**Interviewer：** Greg，感谢你再次来到这里。我觉得我们从没收过你房租，也许之后会给你寄张账单。Greg，你参与了两家非常了不起的公司。你是 Stripe 的第四号员工，也是第一任 CTO。我最近听说 Stripe 处理了全球 GDP 的 1.6%，你一定很自豪。
**Interviewer:** So, Greg, uh, thank you for coming back here. Um, I don't think we ever charge you for rent. So, uh, maybe we'll send you an invoice later, but Greg, you've been part of like two really spectacular companies. Stripe as employee number four and then the first CTO. I just recently heard that they process 1.6 billion, sorry, 1.6% of the global GDP. You must be proud of that.
**Greg Brockman：** 这确实令人惊叹。
**Greg Brockman:** That's amazing.
**Interviewer：** 你应该更自豪的是，OpenAI 现在的周活跃用户已经接近甚至超过十亿了。
**Interviewer:** You must be even more proud of the fact that OpenAI has almost a billion or maybe more than a billion uh in weekly active users at this point.
**Greg Brockman：** 这一切都非常令人兴奋。它展示了技术能够做到什么。
**Greg Brockman:** I mean, it's all it's all very exciting. It shows you what technology can do.
**Interviewer：** 你不仅是联合创始人和总裁，还是 OpenAI 的首席建造者（chief builder）。我听说这是你的头衔之一。
**Interviewer:** And uh you're not just co-founder and president, but you're also chief builder at Opening. I heard that that was one of your titles.
**Greg Brockman：** 我不确定这是否算正式头衔，但别人确实用各种称呼叫过我，就这么说吧。
**Greg Brockman:** I'm not sure if there's ever an official title, but I've been called many things. Let's just say that.
**Interviewer：** 今天在座的都是优秀的建造者。我们从技术栈的最底层开始聊。OpenAI 的业务有多个层面，其中之一是算力。你们在获取算力方面非常激进。为什么？
**Interviewer:** Well, you have a audience of great builders here. So, we'll start from all the way at the bottom of the stack. You Open AAI has multiple stacks to the business, one of which is compute. And you guys have been very aggressive, very aggressive on securing compute. Why is that?
**Greg Brockman：** 从很多方面来说，我们的商业模式非常简单：购买、租赁、建设算力，然后加价转售。就这样。只要利润率为正，你就想扩大规模，因为解决问题的需求、对智能的需求是无限的。而我们现在的 AI 确实能够应对你抛给它的任何类型的问题。
**Greg Brockman:** Well, in many ways, we have a very simple business. We buy, rent, build compute, and we resell it at a margin. That's it. As long as the margin's positive, then you want to scale it because the demand for solving problems, the demand for intelligence, that's unlimited. And the AIs that we have right now really are able to rise to the challenge of effectively any kind of problem that you want to throw at them.
**Interviewer：** 你们有足够的算力吗？
**Interviewer:** Do you have enough compute?
**Greg Brockman：** 没有。
**Greg Brockman:** No.
**Interviewer：** 真的吗？
**Interviewer:** Really?
**Greg Brockman：** 是的，绝对不够。
**Greg Brockman:** Yeah, definitely not.
**Interviewer：** 我刚和 Matt Garmin 聊过，他说 2026 年可用的 GPU 算力约等于零。难道不是你们把算力都买走了吗？
**Interviewer:** I was just with Matt Garmin and he says the GPU compute availability in 2026 rounds to zero. Don't you guys have all of it?
**Greg Brockman：** 我们确实希望更多。我们一直在外面到处寻找更多算力，说真的。我跟你讲，当我们刚推出 ChatGPT 的时候，我记得跟团队开会，他们问"我们该买多少算力？"我说"全部买下来。"他们说"别开玩笑了，认真的，到底买多少？"我说，不管我们多快地增加算力，我保证都跟不上需求。从那以后一直如此。
**Greg Brockman:** I mean, we have we we would love more. We're we're constantly out there hunting for more, honestly. And I'll tell you like when we first launched when we launched CHBT I remember being on a call with my team and they were like all right how much compute should we buy? And I said all of it. And they're like no no no seriously like like come on how much should we buy? I'm like no matter how fast we try to ramp compute I guarantee we're not going to be able to keep up with demand. And that has been true ever since.
**Interviewer：** 这很有意思。从算力往上聊，因为在座大多是创业公司创始人，可能帮不了你们搞更多算力。关于架构和 scaling laws，我们现在处于什么阶段？每年还在翻倍吗？你们在改变架构吗？在研究前沿方面推进什么？
**Interviewer:** Um that's that's fascinating. Moving up from compute since I don't know if much of this audience can help you with securing more compute because most of them are founders of startups. Um about architecture and scaling laws are what are the what are the where are we in the scaling laws? Are they still doubling each year? Are you changing architecture? What's what is what are you guys pushing on the frontier on the research side?
**Greg Brockman：** 首先我想说，scaling laws 是一个深刻而美丽的谜。它们给人的感觉是极其基本的规律，就像一种科学真理。你想想物理学、牛顿定律这些东西，它们就像宇宙的某种真理，而且是实证性的——我们不一定有完整的理论来解释它为什么有效。但对我来说最美的一点是：神经网络的设计理念实际上来自 1940 年代，那时候计算机还不存在。而我们居然能把当年的那些理念拿过来，投入越来越多的算力，模型就会相应地变得更强大，而且一直在持续。没有墙。我觉得这是一件美妙的事情。
**Greg Brockman:** Well, I would say first of all the scaling laws are a deep and very beautiful mystery, right? They feel deeply fundamental. It's like this scientific truth that just like you think about physics and you know Newton's laws and things like that, there's somehow this truth of the universe and they're empirical. Like we don't necessarily have all the theory to explain exactly why it works. But to me, the most beautiful thing is that neural networks were really designed like in the 1940s before they were computers. And somehow we've been able to take the exact ideas that were developed back then and apply increasing amounts of computation and as you pour more compute into the models, they get correspondingly more capable and it just keeps going. There's no wall. And that's I think that's a beautiful thing.
