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Chapter 1: What is the main topic discussed in this episode?
Search is the gateway to the world's information. If you can make it perfect, then that has so many downstream positive implications for the world.
You can kind of think of Google as being synonymous with search, right? It's one of the greatest tech monopolies of the last few decades.
If you want to go really deep into some topic, Google fails. Most people want to understand the world, but they're getting fed information that's just misleading in some way or straight up wrong. And if everyone had information that was accurate, most reasonable people would be reasonable.
We have a family open club, Michael Clodberg, and we wanted to give it web access. And he was like, I recommend Exa.
The world of agents searching is just completely different from human searching. An agent doesn't just want 10 pieces of information. It wants everything. With Exa, you could search something and then get not just 10 results or 100 results, but 1,000 results or 10,000.
How have we, a team that has always been below 100 people, been able to build a search engine that's better than Google in all sorts of ways? Well, it's because... For most of the internet era, search was built for humans. But AI agents search differently.
They need deeper context, more complete information, and the ability to navigate far more complex questions than a traditional search box was designed to answer. That shift is creating an entirely new set of challenges around retrieval, knowledge discovery, and how information is organized online.
Sarah Wang speaks with Exa co-founder and CEO Will Brick about search, AI agents, and the future of information retrieval.
Welcome, Will. Thank you for being here.
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Chapter 2: How did Exa's origins influence its search capabilities?
In fact, you and your co-founder, Jeff, actually started building a mini search engine in college, which is not what I was doing in college. Can you say more about when you started getting interested in search and why you wanted to solve this problem?
Yeah, yeah, sure. So I would say it's a life mission. So since I was a kid, I've cared about finding the highest quality knowledge, right? I was obsessed. And then in high school, I wanted to start a new type of news organization because I thought we're a civilization that got to the moon, we split the atom, and yet we can't understand what's going on at the border or in science news.
Like, why can't we fully understand any topic? And then in college, I was roommates with Jeff, and we were like, we could just build a better search using crowdsourcing the highest quality links. And we did build a pretty solid search. But then five years ago, so in 2021, that's when Transformers started to get really good.
And it suddenly became possible to build a better search engine than Google. And that was a really important opportunity because search is the gateway to the world's information. If you could improve search, if you can make it perfect, then that has so many downstream positive implications for the world across every industry, across every part of human life.
And so it just felt like this huge opportunity that no one was pursuing. And I was like, I'm going to devote my life to this because it's everything I care about is about information. And so, yeah, started EXA and now it's gone. We actually made a lot of progress and we're a lot closer to that mission. There's still a huge amount of things, a huge amount to go.
But yeah, it's been crazy to see how far we've come to achieving that mission that I've been thinking about for years.
Maybe just to probe a little bit deeper, you can kind of think of Google as being synonymous with search, right? It's one of the greatest tech monopolies of the last few decades. The idea that a startup could be better than Google at search is quite amazing. But how do you define perfect search?
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Chapter 3: Why are traditional search engines unsuitable for AI agents?
And what do you see as the limitations of Google? And I'll throw in more recent events because obviously, you know, IO just took place last week and they're very focused on AI mode and how they talk about the idea of information agents and things like that. How do you think about beating old Google, if you will? And then there's, of course, new Google that's evolving.
Yeah, I mean, Google was amazing and is amazing for what it's meant to do, which is like get quick answers to consumers. And now it's like increasingly longer answers. But really, it's focusing on like, what do most of the billions of people in the world search for care about, and like making sure they're really happy. And they do a great job of that. That's what they're optimized for.
That's why they're optimized for human clicks. It's like, you're really tired, you type in a few keywords that make no sense, and Google just magically understands what you're saying. That's magical because it has billions of other people searching similar things. I'm excited by Google too, but like there are certain times when you want something deeper.
And I was actually before starting XR writing a history book, I just got obsessed with history and I wanted to get to the bottom of what did it feel like to live in every period in history going back 5,000 years. I don't know if we ever talked about it.
I want to read this book. It's probably on hold right now.
I would have finished it around now. But at some point I was like, okay, maybe I can build a search engine and then I could automate the building of writing of books. And I feel like that turned out to be true.
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Chapter 4: What challenges do AI agents face in information retrieval?
But anyway, in writing that book, it became extremely obvious that if you want to go really deep into some topic, Google fails. Like Google is great at service level information, which is great for most of the billions of consumers. But if you want to really understand what it was like to live in the Roman Empire, you know, in, like, 100 AD, it's actually quite hard.
And, like, that information is scattered. It's everywhere. But it's, like, you need really, really good search, like, really deep search to understand it. And so that was, like, the first realization that, or one of the first realizations that, wait, like, what if you could have, like, true, perfect understanding of any topic?
