Sergey Brin, Google Co-Founder | All-In Live from Miami
发布时间 2025-05-20 15:08:17 来源
以下是对视频文字稿的总结，重点关注讨论的关键话题：
对话内容主要围绕着谢尔盖·布林在半退休状态后，重新回归谷歌，特别是参与人工智能领域的工作。他强调了人工智能发展的变革性和指数级增长的特性，将其比作互联网的早期阶段，但强调其发展速度更快、影响更深远。与互联网的发展相比，他觉得人工智能的创新速度“令人震惊”。
布林描述了他回归编码，对系统进行小修改并进行实验，以更深入地了解人工智能的各个方面。他对预训练和后训练（特别是思维模型）都表示兴奋，并承认已经取得了巨大的进步。
讨论深入探讨了提示工程和人工智能深度研究的力量。布林解释说，人工智能的超能力在于它能够以人类无法比拟的量和速度执行任务。他用处理数千个搜索结果并进行后续搜索的例子来说明这一点，这对于个人来说在合理的时间范围内是无法实现的。人工智能进行深度研究的能力，比如讨论中涉及的计算每英里死亡率（F1）的例子，展示了人工智能超越初始数据并构建理论的能力，这类似于通常分配给本科生完成的任务。
对话还探讨了人工智能对教育和未来工作的影响。布林承认，人工智能在某些认知任务（如数学和编码）方面已经超越了人类，并对传统教育路径（如大学）的相关性提出了质疑。人工智能快速进行高级计算的能力表明，大学对家长来说可能不再像过去那样重要。布林强调了社交智能和心理韧性的重要性，认为在先进人工智能时代，这些技能可能比死记硬背的知识更有价值。
布林分享了他对机器人技术和硬件的看法，借鉴了谷歌之前与机器人公司合作的经验。他对人形机器人表示怀疑，认为人工智能可以在不同情况下进行调整，而不一定需要模仿人类的形态。他认为，人工智能可以利用模拟和真实世界的数据进行学习，并在各种环境中有效地运行，从而可能使人形形态变得不必要。
对话涉及了人工智能对编程的影响。布林幽默地讲述了谷歌内部关于使用Gemini进行编码的争议，突出了最初禁止在编码任务中使用人工智能的限制。他提倡广泛采用人工智能工具来提高开发人员的生产力，并认为人工智能可以识别有前途的员工。
讨论转向了基础模型的问题。布林认为，存在一种趋同的趋势，即通用模型变得更强大和更通用。虽然专用模型可能对特定任务或研究目的有用，但他认为，长期趋势将是更少、更强大的通用模型。
对话讨论了开源与闭源人工智能。布林承认了开源模型取得的进展，特别是DeepSeek发布的模型，并表示谷歌同时追求开源和专有模型。
对话讨论了人工智能时代的人机交互，布林回顾了搜索框的演变。他谈到了增强现实眼镜的潜力。他坦言，谷歌眼镜发布得太早了，技术还没有准备好。他指出，电池续航问题是需要解决的问题。
布林还分享了一个有趣的轶事，讲述了他在内部聊天中使用人工智能来总结对话、分配任务，甚至识别潜在的晋升候选人。他强调了人工智能能够检测到人类管理者可能忽略的贡献和见解，从而展示了人工智能在增强管理和决策过程方面的潜力。
提到了无限上下文窗口和具有准无限上下文的Gemini构建的用例。布林说，这些领域可能存在内部发展，但他表示总是有发展，问题在于它们的工作效果如何。
在硬件方面，布林指出，谷歌为其Gemini使用了自己的TPU，尽管他们支持Nvidia。他说抽象层还不可用，还有很多事情需要解决。
布林建议采用一种响应时间机制，使语音交互真正值得进行，并且速度与去年相比大幅提高。
Here's a summarization of the video transcript, focusing on the key topics discussed:
The conversation features Sergey Brin, discussing his re-engagement with Google, particularly within the realm of AI, after a period of semi-retirement. He emphasizes the transformative and exponential nature of AI development, likening it to the early days of the web but highlighting its more rapid and profound evolution. He finds the pace of innovation "astonishing" compared to the web's development.
Brin describes his return to coding, contributing minor changes to the system and experimenting to gain deeper insights into various aspects of AI. He expresses excitement about both pre-training and post-training (particularly with thinking models), acknowledging the huge advancements that have come.
The discussion delves into the power of prompt engineering and deep research in AI. Brin explains that AI's superpower lies in its ability to perform tasks at a volume and speed that humans cannot match. He illustrates this with the example of processing thousands of search results and conducting follow-up searches, which would be impossible for an individual to achieve within a reasonable timeframe. The ability to perform deep research, like the discussed example involving calculating F1 death rates per mile, showcases the ability of the AI to go beyond initial data and construct theories, mirroring tasks typically assigned to undergraduate students.
The implications of AI on education and the future of work are also explored. Brin acknowledges that AI is already surpassing humans in certain cognitive tasks, such as math and coding, and raises questions about the relevance of traditional education paths, like college. The ability of the AI to quickly perform advanced calculations suggests college may not be as high of a priority as it has been to parents in the past. Brin emphasizes the importance of social intelligence and psychological resilience, suggesting that these skills may be more valuable than rote knowledge in an era of advanced AI.
Brin shares his thoughts on robotics and hardware, drawing from Google's previous experiences with robotics companies. He expresses skepticism about humanoid robots, arguing that AI can adapt to different situations without necessarily mimicking the human form factor. He believes that AI can leverage simulations and real-world data to learn and operate effectively in various environments, potentially rendering the humanoid form unnecessary.
The conversation touches upon the impact of AI on programming. Brin humorously recounts a dispute within Google regarding the use of Gemini for coding, highlighting the initial restriction against using AI in coding tasks. He advocates for the widespread adoption of AI tools to enhance developer productivity and suggests that AI could identify promising employees.
The discussion shifts to the question of foundational models. Brin suggests a trend towards convergence, with general models becoming more capable and versatile. While specialized models may be useful for specific tasks or research purposes, he believes that the long-term trend will be towards fewer, more powerful general models.
The topic of open source versus closed source AI is addressed. Brin acknowledges the progress made by open-source models, particularly those released by DeepSeek, and states that Google pursues both open-source and proprietary models.
