If you like this episode, just like, share and follow this podcast. Thank you, Back to the show, Welcome.
To the Xai All Hands. We've been 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 of our accomplishments since inception, it's important for bear in mind that XI 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 ten, some cases twenty years old, they have much larger teams they started off with for 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 then all of our competitors combined, we are winning in terms of forecasting,
which is one of the key metrics of intelligence. So up the grock four to twenty forecasting model beat all the other AIS and forecasting, and we've talked many leaderboards.
We've got now a great app with the Imagine, with the core Rock, we've made radical improvements to the x app, and we've launched a Grokipedia which is on its way too far exceeding Wikipedia and ultimately be Audersmatitude 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 it's intended ultimately to be Encyclopedia Galactica, a
distallation of all knowledge, of all knowledge. And we're the first to achieve one hundred thousand, h one hundred GPU training clusters, and we're now about to achieve the first one hundred I should say, one million, h one hundred 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. It's 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'll just chat amongs 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 grow from a single cell and then to a blob of sales. Then you get organ differentiation limbs, you grow a tail, hopefully the ptail disappears, and then you become a baby. You go through these stages, and so we're uh organizing because.
We've we've reached a certain scale.
We're organizing the company h to be more effective at this scale. And naturally, when when this happens, that there's some people who are better suited for the early stages of the company and and less suited for the later stages. And so uh and for the people that have departed, I'd just like to say thank you for a contribution, thank you for getting us this far. And we wish you very well in your future endeavors. So now going on to h the new structure of the company. Uh,
the companies organized in four main application areas. There's there's Grock Main in voice, which is really the main Grock model as much as called Grock 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 Impress ructural ayers. So I'd like to invite members of the teams come up and talk about each of their areas.
Hip. Thanks you long.
So Grock, Maine and Boys are going to be merged into one team. And you know on Boys when anecdote is September twenty twenty four opening, I had this product you could talk to advanced voice mode, and we had nothing, no model, of course, in no product.
We started much after.
That and in a span of 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 open air. And six months fast forward six more months and now we have Grock in more than two billion test las. We have a Rock 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 focus, 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 Rock one point five two Doc three, and we want to really move to a world where it's no longer about this question answering. We want to build and everything up so you should be able to come to it
and really get done whatever you want. You know, ask a leave question, make a slide deck, or you know, solid pizzle stuff like that.
Yeah, 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 you all. And on a 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.
Too.
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 intersellar ambitions, so it's it's not going to be easy, right. So I will say, having come to x Ai, uh, it has been an opportunity for 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 you want to get shipped done, you can get shipped 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 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 it awesome things.
Yeah, So with with the grockmin 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 tit or.
Medicine, any anything, it is useful to you in your in your job. That's essential to understand the universe and make things as useful as possible, like when God gives you an answer that you can.
Count it right, all right, thank you, Thanks.
Everybody. I'm Macro.
So the world changed a lot recently in terms of coding, the coding models. I was always complaining people are trying to convince me to use a coding model, and I was like trusting 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 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 you are. 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 bugio code.
So now we have I do like what we.
Do, like hours of grog code running continuously to make sure that a more complex changed the training system actually works in production. So it's easy to see for us that this is not only about accelerating us ourselves writing code and making us ten x more productive, but we're really on this path for recursive self improvement where the current generation of grock code is training the next generation of Crock code. And we see that this path and
on exponential takeoff here this path will continue. So we are doubling down on coding and making coding one of the highest party efforts in the company. So if you're out there and you're excited about coding, and you're inner 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 h one hundred equivalents to train the best coding medal in the world right.
Now, so please join us.
Yeah, I'm going on a compare with Macro on coding, so it'll become one and more obvious to us, like you know, over time, like we are on the past
to singularity at least on coding. So we decided, like you know, have our best engineer in the company in Macro to lead the coding, and we are building the best coding model for everyone to empower everyone to build and for me, like the man like limiting factor is probably computer and energy where they can run the best model to support everyone, to empower everyone and respects.
Now we are one keeam and we will win on the compute and we are.
Wing with space compute and also like for every engineer right so, if you're like writing kernel, if you're writing a compiler, to think about like whether is still all worth it, maybe you should join us, you know, for coding effort to automate yourself alt the like, to speed it ourselves up.
