Listener Q&A: AI Misconceptions, The Reality of Regulation, Infinite Context, Incumbent AI Execution and Startup Ideas

**Sarah** (0:05)
Hello, No Priors listeners. We're excited to just do another hangout episode with me and Elad 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 Elad, what are people getting most wrong about AI right now?

**Elad Gil** (0:24)
Yeah, I guess 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. The first one is, I feel like a lot of people are treating this as an extension of the last decade of machine learning that we've seen in the convolutional neural network and 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, diffusion-based models, which is statistical physics model for image gen. And then on the language side, we moved to these large language models, which some people are now calling foundation models.
And 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 just the fact that you now have this really interesting 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. Or at least, you know, there's 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 the Journey 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, I think we both started investing or being involved with the area on the general VS side much earlier than that, but the sort of starting shot 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 at, 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.

**Sarah** (2:33)
I actually went on a walk with a growth investor that I actually have plenty of respect for, but they asked the question of like, oh, like, what's working? What should we invest in? Do you have anything that, you know, popped off that we should keep an eye on? I'm like, yes.
And we actually had a very similar conversation around what adoption in the enterprise looks like. And my prediction based on the sort of like, negative point of view that this investor had of, oh, like, it's not enterprise ready, like, this is just a hype cycle. And like, we're not actually going to be investing here. Like we explored it. We met 100 companies. We're done.
So, I think other people will arrive at that conclusion and like any other wave of enthusiasm, you'll see people abandon it as well as the technology.

**Elad Gil** (3:19)
Yeah, I think people just prematurely assuming it's a continuum from before and therefore there's nothing new here. And I just think that's wrong. And I think people will realize that. I actually don't think it's going to, I think there's going to be more hype rather than less coming simply because certain things are really starting to work at scale from a revenue perspective and really quickly. And we've seen the first wave of viral apps in terms of things like lens or other things that really ramped quickly and then went away. But those are signs of real traction and real usage. And so I just feel that it's very early in the hype cycle. There's more to come, but it's a different technology. And I just think people don't appreciate that. Or I should say it's an extension of some technologies in some ways, but fundamentally the capabilities are very different, right? You're still using deep learning, but you know, it really implies a different modality of how these things work. And then I think the last place, the third area that I think people are kind of getting things wrong is the mad rush to call for regulation by people working in the industry strikes me as very unusual and a bit naive in terms of what that actually means.