# (77) LIVE: Sam Altman speaks at BlackRock's US Infrastructure Summit - YouTube
- Transcript ID: `wc3osj2_6ca`
- Source tier: `youtube_auto`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (65/65 chunks)
- Original source: https://www.youtube.com/watch?v=Wc3OSJ2_6CA
- Raw evidence: `data/transcripts/youtube/Wc3OSJ2_6CA.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**
另一个判断标准是：一家大公司的 CEO、一个大国的总统或一位诺贝尔奖得主，什么时候会离不开对 AI 的大量使用，无法正常履行职责？这并不意味着会出现 AI CEO 或 AI 总统，但确实意味着，比如人类 CEO 的角色会发生变化。就我自己的工作而言，它已经大不相同。我们仍然需要一个人为决策负责，运用人类判断力，并具备社会对重要组织领导者所期待的全面理解；但是——
**English / Original**
The other one is when can a CEO of major company, a president of a major country, a Nobel Prize winning scientist, when can they not do their job without making heavy use of AI? This doesn't mean that there will be an AI CEO or an AI president, uh but it does mean that the the role of, let's say, a human CEO, when I think about my job, it's really quite different. You you still do need a person to stand behind decisions and kind of exercise human judgment and all of the understanding that we expect out of someone running a an important organization to do, but
## 00:00:42
**中文**
我职责中的具体工作将越来越依赖 AI，因为人类根本做不到这一切。任何人类 CEO 都不可能与公司的每一位员工、每一位客户交谈，参加每一场会议，同时还是每个领域的专家。因此，我认为这类工作的重心会越来越转向监督一大批 AI：提供监管、判断该在多大程度上信任其输出，以及如何给予指导。当一个人若不高度依赖 AI，就不愿再去管理大型组织时，我认为这也是一个值得关注的——
**English / Original**
the actual parts of my role that I will increasingly have to rely on an AI to do because no human can. You know, no human can talk to no human CEO can talk to every employee at a company, every customer, be in every meeting, be an expert in every field. And so, more and more, I think, of these jobs will be supervising a bunch of AI, um providing oversight, deciding how to like trust the the outputs, how to provide guidance. And that threshold of when you really wouldn't want to be doing your job running a large organization without heavily heavy reliance on AI, I think that's another sort of
## 00:01:25
**中文**
门槛。它可能会晚一点到来，但大概也不会晚太久。——在履行职责时，你发现自己在多大程度上依赖 OpenAI 正在开发的智能体和人工智能？——依赖程度正在极快上升。如果我对商业模式、战略调整或应推出的产品有一个新想法，甚至在拿去与别人讨论之前，我做的第一件事就是询问我们的工具。随着它们获得更多上下文——我认为这正是下一件即将发生的大事——它们——
**English / Original**
interesting threshold. That may take a little bit longer, but probably not a lot longer. >> And And as [clears throat] you do your job, how how much are you finding yourself relying on some of the agents and some of the artificial intelligence that we're developing at Open AI? >> Uh it's ramping incredibly quickly. Um if I have like a new idea for a business model, a strategy shift, a uh a product offering we should do, the very first thing I do, before I even bounce it off somebody else, >> Mhm. is to ask our tools. And as they get more context, and I think this really is like the next big thing to happen, is they
## 00:02:06
**中文**
可以接近掌握公司的完整上下文，访问我们全部内部文档、沟通记录、代码、客户数据等一切资料。这样一来，它们给出的答案、思考——无论你想怎么称呼——质量会越来越高。——好，我们稍微换个话题。两周前，你宣布完成一轮 1,100 亿美元融资。
**English / Original**
can get close to full context of our company, access to all of our internal docs, communication, code, customer data, everything, the quality of the answers, thought, whatever you want to call it, gets better and better. >> Right. >> Okay. So, let's let's shift a little bit. 2 weeks ago, um you announced a $110 billion funding round.
## 00:02:30
**中文**
我问 ChatGPT，这与公开市场上完成过的其他融资相比如何？——我不知道。——规模大约是其四倍。此前史上最大的公开募股，是沙特阿美几年前完成的约 250 亿美元，而公开市场理应是覆盖最广、深度最大的资本来源。
**English / Original**
I asked ChatGPT, how does that compare compare to any other fundraising that's been done in the public markets? >> don't know. >> Uh four times as large. Okay? The largest public offering ever done was roughly 25 billion that Aramco did several years ago. And the public markets are supposed to be the broadest and deepest sources of capital. Okay?
## 00:02:53
**中文**
三家战略合作伙伴是亚马逊、英伟达和软银。请介绍一下这笔融资。为什么它是公司的转折点？还有人问我：这么多钱究竟要花在哪里？——这门生意有许多困难之处，其中最棘手的一点是基础设施极其昂贵，需求量又非常大，而且必须提前很久作出投入承诺。
**English / Original**
Three strategic partners, Amazon, Nvidia, and and SoftBank. Tell us a little bit about this. How is this an inflection point of the company? And one of the questions somebody asked me is, what are we spending all that money on? >> The There is many hard parts of this business, but one of the hardest ones is the infrastructure is so expensive, you need so much of it, and you have to commit so far in advance.
## 00:03:23
**中文**
我从未见过其他任何行业与此相似。历史上当然有过许多资本密集型产业，但展望未来几年，如果增长曲线仍像现在这样陡峭，需求继续如此快速增长，你就必须采取一些非常规做法。
**English / Original**
I have never seen any other industry quite like this. I mean, there have been, clearly, many capital-intensive industries throughout history, but as I look at the heat what's to come in front of us in the future in future years, if the ramp stays as steep as it looks like it is right now, the demand is growing as fast as it as it's growing, you have to do some pretty unusual things.
