2025-05-20 Sergey Brin.All-In Podcast
“We've got a special guest who's going to come join us.
我们有一位特别嘉宾要加入我们。
This always happens.
总是会有这种惊喜时刻。
There he is, Sergey Brin, everybody.
他来了，大家欢迎谢尔盖·布林。
Oh my God. Somebody told me you started submitting code and it kind of freaked everybody out that daddy was home.
天哪。有人告诉我你开始提交代码了，大家都被吓了一跳，感觉“爸爸回来了”。
All models tend to do better if you threaten them.
所有模型在受到威胁时往往表现得更好。
If you threaten them.
如果你威胁它们。
Like with physical violence.
比如说用物理暴力威胁。
Yes.
没错。
Management is like the easiest thing to do with AI.
管理是AI最容易胜任的事情。
Absolutely. It must be a weird experience to meet the bureaucracy in a company that you didn't hire.
确实如此。在一家公司里遇到一群你没有亲自雇佣的官僚，应该是种很奇怪的体验。
But on the other side of it, I would say, it's pretty amazing that some junior muckabee muck can basically look at you and say, hey, go f\*\*\* yourself. No, but I'm serious. That's a sign of a healthy culture, actually.
但从另一个角度看，有个基层的小员工敢直视你说：“去你X的”，其实也挺神奇的。不，我是认真的。这其实是健康文化的一种表现。
You're punching a clock, man. I hear the reports. You and I have talked about it.
你可是打卡上班的啊，我听说了相关报道。你和我也聊过这事。
You're going to work every day.
你现在是每天去上班了。
Yeah, it's been, you know, some of the most fun I've had in my life, honestly. And I retired like a month before COVID hit, in theory. Yeah.
是啊，说实话，这是我人生中最有趣的一段时间之一。我其实在疫情爆发前一个月就“退休”了。
“And I was like, you know, this has been good. I want to do something else. I want to hang out in cafes, read physics books.
“我当时想，差不多了，我想做点别的。我想坐在咖啡馆里读物理书。”
And then like a month later, I was like, that's not really happening. So then I just started to go to the office, you know, once we could go to the office. And actually, to be perfectly honest, there was a guy from OpenAI, this guy named Dan.
然后大概一个月后我就意识到，这根本不现实。所以一旦可以回办公室了，我就开始去上班了。说实话，是OpenAI的一个叫Dan的人促使了我重新思考。
And I ran into him at a little party. And he said, you know, look, what are you doing? This is like the greatest transformative moment in computer science ever.
我在一个小型聚会上遇到他，他说，“你现在在干嘛？这是计算机科学有史以来最具变革性的时刻。”
And you're a computer scientist?
你可是计算机科学家？
I'm a computer scientist.
我是个计算机科学家。
Forget that. Yeah, yeah, yeah. You're a PhD student for computer science.
别谦虚了。是啊，你是读计算机博士的。
I haven't finished my PhD yet, but working on it.
我还没完成博士学业，不过还在继续努力中。
Keep working.
继续努力。
We'll get there. Yeah, technically on leave of absence. And he told me this, and I had already started kind of going into the office a little bit, and I was like, you know, he's right.”
我们会完成的。是的，严格来说我还在休学。他跟我说了这些，而我其实也已经开始回办公室一段时间了，当时我就觉得他说得对。”
“And it has been just incredible, just while you guys all obviously follow all the AI technology. But being a computer scientist, it is, you know, the most exciting thing of my life, just technologically.
“这段时间真是太不可思议了。你们当然都在关注AI技术，但作为一个计算机科学家，我可以说，这是我一生中最激动人心的技术时刻。”
And the exponential nature of this, the pace of it, it dwarfs anything we've seen in our career. It's almost like everything we did over the last 30 or 40 years has led up to this moment, and it's all compounding on itself.
这种指数式的发展速度，远远超过了我们职业生涯中见过的一切。感觉我们过去三四十年做的一切，都是为了走到今天，而且这一切正在以复利方式叠加。”
The pace, maybe you could speak, you know, you had a company, Google, that grew from, you know, 100 users and 10 employees to, now you have over 2 billion people using, I think, six products, or five products have over 2 billion.
关于这种速度，你可能最有发言权。你当年的公司Google，从100个用户、10个员工，发展到现在有超过20亿人在用，可能有5到6款产品各自用户超20亿。
It's not even worth counting because it's the majority of the people on the planet touch Google products. Describe the pace.
这都不值得去数了，因为地球上大多数人都在使用Google的产品。你来形容一下这种增长速度吧。
Yeah, I mean, the excitement of the early web, like I remember using Mosaic and then later Netscape. How many of you remember Mosaic, actually, my weirdo? And you remember there was a What's New page.
是啊，早期网络的那种兴奋感我还记得，我用过Mosaic，后来用Netscape。有多少人记得Mosaic？是不是只有我这个怪人？你还记得当时有一个“What's New”页面吗？
The What's New page is great.
“What's New”页面太棒了。
Like you go to the homepage.
你打开主页。
Two or three new web pages.”
就会列出两个或三个新网页。”
“Yeah, it was like this last week, these were the new websites.
“是的，会显示上周新增的网页。”
Yes.
没错。
And it was like such and such elementary school, such and such a fish tank.
什么什么小学，或者某个鱼缸的网站。
Yeah.
对啊。
And you were like, wow.
你会觉得，哇，太神奇了。
Michael Jordan appreciation page.
迈克尔·乔丹致敬页面。
Yeah, well, whatever it was, these were the three new sites on the whole Internet. So obviously, the web developed very rapidly from there. And that was a very exciting, and then we found smartphones and whatnot.
是啊，不管那时是什么网站，那就是整个互联网的三个新站点。所以很明显，从那以后网络发展得非常快。这一切非常激动人心，后来我们又发明了智能手机等等。
But the developments in AI are just astonishing, I would say by comparison, just because of the web spread, but didn't technically change so much from month to month, year to year. But these AI systems actually changed quite a lot. If you went away somewhere for a month and you came back, you'd be like, whoa, what happened?
但与之相比，AI的发展实在令人震惊。因为互联网虽然普及了，但技术上从月到月、年到年其实变化不大。而AI系统每个月都在变化。你离开一个月回来，都会震惊：“发生了什么？”
Somebody told me you started submitting code, and it freaked everybody out that daddy was home.
