德米斯·哈萨比斯：谷歌人工智能背后的智者

我知道关于它的定义有很多，但 broadly 来说，这种AI指的是和人类一样聪明甚至更聪明的智能。

And I know there's so many different definitions of it, but broadly speaking, this idea of AI that that is as smart or smarter than humans.

我认为，包括OpenAI和DeepMind在内的许多大型AI实验室都在努力推进，并希望达到AGI这一阶段。

And I think that so many of these big AI labs, including OpenAI, including DeepMind, are pushing and hoping to get to this stage of AGI.

到目前为止，他们采用了一种称为大型语言模型的技术。

And so far, they've approached this with a a technique called large language models.

这些AI模型是在海量数据上训练的，但主要是文本数据。

These AI models that are trained on huge amounts of data, but mainly text.

但还有一个流行术语。

But there's this other buzzword.

对吧？

Right?

世界模型。

World models.

这种AI模型能够理解物理世界。

This idea of these AI models that understand the physical world.

这个术语的流行度正在迅速上升。

And this is this this buzzword is really growing in popularity.

对吧？

Right?

是的。

Yeah.

我认为这将成为AI在2026年剩余时间乃至明年的一大主题，因为这里的核心理念是，大语言模型确实已经掌握了语言部分。

And I think this is gonna be a big theme of AI going into the rest of 2026 and even into next year because the idea here is that LLMs sure.

我们已经搞定了语言这一块。

We got the language part down.

它能够模仿人类的说话、交谈和写作方式等等。

It can mimic the way humans talk and and speak and and write and things like that.

但谈到物理世界，我们经常谈论机器人、AI和物理AI。

But when it comes to the physical world, you know, we talk so much about robotics and AI and physical AI.

但它们需要理解物理世界是如何运作的，比如水如何流动、空气如何运动，以及类似的事情。

Well, they need to understand how the physical world works, how water flows, how air moves, and things of that nature.

当你向德米斯提起这一点时，最让我印象深刻的是，他说是的。

And what really struck out to me when you brought this up to Demis, he he said, yeah.

我们确实需要更多地探索这一点。

We do need to start exploring that more.

事实上，他认为LLM和这些世界模型将会开始融合。

And in fact, he sees a world in which the LLM and those world models start to converge.

我认为这就是他使用的词——融合成一种更独特、更强大、更有能力的东西。

I think that was the word he used, converge into something more unique and and more powerful and capable.

这也在AI领袖们之间引发了一场争论，比如在社交媒体上。

This is also a debate that's been playing out among AI leaders, like, on social media.

你可以打开X或者你最喜欢的社交媒体网站。

You could fire up x or your favorite social media site.

让我特别印象深刻的是杨立昆。

And what really struck out to me is Yan Lakun.

他曾多年担任Meta的AI负责人。

He was the head of AI for many years over at Meta.

他最近离开了，去开创自己的事业，因为他被亚历山大·王以及去年夏天那场大规模的人才争夺战所超越。

He recently left to start his own thing because he kinda got superseded by Alexander Wang and that whole big talent wars that happened over last summer.

他在《金融时报》上进行了一次非常有趣的访谈。

He had a really interesting interview in the Financial Times.

他认为大语言模型并不能让我们实现通用人工智能。

He doesn't think LLMs are what's gonna get us to AGI.

正如你所说，大家都在追逐超级智能、通用人工智能，不管你怎么称呼它。

To your point, that's what everyone's chasing, the superintelligence, AGI, whatever you wanna call it.

他的观点是，大语言模型只能带你走一部分路。

His thing is LLMs can only get you part of the way.

你还需要世界模型和其他各种东西。

You need world models and all sorts of other things.

他还严厉批评了Meta没有超越大语言模型的思维框架。

And he kind of harshly criticized Meta for not thinking beyond the LLM.

这似乎也是他离开并自立门户的部分原因。

And that seems to be part of the reason why he left to do his own thing.

看到Meta的一个主要竞争对手Gemini公开谈论这一点，说‘是的’，这真的很有意思。

And it's really interesting to see one of Meta's big competitors, Gemini, just talk openly about it and say them is saying, yeah.

我们需要做这件事。

We're we're we need to do this.

我们需要开始思考这个问题。

We need to start thinking about this.

这能推动许多领域的发展，比如机器人、自动驾驶，以及让这些AI模型和智能系统更好地理解我们，从而给出正确的答案。

It enables so many things from robotics, autonomous driving, and just a better understanding for these AI models and intelligence systems that we're chatting with to get you that right answer.

史蒂夫，你有没有用过聊天机器人，输入一个问题后，它却说：‘嘿，史蒂夫’？

Steve, do you ever use a a chatbot and you put something in and it will say, hey, Steve.

好问题。

Great question.

这是个非常巧妙的想法。

That's a really clever thought.

经常发生。

All the time.

这些聊天机器人全都如此奉承，对吧？它们总是说：‘你真聪明，问了我这么棒的问题。’

It's the sycophancy of all these chatbots, right, where they're like, oh, you're so smart and great at asking me these questions.

是的。

Yeah.

一直如此。

All the time.

没错。

Exactly.

我之所以提到这一点，部分原因就在于此。

Because the reason I bring that up is is partly to this point.

目前对大语言模型的批评越来越多，确实，它们很出色，能提供信息，但当涉及到以大语言模型为基础来创造新想法、新颖想法时，它们是有局限的。

This growing criticism of LLMs is that actually, yes, they're great and they'll give you the information and but actually, when it comes to LLMs as a foundation for being able to create new ideas, novel ideas, there's limitations there.

我认为这正是德米斯所谈论的，也解释了为什么世界模型这一概念正变得越来越流行。

And I think that's partly what Demis was speaking to and why this idea of world models is really growing in popularity.

正如你所提到的，这将在AI的下一阶段中变得非常有趣，而这一阶段对机器人、无人驾驶汽车以及其他许多应用场景都至关重要。

It's gonna be interesting to see how this plays out, as you mentioned, into this next phase of AI where it's key for things like robotics, driverless cars, and many other use cases too.

是的。

Yeah.

而且我会注意到，随着我们继续这个播客，我对当前这个AI时代中的机器人领域持极其悲观的态度。

And I'm you'll you'll notice as we continue this podcast, I'm incredibly cynical about the robotics angle of this AI moment we're living in.

我们看到的这么多机器人，本质上都是提线木偶。

All the so many of the robots we're seeing, they're literally puppets.

它们是远程操控的。

They're teleoperated.

最好的例子当然是特斯拉的Optimus机器人，它最初是一个穿着紧身衣的人在跳舞。

The best example, of course, is the Tesla Optimus robot, which started out as a man in a bodysuit dancing around.

