# (77) Demis Hassabis on AGI by 2030, Curing Every Disease, Life After AGI, and More - YouTube
- Transcript ID: `4tvcheav0d4`
- Source tier: `youtube_creator`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (46/46 chunks)
- Original source: https://www.youtube.com/watch?v=4tVCHeAv0D4
- Raw evidence: `data/transcripts/youtube/demis-hassabis-rundown-2026-4tVCHeAv0D4.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**
我非常相信人类的聪明才智；我们几乎能立即适应。别忘了，我们自身就是通用智能。看看我们用采猎者的大脑在周围建造的一切，令人难以置信。为什么要止步于此？即使是我们今天拥有的工具，在仅仅几年前也是不可想象的；但今天我们已完全适应了它。
**English / Original**
I've got a lot of faith in human ingenuity we're sort of instantly adaptable. We’re general intelligences ourselves, don’t forget. at what we built around us, it’s incredible, with our hunter gatherer brains. Why would we stop here? even the tools we have today, just a few years ago, that would have been unthinkable. But today we've just totally adapted to it.
## 00:00:16
**中文**
哦，是的。你知道，我们与基本达到图灵测试水平的机器人聊天，也制作能创建任何视频的 Omni 模型，而我们的反应有点像：“是啊，这很正常。”真正伟大的科学家与优秀科学家的区别，不在于技术能力，而在于当技术就绪后所具备的创造能力。
**English / Original**
Oh yeah. You know, we chat with bots that are basically Turing test level, and we make Omni models that can create any video, and we're kind of like, yeah, you know, that's kind of normal. what separates the great scientist from the good scientist is not their technical capability. it's that creative capability, after the technology is ready.
## 00:00:33
**中文**
你如何看待普及化？它是否只对富人可用？如何让每个人都拥有它？如何使它最有益于世界？Demis，再次非常感谢你来到这里。和你交谈总是很愉快。很高兴来到这里。那么 Google I/O 显然发布了大量公告。你最兴奋的是什么？哦，哇。
**English / Original**
How are you thinking about democratization? it available just to the rich? How can everyone have it? How can we make it best for the world? Demis, thanks so much for being here again. It's always great talking to you. It's great to be here. So Google I/O, obviously tons of announcements. What are you most excited for? Oh, wow.
## 00:00:51
**中文**
这次——我的意思是，我认为我们发布了很多真正令人兴奋的东西。你可能看到我对 Omni 相当兴奋。我认为这是我们在世界模型上的一个非常有意思的方向，我一直是这一方向的大力支持者。但显然，我认为 Flash 3.5 是重头戏。把它整合到 Anitgravity 2.0 中，我想人们会感到非常惊喜。
**English / Original**
It's, I mean, I think we announced a lot of really exciting things. You probably saw me pretty excited about Omni. I think this is a really interesting direction for us on World Models. I've always been a big proponent of that. But obviously, you know, I think Flash 3.5 is the big story. And integrating that into Anitgravity 2.0, I think people are going to be really pleasantly surprised.
## 00:01:14
**中文**
我认为它是一种了不起的主力模型。当然，还有更多内容即将到来，比如 Pro。是的，基准测试成绩在提升，Spark 带来了智能体。我认为在消费者端，我们真的开始看到生产力提升发挥作用。例如，即使是 nano banana，内容创作者也确实在使用这些工具箱中的新工具制作更多内容，诸如此类。
**English / Original**
And I think it's an amazing kind of like workhorse model. And then of course, you got a lot more to come. You know, with Pro. Yeah, benchmarks are going up, agents are here with Spark. And I think like on the consumer side, we're really starting to see like, productivity gains. Yes. Like kicking in, like even with nano banana for example, content creators are genuinely using these tools new tools in the toolkits to make more content, that sort of stuff.
## 00:01:39
**中文**
我很好奇：以今天的 Omni 为例，与三年前 Gemini 首次发布时相比，你的 AGI 时间线发生了怎样的变化？它是按计划进行、更快了，还是更慢了？我认为它正按计划进行。所以几年前——也许两三年前，我们甚至在采访中谈过——我过去常说是 5 到 10 年。
**English / Original**
I'm curious, like looking at today with like Omni, for example, versus three years ago when when Gemini was first released. Yeah. How have your AGI timelines kind of shifted? Have they, has it gone according to plan or has it gone faster, or slower? I think it's going according to plan. So a few years ago you probably saw maybe we even talked about in interviews I used to say 5 to 10 years, maybe that's 2 or 3 years ago.
