00:00:00
I'm here at Nvidia's annual GTC conference and I'm going to interview
00:00:04
four amazing AI CEOs. Stick with us. >> Our episode is sponsored by the New York
00:00:17
Stock Exchange. Are you looking to change the world and raise capital? Do it at the NYSE. The NYSE is a modern
00:00:25
marketplace and a massive platform built for scale and long-term impact. So if you're building for the future, the NYSE
00:00:33
is where it happens. >> One of the great companies of the AI era is of course Cororeweave. They're
00:00:41
building massive infrastructure for these hyperscalers. And in some ways, Michael Intrader, welcome to the
00:00:47
program. You're the original hyperscaler. you guys got in very early and secured your I don't know which GPUs
00:00:56
you wound up getting but you were very early to this trend. How did you get to it so early and how did you build out
00:01:04
this you know first I guess at the time neocloud? Yeah. So we we didn't really
00:01:09
start it as a Neocloud and I I uh I was uh running an algorithmic hedge fund uh focused on natural gas and uh when when
00:01:19
you build an algorithmic hedge fund um once the algorithms are built you're really just monitoring it and testing
00:01:25
different uh thesises and doing all that. But there's also a lot of downtime
00:01:29
and we got super interested in crypto. Um, and you know, we're pretty nerdy. We
00:01:35
kind of dig under the hood and we started to get interested in the security layer. Uh, we looked at Bitcoin
00:01:40
and the mining for Bitcoin and we didn't like it. We just thought that like there's some brilliant engineer that
00:01:46
built the ASIC and they're probably going to be better at running it than we
00:01:49
are. So, we really began to focus on the GPUs mostly because the GPUs were you can mine Ethereum with them. uh but you
00:01:58
could also do all these other things and really so right from the start we looked
00:02:02
at the compute as an option to be able to deploy our computing power to different use cases and so you know
00:02:12
began the company in 2017 uh you know um spent the first kind of three years mining crypto went through a couple of
00:02:21
crypto winters um because we had come from a hedge fund were, you know, we we have real chops in risk management and
00:02:29
how we think about uh capital and risk exposure and allocation and all of that. And so we were really careful around
00:02:35
that right from the start. So we weathered crypto winter really well um and began to scale the company and
00:02:41
immediately started to look for other use cases that you could use this compute for because crypto was pretty
00:02:47
volatile. >> Yeah. And crypto was a question mark at that time. >> Absolutely.
00:02:51
>> Yeah. I mean Bitcoin was speculative and there were many other specular projects.
00:02:55
the only other people using this type of hardware quants >> medical researchers.
00:03:01
>> So a good way to think about it is like the progression of products that we kind
00:03:05
of started to work on. You know first was uh um crypto but we immediately moved from crypto to CGI rendering and
00:03:12
we built projects that would allow uh um folks that were trying to animate and render images um you know kind of what
00:03:22
makes the movies cool, right? and and uh we started to work on that and then we moved to batch computing and started to
00:03:27
look at medical research and different ways of using the compute to be able to drive science. Um, and we just kind of
00:03:33
kept moving up the stack in terms of complexity uh uh on how GPUs could be used. And ultimately uh in like call it
00:03:43
like 2020 2021 we started to really try to figure out how you can go ahead and use GPUs for neural networks and that
00:03:52
was not something that uh we knew how to do. Um, and so we actually went out and
00:03:56
bought a bunch of A100s and donated them to a uh a group that was working on uh a
00:04:02
Luther AI. They were working on an open- source project with the thought that um
00:04:07
these guys are taking the GPU compute because we're donating it. They can't
00:04:13
really get pissed at us if we're not very good at it initially. And uh that worked out really well because
00:04:17
>> they can't complain about the SLA. >> They they kept telling us like we need
00:04:20
more of this, you got to work on this. And that began to really uh uh give us an understanding of what was necessary
00:04:28
to run scale parallelized computing. And uh you know that that uh um we went through it. I I I kind of feel like
00:04:35
buying those initial GPUs was the tuition we paid to learn how to run this business. And then one of the
00:04:41
interesting things is all of those guys went back to their day jobs because they
00:04:45
were all volunteers working on this. They were like-minded scientists. And when they got to their day jobs, they
00:04:51
were all like, I want that infrastructure. It's built the right way. That's the way that researchers are
00:04:56
going to want to use it. And that launched our our business. It was an amazing story. And
00:05:00
>> so you went from crypto to these researchers into academia and deep research. What's the next card to turn
00:05:07
over in the poker game? >> Yeah. So, so um what became very clear to us very very early on was that the
00:05:15
scaling laws were going to drive and remember this is really back in the you know 2020 2021 before uh uh chatgpt
00:05:24
moment occurred and we began to understand that like computing decommoditizes at scale right like when
00:05:31
when you know anybody can run a GPU but can you run a cluster that's large enough to train a model that can change
00:05:37
the world and that's a different question. And so we really began to think about like how do you go about
00:05:44
scaling up your delivery of this computing to clients, larger and larger clients. And that was the next card to
00:05:51
turn is to think about it from a okay, you know, there's a component of this
00:05:55
that is going to lean into uh our ability to access the capital to be able to deliver our solution to the broadest
00:06:04
possible audience to the most sophisticated consumers of this compute. And and that was really the next card is
00:06:10
thinking about it as a business rather than as a engineering project to be able to deliver the the uh uh the
00:06:17
infrastructure and the software and really everything between you know when you when you're thinking about what we
00:06:22
do, we kind of live above the Nvidia GPUs but below the models. Yeah. and everything in there, all the software,
00:06:30
the integration of software and operations and uh observability and all the things that you need to be able to
00:06:36
build uh a cloud that's purpose-built for this one specific use case, right?
00:06:42
So, we don't we don't do everything. We really focus on one use case which
00:06:46
allows >> you want to do web servers different you got AWS, >> you know what they do a great job. It's
00:06:50
like it's a it's a great solution. It was a brilliant solution to solve a
00:06:53
problem. We just looked at it and said there's a new problem and let's go about
00:06:57
let's go about looking at this problem and try and come up with a solution to
00:07:01
deliver compute that solves that problem. >> And when did the language model start
00:07:05
dialing and calling you for you know capacity? >> Yeah. So uh our our our first uh um well
00:07:13
our our first language model was really a Luther. Um but uh our our first like large commercial uh was inflection. Um
00:07:22
and so you know we work with Mustafa and and and and Inflection and then we we really diversified from there uh into
00:07:31
the hyperscalers into you know uh open AAI across the the the the model uh the foundation models across um you know and
00:07:41
and just kept scaling and scaling with the belief that you know once again the the the decommoditization
00:07:48
of compute the ability to to deliver a solution and the solution is building supercomputers that can change the world
00:07:57
and that's really what we began to focus on. That was the lead into training and
00:08:01
now the world has gone through, you know, this this moment where we've moved
00:08:05
from research into the productization of this. It's it's it's beginning to work
00:08:11
its way in from the the uh the fringe of organizations into the core of what they
00:08:16
do. And you can see that every day in the uh in the amount of inference compute that is being driven through you
00:08:24
know our uh infrastructure layer which is just massive which is just like one of the shows you people are consuming it
00:08:31
not just building models but they're deploying them and and utilizing them. >> I always think of inference as the
00:08:37
monetization >> of the investment in artificial intelligence. So when when when we when
00:08:44
we see our compute being used uh uh to stand up the massive scale of inference that's hitting our compute every day and
00:08:52
like you know inference is when people ask the model a question it comes back with an answer that's an inference or
00:08:58
when you ask the model a question and then to go do something that's inference
00:09:02
right and that's actually where you're you're you have the opportunity to
00:09:06
really drive value outside of the model itself but into the real world and that's really exciting for us. That's
00:09:15
what we like to watch. That's what I like to watch in terms of gauging the health.
00:09:19
>> What chips are those? >> Um so so really uh you know we are we are the tip of the spear in bringing um
00:09:28
the new architecture uh out of Nvidia uh into uh um into commercial production at
00:09:35
scale. Yeah. And uh so when when you know we were the first ones to bring the H100s at scale, we were the first ones
00:09:41
to bring the H200s at scale, first ones with the GB uh 200s, and now you've got
00:09:46
the GB300s. And one of the things that's that's that's amazing and really
00:09:50
fascinating for us is is you know people are using the bleeding edge GPUs to train models as the new architectures
00:09:58
come out and then they take those GPUs and they move them into different experiments and then over time they move
00:10:06
them into inference and they continue to use them in inference for a very very long time.
00:10:11
>> What is the shelf life of a 100 right now? That's been a big debate is I think
00:10:16
for your company for Microsoft and I guess Michael Bur you know who you must have known when you were a quant you
00:10:23
know saying oh my god the whole industry is the sky's falling and then we all
00:10:27
know in the industry that people don't just throw this hardware away that they
00:10:30
find uses for it the street finds its own use for technology so what's the reality of the lifespan of these things
00:10:36
>> so so my my take on the the uh uh the GPU uh depreciation bait is that it's
00:10:43
nonsense Right. It's a debate that is being uh brought to the forefront by uh
00:10:48
some traders that have a short position in the stock and they're trying to uh
00:10:52
talk down. Look, here's what we know, right? Um when when we buy infrastructure, we're a
00:10:59
success based company, right? We're a small company on a relative basis compared to the enormous companies that
00:11:04
we're competing with. And so they come our clients come into us and they buy
00:11:09
compute for five years, for six years. Our average contract is 5 years. So any commentary by anyone either inside or
00:11:18
outside of the industry that this stuff becomes obsolete in 16 months or whatever nonsense they're spewing, it's
00:11:23
it doesn't it doesn't in any way match up with the facts on the ground. The
00:11:28
facts on the ground is they're buying it for 5 years. Right? If and my approach
00:11:32
to this has always been if people are willing to pay me for it, >> it still has value.
00:11:38
>> Correct. >> Pretty simple way of of approaching it. We use a six-year depreciation. Um, we
00:11:43
believe that the GPUs will last in excess of six years, but we felt like that was a fair and reasonable approach
00:11:49
to a technology cycle that's moving at this velocity. Um, the A100s, the ampers
00:11:55
this year, the price has appreciated through the year. >> And why is that? I I think it's because
00:12:00
one of the things that happens is as more installed capacity becomes available, you have new companies that
00:12:08
come into existence that have new use cases that have different size models that are trying to uh build new
00:12:14
commercial ventures that maybe have been blocked out of the H100s and never had an opportunity to run on that. I mean to
00:12:20
make a very simple example for the audience like when you trade in your iPhone after 3 or 4 years you're like
00:12:27
who's going to use an iPhone 12 and it's like have you been to South America or
00:12:32
Africa where you go to the store and you buy an iPhone 12 or you buy the Pixel 7
00:12:38
and it costs $50 that's still got great life left in it. >> Absolutely.
