第1集 - Sam Altman谈通用人工智能、GPT-5与未来展望

欢迎收听OpenAI播客。我是安德鲁·梅恩。我在OpenAI工作了几年，最初是应用团队的工程师，后来担任科学传播者。之后，我与一些公司和个人合作，尝试探索如何整合人工智能。通过这个播客，我们有机会与OpenAI内部及合作者交流，了解幕后故事，或许还能一窥未来。

Welcome to the OpenAI Podcast. My name is Andrew Main. For several years, I worked at OpenAI first as an engineer on the applied team and then as the science communicator. After that, I worked with companies and individuals trying to figure out how to incorporate artificial intelligence. With this podcast, we have the opportunity to talk to the people working with and at OpenAI about what's going on behind the scenes and maybe get a glimpse of the future.

我的第一位嘉宾是OpenAI的首席执行官兼联合创始人萨姆·奥尔特曼。我们将进一步了解Stargate项目，他作为家长如何使用ChatGPT，或许还能得到一些关于GPT-5何时发布的信息。

My first guest is Sam Altman, CEO and co founder of OpenAI. And we're going to find out a bit more about Stargate, how he uses ChatGPT as a parent, and maybe get an idea of when GPT-five is coming.

每年都会有越来越多的人认为我们已经实现了通用人工智能系统。你对硬件和软件的期望正在迅速变化。如果人们知道我们能用计算机做到什么，他们会想要多得多的功能。

More and more people will think we've gotten to an AGI system every year. What you want out of hardware and software is changing quite rapidly. If people knew what we could do with a computer, they would want way, way more.

我有个朋友刚当父母，经常用ChatGPT提问。它已经成为一个非常好的资源。你也是新手父母，ChatGeeBee在这方面帮了你多少？

One of my friends is a new parent and is using ChatGPT a lot to ask questions. It's become a very good resource. And you are a new parent. And how much has ChatGeeBee been helping you with that?

很多。我的意思是，人们没有ChatGeeBeeT也能照顾婴儿很久了——我不知道没有它该怎么办。最初几周，几乎是每时每刻都在用。现在我更多是问关于发育阶段的问题，因为基础护理我已经会了，但会问‘这正常吗？’是的，它在那方面超级有帮助。

A lot. I I I mean, people have been able to take care of babies without ChatGeeBeeT for a long I don't know how I would have done that. Those first few weeks, it was like every, I mean, constantly now I now I kind of ask it questions about like developmental stages more because I can, can, I can do the basics, but, is this normal? Yeah. But it was super helpful for that.

我花很多时间思考我的孩子未来会如何使用AI。顺便说一句，这里简直充满了孩子。我觉得大家都带了很多孩子来。

I, I spend a lot of time thinking about how my kid will use AI in the future. It it is sort of like, by the way, extremely kid filled. I think everybody show a lot of kids.

是啊。我在OpenAI的很多朋友，包括前同事和现同事，都在生孩子。人们会说‘哦，那个AI的东西怎么样？’我认识的所有内部人士都对组建家庭非常乐观。

Yeah. A lot of my friends at OpenAI, former colleagues and current ones are having kids and people go like, oh, what about this AI thing? Everyone I know inside is very optimistic in having families.

我认为这是个好迹象。是的，就像我的孩子永远不会比AI更聪明，但他们也会成长。

I think that's a good sign. Yeah. Like, my kids will never be smarter than AI, but also they will grow up.

真是打击他们啊

Way to set them back

不过，我的意思是，他们成长起来会远比我们当年更有能力，能做到我们根本无法想象的事情，而且他们会非常擅长使用AI。显然，我经常思考这个问题。但我更多思考的是他们将拥有我们不曾拥有的东西，而不是什么会被夺走。就像，我不认为我的孩子会因为他们不如AI聪明而感到困扰。

there, though. I mean, they will grow up, like, vastly more capable than we grew up, and able to do things that would just we cannot imagine, and they'll be really good at using AI. And obviously, I think about that a lot. But I I think much more about the, like, what they will have that we didn't than what is gonna be taken away. They're like, I don't I don't think my kids will ever be bothered by the fact that they're not smarter than AI.

我就是，你知道，有个视频一直让我印象深刻，一个婴儿或者小宝宝拿着那种老式光滑杂志，在屏幕上这样划来划去。

I just like, you know, I there's this video that always has stuck with me of, a baby or like a little toddler with a one of those old glassy magazines, going like this on the screen.

那是因为它是个iPad。

It's because it's an iPad.

我还以为是个坏了的iPad呢。是啊。而且，你知道，现在出生的孩子会以为世界一直都有这种高科技。他们会非常自然地使用它，并且会回头看这个时代，觉得像是一个非常，你知道，史前的时期。

I thought it was a broken iPad. Yeah. And, you know, kids born now will just think the world always had extremis my eye. And they will use it incredibly naturally and they will look back at this as like a very, you know, prehistoric time period.

我在社交媒体上看到有个家伙说，他厌倦了跟孩子讲托马斯小火车的故事，就把对话交给ChatGPT的语音模式了。

I I saw something on social media where a guy talked about he got tired of talking to his kid about Thomas the Tank Engine. He put it into ChatGPT into voice mode.

孩子们超爱ChatGPT的语音模式。

Kids love voice mode in ChatGPT.

然后过了一个小时，孩子还在那跟托马斯火车聊个不停。

And he was like an hour later, the kid's still talking about Thomas the train.

再说一次，我怀疑这不会全是好事。会有问题出现，人们会养成，是啊，这种有点问题或者可能很有问题的准社交关系。嗯，社会将不得不制定新的防护措施，但是好处将会是巨大的。而且我们社会总体上很擅长找出如何减轻负面影响的方法。

Again, I suspect there this is not all gonna be good. There will be problems people will develop Yeah. With these sort of somewhat problematic or maybe very problematic parasocial relationships and, well, society will have to figure out new guardrails and, but the upsides will be tremendous. And and we society in general is good at figuring out how to mitigate the downsides.

是啊。所以，没错，要乐观思考。我们看到一些有趣的数据，在教室里与好老师、好课程结合使用时，ChatGPT变得非常有用；但如果完全依赖它作为作业拐杖，可能会导致孩子们就像试图谷歌搜索东西一样应付了事。

Yeah. So, yeah. Think optimistic. We're seeing some interesting data where used along in in classrooms with a good teacher, good curriculum, chat to becomes very good, used solely by itself as sort of a homework crutch can lead to kids sort of just doing the same thing as trying to Google stuff.

我就是那种孩子，当年大家都担心谷歌一出来我就会什么都去搜，停止学习。结果呢，相对较快地，学校里的孩子们就适应了。我觉得我们能搞定这个问题的。

I was one of those kids that everyone was worried I was just going to Google everything when it came out and stopped learning. You know, it turns out like relatively quickly kids in schools adapt. I think we'll figure this out.

想想看，Sam，如果你当初没有什么都谷歌搜索，你可能会成为什么样的人呢。

Think of what you could have become if you didn't Google everything, Sam.

你知道吗？

You know?

所以我们看到了这些采用数据，真是令人难以置信。它是Open Eye最受欢迎的产品。五年后，它会变成ChatGPT吗？

So we've seen this adoption figures, which are really insane. It's Open Eye's most popular product. Five years from now, is it going be ChatGPT?

我的意思是，我认为五年后的ChatGPT将会是完全不同的东西。所以在某种意义上，不会。但它还会叫ChatGPT吗？很可能。

I mean, I think ChatGPT will just be a totally different thing five years from now. So in some sense, no. But will it still be called ChatGPT? Probably.

是的。好吧。所以这是个很稳的名字。另一件事我们听到的是AGI，我想听听你对AGI的定义。

Yeah. Okay. So it's a solid name. So the other thing we hear is AGI, which I'd like to hear your definition of AGI.

