网络安全与药物研发的物理法则之变

嗨，听众朋友们，欢迎回到 No Priors。今天，我和 Elad 与 OpenAI 的联合创始人兼总裁 Greg Brockman 进行了对话。

Hi listeners, welcome back to No Priors. Today, Elad and I talk with Greg Brockman, the co-founder and president of OpenAI.

我们讨论了 OpenAI 最新的数学成果、Astra，以及为什么 Greg 认为 AI 现在能够生成真正的新知识。我们谈到了药物发现的要点，以及他主张安全支出实际上应该远远超过软件支出，这是一个相当大胆的论断。以下是我们的对话。

We discuss OpenAI's latest math results, Astra, and why Greg believes AI can now generate genuinely new knowledge. We get into drug discovery dots, and his case for why security spending should actually vastly outstrip software spending, which is quite a claim. Here's our conversation with Greg Brockman.

Greg，非常感谢你接受这次访谈。

Greg, thanks so much for doing this.

谢谢你们邀请我。

Thank you for having me.

在 AI 领域，每周都感觉像过了一年，而本周 OpenAI 发生了很多事情。你们向超过十亿人推出了 GPT-6，这非常了不起。在数学方面，你们有一些非常令人兴奋的消息。毫无疑问，内部还发生着各种各样的事情。对于 OpenAI 内部以及更广泛的 AI 世界目前正在发生的事情，你最兴奋的是什么？

So, every week in AI feels like a year, and a lot's happened this week for OpenAI. You rolled out GPT-6 to over a billion people, which is pretty amazing. There's some really exciting news that you have on the math side. There's all sorts of things that undoubtedly are happening internally. What are you most excited about that's happening right now both at OpenAI and in the broader AI world?

看，我认为在 AI 领域，我对 OpenAI 的运作方式有这样的信念：有一句自行车界的名言——它永远不会变得更容易，你只会骑得更快。我认为情况正是如此：活动如此之多，每次你认为自己处于影响力的顶峰，事情的发展方式，你意识到实际上还有另一个层次是可能的。这不仅仅是关于任何一家公司，也不仅仅是关于任何一项技术，而是关于我们作为世界和社会都在共同采用这项技术，最大限度地利用它来改善每个人的生活。

Look, I think that in AI, I have this belief about how OpenAI operates: there's a cycling quote of it never gets easier, you just go faster. And I think that's very much what happens: there's just so much activity, and every time you think you're at peak impact and how things are happening, you realize that there's actually another level that's possible. And it's not just about any one company, it's not just about any one technology, it's really about we as a world and society are all coming together to adopt this technology, get the most out of it to improve everyone's lives.

所以，我认为本周的数学成果代表了一个真正的里程碑。这是一个真正的时刻：如果你思考过去一千年人类生活质量提高的根本驱动力，那就是技术，是科学进步。我认为我们现在有能力大规模加速科学发现。一直存在一个问题：AI 是否真的能够提出新知识？在这一点上，已经无法否认。答案绝对是肯定的，是可能的。现在，还有更多要做。我认为我们还没有提出全新的理论，但我们能够解决这些百年数学难题。

And so, I think to me, the math results this week represent a real milestone. It's a real moment where if you think about the fundamental driver of human quality of life improving over the past thousand years, it's technology, it's scientific advancement. And I think that we now have the ability to massively accelerate scientific discovery. And there's always been this question of is AI actually able to come up with new knowledge? And at this point, it's just impossible to deny. It is definitively yes, possible. Now, there's still more to go. I think we're not yet coming up with whole brand new theories, but we are able to resolve these hundred-year-old math problems.

对我来说，非常令人兴奋的是，这不仅仅是机器给你一个答案。而是人类配备这些工具后能实现以前无法想象的事情。我认为这几乎就像人类的杠杆作用，无论你想做什么，它都在大幅提升。现在，如果你有一个数学问题想解决，你可以解决它。但把同样的能力应用到生物学上呢？如果你是一个小企业主，想要很好地经营你的业务呢？那种针对该领域调整的智能水平，将意味着几乎这些与你的目标、你的愿望一致的不可想象的结果，现在将触手可及。这对我来说太令人兴奋了。

And to me, the thing that's very exciting is it's not just about the machine just gives you an answer. It's really what can a human equipped with these tools achieve that was unthinkable before. And I think it's almost like the leverage of humans to do whatever you want to do, it's going way, way up. Now you can, if you have a math problem you want to go solve, you can solve it. But what about taking that same capability, applying it to biology? What about if you're a small business owner and you want to be able to operate your business well? That level of intelligence that's tuned for that domain, it's just going to mean that almost these unthinkable results that are aligned with your goals, with your desires, are now going to be within the reach of everyone. And that to me is so exciting.

进步的速度也非常惊人，因为如果我回顾一下，过去模型在算术上都有问题，对吧？早期的模型，我记得人们抱怨：“哦，为什么它不会做基础数学？”而现在你们在解决这些高级定理。随着这些事物继续进步，并开始真正影响数学、物理、材料等领域以及各种应用，如果这些模型变得如此聪明，是否有必要关注如何有效地向人们传达这一点，或者你如何看待实际的沟通方面，当你处理一个非常高级的定理时？因为如果你看任何数学领域，只有极少数人能够真正评判一个人产生的定理。

The rate of progress has been pretty amazing, too, because if I look at it, you used to have problems with arithmetic, right? And early models, I remember people complain, "Oh, why can't it do basic math?" And now you're solving these advanced theorems. As these things continue to advance and this starts to really impact areas of math, physics, materials, et cetera, and various applications, is there some need if these models get so smart to focus on how to communicate that effectively to people or how do you think about the actual communication side of this as you do a very advanced theorem? Because if you look at any field of math, only a very small number of people can ever really adjudicate a theorem that's produced by a person.

是的。

Yes.

对吧？所以你就有了这种专业化。

Right? And so you kind of have this specialization that happens.

嗯，顺便说一句，这一直是——我来自数学背景。我以为我会成为一名数学家，我深受伽罗瓦或高斯这样的数学家的启发。这些人在 100 年、400 年的时间跨度上工作。我认为数学的深刻之美在于理解宇宙的结构。所以这不仅仅是——而且总是有这些问题：数学是发明的还是发现的？在我看来，数学的美和意义，在某种意义上并不是客观的。它在观者的眼中。它是吸引我们的东西，有些数学像物理定律，关乎简单性和压缩理论、复杂性理论等等，这些可能更偏向物理，但其中也有重要的属于人的东西。

Well, and this by the way is something that has always been—I came from a math background. I thought I was going to be a mathematician and I was very inspired by mathematicians like Galois or Gauss. These people who were working on like a 100, 400 year time horizon. And I think there's something deeply beautiful about mathematics being about understanding the structure of the universe. And so it's not just about—and there's always these questions about is math invented or discovered? And in my mind, the beauty and the meaning of mathematics, it's not something that is objective in some sense. It's in the eye of the beholder. It's something that appeals to us and there is some math that's like physical laws and it's about simplicity and compression theory, complexity theory and all these things that are maybe more physics, but there's something that is also importantly human about it.

