# (77) Can Google’s New AI Solve Everything? | Titans and Disruptors - YouTube
- Transcript ID: `bhftqxmtozw`
- Source tier: `youtube_creator`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (56/56 chunks)
- Original source: https://www.youtube.com/watch?v=BhfTQXMtoZw
- Raw evidence: `data/transcripts/youtube/demis-hassabis-fortune-2026-BhfTQXMtoZw.json`
> 中文是派生翻译，英文是留存的源文本。中文未复核时，以英文和原始音视频为准。
## 00:00:00
**中文**
10 到 15 年后，我们将进入发现的新黄金时代，我希望是一场新文艺复兴。AI 革命正激烈展开，但我们可能会回看 2014 年 1 月：哈萨比斯将 DeepMind 卖给 Google。他拒绝了 Meta 马克·扎克伯格更高报价；收购令埃隆·马斯克担忧，随后与萨姆·奥尔特曼成立后来的 OpenAI。今天德米斯仍是要击败的对象，领导 Google 全部 AI 项目，包括正快速蚕食 OpenAI 用户基础的 Gemini；业余时间——
**English / Original**
In 10, 15, years time, we'll be in a kind of new golden era of discovery. That's why, I hope a kind of new renaissance. We're in the thick of the AI revolution, but we might look back on January 2014 as one of the most pivotal moments in business history. That was the month that Demis Hassabis sold his AI company, Deep Mind to Google. He rebuffed a higher offer from metas Mark Zuckerberg, and the acquisition scared Elon Musk so much that he decided to launch a Rimmel company with Sam Altman, now called Open AI. Fast forward to today, and Demis is still the one to beat. He runs all of Google's AI initiatives, including Gemini, which is quickly eating away at open AI's user base in his spare time,
## 00:00:40
**中文**
德米斯获得诺贝尔奖，还经营 Isomorphic，目标是用 AI 解决所有疾病。我在达沃斯世界经济论坛与他对谈，了解他认为未来走向何方。我是艾莉森·尚特尔，这是《财富》500 强行业巨头与颠覆者。赞助商消息后回来继续谈。
**English / Original**
Demis won a Nobel Prize, and he runs a startup called isomorphic that wants to solve all disease with AI. I sat down with Demis at the World Economic Forum in Davos to learn where he thinks the future is heading. I'm Allison Chantal, and this is fortune 500 Titans and disruptors of industry. We'll be back with Demis after a quick message from our sponsor.
## 00:01:17
**中文**
这是我参与的每场 CXO 对话主题：寻找高影响、未必光鲜却适于自动化的职能领域，用技术创造效率和创新。随着时间，AI 智能体也会进入面向客户和增长领域。德勤在财务组织中用它处理费用管理、营运资金管理等平凡流程；其他组织在呼叫中心和可自动化的软件开发中使用它。
**English / Original**
This is on the topic of every CXO conversation I'm a part of, and I think the thought process has to be looking for high impact areas that may not be necessarily the most glamorous or high profile functional areas, but are ripe for automation and use of this technology to create efficiencies as well as innovation. And over time, AI agents will be also in customer facing and growth oriented domains. In our case Deloitte, we're using it within our financial organization, looking at very mundane processes like expense management and working capital management. We're seeing other organizations using it in call centers and with software development that can be automated
## 00:02:02
**中文**
关键在于有意为之：逐职能领域推进，与业务、IT 和企业领导层协同，成为有预算、设定 KPI、且真正对智能体能力带来的业务结果和影响负责的主流业务规划工作。
**English / Original**
and comes down to intentionality. And so I think that intentionality in going functional area by functional area, in concert with business and IT leadership and enterprise, it needs to be a mainstream business planning effort that's budgeted, that's KPIs are developed, and there's real accountability for actual business outcomes and impact because of agentic capabilities.
## 00:02:32
**中文**
德米斯，感谢在达沃斯抽时间。很高兴在这里。你 2025 年很重要，也正准备 2026 年；但先让人们了解你。你爱国际象棋，是棋手，也爱天文学；这两者如何带你进入或塑造你对 AI 的思考？
**English / Original**
Demas, we're here at Davos. Thank you for making the time to do this. Great to be here. You've had a huge 2025 it sounds like you're gearing up for great 2026 but before we get into both of those things, I want to just take a step back so people can get to know you a little bit better. One of the things you love is chess. Yes, you're chess master. You also love astronomy, yes. And I'm curious how both of those things took you into AI or shape. How you think about AI? Yeah.
## 00:02:59
**中文**
我从小就喜欢天文学、宇宙学、物理，因为一直关心大问题：宇宙到底发生什么、意识及其本质等。若对大问题感兴趣，就会被物理吸引。
**English / Original**
Well, I've always been interested in things like astronomy, cosmology, physics as a kid, because I've always been interested in the big questions. So you know what's actually happening here in the universe, consciousness, nature of consciousness, all of these types of things. So you get sort of drawn to physics if you're interested in the big questions.
