0:03

Bloomberg Audio Studios, Podcasts,

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radio News.

0:20

Hello and welcome to another episode

0:22

of the Odd Lots podcast. I'm

0:24

Joe Wisenthal.

0:25

And I'm Tracy Alloway.

0:26

Tracy, you know, we've done tons of course

0:29

on like electricity and AI

0:32

and data centers and all that

0:34

stuff, but we've never actually

0:36

done like a well, we've never talked to

0:38

someone who is building data centers.

0:42

Putting it all together, you mean.

0:43

Yeah, putting it all together like what you know,

0:45

just a bunch of you know, I've had consultants,

0:47

so we talked to energy people, but like, how

0:49

does this business of essentially,

0:52

I guess, building a building, putting a bunch

0:54

of chips in there, getting the electricity, and

0:57

then in theory, selling all of that

0:59

at a markup? Like, how does it actually work?

1:01

You know?

1:01

What I was reading recently This is kind of a tangent,

1:04

but not really because we're talking about the

1:06

physical and financial

1:09

process of building these things. But

1:12

I saw this is online. There's

1:14

a guide to the like physical

1:16

Planning around an

1:19

IBM system three sixty

1:21

from like nineteen sixty three or

1:23

something, and it's two hundred

1:26

and thirteen pages long.

1:27

Have you read it yet?

1:28

I did flip through it, there's like there's

1:31

guidance on minimizing vibrations

1:34

obviously, like temperature and humidity and

1:36

stuff like that. I did not read the full two hundred

1:38

pages, but I'm kind of thinking like if

1:41

this is what if this is all

1:43

the thinking that had to go into

1:45

like one computer, albeit a supercomputer

1:48

in the nineteen sixties, but like a pretty

1:50

basic machine. When we look back on it now, how

1:53

much planning and thinking has to go into

1:55

building like these huge cloud servers

1:58

and all their associated infrastry structure,

2:00

both physical and software as well.

2:03

No, totally, and you know, we you know,

2:05

one of the ways that we've touched on this subject

2:07

a little bit is in our conversations with Steve

2:09

Eisman, who's been investing

2:12

at least as far as we know, in a lot of these like

2:14

industrial HVAC companies

2:17

and electricity gear

2:19

companies and stuff like that. So like companies

2:22

that have actually been around for a really long

2:24

time, sort of standard cyclical businesses,

2:27

and then they've like caught the secular tailwind

2:30

because with this boom in AI data

2:32

center construction, suddenly there's

2:34

this sort of continuous bid for all

2:36

their gear and services.

2:37

I'm going to start an anti vibration floor

2:40

maker or something. Do you think that's a viable

2:42

business? Does anyone care about vibrations anymore?

2:44

I am certain that in various

2:47

high tech environments you do not want

2:49

to have vibrations. You know, you have like a valuable

2:52

chips, you don't want them to be like degrading.

2:54

Because people are walking around.

2:55

Yeah, or just you know what, all the machine

2:58

and all your air conditioners and equipment

3:00

and all that stuff, you can't be having that stuff degrade.

3:03

Well, the other interesting thing that's happening

3:05

in the space now, So in addition to

3:08

the physical challenge of building a

3:10

bunch of this stuff, there's also the financial

3:12

aspect of it. And I guess

3:14

as AI becomes more and

3:16

more of a thing, and clearly, as you laid

3:19

out, there's a lot of enthusiasm around the space.

3:21

At the moment, you are seeing a bunch

3:23

of financial entities get interested as

3:26

well. So obviously venture capital has

3:28

been pouring money into the space, but we're starting

3:30

to see some new types of

3:32

financial investments in AI. And

3:35

I'm thinking about one thing in particular,

3:37

and it is the recent GPU

3:40

or chip backed loan that

3:43

was reported by the Wall Street Journal and

3:46

I think we should talk about that aspect.

3:47

Of it too, totally, because one of the things

3:49

that's happening in tech is this big

3:52

sort of shift from like, okay, we're all of

3:54

your costs in the past, where a lot of them

3:57

were sort of op x, the cost of engineers,

3:59

et cetera. And now suddenly tech

4:01

companies have to think about CAPEX for

4:03

the first time, these big upfront costs that

4:05

are in theory going to pay off for a long time, which

4:08

in theory then changes how you should think about

4:10

the financing model.

4:11

Absolutely well, I am.

4:12

Excited to say because we literally do

4:15

have the perfect guest we're

4:17

going to be speaking with, Brian Venturo.

4:19

He is the chief strategy officer at

4:22

core Weave. Corewave. For those

4:24

who don't know, it's probably the company

4:26

right now that people most associate

4:29

with being at the heart of the AI

4:33

data center boom. They have a bunch

4:35

of in video chips, they have investments from in Nvidio

4:38

right here in the sweet spot. As

4:40

you mentioned, one of the interesting things

4:42

that's going on is they not long ago

4:44

announced a debt financing facility

4:47

sit back basically by the GPUs

4:50

that they would acquire, so

4:52

literally the perfect person to understand

4:55

like the business of these

4:57

AI cloud data center

5:00

So Brian, thank you so much for coming in.

5:02

Thanks for having me. It's the second time I've been on the podcast.

5:05

That's right. We talked to Brian years ago.

5:07

It's interesting to think about at

5:09

that time because I think that may have been like twenty

5:11

twenty or twenty one, and the excitement

5:14

then was that these chips could be used

5:16

for crypto mining and other things

5:18

like sort of distributed video editing and stuff

5:20

like that, and then Ethereum

5:23

stopped using mining. But it was sort of fortuitous

5:25

timing because right around then AI went

5:28

crazy and that's probably I

5:30

don't know, in my view, maybe a higher use of these

5:32

chips before we get to that. Do you worry

5:34

about vibration in your data

5:37

center?

5:38

So everywhere that's close to a

5:40

fault line is designed around

5:43

that and is part of code. So you

5:45

know, the engineering firms that help us build these data

5:47

centers have taken all of that into account, and all

5:49

of our racks are you know, seismically

5:53

tuned to make sure that we can withstand

5:55

the normal vibration from the Earth. So

5:58

yeah, it's been something that's been in those annuals

6:00

for a long time. Some of our hardwer

6:02

manufacturers actually have vibration testing labs

6:05

where they put the racks on top of a big

6:07

kind of platform that shakes, and it's pretty

6:09

dangerous and uncontrollable and hard to watch.

6:12

But you know, there's people out there that have been solving

6:14

this problem for decades.

6:15

Now I missed the boat on that business

6:17

choir. It sounds like it's

6:19

been dealt with decades ago. Okay, well,

6:21

actually, why didn't I start with a very

6:23

simple question, which is when

6:26

when you're looking at the business of core

6:29

Weave, so a specialized

6:32

cloud service provider, let's put

6:34

it that way, what are the different components

6:37

that you have to think about? You know, Joe kind

6:39

of alluded to all these different ingredients

6:41

that go into the business, but walk us

6:43

through what those actually are.