所以对我来说，这不仅仅是——我认为很多关于数学的讨论不仅仅是这里有一个证明，没人理解它，太好了，我们解决了问题。这真的是关于推导出的见解是什么，我认为我们将看到——但我认为有希望的迹象——这些 AI 所做的工作中有美丽的见解。我认为我们现在看到的是，我们发表了大约 400 个非常重要的开放问题，现在已解决，数学界正在研究所有这些以寻找见解。如果你读一些人们发布的东西，这些特定问题的领域专家，他们说有这些几乎非常令人惊讶的应用想法的方式，或者令人难以置信——等等，为什么他们在这里使用了那个结果？而出来的不是这些不可理解的定理或证明，顺便说一句，数学中有不可理解的证明的历史。

And so to me it's not just about—I think a lot of the conversation about mathematics is not just about here's a proof, no one understands it, great, we resolved the problem. It's really about what are the insights that are derived and the thing that I think we will see—but I think that there's promising signs of life on—is that there's beautiful insights within the work that these AIs have done. And I think what we're seeing right now is that we published about almost 400 very important open problems that are now resolved and the mathematical community is going through all of that to look for insights. And if you read some of the things that people have posted, domain experts in these particular problems, they say that there's these almost very surprising ways of applying ideas or incredibly—wait, why did they use that result over here? And what's coming out is not these incomprehensible theorems or proofs, which by the way, there's a history of incomprehensible proofs in mathematics.

四色定理就是一个完美的例子。就像，好吧，我们只是让机器检查了一堆情况。这没什么美感。所以我认为我们进入了机器发现的不同阶段。

Like the four color theorem is a perfect example of this. It's like, okay, we just had a machine check a bunch of cases. There's no beauty in that. And so I think we're into a different phase of machine discovery.

我有些乐观地认为，随着模型也获得更好的心理理论，即弄清楚我们人类知道什么以及如何让我们跟随它们走上新理解之路的能力也将被铺平。所以，例如，我是一个数学人，但在学校的某个时候我停止了学习数学，然后我用了 Math Academy，然后我用了 ChatGPT 来帮助我学习新的数学。所以我只是觉得，对知识涨潮的期望，即你可以期望人们理解什么，也会在那里。

I have some optimism that as models also get to better theory of mind, the ability to figure out what we as humans know and how to get us to follow them on the path to new understanding will also be paved. And so, for example, I'm a math person, but at some point in school I stopped learning math and then I did Math Academy and then I did like ChatGPT to help me learn new math. And so I just feel like the expectation for the rising tide of what knowledge you can expect people to understand will also be there.

我认为这是一个非常重要的点，也是我期望并希望从这项技术中看到的、我们开始看到的可能被低估的好处之一：帮助人们知道更多、理解更多、更好地相互理解、更好地理解世界。我认为有技术机制可以支撑 AI 能够解决这些问题，看到领域之间的所有联系，但它实际上如何与你沟通？你如何理解它？实际上，甚至与 AI 安全和 Alignment 也有联系，即让 AI 能够理解它们在做什么。所以有一些我们自 2017 年以来一直在讨论的技术，比如辩论和迭代放大，它们有点像同一枚硬币的两面，你真正考虑的是，如果你有 AI 正在解决人类难以检查的问题。甚至例如，总结一本书就是一个很难检查的任务，对吧？除非你读过整本书，否则很难知道摘要好不好。所以如果一个 AI 只是给你一本书的摘要并说这是摘要，它是好摘要还是不好摘要？这很难，除非你有另一个 AI 试图证明这个摘要是糟糕的摘要。然后如果那个 AI 回来说：“嘿，这是我在书中找到的一段，它有一个明显非常重要的情节点，而摘要中没有体现，”你不必读完整本书就能说：“哦，是的，这确实是一个很好的论点。”所以你意识到有办法训练 AI，这些方法实际上非常符合让人类能够监督、能够坐在驾驶座上，这是基本价值，从 OpenAI 的角度来看对未来很重要的事情，但也非常符合你所说的：你能让 AI 的结果成为让你学到东西的东西，真正为人类量身定制，而不是为了某种抽象的知识而知识。

I think that this is a really important point and one of maybe the underappreciated benefits that I expect from and hope for and we're starting to see from this technology is helping people know more, understand more, understand each other better, understand the world better. And I think that there are technical mechanisms that can underlie the AI that's able to go and solve these problems, see all the connections between domains, but how does it actually communicate it to you? How do you understand it? And actually there's even connections to AI safety and alignment on having AI that we're able to understand what they're doing. And so there are techniques that we've been talking about since 2017, such as debate and iterative amplification, which are kind of two sides of a similar coin where you really think about if you have AIs that are solving problems that are just too hard for humans to check. And even for example, summarizing a book is an example of a task that's really hard to check, right? It's really hard to know is the summary any good unless you've read the whole book. And so if an AI just gives you a summary of a book and says this is the summary, is it a good summary or not a good summary? It's pretty hard unless you have another AI who is trying to prove that the summary is a bad summary. And so then if that AI comes back and says, "Hey, here's this paragraph in the book that I found that has a clearly very important plot point and that is not represented in the summary," you don't have to read the whole book to be like, "Oh yeah, that's actually a very good argument." And so you realize that there are ways to train the AI that are actually very aligned with having humans be able to have oversight, to be able to be in the driver's seat, which is a fundamental value, a fundamental thing that is important for the future from OpenAI's perspective, but also are very much aligned with what you're saying of can you make the results of an AI be something that means you learn something that is really tuned for the human rather than just sort of knowledge for some abstract knowledge sake.

对大多数人来说，数学理论的进步仍然有些抽象，对吧？所以会有“哇，这绝对在今天发生”的感觉。然后还有，你知道，AI for science，有一种说法或信念认为它可以在未来多少年内解决所有疾病，对吧？你如何向前看一步，即我们可能会看到什么中间进展？

For most people, advancements in math theory are still somewhat abstract, right? So there's like, "Wow, this is absolutely happening today." And then also, you know, AI for science, like there's a claim or a belief that it could solve all disease in some number of future years, right? How do you look forward like one step in terms of what we might see in advancements in between?

嗯，我认为我们已经有了几年时间来迭代这项技术，以了解预期的形态。我认为这有点像当你有一个在某个维度上有能力的 AI。如果你理解它为什么在那个维度上有能力，那么所有其他具有类似属性的人类努力领域，你也应该期待类似的结果。所以在我看来，为什么数学是我们设法真正转变的领域，我们取得了如此不可思议的结果？部分原因是我们的 AI 现在处于一定的能力和原始智能水平。部分原因是你可以认为数学是可检查的，但这已经不太正确了，对吧？就像我们真正开始于非常可检查的问题，比如这里有一个书面算术问题，有一些数字答案，所以你可以训练 AI 来检查它是否答对了，因为我们可以非常直接地看到 AI 提出的答案是否与实际答案相同？但对于证明，非常不同。证明很难检查。这实际上准确吗？形式化的证明，好吧，是的，那是可检查的，但自然语言证明，这才是我们真正产生的，要困难得多。但我认为核心洞察实际上是关于数据，对吧？你可以在数学中获得大量数据，在理论物理中获得大量数据。你能在生物学中获得大量数据吗？这显然是受数据限制的，非常困难。现在，幸运的是，我们也一直在研究许多技术，以便能够用更少的数据工作。如果你想想这意味着什么，就像，好吧，我们现在有聪明的模型，如果有大量数据的东西，这是深度学习的基本物理，对吧？算力、数据、算法等于能力，等于结果。我们能够在这些困难的领域做到这一点，人们花了数百年时间，我们最聪明的人都在试图解决这个问题，现在 AI 可以解决它。太棒了。当你把它转移到生物学时，显然我们想要的是药物发现，所有这些生物学及其理解的根本性变化，以改善生活质量，这就是我们想要应用的。问题将是，而且尚未解决，我们有数据吗？我们有能力获取数据吗？所以我认为将会发生的是，我们将采取这些，我们已经这样做了，我们采取这些模型，这些相同的技术，并将它们应用于生物领域。有些领域会很快倒下，对吧？我们会看到曾经非常痛苦的事情现在变得非常容易，大规模加速。但会有其他瓶颈，问题将是，好吧，我们如何缓解这些瓶颈？顺便说一句，其中一些甚至不是真正关于深层生物学理论。例如，监管文件。巨大的瓶颈，对吧？写整个临床试验案例和所有这些事情需要大约一年时间。而你可以用 AI 更容易地写这些东西。所以我认为我们必须审视药物发现过程的每一步。而且会有一些地方根本数据不存在。

