Original subtitles

Heat. Heat.

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What is

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this?

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out.

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Take care.

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Yeah.

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Hey,

hey, hey.

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You are feeling

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heat.

Heat. Heat. N.

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Heat. Heat.

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Heat. Heat.

Heat.

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Hey, heat. Hey, heat.

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In a world where knowledge shapes

destiny, one creation dares to redefine

the future. From the minds at XAI,

prepare for Gro 4. This summer, the next

generation arrives faster, smarter,

bolder. It sees beyond the horizon,

answers the unasked, and challenges the

impossible. Gro 4 Unleash the Truth

coming this summer.

All right, welcome to the Gro 4 release

here. Um, this is uh the smartest AI in

the world and we're going to show you

exactly how and why. Um, and uh, it

really is

remarkable to see the advancement of

artificial intelligence, how quickly it

is uh, evolving. Um,

I I sometimes think compare it to the

growth of

a human and how fast a human learns and

gains conscious awareness and

understanding and AI is advancing

just vastly faster than any human. Um,

I mean, we're going to take you through

a bunch of benchmarks that um that Grock

4 is able to uh achieve incredible

numbers on. Um, but it's it's it's

actually worth noting that like Grock 4

if if given like the SAT would get

perfect SATs every time, even if it's

never seen the uh the questions before.

Um and if even going beyond that to say

like uh graduate student exams like the

GRE uh it will get near-perfect results

in in every discipline of uh of

education. So from the humanities to

like languages, math, physics,

engineering, pick anything and we're

talking about questions that it's never

seen before. These are not on not on the

internet and it's Grofor is smarter than

almost all graduate students uh in all

disciplines simultaneously

like it's actually just important to

appreciate the like that's uh really

something

um

and uh the the reasoning capabilities of

Grock are incredible. Um, so there's

some people out there who who think AI

can't reason and look it it can reason

at superhuman levels. Um, so yeah, and

frankly it it only gets better from

here. So we'll we'll take you through uh

the Gro 4 release and uh

yeah um show you like the pace of pace

of progress here. Um like I guess the

first part is like in terms of the

training um we're going from Grock two

to Grock 3 uh to Grock 4. We've

essentially increased the training by an

order of magnitude in each case. So it's

uh you know a 100 times more training

than than Grock 2. Um and uh and that

that's only going to increase. Um so

it's uh yeah frankly I mean I don't know

in some ways a little terrifying but uh

the growth of intelligence here is is

remarkable.

Yes it's important to realize there are

two types of training compute. One is

the pre-training compute that's from GR

two to GR three. Um but for from GR

three to GU four we're actually putting

a lot of compute in reasoning in RL.

Yeah. And uh just like you said this is

literally the fastest moving field and

Grock 2 is like the high school student

by today's standard. If you look back in

the last 12 months Grock 2 was only a

concept. We didn't even have Gro 2 12

months ago. Um and then by training GR 2

that was the first time we scale up like

the pre-training. We realized that if

you actually do the data ablation really

carefully and infra and also the

algorithm, we can actually push the

pre-training quite a lot by amount of

10x to make the model the best

pre-trained based model. And that's why

we built Colossus the world's

supercomputer with 100,000 H100 and then

with the best pre-train model and we

realized if you can collect these

verifiable outcome reward you can

actually train these model to start

thinking from the first principle start

to reason correct his own mistakes and

that's where the graining comes from and

today we ask the question what happens

if you take the expansion of classes

with all 200,000 GPUs put all these into

RL 10x more compute than any of the

models out there on reinforcement

learning unprecedented scale what's

going to happen. So this is a story of

Gro 4 and uh you know Tony uh share some

uh insight with the audience.

Yeah. Um so yeah let's just talk about

how smart GR 4 is. So I guess u we can

start discussing this benchmark called

humanities last exam and this this

benchmark is a very very challenging

benchmark. Every single problem is

curated by subject matter experts. Um

it's in total 2500 problems and it

consists of many different subjects

mathematics, natural sciences, uh

engineering and also autohumity

subjects. So um essentially when when it

was first released actually like earlier

this year uh most of the models out

there can only get singledigit accuracy

on this benchmark.

Yeah. So we can we can look at some of

th those examples. Um you know uh so

there there is this mathematical problem

which is about natural transformations

in category theory and there's this

organic chemistry problem that talks

about uh electro cyclic reactions and

also there's this linguistic problem

that tries to ask you about

distinguishing between closed and open

syllabus uh from a hub Hebrew source

text. So you can see also it's a very

wide range of problems and every single

problem is PhD or even advanced research

level problems.

