Speaker 1: If you like this episode, just like, share and follow
this podcast. Thank you, Back to the show, Welcome.
Speaker 2: To the Xai All Hands. We've been a very exciting
presentation for you.
Speaker 1: 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.
Speaker 2: What matters. It's not the position at any point in time, but.
Speaker 1: 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.
Speaker 2: No one's even close. So let's go to our team.
Speaker 1: As we grow as a company, a natural thing that
happens is you reorganize the company as it scales up.
Speaker 2: So when you first have a startup, you.
Speaker 1: 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.
Speaker 2: We've we've reached a certain scale.
Speaker 1: 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.
Speaker 2: Hip. Thanks you long.
Speaker 3: 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.
Speaker 2: We started much after.
Speaker 3: 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.
Speaker 4: 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.
Speaker 2: We're going to see work.
Speaker 4: 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.
Speaker 5: To use experience that just works seamlessly.
Speaker 2: Too.
Speaker 6: Being said, we are hiring and we're looking for intelligent
and smart people.
Speaker 2: This is not an easy place to work guys like
this is.
Speaker 6: 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.
Speaker 1: 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.
Speaker 2: So if you're doing engineering or law or tit or.
Speaker 1: 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.
Speaker 2: Count it right, all right, thank you, Thanks.
Speaker 7: Everybody. I'm Macro.
Speaker 8: 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.
Speaker 7: So now we have I do like what we.
Speaker 8: 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.
Speaker 2: Now, so please join us.
Speaker 9: 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.
Speaker 7: Now we are one keeam and we will win on
the compute and we are.
Speaker 9: 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.
Speaker 7: Yeah, I think it's like really a million.
Speaker 9: Year basically with the year to be alive, and I
can already feel the AGI, feel the hi at least
for coding.
Speaker 1: 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.
Speaker 2: And we do expect.
Speaker 1: Code to be stead of the art in two to
three months, so it's happening very quickly.
Speaker 9: Also, do Imagine, so you know, I mean, what will
you do right after post aga?
Speaker 8: Right?
Speaker 9: 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.
Speaker 10: 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.
Speaker 5: So Imagine it's growing extremely.
Speaker 10: 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.
Speaker 2: And just to.
Speaker 10: 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.
Speaker 5: Yeah, Hey, I'm haatin.
Speaker 11: 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.
Speaker 2: Will do it for you.
Speaker 11: 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.
Speaker 1: 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.
Speaker 2: To people leaves in that.
Speaker 1: 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.
Speaker 12: 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.
Speaker 5: And in a sense, it's one of the last few.
Speaker 12: 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.
Speaker 13: 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.
Speaker 5: Like we're actively using these every day.
Speaker 13: 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.
Speaker 5: But you know, to ninety five percent of the world
world software has a GUI.
Speaker 13: So that's like, you know, great representation, and you know,
to truly make people's lives easier, we need to develop models.
Speaker 5: That are capable of solving day to day tasks on GUI.
Speaker 13: So Macrohart, you know, we will emulate a company where
the output is digital and so this is.
Speaker 5: The obvious next step for agents.
Speaker 13: 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?
Speaker 14: Yeah?
Speaker 5: So thank you.
Speaker 2: Yeah. The Macrohart project is over time.
Speaker 15: 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.
Speaker 1: 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.
Speaker 2: The output is digital.
Speaker 1: And this will usher in an age of prosperity lives
which we can barely imagine at this point.
Speaker 2: You need imagine to imagine it.
Speaker 1: 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.
Speaker 5: It's also pretty funny, meant to be a joke. It's
me again.
Speaker 12: 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.
Speaker 5: Also, thank you to anyone in paleal to getting paged.
Speaker 12: 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.
Speaker 5: The team to join.
Speaker 7: Hey everyone, manasiego. Yeah.
Speaker 16: 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.
Speaker 7: Next few months.
Speaker 16: 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.
Speaker 7: Yeah.
Speaker 1: 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.
Speaker 2: Whatever the case may be. And the the.