Well, I think that we've had a number of years in iterations of this technology to see the shape of what to expect. And I think that it's kind of like when you have an AI that is capable in a certain dimension. And if you understand the why it got capable in that dimension, then all other fields of human endeavor that have similar properties, you should also expect similar results. And so in my mind, why is mathematics something that we've managed to really transform in the way that we have had such incredible results? And part of it is that our AI is now at a certain level of capability and raw intelligence. Part of it is that you could think of it as that well, mathematics is checkable, but that's not quite true anymore, right? Like we really started with very checkable problems of here's a written arithmetic problem, there's some numeric answer, and so you have an AI that you can train to just like check if it got the answer right because we can very much just see is the answer that the AI is proposing the same as the actual answer? But for a proof, very different. Proofs are very hard to check. Is this actually accurate or not? A formalized proof, okay, yes, that is checkable, but natural language proof, which is what we're really producing here, much much more difficult. But the thing that actually I think is the core insight is really about data, right? It's that you can get lots of data in mathematics, you get lots of data in theoretical physics. Can you get lots of data in biology? That's obviously something very data bound, like very difficult. Now, fortunately, we also have been working on lots of techniques to be able to work with less data. And if you think about what that means is like, okay, we now have smart models that if there's something where there's a lot of data, and this is the fundamental physics of deep learning, right? Compute, data, algorithms equals capability, equals results. We're able to do it in these hard domains where people have spent hundreds of years of our smartest people all trying to solve the problem, and now the AI can solve it. Amazing. When you transfer that over to biology, and clearly what we want is drug discovery, all of these things that are like fundamental changes in biology and our understanding of it to improve quality of life, that's what we want to apply. The question will be and that is not yet resolved, do we have the data? Do we have the ability to get the data? And so I think that what will happen is that we will take these, and we're already doing this, where we take these models, these same techniques, and apply them in the biological domain. And there's some areas that will fall quickly, right? That we'll just see massive acceleration of things that used to be incredibly painful, now incredibly easy. But there will be other bottlenecks, and there'll be a question of, well, how do we alleviate those bottlenecks? And some of these, by the way, are not even really about deep biological theory. For example, regulatory filings. Huge bottleneck, right? It takes like a year to write up your whole sort of case for a clinical trial and all these things. And it's just like you can write those things in AI much more easily. And so I think that we have to look at every step of the drug discovery process. And there are going to be places where the fundamental data just does not exist.

因此，湿实验室的创新、模拟方式的创新，所有这些都将变得极其重要。但我认为我们会看到这些突破的到来，重要的是要认识到有些东西与数学是共通的，那就是关于科学发现，而有些东西在数据来源方面则非常不同。

And so then innovation on wet labs, innovation on how you actually do simulations, all of this will become incredibly important. But I think that we will see these breakthroughs coming, but it's important to know there are things that are shared with mathematics, that's about scientific discovery, and things that are very different in terms of where the data comes from.

有没有具体的……你提到了生物学，我认为这是一个很好的例子，很多早期研究，比如 AlphaFold 之类的，确实专注于设计分子或理解分子如何结合。正如你所说，开发一种药物需要 15 年，花费 15 亿美元。其中约 5000 万美元用于临床前研究、小鼠研究、分子本身，然后 14 亿美元以上用于临床试验。所以你的观点是，这也关乎 2-3 年与另外 12 年的对比。你的观点非常正确，这些模型的早期重点一直是‘这个东西能否结合’，而不是下游还要担心哪些其他事情才能真正将其转化为对人类事业有用的东西。

Are there specific um you mentioned biology, and I think that's a great example where a lot of the early research, AlphaFold and things like that, were really focused on designing a molecule or understanding how something binds to each other. And to your point, it takes 15 years to develop a drug, it's $1.5 billion. 50 million or so tends to be preclinical, mouse studies, the molecule, and then 1.4 billion plus tends to be the clinical trial. And so your point is and it's also, you know, 2-3 years versus another 12 years. And so your point is really well taken in terms of a lot of the early emphasis of these models has been can the thing bind versus what are all the other things you have to worry about downstream to actually turn it into a productive thing for human endeavors.

在 OpenAI，你们目前如何对不同的科学领域进行优先级排序，比如你们关注哪里，或者你们希望在哪里生成更多数据、更多结果或更多能力，让其他人受益？

How do you at OpenAI currently stack rank the different areas of scientific endeavor in terms of where you're focused or where you want to generate more data or more results or more capabilities that others can benefit from?

看，我认为药物发现是最清晰、最明显的领域，如果你要选一个想要彻底变革的领域的话。而且那些真正能提升每个人生活质量的地方，纯粹是非常重要的，也许是大家长期以来一直在想的 AI 的头号应用，我们显然必须去做。正如你所说，整个流程太长了。我认为我们会发现人类在生物学上非常糟糕，在药物发现上非常糟糕。如果你想一想，对于人类来说，自然语言是我们非常擅长的。它非常人性化，非常契合我们与世界互动的方式。而生物学则完全陌生。但对于神经网络来说，我并不认为有任何理由相信它在自然语言上应该比在生物学上更好。所以，既然我们看到神经网络在自然语言上取得了如此惊人的成果，那么从第一性原理出发，你应该能够真正破解生物学的语言。所以对我来说，这是第一位的，从第一性原理来看它应该可行，就我们所看到的而言，我们在很多方面已经非常接近了

Look, I think that drug discovery is the most clear and obvious area if you get to pick one that you want to fully revolutionize. And just the places where you're going to really uplift everyone's quality of life that is just like so purely important and maybe the top one application of AI that everyone's kind of thinking about for a long time, we clearly got to do it. And to your point, like there's this whole pipeline, it's just so long. And I think we're going to find that humans are incredibly bad at biology, incredibly bad at drug discovery. And if you think about it, there's this property where for humans natural language, we're very well suited to it. It's something that very clearly is very human, it's very well tuned to how we have interacted with the world. Whereas biology, it's totally foreign. But for a neural net, I don't really think there's any reason to believe it should be better at natural language than it should be better compared to biology. And so the fact we're seeing such incredible results with neural nets in natural language, you're just like from first principles, you should be able to really crack the language of biology. And so to me, that is number one, it's like first principles it should work in terms of what we're seeing, we're so close in so many

是的，这真的很令人兴奋。

Yeah, that's really exciting.

是的，我认为可以大幅增加并行性。我认为可以增加，我认为可以缩短许多步骤的持续时间。顺便说一句，药物发现有很多维度，例如，对于某种疾病，甚至有很多人，治愈他们特定疾病的药物已经存在。只是它存在于文献中的某个地方，有一篇论文，如果你能找到那篇与你的病情相关的论文，你就能治愈那个人。这方面有很多先例。我认为可以大规模扩展这类操作。所以我认为短期内的这种影响实际上是非常可能的。嗯，但材料科学是另一个例子，我认为还有一长串其他领域，我们应该看到人类所能实现的事情发生真正的变化。

Yeah, and I think you can massively increase the parallelism. I think you can increase, I think you can decrease so many of these steps in terms of their duration. And I think by the way that there are many dimensions to drug discovery, for example, and of one diseases, um there's even like lots of people who the drug to cure their particular condition exists. It's just that it's just out there somewhere in the literature that there's a paper and if you can find that one paper that's relevant to your conditions, then you'll be able to cure the person. And there's plenty of precedent for it. I think you can massively scale up those kinds of operations. So I think that impact in the short run is actually very possible. Um but material science, another example, and I think there's a long tail of other domains where we should just see real changes in kind of what is possible for humanity to accomplish.

