Nebius Co-Founder on AI Infrastructure Bubbles | How Price Elastic is Demand for Compute

We are in the capital intensive game and

we competing with the most capitalized

companies in the world. Our program this

year is 2025 billion. Our competitors

hyperscalers have eight times bigger.

The AI infrastructure race is on. Capex

spend has never been greater. At the

center of this, Nebus. Today I'm joined

by the co-founder of Nebius, a company

that has scaled to a $66 billion market

cap, going head-to-head with some of the

largest hyperscalers in the world.

>> In the next 6 months, the capital cannot

help. 6 months is too short time. You

have what you have, you need to deliver.

The main threat for Nebios as a business

is the world will be too much

consolidated.

>> Today, we uncover the AI infrastructure

bubble and so much more.

>> It's like a shark. You're alive when you

move, right? So, we have to move. And

I'm thrilled to welcome Ronan Chernin,

>> who has power against Nvidia.

>> Ready to go.

Roman, I am so excited for this, dude. I

think Nebius is one of the most

unbelievable, incredible stories in

terms of what we've seen over the past

few years, but also like, holy [ __ ]

what an exciting few years we have

ahead. So thank you so much for agreeing

to do the show.

>> Yeah, thank you for inviting and uh glad

to be here.

>> Now I would love to start with a

question that I think is at the top of a

lot of people's minds which is like

where are we at in the insertion point

on AI infrastructure? A lot of people

are seeing the capital going oh it's a

bubble and a lot of people are going

it's just the start. How do you think

about the we're at an AI infrastructure

bubble moment right now? No, I I I don't

believe it's a bubble. Uh uh I mean

define the bubble. Do I believe that we

will need tens or hundreds times more uh

to build? I thoroughly believe uh I

probably biased. I would probably not

being in the business that we are doing

if I wouldn't believe. So I think that

we are just at the beginning of this

amazing moment when Jensen calls it like

useful AI and like we just at the

beginning of this kind of real adoption

and honestly we have maybe one use case

that works out of so many of use cases

and the one use case that works like

coding everybody's talking about coding

started working like maybe few months

ago. go just so like let's put it in the

perspective we just few months from the

moment when we've got maybe first use

case that's works in like in the scale

and we start seeing it's applying here

and there and uh I think we'll see

obviously like many many many more use

cases and we will see much more adoption

uh I think that what we see yet is if

you take every single company in the

world uh maybe outside of the fastest

moving startups before the show we we

were speaking with like who is moving

fast enough or not fast enough right so

maybe there are some exceptions but if

you take I think if you take any company

in the world today and you will see

we'll look at the AI adoption there you

will actually see that they start using

AI in a first percent of the volume in

the first percent of the use cases. So

if you take any large company even

pretty advanced technologically

you will see that they just starting and

I'm taking from that that we only only

beginning uh and uh even you even if you

don't believe in what Musk says about

everything in the future space and so on

just practically

uh from from uh enterprise adoption it's

it's just the first steps

>> so we're completely align but it's a

very boring discussion if I just go I

agree with you on on everything. Um my

my question to you on on the back of hey

we've seen coding now work for the last

whatever 6 to 12 months. Yes, but there

is a question that we will move to open

source models locally hosted because the

cost will be too significant for some of

these enterprises to burden and we're

going to see that shift happen soon. If

we do that is both damaging to the

providers open AI and anthropics of the

world and to anas. Why is that

perspective wrong? Uh yeah. So first of

all I think that uh it's not in the

future it's already in the present. Uh

again what we see in a lot of examples

at the moment when our customer or the

product builder gets to the scale they

start uh looking uh to the ways to

improve the economics or accelerate the

growth and so on and this is the way

when they most a lot of them start to

look to alternative models. So the best

way to build today is obviously to build

on the frontier models from great

providers like OpenAI, entropy, Google

because they actually provide and that's

true they provide you the best best

capabilities in the world.

But then when you figure it out the use

case, when you start seeing adoption,

when you see the customer data loop, you

maybe can find the cheaper or

even not cheaper but more quality high

quality way to serve the same use case.

You don't need maybe you don't need the

best in the world universal model but

you can create the specialized model

that in your particular case will work

even better and that's the way where you

need to shift or may consider to shift

from uh frontier closed models to open

source. The most important kind of

cause of those models is not just they

open source but they are tunable they

are trainable. So you can take them and

you can do something you you can

postrain them and you can create the

specialized model that in your

particular case may work better. So

that's we see over the world over over

the use cases but why doesn't it hurt uh

entropic and open AI because in reality

the they move to the next frontier and

to the previous uh point that we

discussed there are so many unsolved

tasks yet or the tasks that not

necessarily have the limited budget uh

uh to be solved And every time we see

and we saw it like with deepseek one

year ago and like we continuous see

seeing it now. Every time we find the

way to solve some task uh more efficient

we just start solving more complex

complex task in the same time and this

is like continuous journey I believe. So

you you kind of you always push the

frontier. You always have the more

complex tasks to to figure out how to

solve. When you figure it out how to

solve them, you can go down and reduce

the price or improve like the quality.

But we have so many unsolved tasks that

entropics and opening and all other

frontier models still have such a not

addressed yet time, not addressed yet

market. they continue kind of

exponentially grow.

>> Do you buy that? These companies are

priced to to perfection in a lot of

cases at a trillion dollars. If the

value that they create is eroded and

they're constantly playing a game of

leaprogging from value to value to value

while open source continuously eats

behind them.