## 00:03:49
**中文**
OpenAI 做的许多事看起来很奇怪：我们在收入到来之前就投入巨资建设基础设施；尝试广告之类的新商业模式，尽管它看起来未必是利润最高的选择；还有很长一串其他事情。但我们有一个根本信念：智能应当充裕。借用能源行业一句未能真正实现的老话，未来最重要的事情之一，是让智能“便宜到无需计量”。我们希望让智能充满世界，让人们把它用于一切；希望下一代把它视为理所当然，甚至无需特意去想。
**English / Original**
Uh OpenAI does a lot things that look weird. We spend a ton of money on infrastructure in advance of revenue. We do new business models like ads that seem like, you know, maybe not the most profitable thing we could do. Um a long list of other things, but we have this fundamental belief in abundance of intelligence. And that one of the most important things in the future is that we make intelligence, you know, to borrow an old phrase from the energy industry that didn't quite work, too cheap to meter. We want to flood the world with intelligence. We want people to just use it for everything. We want this to just be something that the future generation doesn't think about.
## 00:04:29
**中文**
他们会理所当然地认为，智能无处不在，每个人都能按需获得各个领域、数量不限的“天才”。这是我们最重要的指导原则之一，因此也会促使我们采取许多在其他公司看来不那么自然的行动。其中一项，就是摆脱一直以来的算力供给受限状态；如果不改变做法，我们认为自己仍会沿着这条受限轨迹走下去。
**English / Original**
They expect everywhere and everybody has access to like geniuses, as many as they need in any area that they need. And this principle, which is one of our kind of like top guiding principles, does lead to a lot of behavior that would look less natural for other companies. And one of those is we really want to get out of this world that we have been in, that we still think we're on a trajectory to stay on without changing what we do, of always being capacity constrained.
## 00:04:59
**中文**
——你说的供给受限，是指算力吗？——是的。——我经常听你说“算力就是收入”。能谈谈你对此的理解吗？——从根本上说，我们以及其他所有模型提供商的生意，最终都会像是在销售 token。
**English / Original**
>> Right. And capacity constrained, you mean compute? >> Yeah. >> And I've often heard you say a lot that compute is revenues. You want to talk a little bit about how you think how you think about that? >> Fundamentally, our business, and I think the business of every other model provider, is going to look like selling tokens.
## 00:05:22
**中文**
这些 token 可能来自更大或更小的模型，因而成本有高有低；可能使用更多或更少的推理，同样会影响成本。它们可以一直在后台运行、随时尝试帮助你；如果想少付费，也可以只在需要时运行。它们还可能为一个真正有价值的问题投入巨量工作，单个问题耗费数千万、数亿美元，未来甚至数十亿美元。
**English / Original**
You know, they may come from bigger or smaller models, which makes them more or less expensive. They may use more or less reasoning, which also makes them more or less expensive. They may be running all the time in the background trying to help you out. Uh they may run only when you need them if you want to pay less. They may work super hard, you know, spend tens of millions, hundreds of millions, someday billions of dollars on a single problem >> Right.
## 00:05:45
**中文**
但我们看到的未来是：智能会像电力或自来水一样成为公用资源，人们按表向我们购买，再将其用于任何想做的事情。我们看到的需求似乎会继续这样一路攀升。如果供给不足，我们要么无法销售，要么价格变得极高，使智能只流向富人；又或者社会被迫通过某种中央计划来决定如何使用稀缺算力，而我认为这种安排几乎总会产生糟糕结果，例如规定有限的——
**English / Original**
>> that's really valuable. But, we see a future where intelligence is a utility like electricity or water, and people buy it from us um on a meter and use it for whatever they want to use it for. The demand that we see for that seems like it's going to continue to just go like this. And if we don't have enough, we either can't sell it or the price gets really high and it, you know, kind of goes to rich people or society makes a bunch of sort of central planning decisions that I think almost always go badly about, you know, we're going to use our limited
## 00:06:26
**中文**
算力供给只能用于这件事，不能用于那件事。
**English / Original**
compute supply for this and not that.
## 00:06:29
**中文**
所以在我看来，纵观资本主义与创新的整个历史，最好的办法就是让供给充分涌入市场。——满足算力需求的关键部分显然是“星际之门”。这里是一场基础设施峰会，你大约一年前宣布了这个项目。
**English / Original**
So, the best thing to me throughout all the history of capitalism, innovation, whatever you want, is to just flood the market. >> Yeah. And obviously a critical part of addressing that compute demand is Stargate. And this is an infrastructure conference and, you know, you announced that several months ago, or almost a year ago I guess it was, yeah.