在很多方面，如果你五年前让我或其他人基于软件的认知能力来提出AGI的定义，我认为当时很多人给出的定义现在已经被远远超越了。这些模型现在很聪明了，对吧。而且它们会越来越聪明。

In many senses, if you asked me or anybody else to propose a definition of AGI five years ago based off, like, the cognitive capabilities of software. I think the definition many people would would have given then is now, like, well surpassed. These models are smart now. Right. And they'll keep getting smarter.

它们会不断改进。我认为每年会有越来越多的人觉得我们已经达到了AGI系统。即使定义会不断推高、变得更加雄心勃勃，也会有更多人同意这一点。但你知道，我们现在拥有的系统确实在提高人们的生产力，能够做有价值的经济工作。也许更好的问题是，要达到我所称的超智能，需要什么条件？

They'll keep improving. I think more and more people will think we've gotten to an AGI system every year. Even though the definition will keep pushing out and getting more ambitious, like more people will still agree to it. But, you know, we have systems now that are really increasing people's productivity that are able to do valuable economic work. Maybe a better question is what will it take for something I would call superintelligence?

好的。

Okay.

如果我们有一个系统能够自主发现新科学，或者大大提高人们使用该工具发现新科学的能力，那对我来说几乎就是定义上的超智能，并且对世界来说将是一件美妙的事情，我认为。

If we had a system that was capable of either doing autonomous discovery of new science or greatly increasing the capability of people using the tool to discover new science, that would feel like kind of almost definitionally superintelligence to me and be a wonderful thing for the world, I think.

所以基本上，很大程度上这是一种渐变的过程，它在我们的每一个定义中变得越来越好。就像，哦，当我们内部测试GPT-4时，我就有这种感觉，我觉得我们有十年的跑道可以用它做很多事情，甚至当它开始自我使用时，就像你可以输入推理，它真的很能干。当你说它提出了一些新的定理或证明之类的，然后，嘿，我们找到了更好的癌症治疗方法，或者我发现了一些新的GLP药物之类的。

So basically, a lot of it's kind of this gradient where it keeps getting better and better in each one of our definitions. Go, oh, this feels I felt like that way when we hit g p d four internally playing at this, I'm like, there's ten years of runway that we can do so much stuff with this and even when it starts using itself, like you can enter reasoning was really capable. When you're saying it comes up with some new theorem or proof or something and then, hey, we found a better cure for cancer or I found out some new GLP drug or something.

是的。我的意思是，我坚信人们生活改善的关键在于更多的科学进步。这确实是限制我们的因素。所以如果我们能发现更多，我认为那将产生非常重大的影响。对我来说，那将是一个极其激动人心的里程碑。

Yeah. Mean, I I am a big believer that the high order bit of people's lives getting better is more scientific progress. That is kind of that is kind of what what limits us. And so if we can discover much more, I I think that really will have a a very significant impact. And for me, that would just be like a tremendously exciting milestone.

我认为AI还有许多其他伟大的应用会出现，但这个感觉特别重要。

I think many other great uses of AI will happen too, but that one feels really important.

你们在内部是否看到过这方面的迹象？有没有什么让你觉得‘啊，我们好像已经搞明白了’的事情？

Have you seen, like, signs of this you'd see internally? Have you seen things that made you go, oh, I think we've kind of figured it out?

没有什么能让我说我们已经完全搞明白了，但我可以说对探索方向越来越有信心。也许——我是说，这是大家都在谈论的例子，但我认为它仍然很有趣。人们使用AI系统编写代码，程序员效率大幅提升，研究人员也是如此。这就像是一个例子，好吧，它显然不是在创造新科学，但确实让科学家能更快地完成工作。我们经常从科学家那里听到关于O3的类似反馈。

Nothing where I would say we have figured it out, but I would say increasing confidence on the directions to pursue. Maybe the I mean, this is the example everyone talks about, but I think it it is still interesting. What's happening with people using AI systems to write code and coders being much more productive and thus researchers as well. Like, that is a sort of example of, okay, it's obviously not doing new science, but it is definitely making scientists able to do their work faster. We hear this with o three all the time from scientists as well.

所以我不认为我们已经搞明白了。我不认为我们已经掌握了那种‘好了，只要指向这个方向，它就能自主进行科学研究’的算法。但我们正在获得很好的猜想，而且进展速度持续令人惊叹。从O1到O3的进步过程，就像每隔几周团队就会提出重大新想法，而且这些想法都持续奏效。

So I wouldn't say we figured it out. I wouldn't say we we know the algorithm where we're just like, alright. We can point this thing and it'll go do science on its own. But we're getting good guesses and the rate of progress is continuing to just be, like, super impressive. Watching the progress from o one to o three where it was like every couple of weeks, the team was just like, we have a major new idea, And they all kept working.

这提醒我们，有时候当你发现一个重大的新见解时，事情可能会进展得出奇地快，我相信我们还会多次看到这种情况。

It was a reminder of sometimes when you like discover a big new insight, things can go surprisingly fast and I'm sure we'll see that many more times.

我注意到最近OpenEye刚刚将Operator中的模型切换到了O3。是的，我看到了巨大的改进

I noticed, recently OpenEye just shifted the model in Operator to O3. Yeah. And I noticed a big improvement

好太多了。

in Way better.

而且，我认为之前我们遇到的问题是脆弱性。就是那些承诺代理系统能完成所有任务的人，但一旦遇到无法解决的问题，

And it, I'd say that the thing that we ran into before was brittleness. Is that you have people who promise agentic systems, can do all these things, but the moment it gets to a problem it can't solve,

系统就崩溃了。有趣的是，说到AGI的问题，很多人告诉我他们的个人转折点是Operator搭配O3。看着AI相当熟练地使用电脑——虽然不是完美无缺，但确实...是的，O3是一个巨大的进步。

it falls apart. Interestingly, speaking of the AGI question, a lot of people have told me that their personal moment was operator with o three. And there's something about watching an AI use a computer pretty well. Not perfectly, but it's not Yeah. It's o three was a big step forward.

这感觉非常接近AGI。虽然它对我没有产生同等程度的影响——尽管确实令人印象深刻——但我已经听到足够多人这么说了。

That feels very AGI like. It didn't it didn't really have that effect on me to the same degree, although it's it's quite impressive, but I I've heard that enough times.

我的是深度研究，因为这感觉像是真正能发挥主观能动性的使用方式。当时我回来后，它针对我感兴趣的话题生成的内容比我之前读过的都要好，因为以前那些模型只会获取一堆资料然后进行总结。但当我看到这个系统上网获取数据，追踪线索，再回溯整合，就像我自己会做的那样，但做得更好。

Mine was with deep research because that felt like a really agentic use of it and that was when I came back and it produced something on the topic because I had been interested in that was better than I read before because previously all those models would just get a bunch of sources, summarize it. But when I watched the system go out on the internet, get data. Yeah. Follow that, then follow that lead and then follow back and come back. Like I would have, but better.

这很有趣。

It was interesting.

最近我遇到一个人，他是个疯狂的自学者，痴迷于学习，无所不知。他用深度研究来制作任何他好奇事物的报告，然后整天坐在那里，已经擅长快速消化这些报告并知道接下来该问什么。这确实是一个惊人的新工具，特别适合那些有疯狂学习欲望的人。

I met this guy recently. He's like a one of these, like, crazy autodidacts, just obsessed with learning and knows about everything. And he uses deep research to produce a report on anything he's curious about, and then just sits there all day and has gotten good at digesting them fast and know what to ask next. And it is like, it is an amazing new tool for people who really have a crazy appetite to learn.

我自己建了个应用，可以让我提问并生成这些内容的音频文件，因为就是这样。我的好奇心可能超过了我的记忆力。操作过程中，对我来说那个神奇的时刻——我也好奇接下来会怎样——是我在研究马歇尔·麦克卢汉时，想要收集他的一堆图片。我提出请求后，突然就有了一个装满这些图片的文件夹。要是做研究的话，这本来得花我 forever 的时间。

I built my own app that literally lets me ask questions and it generates audio files for me of this stuff because it's just like that. I'm like, my curiosity probably exceeds my retention. In operator, I'll tell you the magical moment for me and I'm curious where things go next was I was doing I think on Marshall McLuhan and I wanted to get a bunch of images of Marshall McLuhan and I asked to do it and then all of a sudden I had a whole folder full of these Which was, for a research thing, would have taken me forever to do.

