Speaker 1: With a round segle and from London and Gerard read
Speaker 1: from Berlin, this is redefining energy.
Speaker 2: Today. On redefining Energy, Child, we're going to talk about
Speaker 2: European sovereign neo cloud.
Speaker 1: Yeah, which is obviously a critical topic in this AI
Speaker 1: revolution that we're currently at the beginnings of.
Speaker 2: Yes, job, because when you see our investment right now,
Speaker 2: it is the biggest capital expenditure cycle in history. We
Speaker 2: are seeing statistics that the kapex is going to reach
Speaker 2: nine percent of global GDP, never seen before the peak
Speaker 2: of the railroad boom in the eighteen eighties or six
Speaker 2: percent of GDP. And the current AI data center built
Speaker 2: is bigger in the US than the rest of the
Speaker 2: construction company. So we are really in extremely out sector.
Speaker 2: But it's very US and when you're in Europe you
Speaker 2: have a different point of view.
Speaker 1: Well, listen, I think we be really honest with ourselves.
Speaker 1: What's happened in Europe is AI regulations have blocked the
Speaker 1: development of AI companies across Europe. So we don't have
Speaker 1: any foundational model companies like the Chatchbts of this world. Yeah,
Speaker 1: and we're playing catch up, and I think there's a
Speaker 1: growing realization that actually, you know, one of the advantages
Speaker 1: that Europe has in particular businesses is they have data
Speaker 1: and do you certainly want that data sitting in another country,
Speaker 1: And even if it's not in another country, you want
Speaker 1: to make sure that that data is not leaking out
Speaker 1: to the rest of the world. There's also growing concern
Speaker 1: that you don't want to be just sticking with all
Speaker 1: your data stuck on AWS or Google or any of
Speaker 1: these other big American players. We need to really make sure,
Speaker 1: as I said, that your data is your data.
Speaker 2: Absolutely so. We have a supper guest, Michelle Butwelle is
Speaker 2: the co founder and CEO of a German complete called Polarize.
Speaker 2: Polarize is one of the few European what we call
Speaker 2: neo cloud and is a partner of Envigad and his
Speaker 2: mission is to build sovereign AI factories high performance across
Speaker 2: Europe and.
Speaker 1: Ron I think what we also want to do is
Speaker 1: we want to thank the BMW Foundation Herbert Kun for
Speaker 1: their support and those of you who don't know the
Speaker 1: foundation what they're all about as uniting leaders across sectors
Speaker 1: to develop solutions that foster innovative economy and a future
Speaker 1: proof society. A key focus is the energy transition and
Speaker 1: climate change, where the Foundation drives international collaboration to accelerate
Speaker 1: the energy transition. Obviously, with rising energy demand from AI
Speaker 1: and data centers, new partnerships, effective collaboration, exchange, a science
Speaker 1: based solutions and strategies are not just the samsual but
Speaker 1: like they're critical really going forward.
Speaker 2: Yeah, for our listeners, we had to learn what the
Speaker 2: neo cloud company is because most of the time we're
Speaker 2: outside the data centers, so I'm more like energy in
Speaker 2: real estate, but here we are going inside the data center.
Speaker 2: So a real definition I just found a neo cloud
Speaker 2: company is a vertically integrated AI era infrastructure provider that
Speaker 2: controls the land power, data center build out and GPU
Speaker 2: cluster end to end offering hyperscal a great compute without
Speaker 2: being a traditional cloud like a WS or Azure. So
Speaker 2: the type of company which are now clouds in the US,
Speaker 2: you have Core with Amnhibius, Cruso, and so they are
Speaker 2: different from what they call the powerkshell the whole one,
Speaker 2: the aliks the advantage the cyrus one aligned data centers,
Speaker 2: you know, which provide the infrastator from the outside, and
Speaker 2: of course you've got another layer the original one, the
Speaker 2: traditional collocation reads Equinique Digital Reality, which are multi tenant.
Speaker 2: It's more retail wholesale focus. And then you've got the
Speaker 2: eper scalers and on the top of that a lot
Speaker 2: of money. The Blackstone, the Kickr, the Brookfield, Stone Peak
Speaker 2: Digital Bridge, they're all financing this absolute crazy build. We'll
Speaker 2: see what you're up as to reply to that US push.
Speaker 1: So let's bring Michelle on the show.
Speaker 2: Michelle, Welcome to the show.
Speaker 3: Thanks for having me.