## 00:03:16
**中文**
我也爱国际象棋、游戏和策略。童年认真下棋训练了大脑，让我思考思考本身和大脑如何运作。我把这些结合，走向 AI 与计算机：AI 既能理解自己的心智，也是科学和理解宇宙的完美工具。哈萨比斯有计算机科学学位和认知神经科学博士，2010 年共同创立 DeepMind，以“解决智能”为雄心目标，团队在 AI 模型上取得重大进展。
**English / Original**
And then for me, for chess, I also love games. Love strategy. Ended up training my own mind by playing chess as a kid, very seriously, and then that got me thinking about thinking and how does the brain work? And then I've combined all that together. That sort of led me to AI and computers and AI being a way to understand our own minds, but also a perfect tool for science and understand the universe out there, equipped with a degree in computer science and a PhD in cognitive neuroscience, Hassabis co founded Deep Mind in 2010 the company launched with an ambitious goal to solve intelligence under Hassabis leadership, the Deep Mind team made significant strides in its artificial intelligence models,
## 00:03:56
**中文**
仅四年后，Google 以数亿美元收购这家研究初创公司。2014 年你以约 5 亿美元卖给 Google，当时是热门交易，Meta 也想要它。我认为这会是商业史最具变革性的时刻之一：它给 Google 建造惊人 AI 机器、走向未来的基础。回望那一刻感觉如何？如何决定，知道它会这么大吗？
**English / Original**
and just four years later, Google purchased the research startup for hundreds of millions of dollars. You first started Deep Mind. You co founded it a number of years ago, and about 2014 you sold it to Google for about $500 million at the time, it was a hot deal. I know meta wanted it too, and from my perspective, I think we're going to look back on that moment as one of the most transformative moments in business history, you've given Google the foundation of which to build an incredible AI machine and really take it into the future. When you look back on that, how do you feel about that moment? How did you make that decision? Did you know it was gonna be such a big moment?
## 00:04:35
**中文**
是的，参与科学的人知道。2010 年创办 DeepMind，15 年多前无人谈 AI，但我们知道；使命是解决智能，再用它解决其他一切，成为首家构建 AGI 的公司，首要应用是科学问题。2014 年拉里·佩奇推动 Google 来谈时，我们知道某种意义上是低卖了，但另一方面——
**English / Original**
Yeah, we did. Actually, those of us, you know, were involved in the science. So it's interesting. We started DeepMind in 2010 which was, you know, 15 years plus ago now. And nobody was talking about AI, but we knew, and we set out with the mission of solving intelligence and then using it to solve everything else. So we wanted to be the first company to build artificial general intelligence. And the main thing we wanted to apply it to was, was solving scientific problems. So when Google came a. Long in 2014 and it was actually driven by Larry at the time. Larry Page, who was the CEO. We knew that in some ways, we were sort of underselling, but on the other
## 00:05:08
**中文**
对我重要的不是钱，而是使命：加速走向 AGI、回答科学问题。Google 的巨大算力会加速这一点，如今可见算力对发展智能多重要。当时我告诉拉里和推动交易的搜索负责人，这可能会成为——如今看似可能是 Google 最重要的收购。
**English / Original**
hand, what mattered to me was not the money. It was being able to it was the mission and be able to accelerate our progress towards artificial general intelligence and answering these scientific questions that that we were, we were trying to solve, and I felt that teaming up with Google would accelerate that, mostly because they had obviously enormous compute power, and we see today that how important that is for developing intelligence. So at the time, I did mention to Larry, and also the head of search at the time, who was driving the deal that this would turn out to be, or they didn't look like it. Now it now it might turn out to be the most important acquisition
## 00:05:42
**中文**
这意义非凡，Google 收购过 YouTube、Android 等重要资产。回看 OpenAI 起源，埃隆和萨姆合作，是因担心 DeepMind 收购后 Google 垄断 AI；这也在当时造就一个巨大竞争者。
**English / Original**
Google has ever done, which is saying something, because they acquire YouTube and their history and Android, they've got a good history of buying important things. And now, if you go back and you look at the origins of open AI, if Elon and Sam got together because they were afraid that Google might now have a monopoly in AI space with the Deep Mind acquisition. So really, that also kind of created a mega competitor at the time.
## 00:06:05
**中文**
这有很多蝴蝶效应。AlphaGo 的成功也是原因：它是首个用今天熟悉的强化学习、深度学习系统成为围棋世界冠军的程序，是重大分水岭。2016 年突破今年正值十周年，它像现代 AI 时代发令枪，OpenAI 及其创始人看了比赛，也想参与。
**English / Original**
Yeah, I guess there's all these sort of butterfly effects that happen. And I think partly also was the success of things like AlphaGo, first program to be the world champion at the game of Go using these kinds of learning systems that we're familiar with today. You know, reinforcement learning, deep learning, at the heart of it, I think that was a big watershed moment as well. In 2016 is actually like the 10 year anniversary of that breakthrough this year. And I think that really started the starting gun for the modern AI era, including things like, you know, open AI and the founders of that watched that match and wanted a piece of that action.
## 00:06:38
**中文**
Google 收购后，DeepMind 屡因 AI 成就上头条。2015 年 AlphaGo 首次击败围棋冠军，后来击败国际象棋、Stratego 和《星际争霸 II》顶尖玩家。2020 年 AlphaFold 2 解决蛋白质折叠问题，以惊人准确度预测序列形成的最终结构；团队已扩展至预测超过 2 亿种结构，全部在线公开。
**English / Original**
Following Google's DeepMind acquisition, the company repeatedly made headlines for its accomplishments in AI in 2015 the company's AI model AlphaGo became the first computer to defeat a champion Go player, and later on, it defeated top players in chess, Stratego and StarCraft two, which is a popular Real Time Strategy computer game in 2020 Google's Deep Mind Alpha Fold two solved the protein folding problem. For decades, scientists had struggled to predict how protein sequence forms in its final structure. The model accomplished this with remarkable accuracy. The team has since scaled its process to predict over 200 million structures, all of which are now available in an online database.