**Interviewer：** 确实很美。有没有更多新的研究或算法正在开发中？因为你提到 1940 年代就有了神经网络，但当时没有算力。现在有了算力，我们只是推进同样的东西，还是有新的架构和新想法出现？
**Interviewer:** That's pretty beautiful. Are there more research or more algorithms that are in the works that because you know in the past we had neuronet networks to your point in 1940s but we couldn't we didn't have the compute for it. Now that we have the compute for it you just are we just pushing the same things or are there new architectures and new ideas coming out?
**Greg Brockman：** 我们确实不断有新想法在驱动我们的工作。说"把 1940 年代的神经网络放进一个吉瓦级数据中心"是过度简化了。我们做了大量创新，持续在改进。有时候是微调——比如你发现数据格式化的方式不太对，这其实影响很大。有时候是更大的变化，比如从 LSTM 到 Transformer 的转变。而且我认为 Transformer 也已经不是 2018 年那篇论文里描述的样子了。创新一直在发生。在那些长期投入研究如何改进架构、改进基础算法、实现范式转换的机构中，我认为 OpenAI 一直走在前面。这是我们持续投入的方向，而且我看到了很多即将结出的成果。
**Greg Brockman:** Yeah. So I would I would think of it as we we absolutely have new ideas that are constantly powering what we do. It's very simplified to say, well, let's take a neural network from the 1940s and, you know, put put it in a gigawatt data center, right? We have made tons of innovations and we constantly are improving things. And sometimes these are micro tweaks like you just realize that the way you've been formatting data was not quite right and that can actually be a very big deal. Sometimes it's larger. You think about the shift from the LSTM to the transformer and I don't think the transformer is, you know, like everyone's moved past the transformer as described in the other 2018 paper. So there's there's constant innovation happening and I think of places that have been perhaps the most invested in long-term research on how to improve the architectures, how to improve the fundamental algorithms and how to get the paradigm shifts. I think OpenAI has been leading the pack there and that's something we continue to invest and I see lots of fruit on the horizon. Got it.
**Interviewer：** 关于模型，OpenAI 有没有一个正式的 AGI 定义？我们接近了吗？Pat 和 Sonia 发表了一个说法，认为我们在功能上已经达到了 AGI。你同意吗？
**Interviewer:** And on the models um does open a have a formal definition for AGI? Are we close? Are we not close? Pat and Sonia published this thing that we are at AGI functionally. Do you agree with that? Do you not agree with that?
**Greg Brockman：** 我们确实有一个正式定义，但在某种程度上，我发现每个人对 AGI 是什么都有自己的直觉。按照我的看法，我认为我们大概走了 80% 的路程。我们拥有聪明的模型，非常有能力。如果你给它所有的上下文——
**Greg Brockman:** Well, we do have a formal definition, but to some extent I one thing I have learned is that everyone has their own intuitions about what AGI is. And maybe you can view it as like according to my view of where we are, I think we're about 80% of the way there in that we have models that are smart. They're very capable. They are able to if you give
**Interviewer：** 它们比你聪明吗？
**Interviewer:** Are they smarter than you?
**Greg Brockman：** 在写软件方面，它们肯定比我更强。如果你给它所有的上下文，那么是的，我认为它们真的非常强大，令人惊叹。在座有人觉得自己写代码比 GPT 5.4 强吗？
**Greg Brockman:** I mean, they're certainly more capable than I am at writing software, right? If you give it all the context, then yes, I I I think that they are they're just so capable. It's it's really remarkable. Like, does anyone here feel better at writing software than GPD 5.4?
**Interviewer：** 哦，
**Interviewer:** Oh,
**Greg Brockman：** 好吧。那写内核呢。即使在底层任务上，我们也在看到巨大的提升。我给你举个例子说明趋势。我的一位系统工程师之前也是类似的情况——他觉得从 GPT 5.1 到 5.2 到 5.3 都没法从模型中获得太多价值。GPT 5 出来后，他一时兴起准备了一份设计文档，是一个非常复杂的系统优化方案，准备第二天交给团队花一周来做。他把文档交给模型，然后去睡觉了。等他醒来，事情已经完成了。模型不仅实现了初始规格，还发现运行很慢，加了性能监测工具，用分析器找出瓶颈所在，然后迭代了多次直到得到优化后的结果。这就是我们现在所处的位置。
**Greg Brockman:** all right. Writing kernels. Um, so even there we're seeing massive gains from Exactly. We're and for some of our internal results um that there we're really seeing if you pour the right kinds of of you know if you have the right setup for your problem then you're able to get really massive results out of very low-level um even low-level tasks. And just to give you one example of how things have been trending um one of my systems engineers also very similarly was like hey like I haven't been able to get value out of the models for GPD5 for 5.1 for 5.2 to as well for 5.3 he on a lark prepare had prepared this design document for a very complicated systems optimization he was about to do he handed it over to the model went to sleep waking up intending to like give this to his team to work on for the next week and when he woke up it was done that the model had actually implemented the initial spec had seen that it was slow had added instrumentation had actually run the code used a profiler to figure out where things were slow and iterated multiple times until it got to an optimized result and like like that is incredible. That's where we are.
