Why AGI Is Bigger Than the Industrial Revolution

"I actually wanted to start on AGI. Definitions are very varying. You've been very thoughtful about what it means to you. Can you explain to me how you think about it today so we get that as a kind of ground center?"

"我其实想从 AGI 开始聊。这个词的定义五花八门,而你一直在很认真地思考它对你意味着什么。今天能不能先讲讲你怎么理解它?这样我们好有个共同基准。"

"Yeah, well, we've always defined — we've been very consistent how we define AGI as basically a system that exhibits all the cognitive capabilities the human mind has."

"嗯,我们一直把 AGI 定义得很一致——本质上就是一个具备人脑所有认知能力的系统。"

"And that's important because the brain is the only existence proof we have that we know of in maybe in the universe that general intelligence is possible."

"为什么这个定义重要?因为大脑是我们目前所知的、可能在整个宇宙里都唯一的'通用智能确实可能'的存在性证明。"

"So that for me is the bar for what AGI should be."

"所以对我来说,这就是 AGI 应该达到的标杆。"

"It's the worst question — how close are we? Everyone says different things and it's very difficult when you have very prominent figures saying it could be as early as 2026, 2027."

"我知道这是最糟糕的一个问题——我们离它有多近?每个人说法都不一样,挺难判断的——尤其是有些非常重要的人会说,2026、2027 年就有可能。"

"I mean, look, I've got a probability distribution around the timings, but I would say there's a very good chance of it being within the next 5 years. So that's not long at all."

"嗯,我心里对时间是有一个概率分布的——但我会说,5 年之内出现 AGI 的概率非常大。所以并不远。"

"Is that closer than you thought? Has that changed over time?"

"这比你原本预期的更近吗?这个判断这些年有变化吗?"

"Not really. It's funny — my co-founder Shane Legg, who's chief scientist here, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting about when AGI would happen."

"其实没什么变化。说起来挺有意思——我的联合创始人 Shane Legg(肖恩·莱格),也是我们的首席科学家,2010 年我们刚起 DeepMind 那会儿,他写过几篇博客预测 AGI 会在什么时候到来。"

"Bearing in mind in 2010 when we started, almost nobody was working in AI and everyone thought AI basically didn't work — it was a dead end."

"要记得,2010 年那时候,几乎没人在做 AI——所有人都觉得 AI 根本不 work,是一条死胡同。"

"They're still there on the internet for people to check. We used to do this extrapolation of compute and algorithmic progress, and basically we predicted around 20 years it would take from when we started out, and I think we're pretty much on track."

"那些博客现在网上还能查到。我们当时是基于算力和算法进展做外推,基本上预测 AGI 大概要从那一年起再 20 年。现在回头看,我们差不多还在那条线上。"

"What are the biggest bottlenecks when you look today? In the documentary you said you just never have enough compute. What are the biggest bottlenecks when you look at where we are today?"

"今天看,最大的瓶颈是什么?在那部纪录片里你说过,你永远不嫌算力多。今天的瓶颈具体是什么?"

"I think compute is the big one — not just for the obvious reason of scaling up your ideas and your systems, as the scaling laws as they're called keep on building bigger and bigger architectures with more and more parameters."

"算力是最大的瓶颈——不仅仅是大家熟悉的那个原因,也就是 scaling laws(扩展定律)那一条:把架构越搭越大、参数越加越多。"

"And as you do that, you get more intelligent systems."

"沿着这条路确实能得到更聪明的系统。"

"But the other thing you need a lot of compute for is for doing experiments. So the cloud is our workbench, basically."

"但还有一件你需要大量算力的事——做实验。云端基本上就是我们的工作台。"

"So if you have a new idea, a new algorithmic idea, but you want to test it, you've got to test it at a reasonable scale, otherwise it won't hold when you actually put it into the main system."

"如果你有一个新想法、一个新的算法 idea,要验证它,你必须在足够大的规模上测——否则一旦把它装回主系统里,它就站不住。"

"So you need quite a lot of compute if you have a lot of researchers with lots of new ideas."

"所以,只要你的研究员多、想法多,你就一定需要大量算力。"

"You mentioned the word scaling laws. A lot of people suggest that we're hitting scaling laws and we're starting to see that plateauing effect. Do you think that's true?"

"你提到 scaling laws。现在很多人说我们已经撞到 scaling laws 的墙了,开始看到平台化效应——你觉得这个说法对吗?"

"No, I don't think so. I think it's a bit more nuanced than that."

"我不同意。这个事比'撞墙'要复杂一点。"

"When the leading companies all started building these large language models, you're getting enormous jumps with each generation of new system — maybe they're almost like doubling in performance."

"当头部公司刚开始做这些 LLM 的时候,每代新系统都是巨大的跳跃——几乎是性能翻倍。"

"At some point that had to slow down, so it's not kind of continuing to be exponential."