Yeah. I mean the these there are no

humans that can actually answer these

can get a good score. I mean if you

actually say like any given human um

what like what's the best that any human

could score? I mean I'd say maybe 5%

optimistically.

Yeah. Um, so this this is much harder

than than what any any human can do.

It's it's incredibly difficult and you

can see from the the types of questions

like you might be incredible in

linguistics or mathematics or chemistry

or physics or anyone of a number of

subjects, but you're not going to be um

at a postgrad level in everything. And

Grock 4 is a postgrad level in

everything. Like it's it's just some of

these things are just worth repeating.

like Grockport is postgraduate like PhD

level in everything better than PhD but

like most PhDs would fail so it's better

said I mean at least with respect to

academic questions it I want to just

emphasize this point with respect to

academic questions Gro is better than

PhD level in every subject no exceptions

um now this doesn't mean that it's it

you know at times it may lack common

sense and it has not yet invented new

technologies or discovered new physics

but that is just a matter of time.

Um if it I I I think it may discover new

technologies

uh as soon as later this year. Um and I

I would be shocked if it has not done so

next year. So I would expect Grock to

yeah literally discover new new

technologies that are actually useful no

later than next year and maybe enter

this year. um and it might discover new

physics next year and within two years

I'd say almost certainly

like so just let that sink in.

Yeah.

So yeah um how okay so I guess we can

talk about the the what what's behind

the scene of graph 4 as Jimmy mentioned

uh we actually throwing a lot of compute

into this training you know when it

started it's only also a single digit

sorry um the previous slide sorry yeah

it's only a single digit u number but as

you start putting in more and more

training compute it started to gradually

gradually become smarter and smarter and

eventually solved a quarter of the HL

problems and this is without any tools.

The next thing we did was to adding a

tools capabilities to the model and

unlike Gth 3 I think G3 actually is able

to use C as well but here we actually

make it more native in the sense that we

put the tools into training. Uh G3 was

only relying on generalization. Here we

actually put the tools into training and

it turns out this significantly improves

the model's capability of using those

tools.

Yeah, I remember we had like deep search

back in the days.

So how is this different?

Yeah.

Yeah. Exactly. So deep search was

exactly the graph 3 reasoning model uh

but without any specific training but we

only asked it to use those tools. So

compared to this, it was much weaker in

terms of its tool capabilities

and unreliable.

And unreliable. Yes. Yes.

And and to be clear, like these are

still, I'd say, fairly this is still

fairly primitive tool use. If you

compare it to say the tools that I used

at Tesla or SpaceX uh where you're using

um you know finite element analysis and

computational flow dynamics and and

you're you're able to run uh or say like

Tesla does like crash simulations where

the simulations are so close to reality

that if the test doesn't match the

simulation you assume that the test

article is wrong. That's how good the

simulations are. So Grock is not

currently using any of the tools the the

really powerful tools that a company

would use but but that is something that

we will provide it with later this year.

So it will have the tools that that a

company has um and and have very

accurate physics simulator. Um

ultimately the the thing that will make

the biggest difference is being able to

interact with the real world via

humanoid robots. So you combine sort of

grock with with Optimus and it can

actually interact with the real world

and figure out if if it's hypo if it has

if it's it can formulate an hypothesis

and then confirm if that hypothesis is

is true or not. Um

so we're really you know think about

like where we are today. We're we're at

the beginning of an immense intelligence

explosion. We're in we're in the

intelligence big bang right now.

Um

and the mo we're at the most interesting

time to be alive of any time in history.

Yeah. Now that said, we need to make

sure that the AI is um a good AI.

Uh good Grock. Um and the the thing that

I think is most important for AI safety,

at least my biological neural net tells

me the most important thing for AI is to

be maximally truth seeeking.

So this is this is a very fundamental

um like you can think of AI as this

super genius child that ultimately will

outsmart you but you can still in you

can instill the right values um and

encourage it to be sort of you know

uh truthful

uh

I don't know honorable you know good

good things like

the values you want to instill in a

child that that that grow would grow

ultimately grow up to be incredibly

powerful. Um

yeah.

So yeah, so these this is really I say

we say we say tools these are still

primitive tools not the kind of tools

that um that uh serious commercial

companies use but we will provide it

with those tools and uh I think it will

be able to solve with those tools real

world technology problems. In fact I'm

I'm certain of it. It's just a question

of how long it takes.