Speaker 1: Well, Michelle, maybe i'd like to kick off here now,
Speaker 1: and I'm going out with I spose a thesis that
Speaker 1: really were in the fifth Industrial Revolution and this is
Speaker 1: the biggest industrial revolution the history of mankind. And there's
Speaker 1: two things. One is it's AI and AI needs alecturously
Speaker 1: and actually a funny way, electricity needs AI. I'd really
Speaker 1: love to firstly ask whether you agree with that view,
Speaker 1: and then secondly just talk about Europe's role in this
Speaker 1: revolution going forward and how you see it.
Speaker 3: Yeah, I totally agree with that, and I would even
Speaker 3: go a little bit further. It's not just an industrial revolution,
Speaker 3: it's also like a revolution of the society because we
Speaker 3: have to adapt to all these things which AI brings
Speaker 3: to us. For example, it changed the way how we
Speaker 3: work in the future. It changed the way how we
Speaker 3: it's gonna spend time, you know, always work in silence
Speaker 3: of forth. So that means yeah, I'm totally agree to that.
Speaker 1: My second part to the question then really is with
Speaker 1: in and around Europe. What's Europe's role now, because again
Speaker 1: stand back and look at it, I see us doing
Speaker 1: a huge amount, particularly in and around learning for national models.
Speaker 1: China going a slightly different approach, which is a more
Speaker 1: open source, and then I sort of see Europe and
Speaker 1: I wonder what Europe is actually doing.
Speaker 3: This is the problem because Europe is not doing much
Speaker 3: at the moment. The biggest issue is if we are
Speaker 3: not starting now, we are missing the train. Because you
Speaker 3: see what is happening in America, you see what is
Speaker 3: happening in China. They're taking a major role in this
Speaker 3: AI business. And it means as soon as the European
Speaker 3: economy starts to adapt to AI, it will it needs
Speaker 3: a choice, and if there's no choice from a European perspective,
Speaker 3: then they will go to an hyperskiller, to an American one,
Speaker 3: or to a Chinese one, and they get sticky and
Speaker 3: then they can't go back anymore or not so easy
Speaker 3: back anymore. And it's not just about sovereignty. Is also
Speaker 3: just it's about how we're gonna give out our datas
Speaker 3: for free to these models to learn to replace us
Speaker 3: in the future.
Speaker 1: And this is a real threat.
Speaker 3: And in my opinion, we're doing a big mistake in
Speaker 3: Europe because we are trying to replicate things from America
Speaker 3: like we did in the European Union. There's a tender.
Speaker 3: The tender says we want to have one hundred thousand
Speaker 3: chips in one giga factory. And to be honest, I
Speaker 3: think this paper was made by a lot of lawyers,
Speaker 3: but no technical guys sitting there and explaining what is
Speaker 3: really needed in Europe.
Speaker 1: So Michelle jumping on that, it's tough because let's go
Speaker 1: back to the fact that it is a revolution that
Speaker 1: we're going through. And I be critical of the European
Speaker 1: Union too, because when I look at their AI Directive
Speaker 1: and all this type of stuff, it just slows down.
Speaker 1: You're trying to regulate something that is changing too quickly
Speaker 1: and we're not going about it in the right way.
Speaker 1: So I'm totally witcher and I suppose the question then
Speaker 1: to ask is well, what should we do?
Speaker 3: First of all, we as polarized trying to build something
Speaker 3: which is not existing at the moment. In Europe, it's
Speaker 3: called the neo cloud, so means a cloud who's specialized
Speaker 3: in AYE for a eyepurpose like AI infrastructure. So we
Speaker 3: need to enable European companies, startups and solence of force
Speaker 3: so that they are starting to build something that they're
Speaker 3: going to invest heavily into data center, into AI. If
Speaker 3: without that, we can't catch up anymore in my opinion,
Speaker 3: So we don't have to regulate everything beforehand, which we
Speaker 3: need to have some form of sandboxes where we can
Speaker 3: start to work with. Startups needs to be supported, scale
Speaker 3: ups needs to be supported. We don't have to look
Speaker 3: just at the big carps. We have to look like
Speaker 3: a mixture of everything. And if we're not starting now
Speaker 3: to trust in ourselves means that we're going to lose.
Speaker 1: On the long end.
Speaker 2: In my opinion, do I understand it, and that's coming
Speaker 2: from somebody who doesn't understand much, is that you've got
Speaker 2: to type of AI work. You've got the work where
Speaker 2: basically you run those big models. I have no idea
Speaker 2: how it works, but it looks like it consumes a
Speaker 2: lot of power. And then you've got what they call
Speaker 2: the inference, which I finally understood what it means. Basically,
Speaker 2: it's the asset management. Once the models are running, so
Speaker 2: do you concentrate on the first and on the second.
