xAI All-Hands February 10, 2026 In a rare public all-hands, Elon Musk lays out xAI's new structure — Grok, Coding, Imagine and Macrohard — the Grok roadmap toward AGI, and the company's long-term interplanetary ambitions.
In a rare public all-hands, Elon Musk lays out xAI's new structure — Grok, Coding, Imagine and Macrohard — the Grok roadmap toward AGI, and the company's long-term interplanetary ambitions.
Transcript Welcome 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 5, 10, 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. So 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 the Grok 420 forecasting model beat all the other AIs in forecasting. And we've talked many leaderboards. We've got now a great app with Imagine with the core Grok, we've made radical improvements to the X app and we've launched a Grokipedia which is on its way to far exceeding Wikipedia. And ultimately the orders of magnitude are 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, of all knowledge, and were the first to achieve 100,000 H100 GPU training cluster. And we're now about to achieve the first hundred, 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. So 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 then to 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. Now, naturally, when this happens, there's some people who are better suited for the early stages of the company and less suited for the later stages. And so for the people that have departed, I'd just like to say thank you for a kind of 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, 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 Macro Hard, 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.
xAI Team
Hey, 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 you could talk to advanced voice mode. And we had nothing. No model, of course, no product. We started much after that and in a span of few months, six months, we developed a 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 GROQ 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 xci. We have small teams, committed, mission focused, lots of compute, and we really, really want to keep pushing same 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
xAI Team
to it and really get done whatever you want. You know, ask a legal question, make a slide deck or, you know, solve
Ashdeep (xAI)
a puzzle, stuff like that.
xAI Team
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 work, the magnitude of 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, 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. That being said, we are hiring and we're looking for intelligent and smart people. This is not an easy place to work, guys.
xAI Team
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 shit done, you can get shit done. There isn't like, organizational overhead getting 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, I just, you know, just, you can do things here, and that's amazing. And I invite more people to come here and just do awesome things.
So with the GROK main, the sort of main foundation model, the intent is that it's. 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 when GROK gives you an answer that you can count on it. Yep, absolutely.
Makro (xAI)
Hey, everybody. I'm Makro. 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, like, 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 you, you can, 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 like 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 code base. 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. So now we have, I do like what we do like hours of GROK code running continuously to make sure that 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 are really on this path for recursive self improvement. Where the current generation of GROK code is, is training the next generation of CROC code and we see that this path 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 either very good at training, modeling or you're a really good low level software engineer, interesting in systems design, this is the place to work. Like we have a million H100 equivalents to train the best coding metal in the world right now. So please join us.
Godon (xAI)
Yeah, I'm Godon, I work paired with Macro on coding. So it become more and more obvious to us like you know, all the time, like we are on a path to singularity at least on coding. So we decided like, you know, have our best engineer in the company, Macro to lead the coding. And we have built the best coding model for everyone, to empower everyone to build. And for me like the main limiting factor is probably compute energy, where they can run the best model to support everyone, to empower everyone. And with SpaceX now we are one team and we will win on the compute and we are winning with space compute and also for every engineer. Right. So if you are like writing kernel, if you're writing compiler, just think about whether it's still worth it. Maybe you should join us, you know, for coding effort, to automate it yourself a little bit, like to speed it yourself up. Yeah, I think it's like really amazing year basically what a year to be alive and I can already feel the AGI. Feel the AGI, at least for coding.
Yeah, yeah. 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. 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 two to three months. So it's happening very quickly.
Godon (xAI)
Yeah. Also do Imagine. So, you know, I mean, what do we all do right after post AGI? 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 have to do the image gen, we'll do the video gen. Like, yeah, look at what we achieved today. Like, you know, like two weeks ago we released like Imagine V1. We actually top a 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, like we actually may build a metaverse before meta. Yeah. I'll also pass to try to talk about like, you know, the metrics. We have the product.
xAI Team
Like Godong 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 Imagen 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 Imagen. And just to reiterate what Elon said earlier, that to the best of our knowledge, that is more than every other provider combined with which again is an astonishing place to be compared to where we were six months ago. We are also generating 6 billion images in the last 30 days. Google recently posted that 1 billion images were generated using Nano Banana in 30 days. So we're six times that. And really the goal is we don't 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 are going to. We're going to speed run. That basically is the goal. Yeah. Hey, I'm hatin. As we keep scaling our model capabilities, building visual worlds that's indistinguishable from reality, we're also building systems that unlocks much more possibility than what we have right now. They will be able to generate the 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. And, and 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 exciting future that we are going to build with ourselves.
