Elon MuskWelcome to the xAI all hands. We've got a very exciting presentation for you. We're going to start off by recapping the incredible progress that the xAI team has made in just two and a half years. It's really remarkable in pursuit of our goal of understanding the universe. So just going over our accomplishments since inception, it's important to bear in mind that xAI is only two and a half years old, basically a toddler, and we've nonetheless achieved an incredible amount in a very short period of time. So our competitors are five, 10, in some cases 20 years old. They have much larger teams. They started off with far more resources, and yet nonetheless we have achieved number one in many arenas in just a few years.欢迎来到 xAI 全员大会。今天我们有一个非常精彩的演讲。首先我们要回顾一下 xAI 团队在短短两年半内取得的惊人进展——这在我们追求理解宇宙这一目标的过程中，真的相当了不起。回顾一下我们自成立以来的成就，有一点很重要：xAI 才成立两年半，基本上还是个蹒跚学步的婴儿，但我们在极短的时间里却取得了令人难以置信的成就。我们的竞争对手有的已经成立五年、十年，某些情况下甚至二十年了，他们有大得多的团队，起步时拥有远比我们雄厚的资源，但即便如此，我们在短短几年内已经在很多领域做到了第一。
Elon MuskSo we've achieved number one in voice, in image and video generation. I think we now at this point are actually generating more images and video, based on the last numbers I saw, than all of our competitors combined. We are winning in terms of forecasting, which is one of the key metrics of intelligence. So our Grok 4.20 forecasting model beat all the other AIs in forecasting. And we've topped many leaderboards. We've got now a great app with the Imagine, with the core Grok. We've made radical improvements to the X app, and we've launched Grokipedia, which is on its way to far exceeding Wikipedia and ultimately be orders of magnitude more comprehensive and more accurate and have more information, as well as video and image data that simply isn't there on Wikipedia. So it's intended ultimately to be Encyclopedia Galactica, a distillation of all knowledge, yeah, all knowledge.我们在语音、图像和视频生成方面做到了第一。根据我最近看到的数据，我们现在生成的图像和视频数量实际上已经超过了所有竞争对手的总和。我们在预测方面也处于领先地位，而预测正是衡量智能的核心指标之一。我们的 Grok 4.20 预测模型在预测上击败了所有其他 AI。我们登顶了众多排行榜。我们现在有了出色的应用——有 Imagine，有核心的 Grok。我们对 X 应用进行了根本性的改进，还推出了 Grokipedia，它正朝着远超 Wikipedia 的方向发展，最终将在全面性、准确性和信息量上超出 Wikipedia 几个数量级，还会包含 Wikipedia 上根本没有的视频和图像数据。所以它的终极目标是成为 Encyclopedia Galactica（银河百科全书），是所有知识的蒸馏——对，所有知识。
Elon MuskAnd we're the first to achieve 100,000 H100 GPU training cluster, and we're now about to achieve the first 100, I should say 1 million H100 GPU equivalents in training. So really an incredible amount of work in a very short period of time. And it's important to consider for competitiveness of any technology company what matters is not the position at any point in time but what is your velocity and acceleration. And if you're moving faster than anyone else in any given technology arena you will be the leader, and xAI is moving faster than any other company. No one's even close.我们是首个实现 100,000 H100 GPU 训练集群的公司，现在我们即将实现——应该说，等效 1 百万 H100 GPU 的训练规模。这在极短的时间内完成了极其大量的工作。对于任何科技公司的竞争力而言，重要的不是某个时间点上的位置，而是你的速度和加速度。如果你在某个技术领域的前进速度比任何人都快，你就会成为领导者，而 xAI 的前进速度比任何其他公司都快。没有人能与我们相提并论。
Elon MuskSo let's go to our team. As we grow as a company, a natural thing that happens is you reorganize the company as it scales up. So when you first have a startup, you might have just a few dozen people and they all just chat amongst themselves. As you grow to several hundred people, you have to then add more structure. Just like an organism that grows from a single, like we all just grew from a single cell and into a blob of cells. Then you get organ differentiation, limbs. You grow a tail. Hopefully the tail disappears and then you become a baby. You go through these stages. And so we're organizing because we've reached a certain scale. We're organizing the company to be more effective at this scale.接下来说说我们的团队。随着公司的成长，一件自然而然的事情就是随着规模扩大而进行重组。当你刚成立一家初创公司时，可能只有几十个人，大家彼此直接交流就好了。当团队增长到几百人，你就需要增加更多架构。就像一个生命体从单个细胞成长起来——我们每个人都是从一个单细胞开始，变成一团细胞，然后出现器官分化、长出四肢，长出尾巴，然后希望尾巴消失，最后变成一个婴儿。你要经历这些阶段。所以我们正在进行组织架构调整，因为我们已经达到了一定规模，需要让公司在这个规模上更加高效地运转。
Elon MuskNow naturally when this happens there's some people who are better suited for the early stages of a company and less suited for the later stages. And for the people that have departed I'd just like to say thank you for your contribution, thank you for getting us this far, and we wish you very well in your future endeavors. So now going on to the new structure of the company. The company is organized in four main application areas. There's Grok main and voice, which is really the main Grok model. That's why it's called Grok main. Then there's a coding specific model. There's an image and video model which is Imagine. And then Macrohard, which is intended to do full digital emulation of entire companies. And then we've got the infrastructure layers. So I'd like to invite members of the team to come up and talk about each of their areas.当然，当这种情况发生时，有些人更适合公司的早期阶段，而不太适合后期阶段。对于那些已经离开的人，我想说的是：感谢你们的贡献，感谢你们把我们带到今天，祝你们在未来的事业中一切顺利。接下来谈谈公司的新架构。公司围绕四个主要应用方向组建：一是 Grok main 和语音，这是核心 Grok 模型，这就是为什么叫 Grok main；二是一个专注于编程的模型；三是图像和视频模型 Imagine；四是 Macrohard，旨在对整个公司进行完整的数字仿真。然后我们还有基础设施层。现在我邀请团队成员上来分别介绍各自负责的领域。
OtherHey, thanks Elon. So Grok main and voice are going to be merged into one team. And you know, on voice, one anecdote is September 2024 OpenAI had this product where you could talk to advanced voice mode, and we had nothing, no model of course in the product. We started much after that, and in a span of a few months, six months, we developed the model in-house from scratch without a bunch of people who knew audio, and had a product that was surpassing OpenAI in six months. Fast forward six more months and now we have Grok in more than 2 million Teslas. We have a Grok voice agent API. You can do all kinds of amazing things. In a span of one year, we went from nothing to being leaders. That kind of stuff is only possible in a place like xAI where you have small teams, committed, mission focused, lots of compute. And we really really want to keep pushing.谢谢 Elon。Grok main 和语音将合并为一个团队。关于语音，有个故事：2024 年 9 月，OpenAI 发布了一个可以和 advanced voice mode 对话的产品，而我们当时什么都没有，当然也没有这样的模型。我们是在那之后很久才开始的，但在短短几个月、六个月内，我们从零开始、完全自研构建了这个模型，没有一堆懂音频的人，却做出了一个在六个月内就超越 OpenAI 的产品。再快进六个月，现在我们的 Grok 已经装载在超过 200 万辆 Tesla 上，我们还有 Grok voice agent API，你可以用它做各种神奇的事情。在短短一年内，我们从零做到了行业领先。这种事只有在 xAI 这样的地方才有可能——小团队、目标明确、使命驱动、计算资源充足。我们真的非常非常想继续推进。