You got to find a lot of problems

continuously dude. That's a that's a

hard life to live. Actually most of the

people are concerned on other side like

will we have a strong enough open source

and strong enough specialized models

environment to to build this floor. Uh I

think that we yet at such a early point

of adoption we have so many unsolved

problems yet that uh um it's just the

matter of the total pie and uh I think

there is uh enough space to solve so

many tasks in the future that it's

enough uh enough pi for uh both frontier

capabilities uh and very tuned

uh models for specific use cases and all

the world of these open source or

specialized models that we can build on

top of them uh to gain these economics

advantages and uh performance advantages

when we know what we need. You said that

like every time we we have the cheaper

model is it hearts uh the business and

my favorite anecdotal story about that

is I think 15 months ago or so there was

this deepseek moment if you remember.

clue. I remember that Nebul stock went

down 40% in one week or so uh uh in

February I think it was February or

March 2024 2025 and anecdotal story the

same the same exact week we probably had

the best week in sales. So uh people on

the market were concerned that market is

going down and like infrastructure

companies like Nebio's

not needed because okay if AI is such so

much cheaper maybe it's bubble but at

the same time we never had the best

commercial week we were pretty early in

our story but that was the best

commercial week in the history of the

company because so many people figured

out that they can run inference in their

production workloads uh with DeepSeek

and economics will work and then like at

the at the same time like Ktor started

growing uh I think they were the first

who really benefited from uh tuning

those models for coding and so on. Every

time we got intelligence cheaper

uh this the same unit of intelligence

cheaper, we are not reducing the

consumption but we increasing the

consumption because we can just solve

more complex tasks with the same budget

or we can finally

uh economically viably solve the tasks

that we already kind of knew that was

solvable but economics didn't work and

we could not scale. So I think it's

quite uh fascinating what's like this

economics improvements to observe.

>> Speaking of kind of Jeav's paradox and

producing more and that yielding more

demand where are you not moving fast

today where you would like to be moving

faster

>> everywhere. So when we think about how

we build a company we talk about it in

the four dimensions. One dimension is

capacity like uh how much megawatts,

gigawatts and the GPUs we deploy. We are

infrastructure company. We need to be

large. If you're not large enough,

nobody needs us to exist. So this is the

the the the physical world expansion.

The team is doing amazing job. uh but

it's never enough and you want to move

as fast as possible and uh uh there are

a lot of complications of the real world

that prevent you to move fast enough

sometimes

uh to launch new data center you need to

go through the entire like supply chain

regulatory and uh fires and waters uh

and uh uh everything that happens in the

real world right so this is one

dimension

uh another dimension

is the product. So you want to move fast

enough to address new types of the

workloads, new types of the customers

that coming to the market. Think about

it. We started as a industry in this AI

journey uh from the people who first of

all built the models right. So there the

that that was like companies like OpenAI

in harpscalers large labs and so on and

what they need from you as an

infrastructure provider is barely

compute like just throw infrastructure

and we see a lot of these large bare

metal deals on the market and we also do

them uh but this is only the first layer

of what we build like scaled physical

infrastructure that customers like

Metal, Microsoft in our case can consume

on a large volumes. This is the first

layer. The second layer is what we

called multiden cloud. Uh still

addressing

um research heavy teams

but now we have hundreds uh or thousands

teams that want to have they don't want

to deal with the physical

infrastructure. They want to deal with

the managed infrastructure. classical

infrastructure as a service in the cloud

terms. You have storage, compute,

networking, virtualized and a good

environment with API, observability,

security, everything that normal teams

expect cloud to have. You log in, you

get your cluster provisioned and you can

start training or run inference if you

need uh and manage your application or

manage your workflow yourself but have

infrastructure figure it out for you.

Right? So still if if the first layer

speaks in megawatt and literally like if

you read announcements someone signed a

large deal with some like Meta or

Microsoft or OpenAI people speak

megawatts there uh so it's like you

deliver the megawatts of compute then

when you speak about this managed cloud

people speak GPU hours because this is

the key unit you sell the efficient

hours you

spent on compute with storage with

complimentary services but you still buy

managed by com but compute then the next

layer that we working is managed

inference when people don't want to go

in terms of GPU hours they don't want to

figure out B200s against H200s against

B300s what is better for particular

workload they don't want to manage the

you know VLM or SGAN deploy themselves

do all the optimizations and here like

our product called Nebio stocking

factory this is a managed inference

platform and again this is the new type

of the customers mostly people who we

call them vertical AI companies or

enterprises so people who actually build

products they don't do models they build

progress products on top of the of the

and this is to your point of specialized

and open source models when they need to

shift from entropic for example or

diversify um the models they use for

that. So, and again this is the new

primitive that we provide or the new

kind of uh new entity that customers

need. Now we speak in tokens. It's not

you pay for GPRs,

you you consume tokens and you can build

your applications not thinking in terms

of the clusters underneath.

And this is where we sit now. But it's

also I think not the final stage of of

where we going because now people build

agentic uh agentic applications agentic

workflows and when you build uh end to

end agent you may not even think in

terms of the particular model and you

not think you may not think in terms of

particular number of tokens that you

want to generate you you want the end

toend task to be uh efficiently

executed.

and provide the expected outcome. And

then the magic that platform can make is

actually think for you which model

better to to use in this particular

call. uh do you need to go to the

smarter model or you know you can ask

two time in the same inference budget

you can request two models uh lighter

models and get you know less smart

tokens and then have the judge model

that chooses the best result or what

size of context you should have and so

on. So this is the next layer when

developer would maybe not even think in

terms of particular

you know types of the tokens but thinks

in terms of end to end execution of

their task.

>> So that's a d layer 4 is a direct

competitor to open router.

>> What we would love to bring on that

level is this the same like what we do

on the layers below is the optimization

engine.

uh you can build your agent in so many

kind of open source or appropriate tools

but then when you need to scale it, you

start thinking about the economics, you

start thinking about reliability,

reliable execution like repeatable

execution. And this is like where it's

not just like model choice problem. It's

not just like the outcome problem, but

it's a system problem. You need to make

it reliable. You need to make it

repeatable. And you need to make it

economically viable. And that's probably

where Nebios could create the value. The

same way like we don't tell people how

to build their applications. We just say

okay, if you need this model to work for

you like with this economics, we will

help you to optimize. The same here. If

you need this agent end to end run with

this budget, with this quality, maybe we

can help you uh to optimize it. And

again, this is just to make it sure it's

a kind of a little bit speculative

thinking about what what's next. It's

not like what we already have, but this

is where we think where we see our

customers evolving and where we think

that we could create the next kind of uh

the next layer of the product ordering.