## 00:02:03
**中文**
我认为我们完全走在轨道上，而且我的置信区间缩小了。因此，如今我认为是 2030 年前后一年。哇。好，2030 年前后一年，这已经很近了。是的，非常近。你们在内部如何准备？首先，过去一年我们在底层做了大量工程工作；当我们宣布这样的东西时，你们看到的是表面。
**English / Original**
I think we're sort of exactly on track. And, and I would say my confidence interval is narrowed. So, you know, these days I'm thinking it's 2030 plus or minus a year. Wow. Okay. So 2030 plus minus a year. That's that's. Soon. Yeah very soon. How are you guys preparing? Like, internally. We we're, you know, first of all, we've got to we've done a lot of engineering work under the hood in the last year, which you see, you see the surface of when we announced things like this.
## 00:02:33
**中文**
但实际上，几乎可以说整个技术栈都必须重写，依次做到 AI 优先、模型优先，而现在是智能体优先。这与普通软件相比是相当大的变化。显然，还有为每天以闪电般速度服务数十亿用户的庞大基础设施，那一切都必须正常运作。
**English / Original**
But actually you can almost think that whole stack had to be rewritten to, be sort of, AI first, model first, and now agent first. So that's quite a big change from like normal software. And obviously there's all this huge infrastructure that serves billions of users every day at lightning speed, and that's all got to work.
## 00:02:53
**中文**
我认为过去一年我们在这方面取得了巨大改进。这就是为什么你能看到 AI 模型的所有优势如此迅速、如此有说服力地流入我们的产品和服务。我认为这很重要。至于 AGI，我认为我们正在按轨道前进；实际上，是按照我们 2010 年创办 DeepMind 时设想的轨道前进。
**English / Original**
And I think, we've made massive, improvements on that over the past year. And that's why you can see all the goodness of, of the AI models flowing through into our product services, you know, so quickly and in such compelling ways. So I think, I think that's big and, but yeah, in terms of AGI, I think we're, we're sort of on track and, actually we're sort of on track from where we thought back in 2010 when we started DeepMind.
## 00:03:21
**中文**
你知道，我的联合创始人 Shane 等人曾公开说过大约是 2028 年、2030 年。我们把它视为一项 20 年使命，我认为我们正按此推进。你认为在实现它之前仍缺少哪些能力？我认为还缺少几样东西。我们仍然有某种“参差不齐的智能”：它们在某些事情上好得惊人。
**English / Original**
You know, people like Shane, my co-founder, was on record saying around 2028, 2030 you know, we thought of it as a 20 year mission, and I think we're on track for that. What do you think the capabilities that are missing are still before we hit it yet? So I think there's still a few things missing. I feel like we still have kind of jagged intelligences in the sense that they're amazingly good at certain things.
## 00:03:43
**中文**
你在 Omni 之类的东西上看到了这一点。我觉得它有点令人震撼。现在我们对这一切有些麻木了，但你可以把它想成用于视频的 nano banana 的第一个版本。我认为像你这样的内容创作者会用它玩得很开心。不过，就它对物理和世界运行方式的理解而言，它并不完美；但如果停下来想想，已经相当令人震撼。
**English / Original**
You're seeing that with things like Omni. I think it's kind of mind blowing. We're a little bit desensitized to all of that now, but it's you can think of it sort of like nano banana for video, this first version. And I think content creators like yourself should have a load of fun with it. But yeah, in terms of, like its understanding of physics and, and and and the way the world works, it's not perfect, but it's pretty mind blowing if you stop and think about it.
## 00:04:07
**中文**
而这些都是我们设想的 AGI 应能做到的标志性能力。当然，这对机器人和眼镜等将极其重要。但另一方面，这些系统仍有很多做不好的事；在智能体方面你能看到这一点。实际上，在过去六个月里，我认为它开始真正对人们的日常生活很有用。
**English / Original**
And, those are all hallmarks of what we imagine an AGI to be able to do. And of course, is going to be amazingly important for things like robotics and glasses. But on the other hand, there's still a lot, the systems fail at still and, and you can see that in the agentic side of things, it's, it's starting to become, I think in the last six months, actually, genuinely really useful to people in their daily lives.