OtherSame story on the chat models. You know, we've always been at the forefront of reasoning, starting from Grok 1.5, Grok 2, Grok 3. And we want to really move to a world where it's no longer about just question answering. We want to build an everything app. So you should be able to come to it and really get done whatever you want. You know, ask a legal question, make a slide deck, or solve a puzzle, stuff like that.对话模型也是同样的故事。我们一直站在推理能力的前沿，从 Grok 1.5、Grok 2 到 Grok 3 都是如此。我们真正想要迈向的是一个不再只是问答的世界。我们想打造一个万能应用，让你来了就能搞定任何事——问法律问题、做幻灯片、解谜题，诸如此类。
OtherYeah. So I really think on the product side, we're really going to see a huge transformation happening in a very short period of time. We're going to see the magnitude of the amount of work that all knowledge workers are going to be able to produce increase tenfold in the next short period of a few months. The models that we are building out are incredibly amazing and we have a lot on the way and we're really excited to share that with you all. And on the product side the goal is to just build that portal that allows you to accomplish all of your work, and how do we amplify everyone to achieve much much more than what they can accomplish alone. And we're building that out and it's going to be an incredibly easy to use experience that just works seamlessly.是的。我确实认为在产品层面，我们即将在极短的时间内看到一场巨大的变革。在接下来短短几个月里，所有知识工作者能够产出的工作量将增长十倍。我们正在构建的模型极其出色，路上还有很多东西，我们非常兴奋地想与大家分享。在产品层面，目标就是打造那个门户，让你能完成所有工作，让我们如何放大每个人的能力，使他们能取得比单独工作多得多的成就。我们正在构建这个，它将是一个极其易用、无缝运行的体验。
OtherThat being said, we are hiring and we're looking for intelligent and smart people. This is not an easy place to work, guys. Like this is, it's a grind, but we have I guess like interstellar ambitions. So it's not going to be easy, right? So I will say, having come to xAI, it has been an opportunity of a lifetime to work among really smart and really passionate people. The vibes here are amazing and it's truly an environment where if you're a smart person and you want to get done, you can get done. There isn't like organizational overhead getting in your way or kind of, I don't know, like having to write docs and all this kind of stuff. You just do stuff. At least for me, you can do things here and that's amazing, and I invite more people to come here and just do awesome things.话虽如此，我们在招人，我们在寻找聪明、有才华的人。这里工作不轻松，真的，这是一场硬仗，但我们有，姑且说是星际级别的野心。所以不会容易，对吧。来到 xAI 之后，我觉得这是我这辈子能和真正聪明、真正充满热情的人共事的难得机会。这里的氛围非常棒，确实是那种如果你聪明、有执行力，就能把事情做成的环境。没有什么组织层面的繁文缛节阻碍你，也不用写一堆文档之类的东西。你就是直接去做。至少对我来说，在这里能把事情做成，这很了不起，我邀请更多人来这里，来做出色的事情。
OtherYeah. So with the Grok main, the sort of main foundation model, the intent is that it's genuinely useful in a wide range of areas. So if you're doing engineering or law or medicine, anything, it is useful to you in your job. That's essential to understanding the universe and making things as useful as possible, like where when Grok gives you an answer that you can count on it.对于 Grok main，也就是主要的基础模型，我们的意图是让它在广泛的领域真正有用。不管你是做工程、法律还是医学，它对你的工作都有帮助。这对于理解宇宙、让一切尽可能有用至关重要——就是那种 Grok 给出的答案让你能够信赖的感觉。
Elon MuskYep. Absolutely. All right. All right. Thank you. Yeah. Thanks.对，完全正确。好的。谢谢。好，谢谢。
MarcoHey everybody, I'm Marco. So the world changed a lot recently in terms of coding. The coding models, I was always complaining, people were trying to convince me to use a coding model and I was testing it and I wasn't really convinced, but as of recently the models, they actually produce good decent quality code. I mean you still need to review and give feedback but it's easy to see how they can accelerate you quite a lot. So it's not only about coding, it's like they understand your intuition much better than before. Like now when I describe a problem I only have to phrase it like I would to another colleague engineer who has already seen the codebase. That's a huge change. Before you kind of need to handhold a toddler to make a change. And they don't only write your code but they also can debug your code.大家好，我是 Marco。最近编程领域发生了很大的变化。对于编程模型，我以前一直有抱怨——人们一直让我用编程模型，我也测试过，但并不是很信服。但最近模型确实能生成质量不错的代码了。当然你还是需要审查和给出反馈，但很容易看出它们能让你的效率大幅提升。而且这不只是关于编程，它们现在对你的直觉理解得比以前好多了。比如现在我描述一个问题，只需要像和一个已经看过代码库的同事工程师说话那样描述就行了。这是一个巨大的变化。以前你得像哄一个学步的孩子一样手把手带着它做改动。而且它们不只写代码，还能帮你调试代码。
MarcoSo now we do like hours of Grok code running continuously to make sure that a more complex change to the training system actually works in production. So it's easy to see for us that this is not only about accelerating ourselves writing code and making us 10x more productive, but we're really on this path for recursive self-improvement where the current generation of Grok code is training the next generation of Grok code, and we see that this path, we're on an exponential takeoff here, this path will continue. So we are doubling down on coding and making coding one of the highest priority efforts in the company. So if you're out there and you're excited about coding and you're either very good at training, modeling, or you're a really good low-level software engineer interested in systems design, this is the place to work. Like we have a million H100 equivalents to train the best coding model in the world right now. So please join us.现在我们让 Grok code 连续跑好几个小时来确保对训练系统的某个复杂改动在生产环境中真正能运行。对我们来说很容易看清楚，这不仅仅是加速我们自己写代码、让我们效率提升 10 倍的事，我们真正踏上的是一条递归自我改进的道路——当前这一代 Grok code 正在训练下一代 Grok code，而且我们看到这条路，我们正处于指数级的起飞阶段，这条路还会继续。所以我们在大力押注编程，将其列为公司最高优先级的工作之一。如果你在外面，对编程充满热情，无论是在训练、建模方面非常厉害，还是对系统设计感兴趣的底层软件工程师，这就是你该来的地方。我们现在有等效 100 万 H100 来训练世界上最好的编程模型。请来加入我们。
OtherUh yeah, I'm good. I will compare with Marco on coding. So it becomes more and more obvious to us, like you know over time, that we are on a path to singularity at least on coding. So we decided to have our best engineer in the company, Marco, to lead the coding, and we'll build the best coding model for everyone, to empower everyone to build. And for me the main limiting factor is probably compute and energy, where they can run the best model to support everyone, to empower everyone. And with SPEC now we are one team and we will win on the compute, and we are winning with SpaceX compute. And also for every engineer, right? So if you are writing kernel, if you're writing compiler, just think about whether it's still worth it. Maybe you should join us for coding effort to automate yourself a little bit, to speed yourself up. Yeah, I think it's a really amazing year. Basically what a year to be alive, and I can already feel the AGI, feel the AI at least for coding. Yeah.嗯，我同意。我会在编程方面和 Marco 比一比。随着时间推移，我们越来越清楚地看到，我们正走在通向奇点的道路上——至少在编程领域是这样。所以我们决定让公司最好的工程师 Marco 来领导编程方向，我们要为所有人打造最好的编程模型，赋能所有人去构建。对我来说，主要的限制因素可能是算力和能源，也就是能运行最好模型来支持所有人、赋能所有人的能力。现在有了 SPEC，我们是一个团队，我们将在算力上取胜，我们正在用 SpaceX 的算力取胜。还有对每一个工程师来说——如果你在写 kernel、写编译器，想想这是否还值得。也许你应该加入我们的编程工作，让自己自动化一点，让自己提速。是的，我认为这是非常了不起的一年。基本上，能活在这个时代太棒了，对于编程，我已经能感受到 AGI、感受到 AI 的存在了。
