Elon's Recipe for Winning the AI Race: Grok5 and Colossus

In this episode, we examine advancements in AI through Elon Musk's xAI, focusing on Grok4 Grok Fast. We discuss Musk's claim that Grok 5 could achieve AGI (artificial general intelligence) and Grok4's impressive benchmark improvements.

We highlight Grok Fast's two million token context window for enhanced efficiency at lower costs. The episode also explores the competitive AI landscape shaped by significant investments from tech giants.

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TIMESTAMPS

0:00 The Rise of Grok 5
1:41 Charting the Path to AGI
3:32 Breakthrough Techniques in AI Training
5:06 The Power of Plain Language
9:49 Grok 4 Fast: A Game Changer
14:21 The Future of AI Accessibility
18:14 Reinforcement Learning Revolution
21:58 Colossus 2 and the Energy Race
26:00 Global Investments in AI Infrastructure
28:15 Closing Thoughts and Future Episodes

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RESOURCES

Josh: https://x.com/Josh_Kale

Ejaaz: https://x.com/cryptopunk7213

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Not financial or tax advice. See our investment disclosures here:
https://www.bankless.com/disclosures⁠

Creators and Guests

Host

Josh Kale

What is Limitless Podcast?

Exploring the frontiers of Technology and AI

Josh:
It's been a big couple of weeks for elon we had a few pretty hit

Josh:
episodes last week talking about starlink talking about the ai5 chip

Josh:
and this week it's just another big breakthrough ejaz this week we're coming

Josh:
out with a lot of new grok and xai news which is pretty exciting i mean one

Josh:
of the leading headlines he said i now think xai has a chance of reaching agi

Josh:
with grok 5 never thought that before and now there's two things that kind of

Josh:
spawn this one which we'll get into a little bit later, which is the Grok Fast model.

Josh:
It is remarkable. It is a full order of magnitude better than anything else for its size.

Josh:
And it is really, really impressive. But the thing we're going to start with,

Josh:
Ejaz, is this chart that we're showing on screen right here,

Josh:
which is the single thing that convinced Elon, wait a second,

Josh:
maybe, maybe just maybe, Grok 5 could actually lead to AGI.

Josh:
And it's because we're seeing this crazy anomaly on the chart where Grok 4 was

Josh:
kind of ahead, but somehow without any new major release, Grok 4 is now way ahead.

Josh:
So Ejaz, can you explain to us like what's going on in this chart?

Josh:
How did they get so good so fast without a major new model release?

Josh:
I mean, this didn't even come from XAI, did it?

Ejaaz:
It's a good question. And no, it didn't come directly from XAI.

Ejaaz:
It actually came from two random AI researchers, one called Jeremy Berman and

Ejaaz:
the other one called Eric Pang, who tweaked Grok 4's model,

Ejaaz:
also known as fine tuning, to basically make it a hell of a lot smarter.

Ejaaz:
And so they put it to the ultimate test, Josh.

Ejaaz:
It's this thing called the Arc AGI benchmark.

Ejaaz:
And for those of you who have not been spending all your time researching benchmarks,

Ejaaz:
the Arc AGI benchmark tests how good your AI model is at being successful.

Ejaaz:
Intelligently human. What I mean by that is it presents the AI model with puzzles

Ejaaz:
that it's never seen before, that it can't possibly have been trained to solve

Ejaaz:
and sees how good it does. Now, Josh, let me ask you this question.

Ejaaz:
Before Grok 4 itself was released, what do you think the highest score was on this benchmark?

Josh:
Lower, but I'm not sure how much lower. I don't know this particular numbers,

Josh:
but maybe I'll guess five to 10% lower than what the best is now,

Josh:
kind of like an incremental improvement.

Ejaaz:
Nope nope nope it was way way

Ejaaz:
lower in fact it only scored between five to eight

Ejaaz:
percent from the top models from open ai google and all those kinds of things

Ejaaz:
and then grok 4 came yeah grok 4 came along and it broke that frontier and scored

Ejaaz:
22 percent guess how much these two models that these two random ai researchers um scored on wait

Josh:
So you're I'm looking at the screen. I'm seeing 79.6%. Is that right?