**Interviewer：** 你会建议在座的创业者怎么做？因为模型越来越强大。我之前问过 Sam 类似的问题：如果你今天在构建产品，两年后新模型出来，功能和能力都变了，你是不是得重建？你是不是得确保不挡在 OpenAI 的路上，因为模型太强了会直接碾过创业公司？你会怎么建议创始人们在这个环境中构建产品？
**Interviewer:** And so how what would you advise all startups here to do because the models keep getting more and more capable? They kind of I've asked this uh when Sam was here in the past and you know what if you're building today do you need to rebuild in two years when a new model comes out because all the functionality and all the capabilities all change around you. Um, do you need to make sure that you're not in Open AI's way because you're going to roll you're just going to run over startups because the models are so much more capable. Um, h how would you recommend a a set of um startup founders to to build in this environment?
**Greg Brockman：** 首先，我建议大力拥抱这些工具。现在的工具已经变得非常有用。即使只看去年 12 月的变化，agentic 编程工具从写大约 20% 的代码变成了写 80% 的代码，这意味着它从配角变成了你工作的主角。我认为我们今年会在所有电脑工作中看到同样的变化。你可以看看 Codex 最近的进展——它正在从一个软件工程师的工具变成任何用电脑工作的人的工具。就在过去一周，我们发布了一系列功能让它更加强大。今天我们刚宣布了一个叫 Chronicle 的新工具，它接入 Codex，能看到你用电脑做的一切，并形成记忆。你问它一个问题，它立刻知道你在说什么。你说"我五分钟前在干什么？"它知道。"这个人刚才说了什么？"它也知道。这让我意识到，你现在花了大量精力向电脑解释正在发生什么。为什么你要向电脑解释发生了什么？这完全没道理。所以我认为未来几年模型会越来越强，我们会有更好的工具框架，能解决越来越难的问题，产生新知识。但现在正在发生一个一次性的转变，核心是关于上下文。你的 AI 能否获得足够的信息？你开了那么多会议，却没让 AI 参加——这对 AI 不太友好。你让它帮忙，但它什么信息都没有。所以我认为关键是要确保 AI 至少在理论上有足够的信息来解决问题，然后相信模型会不断改进。这将是一个持续改进和迭代的循环，要拥抱工具，和朋友交流看他们怎么用。但有一个一次性的投入——现在就是做这个投入的时候。
**Greg Brockman:** Well, first of all, I would say to lean in the tools right now have become incredibly useful. And if you look even over the course of December, I think that we went from these agentic coding tools being like, you know, they're like writing 20% of your code to writing 80% of your code, which means they go from being kind of a sideshow to being the main thing that you're doing. And I think we're doing that across all of the work that people do with computers, all computer work this year. And you can look at the recent progress on codecs. really changing from a tool for software engineers to a tool for anyone who's doing work with a computer. And just over the past week, we've released a bunch of features that just make it so much more powerful and capable. Um, and like one thing we just announced today is a new tool called Chronicle that plugs into codecs where it actually can see everything you're doing with your computer and can form memories of of what's going on. And so you ask it a question, you just it instantly knows what you're talking about. You're like, "Huh, what was I doing five minutes ago?" It knows, right? You're like, "Oh, what was this person talking about?" It knows. It's to me it was this real wakeup call to realize you spend so much of your effort right now just explaining to your computer what's going on. Like, why are you explaining to your computer what's going on? That makes no sense. And so, I think what's going to happen over upcoming years is the models are going to get much more capable. We'll have better harnesses. We'll be able to be able to solve harder and harder problems, come up with new knowledge, all of these things. But there is a one-time shift that's happening now, which is really about context. It's really about Is your AI able to you have all these meetings, you didn't include the AI, you know, that's not very nice to the AI. Like you're asking it to to help you with things and it has no information. So I think really leaning into how do you make sure the AI even has enough information in theory to solve the problem and then trust the models are going to really get there and improve. So I think it will be a constant cycle of improvement and iteration and leaning into the tools and kind talking to your friends and figure how they are using it. but that there is this investment that's a one-time investment that now is the time to make.
**Interviewer：** 在你把这些都设置好之后，OpenAI 内部使用 Codex 的方式和外部有什么不同？
**Interviewer:** And in terms of like let's say we you you set that all up. How do you how is open AI using codecs differently than you think everybody else outside is using it?