## 00:04:30
**中文**
但对普通用户而言，它仍然还不能完全做到“一设定就不必管”。我认为仍有可靠性问题，以及这些系统都会卡住的一些事情。所以需要解决一致性、持续学习等问题。朝这个方向仍需要一些突破。明白了。所以这算是一类研究问题，还是？
**English / Original**
But it's still not quite fire and forget for the average, you know, user. I think it's there's still some reliability and some things, these systems all get stuck on. So those things need to be, you know, consistency, continual learning. There's still some, breakthroughs that are needed in that direction. Gotcha. So this is like sort of a research problem or?
## 00:04:51
**中文**
是的。我们在这类问题上有大量研究。此外，我会说，任何系统的记忆都还不太对；它有点蛮力化，我认为需要更聪明的方案。还有一些领域，例如长期推理和规划；只剩少数几个但至关重要的领域，我仍认为在我们拥有能称为 AGI 的东西之前，必须取得突破。
**English / Original**
Yes. So we have a ton of research on things like that. Also memory still not quite right, I would say in any of the systems it's a little bit brute force, something smarter I think is needed there. So there's a few, you know, long term reasoning and planning. There's a few areas, critical areas, only a handful, that I still think, need to be cracked before we would have something like AGI that we could call AGI.
## 00:05:14
**中文**
是的。我的意思是，现在看到跨设备的个性化真的很酷。我想我们去年谈过这个。今年你们会在夏天随眼镜发布它。你有手表，它还能连接到手机。是的，Fitbit 和所有这些东西。它正慢慢汇聚在一起，但记忆还没有完全到位。
**English / Original**
Yeah. I mean, we're it's really cool to see the personalization across the devices now. I think last year we were talking about this. And this year you guys are releasing it in the summer with the glasses. You got the watches, and it connects to your phone. Yeah. The the Fitbits and all of these things. It's slowly coming together but the memory isn't like fully there.
## 00:05:30
**中文**
对，正是如此。所以它正在汇聚在一起。我认为我们展示的最令人兴奋的事情之一，是今年相较去年在眼镜上取得的进展，而且很快它就会到每个人手中。我自己也迫不及待想拥有它。这一直是我的梦想之一：有一个随你走到哪里都能帮助你的私人助理，协助你的日常生活，消除那些琐碎的行政事务。
**English / Original**
Yeah exactly. So it's coming together. I mean, I think one of the things that was most exciting we showed is the progress we've made with glasses this year compared to last year, and soon is going to be in everyone's hands. I can't wait to, you know, have that myself. It's one of the dreams I've always had a personal assistant that helps you with you everywhere you go and just helps you in your daily life, you know, get rid of mundane admin tasks.
## 00:05:51
**中文**
处理我的电子邮件就会很棒。这样你就有更多时间做一天中想做的事。我认为我们现在真的已经非常接近一些很有吸引力的成果。某种意义上，在我看来，通用数字助理这个想法就是眼镜这一形态一直在等待的杀手级应用。
**English / Original**
And email would be great. You know, dealing with my emails, but it's so you have more time to do what you know, what you want in the day. And I think we're really close now to how things, really compelling things. And in some ways, I think the this idea of a universal digital assistant, in my view, is, is the killer app that the form factor of glasses was always waiting for.
## 00:06:12
**中文**
你知道，Google 在眼镜方面当然有非常深厚且悠久的历史。但我认为这才是真正的杀手级应用，是我们一直在努力构建、如今已近在咫尺的东西，令人非常兴奋。然后，如果你愿意，它还可整合到所有其他设备、你的健康数据及此类信息中。
**English / Original**
You know, at Google, of course, has a huge history and long history in glasses. But but I think this is the actual, killer app. What we've been sort of building towards now and we're on the cusp of and it's very exciting. And then also integrating into all the other devices and your health, data and things like this if you want to.
## 00:06:30
**中文**
然后 Gemini 能帮助你处理这类事情。Gemini 订阅在这种意义上很有吸引力，对吧？因为它真正把一切都捆绑在一起。是的。这正是我认为 Google 全栈能力之美和力量的一部分：从芯片到模型，再到产品。
**English / Original**
And then Gemini being to help you, with those kinds of things. The Gemini subscriptions quite compelling in that way, right. Because it really has everything. Yeah. Bundled into one. And that's one of the, I think beauties and of of the power of the, the full stack we have at Google, right. You know, from the chips to the models to the, to the products.
