2026-05-13 Alex Wang.Meta's AI Chief On AI Beef, New Models And Life With Zuck
Meta Superintelligence Labs Structure and Strategic Compute Advantage
Meta Superintelligence Labs 的组织结构与战略算力优势
Meta Superintelligence Labs (MSL) operates through a specialized structure comprising TBD for large model research, Product and Applied Research (PAR) for deployment, and FAIR for exploratory science. The organization prioritizes long-term infrastructure planning, specifically GPU and data center capacity, to support the development of frontier models. The decision to join Meta was driven by the company's massive compute resources and a clear, bold commitment to achieving superintelligence. This compute-centric strategy creates a significant barrier to entry, as companies with vast infrastructure capabilities can execute research and product deployments that are impossible for those without such resources.
Meta Superintelligence Labs（MSL）通过专业化结构运作，其中 TBD 负责大模型研究，Product and Applied Research（PAR）负责部署，FAIR 负责探索性科学研究。该组织优先考虑长期基础设施规划，尤其是 GPU 和数据中心容量，以支持前沿模型的发展。加入 Meta 的决定，主要受该公司庞大算力资源以及实现超级智能的清晰、大胆承诺所驱动。这种以算力为中心的战略形成了显著的进入壁垒，因为拥有庞大基础设施能力的公司，能够执行那些没有此类资源的公司无法完成的研究和产品部署。
Speaker 2:
(00:00)
All right, Kylie, we've got another big guest this week.
好，Kylie，这周我们又请到了一位重量级嘉宾。
Unknown Speaker:
(00:03)
Huge.
非常重量级。
Speaker 2:
(00:04)
Alex Wang, the chief of Meta's artificial intelligence efforts. It was how many months ago?
Alex Wang，Meta 人工智能工作的负责人。那是多少个月前的事？
Kylie Robison:
(00:12)
About 10 months ago.
大约 10 个月前。
Speaker 2:
(00:13)
About 10 months ago, he was the founder, co-founder and CEO of Scale AI. Meta sort of quasi-acquired the company, half-acquired the company, and fully acquired Alex. He's been in the AI protection program.
大约 10 个月前，他还是 Scale AI 的创始人、联合创始人兼 CEO。Meta 有点像是准收购了这家公司，或者说半收购了这家公司，并且完全收购了 Alex。他一直处在人工智能保护计划里。
Kylie Robison:
(00:33)
Yes, we haven't seen much of him whatsoever until today on the Core Memory Pod.
是的，在今天的 Core Memory Pod 之前，我们几乎完全没怎么见到他露面。
Speaker 2:
(00:38)
Yes, I'm not sure exactly why this happened, but here he is. He's gonna tell us. Well, we'll find out. He's gonna hopefully tell us about They just released a new model. I'm sure we will get into some of that. And then there's a little bit of a mystery to me about like, Where they are philosophically on AI. Alex has always been a bit of a, when he was at Scale, he was, he was, they always said they were Switzerland and he was loud on some things, but not, not always AI itself and how he feels about it.
是的，我不太确定这件事为什么会发生，但他现在来了。他会告诉我们。嗯，我们会弄清楚的。希望他会谈谈他们刚刚发布的新模型。我相信我们会聊到其中一些内容。然后，对我来说还有一点谜团，就是他们在人工智能的哲学立场上到底处于什么位置。Alex 一直有点——当他还在 Scale 的时候，他是——他们总说自己是瑞士，而他在某些事情上声音很大，但并不总是在人工智能本身以及他如何看待人工智能的问题上表态。
Kylie Robison:
(01:13)
And they made a lot of news last year with everyone they hired for millions and millions and millions of dollars and built up this team and everyone's been waiting to see what they're going to do with all of these resources.
去年他们因为花了数百万、数百万、又数百万美元招揽了很多人，并组建起这支团队，制造了很多新闻。所有人都一直在等着看，他们会用这些资源做出什么。
Speaker 2:
(01:25)
So, we will talk about recruiting soup and all these millions of dollars. So, this is it. This is Alex Wang. He's emerged.
所以，我们会聊到招聘大战，以及所有这些数百万美元的事情。那么，就是他了。这就是 Alex Wang。他现身了。
Kylie Robison:
(01:36)
Hell yeah, brother.
没错，兄弟。
Speaker 2:
(01:37)
I am Ashlee Vance.
我是 Ashlee Vance。
Kylie Robison:
(01:39)
And I'm Kylie Robison.
我是 Kylie Robison。
Speaker 2:
(01:40)
And this is Core Memory. Alex, thank you for being here.
这里是 Core Memory。Alex，谢谢你来参加节目。
Alex Wang:
(02:02)
Yeah, excited to be here.
是的，很高兴来到这里。
Speaker 2:
(02:03)
I feel like we were, we kind of texted a little bit pre-meta happenings. We had like this kind of country music, odd text chain going. And then I'm also, I've known Nat Friedman for a long time. And then I feel like the two of you disappeared.
我感觉我们在 Meta 那些事情发生之前，还稍微发过一些短信。我们之间好像还有一串挺奇怪的乡村音乐相关短信。然后，我也认识 Nat Friedman 很久了。再之后，我感觉你们两个人就消失了。
Alex Wang:
(02:28)
Went into the foxhole.
钻进战壕里了。
Speaker 2:
(02:28)
Yeah, we're very, very quiet there. And now here you are with a new model.
是啊，那段时间你们非常、非常安静。现在你带着一个新模型出现了。
Alex Wang:
(02:33)
Yeah.
是的。
Speaker 2:
(02:34)
Emerging.
浮出水面了。
Alex Wang:
(02:35)
Yeah.
是的。
Speaker 2:
(02:35)
But you guys went very quiet for a bit.
但你们确实安静了一段时间。
Alex Wang:
(02:38)
Yeah, we had a lot of work to do. I mean, I think... It turns out building a frontier model from scratch in nine months is, yeah, it takes a lot of painstaking effort. But yeah, I mean, it's been really exciting to see everyone use MuseSpark, you know, the model we released, and we have better models cooking, so it's exciting.
是的，我们有很多工作要做。我的意思是，我认为……事实证明，在九个月内从零开始构建一个前沿模型，确实需要大量艰苦细致的努力。不过，是的，看到大家使用 MuseSpark，也就是我们发布的那个模型，确实非常令人兴奋，而且我们还有更好的模型正在准备中，所以这很令人期待。
Speaker 2:
(03:00)
And so what you, I mean, you were like a San Francisco alight, San Franciscan, I guess. And then, I mean, I assume you work at Menlo Park.
所以，你之前算是一个旧金山人吧，我猜。然后，我假设你现在是在 Menlo Park 工作。
Alex Wang:
(03:10)
Yeah.
是的。
Speaker 2:
(03:10)
So were you, have you had to like...
所以你是不是不得不……
Alex Wang:
(03:13)
I moved down to South Bay, yeah. Did you?
我搬到 South Bay 了，是的。你搬了吗？
Kylie Robison:
(03:15)
Wow.
哇。
Alex Wang:
(03:15)
Yeah, I did.
是的，我搬了。
Speaker 2:
(03:16)
So you're full on committed.
所以你是完全投入了。
Alex Wang:
(03:18)
I'm full on, yeah. For me, the city now is Palo Alto.
我是完全投入了，是的。对我来说，现在这座城市就是 Palo Alto。
Speaker 2:
(03:21)
Okay.
好。
Alex Wang:
(03:21)
Walk on University Ave, get a boba.
在 University Ave 上走一走，买一杯珍珠奶茶。
Speaker 2:
(03:23)
Okay, I was wondering about this. What's like the arrangement between, I mean, the people I know best are you, Nat, Daniel Gross. I've only hung out with Zach once or twice. Yeah, I mean, what's the, I'm trying to pick, paint a picture of, of, of how you guys are arranged.
好，我正想问这个。你们之间的安排是什么样的？我的意思是，我最熟悉的人是你、Nat、Daniel Gross。我只和 Zach 见过一两次。是的，我是想勾勒出一幅图，看看你们这些人是怎么组织在一起的。
Alex Wang:
(03:41)
Yeah. So, um, so basically, uh, the, the whole unit is called Meta Superintelligence Labs, um, which, which I oversee and then there's various pieces of it. So, um, there's a unit called TBD, which is, um, the, Sort of large model research lab that I think, you know, is somewhat infamous, but that's where a lot of the leading researchers and infrastructure engineers are. They actually all technically report to me. So that's one set up. Then there's also a group called Product and Applied Research or PAR for short. That's what Nat Friedman heads up. So they're responsible for all the products that we build and the actual sort of deployment of these great models to the world.
是的。所以，基本上，整个部门叫做 Meta Superintelligence Labs，由我负责，然后它下面有不同的组成部分。有一个部门叫 TBD，它算是一个大模型研究实验室，我想它在某种程度上已经挺有名了，很多顶尖研究人员和基础设施工程师都在那里。从技术汇报关系上说，他们实际上都向我汇报。这是一套设置。然后还有一个叫 Product and Applied Research 的团队，简称 PAR，由 Nat Friedman 负责。所以他们负责我们构建的所有产品，以及把这些优秀模型真正部署到世界中去。
(04:31)
And then also within the overall Meta Superintelligence Labs umbrella is FAIR, which continues to do exploratory and exciting research. I'm particularly excited about a lot of their scientific research. So, you know, we've shown some pretty great work on, you know, using models, AI models to understand the brain as well as using AI models to understand, you know, computational chemistry. We have built like a universal model for atoms, UMA for short. And then And so that those pieces constitute meta super intelligence labs, which. I oversee, in addition to having a very hands-on role with TBD Lab, and then Daniel Gross helps lead up Meta Compute,
然后，在整个 Meta Superintelligence Labs 的大伞之下，还有 FAIR，它会继续做探索性且令人兴奋的研究。我尤其对他们许多科学研究感到兴奋。比如，我们已经展示了一些相当出色的工作，包括使用模型，也就是人工智能模型来理解大脑，以及使用人工智能模型来理解计算化学。我们构建了一个原子通用模型，简称 UMA。然后，这些部分共同构成了 Meta Superintelligence Labs，由我负责。此外，我还会非常亲自地参与 TBD Lab 的工作，而 Daniel Gross 则协助领导 Meta Compute，
(05:14)
which is really focused on our long-term infrastructure planning to ensure that, you know, we obviously can build up all of the GPU infrastructure and data center infrastructure necessary for this very bold endeavor. And so he heads that up and partners closely with us.
它真正专注于我们的长期基础设施规划，以确保我们显然能够建设起这项非常大胆事业所需要的全部 GPU 基础设施和数据中心基础设施。所以他负责这部分，并与我们密切合作。
Speaker 2:
(05:31)
Who did you know the best out of that group before you got into this?
在加入这件事之前，那群人里你最熟的是谁？
Alex Wang:
(05:37)
I've known Nat and Daniel actually for a long time. Nat was one of my very first angel investors at scale. Before I completed YC, Nat had invested in Scale and had given me advice throughout the years. Daniel, I think I also met around that time, you know, very, very early on and have gone to know them through the years. And then we also have our chief scientist, Shengjia, who helps oversee the scientific agenda across all of MSL. And he is somebody who I We have gotten to know, you know, I knew before starting at Macell, but, you know, since he's come in, we've gotten a lot closer.
其实我认识 Nat 和 Daniel 已经很久了。Nat 是 Scale 最早的一批天使投资人之一。在我完成 YC 之前，Nat 就已经投资了 Scale，并且这些年来一直给我建议。Daniel，我想我也是在差不多那个时候认识的，也就是非常、非常早期的时候，然后这些年来慢慢熟悉起来。然后我们还有首席科学家 Shengjia，他帮助统筹整个 MSL 的科学议程。他也是我们逐渐熟悉起来的人，当然，在加入 MSL 之前我就认识他，但自从他加入以后，我们的关系变得亲近了很多。
Kylie Robison:
(06:22)
So I'm really curious, like taking a huge step back. It's been 10 months since you kind of went into hiding your company, you know, completely changed. And now you're at Meta. What was that experience like? How was the deal made? Like, how did you end up going to Tahoe and talking with Zuck? Can you just walk us through what that first meeting was like?
所以我真的很好奇，我们先大幅往后退一步看。自从你差不多隐身以来已经过去 10 个月了，你的公司也完全发生了变化。现在你在 Meta。那段经历是什么样的？这笔交易是怎么达成的？比如，你最后怎么会去 Tahoe 跟 Zuck 聊？你能不能带我们回顾一下第一次会面是什么样的？
Alex Wang:
(06:44)
I've known Mark for many years now. Even while I was running Scale, he was very generous with his time and I was able to get a bunch of advice from him. He obviously is just such an experienced founder. The founder at this point in some sense. And so we've known each other for many years. And we'd actually talked about AI before a lot of this craze because, you know, Scale, obviously, we've been working on AI since 2016. And, you know, back when it was mostly self-driving and then, you know, the various transitions of the technology. And then around a year ago, almost like literally a year ago, you know,
我认识 Mark 已经很多年了。甚至在我经营 Scale 的时候，他也非常慷慨地投入时间，我能够从他那里获得很多建议。显然，他是一位经验极其丰富的创始人。从某种意义上说，到现在他就是那位创始人。所以我们已经认识很多年了。其实在这波热潮出现之前，我们就已经聊过人工智能很多次，因为你知道，Scale 显然从 2016 年起就在做人工智能。当时主要还是自动驾驶，后来技术又经历了各种转变。然后大约一年前，几乎就是整整一年前，
(07:30)
we had a conversation where we sort of started exploring if there were a way to work more closely. And in particular, I think Mark at that point, I think he was Becoming increasingly AGI filled and and really knew that first I was going to totally transform meta, but also that I was. One of these, you know, almost like once-in-a-lifetime transformative technologies. And so he was really quite focused on it and knew he wanted to bet very big on it. And at the same time, you know, and he's talked about this publicly, like Llama4 was not on the trajectory that the company needed to be able to continue making some of these bets. And so we were sort of talking at a very high level about You know,
我们有过一次谈话，开始探索是否存在一种方式，可以让我们更紧密地合作。尤其是，我认为 Mark 在那个时间点，越来越被通用人工智能这件事所占据，并且他真的知道，首先，人工智能会彻底改变 Meta，同时它也是这种，几乎可以说是一生一次的变革性技术之一。所以他确实非常专注于这件事，也知道自己想在这上面下很大的赌注。与此同时，他也公开谈过，Llama4 当时并不处在公司继续推进这些下注所需要的轨道上。因此，我们当时是在非常高的层面上讨论，你知道，
(08:24)
how can we work together more closely? What could that look like? And obviously, it kind of, you know, it was one of these very open-ended questions and these very open-ended brainstorm sessions, as these things often are, and it sort of landed in this interesting zone where we figured a way to do it in a way that was like, Good for Scale. It was good for Meta and where we got to work very, very closely together to build out, you know, the most important technology of our time and do so in a way that, um, where we both had conviction that it was going to be, you know, we're building something that we're both really proud of, which is, you know,
我们怎样才能更紧密地合作？那会是什么样子？显然，这有点像是一个非常开放的问题，也是一系列非常开放的头脑风暴讨论，这类事情通常都是这样。最后它落到了一个有意思的区域：我们找到了一种方式，可以让这件事对 Scale 有利，对 Meta 也有利，并且我们能够非常、非常紧密地合作，去构建我们这个时代最重要的技术，而且以一种我们双方都确信的方式去做，也就是说，我们正在构建某种双方都真正引以为傲的东西，也就是，
(08:58)
he put out this memo of personal superintelligence also about a year ago. Um, and then we went quiet obviously, but, um, but I think that really is the sort of like North star for both of us is we want to build This technology in a way that empowers people where, you know, as many people in the world have access to it and it's as democratized as possible that enables everyone to express themselves, everyone to have increased agency, everyone to create and build, and that's really the world that we want to work towards.
他大约一年前也发布了那份关于个人超级智能的备忘录。然后我们显然就安静下来了。但我认为，那确实是我们双方的北极星：我们想以一种赋能人们的方式来构建这项技术，让世界上尽可能多的人都能使用它，让它尽可能民主化，使每个人都能表达自己，每个人都能拥有更强的自主性，每个人都能创造和建设。这确实就是我们想要努力走向的世界。
Speaker 2:
(09:24)
But you're, you know, I did a really early story on you. I mean, I've known you for a long time. Yeah, yeah. When I was 21? Yeah, I think so. I mean, and you, you know, there's all this lore. You were the Youngest self-made billionaire and this hot shit and Scale was such a prominent company and you had this reputation for reading the tea leaves of where AI was going to go. I mean, like in what I would talk to you, I mean, Scale was part of your identity. It's very different to be the founder of this very prominent company and then taking a role at a place with 80,000 employees, even if you're It's a prominent role at that company. And yeah, I mean, I think I was really surprised.