6:46

Sure, so, there's there's three pieces that as

6:48

a management team, we think are incredibly critical to the

6:50

business. The first is,

6:53

you know, our technology services that we provide on

6:55

top of the hardware, right and this is

6:57

everything from the software layer through the support organization

7:00

to you know, how we work with our customers. This

7:02

isn't the type of thing that you just go plug in

7:04

and it works. In these large supercomputer

7:06

clusters, there may be two hundred thousand infinibank

7:10

connections that connect all the GPUs together, and

7:12

if one of those connections fails for whatever

7:14

reason, the job will completely

7:16

stop and have to restart from its previous

7:18

checkpoints. So, you know, everything that we do

7:20

on the software side and engineering side is to make sure

7:22

these clusters are as resilient and performant

7:25

as they possibly can be to ensure

7:27

you know, our customers can run their jobs, you

7:30

know, increase efficiency and get all

7:33

of the kind of monetary value

7:35

they can out of the chips. So technology

7:37

piece is really hard. It's something that I think

7:39

is very overlooked by the market, but it's

7:42

just as hard as the two other kind of pieces

7:44

that this business stands on. The second

7:46

is, you know, the physical nature of the business

7:49

in that you have to actually build and run these data

7:51

centers and those hundreds of thousands

7:53

connections inside the supercomputers. Like

7:55

somebody has to go put those together and make sure

7:57

they're clean and make sure they're labeled correctly

7:59

to be able to remediate failures. And

8:02

when you're building a thirty two thousand

8:04

GPU supercomputer that is one

8:06

of the fastest three computers in the planet.

8:09

You know, you're running thousands of miles

8:12

of cable inside a very dense

8:14

space, right. These data centers are built

8:16

very tiny to make sure that you can connect

8:18

everything together, and that becomes

8:20

a huge logistical challenge. So, you know,

8:22

the data centerpiece, which we're going to talk more about today,

8:25

is very challenging to design for the use case.

8:28

And then the third piece is how the hell do you finance

8:30

the whole thing? Right, And you know, we've

8:32

been very successful in the

8:34

financing aspect of this, but you know,

8:36

whether you're financing technology operations

8:39

or the physical build of these things, it is an

8:41

incredibly capital intensive business

8:43

and constructing those financial

8:45

instruments to back our business is

8:47

very hard, and we have to be very very thoughtful

8:50

around who the counterparties are, how

8:52

do we think about credit risk, how do our investors

8:54

think about that credit risk, How do we deal

8:56

with contingencies inside the contracts to

8:59

make sure that they are financeable on the scale that we've

9:01

done over the last eighteen months.

9:02

Talk to us a little bit more. We could probably

9:05

talk about data center financing

9:07

credit and have have that be

9:10

a whole episode, but when you think about you

9:12

have to think about your counter party's

9:15

credit risk. Talk to us a little bit about what

9:17

you're who those are, what the type

9:19

of entity is.

9:20

Sure, so I'll get myself in trouble

9:22

if I just start naming them off. Yeah,

9:24

some of them are more public than others. You

9:27

know, I'm going to refer to them as you

9:29

know, hyperscale customers. We

9:31

have AI lab customers, we

9:33

have large enterprise customers.

9:36

We've really constructed our portfolio of business

9:38

around the idea that you know, if we're going

9:40

to build ten billion dollars of infrastructure for somebody,

9:43

we have to know there's a balance sheet we can lean into

9:45

behind it, right, and we're

9:47

the pace at which we've grown. You

9:51

know, our customers are demanding scale

9:53

so quickly that the credit

9:55

of the counterparty is incredibly important

9:57

to find the low cost of capital we have with these ADIT

10:00

facilities we've announced, right, So you know, when

10:02

people talk about how this is a credit facility

10:04

backed by GPUs, it's not really backed

10:06

by GPUs. It's backed by you know, commercial

10:08

contracts with large international

10:11

enterprises that may have triple a credit, right,

10:14

So you know it's it's the framing of the.

10:15

Aid receivables finance.

10:17

Basically it's closer to trade receivables

10:19

financing than it is Hey, we're going to go leverage up

10:21

a bunch of GPUs and see what happens.

10:23

Huh, okay, well walk us through the

10:25

I guess like the sequence

10:28

in some of these financing agreements. So you

10:31

know, if a customer comes to you and they

10:33

say, we want a certain amount of

10:35

compute, can you do this for us?

10:37

And you start going down the process

10:40

of like, okay, what do we need to make this happen?

10:43

What do those like financial agreements

10:45

actually look like. And who's bearing the initial

10:48

risk? Is it the customer? Is it you?

10:51

Good question?

10:52

So when we're approached by a customer, right, you know,

10:54

the ask is typically going to be pretty

10:56

pretty general, and they're going to say,

10:58

hey, we're looking for facity in Q one

11:00

of next year. What's the largest thing you can do? And

11:04

you know, we take that effectively as

11:06

a mandate of okay, hey, you know this customer.

11:08

We're not business.

11:09

But before you know, we're really comfortable with them, we know that

11:11

we're going to get a contract done. We'll go out and we'll

11:13

try to secure an asset to you know, to go

11:15

build it. And we may have it in our portfolio already. We

11:17

maybe it may have been a strategic investment that we made.

11:20

But once we find the data center asset, that's when we go back

11:22

to the customer and say, okay, like we can commit to doing

11:24

this. This is the timeline. We'll structure

11:26

a contract around it. Depending upon

11:28

who the customer is. There may or may not be some credit

11:30

support associated with it around the scaling

11:33

of the you know, that asset, and

11:35

then we'll get a commercial contract

11:37

in place, and we will initially

11:39

fund a large portion of that

11:42

project off of our own balance sheet. Right.

11:44

It's why you also see us raising equity, right,

11:46

is we have to have the capital to accelerate the business.

11:49

And then once we have that and we're making progress,

11:51

you know, think about it as you're building

11:53

real estate. Right, you have a construction loan and then you have a stabilized

11:56

asset loan, and we basically

11:58

fund the construction loan piece off of our balance sheet.

12:00

When we get to a more stabilized asset, that's when we go

12:02

out and kind of do that trade financing

12:04

or trade receivables financing our with

12:06

our partner lenders. You know, they worked with

12:08

us before, they know that these things are going to stand up, They know

12:10

how they perform, and at that point in

12:12

time, it's it's pretty easy for them to underwrite that risk.

12:31

It's funny. Tracy and I had coffee with

12:33

someone yesterday who

12:36

is sort of in the space I want docs here,

12:39

And I was like, what should we ask Brian? And he's like, ask

12:41

him why he won't let my company, why

12:43

I'm still on the waiting list or something, or why he hasn't

12:45

approved my company to use

12:48

core weave. But what are some of the

12:50

bars or the threshold? So you know,

12:52

I apparently there's a lot of demand for

12:54

compute these days. What does it

12:56

take to get in the door and get access

12:59

to some of your chips and electricity?

13:01

So it's it's a great question. It's

13:04

a question that we get all the time from our sales

13:06

teams, right is you know, we're faced a lot

13:08

with a sales team that is incredible

13:10

at delivering product to customer

13:13

and we don't have anything to sell. And it's

13:15

kind of my job. As the strategy

13:18

organization at Core, We've were responsible

13:20

for two things. It's product and

13:22

infrastructure. Capacity, and you

13:24

know, I spend most of my time going out and finding those

13:26

data centers and being able to support those deals and

13:29

the growth that we had over the past twelve months.