Human-computer interaction in the age of AI is discussed, with Brin reflecting on the evolution of the search box. He addresses the potential for augmented reality glasses. He confesses that Google Glass was released too early and the technology was not ready. He notes that battery life issues are something that needs to be addressed.
Brin also makes a humorous anecdote about using AI in internal chats to summarize conversations, assign tasks, and even identify potential candidates for promotion. He highlights the AI's ability to detect contributions and insights that might be overlooked by human managers, showcasing the potential for AI to enhance management and decision-making processes.
The use cases for infinite context windows and a Gemini build with quasi-infinite context is mentioned. Brin says there may be internal developments in those fields but he says there are always developments and the question is how well do they work.
On the topic of hardware Brin notes that Google used its own TPUs for Gemini, although they support Nvidia. He says the abstraction is not available yet and there are a lot of things that have to be addressed.
Brin suggests a response time thing where it is actually worth doing voice and that the speed has increased drastically from just last year.
摘要
(0:00) The Besties welcome Sergey Brin! (0:40) Sergey on his return to Google, and how an OpenAI employee played a role!
GPT-4正在为你翻译摘要中......
中英文字稿 
We've got a special guest who's gonna come join us. This always happens. A nutty ass chicken brain everybody. Oh my god. Somebody told me you started submitting code and it kind of freaked everybody out that daddy was hung. Well models tend to do better if you threaten them. Be threatened. Like with physical violence. Yes. Management is like the easiest thing to do with AI. Absolutely. It must be a weird experience to meet the bureaucracy and the economy that you didn't hire. On the other side of it I would say it's pretty amazing that some junior marketing market basically look at you and say, hey go fuck yourself. But I'm serious. That's a sign of a healthy culture actually. You're punching a clock man. I hear the reports you and I have talked about it. You're going to work every day.
我们有一位特别嘉宾要加入我们。这种事情总是会发生。一只疯狂的小鸡脑袋，天哪。有人告诉我你开始提交代码，结果让大家都有点惊慌，因为老爹很有影响力。不过，威胁模型通常能让它们表现得更好。让它们感受到威胁。比如身体上的威胁。是的。用人工智能来管理是最简单不过的事情了。绝对的。遇到你没有雇佣的官僚体制和经济体系一定是很奇怪的体验。另一方面，我觉得一个初级的营销人员看着你说，"嘿，去你的吧"，这其实是健康企业文化的标志。我是认真的。你在上班打卡。我听过你说的那些报告。你每天都去工作。
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. 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. 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 open AI. This guy named Dan and I ran into a little party and he said, you know, look what are you doing? This is like the greatest transformative moment in computer science ever.
是的，老实说，这是我人生中最开心的一段时光之一。而且理论上讲，我是在疫情爆发前一个月退休的。是的，我当时想，嗯，这还不错，我想做点别的事情。我想在咖啡馆里消磨时光，读物理书。但随后一个月后，我发现事情并不如我所愿。所以，一旦我们可以去办公室了，我就开始上班。要诚实说，当时有个来自OpenAI的家伙，他叫丹。我在一个小聚会上碰到了他，他说，你现在在干什么？这是计算机科学历史上最具变革性的重要时刻。
And you're a computer scientist. I'm a computer scientist. Forget that. I'm a chapter of Google but you're a PhD student for computer science. I haven't finished my PhD yet but working. Keep working. You get there. Technically unleave absence. Right. And he told me this and I'd already started kind of going into the office a little bit and I was like, you know, he's right. And it has been just incredible. Just, well, 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.
你是一名计算机科学家。我也是。但是先不谈这个，我在谷歌工作，而你是计算机科学的博士生。我还没有完成我的博士学位，但正在努力。继续努力，你会成功的。技术上来说，我是暂时离职。没错。他告诉我这些的时候，我已经开始有点去办公室工作了，我觉得他说得对。这段经历真是不可思议。你们显然都在关注人工智能技术。作为一名计算机科学家，从技术上讲，这是我人生中最令人兴奋的事情。
And the exponential nature of this, the pace of it, it works. 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. The pace maybe you could speak, you know, you had a company, Google, that grew from, you know, a hundred users and 10 employees. So, right now you have over two billion people using, I think, six products or five products have over two billion. It's not even worth counting because it's the majority of the people in the planet touch Google products. Describe the pace.
这种情况的指数级增长和速度确实有效。我们在职业生涯中见过的任何事情，几乎就像过去三、四十年的所有努力都在为这一刻做准备，而现在这一切都在自身不断累积。关于这种速度，也许你可以谈谈，比如你曾经拥有的公司——谷歌，从一百个用户和十名员工起步，现在你有超过二十亿人在使用谷歌的产品，我想有五、六个产品用户数都超过了二十亿。这几乎不值得去计算，因为地球上的大多数人都在接触谷歌的产品。你来描述一下这种发展的速度吧。
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 remember there was a what's new page? The what's new page? Great. Like you go to every new web page. To every new web page is it? Yeah, it was like this last week. These were the new websites. Yes. And it was like such and such elementary school, such as such a fish tank. Yeah. Like Michael Jordan appreciation page. Yeah, well, whatever it was, these were the three new sites on the whole internet.
是的，我是说早期网络带来的兴奋感，我记得我用过Mosaic浏览器，后来是Netscape浏览器。你们中有多少人记得Mosaic？可能我是个怪人吧。还记得那时候有一个“What's New”的页面吗？这个页面真不错。你可以浏览每个新上线的网站。每周更新一次，列出当时所有新的网站。像某某小学啦，某某鱼缸啦，或者像迈克尔·乔丹粉丝页啦。无论是什么，那时候整个互联网也就这三个新网站。
So obviously the web, you know, developed very rapidly from there. And that was a very exciting and then we've had smartphones and whatnot. But, you know, this, the developments in AI are just astonishing, I would say, by comparison, just because of, you know, the web spread, but didn't technically change so much from, you know, month to month, year to year. These AI systems actually changed quite a lot, quite a lot, you know, the like, if you went away somewhere for a month and you came back, you'd be like, whoa, what happened?
显然，互联网的发展非常迅速，这让人十分兴奋。然后我们又迎来了智能手机等设备。但是，与互联网相比，人工智能的发展真是令人惊讶啊。因为互联网虽然传播得很广，但技术上并没有月月年年都有很大的变化。而人工智能系统的变化则非常显著。如果你离开一个地方一个月后再回来，可能会感叹：“哇，发生了什么？”
Somebody told me you started submitting code and it kind of freaked everybody out that daddy was home. Okay. Daddy did a PR. What happened? Well, the code I submitted wasn't very exciting. I think I needed to like, add myself to get access to some things and, you know, minor CL here or there, nothing, nothing that's going to win any awards. But, but I, you know, you need to do that to, to do basic things, run basic experiments and things like that.