Yeah, I think it's like really a million.
Year basically with the year to be alive, and I can already feel the AGI, feel the hi 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 do during 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
and you actually bypass even traditional coding. There's there's no that that's an intermediate step that actually will not be needed probably if by i'd say the end of this year.
And we do expect.
Code to be stead of the art in two to three months, so it's happening very quickly.
Also, do Imagine, so you know, I mean, what will you do right after post aga?
Right?
You probably do, like digital life, So that's what we are doing here as well. We have the Imagine team like started pretty much from Squash like six months ago. We have a few people we decided will to do the image and we'll do the video gen like, yeah, look at it. What do we achieved today, Like you know, like two weeks ago. We'll recently Imagine we actually thought of a leader ball 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 months. So yeah, to me, there's like a very high chance, like we actually made view the Metaverse before Mada. Yeah, I'll also pass to try to to talk Abaul, like you know, the metrics we have, the products.
Yeah, yeah, Like like Godong said, it's only been six months since we started working on Imagine, we had 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 except 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 it's 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 fifty 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. We are also generating six billion images in the last thirty days nano You know, Google recently posted that, you know, one billion images were generated using nano Vana in thirty days, So you know we're six times that, right, And really the goal is
it's not like we don't just want to win. We want to win like like over a long period of time and have sustained greatness. And so the goal with Imagine is to take anything that you can you know, imagine and turn it into reality. And so that's that's what we're gonna you know that we're going to speed run that basically is the goal.
Yeah, Hey, I'm haatin.
We As we keep scaling our model capabilities puwing visual worlds that's indistinguishable from reality, we're also puling systems that onlocks much more possibility than what we have right now, never be able to generate the videos that's much longer and 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 ten minutes or twenty 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 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 ourself.
Absolutely, my friction is that most of AI compute is going to be real time, but your understanding and real time video generation and we expect.
To people leaves in that.
It's really emphsizing these points that you know, six months ago we didn't even have. We had basically nothing in very weak in video and image generation and editing, and we're in six months to number one spot and in fact generally more 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 Macrohart and I think people will be pretty impressed with the clock
four point two models coming out. That's a it's a it's a significant improvement, and that's really just that's that's the small version of our new model. So we will have a medium and a large version that are even more intelligent.
All right, Hi everyone, I'm Toby and I work on Macrohart, the most series of all product names. So arguably, giving computers to humans was a good idea, So we're doing the same thing thing for AI. It's kind of like Inception. We're giving computers to computers. So Macrohart is building fully capable digital review 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 and 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 change the field.
Hi everyone, So yeah, my name is 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 bursts to the whole team. I know, the voice team is like killing on that. And you know, this is the reason why we need the compute. You know, we need a large scale computer run these models to boost our own productivity.
But you know, to ninety five percent 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 Macrohart, you know, we will emulate a company where the output is digital and so this is.
The obvious next step for agents.
Macrohard will and the able true end to end orchestration across the desktop, and it will lead to immense economic prosperity. So yeah, we're entering in there 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 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 we 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?
Yeah?
So thank you.
Yeah. The Macrohart project is over time.
Actually, we'll probably be the 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.
That their 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 lives 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 macrohard 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. It's me again.
You might remember me from macrohart in computer used from a long time ago, but I also actually work on a 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 Cork dot Com API authentication, you go to status at xot Ai. This is done by the core product infront team, and a large portion of them actually sit in London and we work with himI over there, so we keep the lights
on peak our four pm every day. We get paged at night when stuff goes down.
Also, thank you to anyone in paleal to getting paged.
This is really important work reliability, security, core product 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.
Hey everyone, manasiego. Yeah.
So, I think one of the main bottlenecks in this next year for these models is gonna be very high quality emails and training data. And one of the ways we solve that is by taking the world's foremost experts in these domains, bringing them here and having them a value with them up. We do this for domains like medicine,
finance law with voice actors. We have video editors who contribute daily to making GROW better and uh yeah, we're going to be continuing to work on very high quality emails over the.
Next few months.
We have some same stuff in you know, the frontier of useful tast and finance and law. You know, we're trying to build evls that are are are useful in training data that that represents useful work and not necessarily proxies of intelligence for a lot of the open source emails to today.