看起来，嗯，我们想想为什么一种药物可能需要 15 年才能上市。嗯，部分原因是你正在进行越来越昂贵的试验，这些试验有一系列不同的安全考虑，最终导致大规模人体试验，而今天的这些试验是由法规规定的，对吧？这是有充分理由的。如果你相信技术会起作用，并且部分数据可以获取或生成，那么一个限制因素似乎将是说服监管机构，我们应该改变对哪些试验被接受以及步骤是什么的期望。

It seems um just we think about why a drug might take 15 years to go to market. Um part of it is you're doing like successively more expensive trials that have a, you know, different set of safety considerations resulting in large-scale human trials and and then these trials today are they're ordained by regulation, right? For a good reason. It feels like one of the limiting factors if you believe the technology will work and parts of the data can be acquired or generated is going to be convincing regulators that we should change the expectations around uh uh around what trials are accepted and what the steps are.

我认为现在有一个时刻，要达到这一点还有更多工作要做，但如果你能真正证明药物发现的物理规律已经改变，那么它当然应该渗透到系统的每个部分。如果你想一想，甚至举一个非常不同的例子，想想自动驾驶汽车，对吧？我们的道路运营和安全等很多事情都是为人类驾驶员量身定制的。如果你达到一个点，自动驾驶汽车比人类驾驶安全 10 倍或 100 倍，对吧？你已经达到了 AI 实际上能够在一个领域中交付结果的地步，这些结果如此明确地是你想要的，那么你当然应该说：“好的，我们如何重新思考整个交通系统？”也许你应该有法规规定只允许在某些地区使用自动驾驶汽车。就像有很多潜在的变化，你需要思考系统试图产生什么，这也需要渗透回法规。当然，法规在世界各地并不统一，对吧？在临床试验方面，澳大利亚和美国的法规非常不同，他们已经看到很多人将试验转移到监管环境更合理的地方，以符合他们想要追求的方式，以及人们认为有效的方式。所以我认为事情究竟应该如何改变，很大程度上取决于我们实际能证明什么，对吧？你是否真的达到了从神经网络获得比实际进行临床试验更好的结果的地步？我们还没有到那一步，但你可以想象我们会达到。

I think there is a moment here where and there's more work to be done to reach this point, but if you can really prove that the physics of drug discovery have changed, then of course it should trickle through to every part of the system. And if you think about I mean even just take a very different example, think about self-driving cars, right? That we have so much of our how roads operate and safety and all these things that are really tuned for human drivers. And if you have if you reach a point where self-driving cars are a 10 times or 100 times safer than than human ones, right? That you've reached that point where AI is actually able to deliver the results in a domain that just are so unambiguously the thing that you want, of course you should then say, "Okay, how do we rethink the whole transportation system?" Maybe you should have regulation that says that you only are allowed to have self-driving cars in certain areas. Like there are a lot of of potential sort of changes to the to the the way that you think about what is even trying to come out of a system that needs to trickle back into regulation as well. And of course regulation is not uniform across the world, right? That there's very different regulations in Australia versus the US when it comes to clinical trials and they're already seeing a lot of people moving their their trials to places where the regulatory environment makes more sense for how how things are how they want to pursue and and you know, what people think is is efficacious for that. And so I think that the exactly how things should change, I think we'll depend a lot on what we actually can prove, right? Is it the case that you actually reach the point that you can get better results from a neural net than from actually running a clinical trial? Like, we're not there, but you can imagine we will get there.

如果它们在有效性或毒性等方面更具预测性，那么显然你应该改变它。

if they were more predictive on efficacy or tox or something, then obviously you should change it.

当然。我认为在社会应该并且我希望会非常投入的领域，可能性已经从根本上改变了，然后让我们很快地围绕它重新定位。

That of course. And I think of areas where I think society should and I hope will be very sort of leaned into the what is possible has fundamentally changed, and let's very quickly then reorient around it.

再说一次，你必须真正思考所有的后果，对吧？以及所有的含义。而且，如果你考虑的是对人的长期研究，以及这个神经网络在预测方面——模拟对于长期结果是否真的准确——很难不至少有一个长时间刻度来对应。但我认为我们会看到大约 80% 的可能很快成为现实。

And again, you have to really think through all the consequences, right? And all of the implications. And one of the things—if you're thinking about long-term studies of people and the question of whether this neural net, in terms of what it's predicting, the simulation is actually accurate for long-term outcomes—it's pretty hard to not have at least one long time tick to correspond to that. But I think we will see like 80% of what is possible come to fruition very quickly.

现在距离 Astra 发布和 DevDay 已经有一段时间了。就内部或外部人们如何使用一些新产品或能力而言，有什么让你感到惊讶的吗？

You're a little ways out from the Astra release and DevDay now. Is there anything in terms of how people internally or externally are using some of the new products or capabilities that surprises you?

嗯，我认为 Dots 真的改变了很多人的工作方式。而 Dots 所代表的东西是一个——

Well, I think Dots has really changed how many of us work. And the thing that Dots represents is a—

简要描述一下 Dots。

Describe Dots briefly.

是的。所以，Dots 是一个主动型智能体。它始终在线，始终工作。你停止工作，它继续工作。而我的 Dots 所做的是——我有一个 Dot，它会留意我本该做的工作，比如我本该发送的消息，我说过要做但还没做的事情。我每天早上醒来，都会有一个列表，就像‘哦，对，这是我的待办事项列表。’而且它经常注意到我从未要求它注意的事情。如果我的日历在日程安排上有冲突，它会指出来。比如‘嘿，你实际上需要提前离开这个会议才能赶上另一件事。’所以，它具有上下文感知能力。它知道我一天在做什么。它理解我们正在进行的项目。它知道我的收件箱里有什么，知道我的 Slack 里有什么，并且能够整合这些信息，它知道我一直在问的问题，所以它知道我最关心哪些领域。我认为，在某种程度上，Dots 只是形态的改变，而不是能力的改变，对吧？它就像一个 Astra 级别的模型。它有自己的车载电脑。但在工作场所中，这种形态的改变加上这种级别的智能，是游戏规则改变者。

Yeah. So, Dots is a proactive agent. It's always on, always working. You stop working, it keeps working. And the kind of thing that my Dots does—so, I have a Dot that's looking out for work that I was supposed to do, like messages I was supposed to send, things I said I would do that I haven't gotten to yet. And I always wake up in the morning, there's like a list of things that I was like, 'Oh, yeah, this is my to-do list.' And it often notices things that I never even asked it to notice. If there's like a conflict on my calendar in terms of scheduling, it'll point that out. If it's like, 'Hey, you actually need to leave this meeting earlier in order to get to the other thing.' And so, it's so contextually aware. It knows what I'm doing with my day. It understands the projects we're working on. It knows what's in my inbox, knows what's in my Slack, and it's able to integrate across these and it knows the questions I've been asking about, so it knows what areas are most top of mind for me. And I think that having—I mean, in some ways, what Dots is is just a change to the form factor, not the capability, right? It's like it's an Astra-level model. It's got its own car computer. But that change in that form factor in a workplace setting with this level of intelligence, it's a game changer.

那么你运行着多个 Dot，还是只有一个来覆盖你所有的后台任务之类的？

And do you have multiple Dots running or you just have one that's sort of covering all your background tasks or anything?

我有一个。我有一个。它是一只小猫头鹰——猫头鹰。你可以要求它更改图标。我就让它处理我工作生活中的所有事情。

I've got one. I've got one. It's a little owl—the owl. You can ask it to change the icon. And I just sort of have it doing everything across my work life.

