Hi listeners, welcome back to No Priors.

Today I'm here with Neil Tuari of

Magnetar Capital. This is a $22 billion

alternative asset manager at the center

of the AI compute buildout. We talk

about the financial innovation

depreciation of GPUs and what's next in

AI compute. Welcome. Thanks so much for

doing this, Neil.

>> Absolutely. You know, really happy to be

here. So you are leading AI

infrastructure at Magnetar. You're at

the center of the buildout, enabling it,

financing it. For any of our listeners

who haven't heard, can you just explain

a little bit what Magnetar is?

>> Sure. Um, so Magnetar has been around

for actually this is our our 20th year.

Uh, we're an alternative asset manager

and that can mean a lot of different

things. Um, but we have three primary

strategies. The first one is private

credit. uh the second one is a venture

strategy and the third is more of a

systematic or quantitative focused uh

public strategy as well. And so I think

you know when when people look at us and

and you know why are we here in this

moment especially on building out AI

infrastructure um I think a lot of it

has to do with kind of our unique lens

on helping to build uh capital intensive

businesses and using creative financing

whether it's venture or other structures

with unique elements and I think we're

going to talk a lot about that but um to

build out uh and optimize the balance

sheets for these capital intensive

businesses. So, I remember hearing about

you guys originally. So, you're the

first investor I think we've ever had on

the podcast. I'm excited about that.

Thank you.

>> Uh I remember hearing about you and

Magnetar initially around I was like

who's this big owner of Corewave and

also um you know helping OpenAI with

some of their early buildouts. When did

you guys first start looking at the

problem and thinking about how to how to

solve it?

>> Yeah, so we actually you know stumbled

across the the compute problem before it

was compute. Um, you know, we met uh

Coreweave back in uh 2021 and that was

when they were actually transitioning

from uh mining Ethereum into uh high

performance compute and at that time it

was using the GPU as a you know an

instrument to mine uh cryptocurrencies

and interestingly that same instrument

could be used for high performance

computing applications. Uh and the first

one was uh visual effects uh which so

think of like things like movies, Marvel

movies and things like that. And so they

were transitioning um at that point

between crypto mining into the first

kind of uh high performance compute use

case. And this is all before AI

>> and so we made our first investment

before the AI trade started. Um but we

added a lot of optionality where you

know we could envision a world where uh

the GPU could be used for a lot of

different high performance kind of

computing applications. I think um you

know AI was on the radar, machine

learning was on the radar for us. Um but

I wouldn't say that we could foresee

everything that happened. we just

happened to be, you know, at the right

place at the right time and we continued

to double down um as the company

progressed and started, you know,

shifting into more workloads that were

machine learning and and kind of AI

training based.

>> Did you have like an existing

significant data center investing

footprint?

>> No, I mean I think you know uh

interestingly at Magnetar there, you

know, we have invested across asset

classes. Um so we we've done a lot of

property investing, real estate

investing as an example. um investing in

energy. We had an energy business

historically and so a lot of the

elements for you know what constitutes a

data center power energy land uh real

estate you know we had a lot of the the

background in those spaces I think we

were new to compute right like that was

a new sector for us and so kind of those

two worlds merging um you know we we

obviously you know came up on the curve

on the compute side but we had a lot of

you know background on um the the

elements that constitute what it means

to build a cloud. So you guys just

really you were in this company, you saw

the demand and you said like it's going

to grow and we're going to make this a

big part of our business.

>> Exactly. I think you know what was

interesting is we made our first

investment in 2021 um and then about a

year later we continued to see expansion

of use cases uh for at that time it was

called high performance compute and then

it was kind of towards the end of 22 the

whole AI uh discussion started and as we

entered 2023

uh coreweave uh started to train models

for open AI

>> um and that's when things really started

growing because the sheer amount of

compute that was needed to train an LLM

this was like the first time had ever

been done. And what was interesting was

what kind of allowed them to take

advantage of that opportunity was the

historical kind of backgrounds of a lot

of the founders uh were in energy asset

management. And when you fast forward to

today and you look and like what is what

constitutes your ability to build a GPU

cloud, it's your ability to manage these

highly complex assets. And it

fundamentally comes down to access to

power and energy. And so they had these

elements with them. They obviously

brought on a lot of talent on the cloud

side. And to put all these together and

at that moment it allowed them to um you

know build very large scale reliable um

clusters for OpenAI and obviously many

other customers since then. And I think

the last comment I'll make is what

really allowed them to kind of win this

market early on was focus on two things.