Yeah.
Yeah, I'd like to say like we're we're shifting from using these sort of common uh Internet emails, which I think are actually not a real indicator of usefulness, to having expert judos in each domain to every domain of engineering, medicine, law.
Whatever the case may be. And the the.
The actual evail is, does the expert in that arena or does our group of experts in that arena human experts agree that GROCK is extremely useful and that the results are correct. That's the that's actually the only EVL it really matters.
Yeah, exactly.
In you'll you'll see this in GROW four twenty. But we've made some improvements because of that type of data in truth seeking and kind of minimizing political bias. Our responses are are much more cogent. Yeah, that's exciting. And we are also working on Grockipedia. So the the goal of Wrockipedia is to create a distillation vol 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 to will one day be cult we've gone from essentially having nothing to around six million articles for context wikipedias around seven million English articles uh and uh yeah, or we're improving on hallucination uh. And our our goal is essentially for Rock five to not have to search out of the data center.
So yep, I guess.
So in the infrad team, we are building the training in France and touring team tooting.
Software for the company. So to give a new example, we were training Rock three.
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 one hundred kh one hundreds at the time and they were just delivered and we.
Didn't quite have the software.
We thought we'd have the software, but then at thirty K scale we realized, actually the software is not quite working, and it took a major almost I was like halfway rewrite off the software because there's so much going on in a data center that you can't actually account for. Switch switches are flapping, little 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 one hundred
k h one hundreds to behave in locksteps. 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 that can happen in the environment. And we did a successfully in what's one of the coolest times in our 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 fifteen 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 Lea and Minn from the RL and inference team.
Hi, I'm Lemming.
So at our team we were on our reinforced Malanning training job and the production inference has a larger scale on the ours and probably as soon in space, and we are kind of already designed a lot of seeing to make it more resilient and scalable, So building a system to scale from hydro kate ships to millions of ships, and we aug every aspect of the stack like paradism pre fiell decault and make resilient to error in them
and the 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 place. And I think actually like very similar to all kinds of things like you.
It's it's very important for you to first see the.
Problem and there you will developed the solutions that Noah else can develop.
Before I'll handle to the tooling team. Hello, I might slip from the tooling team.
Every software needs to have a great interface to be able to make it useful. So as the Tooling team were responsible for building the platform sprameworks and infrassection, 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. There's a place where we collect all of our human data and eventually expect, you know, to build that internal e general platform through which we basically run deployments, run evaluations or like look at like 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 tutests, we should definitely join our team.
So how everyone, I'm you know from the Jacks team.
So now Jacks XAI is a really small team with a couple of engineers 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 hollow world at scale can be complicated. Right, So then we're actually responsible for supporting the entire companies from pre training fundation models rls and also multimodel to scale things to from from first from ten k hundred kent then probably one million, h one hundred equivalents GPO scale, and that we to implement a lot of you know,
practical practical optimizations. We have to uh customize the entire jacks stack from compiler and brunt times, and there will be a lot of interesting problems.
And also if you really want to, uh, you know, obsessed on optimizing uh the entire stack at scale, that we are probably the best place to go, uh because you know, we really have very large scale GIP clusters and we have a lot of interesting problems to work with.
Hey, I'm branding from the Kunnels team.
Basically, the Kunnel team sets at the very bottom of a training and serving stack. Our code runs inside the million current GPUs that we have and if you look inside the gp there's hundreds of thousands of threads and these threads are tend to talk to each other to multiply matrices. Computer tens scores and some of them even
talk to the million other gps 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 large top of performance from these gps. So if you like these low level systems, problems, algorithms, these join us.
But you know, we'll try to bring in uh Heiner and Spencer who are actually at our Tubook compew cluster in Memphis.
And I'm high from the computer network.
Plus such a team we are maybe basic alto by the day. We're here in Memphis in the super computer.
So the data center here in Memphis swum the last tribute class on the planet and it.
Is still growing.
Our drugs keep all this from do and running.
Next Rock and the sir Ai alps us used to work well.
I love to agree.
It's actually just put the mic really close to your mouth because you're the ambient noises high.
It's getting out that you go back.
So I was saying, I'll drop the compute up and running, try the next model of ruck.