## 00:06:50
**中文**
是的。但跳到另一个话题：我认为一年前我们谈过，你说 AI 可能在我们有生之年帮助治愈所有疾病。从那以后，你看到 AI 药物发现有哪些最大的进展？你的时间线改变了吗？我还是会说，时间线没有改变，但它们变得更确定了。
**English / Original**
Yeah. But jumping ahead here, I think a year ago, we spoke and you said that AI could help cure all diseases in our lifetimes. But since then, what are some kind of, like the biggest developments you've seen? In AI drug discovery have your timelines kind of shifted since then? Again, I would say they've not shifted, but they've hardened.
## 00:07:08
**中文**
它们收紧了。因此，可以说我现在对那些时间线更确信。我曾希望在未来一二十年内我们能在医学上取得这些重大进展；现在我非常确信——我不会说百分之百确定，但我非常有信心——会是这样。看看过去一年我们在 DeepMind 的 Gemini for Science 方面取得的进展，尤其是 Isomorphic Labs；我们正努力推动理解化学和生物化学所需的模型，即设计药物所需的专用模型。
**English / Original**
They've. They've tightened. So I'm more sure let's say about those timelines now. Right. I was hoping it would be in the next decade or two we'd make these big advances in medicine. Now I'm, you know, very sure. I wouldn't say I'm certain, but I'm, I'm I'm very, confident that is the case. Seeing the progress we made in the last year on on our Gemini for science side of things at DeepMind, but also especially at Isomorphic Labs, where we're really trying to, you know, push forward, the models, the specialized models that are required to understand chemistry and biochemistry, the kinds of things you'd need to design drugs.
## 00:07:46
**中文**
进展极其顺利。我们刚筹集了一大笔资金，以进一步加速。而且令人兴奋的是，我们现在已有一些测试化合物处于临床前阶段；这意味着它们开始有可能成为真正的疗法。我认为，那真正发生时将是 AI 的最大分水岭时刻。
**English / Original**
And it's going, amazingly well. We just raised a bunch of money to accelerate even further. And, you know, we have excitingly now, some, test compounds in pre-clinical stage, which is, you know, the beginning of them becoming real, real, cures. And I think that actually will be the biggest watershed moment in AI when that happens.
## 00:08:08
**中文**
那么你说“治愈所有疾病”时，是指一次一种疾病，还是有你认为会先出现的特定疾病？是的，我更多是指构建一个平台……把它想成 AlphaFold。但 AlphaFold 只是很小的一块；可能还需要大约六到十二项 AlphaFold 级别的突破。
**English / Original**
So when you say like cure all diseases, is it sort of one disease at a time or are there particular diseases you think come first? Yeah, I'm meaning more like building a platform... Think of it as AlphaFold. But AlphaFold is only one small piece, but, you know, maybe like half a dozen to a dozen other AlphaFold level breakthroughs.
## 00:08:25
**中文**
所以这并不容易。我们正致力于把所有这些整合为一个连续的平台：从感兴趣的靶点、你对疾病作用机制的理解，一直到你想在临床试验中测试的先导化合物。我说的是，将这通常需要约十年的过程压缩至数月，甚至数周。
**English / Original**
So it's not easy, you know, we're working on all those, all together into one continuous platform that goes from the target of interest to the way you think the disease is working all the way to a, lead compound that you want to test in clinical trials. So I'm talking about, sort of compressing that average of, you know, ten years it can take to do that down to months, maybe even weeks.
## 00:08:50
**中文**
当然，仍有临床试验阶段；在得到患者可以服用的实际药物之前，还有所有后续环节，这可能还要花 5 到 10 年。我认为 AI 也能帮助这一过程。但我真正说的只是药物发现引擎，即从疾病画像到一种需要随后测试的潜在疗法的那一部分。
**English / Original**
Of course, there's still the clinical trial stage, and there's all the downstream stuff before you get an actual drug someone can take that, can take another ten, 5 to 10 years. And I think I can help with that too. But I'm really just talking about the drug discovery engine, the bit where you go from the, you know, the disease profile to a potential cure, that then needs to be tested.