Josh:
Is this a 4X multiple on base Grokfor?

Ejaaz:
80%. And this had nothing to do with the XAI team at all. I want you to focus

Ejaaz:
on this chart that I'm showing you right now.

Ejaaz:
And look at my cursor circling around these two orange dots that are off into the distance.

Ejaaz:
You see Grokfor thinking over here, which was basically the heaviest,

Ejaaz:
most expensive model that Elon and the XAI team released.

Ejaaz:
Um when they launched croc 4 and they were just completely beaten by these two

Ejaaz:
models but i'm sure you're probably thinking josh how the hell did these two

Ejaaz:
researchers do that and um you know why aren't they being hired by elon immediately they

Josh:
Don't have the resources of a giant lab.

Ejaaz:
Like they're

Josh:
Competing against i mean if you remember these people are getting billion dollar

Josh:
offers to come work for a single employer and there's a collection of these

Josh:
so how is it that one individual it's being a collection of these people.

Ejaaz:
So these two researchers introduced two novel

Ejaaz:
ways of training their models one is called

Ejaaz:
open source program synthesis and the

Ejaaz:
other is called test time adaptations before

Ejaaz:
i get into an explanation as to how these work i

Ejaaz:
want to remind the audience that what really makes a model really intelligent

Ejaaz:
um is largely part in due to the data that it's trained on people spend so much

Ejaaz:
money i'm talking hundreds of millions to billions of to acquire the best data to train their models.

Ejaaz:
And the reason why this is so important is the model, when it's trying to answer

Ejaaz:
a question, draws on the data that it's been trained on, right? So it's hoping that

Ejaaz:
it can look back on the data that it's been trained on and find the right answer

Ejaaz:
somewhere in all of these tokens and characters, right, Josh?

Ejaaz:
These researchers decided to flip that completely on its head.

Ejaaz:
It's this thing called open source program synthesis where the model designs

Ejaaz:
its own bespoke solutions in real time.

Ejaaz:
So it doesn't even look at the data that it was trained on.

Ejaaz:
It just looks at the puzzle that it's presented with and it tries to break it

Ejaaz:
down into smaller components. So let's say the puzzle has 10 different steps

Ejaaz:
to reach to the end goal, the correct answer.

Ejaaz:
It'll break it down into 10 different little steps, whereas normally a model

Ejaaz:
would just look at the complete set of 10 steps and think, hmm,

Ejaaz:
how do I get from step one to step 10?

Ejaaz:
It just solves each step one at a time.

Ejaaz:
And that was like the massive breakthrough that they made.

Ejaaz:
And if this sounds familiar, you're probably thinking of this technique known

Ejaaz:
as reinforcement learning,

Ejaaz:
which basically has like the model like repeatedly go at a problem over and

Ejaaz:
over again this is pretty similar but it's the next step up in that field

Josh:
Okay got it yeah this this news kind of really annoyed me because of how seemingly

Josh:
simple it was i mean jeremy burman in the case of this i got some examples of

Josh:
specifically how he did it and he he was originally writing in python code but

Josh:
then he switched to just writing instructions in plain english and i think this

Josh:
is such an important thing that a lot of people forget.

Josh:
I mean, myself included, I'm speaking for myself here, that a lot of this really

Josh:
challenging, difficult work with engaging with LLMs is really just done in plain English.

Josh:
You're just writing sentences to a model in hopes that it produces a better

Josh:
output for you. It's not this crazy complex code base, although that exists deep down.

Josh:
But the way that they achieve this is actually just by writing plain English.

Josh:
So I did a little bit of digging. I have a few notes on how it works.

Josh:
And his system, it basically starts by having Grok 4. He chose Grok

Josh:
for is this model of choice it produces 30 english descriptions

Josh:
of rules to transform inputs into outputs so it

Josh:
takes that and then it tests these descriptions on training examples by

Josh:
pretending each is a test and scoring how well

Josh:
they match the correct outputs and then the top five descriptions get

Josh:
revised individually with feedback on mistakes like highlighting the

Josh:
wrong cells and stuff like that and then it combines the elements into

Josh:
the top one to create these pool descriptions so it kind of has this iterative loop

Josh:
where it tests itself it creates more examples it

Josh:
gets better data it confirms that it's the right output and that's

Josh:
generally why you see the actual outputs of this model are

Josh:
a little more expensive but the quality of it is amazing because it

Josh:
just continues to do this like self-iterative loop on itself and

Josh:
get better and better and better again all in plain english so if you are listening

Josh:
to this podcast in english you are fully capable of doing this because you speak

Josh:
the language and this isn't anything crazy it's just very refined um prompts

Josh:
that you're feeding to a model that result in these unbelievable outputs that

Josh:
are now best in the world.

