Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Well. When ASML introduced
Speaker 1: its EUV machines, it helped customers move from seven nanometer
Speaker 1: chips down to the three nanometer chips now used by
Speaker 1: the likes of Nvidia and Apple. Its next big test
Speaker 1: is whether it can transition to what's known as high
Speaker 1: numerical aperture lithography or high NA. These newer EUV machines
Speaker 1: raimed at pushing chips below two nanometers, giving them even
Speaker 1: more capabilities considered crucial for future AI applications. The person
Speaker 1: leading this effort is Chief executive Officer Christophe Fouke. In
Speaker 1: an exclusive interview recorded on November fourteenth at ASML's headquarters,
Speaker 1: Fuke explained why no other company on the planet can
Speaker 1: do what his can.
Speaker 2: Lithography will always be there, right because you can not
Speaker 2: do any sake with lithography, and there will always be
Speaker 2: the wish to get better lithography. Better lithography means better resolution,
Speaker 2: better accuracy, and better productivity, and we're going to work
Speaker 2: basically on those three axes for many, many years. If
Speaker 2: we look at lithography in CML today, thanks to UV,
Speaker 2: we most really know what to do for our customer
Speaker 2: for the next ten to fifteen years and the next
Speaker 2: ten to fifteen years, we'll still see major innovation in
Speaker 2: lithography in order to continue to work with our customer
Speaker 2: on cost and transistor density. So that's the first sake
Speaker 2: we do lone today. We just talked about i NA.
Speaker 2: We may at some point of time or so to
Speaker 2: introduce even higher numerical aperture tool which we call like CURNA.
Speaker 2: That's the point seventy five tool. Basically we hyper and so.
Speaker 1: When does hyper and I come mostly mean over.
Speaker 2: The next decade, So we still have time. We still
Speaker 2: have time, but that's still something we are preparing for
Speaker 2: because as you said, we have to look long term.
Speaker 1: You're also pushing into advanced packaging or three D packaging,
Speaker 1: and this is essentially where you get the components. Instead
Speaker 1: of laying them out flat urban spool style, you build
Speaker 1: them up a bit like a skyscraper. Cort. What is
Speaker 1: the importance of that? How significant will will that be
Speaker 1: for SML and why focus on that packaging?
Speaker 2: Yeah, to reason why this is becoming important. So the
Speaker 2: first thing is our customer are continuing to drive for
Speaker 2: more transistoror density. So the most law we usually refer
Speaker 2: to is calling for doubling transition density every two years,
Speaker 2: and this has been going on for many years. It's
Speaker 2: still keep going. In fact, if you look at AI
Speaker 2: customers you mentioned NVIDA before, they aren't even more than that.
Speaker 2: So they don't want to get the transition density to
Speaker 2: double every two years. They were like the number of
Speaker 2: transistors to go up by a factor of sixteen every
Speaker 2: two years. That's what we have seen happening in the
Speaker 2: last two three years. So you're going completely off Moore's
Speaker 2: law and you need even more transistor Now. We still
Speaker 2: use lithography to try to put as many transistors as
Speaker 2: possible per unit of area, but that's not enough. And
Speaker 2: if you cannot get enough transistor as you show doing this,
Speaker 2: then you also.
Speaker 1: Have to do that. On AI. You made a big
Speaker 1: step by investing almost one and a half billion US
Speaker 1: dollars in Mistral, which is one of the leading large
Speaker 1: language model companies in Europe. Certainly a competitor sort of
Speaker 1: likes about pen Ai and Gemini and Anthropic. You're taking
Speaker 1: about a ten percent stake with that Dale Why.
Speaker 2: Well, we saw AI as also a huge opportunity for
Speaker 2: SML if you look at a SML. We invest a
Speaker 2: lot in R and D four and a half billion
Speaker 2: euro every year to develop our product, and of course
Speaker 2: we invest a lot in hardware. We also invest a
Speaker 2: lot of software, and we saw that when it comes
Speaker 2: to development cycle, AI could help us enormously with the software,
Speaker 2: of course like many company, but for us to be
Speaker 2: able to ship our machine to our customer and have
Speaker 2: people to maintain those machines on the field, we're writing
Speaker 2: a huge amount of procedure. We spend a lot of
Speaker 2: time describing on paper our tool walk so that people
Speaker 2: can deal with it, and they also AI can help
Speaker 2: us to really reduce the time our engineers spend on
Speaker 2: that so that we free them to do other things like,
Speaker 2: for example, free the integration. So we saw a major
Speaker 2: major efficiency opportunity with AI. More importantly, we also believe
Speaker 2: that AI can improve our product because our product generates
Speaker 2: tons of data.