I love this and I I have all of these

notes. Um and uh I just want to actually

go through the four pillars that you

said there. You said number one,

capacity.

>> Yeah.

>> If you had 10x the capacity today,

what would be different? Like could you

sell it overnight?

>> Yeah. Yeah, it's a good question. Not

overnight, but we would definitely we we

definitely have demand for that. uh and

I think the key question for us it's not

uh do we have demand or not but how we

actually build a portfolio of demand

because you have so many customers on

this market that you can balance between

and again to the point of four layers of

the product. You can sell bare metal,

you can sell managed customers, managed

infrastructure, you can sell inference

and maybe in the future you can sell uh

some new layers of product. And I think

what we try to do is to build kind of

quite diversified portfolio of uh

customers. We we we believe that the

higher stack we move the more value

potentially we can create for the

customers and actually the higher stack

we move the bigger population of the

customers we can serve because again

like on bare metal level you have maybe

dozen of the customers in the world that

you can work with on uh managed

infrastructure there are hundreds on

inference there are thousands on a

gentic there will be tens of thousands

of new developers that build it Right. I

Okay. On the kind of customer portfolio,

I love that for the capacity.

Absolutely. You want to be big enough

that you're meaningful, but not too

large that the business relies on them.

With that difficult awareness,

where do you settle on what revenue

concentration with a meta or a Microsoft

you're happy with?

>> It's a great question and I I would say

it's a it's a main question of our

business. uh I mean not nebios even but

the product category and we always told

it and publicly and uh to our investors

and to our customers that we believe

that long-term strategy of Nabios is to

serve as much diversified portfolio as

possible. So we we do the best to to to

have many customers that we work with.

We build the platform. If you in reality

again to serve dozen of the customers of

the world on like of the level of meta

and Microsoft which super advanced and

they have their entire software stack,

they literally need only physical

infrastructure. They bring with they

bring everything they have deploy on

your infrastructure and run right. Uh

you have a tiny tiny uh additional value

that you can provide them above the the

the physical infrastructure. by the way

to to to satisfy them with what they

need on physical infrastructure is quite

a challenge because you can imagine they

are quite demanding and and and and they

need the like the most scaled

infrastructure in the world that exists.

So uh sometimes people say it's

commodity but it's not really commodity

on that scale like nothing commodity

when it comes to the to the real scale.

But again to your point uh uh this is

quite a small population of the

customers that you can work with and you

not necessarily need all the full stack

software to to work with them. So we

intentionally

building and from day zero of nebios we

were building this software stack

because we thought that it's a much more

beneficial for us and if I want to be

pathetic for the world uh to have

someone who can support customers uh not

only on this physical infrastructure

layer but beyond

>> for the long-term protection of the

business. Do you not have to build the

full stack? Because otherwise you become

the capacity provider to these mega

players which will make a [ __ ] ton of

money.

>> Yeah.

>> But you're incredibly concentrated and

very vertically focused.

>> Yeah, I think I I I think so. And again

we don't know where the world will end

up like and in the world of infinitive

demand uh you you may uh sustain

even long-term and midterm uh selling

this like bare

uh whole bare metal kind of uh

contracts. Um but the more competition

let's say you have from the customer

from demand side uh you you can be picky

uh even with the customers you you work

with and work with the customers that um

appreciate like that value uh the

platform that we built more and there

are different customers in the world.

Someone more obsessed about the price,

someone more obsessed about the quality,

someone really want to have much more

advanced platform because they want to

concentrate focus on their platform uh

or product and don't spend time on the

Yeah. Before before we move to number

two being product just staying on

capacity given the insufficient supply

of capacity today if you doubled pricing

would you see any change to demand?

>> Oh it's a it's a it's a difficult

question. Uh we actually raised prices

like just% yeah just couple months ago.

uh uh and we still uh still have fair

fair kind of pipeline pressure let's say

uh uh on supply and again uh if we the

question we we we don't really know

where is the balance uh and I will tell

you why uh it's not only us being greedy

and want to get like as much money and

like then people in the shortage will

still will have to pay for some extent

it works like people needs compute to

build but then there is a point and

especially it's less in in training

because in training it's like oneoff

cost but if you believe that we're

moving to inference and inference is a

uh is the cost of serving the customer

there is a a a level where economics

doesn't work and the economics of the

products of our customers ers if they

work they can grow and then we can grow

with them. It's not like just supply

demand situation and then absolutely

elastic prices. They are elastic for

some extent. uh but we also want to be

meaningful and we want to be thoughtful

kind of what our customers need and by

the way it's not only GPU hour cost it's

all the optimizations you do all the

real we call it TCO total cost of

ownership that you in like and this is

partially why we build the software

platform and I'm sorry come back to

product again and again you want to

speak about capacity but people too much

obsessed about capacity like capacity is

important too too much obsessed about

the nominal price of capacity. You can

price GPU $3, $4 and $5.

And depending on the use case and

depending on on the quality of the

platform, it can create completely

different outcomes for the customer in

real cost.