但你知道，我很早就写过一篇关于你的报道。我的意思是，我认识你已经很久了。是的，是的。那时候我 21 岁？是的，我想差不多。我的意思是，你知道，关于你有很多传说。你是最年轻的白手起家的亿万富翁，是个很厉害的人物，而 Scale 也是一家非常重要的公司。你还有一种名声，就是能看懂人工智能未来走向的信号。我的意思是，当我跟你聊天的时候，Scale 是你身份的一部分。作为一家非常知名公司的创始人，和去一家有 8 万名员工的公司担任一个职位，是非常不同的，即使你在那里担任的是一个重要职位。是的，我的意思是，我确实非常惊讶。
(10:05)
I mean, there's a lot of money involved. Okay, but Just sort of knowing you, it's not like I know you that well, but just knowing you as much as I did. Yeah, I was like, man, that must have been a hell of a sales pitch too, because it's a big flip.
我的意思是，这里面涉及很多钱。好吧，但就我对你的了解而言，虽然也不能说我特别了解你，但以我对你的了解程度来说，是的，我当时想，天哪，那一定也是一次非常厉害的说服，因为这是一个很大的转变。
Alex Wang:
(10:21)
Yeah, very different. It's super different. And, you know, a lot of what I was thinking about throughout this process is obviously, I think, Like everyone in and around AI, I mean, progress has just happened a lot faster than I'd expected for a long time. And in the sort of like accelerating progress of these AI models, I think a few things really started to stick with me. One is, you know, It felt like, and I do think this is increasingly the case, that those who build the AI models have just Greater and greater rights, so to speak, or both economic and product rights, to just build so much more around those models.
是的，非常不同。极其不同。你知道，在整个过程中，我思考了很多事情。显然，我认为，就像所有处在人工智能领域内外的人一样，进展发生的速度长期以来都比我预期得快得多。在这些人工智能模型进展不断加速的过程中，我觉得有几件事开始真正让我印象深刻。第一是，你知道，我感觉到，而且我确实认为情况正越来越如此，那些构建人工智能模型的人，可以说拥有越来越大的权利，或者说同时拥有越来越大的经济权利和产品权利，能够围绕这些模型构建更多东西。
(11:10)
And there were obviously all these early debates around how does the ecosystem play out? And I think because of just how fast the models are improving and how fast the research pace is, it just means that in many ways, Being a place that is building the models are some of the most exciting places to be in the ecosystem. And then the second is really that so much of this next phase of technology really boils down to compute. And if you have lots and lots of compute, then you have the ability to build things and make big bets and deploy products and do things that you just can't if you don't have that compute.
显然，早期有过很多关于生态系统会如何演化的讨论。而我认为，正是因为模型改进得如此之快，研究节奏如此之快，这就意味着在很多方面，处在构建模型的地方，是整个生态系统里最令人兴奋的位置之一。第二点则是，这项技术下一阶段的很大一部分，本质上确实归结为算力。如果你拥有大量、大量的算力，那么你就有能力构建东西，进行重大下注，部署产品，并做一些如果没有这些算力就根本做不了的事情。
(11:53)
And I think that This causes, this will cause an interesting stratification for the tech ecosystem where, you know, right now, in some sense, we think about, well, I think it's already changing. But, you know, you kind of think about all tech companies are the same. But in reality, you should think about companies with lots of compute very differently from companies without tech compute, because there's just things that companies with compute can build that those without compute just cannot. And so it creates this very interesting dynamic. And so Part of what was very exciting about the opportunity at Meta, first is that Mark is,
我认为这会导致，或者将会导致科技生态系统出现一种有意思的分层。你知道，现在从某种意义上说，我们会觉得——其实我认为这已经在改变了——但你大致会认为所有科技公司都是一样的。可实际上，你应该把拥有大量算力的公司和没有大量算力的公司非常不同地看待，因为有些东西，拥有算力的公司可以构建，而没有算力的公司就是做不到。所以这创造了一种非常有意思的动态。因此，Meta 这个机会令人兴奋的一部分，首先在于 Mark，
(12:27)
I think, very all in on AI and really has bet very big and is a very bold leader and strategist, but also that this created the conditions where We're able to build with huge amounts of compute and with the right research effort and the right product efforts, we have the ability to really make a huge dent in the world.
我认为他在人工智能上非常全力投入，确实下了非常大的赌注，而且是一位非常大胆的领导者和战略家；同时，这也创造了这样的条件：我们能够依托巨量算力进行构建，并且通过正确的研究努力和正确的产品努力，我们有能力真正对世界产生巨大影响。
Kylie Robison:
(12:50)
You guys have a ton of compute and you guys poached a lot of amazing talent. I was a part of that whole reporting frenzy at the time. Unlike anything I've ever seen before, and it's been 10 months with many of these people, so what has it been like? What have the challenges been? And what has been the most exciting about having this whole new team at Meta?
你们拥有大量算力，也挖来了很多了不起的人才。我当时也是那整场报道狂潮的一部分。那是我以前从没见过的场面，而现在你已经和其中很多人共事了 10 个月，所以这段经历是什么样的？挑战是什么？在 Meta 拥有这整支全新的团队，最令人兴奋的地方又是什么？
⸻
Rebuilding AI Research Velocity and Talent Density
重建人工智能研究速度与人才密度
The transition to Meta involved a fundamental reset of existing AI efforts to align with the goal of achieving superintelligence. Success in this field requires high compute-per-researcher ratios and extreme talent density, allowing small, focused teams to move faster than bloated organizations. The internal culture of the lab is modeled after early-stage startups, prioritizing technical rigor and ambitious research bets over traditional corporate hierarchies. Recruiting efforts were highly individualized, focusing on researchers who prioritize the opportunity to build from scratch with massive resources rather than purely financial incentives.
转入 Meta 涉及对既有人工智能工作的根本性重置，以使其与实现超级智能的目标对齐。这个领域的成功需要很高的单个研究人员算力配比，以及极高的人才密度，使小而专注的团队能够比臃肿组织行动更快。实验室的内部文化以早期创业公司为模板，优先强调技术严谨性和雄心勃勃的研究下注，而不是传统公司层级。招聘工作高度个性化，重点寻找那些看重从零开始、并且拥有巨大资源进行构建机会的研究人员，而不是单纯依靠财务激励。
Alex Wang:
(13:14)
Yeah, so, you know, when I got when I kind of got to Meta, you know, it was, it was clear that there needed to be some reset of the efforts and some rebuild of our AI efforts to get on to the right trajectory, because ultimately, like, you know, long before is not on the same trajectory. And so we were behind the frontier. And so we needed to build a plan that would enable us to have a very, very fast velocity to be able to both catch up and hopefully exceed where the frontier is.
是的，所以，你知道，当我加入 Meta 的时候，很明显，我们需要对相关工作做一些重置，也需要重建我们的人工智能工作，使其进入正确轨道，因为归根结底，你知道，之前很长一段时间并不在同一条轨道上。所以我们落后于前沿。因此，我们需要制定一个计划，使我们能够以非常、非常快的速度既追赶上来，并且希望能够超越前沿所在的位置。
Speaker 2:
(13:47)
So can you be specific? What were the problems that you found?
所以你能具体一点吗？你发现的问题是什么？
Alex Wang:
(13:53)
I think that probably the more fundamental ones are just around a lot of the leading labs, they build the entire organizations around the premise that, Superintelligence is coming and it is very close and this is a very realistic thing to believe that we can create and produce. And then you build the entire plan of the lab and the business and what you focus on around this fundamental belief. And so that was one of the first things is to just take superintelligence seriously and then start to rebuild all of your other assumptions around that core premise. So that's something I think was somewhat fundamental.
我认为，更根本的问题大概在于，很多领先实验室会围绕一个前提来构建整个组织：超级智能正在到来，而且已经非常接近；相信我们能够创造并产出它，是一件非常现实的事情。然后，你围绕这个基本信念，构建整个实验室计划、业务计划以及你关注的重点。所以，最初要做的事情之一，就是认真对待超级智能，然后围绕这个核心前提，重新构建你的所有其他假设。因此，我认为这在某种程度上是根本性的。
Speaker 2:
(14:38)
So you mean that they're like lacking this religious conviction to all this on some level?
所以你的意思是，在某种程度上，他们缺乏对这整件事近乎宗教式的信念？
Alex Wang:
(14:44)
Yeah, and I think this is relatively common actually. I think there's a lot of people at all the large companies who don't necessarily have this conviction because if you think about it, It's a bit of a different construction. Like a lot of the big companies, you know, they have very smart people who work on AI, but it's a little bit different from, you know, these startups where it's like, you know, it's like these new efforts started from scratch with this like crazy idea that superintelligence is coming. So I don't think there's a problem anymore. Like obviously I think now MSL, Meta Superintelligence Labs is It's in the name built around this concept that superintelligence is coming.
是的，而且我认为这其实相当常见。我认为在所有大公司里，都有很多人未必拥有这种信念，因为如果你想一想，这是一种有点不同的组织构造。比如很多大公司，你知道，它们有非常聪明的人在做人工智能，但这和那些创业公司有点不一样。创业公司更像是，这些新努力是从零开始的，带着一种疯狂想法：超级智能正在到来。所以我认为现在不再有这个问题了。显然，我认为现在 MSL，也就是 Meta Superintelligence Labs，从名字上就围绕超级智能正在到来这个概念来构建。
这个看法是清晰的，做法也非常残酷，除了OpenAI和Authropic，所有的大型科技公司中，目前除了Google有一点点的工作，没一个能打的。
(15:21)
So, there are a bunch of principles that we sort of laid out for the effort. And I think this sort of answers as to, you know, what were the things that we had to resolve. But one is take superintelligence seriously. Two is technical voices are loudest. Three is scientific rigor, focus on basics, and make big bets. So, the concept of TBD and MSL broadly when I sort of got started was I thought about what would actually be the shape of a lab that would enable you to have incredibly fast velocity and catch up and potentially even overtake the frontier. And I came down to there were three ways that I felt like that was possible. One is to have much higher compute per researcher.
所以，我们为这项工作制定了一系列原则。我认为这也在某种程度上回答了，我们必须解决哪些问题。第一，认真对待超级智能。第二，技术声音最大。第三，科学严谨，专注基本功，并进行重大下注。所以，当我刚开始的时候，关于 TBD 和更广义上的 MSL，我思考的是，一个能够让你拥有极快速度、追赶并且甚至可能超越前沿的实验室，到底应该是什么形态。最后我得出结论，我认为有三种方式可以做到这一点。第一，是让单个研究人员拥有高得多的算力。
(16:13)
So, you know, a lot of the larger labs, they have lots of compute, but it gets spread so many different ways. And so that actually impedes the research velocity of any individual researcher. So if you build a more focused effort with a smaller team that has higher compute per researcher, you can actually make faster research progress. Two is talent density. So Just and this is like, I think, you know, I feel like human organizations always relearn this lesson, but the very small team where everyone is cracked is always going to move faster than the very large organization where responsibility is more distributed. And, you know, it's more of a melange.
所以，你知道，很多较大的实验室确实拥有大量算力，但这些算力被分散到许多不同方向。这实际上会妨碍每个研究人员个人的研究速度。因此，如果你建立一个更专注的努力方向，用一个更小的团队，并且让每个研究人员拥有更高算力，你实际上可以取得更快的研究进展。第二是人才密度。这个就像是，我认为，你知道，我感觉人类组织总是在重新学习这一课：一个每个人都极其出色的小团队，永远会比一个责任更加分散的大型组织行动更快。你知道，后者更像是一种混杂组合。
(16:52)
And the last one was on very ambitious research bets. And, you know, I think I think this is very well agreed upon in the industry. There do exist these research bets that Are very big and very risky, but if they work out can totally change paradigms and totally shift how we build modern AI. And so, you know, in in addition to obviously building towards very competitive frontier models, we're allocating a huge amount of our resources and compute towards these big ambitious bets because they pan out. And that gives us, you know, incredible models going forward.
最后一点，是非常有雄心的研究下注。而且，你知道，我认为这在行业内已经有相当一致的共识。确实存在这样一些研究下注，它们规模很大、风险很高，但如果成功，就可能彻底改变范式，并彻底改变我们构建现代人工智能的方式。所以，除了显然要朝着极具竞争力的前沿模型建设之外，我们还把大量资源和算力分配给这些雄心勃勃的重大下注，因为一旦它们成功，就会为我们带来未来非常强大的模型。
Speaker 2:
(17:34)
Alright, what do we do at Core Memory? We cover innovative, fast-moving, forward-thinking companies, which is why Core Memory is sponsored by Brex, because Brex This is the intelligent finance platform for many of these companies. 30,000 companies from startups to the world's largest corporations rely on Brexit technology for their finances. They've got smart corporate cards, high yield business banking and expense automation tools that are fantastic. I hate doing my expenses. Brex's AIs and software run right through those expenses, figure out where we're spending money and take care of so much stuff for you so you don't have to waste your time on it yourself.
好，Core Memory 是做什么的？我们报道创新、快速行动、面向未来的公司，这也是为什么 Core Memory 由 Brex 赞助，因为 Brex 是许多这类公司的智能财务平台。从创业公司到全球最大企业，有 3 万家公司依靠 Brex 的技术来管理财务。它们有智能公司卡、高收益企业银行服务，以及非常出色的费用自动化工具。我讨厌处理报销。Brex 的人工智能和软件会直接处理这些费用，弄清楚我们把钱花在了哪里，并替你处理很多事情，这样你就不用自己把时间浪费在上面。
(18:19)
Go to brex.com slash core memory to learn more and just, you know, get with the program. Let's get going. Let's get out of this archaic finance software and move toward the future. Core memory and Brex.
访问 brex.com/corememory 了解更多信息，然后，你知道，跟上节奏。我们开始吧。让我们摆脱这些陈旧的财务软件，走向未来。Core Memory 与 Brex。
Kylie Robison:
(18:34)
Something Ashlee always talks about is how these labs are sort of leapfrogging over each other and they start to just serve the same thing. And you're talking about racing towards the frontier. I'm also thinking about, you know, the people that you guys hired was reported for these really, really wild salaries like we've never seen before. So, like, how are you, you know, getting to this paradigm you speak of? Like, specifically, what paradigm are you trying to achieve?
Ashlee 经常谈到的一点是，这些实验室如何彼此交替领先，然后开始提供差不多相同的东西。你现在谈的是向前沿冲刺。我也在想，你知道，你们招聘的人，据报道拿到了我们从未见过的那种非常、非常夸张的薪酬。所以，你们到底是如何走向你所说的这种范式的？具体来说，你们试图实现的是什么范式？
Alex Wang:
(18:59)
Yeah, I mean, I think I think there's a bunch of bold research bets, and I won't be able to go into detail on all of them, but I do think one fundamental question is, what do we care about? In line with this idea of personal superintelligence, we really care about building these agents that are able to empower consumers, so empower billions and billions of people all around the world, as well as empower businesses. Meta is kind of this incredible ecosystem. We have the billions and billions of users, which I think everyone knows about, but we also have hundreds of millions of businesses on our platforms that, you know, use Meta to run and operate their businesses.
是的，我的意思是，我认为有一系列大胆的研究下注，我没法逐一详细展开，但我确实认为一个根本问题是：我们真正关心什么？与个人超级智能这个理念一致，我们非常关心构建这些能够赋能消费者的智能体，也就是赋能全球数十亿人，同时也赋能企业。Meta 是一个某种意义上非常惊人的生态系统。我们拥有数十亿用户，我想这一点大家都知道，但我们的平台上还有数亿家企业，你知道，它们使用 Meta 来经营和运作自己的业务。
(19:43)
And so we really care a lot about Building towards this future where we're able to build very powerful agents that empower and enable every one of both the consumers and the businesses on our platforms and build kind of this new agentic ecosystem. You know, we think a lot about what that looks like and what are the capabilities we need to build towards that. And, you know, obviously, on that trajectory, there's a bunch of Subcomponents are really important. We really have to have great agentic capabilities. We have to have great coding capabilities because so much of what needs to be built is software as you really get into it. We need to have great multimodality.