00:12:42
>> Yeah. you know, >> and so look, you know, we we find these amazing use cases, new companies that
00:12:48
have come into existence or existing companies that have integrated new models into their workflow that are able
00:12:55
to use the Ampierce and so they keep buying any GPUs that we have available. And once again, you know, the the
00:13:03
concept that a GPU >> is no longer relevant or commercially viable after 16 more 18 months or two
00:13:10
years. >> Yeah, that's it just it just doesn't make sense. >> It's obviously far. I think sometimes
00:13:14
people get caught up in Moore's law or in just how fast our industry is growing
00:13:19
and that there's so much at stake that big companies are demanding the most recent products. That doesn't mean that
00:13:27
the lifespan has gotten shorter. It means the opportunity and the surface area of the opportunity has gotten much
00:13:33
larger. >> Yeah. Uh one of the things is is like you know the the uh the the industry has
00:13:40
gotten so much attention for the unprecedented scale of capital that is coming to bear on this. And
00:13:49
because of that, there tends to be a incredible focus on the companies that are building on these
00:13:57
most advanced chipsets. And the truth of the matter is is you know even within those companies they have a long tale of
00:14:05
useful life >> to provide inference horsepower to work on other experiments to do less bleeding
00:14:12
edge activity but still needs to be done >> and yeah I mean rendering comes to mind
00:14:16
as well or yeah we're making images on nano banana like there there will be a
00:14:21
use for it. There is a moment in time where maybe the compute to power ratio doesn't make sense. My my expectation is
00:14:29
is obsolescence will be defined by the moment in time where the power in the data center for me will be able
00:14:41
to be repurposed for a higher margin than the existing infrastructure provides. And you know, like I said, I I
00:14:49
fully expect this infrastructure to last in excess of 6 years, but the the the standard in the in in in the space has
00:14:56
really been used with one exception, which is Amazon, which is Yeah, it's 6 years. That's that seems like the right
00:15:02
schedule. I'm not making it up. That's what everybody's using. >> Yeah. And the energy cost is the
00:15:09
opportunity because hey, it's just a we need that space. there's a better uh
00:15:14
reward here and that might get resold that hardware to somebody else who wants it a hobbyist or something. It's
00:15:20
available >> and or it could be sent someplace else where they have more capacity when they
00:15:25
can repurpose it there. But I I I um I kind of feel like, you know, we'll we'll
00:15:29
deal with that part of the business when we get there. What I know right now is it is extraordinarily profitable. It's
00:15:36
very creative to my company to continue to keep the infrastructure that's been
00:15:41
up and running, that's been on these long-term contracts, and as it rolls off, as it's been in use for 5 years,
00:15:47
you know, as it becomes available, I am still able to sell it at a higher price than it was at a year ago. There's
00:15:53
competition now. When you were buying these from Jansen back in the day, yeah, you could buy them and have them
00:15:58
shipped, I would assume, within 30 days or less. nowadays what's the weight like
00:16:04
even for you a loyal old customer and is there a bit of a battle is there politics to who gets the servers like I
00:16:12
you see some like very big names talking about they got to get an allocation is it still a little bit crazy what's it
00:16:18
like to be in that category having to buy something everybody wants >> look uh you know I I uh I I think of it
00:16:25
as an affirmation of the business that we're in right like the fact that we are
00:16:29
attracting competitors the the means that the business is healthy and there's
00:16:33
a lot of people trying to deliver this service because the need for this infrastructure the need to integrate the
00:16:40
infrastructure you know into the software layers to deliver it to artificial intelligence uh either at the
00:16:46
model level or the inference level or the application level or whatever you know level of the five layer cake that
00:16:52
Jensen's you know focused on the the fact that there are more people coming into this it doesn't discourage
00:16:58
me um as far as getting access to the GP CPUs, we show up like everybody else with a um you know, we'd like to buy
00:17:06
here's a PO and we're ready to pay. Um the one what's the wait time like? And
00:17:11
is it just really competitive or not? Because I talked to Jensen about he said I said, "How do you manage all these
00:17:18
like big egos and names and companies trying to buy stuff?" And he said, "Well, they order it and we give it to
00:17:24
them in the order in which they order it." >> Is it really like that?
00:17:27
>> It really is. Right. like you know he he doesn't want to be in the position of
00:17:33
playing favorites or all like that that just seems like a bad place to be with your clients
00:17:37
>> or auctioning them off. Can you imagine? >> Yeah, that would that that
00:17:41
>> that would be crazy. >> Yeah. I don't I'm not sure that would be good
00:17:45
for the long-term business. No. Yeah. So, so our our our approach is >> you might get some sovereigns coming in
00:17:50
and saying I'll pay double. They do that with Ferraris too sometimes. >> I guess these are the Ferraris of
00:17:56
computing, right? >> In a way they are. Yeah. Bugattis. Our >> our our approach is to work with
00:18:02
clients across the entire space to find opportunities that are really interesting companies that can fit into
00:18:09
our contraction contracting requirements where we're going to be able to go out
00:18:14
and structure the debt that we require in order to go out and and uh build infrastructure at this scale. And um
00:18:21
>> how does all that debt work? I that is something that you guys specialize in.
00:18:26
um corporate debt uh I'm in the venture business people are like why should I be
00:18:30
in venture when corporate debt pays so well corporate paper's so huge I'm
00:18:34
curious how this fits in and like what uh interest rate people are paying on you know a billion dollars in
00:18:43
infrastructure what do they pay on that >> yeah so so coreweave has really been the
00:18:49
innovator around a lot of the financing engines that have come to bear on this we did the first GPU based uh loans. Um,
00:18:58
and like I I think it's important or I'm going to try to explain this in a way
00:19:02
people can understand. So what we do is we go out and we find a client. Let's
00:19:07
use Microsoft. You brought them up before, right? And Microsoft comes to us and says, "We'd like to buy some compute
00:19:12
for you." And we say, "Okay, great. We're going to sign a contract." Once I
00:19:15
have a contract in hand, >> then what I do is I create something. It's not a particularly creative name.
00:19:21
It's called the box. Yeah. >> Right. And what I do with the box is I take my contract with Microsoft and I
00:19:27
put it in the box. I go to Jensen and I buy the GPUs, I put it in the box. I take my data center contract, I put it
00:19:34
in the box. And now the box governs cash flow. >> And it has a waterfall of cash flow that
00:19:39
comes into it and goes out of it. And so the way it works is then I build the compute and then I deliver the compute
00:19:46
to Microsoft and they pay the box. They don't pay me, >> right? It goes into the box and the
00:19:51
first thing it does is it pays the data center. It pays the power bill. It pays the interest and the principal and then
00:19:59
whatever's left flows back to us, right? And so it is an incredibly well ststructured, time-tested,
00:20:07
pressure-etested vehicle to be able to borrow money against client paper and all of the other collateral around the
00:20:16
deal. which is why Corewave, which is a company that many people haven't ever
00:20:20
heard of, was able to go out and raise $35 billion in 18 months to build infrastructure at scale. But what's
00:20:27
important to understand is the economics in this box are such that within 2.5 years of a 5-year deal, we have paid for
00:20:37
everything. >> The principal's been paid off. The well the principal's been paid off, the
00:20:42
interest has been paid off. The return into the box is such that we are able to generate returns to our company at the
00:20:50
box level which gives the most sophisticated lenders in the world whether it's banks or private equity
00:20:59
funds or um you know whoever. confidence that they're going to be able to achieve the one rule of
00:21:08
lending, which is give me my money back. >> Yes. Works better when that happens.
00:21:12
>> So, they look at this box and they're like, "Wow, we're really confident we're
00:21:15
going to get our money back." >> And maybe they want 10 boxes. >> That's correct.
00:21:18
>> And if any one box um goes upside down, you can deal with it and it's not as
00:21:24
acute. >> That's correct. And they don't cross-pollinate. They don't cause uh
00:21:29
contagion across the boxes. are all independent and discreet. One, and number two is as you do this and as you
00:21:36
show the lenders how this financing tool and how this financing mechanism works,
00:21:43
what they do is they continue to lend you money at progressively lower rates. And so when you think about our cost of
00:21:51
capital over the last two years, we have dropped our cost of capital by 600 basis
00:21:57
points. >> Wow. It is enormous, right? And so you're seeing a company that is driving
00:22:02
its cost of capital down towards where the hyperscalers borrow, which will enable us to be able to be competitive
00:22:10
with them over time. And we have been extremely uh militant and diligent about feeding,
00:22:19
watering, and caring for those boxes so that we continue to have access to the capital markets in a way that allows us
00:22:25
to build and drive our business. >> Means you has to say no. You have to say
00:22:28
no to maybe some people who want to be in the box. >> Yeah. So, we we look at some deals and
00:22:33
we're just like, you know, they want to buy GPUs for a year and I look at it and
00:22:37
say I I that's not a deal that I can do because it's too short for me to am
00:22:41
advertise the expenses or and so I won't do that. Right. Like once >> and they can go to another provider who
00:22:47
maybe wants to take that risk on who has extra capacity. >> Absolutely. But our business is really
00:22:52
built about around the risk management of being able to get to scale. Because in my mind
00:22:59
during this period of disequilibrium during this period where there are not enough GPUs in the world to uh provide
00:23:06
the compute for all of the different use cases in artificial intelligence the part that's important for me and for my
00:23:13
company is to get enormously large so we can drive down our cost of capital so that we have information flow coming in
00:23:20
from all different parts of the market. large language models, high-speed trading, uh, uh, search, all of these
00:23:27
things. And they're feeding they're feeding information back into us that is
00:23:31
letting us know what the next product we need to build is or where, you know, they need help uh, scaling or what type
00:23:38
of compute they need and all of that information flow is incredibly valuable to us.
00:23:44
>> What What can you tell us about demand? There's been reports of, hey, maybe the
00:23:48
Oracle Starbase thing with OpenAI's been downsized or maybe not and then you know
00:23:56
uh other folks Microsoft is going big and Google's going big Meta's going big
00:24:01
and those people obviously have massive cash flow Apple seems to be MIA they don't seem to want to play you you
00:24:07
you've you've uh you've named a lot of really big companies with really big
00:24:10
balance sheets that have the capacity to drive a lot of demand look I I have been
00:24:15
truly steadfast in this >> for years now for for for four The depth of the demand for the service
00:24:23
we provide has been relentless and overwhelms the global capacity of the world to deliver enough compute to
00:24:33
enable all of the demand for artificial intelligence to be sated and that has been we have been relentless about that.
00:24:42
>> Sounds like Nick's tickets during the Patrick Euing era like they got up to
00:24:46
50,000 people on the wait list. So if magically the weight list went away, if the if the constraint went away and we
00:24:53
just had a large amount of GPUs available, lot of energy available, a lot of data center available, how much
00:25:03
capacity would just all of a sudden come out of the system. >> So so or would be deployed I should say.
00:25:08
>> So remember how we build our our business through this box >> and it's a fiveyear box. So if we had an
00:25:16
air pocket, if if demand were suddenly to disappear because of a technology breakthrough, because of a uh a war,
00:25:24
anything, right? Like like the why from a riskmanagement perspective does not matter. You have to prepare your company
00:25:31
for the what happens if it happens. Yeah. And so by entering into these long-term contracts into entering into
00:25:38
contracts with counterparties that have large balance sheets, you are or we are protecting ourselves and our lenders.
00:25:47
Yeah. >> So that we are confident and they are confident because you can see how
00:25:51
confident they are by the rate that they're charging us continuing to decline that they're ultimately going to
00:25:56
get their money back. And that is the one rule of lending. And so um you know I if
00:26:02
>> but just in terms of the capacity if you were unconstrained and Nvidia Jensen
00:26:06
says hey order as many as you want what would happen >> so um the the it's also important to
00:26:13
understand the constraints aren't just GPUs right electricity it's it's power
00:26:17
shells it's memory it's storage it's it's networking it's optics all of the
00:26:23
things and there's there's various throttles that will limit the >> memory is a throttle right now right
00:26:28
>> oh yeah it Oh yeah, it is. >> Why? How did memory become the throttle?