## 00:07:17
**中文**
哈萨比斯 2024 年部分因该成就获诺贝尔化学奖和爵士头衔。在 Google/Alphabet 下，你能做大量登月项目、冒险试验不立即赚钱却有深刻突破，其中一项获得诺奖。能谈 AlphaFold 及其为何对未来解决疾病如此重要吗？
**English / Original**
Pasabas received the Nobel Prize in Chemistry and a knighthood in 2024 in part for this achievement under Google at alphabet, you've been able to have a lot of moon shots, take risks, try things that haven't necessarily led to money immediately, but have profound breakthroughs, and one of them, you won a Nobel Prize. So I was wondering, congratulations, it's incredible. I was wondering if you could just tell me a little bit more about Alpha fold and why that's such a big deal in terms of how we could be looking at solving diseases moving ahead?
## 00:07:45
**中文**
加入 Google/Alphabet 的好处之一是有资源和时间攻克深层科学问题，AlphaFold 是最佳例子。它解决一项已有 50 年的生物学重大挑战：能否仅从氨基酸序列、即遗传序列确定蛋白质三维结构？这极重要，因为从肌肉到神经元放电，身体几乎一切都依赖蛋白质；知道其三维结构，就——
**English / Original**
Yeah, I think this is one of the benefits of being as part of Google and alphabet, was having the resources and the time to really go after these sort of deep scientific problems. And alphafold, I think, is the best example of that. It's basically a solution to a 50 year old grand challenge in biology of can you determine the 3d structure of a protein just from its amino acid sequence, basically from its genetic sequence? And this is incredibly important, because proteins basically do everything in your body, from muscles to neurons firing. Everything depends on proteins. And if you know the 3d structure of a protein, what it looks like in your body, then
## 00:08:20
**中文**
能部分了解其功能和支持作用。它也关乎疾病：蛋白质可错误折叠，如阿尔茨海默病，导致疾病；因此对药物发现和基础生物学都重要。AlphaFold 解决的是另一位诺奖得主克里斯琴·安芬森约 50 年前提出的问题：从一维氨基酸串直接得到三维结构，理解它如何蜷成球。
**English / Original**
you kind of partially know what the function it does, what it supports. Obviously, it's important also for disease, because things can go wrong with proteins. They can fold in the wrong way, like in something like Alzheimer's, and then that can create a disease. So really important for drug discovery, as well as fundamental biology and alpha fold. It was a solution to this problem that was posed 50 years ago by another Nobel Prize winner, actually Christian and Vincent, that this should be possible, and to go directly from a kind of one dimensional string of amino acid sequence to this 3d structure, this kind of, how does it scrunch up into a ball? And alpha vault was a
## 00:08:54
**中文**
AlphaFold 是答案且极高效。它不仅准确，我们还折叠了科学已知全部 2 亿种蛋白质，并与欧洲生物信息学研究所共同放入大型免费数据库。如今全球超过 300 万研究者每天使用 AlphaFold。
**English / Original**
solution, and it's so efficient. Not only is it accurate, we folded all 200 million proteins known to science, and then we put that on a huge database with the European Bioinformatics Institute and for free into the world for everyone to use. So now over 3 million researchers around the world make use of alpha vault every day.
## 00:09:11
**中文**
令人惊叹。你将其中一部分用于 Isomorphic，这家“副业”初创公司；你同时做两份巨大工作，筹了数亿美元，Google 是支持者。能解释使命吗？你的目标是“解决”而非“治愈”所有疾病；也请说明把药物带入试验有多难。
**English / Original**
Wow. And it's turned into you're using some of it, I believe, for isomorphic, which is a startup that you have, I want to say, on the side. So you're doing two huge jobs at once. Here you've raised hundreds of millions of dollars in Google, of course, as a backer, yeah, for isomorphic. Can you just explain the mission there? And you have some lofty goals, like you say that we're going to solve all disease, yes. You don't say cure, yes. You say solve, yes. And also walk me through how hard it is to get a drug to trial, because that's historically been very difficult.
## 00:09:39
**中文**
这一直是 AlphaFold 背后构想。理解蛋白结构可做基础科学，也可设计具有新功能的新蛋白；可以反向使用 AlphaFold：想要某形状，如何从遗传序列获得？但药物发现中，蛋白质结构仅是很小一部分。平均从了解疾病靶点到上市药物要约 10 年，耗时巨大且——
**English / Original**
That was always the idea behind Alpha fold. So obviously there's a lot of fundamental science that can be done if you understand the structures of proteins, including designing new proteins that do new things. So you can sort of use alpha fold in reverse to sort of go, Okay, I want this particular shape. How do I get it from a genetic sequence? But to do drug discovery, knowing the structure of protein is only. One small part of that whole process. And usually it takes, like, on average, you know, 10 years to to go from understanding a target, you know, for a disease, to all the way to a drug that's ready for in the market. So it's an enormous amount of time and
## 00:10:13
**中文**
成本数十亿美元、十年或更久，多数药物失败，成功率约 10%，因生物学太复杂。我一直梦想、也最早想用 AI 做的是改善人类健康，还有什么更重要？AlphaFold 证明可行。三四年前 AlphaFold 完成后，我们拆分出 Isomorphic，开发围绕它的其他 AlphaFold 级突破，例如更偏化学——
**English / Original**
cost, you know, billions of dollars a decade or more. And most drugs, you know, fail along the way. Is only like a kind of 10% success rate. So it's just incredibly inefficient, because biology is so complicated. So what I've always dreamed about doing, and it was the first thing you know, I wanted to apply AI to, was human health, improving human health. What could be more important use of AI? And alpha fold was the proof point of that this could be possible. And then isomorphic, we spun that out after alpha fold was done so three, four years ago, to develop additional Alpha fold level breakthroughs surrounding Alpha fold so you can think more in the chemistry