Elon MuskYeah. I think actually things will move maybe even by the end of this year to where you don't even bother doing coding. The AI just creates the binary directly, and the AI can create a much more efficient binary than can be done by any compiler. So just say create optimized binary for this particular outcome, and you actually bypass even traditional coding. There's no, that's an intermediate step that actually will not be needed probably by, I'd say, the end of this year. And we do expect Grok code to be state-of-the-art in 2 to 3 months. So it's happening very quickly.是的。我认为实际上到今年年底，事情可能已经演变到你根本不用去写代码的地步。AI 直接生成二进制文件，而且 AI 能生成比任何编译器都高效得多的二进制文件。你只需要说，为这个特定的结果生成优化二进制，你甚至绕过了传统的编程过程。传统编程是一个中间步骤，大概到今年年底就不再需要了，我是这么估计的。我们预计 Grok code 会在 2 到 3 个月内达到最先进水平。所以这件事正在以极快的速度发生。
GaurangYeah. Also do imaging. So you know, I mean, what do you all do right after post a, right? You probably do like digital life. So that's what we are doing here as well. And we have the Imagine team, like started pretty much from scratch like six months ago. We have a few people, we decided we'll do the image gen, we'll do the video gen. Like yeah, look at what we achieved today. Like you know, two weeks ago we released the Imagine v1, we actually topped the leaderboard across like many of them, and people really love our product, love our model, and we have many more releases actually this month and next month. So yeah, to me there's like a very high chance we actually may build a metaverse before Meta. I will also pass to try to talk about the metrics we have on the product. Yeah.是的，我们也做图像。你知道，大家在发布了什么之后第一件事会做什么？可能就是搞数字生活。这也是我们在这里做的事。我们有 Imagine 团队，大概六个月前从零开始。我们只有几个人，决定要做图像生成、视频生成。看看我们今天取得的成就——就两周前我们发布了 Imagine v1，在很多排行榜上都登顶了，用户真的非常喜欢我们的产品和模型，而且本月和下个月还有很多发布。所以对我来说，我们在 Meta 之前建成元宇宙的概率非常高。接下来请介绍一下我们在产品上的指标。
OtherYeah. Like Gaurang said, it's only been six months since we started working on Imagine. We had no code internally for diffusion at all six months ago, and basically now we've launched Imagine on every product surface that we have, including seamlessly integrating into X. So you can open the X app right now, you can long press on any image, you can edit the image, you can make a video out of the image. We also ran a contest recently where we had some really funny submissions that I'm sure many of you have seen. So Imagine is growing extremely extremely fast. And it's because of the speed at which we iterate. Basically we do multiple product updates every day. We do model updates every other week, and effectively what this has led to is now users are generating close to 50 million videos every day using Imagine. And just to reiterate what Elon said earlier, that to the best of our knowledge that is more than every other provider combined, which again is an astonishing place to be compared to where we were six months ago.就像 Gaurang 说的，我们开始做 Imagine 才六个月。六个月前我们内部根本没有任何扩散模型的代码，但现在我们已经在每一个产品面上都上线了 Imagine，包括无缝集成到 X 中。现在你可以打开 X 应用，长按任意一张图片，编辑它，或者把它做成视频。我们最近还举办了一个比赛，收到了一些非常有趣的投稿，我相信你们很多人都看过了。Imagine 的增长速度极其极其之快，这要归功于我们的迭代速度——我们基本上每天做多次产品更新，每隔一周做一次模型更新。这带来的结果是，用户现在每天用 Imagine 生成接近 5000 万个视频。重申一下 Elon 刚才说的，据我们所知，这已经超过了所有其他提供商的总和。考虑到我们六个月前的起点，这个成就真的令人震撼。
OtherWe are also generating 6 billion images in the last 30 days. Nano Banana, you know Google recently posted that 1 billion images were generated using Nano Banana in 30 days, so you know we're six times that, right? And really the goal is, it's not like we just want to win, we want to win over a long period of time and have sustained greatness. And so the goal with Imagine is to take anything that you can imagine and turn it into reality, and so that's what we're going to speedrun. That basically is the goal.在过去 30 天里我们还生成了 60 亿张图片。Nano Banana——Google 最近发布说 Nano Banana 在 30 天内生成了 10 亿张图片，所以我们是他们的六倍。真正的目标不只是赢，我们要长期赢，保持持续的卓越。Imagine 的目标是把你能想象到的任何东西变成现实，这就是我们要快速推进的方向，这基本上就是我们的目标。
HatimYeah. Hey, I'm Hatim. As we keep scaling our model capabilities, building visual worlds that's indistinguishable from reality, we're also building systems that unlock much more possibility than what we have right now. They will be able to generate videos that's much longer than what we have right now, with stories or with souls of your Imagine. And by the end of the year, we likely will be having models that allow you to generate videos of 10 minutes or 20 minutes in one shot without any intervention. You just need to give your imagination, and our model, our agents will do it for you.嗯，大家好，我是 Hatim。随着我们不断提升模型能力，构建与现实无法区分的视觉世界，我们也在构建能释放远比现在更多可能性的系统。这些系统将能生成比现在长得多的视频，带有你 Imagine 的故事或灵魂。到今年年底，我们可能将拥有能让你一次性生成 10 分钟或 20 分钟视频的模型，无需任何干预。你只需要给出你的想象，我们的模型和 agent 就会帮你完成。
HatimAnd moreover, those are the videos we generate. And we're also going to allow rendering those. We're already the fastest in generating the videos and we're going to keep pushing the extreme where we're going to render those videos in real time, and you will be able to imagine, build and interact with your own world, and the world will respond to you in real time. And it is an exciting future that we are going to build with ourselves.而且，这些是我们生成的视频。我们还将允许对其进行渲染。我们已经是视频生成速度最快的，我们将继续推向极限——我们要实时渲染这些视频，你将能够想象、构建并与你自己的世界互动，而这个世界会实时响应你。这是一个令人兴奋的未来，我们要亲手去构建它。
Elon MuskAbsolutely. My prediction is that most of AI compute is going to be real-time video understanding and real-time video generation, and we expect to be the leaders in that. It's worth emphasizing these points that, you know, six months ago we had basically nothing, very weak in video and image generation and editing, and went in six months to number one spot. And in fact generating more videos and images than everyone else combined. We're going to do the same thing with coding and we're going to do the same thing with Macrohard. And I think people will be pretty impressed with the Grok 4.2 model that's coming out. That's a significant improvement. And that's really just the small version of our new model. So we'll have a medium and a large version that are even more intelligent.绝对是这样。我的预测是，未来大部分 AI 算力都将用于实时视频理解和实时视频生成，而我们预计将在这方面处于领先。值得强调的一点是，六个月前我们在视频和图像生成与编辑方面基本上什么都没有，非常弱，但在六个月内做到了第一，实际上生成的视频和图像超过了所有其他人的总和。我们要在编程上做同样的事，在 Macrohard 上也要做同样的事。我认为大家看到即将发布的 Grok 4.2 模型会相当印象深刻，那是一个重大提升。而且那其实只是我们新模型的小版本，我们还会有中等版和大版本，智能程度更高。