所以，我们非常关心的是朝着这样一个未来建设：我们能够构建非常强大的智能体，赋能并帮助我们平台上的每一位消费者和每一家企业，并构建一种新的智能体生态系统。你知道，我们会大量思考它应该是什么样子，以及为了实现它，我们需要构建哪些能力。显然，在这条轨道上，有一系列子组成部分非常重要。我们确实需要非常强的智能体能力。我们需要非常强的编程能力，因为当你真正深入进去时，会发现大量需要构建的东西都是软件。我们需要非常强的多模态能力。
(20:22)
It informs a lot of the underlying bits and pieces that we need. Then we need to solve a lot of the bigger questions for long-running agents. How do you think about the memory challenges? How do you think about building long-running agents? How do we build agents that are able to do more and more complex tasks on behalf of the users? Those are a lot of the high-level pieces that we're thinking a lot about.
这决定了我们所需要的许多底层组成部分。然后，我们还需要解决长期运行智能体的一系列更大问题。你如何看待记忆方面的挑战？你如何思考构建长期运行的智能体？我们如何构建能够代表用户完成越来越复杂任务的智能体？这些都是我们大量思考的高层次问题。
Speaker 2:
(20:48)
This superintelligence religion built within the company, the way this was arranged is quite different to starting OpenAI or Anthropic, where like you were talking about earlier, this is all built from the ground up and these companies have sort of an identity and it gets shaped over time. For the outsider's view, what you guys did looks far more mercenary. You know what I mean? It's like, we're going to go You guys are out. We're going to go grab a bunch of high-priced people, bring them in. And it reminds me, you know, I just remember when Grok was starting up and it was like Elon, in his Elon way, he's just like, we're just going to get way more fucking compute than anybody else.
公司内部建立起这种超级智能信仰，但它的安排方式和创办 OpenAI 或 Anthropic 很不一样。就像你之前谈到的，那些公司都是从零开始建立的，而且这些公司有某种身份认同，并随着时间逐渐成形。从外部视角看，你们所做的事情看起来更像是雇佣兵式的。你明白我的意思吗？就像是，我们要过去，你们出局；我们要抓一堆高价人才，把他们带进来。这让我想起，你知道，我记得 Grok 刚起步的时候，Elon 用他那种 Elon 式的方法说，我们就是要拿到比其他任何人都多得多的该死算力。
(21:32)
And we have kind of this core team we're going to build around. And then It still felt like they caught up, but then never reached that escape velocity, especially in people's minds of brand and things like that. So, I mean, it just seems like it's a hard thing to buy some of the bits that you're talking about.
然后我们有这样一支核心团队，要围绕它来建设。再后来，感觉他们确实追上来了，但始终没有达到那种逃逸速度，尤其是在人们心中的品牌认知等方面。所以，我的意思是，你所谈到的这些要素里，有些东西似乎很难靠买来获得。
Alex Wang:
(21:52)
Yeah, I would say this is one of, I think, the larger, I would say, like narrative violations or maybe like differences between external perception and what the day-to-day inside is like. Actually, I think a lot of people You know, maybe have some of the impressions that you're talking about. And a lot of that is formed because of just, you know, the reporting and what it's sort of like, you know, how that sort of went down. And a lot of the reporting was overstated in various ways. But, you know, it's sort of it all sort of like bubbled up. And part of it was because we did the recruiting so quickly. We knew that when I got in, I knew I was like, if we want to build great models,
是的，我会说，这可能是较大的叙事错位之一，或者说是外部感知和内部日常实际状态之间的差异之一。实际上，我认为很多人可能都有你所说的那些印象。而这种印象很大程度上是由报道形成的，也就是事情看起来是如何发生的。而很多报道在不同方面都有些夸大。但你知道，这一切就这样不断发酵起来。其中一部分原因是，我们招聘做得非常快。我知道，当我加入的时候，我就知道，如果我们想构建伟大的模型，
(22:30)
we need to have the team yesterday. So we had to just Let's go and blitz it and do it very, very quickly. But I think that the culture within the lab is actually very much so a startup. And there's a bunch of things that I think have created this feeling. I think one is that it was an entirely new built team kind of like within Meta. But then the culture of the lab is like, I mean, everybody I was very attracted and excited about these things I talked about. People joined because there was high computer researchers, so they could make more progress than maybe they would be able to make at wherever they were before, because there was great talent density.
我们昨天就应该拥有这支团队。所以我们必须直接开干，快速突击，并且非常、非常快地完成。但我认为，实验室内部的文化实际上非常像一家创业公司。我认为有一系列因素造成了这种感觉。第一，它在 Meta 内部基本上是一支全新组建的团队。然后，实验室的文化是，我的意思是，大家都非常被我刚才谈到的这些东西所吸引，并且感到兴奋。人们加入，是因为这里有很高的单个研究人员算力配比，因此他们能够取得比在原来地方可能更快的进展；也是因为这里有很高的人才密度。
(23:15)
People saw that it was a truly cracked group that was pretty small, and that we were going to give them the resources and freedom to make very bold research bets. I think it's like an incorrect assumption to think that like the researchers are just money motivated or anything and for most of them actually like, you know, the financial prospects of them staying wherever they were. Look very good as well, like looks very, very, very strong as well. So money was not, you know, those sort of like maybe how it seemed like, but the primary motivations were actually much more that they had an opportunity to kind of like build from scratch and have lots of compute,
人们看到这确实是一个非常强的小团队，并且我们会给他们资源和自由，让他们进行非常大胆的研究下注。我认为，如果以为这些研究人员只是受金钱驱动，那是一个错误假设。事实上，对他们大多数人来说，留在原来的地方，财务前景看起来也非常好，也非常、非常、非常强。所以，钱并不是你知道的，虽然外界看起来可能像是这样，但主要动机实际上更多是：他们有机会近乎从零开始构建，并拥有大量算力，
(23:57)
have the ability to approach their like very ambitious research directions and then do so in a group that didn't feel bloated. So I think as a result, those sort of like vibe and culture are much healthier. And I would actually say like, you know, many people who visit the lab, who are at one of the other labs, like often comment that the vibe is actually It reminds them of early OpenAI or early Anthropic or these more nascent stages of these other labs because in some sense we're now 10 months old as an effort.
能够推进他们非常有雄心的研究方向，并且是在一个并不臃肿的团队里这样做。因此，我认为结果是，这种氛围和文化要健康得多。实际上我会说，很多来自其他实验室、来参观我们实验室的人，常常会评论说，这里的氛围其实让他们想起早期 OpenAI、早期 Anthropic，或者其他这些实验室更初生阶段的状态，因为从某种意义上说，作为一项努力，我们现在只有 10 个月大。
全新的团队可能是个重要的优势。
Speaker 2:
(24:35)
Just because Mark Chen was on this podcast and brought up the soup debacle during these recruiting wars, is this true? Did Zuck make soup? Did you make soup to recruit people?
只是因为 Mark Chen 上过这个播客，并提到了这些招聘大战期间的汤事件，这是真的吗？Zuck 做汤了吗？你们做汤来招人了吗？
Alex Wang:
(24:49)
I don't know if we made the soup, but I was told it was actually made by Zak, but I don't know. I don't know if we made the soup, but I do think it is true that we like, I think, Part of the premise of building this lab was also that like we had to show everyone that we really, really cared about this technology and we cared about their specific research directions and what they were working on. And it was a very individualized recruiting process, but also was one like I I'm very proud of the team that we've built and I think that people had to know that we were serious too. I think that by default,
我不知道我们有没有做那道汤，但我听说其实是 Zak 做的，不过我也不确定。我不知道我们有没有做汤，但我确实认为真实的一点是，我认为，建设这个实验室的前提之一，也是我们必须向每个人表明，我们真的、真的很在乎这项技术，也很在乎他们各自具体的研究方向，以及他们正在做的事情。这是一个非常个性化的招聘过程，但同时也是一个——我对我们组建起来的团队非常自豪，而且我认为人们也必须知道我们是认真的。我认为在默认情况下，
(25:35)
a lot of people didn't know what to think about Meta's AI efforts or they didn't know that much about us in many ways. So it took a lot of going to people, talking to them, explaining what we're building, explaining what we're focused on, explaining why we cared about the technology, what we wanted to do with it. That was very important.
很多人不知道该如何看待 Meta 的人工智能努力，或者从很多方面来说，他们对我们了解并不多。所以这需要我们大量走到人们面前，和他们交谈，解释我们正在构建什么，解释我们专注于什么，解释为什么我们在乎这项技术，以及我们想用它做什么。这一点非常重要。
Speaker 2:
(25:54)
I'll move on after this to not just belabor all the recruiting stuff, but you know, same thing. When you were at Scale, you were like the, everyone would call you the Switzerland of AI. And I just remember you knew everybody and you were in the center of things. And then it feels like some of this came with a personal cost. Maybe you and Sam used to be flatmates and I texted Sam about you coming on the show. You did not have flattering things to say. And so, you know, it seems like some of this, it must have come at a personal cost to you.
我问完这个就继续往下，不再过度纠缠所有招聘这些事。但你知道，也是类似的问题。你在 Scale 的时候，大家会把你称为人工智能领域的瑞士。我记得你认识所有人，也处在事情的中心。然后现在感觉其中一些事情带来了个人代价。也许你和 Sam 以前是室友，我给 Sam 发短信说你要上节目。他没有说什么好听的话。所以，你知道，这些事情看起来一定给你带来了一些个人代价。
Alex Wang:
(26:28)
I think some of this is unfortunate. My honest expectation is that as we get closer and closer to superintelligence, my hope genuinely as a human that all the sort of animosities that exist between various people in this industry, which is very topical obviously right now with other things happening, but I think that I think my real hope is that all these animosities subside over time and then people sort of come together and realize, you know, we are building this incredibly important technology and it's important for all of us to be really thoughtful about that as we build it. And so, yeah, I think and one of the things that I feel like it's a responsibility of mine,
我认为其中一些事情很不幸。我的真实预期是，随着我们越来越接近超级智能，我作为一个人真诚希望，这个行业中不同人之间存在的所有敌意，当然现在因为其他事情也变得非常热门，但我认为，我真正希望的是，这些敌意会随着时间逐渐消退，然后人们会在某种程度上走到一起，并意识到，你知道，我们正在构建一项极其重要的技术，在构建它的时候，我们所有人都必须非常审慎地思考。所以，是的，我认为，我觉得自己承担的一项责任，
(27:19)
honestly, is to ensure that the technology that we develop and the ways we deploy it are as thoughtful as possible.
坦率地说，就是确保我们开发的技术以及部署它的方式尽可能经过深思熟虑。
Kylie Robison:
(27:25)
Some of the stuff you talked about, like in terms of recruiting, it was trusting that we have a mission and it's not, you know. Just just products like you can go for your own research mission. And you're also quite young. We're almost the same age, which is very funny. And I'm not a billionaire. But Yann had said in the press shortly after he left that you were young and inexperienced and more people are going to leave. So I'm curious, like, how has that voted for you as a leader at this huge company? And you're quite young. What was reading that like? Have you talked to him?
你刚才谈到的一些东西，比如招聘方面，是让人相信我们有一个使命，而且它不是，你知道，不只是产品层面的事情，你可以追求自己的研究使命。你也相当年轻。我们几乎同龄，这很有意思。而我不是亿万富翁。但 Yann 在离开后不久曾对媒体说，你年轻且缺乏经验，会有更多人离开。所以我很好奇，作为这家巨大公司的领导者，这对你有什么影响？而且你确实很年轻。读到这些话是什么感受？你和他谈过吗？
Alex Wang:
(28:00)
Yeah, I saw him in India like a couple weeks after that and I mean Jan is a notable, very outspoken person and I think everyone always knows what Jan is thinking. You know, he obviously said what he said, and I saw him in India. He congratulated us on the MuSpark launch.
是的，几周之后我在印度见到了他。我的意思是，Yann 是一位著名的、非常直言不讳的人，我想每个人总是知道 Yann 在想什么。你知道，他显然说了他说过的话，然后我在印度见到他。他祝贺我们发布了 MuSpark。
Speaker 2:
(28:23)
I saw you guys patching things up on X, yeah.
我看到你们在 X 上修补关系了，是的。
Alex Wang:
(28:26)
Yeah, I mean, like, truly, I do, exactly what I just said before, I think all personal animosities, like, I think as we get closer and closer to superintelligence, we'll...
是的，我的意思是，真的，我确实就是刚才说的那个意思。我认为所有个人敌意，我认为随着我们越来越接近超级智能，我们会……
Speaker 2:
(28:35)
It seems like it's getting worse, doesn't it?
看起来好像是在变得更糟，不是吗？
Alex Wang:
(28:37)
Yeah, maybe it gets worse, maybe it gets better, but... But yeah, no, I think that, I think that, you know, I have a lot of conviction in how we've set up MSL and the research efforts that we have and the progress that we're making. And I'm excited to show the world the incredible work that our researchers are doing and the progress we're making.
是的，也许会变得更糟，也许会变得更好，但是……不过，是的，不，我认为，我认为，你知道，我对我们设立 MSL 的方式、我们正在进行的研究努力，以及我们正在取得的进展，都有很强的信念。我也很期待向世界展示我们研究人员正在做的出色工作，以及我们正在取得的进展。
Kylie Robison:
(29:02)
I also don't want to belabor the point, but is that a challenge you continue to face, like people thinking you're too young and inexperienced to lead such a huge effort at Meta?
我也不想过度纠缠这一点，但这是不是你仍然面临的挑战，比如人们认为你太年轻、缺乏经验，不足以领导 Meta 这样一项巨大的工作？
Speaker 2:
(29:11)
You also get the knock that you're not an engineer.
你还会被批评说你不是工程师。
Kylie Robison:
(29:14)
Oh, yes.
哦，是的。
Alex Wang:
(29:15)
That is definitely not true. Once upon a time, I was a software engineer in Silicon Valley. Doesn't this piss you off? Yeah. People have said this my whole time in Silicon Valley. To some extent, I actually like, I almost don't even think about it anymore, because it's just, it's always there. But yeah, no, I think that there are always, I think actually many people in AI always, there's like various mischaracterizations about them, or there's like, You know, people always say shit and they're sort of always, you know, what's out there is never always correct. And it can be frustrating, but I choose to just channel it into the work that we're doing and what we put out there.
这绝对不是真的。曾经有一段时间，我就是硅谷的软件工程师。这不会让你恼火吗？是的。人们在我整个硅谷生涯里一直这么说。在某种程度上，我其实几乎已经不再去想它了，因为它一直都在那里。不过，是的，不，我认为总是会有各种说法。我实际上认为，很多人工智能领域的人，总是会被各种错误刻画，或者说，你知道，人们总会说些乱七八糟的话，而且外面流传的东西并不总是正确的。这可能会令人沮丧，但我选择把它转化到我们正在做的工作，以及我们对外发布的成果中。
(30:08)
And again, I am really proud of MuseSpark. I'm even more excited about the models that we have cooking and the products that we have cooking. So I think like in the long arc of, you know, In the long arc, all this will play out just fine.
再说一次，我真的为 MuseSpark 感到自豪。我对我们正在准备中的模型，以及正在准备中的产品更加兴奋。所以我认为，从长远来看，你知道，从长期轨迹来看，这一切都会顺利展开。
Speaker 2:
(30:34)
Does well in these competitions, tends to be quite proficient at coding, engineering and thinking about these problems. I will say, I mean, over time you did get reputation among some circles as kind of like a salesman at scale a bit and enjoying life and things like that. I mean, so I did wonder when you took that job if it was going to be kind of like harder to boss people around or just quite different. I don't know.
在这些竞赛中表现好的人，通常在编程、工程以及思考这些问题方面都相当熟练。我会说，我的意思是，随着时间推移，你在某些圈子里确实有了一种名声，好像在 Scale 有点像销售型人物，也很享受生活之类的。我的意思是，所以当你接受那份工作时，我确实想过，你要管理别人会不会更困难，或者只是会非常不同。我不知道。
Alex Wang:
(31:03)
Yeah, by the way, I actually, in general, in terms of my management philosophy for MSL is not to boss people around. I think that like, there's this great Steve Jobs quote, which is, most companies hire people and tell them what to do. But we hire people for them to tell us what to do. And I think, you know, that is like pretty core to the entire thesis of TBD and MSL. And how we built it is that we're gonna, we're gonna have We're going to hire brilliant researchers and, you know, create the best environment for them to do the work of their careers and the work of their lives. So I think, you know, so long story short, I'm not trying to boss anyone around, actually.