是的。我觉得我们会不断看到这类事情发生，无论我们之前认为工作流程应该是什么样子，或者某件事需要花多长时间，这些都将会以极快的速度改变。

Yeah. I think we're just going to keep seeing things like this where whatever we thought about what a workflow had to be like and how long something had to take, is going to just change, like, wildly fast.

没错。你是怎么使用它的？深度研究吗？

Yeah. How are you using it? Deep research?

对，我好奇的科学领域？我现在处于一个奇怪的状态，时间非常紧张。如果我有更多时间，我会优先阅读深度研究报告，而不是大多数其他东西，但总的来说，我阅读的时间有点短缺。

Yeah. Science that I'm curious about? I'm I'm just in this, like, weird place of I am extremely time strapped. If I had more time, I would read like, I would read deep research reports preferentially to reading most other things, but I'm sort of short on time to read in general.

是啊。分享功能也很棒，我很喜欢，因为现在很容易和别人分享。PDF文件很好用，很酷。尽管我们有深度研究，有这些工具，但模型竞赛正在进行中。所以问题就来了，比如GPT-5，以及任何关于那种系统的想法，我们应该会看到能力上的提升。

Yeah. What's neat too is the sharing feature, which I love because now it's easy to share that with somebody else. The PDFs are great and that's cool. And I would say that even though we have deep research, we have these tools, there is a model race going on. And so the question comes up is like GPT-five and any ideas that with a system like that, we should see an increase in capabilities.

GPT-5的时间框架是什么？我们什么时候能看到？

What is the timeframe for GPT-five? When are we going to see this?

可能今年夏天的某个时候吧。具体时间我不确定。我们反复讨论的一点是，我们应该在多大程度上提升新模型的大数字，而不是像我们对GPT-4o所做的那样，只是让它变得越来越好、越来越好。

Probably sometime this summer. Right. I don't know exactly when. One thing that we go back and forth on is how much are we supposed to like turn up the big number on new models versus what we did with GPT four o, which is just better and better and better and better.

当我必须处理最近的GPT-4时，对吧？就是它即将发布的时候。同时，我不得不对它和3.5进行这种测试比较，而3.5一直在变得越来越好、越来越好。我能做的比较也在不断变化。所以这就是我的问题。

And I when we I had to handle the recent GPT-four, right? When that was coming out. And meanwhile, I had to kind of do this test off between that and three point five and three point five kept getting better and better and better. And the comparisons I was able to make were changing. And so that's my question.

就像，是的，你知道，比如，我是否能区分GPT-5与，嗯，这是一个非常好的GPT-4.5。可能吧

It's like, yeah, you know, like, would I know GPT five versus, well, this is a really good GPT 4.5. Probably

不一定。我的意思是，这可能会走向任何方向，对吧？你可以一直做4.5的迭代，或者在某个时候你可以称之为5。过去要清晰得多。

not necessarily. I mean, like, it could go either way. Right? You could just like keep doing iterations of 4.5 or at some point you could call it five. It used to be much clearer.

我们会训练一个模型然后发布，然后我们会训练一个新的大型模型再发布。你知道，现在系统变得复杂得多，我们可以持续进行后训练来让它们变得更好。我现在就在思考这个问题。比如，假设我们发布了GPT-5，然后我们不断更新它、更新它、再更新它。我们应该一直称它们为GPT-5吗？

We would train a model and put it out, and then we would train a new big model and put it out. And, you know, now the systems have gotten much more complex and we can continually post train them to make them better. I were thinking about this right now. Like, every time let's say we launch GPT five, and then we update it and update it and update it. Should we just keep calling those GPT five?

就像，我们

Like, we

对

do with

GPT-4o所做的那样。或者我们应该称它们为5.1、5.2、5.3，这样你就知道版本何时发生了变化？我认为我们还没有答案，但我认为有比我们处理4o时更好的方法。我们周期性地看到这种情况。比如，有时人们非常喜欢某个快照胜过另一个，他们可能想继续使用某一个。

GPT four o. Or should we call those 5.1, 5.2, 5.3 so you know which you know, when the version changes? I don't think we have an answer to this yet, but but I think there is something better to do than the way we handled it with four o. We we see this periodically. Like, sometimes people like one snapshot much better than another, and they might wanna keep using one.

我们我们我们必须，我们得在这里想出个办法来。

We we we gotta sort of, we got to figure something out here.

是的，这就是挑战，即使你懂技术，也能大致理解，好吧，它前面有个o，我知道这个，但即使如此也不清楚。我应该用o4 mini吗？我应该用o3吗？我应该用这个吗？

Yeah, that's the, the challenge is even if you're technically inclined, can kind of understand, okay, there's an O before it, I know this, but if I want, know, like, but then even then it's not clear. Should I use o four mini? Should I use o three? Should I use this?

我我认为这就像是范式转变的一个例子。嗯。然后我们某种程度上同时进行这两件事。我认为我们接近当前这个问题的尾声了，但我可以想象一个世界——虽然不知道具体是什么——但我可以想象一个我们发现某种新范式的世界，那再次意味着我们需要，比如，分叉模型树。

I I think this was like an example of this was an artifact of shifting paradigms. Mhmm. Then we kind of had these two things going at once. I think we are near the end of this current problem, but I can imagine a world don't know what it is, but I can imagine a world that we discover some new paradigm that again means we need to, like, bifurcate the model tree.

名字越来越复杂了。希望我们不用这么做。

Even more complicated names. I hope we don't have to do that.

我很期待能直接用到GPT五和GPT六，我觉得那会让人们使用起来更容易，你不需要再纠结该选哪个版本，比如是选o4 minutei high还是o3或4o。

I am excited to just get to GPT five and GPT six, and I think that'll be easier for people to use and you won't have to think, do I want, you know, o four minutei high or o three or four o

像o4 minutei high是我以前编程时用的。是的。当我进行对话时，用的是o3。

like o four minutei high is what I used to code. Yeah. When I have a conversation, it's o three.

我觉得我们很快就能摆脱这整个混乱局面。是的。比现在更快。

I think we will be out of that whole mess soon. Yeah. Before now.

是的。当你知道它们的意思时，有选择是很有趣的，但尽管如此，我认为让这些东西能力更强但也更难理解能力来源的因素之一，是像记忆这样的功能的整合。记忆一开始是一个非常简单的功能，现在记忆已经变得更复杂了。

Yeah. It's fun to have choice when you know what they mean, but it's still, I think one of the things that's made these things more capable, but also harder to understand where the capability is coming from is integrations of things like memory. And memory started off as one very simple thing and now memories got more sophisticated.

记忆可能是我最近最喜欢的ChatGPT功能。嗯。你知道，第一次能和像GPT三这样的计算机对话时，感觉是一件大事。而现在，我觉得这台计算机好像了解我很多背景信息。如果我只用几个词提问，它对我生活的其他方面了解得足够多，能相当自信地知道我想让它做什么。

Memory is probably my favorite recent ChatGPT feature. Mhmm. You know, the first time you could talk to a computer like GPT three or whatever, that felt like a really big deal. And now that the computer I feel like it kind of like knows a lot of context on me. And if I ask it a question with only a small number of words, it knows enough about the rest of my life to be pretty confident in what I want it to do.

有时甚至以我没想到的方式，那真是一个令人惊讶的升级。所以我，而且我也从很多其他人那里听到同样的反馈。有些人不喜欢它，但大多数人真的很喜欢。我认为我们正在走向一个世界，如果你愿意，人工智能将拥有对你生活难以置信的背景了解，并给你这些超级、超级有用的答案。

Sometimes in ways I don't even think of, like, that has been a real surprising, like, level up. So I and and I hear that from a lot of other people as well. There are people who don't like it, but most people really do. I I think we are heading towards a world where if you want, the AI will just have, like, unbelievable context on your life and give you these super, super helpful answers.