## 00:06:52
**中文**
它在美国进展如何？另外，我也参观了阿布扎比的“星际之门”，那里的情况怎样？——这份工作有许多很酷的部分，其中最酷的体验之一，就是参观正在建设或运行的超大型数据中心。那些吉瓦级园区的规模实在难以用语言解释。照片上看着已经很大，但当你真正走进去，从一栋楼走到另一栋楼，看到现场约一万人、各类熟练技工各司其职，内部又像宇宙飞船一样，才会发现它确实——
**English / Original**
How's it going in the US and then how is it going cuz I was also Stargate in in Abu Dhabi. >> Yeah. >> Yeah. >> Um Honestly, there's many cool parts of the job. One of the coolest is getting to go visit these mega data centers under construction and operation. >> Yeah. >> Just the scale is, you know, of like these gigawatt campuses. It's it's it's really hard to explain and you see these photos and it's like, okay, it does look big. And then you go there and you're walking through it and you're building to building and it's just, you know, 10,000 people there, all these different skilled trades doing all these different things and you know, it looks like a spaceship inside. It's really quite
## 00:07:30
**中文**
令人难以置信。我们目前正在阿比林的第一个站点训练一个模型；我认为它将成为全球最好的模型，希望还能遥遥领先。此前我在施工期间多次到访，逐渐理解它的规模和惊人复杂度；而如今，OpenAI 的一名研究人员只需输入一条命令并按下回车键，这种转变实在令人惊叹。
**English / Original**
incredible. Um we are training right now on the first site in Abilene, uh what I think will be the best model in the world, hopefully by a lot. And it is it's so amazing to have gone from, you know, many visits while it was under construction and just really internalizing the scale and incredible complexity to the day that one researcher at OpenAI types in like one command and it pushes enter.
## 00:07:57
**中文**
数量惊人的 GPU 随即启动，共同执行一次巨大的计算，非常酷。——在启动和扩大项目的过程中，最令人惊喜的事情和最困难的事情分别是什么？——既有所有预料之中的挑战，也有完全没想到的未知问题。例如，阿比林发生过一次异常天气事件，超出了我们的规划范围，导致系统一度停运。
**English / Original**
Unbelievable number of GPUs spin up and start doing this one huge computation all together. It's very cool. >> And and what have been the most pleasant surprises and what have been the hardest things about getting it up and running and scaling it going forward? >> Uh I mean there have been all of the expected challenges and then the unknown unknowns. Like there was a crazy weather event in Abilene that we hadn't, you know, was outside what we had planned for and that brought things down for a little while.
## 00:08:28
**中文**
供应链也面临各种挑战。任何达到这种规模的项目都会有许多地方出错。当然也有大量事情进展顺利，但要让如此复杂的建设全天候推进，各类问题必然层出不穷。最出乎意料的积极之处之一，是这么多不同组织必须在极短时间内协同完成任务，而大家最终在巨大压力下真正像一支团队一样合作。
**English / Original**
Um there's all of supply chain challenges. Anything at this scale is just like so much stuff goes wrong. Lots goes right, but the just trying trying to build something around the clock with such complexity, there's all the stuff that goes wrong. One of the biggest surprises on the upside is how many different organizations had to come together to do this in an incredibly short period of time and how much we all like ended up working together as one team under a lot pressure.
## 00:09:00
**中文**
——当然，电力需求是很多人最关注的问题。你是否乐观地认为我们能解决这一挑战？先谈美国，随后也可以谈其他地区。——从长期看，我很乐观。——好。——我毫不怀疑，我们会找到建设巨量发电能力的办法。
**English / Original**
>> Mhm. And of course the power demand part of it is the one that a lot of people have have focused on. Um Are you optimistic that we will solve that challenge first in the US and then we can talk about it in in other places? >> Uh In the long term I am. >> Okay. >> Uh I have no doubt we'll figure out how to build huge amounts of power generation.
## 00:09:29
**中文**
AI 当然也会提供帮助。面对燃气、太阳能、核裂变、核聚变等各种能源选择，我对世界最终能够建设并实现的能力很有信心。不过，鉴于我们看到的需求增长，我多少还是希望能出现一个奇迹，让模型每瓦电力的效率大幅提高——
**English / Original**
AI will help, of course. Uh but I think the portfolio the world has in front of it gas, solar, nuclear fission, nuclear fusion, more like we're I feel good about what what we will be capable of and what we will eventually do. >> Mhm. >> Um given the demand growth we are seeing, I am sort of hoping for a miracle in terms of figuring out how we can get way more efficient per watt >> Yeah.
## 00:09:57
**中文**
从而为我们争取时间，建成所需的全部基础设施。过去的效率提升纪录已经令人难以置信。人们会引用各种惊人数据，说明我们的模型乃至整个行业的模型随时间变得多么高效；我认为其中一个特别惊人的例子是，我们的第一个推理模型叫 o1，大约在 16 个月前发布。
**English / Original**
>> with models to give us time to build out all of this infrastructure. Now, the track record there has been incredible. Um people cite whatever amazing statistic they like about how much more efficient our models have gotten over our industry's models have gotten over time, but one that I think is incredible, our first reasoning model was called O1, came out like 16 months ago.
## 00:10:25
**中文**
而最新的 5.4 模型已经把推理能力整合进去。要让最初的模型与 5.4 对同一个难题给出相同答案，成本大约下降了 1,000 倍。——嗯。——也许我对时间线的记忆略有偏差，实际间隔可能更长一点；但无论如何，从 o1 到现在——
**English / Original**
Uh and our latest model, where we've now integrated reasoning, is 5.4. To get the same answer to a hard problem from that first model to 5.4 has been a reduction in cost of about a thousand X. >> Mhm. >> Maybe I was a little bit wrong on the timeline, maybe it was a little bit longer, but in any case, since O1 till now, >> Yeah.
## 00:10:48
**中文**
成本降低了一千倍。在如此短的时间里，这是难以置信的。我认为这说明两件事。第一，我们仍处于这一范式的极早期，对于如何开发、训练模型以及如何高效运行它们，仍有巨大提升空间。
**English / Original**
>> a thousand X. Uh that is unbelievable in a relatively short period of time. And the thing that that the two things that I think that points to, one, we are still so early in in this paradigm and we're still so we have so much more to gain about our understanding of how to develop these models and train them and run them efficiently, >> Mhm.