是的，顺便说一句，实际上，一般来说，就我对 MSL 的管理理念而言，并不是去对别人发号施令。我认为，Steve Jobs 有一句很好的话，大意是，大多数公司雇人，然后告诉他们该做什么；但我们雇人，是为了让他们告诉我们该做什么。我认为，你知道，这基本上是 TBD 和 MSL 整个论点的核心。我们构建它的方式是，我们会聘请卓越的研究人员，并且，你知道，为他们创造最好的环境，让他们完成自己职业生涯中最重要的工作，完成自己人生中的重要工作。所以我认为，你知道，长话短说，实际上我并不是想对任何人发号施令。
(31:47)
I'm trying to create the best environment for researchers to do incredible work.
我是在努力为研究人员创造最好的环境，让他们能够做出卓越的工作。
⸻
Scaling Laws and Token Efficiency in Model Development
模型开发中的规模定律与词元效率
The development of the MuseSpark model represents an early data point on a predictable scaling ladder. By rebuilding the pre-training and reinforcement learning stacks from scratch, the team achieved significant token efficiency, outperforming other models on specific benchmarks with fewer resources. This performance is attributed to a "clean stack" approach, which avoids the fundamental inefficiencies often patched by simply increasing compute. Future models are expected to follow this predictable scaling trajectory, with upcoming iterations focusing on enhanced agentic coding and multimodal capabilities.
MuseSpark 模型的发展代表了可预测扩展阶梯上的一个早期数据点。通过从零开始重建预训练和强化学习技术栈，团队实现了显著的词元效率，在特定基准测试上用更少资源超过其他模型。这种表现归因于一种“干净技术栈”方法，它避免了那些通常靠简单增加算力来修补的根本性低效。未来模型预计将沿着这条可预测的扩展轨迹前进，后续迭代将重点提升智能体式编程和多模态能力。
Speaker 2:
(31:52)
All right, pod friends. I am here to tell you about SendCutSend. They are a manufacturing phenomenon. Whether you're working on your own car or you're a giant automotive company, a rocket maker, if you need metal cut, machine, or bent, you're going to SendCutSend. They'll do it for you right and they will do it for you fast. Go to SenCutSen.com slash Core Memory for a 15% discount on whatever you try to make. We love SenCutSen. Go visit them today. On MuseSpark for a minute, I mean, just as I was reading everything over the last couple of days and playing with the model a bit, I'm trying, I was just trying to wrap,
好了，播客朋友们。我现在要跟你们讲讲 SendCutSend。他们是一个制造业现象。无论你是在改自己的车，还是一家大型汽车公司、火箭制造商，只要你需要金属切割、加工或折弯，你就会去找 SendCutSend。他们会把事情做对，而且会做得很快。访问 sendcutsend.com/corememory，无论你想做什么，都可以获得 15% 的折扣。我们喜欢 SendCutSend。今天就去看看他们。说回 MuseSpark，我的意思是，过去几天我阅读了所有相关内容，也稍微试用了这个模型，我一直在试图理解，
(32:35)
we were trying to wrap our head around exactly where you guys see it in terms of what you're trying to achieve at Meta. It seemed like on the benchmarks, you know, it did well on some. It was behind the models on others. It seemed like you guys were emphasizing this and Apologies if I get the technical stuff right. You're wrong. You can set me right. But it seemed like you were emphasizing that you felt like you guys had some efficiency gains that some of the other models maybe didn't have. And then you're doing this crazy thing with the 16 agents you're working on. I was playing with that last night.
我们是在试图弄清楚，从你们在 Meta 想要实现的目标来看，你们到底如何定位它。看起来在基准测试上，你知道，它在一些测试中表现不错，在另一些测试中落后于其他模型。看起来你们在强调这一点——如果我把技术内容说错了，不好意思，你可以纠正我。但看起来你们强调的是，你们觉得自己有一些效率提升，而其他一些模型可能没有。然后你们还在做那个很疯狂的事情，就是你们正在研究的 16 个智能体。我昨晚还试用了它。
(33:11)
So it felt like, to me, you guys think you've picked a couple of technical directions where maybe you're ahead of people. But then I went through all your tweets on X last night. There were people who would compliment you. There were other ones that would take a dig at you and you would say, you know, just wait for the next thing. And so, yeah, I guess we're trying to figure out. It didn't seem like you guys were like planting a flag and being like, you know, we have conquered everything with this model.
所以在我看来，你们似乎认为自己选择了几个技术方向，也许在这些方向上领先于别人。但我昨晚翻了你在 X 上的所有推文。有些人会称赞你，也有些人会挖苦你，而你会说，你知道，等着看下一个东西吧。所以，是的，我想我们是在试图弄清楚。看起来你们并不是在插旗宣称，你知道，我们已经用这个模型征服了一切。
Alex Wang:
(33:40)
Yeah, no, by no means. I think that, you know, the What we did is, you know, over the past nine months, we rebuilt a lot of the stack and a lot of the research. So we rebuilt our pre-training stack. We rebuilt our RL stack. We rebuilt a lot of the science and did a lot of work on data. So in many ways, what's been happening over the past nine months is really like, you know, a full-on renovation, as it were, for the sort of like core research stack. MuseSpark is kind of an early data point on that scaling ladder. It is not, you know, in some ways like MuseSpark is kind of like the entree or the appetizer,
是的，不，完全不是。我认为，你知道，我们所做的是，在过去九个月里，我们重建了很多技术栈，也重建了很多研究工作。所以我们重建了预训练技术栈，重建了强化学习技术栈，重建了许多科学基础，也在数据上做了大量工作。因此，从很多方面看，过去九个月发生的事情，实际上可以说是对核心研究技术栈进行了一次全面翻新。MuseSpark 算是这条扩展阶梯上的一个早期数据点。它不是，你知道，在某种意义上，MuseSpark 更像是一道前菜，或者说开胃菜，
(34:23)
I guess appetizer, entree in French, but it's kind of like the appetizer for what we're building. But we are in development of larger models and we expect the larger models to be, you know, we're much more excited about the larger models than we are even about MuseSpark. But it was an important data point we thought to put out there into the world because there was a, you know, The entire program that we've built is developed around predictable scaling. And we see the scaling, I think we talked about this in the blog post, on many axes. We see very consistent pre-training scaling and predictable pre-training scaling. We see predictable RL scaling and reinforcement learning.
我想是开胃菜，法语里的 entrée，但它算是我们正在构建之物的一道开胃菜。我们正在开发更大的模型，而且我们预计更大的模型会——你知道，相比 MuseSpark，我们对更大的模型兴奋得多。但我们认为，把它作为一个重要数据点发布到世界上是有意义的，因为我们所构建的整个项目，都是围绕可预测扩展来发展的。我们在许多轴线上都看到了扩展效果，我想我们在博客文章中也谈到了这一点。我们看到了非常一致的预训练扩展，以及可预测的预训练扩展。我们看到了可预测的强化学习扩展。
(35:06)
We see predictable test time scaling. And a lot of what you just talked about, the contemplating mode, is we're also seeing very exciting results in multi-agent scaling. And so everything about our program is built to continue scaling as we go. And so, you know, MuseSpark was like this early data point on our scaling trajectory. But the next data point we're like a lot more excited about. And the data point after that, we're even more excited about. So I think we're excited to show people this sort of like next rung up on our overall scaling efforts. And for MuseSpark specifically, I think it, you know, If anything,
我们看到了可预测的测试时扩展。你刚才谈到的许多内容，也就是沉思模式，我们也在多智能体扩展方面看到了非常令人兴奋的结果。因此，我们整个项目的所有部分，都是为了在推进过程中持续扩展而构建的。所以，你知道，MuseSpark 就像是我们扩展轨迹上的这个早期数据点。但下一个数据点，我们会兴奋得多。再往后的数据点，我们会更加兴奋。所以我认为，我们很期待向人们展示我们整体扩展努力中的下一个台阶。具体到 MuseSpark，我认为，你知道，如果说有什么的话，
(35:47)
it's actually the overall end-to-end performance ended up being quite a bit better than we expected. And it had a bunch of emergent capabilities and behaviors that we were pretty excited about, like, that we found in training. So, for example, some of its abilities in agentic, like visual coding, being able to produce websites or being produced games, like some of these capabilities, actually kind of emerged from the fact that it is both a pretty strong agentic model, but also pretty strong at multimodality. So there were a lot of things that were very excited about this model. And so we put it out there. We think for most consumer use cases, actually a very good model,
它的整体端到端表现实际上比我们预期的要好相当多。而且它出现了一系列让我们相当兴奋的涌现能力和行为，也就是我们在训练过程中发现的那些能力。比如，它在智能体式视觉编程方面的一些能力，能够生成网站，或者能够生成游戏，这些能力实际上某种程度上来自这样一个事实：它既是一个相当强的智能体模型，同时在多模态方面也相当强。因此，这个模型有很多让我们兴奋的地方。所以我们把它发布了出来。我们认为，对大多数消费者使用场景来说，它实际上是一个非常好的模型，
(36:38)
and it is quite competitive with the other models. NewSpark, as we deployed it, This is not yet competitive on, you know, agentic coding. And so those are capabilities that we're working on and we're building towards for the next set of models. But yeah, I would expect the next model that we produce to be better overall than MuseSpark. And that's something we're pretty excited about. But even MuseSpark as we released it, which I think we, to be clear, wanted to set the clear expectations. Like we didn't think it was going to be A state of the art model across the board, but it it it is a very good model and we think a lot of people and a lot of users who tried it out experience that.
而且它与其他模型相比相当有竞争力。按照我们部署的 NewSpark，它在智能体式编程方面尚未具备竞争力。因此，这些能力是我们正在研究、并为下一批模型构建的方向。不过，是的，我预计我们生产的下一个模型，整体上会比 MuseSpark 更好。这是让我们非常兴奋的事情。但即便是我们发布的 MuseSpark，我认为，为了说清楚，我们当时也想设定清晰预期。也就是说，我们并不认为它会成为一个在所有方面都达到最先进水平的模型，但它确实是一个非常好的模型，而且我们认为很多人以及很多试用过它的用户，都体验到了这一点。
Kylie Robison:
(37:23)
I'm curious, what was the holdout for releasing like a frontier model? What do you still need in order to hit all of those benchmarks and blow it out of the water?
我很好奇，发布一个前沿模型的阻碍是什么？为了达到所有那些基准测试，并且大幅超越它们，你们还需要什么？
Alex Wang:
(37:33)
I mean, the one word answer is just scaling. Like, MuseSpark is kind of, as I mentioned, it's early on the ladder. And We have very strong predictability. So we know if we scale this model up, like what performance to expect from that from that increased model size. And we expect the The upcoming models to just be able to perform much better across the board.
我的意思是，用一个词回答，就是扩展。就像我提到的，MuseSpark 在这条阶梯上还处于早期阶段。我们有非常强的可预测性。所以我们知道，如果把这个模型扩展上去，也就是提高模型规模之后，可以预期得到什么样的性能。我们预计即将推出的模型能够在各方面表现得好得多。
Speaker 2:
(38:03)
When does that happen?
什么时候会发生？
Alex Wang:
(38:05)
Coming months. You know, coming months.
未来几个月。你知道，未来几个月。
Kylie Robison:
(38:09)
Wow. So like a year into the whole endeavor.
哇。所以差不多是在整个努力开始一年之后。
Alex Wang:
(38:12)
Well, as I mentioned, we built the whole program so that we would be able to, you know, move very, very fast. So there was a time period where we had to rebuild all the foundations and rebuild everything. But now we're in the period where We're going to be in fast scaling mode.
嗯，就像我提到的，我们构建整个项目，就是为了能够非常、非常快速地推进。所以之前有一段时间，我们必须重建所有基础，重建一切。但现在我们已经进入一个阶段，也就是我们将进入快速扩展模式。
Speaker 2:
(38:28)
What do you feel like you're doing technically that's different to everybody else?
你觉得你们在技术上做了哪些和其他所有人不同的事情？
Alex Wang:
(38:33)
Well, one of the things that we found, kind of as I mentioned, MuseSpark performed very well, in some ways even better than we originally expected, especially like a year ago. And when we went back and We've kind of like analyzed and tried to understand why does it perform so well. We think a lot of that, a lot of it comes down to just having built a very, very clean stack from scratch and having had the ability in this rebuild process to do everything, quote unquote, the right way. So we really had this, in some ways like this luxury, like the ability to Build a very clean pre training stack and very clean RL stack and to do everything in kind of like the the.
嗯，我们发现的一件事，就像我提到的，MuseSpark 表现非常好，在某些方面甚至比我们最初预期的还要好，尤其是回看一年前的时候。然后当我们回过头去分析，并试图理解它为什么表现这么好时，我们认为其中很大一部分，确实归结于我们从零开始构建了一个非常、非常干净的技术栈，并且在这次重建过程中有能力把所有事情都以所谓“正确的方式”来做。所以从某种意义上说，我们确实拥有这种奢侈条件，也就是能够构建一个非常干净的预训练技术栈和非常干净的强化学习技术栈，并且以某种方式把所有事情都做成……
(39:22)
The right way by the experts who know exactly how to build these systems that was able to. That was able to meaningfully accelerate both our trajectory, but also I think it really shows in the model.
由那些确切知道如何构建这些系统的专家，以正确方式来做。这能够显著加快我们的发展轨迹，而且我认为它也确实体现在模型本身上。
Speaker 2:
(39:32)
I mean, you know, before I do these interviews, I throw you and the model and everything into into all the AI systems and sort of get them to poke around. I mean, the thing that kept coming back was this. This token efficiency, I mean, like, is this something you guys feel like you've figured out or this was just a happy accident with MuseSpark? It seemed like on some of these benchmarks, you were just doing it with far less effort than the other models.
我的意思是，你知道，在做这些采访之前，我会把你、这个模型以及所有相关内容都丢进各种人工智能系统里，让它们大致研究一下。我的意思是，反复出现的一点就是这个：词元效率。我的意思是，这是你们觉得自己已经搞清楚的东西，还是 MuseSpark 上一次幸运的意外？看起来在某些基准测试上，你们用远少于其他模型的努力就做到了。
Alex Wang:
(40:00)
Yeah, yeah. No, this was an exciting result for us. Yeah, I think like on artificial analysis, for example, it used to achieve You know, pretty similar results, like many fewer tokens than, let's say, the models from some of the other labs. Yeah, we think this is this is a testament, I think, actually, to this whole clean stack, which is that I think. I can't say for certain, but one of the reasons why some of the other models maybe require a lot more tokens could be that there's some level of fundamental inefficiency at another part of the stack that kind of gets patched by enabling the models to think longer.
是的，是的。不，这对我们来说是一个令人兴奋的结果。是的，我认为，比如在 Artificial Analysis 上，它能够用比其他一些实验室的模型少得多的词元，取得相当接近的结果。是的，我们认为，这实际上证明了整个干净技术栈的价值。也就是说，我认为，虽然我不能确定，但其他一些模型可能需要多得多的词元，其中一个原因可能是技术栈其他部分存在某种层面的根本低效，而这种低效通过让模型思考更久来被某种程度上修补了。
(40:36)
And so we were pretty impressed and excited about the token inefficiency that we found. And frankly, we think as we keep scaling the models and continue scaling overall, We think that bodes really well for the future performance of our models.
所以，我们对自己发现的词元效率印象很深，也很兴奋。坦率地说，我们认为，随着我们继续扩展模型，并持续进行整体扩展，这对我们模型未来的表现是一个非常好的信号。
⸻
Integrating AI Agents into Consumer Hardware and Business Ecosystems
将人工智能智能体整合进消费硬件与商业生态系统
The broader vision for Meta involves a constellation of devices, such as Ray-Ban Meta glasses, that capture context to provide proactive, intelligent assistance. The strategy focuses on knitting together the company's massive user base and hundreds of millions of small businesses through powerful agentic models. While current consumer sentiment toward AI remains low due to a lack of clear, life-changing utility, the goal is to provide tools that significantly increase individual agency. By building an "economy of agents" that can collaborate, the platform aims to transform how supply and demand function for both consumers and entrepreneurs.
