AI 更有帮助。

Yeah. Interesting. And proactively do it. That's such an interesting idea like we need more helpfulness out of our AIs.

是的。

Yes.

这有点像，有些人不是，而开发 AI 的人可能也不是很有帮助的人。我猜，只是和工程师和研究人员待在一起。

Which is kind of a thing like some humans aren't and probably the humans who develop AI are not very helpful people. I would guess just being around engineers and researchers.

你会惊讶的。我认为我们在 OpenAI 有非常非常有帮助的工程师。但如果你想想如何与另一个人合作，对吧？一个你从未合作过的新同事。你需要一点时间，对吧？你慢慢感受他们。你看他们在不同领域如何回应。人不会带着说明书来。而且通常，实际上有时在咨询等领域很有趣，他们真的倾向于迈尔斯-布里格斯性格测试，他们会说，这里有一个快速了解我是谁以及我如何运作的方法。所以人类如何更多地展示自己是有先例的。你有简历。你有某种记录。人们可以打听你的背景。所以我们建立了一种方式，来理解一个人将如何工作，以及如何最好地发挥那个人的优势。我认为，弄清楚什么是 AI 的正确类比，尤其是随着 AI 的变化，我们推出新工具、新产品界面和新模型以及所有这些事情。

You'd be surprised. I think we have very very helpful engineers at OpenAI. But there is something about if you think about how do you work with another person, right? A new co-worker you've never worked with. It takes you a little bit of time, right? you kind of feel them out. You see how they respond in different areas. People do not come with an instruction manual. And often actually sometimes it's interesting in areas like consulting or something where they really lean into like Myers-Briggs and that they do say like here's a quick way to know who I am and how I operate. So there is some precedent for how humans can kind of present a little bit more. You have a resume. You have sort of track record. People can ask for back channels on you. So we have built up a way of how do you understand how a human will work and what the best way is to get the best out of that person and I think sort of figuring out what is the right analog for AI and especially as AI changes and we produce new tools and product surface and new models and all these things.

我认为这将是社会与公司之间非常重要的一种互动。再说一次，我认为我们的北极星应该是简洁。对吧？我们真的应该是一个统一的 AI，让你与电脑互动变得如此轻松顺畅，而不是让你去迁就电脑。电脑应该在那里赋能你，帮助你、服务你。

I think that this will be a very important society-company interplay, and again I think that what our north star should be is simplicity. Right? That we really should be one AI that's unified, that makes it so easy and smooth for you to be engaging with the computer and less wrapping yourself around the computer. The computer should be there to empower you, to help serve you.

对。对。

Right. Right.

业务正在飞速发展。你们覆盖的面非常广。你们如何决定优先在哪些方向做得最深、哪些不做？而且你的角色也在演变和变化，你涵盖了很多事情——研究、产品、商业化、管理等等。你又是如何考虑分配你的时间的？

The business is ripping. You guys have such broad surface area in terms of what you cover. How do you decide in terms of prioritizing where to go deepest, what not to build? And then also your role has also evolved and changed, and you've encompassed so many things — research, product, commercialization, management, etc. How are you also thinking about prizing your time?

嗯，这两者是相辅相成的。对。

Well, they go hand in hand. Yeah.

所以今年的主题是聚焦。

So this year the theme was focus.

我认为我们真的意识到我们不可能什么都做。对吧？我们需要做选择。尤其是我们想达成的一件事，就是我们的使命。对吧？我们要确保 AGI 造福全人类。那么你如何从这个目标反推？哪些领域——比如部署和产品化，实际上确实能强化这一点。对吧？我们确实想把这项技术付诸实践，让它提升每一个人，让人们把它部署到有用的应用里，所有这一切——个人生活、工作生活，全部。非常核心。但当你思考我们所处的这个智能体式编程起飞、这种指数级增长的时刻，哪些领域强化了这一点，哪些只是——你知道，它们在媒体上被贴上了支线任务的标签，但就是不在正轨上，即便它们单独来看是非常令人兴奋的东西？这是我们必须应对的一个非常核心的问题。所以像 Sora 这样的项目——这可能是我们决定取消的项目里最受关注的一个。顺便说一句，非常非常痛苦。

I think that we really realized that we can't do it all. Right? We need to pick. And particularly there's one thing we're trying to accomplish, which is our mission. Right? We want to ensure AGI benefits all of humanity. Now how do you back-solve from that? What are the areas — like deployment and productization is actually something that does reinforce that. Right? That we do want to bring this technology to bear and have it uplift everyone, and people deploying it in useful applications, all of that — personal life, work life, the whole thing. Very core. But how do you, when you think about this moment we're in of this agentic coding takeoff, that exponential — what areas reinforced that and which ones were kind of just sort of, you know, they got labeled a side quest in the media but just were not on track for it, even if they were individually something very exciting? That was a very core question that we had to grapple with. And so things like Sora — that's maybe the highest profile one of these projects that we decided to cancel. Very, very painful, by the way.