It was scale and reliability. And I

think those were the two things that um

are really difficult or a lot of the new

entrance since then because scale has to

do with your access to capital, your

access to energy, power, data center and

then reliability really had to do with

their their ability to manage a giant

fleet of GPUs uh which is actually quite

complicated. um you know whether it's

reliability from you know GPU failures

or software challenges you know building

a fleet that can healthfully be online

all the time at you know 99.9%

reliability is incredibly difficult and

that's something that they had started

back in 2017 2018 time frame and and

they were at the right moment at the

right place with the right technology

stack um to really build um the optimal

cloud for that moment

>> I've definitely experienced that with

you know our portfolio of companies that

are building large training clusters uh

uh it corewave has a reputation for

reliability that not everyone has

reached. Can you just help characterize

if you fast forward like two and a half

three years now like what is the scale

of the problem today?

>> Yeah. So if you look at um kind of capex

right let's starting with that. So capex

for AI compute and infrastructure in

2026, you know, at least from the

hyperscalers is projected to be between

660 and 690 uh billion dollars. And over

the next several years um you know that

scales to trillions of dollars, right?

And so the the scale of the problem is

how do you build um you know that size

of capex efficiently? And I think a lot

of that has to do with not only, you

know, your ability to have access to,

you know, those core elements, um,

energy, power, you know, uh, and and

your ability to have data center space,

etc. But I think one of the things

that's not talked about as much is

capital and access to capital and how is

capital structured. Um, and what I mean

by that is this is, you know, billions

to trillions of dollars of capex

>> and just using equity dollars alone is

not an efficient way to scale this.

That's obviously massive dilution. You

know, there's there's it's not an easy

problem to solve.

>> When we first met, I had like slowly

come to this realization. I was like, I

don't think we should take the dilution

for the cluster.

>> Yeah. Right. Exactly. And so that's

where I think, you know, when you and I

have talked about like structuring and

and I can give a couple examples um if

that's helpful. I think the first one

was DDTL structures or SPV debt

structures that um had a think of it as

like an SPV. Inside of the SPV are the

cap is the capex, the collateral um

which is the GPUs

>> and the contracts themselves. Um and so

in this example, the actual asset or

collateral was not really just the GPUs

themselves. It was really the contracted

cash flows

>> from in this case investment grade

counterparties and so I think the reason

>> this is the consumer of the

>> the consumer of the exactly you know

your Microsofts your your metas etc of

the world

>> and I think the reason um that was done

is is really twofold when when you look

at the scale of the problem uh you know

those particular contracts uh needed

billions of dollars of debt to finance

the capex you know obviously for a nason

and new growing company that's that's

really hard to raise. Um so part of

structuring it this way is ensuring that

you have kind of guaranteed offtake on

the back end to uh minimize the risk for

you know debt holders and I think that's

a lot of what the market got wrong um

especially when there was a lot of press

about this early on where it was

>> there's billions of debt on these highly

depreciating assets and it's extremely

speculative and the what was oftentimes

characterized in the media was uh these

debt structures had GPUs as collateral

and that's like putting a used car as

collateral which is obviously just going

to depreciate incredibly fast. You know

that's a very risky kind of structure

and I think what got missed was the the

GPUs themselves were actually like the

second second or tertiary level of

collateral in those instruments. The

primary collateral uh was the contracted

cash flows from investment grade

counterparties.

Microsoft or Nvidia or somebody like

that saying, "I'm committed to pay you.

I know you can pay me."

>> Take or pay contracts and they're like 5

years in length. So, I think that was

like one feature

>> uh that that's unique to talk about. And

then the second one really has to do

with um the debt itself and how it

amortizes. And so, like in simple terms,

you know, when you have debt, you have

principal and interest and you have to

pay it off over time. And in these

structures typically the payback period

on the capex was roughly 2 to 3 years.