OpenAI 显然有研究努力，然后今天还有消费者、企业和开发者业务、平台业务。你认为会不会出现分离，比如帮助你在工作的助手，像 Dots——我主要这么认为——和消费者助手，比如世界上的 Instincts Muses？

OpenAI obviously has research efforts and then a consumer and an enterprise and a developer business, platform business today. Do you think that there's going to be a separation between, let's say, assistants that help you at work, like Dots, which is how I primarily think of it, and then like consumer assistants like the Instincts Muses of the world?

嗯，有一些——我认为这些东西会融合，毫无疑问。而且我认为在不同环境中有一些非常不同的压力。所以，对于大众市场消费产品，真正弄清楚如何扩展并提供免费或极低成本的东西非常重要，而在工作场所，如果你是生产力工具，如果你是高管、个人贡献者，无论你的角色是什么，那么更容易说追求巅峰智能，而且只要你能获得回报，支付更多是值得的，投入更多算力是值得的。所以，我认为主要区别确实在于成本。我们在 ChatGPT 上看到了这一点，对吧？ChatGPT，我们投入了大量精力来拥有最大规模的免费层，并将记忆等功能带给所有免费用户是一项不可思议的努力，就像一项巨大的工程努力。而以高得多的成本为极少数人实现相同的功能非常容易。所以，我认为我们在个人助手工作空间中看到的是，将会有这些不同的市场细分，根本上将消耗不同数量的算力。所以，有时当算力水平不同时，就意味着产品可能看起来有点不同，对吧？可能一个产品中嵌入了更多功能。当然还有不同的用例。你考虑支付和旅行之类的事情，更多是面向消费者的。但我不认为有任何根本区别，对吧？真的就像是一个 AI 在做事情，只是碰巧在某个方向上有轻微的压力。所以，OpenAI 的优势之一是我们有 12 亿 ChatGPT 消费者，我们也有领先的编码和企业业务。没有其他人同时拥有这两者。所以，我们的很多策略是，让我们统一这些，这样我们就不会在两条战线上竞争。真的是在一条战线上竞争。我认为这也是 AI 的大方向，即模糊消费者和企业的区别。我们将会有企业家的复兴。就像我们已经看到的那样。我的意思是，想象你正在非常具体地看到这一点。我知道有人辞掉工作去创办公司，他们说，如果我没有一个我知道可以依赖的 AI 来处理这么多正常的进入壁垒，我永远不会这样做。所以，我认为我们将看到，就像这些区别已经保持了这么久，我认为它们将会改变。

Well, there is some—I think these things are going to converge, no question. And I think that there are some pressures that are very different in different environments. So, for a mass market consumer product, really figuring out how to scale and to provide things for free or at very low cost is very important, whereas in the workplace, if you're productivity, if you're the executive, individual contributor, whatever your role is, then it is much easier to say peak intelligence and really, as long as you're getting returns to paying more, it's worthwhile to throw more compute at it. And so, I think that is the main distinction is really around cost. And we've seen this in ChatGPT, right? That ChatGPT, we put in a lot of effort to have the largest scale free tier and bringing things like memory to all of our free users was an incredible endeavor, like a real huge engineering effort. And it's just very easy to implement the same functionality at much higher cost for a very small number of people. And so, I think that what we're seeing in the personal assistant workspace is that there are going to be these different market segments where different amounts of compute are going to be expended fundamentally. And so, sometimes when there's different levels of compute, it then means that the product can look a little different, right? That it can be that there's just more functionality that's embedded in one versus the other. And there's of course different use cases. You think about things like payments and travel and things like that are much more for the consumer. But I don't think there's any fundamental difference, right? It's like really just like an AI that's doing things and it's just like happens to be like slight sort of pressure in one way or another. And so, one of the strengths of OpenAI is the fact that we have 1.2 billion consumers on ChatGPT and we have also a leading coding and enterprise business. And no one else has both in house. And so, a lot of our strategy is to say, let's unify these so we're not sort of competing on two fronts. It's really competing on one front. And I think that this is also the general direction of travel of AI is to blur the distinction between what is consumer, what is enterprise. We're going to have this renaissance of entrepreneurs. Like we're already seeing it. I mean, imagine you're seeing this very concretely. And I know people who are quitting their job to go start companies who say, I never would have done it if I didn't have an AI that I knew I could rely on that could take care of so much of the normal barriers to entry. And so, I think that we're going to see just like the distinctions have held for so long, I think they're going to change.

所以，传统上区分企业和消费者世界的一件事是你如何看待 UI。显然，有你说的成本点以及以一定保真度为某人提供东西的底层能力，但另一部分只是用户界面。通常企业环境中的用户界面或访问方式与消费者环境非常不同。正如你所说，你正朝着统一这些层努力。你也一直在思考动态 UI。这一直是人们很早就在谈论的事情之一：你如何动态生成对用户有用的 UI？或者在某些情况下，这些东西会移到后台，只是你聊天的一部分。你认为 UI 将如何为这些不同的用例演变，或者当你处理这两个细分市场时，你如何看待这个问题？

So, one of the things that traditionally has separated the enterprise and consumer world is how you think about UI. Obviously, there's your point on cost and underlying ability to deliver something for somebody at a certain level of fidelity, but the other part of it is just the user interface. And often the user interface in an enterprise setting or how you access it is very different from a consumer setting. To your point, you're looking towards unifying these layers. You've also been thinking quite a bit about dynamic UIs. And that's always been one of the things people have talked about quite early is how do you just dynamically generate a UI that's useful for a user? Or in some cases, these things kind of move into the background and it's just part of your chat. How do you think UI will evolve for these different use cases or how do you think about that as a problem as you sort of address both of these segments?

所以，首先，我认为最好的 UI 是没有 UI。

So, first of all, I think that the best UI is no UI.

嗯。

Mhm.

我认为在 AI 领域，这就是我们前进的方向。你可以和它交谈，可以通过语音交流，可以通过 Slack、iMessage 等任何界面，也可以打电话给它。这 literally 就是你现在可以用 Dots 做到的事情。但个人环境和工作场所环境之间存在一些根本差异。在工作场所，你有管理员，对吧？你有公司的安全团队，有更多的利益相关者，而且这不仅仅是一个为你服务、代表你、你拥有并对其结果负责的 AI。所以，我认为这就是我看到的企业和家庭使用场景之间的主要区别。当然，这也导致了产品上的差异。有时是非常小的事情。比如，当我们发布新功能时，企业希望控制功能的引入时间。因此，我们有管理员控制和开关，所以，你可能会因为身处工作场所而体验到功能较少的情况。这就改变了——好吧，我们需要某种方式向你展示这一点。这又回到了最初的问题：重点是没有 UI，对吧？那么，功能缺失意味着什么？你甚至如何展示它并让人们知道这种情况？所以，这是一个持续的挑战，我认为它不会神奇地消失。我认为你需要拥抱这种复杂性。但对我来说，从根本上说，如果你想一想，ChatGPT 的文本框，这有点疯狂又美丽，对吧？它就是从那里开始的。它是一个文本框。而我们正在走向的地方甚至没有文本框，对吧？就像那就是我们被承诺的 AI。

And I think in AI that is where we're going. There's one of the thing you can talk to and you can speak to it verbally, you can talk to it over Slack, iMessage, any of the surface, you can call it. And that's literally what you can do with Dots, like literally today. Um but there are some fundamental differences between personal setting and a workplace setting. And workplace you have an admin, right? You have kind of your company security team. You have just more stakeholders and it's not just sort of a AI that's for you, that's representing you, that you own and you own its outcomes. And so, I think that that is the main distinction I see between the enterprise and the and the home use case. And of course, this leads to product differences as well. Sometimes they're very small things. Like for example, when we release a new feature that enterprises want control over when it's introduced. And so, I that we have admin controls and toggles and so, you can have a an experience where there's just less functionality available to you as a result of the fact that it is in the workplace. And that does then change the well, we need some way to surface that to you. And this comes back to well, the whole point was that there was no UI, right? So, it's like what does it mean for there to be to be functionality that's missing and how do you even surface it and let people know that that's the case? So, that's an ongoing challenge, and I think that that will not magically go away. I think that you need to lean into that that complexity. Um but yeah, to me, fundamentally, it's it's kind of crazy and beautiful if you think about it, the chat GPT the text box, right? That's where it started. It was a text box. And what we're heading to there isn't even a text box, right? It's just like that is that is the AI we were promised.