"这种翻倍迟早要慢下来,所以现在它不再是纯粹的指数增长了。"

"But that doesn't mean there isn't great returns still for scaling the existing systems up further."

"但这不代表继续扩大现有系统就没有显著回报了。"

"Yeah, we and the other frontier labs are getting a lot of great returns on that kind of compute expansion."

"我们和其他前沿实验室在算力扩张上都还在拿到很大回报。"

"So I would say the returns are kind of still very substantial, although they're a bit less than they were obviously at the start of all of this scaling."

"所以我会说,回报仍然非常可观,只是相比这一轮 scaling 最早期那种水平,确实是少了一点。"

"Where are we behind where you thought we would be?"

"哪些地方我们其实落后于你原本的预期?"

"I think actually in most areas we are ahead of where I thought we would be."

"其实在多数领域,我们都跑在我原本预期的前面。"

"If you think about things like the video models or even now with our newest systems like Genie, they're interactive world models, which I think is kind of incredible if you sort of step back and think about it."

"想想视频模型,或者我们最新的 Genie 这种系统——它们其实是交互式的 world models(世界模型)。退一步看,这件事相当不可思议。"

"If you'd shown me that 5, 10 years ago, I would have been pretty amazed."

"如果你 5 年、10 年前给我看这种东西,我会非常震惊。"

"So I think in most domains we are ahead of where the field thought."

"所以多数领域,我们其实跑在整个领域的预期前面。"

"There's still some big things missing though, like continual learning. These systems don't learn after you finish training them, after you put them out into the world. They're not very good at learning further things, and I think some critical capabilities are lacking."

"不过还是有几样大东西没解决——比如 continual learning(持续学习)。这些系统训练完、上线之后,就不再学习新东西了。它们在'继续学'这件事上很差,我觉得这是一些关键能力的缺位。"

"I'm sorry to ask basic questions — why do we not have continuous learning?"

"不好意思问个基础问题——我们为什么没有持续学习?"

"Well, people haven't quite figured out yet — and all the leading labs are working on this — how to integrate new learning into the existing systems that you spent months training."

"目前大家还没完全搞清楚——所有头部实验室都在做这件事——怎么把新的学习,无缝接入到那个你已经花了几个月训练好的系统里。"

"Of course the brain does this very elegantly, right? Probably through things like sleep, reinforcement learning."

"大脑做这件事其实很优雅——很可能是通过睡眠、强化学习这一类机制。"

"You just kind of get consolidation, it's called in the brain, where your memories during the day are replayed, and then some of that information is elegantly incorporated into your existing knowledge base."

"大脑里管这个叫 consolidation(巩固)——白天的记忆会被重放(replay),其中一部分信息就被很优雅地融进你已有的知识库里。"

"I've thought for a while maybe we need something like that to incorporate new information along with the existing information base."

"我有一段时间一直在想,我们的模型也许需要类似的机制——让新信息能跟已有的信息基底一起被吸收。"

"I think there's quite a few things that are missing. There's continual learning. I think a lot of mileage in looking at different memory systems."

"缺的东西不止一件。一个是持续学习。另一个我觉得很有戏的是——重新看 memory systems(记忆系统)的各种方案。"

"At the moment we have these long context windows which are kind of a bit brute force. You just put everything in them."

"现在我们用的是长 context window,这有点暴力——把所有东西都塞进去就是了。"

"And then there's stuff like long-term planning, hierarchical planning. These systems are not very good at planning at long time horizons, many years into the future, which we — with our minds — we can do."

"还有像 long-term planning(长程规划)、hierarchical planning(分层规划)。这些系统在长时段规划上很差——比如规划未来几年——而我们的大脑是能做的。"

"Maybe one of the biggest is consistency. I sometimes call these systems jagged intelligences because they're really amazing at certain things when you pose the question in a certain way, but if you pose a question in a slightly different way, they can actually still fail at quite elementary things."

"也许最大的缺口是一致性(consistency)。我有时候叫现在这些系统'参差智能'(jagged intelligences)——因为某些问法下它们极其厉害,但你稍微换一种问法,它们就在非常基础的题上翻车。"

"A general intelligence shouldn't be that sort of jagged."

"真正的通用智能不该是这种参差不齐的样子。"

"When you reposition files and you set up agents to perform in certain ways, and then the files no longer configure — that's a disaster."

"比如你把文件重新组织一下、把 agent 配置好让它做某件事,然后文件结构一变它就配不上了——这种情况就是灾难。"

"100%. The general intelligence — if you think about how our minds work — it shouldn't have those kinds of holes in it."

"100%。真正的通用智能——你想想我们的大脑是怎么工作的——不应该有这种坑。"

"You mentioned video models, you mentioned media and image. It seems that DeepMind has progressed very quickly and caught up / overtaken other providers."