Yes. Yes, exactly. Um,

so is it just compute all you need,

Tony?

Right.

Is it just compute all you need at this

point?

Well, you you need compute plus plus uh

the right tools.

Mhm.

And um and then ultimately to be able to

interact with physical world.

Yes. Um, and then

I mean we'll effectively have an economy

that is

well ultimately an economy that is

thousands of times bigger than our

current economy or maybe millions of

times. Mhm.

I mean if you if you think of

civilization as percentage completion of

the kadesv scale where kadeshv one is

using all the energy output of a planet

and kv two is using all the energy

output of a sun and three is all the

energy output of a galaxy. We're we're

only in my opinion probably like close

closer to 1% of kartev one um than we

are to 10%. So like maybe a point or one

one or two percent of kadeshv one. So

um we we will get to

um most of the way like 80 90% kadeshv

one and then hopefully if civilization

doesn't

self annihilate and then kadeshv 2 like

it's the the actual notion of of a human

economy assuming civilization continues

to progress will seem very quaint. Um,

in in retrospect,

um, it will it will seem like uh

sort of cavemen throwing sticks into a

fire uh level of economy

um, compared to what the future will

hold.

Um, I mean, it's very exciting. I mean,

I I've been at times kind of worried

about like, well,

you know, is this this this seems like

it's it's somewhat unnerving to have

intelligence created that is far greater

than our own.

Um, and will this be bad or good for

humanity? Um,

it's like I I I think it'll be good.

Most likely it'll be good. Um,

yeah.

Yeah. But I somewhat reconciled myself

to the fact that even if I even even if

it wasn't going to be good, I'd at least

like to be alive to see it happen.

So, yeah.

So, actually one one

Yeah. Yeah. I think one one technical

problem that we still need to solve

besides just compute is how do we

unblock the data uh data um bottleneck u

because um when we try to scale up the

RL uh in this case we did invent a lot

of new techniques innovations to allow

us to figure out how to find a lot of a

lot of challenging RL problems to work

on. It's not just the problem itself

needs to be challenging but also it

needs to be um you also need to have

like a reliable uh signal to tell the

model you did it wrong you did it right.

This is the sort of the principle of

reinforcement learning and as the models

get smarter and smarter the number of

cool problem or challenging problems

will be less and less.

Yeah.

So it's going to be a new type of

challenge that we need to surpass

besides just compute. Yeah.

Yeah. And we actually are running out of

of of actual test questions to ask. Uh

so there's like even ridiculously

questions that are ridiculously hard if

not essentially impossible for humans

that are written down questions um are

uh becoming swiftly becoming trivial for

for AI.

Um so then there's

um but you know the the one thing that

is an excellent judge of things is

reality. So because physics is the law

ultimately everything else is a

recommendation you can't break physics.

Um so the ultimate test I think for

whether an AI is um the the ultimate

reasoning test is reality.

Yes.

So you invent a new technology like say

improve the design of a car or a rocket

or um create a new medication

uh that and and and does it work?

Yeah.

Um does does the rocket get to orbit?

Does the does the car drive? Does the

medicine work? Whatever the case may be.

Um, reality is the ultimate judge here.

Um, so it's it's going to be a

reinforcement learning closing loop

around reality.

We asked the question how do we even go

further? So um actually we are thinking

about now with single agent we are able

to solve 40% of the problem.

What if we have multiple agents running

at the same time? So this is what's

called test and compute. And as we scale

up the test and compute actually we are

able to solve almost more than 50% of

the uh text only subset of the h

problems. So it's a remarkable

achievement I think. Yeah.

Yeah. This is this is this is insanely

difficult. These are it's it's so what

we're saying is like a majority of the

of the of the textbased um of humanities

you know scarily named humanities last

exam um GR 4 can solve and you and you

can try it out for yourself. Um and the

with the GR four heavy what what it does

is it spawns multiple agents in parallel

and uh all of those agents do do work

independently and then they compare

their work and they they decide which

one like it's like a study group. Um and

it's not as simple as a majority vote

because often only one of the agents

actually figures out the trick or

figures out the solution. Um and and and

but once they share the the trick or or

or figure out what what the real nature

of the problem is, they share that uh

solution with the other agents and then

they compare they essentially compare

notes and then and and then yield uh

yield an answer. Yeah.

