Shawn Wang: belief

Transcript context

…mean, you know. Reasonable, reasonable question. But I think that... I'm excited about getting things useful into people's hands very quickly. Like I like seeing, we talked about this earlier, right? We've moved from the research phase to the engineering phase of AI. It's the first time after having been in this field for maybe seven years, where stuff goes beyond like just kind of a graph, right? Like, like the output of my work before would always be like, Oh, look, there's a graph and like the number is better now, versus we actually get to see, you know, we have a customer. Between Duolingo and two or three of our other customers, we've got three or four customers working on, like, better versions of, like, teaching students, right, tutors or language learning or whatever it might be. And to be able to make that incrementally better and accelerate the time it takes to get there, it just feels to be so much closer to it, to be on the engineering space right now. Whereas I think there's an alternative universe in which I stayed in research and I went to an open AI or, you know, almost everyone from my research, PhD research group apart from Peter, now works at DeepMind. And I think I would have enjoyed that as well. But I really wanted to start a company that built something, like, useful and in production. And I don't even think those companies do that much right now, right? Like, it's only recently that OpenAI has sort of become a product company. They're more of a research company. They're building AGI. And I think that's true of the others. And I think that's amazing and fascinating. And if I had multiple lives, I would love to do that too. But at least right now, I want to be building products and putting them in people's hands. And it just feels a little bit far removed. Yeah. Yeah, makes sense. You know, I think the world's better because you're actually coming at it with a full knowledge of what came before. Yeah, I do think it's a huge advantage. I do think like having a good conceptual understanding, like, like, there's been a lot of people have pivoted into, as you said, LLM ops earlier. And I do think that actually knowing how it works, having a sense of what's going to come next and being able to project forwards and build for it is difficult to do if you don't have a good conceptual understanding of the machine learning. Yeah, yeah, yeah,…

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