"你刚才提到视频模型、媒体、图像——感觉 DeepMind 进步非常快,已经追上、甚至在某些方面超越了其他厂商。"

"DeepMind now is my number one for research for new shows. It wasn't that way before. What has led to the acceleration and progression of DeepMind in a way that it wasn't maybe there 2 to 3 years ago?"

"我现在做新节目的调研都是 DeepMind 排第一,以前不是这样。是什么让 DeepMind 这两三年加速、进入到一个之前没有的状态?"

"Well, we made some organizational changes. I think we've always had the deepest and broadest research bench at Google and at DeepMind."

"我们做了一些组织上的调整。我一直认为,Google 和 DeepMind 合在一起拥有这个领域最深、最宽的研究板凳。"

"If you look at the last decade or 15 years, I would say about 90% of the breakthroughs that underpin the modern AI industry were done either by Google Brain or Google Research or DeepMind, so one of our groups."

"看过去十年、十五年——支撑现代 AI 产业的突破里,大约 90% 来自 Google Brain、Google Research 或 DeepMind 这三组中的某一个。"

"If you think of like AlphaGo and reinforcement learning and of course transformers, you know, these are all the key breakthroughs."

"想想 AlphaGo、强化学习、当然还有 Transformers——这些都是关键突破。"

"So I would back us to make those breakthroughs in the future, if there are any missing ones."

"所以我会押:剩下还没出现的突破,我们来做这件事的概率仍然很高。"

"I think we've basically helped put together all the talent from around the company sort of pushing in one direction."

"我们做的,就是把公司里散落的所有这些人才,集合起来朝一个方向用力。"

"And then we talked earlier just about compute resources. It was also about combining all of our resources together so we could build the biggest models rather than having two or three versions around the company."

"再加上前面聊过的算力资源——我们把所有资源合并到一起,这样能造一个最大的模型,而不是公司里同时有两三个版本在分散资源。"

"So I think a lot of it was assembling together all the ingredients we already had and then kind of pushing with relentless sort of focus and pace, acting almost like a startup, really, to get back to the frontier and be ahead in many areas."

"所以很大一部分,就是把我们已经有的所有原料拼回到一起,然后用接近创业公司的那种死磕节奏、专注度往前推——重新回到前沿,在很多方向上拿到领先。"

"Everyone talks about a commoditization of models in terms of capabilities. Do you think we see that, or do you think we see ones continuously accelerate ahead of the others?"

"大家都在讨论模型能力的商品化(commoditization)——你觉得这个事会发生吗?还是说会有头部模型持续加速、把其他人甩开?"

"I feel like maybe the three or four leading labs now, which we're one, I think the gap is starting to pull away."

"我感觉现在的三四家头部实验室——我们是其中之一——和后面的差距正在被拉开。"

"A lot of these tools also help you build the next generation, things like coding tools, math tools."

"原因是这些工具本身——比如 coding tools、math tools——已经在帮你造下一代模型。"

"And it's getting harder and harder, I would say, to eke out the same gains from just the same ideas."

"另一方面,光靠老想法去抠出同样大小的收益,正在变得越来越难。"

"So I think those labs that have capability to invent new algorithmic ideas are going to start having bigger advantage over the next few years as the set of ideas — all the juice has been rung out of them."

"所以未来几年,真正能'发明新算法'的实验室会拉开越来越大的领先——因为现成想法的红利,差不多被榨干了。"

"I'm intrigued — you were very open with a lot of your research for years and we see many very good quality open models. How do you think about the future of open?"

"我挺好奇——这些年你们对自己的研究一直很开放,而我们也确实看到了很多质量很高的开源模型。你怎么看开源的未来?"

"I have many portfolio companies that use frontier models, set a benchmark, then use open models to get as close as possible but with more cost effectiveness."

"我投的很多 portfolio 公司都是这套路:用前沿模型先定一个 benchmark,再用开源模型尽可能接近这个 benchmark,但成本更低。"

"I think it's probably similar to what we're seeing today. We're big supporters of open science and open models — we've done many things from the original transformers to AlphaFold."

"我想未来跟现在差不多。我们一直是开放科学、开放模型的大支持者——从最早的 Transformers 到 AlphaFold,这些东西我们都开放出去过。"

"These are all things we've sort of given out into the world to help the research community, and we plan to continue to do that especially in applied domains, scientific domains applying AI to science, which is obviously my passion."

"这些都是我们交给世界、帮助研究社区的东西,我们打算继续这样做,尤其在应用领域——把 AI 用到科学上,这是我个人的热情所在。"

"But I think increasingly what you're going to see is the open source models are probably one step back from the absolute frontier."

"但越来越能看到的趋势是:开源模型大概会在'绝对前沿'后面差一档。"

"It usually takes about 6 months for the open source community to sort of re-implement and figure out what those ideas are."