有人告诉我你开始提交代码了，大家都被吓坏了，“爸爸回来了”。
Okay.
好吧。
Daddy did a PR? What happened?
“爸爸”还提了个Pull Request？到底怎么回事？
The code I submitted wasn't very exciting. I think I needed to add myself to get access to some things, and a minor seal here or there. Nothing that's going to win any awards.”
我提交的代码没什么特别的。我只是需要把自己加入进去以获得一些权限，顺便修复了些小问题。不会得奖那种小改动。”
“But you need to do that to do basic things, run basic experiments and things like that. I've tried to do that and touch different parts of the system.
但你得做这些基本工作，比如运行实验等等。我试过这样操作，去接触系统的不同部分。
First of all, it's fun, and secondly, I know what I'm talking about. It really feels privileged to be able to go back to the company, not have any real executive responsibilities, but be able to actually go deep into every little pocket.
首先，这很有趣；其次，我确实懂这些。能回到公司，没有行政管理职责，却能深入每一个小领域，这种感觉很特别。
Are there parts of the AI stack that interests you more than others right now? Are there certain problems that are just totally captivating you?
在AI技术堆栈中，现在有没有某些部分特别吸引你？有没有什么问题让你完全着迷？
Yeah, I started a couple of years ago and maybe a year ago, I was really very close with what we call pre-training. Actually, most of what people think of as AI training, whatever people call it, pre-training, for various historical reasons. But that's sort of the big super, you throw huge amounts of computers at it.”
是的，我从几年前开始，大约一年前，我非常专注于我们所谓的预训练。其实大多数人所说的AI训练，其实历史上叫做预训练。那就是那种要扔进去海量计算资源的大工程。”
“I learned a lot, just being deeply involved in that, seeing us go from model to model and so forth, then running little baby experiments, but kind of just for fun, so I could say I did it. More recently, the post-training, especially as the thinking models have come around. That's been another huge step up in general in AI.
我从深度参与中学到了很多，看着我们从一个模型过渡到另一个模型，然后运行一些小实验，纯粹为了好玩，好说自己动手做过。最近，我更多关注后训练阶段，尤其是“思维模型”的出现，这是AI整体上又一个巨大的飞跃。
So we don't really know what the ceiling is.
所以我们其实不知道上限在哪。
When you explain what's happening with prompt engineering, then to deep research and what's happening there to like a civilian, how would you explain that step function? Because I think people are not hitting the down carrot and watching deep research in Gemini's mobile app, and you got a mobile app and it's pretty great. By the way, I got the fold after you and I were talking about it.
如果你要向普通人解释从提示词工程到深入研究发生了什么，你会怎么描述这个跃迁？因为我觉得很多人根本没点开Gemini手机App里那个“深入研究”的箭头。而你们现在有个很棒的App。顺便说一下，我在和你聊完之后也买了那个折叠机。
Okay, Google kicks serious ass now. Like it actually does what you ask it to do. When you ask it to open up, it does stuff.”
好吧，Google现在真的很厉害。它确实能做你让它做的事。你叫它开启，它就真的开始干活。”
“But the number of threads, the number of queries, the number of follow-ups that it's doing in that deep research is 200, 300? Maybe explain that jump and then what you think the jump after that is.
但在深入研究中，它处理的线程数量、查询数量、跟进操作，有200、300个？你能不能解释下这个跃迁，以及你认为下一个跃迁会是什么？
To me, the exciting thing about AI, especially these days, I mean, it's not like quite AGI yet as people are seeking or it's not superhuman intelligence. But it's pretty damn smart and can definitely surprise you. So, I think of the super power is when it can do things in a volume that I cannot.
对我来说，AI最激动人心的地方，尤其是现在，虽然它还不是人们追求的AGI，也不是超人智能，但它已经非常聪明，时常让人惊讶。它的“超能力”，我认为，是能做出我在人力上无法完成的规模化任务。
Yes.
对。
Right? So, by default, when you use some of our AI systems, it'll suck down whatever top 10 search results, and kind of pull out what you need out of them, something like that. But I could do that myself, to be honest.
对吧？一般来说，我们的AI系统会抓取前十条搜索结果，然后提取你需要的内容，类似这样。但说实话，我自己也可以做到。
Maybe it would take me a little bit more time. But if it sucks down the top 1000 results, and then does follow-on searches for each of those, and reads them deeply, that's a week of work for me. I can't do that.”
可能会花我多点时间。但如果它抓取前一千条结果，然后为每一条结果都做跟进搜索、深入阅读，那对我来说是一个星期的工作量。我做不到。”
“This is the thing I think people have not fully appreciated who are not using the deep research projects. Before we had our F1 driver on stage, I'm a neophyte, I don't know anything about it. I said, how many deaths occurred per decade?
这就是我觉得没用“深入研究”项目的人还没完全理解的东西。在我们请F1车手上台之前，我是个新手，对这没什么了解。我问：“每十年有多少起死亡事故？”
I said, I want to get to deaths per mile driven. At first was like, that's going to be really hard. I was like, I give you permission to make your best shot at it and come up with your best theory.
我说我想要得到“每英里行驶死亡率”。一开始AI说，这很难。我就说，“我允许你尽力尝试，提出你最好的理论。”
Let's do it. And it was like, OK. And it was like, there's this many teams, there's this many races.
我们开始吧。然后它说，“好”，接着它就列出了有多少车队、有多少比赛。
Which model did you use?
你用的是哪个模型？
OpenAI? No, I used Gemini.
OpenAI的吗？不，我用的是Gemini。
Gemini 5.5.
Gemini 5.5版。
The fabulous one. And it was like, let's go. I treat it like I get sassy with it.
超棒的那个。我就说“我们走起”。我跟它说话的时候还挺调皮的。
And it kind of works for me.
而且这种风格对我来说居然挺管用的。”
You know, it's a weird thing. We don't circulate this too much in the AI community. But not just our models, but all models tend to do better if you threaten them.
你知道，这是一件很奇怪的事。我们在AI圈子里并不常公开谈论这个。但不仅是我们的模型，几乎所有模型在被威胁时表现得更好。
If you threaten them?
你是说威胁它们？
Like with physical violence.
比如说用暴力威胁。
Yes.
没错。
But people feel weird about that, so we don't really talk about that.”