现在它成了真正的机器人，但同样，它仍然是远程操控的。

Now it's a real robot, but again, it's teleoperated.

确实有人员在控制室通过互联网操控它，甚至用他们的声音跟你对话之类。

There are literally people at a control room controlling it over the Internet and even using their voice to talk to you and things like that.

所以，我们这些搞机器人的人之前聊过，几周前我们办公室就来过一位，他们说最难的部分不是建造机器人本身，而是训练它。

So we are the robotics people I talked to, we had one in the office just a couple weeks ago and they said the hardest part isn't building the actual robot, it's training it.

而这就是世界模型将发挥作用的地方，让它们能够真正像我们被承诺的那样自主运行。

And that's where these world models are gonna come in so they can actually operate autonomously like we've been promised.

德米斯，你提到了一些正在发挥作用的动态因素。

Demis, you mentioned some of the dynamics at play.

对吧？

Right?

当然，商业竞争就是其中之一。

And competition commercially, of course, is one of those.

我们有OpenAI。

We've got OpenAI.

我们有Anthropic。

We've got Anthropic.

外面还有这么多不同的AI实验室。

We've got all these different AI labs out there.

竞争非常激烈。

It's intense.

Gemini 3 到目前为止获得了非常好的反响。

And Gemini three has had such good reception so far.

但曾经有一段时间，人们质疑谷歌整体以及它在竞争中的能力，我认为那时是个关键点。

But there was a point people were doubting Google as a whole and its ability to compete in the and I'd say a point.

那是在2025年的某个时候，而且并不算太久以前。

It was at some point in 2025, and it wasn't that long ago.

然后，Gemini 3 真正发布后，也给很多人留下了深刻印象。

And then, know, Gemini three really came out and and impressed a lot of people as well.

但这是一个不断变化的领域。

But it's a space that's ever changing.

是的。

Yes.

那么，您现在如何评估这个竞争环境呢？

So how we how would you assess right now the competitive environment?

您怎么

How do

你感觉到了吗？

you feel it?

是的。

Yeah.

嗯，目前的竞争环境非常激烈。

Well, look, it's a ferocious competitive environment at the moment.

我的意思是，很多人告诉我，他们从事科技行业二三十年了，说这是他们见过的最激烈的环境，也许甚至是整个科技史上前所未有的。

I mean, many people were telling me, you know, being in tech for twenty, thirty years, say it's the it's the most intense environment they've ever seen, perhaps, you know, ever in the technology industry.

而且，所有最有能力的参与者——无论是个人、科技巨头、大型科技公司，还是顶尖的初创企业——现在都投身于这个领域，因为我认为每个人都已经明白了我们二十多年来一直知道的事：这确实是最重要的技术。

And and and and, you know, all the, I guess, most capable players, whether it's individual, you know, tech titans or big tech companies or and all the best startups, they're all involved in this space now because I think everyone has understood what we've known for twenty plus years now that this is really the most important technology.

所以这并不意外，但确实很艰难。

So that's sort of to be expected, but it's tough.

但这也令人兴奋。

But it's it's also exciting.

而且，说到游戏，我从小就下国际象棋，曾代表英格兰青少年象棋队参赛。

And, you know, going back to games, I I sort of I've started playing chess when I was very young for the England junior chess team.

所以我从小就在竞争中成长。

So I've kind of been brought up in in competition.

所以，你知道，我很喜欢竞争，幸运的是。

So, you know, I love competition, fortunately.

事实上，从很多方面来说，我就是为了竞争而活。

In fact, many ways I live for competition.

所以，很大一部分我倾向于拥抱这种竞争。

So lot of a big part of me sort of like likes to lean into this.

但另一方面，我心中始终明白，有一件事比公司之间甚至国家之间的个体竞争更重要，那就是更好地引导AGI为世界、为全人类服务。

But on the other hand, the only thing I would say is at the back of my mind, I know there's something much more important than individual competition between companies or even countries, which is overall getting stewarding AGI well for the world, for the whole, you know, for the all of humanity.

我认为，作为AI实验室的领导者，能够对这一领域产生影响的人，都应当在心中时刻牢记这一点，即使我们身处这场激烈的资本主义竞争之中。

And I think that's incumbent of all of us who are leaders of the AI labs and and can have an influence over this is to have that sort of in the front of their minds in amongst this sort of ferocious capitalist competition that we're in as well.

所以这两点同时都是真实的。

So both are true at the same time.

我之前提到过，今年早些时候，人们曾质疑谷歌将如何应对AI。

I mentioned kind of the moment people were questioning what Google was gonna do with with AI earlier in in the year.

是的。

Yeah.

你做了什么不同的事吗？

Did you do anything different?

是的。

Yeah.

我想，如果我们回看过去十年，嗯。

Think look, I I feel like, know, if we go back over the last decade Mhmm.

实际上，你知道，谷歌大脑，特别是谷歌的研究部门，还有DeepMind，当时相当独立，我们几乎发明了今天每个人都在使用的90%的技术，无论是Transformer——当然，这是所有大语言模型背后最著名的架构，还是AlphaGo，它首次在复杂问题上大规模应用了强化学习。

Actually, you know, Google Google Brain, specifically, the research division in Google and DeepMind as it was sort of fairly independent, we kind of invented about 90% of the technologies that everybody's using today, you know, whether it's transformers, of course, most famously, the architecture behind all the LLMs or AlphaGo, you know, sort of introduced reinforcement learning at scale on a really hard problem.

所以我们发明了所有这些技术，但也许从 hindsight 来看，我们在商业化和规模化方面慢了一点。

So we've invented all this technology, but then maybe we were in hindsight, we were a little bit slow to commercialize it and scale it.

而OpenAI和其他公司在这方面做得非常好。

And, you know, that's what OpenAI and others did very well.

在过去两三年里，我认为我们不得不重新回归创业初期的那种精神，变得更坚韧、更迅速，快速推出产品，并取得飞速进展。

And then the last two, three years, I think we've had to come back to almost our startup entrepreneurial roots and be scrappier, be faster, ship things really quickly, and and sort of make really rapid progress.

我认为过去几年的发展，尤其是以我们非常满意的Gemini系列为顶点，Gemini 3作为我们最新的版本，已经让我们重新回到了应有的位置——排行榜的顶尖行列。

And I think what you're seeing over the last couple of years culminating in Gemini, the Gemini series, which we're very happy with Gemini three is, as as you mentioned, our latest version, has sort of put us back at, you know, near the top of, you know, the top of the leaderboards where we feel we belong.

你觉得你们能一直保持这个位置吗？

And you feel like you can stay there?