00:26:32
>> If um memory and uh it has historically been a cyclical business, right? We have seen
00:26:41
these waves of demand driving up the cost for memory and then it collapses and then it drives it up. It's a very
00:26:48
boom and bust business. is cyclical in its nature because the fabs are so capital intensive that people invest in
00:26:55
the fabs, build a ton of capacity and then overbuild if there's any type of turndown. And that we've seen that cycle
00:27:04
again and again. What's happening right now is the confluence of two things,
00:27:09
right? one is is with all the demand for artificial intelligence and the corresponding
00:27:15
demand for compute and the ancillary services around the GPU, the demand is through the roof. That's number one.
00:27:23
Number two is is that >> there was probably an investment cycle that needed to happen back in 2023
00:27:30
that would have brought on the necessary fab capacity to be able to serve. >> Impossible to predict what should happen
00:27:37
just with energy. It's impossible to predict what just happened. And now people are chasing energy. The data
00:27:42
centers are going where the energy is. It's not based on real estate. It's
00:27:46
based on it's and >> where's there's some wind. >> And anytime you you have a uh very cap
00:27:52
not every any time, but many times when you have a uh a capital inensive business like you know building fabs,
00:27:58
you will get this boom and bust cycle just like in energy they overbuild. Yeah. And you know
00:28:03
>> fiber. >> Yeah. I mean there's there's there's a lot of examples of that our approach
00:28:09
>> in some ways when you look at that it's a beautiful aspect of capitalism that
00:28:13
we're able to have a boom bus cycle that we're able to weather it right if you
00:28:18
think just that capitalism from first principles something like that happens and we have too much fiber it creates an
00:28:24
opportunity for Google to buy it all up or the next person >> listen the the the um um you know it it
00:28:30
does it does a lot of things having a boom cycle it clears out the underbrush. will be able to survive and take
00:28:37
advantage of that and it sews the seeds of future business. You put the fiber into the ground which
00:28:47
became the backbone of how you know we watch movies every day and how we you know uh communicate and how we hop on a
00:28:55
zoom and you know co and all of these things were based on that infrastructure that was available to be consumed. Yeah,
00:29:04
people don't recognize this fact if you the the premise of YouTube from the founders who I knew, Chad Hurley and his
00:29:12
other partner. They basically had the realization at this curve storage is coming down so quickly we could offer
00:29:18
free unlimited uploads and bandwidth is coming down. So I guess we don't have to
00:29:23
charge people for sharing a video online. Before that, if your video went viral, people are going to have their
00:29:30
minds blown. But your server would turn off and it would say this person, you know, needs to pay their bill. Yes.
00:29:36
Because they were getting charged for carriage by the megabit going out. >> Yes. I mean it look and and you know
00:29:43
these these the business models change and evolve and you know like you said Moore's law and and and certainly Jensen
00:29:50
will talk about the fact that like what what is going on within the the the accelerated comput dwarfs
00:29:58
>> Moore's law right and all of that is going to lead to >> more opportunity to build more companies
00:30:05
that are going to do things like you two did which has really changed the world.
00:30:09
>> Yeah. I mean the the concept that I I don't know if it was like a million
00:30:14
hours being uploaded every hour or minute but at some point Susan what Jackie rest in peace said told me just
00:30:21
like how much was being uploaded every minute and it made no logical sense and she realized
00:30:28
>> well there's three billion people two or three billion people in the service and
00:30:32
1% upload or 0.1 10 bit bips upload and it's like okay one in a thousand people
00:30:37
upload it's a big it's a big denom denominator like >> I I was uh sitting on a a panel uh with
00:30:43
Sarah Frier, CFO of uh Open AAI and u um she every once in a while uh um she she
00:30:53
really puts out like interesting uh information and so she was talking about the cost of a million tokens when ChatG3
00:31:01
came out and it was $32 and change and now a million tokens cost nine cents. >> Yeah.
00:31:08
>> Right. And so you you just see like like the incredible power of how the capital
00:31:15
markets, how capitalism is uh uh fueling engineering and fueling uh uh competition.
00:31:23
>> It's become recursive now too. I mean these models if you say to the model,
00:31:27
hey make yourself more efficient, spend less money and lower the cost of tokens.
00:31:30
It'll be like okay captain. >> Yeah. >> I don't know if you saw Cararpathy's
00:31:34
recursive >> thing last weekend but it's like now civilians who've never worked in a
00:31:40
language model or done computer science are like, I'm going to try to do something recursive this weekend. You
00:31:44
know, it's one of the things that I that uh uh I talked to, you know, the other
00:31:51
founders about, you know, and it's like when you think about some of the things
00:31:57
that AI does, right, it's lowering the barrier to operations. So if you have a
00:32:03
good idea or a great idea, you can open up your model and you can tell your model, you can vibe code it, you can do
00:32:11
all kinds of different things and create things that never existed before. That's
00:32:16
amazing, right? like that's bringing down this incredible barrier that kept human creativity contained and now all
00:32:23
of a sudden this whole new vector of uh uh you know medical research or different approaches to you know
00:32:31
baseball cards or whatever you want if you've got a great idea if you've got a
00:32:34
new creative idea that's the valuable kernel right now that allows you to to
00:32:40
build new things and to create new things and I just think that's incredibly exciting like you're bringing
00:32:45
the minds of 8 billion in people a tool that allows them to overcome what was insurmountable for
00:32:52
forever >> for humanity. >> Yeah, it's a bright new future. Michael,
00:32:56
appreciate you sharing the uh uh information with us and the vision. I am really delighted to have Arvin Shri Nas
00:33:04
on the program. >> Thank you for having me here Jason. >> It's so great. I want to go through
00:33:09
three stages in which I fell in love with your product. The first phase was I could go in pick my language model if I
00:33:18
wanted to choose open AI, if I wanted to use claude, whatever it was. That was like a real unlock for me. And on the
00:33:25
sidebar sidebar, I noticed you had done essentially like what Yahoo did in the early days, finance, sports, and when I
00:33:36
pulled my nickname up, it gave me a live version of that. When I pulled my stocks
00:33:40
up, it summarized the news in real time. time and I was like, "Wow, this execution's great." And I I kind of made
00:33:46
you my front door, two different models, and it made it easier for me to check it. Then you came out with the Comet
00:33:52
browser and I was like, "Holy cow, I can give this a series of instructions. Go
00:33:56
to my LinkedIn, find everybody from this company, put them into a Google sheet and boom, you were the first out of the
00:34:02
gate with that." And then just the last couple of weeks I had been claw pilled
00:34:07
in using openclaw but you came out with computer and I started using computer and boy it's good uh it's a really
00:34:15
strong start uh allowing me to do repetitive tasks very similar in some ways to co-work from claude uh or
00:34:24
basically an engineer or developer using it. So are are these the evolution of the
00:34:31
company and I should think about it that way. But how do you look at perplexity now? You have a very loyal fan base.
00:34:37
You're making a lot of money. I don't know if you disclose it but I think it's
00:34:40
hundreds of millions to billions. You can tell us but what is perplexity in the face of wow Claude's having a great
00:34:47
run, OpenAI still doing strong. Grock doing very well. Gemini coming on strong. There's like six or seven of you
00:34:53
and uh you just happen to be one of my top twos right now. >> Thank you. So tell me first of all,
00:34:58
first of all, thank you. Thank you so much. Perplexity has always been built for people who are always looking for
00:35:04
the extra edge, the curious people. So it's very natural that you are uh one of
00:35:09
our power users. Uh one common theme for us uh for the last three and a half years is accuracy. Plexity wants to be
00:35:19
the company that's building the most accurate AI. So when you want to give somebody answers, accuracy is very
00:35:25
essential for building trust because only then the user is going to ask the next set of questions. It turns out it
00:35:31
was a great idea to give AI access to the internet to be accurate. So that's
00:35:36
the perplexity ask product. It turns out it's a great idea for AI to have full
00:35:40
access to a browser so that it can be accurate when you task it to go do something that you would do yourself on
00:35:46
a browser. Aentic browsing comet. Now the last phase is it turns out it's a great idea for AI to be given a full
00:35:55
access to a computer so that it can do whatever you do on a computer on its own essentially becoming the computer
00:36:03
itself. an orchestra of everything AI can do today. every single capability each individual AI model has be it GPT
00:36:12
or cloud or Gemini or anything else an orchestra of all those capabilities that what that's what perplexity computer is
00:36:20
and all these sub agents that are running inside computer are the musicians the models are essentially the
00:36:27
instruments and they're like hundreds of models out there each having their own
00:36:32
specialization some are good at coding some are good at writing some are good at multimodal visual synthesis is image
00:36:38
generation, video generation, audio, but what matters is the end output, the music you play. That's the work AI gets
00:36:45
done for you. And that's what perplexity computers. The AI itself is the computer. Now,
00:36:51
>> still lives inside of a browser. Have you considered giving it desktop root
00:36:56
access? That feels like the next place this is going, but that comes with a lot of security issues, a lot of trust
00:37:02
issues. As you mentioned, trust is paramount. getting the right answer is what builds it, but also not getting
00:37:07
hacked and not having it delete your files. So, how do you think about root access to my Windows machine? Obviously,
00:37:14
iOS, they won't let you, but with an Android phone, it would let you. >> Yes.
00:37:18
>> So, do you have that in the works? >> Yes. So, we announced something called
00:37:21
personal computer. Perplexity personal computer that's essentially going to take all the trust and reliability and
00:37:28
the server side execution of perplexity computer but synchronize it with your local computer so that you can use it
00:37:36
from your phone and we're going to do this with the Mac Mini where you synchronize your computer with the Mac
00:37:41
Mini so that becomes your local server all the agent orchestration that has to do with your local private data will run
00:37:48
on that local orchestration loop that runtime with the Mac Mini. Not on your servers, not on anthropics.
00:37:55
>> Exactly. >> Yeah. >> It could still ping Frontier models if
00:37:58
it needs to with your permission, >> but it will be orchestrating everything
00:38:03
on your local hardware. >> Yeah. >> And if it needs to run on the server
00:38:07
side hardware, if you don't want very complicated, longunning stask to be running on your local hardware. Yeah.
00:38:14
>> You can delegate it to run on your server side computer, which is again
00:38:18
only accessible to you and you alone. So that way we're going to bring this perfect hybrid of trustworthy
00:38:25
uh hybrid between local and server side and you >> and you'll make it easy to do. It just
00:38:30
be abstracted. You install one executable, boom, it's done. >> It's it's like open claw for dummies.
00:38:35
Nobody needs to learn how to use it. Nobody needs to manage API keys. Nobody needs to manage separate billing across
00:38:40
like 100 different services. Figure out what you can give access to and not access to. We take care of that.
00:38:47
>> So it's a Steve Jobs way of doing it. you know, end to end integration
00:38:51
>> and and how do you think about local models? I have started running Kimmy 2.5
00:38:56
on a Mac Studio. >> It's not as good as Claude or Gemini or Grock, but you can probably do about 80%
00:39:03
there for free. >> Yeah. >> Essentially. >> Yeah. >> Uh and so that's quite compelling
00:39:08
considering some of my other bills, Claude and and stuff were getting expensive.
00:39:12
>> So, do you have one of those? You started testing on your local Mac Studio. I assume you have a Mac Studio
00:39:18
and you're doing this yourself. Yeah. >> Or now, I don't know if you saw uh Dell
00:39:22
and Nvidia announced a giant workstation. Um is it 3,800? >> Something like that.