TobyAll right. Hi everyone. I'm Toby and I work on Macrohard, the most serious of all product names. So arguably giving computers to humans was a good idea. So we're doing the same thing for AI. It's kind of like Inception. We're giving computers to computers. So Macrohard is building a fully capable digital, real-time, very important, human emulator. So it's able to do anything on a computer that a human is able to do, including using advanced tools in engineering and medicine. So there should be rocket engines fully designed by AI. And in a sense, it's one of the last few remaining areas where AI is significantly worse than humans, which is why I think it's one of the most exciting areas to actually innovate in and actually change the field.好的，大家好，我是 Toby，我在负责 Macrohard，这是所有产品名称中最认真的一个。可以说，把计算机给人类用是个好主意。所以我们现在为 AI 做同样的事情。这有点像《盗梦空间》——我们把计算机给计算机用。Macrohard 在构建一个完全有能力的、实时的（这点很重要）人类数字仿真器，它能在计算机上做任何人类能做的事情，包括使用工程和医学领域的高级工具。所以应该会有完全由 AI 设计的火箭发动机。从某种意义上说，这是少数几个 AI 明显还不如人类的领域之一，这正是为什么我认为它是最令人兴奋的创新领域，真正能改变这个领域。
JohnHi everyone. So yeah, my name is John, and so we're building these strong reasoning models which are now going to control our CLI. Like we're actively using these every day. They are like tremendous productivity boost to the whole team. I know the voice team is like killing it on that, and you know this is the reason why we need the compute, we need the large scale compute to run these models to boost our own productivity. But you know 80 to 90, 95% of the world's software has a GUI, so that's like a great representation, and to truly make people's lives easier we need to develop models that are capable of solving day-to-day tasks on GUI.大家好。我是 John，我们正在构建这些强大的推理模型，它们现在将控制我们的 CLI。我们每天都在积极使用这些，对整个团队来说是巨大的生产力提升。我知道语音团队在这方面做得非常出色，这也是为什么我们需要算力、需要大规模算力来运行这些模型来提升我们自己的生产力。但你知道，世界上 80 到 90、95% 的软件都有 GUI，这是一个很好的说明，要真正让人们的生活更轻松，我们需要开发能够解决 GUI 上日常任务的模型。
JohnSo Macrohard, you know, we will emulate a company where the output is digital, and so this is the obvious next step for agents. Macrohard will enable true end-to-end orchestration across the desktop and it will lead to immense economic prosperity. So yeah, we're entering an era where we need to tackle the hardest of tech problems, but in order to solve this we need to hire the best people. So you know, think of the smartest people that you've worked with and put them forward for a position here. And if you can't think of anybody, like go through your phone book, go through your LinkedIn. You'll be surprised how big your actual network is. And they just need three properties obviously that we want to optimize for. Are they clever? Can they solve hard problems? And the second property is are they driven? Do they have the ambition? Do they want to win? And the third is, are they a nice person? Like do you want to actually work with them? But yeah, so thank you.所以，Macrohard 将仿真一家输出是数字化的公司，这是 agent 的显而易见的下一步。Macrohard 将实现跨桌面端真正的端到端编排，并将带来巨大的经济繁荣。我们正进入一个需要解决最棘手技术问题的时代，但要解决这些问题，我们需要招募最优秀的人才。想想你共事过的最聪明的人，把他们推荐过来。如果想不到的话，翻翻你的通讯录，翻翻你的 LinkedIn，你会发现你的实际人脉比你想象的要大得多。我们想要具备三个特质的人：第一，他们聪明吗？能解决难题吗？第二，他们有驱动力吗？有野心吗？想要赢吗？第三，他们是好人吗？你真的愿意和他们共事吗？好的，谢谢大家。
Elon MuskYeah, the Macrohard project over time actually will probably be our most important project, because what we're talking about is emulation of entire human companies. So when you look at the most valuable companies in the world, they are, their output is digital. So they don't actually make hardware. So it should be possible to completely emulate any company where the output is digital. And this will usher in an age of prosperity the likes of which we could barely imagine at this point. You need Imagine to imagine it. So this is a big deal, and this is why the words Macrohard are painted on the roof of the training cluster. Because that's what it's going to build.是的，Macrohard 项目随着时间推移实际上可能会成为我们最重要的项目，因为我们谈论的是对整个人类公司的仿真。看看世界上最有价值的公司，它们的输出都是数字化的——它们实际上不生产硬件。所以，对任何一家输出是数字化的公司，进行完全仿真应该是可能的。这将开创一个我们目前几乎难以想象的繁荣时代。你需要 Imagine 才能想象它。所以这是件大事，这就是为什么 Macrohard 这个字被涂在训练集群的屋顶上。因为那就是它将要建造的东西。
OtherSo it's also pretty funny.而且这也挺好笑的。
Elon MuskYeah, meant to be a joke.对，本来就是个玩笑。
OtherIt's me again. You might remember me from Macrohard and computer use from a long time ago, but I also actually work on core product infrastructure and API. In fact, this is what I've done for most of my time at xAI. So anytime you use any of our products like grok.com, API authentication, you go to status.x.ai, this is done by the core product infra team. And a large portion of them actually sit in London and we work with Haime over there. So we keep the lights on at peak hour, 4:00 PM every day. We get paged at night when stuff goes down. Also, thank you to anyone in Palo Alto getting paged. There's really important work, reliability, security, core product infrastructure. So if you're really interested in solving difficult distributed problems with like messy data, this is the team to join.又是我。你可能记得我在 Macrohard 和很久以前的 computer use 工作，但实际上我主要负责核心产品基础设施和 API，这是我在 xAI 大部分时间所做的工作。所以每次你使用我们的产品，比如 grok.com、API 认证，或者查看 status.x.ai，这些都是核心产品基础设施团队做的。我们有很大一部分人在伦敦工作，与 Haime 一起。我们维持着每天下午 4 点高峰时段的正常运行，半夜出问题了我们也会被叫醒。另外也感谢在 Palo Alto 被呼叫处理的所有人。可靠性、安全性、核心产品基础设施，这些都是非常重要的工作。如果你对解决具有混乱数据的复杂分布式问题感兴趣，这就是你该加入的团队。