我觉得

I I I feel like

我们当然能保持这个位置。

we can stay there, of course.

是的。

Yeah.

在如此激烈的竞争中，人们显然在热议泡沫的问题。

Amid all this competition, there's obviously a lot of talk about bubbles Mhmm.

在人工智能领域。

In AI.

没错。

Yes.

特别是关于某些公司的估值，这些公司筹集了天文数字的资金，科技巨头在基础设施上花费数千亿美元，而有些公司实际上几乎没有产品，甚至几乎没有盈利能力，却依然筹集了大量资金。

Particularly around valuations of certain companies, companies rating raising astronomical sums of money, the tech giant spending hundreds of billions on infrastructure, and companies out there, quite frankly, raising large sums of money with very little product or or or even very little profitability, if any.

那么，在这种泡沫讨论中，你认为我们现在处于什么阶段？

And so where do you think we are right now in terms of this this kind of bubble discussion?

你认为在人工智能行业，我们正处于金融泡沫之中吗？

Do you think we're in a financial bubble when it comes to AI industry?

我认为这个问题不是非黑即白的。

I think it's not a binary thing, this bubble discussion.

我觉得行业的一些部分可能正处于泡沫中。

I don't I think some parts of the industry might be in a bubble.

在我看来，确实如此，而其他部分可能并非如此。

To me, that's what it looks like, and and others probably not.

从根本上说，人工智能将成为有史以来最具变革性的技术。

You know, fundamentally, AI is gonna be the most transformative technology ever invented.

因此，这一点是支撑一切的基础。

So that's that's that part that underpins everything.

所以最终，这有点像互联网泡沫。

So in the end, it's a bit like the Internet bubble.

最终，互联网是至关重要的，当时也诞生了一些具有代际意义的公司。

In the end, the Internet was critical, and there were some generational companies that were created in during that time.

对吧？

Right?

所以我认为，这几乎是不可避免的。

So I think, you know, that's sort of almost inevitable.

一旦每个人都意识到某项技术的变革性，就会出现过度狂热。

There'll be over exuberance once everyone realizes how transformative a specific technology is.

然后可能会迎来一次清算。

And then there'll be a a probably a reckoning.

而真正有价值的东西将会生存并繁荣起来。

And then the the things that are real will survive and and flourish.

在我看来，情况似乎是，在私募市场中，种子轮融资已经达到数十亿美元，但实际上那里几乎什么都没有。

Where it seems to me is, you know, maybe like in the private markets where there's sort of seed rounds at tens of billions of dollars where basically there's just almost nothing there yet.

从长远来看，这似乎有点不可持续。

And that seems a little bit unsustainable over the long run.

就我而言，我并不太担心泡沫。

As far as I'm concerned, I don't really worry about bubbles.

我的观点是引领谷歌DeepMind。

My my point of view is sort of leading Google DeepMind.

我必须确保无论未来走向如何——无论是像现在这样继续繁荣并呈指数增长，还是出现某种泡沫破裂——我们都能处于有利位置，无论哪种情况都能抓住机遇。

I've got to make sure that what it whichever way it goes, whether it continues to go rosy and exponential like it is now, or there's a bubble that, you know, there's some kind of bubble bursting that we're in the right position to to to win either way and to take advantage of that either way.

我认为，鉴于谷歌的底层业务以及AI如何与之契合，我们已经占据了有利地位，无论未来如何发展都能从中受益。

And I think we've got a good position, given Google's underlying business and how AI fits with that to to to benefit whichever way it goes from here.

一些

Some of

我想，你的一些最大竞争对手是那些在私募市场成功筹集了巨额资金的公司

I guess some of your biggest competitors are the ones who have managed to raise huge sums of money in the private market

到目前为止。

at this point.

所以你

So do you

觉得即使将来出现某种调整，你也能挺过去，是吗？

feel confident that even if there is some sort of correction at some point Mhmm.

你能熬过这一关，对吧？

That, you know, you'll be able to weather it out, I guess?

是的。

Yeah.

我的意思是，你看，这正是谷歌资产负债表以及我们所有卓越产品和平台的价值所在。

I mean, look, you know, that's the whole point of Google's balance sheet and and also all the incredible products that and surfaces that that we have.

我认为，我们有数十款拥有十亿级用户的产品，而人工智能自然融入了所有这些产品中，无论是邮件、办公套件，还是像Gemini这样的新应用。

You know, I think it's, you know, dozens of multibillion user products and and AI kind of naturally fits into all of those products, whether it's, you know, email workspace or or, you know, new things like the Gemini app.

对。

Yeah.

你还提到了其他正在发挥作用的因素。

You mentioned dynamics at play as well.

我们与竞争对手进行了交流。

We talked to competition.

另一个是地缘政治，你也提到了这一点。

The other one is geopolitics, which you mentioned as well.

关于中国，有很多激烈的讨论，

Huge discussions around China, of course,

在这场中美之间的竞争中。

in this kind of competition battle between China and The US.

但曾经有一段时间，人们认为中国及其公司无法开发出强大的人工智能模型和技术。

But, yeah, there was a point where people were discounting the ability of China and and its companies to come up with strong AI models and and and technologies.

但事实上，我们看到DeepSeek的成就，这让我们感到震惊，更不用说像阿里巴巴这样的大型科技公司推出了极具竞争力的开源模型。

But actually, we saw with kind of what DeepSeek did, it kind of brought a bit of shock to our but actually, more than that, some of the big tech companies like Alibaba coming up with some very competitive open source models.

所以中国在这场竞赛中已经领先了。

So China's on out this game.

对吧？

Right?

完全不是。

Not at all.

实际上，我认为它们与美国和西方前沿模型的差距，比我们一两年前想象的要小得多。

And actually, you know, I think they are closer to The US front, you US and West Frontier models than maybe we thought one or two years ago.

此时此刻，它们可能只落后几个月而已。

Maybe they're only a matter of months behind at this point.

有趣的是，它们背后都是非常有能力的团队，比如你提到的深度求索团队和阿里巴巴。

The interesting thing is, and they're very, they're from very capable teams, of course, like the deep sea team and Alibaba you mentioned.

问题是，它们能否在前沿之外实现新的创新？

And the question is is can they innovate something new beyond the frontier?

我认为它们已经证明了自己能够追赶，并且非常接近前沿，还能迅速赶上。

So I think they've shown they can catch up, you know, and and be very close to the frontier and catch up very quickly.

但它们能否真正实现新的突破，比如发明一种新的Transformer架构，从而超越前沿？

But can they actually innovate something new, like a new transformers, you know, that gets beyond the frontier?

这一点目前还没有得到证明。

I don't think that's been shown yet.