这可不是件容易的事。

Not an easy thing to do.

但它在很多方面对于释放业务潜力至关重要，这样我们才能真正专注于把 ChatGPT 的消费者端和企业端整合进 ChatGPT Work。这是另一个我们不得不真正聚焦、真正说“这就是我们要做的”的领域。所以我们思考如何解锁这个时刻的很多方式，就是真正对未来的走向有远见，以及我们如何认为正在涌现的新能力能够最好地通过一个统一的栈来发挥作用，这个栈能跨不同领域、不同人生场景、我们试图聚焦的不同领域运作。而且这很痛苦，对吧？如果你看上半年，我认为有很多指标都没有朝着我们希望的方向走，有很多时候就是告诉团队我们只需要专注于基本功。比如我最喜欢的管理书之一是《比分自会说明一切》。你们读过那本吗？

But it was so critical to unleash the business in many ways, so we could really focus on bringing together the consumer and enterprise side of ChatGPT into ChatGPT Work. That's another area where we've really had to focus and really say this is what we're doing. So a lot of the way that we've thought about this to unlock this moment is to really have vision about where we think the future's going and how do we think that the new capabilities that are emerging can best be brought to bear with a single unified stack that works across the different areas, different walks of life, different areas that we're trying to focus on. And it's been painful, right? If you look at the first half, I think that there was just a lot of metrics that were not looking the direction that we wanted, and that there was a lot of just sort of telling the team we just need to focus on the basics. Like one of my favorite management books is The Score Takes Care of Itself. Have you guys read that one?

读过，Keith 的最爱。

Yeah, Keith's favorite.

对，这是本很棒的书，它就是一本非常赋能的书，因为你会意识到你无法影响结果。你只能影响输入，对吧？你只能影响基本功。所以就专注于那些基本功，对吧？你不会靠说“我想赢超级碗”来赢超级碗。你是靠阻挡和擒抱来赢的。对。所以这就是我们这一整年所做的。而对我自己来说，我在 OpenAI 一直专注于我认为我能推动进展、且没有我就不会发生的最重要的问题。

Yeah, it's a great one, and it just is a very empowering book because you just realize it's like you cannot affect the outcome. You can only affect the inputs, right? You can only affect the basics. And so focus on those basics, right? You don't win the Super Bowl by saying I want to win the Super Bowl. You win it by blocking and tackling. Yeah. And so that's what we have done for this whole year. And for myself, I throughout OpenAI have always focused on whatever is the most important problem that I think that I can move the needle on, that just isn't going to happen without me.

过去两年，是数据中心、基础设施、机器学习工程。

For the past two years, it's been the data centers, the infrastructure, the machine learning engineering.

而这是我们真正花了大量精力把预训练基础设施做到极佳状态的领域。今年则真的是关于业务。真的是关于——好吧，我们已经搞清楚了如何让研究真正顺畅运转。我们搞清楚了如何让基础设施真正顺畅运转，但我们如何真正把这项技术带给世界？我认为这就是我一直在投入大量精力的地方，试图把一堆原本各自平行或交叉运行的职能整合到一起。而这一点，我认为作为创始人，作为一个从一开始就接触过这项业务每一个部分的人，我认为我独特地能够介入并做出改变、做出艰难的决定，真正搞清楚这就是方向。我们走。我的很多风格是我喜欢从战壕里领导。所以我会非常深入地钻进事情的具体细节里，真正不断地问很多问题。其实我的很多风格就是问，嘿，这还说得通吗？我不太明白那个。有时候当事情混乱时，比如过去几天，有些时候就是，我们有个东西，我们得搞清楚怎么去谈论它。我们怎么想它？世界应该怎么想这个？我就说，让我们把所有能摸到大象不同部位的人都拉到一个电话上。我们就在一个 hangout 里过一份 Google Doc，然后就是，这句话说得通吗？等等，我们到底是什么意思？所以就是真正试图提升执行力，有时是小处，有时是大处。