Speaker 3: So we are concentrating on the second because maybe I'm
Speaker 3: gonna make it a little more understandable. Large language models
Speaker 3: needs to be trained first, so that means we have
Speaker 3: to raise the child. We have to give them the
Speaker 3: ability to speak and to think. This is let's say
Speaker 3: training in the world, and we don't have so much
Speaker 3: foundation model, so we don't have so much training in Europe.
Speaker 3: So this is done by OpenAI and by all the
Speaker 3: big guys. And the inferencing is like AI in operation,
Speaker 3: So that means as soon as you use AI, you
Speaker 3: are implementing into your workflows and it starts to do
Speaker 3: something for you. Then it's called inferencing because like consuming
Speaker 3: constantly tokens, and tokens is they your form of energy?
Speaker 3: So our computers are transforming energy into tokens. The tokens
Speaker 3: are consumed by the models, and this is how it operates,
Speaker 3: and we are specialized in inferencing, and because what we
Speaker 3: think is that we don't need to have so much
Speaker 3: foundation mode, because there are a lot of open source
Speaker 3: models out there. We can use these open source models,
Speaker 3: we can fine tune them, we can put them on
Speaker 3: an infrastructure which is sovereign, and then we can start
Speaker 3: to use it in Europe or European companies can use
Speaker 3: it because we need it, because otherwise we can't compete
Speaker 3: with other companies in the world who have this advantage
Speaker 3: of the eye. So we have two choices. One choice
Speaker 3: is we go with foundation model, which are proparatary models
Speaker 3: from the Googles of the world, and then that means
Speaker 3: we're giving the datas into this these models are learning
Speaker 3: from it, and then it could be in the future
Speaker 3: it could be possible that they're going to replace work
Speaker 3: which was done here at European companies. Or we can
Speaker 3: use open source model, we put them on a European
Speaker 3: based infrastructure with European companies and then it has the
Speaker 3: same effect and then the companies can use the I
Speaker 3: as well.
Speaker 1: And this is what we're doing in Polarized Michelle, I
Speaker 1: just want to go back to open source here again
Speaker 1: because if I look and I see all the tests
Speaker 1: and studies that have been done. Now, if we went
Speaker 1: back three years ago, there was like a year's difference
Speaker 1: in terms of the quality of an open source one
Speaker 1: versus a proprietary system, but now it's like one month.
Speaker 1: And so am I wrong to look at it and say,
Speaker 1: who cares if it's proprietary or not? If it's open source?
Speaker 1: Is a way to go going forward? Which would mean
Speaker 1: if I'm a European and I want to catch up,
Speaker 1: I really want to be the fast follower. I don't
Speaker 1: want to be the leader the US who's going to
Speaker 1: do and all this spend, all this money and all
Speaker 1: this foundation. Might I just go and take these open
Speaker 1: source models and I go directly into inference. Am I
Speaker 1: thinking about this right? Wrong? How do you think about it?
Speaker 3: You're absolutely thinking in the right way. So I think
Speaker 3: that the open source models has catched up so fast,
Speaker 3: and they are pretty good, and you have so much varieties,
Speaker 3: and you also have to think how much performance do
Speaker 3: you need? How much parameters are really necessary? If you
Speaker 3: look at the biggest foundation models in the market and
Speaker 3: you see the parameters, I would say in OPO's four
Speaker 3: point zero is not necessary for ninety nine percent of
Speaker 3: the people who are using EYE at the moment, So
Speaker 3: that means you don't need this super super super high
Speaker 3: intelligent parameters. But you can go with open source. You
Speaker 3: can save a lot of costs and you can also
Speaker 3: save your datas and it will be at the end
Speaker 3: of the day, the open source model will your personally
Speaker 3: EYE model in my opinion, and that is super interesting.
Speaker 3: So that means, yes, I'm totally following what you.
Speaker 2: Say if I understand. Well, you say you're a Neil cloud,
Speaker 2: so I know because that's kind of funny. Michael Intrator
Speaker 2: used to be my carbon broker twenty five years ago.
Speaker 2: Well it's not very well the sea of core weave now,
Speaker 2: so you kind of a European core weave. Correct, correct, Yes, Okay.