## 00:09:12
**中文**
并没有特定疾病；我希望它能用于任何类型的疾病。对，对。我其实注意到今天的主题演讲写着，癌症和免疫学优先？所以我们首先从这些领域开始：免疫学、肿瘤学。部分原因显然是，其中一些疾病如此严重；正因为疾病如此严重，临床这一侧的流程会更快。
**English / Original**
And there's no specific disease I hope it could work on any type of disease type. Yeah. Yeah. I actually noticed on the keynote today it said cancers and immunology first? So we're starting on those areas for first immunology oncology. Partly because obviously some of those things are so serious. It's, it's it's more, you know, the clinical side of that is quicker because they're such serious, diseases.
## 00:09:40
**中文**
在那方面，任何能提供帮助的事情都至关重要。你知道，我感到一种真正的需要；我一直觉得，这是我最先、也是最重要地想用 AI 做的事。这确实是不可思议的工作。我想接着问：当技术准备好之后，你如何看待普及化？
**English / Original**
Anything you can do to help is essential there. And, you know, I feel a real need to I've always felt that that's what I first and most importantly, wanted to do with AI. It's it's really incredible work. I guess the follow up question to that is like, after the technology is ready. How are you thinking about democratization?
## 00:10:01
**中文**
它是否只对富人可用？如何让每个人都拥有它？如何使它最有益于世界？我们在 Google 非常关心这些。显然，我们所有的产品都被使用——我不知道，现在大约有 13 个产品拥有十亿用户；Gemini 应用有 9 亿用户。因此，我们非常认真地对待让世界各地、每一个国家的每个人都能使用这项技术这一理念。
**English / Original**
You know, is it available just to the rich? How can everyone have it? How can we make it best for the world? We care a lot about that at Google. Obviously, you know, all our products are used by you know, there's I don't know, 13 products now billion user products, Gemini apps, 900 million. So we are really, take very seriously this idea of democratizing the technology to everyone around the world, in every country.
## 00:10:24
**中文**
Google 一直通过搜索、YouTube 和其主要产品这样做。这也是我们为什么花如此多时间开发 Flash 级别，甚至 Flash Lite 级别的模型：它们非常高效、性能很强，但也便宜、快速且可在任何地方使用。当然，我们自身内部对此也有巨大需求，所以才为所有产品推动这一点。
**English / Original**
And that's what Google's always done with search and and YouTube and its main products. And that's why we spend so much time on these flash level and even flash light level, very efficient, very performant models, but that are cheap and fast and can be used everywhere. Of course we have a big need for that ourselves internally, which is why we push that for all of our products.
## 00:10:44
**中文**
但我们也认为，开发者和构建者也会非常喜欢这种东西。好，让我们快进五年。比方说我们实现了 AGI，并治愈了一些重大疾病。你会把时间集中在哪里？下一项使命是什么？我想有几件事我会考虑。
**English / Original**
But we also think it's the sort of thing that developers and builders are going to really enjoy too. Okay, let's let's fast forward five years. Maybe let's say, and let's say we hit an AGI and we've cured some of the big diseases. Where are you focusing your time? What’s the next mission? Well, it's a couple of things I think I would be thinking about.
## 00:11:02
**中文**
第一，是自己使用 AI 工具，帮助我们理解我的总体科学目标，也就是现实的本质。这是我从小就一直痴迷的事。实际意味着做一些物理学和物理实验，但借助 AI。这是一件事。第二，我想我可能会稍微转向更多哲学主题，例如“作为人意味着什么”。
**English / Original**
One is, using the AI tools myself to help us understand my, my kind of overall scientific goal, which is the nature of reality. Right. That's always what I've been sort of obsessed with since a kid. And really, that means doing some physics and physics experiments, but with the help of AI. So that's one thing. Second thing I think is maybe I would pivot a little bit to more philosophical topics around, you know, what it means to be human.
## 00:11:28
**中文**
这有什么特别之处？我们希望社会如何改变、如何适应这场即将发生的难以置信的转型？当我们开始前往星际时，这对社会和人类处境意味着什么？所以，如果我能做到，我希望在这方面提供一些帮助。谈到人的意义，你对此有什么初步想法吗？
**English / Original**
What's special about that? How do we want society to change and adapt to this incredible, transformation that is going to happen as we, you know, start to travel to the stars and, you know, what does that mean about society and the human condition? So I'd like to maybe try and help out there if I can. Speaking about human meaning, do you have any, like, initial thoughts there?