How long it works? Well like what what

is the e effective kind of uninterrupted

kind of time that you can run there. If

you talk about inference how much tokens

you can extract we we see all these

optimizations that happening that

changes the price of the tokens in order

of magnitude. So people so much speak

about the cost of particular GPU but if

you do the right thing with the model

you can change the price like uh in the

times and this all should work together

uh uh as a system not just as a again if

you speak about role infrastructure then

you can manage only the price but if you

if you if you build the platform and if

you provide the high level of the

service to the customer then you can

extract much more economics not only

from the infrastructure cost structure

right

>> if we move to that second layer then if

we move away slightly from capacity to

GPU hours the product itself

multi-tenant

>> what is the main question that you ask

yourself within that segment if in the

first capacity it's how much revenue

concentration we have what is the big

question in that layer of value

>> what customer needs it's a normal you

know you speak with a lot of product

founders

uh uh and this is the same like what

customer needs at the end of the day how

customers evolve in their needs where is

the demand moving so it's like we see

all this transition from training to

inference we see transition from uh uh

just using the models to building agents

uh and we see the transition from mostly

AI labs uh consuming AI compute to

enterprises coming in game and all the

time if we want to be relevant we need

to follow the changes and this is the

main question which like we we ask us in

the in the product like what should what

customer needs and what is neio's what

is our value that we need to create

because again we are small company we

cannot build everything uh and we need

to be very precise on what we can do

better than others and where the value

that we should focus on uh uh given how

customers evolving.

>> What changes are you seeing in customer

needs that you're not seeing discussed

much in public?

>> Everybody's talking about this moving

from training to inference. I think it's

just very uh uh 100,000 ft like view

because this move means actually people

um build specific products and in those

products they have their economics they

have their trajectory of growth and it's

not just like whatever the same GPU is

just used uh uh for other purposes I

think it it brings the new requirements.

You you need to build your inference

platform. You need to help your

customers not only run inference but

where the model that they inference come

from. Everybody is taking open source

models and fine-tune or them. So how do

we help them and then when they run them

they generate a lot of data. How do we

help our customers like when they

already run their application their

inference to collect the data to create

it and then use it to improve uh uh uh

to improve the model or the application

that they run. So it's a people like

this flywheel analogy like uh you you

you run inference you generate data you

can observe this data then you can

improve the model uh that you run and

kind of continue continue uh um improve

the quality uh of the end product. So I

think uh there are a lot of pieces both

on system level and both on uh um AI

magic level if you want. Uh and I think

the the most fascinating m moment for me

is that I think what we see is that

barrier to build is going down. So we

see more and more customers like

builders coming to the market that not

necessarily AI researchers or not

necessarily inference engineers. Uh and

the value that companies like Nebios can

create is actually to lower the barrier

uh to to build AI enabled enabled

products and AI enabled applications

that really work and incorporate like

hide from the developer all the

complexity of infrastructure, all the

like

some complexity of AI like how you tune

the model or how you optimize the

inference. It's a lot like research

heavy area as well and just let people

focus on their customers and uh and use

case by the way the same way like they

do with uh with the closed ecosystems

like entropics open ais you mentioned

the word differentiation

and and one thing that I was discussing

with my partner before that we have as a

theme we have to discuss and it's within

these layers but you've spoken

extensively about product buildout and

the importance of building the product

underneath capacity when People look at

you versus other Neo clouds, you know,

we look at you versus a corewave. You

both run GPUs, you both have Nvidia

relationships, you both have meta as a

customer.

What's the difference? I don't like

compare with others. The principles we

build are full stack. We call it full

stack integration. And you you can think

about it like full stack down and full

stack up. Full stack down is we're

really deep in physical world. We build

data centers. We built racks and

servers. We built the platform. And then

uh and when you control this kind of uh

things downstream, you can move faster

and you can squeeze more

uh cost and provide more economically

viable solutions for the customers. And

then your vertical integration upstream

is actually what we spoke about like

product and how can you follow the

customer's needs and customer segments

and not be limited by the small

population of the people that just need

infrastructure but really serve kind of

enterprises and product companies uh

with like meet them where they need us.

And this is like I think what we

different and then how it how it like

showing up I would say is again uh less

concentration in the in the in the in

the business more diversified customer

portfolio uh uh we believe long-term uh

better positioning for going to

enterprises where we believe eventually

a lot of demand will come from again now

most of our segment is working it's AI

natives working with AI natives but we

have a huge economics uh huge market of

enterprises existing companies and

someone needs to serve them and uh uh

they will not buy ro compute they will

need platforms they will need tools they

will need uh us to respect their legacy

and being able to work with their more

complex environment they're not nimble

they have data to migrate, they have

systems to integrate and that's that's

the big game and I think that for us

it's kind of the main uh direction to

move.

>> You mentioned the third layer of the

four-pillared stack being managed

inference for people that don't

understand how do you think about this

layer and how would you explain it to

them?

>> Yeah, very simple. uh you you built your

product on whatever call your where you

wipe code.

>> I I'm I'm actually an open AI in a

>> code. Okay, good enough. Uh you built

your great product with OpenAI. Uh you

you cracked the use case uh and you

started growing and you have amazing

traction. The only problem may be that

uh you don't have enough margin or you

want to start applying more aggressively

the data and tune the the behavior of

the model and you cannot do it in the

closed ecosystem. So you you you go to

internet and you read there is there are

a lot of great open source models that

on the benchmarks are close to open AI

and you think oh great it will be 10

times cheaper inference is cheaper I can

tune those models I can apply my data

and my product will be better my growth

will accelerate. So you you go you you

take the weights from hugging face you

take uh some uh engine to run it like

vlm sg lang something and then it

doesn't work uh because

uh you need to to to really extract the

value you expect you need to do like

optimizations you need to deploy it in a

proper way you need not just one GPU

tokens extraction or one host

uh setting but you you have the large

product you run on hundreds of thousand

GPUs already you need all the

orchestration you need the caching you

need uh you need the observability like

your customers ask you like how does it

work and so on so forth and by the way

you had all of that on open AI because

this is like the production service for

you it's you don't think about

infrastructure when you work with open

just subscribe for the the plan you need

and you pay for whatever uh end result

and so that's where you need the product

like token factory uh you token factory

gives you the managed inference with the

open source or specialized models you

can run existing open source vanilla

open source model or you can tune the

model and deploy your own like weights

and then we'll take care about all the

rest we'll apply all the optimization

techniques we'll uh manage the better

economics for you it will be reliable

you don't need to think about the next

100 GPUs where you will find them uh and

so on so forth. It's service. It's like

managed managed service.