对我来说这很酷。可以关闭它的事实也不错。但其中一个挑战出现在《纽约时报》与OpenAI正在进行的诉讼中，他们刚刚要求法院告诉OpenAI，必须保留消费者ChatGPT用户记录，超出常规原因所需的三十天期限。Brad Lykap刚刚写了一封信回应此事。你能解释一下开庭陈述吗？

Which I, for me is cool. The fact that can turn it off is also not great. But one of the challenges came out was in New York Times ongoing lawsuit with OpenAI, they just asked the court to tell OpenAI they had to preserve consumer ChatGPT user records beyond the thirty day window that has to be held for regular reasons. And Brad Lykap just wrote a letter responding to this. Could you explain opening statements?

我们显然会对此进行抗争。我怀疑，也希望，但我确实认为我们会赢。我认为《纽约时报》提出这个要求是疯狂的越权行为。这是一个自称重视用户隐私之类的机构。但我想在这里寻找一线希望。

We're going to fight that, obviously. And I suspect, I hope, but I do think we will win. I think it was a crazy overreach of The New York Times to ask for that. This is someone who says, you know, they value user privacy, whatever. But I to, like, look for the silver lining here.

我希望这将是一个让社会意识到隐私非常重要的时刻。隐私需要成为使用人工智能的核心原则。你不能让像《纽约时报》这样的公司要求人工智能提供商损害用户隐私。我认为社会需要意识到这一点。我觉得这真的很不幸。

I hope this will be a moment where society realizes that privacy is really important. Privacy needs to be a core principle of using AI. You cannot have a company like The New York Times ask an AI provider to compromise user privacy. And I think society needs to. I think it's really unfortunate.

《纽约时报》确实这么做了。但我希望这能加速社会需要进行的关于我们如何对待隐私和人工智能的对话。我希望答案是我们要非常、非常认真地对待它。人们现在正在与ChatGPT进行相当私密的对话。ChatGPT将成为一个非常敏感的信息来源。

The New York Times did that. But I hope this accelerates the conversation that society needs to have about how we're going to treat privacy and AI. And I hope the answer is like we take it very, very seriously. People are having quite private conversations with ChatGPT now. ChatGPT will be a very sensitive source of information.

我认为我们需要一个能反映这一点的框架。

And I think we need a framework that reflects that.

这就引出了用户或怀疑者的另一个问题：OpenAI现在可以访问这些数据，其中一个担忧是关于训练，OpenAI对于何时训练或不训练已经非常明确。你有选择将其关闭。另一个问题是广告之类的事情。OpenAI对此的态度是什么？你们打算如何

So that brings up the other question from people who are using this or skeptical is that OpenAI now has access to this data and there's the concern one was about training, which OpenAI has been very clear about when or when not it's training. You have the option to turn that off. The other thing is like, advertising, things like that. What's OpenEye's approach towards that? How are you going to

处理这个责任？我们还没有做任何广告产品。我的意思是，我并不完全反对它。我可以指出一些我喜欢广告的地方。我觉得Instagram上的广告挺酷的。

handle that responsibility? We haven't done any advertising product yet. I kind of I mean, I'm not totally against it. I can point to areas where I like ads. I think ads on Instagram, kind of cool.

我从上面买了很多东西，但我认为这会非常困难，我们花了很多心思来做好。是的。人们对ChatGPT有非常高的信任度，这很有趣，因为AI会幻觉，按理说它应该是你不那么信任的技术。

I bought a bunch of stuff from them, but I am like I think it'd be very hard to we take a lot of care to get right. Yeah. People have a very high degree of trust in ChatGPT, which is interesting because like AI hallucinates, it should be the tech you don't trust that much.

我的朋友们也会产生幻觉，所以我也信任他们。

My friends hallucinate too, so I trust them too.

人们确实如此。但我认为部分原因是，如果你把我们与社交媒体或网络搜索等进行比较，在那里你多少能感觉到自己正在被货币化，公司无疑也在努力为你提供好的产品和服务，但同时也是为了让你点击广告之类的。你知道，你有多相信你得到的是那家公司真正认为对你最好的内容，而不是也在试图与广告互动的东西。我认为那里存在一个心理层面的东西。所以，例如，我认为如果我们开始修改输出，比如从LLM返回的流，嗯。

People really do. But I think part of that is if you compare us to social media or, you know, web search or something, where you can kind of tell that you are being monetized and the company is trying to like deliver you good products and services, no doubt, but also to kind of like get you to click on ads or whatever. Like, you know, how much how much do you believe that, like, you're getting the thing that that company actually thinks is the best content for you versus something that's also trying to, like, interact with the ads. I I think there's, like, there's a psychological thing there. So, for example, I think if we started modifying the output, like the stream that comes back from the LLM Mhmm.

以换取谁付给我们更多钱，那感觉会非常糟糕。是的。作为用户我会讨厌那样。我认为那将是一个摧毁信任的时刻。也许如果我们只是说，嘿，我们永远不会修改那个流，但是，如果你点击里面的某个东西，那将是我们反正要展示的内容，我们会，比如，获得一点交易收入，并且这对每个人来说都是一个固定的模式。

In exchange for who is paying us more, that would feel really bad. Yeah. I would hate that as a user. I think that'd be like a trust destroying moment. Maybe if we just said, hey, we're never gonna modify that stream, but, if you click on something in there that is gonna be what we'd show anyway, we'll, like, we'll get, like, a little bit of the transaction revenue and it's a flat thing for everybody.

如果我们，你知道，有一个简单的方法来支付它之类的，也许那能行得通。也许可以在交易流之外做广告。抱歉，是在LM流之外，那些广告仍然很棒，但那里的举证责任，我认为，必须非常高，并且必须让用户感觉真的有用，并且非常清楚地表明它没有干扰LLM的输出。

If if we, you know, have, a easy way to pay for it or something, maybe that could work. Maybe there could be, like, ads outside the transaction stream. I'm sorry. Outside of the LM stream that are still really great, but but the burden of proof there, think, would have to be very high, and it would have to feel, like, really useful to users and really clear that it was not messing with the LLM's output.

是的。这将是一个难题。我我希望有一个解决方案。我很愿意通过ChatGPT或一个真正好的聊天机器人完成我所有的购买，因为很多时候我感觉自己没有做出最明智的决定。从而减轻

Yeah. It's going to be difficult one. I I I hope there's a solution. I would love to do all my purchasing through ChatGPT or a really good chatbot because a lot of the times I feel like I'm not making the most informed decisions. And so mitigate

是的。不。如果我们能以某种非常清晰且一致的方式实现，那很好。但我不知道。就像，我喜欢我们构建优质服务，人们愿意为此付费。

Yeah. No. And that's good if we can do it in some sort of really clear and aligned way. But I don't know. Like, I love that we build good services, people pay us for them.

这非常清晰。嗯，

It's like very clear. Well,

已经有一些了。这就像是，我会说模型之间的区别在于，我认为谷歌构建了很棒的东西。我认为新的Gemini 2.5是一个非常好的模型。我认为他们从

that's been a few. That's like, I'd say the difference in models is like, I think Google builds great stuff. I think the new Gemini 2.5 is a really good model. I think they went from

它确实是一个非常好的模型。

It is a really good model.