00:39:28
>> Something like that with 750 gigs of RAM. So, >> what do you think about the desktop
00:39:34
going back to workstation/server? >> Yeah. >> Status. >> I think it's very promising. Um my my
00:39:41
prediction is it'll initially start off as a sub agent. So whatever you need to
00:39:46
go uh like your tax returns, your personal photos, your emails, your your calendar, all that stuff, those local
00:39:55
apps, your personal notes, very personal notes. You can make sure that the models
00:40:00
that access those tokens will be running on your local hardware if you want to, if you're that privacy conscious.
00:40:09
uh and more complicated stuff that accesses your data that's already on the server side. Example, your Google
00:40:15
calendar, yeah, your Gmail. This is personal data still, but an AI runtime can access that through your connector,
00:40:24
your Google calendar connector, your Google Workspace connector, and that could run on the server side because
00:40:29
anyway, the data is on the servers. It's not even lying on your device. >> So, that sort of hybrid orchestration is
00:40:35
where we're headed to. I don't think it's a dichotomy between fully local
00:40:39
versus fully server. Uh it's all about choice. And anyway, when you're on your
00:40:44
phone, uh you want to you don't care actually which server that workloads running from because it's not going to
00:40:49
be able to run on your phone anyway. The chips need to exist on a Mac Studio or a
00:40:54
Mac Mini and or on the server >> or this new Dell that's coming out. And
00:40:58
I I really think the idea of spending $10,000 on a powerful desktop will appeal to people if it lowers their $500
00:41:08
a month >> claude bill. This is an incredible savings. Plus, you get the benefit
00:41:13
>> of privacy and not educating the language models on your personal data.
00:41:18
>> Yes. And it's going to be it's going to be like you're buying a refrigerator,
00:41:21
your your your internet modem. Like the cost for these will eventually go down. >> Yeah. But it's not going to feel like
00:41:28
you're wasting your money. Uh every every home has a lot of other sensors. >> Yeah.
00:41:34
>> That runs your home that'll also be part of this orchestration loop.
00:41:39
>> Yeah. >> So, so that's where it gets exciting because now you can just dictate
00:41:43
something to your phone and that can control your entire home. >> So that's the dream that everybody has
00:41:49
and all that orchestration loop can run on your local hardware, no problem. And I'm curious what you think of the
00:41:56
operating system. What's eventually going to be the operating system of this
00:42:02
workstation? >> AI is the operating system. Like earlier in the traditional operating system, you
00:42:07
execute programmatically. Now you start with objectives, not specific instructions.
00:42:13
>> Right? >> You come up with a highlevel objective. go build this website for me that you
00:42:18
know takes all the transcripts of all in podcast and tracks the stock price just
00:42:22
before the podcast and after. Yeah. >> And charted for the max 7. >> Yeah.
00:42:26
>> And and charted over time you can so that's the objective but individually
00:42:31
it's running a file system a code sandbox access to the internet. It's having like its own HTML tools and like
00:42:38
so I think that's basically where you know models systems and files and connectors are all coming together. You
00:42:44
would think of that as an OS >> except you're operating at an abstraction about that where you're
00:42:50
thinking in terms of objectives. >> Yeah. And does it need to eventually become its own operating system in your
00:42:57
mind? >> It could be like people could think about like yeah I have a my perplexity
00:43:02
computer running all the time whether it essentially it runs on Linux machines right now. Every server side computer is
00:43:09
a Linux machine. Yeah. >> So, I think Mark Anderson tweeted this right after our release that turns out
00:43:15
Linux computers was the right idea. Desktop desktop Linux computers are finally going to work.
00:43:20
>> Yeah. I mean, they're stable. They're customizable and you're not at the mercy
00:43:25
of Apple's desire to contain the experience or Microsoft surface area as for hackers.
00:43:33
>> Exactly. >> You build something rock solid and it does feel like Linux might actually
00:43:38
become the correct >> the eventual winner. It may not need to have a front end.
00:43:42
>> That's the thing. You could you could access the Linux machine on your phone.
00:43:47
>> You could be running iOS or Android. It doesn't matter. >> The actual valuable runtime is running
00:43:53
on Linux on the server. >> You've done great as a consumer company. Lot of love there. Now I'm starting to
00:44:00
see uh corporations with computer starting engaging it. In fact, you'll be happy to know this. Last week, I took
00:44:08
two people in my back office and I said, "Stop working on OpenClaw. Your job is
00:44:13
to do the back office automation at our venture firm only using Perplexity." And
00:44:19
they were like, "Perplexity computer." And they were like, "Oh, okay. Um, it
00:44:24
doesn't talk well in Slack. It doesn't have an agent in Slack." I was like, "It
00:44:28
will. I'm going to see AR and I'll talk to him about that." So, we need a really
00:44:33
strong Slack connector. >> It's already out. >> It is. Okay, great. computer exists as a
00:44:37
Slackbot right now. >> Okay, >> that you can add to your Slack workspace
00:44:40
on enterprise plan >> and our entire company works like that. People are talking more to computer on
00:44:45
Slack to other than to other people. >> In our first volley, we were sending
00:44:49
reports in, but it wasn't interactive. That's perfect. So now you've got your company going in
00:44:56
two different directions. This incredible consumer run you have. How many people are using the product every
00:45:01
month? >> Several tens of millions. So tens of millions of people that's very much
00:45:05
similar to the trajectory of the Google and Yahoo consumer business. Now you've
00:45:09
got corporate. How are you doing on the corporate side? Thousands of companies. >> The fastest growing business for us. Ah
00:45:15
>> it's growing faster than the consumer in revenue and things like computer unlock
00:45:20
entirely new possibilities. For example, we've saved more than $und00 million for
00:45:25
our uh enterprise max customers who are on the highest tier of enterprise. >> Explain what that is. What does it cost?
00:45:31
200 a month per person. >> So there are two tiers. One is the enterprise pro which is $40 a month and
00:45:36
there's the enterprise max which is $400 a month. And that that and and and and
00:45:42
on a computer after you run out of your credits you would pay for the tokens. You pay for the usage.
00:45:48
>> Are you making money on the $400 a month, $5,000 a year one or at this point in time are people going so crazy?
00:45:54
Our uh one thing that Perplexity has is every revenue we make, unlike certain other rapper companies, every revenue
00:46:01
Perplexity makes has positive gross margins. >> Got it. >> Because uh we're not just selling
00:46:06
tokens, >> right? >> Most of our revenue is recurring because people are paying a subscription fee
00:46:11
>> and because we route through multiple different models, we're very efficient
00:46:15
in terms of how we spend on the tokens. because we have all this advantage with rag and orchestration and search. We
00:46:22
don't actually need to blow up the context window of the models. >> Yeah.
00:46:25
>> As a result of that, we have positive gross margins on all the revenue. Every
00:46:30
single penny we make, we make profits on that. But the overall the company is still yet to be profitable, but we're
00:46:35
working towards that. >> You've had the opportunity to exit. A lot of rumors, Apple, other people were
00:46:41
like, "Hey, this is a great team." How many people on the team now? >> About 400.
00:46:45
>> Yeah. You you've got a very coveted team. You obviously understand consumer.
00:46:48
You obviously understand business. It's a product driven organization. Reports
00:46:52
are you declined, but the world's getting hyper competitive here. How do you keep up as
00:46:58
a 400 person organization when you got Sam Alman over here raising a hundred billion dollars, you know, and then you
00:47:06
have Elon putting data centers in space and merging with SpaceX and Twitter. You
00:47:11
have Google with unlimited resources. Amazon getting in the game and obviously Gemini uh very strong product and Google
00:47:20
really good at consumer. I think we'd all agree Facebook and Meta haven't
00:47:25
figured it out yet except maybe for serving us better ads, but they they haven't figured out the consumer case
00:47:30
yet, but they'll copy it. They always do. How do you look at the playing field?
00:47:34
Because the degree of difficulty, this isn't playing checkers or this is like
00:47:40
playing against the 10 best chess players in the world. That's what you have to do every day.
00:47:46
>> So, how do you think about it? Long-term and independent company. Do you think
00:47:49
you'll need to join forces at some point? >> Well, and why didn't you take the deal?
00:47:53
This deals were incredible that you got offered. >> So, one advantage we have that all these
00:47:59
companies you mentioned don't have is the multimodel orchestration. We're like
00:48:04
Switzerland. We don't have to have one horse in the race. If GPT wins, Gemini
00:48:09
wins, Claude wins, Llama wins, it doesn't matter to us. Uh or even open source models can win, no problem.
00:48:16
>> And you have them on the service. You have DeepSeek and Kimmy. >> We have Kimmy, we have Neotron, and we
00:48:22
have uh a lot of usage of Quen, Alibaba Quen. >> Yeah. >> Silently under the hood. So for us like
00:48:29
that advantage of being able to take the best in each model and give the user the
00:48:35
orchestra of everything they can do. I don't think any of the companies you mentioned can do that
00:48:39
>> right nor would they >> nor would they it makes no sense for them. It would be an admission that all
00:48:44
the data centers and capex they've built out mean still couldn't produce them the
00:48:49
best model. And uh Daario uh CEO of Anthropic said recently in an interview uh that models are specializing. Towards
00:48:58
the beginning of last year people thought models are going to commoditize but towards the end of last year people
00:49:04
models started specializing. Even within coding u cloud code and codeex have very
00:49:11
different capabilities. Our iOS engineers love using codeex. Our backend engineers love using cloud code.
00:49:17
>> Yeah. So even within a specialization like coding, models have their own
00:49:22
unique specialtities and there are many other use cases outside coding where different models are good at different
00:49:27
things. Which means the orchestra conductor that has no one model to the horse in the race can win by providing a
00:49:35
very unique value and service to the customer that each of these amazing names that you mentioned cannot. And so
00:49:42
you're buying tokens wholesale from them and then you'll charge customers to do
00:49:48
it or do you think it's all >> we're going to take care of all that
00:49:51
orchestration? >> Yeah. >> So you don't have to manage tokens across different models
00:49:55
>> cuz I authenticate I a couple of my different accounts my pro accounts into
00:49:59
perplexity. But does it I I I don't have enough knowledge to know if you're
00:50:04
abstracting that and people can just search across them and it's part of their perplexity subscription. No, we're
00:50:09
not bundling subscriptions from into other AIS. >> We just ping the models directly.
00:50:14
>> Got it. >> Uh what you get in us is the perplexity or orchestration.
00:50:18
>> Got it. >> The harness, >> right? >> So the when when when when models are
00:50:22
kind of specializing the there's a bigger value in the one who knows how to build a great harness,
00:50:28
>> right? >> That can take the best in each model. >> Does it auto route today or do you still
00:50:32
have the drop down somebody's got to pick >> it? It definitely auto routes the best
00:50:36
model for each prompt, >> but we also give users the flexibility to pick whatever model they want.
00:50:41
>> What do you think of I've seen a bunch of startups hack this together, but
00:50:45
doing the same query across multiple >> We built a thing called model council.
00:50:49
>> Model council. Yeah. >> Yeah. So that's one of the one of the
00:50:52
modes and perplexity where I saw Jensen say in one of the interviews that he he puts the same prompt in five different
00:50:58
AIs and sees what each of them says. >> Yes. >> Like everybody does that. Yeah. But then
00:51:03
you still have to apply your biological computers about your trust or your >> five different doctors.