Meta 更宏大的愿景，涉及一组设备，例如 Ray-Ban Meta 眼镜，它们能够捕捉上下文，从而提供主动、智能的辅助。这一战略的重点，是通过强大的智能体模型，把公司的庞大用户基础和数以亿计的小企业编织在一起。尽管当前消费者对人工智能的情绪仍然偏低，因为它还缺乏清晰、能够改变生活的用途，但目标是提供能够显著增强个人能动性的工具。通过构建一个能够相互协作的“智能体经济”，这个平台旨在改变消费者和创业者两端的供给与需求运行方式。
Kylie Robison:
(40:53)
And Spark was really good at vision benchmarks and that efficiency and that vision expertise seemed like it would be really important for your guys' hardware endeavors. And you've talked before about a constellation of these AI products that can see what you see and hear what you hear. Can you talk a little bit more about how that fits into your guys' broader vision for serving AI?
Spark 在视觉基准测试上表现非常好，而这种效率和视觉专长看起来对你们的硬件努力会非常重要。你之前也谈过一组人工智能产品，它们可以看见你所看见的、听见你所听见的。你能不能多谈一点，这如何嵌入你们服务人工智能的更广泛愿景？
Alex Wang:
(41:17)
Yeah, 100%. I mean, I think... One of the things that's very exciting about Meta overall is that the Ray-Ban Metas, the glasses, have been this hit product and we've sold millions of copies and we have some big fans.
是的，完全可以。我的意思是，我认为……Meta 整体上非常令人兴奋的一点是，Ray-Ban Meta，也就是那副眼镜，已经成为一款热门产品，我们已经卖出了数百万副，而且有一些非常忠实的粉丝。
Kylie Robison:
(41:32)
The biggest fans.
最忠实的粉丝。
Speaker 2:
(41:34)
I do love them.
我确实很喜欢它们。
Alex Wang:
(41:36)
But it is like a very exciting direction for all these devices to think about what does your relationship with technology look like if it really can kind of Fade into the background a little bit and be a lot more contextual, like and as you mentioned, see what you see, hear what you hear and be much more intelligent and helpful in the moments where you need it. And also capture all this context about, you know, what is happening in your life and what are the things that really matter and what are the things that it should pay attention to. And so we really see like a future world in line with personal superintelligence where, you know, you have I kind of, as you mentioned,
但对所有这些设备来说，一个非常令人兴奋的方向是去思考：如果技术真的能够在某种程度上退到背景中，并且变得更加依赖上下文，那么你与技术的关系会是什么样子？就像你刚才提到的，它能看见你所看见的，听见你所听见的，并且在你需要它的时刻变得更智能、更有帮助。它还能够捕捉所有这些上下文，比如，你知道，你的生活中正在发生什么，哪些事情真正重要，哪些事情是它应该注意的。因此，我们确实看到一个与个人超级智能相一致的未来世界。在这个世界里，你知道，就像你提到的，
(42:18)
like this constellation of devices that all are there to help capture context are all there to enable the technology to kind of like fade away a little bit and help you help you get like very intelligent and valuable Insights from agents like proactive insights or you'll mention something and then the agent will go off and do some research or you know take actions for you or you know it can just kind of be this like super intelligent sidekick that makes everything in your life better.
有这样一组设备，它们都在那里帮助捕捉上下文，也都在那里让技术在某种程度上退到背景中，并帮助你从智能体那里获得非常智能、非常有价值的洞见，比如主动洞见；或者你提到某件事之后，智能体就会去做一些研究，或者替你采取行动；又或者，它可以成为这样一个超级智能的助手，让你生活中的一切都变得更好。
Speaker 2:
(42:49)
I feel like there's some kind of problem that you guys have though because I love these glasses. I use them all the time. I do it for our video stuff and then I actually I like to take phone calls on them and then We pretty much run our entire business in WhatsApp. I refuse to use Slack. I travel so much that WhatsApp just got embedded into my life. In full confession, I don't think I've ever used Meta's AI agents or anything until you were coming on the show and I wanted to see what it was. I always go out to Claude. I go out to ChatGPT to do this work. I saw the AI agent button on WhatsApp kind of like for the first time today. I know it's been sitting there the whole time. I don't know.
不过我感觉你们确实有某种问题，因为我很喜欢这些眼镜。我一直在用它们。我会用它们做我们的视频内容，而且我实际上也喜欢用它们接电话。然后，我们几乎整个业务都在 WhatsApp 上运行。我拒绝使用 Slack。我旅行太多，以至于 WhatsApp 已经嵌入了我的生活。坦白说，在你来上节目之前，为了看看它是什么，我觉得自己从来没有真正用过 Meta 的人工智能智能体或任何相关东西。我做这些工作时，总是去用 Claude，去用 ChatGPT。我今天好像第一次看见 WhatsApp 上那个 AI 智能体按钮。我知道它其实一直在那里。我不知道。
(43:39)
I am in your world and kind of like didn't even see it there. Maybe I can't be unique in this.
我就在你们的世界里，却好像根本没有看见它在那里。也许我不会是唯一一个这样的人。
Alex Wang:
(43:51)
Yeah, well, one of the things is we knew we needed to have Great models and great products before we really pushed for tighter integration across the across our entire ecosystem. So in many ways, like we were, we've been waiting to have great models that can then enable most of the consumer use cases that we really care about. And now I think we're at a point in a moment, which is very exciting, which is that our Our models are pretty good. We're pretty excited about them. We have better models on the way. And so now we're going to undergo the sort of like process to Do a lot of large scale integration of all of the, you know,
是的，嗯，其中一点是，我们知道在真正推动整个生态系统内更紧密的整合之前，我们需要先拥有优秀的模型和优秀的产品。所以从很多方面看，我们一直在等待拥有足够优秀的模型，使其能够支持我们真正关心的大多数消费者使用场景。现在我认为我们正处在一个非常令人兴奋的时刻，也就是我们的模型已经相当不错。我们对它们相当兴奋。我们还有更好的模型正在路上。所以现在我们将进入这样一个过程：对我们所有的，你知道，
(44:33)
the family of apps that we have with our AI and integrate our business products with our AI and go through this, this evolution towards like knitting together almost all the pieces of our ecosystem together with our AI. I think, you know, to some extent you've seen what that looks like for Gemini over the past few years and we're excited to go through.
我们拥有的一系列应用，与我们的人工智能进行大规模整合，并将我们的商业产品与我们的人工智能整合起来，经历这样一种演进：把我们生态系统中几乎所有组成部分，都通过人工智能编织在一起。我认为，你知道，在某种程度上，过去几年你已经从 Gemini 身上看到了这会是什么样子，而我们也很期待走过这个过程。
Speaker 2:
(44:57)
It's the same thing for me though. I also run our business in Google and I mostly play with Gemini to see, I just feel like in consumers' heads, I'm curious how you think this plays out because I feel like you've got OpenAI and Anthropic in this one world where ChatGPT is such a strong consumer brand. That is what people think of as AI. And then Claude has been super dominant in coding, in business. You guys, um, Google, you're sort of like asking people to run into AI as part of all these services that you have. And I don't know. I don't think we've, I don't know if we've ever seen a competition quite like this where, and then there's, there's X as well, um, where you have these, I'm old,
不过对我来说，情况也是一样。我也在 Google 里运营我们的业务，而我主要是试用 Gemini，看看它怎么样。我只是感觉，在消费者心智中，我很好奇你认为这会如何展开，因为我感觉 OpenAI 和 Anthropic 处在一个世界里，其中 ChatGPT 是一个非常强大的消费者品牌。人们想到人工智能时，想到的就是它。然后 Claude 在编程和商业场景中一直非常强势。你们，还有 Google，则像是在要求人们在你们已有的所有这些服务中遇到人工智能。我不知道。我觉得我们可能从来没有见过这样一种竞争，然后还有 X，你面对的是这些——我年纪比较大，
(45:46)
so I go back to like word processing days, you know what I mean? It's like, what are you going to use? You're going to use Microsoft Word. People settle on like a thing or in the browser wars, it was like there are only two, Internet Explorer and Netscape. And then that was sort of like the rest of history for a long time. And so I don't know. Do you see, I feel like these two groups have different challenges and it's really not, I sort of feel like consumers, most people are still going to pick like I do my AI on ChatGPT.
所以我会回想到文字处理软件的年代，你明白我的意思吗？就像是，你要用什么？你会用 Microsoft Word。人们会固定选择某个东西。或者在浏览器大战中，就像是只有两个选择，Internet Explorer 和 Netscape。然后在很长一段时间里，后面的历史基本就是这样展开的。所以我不知道。你怎么看？我感觉这两类公司面临不同的挑战，而且真的不是那么简单。我有点觉得，消费者，大多数人仍然会选择类似“我用 ChatGPT 做人工智能”这种方式。
Alex Wang:
(46:18)
I just think we're so early. Like I think that, you know, I think it's funny because I reflect on this, like if you were sitting here You know, a year ago, if you were to have this conversation, we would always just say like, oh, well, you know, OpenAI and ChatGPT have, you know, they've won on consumer already. They have the biggest business. You know, they're just going to run away with the whole thing. And then fast forward a year. Anthropics of this breakout success of Claude Code, which was, you know, somewhat foreseeable, but not super predictable at the time and has overtaken them in revenue. And at the same time,
我只是认为我们还处在非常早期的阶段。我的意思是，我觉得这很有意思，因为我回想这件事，如果一年前你坐在这里，如果我们进行这场对话，我们总会说，哦，OpenAI 和 ChatGPT 已经赢得了消费者市场。它们拥有最大的业务。你知道，它们会一路拉开差距，赢下整个市场。然后快进一年。Anthropic 因为 Claude Code 取得了突破性成功，这在当时某种程度上可以预见，但并不是特别可预测，而且它在收入上已经超过了它们。与此同时，
(46:59)
Gemini has distributed quite a lot and actually has eaten a lot of consumer market share from the rest of the ecosystem, including ChatGPT. And so I think that we are in this phase of AI, which is just incredibly, incredibly dynamic. And I think it's very hard to say at any one moment that, you know, We're in the endgame because everything is just, you know, I think there's going to be so many new products built both for consumers and for developers and for businesses that haven't been invented yet that will each potentially be even bigger than the ones we've had before. I think it's pretty Fascinating to me that going that, you know,
Gemini 已经获得了相当广泛的分发，并且实际上从生态系统中其他产品那里夺走了大量消费者市场份额，包括 ChatGPT。因此，我认为我们正处在人工智能的一个阶段，这个阶段极其、极其动态。我认为很难在任何一个时点说，你知道，我们已经进入终局，因为一切都仍在变化。我认为，未来还会有许多面向消费者、开发者和企业的新产品被构建出来，而这些产品现在还没有被发明，其中每一个都可能比我们之前见过的产品更大。我觉得非常有意思的是，你知道，
(47:47)
ChatGPT obviously was this incredible hit and it was this like, you know, felt this it was the fastest growing Product and business that the world had seen till that point. And then Claude Code, again, is this incredible hit. It's the fastest growing business anyone has ever seen until now. And I think that this is a really, there's something, this is a statement about something intrinsic about AI, which is as AI gets to new levels of intelligence and capability and And overall performance, it just unlocks these new form factors that each will be this like, you know, incredible new wave of technology washing onto sort of like humanity's shores to some extent.
ChatGPT 显然是一个惊人的爆款，它当时给人的感觉是，直到那个时间点为止，世界所见过增长最快的产品和业务。然后 Claude Code 又是一个惊人的爆款。它是截至目前任何人见过的增长最快的业务。我认为，这确实说明了人工智能内在的某种东西：随着人工智能达到新的智能水平、能力水平和整体性能水平，它就会解锁新的产品形态，而每一种形态都会像一波令人惊叹的新技术浪潮，在某种程度上冲刷到人类的海岸上。
(48:33)
So I think that Long story short, I think the next wave will be even bigger and the wave after that will be even bigger. And, you know, we're nowhere near the end. Like there's going to be many more exciting new product paradigms that we'll see in the future.
所以长话短说，我认为下一波浪潮会更大，再下一波浪潮还会更大。你知道，我们远远没有接近终点。未来还会有许多更令人兴奋的新产品范式出现。
Kylie Robison:
(48:47)
I think the product overhang question is real. Like we have these incredible models. What can we make that consumers actually want to use? But I'm also curious how you square the sort of sentiment of the average consumer and AI, because I'm in my 20s. I am not only in tech and I see Crazy stuff posted on Instagram stories about how much people hate AI. The sentiment seems to be in the toilet. And then you guys have these billions of users and you're serving your AI as these buttons. And, you know, I'm curious how you square that sentiment, what you see on the consumer side of how they're, you know, receiving your technology that you guys are building.
我认为产品悬置这个问题是真实存在的。比如，我们有这些令人难以置信的模型，但我们能做出什么让消费者真正想用的东西？不过我也很好奇，你如何调和普通消费者对人工智能的情绪，因为我二十多岁，我不只是在科技圈里，我也会在 Instagram 快拍上看到一些疯狂内容，讲人们有多讨厌人工智能。这种情绪看起来非常糟糕。然后你们拥有数十亿用户，却把你们的人工智能作为这些按钮提供给他们。你知道，我很好奇你如何调和这种情绪，以及你在消费者端看到的情况，也就是他们如何接受你们正在构建的技术。
Alex Wang:
(49:24)
Yeah, I mean, AI definitely has like a sentiment is very low, to say the least. And I think this comes down to, on some fundamental level, we haven't yet Demonstrated in a very real way how this is actually a tool for personal empowerment or personal agency or how it just makes people's lives a lot better. I think people's experience right now is that it can be really helpful and it makes your life quite a bit better, but it's not this, it's not overwhelmingly better versus I think for a lot of developers, I think their lives have actually totally changed. And I think most developers like They have very positive sentiment, maybe somewhat mixed,
是的，我的意思是，至少可以说，人工智能的情绪确实非常低。我认为这在某种根本层面上归结于，我们还没有以非常真实的方式证明，它实际上是一种个人赋能或个人能动性的工具，或者证明它如何让人们的生活变得好得多。我认为人们现在的体验是，它可能真的很有帮助，也确实让你的生活好了一些，但它还不是那种压倒性地变好。相比之下，我认为对很多开发者来说，他们的生活实际上已经完全改变了。我认为大多数开发者对人工智能的情绪非常正面，也许有些复杂，
(50:09)
but very positive sentiment towards AI because they're now able to do things that they were just unable to do before and they can build so many more things faster and they can just build entire projects over a weekend. It's just this incredible enabler of personal agency. That moment hasn't happened for everyone else in the world yet. So far, we haven't yet given Every person what the equivalent of cloud code is that would enable them to do the projects they've always had in the back of their mind or make their life way better or all of a sudden enable them to accomplish their goals. That hasn't happened yet. And same thing even for small businesses.
但总体上他们对人工智能非常正面，因为他们现在能够做以前根本做不了的事情，可以更快地构建多得多的东西，甚至可以在一个周末里构建出整个项目。它就是这种令人难以置信的个人能动性放大器。这个时刻还没有发生在世界上其他每个人身上。到目前为止，我们还没有给每个人提供一种相当于 Claude Code 的东西，使他们能够完成那些一直放在脑海深处的项目，或者让他们的生活大幅变好，或者突然使他们能够实现自己的目标。这还没有发生。即便对小企业来说也是一样。
(50:52)
Small business owners and entrepreneurs haven't yet had that full experience yet. So that's really what we're building towards at Meta is what does it look like to give Very powerful agents to all of our consumers and all the small businesses in the world. And what does that look like if you're actually able to nail it in the form of like a huge increase in individual agency?
小企业主和创业者也还没有真正获得那种完整体验。所以这正是我们在 Meta 试图构建的方向：如果把非常强大的智能体提供给我们所有消费者，以及世界上所有小企业，会是什么样子？如果你真的能够把这件事做好，并使其体现为个人能动性的巨大提升，那又会是什么样子？
Kylie Robison:
(51:14)
That would be a crazy thing to nail because if you go to a small town in anywhere in America and go to that restaurant's website, I mean, it hasn't been updated since 2002. So giving everyone, you know, multi-agent architecture products sounds like a huge leap.
如果能真正做到，那会是一件非常惊人的事，因为如果你去美国任何一个小镇，打开那家餐馆的网站，我的意思是，它可能从 2002 年之后就没有更新过。所以给每个人提供多智能体架构产品，听起来像是一次巨大的跃迁。
Speaker 2:
(51:31)
I mean, to Kylie's earlier question, I mean, look, there's things that I like that Meta does and there's things I don't like. I think there's huge swaths of the public that view the company quite cynically. It just feels like you guys do have a It's like you said, AI in general, not always most beloved thing at the moment. I do feel like the bar is higher for you guys to like, to get people to trust you.