13:32

The company was pretty flat out right

13:34

in building and delivering this infrastructure. You

13:36

know, publicly on our documentation page

13:39

it says that we have three regions. We'll have twenty

13:41

eight regions online by the end of the year. I think

13:43

we delivered eleven of them in Q one alone,

13:45

Right, So we're building at a scale, you

13:48

know, i'd say that almost larger than some of the

13:50

three big hyperscalers. But in

13:53

terms of how do you become a customer of Core,

13:55

it's really relationship driven, right is. We

13:57

want to make sure that we're going to be able to be successfu

14:00

with our customers and have an engineering relationship

14:02

and we're aligned on what they need and.

14:04

We can deliver what they need.

14:05

The last thing that we want is for somebody to walk in the door

14:08

and say, hey, I need this for three weeks

14:10

and two weeks into it, they're unhappy and

14:13

we can't give them what they need to be successful. Right is,

14:15

you know, our customers are making such large

14:17

investments in this infrastructure, that we have

14:19

to have, you know, a lot of conviction

14:22

that we will be successful with them

14:24

and provide a good experience. So it's

14:26

not that we're trying to keep people out, it's

14:28

we're trying to ensure positive experiences

14:30

for people that we do bring on board.

14:32

Do you build complete housed

14:35

facilities or is it all you're

14:37

going to bring your chips and expertise into

14:40

an existing Tier one data

14:42

center and essentially rent floor space from them.

14:44

Yeah, so a year ago it was we

14:47

were effectively just a co location tenant, and

14:49

now we've gone a lot more vertical

14:51

for some strategic builds where

14:54

we're either a partner in the project where we own equity

14:56

and the development company, or we're building the project

14:58

ourselves. We've been scaling that team up

15:01

over the past six months, and we had

15:03

to at our scale to be able to guarantee

15:05

outcomes. Right, is, we were in a position

15:07

where we had data centers getting delayed with things

15:09

that weren't communicated to us, and

15:11

you know, we had to go build the capability to handle

15:14

that situation and you know, make sure we

15:16

can still deliver for our customers.

15:17

One of the differentiators that you and some

15:20

of your colleagues have emphasized previously, is

15:22

this idea that you're designing the

15:24

server clusters kind of from the ground up,

15:27

whereas like other hyperscalers

15:30

maybe are doing it on a sort of different

15:32

mass scale. But can you walk us through

15:34

like what is the benefit

15:37

of doing it that way? And then secondly,

15:40

does that end up being an impediment

15:43

to I guess efficiencies

15:45

or economics of scale and

15:47

how customized Like do you really get here?

15:49

So from a customization perspective, it's

15:52

aggressive, right, And I say

15:54

that because you know, our customers are

15:56

involved in the design of you know, our network

15:58

topology of the East West fabric for the GPU

16:00

to GPU communication, for things

16:02

like cooling. You know, I have customers that toward

16:04

the data centers under construction process

16:06

with me like once a week, and it's

16:10

to the point that they're

16:12

impacting how we build

16:15

the base level networking products to ensure

16:17

they have enough throughput to you

16:19

know, meet their use case needs. Whereas

16:21

in you know, what I what we call the legacy

16:24

hyperscaler installations, It

16:26

maybe they have a couple

16:28

thousand GPUs that are in a data center that was really

16:30

built for CPU computation or

16:33

to provide services to ten thousand customers

16:35

that is really with a much lower base

16:38

expectation of what they're going to be doing. Right,

16:40

So it's things around connectivity

16:42

for storage, it's things around power and cooling,

16:45

It's things around how they want to

16:47

be able to optimize their workloads

16:50

inside of the GPU to GPU communication.

16:52

You know, we have some customers that even customize

16:54

their infiniban fabrics and the size

16:57

of those fabrics and how they connect together. So you know,

16:59

we work with them to really understand what their use case is,

17:01

where they're worried currently and in the future, and

17:03

then design around that. So it's a pretty

17:05

comprehensive program when we're building

17:07

something from the ground up.

17:09

And how much complexity does that introduce

17:11

into the business and does it end up being

17:14

a limiting factor on your growth or

17:16

is demand just so strong at the

17:18

moment that it's not really an issue.

17:20

The customization that we do is typically going to be

17:22

above what our base level offering is, meaning

17:25

the environment will be more performant because

17:27

the customer required it. So it's typically

17:29

not going to be limiting to us from a future

17:32

you know, revenue or resale perspective. It's

17:34

going to make the asset more valuable. But you

17:37

know, we're we're designing our reference

17:39

builds for ninety nine percent of use cases,

17:41

and we're trying to price it efficiently, and then

17:43

when customer wants something above and beyond, you

17:45

know, it impacts price. But for these installations it's probably

17:48

deminimus, right, So you know,

17:50

it doesn't really add a lot of complexity for us

17:52

from a business perspective, so we're

17:54

happy to do it.

17:55

You mentioned that some of the hyperscalers,

17:58

yes they have GPUs, but they like

18:00

built in an environment for

18:03

like legacy CPUs.

18:06

Can you talk a little bit about a just

18:08

the difference between the legacy

18:11

architectures and the new one and then in

18:13

the design, like what kind of bottlenecks you run

18:15

into? Is there issues with labor

18:17

like the types of people who know how to string these

18:19

things together well, or other different

18:22

cooling requirements for this type

18:24

of compute environment that

18:26

did not exist, Like what are what are the challenges

18:29

in building out these sort of like fundamentally

18:32

different environments.

18:33

Yeah, so that that's changed also in the last

18:35

twelve months in that you used

18:38

to be able to take what was an enterprise data center

18:40

and you know, creatively retrofit it

18:42

to be capable of supporting the AI

18:44

workloads to a certain density level.

18:46

Okay, right, Like instead of filling up a cabinet,

18:48

you could put two servers in a cabinet and you could

18:51

meet the power and cooling requirements

18:53

of the installation. It you use

18:55

a lot more floor space, but it was

18:57

doable. One of the incredible things about

19:00

is that they're always pushing the boundary on the engineering

19:02

side, and their next generation of chips

19:04

is largely dependent upon much

19:06

more aggressive heat transfer, and they've introduced liquid

19:09

cooling to the reference architectures. So as

19:11

liquid cooling comes in, it changes

19:13

what type of data center is capable of doing

19:15

this, and it truly requires

19:18

that ground up redesign and

19:20

almost greenfield only build

19:22

to support it. Is you've gone from an environment

19:24

where you could take an enterprise data center

19:26

and deploy less servers per cabinet and get

19:28

away with it to hey, nobody's

19:31

ever built this before. It's at an incredible

19:33

scale and it has to happen on a yearly

19:35

cadence now, so the data center

19:37

industry is in't a full sprint to figure

19:39

out, Okay, how do we do this? How do we do it quickly?

19:42

How do we operationalize it right? And

19:44

you know that's kind of where I've been spending all of my time

19:46

over the past six months.

