Tim Scarfe: commitment

Transcript context

…No. I don't I don't think this exists today. Like, we don't know how to solve problems. I think this is a this this is the honest answer, is that for most of the issues we have today, we just don't have a solution, like, size fits all solution. I promise you, like, incentives right now are such that, like, security is is is a very expensive commodity. Right? Like, if I can convince you to trust my product more than somebody else's, it's yeah. It's it's it's great. This is why we're investing a lot of money in the security. I think with the mail, the the actual issue is the fact that before you develop security tooling, you really need to have something to secure because, like, small every single small detail changes how you build security systems. So unless you know everything about the system and it's kind of frozen in time, you can't really build security. And by the time you have something to secure, it's already too late. And this is the thing that is not unique to ML. It's kinda it was there before. If you look at there is a field of information security economics. It's it's kinda always there. It's like it's a fundamental. First to the market wins, so everyone rush to the market. Early investment in security means you could have spent the resources getting first to the market and getting the network effect, And thus, you kinda don't have incentives to build the security tooling first. And then if you look at statistic around company compromise, you'll find that it takes a number of compromises before the company fails. So they have a bit of time after they conquer the market to actually put the security tooling inside. So it's economic incentives. It's nothing but. But I have to say that I'm quite certain if we knew how to solve stuff, we would solve this. It's just today, we don't know how to do it. In the same way, it's like, if you look at 19 nineties, I promise you, everyone wanted to make sure that their systems are reliable, which is didn't know how to build stuff. We won't spend long on this because we've already filmed all about your model collapse paper in nature. I know there is a lot of, like, folks criticizing saying, oh, this theoretical blah blah blah blah. But if you look around, we use a lot of synthetic data, but we still use a lot of real data. And whenever we need to go and acquire data, there is a massive market in acquiring very specialized data, and you see that improvements still come from humans, the cost of this data is growing, like, for extremely specialized, like, hiring a ton of mathematical PhDs.…

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