Um and the uh structures themselves the

debt was over five years you know four

to five years in length where the entire

debt amortized during the um outstanding

period that the that the debt was out.

And so at the end you ended up with zero

balance uh for the debt and there was no

balloon payment or or anything that was

really due on the back end. And so the

question that often you know comes up uh

is you know isn't that a very risky uh

type of structure because these things

are depreciating incredibly quickly. So

I think know there's there's two

comments here. first is on that

depreciation question. In these kind of

debt structures, it doesn't really

matter because the debt's fully paid off

by the end of the debt term against

committed contractual um you know

contracts from investment grade

counterparties. Um and then at the very

end the the actual upside or residual

value and I know there's a lot of

questions on on residual value is is

held by um you know the uh the cloud

player in this example right Courte

right or or you know any others

>> um and that's a really interesting

prospect because you can see a world

where all of this capex is paid off

incredibly quickly and there's an

opportunity to redeploy it um where you

can redeploy it um without having to pay

for any additional uh debt obviously

against that redeployment.

>> How have the instruments changed?

>> They've changed in several ways where uh

you know the first is you when you look

at these SPVS I think you're starting to

see ways to change the portfolio

construction of who can go inside of one

of these debt structures. And so, you

know, early on in the early days, these

were all only investment grade

counterparties

>> because there was the the space was so

nent, the operators had no experience.

And I think now what you're starting to

see is a blend of investment grade and

non-investment grade. So, like what does

that actually mean? What that means is,

you know, you're you're seeing these

structures with investment grade

counterparties like your hyperscalers

and your other corporates that that are

IG um mixed alongside uh some of the AI

native companies. And so think of the AI

model companies, the labs, software

companies that are building AI startups.

You're seeing those companies get mixed

in alongside um the IG companies to

build a portfolio because now you have,

you know, the the history that you can

do this and now you have structures

where you can kind of balance the risk

uh wi with IG and nonIG. And we're

continuing to see that kind of move to

be able to help finance, you know,

really the model companies and a lot of

these startups. Obviously, that was

difficult to do, you know, three or four

years ago. starting to become easier um

as these companies have more runtime and

ability to uh you know make the compute

fungeible.

>> All are uh portfolio companies that buy

compute tell me it's a supply constraint

market today. One is that true and two

when you think about like uh continuing

to grow your business or grow this

ecosystem like what's going to stop it

like what could slow down a buildout?

>> Yeah. Yeah, I mean I think what's

interesting is uh if you look at like

2023 2024 we were very supply

constrained and the supply constraint

was chips and no one could get access to

chips.

>> Yes, we bought chips.

>> We bought chips, right?

>> And you know there was this thought that

okay there's going to be an overbuild of

chips and then the supply constraints

will go away. Well, you know, fast

forward to 2026 and what we see is, you

know, there is obviously more

availability of chips, but to build and

operate these uh, you know, data centers

requires people, power, infrastructure,

a lot of these things that uh have a lot

of of bottlenecks. And so, actually

taking these chips and then making them

into useful revenue generating assets is

really the bottleneck. It's also not

clear that there is supply of chips at

the latest generation at scale.

>> That's true.

>> Soon, which is how everybody wants them.

>> Exactly. I think, you know, you see um

not only you're starting to see

interesting and not only just the the

high-end players want access to the

latest chips. You're seeing the latest,

you know, obviously startups want access

to those. And I think it has to do with

efficiency. Mhm.

>> Um, you know, one of our friends or one

of your friends as well, Dylan Patel

over at Semi analysis, posted this

interesting article last week on

inference and inference spend an

inference kind of performance. Um,

>> and you know, there's a lot of, you

know, jokes made about Jensen math. Um,

and it was interesting because the

>> seems pretty good at math.

>> He's actually great at math. Um, and so

for the uh hoppers, the H100 or H200

series of GPUs into the black wells, uh,

there was a claim made that it could be

30 times more efficient. And I think the

data from, you know, some analysis

showed that it was 90 to 100 times more

efficient in terms of inference

performance.

>> And so I think part of the the need to

go to these new chips is not is yes,

more computing power, but it's actually

the it can be cheaper to operate more

performance. Price performance. Exactly.

>> Mhm. Yes. My favorite Jensenism is the

more you buy, the more you save.