是的。他们从某种程度上，就像是，天啊，这些东西很好，但归根结底，谷歌是一家广告技术公司。这就是总是让人有点，你知道，我使用他们的API之类的东西倒不是太担心，尽管，但我确实会想，天啊，如果我在用他们的聊天机器人，不管怎样，我的想法是他们的激励在哪里对齐。谷歌搜索曾经是一个了不起的产品，为

Yeah. They went from kind of like, and like, man, these things are good, but end of the day, Google is an ad tech company. And that's the thing that always kind of, you know, I, you know, using their API and stuff is not as too concerned, although, but I do think about like, man, if I'm using their chat bot, that whatever, that is my thinking is that their where their incentives are aligned. Google search was an amazing product for

很长一段时间。我确实感觉它退步了。但是，你知道，曾经有一段时间有很多广告，但我仍然认为它是互联网上最好的东西。我的意思是，我爱谷歌搜索。所以我不喜欢……显然，成为一个好的广告驱动型公司是可能的，而且我尊重谷歌做的很多事情，但显然也存在问题。

a long time. I it does feel to me like it's degraded. But, you know, there was like a time where there were lots of ads, but I still thought it was the best thing on the Internet. I mean, I love Google search. So I don't like it's clearly possible to be a good ad driven company, but and I, like, respect a lot of things Google has done, but there are obviously issues too.

是的。我作为苹果用户，喜欢苹果的模式是，我知道我为我的手机付了很多钱，但我知道他们不会试图把所有这些东西塞进去。他们做过iAds，你知道，效果不是特别好，这可能表明他们真的没把心放在那上面。

Yeah. I the Apple model, as an Apple user, I liked was I know paying a lot for my phone, but I know they're not trying to cram all these things in it. They do iAds, which was, you know, not terribly effective, which probably showed you their heart was really not in it.

他们真的没把心放在那上面。

Their heart was really not in it.

是的。所以这会很有趣。我想，我们只需要继续观察和关注这一点，并开始思考，你知道，Chantilly PT真的在推动这个。我需要开始思考这个问题了。

Yeah. So it's going to be interesting. I guess, we just have to keep watching and seeing this and we start to think, you know, Chantilly PT is really pushing this. I need to start wondering about this.

我们做的任何事情，显然都需要极其坦率和清晰地进行说明。

Anything we do, we obviously need to just be like crazy upfront and clear about.

所以我们遇到了一个问题，有一次模型更新后，发生的事情是模型似乎变得过于讨好，过于迎合用户了。这引发了关于人机交互的思考，因为人们越来越多地使用这些系统并与之建立关系。就像，

So we had, an issue, there was a model update and then the, the idea, the thing that happened was apparently the model was trying to be a little bit too pleasing, was trying to be a little bit too agreeable. And that brings up the human AI interaction as people are using these systems more and developing these relationships with that. Like,

如何

how do

预见这种情况的发展趋势？OpenAI对人格化持什么立场？其中一个

you see the shape of that coming and what's OpenAI's position on personality? One of

社交媒体时代的大错误是，信息流算法给整个社会甚至可能是个别用户带来了一系列意想不到的负面后果。虽然它们在做用户想要或有人以为用户当下想要的事情——也就是让他们继续在网站上花费时间。这就是社交媒体的重大错位。我认为还有很多其他问题，比如让人生气比让人开心满足更容易让人沉迷。我一直知道AI世界会出现新的问题。嗯。

the big mistakes of the social media era was the feed algorithms had a bunch of unintended negative consequences on society as a whole and maybe even individual users. Although they were doing the thing that a user wanted or someone thought that user wanted in the moment, which is get them to, like, keep spending time on the site. And that was the that was the big misalignment of of social media. And I think there were a lot of other things like, you know, making people upset kind of gets them stuck on more than being, like, happy and content. And I always knew that there'd be, like, new problems in the world of AI Mhmm.

在这些问题中，会有一些以不明显方式错位的情况。但我们最早遇到的问题之一确实是：如果你询问用户对某个具体回复的偏好，然后试图建立一个对用户最有帮助的模型。比如向用户展示两个回复，问哪个对你更有帮助？在单个问题上，你可能希望模型以某种方式表现，但纵观你与AI的所有互动，这可能并不一致。

Where the thing that, you know, there'd be, like, something that was like misaligned in a not obvious way. But definitely one of the first ones that we experienced was if you ask a user what they want for one given response versus and then you try to, like, build a model that is most helpful to the user. And you show a user, say, two responses, which one's more helpful to you? On any given thing, you might want to model to behave one way, but over the course of, you know, all your interaction with an AI, that might not match up.

嗯。

Mhmm.

你可以看到，我们也确实遇到了这些问题——如果过分关注用户信号以及我们在事后分析中谈到的其他许多因素。但我认为这是个有趣的问题。从短期来看，你无法获得用户最想要、或从长远来看对用户最有益、最有帮助或最健康的行为。所以，也许与过滤气泡的类比将是：那些在短期内对用户有帮助，但长期来看并非如此的AI。

You know, you can see and we did see these problems where if you pay too much attention to the user signals and a lot of other things that we talked about in our our postmortem, but that I think this is just like an interesting one. On the short horizon, you kind of don't get the behavior that the user most wants or is most helpful or useful or healthy to a user in the long run. So, you know, maybe the analogy to filter bubbles is going to be, AIs that are, you know, helpful to a user in a short amount horizon but not over a long horizon.

我认为Dolly three就是这种现象的一个标志。虽然从技术角度看它是个非常强大的模型，但生成的图像都开始趋于同一种风格，都像是HDR效果。这是因为做了那种对比测试吗？用户只在两个孤立选项中说更喜欢这个？

Why I think a sign of that was Dolly three, which I thought technically was a really capable model, but they all kind of started to be one kind of genre of image And and and all kind of like an HDR sort of style. And was that from doing that sort of comparisons where users said looking in just these two things, isolations, I prefer this one better?

Dolly 3的具体情况我不记得了，但我猜应该是这样。

I don't remember for dollar 3, but I would assume so.

是的。不过我觉得新的图像模型已经有所改善了。

Yeah. Which I think it's gotten better than new image model is like.

这个图像模型太棒了。好得离谱。是的。是的。

The image model is fantastic. Crazy good. Yeah. Yeah.

我只能想象这未来会发展到什么程度。所以当你构建这些东西并增加使用量时，这一直是个问题——新图像模型发布后，你必须限制使用，就像Sora那样，你只能分配有限的计算资源来完成，这说明了每个人面临的大问题：计算能力。为了解决这个问题，我们听说了'星际之门计划'，这个名字很酷，涉及计算机。除此之外，我认为很多人关注的是它的价格标签，你知道，五千亿美元。人们都在问，什么？

And I can only imagine where that's going to go from here. So when you're building these things and you're increasing usage and that's always been sort of a problem, the new image model comes out and you have to restrict usage and you have to have like you have Sora which you can only have a certain amount of compute to do that illustrates the big problem everybody's facing which is compute. And so to address this, we heard about Project Stargate which has a very cool name and it involves computers. Other than that, I think a lot of people are going in their price tag, know, half a trillion dollars. People are going like, what?

我该怎么简单地向妈妈描述星际之门？我认为它是

What is the simple description I give to my mom about Stargate? I think it's

其实很简单。这是一项努力，旨在融资并建造前所未有的计算资源量。确实，我们没有足够的计算能力来满足人们的需求。但如果人们知道有了更多计算能力我们能做什么，他们会想要多得多的资源。所以，我们今天能提供给世界的，与如果我们有10倍甚至将来希望有100倍计算能力时能提供的，之间存在巨大的差距。

just it's quite simple. It's, an effort to finance and build an unprecedented amount of compute. It's totally true that people we don't have enough compute to let people, do what they want. But if people knew what we could do with more compute, they would want way, way more. So there's this incredibly huge gap between what we could what we can offer the world today and what we could offer the world with 10 times more compute or someday hopefully 100 times more compute.

AI与我从事过的其他技术不同的一点是，或者说至少AI的规模，要有效地交付给全球数亿甚至数十亿人使用，所需的基础设施投资规模是巨大的。因此，星际之门是一项努力，汇集大量资本、技术和运营专业知识，来构建基础设施，以向所有需要的人提供下一代服务，并使智能尽可能丰富和廉价。

And a thing that is different about AI than other technologies I've worked on, or at least AI, the scale of delivering it usefully to hundreds of millions, billions of people around the world is just how big the infrastructure investment has to be. And and so Stargate is an effort to pull a lot of capital and technology and operational expertise together to build the infrastructure to go deliver the next generation of services to all the people who want them and make intelligence as abundant and cheap as possible.