## 00:10:49
**中文**
领域。知道蛋白结构后，需要知道设计的药物化合物会在哪里结合、做什么；需建其他 AI 系统预测这些。Isomorphic 进展极好，与礼来、诺华等顶尖药企合作，已有约 17 个活跃药物项目，最终计划数百个。要让人类健康实现真正跃迁，就是在——
**English / Original**
space. So if you now know the structure of a protein, you need to know where the chemical compound you're designing the drug, basically where it's going to bind to the protein, and what is it going to do. And so you need to build other AI systems that can predict all of that. So that's what we've been doing in isomorphic it's going incredibly well. We have great partners with Eli Lilly and Novartis, the best farmers in the world on we have like, 17 drug programs active already, and we plan to go to, you know, eventually be hundreds. And I think this is the way to make real step change progress in human health, is you basically do your search and your hypothesis searching in
## 00:11:28
**中文**
计算机模拟中进行搜索和假设搜索，比湿实验室高效数百、上千倍；只把湿实验室留给验证。当然最终还要人体临床试验确保安全，但几乎所有搜索和设计可在硅中完成。2026 年对 Google 和 Isomorphic 都很大；会在 2026 年初将第一种药物带入试验，可能是癌症吗？
**English / Original**
silico. And that's, you know, hundreds, 1000 times more efficient than doing it in a wet lab. And you save the wet lab part just for the validation step. Of course, eventually you have to test it in, you know, trials, human trials, and all those types of things to make sure everything's safe. But you can do all of your search and design, or almost all of it in silico, that's the plan. And so 2026 you mentioned, is going to be a big year, I imagine, for both Google and for isomorphic. Do you anticipate in early 2026 this could be the moment that you get the first drug to trial, and might it be in cancer, yes.
## 00:12:01
**中文**
我们在癌症、心血管、免疫学等多个领域工作，最终希望覆盖所有治疗领域。我们正建设通用药物发现引擎平台；一些癌症药物已在极早期临床前阶段。若顺利，希望年底开始走向临床试验。
**English / Original**
So we're working on, actually, several spaces, cancer, cardiovascular, immunology, and then eventually we'd like to branch out to all therapeutic areas. We're building a, you know, a general drug discovery engine platform you can think, and we are already in pre clinical trials, very early stage, for some cancer drugs. And then, you know, hopefully by the end of the year, if those are successful, we'll start going towards clinical trials.
## 00:12:24
**中文**
你如何管理自己、时间和团队？无论成功发布备受好评的 Gemini 3，还是把药物送入试验，这看起来是两件完全不同、由不同团队完成的难事；你不能同时在两处，如何做到？
**English / Original**
How do you manage yourself and your time and your teams? Because you're achieving really, really difficult things, whether it's the launch of Gemini three, which was very successful and well received, or it's getting drugs to trial, these are two sounding like very different things, two different teams to run. You can't be in two places at once. How are you doing this?
## 00:12:45
**中文**
如何经营两家公司？我的一项技能是汇集出色、世界级的跨学科团队，我热爱组织管理团队。Google DeepMind 和 Isomorphic 都有惊人团队。以 Isomorphic 为例，我们融合顶尖生物学家、化学家、机器学习和工程人才，跨学科群体会产生很多魔力。Google DeepMind 方面，我们也试图融合最初 DeepMind 的创业世界优点与——
**English / Original**
How are you running two companies? One of my skills is bringing together amazing, world class, interdisciplinary teams. I've loved managing those teams. I love composing those management teams together. And I've got incredible teams, both at Google, DeepMind and isomorphic. And if we take isomorphic, for example, we've blended top biologists and chemists along with top machine learning and engineering. And I think there's a lot of magic happens when you have these kind of interdisciplinary groups. And then if we think about on the Google DeepMind side there, we've tried to blend together the best of the startup worlds, like what we were doing at DeepMind originally, and then at
## 00:13:21
**中文**
跨国规模优势：有惊人产品界面可立即部署 Gemini 3、2 等技术，迅速得到用户反馈、帮助数十亿用户日常生活，非常令人兴奋和有动力。时间管理上，我睡得不多。两小时？再多一点，否则对大脑不好。我尽量睡六小时，但有不寻常的睡眠习惯，白天安排并尽量塞满——
**English / Original**
scale, you know, in a kind of multinational scale, with all the advantages of having these amazing product surfaces that we can immediately deploy, you know, technologies like Gemini, three, two, and immediately get great feedback from users, and also, you know, help in everyday lives of billions of users. So it's amazingly exciting and motivating, actually. And in terms of the way I manage my time is, you know, I don't sleep very much, but, like, a couple hours, yeah, well, if you know a bit more than that, that would be bad for the brain. Yeah, I do try and get six, but I have unusual, you know, sleeping habits I sort of manage during the day. And do try and pack my
## 00:13:58
**中文**
办公室日程，会议背靠背，几乎无间隙。回家与家人待一会儿、吃饭，晚上约 10 点开始第二天工作，至凌晨 4 点，做思考、创造性和研究工作。大约十年来如此，效果很好。
**English / Original**
day in the office with as many meetings as possible, back to back, almost no time, no break between. Then I get home, spend a little bit of time with family, have dinner, and then I sort of start a second day of work about 10pm and go to 4am where I do my thinking and kind of more creative work and research work. And it's worked out, you know, I've done that for about a decade now, and it works well.