有人告诉我，你开始提交代码了，这让大家都感到有些紧张，好像"老爸回来了"。好了，"老爸"提交了一个PR（合并请求）。发生了什么事？其实，我提交的代码并不怎么令人兴奋。我需要把自己添加到某些访问权限中，做了一些小的代码更改，没有什么能获奖的东西。但是，你知道，为了做一些基本的工作、运行基础实验之类的事情，这么做是必要的。
And I've tried to do that and touch different parts of the system so that, you know, so that, first of all, it's fun. And secondly, I know what I'm talking about. It's really feels privileged to be able to kind of go back to the company, not have any real executive responsibilities, but be able to actually go deep into every little pocket. Are there parts of the AI stack that interests you more than others right now? There's certain problems that are just totally captivating you?
我尝试去接触系统的各个部分，首先是为了让过程变得有趣，其次是为了确保我对自己所说的内容有充分的了解。能够回到公司，不必承担任何真正的管理责任，但却能深入了解每一个细节，我真的感到很荣幸。有没有哪个部分的AI技术栈现在特别吸引你？是否有某些问题让你特别着迷？
Yeah, I started, you know, like sort of, I don't know, a couple of years ago and maybe a year ago, I was really very close with what we call pre-training. Yeah. 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 know, you throw huge amounts of computers at it. And I learned a lot, you know, just being deeply involved in that and seeing us go from model to model and so forth and running little baby experiments, but kind of just for fun, so I could say I did it.
是的，我大概在几年前开始接触这个领域，大约一年前，我非常专注于我们所谓的“预训练”工作。其实，大多数人认为的人工智能训练，从历史角度来看，被称作预训练。这是一个庞大的工程，需要投入大量的计算资源。我参与其中，看到我们从一个模型发展到另一个模型，学到了很多东西。我也做了一些小实验，虽然只是为了好玩，但至少我可以说我做过了。
And more recently, the post-training, especially as the thinking models have come around. And that's been, you know, another huge step up in general in AI. So you know, we don't really know what the ceiling is. 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 sort of 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.
最近，特别是在思维模型出现后，后期训练取得了显著进展。这对人工智能来说是一个巨大的飞跃。我们实际上还不了解这个发展的上限。当你向普通人解释提示工程正在发生的事情以及深入研究的发展时，你会如何描述这一突飞猛进的变化？因为我认为很多人并没有点击下拉菜单去观看Gemini移动应用中的深入研究，而那个移动应用做得非常出色。
And by the way, I got the fold after you and I were talking about it. Okay, Google, kick series ass now. Like it actually does what you ask it to do when you ask it to open up and stuff. 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. To me, the exciting thing about AI, especially these days, I mean, it's not like quite a G.I. yet as people are seeking or it's not superhuman intelligence.
顺便说一下，我在我们谈论之后得到了这项折叠功能。好吧，谷歌，现在彻底展现你的实力吧。就像它实际上能在你需要的时候帮你打开并处理事情一样。但在深入研究中，它正在处理的线程数、查询数和后续操作数量是200、300次？也许可以解释一下这个跃升，然后你认为接下来的跃升会是什么。对我来说，AI令人兴奋的地方在于，特别是在当下，它还不是人们追求的那种通用人工智能，也不是超级智能。
But it's pretty damn smart and can definitely surprise you. So I think of the superpower is when it can do things in a volume that I cannot. Yes. Right? So by default, when you use some of our AI systems, it'll suck down whatever top 10 search results will, you know, and kind of pull out what you need out of them, something like that. But I could do that myself, to be honest. Maybe it would take me a little bit more time. But if it sucks down the top, you know, 1000 results and then does follow-on searches for each of those and reads them deeply, like that's, you know, a week of work for me.
但是它真的非常聪明，绝对可以给你惊喜。所以我觉得它的超级能力在于能够在我无法处理的规模上完成任务，对吧？通常情况下，当你使用我们的一些人工智能系统时，它会自动提取出前十个搜索结果，并从中找出你需要的东西，大概就是这样。但老实说，我自己也可以做到这一点，可能会花多一点时间。但是，如果它能提取出前1000个搜索结果，然后为每一个结果进行更深入的后续搜索，并详细阅读分析，那对于我来说就是一周的工作量。
Like I can't do that. 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? And I said, I want to get to deaths per mile driven. And it 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. Let's do it. And it was like, okay.
就像我不能做到那样。这是我认为那些没有参与深入研究项目的人还没有完全意识到的事情。在我们请来一级方程式赛车手上台之前，我对这方面是一无所知的。我问，每十年有多少死亡发生？我还说，我想知道每英里驾驶造成的死亡率。起初，他们觉得这个问题很难。我告诉他们，你可以尽力尝试，提出你最好的解释。让我们试试看。最终，他们说，好的。
And it was like, there's this many teams, there's this many races. Which model did you use it? Open eyes. No, I use Gemini's fabulous. Gemini's fabulous first. The fabulous one. And it was like, let's go. I treat it like I get sassy with it. And it kind of works for me. You know, it's a weird thing. It's like, we drink it in the wine. We don't circle, but it's too much. Yeah. The AI community. But not just our models, but all models tend to do better if you threaten them.
这段话的大意是：
有这么多队伍，有这么多比赛。你用的是哪个模型？是Open AI的吗？不是，我用的是Gemini's Fabulous。就是这个出色的模型。然后就像是，出发吧。我对它带有些许调皮的态度，这对我来说还挺奏效的。这是一种奇怪的事情，就像我们在喝酒一样。我们不绕圈子，但有点太过了。是的，整个AI社区都是这样。但不仅仅是我们的模型，所有的模型在受到威胁时往往表现得更好。
希望这段翻译能帮助你理解原文的意思。
It'd be threatened them. Like with physical violence. Yes. But like, people feel weird about that. So we don't really talk about that. Yeah. I was threatening with not being fabulous. And it responded to that as well. Yeah. So, luckily, you just say like, oh, I'm going to kidnap you if you don't fuck all of them. Yeah, they actually. Can I ask you a more. Hold on, but it went through it. Okay.