**Greg Brockman：** 在 OpenAI 工作的一个好处是你确实能活在未来。你能看到正在涌现的形态，而且我们可以协同设计——我们可以同时调整模型、工具框架和所有东西，以更好地服务我们看到的需求。我们的做法是这样的：我们从软件工程开始，设定了一些明确的准则，比如我们仍然希望有人类对所有合并的代码负责。最终判断"这段代码值不值得合并，结构好不好，会不会让代码库更难维护"，我们要确保有一个人类在签字确认。这种深思熟虑很重要——不是盲目使用，也不是完全拒绝，两个极端都不对。然后我们也在 OpenAI 内部逐个垂直领域推进——财务、销售、IT，都有一个小型专门团队，深入理解该领域，和领域专家合作，构建技能、修改 Codex 的界面等等，把它做好。一旦做好了，我们就会把它外部化，发布给大家使用。我们也开始和一些客户合作，对于那些想走在 AI 最前沿、想参与定义这场变革的人，这是有机会的。会后欢迎来找我聊。核心就是这种意愿——我们真的想拥抱 AI，活在未来，体验一年两年三年后其他人都会经历的事情。
**Greg Brockman:** Well, I think one of the amazing things about being at OpenAI is you do get to live in the future, right? You do get to really see the shape of what's emerging and we can co-design, right? we can really change the models the harness everything together in order to better serve the needs that we see and a lot of the approach we've been taking is so we started with software engineering and we set some clear guidelines for example saying that we still want a human to be accountable for all code that gets merged right so at the end of the day is it a good thing to merge this piece of code is it well structured is going to make our codebase more maintainable we want to make sure there's a human who is signing off to say And that's I think that thoughtfulness of not just saying oh co just blindly use this or you know oh we don't want to use this at all like I think neither extreme is quite right and then we are also going vertical by vertical within open AI to adopt these tools within finance within sales within IT and there we have a small dedicated team who's really deeply understanding the domain working with the people who are the experts in it in order to build skills in order to modify the codeex UI whatever it is that is is needed in order to get it to be good and then that's something we can then once we have it in good shape we will externalize and that we're able to to ship that to all of you and so we are starting to work with certain customers as well so for people who want to be very AI forward and want to be part of defining this revolution that there's a place for that and I'd love to talk afterwards but yeah I think that just this desire to say hey we really want to be AI forward really live in the future and experience what it will be like for everyone else one year two years three years down the road
**Interviewer：** 你们会因为活在未来而用不同的方式组织公司或工程团队吗？回顾历史，我父亲学计算机的时候就他一个人，后来有了长周期软件发布和瀑布模型，Web 和云出现后有了两个披萨的小团队和 Scrum。现在有了编程 agent，你们的组织方式有什么不同吗？
**Interviewer:** do you guys structure your company differently or the engineering teams differently because of of the living in the future. I mean if you have to go way back when my father learned computer science he was just himself and then we had these long software releases that became waterfall and then when the web happened and the cloud happened we had these two pizza teams and we had scrum now that we have these coding agents is it is how do you structure around everything differently?
**Greg Brockman：** 我们还在摸索中，但在某些地方已经看到了变化。比如，现在构建原型的成本非常低。
**Greg Brockman:** I think we're still figuring it out and there's certain places where you really see it. For example, the cost of building a prototype is cheap now.
**Interviewer：** 是的，
**Interviewer:** Yeah,
**Greg Brockman：** 非常便宜。以前做一个仪表板大概要一个人花一周时间，现在直接就做了。所以瓶颈实际上已经转移了，比如转移到共享——你怎么让任何人都能轻松构建一个仪表板、小组件、机器人，然后分享给别人？我们内部也在做这方面的工作，之后会外部化。这就开始对治理提出要求——你的 IT 部门需要能看到所有这些正在执行的线程、被分享的小工具，需要对数据来源有一定的控制权。举个好例子：现在人们开始把内部知识库转换成 wiki。我们内部就有一个很酷的工具。你马上会想到一个问题：如果内部知识库中有人的文档权限设置错了，他们发现"糟糕，我不想让这些信息被看到"，怎么修复？正常情况下你改一下文档权限就行了，但现在有了派生产物。所以你需要某种方式追踪：这个输出文档来自这个源文档，源文档对这部分人不再可见，那派生文档也要失效。你必须在技术架构中内置对信息使用方式的感知。这真正改变了团队之间的关系，改变了瓶颈所在和什么才是困难的事情。
**Greg Brockman:** it's so cheap and if you want to build a dashboard that used to be like h it would take like someone like a week to do it and you just do it now. And so actually a lot of the bottleneck has shifted to things like sharing like how do you and so we we actually have some internal work on this as well that again we will be externalizing of how do you make it really easy for anyone in your enterprise to build a dashboard a widget a bot whatever the thing is and then share it with others and then that starts to really put pressure on having good governance like you want your IT organization to be able to see all these different you know threads of execution that are happening all the little things that are being shared around have some control over data provenence right to really make sure that okay like a good example of this is um I think people are now starting to take their internal knowledge dumps turn them into wiks. We we have some some a really cool one of these internally. And the thing you immediately think about is well, if someone has a document in the internal knowledge base that was accidentally permissioned incorrectly and they realize, oh no, I didn't want this information to be accessible. How do they fix that? Right? So, normally it's they go into the doc, they change the permissions, but now there's these derived artifacts. And so, you need to make sure you have some way of tracking through the system to say, well, this output document came from this source one. The source one's no longer accessible to this audience. let's go and invalidate that as well. And so you have to start really building your technical architecture with awareness of the way that people are going to use this information. And it really changes how teams relate to each other because you can just it really changes where the bottlenecks are and what's hard.
**Interviewer：** 你觉得团队规模会变小很多吗？十年后还会有人类软件工程师吗？
**Interviewer:** Uh do you think team size is going to be a lot smaller? We're going to have still human software engineers in a decade.