DiegoHey everyone, my name is Diego. So I think one of the main bottlenecks in this next year for these models is going to be very high quality evals and training data. And one of the ways we solve that is by taking the world's foremost experts in these respective domains, bringing them here and having them evaluate the model. We do this for domains like medicine, finance, law, we have voice actors, we have video editors who contribute daily to making Grok better. And yeah, we're going to be continuing to work on very high quality evals over the next few months. We have some exciting stuff in, you know, the frontier of useful tasks in finance and law. We're trying to build evals that are useful and training data that represents useful work and not necessarily proxies of intelligence, where I think a lot of the open source evals do today.大家好，我是 Diego。我认为接下来这一年，这些模型最主要的瓶颈之一将是非常高质量的评测和训练数据。我们解决这个问题的方式之一，是把各个领域里世界顶尖的专家带来，让他们对模型进行评估。我们在医学、金融、法律等领域都这样做，我们还有配音演员、视频编辑，他们每天都在为让 Grok 变得更好贡献力量。在接下来几个月里，我们将继续在非常高质量的评测上努力。我们在金融和法律的前沿实用任务方面有一些令人兴奋的工作，我们试图建立代表真实有用工作的评测和训练数据，而不是智力代理指标——我认为很多开源评测现在做的就是那种代理指标。
OtherYeah. I'd like to say, we're shifting from using these sort of common internet evals, which I think are actually not a real indicator of usefulness, to having expert tutors in each domain. So every domain of engineering, medicine, law, whatever the case may be. And the actual eval is, does the expert in that arena, or does our group of experts in that arena, human experts, agree that Grok is extremely useful and that the results are correct? That's actually the only eval that really matters.是的。我想说的是，我们正在从那种通用网络评测转向，我认为那些根本不是真正有用性的指标，而是转向每个领域都有专家导师的方式。每一个工程、医学、法律等领域都是如此。真正的评测标准是：该领域的专家，或者我们的一组该领域人类专家，是否认为 Grok 极其有用、答案是正确的？这才是真正重要的唯一评测指标。
OtherYeah, exactly. You'll see this in Grok 4.20. We made some improvements because of that type of data in truth seeking and kind of minimizing political bias. The responses are much more cogent. So yeah, that's exciting. And we are also working on Grokipedia. So the goal of Grokipedia is to create a distillation of all human knowledge. I kind of like to think of this as like a modern day version of the Library of Alexandria. And in the quest to build Encyclopedia Galactica, which it will one day be called, we've gone from essentially having nothing to around 6 million articles. For context, Wikipedia is around 7 million English articles. And yeah, we're improving on hallucination. And our goal is essentially for Grok 5 to not have to search out of the data center. So yeah.对，完全正确。你会在 Grok 4.20 中看到这一点。由于那种类型的数据，我们在追求真相和减少政治偏见方面做了一些改进，回答更加连贯了。这非常令人兴奋。我们还在开发 Grokipedia。Grokipedia 的目标是创建所有人类知识的蒸馏。我喜欢把它想象成现代版的亚历山大图书馆。在构建 Encyclopedia Galactica（它有一天将以这个名称被人们知晓）的过程中，我们从基本什么都没有，做到了大约 600 万篇文章。作为参考，Wikipedia 大约有 700 万篇英文文章。我们也在改善幻觉问题。我们的目标基本上是让 Grok 5 不必离开数据中心就能搜索。
OtherSo in the ML infra team we are building the training, inference and tooling software for the company. So to give you an example, when we were training Grok 3 we built the pre-training framework for this, and these are some of the coolest systems in my opinion that you can build as a software engineer. So it's like we have 100k H100s at the time and they were just delivered and we didn't quite have the software. So we thought we'd have the software, but then at 30k scale, we realized actually the software is not quite working. And it took a major, almost I would say halfway rewrite of the software, because there's so much going on in a data center that you can't actually account for. Switches are flapping, links are flapping, switches are going down, GPUs are just burning through, you have numeric issues.在 ML 基础设施团队，我们为公司构建训练、推理和工具软件。举个例子，在我们训练 Grok 3 的时候，我们为此构建了预训练框架，在我看来，这是软件工程师能构建的最酷的系统之一。当时我们有 100k H100，刚刚交付，软件还没完全准备好。我们以为会准备好的，但在 30k 规模时，我们意识到软件其实并不完全好用。这需要对软件进行一次重大的——我几乎可以说是一半以上的重写，因为数据中心里发生的事情太多，你根本无法全部预先考虑到。交换机在抖动，链路在抖动，交换机在宕机，GPU 在烧掉，还有数值问题。
OtherAnd it's a system where you want really 100k H100s to behave in lockstep. So a training step is like 5 seconds and you're going 5 seconds in lockstep, but during that 5 seconds, everything can happen. So you need to write a system that makes progress despite all these things that can happen in the environment. And we did this successfully in one of the coolest times in my life, where the system was actually running at the same time my son was born. So that was extra excitement. But these problems, like you don't find anywhere else, like nobody has this kind of compute and also nobody has this kind of talent density. So at the time, to give you a perspective, we were like, in overall team in pre-training we were probably like 15 people, out of that maybe like seven people were working on the actual training system, and we still maintain that talent density in the team. So if you're interested in working on these problems and you don't want to be just like part of a bigger organization where you're one of like a thousand people working on this, then this is the place. Like we are still a very small team. With me is Leon Min from the RL and inference team.这是一个你希望 100k H100 保持锁步运行的系统。一个训练步骤大概 5 秒钟，你们要在这 5 秒内保持锁步，但在这 5 秒里什么都可能发生。所以你需要编写一个即使在所有这些环境干扰下也能取得进展的系统。我们成功做到了，而且发生在我人生中最棒的时刻之一——那个系统实际上是在我儿子出生的同时运行的。额外的兴奋感。这些问题在其他任何地方都找不到，因为没有人有这种算力，也没有人有这种人才密度。当时，给你一个参考，我们在预训练整个团队里大概只有 15 个人，其中大概七个人在实际的训练系统上工作，而我们在团队里仍然保持着这种人才密度。如果你有兴趣解决这些问题，不想只是成为一个有一千人工作的大型组织里的一员，这就是你该来的地方。我们仍然是一个非常小的团队。接下来有请 RL 和推理团队的 Leon Min。
Leon MinYeah. Hi, I'm Leon Min. So at our team, we run a reinforcement learning training job and production inference at the largest scale on the earth and probably soon in space. And we are kind of already designing a lot of things to make it more resilient and scalable. So we're building a system to scale from 100k chips to millions of chips. And we optimize every aspect of the stack like parallelism, prefill, decode, and make it resilient to every known and unknown hardware failure. So if you are system hackers obsessed with extreme performance and reliability, here is where you'll find the most interesting problems to work with. And I think actually, very similar to all kinds of things, it's very important for you to first see the problem and then you will develop the solution that no one else can develop before. Yeah. Okay. I'll hand over to the tooling team.对，大家好，我是 Leon Min。我们的团队在地球上最大规模运行强化学习训练任务和生产推理，而且很快可能在太空中也是如此。我们已经在设计很多东西使其更具弹性和可扩展性。我们正在构建一个从 100k 芯片扩展到数百万芯片的系统，并对整个栈的每个方面进行优化——并行性、prefill、decode——使其对所有已知和未知的硬件故障都具有弹性。如果你是痴迷于极致性能和可靠性的系统黑客，这里有你能遇到的最有趣的问题。我认为，就像很多事情一样，最重要的是你要先看到问题，然后你才会开发出别人之前开发不出来的解决方案。好的，我把话筒交给工具团队。