## 00:14:18
**中文**
我无法想象凌晨四点有创造力。是啊，我凌晨一点左右才活过来。2023 年，Google 面对其他快速增长搜索引擎和 ChatGPT 发布的竞争，决定将 DeepMind 和 Brain 两个 AI 团队合并到哈萨比斯领导下，目标是启动下一代 AI。你很会激励团队做难事；如何解决管理摩擦、让团队重新快速交付？当时感觉 Google 在 AI 上有些昏昏欲睡。
**English / Original**
I can't imagine being creative at four in the morning. But, yeah, I come alive at about 1am in 2023 Google was facing increasing competition from other rapidly growing search engines and from the launch of chat GPT. In the same year, Google pivoted to merge two of its AI teams, Deep Mind and Brain under Hassan. This is leadership, and the goal was clear, jump start the next generation of AI. You're clearly good at motivating teams to do hard things. I know in 2023 a decision was made at Google to put two different AI teams under you. How did you work out management kinks there and get the team shipping again? Because there was this feeling that Google was a little bit asleep
## 00:14:58
**中文**
我们有两个世界级组织：原 DeepMind 和 Google Brain。我们往往没有得到足够认可：我认为现代 AI 产业约 90% 建在这两组之一的技术或发现上，从 Transformer 到 AlphaGo、深度强化学习。我们仍有最深、最广研究梯队，人才远胜世界其他地方。但两个组变复杂，特别在——
**English / Original**
at the wheel for AI. And I'm curious if. You think that's true, and how you got them to wake up? Yeah, well, we had two world class groups in original Deep Mind and Google Brain. And actually, I think often, as a collector, we don't get enough credit for the fact that, you know, I think about 90% of the modern AI industry is built on technology or discoveries made by one of those two groups, from transformers to AlphaGo and deep reinforcement learning. So we have, when we still have, I think, the deepest and broadest research bench. So we have incredible talent, I think, better than anywhere else in the world, by a long way. But it was getting complicated having two groups, especially given the
## 00:15:37
**中文**
这个缩放时代所需算力背景下，因此必须合并，让所有人才在 Gemini 单一项目上协作。即使 Google 也没有足够算力同时做两个前沿项目，所以需合并资源。我很协作、对不同工作方式开放，始终想改进。我的座右铭之一是日语“改善”（Kaizen），即持续自我改善；我始终处于——
**English / Original**
amount of compute needed in this scaling era. So that was really why we had to put the two groups together, so we could pull all of the talents together working on a single project in Gemini. But also, even someone like Google didn't have enough compute to have two frontier projects under one house, so we needed to combine all of our resources together. I'm very collaborative person. I'm very open minded about different ways of working and try to I'm always looking to improve as well. Like one of my watch words I live by is this Japanese word Kaizen that I love, which is sort of striving for continual self improvement, and that's what I always try and do. I'm always in
## 00:16:13
**中文**
学习模式。也许我喜欢造学习机器，因为自己爱学习；无论多专家，总有可学。合并两组并融合两种文化的优点很成功，成果正显现。Google DeepMind 被我们视作 Google 的引擎室，像接入这家伟大公司的核电站。让我自豪的一件事是建立交付文化。
**English / Original**
learning mode. Maybe, perhaps that's why I like building learning machines, because I like learning, and there's always something you can learn, no matter how expert you are at what you do. And bringing the two groups together and trying to combine the best of both cultures has been great, and I think we're reaping the rewards of that now and now Google Deep Mind is really we, the way we think about is like the engine room of Google. So we're sort of powering it's like the nuclear power plant that's plugged into the rest of this amazing company in Google. And I think one of the things we did is, one of the things I'm very proud of, is getting the shipping culture
## 00:16:45
**中文**
重新发现 Google 10 到 15 年前的黄金时代，承担经过计算的风险、快速交付、保持创新；同时对向世界推出的东西——工程或科学——保持审慎、科学和严谨，我认为目前都很顺利。
**English / Original**
going and sort of rediscovering, I guess, the golden era of Google back 1015, years ago, and taking risk, calculated risk, shipping things fast, and being innovative. And I think that's all working out really well now, whilst at the same time being thoughtful and scientific about and rigorous about what we put out in the world, whether that's engineering or scientifically.
## 00:17:05
**中文**
我希望我们做对了平衡。你提到回归 Google 黄金时代，创始人似乎也回来了，至少谢尔盖重新参与。与他合作 Google AI 如何？很好，拉里也以不同方式参与。
**English / Original**
And I think, and I hope, you know, we're getting that balance right. Yeah, and you mentioned kind of going back to the golden era of Google, so much so that the founders, at least Sergey seems like he's back involved. How is it like working with him on AI and Google? It's been great, and Larry is too, in different ways.
