That's a really, really interesting question actually. Let me start with SLM because what's interesting about SLM is this word life cycle. Because if you just take a chip and you say, put it in a phone or put it in a car, we all know which one has the longer life cycle. And so -- and in the case of the car, you have, of course, safety that is part of it.
And so suddenly, the ability to put inside of the chip sensors and the diagnostic system that, by the way, gets trained by AI so that a chip can self-diagnose as, well, I'm not feeling so good. You better start stopping the car, so to speak, is going to be a very high value. Maybe even more practical immediately is in cloud centers, where people run compute at the max speed and they want to know is a certain set of processors going to go down so that I replace them before it happens. So in essence, preventive maintenance.
And so in that sense, SLM is interesting because we touch these chips literally at the early days of even what are the type of transistors. So very minute physics, but now we also have very meaningful interaction with very large companies that are exactly in those verticals I described.
Now DSO.ai, I think, is breakthrough technology. And it's always difficult to compare something that we did over 30 years ago. But the early days of Synthesis had something similar, which is it took a set of human tasks where complexity just was outrunning the human and automated it. And overnight, we could do circuits that were better than what a human could do in a fraction of the time, and were faster and smaller.
Now we're talking of entire chip pieces, very large designs with many, many different constraints. And the fact that we can take tasks that take people multiple months and bring them down literally to a few weeks with fewer people and, in the last few quarters, even better results certainly sounds very similar. But to me, it's sort of essentially 30 years later, many orders of magnitude more complexity. And I think it fits well the very moment where the semiconductor industry will want to do many more chips for all these verticals.
And so we use sometimes the front line of using AI to design AI chips, but that is exactly what this is. And it's exciting. We're just at the beginning of that, but the impact is already economically felt by the users.