这会对他们造成威胁，比如说身体上的暴力威胁。是的，不过大家对这类事情通常感到不安，所以我们并不经常讨论这个话题。我会用“不够出色”来威胁，他们对此也会有反应。所以，有时候你可以说，“如果你不全都搞定，我就绑架你。”他们真的会这样反应吗？不过等等，它确实经历了这样的情况。
And it literally came up with a system where I said, I think we should include practice miles. So let's say there's a hundred practice miles for every mile in the track. And then it literally gave me the death's per mile estimated. And then I started cross referencing it and I was like, oh my God. This is like somebody's term paper for undergrad. You know, like, whoa. Doug, in minutes. It's.
它真的提出了一个系统，我说，我认为我们应该包括练习英里数。假设每一条赛道英里有一百个练习英里。然后，它竟然给出了每英里的死亡率估算。我开始交叉参考这些数据，然后我简直目瞪口呆。这就像是某个本科生的期末论文，简直难以置信。Doug，这在几分钟内就做到了。
Yeah, I mean, it's amazing. And all of us have had these experiences where you suddenly decide, okay, I'll just throw this day. I don't really expect it to work. And then you're like, whoa. That actually worked. Yeah. 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?
是的，我的意思是，这很神奇。我们都有过这样的经历，你突然决定，好的，我就随便试试这一天吧，不太指望会有什么效果。然后你会觉得，哇，竟然真的成功了。是啊。那么，当你有这样的时刻，然后回归到作为一个父亲的日常生活中时，你有没有想到，我的孩子们将来会做些什么？他们的学习方式正确吗？我是否应该彻底改变他们现在正在做的一切？
Have you had any of those moments yet? Yeah. I mean, 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, middle school. And, you know, I mean, the AIs are basically, you know, already ahead, you know. I mean, obviously there's some things AIs are particularly dumb at and they, you know, it makes certain mistakes human would never make. But generally, you know, if you talk about like math or calculus or whatever, like they're pretty damn good. 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, okay, he's whatever my son's going to go on to whatever from sophomore to junior and what is he going to learn.
你有没有经历过这样的时刻？有。我是说，我真的不知道该怎么想，说实话。我并没有什么神奇的方法。我有个孩子正在上高中、初中。现在，人工智能基本上已经超前了。当然，有些事情人工智能确实很笨，还会犯一些人类绝不会犯的错误。但总的来说，如果你谈论的是数学或微积分之类的，它们真的非常厉害。它们可以在数学竞赛和编程竞赛中战胜一些顶尖的人类选手。然后我想，我的儿子从高二升到高三，他会学到些什么呢？
And then I think in my mind, and I talked him about this, well, what is the AI going to be in my ear? Exactly. Yeah. Yeah. And it's like comparable, right? Obviously, the AIs where you would tell your son, look, don't or not, not yet. 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. I guess maybe I got lucky and I worked out to be, you know, useful in the world. I don't know, I guess I think, you know, my kids should do what they like. Hopefully it's somewhat challenging and they can, you know, overcome different kinds of problems and things like that.
然后我心里想着，也跟他说了这个问题：AI到底会如何在我身边存在？确实，这种情况可能会让人联想到，不是吗？显然，你会跟你的儿子说，不要或者还没到时候。我不确定你是否可以围绕这个来规划你的人生。我自己当初也没有特别计划要成为一名企业家或者其他什么。我只是学了数学和计算机科学。也许我比较幸运，这些技能最后变得对社会有用。我认为我的孩子们应该去做他们喜欢的事情。希望这些事情有一定的挑战性，他们能够克服各种问题和困难。
What about specifically college? Do you think college is going to continue to exist as it is today? 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? 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, but at the forefront. As a parent, I think a lot about, hey, so much of education in America and 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.
关于具体的大学呢？你认为大学将会以今天的形式继续存在吗？我的意思是，即便在人工智能挑战出现之前，大学似乎已经在经历一场变革。人们在质疑：“值得吗？我是否应该多关注职业教育？什么才是真正有用的？”我们已经进入了这样一个情境，开始对大学提出各种疑问。是的，我想，人工智能显然是站在最前沿。作为一个家长，我常常思考，美国的教育系统中，中产阶级和上层阶级非常关注上的哪所大学，以及如何进入。然而，最近我越来越觉得，他们不一定需要上大学，从根本上说。
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. Be socially well adjusted, psychologically deal with different kinds of failures, you know? Enjoy a few years of exploration. Yeah. Yeah. I asked 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, like, learning curves in hardware.
你知道，我的儿子即将升入高三，他现在的全部心思就是希望去一所东南联盟（SEC）学校，因为他喜欢那里的文化。如果是在两年前，我可能会很慌张，想着我是不是应该帮助他进入不同的学校。但现在我的想法是，这其实是他能做的最好的选择。能够在社交上适应良好，心理上能处理各种失败，并享受探索的几年生活，这些都是很重要的。你提到了硬件。几年前，谷歌曾拥有Boston Dynamics，或许当时有些超前。但这些系统正在通过视觉信息和感官信息学习，并逐渐学会如何适应周围环境。这种方式正在硬件领域引发一些相当深刻的学习曲线。
And there's dozens of like, 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? I mean, I think we've acquired and the later sold like five or so robotics companies and Boston being one of them. I guess if I look back on it, we built a hardware. We also had this more recently we built out everyday robotics internally and then later had to transition that. 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, you know, to make them truly useful. And presumably one of these days that'll no longer be true. Right.
现在有几十个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？我们是在一个关键时刻，还是说这一年真的开始有突破了呢？我想说，我们曾经收购并后来出售了大概五家机器人公司，包括波士顿的一家公司。如果回顾过去，我们确实开发了一些硬件。最近，我们还在公司内部建立了一些日常用的机器人系统，后来也不得不进行转型。机器人的设计都很酷，但软件却总跟不上。每次我们尝试使它们变得真正有用的时候，总是遇到软件的问题。但大概在不久的将来，这种情况会有所改善，对吧？
But have you seen anything lately that you do? Yeah. Do you believe in the humanoid form factor robots? Or do you think that's a little overkill? 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 robotics startups and later sold them. 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. I personally don't think that's given the AI quite enough credit.
