ASML CEO Sees AI Spending 'Arms Race'

Lithography will always be there, right? Because you cannot do anything without lithography and there will always be the wish to get better lithography. Better lithography means better resolution, better accuracy and better productivity. And we're going to work on those three axes for many, many years. If we look at lithography, today, thanks to EUV, we mostly know what to do for our customers for the next 10 to 15 years, and we'll still see major innovation in lithography in order to continue to work with our customers on cost and transistor density. So that's the first thing we do know today. And we may at some point introduce an even higher numerical aperture tool, which we call Hyper NA.

Most likely for the middle of the next decade, so we still have time. We still have time, but that's still something we are preparing for because, as you say, we have to look long term.

Yeah, there are two reasons why this is becoming important. First, our customers are continuing to drive for more transistor density. So the Moore's Law we usually refer to calls for doubling transistor density every two years. This has been going on for many years and still keeps going. In fact, if you look at AI customers, and you mentioned Nvidia before, they want even more than that. They don't want transistor density to double every two years; they would like the number of transistors to go up by a factor of 16 every two years. That's what we have seen happening in the last two to four years. So you're going completely off Moore's Law and you need even more transistors. Now, we still use lithography to try to put as many transistors as possible per unit of area, but that's not enough.

Well, we saw AI as a huge opportunity for ASML. If you look at ASML, we invested a lot in R&D, four and a half billion euros every year to develop our product. Of course, we invest a lot in hardware, and we also invest a lot in software. We saw that when it comes to development cycles, AI could help us enormously with the software, like many companies. But for us to be able to ship our machines to our customers and have people maintain them in the field, we write a huge amount of procedures. We spend a lot of time describing on paper how to work so that people can deal with it. AI can help us reduce the time our engineers spend on that, freeing them to do other things like integration. So we saw a major efficiency opportunity with AI. More importantly, we also believe AI can improve our product because our product generates tons of data.