当我思考如何让一个没有 UI 的产品更好时，这得由你来说，但我认为包括：我能投入多少算力？模型本身有多好？有记忆系统。还有，我不知道，关于行为的决策。模型在校准方面有多好，比如它们应该对某件事思考多努力。现在，

When I think about what make make a product that doesn't have a UI better, there's like you tell me, but I think there's like how much compute can I spend on it? Um how good is the model itself? Uh there's a memory system. There's like I don't know, decisions about behavior. Um how good are models at being calibrated on uh you know, how hard they should think about something. Now,

这是我们越来越擅长的事情吗？随着 GPT-6 向所有人发布，我们现在有能力让 AI 去深入思考一个问题，它实际上会先给你一个快速答案，然后继续思考，再给你更正确、更全面的答案。另外，关于没有 UI 这一点，也接着你之前说的，我们推出了智能 UI。所以，现在 AI 可以即时生成生成式 UI。因此，我认为即使没有固定的 UI，没有按钮，没有菜单需要学习，拥有一个能够实际打造出视觉上完全符合你需求的结果的 AI。比如你想想甚至像航班预订这样的事情，你需要一个复杂的 UI，对吧？这样你可以看到所有不同的选项。对于你可能问的任何问题，比如如何组装自行车之类的，你可能想要一些非常定制的应用，通常一个人会花一个月去写，当然，你永远不会雇人为你写一个一次性的应用来帮助组装自行车，但让 AI 在你需要的那一刻实时为你做这件事是完全没问题的。所以，我认为这些方面，是的，我们现在处于一个 AI 越来越多地为你塑造自己的世界，对吧？用户界面正在变成你需要的那个东西，在你需要的时刻。这是我们很多关注的重点，我们在思考产品的各个方面都在这样做。

Is that something we're getting very good at? And with the release of GPT-6 to everyone, that we have the ability now for the AI that's going to go think hard about a problem, it actually give you a quick answer, and then keep thinking, and then give you the more correct the more fulsome answer. And also to the point of no UI, like and and to the thing you you were saying earlier, too, is that we introduced intelligent UI. So, now the AI can do generative UI on the fly. And so, I think that even though there's no fixed UI, there's no buttons, there's no menus to learn, having an AI that can actually craft the result to be visually exactly what you want. Like you think about even something like flight booking, you want kind of a complex UI there, right? So, you can see all the different options. And that for any question you might ask, like how do I assemble a bicycle or something, you might want some very sort of bespoke app that normally a person would have gone off and written for a month, and of course, you're never going to hire someone to write a one-off app for you to help assemble a bike, but totally fine for an AI to go do that for you in real time, the moment you need it. And so, I think that there is something about these like we're yeah, we're now in a world where the AI is molding itself much more to you, right? That the user interface is becoming exactly the thing you need at the moment you need it. And that is a lot of our focus, and we're doing that across every aspect of how we think about the product.

最近关于 AI 的讨论中最大的转变之一是关于网络安全的，以及如何构建能够抵御 AI 驱动的网络攻击的安全系统。所以我有点好奇你是如何思考这个问题的，如何构建护栏，或者如何处理网络安全的各个方面。

One of the biggest shifts in the discourse around AI recently has been around cyber, and how do you build secure systems that are resistant to AI-driven cyber approaches. And so, I'm just a little bit curious how you're thinking about that, how you're building guardrails, or how you're approaching the different aspects of cybersecurity.

嗯，首先，我确实认为网络安全的物理规律正在改变，我们正处于所谓的“防御者窗口”。我对这个窗口的理解是，我们拥有峰值的前沿 AI 能力，可以帮助防御者保护自己。这些能力尚未被广泛击败，但被广泛击败的能力正在逐步增强，你可以从世界各地提供商每次发布的模型看到这一点。所有人都可以使用的网络能力正在提高，你开始看到一些影响的领先边缘，对吧？例如，你开始看到在韩国，一些知名银行遭到由不同模型驱动的高调黑客攻击，我认为关键的一点是防御者需要利用这段时间。因此，我们一直在非常努力地通过我们的 Daybreak 计划负责任地向防御者提供访问权限，使他们能够使用我们的峰值智能，使用非常强大的模型来发现和修复其系统中的漏洞。有一点非常重要，需要认识到这些缺陷一直存在，对吧？每一行人类编写的代码，在某种程度上你都必须问，这是安全的吗？开发者是否接受了正确的培训？然后你开始意识到我们构建系统的方式从根本上是不安全的。我们一直知道这一点，对吧？从来没有一个 CISO 拥有过多资源。

Well, first of all, I do think the physics of cybersecurity are changing, and we're in what we call the defender's window. And the way that I think about this window is that we have peak frontier AI capabilities that can help defenders secure themselves. They're not broadly defeated yet, but that the broadly defeated capabilities are ratcheting, and you can see this from each model release to model release from providers across the world. The capabilities on cyber that are available to everyone are are going up, and you're starting to see the leading edges of some of the impacts, right? You're starting to see, for example, in South Korea that there were some high-profile hacks of of of banks that were driven by different models, and I think that the picture that is so critical is that defenders need to use this time. And so, we have been working very hard to be able to responsibly give access through our Daybreak program to defenders to be able to use our peak intelligence to use very capable models to find and fix vulnerabilities in their systems. And one thing that is very important to realize is these flaws have existed, right? It's It's every line of human-written code in some ways that you have to ask, is this secure? Was the developer trained in the right way? And you just start to realize that the way in which we built systems were kind of fundamentally insecure. We always knew it, right? There's never been a CISO who's been over-resourced.

是的。

Yeah.

对吧？你看看 XKCD 的漫画，就像全世界的基础设施都依赖于一个开源项目，由一个家伙在地下室写的，对吧？或者是 Heartbleed 或任何早于所有这些 AI 问题出现的事情。

Right? You You look at XKCD in the comic of like all the world's infrastructure reliant on like one open source project written by one dude in his basement, right? Or Heartbleed or anything that uh predated like all these AI issues.

确实如此。所以我认为，对我来说，而且我认为非常重要的是，要认识到这些确实是人的问题，对吧？这是我们选择构建互联网的方式。安全是被附加上的，对吧？我们有过高调的事件，对吧？你想想早期的蠕虫和病毒。然后人们开始意识到，我们需要病毒扫描软件，对吧？我们需要为请求加密，你知道，HTTP 应该通过 HTTPS。是的，所有这些机制都是我们为了事后添加安全而走了较慢的路线。就像互联网真的是为了韧性而构建的，不一定是为了安全。我认为我们现在将看到的是，很多债务，很多技术债务将浮出水面。是时候让我们偿还它了。作为一家公司，我们正在做的是，我们真的在努力帮助应对这个时刻。

Exactly. And so I think that to me, and I think very importantly, it's very important to realize that these are really human issues, right? That these are the way that we chose to build the internet. It was security got kind of bolted on, right? And we had high-profile incidents, right? You think about early worms and viruses. And then people started to realize, well, we need virus scanning software, right? And we need to I have encryption for I for requests, you know, over HTTP should be over H. Yes, all of these mechanisms were ones where I think that we kind of took the slower route in order to add security on afterwards. Like the internet's really been built for resilience, not necessarily for security. And I think that we're what we're going to see now is a moment where a lot of that debt, a lot of that tech debt is going to come to the surface. It's time for us to pay it down. And that as a company, we are doing we are really leaning in to trying to help with this moment.

