background model. So, what would it take for end-to-end models to actually become the dominant paradigm? >> My intuition is at this point it's going to be mostly a post-training question. Which is creating the right synthetic in situ data and finding the right reward models. And this one is very interesting because when you look at text models computing the reward for a verifiable problem it can be exactly computed and so you can scale it very well the post-training. Asking the question, is this conversation natural? Very hard, right? To find an objective automatic metric of that. What does natural mean? Is it that the pauses were at the right time? Was it that the tone was right? Is it what was said that was meaningful? How do you pick the best out of two 10-minutes conversations? >> In the limit, the end-to-end systems are going to catch up, but what do you think that timeline is? >> Being a bit pessimistic, I would say 1 or 2 years. We're going to see very impressive stuff in the next year or so. And then I will say we're going to see a completely new wave of changes. With two things I think. The first one being miniaturization, so being able to make this stuff run on device. Maybe most importantly, integration in robots. Or anything that is not a smartphone or or a computer.