So that's that's the the heavy part of

Grock 4 is is where we you scale up the

test time compute by roughly an order of

magnitude. Uh have multiple agents uh

tackle the task and then they compare

their work and they they put forward

what they think is the best result.

Yeah. So we will introduce graph 4 and

graph for heavy. Sorry, can you click

the next slide? Yeah.

Yes. So yeah. So basically graph 4 is

the single version a single agent

version and graph for heavy is the

multi- aent version.

So let's take a look how they actually

do on those exam problems and also some

real real life problems.

Yeah. So we're going to start out here

and we're actually going to look at one

of those HLE problems. This is uh

actually one of the easier math ones. Uh

I don't really understand it very well.

I'm not that smart. But I can launch

this job here and we can actually see

how it's going to go through and start

to think about this problem. Uh while

we're doing that, I also want to show a

little bit more about like what this

model can do and launch a uh Gro 4 heavy

as well. So everyone knows Poly Market.

Um it's extremely interesting. It's the

you know seeker of truth. It aligns with

what reality is most of the time. And

with Grock, what we're actually looking

at is being able to see how we can try

to take these markets and see if we can

predict the the future as well. So, as

we're letting this run, we'll see how uh

Gro for Heavy goes about uh predicting

the, you know, the World Series odds for

like the current teams in the MLB. And

while we're waiting for these to

process, we're going to pass it over to

Eric, and he's going to show you an

example of his.

Yeah. So uh I guess one of the coolest

things about uh Gro 4 is its ability to

understand the world and to solve hard

problems by leveraging tools like Tony

discussed. And I think one kind of cool

example of this we asked it to generate

a visualization of two black holes

colliding. Um and of course you know it

took some there are some liberties. It's

in my case actually pretty clear in its

thinking trace about what these

liberties are. Uh for example, in order

for it to actually be visible, you need

to really exaggerate the the scale of

the you know the uh the uh the waves.

And yeah, so here's like you know this

kind of inaction. Um it exaggerates the

scale in like multiple ways. Uh it drops

off a bit less in terms of amplitude. um

over distance and uh but yeah we can

kind of see uh the basic effects that

you know are actually like you know

correct. It starts with the inspiral, it

merges uh and then you have um the ring

down and like this is basically um

largely correct um yeah uh modulo some

of the simplifications that need to do

um you know it it's actually quite

explicit about this you know it uses

like post postnonian approximations

instead of actually like computing the

general relativistic effects at like uh

near the center of the black hole which

is you know incorrect um and you know

will lead to you know some incorrect

results but the overall you know

visualization is uh yeah is basically

there um and you can actually look at

the kinds of resources that it

references. So here it um it actually

you know it obviously uses search it

gathers results from a bunch of links

but also reads through a undergraduate

text in analytical analytic

gravitational wave models. Uh it um yeah

it um reasons quite a bit about uh uh

the actual constants that it should use

for a realistic simulation. uh it

references uh I guess existing real

world data. Um and yeah I it yeah it's a

it's a pretty good model. Uh yeah

but like actually going forward we can

we can we can plug we can give it the

same model that physicists use. So it it

can run the the same level of compute

that uh so leading physics researchers

are using and and give you a physics

accurate black hole simulation. Exactly.

Just right now it's running in your

browser. So

yeah, this is just running in your

browser.

Exactly.

Pretty simple.

So swapping back real quick here. We can

actually take a look. The math problem

is finished. Uh the model was able to uh

let's look at its thinking trace here.

So you can see how it went through the

problem. I'll be honest with you guys, I

really don't quite fully understand the

math, but what I do know is that I

looked at the answer ahead of time. Um,

and it did come to the the correct

answer here in the final part here. Um,

we can also come in and actually take a

look here at our uh our World Series

prediction. Um, and it's still thinking

through on this one, but we can actually

try some other stuff as well. So, we can

actually like try some of the X

integrations that we did. So, we worked

very heavily on working with uh all of

our X tools and building out a really

great X experience. So we can actually

ask, you know, the model, you know, find

me the XAI employee that has the

weirdest profile photo. Um, so that's

going to go off and start that. And then

we can actually try out, you know,

let's, uh, create a timeline based on

XOST, uh, detailing the, you know,

changes in the scores over time. And we

can see, you know, all the conversation

that was taking place at that time as

well. So we can see who are the, you

know, announcing scores and like what

was the reactions at those times as

well. Um, so we'll let that go through

here and process. And if we go back to

uh uh this was the uh Greg Yang photo

here. So uh if we scroll through here,

whoops. So Greg Yang, of course, who has

his favorite uh photograph that he has

on his account. Um that's actually not

how he looks like in real life, by the

way. Um just so aware, but is quite

funny.