19:48

Can I ask a really basic question, and

19:50

we've done episodes on this, but I would

19:52

be very interested in your opinion, But

19:55

why does it feel like customers

19:58

and AI customers in particular, are

20:01

so I don't know if addicted

20:03

is the right word, but like so devoted

20:05

to in Nvidia chips, Like what

20:08

is it about them specifically that

20:11

is so attractive? How much

20:13

of it is due to like the technology

20:15

versus say, maybe the interoperability.

20:18

So you have to understand that when you're

20:20

an AI lab that has

20:22

just started and it is a

20:25

it's an arms race in the industry to deliver product

20:27

and models as fast as possible, that it's

20:29

an existential risk to you that

20:32

you don't have your infrastructure be

20:36

like your Achilles heel. Right, And

20:38

and Vidia has proven to be a

20:41

number of things. One is they're

20:43

the engineers of the best products, right.

20:47

They are an engineering organization

20:49

first, and that they identify and solve problems.

20:51

They push the limits. You know, they're willing to

20:53

listen to customers and help you solve problems

20:55

and design things around new use cases.

20:58

But it's not just creating good hardware.

21:01

It's creating good hardware that's scales and they

21:03

can support at scale. And when you're building

21:05

these installations that are hundreds of thousands of components

21:08

on the accelerator side and the infinband link

21:10

side, it all has to work together well. And

21:13

when you go to somebody like in Video that

21:15

has done this for so long at scale, with

21:17

such engineering expertise, they eliminate

21:20

so much of that existential risk for these startups. Right.

21:22

So when I look at it and I see some of these smaller

21:25

startups saying we're going to go a different route, I'm like, what

21:27

are you doing? Right? You're taking

21:30

so much risk for no reason here? Right,

21:32

this is a proven solution, it's the best

21:34

solution, and it has the most community support,

21:37

right, Like go the easy path because the venture

21:39

you're embarking on is hard enough.

21:41

Is it like the old what was that old adage?

21:44

Like no one ever got fired for buying Microsoft?

21:46

Is it like no, yeah, or IBM

21:49

something like that.

21:50

But the thing here is that it's not even

21:53

nobody's getting fired for buying the tried

21:55

and true and slower moving thing. It's

21:58

nobody's getting fired for buying the tried, true

22:00

and best performing and you know bleeding

22:02

edge thing.

22:03

Right.

22:03

So I look at the folks that are

22:05

buying other products and investing and other

22:08

products almost as like they're trying. They

22:10

almost have a chip on their shoulder and they're going against the mold

22:12

just to do it.

22:14

There are competitors to in video

22:16

that they claim cheaper or

22:18

more application specific

22:21

chips. I think Intel came

22:23

out with something like that. First of

22:25

all, from the core weave perspective,

22:28

are you all in on in video hardware?

22:31

We are?

22:32

Could that change?

22:33

The party line is that we're always going

22:35

to be driven by customers, right, and

22:37

we're going to be driven by customers to the

22:40

chip that is most performant, provides

22:43

the best TCO, is best supported

22:46

and right now and in what I think is

22:48

the foreseeable future, like I believe

22:50

that is strongly in video.

22:52

Think about okay, maybe one day you guys IPO

22:54

And I'm looking through the risk factors, and one of

22:56

the risk factors, right, we have a heavy

22:59

reliance on in video chips. There is a risk

23:01

that a competitor thing, what would it take

23:03

for one of these competitors

23:05

that does ostensibly over cheaper or hardware

23:08

or perhaps lower electricity

23:10

consumption in your view, To

23:13

make one of those risk factors real.

23:15

I think that they'd have to be willing to quote

23:18

unquote buy the market. And when

23:20

I say that, I mean they'd have to subsidize their hardware

23:23

to get a material market share.

23:26

And from what I've seen, there's no one else that's really

23:28

been willing to do that so far.

23:30

And what about Meta with Piedtorch

23:32

and all their chips.

23:33

So they're in house chips. I think

23:36

they have those for very very specific production

23:38

applications, but they're

23:40

not really general purpose chips, okay,

23:43

right, And I think that when you're building something for general

23:45

purpose and there has to be flexibility in the use case.

23:48

While you can go build a custom AASIC to solve

23:50

very specific problems, I don't think

23:52

it makes sense to invest in those to go

23:55

to be a five year ass set if you don't necessarily know what you're

23:57

going to do with it.

23:58

So you talked about the advantages

24:01

of Nvidia hardware like the chips

24:03

themselves, but one of the things you sometimes hear

24:06

is that those same chips might perform differently

24:09

in different clouds. So what is

24:11

it that you can do to sort

24:13

of boost the performance of the same chip

24:16

in your structure or

24:19

ecosystem versus say an AWS

24:21

or someone like that.

24:22

Sure, a great question. We do a lot of work around

24:24

this internally and it's a big part

24:26

of our technical differentiation. And

24:29

what we call it internally is mission control. And

24:31

mission control is effectively a portfolio of

24:33

different services that we run on our infrastructure

24:36

to make sure that these incredibly complex

24:38

supercomputers are healthy and performant

24:41

and are optimized, you

24:43

know, where we take a lot of that responsibility

24:46

off of our customer engineering teams, right,

24:48

And it sounds like that might be an easy

24:50

lift, but when you're running supercomputer

24:53

scale, you know you need a team of fifty to

24:55

do that, right, So we provide a ton of software automation

24:57

around that, providing that health checking

24:59

and observed ability to our customers. But

25:01

it's also the engineering engagement, right, is

25:04

you know, working with our customers to understand, Okay,

25:06

what are you doing, what's the best way to optimize

25:08

this, how do we you know, how did we design

25:10

the data center to be more performant, to make sure

25:12

your storage solution was correct, Your networking

25:15

solution was correct. So it's not just

25:17

a hey core we've provides

25:19

like this one little thing that makes it better. It's

25:21

the comprehensive solutions, starting from the data

25:24

center design, through the software automation

25:26

and health checking and monitoring, via mission control,

25:28

via the engineering relationships that really add

25:30

that value.

25:31

Let's talk about electricity, because this has become

25:34

this huge talking point that this is the major

25:36

constraint and now that you're becoming more vertically integrated

25:39

and having to stand up more of your operations.

25:42

We talked to one guy formerly at Microsoft

25:44

who said, you know, one of the issues that there may

25:47

be a backlash in some communities who don't

25:49

want, you know, their scarce

25:51

electricity to go to data centers when

25:53

they could go to household air conditioning. What

25:55

are you running into right now or what are you

25:57

seeing?

25:58

So we've been very very selective

26:00

on where we put data centers. We don't

26:02

have anything in Ashburn, Virginia, right and the Northern

26:05

Virginia market, I think is incredibly saturated.

26:07

There's a lot of growing backlash in that market

26:09

around power usage and you know,

26:12

just thinking about how do you get enough diesel trucks in

26:14

there to refill generators that they have a prolonged

26:16

outage.

26:17

Right.