但大家觉得那样很奇怪，所以我们并不怎么讨论这个。”
“I was threatening with not being fabulous, and it responded to that as well.
我威胁说如果你不出彩就不理你，它也有反应。
Yeah, historically you just say, oh, I'm going to kidnap you if you don't blah, blah, blah. They actually.
是啊，以前你就说：“如果你不怎么怎么样，我就要绑架你了。” 它们真的会反应。
Can I ask you a more specific question?
我可以问一个更具体的问题吗？
Hold on.
等一下。
But it went through it, and it literally came up with a system where it said, I think we should include practice miles. So let's say there's a 100 practice miles for every mile on the track, and then it literally gave me the deaths per mile estimated, and then I started cross-referencing and I was like, oh my God, this is like somebody's term paper for undergrad. You know, like, whoa, done, in minutes.
但它还是继续做了，它真的想出了一个体系，说：“我认为我们应该包括练习赛的里程。” 假设每圈正式赛道后面有100英里的练习，然后它直接给出了每英里死亡率的估算。我开始交叉验证，结果我简直惊呆了，这就像一个本科生的期末论文。几分钟就搞定了。
Yeah, I mean, it's amazing. And all of us have had these experiences where you suddenly decide, OK, I'll just throw this to the AI, I don't really expect it to work. And then you're like, whoa, that actually worked.”
是啊，这真的很惊人。我们都有过这样的经历，你突然决定，把这个问题丢给AI，没太大期望，然后AI居然真的搞定了。”
“So as you have those moments and then you go home to your just life as a dad, have you gotten to the point where you're like, what will my children do? And are they learning the right way? And should I totally just change everything that they're doing right now?
所以你在经历这些AI神奇时刻之后，回到家过上爸爸的日常生活，你有没有开始想：“我的孩子将来该怎么办？他们现在学的方式对吗？我是不是应该彻底改变他们正在做的一切？”
Have you had any of those moments yet?
你有没有经历过这样的思考？
Yeah, I mean, I look, I don't really know how to think about it, to be perfectly honest. I don't have like a magical way. I mean, I see I've a kid in high school and middle school and, you know, I mean, the AIs are basically, you know, already ahead, you know.
有啊。说实话，我真的不知道该怎么想这个问题。我也没有什么神奇的答案。我有一个孩子在高中，一个在初中，说真的，AI基本上已经在很多方面领先他们了。
I mean, obviously there's some things AIs are particularly dumb at and they, you know, they make certain mistakes. A human would never make. But generally, you know, if you talk about like math or calculus or whatever, like, they're pretty damn good.
当然有些事AI还是很蠢，会犯一些人类永远不会犯的错。但整体来说，比如数学、微积分之类的，它们真的很厉害。
Like they, you know, can win like math contests and coding contests, things like that against, you know, some top humans. And then I look at, you know, OK, he's whatever. My son's going to go on to whatever from sophomore to junior.”
它们甚至可以在数学竞赛、编程竞赛中击败人类高手。然后我看看我儿子，嗯，他现在从高二升高三。”
“And what is he going to learn? And then I think in my mind and I talked to him about this. Well, what is the AI going to be in one year?
那他明年会学到什么？我心里就会想，我也和他讨论过，那AI一年后会发展到什么程度？
Exactly.
对啊。
Yeah. And it's like not comparable, right?
没错。这根本就不是一个可以比较的节奏，对吧？
Obviously, the AI is where you would tell your son, look, don't or not yet.
你会不会告诉你儿子：“看，别现在做这事，AI马上就能干了。”
I don't know if you can like plan your life around this. I mean, I didn't particularly plan my life to like, I don't know, be an entrepreneur or whatever. I was just like math and computer science.
我不知道你能不能围绕这个来规划人生。我自己也没有刻意规划过人生要成为什么，比如创业者之类。我就是喜欢数学和计算机科学。
I guess maybe I got lucky and it worked out to be useful in the world. I don't know. I guess I think, you know, my kids should do what they like.
我猜我可能运气不错，刚好这些在现实世界变得有用了。我不知道。我觉得我的孩子们应该做他们喜欢的事。
Hopefully, it's somewhat challenging and they can, you know, overcome different kinds of problems and things like that.
希望这些事情有一定挑战性，他们能从中学会解决各种问题之类的。”
What about specifically the...
那如果具体谈谈……
What about college?
比如说上大学呢？
Do you think college is going to continue to exist as it is today?”
你觉得大学还会像现在这样存在下去吗？”
“I mean, it seems like college was already undergoing this kind of revolution even before this sort of AI challenge of people are like, is it worth it? Should I be more vocational? What's actually going to be useful?
我觉得大学在AI挑战出现之前就已经在经历一场变革了。很多人开始质疑：这值得吗？我是不是该走职业路线？什么才是真正有用的？
So, we're already kind of entering this kind of situation where there's sort of questions asked about colleges. Yeah, I think, you know, AI obviously puts that at the forefront.
我们其实已经进入这样一个阶段，开始质疑大学的作用。而AI毫无疑问把这些问题推到了最前沿。
As a parent, I think a lot about, hey, so much of education in America, in the middle class, upper class, is all about what college, how do you get them there? And honestly, lately, I'm like, I don't think they should go to college. Like, it's just fundamentally...
作为家长，我经常思考，美国教育系统，特别是中产和上层阶级，都在围绕“上哪所大学”展开。但说实话，最近我有时会想，也许他们根本就不该上大学。从根本上来说……
You know, my son is a rising junior and his entire focus is he wants to go to an SEC school because of the culture. And two years ago, I would have panicked and I would have thought, should I help him get into a school, this school, that school? And now I'm like, that's actually the best thing you could do.
我儿子马上升高三，他现在一门心思想去一所SEC学校，就因为喜欢那里的文化。如果是在两年前，我肯定会很焦虑，想着该怎么帮他申请名校。但现在我反而觉得，那其实是他最好的选择。
Be socially well-adjusted, psychologically deal with different kinds of failures, you know.
学会社会适应，心理上能面对各种失败，这些才是关键。”
Enjoy a few years of exploration. Yeah”
享受几年探索的时光吧。是啊。”
“Yeah.
是的。
Sergey, can I ask you about hardware? You know, years ago, Google owned Boston Dynamics, maybe a little bit ahead of its time. But the way these systems are learning through visual information and sensory information and basically learning how to adjust to the environment around them, is triggering these kind of pretty profound learning curves in hardware.