Speaker 2: What we do know from the energy guys is basically
Speaker 2: what Rowan is doing created by Quinn Brooke, fantastic company,
Speaker 2: but the others it's basically all outside the data center.
Speaker 2: So you know, they arrived, they put a terrain, they
Speaker 2: build the all infrastructure, and basically they deliver you or
Speaker 2: they deliver somebody a building which is ready to be
Speaker 2: powered and cool and everything. And that's where you come
Speaker 2: in explain a bit what you're doing.
Speaker 3: No, it's in fact, it's not where we are coming.
Speaker 3: We'recoming a stage earlier. Because if you look at the
Speaker 3: actual data center industry in the world, and it's mainly
Speaker 3: let's say cloud business like storage and so on and
Speaker 3: so force, it was like that the infrastructure, so the
Speaker 3: real estate building was thought from real estate guys, and
Speaker 3: then the computing which was implemented there was staying in
Speaker 3: the same shape for years, for maybe ten fifteen years.
Speaker 3: So inside the data center you had like a real
Speaker 3: estate part, and then in the in the real estate part,
Speaker 3: we had like a standard of some form of it
Speaker 3: called rex. It's like shelfs and then these shelvels had
Speaker 3: around fifteen to twenty kilowat each shelf today and we
Speaker 3: are seeing it the other way around because what we
Speaker 3: have understand and this is why we are building the
Speaker 3: data center ourselves, because at the moment, there are no
Speaker 3: co location data center which are suitable for the II infrastructure,
Speaker 3: so to say, because you have to think backwards, you
Speaker 3: have to think from the it back to the real estate.
Speaker 3: And there's a lot of differences between the old data
Speaker 3: center economy and the old data center economy.
Speaker 1: It will not be ending. It will stay as it is.
Speaker 3: We're still going to consume data is We're still going
Speaker 3: to have Internet, We're still going to have storage, and
Speaker 3: then we'll also have a gross every year. And then
Speaker 3: there is a new form of data center. These are
Speaker 3: the AI factories, So we are not calling them any
Speaker 3: more data centers. We are calling them AI factories, and
Speaker 3: these AI factory are totally different constructed. First of all,
Speaker 3: we are not going with fifteen kilowatt uperrac. We are
Speaker 3: going with up to one hundred and fifteen more. And
Speaker 3: we have to think not just about the generation of
Speaker 3: compute today, we also have to think about the generation
Speaker 3: of compute of tomorrow because the iteration of compute is
Speaker 3: going so fast. So every GPU model it will be
Speaker 3: replaced in the next three to five years. So we're
Speaker 3: going to have an efficiency every three to five years
Speaker 3: in GPUs, and we're going to consume more energy per
Speaker 3: GPU in the same amount of time. It always doubles
Speaker 3: the town sometimes even triples, and that needs to be
Speaker 3: sought as well. So if you're going in a classical
Speaker 3: data center, and if you don't think about all these
Speaker 3: different elements you're going to be outdated super fast in
Speaker 3: the data center world. So that means you need to
Speaker 3: think differently in THEI factories than you did in the
Speaker 3: past with normal data centers. Normal data center are not
Speaker 3: suitable for AI, and that means there's a totally new
Speaker 3: classification of data center coming into the market with a
Speaker 3: big hunger of energy, but with a lot less space.
Speaker 3: So for example, we are putting per square meter a
Speaker 3: lot more energy than a normal classical data center, So
Speaker 3: it means we can build in two thousand square meters,
Speaker 3: we can build twenty twenty five megawats.
Speaker 1: Very interesting you're saying about energy there. But I'm going
Speaker 1: to ask now, just coming from the energy side, when
Speaker 1: you look at data centers going forward, are we going
Speaker 1: to build big, huge ones or are they going to
Speaker 1: be small? In other words, are we going grade edge?
Speaker 1: And if it is grid edge, jesus fifty megawats, twenty
Speaker 1: megawards five one? How do you see the future in
Speaker 1: Europe of building these data centers or sorry AI factory,
Speaker 1: sorry AI factories, no whorries.
Speaker 3: So I see it in the same way like we're
Speaker 3: producing energy today, so we have like big new power
Speaker 3: plants in Europe. We have smaller coal plants, we have
Speaker 3: gas plants, we have windmills, We have a lot of
Speaker 3: different power production all over Europe, which is necessary because
Speaker 3: you need sometimes a really big power plant to produce
Speaker 3: a lot of energy because you have a region, we
Speaker 3: have big cities and so on, and the force whereas necessary.