26:17

So I think that there's some markets where

26:19

it's just like okay, like to stay away from that, and

26:22

when the grids have issues and

26:25

that market hasn't really had an issue yet, it

26:27

becomes an acute problem immediately. Like just think

26:29

about the Texas power market crisis

26:31

back in I think it's twenty twenty one, twenty

26:34

twenty, where the grid wasn't really set up to be able

26:36

to handle the frigid temperatures

26:38

and they had natural gas valves that were

26:40

freezing off at the natural gas generation

26:43

plants that didn't allow them to actually come

26:45

online and produce electricity no matter how high

26:47

the price was. Right. So there's there's going

26:49

to be these acute issues that you know, people

26:51

are going to learn from and the regulators are going to learn from

26:54

to make sure they don't happen again. And we're

26:56

kind of citing our our plants and

26:58

markets where our data centers and markets where

27:00

we think the grid infrastructure is capable of handling

27:02

it right, And it's not just is there

27:05

enough power, it's also on things.

27:07

You know, AI workloads are pretty

27:09

volatile in how much power they use, and they're

27:11

volatile because you know, every fifteen minutes

27:13

or every thirty minutes, you effectively stop

27:15

the job to save the progress you've

27:17

made, right, and it's so expensive

27:20

to run these clusters that you don't want to lose hundreds

27:22

of thousands of dollars of progress, So they

27:24

take a minute, they do what's called checkpointing, where

27:26

they write the current state of the job back

27:28

to storage, and that checkpointing

27:31

time, your power usage basically goes from one hundred

27:33

percent to like ten percent, and then

27:35

it goes right back up again when it's done saving it. So

27:38

that load volatility on a local

27:40

market will create either voltage spikes

27:42

or voltage SAgs, and a voltage sag

27:45

is what you see is what causes a brown out

27:47

that we used to see a lot of times when people turn their cognitioners

27:49

on and it's thinking through, Okay, how do I ensure

27:52

that, you know, my AI installation

27:55

doesn't cause a brown out when people are turning their

27:58

you know, during checkpointing, when people are turning the

28:00

air conditioners on. Like that's the type of stuff that

28:02

we're thoughtful around, like how do we make sure we don't do this right.

28:05

And you know, talking to engineerings and

28:07

in Video's engineering expertise, like they're

28:09

working on this problem as well, and there they've

28:12

solved this for the next generation. So

28:15

it's everything from is there enough power there? What's

28:17

the source of that power? You know, how clean is

28:19

it? How do we make sure that we're investing in solar

28:21

and stuff in the area to make sure that we're not

28:23

just taking power from the grid. To also

28:25

when we're using that power, how is it going to impact the consumers

28:28

around us?

28:29

I want to ask you more about what in Nvidia

28:31

is doing, but just on that note, what's

28:33

the most important metric for

28:36

evaluating a data center's

28:38

quality or performance? Is it like

28:41

days without brownouts or an

28:43

interrupted power supply, or is it measures

28:45

of efficiency like power usage effectiveness

28:48

or something like that. If I'm serving a bunch

28:50

of data centers, I want to pick a good one. What

28:52

should I be looking for?

28:53

So right now, the market's pretty thin, So

28:56

right now.

28:58

Options Okay, I

29:00

imagine I'm like the biggest customer on earth

29:02

and I can get in anywhere. What should

29:04

I be looking for?

29:06

So it's the first thing

29:08

goes back to the electricity piece, right, is

29:10

the grid stable? Is there enough power supply?

29:13

You know, is there excess renewable generation

29:15

in the area that doesn't have the ability to make it

29:17

too downstream consumers? Right? A lot of the

29:19

renewables that we have in the US are built

29:21

in places that don't necessarily have the consumers.

29:24

So you're citing these data centers

29:26

in places where you have this excess supply,

29:29

So that that's the first piece, right, is how

29:31

good is the electricity supply? And how

29:34

angry are the people around me going to be if I take it? Now?

29:37

You go from there into everything else is

29:39

kind of solvable, right, And the way

29:41

that you design it, and if you're building a green field,

29:43

it's okay. You know what type of ups systems

29:46

am I putting in? Are they capable of handling

29:48

that load volatility?

29:50

You know?

29:50

How am I thinking about my cooling solutions?

29:54

There's been a big shift to liquid

29:56

cooling, right, and liquid

29:58

cooling from a PE perspective, isn't

30:00

a thirty to forty percent decrease

30:03

in electricity utilization like people think?

30:05

It's more like sixty to seventy percent, right,

30:08

And the reason for that is it's not just the

30:11

efficiency of the data center plant.

30:14

It's also that now if you're not cooling things

30:16

with air, you don't have to run the fans inside the servers

30:18

as well. And for these AI installations,

30:21

because they're so dense, the fans consume

30:23

a lot of energy. Right. So everything

30:25

that we're building now is a combination of liquid

30:27

and air cooling, right. And the liquid

30:29

cooling piece has solved the PUE issue,

30:32

right, And we're everything we're doing is trying

30:34

to say, Okay, how much power

30:36

can we use only for running our critical

30:39

IT operations versus

30:42

cooling the environment making sure the environment's

30:44

running correctly from a resiliency perspective, And

30:47

there's been big strides made there over the last whole months.

31:06

Does colocation trump

31:08

grid reliability? Like if I'm Elon

31:11

Musk building some sort of

31:13

new AI thing as I think he's doing

31:16

in Texas, say like,

31:18

am I just going to have to find a data center

31:20

in Texas? Or how much flexibility do

31:22

I have to use one

31:24

further away?

31:25

So great question, it's

31:28

it's a different answer for different use cases

31:31

at different times. And right

31:33

now, you know, we were in the middle of this rush

31:35

to train whether they're open

31:37

source or proprietary foundation models at

31:39

the largest, most valuable companies in the world, and they're

31:42

mostly worried about access to contiguous

31:45

compute capacity. Right, how much compute

31:47

can I get in one location, all connected together

31:49

so I can go faster than the next guy. But

31:52

when the models are trained, they

31:54

want that compute to then be local to their

31:56

customer base, right, is how do they take it

31:59

from the middle of nowhere and then go serve it

32:01

in the metropolitan markets. And as the

32:03

use cases are more distilled and they get more

32:05

real time, think like the

32:08

type ahead suggestions that you get in your Gmail

32:10

account right as you're typing something, and it's getting

32:12

better and better. It's you know, that's

32:14

an AI model somewhere like predicting

32:16

what you would want to say next, And they

32:19

want to make sure that's delivered at human speed.

32:21

So that human speed is a

32:24

latency consideration. Right as

32:26

you're citing those GPUs and you're citing that compute

32:28

to be locals to the people that are using it. So that

32:32

move has started probably

32:34

four months ago where we saw customers

32:37

finally becoming concern around latency for

32:39

their serving use cases. So initially

32:41

training people don't really care where it is cheap

32:43

power, reliable grid. They just need

32:45

it all contiguous and they need it fast. And then

32:48

down the road as their applications find

32:50

success, they're more worried about where the compute is for their customers.

32:53

What are some of the areas that are going to be the next

32:55

Northern Virginia when it comes to data

32:57

center clusters.

32:59

So I think we're seeing it in Atlanta

33:01

already, where Georgia has

33:03

paused or has attempted to pause some

33:06

of their tax incentives around it because they want to make

33:08

sure they do grid studies. I

33:11

think that we're we're probably going to see

33:13

it in some of the other hotspots.

33:14

You know.