谢尔盖，我能问你一个关于硬件的问题吗？你知道，很多年前，Google曾拥有波士顿动力公司，可能有点超前了。但如今这些系统通过视觉信息和感官信息进行学习，并且学会如何适应周围环境，这在硬件上引发了一些非常深刻的学习曲线。
And there's dozens of startups now making robotic systems. What do you see in robotics and hardware? Is this a year or are we in a moment right now where things are really starting to work?
现在有几十家创业公司在做机器人系统。你怎么看待机器人和硬件的发展？今年是关键之年吗？我们现在是否正处于真正开始见效的时刻？
I mean, I think we've acquired and later sold like five or so robotics companies and Boston being one of them. I guess if I look back on it, we built the hardware. We also had this more recently, we built out everyday robotics internally, and then later had to transition that.
我想我们曾经收购又出售了大约五家机器人公司，波士顿动力就是其中之一。如果回顾过去，我们确实造了硬件。更近期我们在内部也组建了“日常机器人”团队，后来也进行了调整。
You know, the robots are all cool and all, but the software wasn't quite there. That's every time we've tried to do it to make them truly useful. And presumably one of these days that will no longer be true.
机器人都很酷，但软件还没跟上。每次我们尝试让它们变得真正有用时，软件总是拖后腿。也许哪天这将不再是问题。
Right. But have you seen anything lately that...”
没错。但最近你看到什么新进展了吗……”
“Yeah. And do you believe in the humanoid form factor robots or do you think that's a little overkill?
对了，那你相信人形机器人这种形态吗？还是觉得有点过头？
I'm probably the one weirdo who doesn't, who's not a big fan of humanoids, but maybe I'm jaded because we've, you know, we at least acquired at least two humanoid robotic startups and later sold them.
我可能是那个不太看好人形机器人的怪人吧，可能是因为我们之前至少收购过两家人形机器人初创公司，后来又卖掉了。
But the reason is, I mean, the reason people want to do humanoid robots for the most part is because the world is kind of designed around this form factor. And, you know, you can train on YouTube, we can train on videos, people do all the things.
原因是，大多数人想做人形机器人，是因为这个世界本来就是围绕人形设计的。而且你可以用YouTube视频进行训练，因为视频里人类做了所有动作。
I personally don't think that's given the AI quite enough credit. Like, AI can learn, you know, through simulation and through real life pretty quickly how to handle different situations.
但我个人认为，这其实有点低估AI的能力了。AI可以通过仿真和现实生活快速学会处理各种情况。
And I don't know that you need exactly the same number of arms and legs and wheels, which is zero in the case of humans, as humans, to make it all work.
我不觉得非要有和人类一样数量的手脚或者轮子（人类是没有轮子的），系统才能正常工作。
And so I'm probably less bullish on that. But to be fair, there are a lot of really smart people who are making humanoid robots. So I wouldn't discount it.”
所以我对这方向没那么乐观。但公平地说，现在确实有很多聪明人正在开发人形机器人，所以我也不会完全否定它。”
“What about the path of being a programmer? That's where we're seeing, with that finite data set, and listen, Google's got a 20-year-old codebase now, so it actually could be quite impactful. What are you seeing literally in the company?
那程序员这个职业的未来呢？在当前有限的数据集背景下，这可能是个关键领域。Google现在的代码库已有20年历史，这其实很有影响力。你在公司内部看到的情况是什么？
Are the 10x developers always this ideal that you get a couple of unicorns once in a while? But are we going to see all developers? Their productivity hit that level, 8, 9, 10, and they're just going to...
那种“10倍程序员”一直都是个理想——偶尔能遇到一两个天才。但未来是否所有程序员的效率都会达到8、9、10倍的水平，他们都能……
Or is it going to be all done by computers, and we're just going to check it and make sure it's not too weird? Because it could get weird.
还是说将来全都由计算机完成，我们只是检查一下代码别太离谱？因为那确实可能变得怪异。
If you vibe code, yeah.
如果你用“情绪写码”的方式，是的。
I'm embarrassed to say this. Like recently, I just had a big tiff inside the company because we had this list of what you're allowed to use to code and what you're not allowed to use to code, and Gemini was on the no list.
说出来有点丢人，我最近在公司里还为这事大吵了一架。我们有一份允许用来写代码的工具清单，还有一份禁止使用的清单，而Gemini居然在禁用名单上。
Oh, you have to be pure. You can't...
哦，你必须“纯净”编程，不能……
I don't know. For like a bunch of really weird reasons that it would like boggled my mind that...
我也不知道，为了一些我完全搞不懂的奇怪理由……
You couldn't vibe code on the Gemini code.”
你居然不能用Gemini来“情绪写码”。“
“I mean, nobody would like enforce this rule, but there was this, you know, actual internal web page for whatever is historical reason. Somebody had put this and I had a big fight with them. Like, you know, I cleared it up after a shocking long period of time.
其实也没有人真的去执行这个规定，但我们内部真有一个网页，不知出于什么历史原因，把这条禁令写在那里了。我和他们大吵了一架，最后花了惊人长的时间才解决这个问题。
宏大叙事但偏离本质的企业文化。
You escalated to your boss.
你把问题上报给了你的老板？
Oh, I definitely told Twitter about it and I...
哦，我当然在Twitter上抱怨了这事……
Sorry, I don't know if you remember, but you got super voting foundership.
抱歉，我不知道你记不记得，你可是有超级创始人投票权的。
You are the boss.
你就是老板啊。
You can do what you want. It's your company still.
你想干嘛都行，这公司还是你的。
No, no, he was very supportive. I was more like, I was like, I talked to him, I was like, I can't deal with these people. You need to deal with this.
不不，他其实非常支持我。我更多是和他说：“我实在受不了这些人，你得来处理。”
Like, I just like, I'm beside myself, so that they're like saying we can't.
我真是要疯了，他们居然说我们不能这么做。
It's weird that there's bureaucracy, like in a company, it must be a weird experience to meet the bureaucracy in a company that you didn't hire.”
在一家你没有亲手招聘员工的公司里遇到官僚主义，确实是一种很奇怪的体验。”
“But on the other side of it, I would say, it's pretty amazing that some junior muckety muck can basically look at you and say, hey, go f*** yourself. No, but I'm serious, that's a sign of a healthy culture, actually.