>> With Token Factory, you run on 60 open-

source models. And you said before about

cutting inference cost by up to 70%

through optimization.

Can I ask a dumb question which is how

do you actually make a token cheaper?

>> Yeah. So the it's not the magic again.

you take the model uh the b like some

baseline model and then you can optimize

it for particular scenarios that uh that

you have. So you can do

actually you can distill the model you

can make the same like the smaller model

that works uh uh with the same quality

you can do spec decoding you can

optimize caching uh uh and so on so

forth. So you take the model and out of

this model you actually build a system

that in your particular case works with

your with with your requirements with

optimized economics. And by the way, one

of the things that uh also um I think

important for customers to use managed

platforms like token factory, the models

are changing every week, every month

like right today maybe minimax 3 was

released and there is ultra that was

announced released. So, and this happens

every few weeks and every time the new

model released, uh, it may work better

on some benchmarks and maybe not like on

other benchmarks and so on. And you want

to have flexibility. You want, uh, you

want someone to support you on

experimenting and actually adopting the

new best models for your use case every

time they come online. And then like the

platforms like ours uh actually again

abstract from you all the work that you

need to do to actually like change from

one model to another to benchmark all of

them and so on. So you you you can be

sure you can be sure that you will be on

the frontier like every time something

new is happening it will be in the

platform you will be able to test it you

if it works better for your use case you

will be able to switch and it all will

be kind of smooth and transparent for

you

>> does the pace of model development

sustain like you said there I would

argue respectfully you said every couple

of weeks I'd say every couple of days

there's new does that sustain in 5 years

time are we seeing that level of

iteration?

>> Well, I don't know. It's a good chances

that we'll continue to see a lot of

niche models uh show up and improved. Uh

I don't know again I'm a believer that

we quite far from the wall and we will

see a lot of like uh models improvement

uh happening. I think that what we also

see is

much more new like modalities and

specialized models uh coming in game. So

we speak about this frontier alms but

there is entire world of life science

models robotics

uh world models video models image

models uh so and they all have their own

use cases as well and we see more and

more like small specialized models

particular use cases coming like very

much optimized. Just this morning, I

spoke with a team here in Israel that

develops uh uh cyber defense uh

foundational model like the model that

optimized for to build uh cyber defense

uh agents and again they don't start

from the scratch. they they take some of

the foundational like uh some of the

open source foundational model but then

they train it for the particular case

optimize for the quality and the latency

that needed in this like cyber defense

use cases and I think we'll continue to

see it we'll see a lot of specialized

bolt post trained models that still need

uh optimized inference and optimized

like uh infrastructure around them uh to

let customer use

Can I ask going back to token factory on

token costs and token usage? What are

you seeing that you don't think other

people are talking about enough? What

has shocked you recently? Again I I

think everybody is speaking the same

thing like how fast it's growing uh uh

when we see this uh uh trajectories of

some companies like entropic and cursor

and cognition in coding and now we see

start seeing in other verticals as well

uh some uh healthcare examples some uh

financial uh use cases I think uh I

think it's like quite amazing what it

what's interesting is to see how like

noni startups are moving. So we I can

give an example. We we have the customer

of uh revolute uh and uh when we started

working with them I think 99%

of their budget inference budget was in

uh uh closed models in open AI

uh and they started to crack some of the

use cases and some of them didn't work

for them economically. So they

practically couldn't replace the humans

or cannot enhance the humans uh in the

in the use cases they wanted to address

and they started moving to open source

models but it didn't move fast for them

because they had to spend time on

building the entire engine internally in

the company and first of all they were

focusing on evaluations. So, and I think

this is something that people

underestimate

how important to build kind of the

foundation for improvements and

experimentation engine. Uh when you

understand as a company, as a team what

is good for you because again like you

you you you close some use case it works

but then you want to change the model.

How do you know you don't uh you don't

ruin the quality? you need to have like

metrics, you need to have a valve

mechanism, you have you need to have

this CI/CD process uh established for EI

development.

And I think that what we see a lot of

customers like Revolute, they have this

foundational investments that need to do

in the understanding of how to evolve

the models, how to actually safe safely

integrate them in their production

processes.

But when they solved these foundational

problems, they start growing

exponentially.

And I wouldn't

uh underestimate

how fast those customers can grow when

they build the system that let them ship

fast. And ship fast means they know how

to evolve. They know how to make a

decisions.

And this is something that we see across

a lot of customers. They have this you

can call it foundational investments or

cold start problem. How to start

shipping. But when they solve it, they

start to grow exponentially and they can

use different models. They can build

much more products inside the company

and so on so forth. And I think this is

this is something that kind of when you

look from outside you kind of oh they

are not growing they start small they

take time and so on. But this is in a in

a if the company has a strong team they

build this foundation and then they

start growing exponentially and I think

we'll see a lot of explosive growth

in enterprises in the digital like in

the cloud companies in cloudnative

companies like revolute Shopify pro

booking.com when they solve this cold

start problem they build the system how

to ship and then they will grow like in

their AI adoption like crazy.

>> How much more do you think Revolute will

pay you in 3 years time?

>> I don't know. I don't know. No, I don't

want to speak about that. No, but I I

can say that like they in total I think

they they grow times like they grow like

this. We we all see this AI companies

reporting IR growth, right? For them

it's not IR, it's like their budget. But

I think that the most advanced companies

their AI budget and it's not like this

fake or not fake like this more all this

maxaxing kind of race uh we see it like

how they do it in the in the production

workload. So they they grow the same

pace like this uh AI native companies

reporting they are growing their AI

whatever uh consumption equal to their

IR. So the companies like Revolute,

they're growing the same exponential uh

trajectory.