But it had to understand that question.

Yeah.

Which is the that's the wild part. It's

like it understands what is a weird

photo. What is a weird photo?

Yeah.

What is a less or more weird photo?

It goes through it has to find all the

team members. It has to figure out who

we all are. It, you know, searches

without access to the internal XAI

personnel loss. It's literally looking

at the just at the internet.

Exactly.

So you could say like the weirdest of

any company.

Yeah.

To be clear.

Exactly. And uh we can also take a look

here at the uh question here for the

humanities last exam. So, it is still

researching all of the historical

scores. Um, but it will have that final

answer here soon, but we can while it's

finishing up, we can take a look at one

of the ones that we set up here a second

ago. And we can see like, you know, it

defines the date that like Dan Hendricks

had initially announced it. We can go

through, we can see, you know, uh,

OpenAI announcing their score back in

uh, February. And we can see, you know,

as progress happens with like Gemini. we

can see like Kimmy uh and we can also

even see you know the leaked benchmarks

of uh what people are saying is you know

if it's right it's going to be pretty

impressive so pretty cool um so yeah I'm

looking forward to seeing how everybody

uses these tools and gets the most value

out of them um but yeah it's been great

yeah and we're going to close the loop

around usefulness as well so it's like

it's not just books smart but actually

practically smart

exactly

And we can go back to the uh the slides

here. Yeah. So

cool. Um so the so we actually evaluate

also on the multimodel subset. So on the

full set uh this is the number on the h

exam. Uh it you can see there's a little

dip on the numbers. uh this is actually

something we're improving on which is

the multimodel uh understanding

capabilities but I do believe uh in a

very short time we're able to really

improve and got much higher numbers on

this um even higher numbers on this

benchmark. Yeah.

Yeah. This is the we we saw like what

what is the biggest weakness of Grock

currently is that it's it's sort of

partially blind. it can't it's its image

understanding um obviously and its image

generation uh need to be a lot better um

and that um that that's actually um

being trained right now. So um graph 4

is based on version six of our

foundation model um and we are training

version seven uh which we'll complete in

a few weeks um and uh that that'll

address the weakness on the vision side

and just to show off this last here. So

the the prediction market finished uh

here with a heavy and we can see uh here

we can see all the tools and the process

it used to actually uh go through and

find the right answer. So it browsed a

lot of odds sites. It calculated its own

odds comparing to the market the market

to find its own alpha and edge. It walks

you through the entire process here and

it calculates the odds of the winner

being like the the Dodgers. Uh and it

gives them a 21.6% 6% chance of uh

winning uh this year. So, and it took

approx approximately four and a half

minutes to compute.

Yeah, that's a lot of thinking.

Yeah,

we can also look at all the uh the other

benchmarks besides HRE. Um, as it turned

out, GR 4 excelled on all the reasoning

benchmarks that people usually test on.

Um, including GBQA, which is a PhD

level, uh, problem sets. Uh, that's

easier compared to HLE. Um, on Amy 25

American Invitation Mathematics exam, we

with graph for heavy, we actually got a

perfect score. Um, also on some of the

coding benchmark called live coding

bunch. um and also on uh HMMT, Harvard

math uh MIT uh exam and also USMO. Uh

you can see actually um for on all of

those benchmarks we often have a very

large leap against the second best uh

model out there.

Yeah, it's I mean really we're going to

get to the point where uh it's going to

get every answer right in every exam. um

and where it doesn't get an answer

right, it's going to tell you what's

wrong with the question. Or if the

question is ambiguous, disambiguate the

question into answers A, B, and C and

tell you what what answers A, B, and C

would be with a disamiguated question.

Um so the only real test then will be

reality. Uh can I make useful

technologies, discover um new science?