**Greg Brockman：** 十年是很长的时间，这项技术的天花板确实很难真正内化理解。我认为很明显"公司是什么"会在很多方面发生变化。我们会看到独立创业者（solopreneur）构建令人难以置信的企业。任何有愿景的人都将能够实现它。大家的工作会在很多方面变得更轻松、更有趣。但也可能竞争更激烈，因为每个人都有这些强大的工具。所以找到你的独特定位和独特角度可能会成为最核心的事情。目前我们组织大型团队的方式几乎只有一种——有团队、有管理层级、有职责范围、有层级架构。也许这可以改变。也许可以变成更扁平的小团队做出惊人的事情。我们现在就在数学领域看到这种现象——网上的个人用 GPT 5.4 Pro 在解决未解决的数学难题。通常你需要一个数学团队，现在他们一个人就在做。
**Greg Brockman:** Well, decade is a long time from now and that the ceiling on this technology is hard to is really hard to internalize. I think that it is clear that what a company is will change in a lot of ways. I think that we're going to have this ability for solarreneurs to build very incredible businesses. And so, anyone who has a vision, I think we'll be able to realize it. I think the jobs that you all have will become way easier in a lot of ways, way more fun now. Might be more competitive too, right? Because everyone's going to have these amazing tools. And so really figuring out what is your niche, what is your unique angle is probably going to become kind of the most important core. But a lot of how we run organizations right now and it's there's almost only one way to organize large groups of people where you have teams and you have management structures and you have scopes and you have these hierarchies and all these things. Maybe that can change. Maybe you can be much more flat small teams that can really just do incredible things. Like we're seeing it right now in mathematics where these individuals on the internet are using GPD 54 Pro to solve these unsolved math problems. Normally you need a math team and they're just doing it.
**Interviewer：** 是啊，我儿子是个数学迷。我刚告诉他也许应该学点数学以外的东西。
**Interviewer:** Yeah, my my son's a math nerd. I just told him that maybe you should be studying something else besides math.
**Greg Brockman：** 但这恰恰是值得思考的问题。想想 AlphaGo 的第 37 手，那一步改变了人类对围棋的理解。但令人惊讶的是，它让这个游戏对人类来说变得更有趣、更重要了。也许在其他领域也会如此。
**Greg Brockman:** But I Well, but see this is the question, right? is if you look at something like Alph Go, you know, move 37, this move that just like changed humanity's understanding of of the game. But the thing that was surprising is it made the game more interesting and important for humans. And maybe that'll be true for for these other domains, too.
**Interviewer：** 确实。在构建生产级 agentic 工作流时，常见的失败模式是什么？你看到创始人们经常犯哪些错误？
**Interviewer:** True. What about uh common failure modes when you're building when you're you're building with uh production agentic workflows? What do you what do you see as the common things that founders get wrong and they're building incorrectly these days?
**Greg Brockman：** 这些模型非常强大，要想用好它们需要深思熟虑。我们一直在投入安全原语（security primitives）、可观测性（observability）、治理等方面。给你讲个有趣的小故事：我在用 Codex 工作时，让它安装某个人写的开源包，遇到了错误。我说"在 Slack 上 ping 一下那个人问问。"它就 ping 了。两分钟后它说"等太久了，我已经升级给他的经理了。"它真的 ping 了那个人的经理。你想想，一方面这是模型做的一件合理的事——它很主动，在尝试解决我的问题，不是干坐着等指令。但另一方面，也许应该再多等一会儿，也许应该先问问我。所以我认为这些关于情商的问题很值得思考。模型在某些地方已经做得很好了。比如之前人类在审批时就是一路点"批准、批准、批准"——
**Greg Brockman:** Well, I think that these models, they have such power and really understanding how to operate them well takes thought and so we've been investing a lot in primitives, security primitives, observability, having again good governance, things like that. Um, but just to give you one anecdote that I think is evocative um I asked, so I was working with my codeex. I asked it to install some package that someone had open written ran into an error. I was like, "Oh, ping that person on Slack and ask them for help." So, it pinged the person on Slack. Two minutes later, it said, "This is taking too long. I've escalated to the person's manager." And it actually pinged the person's manager. And and you realize it's like on the one hand, it's kind of a reasonable thing for the model to do. It's being proactive. It's trying to solve my problem. It's like, you know, not just sitting around waiting to be told what to do. But on the other hand, like, you know, maybe should have taken a little bit longer. Maybe should have checked with me. And so I think that really thinking about these questions where we're still building up the EQ of the model and that in some places it's getting very good. For example, clicking approve, approve, approve is kind of where we've been. And humans are not very good at that either, right? It's like
**Interviewer：** 人就是会默认通过。
**Interviewer:** they just they just default.
**Greg Brockman：** 就是默认通过。所以现在我们开始让 AI 来判断：这是不是高风险操作？这个应该升级处理，那个可以自动批准。这让你意识到，人类注意力将成为一种极度稀缺的资源。现在"执行"变得容易了。"这是不是好事？这是不是我想要的？这是否符合我的价值观和意愿？"——这将成为最重要的瓶颈。所以我认为构建系统时要把这一点考虑进去，真正思考人的因素，这是最重要的事情。
**Greg Brockman:** They just default. And so now we're starting to have AIs that can actually take care of flagging is this a high-risis risk action? Hey, this one should be escalated. This one's okay to auto approve. And it really makes you realize that human attention is going to be this incredibly scarce resource, right? The doing of things now is easy. The is this a good thing? Is this what I wanted? Is this aligned with my values, with my desires, that is going to become the single most important bottleneck? And so I think building systems that take that into account and really think about the human factor like that's the most important thing to do. Now
**Interviewer：** 另一个人的因素是安全。你会建议人们怎么在 AI 时代思考安全问题？我们不断听到数据泄露的消息，最近 Vercel 就出了事。而且这些模型非常擅长发现安全漏洞。你会怎么建议大家用模型来发现安全问题？
**Interviewer:** another human factor, security. um how would you advise people to think about security in this world of AI and just heard about breaches left and right with Versel recently and then and these models are incredibly powerful at finding security holes. So how how would you recommend people here use the models to to find those security issues?