>> So I always push back on people who

proclaimed that open source would be a

credible threat to the largest model

providers because I said listen the

biggest enterprises want reliability.

They want security and most of all they

want ease. They don't want to be

tinkering around with all the

architecture and [ __ ] beneath the

surface. What you're telling me is

you're able to be all of that to allow

them to pipe away from those providers

and have a cheaper better experience

because you take away the plumbing.

Correct.

>> Yes. But I again I think it's not about

my point is closed models with open

source models. It's not about like

reliable or not reliable. Again the work

of the companies like Nabio to make

possible like as you say not think about

plumbing if you want to use alternative

models but I think it's about

capabilities again I think that closed

source models like frontier models are

great and they will become even better

and they will solve so many problems

that we don't solve yet

and we we have such a diversity of the

use cases we want to solve.

that there will be market for the

smartest models of the world, the

fastest models of the world, the in

between models of the world. Smart

enough but cheap enough and you as a

customer will be able to just pick the

right, you know, the the right source of

token

uh for each particular uh task. And back

to the agentic uh layer point maybe it's

even won't be the customer kind of task

to choose the like which model to call

now it will be the engine that knows uh

all the capabilities like all the models

underneath and then uh when you go to

open AI and you do the research you

don't think in terms of how many loops

you want it to make. You don't think in

terms uh when it should go to level lm

and when it should go to search. You

don't think should it now call like

which prompt to to call. Right? It's

it's happening. You just you give a task

there is an engine

uh the reasoning uh engine that decides

how to run this task and you got the

result. So I think that a lot of

enterprise cases a lot of these agentic

tasks will be solved in the same way

when it's not you as a developer that

focusing on customer need will need to

kind of orchestrate all these tokens and

models and then we will need all the

models the smartest one for the most

complex kind of intelligence

and the fast models that can do like

quick iterations.

And again we don't speak even about all

the modalities and like what we'll need

in the physical AI world and so on. So I

think again my point we will have enough

of PI for

different models and what we need to do

as a as a infrastructure company uh is

just help for extent we can to make

developers comfortable how they use all

these opport all these capabilities that

models provides because as you as you

rightly said it's not about like model

capabilities. It's uh it's not only

about model capabilities. It's about

like not plumbing, getting them working,

getting them optimized, getting them

reliable. When we look at the explosion

of models and the specialization of

models like you said there and how many

will be built and the depth across

different use cases

sadly the one thing that is quite clear

is that Europe does not have anywhere

near the model buildout that we've seen

both in the US and in China. How

important do you think it is that

nations have their own sovereign models?

It looks like the world is divided or uh

we we can we we may not like it. H and I

think that having good enough

foundational models

uh available for the big parts of the

world is important. And I think here in

Europe uh we or at least at this part of

the world we should think uh how we

have enough capabilities available

uh here and I think that we had a lot of

conversations over the last couple of

years in like about the serverity and so

on all this like sovereign AI agenda and

I think it was too much concentrated

around like again megawatts and and

power rather than on what we have on the

build builder layer right and I think

that megawatt will come uh I think that

it's it's uh what what we in Nebios

always told is we will build

infrastructure the companies like us

will build infrastructure if we have

demand and demand is coming from the

builders and I think that uh what we

need to care about here is to have more

great companies like lovables, black

forest labs, I don't know, mist drives

of the world and we have enough people

that invest in research, have enough

people that invest in the products uh

and then they will create enough of

demand and there will be enough of

flywheel again to have a good enough

models if we need. So I think this is

something that like we should care

about. Where is the most interesting

area to invest today? Okay, I'm giving

you four options.

Infrastructure,

horizontal model, vertical model,

application layer.

Uh I mean we built infrastructure. So uh

uh uh we are quite happy here. I think

it's a good place to be in the current

world. I think that uh even though like

we we for some extent we are building

kind of the easiest part not in a way uh

it's complex execution but we kind of

know what's needed and our customers

help us to understand what's needed. I

think the most amazing people in this

industry are those who take a risk to go

and build uh enduser products in my view

and they actually drive the most of uh

uh a most of growth here like people who

take a risk like the real like the real

risk of building something people would

need or not need. Uh I think this is the

most the heroes uh of our like AI

journey. Speaking of heroes of AI

journeys, before I do a show, I go and

speak to I'm very fortunate. Now, you

mentioned earlier I've interviewed some

some big people. I go and speak to some

of those big people. A theme that did

come up when I was speaking to them was

the relationship with Nvidia. And is a

marriage a marriage if one has more

power than the other? How do you think

about the the power dynamics in a

relationship with Nvidia when they have

so much power? We look at this in a very

simple manner. We just need to build

what we build. Uh we need to build uh

our product. We need to tell our story

and then uh the rest will complement it.

Uh I think what is the most fascinating

uh Nvidia is still for big extent is an

engineers driven company and I think the

best thing you can do to get respect

from Nvidia it's my read uh they may

have a different uh uh point of view but

if engineers in Nvidia respect uh your

engineers you will have the right

foundation for relations let's Okay. And

I think that uh we managed to prove uh

again and again that we

know what we build and we have a strong

engineering team and I think that they

see it and they respect it and we have a

lot of like engineers to engineers

relations on physical like on a on a

hardware level on the software layer on

the inference platform layer and the

better engineers in Nvidia think about

you, the better

uh relations and partnership uh I think

it's enables and uh and again we may be

maybe wrong thinking this way but uh but

but but that that's what we see like we

can do and uh we we we just focus on

being reasonable and being kind of

focused on the long-term value. It

sounds like fluffy. Everybody say it.

But uh just do do your [ __ ] job at

the at the end of the day, right?