>> Exactly. It's actually true.

>> Yeah. Um crazy. Um help me address like

this uh criticism around circular

financing.

>> Yeah, I know. Um it's obviously a topic

dour and I think you know the way we see

it and frame it really has to do with

the demand signals um and who are the

eventual buyers and and how is this

being used? And so at least from what

our perspective, we we continue to see

uh insatiable demand. Um and if you go

back to, you know, the previous kind of

big tech buildout back in the early

2000s, there was obviously a lot of

fiber that was being built and you had

dark fiber, you know, in in an overbuild

happening. And I think what you see here

is I I have, you know, you don't see any

dark GPUs, any GPU. Exactly. Any GPUs

used. Yeah.

>> Um and then number two, you're starting

to see uh actual economic value. Um so I

think last year enterprise AI had about

37 billion of total TAM. Um and it's

continued to grow like crazy and at

least personally and and I'm sure you

see this too, but I use these tools all

the all the time and I find incredibly

valuable, right? The actual tokconomics

of positive uh ROI is is actually here

now I think from our perspective. Um and

so that the circularity you know comment

I think applies when you're building um

you know speculative uh compute and

capacity uh or if you're you know purely

doing vendor financing and it's you know

you're trying to do some type of you

know unique some type of you know

revreck type item related to that and

that that's not what we see like what we

see is financing to support to build out

the demand against uh use cases that are

very positive in their ROI and so like

our perspective is that that's uh you

know not a real real concern that we

have um and and it really has to do with

who are the ultimate buyers here.

ultimate buyers have been and at scale

the hyperscalers they're deploying this

uh at scale and the economics are

positive uh when you look at a unit

economic basis in terms of uh deploying

intelligence um and I think we're at a

moment in time where you we're really

starting to see that

>> in my own experience um I have been a

heavy AI user for several years

>> but reasoning advances the ability to

scale up inference especially around

code

>> means I'm up against my max limit all

the time in a way That was not true uh

uh uh initially. How does the inference

workloads actually growing? I mean it's

a it's a good demand signal that there

is value but how does that change your

business?

>> Yeah. So I think one thing that's

interesting that we're seeing is

obviously there's been the shift from

training to inference you know over the

last few years that that split continues

to grow on the inference side as usable

uh and ROI positive applications get

developed. I think the two things I see

on the inference side now is um

inference has is a lot more complex than

I think initially thought and what I

mean by that is it's not as simple as um

you you train a model and then you it's

easy to inference it in some certain

cases you can do that on similar

infrastructure but there are issues

around latency um fungeibility of that

uh and and really optimizing the cost of

your compute on the inference side um

how do you manage uh you know peaks of

inference demand and and obviously it's

not linear like training you your GPUs

are on all the time you know 100% of the

time and so with inference you have a

lot more variability

>> um and so there's a lot more nuances uh

in in optimizing inference I think the

second thing that's observed um that

I've seen is uh inference is definitely

a memory problem a memory throughput

problem um you know on the inference

side you know you have these kind of

phases called prefill and and decode,

right? And how you optimize that across

a fleet of GPUs is actually unique

technical problem.

>> Um, and then the third is what I would

say is distribution.

>> Um, you know, a lot of times training

infrastructure is is quite centralized.

What you're seeing with inference is in

many use cases as this becomes more

ubiquitous, you're going to have more

and more decentralized

uh, inference clusters. And actually one

of my favorite companies is one of your

companies, B 10, which is really, you

know, optimizing distributed inference

at scale. And I think one thing that's

interesting when you look at companies

like that and and other inference clouds

is how do you optimize the uh compute

and and build out these clusters that

could actually look very different than

a training cluster where training

cluster might be 50, 100, 150 megawws in

one kind of four walls. Mhm.

>> I think you're starting to see

distributed inference which could be,

you know, four or five megawatts and

five separate data centers and stitching

them together in different areas, right?