但是你最近有没有看到什么让你感兴趣的东西？有。你相信类人形机器人的发展吗？还是觉得有点过头了？可能我是个不太喜欢类人形机器人的另类。但也许是因为我们至少收购了两家类人形机器人创业公司，后来又把它们卖掉了。原因是，大多数人想做类人形机器人主要是因为这个世界基本上是围绕这种形态设计的，而且你可以在YouTube或视频中进行训练，人们就是这样做各种事情的。我个人认为，这对AI的能力评价是不够的。
Like AI can learn, you know, through simulation and through real life pretty quickly how to handle different situations. 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 in it. 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.
人工智能可以通过模拟和现实生活快速学习如何应对不同情况。 我认为，你不需要和人类一样数量的胳膊、腿和轮子（对于人类来说是零），就可以使人工智能运作良好。所以，我可能对此不太乐观。不过，公平地说，有很多非常聪明的人正在制造类人机器人，所以我不会完全否定这一点。
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 code base now. So like it actually could be quite impactful. What are you seeing, like literally in the company, you know, are the KENX developers always just like ideal that you can, you know, you get a couple of unicorns once in a while. But are we going to see like all developers, like, you know, their productivity hit that level 8, 9, 10 and they're just going to, or is it going to be all done by computers?
关于成为程序员的道路呢？这就是我们在有限的数据集上所看到的现象。要知道，谷歌现在的代码库已经有20年的历史了，所以这实际上可能会产生相当大的影响。你在公司内部看到的情况是怎样的？是否所有的KENX开发者总是很理想化，你知道，有时候会出现一些非常优秀的“独角兽”开发者。但是否我们会看到所有开发者的产能都能达到那种8、9、10的水平，还是说一切都会由计算机完成？
And we're just going to check it and make sure it's not too weird. Because it could get weird if you vibe code, yeah. I'm embarrassed to say this. Okay, I like recently, I just had a big tiff inside the company because we have this list of what you're allowed to use to code and what you're not allowed to use to code. And the Gemini was on the nildest. You have to be pure. You can't. I don't know for like a bunch of really weird reasons that it was like boggled my mind that, you know, this vibe code on the Gemini code. I mean, nobody would like enforce this rule, but there was this, you know, actual internal web page for whatever is historical reason.
我们打算检查一下，并确认它不会太奇怪。因为如果是在“vibe code”（一种编程风格）上使用，可能会变得很奇怪。说实话，我有点尴尬。最近，我在公司内部因为一件事闹了不小的矛盾。我们有一个列表，规定了哪些东西可以用来写代码，哪些不可以。而“Gemini”这项被列在了禁止使用的清单上。你需要保持纯粹，不能......原因有很多，而且有点奇怪，让我很困惑，为什么在“vibe code”中过滤掉了"Gemini code"。虽然没人会真正强制执行这条规则，但出于某些历史原因，这个规定在我们内部网页上确实存在。
Somebody had put this and I had a big fight with them. I, you know, I cleared it up after a shock. Did you tell your boss period of time? You escalated to your boss. Oh, I, I definitely told. I'm whatever I do. I'm like, I don't know if you remember, but you got super-photic founders. You are the boss. You can do what you want. It's your company still. No, no, it was, he was very supportive. It was more like, I was like, I talked to him. I was like, I can't deal with these people.
某个人放了这个东西，我跟他们大吵了一架。你知道，我被震惊之后解决了这个问题。你和你的老板说了吗？你向你的老板汇报了情况。我当然告诉他了。不管我做什么，我就像，我不确定你是否记得，但你是很有远见的创始人。你是老板，你可以做你想做的事情，公司还是你的。不，不，他很支持我。更像是，我跟他说，我不能再和这些人打交道了。
You need to deal with those like, I just like, I'm beside myself that they're like saying. Did you hear that there's bureaucracy like in a company that you find must be a weird experience to meet the bureaucracy in a company that you didn't hire? No, but, but on the other side of it, I would say it's pretty amazing that some junior Muckity Muck can basically look at you and say, go fuck yourself. But I'm serious. That's a sign of a healthy culture, actually. I guess so.
你需要处理这些事情，我真的是感到不知所措，他们居然这么说。你听说过公司内部有官僚作风吗？遇到这种在你没有参与招聘的公司里出现的官僚作风真是种奇怪的经历。不过，从另一面看，我觉得挺棒的是，即使是一些小职员也敢理直气壮地对你说，“去你的”。说真的，这其实是健康企业文化的体现。我想是这样的。
Anyway, it did get fixed and people are using it. They got fired. That person's working in Google's Siberia. No, we're trying to roll out every possible kind of AI. And trying external ones, you know, be whatever the cursors of the world, all those, to just see what really makes people more productive. I mean, for myself, definitely makes me more productive. Because I'm not coding.
无论如何，问题已经解决了，人们已经开始使用该系统。有人被解雇了。有个人在谷歌的西伯利亚分部工作。不，我们正在尝试推出各种可能的人工智能，包括那些外部的，因为我们想看看到底什么能真正提高人们的生产力。对我个人来说，人工智能确实让我更有效率。因为我不再编程了。
Do you have a 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. Like, is the number of foundational models in the future, Sergei? A multiple of what they are today, the same, something in between.
你们是否拥有许多基础模型？从未来三年的角度看，这些模型会不会开始分化并变得高度专业化？比如，除了通用模型和推理模型之外，或许会有一个专门用于芯片设计的模型。显然，还有一个专门用于生物前体设计和蛋白质折叠的模型。Sergei，你认为未来的基础模型数量会是现在的几倍、保持不变，还是介于两者之间？
That's a great question. I kind of, if I, 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. 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 one of our RNNs for text and speech and stuff.
这是个好问题。我有点儿，如果说，我的意思是，我不知道，你们也可以像我一样猜测。但是如果让我猜的话，事情确实越来越趋同。总体来说，这在机器学习领域是普遍现象。以前有各种各样不同的模型，比如用于视觉处理的卷积神经网络，还有用于文本和语音的循环神经网络等。
And, you know, all of this has shifted to transformers basically. And increasingly, it's also just becoming one model. Now, we do get a lot of them occasionally. We do specialized models. And it's definitely scientifically a good way to iterate. We have a particular target. You don't have to like do everything in every language and handle whatever, both images and video and audio and in one go. But we are generally able to, after we do that, take those learnings and basically put that capability into a general model.