33:16

You know, you see aws up in Oregon who

33:18

is trying to find creative alternative

33:20

ways to power their data centers from

33:23

non grid generation to alleviate some concerns

33:25

there. But you

33:27

know, I think that the market

33:29

has to solve this problem. And

33:31

you know, you're starting to see some of the startups around

33:34

nuclear generation in you

33:36

know, the small reactors at the data center

33:38

level. As people are you know, being

33:40

thoughtful for five to ten years from now, do.

33:42

You have any influence on the

33:44

type of power being built in

33:47

certain areas? You know, could you say to

33:49

a utility company of some sort, we're

33:52

here, we need access to energy,

33:54

but we want it to come in a particular

33:57

form.

33:57

So you can. But you have to understand that

33:59

the investment cycles and the physical build

34:01

cycles for those are so much longer than you

34:04

know how quickly our customers need

34:06

infrastructure, right. So you may go to a market

34:08

and say, hey, we're going to be here over the next ten years,

34:10

we'd like you to install X y Z, you know, renewable,

34:13

and they're happy to do it. It's just that

34:15

you have to find a medium term solution while

34:17

that's being built.

34:19

I'm going to ask a question. So there was a news

34:21

story, and maybe you won't comment on the

34:23

news story, specifically about core

34:25

Weave having made a one billion dollar offer

34:27

for a bitcoin miner called core

34:29

Scientific, apparently

34:32

was rejected. According to things I've read in

34:34

the news. Setting aside this

34:37

deal, there's you know, there used to

34:39

be a lot of crypto mining and then ethereum

34:42

went from proof of work to proof

34:44

of steak and that all basically disappeared overnight.

34:46

There are still bitcoin miners. I never

34:49

get the impression it's like that great of business.

34:51

But whatever are there bitcoin

34:53

miners that have latent value

34:55

in the fact that they I mean,

34:57

I know those chips don't the bitcoin mining

34:59

chip, the actual acis don't work for AI

35:02

because all they are is bitcoin mining

35:04

chips. But are there by dint

35:06

of their access to electricity, space,

35:08

et cetera, is there a fair amount

35:11

of latent value in the

35:13

general physical structures that they've built

35:15

for the mining.

35:16

So I'm just not going to answer your question at all.

35:19

I'm gonna go on a tangent.

35:20

Okay, that's fine.

35:21

So I think that when

35:23

I think about core Weave and what our

35:25

mission is, it's to find

35:28

creative solutions to problems in in

35:30

you know, various markets, and those

35:33

various markets can be blocking for us

35:35

and our customers to.

35:36

Achieve our goals.

35:37

So if power is a concern

35:39

for us, and power availability

35:41

and substations and substation.

35:43

Transform, coin miners definitely have access to power.

35:46

That that is true.

35:47

I'm just stating fact you could keep

35:49

doing it.

35:50

So you know, as we go and we try

35:52

to solve these problems, you know, we're

35:55

going to go to places that others

35:57

may not have thought of, and we're

35:59

going to go do due diligence and I'm

36:01

going to personally go and walk the sites and I'm

36:04

going to you know, look through and see,

36:06

okay, can we.

36:07

Pull this off?

36:08

And we're going to get our engineering partners in

36:10

to help us design retrofits. And

36:13

you know, we're going to do deals with the companies

36:15

that we believe have the ability to provide

36:17

us value.

36:19

Since we're doing stuff in the news. This

36:22

has been in the news for a while, so it doesn't really count.

36:24

But the new Nvidia

36:26

chips, the GB two hundreds,

36:29

what will those do for core weave

36:31

and when would you expect to get them?

36:33

What will they do for us? It's more about what they're

36:35

going to do for our customers, right, and

36:38

I think.

36:38

That they are.

36:41

This is a great question. They

36:44

are going to open up a

36:46

lot of both training and inference

36:48

use cases in the AI side

36:51

that I think our customers have

36:53

been blocked by UH with

36:56

the existing generation in that

36:58

you're now able to think seventy

37:01

two of these GPUs together to work almost

37:03

as one unit, and previously that

37:05

was limited to eight. They have

37:07

a much larger what's called the frame buffer, which

37:09

is how much memory that's usable for their matrix operations.

37:13

So you know, I think that we're going to see

37:15

a lot of new use cases show up for this stuff,

37:17

but I think it extends well

37:19

beyond AI as well, and

37:22

it's going to be a lot more useful for things like scientific

37:24

computing. One of the things

37:26

that has me really excited is the computational

37:29

fluidynamics and I'm specifically

37:31

thinking about the uses for that in F

37:33

one under the new regulation in twenty twenty

37:35

six. I'm excited for the

37:38

new platform. I think in a year and a

37:40

half people are going to be using it for things that are different

37:42

than anybody expects today. And

37:45

that's to me. The pace at which

37:48

this is changing is the piece that's really cool.

37:50

Wait, I'm sorry, I hate sports.

37:52

What's the six? Explain

37:55

how the invidio is.

37:56

Yeah, So the F one platform,

37:58

they have very tight restrictions around what type

38:00

of compute and how much compute you can use to do aerodynamic

38:03

testing in your cars, and you can either

38:05

do real life testing in a wind tunnel or you can

38:08

do it through CFD analysis. And

38:11

what are the great uses for the you

38:13

know, the Grace Blackwell and the Grace Hopper architectures.

38:16

Impairing that Grace super chip with

38:18

the GPU is they're great for CFD

38:21

workloads, right, and the.

38:23

DAFD stands for computational fluid

38:26

dynamics yep, yep.

38:27

And the regulations around the existing

38:29

program in F one are they're only able

38:31

to use CPUs. They have very like specific

38:34

limitations around it. But there's been a lot of talk of

38:36

that changing for twenty twenty six

38:38

car models, and for me, like,

38:40

that's pretty cool and I'm gung

38:43

ho excited about possibly supporting

38:45

that.

38:46

That does sound very fun. I

38:48

want to get back to actually the financing a little

38:50

bit because I guess two

38:52

questions. So the logic

38:55

of why you would borrow

38:57

money both I guess for

38:59

the equal position of chips, and the chips

39:01

are sort of collateral, but I understand they're not really

39:04

chip back loans per se.

39:07

A. Do you see your clients

39:09

getting more into debt financing

39:12

rather than equity financing. I mean, there's a whole

39:14

generation of software companies

39:17

from the Zerp era that was just you know, all

39:20

equity and never had any debt at all,

39:22

and they never really had to think about like their

39:24

compute costs, or they did, but not

39:26

as much. Do you think

39:29

that will rise their own use of

39:31

debt instead of equity in terms of their own

39:33

financing. And another topic

39:35

we talk about a lot on the show private credit, like

39:38

there is there an emergence of an ecosystem

39:40

of lenders for whom this is

39:43

going to become a specialty of some

39:45

sort.

39:46

So the first piece of the question, I don't believe

39:48

that the venture backed kind of AI lab

39:50

startups will ever take on debt in this type

39:53

of environment, largely

39:55

because they don't have the collateral to back

39:57

it. If they're buying cloud services to run their infrastructure.