And that looks very different from a

kind of power perspective, how you, you

know, the software matters a lot more

when you're doing like distributed

inference. And then in terms of your

question how it impacts us I think one

of the things that we've been you know

focused on is um you know where we

started this conversation with you on um

financing compute that was really

obviously uh it started with mostly

training um a lot of those hyperscalers

are now doing a lot of inference on that

same infrastructure but these are

investment grade counterparties you know

it's easy to it's easier to lend uh

money to build out these clusters to

those customers I think now that you

have this new crop of inference clouds

and application layer companies that are

needing tons of inference. I think the

the key question that we're really

focused on is how can we finance the

next build which is distributed

inference. Um and maybe the last you

know one or two takeaways would be uh

one thing I'm seeing is you know for

every application layer company out

there the highest line item from cogs is

compute

>> um and then the inference companies and

inference clouds out there most of them

are um purchasing up compute from either

other clouds or unused act uh capacity

and when you look at like margins for

that you've got like layered margins

>> and so there's a push to kind of own

your own infrastructure

>> um to really drive and increase you know

uh profit margins but also it's the

ability to kind of have control of your

own destiny and I think a lot of folks

are starting to the application layer

companies and inference clouds are

grappling with how can we build and own

and operate our own infrastructure um

and that's something I'm I'm really

looking into

>> I am too and I think one of the things

that uh is going to make a big

difference in this ecosystem is like can

the inference clouds like base And can

they deliver reliability that you would

expect from a a cloud like a traditional

cloud?

>> Um because the like uh distributed data

center operations that you know they

consume today do not offer that

reliability. Right.

>> And the other thing that's interesting

is um you know this is additional

reporting from last week. Um if you're

familiar with silicon data they they put

together a lot of you know data on spot

pricing and price per token performance.

This is Kerman Lee's company. And one

thing that that I think was really

interesting in some some in an article

she uh published last week uh had to do

with how two pieces of compute that look

identical on paper have wildly different

performances. Everything from

reliability to cost to speed. And I

think as you distribute um you know have

distributed inference, how do you m um

you know mash together very different

types of compute and try to optimize for

reliability I think is super

interesting. Um and that gets to kind of

one thing I I find really interesting

that Nvidia is doing is is this concept

of AI factories

>> and building AI factories um you know

behind corporates and AI companies. And

maybe the way I unpack that is you've

got kind of more large monolithic cloud

players, the hyperscalers and the

neoclouds that are building large scale

um you know cloud environments. uh and a

lot of where I think Nvidia and others

see this going is yes those are going to

be important components and those are

going to be huge markets but corporates

fortune you know 500 AI companies that

use a ton of compute will want dedicated

AI factories associated with workloads

that they run and that they have control

over. And so I think you're starting to

see, you know, the early indications of

how do you finance and build out uh

almost think of like literally AI

factories that sit on prem with a

company that can operate their

workloads.

>> Uh

>> you're talking about my Mac mini farm.

>> Exactly.

No, but but all joking aside, I I think

one thing that is another supporting

factor for use of all of the compute we

have is and and can create over the

coming years is um power is clearly the

limiting factor.

>> Um it's easier to get more power in

smaller

>> units. Yeah,

>> I think that as inference demand is

growing these uh anyone who has uh

usable compute for inference is going to

find a lot of partners for offtake.

>> Exactly.

>> Okay, let's look at the future a little

bit while we while we have 10 minutes.

Um uh let's talk about the the macro.

Like people talk about energy, they talk

about um natural gas, uh the grid, the

slowness of nuclear. like what do you

think about over the next 6 or 12

months?

>> Over the last year, I've been spending a

ton of time in the power and energy

markets um and looking at interesting

solutions that can help scale power, you

know, for the gap that we see. I think a

few observations that we've seen. The

first is um we do have a power problem,

but I think it's a bit more nuanced than

than a lot of the reporting out there

where

>> it just we can't generate.

>> We can't generate. Yeah. I think there's

actually quite a bit of stranded power

across the grid across the country. And

what I mean by that is, you know, a lot

of the utilities are built in a way

where they're focused on peak power,

right? So they've got natural gas

peakers and they're focused on, you

know, providing peak power for those

moments where demand is is kind of off

the charts. Um, and that's obviously

only for a few days out of the year. So

there's lots of generating assets out

there. Uh, the question is they're a bit

stranded, right? And so there's kind of

I I look at the power problem as being

kind of multiplefold. The first one is

how can you take the power we have on

the grid and actually make it usable.