举个例子，我们承诺投入 10 亿美元，帮助一线防御者获取我们的模型并将其用于防御。我们正在研究并试图找出各种漏洞。然后思考，我们如何帮助世界应对？如何协调事件响应？如何与政府合作？如何与社区合作，以尽可能好地解决这些问题？帮助人们度过难关。但我认为，人们还没有真正意识到网络安全的物理规律将发生多大变化。我认为我们最终会——我认为有机会最终建立一个更加安全的世界，对吧？想想看，AI 让你能够以前所未有的方式偿还技术债务。你可以做软件形式验证之类的事情。这些模型显然擅长证明事物。为什么不证明软件的安全属性呢？这项技术已经为人们提供了可能，但工作量太大了。

So, for example, we have a billion-dollar commitment that we've made to help frontline defenders get access to our models and utilize them for defense. We're studying and trying to find different vulnerabilities out there. And then thinking about, how do we help the world respond? How do we coordinate incident response? How do we work with government? How do we work with the community in order to land these as well as possible? To help people navigate through. But I think that people have not yet really internalized how much the physics around cybersecurity are going to change. I think we will end up—I think there's an opportunity to end up with a much more secure world, right? You think about the fact that AI lets you pay down your tech debt in a way that was just previously unthinkable. The fact that you could do things like formal verification of software. These models are obviously great at proving things. Why not prove security properties of software? That's something that the technology has kind of been there for people, but it's just so much work.

是的，是的。

Yeah, yeah.

但没有人对任何重要的事情这样做。所以我认为所有这些都可以改变。我认为我们现在正在走向的世界——我们在 OpenAI 内部已经这样做了——每次新模型发布，你都需要应用它来保护自己，找到你的漏洞，然后饱和。例如，我们让大约 25% 的生产工程师说：“你们的所有项目都暂停。你们现在只做安全。”然后拿出我们当时最智能的模型，让它攻击我们所有的基础设施，对一堆东西进行红队测试，构建了一些工具，以便随着时间的推移很容易再次这样做。我们发现了许多问题，修复了它们，并且在某个时候，我们饱和了该模型能够发现的问题。这是一个非常好的故事，对吧？就像我认为要意识到的是，我们将会有下一个模型，我们将不得不重复这个过程，并且这将持续下去。但在某个时候，再次，我认为如果你考虑一下，正在编写的代码，实际上现在很多都是 AI 辅助的，我们可以训练这些模型，而且我们正在训练这些模型从一开始就编写安全的代码。所以我认为通过以正确的方式使用和应用这些 AI 模型，我们可以拥有从根本上更安全的系统。

But no one does it for anything significant. And so I think all of that can change. And I think that the world we're moving to right now—and we've done this internally within OpenAI—every time a new model drops, you need to apply it to secure yourself, find your vulnerabilities, and then saturate. And so for example, we took about 25% of our production engineers and said, "All of your projects are on hold. You are just securing now." And took our peak intelligence model at the time, had it poke at all of our infrastructure, red team a bunch of things, built some tools so that it's easy to do it again over time. And we found a number of issues, we fixed it, and at some point we saturated the issues that this model was capable of finding. And that's a very good story, right? It's like I think that the thing to realize is that we're going to have a next model, we're going to have to repeat that process, and that will continue. But at some point, again, I think if you think about, well, the code that's being written, we're actually so much of it is now AI-assisted, we can actually train these models, and we are training these models to write secure code from the beginning. And so I think we can have sort of fundamentally more secure systems through the use and applications of these AI models in the right way.

所以听起来基本上会有一段时间，也许我们会看到更多这样的网络攻击、更多事件等，因为人类或人们正在追赶，他们的代码库变得越来越安全，使用更多 AI 生成的代码，这些代码被预设为更安全。因此，我们可能会经历一段过渡期，你知道，攻击和防御都会增加，然后防御会赶上。我的意思是，这是我总体上在行业中听到的共识，而且似乎至少是最有可能的前景。

So it sounds like basically there's going to be a period of time where maybe we're going to see more of these cyber attacks, more incidents, etc., as humanity or as people sort of catch up in terms of their code bases getting more and more secure, using more AI-generated code that's sort of pre-set to be more secure. And so we'll probably end up with some transitionary period over time where, you know, we'll have more offense and defense, and then defense will catch up. I mean, that's kind of the consensus I've been hearing in the industry in general, and it seems like the most likely forward case at least.

是的，看，我对我们如何度过这一刻非常乐观。我认为这确实需要协调一致的响应，对吧？这真的需要防御者内化网络安全的物理规律正在如何变化，存在这个窗口，而这个窗口不会永远持续，对吧？你应该考虑——我们正在转向这样一个世界，我们组织的安全既与他们已经做对的基础有关，这是一次性投资，也与他们用来防御自己的网络能力水平有关。当我们转向那个世界时，我认为我们可以处于防御者能够超越攻击者的世界，但这需要很多改变，对吧？这需要改变我们如何看待每个组织的运营，以及我们如何共同思考关键基础设施，并为那些今天还没有这些资源的人提供资源。

Yeah, look, I have a lot of optimism about how we can navigate this moment. I think it does require a coordinated response, right? It really requires defenders to internalize how the physics of cybersecurity are changing, that there is this window where that window is not going to last forever, right? And that you should think about—we're shifting to this world where the security of our organization is related to both the fundamentals that they've gotten right, which is kind of one-time investment, but also the level of cyber capability they've used to defend themselves. And as we shift to that world, I think that we can be in a world where defenders are able to outstrip attackers, but it is going to take a lot of change, right? It's going to take a sort of change in terms of how we think about every organization operating, and how we collectively think about critical infrastructure and resourcing those that have sort of got, you know, sort of not had those resources today.

我认为你的例子，我的意思是，你对此的描述就像是，比如说，互联网范围内的技术债务，然后你在内部对此的回应非常好，因为就像，好吧，我参与过一些公司，你知道，技术债务非常痛苦地解决。说我们要拿走 25% 的工程师，或者上帝保佑，你知道，我们要冻结一个季度的功能，直到我们修复一些真正需要修复的东西。那是——这感觉不像是世界已经决定的状态。嗯，当你思考整个世界在安全投资方面的位置时，你知道，传统上它大概是，哦，也许是 IT 预算的低个位数百分比。比如，你认为在这个时刻应该是多少？而且，是所有这些其他东西，你从网络工具角度投资的东西，网络、端点、传统企业安全变得不那么相关了，还是我们只需要花两倍的钱？

I think your example, I mean, your description of this is like, let's say, like internet-wide tech debt, and then your response to it internally is a really good one, because like, okay, I've been part of companies where, you know, tech debt is very painful to address. And to say, we're going to take 25% of the engineers, or God forbid, you know, we're going to do a feature freeze for a quarter until we fix some stuff that really needs to be fixed. That is—That doesn't feel like the state that the world has decided on quite yet. Um when you think about where the entire world is from a security investment perspective, uh you know, traditionally it's sort of it's like, oh, maybe it's like low single digits percent of the IT budget. Like, what do you think it should be at this moment of time? And like, is it all of all of this other stuff that you were investing in from a cyber tooling perspective, network, endpoint, traditional corporate security is less relevant, or we just need to go spend twice as much?

我有一种感觉，网络支出、安全支出实际上应该远远超过软件支出。就像，我真的认为尤其是——

I have a feeling that cyber spend, security spend, should actually vastly outstrip software spend. Like, I really think that especially—

是的，我真的相信。我可能不对，但当我看着我们将要进入的世界，软件在某种程度上正在成为进入门槛，没错。它——

Yeah, I really believe it. And I may not be right, but when I look at the world we're going to be in, where software in some ways is becoming like the barrier to entry exactly. It's—

几乎免费。

Free-ish.