## 00:11:49
**中文**
我知道你在这一领域钻得很深。你是否想过，AGI 之后人类意义还会剩下什么？我认为，在我看来，这一切仍有待书写。下一代人将原生地伴随着这些工具成长，就像我最初伴随着计算机和编程成长一样。
**English / Original**
I know you're very deep in the weeds here. Have you thought about, like, what's left for human meaning after AGI at all? Well, I think that's it's all still to be written in my view. You know, I think the next generation, they're going to be growing up natively with these tools, just like I did with computers and programing in the first place.
## 00:12:06
**中文**
然后我们创造了这个世界，我迫不及待地想看到今天的学生会怎么做。他们伴随这类工具、以及某种意义上的所有这些超能力成长，并把它与数学和计算机科学等传统技能结合起来；我认为至少在我能看见的未来十年，这仍然会相关，以便你能真正理解这些系统如何运行，从而最好地利用它们。
**English / Original**
And then we created this world that I can't wait to see what the students of today are going to do. Growing up with these sort of tools and all my superpowers in a way, and combining that with traditional skills in math and computer science, I think that's still going to be relevant, at least as far as I can see the next ten years, so that you can really understand how these systems work so you can take best advantage of them.
## 00:12:29
**中文**
然后，我认为将会创造出令人惊叹的新事物。我非常相信人类的聪明才智：我们几乎能立即适应。别忘了，我们自身就是通用智能。看看我们用采猎者的大脑在周围建造的一切，令人难以置信。为什么要止步于此？你知道，我们非常善于适应工具。
**English / Original**
And then, I think amazing new things are going to be created. I, I've got a lot of faith in human ingenuity where we're sort of instantly adaptable. We’re general intelligences ourselves, don’t forget. Look at what we built around us, it’s incredible, with our hunter gatherer brains. Why would we stop here? You know, it's like we're amazing at adapting to tools.
## 00:12:49
**中文**
看看我们看待今天工具的方式：这些我们今天惊叹不已的 AI 工具，仅仅几年前还是不可想象的；但今天我们已完全适应了它。哦，是的。你知道，我们与基本达到图灵测试水平的机器人聊天，也制作能创建任何视频的 Omni 模型，而我们的反应有点像：“是啊，这很正常。”
**English / Original**
Look at the way we we see even the tools we have today, these AI tools we're marveling at today, just a few years ago, that would have been unthinkable. But today we've just totally adapted to it. Oh yeah. You know, we chat with bots that are basically Turing test level, and we make Omni models that can create any video, and we're kind of like, yeah, you know, that's kind of normal.
## 00:13:07
**中文**
所以我认为，这就是人类适应能力好的一面，而且我确信未来也会如此。我喜欢用 Waymo 作类比：你第一次乘坐 Waymo，会在车里拍视频，觉得不可思议——对，无人驾驶汽车。但第二次乘坐时，就会想：“哦，这是不是已经有点正常了？”
**English / Original**
And so I think that's the good side of human adaptability. And I'm sure that will be the case. Going forwards too. It's kind of the Waymo analogy I like where like you try Waymo once and you know, you're taking a video in the car. And it's insane. Yeah. Driverless car. But then you do it a second time and it's like oh is this kind of normal.
## 00:13:27
**中文**
奇怪的是，它变得正常了，对吧？现在这些 AI 工具也是如此。但同样，这正是一个标志：因为我们的大脑是通用的，而且极为通用，尽管它们是为特定目的——采猎——而进化的。但我们是制作工具的物种，这也是我们与其他动物的区别。
**English / Original**
It's weird that it's normal. Right. It's the same with these AI tools now. But again, that's the sign. That's because our brains are general And so general, even though they were evolved for a particular purpose, you know, Hunter gathering. But we’re a toolmaking species. And that's what separates us from the other animals.
## 00:13:43
**中文**
我认为，这把我们带到了今天的现代文明，而 AI 只是下一步。随着 AI 能力越来越强，你认为哪些人类技能会变得更有价值？我认为以这种方式看，至少短期内非常清楚；至于十年之后，则更难预测。
**English / Original**
And I think that's, led us to where we are today with modern civilization. And I think AI is just the next step. What human skills do you think get more valuable as AI increasingly gets more capable? Well, I think it's very clear in this way that certainly in the short term it's harder to predict out, you know, ten years from now.