Speaker 3: I see the same in AI. So of course we're
Speaker 3: going to have one hundred megawatter one hundred and fifty
Speaker 3: megawatt big AI factories, but we're going to also have
Speaker 3: like twenty megawat factories. We're going to have fifty megawatt factories.
Speaker 1: And what we think, we.
Speaker 3: See it as a and we call it giga grit
Speaker 3: in polarized. So we don't need these big centralized single
Speaker 3: point of failures in my opinion. So what we need
Speaker 3: is all the mixture of different form of AI factory
Speaker 3: which fits into the region and into the market. For example,
Speaker 3: if you're living in Munich and we have one data
Speaker 3: center or one AI factory in Munich where we are
Speaker 3: running ten thousand GPUs, it has around twenty five megawatts,
Speaker 3: so it's it's in terms of data center as standard
Speaker 3: data centers quite huge, but in terms of AI factories
Speaker 3: is pretty small and with this ten thousand GPUs, we
Speaker 3: have doubled the AI capacity in Germany at the moment.
Speaker 3: That means at the same time there is a lot
Speaker 3: more hunger for GPUs and AI factory, so we need
Speaker 3: to build them fast. And in my opinion, there's a
Speaker 3: lot of options in the market if you want to
Speaker 3: go fast, because if you build a two hundred megawat
Speaker 3: or even bigger AI factory, it takes time because the
Speaker 3: grid connection is not so easy. You need to find
Speaker 3: the right point to connect, then you need to build it.
Speaker 3: Then you need to have all this residancy. You need
Speaker 3: to have a lot of things to build these huge factories.
Speaker 3: But if you find an old production fabric which already
Speaker 3: has twenty or ten or fifteen mega what's in place,
Speaker 3: and you are capable to retrofit it, and you have
Speaker 3: the creativity how to transform a former whatever factory it was,
Speaker 3: into an AI factory and using the same infrastructure which
Speaker 3: was already there, and you just upgraded, you just put
Speaker 3: it in a way that is suitable for an AI factory,
Speaker 3: and then you have a very fast and a very
Speaker 3: efficient AI factory. And it's also in my opinion, so
Speaker 3: that you don't have to build everything from Squattionally you're
Speaker 3: saving a lot of cou two for example, and so
Speaker 3: and so forth. And we have in Europe and especially
Speaker 3: in Germany, we have so much potential sites and we
Speaker 3: see that every day which could be transformed into an
Speaker 3: AI factory and being a super efficient and a super
Speaker 3: fast way forward. So there is enough options, but the
Speaker 3: option needs some creativity from companies like US or others.
Speaker 3: And if you just go the normal way like we
Speaker 3: did it in the normal data center world, for example
Speaker 3: in Frankfurt. So we have in Frankfort a lot of
Speaker 3: data centers because we have there the International and the
Speaker 3: Transatlantic Internet KNOT and there's DCAKES which is one of
Speaker 3: the biggest Internet distribution not in the world. This is
Speaker 3: why everybody wants to be in Frankfort. But if you
Speaker 3: want to build a data center at Frankfoot, it will
Speaker 3: take you at least six seven eight years until you
Speaker 3: get the grid concert and it will cost you forty
Speaker 3: percent more than other regions, and that will be too
Speaker 3: late in my opinion. If you're going to go the
Speaker 3: same root.
Speaker 2: It's a very interesting approach. It's a very German approach decentralization.
Speaker 2: But if you cross the border and you are in France,
Speaker 2: where everything is stopped down, decided by the president, the
Speaker 2: only thing they're interested is to sign seventy five billion
Speaker 2: with a soft bank. Who's going to deliver or not?
Speaker 2: Is it compatible? Or who's right, who's wrong, everybody's right?
Speaker 2: How does it work?
Speaker 3: It is compatible because if you look at the reality
Speaker 3: in France, the grid connection times is even longer than
Speaker 3: sometimes for example, if you look in Paris region or
Speaker 3: Bordeaux region or other bigger regions, it's a nightmare how
Speaker 3: long it takes until you get a real data center
Speaker 3: on the ground at the end of the day. In
Speaker 3: a more centralized country like France, they're going to have
Speaker 3: the same promise and there are the same options. You
Speaker 3: also have a lot of industries which are not performing
Speaker 3: and in the same way, which can be retrofitted into
Speaker 3: two AI factories. And we see that all over Europe,
Speaker 3: so in Spain, in England, and we have projects in
Speaker 3: Spain we haven't we have also a project in England.