AshishHello. I'm Ashish from the tooling team. Every software needs to have a great interface to be able to make it useful. So as the tooling team we are responsible for building the platforms, frameworks and infrastructure which is required for humans as well as agents to be able to use our products. We started by building out the human data platform. This is a place where we collect all of our human data and eventually expanded on to build our internal engineering platform, through which we basically run deployments, run evaluations, or like look at what training results exist. So if you really care about building a good interface or providing a really useful framework for researchers, for agents as well as our tutors, then you should definitely join our team.你好，我是 Ashish，来自工具团队。每个软件都需要有一个好的界面才能发挥作用。作为工具团队，我们负责构建平台、框架和基础设施，供人类和 agent 使用我们的产品。我们从构建人类数据平台起步，这是我们收集所有人类数据的地方，后来逐步扩展，构建了我们的内部工程平台，通过它我们可以运行部署、运行评测，或者查看训练结果。如果你真的在乎为研究人员、agent 以及我们的导师打造好的界面或有用的框架，那么你绝对应该加入我们的团队。
OtherSo hi everyone, I'm from the Jax team. So now Jax at xAI is a really small team with a couple of engineers that working on Jax GPU to optimize our ultra large scale GPU training. So you can imagine that training at scale can be very complicated. Even if you run hello world at scale it can be complicated, right? So then we are actually responsible for supporting the entire company, from pre-training foundation models, RLs, and also multimodel, to scale things from first from 10k, 100k, then probably 1 million H100 equivalent GPU scale. And to implement a lot of practical optimizations we have to customize the entire Jax stack, from compiler and runtimes, and there will be a lot of interesting problems. And also if you really want to obsess on optimizing the entire stack at scale, we are probably the best place to go, because we really have very large scale GPU clusters and we have a lot of interesting problems to work with.大家好，我来自 Jax 团队。xAI 的 Jax 现在是一个非常小的团队，只有几个工程师，专注于 Jax GPU 优化，用于我们超大规模的 GPU 训练。你可以想象，大规模训练会非常复杂，就算是在大规模上跑 hello world 都可能很复杂。所以我们实际上负责支持整个公司，从预训练基础模型、RL，到多模态，将规模从最初的 10k、100k 一路扩展到可能的等效 1 百万 H100 GPU 规模。为了实现很多实用的优化，我们必须定制整个 Jax 栈，从编译器到运行时，有很多有趣的问题。如果你真的想痴迷于对整个栈进行大规模优化，我们可能是最好的去处，因为我们确实有非常大规模的 GPU 集群，有大量有趣的问题可以探索。
PranjalHey, I'm Pranjal from the kernels team. Basically, the kernel team sits at the very bottom of a training and serving stack. Our code runs inside the million equivalent GPUs that we have. And if you look inside the GPU, there's hundreds of thousands of threads. And these threads are trying to talk to each other to multiply matrices, compute attention scores, and some of them even talk to the million other GPUs that we have. And this is the low-level system that we have. And we like optimizing every single microsecond in this. And we care deeply about squeezing every last drop of performance from these GPUs. So if you like these low-level systems problems, algorithms, please join us.嗨，我是 Pranjal，来自 kernels 团队。基本上，kernel 团队处于训练和服务栈的最底层。我们的代码运行在我们拥有的等效百万 GPU 内部。如果你深入 GPU 内部，有成千上万个线程。这些线程相互通信来做矩阵乘法、计算注意力分数，有些线程甚至还要和我们拥有的另外一百万颗 GPU 通信。这就是我们拥有的低层级系统。我们喜欢优化这里面的每一个微秒，我们深切关心如何榨干这些 GPU 的每一滴性能。如果你喜欢这种底层系统问题和算法，请来加入我们。
Elon MuskLet's see. Now we'll try to bring in Hiner and Spencer who are actually at our supercomputer cluster in Memphis. Hey Hiner.好，现在我们来连线 Hiner 和 Spencer，他们实际上在我们位于孟菲斯的超级计算机集群现场。Hiner，你好。
HinerHey, I'm Hiner from the compute network infrastructure team. We are mainly based in Palo Alto but today we're here in Memphis in the supercomputer. So the data center here in Memphis, the largest training cluster on the planet, and it is still growing. Our job is to keep all this for you up and running the next version of Grok and serve AI output to our users.嗨，我是 Hiner，来自计算网络基础设施团队。我们主要在 Palo Alto 工作，但今天我们在孟菲斯的超级计算机这里。孟菲斯的这个数据中心是地球上最大的训练集群，而且还在持续增长。我们的工作是让这一切保持运转，来训练下一个版本的 Grok，并向用户提供 AI 输出。
Elon MuskActually just put the mic really close to your mouth because the ambient noise is high.把麦克风靠近你嘴边一点，因为环境噪音很大。
HinerIt's getting a lot. Let me go back. So I was saying our job is to keep the computer up and running to train the next model of Grok and serve AI to users. So for GPUs to work well a lot of ingredients have to come together, mainly software and hardware. So there's all these GPUs, CPUs, nics, switches, and hundreds of thousands of operating systems running as one big supercomputer. And what we need is folks who really understand the nodes, really understand how they make, and really understand how computers work on a deep level. If that is you, reach out on X, and I'm handing over to Dan.好多了。让我重新说。我刚才说的是，我们的工作是让计算机保持运转，来训练 Grok 的下一个模型，并向用户提供 AI 服务。要让 GPU 工作良好，很多要素必须到位，主要是软件和硬件。有所有这些 GPU、CPU、网卡、交换机，还有成千上万个操作系统作为一个大型超级计算机运行。我们需要的是那些真正理解这些节点、真正理解它们如何构成、真正从底层理解计算机如何工作的人。如果你是那样的人，在 X 上联系我，我把话筒交给 Dan。
DanAll right. So we have 300,000 GB300 platform GPUs here today. We're still growing, still building. 847 miles of fiber per data hall. 12 data halls. If you want to be part of the world's largest supercomputer, come join us.好的。我们今天这里有 300,000 块 GB300 平台 GPU，还在继续增长，还在继续建设。每个数据厅 847 英里光纤，共 12 个数据厅。如果你想成为世界上最大的超级计算机的一部分，来加入我们吧。
Elon MuskAll right. So it's quite marvelous what we've been able to do in less than one year's time here. Once we're completely finished, we'll have north of a gigawatt of power online and running. We'll have the largest Tesla Megapack system in the world, larger than Hawaii or South Australia. And Zach is really quickly going to talk a little bit about actually constructing the data center.好的。我们在不到一年的时间里完成的事情真的相当了不起。完全建成之后，我们将有超过 1 gigawatt 的电力在线运行。我们将拥有世界上最大的 Tesla Megapack 系统，规模超过夏威夷或南澳大利亚的。Zach 接下来会简短地介绍一下数据中心的实际建造情况。
ZachYeah, so behind me you can see data hall 11. So one of the most incredible things about what we're doing here at Macrohard is how fast we do it, right? So like they were saying before, over 850 miles of fiber at every single data hall, over 27,000 GPUs and over 200,000 connections. So all of this that you can see behind me was put up in less than six weeks. We do that over and over and over again. We massively parallelize it. It's pretty much the most complex and consistent type of engineering, design and construction project you can possibly imagine. So come join us.是的，你可以看到我身后的数据厅 11。我们在 Macrohard 这里做的事情中最令人惊叹的之一，就是我们的速度。就像他们之前说的，每个数据厅超过 850 英里的光纤，超过 27,000 块 GPU，超过 200,000 个连接。你在我身后看到的这一切，是在不到六周内建起来的。我们一遍又一遍地这样做，大规模并行推进。这大概是你能想象到的最复杂却又最一致的工程、设计和建造项目。来加入我们吧。