00:51:13
>> Five different doctors trying to figure it out. >> Exactly. So it's dumb.
00:51:16
>> So the model council is a feature we built where it will not just give you
00:51:20
the answers of each model, but it will tell you exactly where they agree, where they disagree, and where the nuances
00:51:24
are. >> And that's in the interface. Model council, I didn't know it was there.
00:51:27
>> It's there. >> I mean, you you released product at a pretty great cadence, huh? How where did
00:51:32
you learn that and what's your philosophy of shipping product? >> Our philosophy is like speed is our
00:51:38
mode. Like you know again one of the things that big companies cannot do is move at the speed we do serve customers
00:51:43
at the speed and qual it's it's very hard to maintain quality speed and trust
00:51:47
at the same time. >> Yeah. >> Like Apple takes a long time to ship anything
00:51:51
>> because they're very worried about people not trusting them. >> Yeah.
00:51:54
>> Uh and so some companies are bureaucratic and they just take forever to ship something. They don't maintain
00:52:00
what they ship. They may make a big deal about an event but nobody even knows how
00:52:04
to go and use that feature. >> Yeah. They get abandoned. >> Exactly. So, Perplexity has those
00:52:08
advantages for being very small. And towards the end of last year, we found that like AI coding tools have made it
00:52:14
much faster for us to ship things >> which is honestly one of the reasons why
00:52:18
we built computer because now even non-engineers are shipping code here by just pinging a slack bot and asking it
00:52:24
to fix bugs. >> So, this the the iteration has just been like exponential. The the moment I had
00:52:30
where I became clawilled was when I was working with it and I was like, "Hey, I
00:52:36
want to build my network. I know these 20 people in Japan. I had dinner with them during my recent trip. I want to
00:52:41
know who they know. So, check out LinkedIn and other things and who they're associated with and make me like
00:52:46
a mind map of it. And then the next trip I want to meet with the next circle of,
00:52:52
you know, those connections." So, I started asking like, "Okay, I got the
00:52:55
results." I was like, "Great." Um, and they said, "Where do you want me
00:52:59
to put them?" And uh, I was like, "Well, where can you put them?" And it said,
00:53:02
"Well, I can put it in a Google sheet. I can put it in notion table. I can put it
00:53:05
here. I can give you a PDF. I can give you a CSV file. Or I could write you a CRM." And I was like, "Yeah, sure. Make
00:53:12
me a CRM system." And it may a CRM system. >> And I think that becomes, and I think
00:53:18
maybe one out of a thousand people working with AI have had that experience. Maybe it's one in 10,000.
00:53:24
Where your agent says, I'll make you bespoke software. >> Yeah. >> Have you had that yet? And and do you
00:53:30
see that as a part of computer that when a person needs a spreadsheet, you don't
00:53:35
launch Excel or Google Sheets, you just pop up a spreadsheet? >> Yeah. Well, we have a board meeting
00:53:42
tomorrow. >> Okay, I'll come. >> And and so >> I'll pitch it to the board.
00:53:45
>> Sure. >> Uh our computer computer made the memo. >> Oh, wow.
00:53:50
>> Yeah. And um we had a partner meeting to pitch a partnership idea and uh earlier
00:53:56
we would have a design team do the whole deck. >> Yeah. >> Computer just oneshotted it. Uh I had a
00:54:02
press briefing with a bunch of journalists. My comm's person would >> Sorry about that. Brutal.
00:54:07
>> And then my comms person would usually u give me a memo what to say. >> Computer one-shoted him.
00:54:13
>> So >> it's crazy. And it's the context is so good because the memor is getting
00:54:18
better. Yeah. Yeah. >> So it's like I know that journalist from the last time.
00:54:24
>> I know the board meeting. I have all the previous decks. >> When did that happen?
00:54:30
>> I think it it happened with Opus 45 >> Opus 45. That was a inflection point
00:54:36
when models were started being amazingly good at orchestration and reasoning and
00:54:42
tool calls and cloud code brought in this new idea in AI that everything can happen inside a sandbox, a console, a
00:54:51
terminal with access to tools where tools are just command line tools. >> Yeah,
00:54:56
>> they don't even need to have graphical user interface. So when you did that and
00:55:00
when you organize around files and sub aents and skills and CLIs, the model started be becoming very good at
00:55:09
handling the context. So the context window no longer became a problem. It just put whatever necessary into the
00:55:15
context whenever it wanted to and dumped dumped them away when it wanted to. >> Yeah.
00:55:19
>> And that made it like suddenly so good at doing very long orchestration tasks.
00:55:26
>> Yeah. It's it's pretty crazy. I have every episode of this week in startups,
00:55:30
all the transcripts and then all of all in >> that was one of the tasks I did by the
00:55:33
way I can send it to you. I asked it I want you to download every all-in podcast.
00:55:38
>> Yeah. >> U since the beginning and I want you to take a mention of all the public
00:55:44
companies they mentioned during the episode. >> Yes. >> I want you to have a histogram of the
00:55:48
counts and I also want you to chart it across time and then I want you to analyze the impact on the stock price
00:55:55
>> and the sentiment of what we said. Exactly. And it did like it clearly said,
00:55:59
>> "Are we moving stocks >> around Google's stock going up?"
00:56:02
>> Yes. >> Prior to that, you guys were talking a lot about Google. >> Yes.
00:56:06
>> And it clearly >> And I said I made a bet publicly on the thing. I said, "I am buying a bunch of
00:56:10
Google because I believe even though they're behind, >> it's because they're too precious." You
00:56:15
were kind of mentioning a company that might be too precious at times and doesn't release.
00:56:18
>> I was like, "That's that company. They need to release more." Yeah.
00:56:21
>> And uh I told Sergey, I was like, >> like >> give us the good stuff. started giving
00:56:27
us the good stuff. >> It literally gives you the timestamps of every single and then I can go click on
00:56:31
it and actually hear >> exactly >> that moment. >> Yeah. >> Sweet.
00:56:35
>> Yeah. So that's when that's when I was like damn like >> this I would have had somebody do this
00:56:40
as a weekl long project. >> It would have been 10 hours a week of researcher. I I'm experiencing the same
00:56:46
thing when I do research notes. I've created my own uh like mega prompt. >> Yeah. and it will go and like tell me
00:56:57
where you worked before and who's in your circle, who your competitors are, who your friends are, blah blah blah,
00:57:02
and then go find I try to find old podcast is one of my secrets. If you're an interviewer watching, I try to find
00:57:09
what was the person talking about 5 years ago, 10 years ago, and then over 10 years ago. And I've gone into
00:57:14
interviews now with Michael Dell and talked about things he was talking about in the '9s. Yeah.
00:57:19
>> And it finds me some ancient stuff. Like you would pay a research or a producer,
00:57:24
>> you know, $70,000 a year, $80,000 a year to do this and they would have done a
00:57:29
third of the job in 10 times longer. >> It's really gotten weird just in the
00:57:35
last 6 months. What do you think the next 6 months looks like? >> I think the the dream that what we are
00:57:40
going to try to do is help businesses run as autonomously as possible. You know, everybody talks about this AI is
00:57:47
going to create this one person $1 billion company. Some people say it's already happened because people pay
00:57:53
researchers like 1 billion, but it's not truly moving the GDP by 1 billion. It's
00:57:58
not truly creating new value. So the best way to do that is to actually help a small business people who would
00:58:04
otherwise drive Ubers for like yes >> extra passive income to like buy like a
00:58:10
Mac mini set up perplexity personal computer and run their business on that or like run it on the server it doesn't
00:58:16
matter uh and actually make real money. >> Yeah. >> Hundreds of thousands or even millions a
00:58:20
year >> and uh grow it. >> Have computer go and run your ad campaigns on Instagram or Google. I mean
00:58:28
>> integrate with SEM and SEO tools, find new users and uh integrate with Stripe,
00:58:34
charge them, ship new features, have your own like intercom integration for customer support and like have this all
00:58:41
working well. You can be sipping wine in Napa. That's the dream that you know it
00:58:45
feels awesome to say. Everybody thinks AI is already there. It's not there yet.
00:58:49
Someone has to do that hard work. >> Yeah, >> that's what we want to do. Yeah, it it's
00:58:53
a great vision because when I watched startups 20 years ago, there were so many check boxes they had
00:59:00
to do. I have to find an office space. I got to put up a bunch of servers. I I got to hire hire an HR firm. I I got to
00:59:07
hire a PR person. All this stuff. And now I talk to young founders. They got a three-person team. They've come out of
00:59:13
A16Z, my program, Launch Accelerator, whatever it is, Y Combinator. And I'm like, "Okay, you raised a half million,
00:59:19
you raised a million. Who are you hiring?" And they're like, "Um, I don't
00:59:23
know if we need to hire anybody." I'm like, "If you could hire somebody, would
00:59:26
you hire?" They're like, "Well, I do my own HR. I have this partner." And
00:59:30
they're I'm like, "How are you doing hiring anyway?" And they're like, "Well,
00:59:34
I put out an ad and then uh it sorts and ranks the candidates and then it emails
00:59:40
the top 10, asks them a bunch of questions, and then I meet with the last two." And I'm like, "That's what a
00:59:44
recruiter did." >> Like, the entire recruiting job has been abstracted. And like a a tool like
00:59:49
computer is going to make that even faster. >> Much work to do. Uh lot of connectors, a
00:59:55
lot of specific workflows. People don't want to like learn how to write like,
01:00:00
you know, essay long prompts. You know, it needs to be so quick and fast and autonomous. You just set it up and done.
01:00:06
>> And you have an idea, you can turn it into a business and start making money.
01:00:09
>> Yeah. It's it's an incredible future. Uh and it feels like it's right here. Do
01:00:13
you how do you think about job displacement? is you're actually making the tool that enables people
01:00:20
>> to be a solo entrepreneur and get to a million in revenue, but it's also the
01:00:23
same tool that doesn't require them to hire. And we've had this debate a
01:00:26
million times on the podcast. >> Do you I'm wondering if like me, you have
01:00:32
moments where you're like, "Oh my god, this is really terrifying." Yeah.
01:00:35
>> A lot of people are going to lose their jobs really fast. >> Yeah.
01:00:38
>> And then, oh my god, you can learn any skill you want and all the things that
01:00:42
were hard are now easy. >> Yeah. I I go back and forth. I'm 70 80% super positive about this, but I do
01:00:49
worry about like 20% of the time I'm a little worried. Yeah. Where do you sit?
01:00:53
>> I mean, America has always been about like entrepreneur entrepreneurship,
01:00:57
right? Like we we've been about like trying to build new things, discover new
01:01:00
things, go explore. >> Uh I think this whole like Henry Ford came and built factories and brought in
01:01:06
jobs and things like that and like put people into a box. But u I think the reality is people most people don't
01:01:15
enjoy their jobs. They're doing it for they hate them. >> Exactly. >> So there is suddenly a new possibility a
01:01:20
new opportunity to go use these tools, learn them and start your own mini business. And if it pays for your needs
01:01:27
for year or multiple years and lets you have a high quality life and good work life balance and true feeling of agency
01:01:34
and ownership and passion to like get your ideas out there. I think that is even if there is temporary job
01:01:41
displacement to deal with that sort of glorious future is what we should look forward to.
01:01:45
>> I I I think you're exactly right. If there will be some displacement, but
01:01:49
then there's also going to be so many opportunities open up and it requires the individual to not be passive.
01:01:55
>> Exactly. >> They have to be rugged individualists. They have to be resilient. Yeah.