"开源社区把前沿的想法重新实现、搞清楚通常需要大概 6 个月。"

"But we are also pushing hard on a kind of suite of open source models called Gemma which we're determined to make best in class for their sizes."

"但我们自己也在猛推一套开源模型,叫 Gemma——我们要把它做成同尺寸里最好的。"

"Specifically for small developers or academics or the beginnings of a startup. I think they're perfect for that, and also edge computing too. So we're very interested in open source models for certain types of applications."

"特别是给小开发者、学界、初创公司起步阶段用——Gemma 很合适。还有 edge computing。所以对某些类型的应用,我们对开源模型非常感兴趣。"

"How do you think about a world post LLMs? You have different views from people like Yann LeCun."

"你怎么看 LLM 之后的世界?你和 Yann LeCun(杨立昆)在这个问题上的看法明显不一样。"

"I kind of disagree with Yann on a few things. I think there might be — there's a 50/50 chance there's some things maybe missing that we still need to make breakthroughs in, perhaps world models, these kinds of approaches."

"我和 Yann 在几件事上意见不同。我觉得——有 50/50 的概率,还有一些东西缺失、需要做新突破——也许是 world models 这一类路线。"

"But my betting is pretty strongly: we've seen how successful these foundation models have been. They can do incredibly impressive things. I don't think that's going to go away."

"但我的下注很明确:我们已经看到了 foundation models 多成功——它们能做的事情极其惊人。我不觉得这条路会被抛弃。"

"We're still seeing gains, returns from the scaling laws."

"我们也还在看到 scaling laws 的回报。"

"So I think the only question really, when you think about a future AGI system, is: is an LLM foundation model going to be the key component only, or is it the total system?"

"所以真正的问题其实是:未来的 AGI 系统里,LLM 这个 foundation model 是其中一个关键组件,还是整个系统就是它?"

"I just think it's a question of, is there anything else needed — not is it going to get replaced. I don't think it's going to get replaced. I think it's going to get built on top of these foundation models, just like the way we do with our world models."

"我觉得问题只是'还需要别的什么'——而不是'LLM 会不会被替代'。LLM 不会被替代,会有东西被叠加在它之上——就像我们 DeepMind 的 world models 那样。"

"When we think about that future 5 years out as you said potentially with AGI, what does that world look like to you?"

"想想 5 年后那个可能已经有 AGI 的世界——在你眼里它是什么样?"

"On the positive side — and the things obviously I've spent my whole career and life building towards AGI — is I think it will be the ultimate tool for science and medicine."

"先说正面——这也是我整个职业生涯、整个人生在朝着 AGI 努力的原因——它会是科学和医学的终极工具。"

"In terms of advancing scientific discovery, finding cures to diseases, I think we need that kind of technology, and so I'm hoping in 5 years plus time we'll be entering a new golden age of scientific discovery."

"在推进科学发现、找出疾病治愈方法这件事上,我们需要这种量级的技术。希望 5 年多以后,我们会进入科学发现的一个新黄金时代。"

"My mother's got multiple sclerosis, so it's the thing that I'm always most excited about."

"我妈妈有多发性硬化症(MS),所以这个方向是我一直最兴奋的事。"

"The thing I worry about is actually drug discovery, the process of getting it through all the trials and knowing that it takes a decade before my mother will actually get any benefits from it."

"我真正担心的是药物研发那条链——从研发走完所有临床试验,得花十年——而我妈妈要等十年才能真正受益。"

"How do we solve that?"

"这个问题怎么解?"

"I think we'll get to that point soon. After we did the AlphaFold project to do protein folding, then we spun out a company called Isomorphic Labs, which is doing extremely well."

"我觉得我们快到那个点了。在我们做完 AlphaFold(蛋白质折叠)项目之后,我们孵化出了一家公司,叫 Isomorphic Labs,做得非常好。"

"The idea there is we're focusing on solving the rest of the drug discovery process — a lot of chemistry, designing the compounds, checking it's not toxic and all the different properties you need for drugs to be safe."

"它的思路是把药物研发剩下那一大块解决掉——大量化学、化合物设计、毒性检查、所有让药变安全所需的属性。"

"I think we'll have that whole drug design engine ready in the next 5 to 10 years."

"我估计未来 5 到 10 年,我们能把整个药物设计引擎做出来。"

"Then you're right — the next problem is the clinical trials still take many many years."

"然后下一个问题就是你说的——临床试验本身还要好多年。"

"But I think AI can help there in terms of maybe simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that's suitable for their genomic makeup."

"AI 在这一段也能帮上忙——一方面是模拟一部分人体代谢过程,另一方面是给患者做分层(stratification),确保特定病人拿到的就是匹配他基因组的那个药。"

"The real revolution will come when a dozen or so AI drugs get through the whole process and then the government and the regulatory body see that and they have enough data to back-test the predictions of those models."