在你看来，这会因为技术获取受限而变得困难吗？比如先进芯片的获取？

Is that gonna be, in your view, difficult because of restrictions on access to technology, like leading edge chips, for example?

不会。

No.

我觉得这更多是一个心态问题。

I think it's more a mentality issue, you know.

我认为，至少西方领先的实验室，比如我们自己，已经培养了这种文化——你可以把DeepMind看作是现代版的贝尔实验室，鼓励创新和探索性研究，而不仅仅是扩大当前已知的技术规模。

So I think it's something that at least the leading labs, the leading frontier labs in the West have nurtured, I can say for ourselves, you know, we you can think of DeepMind as a bit like a try to be a modern day Bell Labs and encourage innovation and exploratory innovation, not just scaling out what's what's known and and today.

当然，这已经非常困难了，因为你需要世界级的工程能力才能做到这一点。

And, of course, that's already very difficult because you need world class engineering already to be able to do that.

而中国肯定具备这种能力。

And and China definitely have that.

问题是，科学创新部分呢？

The question is, is the scientific innovation part?

发明新东西比复制东西难大约一百倍。

That's a lot harder to, you know, to invent something is about 100 times harder than it is to to to copy it.

所以下一个前沿真正的问题是，我目前还没有看到这方面的证据，但这非常困难。

So the question that's the next frontier really is is and I haven't seen evidence of that yet, but it's very difficult.

史蒂夫，对我来说，这段对话中最引人注目的部分之一就是关于中国。

So one of the most striking parts of that part of the conversation for me, Steve, was around China.

我曾经在中国生活了三年多，为CNBC报道中国科技行业。

I used to live in China for just over three years, report out of China for CNBC covering the tech sector there.

最近，有一种日益增长的观点认为，中国在人工智能方面远远落后于美国，原因有很多。

And there was this growing view recently that actually China's so far behind The US when it comes to AI for for multiple reasons.

其中一个原因是，它可能无法获得最先进的芯片，因此其产业可能会落后。

One of those is that, oh, it may not be able to get its hands on the most advanced chips, so its industry could fall behind.

有一种观点认为，中国根本没有创新，也不像美国公司那样拥有充足的资本。

One view is that it's just not innovating, and it doesn't have the capital the way US companies do.

但真正有趣的是，德米斯说，他认为中国的AI模型仅比美国落后几个月。

But actually, what was really interesting from Demis is he said that he believes Chinese AI models are are just months behind where The U US is.

所以实际上并没有落后太多。

So actually not far behind.

还记得去年DeepSeek震惊了世界和市场吗？这表明中国已经参与其中。

And remember when last year we had DeepSeek really shock the world and markets, it showed, I think, China is in the game.

自那以后，虽然DeepSeek没有像刚推出时那样引起巨大轰动，但阿里巴巴——中国最大的科技公司之一——已经在这方面成为领导者。

And since then, whilst DeepSeek hasn't quite made the waves it did when it first kind of came out, Alibaba, one of the world's biggest one of China's biggest tech companies, has been a leader there.

它开发了一些非常有趣的模型，如果你查看开源社区，比如Hugging Face这样的网站，就会发现阿里巴巴的模型是其中最受欢迎的之一；我接触过的领域专家都说，它们是全球最先进的一批模型。

It's developed some really interesting models, which if you look at the open source communities such as on a site called Hugging Face, you see Alibaba's models are amongst some of the most popular experts who I've spoken to in the space say they're amongst some of the most advanced in the world.

所以你确实看到了这一点。

So you are seeing that.

我可以告诉你，根据我在那里生活和工作的经历，中国企业行动非常迅速。

And one of the things I can tell you just from living and working out there is Chinese companies move fast.

它们拥有专业知识，也能够创新。

They have the expertise, and they can innovate.

因此，你不能把它们排除在这场AI竞赛之外。

So you can't discount them out of this kind of AI race.

但也要考虑Dennis的观点，他说尽管中国公司正在追赶，并且深度参与这场竞赛，但它们尚未证明自己具备做出重大突破的能力。

But also take Dennis' point that he said whilst the Chinese companies are sort of catching up and and and they're very much in this race, one thing they haven't proven is their abilities to kinda make these big breakthroughs.

所以，我觉得这是一个非常有趣且细致的观点。

So, you know, I thought that was a really interesting and nuanced view.

我想，史蒂夫，你注意到的另一点是丹尼斯关于泡沫和AI泡沫的评论。

I guess the other part here, Steve, is something you picked up on is Dennis' comments on bubbles and AI bubbles.

是的。

Yeah.

顺便说一下，让我们先回到他之前提到的关于月份的说法。

And that and by the way, just talking let's go back to what he said first about the months thing.

一年前，DeepSeek的出现不仅仅意味着中国能够做出一个优秀的大型语言模型或聊天机器人。

DeepSeek a year ago, it wasn't just about the fact that China can do it and make a really good large language model or a chatbot.

更重要的是，他们甚至在没有使用最强大的NVIDIA芯片的情况下完成了这一点，这也让市场感到震惊。

It was also the idea that they did it without the most powerful NVIDIA chips that kinda rattled the markets as well.

而我们现在在美国看到的情况是，阿琼，美国正试图限制中国获取这些NVIDIA芯片的能力。

And that's what we're seeing here in The United States now, Arjun, is trying to limit China's ability to get those NVIDIA chips.

现在有很多讨论，说他们或许能获得H200芯片，虽然这些芯片不是最好的，但可能比中国目前能接触到的要好一些。

There's all this talk about maybe they'll get those h 200 chips, which aren't the best chips, but they're better probably than what China has access to.

然后你就涉及到整个走私问题了。

And then you get into the whole smuggling thing.

但根据丹尼斯的观点，如果他们没有完全获得这些芯片，真的落后了几个月，那就会让人质疑英伟达在芯片领域的突出地位和主导性。

But to Dennis' point, you know, if they really are months behind without full access to these chips, you know, that kind of questions NVIDIA's prominence and dominance in the chip space as well.

不过，是的，你提到的关于泡沫的观点也非常有趣，因为你当时问他了。

But, yes, what what you said about the bubble is also super interesting too because you asked him about that.

我们现在是在泡沫中吗？

Are we in a bubble?

你怎么看？

What do you think?

所有这些类似的问题。

All this sort of things.

他基本上说，我们就是谷歌。

And he basically said, we're Google.

我们很富有。

We're rich.

这无关紧要。

It doesn't matter.

我们有钱。

We have the money.

我们有足够的自由现金流来承担这笔开销。

We have the free cash flow to spend this.

我们的资产负债表是我们的超级优势。

Our balance sheet is our superpower.