但从另一个角度看，我得说，有一个基层小员工能看着你说‘去你X的’，其实挺了不起的。说真的，这其实是健康企业文化的体现。”
I guess so. Anyway, it did get fixed, and people are using it.
我想是吧。反正问题后来解决了，而且大家现在都在用了。
So they got fired.
所以他们被炒了？
That person's working in Google Siberia.
那人现在被调去“谷歌西伯利亚”了。
所有白痴、所有的岳不群都认为自己是对的，Google的文化中缺少洞察力。
No, we're trying to roll out every possible kind of AI, and trying external ones, you know, Adobe, whatever, the cursors of the world, all of those, to just see what really makes people more productive. I mean, for myself, definitely makes me more productive, because I'm not coding...
不是啦。我们现在尝试推出各种可能的AI，包括用外部的，比如Adobe、Cursor这些产品，就是想看看什么能真正提升生产力。对我个人来说，这确实提高了我的效率，毕竟我现在自己也不写代码了……
Do you think the number of foundational models, like if you look three years forward, will they start to cleave off and get highly specialized? Like, beyond the general and the reasoning, maybe there's a very specific model for chip design. There's clearly a very specific model for biologic precursor design, protein folding.
你觉得未来三年基础模型的数量会增加并变得高度专业化吗？除了通用模型和推理模型之外，会不会出现专门为芯片设计、或者生物前体分子设计、蛋白质折叠等任务开发的模型？
Like, is the number of foundational models in the future, Sergey, a multiple of what they are today, the same, something in between?
谢尔盖，你觉得未来的基础模型数量是今天的数倍？一样多？还是介于两者之间？
That's a great question. I kind of... If I...”
这是个好问题。我有点……如果我要猜的话……”
“I mean, look, I don't know. Like, you guys could take a guess just as well as I can. But if I had to guess, you know, things have been more converging.
说实话，我也不知道。你们猜的可能也不比我差。但如果让我猜，现在的趋势是收敛。
And this is sort of broadly true across machine learning. I mean, you used to have all kinds of different kinds of models and whatever, convolutional networks for vision things. And, you know, you had whatever RNNs for text and speech and stuff.
这种趋势在整个机器学习领域都是普遍存在的。过去有各种不同类型的模型，比如视觉任务用卷积网络，文本和语音任务用循环神经网络。
And, you know, all this has shifted to transformers, basically. And increasingly, it's also just becoming one model. Now, we do get a lot of oomph.
而现在，这些基本都转向Transformer架构了。而且越来越多地，我们在朝着单一模型的方向发展。当然，我们确实能从中获得很多能量。
Occasionally, we do specialized models. And it's definitely scientifically a good way to iterate when you have a particular target. You don't have to, like, do everything in every language and handle whatever, both images and video and audio in one go.
偶尔我们也会做一些专用模型。如果你有明确的目标，这在科研上是一个很好的迭代方式。你不需要每次都处理所有语言，或者图像、视频、音频全部打包。
But we are generally able to, after we do that, take those learnings and basically put that capability into a general model. So, there's not that much benefit. You know, you can get away with a somewhat smaller specialized model, a little bit faster, a little bit cheaper, but the trends have not gone that way.”
但我们通常会在做完这些之后，把学到的能力整合回通用模型中。所以专用模型的优势也就不那么明显了。你确实可以做一个更小、更快、更便宜的模型，但总体趋势并不朝那个方向发展。”
“What do you think about the open source, closed source thing? Has there been big philosophical movements that change your perspective on the value of open source? We're still waiting on this, you know, open AI, open source drop.
你怎么看开源和闭源的问题？有没有什么哲学层面的转变影响了你对开源价值的看法？我们现在还在等OpenAI的开源模型发布。
I mean, we haven't seen it yet, but theoretically, it's coming.
虽然现在还没看到，但理论上应该快来了。
I mean, have to give credit to where credits due. I mean, DeepSeq released a really surprisingly powerful model when it was January or so. So, that definitely closed the gap to proprietary models.
我得承认，有些人确实做得不错。比如DeepSeek在一月份左右发布了一个令人惊讶地强大的模型，确实缩小了与专有模型之间的差距。
We've pursued both. So, we released Gemma, which are our open source or open weight models, and those perform really well. They're small dense models, so they fit well on one computer.
我们两方面都在推进。我们发布了Gemma，这是我们开源（或说开放权重）的模型，性能相当不错。它们是小型密集模型，适合运行在单机上。
And they're not as powerful as Gemini. But I mean, the jury is out. Which way is this going to go?”
当然它们不如Gemini强大。但说到底，现在还不好说，未来会走哪条路。”
对乔布斯、巴菲特来说，这种问题显而易见，普通人怎么努力都是想不明白，Google缺少洞察力，但好在没有因为缺少安全感的算计，比如，Amazon、Meta。
“Do you have a point of view on what human computing interaction looks like as AI progresses? Because it used to be, thanks to you, at the search box, you type in some keywords or a question, and you would click on links on the Internet and get an answer. Is the future typing in a question or speaking to an AirPod or talking?
你怎么看人机交互的未来？过去，多亏了你，人们通过搜索框输入关键词或问题，然后点击网页获取答案。那未来是打字提问？还是直接对着AirPods讲话？
Or thinking.
还是用“意念”？
Or thinking. Or like, what's the... Yeah, and then the answer is just spoken to you.
或者用意念。然后答案就直接说给你听。
I mean, by the way, just to build on this, it was Friday, right? Neuralink got breakthrough designation for their human brain interface. I mean, that's a very big step in allowing the FDA to clear everybody getting an implant.
顺便提一下，就在上周五吧，Neuralink获得了人脑接口的突破性进展认定。这意味着FDA正在为全民植入铺路，这是非常重要的一步。
Yeah. And is it like, if you could just summarize what you think is kind of the most commonplace human-computer interaction model in the next decade or whatever, is it a, you know, there's this idea of glasses with a screen and glasses, and you tried that a long time ago. Yeah.
是的。如果让你总结一下，未来十年最常见的人机交互模式会是什么？是那种带屏幕的眼镜吗？你当年不是试过这个了吗？对。
I kind of messed that up, I'll be honest. Got the typing totally wrong on that.
说实话，那次我确实搞砸了。当时我们在输入方式上完全搞错了。”
Early again.
又早一步了。
Yeah.