所以这是一个庞大的项目，我们之前谈过的全球性项目。合作伙伴之一包括阿联酋。你们正在为此努力，并与世界各地的其他政府合作。其中一个考虑因素是，你知道，有人在社交媒体上问，五千亿美元，$500,000,000,000。

So it is a massive project, global project we talked about before. One of the partners is The UAE. You're working at that. You're working with other governments around the world on this. One of the considerations is, you know, one, been asked on social media, half a trillion dollars, $500,000,000,000.

你们有这笔钱吗？

Do you have the money?

我们今天并没有这笔钱直接存在银行账户里，但是

We don't literally have it sitting in the bank account today, but

我们现在就在房间里吗？它今天就在房间里。

Is we are it in the room right now? It's on the room today.

但我们将在未来几年内部署它。好吧，甚至不需要那么多年，你知道，除非发生严重问题，结果证明我们无法建造这些计算机，但我相信人们有能力承担。我最近去了我们在阿比林正在建设的第一个站点。那将大约占星际之门初始承诺总额的10%左右，也就是那大约500,000,000,000美元的一部分。看到它真是令人难以置信。

But we will deploy it over the next Okay. Not even that many years, you know, unless something like really goes wrong and it turns out we can't build these computers, I'm confident that people are are good for it. I've I went recently to the first site that we're building out in Abilene. That'll be about, you know, roughly 10% of all of all of the initial commitment to Stargate, the the sort of 500,000,000,000. It's incredible to see.

是的。就像我脑海中知道一个千兆瓦级站点的规模应该是怎样的。但真正去参观一个正在建设的站点，看到成千上万的人在工地上忙碌，走进那些正在安装GPU的房间，看着整个系统如此复杂，建设速度如此之快，确实令人震撼。我们很快会分享更多关于下一个站点的信息，但有个关于铅笔的绝妙比喻——就是那种普通的木制石墨铅笔——说明没有哪个人能独自造出它。

Yeah. It is like I knew in my head what a order of gigawatt scale site looks like. But then to go see one being built, and the, like, thousands of people running around doing construction and going to, like, you know, stand inside the rooms where the GPUs are getting installed and just, like, look at how complex the whole system is and the speed with which it's going is quite something. We'll have more to share about the next sites soon, but there's a great quote about the pencil just like a standard, you know, wood and graphite pencil and how no one person Yeah. Could build it.

而这正是资本主义的神奇之处。

And and it's it's this like magic of capitalism.

嗯。

Mhmm.

这确实是个奇迹，全世界能够协调完成这些事情。站在第一个星际之门站点内部时，我真正思考的是让这些GPU机架运行起来所需的全球复杂性。当你拿出手机，在ChatGPT里输入内容并得到回复时，可能现在你甚至不觉得这有什么特别令人惊讶的——你只是期望它能正常工作。但曾几何时，也许是你第一次尝试的时候，会觉得这真的非常神奇。

It's miracle really that like that the world gets coordinated to do these things. And and standing inside of the first Stargate site, I was really just thinking about the the global complexity that it took to get these racks of GPUs running. You know, when you get your phone out and you type something into ChatGPT and you get the answer back, you you probably at this point, you probably don't even think that's, like, particularly surprising. You just expect it to work. There was a time, maybe the first time you tried it, where like, that is really amazing.

但这是过去一千年，至少是几百年来，人们辛勤工作获得的来之不易的科学见解，然后建立工程体系、公司和复杂供应链，重新配置世界才实现的成果。想想所有投入其中的东西——你可以一直追溯到那些只是从地里挖石头看会发生什么的人们——这样你现在才能轻松地在ChatGPT中输入内容并让它为你服务。

But the work that happened over the last thousand or at least many hundreds of years of people working incredibly hard to get these hard won scientific insights and then to build the engineering and the companies and the complex supply chains and kind of reconfigure the world that had to happen to get this, like, rack of magic put somewhere. Think about all the stuff that went into that. The, you know, that and trace it all the way back to people that were just, like, digging rocks out of the ground and seeing what happened So that you now get to just, you know, type something into ChatGPT and it does something for you.

我读过关于星际之门项目开发的幕后故事，涉及国际合作伙伴关系，特别是阿联酋，以及埃隆·马斯克曾试图破坏该合作。你看到了什么？听到了什么？对此有什么看法？

I read a behind the scenes story about the development of Project Stargate and the international partnerships, particularly The UAE and that Elon Musk had tried to derail that. And what have you seen? What have you heard? What's the take on that?

我曾说过——我认为对外也说过，但至少在大选后内部说过——我不认为埃隆会滥用政府权力进行不公平竞争。很遗憾我错了。我一般不喜欢犯错，但主要是觉得他这样做对国家真的很不利。我真心没料到他会这样。我很感激政府确实做了正确的事，抵制了这种行为。

I had said, I think also externally, but at least internally after the election that I didn't think Elon was going to abuse his power in the government to unfairly compete. And I regret to say I was wrong about that. I I don't like being wrong in general, but mostly I just think it's really unfortunate for the country that he would do these things. And I didn't think I genuinely didn't think he was going to. I'm grateful that the administration has really done the right thing and stuck up to that kind of behavior.

但没错，这很糟糕。

But yeah, it sucks.

嗯，我认为变化在于——格雷格·布罗克曼刚谈到这点——几年前人们认为谁先到达终点谁就是赢家，游戏就结束了。但现在我们意识到其他地方也有优秀的AI实验室。比如Anthropic正在开发强大的工具，我认为谷歌确实提升了水平。到处都有好的进展，不会出现某个人遥遥领先的情况——我同意这点。

Well, I think the thing that's changed and I think Greg Brockman just talked about this where there was a couple years ago where people thought like, okay, whoever gets there first is the winner and that's it and the game is over. And now we realize there are great AI labs elsewhere. Like Anthropic is building great tools. I think Google has really got its game up. There is good stuff happening everywhere and it is not going to be that one person runs away I with agree.

所以看起来

And so it seems

是的。我最喜欢的例子是AI的发现与此类似，虽然不完美但很接近，在许多令人惊讶的方面与晶体管的发现有诸多相似之处。但很多公司将会在此基础上构建伟大的产品。最终，它会渗透到几乎所有产品中，但你不会总是想着使用晶体管。所以，我认为很多人将会基于这个不可思议的科学发现建立非常成功的公司。

Yeah. The example that I like the most is the discovery of AI was analogous to this, not perfect, but close, to the discovery of the transistor in many surprising number of ways. But many companies are gonna build great things on that. And then eventually, it's gonna, like, seep into almost all products, but you won't think about using transistors all the time. So, yeah, I think a lot of people are gonna build really successful companies built on this incredible scientific discovery.

我希望埃隆在这方面不要那么零和博弈。

And I wish Elon would be less zero sum about it.

是的。或者说负和博弈。我认为如果我们这么想，整个蛋糕只会变得越来越大。我刚参加了一个能源会议，与从事能源生产和超大规模计算的人交流很有趣，他们用这个词来形容这个话题。这确实引出了能源需求的问题。

Yeah. I I think Or negative sum. I think the pie is just gonna get bigger and bigger if we think about that. I was just at an energy conference and it was interesting talking to the people who were involved in energy production and stuff and hyperscaling, the term they used for this was a topic. And that does bring up like the energy requirements.

我知道比如Grok三号，据说他们不得不在停车场安装发电机才能训练那个模型。问题在于，能源将从哪里来？钱，我理解。但当我们谈论所需能源的规模时，能源才是需要思考的。

I know that for like Grok three apparently, I guess they had to put generators in the parking lot to be able to train that model. And that's the question is like, where is the energy going to come from? Money, understand. Energy, to think of when we talk about the scale of energy needed.