01:01:59
>> And they have to be resourceful. And I think once you start playing with these
01:02:02
tools, that's what happens. >> Exactly. you you all of a sudden feel like
01:02:06
>> it brings out the best in you if you truly are in a good space. >> Yeah.
01:02:09
>> Yeah. >> I today uh Comet for iOS is out. >> Yeah. >> I'm a Comet super fan. I required
01:02:17
everybody. You were nice enough when I I emailed you. I was like, "Can you send
01:02:19
me some licenses?" You sent You don't may not remember. You sent me a bunch of
01:02:22
licenses. I said, "Everybody put this on because it was $300 a month when you
01:02:26
first came out with the common browser. Now it's free, I think, for all users.
01:02:30
>> Highly recommend it. Highly recommend getting a pro account. It's only 20
01:02:34
bucks a month to get into perplexity, which is a joke. So, you can get on board for nothing, less than a dollar a
01:02:39
day. >> But what does iOS allow me to do? And and how does it connect to computer?
01:02:45
Because that's another thing I'm having. >> Yeah. >> Cloud code. Uh computer, there's not a
01:02:51
good enough integration with this mobile device yet. >> Yeah. So, computer is already on the
01:02:56
perplexity app. So, you can just toggle the computer and start using it. uh comet's uniqueness and perplexity for
01:03:03
the company uh and and and the strategy is the fact that you can control the browser. So the browser also becomes a
01:03:10
tool for computer >> just like your Google workspace and all these other things. uh until the whole
01:03:17
world is organized around CLI and tools. >> Yeah, >> there's still a lot of tasks we have to
01:03:22
do manually on the web on the browser. Open tabs, fill up forms, click on things, upload stuff, all that stuff. If
01:03:29
you want to automate, you need a browser. You need an AI that can natively control the browser. So that is
01:03:35
comet. And that's why no matter how many other tools in the market exist like
01:03:40
open claw or like claw co-work >> executing tasks on a browser on the server side along with all the other
01:03:47
things is something uniquely perplexity can do. >> Yeah. My dream is that you'll create an
01:03:53
Android app that roots my Android phone. >> Yeah. >> And that you just take over and see
01:03:58
everything because one of the blockers I have now is some of the websites have gotten a little pnicity.
01:04:05
>> Yeah. I don't want to mention too many, but Reddit, LinkedIn. >> Yeah.
01:04:10
>> And like they're just I I am a great Reddit user. I'm a great LinkedIn
01:04:15
supporter, but sometimes like I need to get my inmail. >> Yeah. >> From my LinkedIn and I just need to, you
01:04:22
know, find seven people at company. I is there going to be a solution >> between the LinkedIn and Reddits of the
01:04:29
world and the claws and perplexities? Is how is that >> I mean >> negotiation going? You don't have to
01:04:35
speak about any specific ones unless you want to, >> but it feels like there's got to be a
01:04:39
solution >> and I'm willing to pay for it as a user. I'm willing to play Reddit to allow my
01:04:44
bot to show up and behave properly. >> Well, I I I cannot speak about any particular company, but we are happy to
01:04:51
work with anyone, right? So, um I think with with Comet, our idea is to give people the flexibility to set things up
01:04:58
on their own. >> Yeah. and uh any um official APIs that anyone's willing to offer, we're always
01:05:04
happy to put that as part of computer. Here's what I think should happen. Let
01:05:08
me see if you agree. Um and this is for Steve Huffman at Reddit. I go on Reddit. I do a pro account for
01:05:18
20 bucks a month. And when I do that, I can authenticate whatever tool I want um
01:05:24
to do a series of well- behaved things a certain number of times a day. >> Yeah.
01:05:30
>> So, it's not unlimited. I'm not going to scrape the whole site, but I would like
01:05:33
it to just let Perplexi or computer go and just tell me, hey, >> what are people saying on the this
01:05:40
weekend startups and all-in subreddits? Summarize it for me so I get the customer feedback. And I would literally
01:05:46
name my uh agent and I would say I it won't post on my behalf. It won't vote on my
01:05:53
behalf. Just needed to do a couple of little readonly things. This would be an easy solution. Or LinkedIn I would like
01:05:58
if you I have I already pay LinkedIn like 50 bucks a month. Like they should just let the $50 a month one work with
01:06:04
computer. >> Yeah, absolutely. I mean, >> okay, this is for Satia Nadella. Let
01:06:09
LinkedIn work with Perplexity and the other players and we'll pay you extra.
01:06:14
>> Perfect. It's a revenue stream. Don't you think API access for our customers
01:06:17
is a revenue stream? >> I think so. I think so. I think I think fundamentally giving users a choice
01:06:23
>> and setting it up as a win-win for both the business and the user >> Yeah.
01:06:26
>> is where the world should head to. >> And and I I would say the same thing
01:06:30
applies to any any website in the world. Like if if you want an AI to use it on your behalf, it should be okay for cuz
01:06:36
that's what the user wants. >> I mean, I have a paid New York Times subscription. like let me go in there
01:06:42
and do you know whatever 100 searches a day, a week, a month, whatever they choose, but that would make the
01:06:49
subscription that much more sticky. >> Exactly. >> Uh all right, Arvin, love the product.
01:06:55
Anybody at home, >> it's just tremendous. Go learn computer and get the Comet browser. It has
01:07:01
changed my business for the last two years. Love the product and we'll have you back soon when you launch your
01:07:07
operating system and come up with your own server and desktop server but business is the focus. Yeah.
01:07:13
>> Yes. >> All right. Great seeing you. >> We have an amazing guest Arthur
01:07:16
Manchester here the CEO of Mistral AI. How are you doing sir? >> Great. Thank you for having me.
01:07:22
>> And so you're here at Nvidia's big conference, big announcement. You're going to be
01:07:30
working with Nvidia to build models. uh to open source them. What is the uh big announcement here?
01:07:37
>> Well, we're announcing that we are going to be training the next generation of
01:07:41
frontier models with uh with Nvidia. Um it's something that we've been doing
01:07:45
before with Nvidia with MLMO, something we did like 18 months ago. And the point
01:07:49
for us is really to be able to produce the best open source models out there so that we can actually use those assets to
01:07:55
specialize them through products that we do for our customers like Forge that helps us customize the models for the
01:08:02
enterprise we work with in engineering in physics in science uh in making them better at certain languages when we work
01:08:08
with governments etc. >> And and Michel obviously based in uh France you're the leading AI company
01:08:15
there. What's it like running the company and building a large language model in Europe? Obviously, there's
01:08:20
regulations and all kinds of considerations. Privacy, the French are known for protecting privacy. In the
01:08:26
United States, we're known for taking it away. How is the landscape there and
01:08:31
what do you have to deal with there that maybe you wouldn't have to deal with in
01:08:34
America? And what's the pros and the cons? I'd say first, we have 25% of our
01:08:38
business in the US. Uh, and 25% of our researchers are actually here. So I actually spend a lot of time here as
01:08:44
well as in France as well as in the UK in Singapore where we are. So of course it's it's different markets. Uh it's
01:08:51
markets where you have language which is a topic uh where there's much more manufact manufacturing is a bigger piece
01:08:57
of the cake than it is here. uh and I'd say the our strength has been to also
01:09:02
work with European companies that are a bit lagging behind uh and that wants to adopt the technology to to leap forward
01:09:09
and we've been able to do that through a forward deployment engineering engagement through our forge product for
01:09:13
our studio product that allows to deploy agents that do end to end automation but
01:09:18
on top of that the thing that we have announced today like forge is something that is actually being used today uh
01:09:23
with customers in the US because they come to us with uh needs for post training for making mod specifically
01:09:29
good at financial services and what's happening is that we have this product
01:09:33
and we can bring the models to specialize them as well. >> And so your belief is specialized
01:09:39
verticalized models healthcare finance engineering different verticals will win the day or a a global model will win the
01:09:47
day that does everything. >> Well you need general purpose models to do the orchestration parts etc. But at
01:09:53
some point you enterprises sits on a lot of intellectual property on a lot of signals coming from physical systems
01:10:00
from factories from tools and the it's actually not trivial to connect those
01:10:05
systems to connect those data to models that are closed source. If you have open
01:10:09
models you can actually add uh new parameters you can make a lot of deeper things that you cannot do with closed
01:10:14
models. You can also and that's what something that we do. We don't we not
01:10:18
only do we work at the model side but also at the orchestration side. We see it with subject matter experts to
01:10:23
understand their needs and we build business applications that are fully bespoke to their needs by modifying the
01:10:28
models but also modifying the harness on top etc. So we believe that eventually building on open source technology is a
01:10:35
way to save cost is a way to have better control because you can sit the thing on
01:10:38
every cloud that you want on your hardware if you want you can deploy it on the edge if you want and eventually
01:10:43
uh from a from a customization perspective and from leveraging your decades of IP that you've been acrewing
01:10:49
in financial services in heavy manufacturing like companies like SML for instance they do benefit from
01:10:54
working with us because we take their data and we build models that are specifically good for their
01:10:59
>> um just training data using experts to come in and refine a model. Most people
01:11:05
don't know this business that well, but this has become a very large part of the
01:11:10
industry. Obviously, scale AI was doing it. They went to Facebook, lost a lot of
01:11:14
the customer base who didn't want to uh send their data, I guess, over to Meta.
01:11:19
Uh we're investors in a company called Micro One that's doing pretty well in
01:11:22
this space. There's other folks doing it. explain to the audience what you're
01:11:27
doing specifically for companies and how this training works in a verticalized way and then how you silo that data
01:11:33
because if you're working with one customer in aerospace or fintech they might have a need set but they may not
01:11:39
want that training to go to a competitor. I can use a few examples. I think overall the data segregation is
01:11:47
super important and the way we have solved that is through a portable platform. So our technology is a set of
01:11:52
services, a set of training tools, a set of data processing tools that I can take
01:11:57
and that I can put on the infrastructure of my customers. So suddenly from an IT
01:12:01
perspective and when we talk to the CIOS, they realize that from security perspective, the flow of data doesn't go
01:12:07
there's no data flow coming back to Mistral because everything stays there. Now uh the way we we then use that
01:12:13
technology that has been deployed is that we're going to be working with uh the team that is doing uh image scanning
01:12:20
and default detection with ISML for instance and we're going to be sending forward deployment engineers scientists
01:12:26
they're all PhDs they know how to train models and they spend some time with the
01:12:29
subject matter experts that can explain how an image is being detected what how do you def detect defaults etc and based
01:12:36
on that we're going to work out what kind of data needs to be used to train the models that it's going to solve the
01:12:41
task in itself. And so the we we send the technology typically we send a little bit of scientists because uh you
01:12:49
do need that expertise transfer and that knowledge transfer in between our teams
01:12:53
and the vertical experts and then we make sure that eventually our team no longer needs to be there to retrain the
01:12:58
models to get more data access etc. So that combination of data segregation, expertise transfer, knowledge transfer
01:13:05
is the one thing that makes us quite unique and allows us to serve the most critical use cases, the most critical
01:13:10
processes in industries that actually need to take their data and put it into models for it to work. Yeah, this seems
01:13:17
to be once the entire open web, what was available legally, gray market, etc. I wouldn't have you comment on that
01:13:26
controversy. Uh but we we kind of exhausted what's in the open crawl. Yeah, >> we have.