Josh:
That's the coolest part to me, EJS. I don't know about EJS.

Ejaaz:
No, no, I agree. And it reminds me of Andrew Carpathy's hit tweet three months

Ejaaz:
ago where he goes, the new number one programming language

Ejaaz:
Turned out to be English. It's English. Right?

Ejaaz:
And kind of like to emphasize, again, how important this is.

Ejaaz:
This isn't just another frontier breakthrough of another benchmark.

Ejaaz:
I'm talking about the hardest benchmark that has just been 3x'd by two random researchers.

Ejaaz:
Right? This is, again, puzzles that are problem sets that an AI model has never

Ejaaz:
seen before. Typically, when you put an AI model up against a benchmark,

Ejaaz:
it has some kind of context.

Ejaaz:
Kind of think of yourself taking an exam at school or at university.

Ejaaz:
You can look at past papers. You can look at books. You kind of know what topics

Ejaaz:
they're going to talk about.

Ejaaz:
This is completely foreign to an AI model. And therefore, it is the hardest test.

Ejaaz:
So to have something achieve this almost feels like, and Josh,

Ejaaz:
I hate to say it, but I have to say it, like AGI.

Ejaaz:
And I think the fact that none other than Elon himself was taken completely aback by this.

Ejaaz:
I mean, again, to reiterate the tweet, I now think XAI has a chance of achieving

Ejaaz:
AGI with Grok5, never thought that before.

Ejaaz:
And the fact that he is now saying, hey, by the way, Grok5 starts to train in

Ejaaz:
a few weeks and you know what?

Ejaaz:
I think it's going to be out by the end of this year.

Ejaaz:
I think just speaks to the importance of this development.

Josh:
Yeah, I think one of the things that was really startling for me was the realization

Josh:
of how little resources it takes to get so good. And then I was wondering, well, why?

Josh:
Clearly, this isn't anything super novel, although they did do some unique training frameworks.

Josh:
And I think the reason that I, the conclusion that I came to was just scale.

Josh:
I mean, the cost per query, the cost per token of these new super high end models

Josh:
that just came out is very high.

Josh:
And you can't really scale that to a lot of people because the companies are

Josh:
just resource constrained.

Josh:
So it leads me to believe and leads me to think, well, what happens when a company

Josh:
with a lot of resources dedicates all of their brainpower to this specific type

Josh:
of reinforcement learning, like we're going to see with Grok5,

Josh:
and they do so in a way that's compressed enough, that's efficient enough to

Josh:
actually run it at scale on the servers without melting everything down without

Josh:
charging $1,000 a month per membership.

Josh:
And I think that's probably what we see with Grok5 is this new juiced up reinforcement

Josh:
learning, but efficient and actually built for scale.

Josh:
I mean, even if it just launches at the specs of these two individual researchers,

Josh:
that's a huge win because that's an incredible model.

Ejaaz:
Yeah. And it's open source. It's open source and available for everyone.

Josh:
It's pretty remarkable. Yeah. So I think very interesting things coming.

Josh:
If I was a betting man, I would be betting big on Grok5. I think they very much

Josh:
see a solution that people really want.

Ejaaz:
I was just thinking about why

Ejaaz:
both of us are finding this development both amazing, but really annoying.