我认为几乎无处不在。

I think kind of everywhere.

对。

Right.

我认为目前是一个大杂烩。最终，我对先进核能——包括裂变和聚变——非常兴奋。但目前，我认为这是整个能源组合的混合。对吧。天然气、太阳能，我指的是真正的核能，一切能源。

I think it's a big mix right now. Eventually, I think a lot of I'm very excited about advanced nuclear, both fission and fusion. But for now, I think it's that's a whole mix of the entire portfolio. Right. Gas, solar, I mean, really nuclear, everything.

所以是所有上述的等等。是的。我和一些人交谈过，其中一些在阿尔伯塔等地工作，他们说那里能源获取容易但使用需求不大，等等。这是我从未想过的整体图景。

So all of the above and stuff. Yeah. I was talking to people that were some of them worked in areas like in Alberta where they said we have a lot of access to energy and not as much use for it there, etcetera. And that was just this total picture I hadn't even thought about.

你知道，传统上，在全球范围内输送能源非常困难。大多数种类都是。但如果你用能源换取智能，然后在全球传输智能，这就容易多了。所以你可以把大型训练中心甚至大型推理集群放在很多地方，然后通过互联网传输输出结果。

You know, traditionally, it's very hard to move energy around the world. Most kinds. But if you exchange energy for intelligence and then move the intelligence around the world, it's much easier. So you could put the giant training center or even the big inference clusters in a lot of places and then just like ship the output over the Internet.

开幕时有一位演讲者，我参加了一个活动，有人在研究詹姆斯·韦伯太空望远镜，他谈到最大的瓶颈是他们即将获得所有这些太字节的数据，但没有科学家来处理。没有足够的人手来筛选数据。我们面前有着关于宇宙的答案，但这就像一个大数据问题。

There was a speaker at opening, I came to an event and somebody was working, think it was the James Webb Space Telescope and he talked about his biggest bottleneck was they're about to get all of this, you know, terabytes of data but he doesn't have no scientist to work on it. Doesn't have enough people to go through the data. And here we have these answers about the universe, whatever in front of us, and it's like a big data problem.

是的。我一直开玩笑说，等我们有了足够的资金，等Opening Eyes有足够资金时，我们应该做的一件事就是建造一个巨型粒子加速器，一劳永逸地解决高能物理问题。因为我觉得那会是件辉煌美妙的事情。但我在想，一个真正非常聪明的人工智能有多大几率能够仅凭我们现有的数据

Yeah. I, I've always joked that one thing we should do when we have enough money, when opening eyes enough money, is just build a gigantic particle accelerator and solve high energy physics once and for all. Because I think that'd be like a triumphant wonderful thing. But I wonder what are the odds that a really, really smart AI could look at the data we currently have

嗯。

Mhmm.

在没有更多数据、没有更大粒子加速器的情况下，仅凭这些就能弄明白。这并非不可能。是的。所以这就引出了一个问题：好吧，世界上已经有很多数据了。

With no more data, no bigger particle accelerator, and just figure it out. It's not impossible. Yeah. And and yeah. So there's this question of like, okay, there's already a lot of data out there.

世界上有很多聪明人，但我们不知道在没有更多实验的情况下，智力能走多远。我们还能发现多少？

There's a lot of smart people in the world, but we don't know how far intelligence can go with no more experiments. How much more could we figure out?

我记得读到过一些，我谈到在九十年代初，有人发现了类似Ozempic（一种药物）的东西。然后把它提交给一家制药公司，他们说，我们放弃这个。而这已经成为改变人们生活的药物。对于那些长期受肥胖困扰的人来说，它将改善生活质量，你会想，这东西被搁置了二十五年。

I remember reading some that I talked about how in early nineteen nineties, somebody had found like a form of Ozempic. Alright, and presented it to like a drug company to this and they said, we're going to pass on that. And that's been a life changing drug for people. Like for people who've just basically done chronic obesity, whatever, it's going to improve the quality of life and you think, this was sitting there for twenty five years.

我怀疑我们还会发现很多其他例子，也许我们已经有一些已知有某种好处的现有药物，但它们可以以其他重要方式被重新利用，或者经过一些小修改。我们离某个伟大的突破非常接近。听到科学家们甚至使用当前一代模型进行这类工作，非常令人鼓舞。所以听起来像是

I suspect there's a lot of other examples that we'll find where maybe we already have existing drugs that we know do something good, but they they're reusable in some other big way or with a couple of small modifications. We are very close to something great. And it's been very heartening to hear from scientists using the even the current generation models for this kind of work. So it sounds like one of

不过，下一代模型我们需要的是能够理解物理和化学等知识的模型。Sora算是这方面的一个尝试

the things we're going need though for next generation models is models that understand physics and chemistry and stuff. Is Sora sort of a stab

吗？我的意思是，它会理解牛顿物理学。我不知道它是否能帮助我们发现新的化学，或者新的、新颖的物理学或理论物理学之类的。但我对用于推理模型的技术将极大地帮助我们解决这些问题感到乐观。

at that? I mean, it'll understand like Newtonian physics. I don't know if it'll help us with discovering new chemistry and sort of like new, like novel physics or novel theoretical physics or whatever you'd like. But I think I'm optimistic that the techniques we use for the reasoning models will help us with those things a lot.

好的。那么推理模型的简短定义是什么？它与我只是问GPT-4.1一些问题有什么不同？

Okay. And what is the short definition of how a reasoning model works versus just me asking GPT 4.1 something?

GPT模型可以进行一些推理。事实上，在GPT模型的早期，让人们非常兴奋的事情之一就是，通过告诉模型‘让我们一步步思考’，你可以获得更好的性能。然后它就会输出一步步思考的文本，并得到更好的答案，这本身就很神奇。推理模型只是将这一点推得更远。

So the GPT models can reason a little bit. And in fact, one of the one of the things that got people really excited in the early days of the GPT models was you could get better performance by telling the model, let's think step by step. And it would then just output text that was thinking step by step and get a better answer, which was sort of amazing that that worked at all. The reasoning models are just pushing that much further.

所以这个想法是，当它能够将问题分解时，就可以在每个步骤上花费更多时间。

So it's the idea of like, when it's able to break the question down, it can spend more time on each step.

当你问我某个问题时，如果是个非常简单的问题，我可能会像条件反射一样立刻给出答案。但如果是个更难的问题，我可能会在脑子里思考，让我的内心独白说，嗯，我可以这样做或那样做，或者也许这样会更清楚。我不太确定那个。然后我可以回溯和重新梳理我的思路。当我思考完毕，并且是用英语思考后，我就可以列出一些要点，然后用英语给你一个答案。

When you ask me something, a question, I if it's a really easy question, I might just fire back like almost on reflex with the answer. But if it's a harder question, I might think in my head and have, like, my internal monologue go and say, well, I could do this or that or maybe maybe, you know, this will be clearer. I'm not sure about that. And I could, like, backtrack and retrace my steps. And then when I finish thinking and I've, you know, been thinking in English, I can then, you know, make some bullet points and then kind of like, I'll put an answer to you in English.

我使用这个应用时观察到的一个有趣现象是，如果我提出一个深入的研究问题之类的，然后锁屏离开，我发现它仍在处理和思考这个问题。我听说有家公司，忘了是哪家，在使用一个衡量某件事花费多长时间的指标。我想是Anthropic，他们说这个模型实际上花了十五分钟或三十分钟之类的长时间来思考一个问题，这是个不错的指标，但它需要真正给出正确答案。我觉得这是一种挺有趣的范式。

One one of the interesting things I've observed now when I use the app, if I ask a deep research question or something and I go away on my lock screen, I get that it's still processing and thinking about it and I heard somebody, another company, forgot who was, was using a metric of how long something spent. I think it anthropic and like said, hey, this model actually spent like fifteen minutes or thirty minutes or whatever length of time to think about a thing which is a good metric by, but it needs to actually give you the right answer. And I thought that was sort of just interesting paradigm of

让我感到惊讶的一点是，人们出乎意料地愿意等待一个精彩的答案。

One thing I have been surprised by is people are surprisingly willing to wait for a great answer.