**Greg Brockman：** 我认为答案有几个层面。互联网一直是一个安全问题越来越重要的地方。想想从早期经历 90 年代的病毒、蠕虫、恶意软件，我们已经度过了那个阶段。我认为我们正在走向一个最终更安全的状态，但这需要整个互联网层面的努力。所以很多时候就是要拥抱这项技术——让这些模型扫描你的代码库，用于端到端的红队测试，它们能做很多事情。我们在思考未来模型改进时，也在研究如何利用可信访问计划（trusted access programs），如何利用那些真正关心防御和让互联网更安全的社区。每个人都有角色可以扮演。但最关键的一点是认识到：这些模型非常强大，但不是魔法。它们是整体安全韧性生态系统的一部分。我认为我们作为社会、每家公司都有责任去思考如何整合这些工具，使其带来更多保障——无论是评估某个补丁，还是确保及时滚动更新。还有很多工作要做，但我对未来方向很乐观。
**Greg Brockman:** Well, I think there's a couple levels to the answer. I do think that this is I think that the internet has been a place where security has been just like a a ratcheting important concern over time. You think about where it started going through the '9s with viruses and worms and malware and those things and we've moved past that. Um I think we are also moving now to a much more ultimately secure regime, but it does require kind of a internetwide effort to get there. And so a lot of this honestly is just again leaning into the technology having these models. They can scan your codebase. they can actually be used for end-to-end red teaming. Like there's a lot that can be done with them. And a lot of how we're thinking about further models and improvements there is really leaning into how do we how do we actually sort of leverage trusted access programs? How do we leverage the community of people who really care about being defenders and making the internet more secure? I think that's something where everyone has a role to play and can participate. But the number one thing is just sort of recognizing that these models are very powerful, but they're not magic, right? that they are just like a part of the overall resilience ecosystem. And I think that we as a society and I think every company again really contributes to this have something to build in terms of how do we how do we incorporate these in a way that results in more assurance and more sort of certainty on the impacts of of whether it's a particular patch that you're taking, whether it's thinking about how do you make sure that you're um yeah just sort of rolling in updates quickly as they're being released. Um, so I think that there's a lot of work to be done, but I have a lot of optimism for where this is going.
**Interviewer：** 来聊聊速度。一切似乎越来越快。我们处在加速变化的世界中。你之前走上来的时候我们就在聊怎么跟上节奏。你怎么跟上这些加速变化？你会建议大家怎么跟上？
**Interviewer:** Um, let's switch to speed. Seems like things are moving faster and faster and faster. We're in the world of accelerating change. We were talking about it when we when you uh you were walking up here around how how you're trying to keep up with things. How how do you you keep up with all the accelerating change? How would you recommend everybody here keep up with everything that's changing?
**Greg Brockman：** 我认为这就是新常态，而且某种程度上这不完全是因为 AI。过去二十年技术一直是这个趋势——做事的人更多了，做事比以前更容易了。进入门槛降低意味着也更容易创造价值、取得巨大成功。所以我认为要保持对变化的感知，了解什么在改变。而且始终要从同一件事开始：亲自体验这项技术。听别人描述 AI 和自己使用 AI 是非常不同的。AI 的美妙之处在于它非常直觉化——这正是重点。不再是你要扭曲自己来适应机器，而是机器来适应你。它在为你工作，你只需要提出要求它就会执行。所以我认为核心技能就是把握住什么在变化、什么成为可能、模型在哪里还有差距。这将在很大程度上决定公司未来的成败。
**Greg Brockman:** Well, I think this is the new normal and I think to some extent it's not really because of AI. I think it's just been the trend of technology for the past two decades. There's more people doing things. It's easier to do things than ever. Barrier to entry goes down means it's also much more easy to build value, right? To have great successes. And so I think that really trying to keep your ear to the ground and understand what's changing. And to some extent it always starts with the same thing which is play with the technology yourself. Like it's very different to hear AI described versus to use it. But the beautiful thing about AI is it's so intuitive. Like that's the whole point is that rather than have the machine be something you have to contort yourself to, the m machine canorts itself to you, right? It's doing work for you and it should be something where you ask it and does something. And so I think that just really trying to just get your finger on the pulse of what's changing, what's possible, where the models lag. That is I think the core skill that is going to really determine a lot of the success of of companies in the future.
**Interviewer：** 另一方面，你们也会为了配合安全研究而推迟发布模型。这和尽快发布是相反的。你们也在负责任地做事。你怎么看待这种平衡？你们处在竞争环境中，想尽快发布，但也想做正确的事。
**Interviewer:** And then on the flip side of that, you guys have held up held back models to work with security agents. So it's like the opposite of like going as fast as possible. So um you're doing things responsibly too. So how do you like think about the balance because you're in a competitive environment, you want to ship as quickly as possible and yet you're trying to do the right thing as well.