如果出于某种原因我们需要削减开支，我们也可以做到，而且会没事的。

If for some reason we need to rein back the spending, we can do it and we'll be fine.

但你猜谁做不到这一点？

But guess who can't do that?

那是OpenAI和Anthropic。

That's OpenAI and Anthropic.

另外两个领军者，xAI，我们也可以把它们算进来。

The other two leaders, x AI, we can throw them in here too.

他们的整个模式是必须不断融资，直到最终能展现出收入和收入增长，从而无需持续融资就能自我维持。

Their whole thing is they have to raise money indefinitely in order to get to the point where they can finally show some revenue and and revenue growth to sustain themselves without continuous fundraising.

如果资金来源开始枯竭，OpenAI 和 Anthropic 将面临极大风险。

If things start to dry up, OpenAI and Anthropic are at extreme risk.

谷歌、微软、Meta 都有充足的现金流，可以转向其他项目。

Google, Microsoft, Meta, they have the cash flow to move on to another project.

Meta 已经在元宇宙上这么做过。

Meta's already done it with the metaverse.

这些公司能够轻松转型，因为它们早已拥有高利润率的核心业务。

These companies can pivot very easily because they had these big high margin businesses already.

德米斯，很多人可能忘了谷歌的 AI 能力有多少来自 DeepMind 以及你和你的团队。

Demis, there's a lot of people, I guess, forget how much of Google's AI capabilities come out from DeepMind and and yourself and your teams.

你如何与谷歌合作？

How do you work with Google?

人们对这一点非常感兴趣。

There's a lot of fascination around that.

桑达尔·皮查伊有一天会打电话给你，说‘嘿，德米斯，我们需要这个东西，或者我们有个关于Gemini或其他AI产品的想法’吗？

Does Sundar Pichai call you up one day and say, hey, Demis, we need this thing or we have this idea for Gemini or for some other AI product.

你能把它做出来吗？

Can you build it?

这种关系是怎样的？

How how is that relationship?

是的。

Yeah.

过去三年，我们把所有东西整合成了谷歌深度思维这一单一实体，所有谷歌的AI研究都集中在这里，它融合了谷歌研究、谷歌大脑和深度思维。

So the last three years, we've combined everything together as into Google DeepMind, this this one entity that that that all the AI research at Google goes on in, and it's a kind of combination of Google Research, Google Brain, and and and DeepMind.

我领导这个团队，你可以把它想象成谷歌的引擎室。

And I run that group and it's it's like the engine room of Google, you should think of it like that.

所有的AI技术都是由我们的团队开发的，然后扩散到谷歌各个令人惊叹的产品中。

So all the AI technologies is done by this group, by our group, and then it's diffused across, you know, all of these incredible products right across Google.

过去几年，我们一直在构建这个基础架构。

And the last couple of years, we've been building that backbone.

所以不仅仅是模型，还要重新架构谷歌的整个基础设施，以便这些技术能够极其迅速地上线。

So not just the models, but also almost in re architecting the entire infrastructure of Google so that it can, you know, these things can ship incredibly quickly.

这些模型会部署到所有主要平台上。

These models, so my SIM ship to all the main surfaces.

所以，当我们发布新的Gemini模型时，第二天甚至当天就会出现在搜索中。

So, you know, when we release a new Gemini model, it's there the next day or the same day in search.

而且这一直进行得非常顺利。

And and that's been going really well.

我想说，我们真正进入状态是在Gemini 2.5模型上。

Think I would say we really got into our groove with the 2.5 Gemini models.

在过去大约一年里，这个过程已经变得非常顺畅了。

And and for the last sort of year, that's been coming really a a smooth process now.

我想在未来十二个月内，你会看到更多这样的进展。

And I think you'll see that more over the next next twelve months.

因此，我们把自己视为并描述为这一过程的引擎室。

And so, you know, we think of ourselves as the and describe ourselves sort of as the engine room for that.

而且，你懂的，桑达尔和我几乎每天都会讨论战略问题，比如技术该往哪里发展，以及谷歌整体需要什么。

And, you know, Sundar and I pretty much talk every day about strategic things and where should the technology go and what does the wider Google need.

然后，我们会根据这些讨论每天调整路线图和计划，同时始终牢记长期目标——率先、快速且安全地实现通用人工智能。

And then, you know, we adjust the roadmaps and the plans, you know, on a daily basis, whilst keeping in mind the long term goals of, you know, getting to AGI first, fast and safely.

因此，我们可以期待更多新事物、新AI工具的出现，并且这些工具会迅速部署到谷歌的整个产品体系中，这都得益于这种关系。

So we should we should expect more of the ability to come up with with new things, new AI tools, and that be shipped across the Google portfolio, etcetera, because of that kind

你在这段关系中所做的这种改变。

of change you've made in

这种关系。

that relationship.

所以这是一个极其紧密的迭代循环，而且我们都使用同一套技术栈，

So it's an incredibly tight sort of iteration loop and and and and, you know, we're all on the same tech stack and

等等。

so on.

你所构建的很多内容都进入了谷歌的产品，但我知道你们也帮助像三星这样的公司，在他们的智能手机等产品中构建一些AI工具，这类工作也是这样进行的。

A lot of what you're building is going into Google products, but I know kinda covering companies like Samsung, you help companies like Samsung to build out some of the AI tools within, you know, their smartphones, for example, and and that kind

这样的事情。

of thing as well.

好吧，正如你提到的，我们与许多合作伙伴合作，我们非常自豪我们的技术被这些合作伙伴选用，因为他们看到了它的强大能力。

Well, look, we work with a lot of partners as you as you mentioned, you know, we're very proud of the fact that our technology selected by those partners, because they see how capable it is.

说到三星和其他设备时，情况也是如此。

And, and actually, you know, it comes to Samsung and other devices.

我认为有一种非常有趣的方式。

I think there's a really interesting way.

我对边缘计算以及这些模型在边缘设备上更快运行的想法非常感兴趣，这些设备包括手机，也包括我们正在研发的新设备，比如眼镜，还有像Warby Parker这样的合作伙伴所关注的智能眼镜。

I'm very interested in the idea of edge compute and and faster versions of these models working on these edge devices, be those phones, but also new devices like glasses that we're working on And, you know, partners like Warby Parker, and the idea of smart glasses.

我知道谷歌长期以来一直在研究智能眼镜，但我觉得现在我们终于有了一个杀手级应用，那就是通用助手的概念。

And I think Google's worked on smart glasses for a long time, as you know, but I think that they, you know, finally, we have the killer app, I would say for it, which is this idea of a universal assistant.

它能在你的日常生活中帮助你。

And and and and sort of helping you in your everyday life.