And and a lot of that has to do with

flexibility and storage. And so we've

been spending a lot of time looking at

an energy in the energy storage business

and distribution. How can you store

unused capacity, peak demand shave uh

capacity, store it and then distribute

it when it's needed.

>> Um we made an investment in a company

called Taurus. I think I I mentioned to

you which is building like this

distributed utility layer uh almost like

this mesh infrastructure to um takes to

store excess capacity or store capacity

from a variety of of sources and then

distribute it at the time when it's

needed. And so I think that's kind of a

critical layer that that needs to be

built. Um and then longer term there is

a generation problem but I think in the

shorter term it's really it's more on

the distribution and storage. Uh and

then um the other piece I would say is

you know the true bottleneck um at least

in the short term the next 6 to 12

months is is incredibly I don't want to

use the word simplistic but it's things

like uh structural steel it's uh finding

electricians uh that can you know build

>> sorry there's you can't get enough steel

>> you can't get enough steel you can't

>> this is not something I was aware of

like you can't get steel you can't get

uh you can't find enough electricians to

build out you know the power

infrastructure uh substate

transformers, air chillers. These are

like very specific power infrastructure

needed to just get to a point where you

can start to build a powered shell on a

piece of land. And so the bottlenecks in

the short term really are uh people

equipment. Um and then the other

interesting thing is that on the

generation side, what you're seeing is

regulatory obviously is is a big

challenge. And so there's a combination

of bring your own capacity. There's a

lot of that that's that's interesting

right now. And so a site that can

potentially grow to 50 megawatts might

start with only 10 megawatts of grid

interconnect, but can you add solar net

gas um turbins, put these various bring

your own capacity kind of pieces of

technology together to make that site

usable? And so I think a lot of what's

being looked at and a lot of what I'm

looking at right now is really on on the

bring your own capacity at least in the

short term. Yeah, I think um if people

don't know the uh origin story of Crusoe

and Flur gas, like it's actually really

interesting as an example of, you know,

there is actually lots of energy, lots,

you know, some energy out there and, uh

you can make much more of it consumable.

>> Yep. Exactly.

>> Couple topics to hit before we lose you.

Um

>> uh new players, how do you think about

the sovereigns and what they're doing in

their buildouts? Yeah, I think um

>> they seem to be able to fund themselves

just like

>> Exactly. Right. Um you know, you saw the

news from India last week. Uh obviously

a lot of the news in the Middle East,

Southeast Asia.

>> I think you know, we're continuing to

see that sovereigns view compute and AI,

you know, as and and even we do here in

the in the United States as as as a

matter of national security.

>> Um and obviously the funding of those

clusters is is very different than

funding like a private cluster. And so

you've got, you know, government capital

that can be used for that. I So I think

there's two things that, you know, I

find interesting in that space. I think

one is who are the partners um that are

going to build those that capacity

>> and what are the cyber security kind of

implications and environments for that.

And so those those are the two nuances I

think with sovereigns is they need to

find players that can rapidly scale

compute um in the in their countries.

and often times they don't necessarily

have these players that know how to

build and scale GPU comput

and help build you know sovereign

ecosystems around the world and then

there's a matter of cyber security and

how do you make it into a a a truly um

you know safe ecosystem for for those

sovereigns and so I think there's a lot

of work to do still on the cyber side um

especially as you look at you know

scaling sovereign AI

>> what is your thinking on physical AI

it's another you know if it works capex

intensive

Absolutely. And you know, maybe I'll

just take a second to say one of the

things that we observed um from 2010 to

like, you know, the early 2020s was we

were in a very capital asset light mode

of build. Like SAS was, you know, you

never heard Magnetar and SAS, right?

Because it was just purely asset light.

>> Compute and everything we saw starting

in, you know, 2021 is asset heavy.

That's where you started hearing a lot

more about us. And I think physical AI

is actually an extension of that. And so

what you're seeing is part of the reason

I think and I think we all have scars

from the 2010s of hardware companies

that did not make a lot of money for us.

Part of the scars was it was so

difficult to scale hardware companies.