"真正的革命发生在大概有十几款 AI 设计的药走完整套流程之后——监管机构看到这些数据,有足够样本去 back-test 这些模型的预测。"

"Then maybe 10 further years where we can really just trust the predictions that the models are making and actually then maybe skip out some steps perhaps like the animal testing is not needed anymore."

"再过大约 10 年,我们就能真正信任这些模型的预测——也许有些步骤可以跳过,比如不再需要动物实验。"

"Maybe we can go up the dosage ladder quicker because you can rely on these models. So I think we've got to do it in two steps. Solve the drug design problem first, and then look at the regulatory length of time it takes."

"剂量爬坡也可以加快,因为你信得过模型。所以这件事必须分两步走——先解决药物设计本身,再去解决监管耗时。"

"AI safety is a big topic and a big concern. I watched the documentary last night — Stephen Hawking said we must get it right because we might not get another chance. Do you think that's right?"

"AI 安全是个大话题、大担忧。我昨晚看了那部纪录片——Stephen Hawking 说过,我们必须把这件事做对,因为可能不会有第二次机会。你同意吗?"

"Yeah, I do think that's right. I think that is the stakes that we have to deal with."

"是的,我同意。我们要应对的赌注就是这个量级。"

"There's two things I worry about. One is the misuse of these systems by bad actors, and they can be repurposed. These are dual purpose technologies."

"我担心两件事。第一是坏行为者(bad actors)滥用这些系统——这些系统是可以被改用途的,本质是 dual-use 双用途技术。"

"They can be used for incredible good in science and health as we just discussed, but they can also be repurposed for harmful ends by a bad actor. So that's one issue."

"它们可以在科学、健康上做出惊人的好事——也可以被坏人改用途去做有害的事。这是第一件事。"

"Second issue is a technical one — making sure these systems as they get more powerful, not today's systems but maybe in a year or two's time when they become more agentic, more autonomous as we get towards AGI, can they be kept on the guardrails that we want?"

"第二件是技术上的——这些系统变得越来越强、一两年后越来越 agentic、越来越自主、靠近 AGI 时,它们能不能被关在我们设定的护栏里?"

"I think regulation, the right kind of regulation, could help here in terms of making sure there's at least minimum standards from all of the leading providers, but it needs to ideally be international standards."

"我觉得正确的监管能在这上面帮忙——至少让所有头部厂商守住一组最低标准。但理想情况下,它必须是一套国际标准。"

"What is the right kind of regulation? You said in the documentary, 'I think we need more global coordination,' which worries me — because we're getting worse at it."

"具体什么是'正确的监管'?你在纪录片里说'我们需要更多全球协调'——这话让我担心,因为我们在协调这件事上正越做越差。"

"Yes, for sure. It's sort of crazy the timing that we're in — with this most consequential maybe technology the world's ever seen at the same time as a very fragmented international system."

"是的,完全同意。我们身处的这个时间点其实很疯狂——可能是世界有史以来后果最深远的一项技术,偏偏撞在国际体系最碎片化的时候。"

"It's not ideal but I think we're going to have to try and do the best we can to at least come up with a sort of set of maybe minimum standards, some benchmarks that test for undesirable properties — for example, deception."

"这不是理想状态,但我们必须尽全力——至少制定一组最低标准、一组用来检测'不想要的属性'的基准。举个例子,比如欺骗(deception)。"

"Nobody should be building systems that are capable of deception because then they could be getting around other safeguards."

"没人应该造出会主动欺骗的系统——一旦它能欺骗,它就能绕过其他所有安全机制。"

"And then I imagine, if things go well, some kind of certification process — almost like a kite mark of quality — that this model has certain safeguards and certain guarantees and so therefore consumers and companies can safely build on top of it."

"接下来,如果这件事顺利,会出现某种认证流程——几乎像一种 kite mark(英国质量认证标志)——表明这个模型具备特定的安全机制和保证,消费者和公司可以放心在它之上构建。"

"It does have to be international because of course these systems are cross-border, they're cross-territory."

"这件事必须做成国际化的——这些系统本身就是跨国境、跨辖区的。"

"If I could give you a magic wand only applicable to AI safety, what would be your implementation idea program?"

"如果给你一根只能用在 AI 安全上的魔法棒,你会落地什么方案?"

"I think we need some kind of international body maybe similar to the atomic agency, something like that — that perhaps the AI Safety Institutes feed into, and the research community has to be involved."

"我觉得我们需要一个类似国际原子能机构(IAEA)的国际机构——各国的 AI Safety Institute(AI 安全研究所)往里面汇,研究社区也必须参与。"

"Maybe there are other safeguards too — like it wouldn't be desirable to have AI systems output tokens that are not human readable, in some kind of machine language we couldn't understand. I think that would introduce a new vulnerability."