## 00:11:13
**中文**
我们当时、乃至现在，仍在用一些很笨拙的方式做事，未来会越来越好。第二，人类的创造力，以及在约束条件下运作、寻找解决办法的能力，几乎总会带来超出预期的惊喜。进步不只是模型本身变好了；内核工程师帮助编写了效率更高的内核，电力工程师和数据中心设计人员也找到了更高效的办法。也就是说，远不止模型团队，各领域的人都在响应挑战，共同提升效率。
**English / Original**
>> that we we have were then and still are doing things in dumb ways and will get better and better. And two is that human ingenuity and the ability to operate in constraints and to find ways to solve problems almost always surprise you on the upside. So, it's not just that the models have gotten better, it's that we have figured out, you know, kernel engineers came to help figure out how to write more efficient kernels and like power engineers and people that design data centers found more efficient ways to do that. So, people are answering the call well beyond just the model side to make this more efficient.
## 00:11:49
**中文**
——OpenAI 显然是英伟达、AMD 等公司的大客户，我想我们也与亚马逊签署了一份大合同。但你们还在自研芯片。——是的。——背后的考虑是什么？——我们做的是纯推理芯片。——明白。——我们的考虑是，为解决眼前问题，需要一款专用芯片。它未必是速度最快的推理芯片，但要成为成本最低、并且在现实约束下每瓦效率最高的推理芯片。鉴于未来我们预见的智能体需求，这会非常重要。
**English / Original**
>> Right. And then of course um obviously Open AI is a big customer of the Nvidias and AMDs of the world, but uh you know, I think we signed a big contract with Amazon. But we're building our own chip. >> Yeah. >> What's what's the thinking behind that? >> Uh so, the chip we're doing is inference only. >> Okay. >> And the thinking behind it is that on this, you know, come rise up to solve problems in front of us, um we think that a specialized chip to be not necessarily the fastest inference chip, but the cheapest inference chip, the most efficient per watt given the constraints we see in front of us, is going to be important for all of the agent demand we see in the future. So,
## 00:12:29
**中文**
这是一项带有明确取舍的押注，芯片功能范围有限；但在能源受限的世界里，它所擅长的事情会非常重要。——现场不一定每个人都熟悉 AI，你能否解释推理芯片与训练芯片的区别？——抱歉，我本应先解释这一点。
**English / Original**
it's a it's an opinionated bet, it's a limited chip, but the thing that it does in a world where we're energy constrained, I think it'll be very important. >> Maybe you can explain because I am not sure everybody in the audience is uh AI proficient, what the difference between an inference chip and a training chip. >> Sorry, I should have done that first.
## 00:12:47
**中文**
目前 AI 工作负载主要分为两个阶段。最终两者会融合，成为一个连续过程；但眼下首先要训练模型：数量庞大的 GPU 针对海量数据连续计算数周或数月。你可以把它类比成人用人生前 22 年接受教育：从婴儿时期让东西掉下来、观察它坠落开始学习，直到大学物理阶段非常细致地理解背后的原理。此后，如果让模型解决一道物理题，这就——
**English / Original**
So, there there are two main phases to uh AI workloads. Now, they're going to blend together eventually and it'll all be one continuous thing. But right now, first we train a model, you know, a gigantic number of GPUs crunch for weeks or months on a bunch of data, and you can think of that like maybe it takes you 22 years in life to get your education, and you learn a bunch of stuff starting when you're a baby and you drop things and see they fall, and then eventually like in college physics understanding like at a very detailed level what's going on there. Um and And after that, if you ask a model to solve a physics question, it that's
## 00:13:29
**中文**
叫作推理，而且效率相当高。人类也是如此。人们说“这些 AI 模型效率真高”时，通常是在拿人类训练所需的 22 年，与成年人解答一道物理题所需的一秒钟相比。如果把模型解题与人类解题直接相比，模型现在很可能已经更节能。训练则需要极其庞大的处理能力，最终生成的其实只是一个数字文件；之后你可以向它提出问题并获得回答。
**English / Original**
called inference, uh and that's quite efficient. But this is true for people as well, and when people talk about, "Oh, these AI models are so efficient." They're usually comparing the 22 years that a human takes to train to the 1 second that an adult takes to solve a physics problem. If you compare the, you know, the model solving the physics problem to the human solving physics problem, actually the models already probably are more energy efficient. Um but training is this hugely massive amount of processing power that then produces really just like a file of numbers that you can then pose a question to and get a response.
## 00:14:07
**中文**
——你对芯片研发进展乐观吗？——是的。我们应能在今年年底前让首批芯片实现规模化部署，再过几个月就会拿到第一批芯片，目前看来效果会很好。——太棒了。今天早上，你们刚刚宣布与北美建筑工会达成新合作，为熟练建筑工人拓展培训路径；这也是今天多场讨论的主题。能否介绍一下你与肖恩·麦加维达成了什么协议？
**English / Original**
>> And are you optimistic about the progress we're making on the chip? >> Yeah. Um we should have the first chips deployed at scale uh by the end of this year. We should have the first chips back just in a few months now. Um and looks like it'll be really good. >> Fantastic. So, you announced a new partnership this morning, actually, with the North American Building Trades Unions to expand training pathways for skilled construction, which has been a subject of my many of the discussions today. Can you tell us a little bit about what you and uh Sean McGarvey >> Yes.
