How do we go from search engines to answer engines? With Perplexity AI’s Aravind Srinivas and Denis Yarats

**Aravind Srinivas** (0:07)
So, for factual accuracy, our first step towards that was making sure you can only say stuff that you can cite.
It's not just that we want to retrofit citations into a chatbot, that's not what perplexity is. In fact, it's more like a citation-first service.

**Sarah Guo** (0:27)
This is the No Priors podcast. I'm Sarah Guo.

**Elad Gil** (0:30)
I'm Elad Gil.

**Sarah Guo** (0:31)
We invest in, advise and help start technology companies.

**Elad Gil** (0:34)
In this podcast, we're talking with the leading founders and researchers in AI about the biggest questions.
Watching. With advances in machine learning, the way we search for information online will never be the same. We're back again to talk about the future of search. This week on the No Priors podcast, I'm excited to introduce our next guest from Perplexity.ai AI. Perplexity is a search engine that provides answers to questions in a conversational way and hints at what the future of search might look like. Aravind Srinivas is co-founder and CEO of Perplexity.ai. He's a former research scientist at Open AI and completed his PhD in computer science at UC Berkeley.
Denis Yarats is a co-founder and Perplexity's CTO. He has a background in machine learning, having worked as a research scientist at Facebook AI and also as a machine learning engineer at Quora. Aravind and Denis, welcome to the podcast.

**Aravind Srinivas** (1:29)
Thank you for having us, Yura.

**Denis Yarats** (1:30)
Thanks.

**Elad Gil** (1:31)
Thanks so much for joining.
So the two of you alongside Andy Konwinski created Perplexity around August or so of 2022 Aravind, do you want to give us a little bit of a sense of why you started this company and what the core thesis of Perplexity.ai is?

**Aravind Srinivas** (1:45)
Yeah, sure. Actually, Elad, you're our first ever investor who offered to invest in us. So over the founding days, in fact, I remember the first ever idea we talked about in Noi Valley where we're sitting in the open space opposite Martha and I was telling you, oh, it'd be cool to have a visual search engine. The only way to disrupt Google was to not do text-based search but to actually do it from camera pixels. And you were like, this is not going to work. You need to think about distribution. And search was always the core motivation for me and Denis and many others at the company. We were just bouncing around ideas and then I was still at Open AI around the time and then we left and Denis also was still at Meta. Then he also left and Andy came in to help us sort of incorporate and get the company rolling. So that's sort of how the company started. The space of L and this was exciting. Generative models were really exciting.
And in general, we were motivated about search, whether it be a general search or vertical search.
And we were bouncing around several different ideas.
One of the ideas that you gave us was the working on text to SQL. And we were pretty excited about that and started prototyping ideas around that. And I think Denis also was hacking with us on building a Jupyter Notebook extension with Copilot for every cell. And then we were trying it with SQL around databases. But it's all like a bunch of nonlinear pathways to eventually get to where we are right now.

**Elad Gil** (3:21)
Yeah, absolutely. And hopefully I caveated whatever feedback I gave with. I'm probably wrong, but since I think I'm often wrong on directions.

**Aravind Srinivas** (3:29)
No, I think whatever you said still applies. Search is tremendously a distribution game as much as a technology game.

**Elad Gil** (3:36)
Yeah, I think one of the really impressive things about perplexity is the rate of iteration.
And to your point, you've gone through things like text to SQL, co-pilot for the next gen data stack. And I've always been impressed by how rapidly you've just been able to point in a direction, iterate really fast, prototype something, see if it's working and then move on to the next thing. And to your point, you always had search in the back of your mind. I remember even as you're prototyping these things, you were talking about indexing aspects of Twitter or other sort of data feeds and then providing search on top of them. How did you end up building a team that can iterate that rapidly as well as a culture of fast iteration? Like other specific things that you all do as a team to help reinforce that?

**Aravind Srinivas** (4:15)
Yeah, I'll take the first part of this question and then also let Denis answer this because he's a big part of why this is happening. We both are basically from an academic background. So in general, the culture and academia is to, you have hundreds of ideas and you just need to try them out pretty quickly, run a lot of experiments really quickly and get the results and iterate. So we come from that background, both of us. So that's not really new to us. It's just that when it comes to trying out in products, it's not just a result you get from running an experiment. You actually have to go to users and make them use it and talk to companies or customers or potential people in a company who will be using a product and get feedback. So there's that aspect of operational work that needs to be done to get results for experiments. And there's this aspect of quickly doing engineering to get it to a state where you can show it to people. So both of these things had to come together, and that's why the company exists.