No Priors Ep. 20 | With Sarah Guo and Elad Gil

Hello, No Priors listeners. We're excited to just do another Hangout episode with me and Elade and answer listener questions. I think a fun one to start with would always be a place where we're disagreeing with the market, so I'll ask Elade, what are people getting most wrong about AI right now?

Yeah. I guess there's- there's two or three things that I wouldn't say they're necessarily getting wrong, but I just feel there's some misconceptions about. Um, the first one is, I feel like a lot of people are kind of treating this as a- as an extension of the last decade of machine learning that we've seen, in the sort of convolutional neural network and, um, RNN world. And everybody keeps talking about it as if it's that old world, and they keep emphasizing certain aspects of data and other things which are important but not as important as they used to be. And in reality, we've had a technology disruption. We've shifted to two very different architectures. Uh, diffusion-based models, which is a statistical physics model for image gen, and then, um, on the language side, we moved to, um, these large language models, which some people are now calling foundation models. And, you know, fundamentally, that's different from the prior wave of NLP in terms of capabilities, in terms of the way it works, but also in terms of insights around things like, you know, just the fact that you now have this really interesting, like, chain of logic or chain of thought style processing of information, and the ability to act and synthesize information in a way that never existed before for NLP, for example. And so I think one big misunderstanding is, "Oh, this is just ML, and we've been doing ML for 10 years, and it's the same thing," and it's totally different. So I think that's one big sort of area that I keep seeing people get things wrong. Um, or at least that, you know, there's these- these- these misassumptions. Second is I keep getting pinged by people saying, "Hey, what's working? What's working?" And there are a few things that are truly working at scale. You know, OpenAI and Midjourney and a few other things. But the reality is, it's been six months since ChatGPT came out and most people became aware of this. You know, like, um, I, you know, I think we both started investing or being involved with the area, uh, on the generative AI side much earlier than that. But the sort of starting shop for the industry was six months ago, and then GPT-4 came out maybe three months ago. And so everybody's acting as if this is an old thing, and again, I think this ties into the prior point. This is not a normal extension of what NLP used to be like. This is a fundamentally new set of capabilities. And so when people are saying, "Well, look, no enterprises are adopting it very much yet," you're like, "Well, it's been six months since most people realized this was that important," and six months is one planning cycle for a big enterprise, right? So people are just planning what to do. So I think that's a second one.