Dylan Patel: belief

Transcript context

…The same guy as Kylo Ren. Yes, he had the swarm paper and petal. That research is super cool. The universities will never have much compute, but like, hey, to prepare to do things to, you know, all these sorts of stuff, like they should try to build, you know, super large models. Like, you look at what Tsinghua University is doing in China, actually, they open sourced their model to I think the largest like by parameter count, at least open source models. I mean, of course, they didn't train it on much data, but it's like, you know, it's like you could do some cool stuff like that. I don't know. I think there's a lot that people can focus on. One, scaling out a service to many, many users. Distribution is very important. So figuring out distribution, right? Like figuring out useful fine tunes, right? Like doing LLMs that OpenAI will never make, sorry for the crassness, a porn DALL-E 3, right? Open source is doing crazy stuff with stable diffusion, right? Right? Like, I don't know. Yeah, but it's like, it's like, and there's a legitimate market. I think there's a couple of companies who make tens of millions of dollars of revenue from LLMs or diffusion models for porn, right? Or, or, you know, that kind of stuff. Like, I mean, there's a lot of stuff that people can work on that will be successful businesses or doesn't even have to be a business, but can advance humanity tremendously. That doesn't require crazy scale. How do you think about the depreciation of like the hardware versus the models? If you buy a H100, sure, the next year's is going to be better, but like at least the hardware is good. If you're spending a lot of money on like training a smaller model, it might be like super obsolete in like three months. And you've got now all this compute coming online. I'm just curious if like companies should actually spend the time to like, you know, fine tune them and like work on them where like the next generation is going to be out of the box so much better.…

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