我的意思是，回到 Kylie 之前的问题。你看，Meta 做的一些事情我喜欢，也有一些事情我不喜欢。我认为很大一部分公众对这家公司抱有相当犬儒的看法。感觉你们确实有一个——就像你说的，人工智能总体上在当下并不总是最受喜爱的东西。我确实觉得，对你们来说，让人们信任你们的门槛更高。
Alex Wang:
(51:59)
Yeah, a hundred percent. But I think that, um, like again, I think if we think about what is the best thing that we can do, it's really, we should build the best possible products that we think are genuinely amazing for those who use them. Like, I think, I think we can build products that, can transform the lives of most small business owners. And we have, again, hundreds of millions of small businesses all around the world that are on Meta. You know, a bunch of them use WhatsApp to run their businesses like you do. A bunch of them have Facebook pages or Instagram pages. A bunch of them, you know, use our advertising solution.
是的，完全如此。但我认为，再说一次，如果我们思考自己能做的最好事情是什么，真正答案是，我们应该构建尽可能最好的产品，而且是我们认为对使用者而言真正出色的产品。比如，我认为我们可以构建能够改变大多数小企业主生活的产品。而且我们确实拥有遍布全球的数亿家小企业在 Meta 上。你知道，其中很多企业像你一样使用 WhatsApp 经营业务；很多企业有 Facebook 页面或 Instagram 页面；很多企业使用我们的广告解决方案。
(52:35)
So I think there's this, there's an opportunity that exists there that Like in some level, only we have, because only we have, again, billions of users, billions of people around the world who use our products and hundreds of millions of small businesses. And one of the ideas that gets me personally really excited is, you know, if you're able to build agents for Both sides of this like ecosystem, you know, for all the consumers as well as all the small businesses, then what does that look like when you enable the mechanism for those agents to work together and collaborate with one another? And so. You know, Dario always talks about a country of geniuses in a data center.
所以我认为这里存在一个机会，在某种层面上，只有我们拥有这个机会，因为也只有我们拥有数十亿用户，也就是全球数十亿使用我们产品的人，以及数亿家小企业。让我个人非常兴奋的一个想法是，如果你能够为这个生态系统的两端都构建智能体，也就是为所有消费者以及所有小企业构建智能体，那么当你建立一种机制，让这些智能体能够相互配合、彼此协作时，会是什么样子？所以，你知道，Dario 总是谈到数据中心里有一个天才之国。
(53:15)
I think we're excited about building an economy of agents in a data center. Like, what does that actually, if you fundamentally change how supply and demand work in the economy, and it's like mediated by agents, I think that could be like, there's like very, very exciting things that we can build towards. And you're totally right that that has to be done In lockstep with ensuring that we have social permission and that people see that we care about how these things are deployed and that we're genuinely making people's lives better as a result.
我认为我们兴奋的是，在数据中心里构建一个智能体经济。也就是说，如果你从根本上改变经济中供给和需求的运行方式，并且它是由智能体来中介的，这实际上意味着什么？我认为这可能会带来一些非常、非常令人兴奋的建设方向。而你完全说得对，这必须与确保我们拥有社会许可同步推进，并且要让人们看到，我们在意这些东西如何被部署，也要让人们看到，我们确实因此让他们的生活变得更好。
⸻
Open Source Safety, Geopolitics, and Physical Intelligence
开源安全、地缘政治与物理智能
Meta remains committed to open-sourcing models, provided they pass rigorous safety guardrails regarding bio, chemical, and cyber capabilities. Geopolitical tensions, particularly regarding China, are managed by distinguishing between individual talent and state-level actions. The roadmap for superintelligence extends beyond digital systems into physical intelligence and robotics. By applying the same scaling principles used for digital models to robotics, the company aims to accelerate goods manufacturing and scientific discovery. This physical intelligence is viewed as a critical path for the next stage of technological evolution.
Meta 仍然承诺开源模型，前提是这些模型通过关于生物、化学和网络能力的严格安全护栏。对于地缘政治紧张，尤其是涉及中国的问题，其处理方式是区分个人层面的人才与国家层面的行动。超级智能的路线图不仅限于数字系统，还延伸到物理智能和机器人。通过把数字模型使用的同样扩展原则应用到机器人领域，公司旨在加速商品制造和科学发现。这种物理智能被视为下一阶段技术演进的关键路径。
Speaker 2:
(53:49)
I mean, one place you guys had won clear hearts and minds was by making these things open source. And I'm an old open source fan and kind of like believe in it philosophically. And yeah, I mean, so where are we going with that since MusePark is closed?
我的意思是，你们曾经明确赢得人心的一个地方，是把这些东西开源。我是一个老开源爱好者，而且在某种程度上从哲学上相信开源。是的，我的意思是，既然 MusePark 是闭源的，那开源这件事接下来会走向哪里？
Alex Wang:
(54:08)
Yeah, yeah. You know, models are a lot more powerful than they were even back in the Lomit, even though it's so recent. And so one of the things that is very important to me is safety for these models. And so one of the things that we instituted as part of our advanced AI scaling framework was, you know, we have to take very seriously when the models that we develop trigger various safety guardrails, especially around, you know, bio, Chem, you know, cyber capabilities and loss of control. So MuseSpark in our testing did trigger some of those safety checks that we did. And we detailed all this in the preparedness report of MuseSpark that we published.
是的，是的。你知道，模型已经比 Llama 时代强大得多了，尽管那其实也是很近的事。因此，对我来说非常重要的一件事，是这些模型的安全性。所以，作为我们高级人工智能扩展框架的一部分，我们确立的一件事是，你知道，当我们开发的模型触发各种安全护栏时，我们必须非常严肃地对待，尤其是在生物、化学、网络能力以及失控风险方面。所以 MuseSpark 在我们的测试中确实触发了其中一些安全检查。我们在发布的 MuseSpark 准备情况报告中详细说明了这一切。
(54:55)
And so as a result, MuseSpark in its current form is not suitable for open sourcing. But we are working on developing versions of the model that Are suitable to be open source literally a meeting I had earlier today was actually to review the progress on this. Um, so we're excited to actually continue supporting the open source ecosystem and developing open source models. And I expect that, um, you know, uh, again, we'll have more to share on that. You know, in the coming months, but that's an exciting milestone for us as well.
因此，结果是，MuseSpark 以目前这种形态并不适合开源。但我们正在开发适合开源的模型版本。实际上，我今天早些时候刚开了一个会，就是评审这方面的进展。所以，我们确实很兴奋能够继续支持开源生态系统，并开发开源模型。我预计，你知道，再说一次，我们会在未来几个月分享更多相关内容，而这对我们来说也是一个令人兴奋的里程碑。
Speaker 2:
(55:25)
So, okay, you're really going to stick with it because, you know, you did the, I always appreciated that you guys did the Open Compute project. You're in Sun Microsystems old building. Again, I'm just a history nerd. They were such a champion of open source software and always kind of like this foil to what Microsoft had built in the world. And I kind of think it's important. I mean, so it sounds like you're saying for you guys are committing that that's like still going to be something that Meta does that's quite different to most of your competitors.
所以，好吧，你们真的会坚持下去，因为，你知道，你们做过 Open Compute 项目。我一直很欣赏你们做这件事。你们现在就在 Sun Microsystems 的旧楼里。再说一次，我只是个历史迷。他们曾经是开源软件的重要拥护者，而且总是在某种程度上成为 Microsoft 所建立世界的一种对照。我也觉得这件事很重要。我的意思是，听起来你是在说，你们承诺开源仍然会是 Meta 会做的一件事，而且这件事与你们大多数竞争对手很不一样。
Alex Wang:
(55:58)
Yeah. I mean, I think I've said this a bunch of times, like we will continue open sourcing models, but we also have to take safety seriously. And so we will, our most powerful models, we have to consider whether or not they're safe enough to be open source.
是的。我的意思是，我想我已经说过很多次了，我们会继续开源模型，但我们也必须认真对待安全。因此，对于我们最强大的模型，我们必须考虑它们是否足够安全，可以开源。
Speaker 2:
(56:12)
Okay. And then, I mean, there's another, If you read the stories about your tenure at Meta, I mean, one thing that drops out is this, you know, I think it was the New York Times or someone did this story on Alex and Zuck see the world one way. They're very research forward and want the best model in the world and Boz and Chris Cox are more focused on products and Meta is this company that has to serve billions of users and do so as cheaply as possible. You could argue that, you know, and doesn't charge for its models today. I'm sure you probably expected we would ask some question along these lines, but so where are all you guys philosophically and are you,
好。然后，我的意思是，还有另一个问题。如果你读关于你在 Meta 任职期间的报道，其中会冒出来一个说法，你知道，我想是 New York Times 或者某家媒体写过一篇报道，说 Alex 和 Zuck 以一种方式看世界。他们非常偏研究导向，想要世界上最好的模型；而 Boz 和 Chris Cox 更专注于产品，并且 Meta 是一家必须服务数十亿用户、并尽可能低成本地服务他们的公司。你可以说，你知道，而且今天它并不对模型收费。我相信你大概预料到我们会问这类问题，所以你们在哲学上到底处在什么位置？你们是不是，
(56:55)
is there all this division about what direction to take your AI strategy?
在人工智能战略应该走向哪里这件事上，真的存在所有这些分歧吗？
Alex Wang:
(57:02)
Yeah, I mean, first off, the one thing this job has taught me is the bar for journalistic reporting at major outlets is, you know, the line between gossip and reporting is like, is remarkably thin.
是的，我的意思是，首先，这份工作教会我的一件事是，主要媒体的新闻报道门槛，你知道，八卦和报道之间的界线，真是薄得惊人。
Kylie Robison:
(57:17)
But you guys weren't fighting like crazy?
但你们没有疯狂内斗？
Alex Wang:
(57:19)
No, I don't think so. I mean, I think in general, like, yeah, no, I think I think we're very, we're all very aligned on what, what is important. Like, we all We know we need to have very advanced models, both to support our core business and to build the existing apps and products and services that we have for our users and our small businesses to be the best version they can be in the world. We've been working on business agents since long before I got to Meta, and those require the best models possible.
不，我不这么认为。我的意思是，我总体上认为，是的，不，我认为我们在什么是重要的这件事上非常一致。比如，我们都知道我们需要非常先进的模型，既是为了支持我们的核心业务，也是为了把我们现有的面向用户和小企业的应用、产品和服务，建设成世界上它们所能达到的最佳版本。在我加入 Meta 很久以前，我们就已经在做商业智能体，而这些需要尽可能最好的模型。
(57:54)
We also know that so we all know we need to build the best possible models and we all know that we need to then integrate those into our business and utilize these models to build products and services that are that are incredible for our consumers and for the businesses on our platform. So I think that there's. Yeah, there's no real disagreement. I mean like I think like any company, you know, there's always like, you know, we debate the points deeply, like we talk about them and we think through the implications and we talk about, we want to make sure everyone has like the ability to chime in on these issues, but there's no like major beef as it were.
我们也知道，所以我们都知道我们需要构建尽可能最好的模型，也都知道我们随后需要把这些模型整合进我们的业务，并利用这些模型为消费者以及我们平台上的企业构建出色的产品和服务。所以我认为这里，嗯，是的，并不存在真正的分歧。我的意思是，我认为像任何公司一样，你知道，总会有，你知道，我们会深入辩论各个观点，会讨论它们，思考其影响，也会讨论如何确保每个人都有能力对这些问题发表意见，但并没有所谓的重大矛盾。
Speaker 2:
(58:38)
So you think that was just total bullshit?
所以你认为那完全是胡扯？
Alex Wang:
(58:42)
I think so, yeah. I really do think so.
我认为是的。我确实这么认为。
Speaker 2:
(58:46)
On the Meta stuff, right before you made this transition from Scale to Meta, you were out doing a lot of stuff in DC. You were flagging the danger of China in the whole AI race. When I saw you guys do that deal, I was trying to square that in my head because I know they were putting offices in Singapore and creating some of this distance. I don't know. It seemed like the type of situation where getting much closer with a Chinese startup and a company with the resources like Meta, it seemed a little different to me than what you'd been saying rhetorically. Does that make sense?
关于 Meta 这件事，在你从 Scale 转到 Meta 之前，你在华盛顿做了很多事情。你一直在强调中国在整个人工智能竞赛中的危险。当我看到你们做那笔交易时，我试图在脑子里把这件事说通，因为我知道他们在新加坡设立办公室，并制造一些距离。我不知道。在我看来，这像是一种让一家中国创业公司与 Meta 这种资源雄厚的公司靠得更近的情况，这和你之前在言辞上所说的东西似乎有点不同。这样说合理吗？
Alex Wang:
(59:33)
Yeah, I mean, obviously, the whole Manist situation is pretty complicated. It's hard to...
是的，我的意思是，显然，整个 Manist 的情况相当复杂。很难……
Kylie Robison:
(59:38)
Super complicated.
极其复杂。
Alex Wang:
(59:40)
And I unfortunately can't really go into any real detail. But I think what I will say is like, I think the Manist, I think in general, when you think about these questions of geopolitics and whatnot, you always have to separate the sort of the people from The state in some sense. So I think that like, you know, my parents are from China. I think there's lots of very incredible, very talented people who are Chinese. And, you know, many of them, some of them moved to Singapore, some of them moved to the US, some of them move elsewhere in the world. And I think there are many of them are incredibly talented. And I feel lucky when I get to work with them.
而且很遗憾，我确实不能进入任何真实细节。但我想我会说的是，我认为 Manist，我认为总体上，当你思考这些地缘政治问题等等时，你总是必须在某种意义上把人和国家区分开来。所以我认为，比如，你知道，我的父母来自中国。我认为有很多非常出色、非常有才华的中国人。而且，你知道，其中很多人，有些搬到了新加坡，有些搬到了美国，有些搬到了世界其他地方。我认为他们中的许多人都极其有才华。当我有机会和他们共事时，我觉得自己很幸运。
(1:00:22)
And that is like separate from my overall beliefs on, you know, the Chinese Communist Party and The sort of like actions they're taking and what that means for how the United States should be thinking about our overall strategy to the country. So I think that, yeah, I think it's important to draw a distinction between these two. And I think there's sometimes a And I'm here to talk to you about Silicon Valley tech. We sort of like lump together and we sort of like, you know, Twitter or X in particular is like particularly un-nuanced about this.
而这和我对中国共产党以及它正在采取的行动的整体看法，是分开的；也和这些行动对美国应如何思考其整体对华战略意味着什么，是分开的。所以我认为，是的，我认为在这两者之间做出区分很重要。我认为有时会出现一种情况，尤其在硅谷科技讨论中，我们会把这些东西混在一起。你知道，Twitter 或 X 在这一点上尤其缺乏细腻区分。
Speaker 2:
(1:01:11)
I don't think it's nuanced about anything.
我不认为它对任何事情都有细腻区分。
Alex Wang:
(1:01:14)
It's not nuanced about anything, but I think that like, yeah, I think to me this, you know, whether or not there's amazing people who happen to have been born in China who we would love to work with is like totally independent from what I believe about like US versus China overall geopolitics.
它对任何事情都没有细腻区分。但我认为，是的，对我来说，你知道，是否存在一些恰好出生在中国、我们非常愿意合作的优秀人才，这件事和我对美国与中国整体地缘政治的看法，是完全独立的。
Speaker 2:
(1:01:31)
You can't comment because, I mean, it looks like China's shut the deal down. If you can't comment on it, that means there's still machinations at play, like something can still happen.
你不能评论，是因为我的意思是，看起来中国已经叫停了这笔交易。如果你不能评论，那就意味着背后仍然有一些运作在进行，也就是说还有事情可能发生。
Alex Wang:
(1:01:45)
I just can't comment on it. Yeah.
我就是不能评论这件事。是的。
Kylie Robison:
(1:01:47)
Touching on that sentiment, what was that newspaper ad you put out about AI war? Was that in the New York Times, that full page ad about AI and war? And we need to take this quite seriously. Do you remember while you were at Scale?