And that's an area where we really spent a lot of effort to get our pre-training infrastructure into great, great shape. This year it's really been about the business. It's really been about the — okay, we've figured out how to get the research really humming. We figured out how to get the infrastructure really humming, but how do we really bring this technology to the world? And I think that that's where I've been really putting a lot of my efforts, in trying to bring together a bunch of functions that were otherwise kind of running in parallel or crosswise. And that is something where I think as a founder, as someone who has kind of touched every part of this business from the beginning, I think I've been uniquely able to go in and make the changes, make the hard decisions, and really figure out this is the direction. Let's go. And a lot of my style is that I like to lead from the trenches. And so I get very deep in the weeds on what the thing is and really try to keep asking a lot of questions. Like that's actually a lot of my style is just asking like, hey, does this make sense still? I don't quite get that. Sometimes when things are confused, for example, over the past couple days, there have been times when it's just like, we've got a thing, we got to figure out how to even talk about it. How do we think about it? How should the world think about this? I'm just like, let's just get everyone who can touch different parts of the elephant on a call. We're like going through a Google Doc on a hangout and just kind of like being like, does this line make sense? Wait, what do we really mean by this? And so really trying to uplevel execution sometimes in small ways and sometimes large.

对。不，这太棒了。顺便说一句，这正是正确的运作方式。

Yeah. No, that's fantastic. By the way, exact right way to operate.

对。

Yeah.

你知道明年会聚焦什么，或者现在在准备什么吗？

Do you know what the next year will focus on, or prep for now?

嗯，你看，我认为业务是一个巨大的领域，我认为我们在真正提升执行的每一个部分方面还没有完成。所以我认为那里还有很多要做。但我也认为我们正在进入 AI 发展的一个新阶段，对吧？我称之为，我们也称之为，我们现在处于 AGI 时代。我认为这是可以争论的。是这个模型、上一个模型、还是下一个模型？这不重要。关键是，我们正处于一个新阶段，在这个阶段，安全、保障、对齐——真正思考这些事情，不只是在部署时，而是一路回溯到开发时、评估时——这是客观的。这至关重要。这必须发生。这是我们使命的核心。这是我们需要做的事情的核心。所以我花很多时间思考的是，确保我们有没有所有正确的流程？我们是不是在谈论正确的事情？我们有没有在运营层面、在实践层面真正带领我们走向那种我们视为使命核心的安全与保障保证的计划？

Well, look, I think that the business is a huge area that I think we're not done yet with really upleveling every part of execution. So I think there's a lot more to do there. But I also think we are moving into a new phase of AI development, right? I call this, and we call this, we're now in the AGI era. And I think that that is something that you can debate. Is it this model, previous model, next model? It doesn't matter. The point is that we are in a new phase where safety, security, alignment, really thinking about these things not just at deployment time, but all the way back at development time, evaluation — it's objective. This is critical. It must happen. This is core to our mission. This is core to what we need to do. And so a lot of what I spend my time thinking about is making sure do we have all the right processes? Are we talking about the right things? Do we have plans that really at an operational level, at a practical level, lead us to the kind of security and the kinds of safety guarantees that we view as core to our mission, what we need to do.

所以我认为，OpenAI 过去五年的主题一直是更深的代码、更深的交织，这些功能表面上可能非常不同，对吧？从市场推广到长期研究再到芯片设计，通过以连贯的方式构建这些，让每个人都有上下文，对吧？他们有点明白我如何融入整体图景，我们在尝试做什么，以及我们想要达到的最终结果是什么。就像那样，这是必须发生的。所以我认为我将关注的领域将由最需要这种交织的领域决定，而且我认为随着时间的推移，我看到我们越来越协调一致，同步前进。

And so I think that again the theme of OpenAI certainly for the past five years has been deeper codes, deeper intertwining across these functions that are maybe on the surface very disparate, right? All the way from go-to-market to long-term research to chip design, by building these in a coherent way where everyone has context, right? They kind of understand how do I fit into the overall picture, what are we trying to do, and what is the end outcome we want to achieve. Like that is what has to happen. So I think that the areas that I will focus on will be dictated by the areas that most need that intertwining, and I think that I see us moving more and more in concert, in lockstep as time goes on.

是的。呃，我们可以聊一整天，但我们得准时结束。呃，我觉得这是个很好的收尾点。Greg，非常感谢你来参加播客。

Yeah. I uh we could go all day, but we have a hard stop. Uh I think this is a great place to wrap. Greg, thank you so much for coming on the podcast.

谢谢。

Thank you.

太好了。非常棒。

Great. Fantastic.