## 00:17:20
**中文**
拉里更偏战略；谢尔盖深入细节，在 Gemini 等项目上编程，看到他们参与很棒。你会给谢尔盖派代码任务？不，他自己选择做什么；但看到他在办公室推动某些方向很棒。创始人深度参与更容易。我作为 Google DeepMind 联合创始人，也仍像创始人一样思考该做什么、战略上选择什么；这是我认为——
**English / Original**
Larry, more strategically. Sergey has been in the in the weeds, programming away, you know, and things like Gemini, and it's been fantastic seeing them engage, putting him to work. Are you like Sir Gandhi was code? Right? No, it's more like he chooses what to work on, but it's, it's, it's, it's great see, you know, seeing him in the office and pushing things in certain directions. And it's easier if the founders are kind of heavily involved, and I still act as well like as a co founder of Google Deep Mind, of of like as a kind of founder or two, right, in terms of like, what we've got to do, and strategically, what we pick to do. And that's something I think
## 00:17:51
**中文**
过去 10 到 15 年学会的事：面对“解决所有疾病”或“构建 AGI”等雄心目标，选择同样雄心却是路径点的中间目标。AlphaGo、AlphaFold 等 Alpha 项目和现在 Gemini 上，我们历史上做得很好。任何非常雄心的科学或工程项目都必须拆成可管理步骤，以看到自己走对方向；我认为我们用正在建的——
**English / Original**
I've learned to do well over the last. You know, 1015, years is when you have some ambitious goal, like solve all disease or build AGI. What are the intermediate goals that are also very ambitious, but there are kind of way points. What are the right ones to pick? And I think we've done that historically pretty, pretty well with most of the Alpha projects, Alpha Go, alpha fold and so on. And then now Gemini, and I think that's really critical, actually, for any very ambitious scientific and engineering project, is breaking it down into manageable steps so that you can see you're on the right direction. And I think that we're very clearly are, I think, with the
## 00:18:25
**中文**
技术显然走在正确方向。过去几年不可思议，我们正进入状态，外部世界包括华尔街和股价也开始感到。2025 年第一季度后，Google 在 AI 搜索上是否面对创新者窘境的疑问得到回答；受 AI 开发、爆红图像模型 Nano Banana 和 Gemini 3 推动，股价飙升，年末 Alphabet 上涨约 65%，是 2009 年以来最佳表现，并——
**English / Original**
technology that we're building, and it's been an incredible couple of years for us, and I think we're getting into our groove, I would say, and I think other people and the external world are starting to feel that, you know, including things like Wall Street and the share price in 2005 any questions about whether Google was facing an innovator's dilemma when it came to AI in search were answered after the first quarter, its shares skyrocketed, driven in part by advancements it made in AI development, including the launch of Google's viral image generation model nano banana and Gemini three alphabet. Shares rose about 65% by the end of the year, marking its best performance since 2009 and
## 00:19:01
**中文**
成为“七巨头”中表现最佳股票。Gemini 3 盛大发布和其他发布使 OpenAI 进入“红色警报”（虽称经常发生），还有与苹果的巨额交易；内部发生什么？如何为团队设 KPI，并让 2026 年势头持续？对我而言，始终从研究开始，即——
**English / Original**
making it the top performing stock among the Magnificent Seven. It definitely seems like there must have been some sort of KPI measurement charge ahead unifying moment, because, I mean the launch of Gemini three, much fanfare, among other launches that caused open AI to go to a code red, which they claim happens all the time, like, Okay, sure. And then you have this huge monster deal with apple that is monumental, I think, for the industry. So I'm curious what happened internally, behind the scenes. How did you set those KPIs for the team? And then how are you setting them to keep the momentum in 2026 Well, look, I think for me, it always starts with the research, like
## 00:19:37
**中文**
拥有最好的模型，由基础研究供给；随后尽快反映到产品，做好营销和分发。但模型若非同类最佳、最先进，其他都不重要。因此先聚焦 Gemini，也聚焦 Nano Banana 图像模型（非常爆红）、Veo 视频模型和世界模型；不只有大语言模型，我们各方面都处于前沿。然后是——
**English / Original**
having the best models in this case, and obviously fundamental research feeding into that, and I always believe you then need to reflect that obviously as quickly as possible in your products, and then you've got to get your marketing and distribution right. But none of it matters if your models aren't best in class, in state of the art. And so that's what we focused on, first with the Gemini models, but also our other models, things like nano banana, our image. Model, which went super viral, and that was a big part of their success last year, our video model, VO, our world model. So there's more than just large language models, and we're kind of, you know, state of the art on all of those. And then it was about
## 00:20:12
**中文**
内部理顺，某种意义重建 Google 基础设施，让最新模型力量迅速进入 Search、YouTube、Chrome、Gemini 应用等旗舰产品。这对全行业都新，需要时间围绕其重构。Google DeepMind 再次作为引擎室，为全组织提供引擎；用一年到十八个月完成，如今正见成果，仍有更多——
**English / Original**
sorting things out internally, almost rebuilding the infrastructure in some way of Google, so that you could reflect very quickly the power of the latest models into the lighthouse products, including Search, YouTube and chrome, all these amazing surfaces that we have as well, of course, as the Gemini app, it was new for everyone in industry. And I think, you know, takes a little while to kind of re architect things around that. And very much, you know, again, this idea of of Google DeepMind being the engine room, providing the engine for the rest of the organization to use. And I think that took a year, 18 months, to get right, but I think we're seeing the results of that now, and I think there's still more
## 00:20:50
**中文**
可做，速度还可更快。还要灌输强度、节奏、专注文化，只关注重要事项、剔除干扰。最后，尤其在嘈杂世界中，持续做出好的、理性决策，长期少戏剧性，会产生惊人的复利。我们正建立很大势头，希望今年会更多显现。正如前面提到，卖——
**English / Original**
to go, by the way, and we can have even faster velocity. And I think the other thing is just also instilling this culture of intensity and pace and focus and really focusing only on the things that matter, and kind of cutting out distractions. And then maybe the final thing I would say is, I think there's a lot to say, especially in today's very noisy world, to just consistently deliver good decisions, good rational decisions, and over time, minimal drama. And then I think it's just amazing how much that compounds over time. And you know, I think we're building a lot of momentum now, and I think hopefully you will, we'll see that even more this year, sort of like we mentioned before, the decision to sell
## 00:21:29
**中文**
DeepMind 给 Google 是商业中重大变革时刻；若现在成功，或许是最大商业转型。作为领导者，确保其对社会、劳动力和 Google 都好，如何压在你身上？这有创新者窘境：搜索之王靠广告有巨大商业模式，若你成功——
**English / Original**
DeepMind to Google is a monumental moment, transformational moment in business, if you're successful now, I think that will be perhaps the biggest transformation in business. How does that weigh on you to make sure that you as a leader, are driving this in a direction that's good for society, good for the workforce, good for Google, because it is a little bit of an innovator's dilemma where this is the search King, huge business model based on ads if you're successful.