## 00:14:00
**中文**
但我会说，在未来五年，品味、设计感、原创思维、能把不同学科综合在一起的能力，以及知识和技能上的广博视野，对我而言都非常明显。我认为那些兼具创造力和技术能力的人——像你这样的人——将处在极其有利的位置。
**English / Original**
But I would say in the next five years, it's very obvious to me that taste, design sensibility, original thinking, being able to synthesize different subjects together, I think being quite broad minded in your knowledge and skills, I think it's going to be amazing people who are kind of creative and technical, people like yourself, I think, are going to be in an amazing position.
## 00:14:24
**中文**
拥有这些技术工具，同时把它们与创造力结合起来，无论在艺术形式还是科学形式中。你知道，我一直说，伟大科学家与优秀科学家的区别不在技术能力，因为如果你是职业科学家，技术上显然已经非常出色；区别在于创造能力、那种品味和判断力。
**English / Original**
With these technical tools, but then combining it with creativity, whether that's in, in, in the arts form or the science form. You know, I've always said what separates the great scientist from the good scientist is not their technical capability. Because if you're professional scientist, you're obviously very good technically, it's that creative capability, that sort of taste and judgment.
## 00:14:44
**中文**
我认为，这也将处于前沿：无论你是在构建产品、制作电影还是游戏，如何真正与为之创作的其他人产生情感连接？那不会来自工具或 AI 工具。我认为那将来自人类创作者和人类专家。
**English / Original**
And I think, that's going to be at the fore also, connecting with your audience, whether you're building a product or making a film or game, you know, how do you really connect emotionally with the other human beings that you're making this for? That isn't going to come from the tool, the AI tools. I think that's going to come from the from the human creator and the human expert.
## 00:15:04
**中文**
对。所以是品味和判断力，以及继续学习所需的技能。是的，将两者结合。是的，因为你越深入地理解技术，就越能更好地利用它。把这一点放在最前面。好，我们时间快到了。但我想问的一件事是：新闻周期非常聚焦于智能体和 AI，不过近期有没有什么你一直在想、却被低估了的特定事情？
**English / Original**
Right. So taste and judgment and continuing to learn the skills, the skills needed. Yeah. Combining the two. Yes. Because the deeper you understand the technology, the more you're going to better take advantage of it. And keep that at the forefront. Right. All right. So we're almost out of time. But the one thing I want to ask you is the news cycle is very much focused around agents and AI, but is there anything specific that you think is just been on your mind recently that is under hyped?
## 00:15:32
**中文**
我对这个新的智能体时代非常兴奋。你可以看到我们正全面投入其中。整个 I/O 实际上都围绕这个主题，对吧？有 Spark、Antigravity 和新的 Flash。但当然，我们也必须考虑其中的安全层面。我认为我们特别擅长的一件事，是把真正令人惊叹的前沿能力与可靠性和稳健性结合起来，并把它们连接到人们已经熟悉和喜爱的产品生态系统。
**English / Original**
Well, I think we got a I'm very excited about this new agentic era. And you can see us leaning into that. The whole of I/O is really about that, right? With spark and Antigravity and the new flash. But of course, we've also got to think about the security side of that, too. You know, that's one of the things I think we can do really well is combine really amazing frontier capabilities, but with reliability and robustness as well as connecting to, you know, the ecosystem of products people already know and love.
## 00:16:00
**中文**
所以我认为这是下一阶段。但我确实担心：你已经在一些模型的网络安全担忧中略微看到了这一点。我认为这只是我们需要确保防范的一些问题的开始；与此同时，我们也应为所有惊人的机会感到兴奋。
**English / Original**
So I think that's the next phase. But I do worry about, you know, you're seeing it a little bit with the cyber worries about, some of the models. And I think that's just the beginning of some of the issues that we need to get that we need to make sure we guard against, as well as getting excited about all the amazing opportunities.
## 00:16:20
**中文**
所以这多少萦绕在我心头。也许现在是时候努力推动一些标准，以及也许推动国际合作了。非常感谢你的时间。交谈总是很愉快。是的，一如既往，很高兴和你交谈。谢谢你做这期节目。谢谢。
**English / Original**
So I think that's playing a little bit on my mind. Maybe this is the time now to, you know, to try and push some standards and maybe international cooperation. Well, thanks so much for your time. It's always pleasure. Talking. Yeah. Lovely talking to you, as always. Thanks for doing this. Thank you.