40:00

And you may see some that start

40:02

to buy their own infrastructure and to do that themselves,

40:05

but it is a herculean task to do

40:07

this at scale. Right, There's a reason why clouds

40:09

exist is that there's a lot of complexity that they

40:11

abstract away. On the second question

40:13

around are is there a private credit sector

40:15

that's going to be built to do this? I think that

40:18

it's more you're seeing public lenders

40:20

that are extending into the private credit

40:22

space because the opportunities are there. And

40:25

I'm going to give you the party

40:27

line answer that my CEO gives all

40:29

the time is that you know, as we're

40:31

thinking about financing our business, the

40:33

biggest thing for us is our cost to capital, and

40:36

we're always going to do the things that provide us the lowest

40:38

cost of capital. And you know the lenders

40:41

that we work with, including Blackstone, that

40:43

have been so wonderful for us, you know, them

40:45

extending on the private credit side as

40:47

we go to the public markets because we're

40:49

dragged there by cost of capital concerns, I

40:52

would expect them to be involved as well, right,

40:54

So, I think it's a continuation of the business

40:56

they've been doing in the public markets, just kind of extending into

40:58

this capital intensive business.

41:00

Wait, what was I guess you

41:02

can't get into specific details, but

41:04

my impression was for these types

41:07

of loans that the interest rate is usually

41:09

higher than like a basic bank

41:11

loan or say issuing a

41:13

corporate bond.

41:15

I would definitely say our cost of capital is lower

41:17

than some of the corporate issuance is out there, Okay,

41:20

but you know it's definitely

41:22

higher than if our cost of capital today

41:24

is definitely higher than if we were republican public entity.

41:27

But specifically on the GPU backed

41:29

loans, and I know you keep saying it's not really a

41:32

GPU back loan, but that's sort of

41:34

an uphill battle to call it trade

41:36

receivables financing instead. It sounds

41:38

so much better that way, I know, I know, but like

41:41

on that in particular, Okay,

41:43

there's collateral, so maybe that brings

41:45

the overall like borrowing rate down.

41:47

But on the other hand, it's kind of a new thing, new

41:49

structure. How does that compare

41:51

with more traditional types of finance.

41:53

Yeah, so you know that every

41:56

credit facility that we do, the cost of capital

41:58

declines, and it's declining

42:00

because it's the execution risk

42:02

and the ongoing concern risk are reduced. Right.

42:04

And you know, when we first did this, people

42:07

like you guys are crazy. You have no history of execution.

42:09

And as we've gone through and we've done it,

42:12

like now there's a path that everybody that's

42:14

underwriting these loans now understands. Okay, this is what happens,

42:16

this is how it reforms, This is what we should

42:18

expect from the customers. This is what we should expect from receivables.

42:20

They get more comfortable, they're willing to do it at more aggressive

42:23

rates, right, so that the risk premium

42:25

associated with it has just decreased over time.

42:27

Got it.

42:27

I just have one last question I sort

42:29

of touched on it earlier. But Okay, we know that power

42:32

is scarce. We know that, you

42:35

know, there's not an infinite number of

42:37

Nvidia chips et cetera. Like those

42:39

are quite scarce for

42:41

the other stuff. You know, we've done episodes in the

42:44

past like talking about like just generic

42:46

electrical gear components, and we've certainly done

42:48

a lot on like labor shortages. What

42:50

are you seeing on that front sort of like simple

42:53

gear and the sort of basic building

42:55

blocks of a new construction and

42:57

how difficult that is to acquire. Verse

43:00

to say, if you were doing this, you know you started

43:02

in twenty seventeen, I imagine a lot of the things were more

43:04

plentiful back then.

43:05

Yeah, so it's not even that they're less

43:08

plentiful today than they were. You know, the lead

43:10

times were always the lead times for this

43:12

electrical gear. It's that there was capacity

43:15

to go buy off the shelf, right

43:18

there was inventory in the data center market. And the inventory

43:20

is basically gone. And you know, I

43:22

see deals today that get brought to me

43:24

and there's seven people bidding on the same deal

43:26

and they're all trying to sell it to like similar customers.

43:29

So the market has gotten pretty thin. So

43:31

now you're looking at it, going Okay, my only

43:33

option here is for new built, and you're

43:36

looking at lead times that haven't really shifted

43:38

that much on things inside of the data center.

43:41

The substation transformers are multiple

43:43

years out, and part

43:46

of that reason is that it takes a year for them

43:48

to cure after they're manufactured. Like, there's

43:50

no getting around that, there's no speeding that piece up.

43:52

I mean, it takes a year.

43:53

You when the transformer is built,

43:55

that's taking on so much power that

43:58

whatever the process is, it has to sit for

44:01

a year and harden before it's able

44:03

to take on that electrical load. So even if

44:05

you went and said, hey, I'm going to build ten more of these this year,

44:07

it's still a year away before you can use them.

44:09

Huh right.

44:10

And those are the types of things from a manufacturing

44:12

perspective you just can't get around, and it takes

44:15

time for the supply chain to catch up. But you

44:17

know, the problems that I'm solving on a day to

44:19

day basis in these builds isn't even

44:21

around the substation transformers. It's around

44:23

like small components that somebody missed it when they

44:26

ordered the gear sixteen weeks ago. And

44:28

now you have to go scramble and call in favors

44:30

across the country of Hey, who has this part? I need

44:32

it by tomorrow because I have fifty thousand

44:34

GPUs that are blocked by this one little thing, right,

44:37

So it's a lot of it is logistical

44:39

and human coordination and solving dumb problems

44:41

in real time.

44:42

Ryan Venturro, thank you so much for coming

44:45

on odd Laws. That was fantastic. Thanks for having

44:47

me, Tracy.

45:00

I'm really glad we did that conversation

45:03

because there are a number of these sort of like big

45:05

picture ideas in there that we've

45:07

sort of hit on of course, about data centers

45:09

and AI and electricity consumption, and

45:11

it was really interesting to hear some of them.

45:14

So, like, for example, just

45:16

this idea of like northern Virginia

45:18

is out and like needing this sort of hunt

45:21

to find these spots in

45:23

the country where there is ample

45:25

electricity and basically

45:28

nobody local is going to get upset at you for

45:30

using it.

45:31

Yeah, no one will come out with pitchforks. The thing

45:33

that stood out to me from a bunch

45:35

of these conversations at this point is the

45:37

arms race aspect of it, and how

45:40

urgent building out AI

45:42

is for a lot of these companies, and then

45:45

there seems to be this mismatch

45:47

between the immediate need

45:49

for scale and compute

45:52

and energy now

45:55

versus these really long timelines

45:58

of actually building the stuff out and Brian

46:01

mentioning the substation transformers

46:04

taking a care of cure.

46:05

I had no idea about that.

46:06

I didn't know that either. But that's a really good example.

46:08

That's super interesting, and of course now

46:10

we have to do a how do you build a

46:13

substation transform.

46:14

How do you cure a substation transformer?

46:16

Totally? I mean maybe this is probably something that electrical

46:18

engineer is not interesting to them at all, But

46:20

for me, I did not realize that there was this

46:22

one year long, one

46:25

year long curing process. You

46:27

know, I think there are like a couple other

46:30

things that now I want to talk

46:32

more about, so I'm interested. I

46:34

mean, like Coreweave is an in video company.