没错。它变得如此便宜。所以，当然，确保——你不只是想要软件，对吧？你想要可靠的、安全的、你可以信任的软件。所以，对我来说，软件的韧性是那种——

Exactly. It's becoming so cheap. And so, of course, making sure—You don't just want software, right? You want software that is reliable, that is secure, that you can believe in. And so, to me, the resilience of that software is the sort of—

那就是价值。

That's the value.

没错。那将变得更加重要。所以，我认为如果我们有这个时刻，我们有机会，你知道，去——你提到了 XKCD 的那个小方块，在某个开源维护者的底部。就像——就像那从来没有任何意义。

That's exactly. That's going to become even more important. So, I think if we have this moment, we have this opportunity to, you know, to—You pointed to the XKCD with the little block at the bottom of some open source maintainer. Like—Like that never made any sense.

嗯。

Mhm.

我们之所以有那个世界，是因为一些物理规律，对吧？比如编写软件和编写可靠软件有多难。而且每个人都有供应链，但很少有公司审计他们的供应链，对吧？我们已经看到供应链漏洞，有人上传了一个恶意包，然后许多公司受到打击。所有这些都需要改变，而用人类软件开发人员完全不可能改变，因为人太少了。很少有人拥有这种非常 obscure 的专业知识，比如你知道，了解不同的包和不同的安全技术。

And the reason we had that world was because of some of this physics, right? Of kind of how hard it is to write software and to write reliable software. And that everyone has the supply chain and so few companies audit their supply chain, right? We've seen this with supply chain vulnerabilities where someone uploads a malicious package and then a number of companies get hit. All of that needs to change and it was totally impossible to change with human software developers cuz there's so few. There's so few people who have this expertise in this very obscure thing of like you know, sort of knowing different packages and like different security techniques.

现在像 Socket 这样的软件公司会做这样的事：扫描你的开源供应链，尝试发现漏洞并为你识别出来。或者，你知道，还有人为因素：维护者突然接管一个项目，然后安装一些对使用该包的人有害的东西。所以，看到第三方生态系统也在发展以支持这一过程，这种转变真的很有趣。

Software now like Socket and others who will do this as an example where they'll scan through your open source supply chain and try and find vulnerabilities and identify them for you. Or, you know, there's the human element of it where a maintainer would suddenly take over a project and then install something that would be malicious relative to people then using that package. So, it's really interesting to see that shift in terms of the third-party ecosystem as well developing to support that process.

OpenAI 和其他公司在这个市场上所做的。

OpenAI and others are doing in terms of this market.

正是如此。我认为这非常重要。我们正在与社区一起做这件事。我们与 CrowdStrike 等公司合作，我认为这又是一个协调一致的响应，但我们确实将其视为 OpenAI 引领的核心优先事项，对吧？对我们来说，因为我们拥有前沿模型，我们拥有巅峰智能模型。当然，在网络安全领域，我们认为有可能带来积极的变革潜力。但这意味着我们作为一个公司，我们一直在经历安全转型，对吧？在过去几个月里，我们谈到了开发过程中的安全、保障、对齐，但我们也在思考如何真正帮助世界变得更安全，帮助人们度过整个时期。

Exactly. I think it's so important. So, we're doing this with the community. We partner with companies like CrowdStrike and I think that it is again a coordinated response, but we really view it as a core priority for OpenAI to lead, right? For us to really because we have the frontier model, we have the peak intelligence model. Certainly in cybersecurity, we think that there is transformative potential for the good possible. But, that means that we as a company and we've been going through a security transition, right? That over the past couple months we've talked about safety security alignment of our development process, but we're also thinking about that for the world of how to really help the world become more fundamentally secure and help people through this whole period.

关于 OpenAI 的一点是，它一直非常支持人类，非常支持人类能动性和人类进步。让我们用 AI 来改善每个人。我认为 OpenAI 最初的 premise 是，让我们拥有一些能够制衡其他公司的东西，创造出能够以积极方式广泛普遍使用的 AI。

One of the things about OpenAI is that it's always been very pro-human and very pro-human agency, human progress. Let's use AI to better everyone. I think the original premise of OpenAI was let's have something that can counterbalance other companies in terms of creating AI that can be used very broadly and universally in a positive way.

这种精神在 OpenAI 是否延续了，或者你在 AI 社区的背景下如何看待这一点？

Has that ethos continued at OpenAI or how do you think about that in the context of the AI community?

我认为这种精神是我们使命的核心，对吧？确保 AGI（通用人工智能）惠及全人类。其中“人类”非常关键。我们认为人类应该牢牢掌握方向盘，应该控制未来，这项技术应该增强人类能动性。

I think that ethos is very core to our mission, right? To ensure AGI benefits all of humanity. And the humanity there is very key. We think that humans should be firmly in the driver's seat, should have control over the future, and that this technology should amplify human agency.

即使模型在能力上超越人类。

Even as models surpass humans in capability.

我认为这绝对关键，对吧？我认为，要记住这些模型非常参差不齐，对吧？有些地方这些 AI 显然比今天任何人都更有能力，对吧？甚至 GPT-4 知道的比任何人都多。但非常重要的一点是，我想起了例如史蒂夫·乔布斯为什么在 iMac 上放一个把手的故事，对吧？这样人们就能感觉到他们可以控制机器，对吧？你总是可以把它捡起来扔出窗外。我认为同样的精神需要应用到 AI 上。人类在方向盘后的精神。这些像人类判断、人类价值观，也许你应该想想未来人类在某些领域、某些维度将如何运作，不管是什么，会变得更像 CEO，或者像董事会成员。但重要的是我们拥有那种程度的能动性，我认为我们将建造机器，就像，你知道，如果你是一个 CEO，你显然会有领域专家。你显然会有团队，他们拥有跨这么多领域的知识，甚至可能在许多方面有比你更好的想法。但根本问题是，你快乐吗？这是你想要的吗？这是让你的生活更好的东西吗？对我来说，这是我们所有人都应该集体思考的，不仅仅是关于 AI，而是关于技术。对吧？技术是为了提升人类文明。它是生活质量改善的最根本驱动力，这就是我们应该从 AI 中寻求的。这是 OpenAI 的信念。

And I think this is absolutely critical, right? That I think that the picture of and there Keep in mind these models are very jagged, right? That there are places where obviously these AIs are more capable than anyone today, right? Even GPT-4 knows far more than any person. But the thing that is so important, and I'm reminded for example of the There's the story of why Steve Jobs put the handle on the iMac, right? So that way people could feel that they could control the machine, right? That you can always pick it up and throw it out the window. And I think that the same spirit needs to apply to AI. The fact of the spirit of humans are in the driver's seat. That these like human judgment, human values, and maybe you should think of how humans will operate in certain fields, in certain dimensions in the future, whatever it is, is become more like CEOs, or like board members. But it's important that we have that level of agency, and that I think that we're going to build machines just like, you know, if you're a CEO, you're clearly going to have domain experts. You're clearly going to have teams that are going to have a knowledge across so many things and maybe even better ideas than you on many different axes. But fundamentally, the question is are you happy? Is this what you wanted? Is this what makes your life better? And that to me is something that we should all collectively it's not just about AI, it's about technology. Right? Technology is to lift up human civilization. It's the single most fundamental driver of improvement in quality of life and that is what we should be seeking from AI. That is the belief of OpenAI.

非常感谢你接受这次采访，Greg。

Thank you so much for doing this, Greg.

是的，感谢你们的时间。谢谢你们两位。非常感谢。

Yeah, thanks for your time. Thank you, both. Appreciate it.

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