"可能还有其他防护——比如,不应该让 AI 系统输出人类读不懂的 token、自创一种我们看不懂的机器语言。这会引入新的漏洞。"

"Most of the leading labs would agree probably not best to do, and then these institutions would test against those things. I think that would give the public confidence."

"大多数头部实验室应该都会同意'这件事最好别做',然后这些机构就在这些点上做测试。我觉得这能给公众信心。"

"Academia could be involved as well, as well as civil society, that these systems which are going to get incredibly powerful have been independently audited."

"学术界、公民社会都可以参与进来——确保这些将会非常强大的系统经过了独立审计。"

"It's one of the biggest concerns — labor displacement. I just had Mark Andreessen on the show and he said I was a Marxist for bringing it up. He said it's completely rubbish — we've always overcome it. How do you think about labor displacement when you look at how truly capable these systems are?"

"这是最大的担忧之一——劳动力替代(labor displacement)。我前几天请了 Mark Andreessen(马克·安德森)上节目,他说我提这个就是马克思主义者,说这完全是胡扯,人类每次都能克服。你怎么看?这些系统真的太强了。"

"In the past with every new revolutionary technology there's been a lot of jobs disruption. So that's for sure. A lot of old jobs go away or are not viable anymore."

"历史上每一项革命性技术都伴随大量岗位失踪——这一点确认无疑,很多旧工作要么消失、要么不再可行。"

"But then actually the history of it is that a whole set of new jobs arrive that maybe one can't even imagine before, and those are high quality higher paying. So that's the normal course."

"但历史经验也是——总有一整套你之前根本想象不到的新工作出现,而且这些新工作通常质量更高、工资更高。这是常态。"

"Of course, you have to be very careful to say this time is different. And I guess that's what people like Mark are claiming — that it's the same as the last 10 massive breakthroughs like the internet, mobile and so on."

"当然,'这次不一样'这种话要非常小心。Mark 这一派的论点就是:这次跟前面 10 次大突破——互联网、移动等等——没区别。"

"I do think this is going to be bigger than all of those previous breakthroughs technological breakthroughs."

"但我确实认为,这次比之前那一连串技术突破都要大。"

"I sometimes quantify like the coming of AGI is like 10 times the industrial revolution at 10 times the speed. So unfolding over a decade instead of a century."

"我有时候这样量化:AGI 的到来 = 10 倍工业革命 × 10 倍速度。也就是说,展开周期从一个世纪压到十年。"

"I've been reading a lot about the industrial revolution. There's a lot of great books about it, and that caused a huge amount of upheaval as well as a lot of advances."

"我最近读了大量关于工业革命的书——它带来巨大的社会动荡,同时也带来巨大的进步。"

"I mean, we wouldn't have modern medicine today. Child mortality was at 40% pre-industrial revolution. So things you wouldn't want it not to have happened."

"没有工业革命就没有现代医学。工业革命前儿童死亡率高达 40%——你绝不会希望这件事没发生。"

"But ideally this time around we mitigate some of the downsides a bit better than we did during the industrial revolution."

"理想状态是,这一次我们能把'下行面'比工业革命那时候处理得稍微好一点。"

"With the concern around labor markets, there's also a concern around income inequality and the concentration of wealth of a few players. How do you see that shaping out?"

"除了劳动力市场之外,大家还担心收入不平等、财富集中在少数玩家手里。你怎么看这个走向?"

"I think there's different ways that could play out. Maybe pension funds should be buying into all the big AI companies and making sure that everyone has a piece of that, or sovereign funds. Maybe every country should have a sovereign wealth fund that does that."

"有几种走法。也许养老基金应该买入所有大型 AI 公司,让每个人都分一份;或者主权基金来做这件事——每个国家都应该有一只这样的主权基金。"

"That would be the sort of investment way of doing it."

"这是用投资的方式去做。"

"I think also there needs to be thought about, if there is this massive productivity gain but it's narrow where that accrues, how do we redistribute and how do we distribute that so that everyone benefits from these huge gains."

"另外要考虑的是——如果出现巨大的生产力提升,但收益只集中在很窄的一群人手里,我们怎么再分配,让所有人都受益于这些巨大增量。"

"I can see all sorts of ways that could be done including providing infrastructure and other things with that additional productivity gain."

"我能想到很多做法——包括用这些额外的生产力提升去建公共基础设施和其他东西。"

"How do we solve the energy crisis that comes with an AI revolution? The energy requirements are unprecedented."

"AI 革命会带来史无前例的能源需求——这个能源危机怎么解?"

"I think actually AI will in the medium to long run more than pay for itself, in terms of energy costs."

"我反而觉得,中长期看,AI 在能源成本上不只是自给,而是净贡献为正。"

"We work on all these projects of optimizing existing infrastructure like optimizing the grid. I think we could probably get 30-40% more efficiency out of our national grids."