>> I'm going to title this Roman. Just do

your [ __ ] job.

>> No. What what else we can do? I mean,

it's not we are we we we we are in such

a race and we just can do we can do the

best to do our work better. I think

that's that that Yeah.

>> Just do your [ __ ] job. I Jose I know

it's funny. I like it. Huh? But like

what's the hardest part of just doing

your [ __ ] job today?

>> Four dimensions. Uh build scale, build

product,

work with customers. It's actually like

two dimensions. We discussed like scale

and product. The thought is customers.

We are in the field business. We cloud

is the we we like to say that cloud is

post sales business. When you sell, you

sell the promise and then the c you need

to satisfy the customer and working with

the customers, covering the customers,

having this strong customer engineer

like customerf facing engineering team,

FDE team. This is the third dimension.

Go talk to your customers. Make sure

that they know you, that you know them.

This is the third dimension. And the

fourth, the most boring but also the

most exciting is the capital. We are in

the capital intensive game and we

competing with the most capitalized

companies in the world.

>> If if I gave you unlimited budget, what

would you do differently?

>> Uh build faster. That's that's very

easy.

>> Build what faster?

>> Yeah, data centers and fulfill them with

GPUs. Like just build faster. Our copics

program this year is 2025 billion. our

competitors hyperscalers have like 10

times like eight times bigger. If I

would have like uh 10 times bigger

capital, I would just build more data

centers and fulfill them with GPUs

faster and uh serve more customers.

That's what we started with like what

would I do if I had like 10 times more

supply? I would have I would move

faster. Gavin Baker said, I think quite

intelligently, that permitting and

regulation and the delayed buildout of

data centers has actually helped because

if I enabled you to build 10x the data

centers today, it would actually create

the glut. Yeah, it's it's actually a

great question and uh

and like our investors sometimes ask us

like what is the main bottleneck and the

main bottleneck again it's all it's it's

it's everything but if you you need to

look at this from the time time span

perspective again in the six in the next

6 months the capital cannot help like

you 6 months is too short time you you

have what you have you need to deliver

then in the next 12 months You can

accelerate something but again it's more

like capacity constraints and in the

next 12 months we can accelerate uh with

the capital or with execution something

but but then in 24 months you definitely

can unlock so many things and you can we

are not building one data center. It's

also important to understand we are

building the portfolio the portfolio of

capacity and the more execution power we

have the more capital we have we can do

the things in parallel we can unlock

like that's why we do how we do we

secure power and land then we build data

centers then we fulfill them with GPUs

every next stage requires more capital

but we do as much as possible in advance

to make sure that when we will be on the

next stage we already have power secured

when we will have enough capital to

deploy in GPUs we will have data centers

that up and running so it's like phases

of investments and again the bottlenecks

are different on the different time span

perspective so obviously if you have

more capital you can move faster not in

6 months but in whatever 18 24 months

for sure

>> can I ask you when you think about the

the the data center build out there that

we're seeing more and more public angst

towards AI. Eric Schmidt's getting booed

off stage. Um not because of the content

but because of the AI inventions. Um and

we're seeing like public resentment

towards data centers. I think 40 out of

100 now are not being built when they go

through planning and approvals.

How do you think about and reflect on

that internally?

This is the environment we need to work

in. So again there are two sides of the

thing. One is how we think pragmatically

as a business. Uh that's what I said we

we think about it as a portfolio of the

projects. We need to make sure that we

are like overs subscribed if you want

and if one data center will be delayed

we will still deliver enough capacity to

our customers and most of the customers

they are not locked in one physical

location. And they just like it's a

cloud uh we we can build uh in different

places and then bring the workloads

where we have capacity and but this is

the pragmatical side of the things. Then

what we obviously see that communities

and uh the local authorities require the

companies like us to work closely with

them and explain and uh show what what

what we do and work with them on their

concerns and like address them. This is

the reality. I mean uh you can compare

it uh when Uber uh started growing and

in many places there was the push back

right so oh what's happening it's

something new we it's moving too fast we

didn't didn't expect it to move so fast

and so on and I think that you you you

go and work and you explain and it's a

it's a it's just a part of your of your

duty to engage and work with the new

communities that become dependent on you

and they have concerns and sometimes

they just they have concerns because

they not educated enough. Sometimes they

have rational concerns that you can

address and

the same do your job.

>> Do you think you've done a good job at

it so far?

>> We come from the place we we we always

we always think that we didn't do

enough. Uh I think that we we got quite

a progress uh in the in the places where

we when we started building. Um

historically

uh we had more experience in Europe. Uh

we now like probably 70 75% of the new

capacity that we built midterm is in US.

So we built a lot of presence uh on the

ground and in like to communicate with

those local communities in US and we try

to do the best job. Yeah. We we need to

do better always but we we are moving.

>> Can you help me on another one? We

laughed earlier when we said about

space. Data centers on planet earth is a

very difficult logistical buildout. Data

centers in space. I love technology. I'm

an optimist. I hope it

is that [ __ ] nuts.

>> I think everything we see is [ __ ]

nuts.

No, so many smart my my my view is very

simple. Uh so many smart people now

working to make it happen. So most

likely

uh I I may be less pessimistic that

we'll see I I don't know what is there

like we'll build more in space than on

earth in 3 years. my view I I'm humble

enough to say that so many smart people

are trying to solve uh this uh this task

and uh bring compute to the space that

why wouldn't I believe it will happen

and I think there are a lot of

challenges still like a lot of like a

lot of things to figure out

but if someone would said say us that uh

even 3 years ago that we will build like

multi- gigawatt data centers and it it

will be like large interconnected

compute clusters. Would you believe I I

I didn't think like that and it's we are

here it's it's routine.

>> Um I want to do a quick fire with you.