你知道，这一切基本上都转向了变压器（transformers）。而且越来越多地，只需一个模型就可以涵盖很多东西。当然，有时我们确实会使用很多不同的模型，并开发一些专门的模型。这在科学上无疑是一个很好的迭代方式，我们有特定的目标。这样就不需要每种语言、每种情况都处理，也不必一次性处理图像、视频和音频。但是，通常在我们完成这些专门模型后，能够将这些学习的成果融入到一个通用模型中。
So there's not that much benefit. You know, you could, you can get away with somewhat smaller, specialized models, a little bit faster, a little bit cheaper. But the trends have not gone that way. 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?
因此，好处并不是那么大。你知道的，你可以使用更小一些、专门化的模型，它们速度更快、成本更低。但趋势并没有朝那个方向发展。你怎么看待开源和闭源的事情？有没有什么重大的哲学运动改变了你对开源价值的看法？
We're still waiting on this open AI. Open source. We haven't seen it yet, but theoretically it's coming. I mean, have to give credit to where credits do. I mean, deep seek released really surprisingly powerful model when it was January. Or so, so that definitely closed the gap to proprietary models. We've pursued both. So we released Jema, which are our open source or open to eight models. And those perform really well. They're small dense models, so they fit well on one computer. And they're not as powerful as Gemini. But I mean, the jury's out.
我们还在等待这个开放的人工智能项目。开源的。虽然我们还没见到，但理论上它会出现。我是说，我们必须给予应该得到肯定的人以认可。比如，Deep Seek在大约一月份发布了一个非常强大的模型，这确实拉近了与专有模型的差距。我们同时追求这两个方向。所以我们发布了Jema，这是我们的开源模型，表现也非常好。这些是小而紧凑的模型，因此可以很好地适应在一台计算机上运行。尽管它们不如Gemini强大，但结果如何还有待观察。
Which way is this going to go? Do you have a point of view on what human computing interaction looks like as AI progresses? It used to be thanks to you. As a 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 a ear pod or thinking or thinking? Or like, what's the, what's the, yeah, and then the answer is just spoken to you. I mean, by the way, just to build on this, it was Friday, right? Newerling got breakthrough designation for their human brain interface. That's a very big step in allowing the FDA to clear everybody getting it implanted.
这会如何发展？关于AI进步过程中人与计算机交互的未来，你有什么看法吗？过去，我们通过你这个搜索框，只需输入一些关键词或问题，然后点击互联网上的链接就能得到答案。未来的方式会是怎样的呢？是输入问题，还是对着耳机讲话，或者仅仅是通过思考来获取答案？然后答案会直接以语音方式告诉你。顺便说一下，为了补充这一点，周五的时候，Neuralink获得了关于他们人脑接口的突破性指定地位。这是让FDA批准每个人都能植入这一技术的一个巨大进步。
Yeah, and is it like, if you could just summarize what you think is kind of the most common place human computer interaction model in the next decade or whatever, is it a, you know, there's this idea of glasses with a screen in glasses and you tried that a long time ago. Yeah, I kind of messed that up. I'll be honest. Got the typing totally wrong in that. Early again. Yeah. Right. But early. There are a bunch of things I wish I'd done differently, but honestly, it was just like the technology was 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.
好的，如果可以总结一下你认为未来十年或类似时间里最常见的人机交互模式，它会是什么样的？比如，有种想法是在眼镜上装屏幕，这个你很早以前尝试过。老实说，当时我搞砸了，打字完全搞错了，又早了一步。是的，确实太早了。有很多事情我希望当时能做得不一样，但其实当时的技术对于谷歌眼镜来说是成熟的。不过，现在这样的技术感觉更合理了一些。当然，电池续航能力的问题仍然存在，我认为我们和其他人需要共同克服这个问题。
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, yeah. I don't know if it's very hard to get. But do you have any, sorry, just let me ask about this. There was a comment that Larry made years ago that humans were a stepping stone in evolution. Okay. Can you comment on this? Like, do you, do you think that this AGI superintelligence or really silicon intelligence exceeds human capacity and humans are a stepping stone in, you know, progression of evolution?
但我觉得这是一个很酷的形式。我的意思是，当你提到10年时，很多人认为奇点大概还有五年就会到来。所以你能看到未来的能力，是的。我不知道这是不是很难理解。不过，我想问一下，有人提到拉里多年前说过，人类是进化过程中的踏脚石。你能对此发表评论吗？比如，你认为这种人工通用智能的超级智能或真正的硅基智能会超越人类的能力吗？人类在进化的进程中只是一个踏脚石吗？
Boy, I think like sometimes us nerdy guys go and get in heaven too much wine. I know one of them. I've had two glasses and I'm ready to go. I need to explore for their conversation. Human implants, let's go. I mean, I guess we're starting to get experience with these AIs that can do certain things, you know, much better than us. And they're definitely, you know, 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?
男孩，我觉得有时候我们这些书呆子碰上好酒有点上头。我认识其中一个。喝了两杯酒，我已经准备好出发了。我想要去探讨他们的对话。人类植入物，走吧。我的意思是，我想我们开始有了一些关于这些人工智能的经验，它们在某些方面确实比我们做得更好。而我自己在数学和编程方面的技能，感觉现在不如求助于人工智能，那我对此有什么感觉呢？
I mean, it doesn't really bother me. You know, I use it as a tool. So I feel like I've gotten used to it. But you know, maybe if they get even more capable in the future, I'll look at it differently. Yeah, there's a lot of insecurity maybe. I guess so. As an aside, management is like the easiest thing to do with AI. Absolutely. And I did this, you know, at Gemini on some of our, you know, work chats kind of like Slack, but we have our own version.
我的意思是，这实际上并不困扰我。你知道的，我把它当作一种工具来使用。所以我觉得我已经习惯了它。但是，你知道，如果将来它们变得更强大，也许我会有不同的看法。是的，可能会有很多不安。我想是这样的。顺便说一下，管理是用AI做起来最简单的事情。完全没错。在Gemini的时候，我就在我们类似于Slack的工作聊天工具上做过这类事情，只不过我们有自己的版本。
We had this AI tool that actually was really powerful. We unfortunately, anyway, temporarily got rid of it. 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, okay, summarise this for me. Okay, now assign something for everyone to work on. And then I would paste it back in so people didn't realise it was the AI. That's all I admitted it had pretty soon. And there were a few giveaways here or there, but it worked remarkably well.