Speaker 1: 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.
Speaker 5: Yeah, exactly.
Speaker 16: 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.
Speaker 7: So yep, I guess.
Speaker 8: So in the infrad team, we are building the training
in France and touring team tooting.
Speaker 7: Software for the company. So to give a new example,
we were training Rock three.
Speaker 8: 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.
Speaker 5: Didn't quite have the software.
Speaker 8: 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.
Speaker 7: So if you're interested in working on these problems and.
Speaker 8: 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.
Speaker 7: Like we are still a very small.
Speaker 8: Team with me is Lea and Minn from the RL
and inference team.
Speaker 2: Hi, I'm Lemming.
Speaker 17: 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.
Speaker 2: It's it's very important for you to first see the.
Speaker 17: Problem and there you will developed the solutions that Noah
else can develop.
Speaker 7: Before I'll handle to the tooling team. Hello, I might
slip from the tooling team.
Speaker 18: 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.
Speaker 7: So how everyone, I'm you know from the Jacks team.
Speaker 19: 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.
Speaker 14: 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.
Speaker 7: Hey, I'm branding from the Kunnels team.
Speaker 18: 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.
Speaker 20: But you know, we'll try to bring in uh Heiner
and Spencer who are actually at our Tubook compew cluster
in Memphis.
Speaker 7: And I'm high from the computer network.
Speaker 5: Plus such a team we are maybe basic alto by
the day. We're here in Memphis in the super computer.
Speaker 21: So the data center here in Memphis swum the last
tribute class on the planet and it.
Speaker 3: Is still growing.
Speaker 22: Our drugs keep all this from do and running.
Speaker 21: Next Rock and the sir Ai alps us used to
work well.
Speaker 2: I love to agree.
Speaker 1: It's actually just put the mic really close to your
mouth because you're the ambient noises high.
Speaker 2: It's getting out that you go back.
Speaker 21: So I was saying, I'll drop the compute up and running,
try the next model of ruck.
Speaker 2: And serve the so use us.
Speaker 21: 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.
Speaker 1: Right.
Speaker 22: 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.
Speaker 5: Data Holls.
Speaker 22: You want to be part of the world's largest supercomputer,
come join us, all right.
Speaker 23: So it's quite marvelous what we've been able to do
in less than one year's time.
Speaker 2: Here we have.
Speaker 23: Once we're completely finished, we'll have north of a giggle out.
Speaker 7: Of power online and running.
Speaker 23: 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.
Speaker 24: 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.
Speaker 5: We do that over and over and over again.
Speaker 24: We massively parallelize it. It's pretty much the most complex
and consistent type of engineering, design and construction project you
possibly imagine.
Speaker 2: So come join us. Yes, you know.
Speaker 23: 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.
Speaker 2: All right, thank you, all.
Speaker 1: 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.
Speaker 2: Compute faster than anyone else.
Speaker 1: 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.
Speaker 2: Yeah, this is what it looks like.
Speaker 1: 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.
Speaker 2: So all of this will be training the models that
you experience.
Speaker 1: 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.
Speaker 25: 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.
Speaker 2: Uh.
Speaker 25: 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.
Speaker 2: We just crossed a.
Speaker 25: Billion dollars in ar R.
Speaker 2: There. I think with the X.
Speaker 25: 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.
Speaker 2: And yeah, So.
Speaker 1: 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.
Speaker 19: Uh.
Speaker 1: 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.
Speaker 2: You don't you don't have to go to the core product, and.
Speaker 1: 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.
Speaker 2: Like says, you'd have to expand roughly.
Speaker 1: 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.
Speaker 2: So the.
Speaker 1: 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.
Speaker 2: A few percent of the Sun's energy.
Speaker 1: 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.
Speaker 2: Thank you. Wow, epic end to a great presentation.
Speaker 5: Yes it's done.
Speaker 2: If that's all he wanted to say, catch you, light up.
Speaker 5: I'm going to share a few of my
Speaker 10: K type Thanks for listening, See you in the next episode.