## 00:14:39
**中文**
——他稍后也会上台，所以我们先听听你的版本，再看看他的说法是否一致。——我们之前谈到过，世界过去也多次讨论 AI 基础设施的需求：我们需要实体基础设施，包括发电厂、输电线路、数据中心机房、冷却设备，当然还有机架、GPU 以及其中所有设备。
**English / Original**
>> agreed and what's he And he's going to be on stage later on. So, let's hear your version of it and then see if his version is the same. >> So, we talked earlier, and we've talked The world has talked many times in the past about the need for AI infrastructure, the need for the world to have the physical infrastructure, power plants, transmission lines, data center halls, chillers, obviously the racks and the GPUs and everything that goes inside of those.
## 00:15:12
**中文**
我真希望每个人都有机会参观一次这种超大规模数据中心。因为当你向 ChatGPT 提问并立即得到答案时，很难想象生成答案背后所需设施的规模，以及实现这一切究竟需要什么。人们会谈到我们在各种环节受到限制。
**English / Original**
Um and I I I sort of wish everyone would go visit at some point one of these mega-scale data centers because it it's hard you know, when you ask ChatGPT a question, you get your answer back, and it's really hard to visualize the scale of what it took to make that or what it takes to make that happen. Uh people talk about all of these different places that were limited.
## 00:15:39
**中文**
有时瓶颈是涡轮机数量，有时是变压器、存储芯片工厂或数据中心建设，等等。所有这些环节都有一个共同点：它们都是极其复杂的实体基础设施，需要大量熟练技术工人来完成。无论供应链在某个时点卡在哪里，当我询问怎样才能加快建设时，答案总是需要更多熟练技工，来建设我们共同依赖的全部基础设施。我认为这些会是非常出色的工作岗位。
**English / Original**
>> Mhm. >> We are limited in the, you know, number of turbines or now it's the voltage transformers or it's the memory fabs or it's building the data centers, whatever. All of these things have one thing in common, which is they are massively complex physical infrastructure that require skilled tradespeople uh and a lot of them to do. And no matter where the choke point in the supply chain is at any one time, when I talk to people about what it would take to accelerate it, um it is more skilled trades workers to build out all of this infrastructure that we all depend on. I think these will be incredible jobs.
## 00:16:18
**中文**
不仅如此，我认为它们还会奠定美国下一代基础设施和经济繁荣的根基。我们非常高兴能够携手合作，加快这一进程。——顺便说一句，贝莱德也认同这一观点。你们今天宣布这项合作令人振奋，我们认为这是非常棒的消息。
**English / Original**
Uh more than that, I will I think they will lay the foundation for the next generation of American infrastructure and economic prosperity. And we are thrilled to get to work uh together to to drive that faster. >> Uh just so you know, we at BlackRock share that view, and we think it's really exciting that that you announced this partnership today. We think that's really really terrific.
## 00:16:40
**中文**
——我想大家还关心另一个问题：与中国的竞争目前处于什么位置？我猜我们领先，但并不确定，你可以告诉我们真实情况。为了确保继续领先，我们需要做什么？——我先讲一个总体框架，再更具体地回答这个问题。
**English / Original**
Uh I think another question that's on people's minds is competition with China. Where do you think that stands? Uh what what do we need to do to make sure I assume we're ahead, but I don't know. So, you might tell us whether we are or not. What do we need to do to make sure we stay ahead? So, a a general framework thought first, and then I'll answer the question more specifically.
## 00:17:07
**中文**
我认为，深度学习的发现更接近发现一种元素或物理学的基本属性，而不像某种秘密技术。这意味着，最终——而且这个“最终”可能不会很久——使模型具备强大能力的基本思想会被简化，并为人们广泛理解。
**English / Original**
>> Mhm. >> I think the discovery of deep learning is is closer to discovering an element or a fundamental property of physics than it is of like a secret technology. Um and that means that eventually, and eventually probably not being very long, the fundamental ideas that make a model so capable will be simplified. They'll be very well known.
## 00:17:37
**中文**
就像我们已经理解物理学的大部分规律一样，我们也会把人工智能的重要机制理解为科学原理。大约七年前，OpenAI 发表缩放定律时，我们开始真正认识到这一点：投入模型的资源与模型智能之间存在一种如此可测量而优美的相关性，当时让人汗毛直立，却也清楚显示这里存在某种基础性的科学规律。
**English / Original**
And just like we kind of understand how big parts of physics work, we will understand as a scientific principle how big parts of artificial intelligence work. Um this is this really we started to appreciate this with the scaling laws uh that OpenAI published maybe seven years ago now. Um there was such a measurable beautiful correlation between the resources that go into a model and the intelligence of that model that it kind of felt like hair-raising, but clear at the time, that there was just something fundamental going on here as a scientific principle.
## 00:18:16
**中文**
此后我们发现了许多细节，未来还会发现更多；但和其他科学前沿一样，这套知识正在随时间推移而简化、变得更清晰。最终，这套方法会成为人们充分理解的科学原理，不会像某些技术那样成为商业秘密。
**English / Original**
>> Mhm. >> Now, there have been a lot of details we've discovered since. There will be more to come, but like other scientific frontiers, it is simplifying and becoming more clear over time. And eventually, this recipe will be well understood as a scientific principle. It will not be a trade secret in the sense that other things have been.