Speaker 3: The grids in England, it's also like a very bureaucratic
Speaker 3: and not an easy way to go. So that means
Speaker 3: We have that all over Europe, the same problems, the
Speaker 3: same kind of grid constraints, and if you look at
Speaker 3: Scandinavia where it's a little bit better, but still also
Speaker 3: Scandinavia is now catching up in bureaucratic processes and not
Speaker 3: being so easy anymore than there was in the past.
Speaker 1: Michelle, I like your approach and I think, okay, yeah,
Speaker 1: using existing infrastructure makes a lot of sense to me.
Speaker 1: Talk a little bit about the client perspective, In other words,
Speaker 1: why would a client go to you rather than go
Speaker 1: to Google or Microsoft or someone who they trust and
Speaker 1: they know for many years. In other words, what is
Speaker 1: it they differentiates you from them?
Speaker 3: I think there are three reasons. First of all, we
Speaker 3: are not more expensive than taking a hyperscilla. Second, we
Speaker 3: are sovereign, means all our clients are protected against the
Speaker 3: US out So that means that a government can decide
Speaker 3: to look into your datas, they can even decide to
Speaker 3: shut you down. And that so is we have no
Speaker 3: interest in your data. We don't drive our own models,
Speaker 3: we don't use your data to learn from it, we
Speaker 3: don't feed our own machine. So that means everything stays
Speaker 3: with the client and you can be sure, of course
Speaker 3: they are at form of legal frameworks where you can
Speaker 3: protect yourself, but at the end you don't know how
Speaker 3: the hyperskillers are using your data for their own purposes
Speaker 3: and own good And we know in the past Amazon
Speaker 3: had a marketplace, so everybody could bring his services and
Speaker 3: goods to this marketplace and sell it over Amazon, and
Speaker 3: then Amazon learned from it what are the best products,
Speaker 3: which products can be sold very fast, and which are
Speaker 3: super interesting to sell, and then they sold it themselves.
Speaker 3: So why should they not use your company data to
Speaker 3: train and to understand how they can make money with this.
Speaker 3: This is something every company in Germany, in Europe needs
Speaker 3: to think about that. So this is why the usp
Speaker 3: of Polarized is clearly that we have the same quality
Speaker 3: of service in AI. We have no intention to use
Speaker 3: your data for anything, and we are fully compliant to
Speaker 3: the European Union laws to GDPR and we have nothing
Speaker 3: to do with the US Cloud Act. And I also
Speaker 3: have to say, of course, we as Polarized are not
Speaker 3: so big at the moment, so we are trying to
Speaker 3: do our best. But what we do, for example, is
Speaker 3: we do some form of partnerships, for example with Telecom,
Speaker 3: the German Telecom who are building the trust layer, who
Speaker 3: are giving us a lot of potential into the market,
Speaker 3: but also with others big European companies or potential cloud
Speaker 3: champions in the future, like the Schwartz group, where we
Speaker 3: are delivering the layers, they need to repackage it and
Speaker 3: to sell it to their customers.
Speaker 2: But at the end of the day, what you decide
Speaker 2: to put the architecture, whether you put upper or Blackwell
Speaker 2: or Rubine or whatever else, that's your decision. Does the
Speaker 2: client to tell you that's the cheap I want?
Speaker 3: There is different layers. So we have one layer that
Speaker 3: is a data center, the I factory. Then the next
Speaker 3: layer is the compute means what type of GPUs, what
Speaker 3: type of storage. On top of it, we have a
Speaker 3: layer which is called core, which is like a software
Speaker 3: layer where a customer can just consume virtualized server and GPUs,
Speaker 3: and on top of it we have AIS a service.
Speaker 3: It means we have a platform where we are hosting
Speaker 3: different open source models and you as a consumer can
Speaker 3: directly log into the platform. You can choose your model
Speaker 3: and then you can consume tokens and your pay us
Speaker 3: per token. So if we're selling to a customer the
Speaker 3: GPU layer, which is called bad metal, then of course
Speaker 3: the customer will tell us what kind of GPUs and
Speaker 3: what kind of compute he wants. But if we sell
Speaker 3: it through Core or through our platform, then the customer
Speaker 3: they don't need to understand the infrastructure be lowered because
Speaker 3: they don't need a GPU. Nobody wants a g We
Speaker 3: just want to have the AI in operation to use
Speaker 3: it for two purposes, to make more money or to
Speaker 3: make my company more efficient. These are just the main
Speaker 3: drivers at the end of the day. So that means yes,
Speaker 3: sometimes if we are selling it to a bigger client
Speaker 3: like Telecom, then they choosing the it. They telling us
Speaker 3: what kind of a t they want. But if we
Speaker 3: are selling Core or Drive, then we are deciding what
Speaker 3: kind of a TV you're going to put into the
Speaker 3: data center.