01:13:31
>> And and it's time to actually either make synthetic data or actually
01:13:37
use experts. Do you believe in synthetic data and where does that work and where
01:13:41
does it fail? We use synthetic data as a way to warm up the models. It's a way to
01:13:46
actually be quite efficient at the beginning. If you have a large model and you want to train a small model, you
01:13:50
would you will use your large model to pro to process and to produce a lot of synthetic data at the beginning. uh and
01:13:57
then but eventually you do need to have human signal. Uh so the human signal is something that is always a bit costly to
01:14:02
acquire because you need to talk to the experts they need to give feedback to the machines and so at the beginning
01:14:08
synthetic data allows you to do the compression to to further compress the models. At the end you do need to go and
01:14:13
get data that is uh produced by humans. So yeah, it's a it's a way to have uh
01:14:18
it's it's mostly an efficient way of training models to have big bigger models that are used as as teachers for
01:14:25
smaller models, but it's not enough. And so you also need human signal. Arthur,
01:14:28
we've seen um an incredible explosion. We're sitting here on AO52 after OpenClaw, the year of our Lord, 52
01:14:37
days. when you first saw Open Claw and saw the reaction of hackers, founders, startups, CEOs, just the
01:14:47
amount of energy and it racing to the top of GitHub with the most number of stars and likes and and all these
01:14:54
contributors. What did that say to you as an executive in the space who's been
01:14:59
grinding on this for many years? What what does that openclaw moment mean? Well, it resonated a lot with what we
01:15:05
were doing with our customers uh because pretty quickly uh enterprises realized that if they wanted to make some gains
01:15:12
with artificial intelligence geni, they would need to automate full processes. And to automate a full process as an
01:15:18
enterprise, well, you can use open cloud, but it's going to be uh it's actually not really enough because you
01:15:22
you have data problems, you have governance problems, you can't observe uh the process that is running and you
01:15:28
can't can't control it in um in many cases when you run a KYC process. So if
01:15:32
you're HSBC for instance, one of our customers, uh you will want to have deterministic gates that are going to
01:15:38
always do the same thing in a way that is observable and that you can guarantee the CIO that it's always going to go
01:15:44
through these gates and that's not something that Openflow is providing because it doesn't have this the kind of
01:15:50
primitives that you need to work on collective productivity, observable productivity and to work on mission
01:15:55
critical systems. On the other hand, uh the autonomy it gives and the autonomy it brings to to people that are just
01:16:02
individuals that are hacking together things is a way to also show to enterprises that if you set up the right
01:16:07
control plane, if you set up the right sandboxes, if you connect to the right data sources, if you make sure that you
01:16:13
your access controls are well respected, then you can actually unleash the power
01:16:17
of agents doing things for your employees and that's going to work. Work on the platform cuz otherwise you will
01:16:23
not be at ease when you're sleeping. It is um definitely something you have to
01:16:26
be thoughtful about. When I installed it, I gave it just for my agent root access to my Google Docs and my G Suite,
01:16:35
my notion, my Zoom and uh my notion and uh GCAL, everything. And then I realized, wow, I can with my enterprise
01:16:45
edition of Gmail essentially, I can just summarize for my entire 21 person investment company every conversation
01:16:52
going on in Gmail and then correlate it with every conversation in Slack. And then I realized, oh my gosh, there's
01:16:59
compensation discussions going on. There's a person on a PIP who we put them on a perform performance
01:17:05
improvement plan perhaps or something like that. Oh, I have to make sure nobody else can access this because the
01:17:12
power comes from giving it access to data. But with great power comes great responsibility and I think people are
01:17:19
learning that in real time. Yeah, it's a big problem because the enterprise data
01:17:23
is not a single thing that you want to put into a single system that is going to be accessible by by everyone and so
01:17:28
you need to have this layer that actually understands what is the is what is in the data. you need to have a
01:17:34
semantic of what can actually be proposed to uh HR or what can be proposed to uh engineering and typically
01:17:42
compensation is one of these things you want to make sure that the compensation data does not flow back to all of the
01:17:47
all of the enterprise because you're going to have a lot of problems uh if if
01:17:50
that's the case and so what you actually need and which is hard to do is what we
01:17:54
call context engine so a mapping of where the data sits that comes with a certain number of metadata that is
01:18:00
telling you that this data is actually not accessible to part of the company and if you actually have someone in
01:18:06
engineering that is asking for something related to comp the thing is actually going to tell you look you actually
01:18:11
can't access that data so so that's uh that's hard it's actually hard you need
01:18:16
to rethink entirely the way your IT systems are being connected and uh at some point you also need to think about
01:18:21
your management because your influ your information flow is completely different
01:18:25
today uh if you're connecting agents together with your data sources than it
01:18:29
used to be and suddenly maybe you don't need that manager whose only purpose was
01:18:33
to take information from the bottom and put the information on top etc. So there's some IT problems to solve and
01:18:39
you need the right primitives, you need sandboxes, you need airback based access
01:18:44
control and these kind of things and uh you have change to do. You you need to rethink your entire customer service uh
01:18:50
department cuz suddenly you actually don't need that much transfer of information operated by humans.
01:18:55
>> All right. Uh you have to go. You got a flight to catch. It is so great to see
01:18:58
you Arthur. Continued success with Mishril. >> Thank you very much. Cheers. I'm really
01:19:02
lucky to have Daniel Roberts here. He's the co-CEO and co-founder along with his
01:19:07
brother of Iron. They are a publicly traded company. They started in BTC. Welcome to the All-In Interview program.
01:19:14
>> Thanks, Jason. Pleasure to be here. >> Yeah. And so you started in Sydney. You
01:19:19
and your brother um was seven, eight years ago. And you got in early on Bitcoin and all these Bitcoin monitor uh
01:19:28
miners wanted to have data centers. Huh. >> Yeah, that that's directionally right.
01:19:32
So the thesis we saw was this explosion of the digital world, the growth in the online and at some point the real world
01:19:39
was going to struggle. So we set about to build out largecale data centers. Yes, the first use case was Bitcoin
01:19:45
mining. But as we said to our seed investors, use that to bootstrap the platform, generate cash flow, layer in
01:19:52
higher and better use cases over time as they emerge. Here we are today with AI,
01:19:56
we are swapping out all the Bitcoin for AI chips. When did you first start seeing the demand in the company shift
01:20:02
from hey Bitcoin miners we need some H100s whatever it is uh to hey we're this nonprofit open AAI hey we're this
01:20:13
research lab we need some AI compute when did that start hitting >> look we had a bit of a false dawn I
01:20:19
would say back in 2020 we signed anou with Dell to start bringing out customers and compute but in hindsight
01:20:25
it was too early so we went back to Bitcoin kept bootstrapping in the platform. Look, I would say about 2
01:20:30
years ago and month by month, the demand just continues to escalate. >> And you were in so early that when you
01:20:37
were looking at data center space in the United States, you were one of one looking at the
01:20:43
space, one of two or three people looking at the space, they they were trying to sell you on space. Yeah.
01:20:49
>> Yeah. So, we actually develop the data centers ourselves. So, we go and find
01:20:53
the land, we go and get the permits, we go and apply for grid connections. And we were doing it at a scale that just
01:20:59
amazed people at the time. Like 750 megawatts is our flagship Texas site four years ago was unheard of. In the
01:21:06
middle of the desert, we're building these big data centers. The traditional
01:21:08
data center industry going what are you guys doing? We're saying we believe in
01:21:12
the future digitization, high performance computing and obviously now today it's paying dividends.
01:21:17
>> Yeah. I don't think anybody could have predicted when chat GPT came out, Open
01:21:22
Claw recently as a turning point. Um, and then you know, Microsoft, Google, and everybody embracing this. Uh, and
01:21:30
that's your big partner, Microsoft. >> Yes, Microsoft's one of our early
01:21:34
partners. We signed a $9.7 billion contract with them late last year, but as I was explaining to you before the
01:21:41
show, that's 5% of our capacity. So, things are busy at the moment. >> Yeah.
01:21:46
>> And when you do these buildouts, the big conversation today is not is no
01:21:52
longer the number of GPUs putting in. It's just power. Power is the uh constraint today. Yeah.
01:21:59
>> Look, for many of the industry it is, but for us, because we started 8 years
01:22:03
ago tying up all this land and power, it's not. So, we've got 4 1/2 gawatt.
01:22:08
For context, that's almost as much power annually as the Bay Area uses in its
01:22:13
entirety each. Wow. It's huge. So, for us, the hurdle or the constraint is really time to compute. And that's
01:22:21
emerging across the industry as well. And time to compute means trades people coming to West Texas living in a a
01:22:30
trailer that you set up to then break ground on a data center, build foundations, build water cooling
01:22:38
systems. Like this is hard manual labor going on. Yeah, >> exactly. And this is the whole real
01:22:44
world challenge to respond to these digital exponential demand curves that are unconstrained by the real world in
01:22:50
terms of their appetite. And it just compounds. You need thousands of people out in these locations that haven't
01:22:56
supported it. You put stress on supply chains. We're seeing what's happening
01:22:59
with the memory, every aspect of it. So, it's just permanent whack-a-ole, permanent solving fires to try and be
01:23:05
bring online this compute. >> And you get to spend time there. >> What's it like when you set up a town or
01:23:13
you bring a thousand people or 2,000 people to what's a pretty much remote small town? you I'm assuming that like
01:23:21
when you bring a thousand there might only be 500 living there right now. So what are those towns like? I'm it sounds
01:23:28
to me like something out of like the gold mining era when people first you know uh went and and were prospectors
01:23:35
prospecting town >> pretty pretty much. I mean the barbecue's great that was a draw card
01:23:40
but apart from that uh look we've always had a policy of hiring local supporting
01:23:44
the local community. Uh this year we're hitting a million dollars in community
01:23:48
grants cumulatively. That's things like local playgrounds, supporting the fire
01:23:52
departments, but we will hire locally. Once we can't find that trade locally,
01:23:56
we will expand the radius by 20 mi and hire out of that and so on and so on for us.
01:24:00
>> That's very thoughtful. Yeah. And and these folks are coming say an
01:24:04
electrician or a construction worker. They're coming having built houses or you know uh maybe building um corporate
01:24:15
offices and now they come for a tour of duty here and the salaries go up massively but they got to leave their
01:24:22
family for a 3-month tour or something. >> Yeah. Yes and no. Because typically
01:24:26
where we locate is where there's heavy electrical infrastructure. Where there's
01:24:31
heavy electrical infrastructure is typically where old manufacturing and industry has closed down. Ah,
01:24:37
>> so we go in, leverage that sunk capex, rehire, retrain local workforces and
01:24:43
bring a new industry to town in these data centers. H >> has has that workforce now been
01:24:48
completely depleted and we need to train another generation, a younger generation
01:24:53
to be generation tool belt and really embrace the trades >> 100%. We're partnering with
01:24:58
universities, trade colleges. Absolutely. And you go to a trade school, you got you go to a college,
01:25:05
people are getting degrees in philosophy and English literature, they're going
01:25:10
50k a year in debt, 200k a year in debt. What's the starting salary for a trades
01:25:15
person working on a data center doing electrical or construction or HVAC? What's the ballpark range?