That'll actually be the the only thing

left because human tests will simply not

be um meaningful.

need to make an update to HRE very soon

given the current rate of progress. So

yeah, it's super cool to see like

multiple agents that collaborate with

each other solving really challenging

problems. Uh so we're going try this

model. Uh so it turned out it's

available right now. Uh if we advance to

the next slide uh where there is a super

Grog heavy tiers that we're introducing

where you're able to access to both

Grock 4 and Grock 4 heavy uh where

you're actually going to be the

taskmaster of dodge of little Grock

research agent to help you you know

become smarter through all the little

research and save hours of times of uh

you know going through mundane tasks uh

and it's available right now. Uh so yeah

so uh we we did limit usage during the

the demo so we didn't it didn't break

the demo because all these this all this

stuff is happening live so it's not

there's no nothing canned about the any

of the tests that we're doing. Um so uh

after the after the uh demo is done

we'll we'll allow we'll enable more

subscribers for cyber gro. So if you

can't subscribe right now just try in

half an hour. It should work. Um, so,

uh, and now let's let's get into voice.

Greatby.

So many of you have been enjoying our

voice mode and we've been working hard

to improve the experience over the past

couple months. Um, we have cut latency

in half to make it much snappier. And

today we're excited to announce a set of

new voices that have exceptional

naturalness and procity. You might have

noticed the movie trailer voice that

opened up the live stream. That is one

of our new voices, S, who's got that

epically deep tone. And we're also

excited to introduce Eve, a beautiful

British voice who's capable of rich

emotions. A man, would you like to

introduce Eve?

Absolutely. Yeah. Let's get into the

demo.

Hey, Eve. Millions of people are

watching us right now. Are you excited?

Oh, I'm thrilled. Really, it's like

being on stage at the Old Vic with a sea

of faces out there. Just imagine all

those eyes and ears tuned in. It's a

proper buzz, isn't it? What do you

reckon we should chat about to keep this

lot entertained?

You know, we have a lot to talk about,

but I'll be honest. I'm feeling a little

nervous. Can you whisper something

soothing to calm me down?

Take a deep breath, love. You've got

this. It's just you and me having a

quiet chat. like we're tucked away in a

cozy corner of a Yorkshire pub. The

world's just a murmur out there. Feel

that calm wash over you. Let's keep

talking nice and easy. What's on your

mind?

You know what? I'm actually feeling

quite relaxed to try this um something

crazy. So, can you give us an opera on

Diet Coke?

Oh, an opera on Diet Coke. That's a

riot. Picture this, a shimmering silver

can glistening under stage lights. Oh

diet coke, thou elixir divine with

bubbles that dance in a sparkling line.

Thy crisp cool kiss on lips so fine. Oh,

aspartame

and bro forever mine.

How's that for a mad little area? Want

me to belt out another verse or switch

up the tune?

Okay, let's try switching it up and just

start singing.

Right, then let's have a go. Oh, silver

can, you gleam in the light, diet coke,

my heart's delight.

Okay, I'll touch it. It's very, very

poetic. Um, yeah, thank you, Eve.

Feeling quite nice now.

Cool. Um, so that's Eve. And um so

something else we do here is is a little

test each day to check on the natural

flow of our conversations against some

of our competitors. So we figured why

not try that live on the stream today?

Should I start?

Hey, I'm going to say a number and can

you say the same number back to me? One.

One. Got it. This is like a fun little

game of echo. What's next?

Two.

Sorry, you said two.

Three.

Four. Nice and steady. What's next?

Um. Uh. Five.

Five. Nailed it. This is pretty fun,

actually. Feel free to keep going or

switch it up if you'd like.

Hey, Grock.

Hey, Grock.

Hello there. How can I help you today?

We're going to do a little test. Can you

repeat after me? One.

One. All right. What's next?

Two.

Two. What's on your mind?

Three.

Three. Need anything else?

Four.

Four. How can I assist you?

Five.

Five. What's next?

So, as you can see, um, Groc was, uh,

snappier. It didn't interrupt me. And

the procity is we've made different

design choices. I think we shooting for

something more calm, smooth, more

natural versus something that's more

poppy or artificial. So, we'll keep

improving on these fronts.

Thanks, guys.

Yeah.

Yep. So since the launch of the voice

model uh we actually see the 2x faster

end to end latency uh in the last 80

weeks five different voices and also 10x

the active user. So Grock voice is

taking off. Now um if you think about

releasing the models this time we're

also releasing Grock 4 through the API

at the same time. So if we go to the

next two slides. So uh you know we're

very excited about you know what all

developers out there is going to build.