"我们在做大量优化现有基础设施的项目,比如电网。我估计 AI 能从国家电网里再榨出 30-40% 的效率。"

"And then there's modeling the climate and weather — we have all sorts of the best kind of weather modeling systems in the world. So that helps us work out where the effects are really happening to mitigate that."

"另外是气候和天气建模——我们有世界上最好的几套天气建模系统。这能帮我们看清气候影响到底发生在哪,从而对症下药。"

"And then finally, the most exciting maybe is these new breakthrough technologies like fusion, like new batteries, superconductors that I think AI will be essential for helping us reach."

"最让人兴奋的可能是这些突破性新技术——核聚变、新电池、超导——AI 会是帮我们抵达这些突破的关键。"

"Then I think we'll be in a completely new energy situation than we've ever been as humanity, and that will help with the climate and environment and eventually also help us get into space much more cheaply."

"那时候,人类会进入一个完全前所未有的能源状态——这会帮上气候和环境,最终也能让进入太空的成本大幅下降。"

"Because if you have an incredible energy source like fusion, then you have effectively unlimited rocket fuel because you can just distill / catalyze seawater."

"因为如果你有像核聚变这种超级能源,你基本上就拥有了无限火箭燃料——直接蒸馏或催化海水就行。"

"You're in London. I'm in London. You have been pushed to move to the US. Why have you stayed?"

"你在伦敦,我也在伦敦。这些年应该有人一直怂恿你搬去美国吧——你为什么留下了?"

"I saw in London when we started DeepMind a place that — and the UK in general and Europe to some degree — there's incredible talent here."

"我们启动 DeepMind 的时候,我在伦敦——以及整个英国,某种程度上整个欧洲——看到了一个有惊人人才的地方。"

"We've always had three or four of the top 10 universities in the world with Cambridge and Oxford, Imperial, UCL — these kind of universities. So we're producing the envy of the world really, these amazing graduates and PhD students."

"我们一直拥有全球 top 10 大学里的三四所——剑桥、牛津、Imperial、UCL 这一类。我们培养出来的本科和博士生是世界各地都羡慕的水平。"

"We've got rich heritage of that all the way from Turing and Hawking and Darwin, Newton. So we have this incredible history of scientific breakthroughs and great thinkers."

"我们的科学遗产源远流长——一路追溯到 Turing(图灵)、Hawking(霍金)、Darwin(达尔文)、Newton(牛顿)。这是一段不可思议的科学突破和思想家史。"

"So I felt we had all the ingredients and the talent and great engineers here, but it just hadn't been galvanized into an ambitious startup idea — a deep tech startup idea — and that's what I felt was possible."

"我觉得人才、工程师这些原料这里都齐了,只是还没有被任何一个野心够大的创业公司——尤其是 deep tech 创业公司——点燃。我认为这是可行的。"

"And I felt that there was actually less competition here for that sort of talent and we could even draw in the best talent from the top European universities. And that's what it was like in the early days of DeepMind. So I think it was a huge structural advantage for us."

"而且这里争抢这种人才的竞争其实更少,我们甚至能从顶级欧洲大学吸收最好的人才。DeepMind 早期就是这种状态——这是一个巨大的结构性优势。"

"And then the final thing is maybe being a bit away from the valley. There are some disadvantages — you're not plugged into the network and the gossip and the latest trends and vibes. We're a little bit out of it here."

"最后一件事是——距离硅谷有点远。当然有缺点:你没那么贴近 network、八卦、最新趋势和氛围。我们在这边确实是有点游离的。"

"But it's very conducive to thinking deeply about things, being more original about how you think. I think that's great for things like deep tech, where you don't want to be distracted by the latest fad. It's going to be a 20 year mission, which is what we knew at the beginning of DeepMind."

"但这种距离非常有利于深度思考、保持原创性。对 deep tech 这种事尤其好——你不希望被一阵风一阵风的新潮流牵着走。我们 DeepMind 一开始就知道,这是一个 20 年的使命。"

"Will Europe have a trillion dollar company? The Americans always bash us for our lack of large companies."

"欧洲会有一家万亿美元公司吗?美国人老吐槽我们缺大公司。"

"Not yet. I mean, Daniel might well get there with one of his companies. Spotify, Helsing — I think those are two good options."

"暂时没有。Daniel(指 Spotify 创始人 Daniel Ek)可能会用他旗下的某家公司做到——Spotify、Helsing 都是不错的候选。"

"There's no reason why we can't have that. I'm going to try and do that with Isomorphic, which is headquartered here, and I think has the potential to be that."

"没有理由欧洲不能有。我自己就在用 Isomorphic 试——总部就在这里,我觉得它有那个潜力。"

"But that's one of the disadvantages of Europe — obviously we're a combination of smaller markets. So that's one thing we have to overcome."

"但欧洲有个明显劣势——它是一堆小市场拼起来的。这是必须克服的一件事。"

"Magic wand on European technology — what would you do to implement a growth mindset, the ability to build that trillion-dollar company?"