## 00:18:36
**中文**
从技术史来看，我最喜欢的类比是晶体管。晶体管同样是一项基础科学突破：极难发现，发现过程也带有偶然性，而且我们花了一段时间才完善它。但一旦理解之后，其科学原理便对所有人清晰可见。围绕它仍有大量运营诀窍，例如台积电至今能做到一些全球其他公司无法做到的事情。因此，我预计 AI 的工业流程也会产生许多竞争优势；模型与工作流程、训练数据以及其他面向终端使用环节的整合——
**English / Original**
Now, the analogy I like best historically from technological history is the transistor. >> Mhm. >> Transistor was also a sort of fundamental scientific breakthrough, very hard to discover, kind of chancy to discover, took us a little while to refine our discovery, but once we understood it, the scientific principle was clear to everyone. There were still massive amounts of operational knowledge that went around that. TSMC can still do things that no one else in the world can do, and you know, I expect the industrial process around this to have a lot of advantages, uh competitive advantages. I also expect that the integration in workflows and training data and other end usability of
## 00:19:18
**中文**
同样会形成显著差异。也许最重要的是，差异将取决于谁拥有基础设施，以及拥有多少。不过我认为，最根本的科学原理会广为人知，甚至可以全部印在一件 T 恤上。就目前而言，全球能力最强的前沿模型由美国领先。
**English / Original**
models, there will be a lot of differentiation there. Maybe most of all, I expect that it'll be differentiation on who has the infrastructure and how much of it. But the fundamental scientific principles I are going to be well known and they will fit on a t-shirt, I think. Um In terms of where we are, uh the most capable models in the world, the frontier, the US is leading on.
## 00:19:43
**中文**
——而在低两代模型的低成本推理使用方面，中国领先。基础设施目前由美国领先，但中国推进得快得多。至于工业化、产品化——不管你怎么称呼——美国在闭源领域领先，中国在开源领域领先。总体而言，我认为美国可能仍然领先。
**English / Original**
>> Mhm. >> Um the cheapest inference usage for a two generation earlier model, China is leading on. >> Mhm. >> Um infrastructure, the US is currently leading on, but China is moving much faster on. And that kind of industrialization, productization, whatever you want to call it, I the US is leading in closed source. China's leading in open source. Uh I think the US is probably leading overall.
## 00:20:08
**中文**
——你最近去了印度，听起来你对印度看待这些挑战与机遇的方式非常兴奋。——与印度初创企业交流、了解它们如何使用这项技术，让我大为震撼。刚到那里时，有人递给我一份印度市场简报，上面写着：短短几个月，Codex 在印度的使用量增长了十倍。
**English / Original**
>> Mhm. And you were recently in India. And um sounded like you were very excited about how India is thinking about the challenges and the opportunities. >> I was blown away uh talking to Indian startups and how they are using this technology. Um I like got there and someone like handed me a briefing sheet for India and it was like, you know, Codex usage in India has like 10x'd in some small number of months.
## 00:20:38
**中文**
我的第一反应是：这肯定是数据错误，不可能是真的。但它确实是真的。随后我与这些初创企业交谈，发现那里正在发生的事情比美国还更激进。人们会说：“世界已经不同了。你们谈论一人创业公司，而我想做的是零人创业公司。我只想写一个提示词，让它创建整个公司：编写软件、做客户支持、处理法律事务等等，然后我就去度假。”印度的大公司则会直接问：“我们能从你们这里购买多少算力？可以预订多久？现在能不能谈妥？在你同意之前，我们不会让你离开这个房间。”
**English / Original**
And I was like, that's got to be a bug. Like that >> That can't be right. >> It can't be right. Um but it was true. And the And then I started talking to these startups and it's an even stronger version of what's happening in the US of people saying, "Hey, the world is different. Um you know, you talk about a one-person startup. I'm trying to build a zero-person startup. I'm trying to just write a prompt that's going to make my whole startup and, you know, write my software, do my customer support, do my legal stuff, whatever, and then I want to go on vacation. Um and the companies big companies in India that were just saying like, "How much capacity can we
## 00:21:20
**中文**
那种进取程度、行动速度，以及对 AI 将重塑印度商业格局的信念，确实令人印象深刻。
**English / Original**
buy from you? How long can we reserve it for? Can we negotiate this right now? We're not going to like let you leave the room until you agree on this with us." The just the level of aggression and speed and sort of a belief that AI was going to reshape the business landscape in India was was really quite impressive. Um Yeah.
## 00:21:43
**中文**
——这与我们同美国客户的交流不同吗？——方向相同，但他们似乎走得更远一点，行动也更快。——你说过“威权式 AI”与“民主式 AI”有所不同。你指的是什么？其中利害何在？
**English / Original**
>> Is that different from when we when we talk to customers in the US? >> Uh it's it's the same vector, but they seem a little further along. They're moving faster. >> The other The other comment you I think I saw you made was that there's a difference between autocratic and democratic AI. What did you mean by that, and what what do you think is at stake?
## 00:22:09
**中文**
——有时会出现一种技术变革，对社会的重塑程度如此之深，以至于关于它的决策不能只由恰巧在开发这项技术的少数公司作出。我非常信奉资本主义，非常看重企业的权利，也坚信政府不应过度干预。
**English / Original**
>> Once in a while, I think a technological shift comes along that reshapes society to such a degree that it does not Decisions about it do not belong to the handful of companies that happen to be developing it. I am a huge believer in capitalism. I am a huge believer in the rights of companies. Um I am a huge believer in governments shouldn't interfere too much.