**Greg Brockman：** 从价值观层面来说，OpenAI 的核心是把 AI 的力量交到人们手中。我们相信人们可以用正在被创造的工具来建设未来，但需要以深思熟虑的方式来做。我们会认真考虑两面：好处是什么、风险是什么、如何最大化好处、如何降低风险。在网络安全和生物安全领域，我们非常审慎。我们在这些方面的风险缓解和可信访问计划上已经工作了很长时间。我们看到未来的模型会在所有能力维度上持续变得更强大。上周我们宣布了扩展网络安全可信访问计划——顺便问一下，在座有人申请了吗？我看到一两只手……你们更多人应该申请，这个计划很好。我们真的需要帮助，因为让值得信赖的、负责任的、真正想要推动模型发展的人参与进来非常重要，这会给所有人带来好处。接下来几周我们会有更多关于扩展计划的公告。而且当我们向所有人发布模型时，我们会考虑风险缓解措施，在尽可能广泛地提供能力和确保我们考虑到风险之间取得平衡，保持可观测性，确保部署产生最大的正面影响。简短的回答是：这是我们使命的核心。我们非常关心我们所做之事的影响，不是孤立地构建技术，而是需要整个社区、整个世界的努力来达到我们需要到达的地方。
**Greg Brockman:** Yeah, I think at a values level like what OpenAI is about like we really want to put the power of AI in people's hands. like we believe that people can we want to empower people to build the future with the tools that are being created but we need to do that in a thoughtful way right that we really think about both sides of here are the benefits here's the risks how do you maximize the benefits how do you mitigate those risks and I think that in cyber security and in biocurity those are areas where we're very thoughtful we've been building we've been working on these kinds of both mitigations and trusted access programs for quite a long time and that what we see coming is models that are going to be increasingly powerful and capable in a continuous way across all dimensions of capability and the you know we announced last week uh the expansion of our trusted access for cyber program by the way has anyone here applied no one oh I see one hand two hands okay more of you should apply it's great um we really need help because and and it's very important that people who are trustworthy and responsible and really want to push these models are participating in this because that is how that's going to pay dividends for everyone. Uh we're going to have more to announce over upcoming weeks on how we're expanding the program. But and also when we release models to everyone kind of the mitigations that that we have and how we're going to tune those to be both to really balance right to really try to bring these capabilities as broadly as possible while also making sure that the ones that are you know that we're thinking about the risks and and able to uh to have some observability over them and to ensure that that this is maximally positive in terms of deployment. So I think the short answer is like it's core to our mission. We care a lot about the impacts of what we're doing, not just building the technology in isolation, but it is a whole community, a whole world effort to really get to where we need to be
**Interviewer：** 现在从模型往上到应用层——这是在座很多人正在构建的层面。OpenAI 怎么决定在应用层构建什么、不构建什么？
**Interviewer:** on um now moving up from the models to the application layer, which is what a lot of people here are building. How do you how does OpenAI decide what in the application layer you're you're going to build and what you're going to leave out?
**Greg Brockman：** 大家可能最近经常看到"聚焦"这个词被用来形容 OpenAI，这可能是第一次。
**Greg Brockman:** Well, people have probably seen the word focus being applied to OpenAI uh quite a lot recently, possibly for the first time.
**Interviewer：** 微笑
**Interviewer:** Smiling
**Greg Brockman：** 有一阵子了。
**Greg Brockman:** in a while.
**Interviewer：** 嗯
**Interviewer:** And um
**Greg Brockman：** 这个词也被用来形容她了。确实很难，因为 AI 领域充满机会——你能想到的任何事情都会很棒，毫无疑问。但我们作为一家公司，无论建多少算力、有多少人，能做的事情是有限的。所以我们一直在思考：最聚焦的策略是什么？也许是二八法则，覆盖我们认为能产生最大影响的空间。我认为现在很明确，我们正在经历 agentic 的转变。这不仅仅是企业级和消费级的区别。我们显然在认真对待企业市场——向大公司销售，建立整套销售能力和流程。但消费级产品的含义也会改变。它是一个很宽泛的术语，涵盖多种东西。而其中关于不仅是生产力、还有关于目标——实现你的目标、甚至帮你弄清楚目标是什么、让 AI 能主动地去做这些事——这一切其实是同一件事。最终我们在尝试构建一个你可以对话的 AGI，它有所有的上下文，你可以在个人生活和工作中使用，它值得信赖。你可以向它寻求建议，获得有用的信息——健康、财务、职业规划，所有这些都汇聚成一个愿景。这意味着我们不得不做出一些痛苦的决定放弃某些事情。但我想说的是，这就是我们看待事物的视角，凡是有助于这个统一愿景的，你都可以期待我们去追求。
**Greg Brockman:** it's been applied to her, too. And it's it's hard because the field of AI is one of opportunity, right? It's like anything you're going you can imagine is going to be great. No question. It's going to be great. And we as a company, as a single company, no matter how much compute we build, no matter how many people we have, are only going to be able to do so much. And so a lot of where we've been how we've been thinking about things is what is the sort of most focused strategy that covers the parts of the space, you know, maybe it's an 8020 or just like the parts of the space that we think we can have most impact on. And I think there it's very clear right now we're going through this agentic transition. And so products that are and it's not just about enterprise versus consumer, right? So it's like clear we are being very serious about enterprise like we're selling to to big companies and and building a whole muscle and sales motion there. But consumer what consumer is is going to change, right? It's kind of a very broad term that buckets in multiple things. But that the slice of consumer that's about not just productivity but about goals about achieving your goal about even knowing what is your goal being able to elicit that and having an AI that can proactively do that. It's all kind of the same thing. Like in the end we're trying to build an AGI that you can talk to that has all this context that you can use in your personal life, your work life that's trustworthy, right? That you can go to it for advice and give you useful information, maybe health information or maybe about finances or uh you know about if you're trying to figure out what to do with your career. like all of these things they all kind of ladder into one thing and it's meant we had to make some very painful decisions about what not to do but I think I would just say that that's the aperture that we look at things through and that things that acrue to that singular vision of what we want to build you should expect us to pursue.
**Interviewer：** 明白了。你觉得几年后我们还会用命令行和 agent 来编程吗，还是会完全改变？
**Interviewer:** Got it. Um do you think we'll be coding with command lines and agents in in a few years or it's going to be completely changed?