我们曾经有一个非常强大的人工智能工具。但很不幸，我们暂时放弃使用了。我觉得我们会重新启用它，并让大家使用。这个工具可以读取所有的聊天内容，并回答相当复杂的问题。比如，我会让它帮我总结内容，或者给每个人分配任务。然后我把生成的内容发出去，这样大家就看不出是AI做的。不过，我很快就承认了这个事实，虽然有时有一些小线索透露了出来，但效果还是相当不错的。
And then I was like, well, who should be promoted in this chat space? And actually picked out this woman, this young woman engineer who like, you know, I didn't notice she wasn't very vocal, particularly in that group. PRs kicked out. 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 at all these things. Wow. I think that ended up happening actually.
然后我就想，到底应该在这个聊天室里推荐谁呢？最后，我选中了一个年轻的女工程师。你知道吗，我之前没注意到，她在这个小组里并不是很活跃。拉票这种事没有发生。其实是有些事情被AI检测到了。我去问了她的经理，他说：“是啊，你说得对。她确实在很多事情上都非常努力。”结果，事情就是这样发生了。
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. Do you think that there's a use case for like an infinite context link? Oh, 100%. I mean, all of Google's code base goes into the infinite. Yeah, for sure. You should have access to the infinite. Yeah, it's painful. Yeah. And then multiple sessions so that you can have like 19 of these things, 20 of these things running. Or it just evolved into real time. Eventually it'll evolve itself.
所以，我也不知道，我想经过一段时间后，你就会理所当然地觉得自己可以做到这些事情。我不知道，这真的不太确定。你认为有一个无限上下文链接的用例吗？哦，绝对有。我是说，谷歌的所有代码库都进入了无限制中。是的，没错。你应该可以访问这种无限制的东西。是的，这很让人头疼。而且还可以使用多个会话，这样你就可以同时运行19个、20个这样的东西。或者说，它最终会逐步演化为实时的。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 just and dig your own. You're all that what you're talking about. But yeah, for sure, there's no limit to use of context and there, you know, there are a lot of ways to make it larger and larger. There's a rumor that internally there's a Gemini build that is a quasi infinite context line. Is it is it a valuable thing? Like, I don't know. It, well, you say what you want to say, but I mean, for any such cool new idea in AI, there are probably five such things internally.
是的，我的意思是，我想如果那些东西是“一切”，那么理论上你只需要一个。你只需要以某种方式自己去挖掘你谈论的东西。当然，在使用上下文方面是没有限制的，有很多方法可以让它越来越大。有传言说内部有一个名为Gemini的版本，它有一个近乎无限的上下文线。这个东西有价值吗？我不太确定。你可以随便表达你的观点，但我的意思是，对于任何这样酷的新AI理念，内部可能都会有五个类似的项目。
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. 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'll get abstracted? There'll be a transpiler and it'll be Nvidia plus 10 other options. So who cares? Let's just go as fast as possible. Well, we mostly for Gemini, we mostly use our own TPUs.
那么，问题是它们到底表现如何？是的，我们确实在智能、上下文、速度等各个方面都在极限上探索。至于硬件呢？比如说，当你们开发东西时，会考虑要有连接到英伟达的路径吗？还是你们觉得最终这些都会被抽象化，会有一个转换器，并且除了英伟达，还有其他10个选项。所以不用担心，尽可能提高速度就好。实际上，对于Gemini，我们主要使用自己的TPU。
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. But, you know, 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 and the communication works and so forth are actually pretty big factors.
所以，我们也支持Nvidia，并且是Nvidia芯片的大买家之一。我们在Google Cloud中为客户提供这些芯片，此外还有TPUs。目前，我们最好不要太抽象化这些问题，或许有一天AI会替我们简化处理。不过，考虑到这些模型需要进行的大量计算，你实际上需要非常仔细地考虑如何进行每个步骤，以及使用什么类型的芯片、内存如何运作、通信如何进行等，这些都是非常重要的因素。
And it actually, yeah, maybe one of these days the AI itself will be good enough to reason through that today. It's not quite good enough. 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. Nope. Let's say that again, insured a bullet points. Nope. I want to focus on this definitely. It's so quick now.
事实上，也许有一天，AI 本身会足够强大到可以推理出这些内容。但目前它还不够好。我不知道你们使用界面时有没有类似的经历，但我发现自己在使用桌面或手机时，总是立刻进入语音聊天模式，并对它说，不，不是这个问题，是这个问题。不，不是这样，我要用要点列出来。不，我要重点关注这个方面。现在速度真是太快了。
Last year it was unusable. It was too slow and now it like stops. Okay. And then you sell it. I would like what I want to go to. I don't want to type. I want to use voice. And then, concurrently, I'm watching the text as it's being written on the page and I have another window opening. I'm doing Google searches or second queries to an LLM or writing a Google doc or a notion page or typing something. It's almost like that scene in a 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.
去年这个设备几乎无法使用，因为它太慢了，现在更是卡住了。不过，然后你把它卖了出去。我想要的是那种我可以直接语音输入的设备，不用打字。同时，我希望能看到文本在页面上被逐字写出来，还可以打开另一个窗口进行Google搜索，或者向大型语言模型（LLM）发出额外的查询，甚至在写Google文档，或者编辑Notion页面，或者打字。这就像《少数派报告》里的场景，主角戴着手套操作，或者像《银翼杀手》里他在公寓里说"放大，放大，左边一点，右边一点"的场景。
And there's something about these language models and their ability to the response time, which was always something you focused on response time. Is there like a response time thing where it actually is worth doing voice and where it wasn't previously? Everything is getting better and faster. So if we're smaller models and are more capable, there are better ways to do inference on them that are faster. You can also stack them. This is Niko's company, 11 Labs. It's an exceptional TTS, STT stack.
这些语言模型在响应时间方面有一些特点，这是你一直关注的点。对语音技术来说，是不是现在有一个值得注意的响应时间的变化，而之前并没有？现在一切都在变得更好、更快。如果模型更小且更强大，就可以用更快的方法进行推理，还可以将它们堆叠使用。Niko的公司，11 Labs，就是一个优秀的TTS（文本转语音）和STT（语音转文本）技术组合。
There are other options. Whispers are really good at certain things. But this is where I kind of believe you're going to get this compartmentalization where there'll be certain foundational models for certain specific things. You stack them together, you kind of deal with the latency. And it's pretty good because they're so good. Whisperer and 11 for those speech examples that you're talking about are fucking kick ass. They're acceptable. Wait till you turn on your camera and it sees your reaction to what it's saying.