是啊。
Right, right, but early.”
对，对，但确实太早了。”
“There are a bunch of things I wish I had done differently, but honestly, it was just like the technology wasn't ready for Google Glass. But nowadays, these things I think are more sensible. I mean, there's still battery life issues, I think, that, you know, we and others need to overcome.
有很多事我希望当初能做得不同一些，但说实话，当时技术还没准备好，比如Google Glass。而现在，我觉得这类设备开始变得更合理了。当然，我觉得电池续航的问题仍然存在，我们和其他厂商都需要克服。
But I think that's a cool form factor. I mean, when you say 10 years, though, you know, a lot of people are saying, hey, the singularity is like five years away. So, your ability to see through that into the future.
不过我觉得那种形态很酷。你说10年嘛，但很多人说奇点就在5年之后。所以你要看得穿这些，预测未来。
Yeah.
是啊。
I mean, it's very hard.
这确实很难。
But do you have anybody else?
不过你有没有谁……
Sorry, just let me ask about this. There was a comment that Larry made years ago, that humans were a stepping stone in evolution. OK.
抱歉，我想问一下，有句话是拉里（Page）几年前说的：人类是进化过程中的垫脚石。对吧。
Can you comment on this? Like, do you think that this AGI, super intelligence, or really silicon intelligence, exceeds human capacity and humans are a stepping stone in progression of evolution?
你怎么看这句话？你认为AGI、超级智能，或者说硅基智能会超越人类能力吗？人类会是进化历程中的垫脚石吗？
Boy, I think sometimes us nerdy guys go and get have a little too much wine and chitter-chat. I've had two glasses.
老兄，我觉得有时候我们这些宅男喝多了点红酒就开始瞎聊。我已经喝了两杯了。
I'm ready to go.
我准备好聊点猛料了。
I need some more for that conversation.
要讨论那种话题，我可能还需要再来几杯。
Human implants. Let's go”
人脑芯片植入，来吧”
“I mean, I guess we're starting to get experience with these AIs that can do certain things much better than us. They're definitely, with my skill of math and coding, I feel like I'm better off just turning to the AI now, and how do I feel about that? I mean, it doesn't really bother me.
我觉得我们现在开始有机会接触到那些在某些领域比人类更强的AI了。以我擅长的数学和编程来说，我现在觉得直接交给AI可能效果更好。我怎么想？其实我并不觉得困扰。
I use it as a tool. So I feel like I've gotten used to it. But maybe if they get even more capable in the future, I'll look at it differently.
我把它当成工具来用，我已经习惯它了。但也许将来AI能力更强的时候，我的看法会变。
Yeah, there's a moment of insecurity maybe.
是啊，可能会有些不安的时刻。
I guess so. As an aside, management is like the easiest thing to do with AI. Yeah, absolutely.
我想是的。顺便说一句，管理是AI最容易胜任的事之一。是啊，绝对没错。
I did this at Gemini on some of our work chats, kind of like Slack, but we have our own version. We had this AI tool that actually was really powerful. We unfortunately, anyway, temporarily got rid of it.”
我在Gemini内部的聊天系统上试过，类似Slack，但是我们自己的版本。当时有个AI工具特别强大。可惜后来我们暂时把它下线了。”
“I think we're going to bring it back and bring it to everybody. But it could suck down a whole chat space and then answer pretty complicated questions. So, I was like, OK, summarize this for me, OK, now assign something for everyone to work on and then I would paste it back in, so people didn't realize it was the AI.
我觉得我们会把它重新上线，开放给所有人。它可以读取整个聊天空间的内容，然后回答非常复杂的问题。我当时就让它总结一下聊天内容，然后给每个人分配任务，再把AI的结果粘贴回去，大家还以为是我写的。
I admitted it pretty soon and there were a few giveaways here or there, but it worked remarkably well. And then I was like, well, who should be promoted in this chat space? And I actually picked out this woman, this young woman engineer who, like, you know, I didn't even notice she wasn't very vocal, particularly in that group.
我很快就承认了是AI干的，虽然有些小线索暴露了。但它效果出奇地好。后来我又问：“这个聊天室里谁应该被提拔？” AI居然选出了一位年轻的女工程师，我之前都没怎么注意到她，因为她在群里不太发言。
But her PRs kicked ass?
但她的代码提交很猛？
No, no, it was like, and then, I don't know, something that the AI had detected, and I went and I talked to the manager, actually, and he was like, yeah, you know what? You're right. Like, she's been working really hard, did all these things.
不是，只是AI检测到了一些模式之类的。然后我就去找她的主管聊了聊，他说：“你说得对，她确实很努力，做了很多事。”
比人类更强大的注意力，创始人有一些洞察力但不足以形成正式的文化。
Wow.”
哇。”
“I think that ended up happening, actually. So, I don't know, I guess after a while you just kind of take it for granted that you can just do these things. I don't know, it hasn't really.
后来她好像真的得到了提拔。所以我觉得久而久之你就会习惯AI可以做这些事了。我不知道，也没觉得这有什么不对。
Do you think that there's a use case for like an infinite context link?
你觉得有没有无限上下文这种用法的前景？
Oh, 100 percent. I mean.
当然有，百分百有。
All of Google's code base goes in one day.
Google的整个代码库可以在一天之内全部导入进去。
But sure, you should have access to.
但当然，用户应该能访问那种能力。
Quasi-infinite.
准无限上下文。
Yeah.
是的。
Stateful.
有状态的。”
Yeah.
是啊。
And then multiple sessions so that you can have like 19 of these things, 20 of these things running in.
然后可以同时开启多个会话，比如说有19个、20个并行运行。
Or just evolve it in the real time. Eventually, it will evolve itself.
或者直接实时演化它，最终它会自我演化。
Yeah. I mean, I guess if those everything, then you can have just one in theory, you just need to somehow. Tell it what you're talking about.
是啊。我想如果它能处理所有事情，那理论上你只需要一个模型，只是你得以某种方式告诉它你在讲什么。
But yeah, for sure, there's no limit to use of context. And there are a lot of ways to make it larger and larger.
不过可以肯定的是，使用上下文是没有上限的，而且有很多方法可以让它变得越来越大。
There's a rumor that internally there's a Gemini build that is a quasi-infinite context. Is it a valuable thing? Like, I don't know.