我认为所有主要的设备厂商都会对这种技术感兴趣。

And I think all the all the all the big device players are gonna be interested in that type of technology.

德米斯，我们只剩下几分钟了，但我确实想问一下，2014年谷歌收购DeepMind时，我还是个刚入行的科技记者。

Demis, we've only got a few minutes left, but I do wanna ask a little bit about, I was a brand new tech reporter when Google bought DeepMind twenty '14.

我记得当时那笔交易金额是4亿英镑。

I think it was a £400,000,000 deal back then.

那时候很多人都不知道你们是做什么的。

So many people didn't know what you what you did.

为什么谷歌要收购这家英国公司？

And why is Google buying this British company?

这里到底发生了什么？

What's going what's going on here?

你有没有回过头想过，也许我们当初该保持独立？

Do ever look back to that and and think, oh, maybe we should have stayed independent at all?

是的。

Yeah.

还是你对现在的结果感到满意？

Or you happy with how things have turned out?

嗯，你看，我知道这很有趣。

Well, look, we I knew it's funny.

当时负责搜索的负责人是艾伦·尤斯塔斯，他和拉里一起负责这件事。

So so the the head of search at the time, Alan Eustace, he he was sort of in charge with Larry.

拉里·佩奇当时是CEO，他支持这笔交易。

Larry was sponsoring the the Larry Page was sponsoring the the deal as he was CEO at the time.

但艾伦·尤斯塔斯被委派为搜索部门负责人来促成这笔交易。

But Alan Eustis was delegated head of search to kind of close the deal.

我确实告诉过艾伦，这将是谷歌有史以来最重要的收购，这可非同小可，毕竟他们之前还收购了YouTube、AdWords等其他项目。

And I did tell Alan that this would be the most important acquisition Google ever made, which is which is quite something given they've you know, there's YouTube and and AdWords and other things that they they previously acquired.

但我当时就意识到这有多重要，也明白它与谷歌的使命——组织全球信息——有多么契合。

But I kind of knew how important this was gonna be and also how good a fit it was with Google's mission, which is organize the world's information.

人工智能与组织和理解信息天然契合。

And AI is a very natural fit to that and organizing and understanding information.

还有什么工具比人工智能更适合做这件事呢？

I mean, what better tool than AI for that?

所以我早就知道这会是一个自然的契合。

So I kind of knew that would be a natural fit.

而且我们当时也意识到，这个技术的价值可能已经涨到了我们出售价格的上百倍、甚至上千倍。

And we sort of knew that this, you know, maybe it's now worth, I don't know, a 100 x, thousand x of, you know, of what of what we sold it for.

但关键是，我当时想回到科学研究本身，推动研究向前发展，因为2014年时这项研究还非常初期。

But the thing is, I I wanted to get back to the science at the time and and and push forward the research, which was still very nascent back in 2014.

而且必须承认，谷歌是当时世界上为数不多能认识到这项技术重要性、预见其未来潜力、并理解它今天所代表意义的公司之一，尤其是当时的拉里。

And and, you know, fair play to Google is they were one of the few companies in the world, I think, that could recognize, specifically Larry at the time, how important this technology was gonna be, what it could become, and what we see it for it today.

如果没有谷歌的支持和他们能提供的海量计算资源，我们不可能取得AlphaGo、AlphaFold以及我们所做的一切科学成就。

And I don't think we could have done the the great work we did with AlphaGo and AlphaFold and all the science we've done.

如果没有他们的支持和他们能提供的海量计算资源，我们不可能取得AlphaGo、AlphaFold以及我们所做的一切科学成就。

And if we hadn't had their backing and the amount of compute that they could bring to to play.

所以我一点遗憾都没有。

So I don't have any regrets at all.

所以科技CEO、AI领域的CEO，如今成了世界上的新明星。

So tech CEOs, AI CEOs, new rock stars of the world.

我在这里欧洲见过英伟达的CEO黄仁勋，你

I've seen Jensen Huang here in Europe and the CEO of NVIDIA, you

也知道，他被所有人围着走。

know, being followed around by everyone as well.

詹森最近说，你和他经常交流。

Jensen, I think, said recently that that you and him talk.

他对Nano Banana和新的图像生成技术也给予了高度评价。

He had great things to say about Nano Banana and the new image generation too as well.

你们都聊些什么？

What what do you what do

你们讨论什么？

you guys discuss?

哦，我们聊了很多，詹森真的很棒。

Oh, we discussed I mean, Jensen's great.

你知道，他是一位了不起的先驱。

You know, he's an incredible pioneer.

而且，有人，你知道，我钦佩他坚持自己的愿景长达二三十年之久。

Also, somebody, you know, I admire him for sticking to his vision for twenty, thirty years now.

事实上，我早在九十年代就开始使用GPU，当然是为了玩游戏，同时也用于编写图形引擎和物理引擎。

In fact, I first started using GPUs in the nineties on for gaming, of course, for for for for writing graphics engines and physics engines.

所以很有趣的是，我的早期游戏经历，甚至当时推动的硬件，如今竟意外地用于人工智能了。

So it's funny that it's come full circle to me that that, you know, my my early gaming days, even the hardware that was pushed then is now useful for AI ironically.

但是的，我们讨论过，他对科学和AI在科学中的应用非常感兴趣。

But yeah, we talk about he's very interested in science and AI for science.

实际上，你知道，AlphaFold是在GPU上训练的。

And actually, you know, AlphaFold was trained on GPUs.

所以我们和他都非常喜欢AlphaFold以及我们在药物发现领域所做的工作。

So we and he loves AlphaFold and the work that we're doing, you know, in drug discovery.

因此，我们主要讨论AI在科学中的应用。

So we mostly talk about AI for science.

我知道许多数据中心都是基于NVIDIA系统构建的。

I I know a lot of the data centers are built in NVIDIA systems.

是的。

Yep.

但我知道谷歌也有自己的张量处理单元，也就是TPU芯片。

But I know Google also has its its tensor processing units, TPU chips.

那里有竞争性的友好关系吗？

Is there any kind of competitive friendliness there?

对。

Yeah.

好吧，你看，

Well, look,

我们很幸运，我们有自己的TPU，我们非常喜爱它们。

we we are lucky we have our own we love our TPUs.

我们通常在内部使用它们来训练我们最好的模型。

We we generally use them internally for training our our best models.

实际上，我们发现来自顶尖AI团队的市场需求很大，他们正试图构建大型模型或运行超大规模的AI模型。

And actually, found there's a big demand for that from the elite AI teams who are trying to build large models or serve very large AI models.

它们是专门为这个设计的。

They're specifically built for that.