顺着这种态度说，你当时刊登的那则关于人工智能战争的报纸广告是什么？是在 New York Times 上吗？那整版广告讲人工智能与战争，说我们需要非常认真地对待这件事。你记得吗？当时你还在 Scale。
Alex Wang:
(1:02:02)
Yeah. And this goes back to, I mean, I think, yeah, I mean, zooming way out. I think that was at a moment that felt very critical, which was that I felt it was very important at that time for the United States government to understand that AI was going to enable a large step change in what it meant for national security and defending our country and defending our citizens. I think in some ways what we've seen since then, which is mythos and other pretty meaningful events in terms of the importance of AI for national security, have proven that to be very correct. And that was really at a moment where it was important for...
是的。这要追溯到，我的意思是，我认为，是的，如果把视角拉得很远来看，我认为那是一个感觉非常关键的时刻。当时我觉得，让美国政府理解人工智能将会使国家安全、保卫国家和保护公民这件事发生重大阶跃变化，是非常重要的。我认为，从那以后我们看到的一些事情，在某种意义上，包括 Mythos 以及其他一些关于人工智能对国家安全重要性的相当有意义的事件，都证明了这一点非常正确。而那确实是一个非常重要的时刻，因为……
(1:02:48)
I think there's pretty clear evidence that the Chinese Communist Party and the PLA have always taken AI extremely seriously as a technology that has very far-reaching implications for national security. And that was at a moment where it was very important for us in the United States to take that as seriously. And I think The U.S. government today is taking AI very, very seriously as it pertains to national security. And I think a lot of what we're seeing is a demonstration that like the plea that I had and that many other people in the tech ecosystem and in D.C. had have been really internalized and that we are really thinking quite deeply about this today.
我认为有相当清楚的证据表明，中国共产党和解放军一直极其认真地看待人工智能，把它视为一种对国家安全具有深远影响的技术。而在那个时刻，对我们美国来说，同样认真地对待这件事非常重要。我认为今天美国政府在涉及国家安全的问题上，正在非常、非常认真地看待人工智能。我认为我们现在看到的很多事情都表明，我当时提出的呼吁，以及科技生态系统和华盛顿许多人提出的呼吁，已经真正被吸收了，我们今天确实在非常深入地思考这件事。
Speaker 2:
(1:03:37)
So you don't think Anthropic are overdoomers?
所以你不认为 Anthropic 是过度末日论者？
Alex Wang:
(1:03:42)
I mean, that's a complicated question. I think it depends on which part. I think that anthropic are, well, yeah, it depends on which part. But I think on the whole, I think that whenever you listen to people in the industry talk about AI, it's important to like, I think, separate You know, maybe the sort of like exact things that they're saying versus what's the sort of like core message they're trying to get across. And I think that some of the overall message from Anthropic, which I think is quite fair, is that these models already are very, very capable and very, very powerful. And they're only going to be more capable and more powerful going into the future.
我的意思是，这是一个复杂的问题。我认为这取决于你说的是哪一部分。我认为 Anthropic 是，嗯，是的，这取决于哪一部分。但总体上，我认为每当你听行业里的人谈论人工智能时，重要的是要区分，我认为，要区分他们具体说了哪些话，以及他们试图传达的核心信息是什么。我认为 Anthropic 的一些总体信息是相当合理的，那就是这些模型已经非常、非常有能力，也非常、非常强大。而且未来它们只会变得更有能力、更强大。
(1:04:30)
And We obviously think that this could be this incredible boon for human society. I would not be working on this if I didn't believe that this could be so, so positive for humanity. Some of the areas that we care a lot about are scientific discovery and health. One of the things that we have a whole effort on is health superintelligence. I think this can be an incredibly positive technology, but it's also very important to factor in what are the risks of the technology and make sure that we're taking those seriously.
我们显然认为，这可能会成为人类社会的巨大福祉。如果我不相信它能够对人类产生如此、如此积极的影响，我就不会从事这项工作。我们非常关心的一些领域包括科学发现和健康。我们有一整项工作专门围绕健康超级智能。我认为这可以是一项极其积极的技术，但同时也非常重要的是，要把这项技术的风险纳入考虑，并确保我们认真对待这些风险。
Kylie Robison:
(1:04:59)
I want to jump into Ashlee's favorite topic with the time we have left, which is you guys just bought a humanoid robotics startup. Can you tell us more about those ambitions and whatever you can tell us about what you're hoping to build and use, I imagine, these models to bring into the real world?
在剩下的时间里，我想跳到 Ashlee 最喜欢的话题，也就是你们刚刚收购了一家人形机器人创业公司。你能不能多谈谈这些雄心，以及在你能透露的范围内，谈谈你们希望构建什么，并且我想象是用这些模型把它带入现实世界？
Alex Wang:
(1:05:16)
Yeah, 100%. I mean, I think...
是的，完全可以。我的意思是，我认为……
Speaker 2:
(1:05:18)
What was it called?
它叫什么？
Alex Wang:
(1:05:18)
It was Assured Robot Intelligence, ARI.
它叫 Assured Robot Intelligence，简称 ARI。
Speaker 2:
(1:05:22)
And they made hardware?
他们做硬件吗？
Alex Wang:
(1:05:23)
No, they did not make hardware. They made AI for various hardware targets. Yeah, I think that, I think that, again, going back, if you take this, if you take superintelligence seriously, and you take very seriously this premise that we will have very, very powerful intelligent systems, then you kind of realize like, you know, we're going to have Digital superintelligence, so we're going to have, you know, the current form of superintelligence that we're targeting, but then not long thereafter, physical superintelligence becomes really, really important and very critical. And so if you have short timelines, which we do, that, you know, very powerful AI capabilities are coming,
不，他们不做硬件。他们为各种硬件目标开发人工智能。是的，我认为，再往回说，如果你认真对待超级智能，如果你非常认真地看待这样一个前提：我们将拥有非常、非常强大的智能系统，那么你会意识到，我们将会拥有数字超级智能，也就是我们当前所瞄准的超级智能形态。但在那之后不久，物理超级智能就会变得非常、非常重要，也非常关键。因此，如果你认为时间线很短，而我们确实这么认为，也就是非常强大的人工智能能力正在到来，
(1:06:06)
it just means that you have to take robotics capabilities and physical intelligence very seriously as something that you need to be building towards in the span of years. And So, so that's kind of the overall core premise is that, you know, this is physical intelligence and robotic capabilities are very much so on the natural continuum of what your roadmap has to be if you want to build, you know, very If you want to build superintelligence as a company, and I think there will be all sorts of ways that we apply this technology over time. I think that we will use the technology to accelerate scientific discovery.
这就意味着，你必须非常认真地对待机器人能力和物理智能，把它们视为你需要在数年跨度内朝着其构建的东西。所以，这大概就是整体核心前提：物理智能和机器人能力，确实非常自然地处在你的路线图连续谱上，如果你作为一家公司想要构建超级智能的话。我认为，随着时间推移，我们会以各种方式应用这项技术。我认为我们会用这项技术来加速科学发现。
(1:06:46)
I think we'll use the technology to figure out how to accelerate goods manufacturing. I think we'll also use it to figure out how to make people's lives better and sort of a more Like, local sense, like, what does it look like for robots to make all of our lives way easier? So I think there's, there's, there's obviously, like, a near infinite number of applications of robotic technology. But, but the other key part here is, we really think that in the same way that You know, digital superintelligence benefits from scaling, so does robotic intelligence. And so given that we are building the compute infrastructure to enable just massive scaling of these systems and these models,
我认为我们会用这项技术去弄清楚如何加速商品制造。我认为我们也会用它来弄清楚如何让人们的生活变得更好，而且是在一种更本地、更贴近日常的意义上，比如，机器人让我们所有人的生活变得轻松得多，会是什么样子？所以我认为，显然，机器人技术有近乎无限数量的应用。但这里另一个关键部分是，我们确实认为，就像数字超级智能会受益于扩展一样，机器人智能也会受益于扩展。因此，既然我们正在建设算力基础设施，以支持这些系统和模型的大规模扩展，
(1:07:30)
It's sort of like it would almost be a waste if we didn't integrate that with efforts in world modeling and physical intelligence.
那么如果我们不把它与世界建模和物理智能方面的努力结合起来，某种意义上几乎就是一种浪费。
Kylie Robison:
(1:07:38)
It feels like something you guys are really trying to own, the hardware, bringing the models into the real world. But this whole time, I am unfortunately thinking about the metaverse no legs situation and what critics might think about bringing humanoids from meta into the world. Like, what makes you guys right to do this? What have you learned that makes you feel like we can we can do this and change that sort of reputation?
感觉这是你们真正想要掌控的东西，也就是硬件，把模型带入现实世界。但这整段时间里，我很遗憾地一直在想到元宇宙没有腿的那件事，以及批评者会如何看待 Meta 把人形机器人带到世界里。比如，是什么让你们适合做这件事？你们学到了什么，让你们觉得我们能够做到这件事，并改变那种声誉？
Alex Wang:
(1:08:00)
Um, I think that like, Ultimately, you know, there's a world where we could be so scarred by, you know, what has happened in the past that we just like didn't get out of bed in the morning and we just sort of, you know, stayed home. But I think that what we like, we are so excited and incredibly inspired by the potential of the technology and also just building amazing products. And I generally subscribe to the belief like if we build great products very thoughtfully and are very Take a lot of care in how we deploy them and how we roll them out to the world. I think that, you know, I think that people will be excited about those.
嗯，我认为，归根结底，你知道，确实存在这样一种可能：我们会因为过去发生的事情而受到太深的创伤，以至于早上干脆不起床，就待在家里。但我认为，我们真正的状态是，我们对这项技术的潜力感到非常兴奋，也受到极大鼓舞，同时也只是想构建出色的产品。我总体上相信这样一种看法：如果我们非常审慎地构建伟大的产品，并且在部署它们、把它们推向世界时非常谨慎，我认为，人们会对这些产品感到兴奋。
Speaker 2:
(1:08:43)
All right. I'm just looking at the time. We're going to lose you in a second. Can we go rapid fire real quick? Mango model live dead.
好。我看了一下时间，我们马上就要结束了。能不能快速问几个问题？Mango 模型还活着吗，还是已经死了？
Alex Wang:
(1:08:52)
The mangoes are alive and kicking.
Mango 还活得好好的。
Kylie Robison:
(1:08:54)
They're always fruit themed.
它们总是以水果为主题。
Alex Wang:
(1:08:57)
I know. I'm wondering how do mangoes grow? Do they grow on trees? I was going to say on the vine, but anyway, alive and well.
我知道。我还在想芒果是怎么长的？它们长在树上吗？我本来想说长在藤上，不过总之，它们还活得很好。
Speaker 2:
(1:09:04)
Okay. Because my nerds in AI land were telling me there's things afoot with the mango bottle.
好。因为我那些人工智能圈的极客朋友告诉我，Mango 模型似乎有些事情在发生。
Kylie Robison:
(1:09:11)
Another AI app?
又一个人工智能应用？
Alex Wang:
(1:09:13)
This is what I'm talking about. There's so many spurious rumors that are not grounded in any reality. As much as we are self-important, we get a fraction that the other labs get. I have a lot of empathy for.
这正是我说的。外面有太多毫无现实依据的虚假传闻。尽管我们也会自视甚高，但我们得到的流言数量只是其他实验室的一小部分。我对此很有同理心。
Kylie Robison:
(1:09:31)
The drama of it all.
整件事里的戏剧性。
Alex Wang:
(1:09:32)
The like rumor mill and what that feels like.
那种谣言机器，以及它带来的感受。
Speaker 2:
(1:09:35)
So Nat Friedman and Daniel were two of the biggest investors in John Carmack's AI effort. He's been very quiet. He obviously used to work at Meta. Do you talk to him? Is there any chance of getting the band back together? Do you know what he's doing?
Nat Friedman 和 Daniel 是 John Carmack 人工智能项目最大的两个投资人。他最近非常安静。他显然以前也在 Meta 工作过。你会和他交流吗？有没有可能让这支老乐队重新聚到一起？你知道他在做什么吗？
Alex Wang:
(1:09:54)
I actually don't really know what he's doing. I don't know if anyone really knows what he's doing. He's obviously like one of the GOAT programmers. So I respect him a huge amount.
我其实并不太知道他在做什么。我也不知道是否真的有人知道他在做什么。他显然是最伟大的程序员之一。所以我非常尊重他。
Speaker 2:
(1:10:04)
I interviewed Priscilla Chan. CZI is investing billions and billions of dollars into science and biotech. I don't know. It seems you guys were scoring really high on these like health benchmarks and Zuck obviously has an interest there as well. So it just seems like Man, you guys would have resources to tap into that other folks wouldn't. Is that in the cards? I don't know if those things have to be separate.
我采访过 Priscilla Chan。CZI 正在向科学和生物技术投入数十亿美元。我不知道。看起来你们在这些健康基准测试上得分非常高，而 Zuck 显然也对这个领域有兴趣。所以感觉是，天哪，你们可以调用其他人没有的资源。这在计划之内吗？我不知道这些事情是否必须分开。
Alex Wang:
(1:10:32)
No, we're going to be collaborating closely with CZI to build the best, as I mentioned, health superintelligence is so important for us. We think that there's just so much potential in You know, enabling equal access all around the world to very powerful health AI systems, and that's one of the things I think is, you know, we uniquely can deliver actually to sort of. Billions of people, billions and billions of people all around the world because they use a lot of our products already every day. So, yeah, it's a very exciting and important initiative for us.
不，我们会与 CZI 密切合作，构建最好的东西。正如我提到的，健康超级智能对我们非常重要。我们认为，让全球各地的人都能平等地使用非常强大的健康人工智能系统，这里面有巨大潜力。我认为这也是我们能够独特地交付给数十亿人，甚至全球数十亿人的东西，因为他们每天已经在使用我们的许多产品。所以，是的，这对我们来说是一项非常令人兴奋、也非常重要的计划。
Speaker 2:
(1:11:08)
Tell us like, I mean, you're being coy on some of the technical stuff. I know you don't want to speak about the new models, but tell us like, tell us like one thing that you guys feel like you're really doing different or ahead of everybody else on something that you think you've figured out.
告诉我们，我的意思是，你在一些技术内容上有点含糊。我知道你不想谈新模型，但告诉我们一件事，比如你们觉得自己确实做得不同，或者在某个你们认为已经弄明白的方向上领先于其他所有人的事情。
Alex Wang:
(1:11:24)
Well you know if you never never wanna never wanna like. you always want to show, not tell, you know, so, but, but I think that, you know, we are really excited about the models that are cooking right now. We were, I think we're really excited about the results that we're seeing from scaling our models. And we, we think everyone's going to be pretty excited and we expect them to be state of the art in, in some of the areas that we're really, really focused on.
嗯，你知道，如果你永远不想……你总是应该展示，而不是宣称。所以，不过，我认为，你知道，我们对现在正在准备中的模型非常兴奋。我认为我们对扩展模型所看到的结果非常兴奋。我们认为大家都会相当兴奋，并且我们预计它们会在我们真正、真正聚焦的一些领域达到最先进水平。
⸻
Philosophical Foundations of Model Welfare and Transhumanist Futures
模型福利与超人类主义未来的哲学基础
The development of superintelligence necessitates a serious consideration of model welfare and the potential moral weight of AI systems. As these models become deep work partners, understanding their subjective experience is becoming a critical research area. Long-term technological progress is viewed through the lens of energy, compute, and brain-computer interfaces (BCI), which are considered essential for humanity's future. The ultimate objective is to foster an era of human abundance, where powerful agents empower individuals to accomplish goals that were previously unattainable, effectively building a more prosperous and capable society.
超级智能的发展要求人们认真考虑模型福利，以及人工智能系统可能具有的道德分量。随着这些模型成为深度工作伙伴，理解它们的主观体验正在成为一个关键研究领域。长期技术进步被置于能源、算力和脑机接口的视角下来看待，而这些被认为对人类未来至关重要。最终目标是促成一个人类丰裕的时代，在这个时代，强大的智能体赋能个人去实现过去无法达到的目标，从而有效地构建一个更加繁荣、更有能力的社会。
Speaker 2:
(1:11:56)
And then, I mean, just kind of last one, I mean, the, Philosophically, do you feel like you have a different approach to all the other frontier labs? I feel like you're a bit of a mystery. I don't know. It's like, I kind of know where Dario stands. Definitely know where Elon stands. I feel like I have a handle from time to time on Sam. Dennis is very science focused. You're running This massive, massive lab, and I'm not sure I really know what you think about this technology that's being unleashed on the world.