## 00:22:36
**中文**
但我认为，这是少数例外情形之一：社会对这项技术将产生何种影响拥有正当利益。互联网出现时也是这样的时刻，而我认为当时并非一切都处理得正确；希望我们能从中吸取经验，做得更好。如果 AI 公司所说的未来成真——我也相信它会成真——这项技术将重塑经济、重塑地缘政治力量，并且——
**English / Original**
But e- I think this is one of those exceptional times where society has a legitimate interest in what the impact of this technology is going to be. I think the internet was one of these times, too, and I don't think we got all of that right. But I'd like us to learn what we can do better. And if what the AI companies say, and I also believe it comes true, and that this is going to reshape the economy, this is going to reshape geopolitical power, this is going to
## 00:23:16
**中文**
改变我们每个人的生活方式，那么它的使用方式就不应由公司或某届政府强加特定意志。我认为这应取决于人民的意愿，并通过民主程序来实现。像我们这样的公司，正以前几代公司从未经历过的速度，迅速转变为某种关键基础设施提供者。
**English / Original**
change how we all live our lives, um then I don't think it should be up to companies or a government to impose a particular will on how this is going to get used. Um I think that this belongs to the will of the people, uh working through the democratic process. And companies like ours, I think are quickly, more quickly than companies of previous generations have had to do, moving into a a sort of critical infrastructure role.
## 00:23:50
**中文**
在这个角色中，我们必须说明：我们创造了这项技术，是这方面的专家，因此应拥有真正的发言权；对于技术的局限、哪些领域尚不适合使用，以及哪里可能造成重大伤害，我们也有自己的意见和理解。但规则与限制必须由社会通过民主程序共同商定。鉴于技术发展如此之快，如果民主程序也能运行得更快当然很好。企业和政府需要能够依靠我们这样的公司，将技术集成并投入使用。
**English / Original**
>> Right. >> Where we have to say, um we create this technology. We are experts in it, and we should have a real voice, and we have a we have opinions and understanding of where its limitations are, and where it's not ready to be used, and where great harm could come. Uh but the rules, the limitations have to be agreed upon by society through this process, and because the technology is moving so fast, it'd be great for that democratic process to run a little bit faster. But companies, governments, uh need to be able to depend on companies like ours to integrate and be able to use the the technology.
## 00:24:31
**中文**
——回到全球 AI 竞赛，你认为美国最脆弱的环节在哪里？——我想到三点。第一，人们已经大量讨论全球供应链依赖和美国基础设施问题。我没有什么更新颖或深刻的观点，但无论怎样强调它令我多么担忧，都不为过。
**English / Original**
>> Okay. So coming back to the global AI race, where where do you think the US is the most vulnerable? >> Three three things come to mind. Uh one, there's been a ton of noise made about the global supply chain dependence and US infrastructure. I don't have anything new or deep to say there, but I can't overstate how scary this is to me.
## 00:25:06
**中文**
如果我们在基础设施上落后且无法追赶；如果全球化以其可能发生的任何一种方式瓦解，而我们又无法较为独立地继续建设 AI 基础设施，这将是一个重大弱点。我谈不上厌恶、但确实不满意我们目前在全球体系中的位置。
**English / Original**
>> Yep. >> Um the if we fall behind on infrastructure and can't catch back up, um if if if globalization falls apart in any of the many ways it could, >> Mhm. >> uh and we are not able to fairly independently keep building AI infrastructure, that that seems like a big vulnerability and I don't I don't hate, but I don't love our global position right now.
## 00:25:34
**中文**
第二，这是一个竞争激烈的世界。如果我们在 AI 的经济应用方面行动不如其他国家快，就会失去作为经济强国所拥有的优势。这取决于企业采用 AI 的速度、科学家采用它的速度以及政府采用它的速度。从积极一面看，我认为这是数代人才遇一次的机会，可以真正改善经济，并借助这一全新的巨大财富源泉，重写社会中一些已经失效的规则。
**English / Original**
>> Mhm. >> Um the second is if we don't It's a competitive world and if we don't move as quickly as other countries on economic adoption of this, >> Mhm. >> uh then I think we will lose the advantage that we have from being the economic powerhouse that we are. And this is about how quickly companies adopt it. This is about how quickly our scientists adopted this, how quickly our government adopts it. Uh this is like on the positive side, I think this is a once-in-many-generation opportunity to really improve the economy, really rewrite some of the rules of society that aren't working in light of this new incredible wealth fountain we
## 00:26:16
**中文**
所拥有的。
**English / Original**
have.
## 00:26:17
**中文**
因此，我能设想一种未来：这不但完全不是劣势，反而成为我们最大的竞争优势。但我并不认为美国目前显然走在理想轨迹上。还是那句话，我并非不满，只是认为速度可以更快，同时也能看到许多潜在阻力。例如，AI 目前在美国并不太受欢迎，数据中心被指责推高电价。
**English / Original**
Um so I can see the world where this is not a disadvantage at all, but this becomes our biggest competitive advantage. I don't I don't think it's like super obvious that we're on the trajectory we want to be there now. Again, I don't hate it. I just I think it could move faster and and you can see a bunch of potential headwinds, you know, like AI is not very popular in the US right now. Data centers are getting blamed for electricity price hikes.
## 00:26:47
**中文**
几乎每家公司裁员时都会归咎于 AI，不论裁员是否真的由 AI 导致。此外，政府与企业之间的相对权力也正在经历一场真正的辩论。因此这里有很多事情同时发生。第三个方面，则是技术向世界其他地区的扩散。
**English / Original**
Almost every company that does layoffs is getting is blaming AI whether or not it really is about AI. >> Yeah. >> Um there is this real debate about the relative power between governments and companies going on. So So there's like a lot of There's a lot of stuff happening there. And then the third category is uh diffusion into the rest of the world.