所以TPU比GPU更偏向于特定用途。

So TPUs are sort of they're a little bit more special case than GPUs.

你可以把GPU看作是更通用的。

You can think of GPUs as being more general.

所以，当你想探索一些新架构，比如AlphaFold，或者某个新应用时，我们可能会使用GPU。

So, you know, maybe we would use a GPU when we're trying to explore some new architecture like AlphaFold was or some new application.

但当我们想要在已知的领域实现最大规模时，定制芯片会高效得多。

But then once we're when we're trying to sort of scale to the maximum things we know, then, you know, custom silicon can be a lot more efficient.

所以我们很幸运，两种都有。

So we're lucky we have we have both.

我们在谷歌和DeepMind都能用到这两种芯片。

We get to use both here at at Google and DeepMind.

太好了。

Great.

德米斯，展望未来，你显然非常专注于科学以及人工智能在创造新药突破、发现新疾病等方面的巨大潜力。

Demis, just looking to the future, you're obviously so focused on science and the potential for AI to create new drug breakthroughs, do discover new diseases, lots of potential things there.

你也

You've also

当然还有同构实验室。

got isomorphic labs, of course, as well.

在通往你所构想的AI解锁科学领域所有这些突破的道路上，我们现在进展到哪一步了？

Where are we on this path to your your vision of AI unlocking all of these these kind of breakthroughs in the world of science?

嗯，你看，

Well, look,

我总是把AlphaFold视为迄今为止AI应用于科学的最佳例子。

I I I love I always point to AlphaFold as probably the best example so far of AI applied to science.

嗯。

Mhmm.

我对这个项目感到非常自豪。

You know, I'm very proud of that project.

我们解决了蛋白质折叠这一长达五十年的科学重大挑战，即蛋白质的三维结构问题，全球超过三百万研究人员正在他们的关键工作中使用这一成果。

And, know, you we solved a fifty year grand challenge in science of protein folding, how the structure of three d structure proteins, and over 3,000,000 researchers around the world are using it in their critical work.

因此，我无法想象还有比这更具变革性的技术。

So I can't imagine a more transformative sort of technology.

我所期待的是，能看到十几个类似AlphaFold的成果，每一个都能彻底改变其所在领域的科学或数学研究。

And what I would love is to see have better point to a dozen alpha folds and, you know, each of them revolutionizing their area of science or mathematics.

我认为我们已经在这条道路上取得了良好进展。

And I think we're well on the way to that.

我们目前正在材料科学、物理学、数学、天气预测等领域推进六七个类似项目。

And we're working on half a dozen projects like that in material science, in physics, in in math, in weather prediction.

我认为，如果未来十年人工智能发展顺利、进步良好，并且我们以正确的方式使用它，将开启科学发现的新黄金时代。

And and I think the the next ten years, if AI goes well and progresses well, and we use it in the right way, could usher in a new golden age of scientific discovery.

那

What

你认为2026年人工智能领域最大的突破会是什么？

do you think are gonna be the big things in AI in 2026?

你认为会有什么重大的突破或进展吗？

Any big breakthroughs, any big progresses that you think will happen?

代理系统。

Agentic systems.

系统能够

Systems are able to

更自主地完成任务，并开始变得足够可靠以发挥实际作用。

do things more autonomously are gonna start becoming reliable enough to be useful.

我认为在未来十二到十八个月内，机器人领域会出现一些非常有趣的发展。

I think we're going to see some really interesting things in robotics in the next twelve to eighteen months.

我们正在为Gemini机器人项目开展一些极具雄心的工作。

We're working really hard on some very ambitious projects with Gemini Robotics.

最后，也许是在设备上的AI系统，我认为它们将开始在现实世界中变得真正有用。

And then finally, maybe, you know, AI systems on devices, I think we're going to start seeing them really useful in the in the real world.

而我最期待的可能是进一步推进世界模型，使其更加高效，以便实际应用于我们通用模型的规划中。

And then maybe the thing I'm most excited about is advancing world models further, making them more efficient so they can actually be used maybe for planning in our general models.

很好。

Great.

德米斯，我将把你的最后一个回答视为我们下次再聊时的预告片，希望今年还能有机会再交流。

Demis, I'm going to take that last answer as a sort of teaser trailer for the next time you and I get to catch up, hopefully, at some point this year.

谢谢你。

Thank you

非常感谢你来参加我的节目，德米斯。

so much for joining me, Demis.

谢谢你邀请我。

Thanks for having me.

所以，史蒂夫，在对话的最后部分，我觉得有趣的是DeepMind这个实体与更广泛的谷歌业务之间的关系。

So, Steve, just in that final part of the conversation, I thought what was interesting is the relationship between kind of the DeepMind entity and the broader Google business.

德米斯提到他每天都会和谷歌或Alphabet的首席执行官桑达尔·皮查伊交流，他们之间的整合程度越来越高。

And there was a part where Demis was saying he speaks to Sundar Pichai, the CEO of Google or Alphabet every day and and how sort of more integrated they've become.

我认为，如果从这场AI竞赛的角度来看，这向我传递的信息是：谷歌显然已经找到了如何快速将AI产品推向市场的方法。

And I think if I'm thinking about that in this AI race, what that signals to me is that Google has clearly figured out how to become speedy at getting AI products to market.

但你也得想想所有这些谷歌产品。

But also, you gotta think about all these Google products.

对吧？

Right?

无论是Chrome、Gmail，还是其他任何产品，它们都希望谷歌开发的AI技术能全面渗透到这些产品中。

Whether it's Chrome, whether it's Gmail, whatever it might be, they are wanting whatever Google AI is being developed to spread all across all across those products.

这让他们能够 instantly 利用一个极其庞大的用户群体来推广这些产品。

That gives them an absolutely mammoth user base to kind of almost instantly tap into with some of these products.

我一直这么说，已经好一段时间了。

And I I've always I've said this for for a while now.

我认为谷歌最大的优势之一，就是当你想到安卓操作系统以及它庞大的全球市场份额——大约70%时，这意味着有海量的人和设备可以快速部署和使用谷歌AI。

I think one of Google's biggest strengths really is that when you think about the Android operating system and and, you know, how large it is, 70% odd market share globally, you know, that is a huge amount of people and devices where Google AI could be effectively installed on and used quickly.

所以，我认为他们在推向市场方面处于有利地位。

So they're in a good position in terms of going to market, I think.

而且，DeepMind与谷歌整体业务之间的这种关系，对于谷歌在更长期内维持任何成功都至关重要。

And and and, clearly, this relationship between DeepMind and the broader Google business is gonna be integral for Google to sustain any success over the over the longer run here.

是的。

Yeah.