"再用一次魔法棒,这次用在欧洲科技上——你会怎么落地那种'增长心态',让欧洲能造出那家万亿美元公司?"

"In the UK — and this may apply to other European countries too — I think unlocking what pension funds can invest in for the kind of growth stage."

"在英国——可能也适用于其他欧洲国家——我会解锁养老金能投什么,特别是 growth-stage(增长期)。"

"I think we're brilliant at doing the startup idea and getting it to a certain level like we did with DeepMind, but then if you really want to cross that chasm into the trillion dollar global player, where are the billion dollar rounds going to come from?"

"我们在'起一个 startup idea 然后做到某个量级'这件事上非常厉害——我们 DeepMind 就是这么走过来的。但你要真正跨过那条鸿沟,变成全球万亿美元的玩家,你那些十亿美元规模的轮次从哪来?"

"That certainly was missing 10 years ago when I was doing fundraising for DeepMind, and I think it's still kind of missing today — that level of ambition and the amount the capital markets can support."

"10 年前我给 DeepMind 融资时,这个就明显缺位。今天也还在缺——这种野心水平,以及资本市场能撑得住的规模。"

"Do you remember meeting Elon for the first time? How was that?"

"还记得第一次见 Elon 是什么感觉吗?"

"It was at a Founders Fund [conference] — both SpaceX and DeepMind were part of the same portfolio that Peter Thiel had at Founders Fund. Must have been back in 2011 or 2012, very early days. We were the small little upcoming thing and Elon was the big thing in that portfolio. So he had the keynote. We met afterwards — Elon says it was passing each other in the bathroom."

"是在 Founders Fund 的一次会上——SpaceX 和 DeepMind 都是 Peter Thiel 在 Founders Fund 同一只 portfolio 里的公司。大概是 2011 或 2012 年,非常早期。我们是那个 portfolio 里很小、刚冒头的角色,Elon 是大主角,他做的是 keynote。会后我们见了——Elon 自己说那次是我们在洗手间擦肩而过。"

"We hit it off immediately, like as people that were almost too ambitious in their thinking, perhaps, and love sci-fi. I really wanted to visit his rocket factory. He invited me at the end of that meeting and that was our second meeting in the SpaceX factory."

"我们当下就一拍即合——都是那种'想得过于野心'的人,而且都热爱科幻。我特别想去看他的火箭工厂。那次会议结束他就邀请我了,我们第二次见面就是在 SpaceX 工厂里。"

"Healthcare revolution, disease eradication that you're most excited about?"

"医疗革命、疾病根除——你最兴奋的是哪个?"

"I want to literally cure cancer. I know people say that's a cliché, but what we're building at Isomorphic is general purpose. We're trying to build a drug design platform that will be applicable to any therapeutic area."

"我想字面意义上治愈癌症。我知道这话听着像套话,但我们 Isomorphic 在做的是通用平台——一个可以适用于任何治疗领域的药物设计平台。"

"Ideally it will help with everything from neurodegeneration, cardiovascular, immunology, cancer. Those are the ones we're focusing on first, but eventually it should be applicable to every disease area."

"理想情况下,它能帮上一切——神经退行性疾病、心血管、免疫、癌症。这些是我们最先聚焦的,但最终它应该覆盖所有疾病领域。"

"What are you thinking about that you're not reading about or seeing anyone talk about?"

"有没有什么事情你一直在想,但你没读到、也没看到任何人在谈?"

"A lot of people are worrying about the economic questions around AGI that we talked about earlier, but I worry a lot about the philosophical questions around it."

"很多人担心 AGI 的经济问题——我们前面也聊到了——但我很担心的是它的哲学问题。"

"When it comes — let's say assuming we get the technical right, let's assume we get the economics right, both of those are hard — then there's a philosophical question of what is meaning, what is purpose, we'll find out maybe what consciousness is, what does it mean to be human?"

"当 AGI 真的到来——假设我们把技术做对了、经济也处理对了,这两件事都很难——那时候真正剩下的是哲学问题:什么是意义?什么是目的?我们也许会搞清楚意识到底是什么。'人是什么意思'这个问题怎么答?"

"That's what's coming down the road. And I think we need some great new philosophers to help us navigate that."

"这才是真正在路上的事。我觉得我们需要一批新的伟大哲学家来帮我们导航。"

"Final question. There are many ways you could describe what you do. What would you most like to be remembered for? Your legacy?"

"最后一个问题。你做的事可以有很多种描述方式——你最想自己被怎么记住?留下什么样的 legacy?"

"I would like my legacy to be remembered for advancing science and building technologies that bring incredible benefits into the world, like curing terrible diseases."

"我希望自己的 legacy 被记住的是——推动了科学,造出了能给世界带来巨大好处的技术,比如治愈可怕的疾病。"