还有其他选择。Whispers在某些方面表现得非常出色。但在这里，我相信会出现某种分类情况，即针对某些特定任务会有特定的基础模型。你可以把它们组合在一起，这样就能解决延迟问题。因为它们本身表现得非常好，所以效果还不错。Whisperer和11在你提到的语音示例中表现非常出色，已经足够好了。等到你打开相机，它能检测到你对它所说内容的反应时，就更精彩了。
And you go, and before you even say that you don't want it, you put your finger up. It's pauses. Oh, did you want something else? Oh, I see you're not happy with that result. It's going to get really weird. It's 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. The drive is well. I don't feel like I could, I mean, I would get it. It's output in my headphones.
你走过去，还没开始说你不想要它，你就已经举起手指暂停了。哦，你是不是想要点其他东西？哦，我看你对这个结果不太满意。情况可能会变得很奇怪。这很有趣，但我们有很大的开放共享办公室，所以工作期间，我不能太多使用语音模式。我通常在开车时使用。开车的时候，我觉得可以，我是说，我可以在耳机里听到它的输出。
But if I want to speak to it, then everybody's listening to me. It's weird. I just think that'll be socially awkward. But I should I should that. In my car ride, I do chat to the AI. But then it's like audio and audio out. Yeah, but I feel like I honestly, maybe it's a good argument for a private office. I should spend more time with you guys. I think you could talk to your manager. They might get one. I like being out in the. No, that's the good. I like to get them off. Everybody. Yeah. But I do think that there's this, hey, I use the case that I'm missing a chase probably figure out how to try more often.
如果我想和它说话，大家都在听我说的内容。这感觉很奇怪，我觉得会有些社交尴尬。但我应该尝试这样做。在开车时，我会和AI聊天，那只是一个声音对话而已。但我觉得，也许这是个设立私人办公室的好理由。我应该多花些时间和你们在一块。我认为你可以和经理谈谈，他们也许会给你安排一个私人空间。我确实喜欢在外面的感觉。不过，我确实觉得，我可能在某种情况下错过了一些东西，应该想办法更多地参与其中。
If people want to try your new product, is there a website they can visit? Special code or go check it. 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. It really is the best models. I think it will. And you should use 2.5 pro. 2.5 pro. Pay the fee. It's a, you got to pay, right? Yeah, you got a few queries. You got a few prompts for free, but if you do it a bunch, you need to just going to make all these free.
如果人们想尝试你的新产品，有没有网站可以访问？有没有特别的代码或者其他方式？其实，说实话，有一个专门的Gemini应用程序。就像你用Google导航一样，你可以下载Gemini这个应用。这个应用真心不错，拥有最出色的模型。我觉得它确实是。目前，你应该使用2.5 pro版本。这个2.5 pro需要付费，对吧？是的，你可以免费尝试几次，但如果你经常使用，那就需要付费来获得全部功能。
20 bucks a month. Yeah, it's free. You got a vision for making a free and throwing some ads on the side. Yeah, once you've got a hardware cost, the whole thing will be free. Okay, it's free today. Without ads on the side, you just get 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. But wait three months and then the next generation.
20美元一个月。对，是免费的。你有一个让它免费的计划，然后在旁边放一些广告。是的，一旦有了硬件成本，整个东西就会是免费的。好吧，今天是免费的。没有旁边的广告，你只能获得一定数量的顶级型号。我觉得我们可能总是有顶级型号无法一开始就提供无限量给所有人。但是，等三个月，然后就会有下一代产品新品推出。
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. Oh, and also really good AI advertising. I just, I don't think we're going to like 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. But as we go to the next generation, it's like every time we've gone forward to generation, then the sort of the new free tears usually as good as the previous pro tier and sometimes better.
对我来说，当我在询问大量问题时，侧边栏上有一个可能感兴趣的内容实时更新列表会很有帮助。另外，还有非常好的人工智能广告。我认为，我们不会随便就让大家免费使用最新最强大的模型，因为这些模型需要大量计算资源。但随着我们进入下一代，每次升级后，新的免费版本通常都能达到之前付费版本的水平，有时甚至更好。
All right, give it up for Sergey Brent. Thank you. Okay, thanks everybody for watching that amazing interview with Sergey Brent and thanks Sergey for joining us in Miami. 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 all-in.com slash events to apply.
好的，请大家为谢尔盖·布伦特鼓掌。谢谢你们。感谢大家观看与谢尔盖·布伦特的精彩访谈，也感谢谢尔盖加入我们在迈阿密的活动。如果您想参加我们的下一个活动，那就是在洛杉矶举行的All-In峰会。今年是All-In峰会的第四年。请访问all-in.com/events进行申请。
A very special thanks to our new partner, OKX, the new money 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 and OKX launched their new crypto exchange here in the US.
非常特别的感谢我们的新合作伙伴——OKX，这款新的理财应用程序。OKX曾是迈凯伦F1车队的赞助商，而该车队在迈阿密的比赛中获胜。特别感谢Hyder和他的团队，这真是一个了不起的合作伙伴和团队。我们非常享受与大家共度的时光，OKX也在美国推出了他们新的加密货币交易所。
If you love all in, go check them out. And a special thanks to our friends at Circle. They're the team behind USDC. Yes, your favorite stablecoin in the world. USDC is a fully backed digital dollar, redeemable one for one for USD. 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 else. Go check out USDC for all your stablecoin needs and special thanks to my friends, including Shane over a polymarket Google Cloud, Salana and VVNK.
如果你喜欢全面了解，请去看看。特别感谢我们的朋友Circle团队，他们是USDC背后的团队。对，就是你最喜欢的全球稳定币USDC。USDC是完全有资产支持的数字美元，可以一比一兑换成美元。它为了速度、安全和规模而开发。他们刚刚宣布了Circle支付网络，这是一个企业级基础设施，旨在弥合数字经济与过时金融系统之间的差距。无论你的稳定币需求是什么，去了解一下USDC吧。特别感谢我的朋友们，包括Polymarket的Shane、谷歌云、Solana和VVNK。
We couldn't have done it without y'all. Thank you so much. You should all just get a room and just have one big huge or two because they're all just like this like sexual tension that we just need to release that out.
没有你们，我们不可能做到这一点。非常感谢你们。你们应该找个地方好好聚一下，因为这里有一种需要释放的紧张气氛。