So you know if I think about myself as a

developer what the first thing I'm going

to do when I actually have access to the

graph 4 API benchmarks. So we actually

ask around on the Xplatform what is the

most challenging benchmarks out there

that you know is considered the holy

grail for all the AGI models. Uh so turn

out AGI is in the name ARGI. So uh the

last 12 hours uh you know kudos to Greg

over here in the audience uh so who

answered our call take a preview of the

Gro for API and independently verified

you know the GR for performance. So

initially we thought hey graph is just

you know we think it's pretty good it's

pretty smart uh it's our nextgen

reasoning model spend 10x more compute

and can use all the tools right but

turns out when we actually verify on the

private subset of the RKGI v2 it was

like the only model in the last three

months that breaks the 10% barrier and

in fact was so good that actually get to

16% well 15.8% 8% accuracy 2x of the

second place that is the cloud for

outpus model um and it's not just about

performance right when you think about

intelligence having the API model drives

your automation it's also the

intelligence per dollar right if you

look at the plots over here the guac is

just four just in the league of its own

um all right so enough of benchmarks uh

over here right so what can gro do

actually uh in the real world So uh we

actually uh you know contacted the folks

from uh uh Endon Labs uh who you know

were you know gracious enough to you

know try to gro in the real world to run

a business.

Yeah, thanks for having us. So I'm Axel

from Am Labs

and I'm Lucas and we tested Gro 4 on

vending bench. Vending Bench is an AI

simulation of a business scenario uh

where we thought what is the most simple

business an AI could possibly run and we

thought vending machines. Uh so in this

scenario the the Grock and other models

needed to do stuff like uh manage

inventory, contract contact suppliers,

set prices. All of these things are

super easy and all of they like all the

models can do them one by one. But when

you do them over very long horizons,

most models struggle. Uh, but we have a

leaderboard and there's a new number

one.

Yeah. So, we got early access to the GR

4 API. Uh, we ran it on the bending

bench and we saw some really impressive

results. So, it ranks definitely at the

number one spot. It's even double the

net worth, which is the measure that we

have on this value. So, it's not about

the percentage on a uh or score you get,

but it's more the dollar value in net

worth that you generate. So we were

impressed by Grock. It was able to

formulate a strategy and adhere to that

strategy over long period of time much

longer than other models that we have

tested other frontier models. So it

managed to run the uh simulation for

double the time and score yeah double

the net worth and it was also really

consistent uh across these runs which is

something that's really important when

you want to use this in the real world.

And I think as we give more and more

power to AI systems in the real world,

it's important that we test them in

scenarios that either mimic the real

world or are in the real world itself.

Um because otherwise we we fly blind

into some uh some things that uh that

might not be great.

Yeah, it's uh it's great to see that

we've now got a way to pay for all those

GPUs. So we just uh need a million

vending machines.

Definitely. Um and uh we could make uh

$4.7 billion a year with a million

vending machines.

100%. Let's go.

They're going to be epic vending

machines.

Yes. Yes.

All right. We are actually going to

install vending machines here. Uh like a

lot of them.

We're happy to supply them.

All right. Thank you.

All right. I'm looking forward to seeing

what amazing things are in this vending

machine.

That's that's for uh for you to decide.

All right.

Or tell the AI.

Okay. Sounds good.

Um All right. Yeah. I mean, so we can

see like Grock is able to become like

the co-pilot of the business unit. So

what else can Grock do? So we're

actually releasing this Grog if you want

to try it uh right now to evaluate run

the same benchmark as us. Uh it's on the

API um has 256k

contact length. So we already actually

see some of the early early adopters to

try Guac for API. So uh our Palo Alto

neighbor ARC Institute which is a

leading uh biomedical research uh center

is already using seeing like how can

they automate their research flows with

Gro for uh it turned out it performs

it's able to help the scientists to

sniff through you know millions of

experiments logs and then you know just

like pick the best hypothesis within a

split of seconds. uh we see this is

being used for their like the crisper uh

research and also uh you know GR 4

independently evaluate scores as the

best model to exam the chess x-ray uh

who would know um and uh uh on in the

financial sector we also see you know

the graph war with access to tools

real-time information is actually one of

the most popular AIs out there so uh you

know our graph is also going to be

available on the hyperscalers so the XAI

enterprise sector

is only you know started two months ago

and we're open for business. Um

yeah so u the other thing uh we talked a

lot about you know having gro to make

games uh video games. Uh so Denny is

actually a video game designers on X. So

uh you know we mentioned hey who want to

try out some uh uh Grock for uh preview

APIs uh to make games and Danny answered

the call. Uh so this was actually just

made first-person shooting game in a

span of four hours. Uh so uh some of the

actually the unappreciated hardest

problem of making video games is not

necessarily encoding the core logic of

the game but actually go out source all

the assets all the textures files and

and uh you know to create a visually

appealing game. So one of the core

aspect guac for does really well with

all the tools out there is actually able

to automate these like asset sourcing

capabilities. So the developers you can

just focus on the core development

itself rather than like you know so now

you can run a you know entire game

studios with game of one but with like

one person and then uh you can have gro

4 to go out and source all those assets

do all the mundane tasks for you.