OtherYes. You know the other really awesome thing about this is that everything is completely vertically integrated within this team. From architecture, mechanical, electrical, structure, all the disciplines. And we also care a lot about efficiency while we're designing all of this too. So it's not just about getting the most compute online the fastest, but also achieving the highest PUE in the industry, of using as much power smoothing technology as we can and being really good partners in the community here in Memphis, with the Tesla Megapacks that we have going. You can check them out, xAI Memphis. Back to you, Elon.是的。这里另一个真正了不起的事情是，这个团队里的一切都是完全垂直整合的，从建筑、机械、电气、结构，所有专业学科。在设计这一切的过程中，我们也非常注重效率。所以不只是以最快速度让最多算力上线，还要在行业内实现最高的 PUE（电能使用效率），尽可能使用更多的电力平滑技术，并真正成为孟菲斯这里社区的好伙伴——我们配备了 Tesla Megapack 来做这件事。你可以看看，xAI Memphis。回到你了，Elon。
Elon MuskAll right. Thank you. All right. So that was live live from the front lines in Memphis. So fundamental to any AI company's success is the compute advantage. And what we've demonstrated over and over again is that xAI can actually deploy more AI compute faster than anyone else. And actually as Jensen Huang, CEO of Nvidia, has said many times in interviews, there is no one faster at getting AI compute online than xAI. So congratulations guys.好的，谢谢。这是来自孟菲斯前线的实时直播。对任何 AI 公司的成功来说，算力优势都是根本。而我们一次又一次证明了，xAI 能比任何人更快地部署更多 AI 算力。事实上，正如 Nvidia CEO Jensen Huang 在许多采访中多次说过的，在让 AI 算力上线速度方面，没有人比 xAI 更快。恭喜大家。
Elon MuskYeah, this is what it looks like. So that's really phase one, which is 330,000 Grace Blackwells with Macrohard written on the building. That's not an image edit. It actually is on the roof of the building. And then Macrohard will be the building that you can see which has got the Macrohard with the rockets on it. And that will be another 220,000 GB300s. So all of this will be training our models that you experience. So it's absolutely fundamental obviously to have large scale training compute in order to get the best models.对，这就是它的样子。那是阶段一，330,000 块 Grace Blackwell，建筑上写着 Macrohard。这不是图像处理，那个字真的就在楼顶上。然后 Macrohard 将是你看到那栋楼，上面有 Macrohard 字样和火箭图案，那里还将新增 220,000 块 GB300。所有这些将用来训练你们体验到的我们的模型。显然，拥有大规模训练算力对于获得最好的模型来说是绝对根本的。
Elon MuskYeah, I'm sort of reminded of the Jose meme where you see one guy digging and there's like seven people watching. And one of the big differences between xAI and other companies is we are actually Jose.是的，这让我想起了 Jose 那个梗——你看到一个人在挖掘，旁边站着七个人在观看。xAI 和其他公司一个很大的不同，就是我们实际上是那个 Jose。
NikitaHello. All right. I'm Nikita. You might know me as a part-time shitposter, full-time customer support for X. So we're now reaching over a billion people across our family of apps. Every time news breaks, it just becomes evident that this is the most important communication tool of our time. It's where the most influential people come convene, it's where truth is crystallized. Everything is downstream of X. The reason they say this is going to hit Facebook in a week, it's because it happens here. And I think we're only beginning to realize its full potential. We had a remarkable year for the app. We rolled up our sleeves and got a ton done. January was our biggest month ever for the app in terms of engagement. And then February is on track to beat that. Much of the credit lies with the algorithm team. They've been putting in crazy hours. And it's clearly paying off, but there's still a huge amount of work to be done.大家好。我是 Nikita。你可能认识我是兼职发帖子、全职给 X 提供客户支持的那个人。我们现在覆盖着跨我们家族应用超过 10 亿用户。每当重大新闻爆发，就会变得很明显——这是我们这个时代最重要的通信工具。最有影响力的人都在这里汇聚，真相在这里被提炼。一切都是 X 的下游产物。他们说某件事会在一周内登上 Facebook，是因为它先在这里发生。而且我认为我们才刚刚开始意识到它的全部潜力。我们这款应用度过了非常精彩的一年。我们撸起袖子，做了大量的工作。一月份是我们 App 有史以来在互动方面最大的一个月，二月份的轨迹是会超越一月份的。大部分功劳要归于算法团队，他们投入了疯狂的工时，而且成效显然非常好，但还有大量的工作要做。
NikitaOn the top of funnel side, first-time downloads are up over 50% every month. And we're exhibiting right now like basically the growth rates of an early stage consumer product. We also made a ton of headway in solving one of the like 20-year-old problems of the app, which was ramping up new users. New users are now spending 55% more time per day in the app than they were six months ago. And on the core product side, we're hitting our stride too. Not only did we rebuild the algorithm, we rebuilt our onboarding flows, and we're seeing double-digit increases on all our key metrics. We rebuilt notifications, our web browser, Xchat. Basically every surface of the app has been rebuilt to be better than ever. And it's clear that if we're focused, we can move mountains and evolve this platform. Just last month we did a little push on articles. And articles published are up 10x. Articles read are up 17x.在新用户获取方面，首次下载量每月增长超过 50%，我们现在展现出的增长率基本上就像一个早期消费产品的样子。我们在解决这款 App 一个大约二十年老问题上也取得了很大进展——新用户引导。新用户现在每天在 App 上花费的时间比六个月前多 55%。在核心产品方面，我们也进入了节奏。不只是重建了算法，我们还重建了用户引导流程，所有核心指标都有两位数的提升。我们重建了通知、网页浏览器、Xchat，基本上 App 的每一个界面都被重建得比以前更好。很明显，如果我们专注，就能移山填海，让这个平台不断进化。就在上个月，我们在文章功能上发力推了一把——发表的文章数量增加了 10 倍，阅读的文章数量增加了 17 倍。
NikitaAnd on all other fronts, like over the holidays, we did a big push on subscriptions. We just crossed a billion dollars in ARR there. I think with the X app, you know, there's very few unknowns, like the path for us to win and become the number one app in the world. We know what to do. The ball's in our court, it's for us to win and it's just a matter of us executing.在其他所有方面，比如假日期间，我们大力推动了订阅业务，我们刚刚突破了 10 亿美元的 ARR（年度经常性收入）。关于 X App，我认为不确定的东西已经很少了——我们要赢、成为世界第一 App 的路径，我们知道怎么做。球在我们手里，赢是我们的责任，关键就在于我们执行。
Elon MuskYep. And yeah, so we've evolved what used to be the old Twitter DM stack, which was unencrypted, basically just text, to a fully encrypted messaging system that allows you to do audio and video calls. Has, you know, all the things you'd want from any messaging app. The disappearing messages, screenshot blocks, like there's a whole, all the features that you'd want in an app. And we will be open sourcing the code for this in the next few months, as we are open sourcing the recommendation algorithm code so people can actually see what we're doing. Nothing beats transparency for believing in a company. So we're going to be the only recommendation algorithm that actually open sources. So you can see what it does and how it's evolving.对，我们把过去的旧 Twitter DM 系统——那个未加密、基本上只有文字的东西——演进成了一个完全加密的消息系统，支持音频和视频通话，拥有你在任何消息 App 上想要的一切。消失的消息、截图拦截——所有你想要的功能都有。我们将在接下来几个月内开源这套代码，就像我们正在开源推荐算法代码一样，让大家能真正看到我们在做什么。对于建立对一家公司的信任，没有什么比透明度更有效的了。我们将是唯一一个真正开源推荐算法的平台，让你可以看到它做什么、如何演进。