46:37

It's not owned by Video, but you know it's joined

46:39

at the hip in many respects. So how

46:41

difficult is it going to be either

46:44

for some other maker of

46:46

chips, whether it's an Intel

46:48

or some other maker of software

46:51

environments, whether it's Meta

46:53

and PyTorch going against Kuda

46:56

or whatever, like that's a really interesting

46:59

question to me, Like, you

47:01

know, we have to do more essentially on

47:03

like how much of a lock and video really

47:05

has on this industry.

47:06

Yeah, this seems to be the really big

47:08

question. And then the other thing I was thinking

47:11

about, and I know Brian emphasized

47:13

this and other Core Weave executives

47:15

have emphasized this before, but this idea

47:17

that hyperscalers maybe are

47:20

starting from a point of being disadvantaged

47:23

because they have to retrofit

47:25

all this old infrastructure for

47:28

this new AI technology totally,

47:30

and like I can see that. But on

47:32

the other hand, these are insanely

47:35

impressive companies. You are

47:37

explicitly trying to compete against

47:39

Core Weave in this business, and they're

47:41

not going to stand still. And so I guess

47:44

there's an open question over how much progress

47:46

they're making or how fast that progress

47:48

is actually happening.

47:49

Right, Large companies

47:51

always are going to have some challenges when

47:54

there's like a new model or something. But

47:56

these companies have all the money in the entire

47:58

world, right, and they also have all you

48:01

know, one of the things that Brian said is like they if

48:03

they were if one of them are going to do it, they would

48:05

have to go out and to buy a big chunk of the market,

48:07

which again they have all the money in the

48:10

entire world. So theoretically, whether

48:12

it's the big companies and retrofitting

48:14

the clouds or building new clouds, or

48:16

you know a lot of them like a Google, even if

48:19

they're for now using their TPUs

48:21

internally primarily like, it

48:23

does seem like in theory the opportunities

48:25

out there, particularly with the

48:28

the sky high amount you

48:30

know, valuation that a company like in

48:33

video is getting.

48:34

Oh yeah, you mentioned the sky high valuation. That

48:36

was something that also stood out to me, just

48:38

on the financing side. So this idea of

48:41

you know, the debt financing deal that

48:43

they did, and I'm

48:45

not going to call it trade receivables because.

48:47

No one GPU backed loan.

48:49

Yeah, no one will be interested when we start talking

48:51

about trade receivables. But the GPU

48:53

back loan. This idea that like, okay,

48:55

it's a new structure, but the more

48:58

you do it, the more the cost of particular

49:00

capital starts to fall, the more the market gets

49:02

comfortable with it. I mean, we can talk about whether

49:05

or not it's priced correctly for

49:07

a new type of unfamiliar risk,

49:10

but it does seem like that

49:12

might be a new avenue for the

49:14

vast amounts of capital that are needed for

49:16

this business.

49:17

So one, it's interesting to think

49:19

about the idea that, like, you

49:21

know, I don't think it's like totally true.

49:24

You know that if you need compute at scale

49:26

for AI, that you don't just get

49:28

to call up core weave and get it, and you

49:30

actually have to prove that you're going to be a

49:33

good customer and so like have something

49:35

that is probably going to be sustainable, have

49:37

the balance sheet capacity. So this

49:39

even if the sort of software the end

49:42

users aren't themselves raising

49:44

debt, it does sound like they have to have a

49:46

lot of equity upfront

49:49

just so that they're perceived as

49:52

a sustainable, viable customer

49:55

for a company like corewev. I also thought on

49:57

the electricity front, like obviously

49:59

we talk all the time about just sort of the raw

50:01

demand for electricity. But

50:03

this idea what he said, and I hadn't heard anyone

50:06

say it that the runs the modeling

50:08

runs stop everyone do you say thirty minutes

50:10

and have to be saved. Oh yeah, And so you have this

50:12

big variability at times, and that

50:14

creates its own specific issue

50:17

because it's not just steady state flow of

50:19

electricity and solving for that.

50:21

That's probably another area in

50:23

which the legacy data

50:26

centers or cloud companies. Perhaps

50:29

my guess would be that they're just sort of the demand

50:31

is more constant and therefore

50:34

something that would be a novelty for them.

50:36

Just thinking about the financing more, I do kind of

50:38

wonder how much of this is like AI

50:40

built on top of AI on top

50:43

of AI. Like, yeah, to the

50:45

point where if if the

50:47

bubble were to burst, or if

50:49

funding was suddenly pulled from a bunch

50:51

of these startups, like what would

50:53

that mean for core weaves

50:56

financing? And what would that mean

50:58

for black Rock, which lent money

51:00

based on the GPUs that the clients

51:02

are taking on, who might not be there anymore.

51:05

I don't know.

51:05

By the way, have you ever looked at a chart of riot

51:08

lockschain?

51:09

Oh no, not

51:11

for a while?

51:12

Yeah, well, I mean they're still there as a minor, but

51:14

like here we are in the midst of this pretty

51:16

big crypto bal run. I mean, I guess it's cooled a

51:18

little bit, but and that stock is done terribly

51:21

so it's interesting to wonder, and apparently

51:24

it doesn't seem like anyone's made a bid for them. But

51:26

it is interesting to wonder, like,

51:28

Okay, those chips are useless for

51:33

AI because they

51:35

don't work for that, but you know, they do

51:37

have capacity and they do have

51:40

electricity agreements already in

51:42

place. So it does make you wonder whether,

51:44

like some of the bitcoin mining companies which aren't

51:46

really getting a very the

51:48

market is not excited about them, clearly,

51:50

even in the midst of this crypto bal run.

51:53

Maybe they should go back to being a

51:55

diagnostics company. That's what they were

51:57

before, is it. I think so. I

52:00

think they're one of the ones that changed their name and

52:02

then like there something including blockchain,

52:04

and then their shares went up enormously and

52:06

now they're back down.

52:07

Well they have been. Riot Platforms

52:10

has been around, Okay, now I'm curious.

52:14

Yeah, so it's a bitcoin mining company, but it's

52:16

been the stock has been around since two thousand and three.

52:19

So pretty clearly, uh,

52:22

pretty clearly they were in some other business. I don't

52:24

know what.

52:24

Yeah, I'm looking on the terminal, it says Riot Blockchain,

52:26

formerly Bioptics, has

52:29

ditched the drug diagnostic machinery

52:31

business for the digital currency trade.

52:34

Well, there you go. So if you have some sort

52:37

of computing power or something. I don't know what they were doing

52:39

before, but maybe it is interesting to think about.

52:41

Maybe some of the option value for some of

52:43

these miners isn't there. Non

52:46

is in all the infrastructure other than the

52:48

bitcoin mining operation.

52:50

Maybe we should put in a bid.

52:51

Let's do it.

52:52

We can crowdfund and start

52:54

our own business. Okay, maybe we should leave it there.

52:57

Let's leave it there.

52:57

This has been another episode of the All Thought

53:00

podcast. I'm Tracy Alloway. You can follow

53:02

me at Tracy Alloway and.

53:03

I'm Joe Wisenthal. You can follow me at

53:05

the Stalwart. Follow our guest Brian Venturo.

53:08

He's at Brian Venturo. Follow

53:10

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53:12

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53:15

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53:17

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53:19

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