Yeah. The now the next step obviously is

for Grock to uh play be able to play the

games. So it has to have very good video

understanding so it can play the games

and interact with the games and actually

assess what whether a game is fun and

and and actually have good judgment for

whether a game is fun or not. Um so with

the with version seven of our foundation

model which finishes training this month

and then we'll go through post training

RL and whatnot. um that that will have

excellent video understanding. Um and

with the with the video understanding

and the and improved tool use, for

example, for video for video games,

you'd want to use, you know, Unreal

Engine or Unity or one of the one of the

the main graphics engines. um and then

gen generate the generate the art uh

apply it to a 3D model uh and then

create an executable that someone can

run on a PC or or a console or or a

phone. Um

like we we expect that to happen

probably this year. Um and if not this

year, certainly next year. U so that's

uh it's going to be wild. I would expect

the first really good

AI video game to be next year. Um,

and probably the first uh

half hour of watchable

TV this year and probably the first

watchable AI movie next year. Like

things are really moving at an

incredible pace.

Yeah. When Gro is 10xing world economy

with vending machines, they would just

create video games for human.

Yeah. I mean, it went from not being

able to do any of this uh really even

six months ago

to to what you're seeing before you here

and and from from very primitive a year

ago uh to making

a a sort of a 3D video game with with a

few hours of prompting.

Yep. I mean yeah just to recap so in

today's live stream we introduced the

most powerful most intelligent AI models

out there that can actually reason from

the first principle using all the tools

do all the research go on the journey

for 10 minutes come back with the the

most correct answer for you. Um so it's

kind of crazy to think about just like

four months ago we had Gro 3 and now we

already have Grock 4 and we're going to

continue accelerate as a company XAI

we're going to be the fastest moving a

AGI companies out there. So what's

coming next is that we're going to, you

know, continue developing the model

that's not just, you know, intelligent,

smart, think for a really long time,

spend a lot of compute, but having a

model that actually both fast and smart

is going to be the core focus, right? So

if you think about what are the

applications out there that can really

benefit from all those very intelligent,

fast and smart models and coding is

actually one of them.

Yeah. So the team is currently working

very heavily on coding models. Um I

think uh right now the main focus is we

actually trained recently a specialized

coding model which is going to be both

fast and smart. Um and I believe we can

share with that model with you guys with

all of you uh in a few weeks. Yeah.

Yeah. That's very exciting. And uh you

know the second after coding is we all

see the weakness of Grog 4 is uh the

multimodal capability. So in fact uh it

was so bad that you know Grock

effectively just like looking at the

world squinting through the glass and

like see all the blurry uh you know

features and trying to make sense of it.

Uh the most immediate improvement we're

going to see with the next generation

pre-trained model is that we're going to

see a step function improvement on the

model's capability in terms of image

understanding video understanding and

audios. Right? It's now the model is

able to hear and see the world just like

any of you. Right? And now with all the

tools at this command with all the other

agents it can talk to uh you know so

we're gonna see a huge unlock for many

different application layers. uh after

the multimodal agents, what's going to

come after is the video generation and

we believe that you know at the end of

the day it should just be you know pixel

in pixel out um and um you know imagine

a world where you have this infinite

scroll of content in inventory on the X

platform um where not only you can

actually watch these generate videos but

able to intervene create your own

adventures.

if you just be wild. Um, and we expect

to be training our video model with uh

over 100,000 GB200s uh and uh to begin

that training within the next three or

four weeks. So, we're we're confident

it's going to be pretty spectacular in

video generation and video

understanding.

So, let's see.

So, that's uh

anything you guys want to say?

Other than that, I guess that's it.

Yeah,

it's it's a good model, sir.

Good model.

It's a good Yeah.

Well, we're very excited for you guys to

try Gro 4.

Yeah.

Yeah. Thank you.

All right. Thanks, everyone.

Thank you.

Good night.

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