Absolutely. 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 leads in that. It's worth emphasizing these points that,
know, six months ago we didn't even have. We had basically nothing in 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 macro hard. And I think people will be pretty impressed with the Grok 4.2 model that's coming out. It's a significant improvement. And that's really just, that's the small version of our new model. So we'll have a medium and a large version that are even more intelligent. All right.
Toby (xAI)
Hi everyone, I'm Toby and I work on macroheart, the most series 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 macroheart 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 they 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, change the field.
John (xAI)
Hi everyone. So, yeah, my name's John and yeah, 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, like 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, you know, 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, world software has a GUI. So that's like, you know, great representation and you know, to truly make people's lives easier, we need to develop models that are capable of solving day to day tasks on gui. So macro hard, you know, we will emulate a company where the output is digital. 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 for your LinkedIn, you'll be surprised like 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.
Yeah, the macquart project is over time Lecture 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 that where the output is digital and this will usher in an age of prosperity likes which we can barely imagine at this point. You need imagine to imagine it. So this is a big, this is a big deal. And this is why the words macro hard are painted on the roof of the training cluster, because that's what it's going to build. It's also pretty funny. Meant to be a joke.
Toby (xAI)
You might remember me from MacroHub 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 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 Jaime over there. So we keep the lights on at peak hour, 4pm 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, corporate infrastructure. So if you actually, if you're really interested in solving difficult distributed problems with like messy data, this is the team to join.
Diego (xAI)
Hey everyone, my name is Diego. Yeah, 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've solved that is by taking the world's foremost experts in these respective domains, bringing them here and having them evaluate them up. 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 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 that a lot of the open source evals do today.
Yeah, I'd like to say like 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.
Diego (xAI)
Yeah, exactly. You'll see this in Grok420 but we made some improvements because, 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 Graphopedia. So the goal of Rockypedia 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 Rock5 to not have to search out of the data center.
Makro (xAI)
Come on. So in the ML Infra team we are building the training, inference and tooling team tooling software for the company. So it's giving you an example. When we were training Grok 3, we built the pre training framework for this and these are some of the coolest system in my opinion that you can build as a software engineer. So it's like we have 100k h 100s 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, switches are flapping, links are flapping, switches are going down, GPUs are just burning through, you have numerics issues and it's a system where you want really 100k h 100s to behave in lockstep. So a training step is like five seconds and you're going five seconds in lockstep. But during that five seconds everything can happen. So you need to write a system that makes progress despite all these things, things that can happen in the environment. And we did this successfully and it was one of the coolest times in my life where the system was actually running and it was 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 an overall team in pre training we were probably like 15 people and 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 Lian Min from the RL and Inference team.
Hi, I'm Lian Min. So at our team we run our reinforcement learning training job and the production inference at a large scale on earth and probably soon in space. And we are kind of already designed a lot of things to make it more resilient and scalable. So our building a system to scale from 100k chips to millions of chips and we optimize every aspect of the stack like parallelism, pre fill, decode and make resilient to every known and unknown hardware failure. So if you are system hackers obsessed with extreme performance and reliability, so here is, you will find the most interesting problems to work with and I think actually like very similar to all kind of things like you, 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. Okay, I'll hand over to the tooling team.
Ashdeep (xAI)
Hello, I'm Ashdeep 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 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.