01:25:22
>> Uh, look, I won't talk specifics, but they they are going up. The price is
01:25:26
going up. Depends on the level, but yes, there is a rush for good. hearing 150 to
01:25:31
like 300K. Am I in the ballpark? >> The lower end directionally, you're
01:25:35
right. Yeah. >> Yeah. I mean, it's incredible when you think about it. There's concern about,
01:25:40
hey, AI taking jobs and then on this other side of the ledger can't find enough talent to to to service it. Talk
01:25:48
to me about energy sources and how you think about that. Uh, President Trump, Chris Wright, the administration that
01:25:56
kind of started with, hey, clean, beautiful coal. Year two, they're like, "All sources matter." Nuclear,
01:26:02
obviously, nack gas is plentiful in that area. We obviously got a lot of oil. People don't know this about Texas in
01:26:08
the United States, the number one uh source of solar installations. Yeah. >> Yeah. Talk to us about energy.
01:26:15
>> So, so our our philosophy has been sustainability from day one. We have
01:26:18
used 100% renewable energy since inception. >> What? >> 100%. >> Wait, how is that possible? It's
01:26:25
>> We use hydro in British Columbia. We use wind and solar in West Texas. In West
01:26:29
Texas, where we're located, there's around 45 to 50 GW of wind and solar.
01:26:34
>> Yeah. >> The transmission line to export that down to the load centers in Dallas and
01:26:38
Houston is 12 GW. >> Oh. >> So you go and locate to the source of lowcost excess renewable energy,
01:26:44
monetize it into this digital commodity, export it at the speed of light as token.
01:26:49
>> Great arbitrage. And the wind is producing a lot, but it it's harder to
01:26:53
get from those areas where people are willing to put up. I mean, people don't
01:26:57
understand how big West Texas is. It is an incredible amount of land. And you're
01:27:02
coming from Australia where also on the west side, people don't understand exactly how much just
01:27:09
pure nature land there is. Yeah. Undeveloped. >> So much land. And the issue is distance.
01:27:13
You've got to spend billions of dollars on this transmission connection infrastructure to move that power to
01:27:18
where people actually want it. You can build wind farms, you can build solar farms, but if you build it in the desert
01:27:23
and no one can use it, then what's the point? So the whole opportunity for our
01:27:26
industry is to go to the source of that power and monetize it. >> So the data centers follow the wind
01:27:33
turbines, the solar installations. How do you think about batteries and are you able to put those online? Because
01:27:40
obviously you're going to have periods where, hey, it's not a windy day. In
01:27:43
Texas, we have very few days when it's overcast, so that problem's pretty much
01:27:47
solved. But you're going to have 50 days where the sun's not beating down. So,
01:27:51
how do you deal with the demand and and and softening that duck curve? >> We don't need to.
01:27:56
>> The utility does that on our behalf. So, this is why these grid connections are
01:28:00
so scarce, so hard to get, and so highly valued because once you get that grid connection, the utility underwrites all
01:28:06
of that variability. They guarantee you 24/7 reliable power. >> Got it. So, on their side, they're
01:28:13
figuring it out. something goes down and they could fall back even though you're
01:28:18
100% committed to renewables if they needed to fall back to gas or whatever they have that ability out there. So you
01:28:24
have that as a backup. >> A lot of talk about or a debate. Are we getting ahead of our skis? Are people
01:28:32
slowing down? There was some talk about the OpenAI project maybe downscaling a little bit. Is OpenAI a partner as well
01:28:38
or >> uh can't comment. >> Can't comment. Okay. So we'll we'll read
01:28:42
into that whatever we want. But are there pockets where people are saying, "Hey, let's slow down." Or is it
01:28:49
still gang busters? >> It's right up the end of the spectrum. It's gang busters. We we cannot meet
01:28:55
demand. That's why the whole industry now is around time to compute. There are
01:28:59
no idle GPUs in the world sitting in a data center. >> Yeah. And what's your take on when
01:29:06
software makes and this is a big uh discussion from Jensen himself during his two and a half hour keynote
01:29:13
yesterday. Uh we're sitting here Wednesday. I think he did his keynote on Tuesday. He was talking about hey
01:29:18
software is going to make it 50 times more uh you know lower the cost of tokens 50x and then you have um
01:29:25
transport also contributing to that. When do you think the curve goes from parabolic to simply growing at a
01:29:33
ridiculous level? Is is there a slowdown coming or how are you planning for the future?
01:29:38
>> Look, I think it's actually the opposite. I think it feeds on itself.
01:29:41
So, I'll give you one example. You go into chat GPT today and you generate an
01:29:45
image. You enter to the prompt. It's like the dialup internet days. >> It is
01:29:49
>> right. It takes minutes and you're like, I better get this prompt right.
01:29:52
>> Yeah. >> Finally, 2 minutes later, it comes. Now, I'll give you an example. If we 10x the
01:29:56
amount of compute available, which is an enormous task from where we are today, and those images take 5 to 10 seconds,
01:30:02
are we going to generate more or less images? >> Oh, many more. Uh, this is Jevans
01:30:07
paradox. This is the theory of induced traffic. You know, you build a couple more lanes, people start to think, well,
01:30:13
maybe the uh distance from Bondai Beach to to the central business district in Sydney terms would be an acceptable
01:30:20
commute. >> Love the analogy. >> Yeah. Uh, so what do you think about or
01:30:25
or what are you seeing? I mean, we're here at Nvidia. Obviously, they make the
01:30:29
leading edge chips. They just bought Grock, so now you've got, you know, two
01:30:33
of the leading edge chips uh coming out of the same company, but custom silicon becoming a big discussion. Has that
01:30:40
started to land in the data centers yet? Obviously, Google, don't know if they're
01:30:44
a customer, you can tell us, but they're making custom silicon. Amazon is making
01:30:48
custom silicon. Meta is making custom silicon. Talk to me about that revolution and is it actually making it
01:30:55
to the data centers yet? >> Look, it to various degrees it is. They're promoting their products.
01:30:59
They're trying to tie up data center capacity. So yes, there's multiple silicon looking for homes. I think I
01:31:05
think it's fair to say Nvidia has a massive head start. The ecosystem they've incubated the standards that
01:31:11
they're setting. So I would say the safest pathway to build out at scale early is to follow the Nvidia road maps.
01:31:17
But absolutely over time we are seeing these chips emerge. >> A and in terms of desktop computing I
01:31:25
don't know if you saw the announcement that um Dell and Nvidia are making a really powerful desktop 750 gigs of RAM
01:31:34
lot of power. You're going to be able to run some local models open source and
01:31:39
with openclaw and open source coming from uh Kimmy and a bunch of the models out in China.
01:31:47
has the hacker group, which I think you started in like I did probably in similar time periods. People are
01:31:53
starting to get really obsessed with having a 10 or $20,000 desktop setup and running this local. What do you think of
01:31:59
that trend? I'm curious. >> Yeah, I mean the breakthroughs we're seeing in software, the way it's
01:32:03
distributing power to every man in every and woman in every house and their ability to code and use products like
01:32:09
open core, the generation of demand and appetite for compute at a local level all the way through to these mega data
01:32:15
centers. It's absolutely real and as we see the emergence of agents using more
01:32:20
and more as we see autonomous vehicles and other automation, robotics, it's absolutely going to compound.
01:32:26
>> And what about nuclear? Uh the Trump administration really seemed to flip the switch on a
01:32:35
growing uh belief that hey wait, nuclear is pretty great. It's clean. It's the
01:32:41
original renewable in a way. Uh, and these new modular reactors have nothing to do with Chernobyl, Fukushima, or
01:32:48
Three-Mile Island. They're much safer. They're a completely different architecture. Have the Have those
01:32:54
started to land yet? And are you since you followed correctly in in the great state of Texas where I'm from, you
01:33:00
followed correctly that time, are you following nuclear? >> I I think you have to. I think the
01:33:05
reality is it's going to take a decade, a bit longer by the time big projects
01:33:09
can come into commissioning, but now is the time to start that conversation. and
01:33:13
put in place policies, mobilize capital, and start that ball rolling. >> Yeah. Have you do you have a data center
01:33:19
going up near nuclear? >> No, not at the moment. >> Not at the moment. But you're actively
01:33:24
tracking that activity cuz >> Yeah, this seems uh pretty inevitable. Yeah,
01:33:30
>> feels like it. >> And if that happens, what impact does it have on your industry? If if you could
01:33:35
because obviously it's happening in China and people always put the Bitcoin miners they were like the canary in the
01:33:41
coal mine near the hydro dams and near the nuclear where there was excess capacity. What impact do you think this
01:33:48
has if you could actually have small modular reactors next to data centers? >> Well, I I think it just opens up the
01:33:54
market and enhances the US's competitive advantage in this space. Like AI is
01:33:57
inevitable, robotics is inevitable. The reality is the correlation between human
01:34:02
progress and energy consumption is really really high over a very long time period. So if we can find a way to
01:34:09
unlock new generation, clean generation as nuclear and locate that more at the source and enable more compute on a
01:34:15
distributed basis, all those use cases we just discussed become easier, more fluid, faster and then you get that
01:34:22
positive flywheel around Jebans's paradox and demand. Talk to me about the architecture today of
01:34:29
Ethernet and data moving between data centers within data centers. That backbone is going through a paradigm
01:34:38
shift as well. Yeah. >> Yeah. Yeah, it is. And Jensen coins coins the term the data center is the
01:34:43
new computer. >> So you need to step back and you say right this big building is essentially
01:34:48
the old desktop PC we had under our desk at home. You go right how does that work? So all the cabling, the latency,
01:34:56
the number of hops between each GPU, how they talk to each other, the fabric around Infiniband, Ethernet, it's
01:35:02
absolutely critical because every millisecond matters in terms of performance of that cluster.
01:35:07
>> Yeah. And where do you think uh or or what do you think of Elon's uh vision?
01:35:16
It's obviously a a longer term vision of putting data centers in space and there's a couple other people working on
01:35:21
it as well. Yeah, I mean it's very hard to argue with Elon. He's been very right
01:35:27
on a number of things for a very long time. I think sitting here today, it feels exceptionally difficult given the
01:35:32
cost of moving things to space, the challenges around radiation. There's a huge amount of engineering challenges,
01:35:39
but that's never scared Elon before. So, I'm not >> qualified and he's he he's inevitably
01:35:44
right, but sometimes he's late. He might be late to the party. He might be late
01:35:49
to the dinner party. you might show up at dessert, but generally uh he nails it. How much of an issue is getting the
01:35:56
data out of the data center to consumers today? Is that not something people are
01:36:02
worried about when you're building something out in West Texas, all that data, fiber, all that's been taken care
01:36:09
of or does that become a gating issue at some point? So this was one of the big myths that we had to bust when we
01:36:15
started this business because everyone said data centers must be located close to population centers, metropolitan
01:36:20
areas. Latency is really important and we say yeah that's right latency is important but the reality is in the US
01:36:26
Texas especially there is fiber everywhere underneath the ground lots and lots and lots of it. And when you
01:36:32
look at latency from our site in the middle of the desert in West Texas down to Dallas, the big carrier hotel, six
01:36:39
millisecond roundtrip latency. What's six milliseconds? There's a thousand
01:36:44
seconds milliseconds in a second. Yeah, >> we're talking six. >> It's it's adjacent.
01:36:49
>> Yeah, it's it's not even uh Yeah, it's definitely not material. Uh listen,
01:36:54
continued success uh and uh you're hiring >> a lot of people. >> Yeah. Yeah, I think we got 129 job
01:37:03
advertisements up at the moment. >> All right, so everybody go to the Iran
01:37:06
website. Uh, and listen, company's doing fantastic. Thanks for spending some time
01:37:11
with us here at Allin GTC. >> Thanks, Jason. >> Appreciate it. I'm going all in.