Speaker 1: Are we in an AI bubble?
Speaker 2: Well, you know, when people talk about AI bubble, I
Speaker 2: think I don't know exactly what they mean, and usually
Speaker 2: I say, there's two ways to look at it. If
Speaker 2: you look at the industry, I don't think there is
Speaker 2: a bubble. So the impact of AI in industry is
Speaker 2: just starting and the impact on the industry will be
Speaker 2: positive for many, many years to come. So there there
Speaker 2: is no bubble. I think that the value of AI,
Speaker 2: the industrial value of AI, is extremely high, and this
Speaker 2: will be developing over time. There's no bubble because, like
Speaker 2: I said, we're just starting. Sometimes people talk about a
Speaker 2: bubble in a reference to the stock market because we
Speaker 2: have seen some company getting extremely high valuation as a
Speaker 2: resource of the excitement of AI.
Speaker 1: There.
Speaker 2: I think what you will see is more players coming
Speaker 2: over time. So initially a few company where I would
Speaker 2: say very much the only winner out of AI because
Speaker 2: of the demand. Because industry, the industrial demand will be
Speaker 2: so high, we need more players, and you will see
Speaker 2: more and more company designing chips, designing AI, product manufacturing chips,
Speaker 2: et cetera, et cetera. That could create, of course some
Speaker 2: change on the stock market, but you know the two
Speaker 2: are a bit unrelated.
Speaker 1: Because I can make me three hundred million dollars just
Speaker 1: this year along from hyper scales, maybe four hundred million
Speaker 1: next year. Yeah, that translates into real orders for your
Speaker 1: kids in the years ahead. You start to see it over.
Speaker 2: Time, and sometimes you know the last few months. I
Speaker 2: used to joke with some of our investors because they
Speaker 2: told us, well, you know, two ndred billion here, five
Speaker 2: ulred billion here. I say, well, I still don't have
Speaker 2: the equation to translate doorse order into order for us,
Speaker 2: and it takes a bit of time, but you're right.
Speaker 2: Over time that demand translate into cheap demand to our customer.
Speaker 2: This translate into a need for more capacity, and this
Speaker 2: translate into demand for ISML and our peers in the industry.
Speaker 1: The infrastructure span from the hyper scales and the kind
Speaker 1: of deal making. The open AI has done almost a
Speaker 1: trillion dollars worth of deals committed just by that one
Speaker 1: company alone, which isn't making a profit this year.
Speaker 2: That makes sense to you, Well, I think the investment
Speaker 2: makes sense overhaul, because you cannot play in AI without
Speaker 2: investing in hyperscaler. And there may be even a few
Speaker 2: cycle of investment because the investment today is done on
Speaker 2: certain chips. The chips in two three years from now,
Speaker 2: if you look at Nvidia announcement, there will be a
Speaker 2: lot more powerful and people may be tempted again in
Speaker 2: twenty twenty seven to invest again in Hyperscaler to make
Speaker 2: use of those chips, so you have different cycle and
Speaker 2: you have a bit of a harm rest because if
Speaker 2: you don't invest today, you're out right. It's true for company.
Speaker 2: I think it's true for government. You cannot not play
Speaker 2: in AI and I think that's why you have this
Speaker 2: huge backlog of demand.
Speaker 1: What you touched on on one part of this, which
Speaker 1: is that the pace of innovation, and it makes me
Speaker 1: think of depreciation around some of these assets, particularly when
Speaker 1: you spend heavily on Blackwell and then we've got Rubin
Speaker 1: coming up for it as one example. Good news for
Speaker 1: you because you benefit from that increased investment that's gonna
Speaker 1: have to follow that, but some are gonna get burned
Speaker 1: by that depreciating asset, presumably.
Speaker 2: Could be I think, you know, it's a bit what
Speaker 2: I said. You have a certain landscape when you start.
Speaker 2: The portity is big. You would see more and more players.
Speaker 2: Will everyone win, I don't know, but the importity is
Speaker 2: such that there would be more and more people entering
Speaker 2: the business, and time would say makes the right turn,
Speaker 2: or makes the right decision, the right investment, et cetera,
Speaker 2: et cetera. But the opportunity will I think on the
Speaker 2: short term you will see first more player, and then
Speaker 2: at some point you may see a consolidation or a change.
Speaker 2: This is very naturally in this scale of industry, but
Speaker 2: it's too big for people to not want to play
Speaker 2: in today.
Speaker 1: As a malcio, Christopher Fouquet