Yulong (xAI)
So hi everyone, I'm Yulong from the JAX team. So now JAX at XAI is a really small team with a couple of engineers that working on Jack's GPU to optimize our ultra large scale GPU training. So you can imagine that training at scale can be very complicated. Even you run hello world at scale, it can be complicated. Right? So then we are actually responsible for supporting the entire companies from Pre Training foundation models, RLS and also multimodal to scale things first from 10k 100k, then probably 1 million h 100 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 you know, we really have very large scale GPU clusters and we have a lot of interesting problems to work with.
Pranjal (xAI)
Hey, I'm Pranjal from the kernels team. Basically the kernel team sits at the very bottom of our training and serving stack. Our code runs inside the million equivalent
Pranjal (xAI)
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.
Pranjal (xAI)
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 stop of performance from these GPUs. So if you like this low level systems problems, algorithms, please join us.
As you know, we'll try to bring in Heiner and Spencer who are actually at our supercomputer cluster in Memphis. Hey Heiner.
xAI Team
I'm I'm Heimer from the Computer Network Infrastructure team. We are mainly based in Palo Alto, but today we're here in Memphis in the superb here. So the data center here in Memphis swung the largest GPU cluster on the planet and it is and still growing our job to keep all this from you up and running and serve AI output to our users to work well a lot of ingredients
actually just put the mic really close to your mouth because the ambient noise is high.
xAI Team
It's getting too loud. Let me go back. So I was saying our job is to keep the computer up and running, train the next model of work and serve AI. So users, so what you've used to work. Well, a lot of ingredients have to come together, mainly software and hardware. So there's all these chippy CPUs, NICS switches and hundreds of thousands of operating systems running as one big supercomputer. And what we need is folks to really understand Linux, really understand RDMA 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. So we have 300,000 GB, 300 platform GPUs here today. We're still growing, still building. 847 miles of fiber per data hall. 12 data halls. You want to be part of the world's largest supercomputer, come join us.
xAI Team
So it's quite marvelous what we've been able to do in less than one year's time here. We have, 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. So behind me you can see data hall 11. So one of the most incredible things about what we're doing here at Macroheart 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 them. It's pretty much the most complex and consistent type of engineering, design and construction project you can possibly imagine. So come join us. Yes. 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 POE in the industry, 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 at XAI Memphis. Back to you.
All right, thank you. All right, so that was live from the front lines in Memphis. So fundamental to any AI company 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 of CEO of Nvidia has said many times in interviews, there is no one faster at getting AI compute online than xai. So congratulations, guys. Yeah, this is what it looks like. So that's really phase one, which is 330,000 Grace Blackwell's with Macrohot written on the building. That's not an image edit. It actually is on the roof of the building. And then Macrohota will be the building that you can see which has got the macrohota with rockets on it and that'll be another 220,000 GB3 hundreds. So all of this will be training the models that you experience. So. So it's absolutely fundamental obviously to have large scale training compute in order to get the best models. Yeah, I'm sort of reminded of the Jose mean 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.
Nikita (xAI)
I'm Nikita. You might know me as a part time ship poster. 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, the most influential people 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 is 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 on 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. And 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, you know, 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.
Yep. 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, screen, 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, 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, what it does and how it's evolving. With, with Grokchat 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, in, in Grokchat, 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, you can do that. You don't, you don't have to go to the core product and it will have desktop sharing and multi user so you can do, you can do video calls with lots of people. It's really intended to be a fully functional communication system with X Chat for X Money. We're, we've actually had X Money live in closed beta within the company and we expect in the next month or two to go to unlimited 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 applied, 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 we make it more and more useful, we'll obviously give people reasons, compelling reasons, to use the app every day and have. My expectation is well over a billion daily active users. So 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 is to accelerate humanity's future in understanding the universe and extending the light of consciousness to the stars. So 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, quote, 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. 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 1,000,000th of our Sun's energy. And then going beyond that, extending to the galaxy and, and maybe someday even to other galaxies. So 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 in 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 1,000 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. It'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 shoom, shoom, 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 meet, see some civilizations that lasted for millions of years and we'll find the remnants of, of ancient alien civilizations. But the only way we're going to do do that is if we go out there and we explore. And this is the path to making it happen. Thank you.