有传言说你们内部的某个Gemini版本是准无限上下文的。这是一件有价值的事吗？我也不太确定。
Say what you want to say, bud.”
说出你真正想说的吧，兄弟。”
“I mean, for any such cool new idea in AI, there are probably five such things internally. And, you know, the question is, how well do they work? And, yeah, I mean, we're definitely pushing all the bounds in terms of intelligence, in terms of context, in terms of speed, you know, you name it.
说实话，任何AI领域的新奇概念，在我们内部可能都有五种类似的探索。问题是：它们到底有多好用？我们确实在全面推进所有边界——不管是智能、上下文还是速度，各方面。
And what about the hardware? Like, when you guys build stuff, do you care that you have this pathway to NVIDIA? Or do you think eventually that will get abstracted and there will be a transpiler and it will be NVIDIA plus ten other options?
那硬件方面呢？你们在构建系统时会在意你们对NVIDIA的依赖吗？还是你觉得未来这些都会被抽象掉，通过某种转换器同时支持NVIDIA和其他十种方案？
So who cares? Let's just go as fast as possible.
所以谁在乎呢？我们就尽可能快地推进吧。
Well, we mostly, for Gemini, we mostly use our own TPUs. So, but we also do support NVIDIA and we were one of the big purchasers of NVIDIA chips and we have them in Google Cloud available for our customers in addition to TPUs. At this stage, it's for better for us, not that abstract and maybe someday the AI will abstract it for us.”
我们在Gemini项目中主要使用我们自己的TPU。但我们也支持NVIDIA，我们是NVIDIA芯片的大客户之一。除了TPU，我们在Google Cloud也为客户提供了这些NVIDIA资源。目前阶段来说，对我们来说还不能完全抽象，也许未来AI会帮我们实现这点。”
“But given just the amount of computation you have to do on these models, you actually have to think pretty carefully how to do everything and exactly what kind of chip you have and how the memory works, the communication works and so forth are actually pretty big factors. It actually, maybe one of these days, the AI itself will be good enough to reason through that. Today, it's not quite good enough.
考虑到运行这些模型所需的计算量巨大，你其实必须非常仔细地思考怎么做每一件事，用的是什么芯片，内存怎么调度，通信如何运作，这些其实都是非常重要的因素。或许哪天AI本身会足够聪明，能够推理这些复杂问题，但今天还不够。
I don't know if you guys are having this experience with the interface, but I find myself even on my desktop and certainly on my mobile phone going immediately into voice chat mode and telling it, nope, stop. That wasn't my question. This is my question.
我不知道你们有没有这样的体验，我现在甚至在桌面电脑上，特别是手机上，经常直接进入语音聊天模式，对它说：“不对，停，这不是我的问题，这是我的问题。”
Nope. Let's say that again in short of bullet points. Nope.
不对。用简短的要点再说一遍。不对。
I want to focus on this.
我想重点关注这个部分。
Definitely.
完全同意。
It's so quick now. Last year was unusable. It was too slow and now it like stops.
现在真的很快了。去年根本没法用，太慢了。而现在它甚至会自动停下来等你。
Okay.
没错。
And then you saw it. I would like bullet points.
你就会发现它在响应中会给你列出要点。
It's what I want to go to. I don't want to type. I want to use voice.”
这就是我想要的。我不想打字，我想用语音。”
“And then concurrently, I'm watching the text as it's being written on the page and I have another window open and I'm doing Google searches or second queries to an LLM or writing a Google doc or a Notion page or typing something. So it's almost like that scene in Minority Report where he has the gloves or in Blade Runner where he's in his apartment saying zoom in, zoom in closer to the left, to the right. And there's something about these language models and their ability to the response time, which was always something you focused on response time.
然后我一边看着页面上文字的实时生成，一边在另一个窗口里进行Google搜索、向大模型发第二个问题、或者写Google文档、Notion页面、或者打字。这简直像《少数派报告》里的场景，他戴着手套操作界面，或者像《银翼杀手》里他在公寓里说“放大，左边一点，右边一点”。这些语言模型的响应时间确实变得非常关键，而响应时间一直是你们关注的重点之一。
Is there like a response time thing where it actually is worth doing voice and where it wasn't previously?
是不是响应速度快到一个程度，让语音变得真正值得用了，而以前其实并不值得？
Everything is getting better and faster and so forth. Smaller models are more capable. There are better ways to do inference on them that are faster.
所有东西都在变得更好、更快。小模型的能力也越来越强，而且推理的方法也越来越快。
You can also stack them like, you know, this is like Nico's company, 11 Labs. It's an exceptional TTS, SDT stack. Like there's, I mean, there are other options.”
你也可以把多个模型堆叠使用，比如Nico的公司11 Labs就是这样。他们的文本转语音、语音检测堆栈做得非常出色。当然，还有其他方案。”
“Whisper is really good at certain things, but this is where I kind of believe you're going to get this like compartmentalization where there'll be certain foundational models for certain specific things. You stack them together. You kind of deal with the latency.
Whisper在某些方面确实很强，但我认为未来会出现功能模块化，每个特定任务有专属基础模型，然后你把它们组合起来，处理好延迟问题。
And it's like pretty good because they're so good. Like Whisper and 11 for those speech examples that you're talking about are kick-ass. I mean, they're exceptional.
而且这样效果非常好，因为这些模型本身就很强。Whisper和11 Labs在你提到的语音场景下表现非常出色，简直太棒了。
Well, wait till you turn on your camera and it sees your reaction to what it's saying. And you go, and before you even say that you don't want it or you put your finger up, it pauses. Oh, did you want something else?
等哪天你打开摄像头，它看到你对它说话时的表情反应，甚至在你还没说“不”，只是抬手，它就暂停了：“哦，你是不是想要别的？”
Oh, I see you're not happy with that result. You know, it's going to get really weird.
“哦，我看你对这个结果不太满意。” 你知道吧，那时候会变得非常诡异。
It's a funny thing, but we have the big open shared offices. So during work, I can't really use voice mode too much. I usually use it on the drive.
很搞笑的是，我们办公室是开放式的。所以工作时我没法经常用语音模式。我一般开车时才用语音。”
The drive is incredible.
开车时的体验真的太棒了。
I don't feel like I could. I mean, I would get its output in my headphones, but if I want to speak to it, then everybody's listening to me. It's weird.”