Ejaaz:
And I think it's because to some degree

Ejaaz:
we both believe that in order for AI models today

Ejaaz:
to get to AGI we would need to completely re-architect

Ejaaz:
how they're designed you know transformers was

Ejaaz:
the big breakthrough that's why models that we know and use today

Ejaaz:
are so smart but it's not as

Ejaaz:
smart as we expected it and there was this kind of like lag of improvement and

Ejaaz:
now we suddenly see a 3x improvement where this model is kind of breaking this

Ejaaz:
leading benchmark and so now I I think I'm starting to believe that maybe if

Ejaaz:
we invest hundreds of billions of dollars in the post-training part,

Ejaaz:
where typically we've been investing in the pre-training, in the compute,

Ejaaz:
but if we invest it in the post-training, we may clearly reach AGI before redesigning

Ejaaz:
the entire thing up front.

Ejaaz:
Does that resonate with you, Joshua? Or am I just, do I sound crazy?

Josh:
No, it does. It does. It's funny because we frequently record the show and you

Josh:
expect to be surprised and then something happens.

Josh:
You're like, oh my, I wasn't expected to be surprised in that way. and this

Josh:
is one of those things where i mean i wasn't expecting to see a new leader in

Josh:
between major model releases from an independent researcher so the fact that

Josh:
this is even possible really just blows the doors off of a lot of expectations

Josh:
i had and this isn't even the only interesting news this week from the xai team

Josh:
because they released new model alert grok for,

Josh:
fast let me tell you each as when i saw how this model worked i was like whoa

Josh:
this is um again blown away super.

Ejaaz:
Impressed can you run us through some of the highlights please

Josh:
This spec sheet yeah so first of all the leading headline

Josh:
two million token context window is outrageous i think the current leader is

Josh:
google with the gemini model they have 2.5 pro and flash both of them i believe

Josh:
have a million tokens uh this is two million tokens of context for those that

Josh:
aren't aware context is the basically active memory of a language model it's the more context you can,

Josh:
collect, the more clarity it has into the actual data that it's talking about

Josh:
and conversing with, you want that number to be bigger.

Josh:
This is the biggest by far, by a doubling.

Josh:
So that's a really important headliner. The second one, probably even more outrageous,

Josh:
47 times cheaper than ROC4.

Josh:
Which is crazy because when you look at it on

Josh:
the scale below if you can scroll down just a little

Josh:
bit grok 4 is right in line with every other great

Josh:
model it is a grok 4 fast is just beneath

Josh:
o3 it's above deep seek it's above cloud 4 sonnet it's a above cloud 4 opus

Josh:
it's just like this remarkable model that is better than a lot of the leading

Josh:
models but 47 cheaper than the base model and i think that's going to be a pretty

Josh:
interesting thing when we get into like scaling these models and using them for code.

Josh:
EJ, as we talked last week about how good the Grok model is for coding because

Josh:
it was so cheap and so effective.

Josh:
This is another case of that. And the way they did that, I was so interested

Josh:
in how they were able to come up with like the secret sauce to do it.

Josh:
And basically what they did is they taught to model to spend its brainpower

Josh:
only on tools when it helps.

Josh:
So they use this like large scale reinforcement learning to train Grok for fast

Josh:
to choose when to think in depth and when to answer questions quickly.

Josh:
So what that resulted in was about 40% what we're seeing here on the screen.

Josh:
40% fewer thinking tokens on average than we've gotten from the previous model,

Josh:
which is a significant difference. Oh, and by the way, it's number one on Elmarina.

Josh:
So this was crazy. EJs, what were your reactions when you saw the team drop this?

Ejaaz:
I already thought these tokens were cheap. I thought these models were cheap enough.

Ejaaz:
Do you remember when OpenAir released GPT-5? They kept flexing GPT-5 mini saying,

Ejaaz:
hey, you now have the power of our previous best model, but actually it's more intelligent.

Ejaaz:
And it's like, I think it was something like five times cheaper.

Ejaaz:
And I was like, holy shit, holy crap. I was like, that is like crazy magnitude.

Ejaaz:
And now it's like, now we've got 47X cheaper than Grok 4.

Ejaaz:
Grok 4, by the way, was already cheap compared to some of the Frontier bottles.

Ejaaz:
So I don't know how far this can go, but kind of zooming out,

Ejaaz:
I have never been more confident that,

Ejaaz:
than now that cutting edge super intelligence will be available for anyone and everyone.

Ejaaz:
This isn't going to be some kind of closeted technology where only the rich

Ejaaz:
can buy devices and run it.