So I say a short statement you give me

your immediate thoughts. What job does

not exist today that you think will be

very common in 5 years time? One thing

that obviously happening is we

democratizing what people like called

being developer right now each of us

can be a developer and like what what I

mean being developer is to convert the

idea in some digital digital asset. So

and I hope that again we have to be

optimist here and I hope that uh this

democratizing of building like letting

each of us being builder will open up so

many opportunities and like that we even

don't imagine yet when we will give like

millions of new people tens of millions

of new people's ability just to convert

their idea into something that works

very easily. we will see a lot of new

businesses and a lot of new ideas kind

of just uh un like coming in life and

they will create a lot of new works that

we don't even think exist. So it's like

second you know uh uh second orital of

uh uh of all this kind of democratizing

of the building also what is challenging

and what will need to be changed and I

think it's like as risky as opportunity

as risk as an opportunity is how the

education will change because

uh now when everybody has access to

intelligence what should people learn

you definitely don't need them to learn

the facts. Everything is available. Like

all the knowledge is kind of available.

Like how do you really like train people

to think when they don't need to think

so much? How to teach people to

continuously change like many

professions will be not stable? How do

you how do you help people to find

themselves in the changing environment

and actually like

think and learn the new concepts

constantly. I think this is this is

something that very like a lot of gives

a lot of new opportunities but also like

creates a lot of risks.

>> You you mentioned you know you have um

two teenage uh daughters. Uh what do you

advise them that they're entering the

workforce in the next 10 years. What do

you advise them?

>> No, I what I literally tell them is I

think two things will be needed. I don't

know what will be needed, but I'm sure

that two things will be needed. One is

like being able to communicate with the

people with empathy with emphatic

communications. So like understand

humans like communicate with humans and

being empathic. And the second is uh uh

creativity like uh uh all the the art. I

hope that the art in in in a way will be

will exist. So I think that all the hard

skills that I thought 10 years ago will

be needed when I thought that the most

important thing they need to learn is

math and uh engineering now I'm far from

this belief and I'm quite happy they

much more in the soft skills than than I

was when I was a kid and uh I again like

understand like being able to

communicate with humans understand the

humans and be emphatic to the humans and

have this creativity uh idea like being

able to try new things and like be

creative. I think this two if you can

help your kids to develop those uh I I

think they will in 10 years they will be

in demand.

>> There's a question of how do you teach

creativity um but I completely agree

with you. The big finish this sentence.

The biggest threat to Nabius is not

competition but dot dot dot

>> uh but consolidation in general. Yeah. I

think that the main threat for Nebios as

a business is the world will be too much

consolidated again like like we

discussed we try to be diversified like

we try to solve like problems of

different customers and have different

customers on different layers. If you'll

end up in the world where I don't know

three five superm models, super

companies, super empires control the

world then nebios or companies like

nebios will be needed only to help them

maybe serve their needs on physical

layer. Um so I think that the in general

the consolidation is our main threat.

The the more world democratized the more

world diversified the more we need as a

business. Do you think that's likely?

We're seeing the we're seeing the

concentration of value to fewer and

fewer players. We're seeing the opposite

of diversification.

>> I hope it will not happen. As a

business, I think that it's better for

us as a humans as well. Uh for you and

me, the world will be become like remain

uh quite diversified in a different

manners. And uh uh I'm optimistic here.

I think that there are so many people

that

want to build something indep

independently

let's say like there is a lot of people

with the need to try things and build

new things that it's organically creates

this pressure and organically creates

more diversified world so hopefully will

will remain

>> penultimate one Leo Ashen Brener is a a

famous investor right now has huge cult

following. Um he recently disclosed a

very large position for him. 5.3% of the

company I think it's 15% of his

portfolio.

>> How do you guys sit internally? Are you

like yeah go Leo? I wouldn't say that we

didn't me like notice it obviously like

everybody noticed it and like the the

the the stock jumped and like it was a

big news in the around. Uh again I think

that we take it as a justification of

what we do. Uh and then you you got this

justification you say yourself okay

those people they give you a credit

that you will execute. It's uh uh I I I

I come back again and again to what we

do is post sale business. Every time we

sign a deal, every time someone invests

in us, they give us a credit and

opportunity to deliver. Then go back to

your job and deliver. And I think that

we are in a such a market where

emotional market as well that you should

keep keep yourself like down to the

ground. Remember that all this growth uh

all these credits that customers give

you. It's opportunity to deliver. Go to

do your job. Uh at

>> you're such an Israeli. Americans would

be like yeah go. You're like

I I think I'm Russian in this way. Like

Russians always know that uh things like

you need to you need to look in the uh

very pragmatically and uh you know

Russians always with this like faces

like always expect something will happen

and you you need to be you need to be

ready uh you need to be ready. So no I I

I I I I I I think that uh it's really

important part that comes uh from our

CEO also uh uh and founder Ki you wake

up and it's it's a new customer new day

you need to deliver nothing is

guaranteed just you need to you need to

concentrate on the work and I know how

much effort team is putting on things to

work and how much depends on every day's

dedication and how much how fast market

is moving and to stay relevant you need

to continue moving in the same pace or

try to move in the same pace with the

market and uh again you I think that on

a romantic note I I would say that we

could celebrate a little bit more but we

just don't have time to use opportunity

actually to say kudos to the team I I

don't think we celebrate enough and I I

I think that We I think it's right we

are not relaxed but I think we could

celebrate a little bit more uh and just

give the team like more uh more respect

and like how much uh how much is done

and it was not easy and it's still not

easy uh and it will not be easy but yeah

never stop uh we we cannot stop like you

you it's like

you it's like a shark you're alive when

you move Right. So, uh, this famous

thing. So, we have to move.

>> On that note, I cannot thank you enough

for joining me and for putting up with

my very meandering questions. You've

been fantastic, Roman. So, really huge

thank you.

>> Thank you. And, uh, too kind to me.