但我觉得我自己不太行。我的耳机可以听到AI的回应，但如果我要跟它说话，那办公室里所有人都能听见我说什么。感觉太奇怪了。”
“I just think that would be socially awkward. But I should do that. In my car ride, I do chat to the AI, but then it's like audio in, audio out.
我就是觉得那会很社交尴尬。但我应该多试试。我开车的时候确实会和AI对话，但就是音频输入、音频输出。
But I feel like I honestly, maybe it's a good argument for a private office. I should spend more time like you guys are.
但说真的，我现在有点觉得，这是我该争取一个私人办公室的好理由。我应该像你们那样多花点时间去尝试。
You could talk to your manager. They might get you one.
你可以去跟你的主管谈谈。他们可能会给你安排一个。
I like being out in the bullpen, so to speak. I like being with everybody. But I do think that there's this AI use case that I'm missing.
我其实挺喜欢坐在开放工位区的。我喜欢和大家在一起。但我确实觉得我错过了一个重要的AI使用场景。
I should probably figure out how to try more often.
我可能应该想办法更频繁地试用。”
If people want to try your new product, is there a website they can visit? Special code? Go check.
如果人们想试试你们的新产品，有没有什么网站？有邀请码吗？可以去哪里看看？
I mean, honestly, there's a dedicated Gemini app. If you're using Gemini just like you're going to the Google navigation from your search, just get to download the actual Gemini app. It's kick-ass.
说真的，我们现在有专属的Gemini应用。如果你只是从搜索入口用Gemini，不如直接去下载正式的Gemini应用。它真的非常棒。
It really is the best models.
它真的是我们最强的模型之一。
I think it is.
我也这么觉得。
You should use 2.5 Pro.
你应该试试2.5 Pro版本。
2.5 Pro.
2.5 Pro。
You got to pay, right? Yeah. You got a few prompts for free, but if you do it a bunch, you need to pay.”
要付费的吧？对，有一些免费提示次数，但你用多了就要付费了。”
“You're just going to make all these free, right?
你们打算把这些全免费吗？
It's like 20 bucks a month.
现在大概是每月20美元。
Yeah, it's great.
对，很不错。
You got a vision for making it free and throwing some ads on the side?
你们有没有想过免费+加点广告的模式？
Yeah.
有的。
One step down in hardware costs, the whole thing will be free.
只要硬件成本再降一步，这一切就能免费了。
Okay. It's free today without ads on the side, you just got a certain number of the top model. I think we're likely are going to have always now top models that we can't supply infinitely to everyone right off the bat.
好吧，现在的确是免费、没有广告，只不过是限制顶级模型的使用次数。我觉得我们今后也可能总会有一些顶尖模型，没法一开始就无限量免费开放给所有人。
But wait three months and then the next generation.
不过等三个月，下一代模型就来了。”
It seems to me like if I'm asking all these queries, just having a little on the sidebar of things I might be, a running list that changes in real time, of things I might be interested in to get it for free.
我觉得如果我在问很多问题，那右边有一个实时更新的侧边栏，列出我可能感兴趣的内容，作为免费使用的条件，那挺合理的。
I'm all for really good AI advertising. I just, I don't think we're going to necessarily our latest and greatest models, which are, take a lot of computation. I don't think we're going to just be free to everybody right off the bat.”
我完全支持高质量的AI广告。但我不认为我们会直接让所有人都能免费使用我们最新、最强的模型，因为它们计算量实在太大了。
“But as we go to the next generation, it's like every time we've gone forward to generation, then the new free tier is usually as good as the previous pro tier and sometimes better.
不过，每次我们进入下一代产品时，新的免费版本通常和之前的Pro版差不多好，有时甚至更好。
All right. Give it up for Sergey Brin.
好吧，掌声送给谢尔盖·布林。
Thank you.
谢谢大家。”
Okay. Thanks everybody for watching that amazing interview with Sergey Brin. And thanks Sergey for joining us in Miami.
好的，感谢大家收看这次精彩的谢尔盖·布林专访。也特别感谢谢尔盖来到迈阿密与我们见面。
If you want to come to our next event, it's the All-In Summit in Los Angeles, fourth year for All-In Summit. Go to allin.com/events to apply. A very special thanks to our new partner OKX, the new money app.
如果你想参加我们下一场活动，那就是在洛杉矶举行的 All-In 峰会，这将是All-In峰会的第四年。请访问 allin.com/events 报名。特别感谢我们新的合作伙伴 OKX，这是一款全新的金融App。
OKX was the sponsor of the McLaren F1 team, which won the race in Miami. Thanks to Hyder and his team, an amazing partner and an amazing team. We really enjoyed spending time with you.
OKX 是迈凯伦F1车队的赞助商，这支车队刚刚赢得了迈阿密大奖赛。感谢 Hyder 和他的团队，优秀的合作伙伴，卓越的团队。我们非常享受与你们共度的时光。
And OKX launched their new crypto exchange here in the US. If you love All-In, go check them out. And a special thanks to our friends at Circle.
OKX 还在美国推出了新的加密货币交易所。如果你是 All-In 的粉丝，一定要去看看他们。还要特别感谢我们在 Circle 的朋友们。
They're the team behind USDC. Yes, your favorite stable coin in the world. USDC is a fully backed digital dollar, redeemable one-for-one for USD.”
他们就是 USDC 背后的团队。是的，USDC 是你最喜爱的稳定币。USDC 是一种全额储备的数字美元，可按 1:1 兑换为美元。”
“It's built for speed, safety and scale. They just announced the Circle Payments Network. This is enterprise-grade infrastructure that bridges the gap between the digital economy and outdated financial reality.
它为速度、安全性和扩展性而生。他们刚刚发布了 Circle 支付网络。这是企业级基础设施，旨在弥合数字经济与过时金融现实之间的鸿沟。
Go check out USDC for all your stable coin needs. And special thanks to my friends, including Shane over at Polymarket, Google Cloud, Solana and BVNK. We couldn't have done it without y'all.
如你有稳定币相关需求，记得去了解 USDC。还要特别感谢我的朋友们，包括 Polymarket 的 Shane、Google Cloud、Solana 和 BVNK。没有你们，我们无法做到这一切。
Thank you so much.
非常感谢大家。
And it's said we open source it to the fans, and they've just gone crazy with it.”
据说我们把它开源给粉丝后，他们就彻底嗨翻了。”
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