Ejaaz:
I think anyone and everyone will have fair game access to this.

Ejaaz:
And think about the dynamics that that changes up, Josh.

Ejaaz:
Like you can have someone in the complete middle of nowhere with a cell phone

Ejaaz:
attached to Elon Musk's new 5G Starlink satellite that's beaming down to him.

Ejaaz:
And he could kind of produce something that the world ends up using because

Ejaaz:
he has access to this cheap model that is actually super intelligent and can

Ejaaz:
be used to create whatever crazy invention that he has or she has that dreams

Ejaaz:
up. I just think this is insane.

Josh:
Yeah, the efficiency improvements are the thing that's always most exciting

Josh:
to me because, I mean, as we get more cheaper tokens and as the tokens become

Josh:
more portable and lightweight, I mean, you could have the world of knowledge

Josh:
on your phone even without necessarily an internet connection because these

Josh:
models are getting so lightweight, so condensed.

Josh:
Um so effective it's like it's really it's

Josh:
unbelievably impressive and what i was really interested in

Josh:
is comparing this to the other models because i know google was

Josh:
kind of doing a similar thing they were leading along the frontier and oh yeah

Josh:
here's this post from gavin baker that i love because it shows how google has

Josh:
kind of dominated this thing called the pareto frontier and on the chart you

Josh:
can very clearly see how there's this kind of arc that hugs the outer bounds

Josh:
of all of the models and it shows that like gemini pro has been really good on a few

Josh:
So I briefly want to just talk about the Pareto Frontier concept because it's

Josh:
really interesting and it will explain to you exactly why Grok4Fast is way out there.

Josh:
It totally shattered what it is. So, I mean, basically, it's funny.

Josh:
I was doing a little bit of research on this and the Pareto Frontier is done

Josh:
by an Italian economist named Velfredo Pareto.

Josh:
So I just thought that was a fun fact because great name. Basically,

Josh:
it comes from the economist and decision theory.

Josh:
And it's a way to decide optimal trade-offs when you have multiple objectives

Josh:
you're trying to achieve all at the same time. So imagine you're trying to optimize

Josh:
two things that might conflict a little bit, like you want to make a product

Josh:
as powerful as possible, but also inexpensive as possible, like these models.

Josh:
So in this scenario, there's a set of best possible solutions where you can't

Josh:
improve one aspect, like the power, without making the other aspect, like the cost worse.

Josh:
And what we're seeing in this chart here is Google has made a series of those

Josh:
decisions, those tradeoffs that

Josh:
have led to the absolute Pareto optimal outcome along this outer band.

Josh:
What grok has done is they actually made a new trade-off

Josh:
that isn't necessarily a trade-off it's more of an innovation that allows

Josh:
them to unlock this perceived frontier this limiting factor

Josh:
that was on the outer band and just shatter it and create a new pareto

Josh:
optimal trade-off using these best things and they did that by doing a lot of

Josh:
magic but basically what they have now is they have a really smart model that

Josh:
actually sits above gemini 2.5 flash and not too far below the pro model but

Josh:
it is a order of magnitude cheaper and i think that's where that outlier that

Josh:
cost effectiveness is really unbelievable when it comes to,

Josh:
um distributing these tokens widely so now if you're writing code if you're

Josh:
creating an application if you're just if you're paying for tokens this is very

Josh:
clearly the model you want to use.

Ejaaz:
What you just described is elon and

Ejaaz:
xai literally charting a new path which

Ejaaz:
is kind of like um very behavioral of elon in general um and another thing that

Ejaaz:
i thought was really cool about this is the reinforcement learning infrastructure

Ejaaz:
team was kind of key behind getting this model as fast and as cheap and as efficient

Ejaaz:
as we're describing it, right, Josh?

Ejaaz:
They used this kind of like agent framework, which was extremely compatible

Ejaaz:
with the infrastructure that they used to train and iterate on this model in the first place.

Ejaaz:
And what I wanted to point out here is there's a theme between the two topics

Ejaaz:
that we've discussed so far on this episode, Josh.