## 00:21:57
**中文**
它确实是经典创新者窘境，我认为目前应对得不错，搜索比以往更成功。但若我们不自我颠覆，别人会，因此最好抢先、按自己的条件做。责任感不只在 Google，从 DeepMind 以前和学术生涯就有。因为我和首席科学家谢恩——
**English / Original**
Yeah, I don't know Sure. Well, look, I mean, it is a classic innovator's dilemma. I think we've navigated it pretty well so far. And search is more, you know, is more successful than ever. But also there's this aspect of like, if we don't disrupt ourselves, someone else will. So we, you know, you're, you're better off sort of being ahead of that, I think, and kind of doing it on your terms. And so I think that's what we found in terms of responsibility. I feel that, you know, I felt that way since, not just at Google, but before that deep mind, and before that, even in my academic career. Because if we, you know, myself and Shane, especially, our chief scientists, you know,
## 00:22:33
**中文**
创办 DeepMind 时，虽像异想天开，却真相信能造 AGI，也理解其对世界会多具变革性：可帮助科学、人类健康、能源等。但也有风险，是双用途技术；有害行为者、坏人——
**English / Original**
when we started Deep Mind, we it seemed like a fanciful idea, but we really believed that it would be possible to create artificial general intelligence, and we understood, what I think more and more people understanding now is how transformative to the world that would be, but also, of course, amazing for things like science and human health and maybe helping with energy and so on. But also, there are risks. It's a dual purpose technology. You know, harmful actors. You know bad actors.
## 00:22:57
**中文**
可用于有害目的；最终随着技术更自主、更具智能体性、接近 AGI，也有技术风险。我很担心这些。还需让经济引擎运转，以资助研究、AlphaFold 等并免费给世界；创造和雇人做 AlphaFold 很昂贵，但我们做很多这类事，想为世界做更多——
**English / Original**
Could use it for harmful ends, and eventually, as AGI becomes AI, but technology becomes more autonomous, more genetic, and we get towards AGI, there's technological risk too. And so I worry a lot about all of those things. And I also, of course, you know, we have to make sure that the the engine and the economic engine works as well, so we have enough money to fund our research and fund things like alpha fold and give it to the world for free. You know, that's not easy. It costs a lot of money to create something like and hire the researchers to create something like alpha fold, but we do a lot of things like that, and I want to do more things like that for the world,
## 00:23:29
**中文**
但这要求商业上也成功。要平衡。Google 有平台展示 AI 如何负责任、有益于全社会地部署。所有生产 AI 的前沿实验室都可选择：用于医学、减轻行政负担、帮助贫困，还是用于剥削？我认为我们将——
**English / Original**
but that requires us to be successful also on the commercial side. So I think, you know, there's a balance to be had there, but the responsibility, I think part comes as well as, and I feel like we can do this at Google as well is we have the platform to show how AI can be deployed in a responsible way and a beneficial way for all of society. And, you know, all of us who are frontier labs producing AI, we have choices about, what should we use AI for? Are we going to use it for things like medicine and for alleviating, you know, administration and helping with things like poverty, or we're going to use it for exploitative things, and I think that we're
## 00:24:07
**中文**
努力成为 AI 一切好处的榜样。这不意味着不会犯错；技术如此初生复杂，肯定会，但会尽可能审慎和科学。工作中一贯的科学严谨很重要，最终这是科学事业。我希望我们重视的可靠性、安全性会体现在产品中，市场也会奖励，因为——
**English / Original**
going to try and be a role model for all the good things that can come with AI doesn't mean we won't make any mistakes. We will do because it's such a nascent and complex technology, but we'll try and be as thoughtful as possible as we can with it, and we'll try and be as scientific about it as possible, too. The scientific rigor we bring to our work, and always have, I think, is going to matter here a lot. I mean, it's a scientific endeavor in the end. And then, you know, I hope that what we you know, the kind of reliability and security and safety that we like to work on, will come through in our products. And then I think the market will reward that. Because
## 00:24:41
**中文**
随着企业采用技术更复杂，大银行、保险、健康或医疗公司会想要保证：引入的 AI 系统会做什么。AI 更商业化的一项好处，是存在强大的商业激励去追求稳健、可靠和安全，这也是 AGI 进入世界前所需的一切。
**English / Original**
if you think about enterprises that use these technologies, as they get more sophisticated, they're going to want to know, you know, if you're a big bank or or, you know, insurance company, whatever health, health company, that medical company that you you have some guarantees about what your AI systems that you're bringing in are going to do. And. And so I think that could be a good aspect of AI becoming very commercial, is that there'll be sort of commercial incentives to be robust and reliable and secure, and all the things that you'd want actually in preparation for AGI coming into the world.