Um you know because the software was so

difficult to build. You needed to spend

so much money building the hardware. The

software was an afterthought. What

you're seeing now is now that you have

more generalpurpose uh software via AI

uh it can make the hardware easier to

scale because you have you know software

that can be you know can interact with

more more hardware and so I think the

natural kind of extension of what we see

is kind of what happened in the compute

markets where you really needed flexible

capital where it wasn't just equity it

was debt and you know a variety of

project finance to really scale capex

you're going to see that same kind of

need uh in physical AI and it simply has

to do with capital intensity right you

know on the compute side for like

cororeweave as an example they needed

billions of capital to scale uh you know

that cloud and I think whether it's a

robotics company or whether it's a you

know uh a manufacturing uh focused

company drones defense all of these

areas are incredibly capital intensive

and then now that you add AI into them I

think it can help them scale faster uh

quite frankly and uh capital intensity

is still there. And so there's a moment

in time now where you're going to have

to really look at optimizing balance

sheets um for physical AI to really grow

and scale.

>> I think to your point of how the um

early AI compute contracts were

structured um

I I went from you know learning to be an

investor in an era and an environment

where robotics was a great way to lose a

lot of money for a long period of time.

you remember that.

>> Um, now I sit on the board of two

robotics companies. So, let's hope it's

not true anymore. But I I'd say like

it's just a question of capability to me

like you know whether it's in the home

or in industrial settings where like it

is simply not a good human job or we

don't have the labor.

>> Yeah.

>> Um, you are going to have if I I think

the products will support investment

grade buyers

>> who are going to have contracts that say

like we want it and you can raise debt

against it.

>> Exactly. Right.

>> Um and so I think actually that that

feels of a very similar um shape. Last

question for you because it is so

timely. What do you make of the general

capital rotation out of out of software

the end of software and it's all it's

all infrastructure labs and AI natives I

guess.

>> Yeah. Yeah. It's interesting to see that

every day there's another industry that

kind of tanks whether it's you know you

saw the wealth advisor tank for a few

days you saw the consulting consulting

companies you saw real estate payments

real estate right. I mean I think what

you're seeing at least is at least in my

view what I saw was towards the tail end

of 2025 and into 2026 like there was at

least in my view a big step up in

performance of usable AI and I think you

know what Anthropic was doing really and

claude and like we use it all you

obviously we use all the models but you

know there was a definite step up in

performance in making AI usable and

seeing that it can you know truly

disrupt these you know nonAI native

industries

Uh I think the reaction and rotation out

of each of these names is a bit much

because when you I think there's there's

two factors I look at. One is when you

look at valuations as an example, I

think um from a free cash flow

perspective, SAS companies are are are

valued at at the lowest they've been in

in in years, you know, and there's a

huge margin difference between, you

know, what those rev multiples are today

and what what they've been in the past.

And so free cash flow margins have

steadily increased significantly for SAS

as a whole over the last four or five

years and revenue multiples have stayed,

you know, you know, the same or gone

down.

>> And so to me that's a bit of an

exaggeration because it really has to do

with individual names versus sectors.

And I think that's kind of at least my

take is like in all of these sectors

there are individual names that will

learn how to maximize their, you know,

uh, value using AI and there's those

that won't. Uh but what's happening

right now is there's you know a hammer

being hit across all names and not you

know specific individual names that

might not be using it as well. Um and

then the second point at least you know

my view is there are a number of

applications that you know on paper

sound really interesting like oh AI

could just rebuild Slack or it could

rebuild Salesforce or could rebuild you

know X Y and Z. I think you know the

it's not just the product it's the way

that's integrated across multiple

services and systems across the

enterprise that is a lot more difficult

to just replicate

>> than I think some of the public markets

are are kind of reacting to

>> and I do think there's um you know

fundamental question in addition to what

you said which I agree with of like does

anybody want to rebuild it and own it

and uh you know there are to your point

of like within the software sector in

particular Um there are companies where

uh uh they're structurally more

protected than there are companies that

are at more risk, right? And I I think

it's as simple as like you got to go

select.

>> Yeah, exactly.

>> Um this has been so fun. Thanks so much,

Neil.

>> Yeah, I really appreciate.

>> Congratulations on all the innovation

and uh on building out all the compute.

>> Awesome. Thank you. Good to be here.

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