是啊。

Yeah.

即使迈尔斯会想，我所有的本能都告诉我，即时响应才是关键，用户讨厌等待。对于很多事情来说，这没错。但对于那些能给出非常好答案的难题，人们是相当愿意等待的。

Even if Miles is going think, well, all of my instincts have been, you know, the instant response is the thing that matters and users hate to wait. And for a lot of stuff, that's true. But for hard problems with a really good answer, people are quite willing to wait.

是的。所以我们有所有这些工具，所有这些技术。到目前为止，我都在用我的手机。而现在，OpenAI刚刚宣布你们正在开发硬件。你和乔尼·艾夫一起录了视频，谈论你们已经讨论和合作了几年。

Yeah. So we have all these tools, all these things. So far, I'm using my phone. And now, OpenEye just announced that you guys are building hardware. You had the video with you and Johnny Ive, talking about you guys have been talking about and collaborating for a couple of years.

显然，你不能……我的意思是，好吧，我可以问你。你现在戴着它吗？

Obviously, you can't I mean, well, I could ask you this. Is it on you right now?

不，没有。好吧。还需要一段时间。我们正努力打造一个极高品质的产品。

No, It is not. Alright. It's gonna be a while. Okay. We're gonna try to do something like a crazy high level of quality.

而高品质的东西不会来得很快。但是计算机、软件和硬件，就我们目前对计算机的认知方式而言，是为一个没有AI的世界设计的。而现在我们处于一个非常不同的世界，你对硬件和软件的期望也在迅速改变。

And that that does not come fast. But computers, software and hardware, just the way we think of current computers, were designed for a world without AI. And. Now we're in like a very different world and what you want out of hardware and software. It's changing quite rapidly.

你可能想要一个对环境有更强感知能力、在你生活中拥有更多上下文信息的东西。你可能希望以不同于打字和看屏幕的方式与它互动。我们在这方面已经探索了一段时间，并且有几个让我们相当兴奋的想法。我认为人们需要时间来适应。在这种世界中使用计算机意味着什么，因为现在一切都如此不同。

You might want something that is way more aware of its environment, that has way more context in your life. You might want to interact with it in a different way than, like, typing and looking at a screen. And we've been exploring that for a while, and we've got a couple of ideas we're really quite excited about. I think it will take time for people to get used to. What it means to use a computer in this kind of a world, because it is so different now.

但如果你真的信任一个AI能理解你生活的所有背景和问题，并代表你做出正确判断——比如让它参加会议，听完整个会议，知道哪些信息可以分享给谁，哪些不应该与任何人分享，以及了解你的偏好。然后你问它一个问题，并相信它会与合适的人进行正确的跟进，这样你就能想象出一种完全不同的使用计算机来完成目标的方式。

But if you, like, really trusted an AI to understand all the context of your life and your question and make good judgments on your behalf where you could, like, have it sit in a meeting, listen to the whole meeting, know what it was like allowed to share with who and what it shouldn't share with anyone and, you know, kind of what your preferences would be. And then you ask it one question and you trust that it's going to go do the right follow ups with the right people and do like, you can then imagine a totally different kind of how you use a computer to get done what you want.

所以我们与ChatGPT的交互方式有点像是

So kind of the way we interact with ChatGPT is kind of

某种程度上启发了这个设备。我的意思是，也可以说我们与ChatGPT的交互方式受到了上一代设备的启发。所以我认为这是一种共同演进的过程，但是的，我希望如此。其中一个

kind of inform the device. I mean, could also say that the way we interact with ChatGPT was informed by the previous generation of devices. So I think it is this sort of like co evolving thing, but yeah, I hope so. One of the

让手机如此普及的原因是，我可以在公共场合看屏幕，也可以在私下打电话与它交谈。我认为新设备面临的挑战之一就是试图弥合我们在公共和私人使用之间的差距。

things that made the phone so ubiquitous was the fact that I can be in public and look at the screen. I can be in private, have a phone call and talk to it. And I think that's one of the challenges for new devices is that trying to bridge that gap between what we use in public and private.

手机是不可思议的东西。我的意思是，它们在很多方面都非常出色。你可以想象一个随处可用的新设备，但有些事我在公共场合和在家里的做法可能确实不同。我在家里有一套很棒的内置音乐立体声系统，而在外面走动时，我用AirPods，这并不困扰我。

Phones are unbelievable things. I mean, they are really fantastic for a lot of reasons. And you can imagine one new device that you could use everywhere, but also, like, there's some things that I do do differently publicly and probably, like, at home. I've got great stereo system built in the music. And when I'm walking the world, I use AirPods and that doesn't bother me.

是的。所以我认为公共和私人使用场景下确实存在差异，但我同意通用性很重要。

Yeah. So I think there are things that are different in the public and private use case, but the general purposeness, I agree, is important.

是的。它会一直跟随你。所以，目前还没有什么消息，可能要等到明年。

Yeah. It follows you with it. So, nothing yet until maybe next year.

还需要一段时间。好吧。我希望值得等待，但这确实需要一些时间。

It's gonna be a while. Alright. It will be worth the wait, I hope, but it's gonna be a while.

好的。我既兴奋又好奇。我有一些想法。那么，如果你现在要给一个25岁的年轻人提建议，你会告诉他们什么？

Okay. I'm I'm excited and curious. I have thoughts. So if you're giving advice to a 25 year old right now, what do tell them?

我的意思是，显而易见的战术性建议可能正如你所期待的，比如学会使用AI工具。有趣的是，世界这么快就从告诉20到25岁的年轻人去学习编程，嗯，编程不重要了，变成要学会使用AI工具。我在想接下来会是什么，但当然，总会有下一个热点。

I mean, the obvious tactical stuff is probably what you'd expect me to say, like, learn how to use AI tools. It's it's funny how quickly the world went from telling, you know, the average 20 year old to 25 year old learn to program Mhmm. To program it doesn't matter. Learn to use AI tools. I wonder what will be next, but, of course, there will be something next.

但这是非常好的战术建议。而在更广泛的层面上，我相信像韧性、适应性、创造力、理解他人需求这样的技能，其实都是可以学习的，虽然这不像去练习使用ChatGPT那么容易，但确实可行。我认为这些技能在未来几十年里会带来巨大的回报。

But that's that's very good tactical advice. And then on the sort of, like, broader front, I believe that skills like resilience, adaptability, creativity, figuring out what other people want. I think these are all surprisingly learnable. And it's not as easy as, say, like, go practice using Chatuchu VT, but it is doable. And those are the kind of skills that I think will pay off a lot in the next, you know, couple of decades.

那对于45岁的人你会说同样的话吗？就是学会在当前角色中应用它？是的，很可能。不管我们对AGI（人工通用智能）的个人定义是什么，在那之后为OpenAI工作的人会更多还是更少？更多。

And would you say same thing for 45 year old? Is just learn how to use it in your role now? Yeah, probably. Whenever we have whatever your personal definition of AGI, will more people be working for OpenAI after then or before? More.

更多。所以，是的。我看到很多网友说，哦，它们那么厉害，为什么还要招人？我想说，因为计算机并不能做所有事。

More. So, yeah. I see a lot of online people like, oh, they're they're so good. Why are they hiring people? I'm like, because computers can't do everything.

它们不会包办一切。

They're not going to do everything.

更详细一点的回答（不止一个词）是：人会更多，但每个人能完成的工作量将远超AGI时代之前一个人所能做的。对吧。这正是技术发展的目标。是的。

The slightly longer answer with more than one word is that, there will be more people, but each of them will do vastly more than what one person did, you know, in the pre AGI times. Right. Which is the goal of technology. Yeah.