**Greg Brockman：** 我觉得我们现在的工作方式是很不自然的。我们都坐在这个方盒子后面敲键盘。很明显我们的身体不是为此设计的——腕管综合征、驼背，诸如此类。我不觉得我们真的想要这样。我觉得我们——
**Greg Brockman:** I mean, I think that we're in a very unnatural state right now for how we work. Like, we all sit behind this box and kind of type away. And it's very clear our bodies were not designed for this. We got our carpal tunnel and our, you know, hunch shoulders and all these things. And I don't think we want that. I don't think any of us wanted that. Like I think that we
**Interviewer：** 想要更多自由时间。
**Interviewer:** want more free time.
**Greg Brockman：** 我们想要更多。但甚至不完全是自由时间的问题。你想花更多时间和你爱的人在一起，是的。你想花更多时间和人交流、想出精彩的愿景，或者只是思考你对什么感到兴奋，或者只是了解自己。所以，你想当一个管理十万个 agent 的组织的 CEO 吗？这其实听起来挺好的。我认为我们都将能完成更多的事情。但具体操作方式的变化，就像从用鹅毛笔手写所有东西，变成发一条短信然后有人代替你去执行你的目标一样巨大。
**Greg Brockman:** We want more. But it's it's not even about free time necessarily, right? It's like you want to spend more time with your loved ones. Yes. you want to spend more time like talking to people and like coming up with like brilliant visions or just like what you're excited about or just understanding yourself. So, it's kind of like do you do you want to be a CEO of an organization of like 100,000 agents? Like that actually seems pretty good. And I think that we're all going to be able to get so much more done. But the mechanics of it are going to feel as different as like going from having to write out things with, you know, by hand with a quill or something to being able to uh, you know, just send a text message and have people go and and, you know, working on your behalf on your goals.
**Interviewer：** 好的，我们聊了算力、模型、安全、agent 和应用层。来聊聊前沿。模型什么时候能好到推动科学前沿？在物理 AI 方面——之前 Jensen Huang 也来过这里。看起来 LLM 在数字智能方面有很好的 scaling law，但在机器人、物理智能、生物学和科学的某些方面没那么强，可能因为这些问题更难验证或验证需要很长时间。你怎么看科学和物理 AI 的进展？
**Interviewer:** All right, we talked about compute, we talked about uh model and security and agents and app layer. Let's talk about frontier. When when are the models going to be good enough to push the frontiers of science, physical AI? Seems like we had Jen Fam here. It seems like LMS have been a great scaling law for digital intelligence. It hasn't been as strong for robotics, for physical intelligence, for aspects of biology and science where the problems are probably a lot harder to verify or takes a long time to verify. Well, how are you keeping track of science and and physical AI in in the world?
**Greg Brockman：** 科学是我们真正在投入的一个领域，我们看到了通向惊人进展的路径。我们开始有了一些初步成果。我认为在预测六个月或一年后会发生什么的时候，始终以当下正在发生的事情为基础很重要。比如，我们有一个物理学结果：我们的 AI 提出了一个非常优美的公式，而研究这个问题很长时间的物理学家们原本认为这完全不可能——可能是一个无解的问题。这很重要。这些严肃的物理学家认为这是朝着量子引力等问题的答案迈出的一步——虽然还没到那里，但这比几个月前前进了一大步。这让你真的好奇一年后我们会走多远。像生物学这样的领域确实和物理学、数学不同——你得离开美丽的模拟世界，面对混乱的现实。但我认为我们已经在其他领域学会了如何处理混乱的现实。软件工程就是一个很好的例子——我们意识到仅仅做出能解决编程竞赛的东西是不够的。你需要一个见过真实世界混乱代码库的系统，要应对人类以各种方式打断它、对抗性地折腾它。所以我认为在科学领域我们会看到一场真正的复兴。也许今年会有一些大成果。明年我觉得会是一个疯狂的时期。
**Greg Brockman:** Well, well, science is one domain that we're really leaning into and we see line of sight to really incredible progress. And we're starting to have some signs of life and I I think it's always important to ground in what is happening today when trying to predict what will happen six months, a year from now. So for example, we had a physics result where our AI came up with this very beautiful formula that physicists who've been working on this for quite some time thought was totally impossible. Thought it was like maybe an unsolvable problem and like it's pretty significant, right? It's like real serious physicists who um who who really view this as a step towards really being able to get to um to to some sort of answer for quantum gravity and all these things. not there but it's a step that's much bigger than where we were just a couple months ago and so it makes you really wonder a year from now like how far will we have traveled now things like biology that they are different from physics and math right that they are you got to leave your beautiful simulated world and you know deal with messy reality but I think we've been learning how to deal with messy reality in other domains software engineering is a perfect example where we've really realized that just building the thing that solves competition you know, programming competitions like that's not enough. Like you need something that's seen real world messy code bases, humans interrupting it different ways, like this adversarial banging at it. And I so I think that that on science I expect we're going to see a real renaissance. You know, maybe we'll see some big results this year. Next year I think is going to be a totally wild wild time.
**Interviewer：** 我们活在有趣的时代。我保证让你准时离开，因为你是个大忙人。在你走之前，还有一分钟。既然你现在没什么时间，但很快就会有很多时间了——你和 Anna 平时做什么消遣？
**Interviewer:** We live in interesting times. Um I I promise that I get you out on time because you're a busy man. Um before we let you leave, we got one minute on the shot clock. What since you have no time but soon you will have lots of time. Um what do you and Anna do for fun?
**Greg Brockman：** 消遣嘛，和大家一样——看电影、去徒步之类的。时间确实不太够，也许 AGI 之后会有更多时间吧。但你总得——
**Greg Brockman:** H fun I mean same as anyone like like to watch movies, go on hikes, those kinds of things. Um you know not as much time for it as as maybe we'll we'll hopefully have post AGI. Um but you got to