Elon MuskWith Grok chat it will also be open source so you can actually see if there are any vulnerabilities. There will be no hooks for advertising or anything else like that in Grok chat, which is really intended to be a generalized communication system. And in the next few months we'll be releasing a standalone X chat app. So if you just want to do messaging you can just do that. You don't have to go to the core product. And it will have desktop sharing and multi-user, so you can do video calls with lots of people. It's really intended to be a fully functional communication system with XChat.Grok chat 也将开源，这样你可以真正看清是否存在任何漏洞。Grok chat 里不会有广告钩子或其他任何类似的东西，它真正意图成为一个通用通信系统。在接下来几个月里，我们将发布一个独立的 X chat 应用。如果你只想发消息，就直接用那个就行，不必进入核心产品。它将支持桌面共享和多人模式，你可以和很多人进行视频通话。XChat 真正旨在成为一个功能完整的通信系统。
Elon MuskFor X Money, we actually had X Money live in closed beta within the company. And we expect in the next month or two to go to a limited external beta, and then to go worldwide to all X users. And this is really intended to be the place where all the money is, the central source of all monetary transactions. So it's really going to be a game changer. And the reason we say 1 billion users, is actually over a billion users, is that while our monthly users are on average around 600 million, the number of people who have the X app installed is well over a billion. It's just that most people only occasionally come to the X app when there's some major world event. But as we give people more reasons to use the X app, whether it's for communications, for Grok, or for X money, whatever the case may be, we want it to be such that if you wanted to you could live your life on the X app. And as you make it more and more useful, we'll obviously give people compelling reasons to use the app every day, and my expectation is well over a billion daily active users.对于 X Money，我们实际上已经在公司内部进行了 X Money 的封闭测试。我们预计在接下来一两个月内进入有限的外部测试，然后向所有 X 用户全球开放。这真正旨在成为所有钱的所在地，所有货币交易的中心来源。所以这真的会是一个游戏规则改变者。我们说 10 亿用户，其实是超过 10 亿——虽然我们的月活用户平均在 6 亿左右，但安装了 X App 的人数远超 10 亿，只是大多数人只在有重大世界事件时才偶尔打开 X App。但随着我们给人们更多使用 X App 的理由——无论是通信、Grok，还是 X Money，不管什么情况——我们希望它变成那种你可以在 X App 上过自己生活的地方。随着你让它越来越有用，我们显然会给人们每天使用这个 App 的充分理由，我预期日活用户将远超 10 亿。
Elon MuskSo now, in order to understand the universe you must explore the universe. There's only so much you can learn from just being on earth with telescopes and colliders on earth. Ultimately you have to go out there and you have to explore the universe to understand it. And that's the motivation behind the combination of SpaceX and xAI. It's to accelerate humanity's future in understanding the universe and extending the light of consciousness to the stars.所以，为了理解宇宙，你必须去探索宇宙。仅仅待在地球上，靠望远镜和地球上的对撞机，你能学到的东西是有限的。最终你必须走出去，去探索宇宙才能理解它。这是 SpaceX 与 xAI 结合背后的动机——加速人类理解宇宙的未来，将意识之光延伸至群星。
Elon MuskSo in the grand scheme of things, when you look at how much energy Earth is actually using for civilization, we're only right now using, call it roughly 1% of the potential energy of Earth. And if we wanted to use even a millionth of the sun's energy, that would be roughly a million times more energy than civilization currently uses. The only way to access that energy, the energy of the sun, is to extend beyond Earth. Earth is really a tiny tiny dust mote in a vast darkness. You know, the sun is 99.8% of all mass in the solar system. So you have to expand beyond the tiny dust mote that is earth to make any significant dent in using the sun's energy. Like I said, you'd have to expand roughly a million times just to get to 1 millionth of our sun's energy, and then going beyond that, exploring, extending to the galaxy and maybe someday even to other galaxies.从宏观来看，当你看地球实际上为文明消耗了多少能量，我们现在大概只用了地球潜在能量的 1%。如果我们想用哪怕太阳能量的百万分之一，那大约是文明当前使用能量的一百万倍。获取太阳能量的唯一途径是延伸到地球之外。地球在宏大的黑暗中真的是一粒极其微小的尘埃。你知道，太阳占太阳系所有质量的 99.8%。所以你必须超越这粒地球的微小尘埃，才能在利用太阳能量方面有任何显著作为。就像我说的，光是要达到太阳能量的百万分之一，你就得扩张大约一百万倍；然后再继续，去探索，延伸至银河系，也许有一天甚至到其他星系。
Elon MuskSo the next step beyond Earth data centers is our Earth orbital data centers. And we'll be launching with SpaceX orbital data centers at the 100 to 200 gigawatt per year level. Not cumulative, I mean per year. And ultimately we see a path to maybe launching as much as a terawatt per year of compute from Earth. But what if you want to go beyond a mere terawatt per year? In order to do that, you have to go to the moon. So by having factories on the moon building AI satellites and having a mass driver, which is the kind of thing you really only learn about, or read about, in science fiction, but we're going to make it real. We're actually going to have a mass driver on the moon. And if you do that, you can go several orders of magnitude greater. You can go to a thousand gigawatts or more per year. And ultimately get to maybe a millionth and then a thousandth and maybe even a few percent of the sun's energy.超越地球数据中心的下一步，是我们的地球轨道数据中心。我们将与 SpaceX 一起以每年 100 到 200 gigawatt 的速度发射轨道数据中心。不是累计，我的意思是每年。最终我们看到一条可以以每年高达 1 terawatt 的速度从地球发射算力的路径。但如果你想超越区区 1 terawatt 每年呢？要做到这一点，你必须去月球。通过在月球上建立工厂来制造 AI 卫星，并使用质量加速器——这种东西在科幻小说里你才会了解到或读到，但我们要把它变成现实。我们真的要在月球上建造一个质量加速器。如果你这样做，你可以超越几个数量级，可以达到每年 1000 gigawatt 甚至更多，最终可能达到太阳能量的百万分之一、千分之一，甚至百分之几。
Elon MuskIt's difficult to imagine what an intelligence of that scale would think about. But it's going to be incredibly exciting to see it happen. I really want to see the mass driver on the moon that is shooting AI satellites into deep space. It's going like just one after the other. I can't imagine anything more epic than a mass driver on the moon and a self-sustaining city on the moon, and then going beyond the moon to Mars. Going throughout our solar system and ultimately being out there among the stars and visiting all these star systems. Maybe we'll meet aliens. Maybe we'll see some civilizations that lasted for millions of years, and we'll find the remnants of ancient alien civilizations. But the only way we're going to do that is if we go out there and we explore. And this is the path to making it happen. Thank you. Thank you.很难想象那个规模的智能会思考什么。但看着它发生将会无比令人兴奋。我真的非常想亲眼看到月球上的质量加速器把 AI 卫星一颗接一颗地射入深空。我无法想象还有什么比月球上的质量加速器和月球上的自给自足城市更史诗级的事情，然后超越月球去往火星，穿越整个太阳系，最终在那里，在群星之间，造访所有那些恒星系统。也许我们会遇到外星人，也许我们会看到存续了数百万年的文明，我们会发现古代外星文明的遗迹。但实现这一切的唯一方式是我们走出去、去探索。这就是让它成真的路径。谢谢大家，谢谢。