仅就安卓平台而言，三星作为最大的安卓手机制造商，已经将Gemini作为他们的主要聊天机器人。

And on the Android front alone, I mean, Samsung, the biggest manufacturer of Android phones, they're already putting Gemini is their main chatbot.

Gemini是他们的主要人工智能。

Gemini is their main AI.

我有点惊讶三星没有尝试自己开发，而他们过去曾经这样做过。

I'm I was a little surprised Samsung didn't try to build their own, which like they have in the past.

但他们已经完全全面投入Gemini了。

But, no, they've completely gone all in on Gemini.

他们正在与谷歌合作开发新的混合现实头戴设备。

They're partnering with Google on those the new mixed reality headset that they have.

他们还正在与Warby Parker等公司合作开发即将推出的眼镜。

There are some upcoming glasses that they're working on in partnership also with companies like Warby Parker to design them.

所以，三星确实全面采纳了这一技术，这对Gemini来说是一个巨大的平台。

So, yeah, Samsung has, like, really adopted this, and that is a huge platform for Gemini.

就只是那样。

Just just that.

就只是三星这一方面。

Just the Samsung angle of it.

他们已经拥有的巨大市场份额非常好。

Just that huge market share they already have is is great.

接下来我们聊聊苹果。

And then let's talk about Apple.

Gemini 实际上将成为我们几个月后期待的全新 Siri 的核心引擎。

Gemini is actually gonna be the engine that powers this new version of Siri we're expecting in just a couple months time.

他确实谈到了对 Gemini 在更多设备上普及的兴奋之情。

He did talk about his excitement to see Gemini kinda spread on more devices.

所以我认为苹果做出这个决定非常明智，意识到自己无法独立开发，索性效仿三星，直接整合这种经过验证的技术。

So I think it's a really smart move by Apple to kind of realize it can't build this on its own and honestly do what Samsung is doing and say, okay, let's just integrate this proven technology.

我们和谷歌已经有着良好的合作关系。

We already have a great relationship with Google.

这真的是我多年来见过的完全不同的一种谷歌，以前你看到的是许多不同的团队在做同一件事。

And this is honestly a different kind of Google that I've been seeing that I've seen for so many years where you had so many different groups kind of working on the same thing.

我的意思是，这次大规模重组，德米斯获得了对所有人工智能业务的控制权，之前谷歌内部有多个团队在研究人工智能，彼此之间还互相冲突。

I mean, this big reorg and and Demis got all that control over all of AI, there were multiple groups within Google working on artificial intelligence kind of bumping against each other.

苏达尔·帕查伊非常明智地表示，这是一个至关重要的时刻。

And Sudar Pachai was really smart saying, we gotta this is a huge moment.

我们必须重新组织一切。

We gotta reorganize everything.

他把所有业务都整合到德米斯·哈萨比斯旗下，并并入了DeepMind。

He folded everything under Demis Hassasas and and put it into DeepMind.

我们现在就处在这个状态。

And that's where we are now.

这在2025年带来了巨大的回报，尤其是Gemini 3的推出。

And it's it's really paid off in 2025 in a big way with Gemini three.

是的。

Yeah.

在人工智能领域，消费者市场正变得越来越激烈，尤其是正如你之前提到的，关于泡沫的讨论，像OpenAI这样的竞争对手，虽然谷歌拥有雄厚的资产负债表、强劲的现金流和庞大的用户基础，并且持续创新。

And and that consumer space really is getting more and more intense when it comes to the the AI side of things, particularly as, you know, you mentioned before when you were talking about some some of the talk about bubbles, these competitors like OpenAI, you know, Google has big balance sheet, strong cash flow, and it has a huge user base of users and continues to innovate.

我认为，鉴于这次重组以及谷歌如今展现出的这种速度，这将给OpenAI带来巨大的竞争压力，特别是在2026年的消费者市场方面。

And I think this really does, given the kind of that reorg and and this kind of speed you're seeing now from Google, I think this is adding gonna add a lot of competitive pressure onto OpenAI, particularly on the consumer side in 2026.

所以一切尚无定论。

So it's all up for grabs.

是的。

Yeah.

我预计今年OpenAI会推出大量不同的产品。

And we're gonna see a lot of different stuff I I anticipate from OpenAI this year.

他们会把所有能想到的东西都扔到墙上，看看哪些能粘住，因为他们给自己施加了巨大压力，必须产生巨额收入，以兑现他们关于与甲骨文等合作建设大型数据中心的种种承诺。

They're gonna throw all the spaghetti at the wall they can to see what sticks because they've put enormous pressure on themselves to generate enormous amounts of revenue in order to fulfill all of these promises they made about, you know, capital expenditure build out of these big data centers with Oracle and all these sorts of things like that.

除非他们能更好地、更有效地将这些技术产品化，否则他们承诺的巨额支出根本无法实现。

It cannot happen, all these committed spending they have, unless they productize it better and more effectively.

但顺便说一句，我们也在Meta身上看到了类似的情况。

But, like, to your point, we're seeing this with Meta, by the way.

Meta 拥有利用其用户基础的巨大机会，但目前还没有像谷歌那样找到有效的方式。

Meta has a huge opportunity to leverage its user base, and it hasn't figured out how to do that in the way Google has.

所以目前来看，谷歌感觉自己处于领先地位。

So right now, Google feels like they're kind of on top of things.

嗯，你看。

Well, look.

这个关于 DeepMind 的系列节目的第二部分将于下周发布，我们将采访 DeepMind 的首席运营官莉拉·易卜拉欣。

Part two of this miniseries on DeepMind is gonna be out next week, and we're speaking to Lila Ebrahim, who is the COO over at DeepMind.

别忘了收听。

So catch that.

如果你对这期节目有任何评论或想法，请随时联系我们。

And if you got any comments or or thoughts about this episode, please reach out to us.

我想你几乎可以在所有平台找到我们。

You can reach us pretty much everywhere, I think.

你在多个社交媒体平台上都有账号。

You're you're on multiple social media platforms.

你是个搞商业的。

You're a business guy.

商人。

Guy.

是的。

Yeah.

我们无处不在

We're all over

啊。

the place.

不。

No.

是Instagram。

It's Instagram.

我七年前半就退出了Instagram，从不后悔。

I quit Instagram seven and a half years ago, and I do not regret it.

哇。

Wow.

太棒了。

That's amazing.

是的。

Yeah.

不再无休止地刷负面新闻了。

No more doomscrolling.

太好了。

Love it.

这个家伙也不再无休止地刷负面新闻了。

No more doomscrolling for this guy.

感谢大家收听和观看。

Thank you all for listening and watching.

我们下次再见。

We'll catch you next time.