Speaker 2: Michelle.
Speaker 1: One thing that I see in my work every day
Speaker 1: really is that the people driving AI demand for data
Speaker 1: centers in Europe are all the hyperscalters in the US.
Speaker 1: I don't see any European players actually doing anything.
Speaker 3: Yeah, that's correct, And this is the problem because it
Speaker 3: has two elements. One element is that the hyperskillers are
Speaker 3: going to consume most of our infrastructure, so that means
Speaker 3: they're blocking the infrastructure as well because they need a
Speaker 3: lot of energy for their training clusters. And the second
Speaker 3: element is that we are not really investing into it
Speaker 3: because we as Europeans, we are investing in balance sheets
Speaker 3: and we are not investing in ideas. Means the hyper
Speaker 3: skillers they have no problem in buying trillions and billions
Speaker 3: of euros for GPUs and training them models, but they
Speaker 3: also don't know what's coming in the next four or
Speaker 3: five years.
Speaker 1: But they have an idea.
Speaker 3: In Europe with the big tail coos, the big cloud
Speaker 3: players in Europe, they are not investing at the moments
Speaker 3: so heavy, and this is why everybody is looking at
Speaker 3: the governments and is looking at the European Union to
Speaker 3: subsidize some part of the capital expansion. And this is
Speaker 3: a problem because that makes us super super slow. It
Speaker 3: doesn't give us the real opportunity to grow fast into it.
Speaker 3: And we as a company, we see that. So for us,
Speaker 3: it's super heavy to collect the billion for example or
Speaker 3: one hundred millions, so which is a lot more easier
Speaker 3: if you're an American company with this core weave for
Speaker 3: examples to core weave or n Scale or anibio. They
Speaker 3: have collected so much money and if we polarize, if
Speaker 3: it goes out and tries to collect the same amount
Speaker 3: of money, it's not.
Speaker 1: So easy for us.
Speaker 3: And this needs to be changed in Europe. So we
Speaker 3: need to have more capital flow, We need to have
Speaker 3: more risk appetite in here, because if we are missing
Speaker 3: this revolution and that means we need to invest in it,
Speaker 3: then we have a big, big problem.
Speaker 1: In my opinion. The one thing I was just going
Speaker 1: to comment on that Machell is well, it all comes
Speaker 1: down off take is what it comes down to. If
Speaker 1: you have a client, there's no problem then actually getting
Speaker 1: finance for structure, infrastructure financing. So I suppose I'm trying
Speaker 1: to get my head around, like if I'm let's take
Speaker 1: the example Deutsche Telecom. They have t systems, right, so
Speaker 1: why aren't they doing more? I mean, sorry, they have
Speaker 1: the customers already. I'm trying to get my head around that.
Speaker 1: I can understand you as a startup, but there's a
Speaker 1: whole pile of big existing system integrators out there, so
Speaker 1: why aren't they doing this?
Speaker 3: Well, first of all, that the European Union is different.
Speaker 3: We are mainly driven by SMBs and SMBs they can't
Speaker 3: sign with you a five years contract on several millions.
Speaker 3: They need to start now somewhere, means they're going to
Speaker 3: have short term contracts. And these short term contracts in
Speaker 3: these lets are not exactly knowing how much consumption they're
Speaker 3: going to have in the next let's say one two years,
Speaker 3: will lead us to not having the financing option in here.
Speaker 3: And the big corps they're at the moment looking on
Speaker 3: prem solutions or they have no fast solution because what
Speaker 3: they want is they want to have compute now. So
Speaker 3: means they're also working then with hyper skills at the
Speaker 3: end because we don't have the infrastructure. So of course
Speaker 3: we're trying our best to convince the market and the
Speaker 3: capital market that it's a good idea to invest into
Speaker 3: compute today, even if you don't have a five years contract,
Speaker 3: because consumption will come and it will come in a
Speaker 3: hockey stick and not in a slow growth scenery. And
Speaker 3: if you don't have an offer, and if you don't
Speaker 3: have the infrastructure and you don't have the compute, then
Speaker 3: you have no chance to serve this customer, and the
Speaker 3: customer will then find another solution.