## 00:27:11
**中文**
世界主要会建立在美国的 AI 技术栈之上——无论芯片、模型还是应用——还是我们会出台一系列政策，使这种选择变得更困难？——听起来，你认为如果得到恰当使用和采用，AI 可能成为一轮巨大生产力繁荣的基础。
**English / Original**
>> Mhm. >> Is the world mostly going to build on the American AI tech stack, chips, models, applications, whatever, or are we going to enact a set of policies that make that >> harder? >> It sounds to me like you think that AI could be the foundation of an immense productivity boom. If prop- if properly used and if if adopted.
## 00:27:34
**中文**
——当然。不过我认为，我们衡量这种繁荣的方式将不得不改变。——请解释一下。——我能设想一个世界：生产力出现惊人繁荣，生活质量不断提高，我们口头上所说在意的大多数事情也越来越好，但 GDP 按照现有衡量方式却持续下降。
**English / Original**
>> Yes, for sure, although I think the way we measure that is going to have to change. Um >> Explain. >> I can see a world where we have an incredible productivity boom. Quality of life goes up and up. Uh most of the things that we think we we say we would care about get better and better. And yet GDP and the way we currently measure it goes down and down.
## 00:28:04
**中文**
比如说，世界长期处于通缩之中。我不知道生活在一个永久通缩的世界意味着什么；也不知道当越来越多的经济能力——抱歉，是智力能力——存在于数据中心而非外部世界时，该如何理解 GDP，以及 GDP 与生活质量之间的相关性。
**English / Original**
Like deflation for a very long period of time. >> Right. >> And I don't know what it means to like live in a forever deflationary world. I don't know what it means to think about GDP in a and GDP's correlation of quality to quality of life in a world where, you know, more of the economic capability or the sorry, intellectual capability is inside of data centers than outside of them.
## 00:28:30
**中文**
也许我们很快就会找到答案。我认为未来几年会出现大量讨论：我们究竟应该衡量什么？——你认为我们正在用正确方式思考这些挑战和问题吗？相关讨论已经开始了吗？——是的，我认为已经开始。
**English / Original**
>> Mhm. >> But you know, maybe we're going to find out. And And I think there's going to be a lot of debate in the coming years about what the right thing to measure is. >> Do you think we're thinking about these challenges and issues the right way? Are we having that Are we starting to talk about that? >> I I think we yes, we're starting to.
## 00:28:54
**中文**
如果存在一个容易达成共识的答案，我们早就把它付诸实施了。所以，我认为没人真正知道该怎么办。社会长期以来依赖的所有这些事物，正在同一时间受到质疑。几周前我在网上看到一句话，一直萦绕在脑海里。大意是：数百年、乃至数千年来，我们学会了很多如何构建社会、管理稀缺的知识。
**English / Original**
Um if there was an easy consensus answer, we would >> [laughter] >> have done it by now. So, I don't think anyone knows what to do. Um the All of these things that we've depended on for so long as a society are sort of coming into question at the same time. Uh there was this quote that I saw on the internet a few weeks ago. It's really stuck in my head. It was something like for for centuries, maybe millennia, we have learned a lot about how to structure society to manage scarcity.
## 00:29:25
**中文**
但当我们不得不迅速学习如何管理充裕时，这些知识几乎全都派不上用场。这会真正改变资本主义的运行方式。资本主义还依赖劳动与资本之间某种程度的力量平衡；但如果在许多现有工作中，人很难比一块 GPU 更能干，平衡就会改变。我知道时间到了，不过我列了大约十项这种同时发生的变化。
**English / Original**
>> Mhm. >> Almost none of that helps us as we have to quickly learn towards managing abundance. >> Mhm. >> Uh So, that's like a real change to how capitalism has worked. Um Capitalism has also depended on this balance between somewhat of a power balance between labor and capital. But, if it's hard in many of our current jobs to outwork a GPU, then that changes. And I see we're out of time, but I had like a list of 10 things like that changing all at once.
## 00:29:55
**中文**
长期而言，我并不悲观看待就业；我认为我们会找到新的事情可做。我当然也不长期看衰资本主义，我对此深信不疑。但在抵达那个由我们共同重新定义的新制度和惊人繁荣之前，未来几年将经历痛苦的调整。一路上会出现一些非常激烈、令人不适的辩论。
**English / Original**
I I'm not a long-term jobs doomer. I I think we will figure out new things to do. I'm also certainly not a long-term capitalism doomer. I believe in it very very deeply. Um But, I think the next few years are going to be a painful adjustment as we get to this future we all get to redefine of what the new system and just this incredible prosperity looks like. And there are going to be some very intense and uncomfortable debates on the way there.
## 00:30:24
**中文**
——好的，萨姆，谢谢你带来如此深思熟虑的讨论。我要说的是：五年后，我们会再次回到这里，看看届时身处何方，又是如何一路走过来的。——说定了？——说定了。——我很期待。谢谢。谢谢大家。——［掌声］现在短暂休息。
**English / Original**
>> Okay. So, thank you, Sam, for that very thoughtful discussion. Here's what I will say. 5 years from now, we will come back here and we will see where we are and how we have navigated our way through this. >> Deal? Deal. >> I look forward to that. Don't drink. Thank you. Thank you. Thank you. >> [applause] >> We'll now take a short [music] break.
## 00:30:52
**中文**
请大家前往东侧贵宾休息室享用茶点，并在 15 分钟后回到座位。谢谢。［音乐］
**English / Original**
Please join us in the East Green Room for refreshments and return to your seats in 15 minutes. Thank you. >> [music] [music] [music]
## 00:31:40
**中文**
［音乐］
**English / Original**
[music]
## 00:34:02
**中文**
——嗯。
**English / Original**
>> Mhm.