And serve the so use us.
So, but you used to work well and at in greens have to come together, mainly soft yeah and hotware. So there's all these tb TPUs, nicked switches and hundreds of thousands of operating systems running. Is one take supercomputer and what we need Sparks to re understand the notes, re understand that he may and re understand how computers work. Brond deep level that is, you reach album X and I'm heading over ten A.
Right.
So we have three hundred thousand TV, three hundred platforms us here today, still growing, still building eight hundred and forty seven miles of fiber per Data Hall twelve.
Data Holls.
You want to be part of the world's largest supercomputer, come join us, all right.
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 giggle out.
Of power online and running.
We'll have the largest tessil of megapack system in the world margin than Hawaii or South Australia. And Zach is really quickly going to talk a little bit about actually constructing the data is.
So behind me you can see Data Hall eleven. So one of the most incredible things about what we're doing here at Macrohart's how fast we do it right, So, like they were saying before, over eight or fifty miles of fiber and every single data hall, over twenty seven thousand jews and over a two hundred thousand 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 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 PUE in the industry of
using as much power smoothing technology as we can. I'm being really good partners in the community here in Memphis with the toss of micropark that we have going. You can check them out. XAI Memphis back to you with.
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 Justin Wong of CEO and Vidia 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 a really phase one, which is three hundred and thirty thousand Grace Blackwells with macrohart on the building that's on an image that it actually is on the roof of the building. And then macro Hotter will be the building that you can see which has got the macro Hotter with the rockets on it, and that'll be another turned twenty thousand GP three hundreds.
So all of this will be training the models that you experience.
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 Amian 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.
Hello, all right, 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 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 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 fifty percent 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 and solving one of the like twenty year old problems of the app,
which was ramping up new users. New users are now spending fifty five percent more time per day in the app than they were six months ago. And on the core product side, we're hitting our stride to 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, U x chat, 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.
Uh.
Just last month we did a little push on articles and articles published are up ten x, articles read are up seventeen x. And on all other fronts, like over the holidays, we did a big push on subscriptions.
We just crossed a.
Billion dollars in ar R.
There. I think with the X.
App, you know, the there's very few unknowns, like the path for us to win and become you know, the number one app in the world. Uh, We're it's it's we we know what to do. The balls in our court. Uh, it's it's for us to win, and it's just a matter of us executing yep.
And yeah, So.
We've evolved the 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 uh you know, all the things you'd want from any messaging app for the disappearing messages, screen screenshot blocks, like, there's a whole all the features that you'd want want in an app. And we 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.
Uh.
Nothing beats nothing beats uh, transparency for believing in a company. So we're going to be the only recommendation algorithms that actually open sources so you can see what it, what it does, and how it's evolving. With with crock 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 in grock Chat, which is really intended to be a generalized communication system, and in the next few months we'll be releasing a standalone UH 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.
We'll 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 communications system with x chat. For x money, we're UH. We've actually had x money UH live in closed beta within the company, and we expect in the next month or two UH to go to UH a limited external beata, 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 it's a it's really going to be a game changer. And the reason we say one billion user is actually over a billion users is that while our monthly users are on average around six hundred million, the number of people who have the X app and sold this 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 rock, or for X money, whatever the case may be. We wanted to be such that if you wanted to, you could live your life on the X app. And as we make it more more useful, we'll obviously give people reasons, compelling reasons to use the app every day and have my expectations well over a billion daily active users 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 clatters 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 space 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 called it roughly one percent 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 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 ninety nine point eight percent 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 says, you'd have to expand roughly.
A million times just to get to one millionth of our Sun's energy, and then going beyond that exploring, extending to the galaxy 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 one hundred to two hundred gigawat per year level, not cumulative, I mean per year. And ultimately we see a path to maybe launching as much as a terror wat per year of compute from Earth. But what if you want to go beyond a mere terror wide 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 need to 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 one thousand gigawats 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.
Is simple 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 to like shoo 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 going being out there among the stars and visiting all these star systems. Maybe we'll meet aliens, uh, 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. Wow, epic end to a great presentation.
Yes it's done.
If that's all he wanted to say, catch you, light up.
I'm going to share a few of my
K type Thanks for listening, See you in the next episode.