Speaker 1: Ye well, let me just thank you for coming on
Speaker 1: the show. It's been great really to have this discussion
Speaker 1: with you.
Speaker 3: Thank you for having me.
Speaker 1: It was a pleasure. Joe, I've shut up a foller
Speaker 1: on that we're going to the biggest industrial revolution in
Speaker 1: the history of mankind and Europe is struggling to catch up.
Speaker 1: We need entrepreneurs like Michelle to enable us to do that.
Speaker 2: Okay, John, I'm going to try to take a Scottish accent,
Speaker 2: which is going to be a bit difficult. And to
Speaker 2: quote Sean Connery in The Untouchables, Europe's bringing a knife
Speaker 2: to a gunfight.
Speaker 1: I was actually, I don't even think it's a knife.
Speaker 1: I think it's a baton, because a knife is sharp.
Speaker 1: I think you're right on that.
Speaker 2: What I learned is that the lms are getting really commoditized,
Speaker 2: So I wonder why they're putting so much money into
Speaker 2: developing those new lms, considering the one who are open
Speaker 2: on the market now almost the same quality a few
Speaker 2: months later. What was, of course much more important was
Speaker 2: the attitude of the US government. So you have that
Speaker 2: thing called the Cloud Act, which basically the US government
Speaker 2: can have access to every data, and of course they
Speaker 2: can block as well. We've seen the recent blocking of
Speaker 2: entropy the fable five AI model. So having a European
Speaker 2: base is absolutely critical.
Speaker 1: Yeah, but I want to talk about those fundacial models.
Speaker 1: I think the foundational models by the nation are foundational
Speaker 1: that the basis for everything that comes on top. And
Speaker 1: I know the European approach is let's focus on the
Speaker 1: applications that can be built using those foundational models. Well,
Speaker 1: there's an issue with that, and I'll give you a
Speaker 1: really simple example. There's a whole part of business across
Speaker 1: the world that have come out to help lawyers across
Speaker 1: the world us AI. Most of them have actually been
Speaker 1: based on cloud and then guess what cloude for legal concersions.
Speaker 1: So the question I have to ask myself where's the power?
Speaker 1: Is the power the foundational model guys or the guys
Speaker 1: as an application. That's the concern I'd have from European perspective,
Speaker 1: as we just don't have those foundational moments. Now. I
Speaker 1: know we are moving open source in this, that and
Speaker 1: the other thing, but still, yeah, we have a lot
Speaker 1: of work to do in Europe. That's what I would say.
Speaker 2: It is still a gigantic bet. If you look at
Speaker 2: the cash furnace that are all those investment in those models.
Speaker 2: Big Deck used to print money. Now they burn it
Speaker 2: and all their cash flow is gone. And they are
Speaker 2: doing equity raise and they are raising debt. They have
Speaker 2: of balance sheet leverage SPV. They are trillions trillions committed
Speaker 2: to AI. It's a ginomous bet.
Speaker 1: We're in a revolution, and revolution there are going to
Speaker 1: be big winners and they're also going to be big losers. Yea.
Speaker 1: That means also from a capital perspective, the same thing.
Speaker 1: Go back to the start of the twentieth century. Yeah,
Speaker 1: And I mean, if you're producing horse and carts, you
Speaker 1: were gone. Right, that's the world we're in today, right,
Speaker 1: it really is, but it's bigger. And I think the
Speaker 1: other thing that's that is clear is it's the speed
Speaker 1: of change, which is really really difficult. You know. So
Speaker 1: I think both of us agree that there's a big
Speaker 1: bubble here, But exactly when it's gonna burst and how
Speaker 1: it's gonna burst, and who's gonna win who's not, that's
Speaker 1: not clear to anyone. And in fact, I might have
Speaker 1: an opinion today and actually I'm changing my opinion and
Speaker 1: tomorrow and then I have another one. The day after that,
Speaker 1: because the speed of change is just so breathtaking. I
Speaker 1: think we agree on one thing wrong. My god, it's
Speaker 1: good to good time to be in the electricity spos
Speaker 1: because whatever happens, you need electricity.
Speaker 2: Okay, it's time to thank Michelle for coming on the show.
Speaker 2: Fascinating conversation, and to thank the BMW Foundation a berquant
Speaker 2: for inspiring us. Those episodes so more AI than energy,
Speaker 2: but still everything is linked.
Speaker 1: Absolutely good and I look forward to seeing you next week.
Speaker 2: Okay, yeah, cheers.
Speaker 1: Thank you for listening to Redefining Energy. Don't forget to
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