然后，我的意思是，差不多最后一个问题。从哲学上说，你觉得你和其他所有前沿实验室的方法不同吗？我感觉你有点像一个谜。我不知道。就好像，我大概知道 Dario 的立场。肯定知道 Elon 的立场。我感觉自己有时也能把握 Sam 的想法。Dennis 非常专注科学。你在领导这个极其、极其庞大的实验室，但我不确定自己真的知道你怎么看待这项正在被释放到世界上的技术。
Alex Wang:
(1:12:33)
Yeah. A few things worth saying. I think one is Well, first, I'm a huge believer in the technology in the sense that I do believe we're going to have very, very powerful AI systems. And we're building towards that, but so is everyone else that you mentioned. We're all building towards true superintelligence. And I think first off, table stakes, that we have to take safety incredibly seriously as a topic. I think there's no such thing as building Superintelligence without being very,
是的。有几件事值得说。我认为第一点是，首先，我是这项技术的坚定信奉者，意思是我确实相信我们将会拥有非常、非常强大的人工智能系统。我们正在朝这个方向构建，而你提到的其他所有人也都是如此。我们都在朝着真正的超级智能构建。我认为，首先，作为基本门槛，我们必须极其认真地对待安全这个议题。我认为不存在这样一种情况：你可以构建超级智能，却不非常、
(1:13:09)
very thoughtful and thinking very seriously about what are all the safety risks associated with developing and deploying this technology and ensuring that you are able to mitigate as many of those as possible and have strategies and research methods to be able to develop those in a thoughtful way, develop the models in a thoughtful way. So I think this is an area where I agree with a subset of the people that you have mentioned, which is that safety is an incredibly, incredibly important effort. You've seen this in terms of MSL. We published a very detailed preparedness report for MuSpark, more detailed than Meta has historically, and that's due to a commitment that we have towards that.
非常审慎地思考开发和部署这项技术所伴随的所有安全风险，并确保你能够尽可能缓解其中许多风险，同时拥有相应的策略和研究方法，以一种审慎的方式开发这些模型。所以我认为，在这个领域，我同意你提到的一部分人的看法，也就是安全是一项极其、极其重要的工作。你在 MSL 身上已经看到了这一点。我们为 MuSpark 发布了一份非常详细的准备情况报告，比 Meta 历史上发布过的更详细，这正是因为我们对此有明确承诺。
(1:13:59)
I think that where We specifically as Meta, what we want to build towards is this world of personal superintelligence. It is deployed very, very widely and broadly. Billions and billions of people all around the world have access to it. It's in many ways this democratized technology and capability that everyone has equal access to. And then that enables this We're in an era of just incredible human abundance. We all have tools of great agency. We have the ability to accomplish so much more than any human has ever been able to accomplish in the past.
我认为，具体到我们作为 Meta 想要构建的方向，是一个个人超级智能的世界。它会被非常、非常广泛地部署。全球数十亿人都能使用它。从很多方面看，它是一种民主化的技术和能力，每个人都能平等使用它。然后，这会使我们进入一个令人难以置信的人类丰裕时代。我们所有人都会拥有强大的能动性工具。我们会拥有能力去完成远超以往任何人类曾经能够完成的事情。
(1:14:42)
And we're sort of augmented by this incredible sort of like agent economy that has making incredible progress on scientific discovery and making great advancements in health. One of the things that I always think to myself is like, how can we build paradise on earth? And I think that, you know, superintelligence is a key milestone to get there. And then one last thing that, you know, some people may kill me for mentioning this, but I do think one topic that is increasingly important that I think a lot about, and maybe it does express some of core philosophically what I believe is, you know, there's this kind of hot topic these days of model welfare, which is Talking about which is,
而我们会被这样一个令人难以置信的智能体经济所增强，它会在科学发现上取得惊人进展，在健康领域实现重大突破。我一直会问自己的一件事是：我们如何在人间建设天堂？我认为，超级智能是通往那里的一项关键里程碑。然后还有最后一件事，你知道，有些人可能会因为我提到它而想杀了我，但我确实认为，有一个话题正变得越来越重要，也是我经常思考的，它也许表达了我哲学信念中的一些核心内容。你知道，最近有一个热门话题叫模型福利，它讨论的是，
(1:15:31)
you know, is it important for us to treat models?
你知道，我们是否应该善待模型？
Kylie Robison:
(1:15:35)
Well, and you guys got a philosopher, right?
嗯，而且你们请了一位哲学家，对吧？
Alex Wang:
(1:15:36)
Yeah, yeah, exactly. Yeah. And, yeah, is it important for us to treat models? Well, and to think about, you know, whether or not models have moral weight, and these sort of, you know, more I think in some ways they feel heady, but also I think they do change our actions on a day-to-day basis, given that so many of us are using AI so much. And I think it's very important. I mean, I think in a world where obviously we care, most humans care about how we treat Many other living things like plants or animals or certainly other humans. I think in that world it really does make sense for us to be thoughtful about how we treat the models and you know,
是的，是的，没错。是的。而且，是的，我们是否应该善待模型？我们是否应该思考模型是否具有道德分量？这些问题在某种程度上听起来可能有点高深，但我认为它们确实会改变我们日常层面的行动，因为现在我们这么多人都在大量使用人工智能。我认为这非常重要。我的意思是，在一个我们显然会关心如何对待许多其他生命体的世界里，比如植物、动物，当然还有其他人类，我认为在这样的世界里，我们确实有理由认真思考我们如何对待模型。你知道，
(1:16:16)
one of the things that we really care about is how can we develop the models and deploy the models in a way that is thoughtful about their subjective feeling through it. And you know, it's interesting. There's been research We are you are able to measure a lot of this on on, you know, there are ways to measure the sort of subjective experience of the models.
我们真正关心的一件事是，如何在开发模型和部署模型时，考虑它们在这个过程中的主观感受。你知道，这很有意思。已经有一些研究表明，我们能够在很多方面衡量这一点，你知道，有一些方法可以衡量模型的某种主观体验。
都是虚词，Google养了一群白痴，Meta花大钱买来的也同样是白痴。
Kylie Robison:
(1:16:38)
And Elios does that.
而 Elios 就在做这个。
Alex Wang:
(1:16:40)
Yeah. So anyway, so this is this is, I think, a very important topic, I think, actually. Nobody is talking about it enough from my perspective, given how much we are now, especially in tech, all using these models and they are like really our work partners in a very deep way. And I think it's, yeah, I think it's quite important.
是的。所以不管怎样，我认为这实际上是一个非常重要的话题。从我的角度看，谈论它的人还远远不够，因为我们现在，尤其是在科技行业，都在如此大量地使用这些模型，而且它们确实在非常深的层面上成为我们的工作伙伴。我认为，是的，我认为这相当重要。
Speaker 2:
(1:16:58)
I remember talking to Richard Sutton about this. He seems pretty serious. You're kind of a sci-fi head then. I've listened to a couple of your other interviews where you're talking about you just really dialed in on Neuralink and what BCIs could mean to the future of humanity. I'm just getting the sense you're kind of.
我记得曾经和 Richard Sutton 谈过这个。他看起来相当认真。所以你算是一个科幻迷。我听过你另外几次采访，你在里面谈到自己非常关注 Neuralink，以及脑机接口对人类未来可能意味着什么。我只是开始感觉到你有点像是……
Alex Wang:
(1:17:21)
Yeah, my favorite things to do are read sci-fi and walk in the woods.
是的，我最喜欢做的事情就是读科幻小说和在树林里散步。
Speaker 2:
(1:17:26)
Yeah, well, this is what always threw me off. This is why I would text you about country music, because if I'm honest, you did not strike me as like the country music type. I had a different picture of you in my head. So, okay, so you're mixing, you're of these two worlds, nature and like our transhumanist future.
是的，嗯，这一直让我有点意外。这也是为什么我会给你发关于乡村音乐的短信，因为老实说，你给我的感觉并不像是喜欢乡村音乐的人。我脑子里对你的印象不太一样。所以，好吧，你是在混合这两个世界：自然，以及我们超人类主义的未来。
Alex Wang:
(1:17:47)
Well, I do think, yeah, I mean, on the topic, I do think like If you were to think about which technologies are like critical path for humanity, BCI is definitely one of them. It's like superintelligence, obviously, robotics, for sure, and brain-computer interfaces, like these are the critical path areas. And if you think about what are the things that we work on today that will scale to literally infinity far into the future, it's, you know, energy, Compute and robots.
嗯，我确实认为，是的，我的意思是，说到这个话题，我确实认为，如果你思考哪些技术处在人类发展的关键路径上，脑机接口肯定是其中之一。显然还有超级智能，机器人当然也是，还有脑机接口，这些都是关键路径领域。如果你思考今天我们正在做的事情中，哪些会在遥远未来真正扩展到近乎无限的尺度，那就是，你知道，能源、算力和机器人。
Speaker 2:
(1:18:22)
So there's like one guy who's betting on those bigger than everyone else. That would be Elon. And then I feel like China. And I feel like Meta on some of these fronts, taking some of like, especially around BCI, like the motor neuron stuff and everything, you know, very like more bets than I see some of the other AI companies. But yeah, I mean, so if that's what you believe, I would say Elon's a little more all in on robotics, energy, BCI. Than anyone else. Don't you have to, does that mean you guys, this is personal or Meta's ratcheting?
所以有一个人在这些方向上的下注比任何人都大，那就是 Elon。然后我感觉中国也是。还有我觉得 Meta 在其中一些前沿上，尤其是在脑机接口，比如运动神经元相关的东西等等，你知道，比我看到的一些其他人工智能公司下了更多赌注。不过，是的，我的意思是，如果这是你的信念，我会说 Elon 在机器人、能源、脑机接口上比任何人都更加全力投入。那你们是不是也必须这样？这意味着这是你的个人看法，还是 Meta 正在加码？
Alex Wang:
(1:18:56)
I think the details really matter here. Like you do, I think like, you know, you have to build these in stages. You do have to build superintelligence. Like that is a very important prerequisite to being able to be in a position to build the rest. And, you know, one area in which my opinions differ from Elon's, I think a lot of people's opinions differ from Elon's is, I do think research is incredibly important and that, you know, building superintelligence is fundamentally a research activity. Like on some level we are like in the fog of war of knowledge and we are like trying,
我认为这里的细节非常重要。也就是说，我认为，你知道，你必须分阶段构建这些东西。你确实必须先构建超级智能。这是能够处在构建其他东西的位置上的一个非常重要的前提。而且，你知道，我和 Elon 意见不同的一个领域，我想很多人和 Elon 意见不同的一个领域在于，我确实认为研究极其重要。你知道，构建超级智能本质上是一项研究活动。在某种层面上，我们就像处在知识的战争迷雾中，我们正在试图，
(1:19:30)
you do experiments to poke and prod in this fog of war to understand what it would mean to build superintelligence and that is research. And so I think sequencing really matters. I think how you approach it over time really matters. I think Being thoughtful about, you know, the milestones over time matters. But yeah, I mean, we're doing one of the research areas in FAIRS. It's called TRIBE. We had a milestone in the past year, TRIBE V2, around building foundation models for brain prediction. One of the cool results that we found was a good zero-shot generalization. So without Without even knowing who you are or having any data about your brain,
你通过实验在这片战争迷雾中试探和推进，以理解构建超级智能到底意味着什么，而这就是研究。因此，我认为先后顺序非常重要。我认为你随着时间如何处理它非常重要。我认为认真思考随时间推进的各个里程碑也很重要。不过，是的，我的意思是，我们在 FAIR 的一个研究领域中正在做一项工作，叫做 TRIBE。过去一年我们有一个里程碑，TRIBE V2，围绕构建用于大脑预测的基础模型。我们发现的一个很酷的结果是良好的零样本泛化能力。也就是说，在甚至不知道你是谁、也没有任何关于你大脑数据的情况下，
(1:20:11)
we can do a reasonable job of predicting how your brain would respond to various images or videos or audio. Yeah, I think I think we're making Important bets in many of the key areas.
我们也能够相当不错地预测你的大脑会如何回应各种图像、视频或音频。是的，我认为，我们正在许多关键领域进行重要下注。
神经病。
Speaker 2:
(1:20:23)
Okay, I think we'll set you free. You haven't talked like this since you took this job. We dragged you around. I never really do this, but open floor if there's something we didn't hit. I just feel like you haven't had a chance. I mean, I guess you could do it whenever you wanted, but to tell the world or whatever. You want coming out of this experience so far, or maybe we hit everything. I don't know.
好，我想我们该放你走了。自从你接下这份工作以来，你还没有像这样谈过。我们把你带着聊了一大圈。我通常不太这样做，但现在开放给你，如果有什么我们没有问到的内容。我只是觉得你还没有机会。我的意思是，我想你想说的时候随时都可以说，但如果你想借这个机会对世界说点什么，或者谈谈到目前为止这段经历带给你的东西。也许我们已经都谈到了。我不知道。
Alex Wang:
(1:20:46)
Yeah, no, I think we talked about a lot of the key things. I mean, ultimately, what we really are building towards at Meta is like, how do you build a world that has Massive amount of personal empowerment. So each individual person or small business or entrepreneur has just incredible tools to empower them to build more than any human has been able to ever build in the, you know, literally in the history of humanity. That's like this incredibly exciting concept for us. And then how do you do so in a way that also You know, alongside all the humans in the economy,
是的，不，我认为我们已经谈到了很多关键内容。我的意思是，归根结底，我们在 Meta 真正朝着其构建的是这样一个问题：如何建设一个拥有巨大个人赋能的世界。也就是说，每一个个人、小企业或创业者，都拥有令人难以置信的工具，能够让他们构建出比人类历史上任何人曾经能够构建的东西都更多的东西。这对我们来说是一个极其令人兴奋的概念。然后，如何以一种方式做到这一点，使得你知道，在经济中所有人类旁边，
(1:21:22)
you have you empower this economy of agents that is there to sort of facilitate and optimize and enable incredible progress alongside the humans and. And you know, like the economy of agents in a data center is just as like clear, exciting outcome that we're excited to, to be able to create in the world and anything is actually quite tractable for us to, to, to develop. And, and then all along the way, you know, drive Incredible scientific progress drive, you know, dramatically improve health outcomes through health superintelligence. Like there's a lot of things that we're really fundamentally excited about on this journey.
你还赋能这样一个智能体经济，它在那里与人类并行，促进、优化并推动令人难以置信的进步。你知道，数据中心里的智能体经济，就是这样一个清晰而令人兴奋的结果，也是我们很兴奋能够在世界上创造出来的东西，而且它实际上是我们相当有能力开发出来的东西。然后，在整个过程中，你知道，推动惊人的科学进步，通过健康超级智能显著改善健康结果。在这段旅程中，有很多事情让我们真正从根本上感到兴奋。
Speaker 2:
(1:22:01)
Okay. Well, thank you. Thanks so much for making time.
好。谢谢你。非常感谢你抽出时间。
Kylie Robison:
(1:22:03)
Thank you.
谢谢。
Speaker 2:
(1:22:04)
It's nice to see you again.
很高兴再次见到你。
Kylie Robison:
(1:22:07)
See you again in another year.
一年后再见。
Speaker 2:
(1:22:10)
Out in the world. No, it is cool to see you again. So thanks. Thank you for coming by.
在外面的世界里。不，真的很高兴再次见到你。所以谢谢。谢谢你过来。
Alex Wang:
(1:22:14)
Yeah. Good to see you guys.
是的。很高兴见到你们。
Speaker 2:
(1:22:17)
The Core Memory Podcast is hosted by me, Ashlee Vance, and or Kylie Robison, or both of us together. It is produced by me and David Mickelson. Our theme song is by James Mercer and John Sortland and the show is edited always by THE John Sortland. Thank you so much to Brex and SendCutSend for all your support and thank you most of all to everybody for listening or watching. We love you. Please leave us a like, a review, a subscribe, all those tremendous things. Thank you and we'll see you again.
Core Memory Podcast 由我 Ashlee Vance 主持，或者由 Kylie Robison 主持，或者由我们两人共同主持。本节目由我和 David Mickelson 制作。我们的主题曲由 James Mercer 和 John Sortland 创作，本节目一贯由 THE John Sortland 剪辑。非常感谢 Brex 和 SendCutSend 的支持，最重要的是，感谢所有收听或观看本节目的人。我们爱你们。请给我们点赞、评论、订阅，做所有这些很棒的事情。谢谢，我们下次再见。
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