Ejaaz:
Number one, when we described the two models that the researchers created that

Ejaaz:
broke the ArcGIS benchmark, they specifically used a technique which used reinforcement

Ejaaz:
learning, a new reinforcement learning technique.

Ejaaz:
And the reason, if you remember, why Jeremy Berman picked Grok4 specifically

Ejaaz:
was he said it was the best reasoning model because in the way that had been

Ejaaz:
trained via reinforcement learning.

Ejaaz:
And now we're seeing yet again, this GrokFast model achieving what it can because

Ejaaz:
of reinforcement learning.

Ejaaz:
So I'm seeing a theme or noticing a theme here where XAI and Elon are basically

Ejaaz:
the leaders in reinforcement learning,

Ejaaz:
which i think is going to probably play in their favor maybe it's a hint that

Ejaaz:
the models that are going to be closest to agi that are the quickest that are

Ejaaz:
the cheapest are embedded in reinforcement learning techniques that are just

Ejaaz:
completely breakthrough

Josh:
Yeah it seems like they the team really reasons

Josh:
i mean this is a core elon uh notion but

Josh:
like they really do reason from first principles and what's important and what matters and

Josh:
you're seeing that throughout the entire product as they advance and

Josh:
i think what's really exciting what i'm most stoked about um

Josh:
for this show in general is is to compare this

Josh:
next round of models like will gemini 3

Josh:
and grok 5 like how are they

Josh:
going to compete with each other because those are both going to be remarkable

Josh:
models and it seems to me like those are like those are currently the top dogs

Josh:
i mean as far as gpt5 was kind of a little bit of a miss anthropic's been a

Josh:
little bit quiet uh gemini and xai are on fire but this also there was there

Josh:
was one last thing of news before we sign off today well i.

Ejaaz:
Was I was gonna say, like I'm highlighting this sentence here for those who are just listening.

Ejaaz:
And it says, you know, we built this reinforcement learning infrastructure team

Ejaaz:
with a new agent framework to help train Grok4 fast.

Ejaaz:
But specifically so that we can harness the power of Colossus 2.

Ejaaz:
And if I remember correctly, Josh, there was some breaking news around Colossus

Ejaaz:
2. Elon was getting into some fights. Can you walk us through it?

Josh:
Yeah, it's funny. There was this whole report from SemiAnalysis,

Josh:
which does a really great job. I would highly recommend checking them out.

Josh:
And they released this report on the XAI data center build out.

Josh:
And it was so funny to see, because a lot of times you just see satellite pictures

Josh:
or you read headlines and you're not really sure what's going on.

Josh:
The sole purpose of SemiAnalysis is to actually have boots on the ground,

Josh:
check the satellite images, and look at it with a scientific engineering point

Josh:
of view where they actually understand what is going on.

Josh:
And they shared their findings in one of these articles. And I found one of

Josh:
these stories was so funny because it's such a testament to how the XAI team

Josh:
works, where they were having problems with their energy generation in Memphis,

Josh:
Tennessee, because people were complaining and they were having a tough time getting permits.

Josh:
And the core crux of every large AI data center is energy.

Josh:
So they were like, this is unacceptable. well, we need energy immediately.

Josh:
So what do they do? Well, they jumped over the state lines.

Josh:
They went over to Mississippi a couple of miles down the road and they built

Josh:
these new generators right down the road across the state line.

Josh:
They got the permits they needed.

Josh:
They said, we don't, you don't want us, Tennessee. We'll just go right over to Memphis.

Josh:
You could see here, they took the power lines, they ran them back into Tennessee

Josh:
and now they're powering the data center.

Josh:
And part of the article was, was this funny story, but part of the article also is,

Josh:
is colossus 2 um being built

Josh:
in the sheer scale that colossus 2 is going to be and it's going

Josh:
to be over a gigawatt of energy um which

Josh:
is i don't know how many hundreds of thousands of homes is

Josh:
going to power but this is like a remarkable amount of power and a tremendous

Josh:
amount of gpus and they're planning to make these all coherent and they're using

Josh:
them exclusively i believe to train this new grok 5 model so as this new training

Josh:
center comes online they will be using this new cutting-edge world's largest

Josh:
supercomputer to train the world's perceivedly best model.