## 00:25:15
**中文**
展望今年，AI 故事和成就会是什么？我每年都说 AI 每年都关键；对前沿科学工作者而言，几乎每年都像十年，今年也不例外，强度很高。但也要偶尔抬头看战略图景。至少对我们，Gemini 3 已跨越分水岭；希望用过的人能感到它如今非常能干，我也在使用它——
**English / Original**
So when you look at the year ahead, what do you think the story of AI will be? What will we achieve? Well, I think this year, I mean, every I say this, every year that, you know, every year is pretty pivotal in AI, and it feels like, at least for those of us in science, working at the at the coalface, you know, like 10 years, almost happens every year. And I think this year will be no different. It's very intense. But you've also got to, kind of, every now and again, look up at the strategic picture. I think that at least for us, with Gemini three, we cross the watershed moment, in my opinion. And hopefully, those of you who've used it will feel that in that it's very capable now, and I'm certainly using it
## 00:25:51
**中文**
帮助日常研究、总结和编程。我认为系统现在也许已准备构建智能体；全行业谈了很多智能体、自主系统和委派完整任务，可能到年底会真正开始看到。很兴奋助理随你进入现实世界，或许在眼镜中。我们有智能眼镜大项目，AI 技术刚好接近可行，它或许是眼镜的杀手级应用；我认为——
**English / Original**
in my everyday life to help me with my research and summarizing things and doing some coding. So I think that these systems are now ready to maybe build agents. We've talked the whole industry's talked a lot about agents and more autonomous systems and delegating whole tasks to them. But I think maybe by the end of this year, we'll really start seeing that. I'm very excited about assistance coming into the real world with you, maybe on glasses. We have a big project on smart glasses. I think that the AI technology is only just about there to make that actually viable. And I think that could be a kind of killer app for force for glasses, you know, I think that
## 00:26:28
**中文**
把它带入世界还包括机器人学；机器人仍需更多研究，但未来约 18 个月也会有突破时刻。我们大力推进所有这些领域，也持续改进 Gemini。
**English / Original**
part of bringing that into the world, also robotics, I think is gonna, I still think there's more research to be done on robotics, but I think over the next 18 months or so, I think we're gonna see kind of breakthrough moment in robotics too. So all of these areas we're pushing very hard on as well as, of course, improving Gemini itself.
## 00:26:43
**中文**
那是眼镜吗？不是，如果是我会买。计算机并非为 AI 及 AI 能做的一切而构建，未来形态听起来像眼镜，它只是方案之一吗？我们内部谈“通用助理”：在日常生活极有帮助，推荐新事物、丰富生活、处理行政工作等，并跨越所有终端：电脑、浏览器、手机，以及——
**English / Original**
Those are the glasses, right? No, they're not. I would buy those if they were. They look yeah. I was going to ask you about the future form of how computers were not built for AI, and all the things that AI can do. What do you think is the future for sounds like glasses? And do you think would just be one of the solution? I have this side notion of, we talk about this notion internally of a universal assistant, and what we mean by that is an assistant that's super helpful in everyday life, recommending new things, enriching your life. And, you know, dealing with admin, all of these types of things, but it goes across all the surfaces. So it's this on your computer, on your browser, on your phone. And
## 00:27:21
**中文**
新设备如眼镜。它是同一个助理，理解你不同对话间的上下文，无论在车里还是办公室；若你希望，可全部整合，帮助改善生活各方面。也许明年圣诞或假期我们都能有 Google 眼镜。那就是想法。你们以前只是太早了。是的，Google 常开创空间——
**English / Original**
then I think there'll be new devices too, like glasses, and it will be the same assistant that kind of understands your context across the different conversations you've had, whether that's in your car or in your office. And if you want it to, you know that can all be integrated together. And I think help you, you know, improve your life across all those different aspects of your life, maybe for Christmas next year, the holidays next year, we can all get our Google Glasses. That's, that's the idea you guys were just way too early, I think before when they I think, you know, and like a lot of things we've done at Google, we maybe were, you know, we pioneered all these spaces,
## 00:27:53
**中文**
回看眼镜也许太早：不仅技术上难做得不笨重，也缺少杀手级应用。AI 数字系统可能就是它。最后一个问题：你最大、最大胆的预测是什么？看向未来，是 AI 解决所有问题的富足世界吗？
**English / Original**
perhaps a little bit too early, in hindsight, with glasses, both the technology, the technology of making them not too chunky and things, but also, I think it was missing the killer app. And I think an AI digital system could be that. Yeah, amazing, yeah. Well, I just one last question for you. I want to ask your biggest, boldest prediction for how AI will transform the world when you look ahead. I know you said 10 years is one year now it's true, yes, but when you're looking ahead, are you like the abundance world where AI can solve all of our problems, like, what does it look like?
## 00:28:23
**中文**
如果做对，10 到 15 年后我们会进入发现的新黄金时代、新文艺复兴。人类健康将革命性改变，医学不再像今天；个性化医疗成为现实。AI 将解决许多科学大问题，如新材料、聚变、太阳能、最优电池和能源危机；随后进入激进富足世界，可用这些能源——
**English / Original**
I think done right? We will be an incredible, you know, in 1015, years time, we'll be in a kind of new golden era of discovery. That's why I hope a kind of new renaissance. And I think human health will be revolutionized. It won't medicine won't look like it does today. I think that personalized medicine, for example, will be a real reality, and I think we'll have solved, used these AI technologies to solve many big problems in science, and things like new materials, maybe help with fusion or solar or optimal batteries, some way of solving the energy crisis. And then I think we'll be in a in a world of radical abundance, where we can use those energy sources to,
## 00:29:01
**中文**
前往星际、探索银河系。
**English / Original**
you know, travel the stars and explore, you know, the galaxy.
## 00:29:05
**中文**
我认为这将是我们的命运。太美妙了。谢谢，希望你建成它，也感谢所有努力。很高兴交谈。彼此彼此。
**English / Original**
That's, that's what I think our destiny is going to be amazing. Well, thank you. I hope that that's what you build. And thank you for all your efforts on it. Great to talk to you. Likewise.