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Their wordsThe amount of data that we use to train models is going to continue to scale to the point where we're no longer limited… Training is no longer limited by… Data is now limited by compute. And the reason for that is most of the data is synthetic.
Their wordsAnd if you look at the robo taxis that are that are even on the streets today, it's taken 10 years for that to happen. Look at all the maintenance crews and all of the the the various, you know, hubs that they're in where you have to take care of these robo taxis and just imagine we have a billion robots.
Their wordsI don't love the other methods, which is continuous improvement. The problem with continuous improvement, it… First of all, you should engineer something from first principles at the speed, you know, with speed of light thinking. Limit it only by physical limits, and physics limits. And after that, of course you would improve it over time.
Their wordsComputing is not like that. There's a reason why the x86 still exists. There's a reason why ARM is so sticky. These ecosystems, these ecosystems are hard to replace. It costs an enormous amount of time and energy and most people don't want to do it.
Their wordsThey come to a computing platform because the install base is large. Because a developer, like anybody else, wants to develop software that reaches a lot of people. So, the install base is, in fact, the single most important part of an architecture.
Their wordsthe ability for general purpose computing to continue to scale has largely run its course and the only the the not the only way but the the way to do that is through domain specific acceleration
Their wordsYeah, we we could do all of those things. Um it's just not better. And we simulate it all. they're in our simulator provably worse and so we wouldn't do it.