Josh:
But I found this funny because the day that this article came out,

Josh:
there was another post from another CEO of a very prominent company saying,

Josh:
hey, wait a second, we have something a little bit bigger.

Josh:
Than Colossus 1 currently. And that was from Microsoft's CEO, Satya Nadella.

Josh:
And he had this post where he said they just added over two gigawatts of new energy capacity.

Josh:
So Ejaz, this is just a really crazy brawl between these people who are building

Josh:
larger and larger AI data centers.

Josh:
And it eventually leads to the big news that just dropped a little earlier today.

Josh:
But do you have any commentary before we get to the huge number?

Ejaaz:
Yeah, there's actually one thing I wanted to point out, which is when Elon first

Ejaaz:
announced that he was building out this Colossus 2 data center,

Ejaaz:
it made headlines that it cost $20 billion.

Ejaaz:
And everyone thought it was crazy. Everyone was yelling, this is an AI CapEx bubble.

Ejaaz:
There is no products that prove that all this investment makes sense.

Ejaaz:
And now you have Satya Nadella, CEO of Microsoft, announcing that he's probably

Ejaaz:
going to be investing twice as much of that to build two gigawatts of new capacity.

Ejaaz:
Again, validating that there is a need for energy and compute to train these new models.

Ejaaz:
Don't forget that Microsoft last week acquired a random European data center for,

Ejaaz:
I think it was about, what, $10 billion, which caused its stock price to 3x

Ejaaz:
because it itself wasn't worth that much at the time of the reporting happening.

Ejaaz:
And then it leads us to the even bigger announcement, which released this morning,

Ejaaz:
which is NVIDIA will be investing not one,

Ejaaz:
not 10, not 20, but $100 billion in OpenAI over the next couple of years.

Ejaaz:
And you might be asking why?

Ejaaz:
Well, it's because OpenAI is going to be investing in so many data centers that

Ejaaz:
is going to produce so much power. I don't know how many gigawatts.

Ejaaz:
I think it's actually 10 gigawatts which is 10x colossus 2 5x um uh fair water

Ejaaz:
which is satya nadella's thing for all my mathematician mathematician fans out

Ejaaz:
there it is just crazy josh are we in a bubble or is there a need for all of this

Josh:
So here's the thing as i keep going back

Josh:
and forth about the bubble conversation because a hundred billion

Josh:
dollars is such an outrageous amount of money to spend on making

Josh:
what is already a remarkable language model even more

Josh:
remarkable um like the product is great and

Josh:
at least me personally as a user of these

Josh:
products i'm definitely getting closer to a wall of things that i use them for

Josh:
where if a model is marginally smarter my experience doesn't get that much better

Josh:
um but i was so that's like one school of thought and then the other is thinking

Josh:
well this is probably the only thing we'll ever need to spend money on going forward ever.

Josh:
So it makes sense to throw all of it at it now.

Josh:
Because in the case that you do solve AGI, you get hyperintelligence,

Josh:
it solves all of your problems. And it gives you the better questions to ask

Josh:
in order to solve better problems. So it really...

Josh:
It would appear, assuming that we continue on this trajectory of improvement,

Josh:
that it makes sense to take every disposable dollar you can to get better and better compute.

Josh:
And this will probably just extend forever. As we are able to harness more energy

Josh:
from the sun, from nuclear energy, a lot of that new energy and compute will

Josh:
just go to making better AI, which will then serve better downstream effects for how society works.

Josh:
So is it a bubble on the long term? I think absolutely not on the short term.

Josh:
I don't know. Where do you get the revenue from?

Josh:
I don't know. I mean, it's a ton of money, but you know what?

Ejaaz:
I think the reason why you and I feel this disassociation between the amount,

Ejaaz:
how large these numbers are in investing in infrastructure versus what we're

Ejaaz:
actually seeing is we're not going to be seeing AGI before some other fields

Ejaaz:
or some other professions see it first, right?

Ejaaz:
The clear example is coding. Coding has just

Ejaaz:
been on an absolutely exponential improvement rate

Ejaaz:
that has beaten out any other ai feature ever you now

Ejaaz:
have ai models that can code as well

Ejaaz:
as a senior staff engineer which is getting paid like

Ejaaz:
300 to 500 a year right um so my guess is this investment is worth it um my

Ejaaz:
guess is the investment is going to come to fruition in professions in use cases

Ejaaz:
in jobs that we won't see but we'll maybe talk about or see the kind of like effects.

