Edwin Chen: commitment

Transcript context

…Yeah, which is kind of what we're doing now, so that's really interesting. This might be the last step until we hit AGI. Along these lines, something that's really unique to Surge that I learned is you guys have your own research team, which I think is pretty rare, talk about just why that's something you guys have invested in and what has come out of that investment. Yeah, so I think that stems from my own background. My own background is as a researcher. And so I've always cared fundamentally about pushing the industry and pushing the research community and not just about revenue. And so I think what our research team does is a couple different things. So we almost have two types of researchers at our company. One is our forward-deployed researchers who are often working hand in hand with our customers to help them understand their models. So we will work very closely with the customers to help them understand, "Okay, this is where your model is today. This is where you're lagging behind all the competitors, these are some ways that you could be improving in the future, given your goals, and we're going to design these data sets, these evaluation methods, these training techniques to make your models better." So this very collaborative notion of working with our customers being researched by themselves, just a little bit more focused on the data side, and working hand on hand with them to do whatever it takes to make them the best. And then we also have our internal researchers. So our internal researchers are focused on slightly different things. So they are focused on building better benchmarks and better leaderboards. So I've talked a lot about how I worry that the leaderboards and benchmarks out there today are steering models in the wrong direction, so yeah, so the question is, how do we fix that? And so that's what our research team is focused focused really heavily on right now. So they're working a lot on that. And they're also working on these other things like, "Okay, we need to train our own models to see what types of data performs the best, what types of people perform the best." And so they're also working on all these training techniques and evaluation of our own data sets to improve our data operations and the internal data products that we have that determine what makes something good quality. It's such a cool thing because I don't think basically the labs have researchers helping them advance AI. I imagine it's pretty rare for a company like yours to have researchers actually doing primary research on AI.…

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