Ejaaz:
Maybe it's in science where we create a new drug that cures cancer or whatever that might be, right?

Ejaaz:
I think different types of professionals will see AGI and reap the rewards of

Ejaaz:
this investment before average consumers see it.

Ejaaz:
And then I think the other thing that I want to mention, Josh,

Ejaaz:
is this isn't specific to US or Western spending.

Ejaaz:
In fact, our foes over the seas in China or in Asia have been working on this

Ejaaz:
for like the last five years.

Ejaaz:
They've been building out massive data centers, which I think has like build

Ejaaz:
up an aggregate of like 300 gigawatts over the next five years, at least.

Ejaaz:
And they've been investing in this so heavily. So it's not just a Western thing.

Ejaaz:
It's an Asian thing as well. China is investing so heavily in

Ejaaz:
this if this is a bubble if we are completely

Ejaaz:
wrong this will be the uh biggest most highest profile l that the world has

Ejaaz:
taken it's not just going to be a us thing it's not just going to be a map something

Ejaaz:
it's not just going to be a sam altman thing it's going to be an everyone's

Ejaaz:
involved type of thing kind of like world ending event yeah

Josh:
Too big to fail so i i do love

Josh:
this incentive structure where everyone is incentivized to make it work because

Josh:
everyone's equally at risk in terms of their exposure to the technology so that

Josh:
i think i could be happy to sleep at night where at least u.s and china are

Josh:
aligned in one thing in which they want to achieve agi they want the smartest

Josh:
models they're going to make their money pay off the best they can so,

Josh:
All the power to them. But I think, is that a wrap for us today,

Josh:
EJ? We got anything else?

Ejaaz:
That is a wrap,

Josh:
Josh. That's it. That's a wrap on our little like XAI mini episode.

Josh:
There was one fact that I wanted to just do a little like fun fact check, which is a gigawatt.

Josh:
And according to Grok, it powers approximately 750,000 to 850,000 average US homes per one gigawatt.

Josh:
So the scale we're talking is like a tremendous amount of gigawatts.

Josh:
I mean, this NVIDIA project is 10 of those, which means that's about,

Josh:
I mean, on the high end, eight and a half million U.S.

Josh:
Homes can be powered by a singular data center. So we're going to hope this works out.

Josh:
I think right now it seems like, I mean, Grok is cooking. The XAI team is on

Josh:
fire and they are in between models.

Josh:
I cannot wait until they get this new Colossus training cluster up or even Microsoft's.

Josh:
I mean, Microsoft's got a huge cluster.

Josh:
What are you doing with it, dog? Like, let's see. Let's see your stats.

Josh:
Let's see your numbers. Put a number up on the ArcGIS leaderboard. um

Josh:
but yeah i think that's that's a wrap on all the fun exciting new

Josh:
things about xai the comment section is by energy stocks yeah

Josh:
by energy stocks um i we read

Josh:
all the comments i read every single comment i try to reply to them too so i

Josh:
would love for you to share either what you think about the show or or who

Josh:
you think is winning this ai race currently do you like are

Josh:
we just kind of like do we have elon derangement syndrome are

Josh:
we just kind of like obsessed with everything he builds or is this it feels

Josh:
like it's pretty grounded i feel like we have some good examples about how well

Josh:
they're doing so i'd love to hear if you agree or disagree that

Josh:
would be a fun little thing for the comments but anyway that's a wrap on today's episode

Josh:
we have a couple more exciting ones coming this week so buckle up uh

Josh:
the next one the next one coming i think ejaz myself

Josh:
and we might even have a guest for that episode we'll be probably in an all-out

Josh:
brawl it's good that we're recording remotely because we might like the blood

Josh:
could possibly be drawing so yeah buckle up for for that one there's a lot to

Josh:
look forward to this week but that's it for this week this episode so thank

Josh:
you so much for watching as always please don't forget to subscribe like comment,

Josh:
all the fun things, share it with your friend.

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