And so, yeah, okay, so, like, Google has been changing their search engine a little bit. I would say I've seen many Google IOs now at this point at Exa. Every Google IO, I'm like, okay, they say they're changing search, and they do.
Yeah.
But they change it more to be valuable for the consumer type use cases, which for me, it's like there are so many different use cases that go beyond that. There's like really deeply understanding the Roman Empire, but there's also like finding every competitor to your company. And right now, Google is just not good at that.
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Chapter 5: How is Exa building a better search engine than Google?
No matter how many changes they make, like you don't trust Google to find you literally every competitor to your company, whether it's in Europe or Asia. You don't use Google for recruiting. And this announcement doesn't change that.
Like you're not going to go, you say, hey, Google, I'm looking for machine learning engineers in San Francisco who have a background at startups because it's not built for that kind of thing. So there is an opportunity to build like a new type of search engine that's meant for extremely like deep, complex queries that businesses really care about and agents really care about.
Yeah, absolutely. And I mean, to the extent you talked about starting to build EXA five years ago, and then it feels like in the last five years, the world has completely changed, which is actually, in my opinion, a great thing to happen to you and to EXA while you're building because you can bring in this new technology versus trying to either...
fight it or have some sort of innovator's dilemma, maybe share a little bit more on why you decided to build Exa from the ground up and what parts were the hardest or have been the hardest? And then how has it changed as the world of LLMs have changed?
building it from the ground up, basically we were like, in 2021, we could build a better search engine than Google. I don't care how long it takes. I guess we were young, high energy, just like ready to do anything, devote our lives to this. Then there was a thought experiment that really excited me, which is that, wait, I could totally build a better search engine than Google right now.
And here's how I would do it. For every query, I would take all the trillion documents on the web and I would run GB3 over it.
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Chapter 6: What role does data access play in AI-driven search?
I would say, does this document match the query? Does this document match the query? And then it would filter it down to the top 10 documents. And that would be better than Google. The problem is that would cost like, you know, $10 billion per query. And so then it became an optimization problem.
But at least there was like an existence proof that it's possible to build a better search engine on Google. And that was very inspiring for me. So then it was like, okay, like, how do we optimize the hell out of that? And transformers had gotten really good at the time. And Google wasn't really leaning into it.
And we just had this deep belief in the bitter lesson, maybe more than Google for search, which is that like, if we could develop systems, like neural systems, where you pour more data into it, it just gets better and better for the thing we're optimizing for, then you could actually just totally be better than Google.
When we released this to the world in November 2022, like it was actually shocking. And Andre Carbethy retweeted it. It was pretty popular on Twitter. It was like this new way to find information. It was the first time people were like, holy cow, it's possible to find things beyond Google. And then, by the way, two weeks later, Chachpiti came out.
So then people realized, okay, there's another new way of finding information outside Google with LLMs. And that was very critical to us. So like we released our first search engine to the world in a year and a half after starting X on November 2022. Two weeks later, Chachpiti came out. Actually, to me, I was at nerves when I saw the announcement. I played with it.
I was like, this feels like GB3, but a little bit like better UI. And I went back looking at research papers. But to the world, I think it was the first time they met this new creature. And it was just easy to use, which was a very big learning, which is like, okay, you make something easy to use. It's very important, obviously. But anyway, so then AI started really taking off.
And then early 2023, people started asking us for API access to our search engine that they had used based on that Twitter announcement in November 2022. And that's when we realized, oh, wait, we could start serving this search engine to these Not quite agents because that wasn't the term at the time, just these AI products, these AI workflows. And they're going to want comprehensiveness.
They're going to want to search in these more complex ways. All the ways that we as nerds in 2021 wanted to search, agents were very similar. So that was another interesting realization was like, I'm not a normal consumer. I want to get really deep into any topic and so do agents. And so it's cool that we were building a search engine for ourselves.
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Chapter 7: How does the agent economy impact search infrastructure?
It ended up being the exact same search engine for agents or very similar.
This is a big paradigm shift. Even as investors, we're thinking like, hey, it's not humans who are deciding the dev tool that wins. It's actually agents. Personal anecdote, I think I told you this one, but we have a family open club, Michael Clodberg, and we wanted to give it web access. And he was like, I recommend Exa.
Before we invested, I was like, sure, I'll go with whatever you recommend, right? And so it sounds like there's this nice dovetailing of how you were intending to build Exa in the first place to what agents want. But how do you think about what agents want, right? That's sort of the holy grail right now of, hey, I don't care what database I'm using. My agent's going to select Convex or Supabase.
These are entire tailwinds that are making some of these companies. How do you optimize for that? Think about that?
I've been thinking about this for a long time since there were the first agents, right? Like, we were the first search engine. Like, we were an early search engine, and the first AI products came to us because they were like, okay, they could be a search API. And so I've been thinking about this for a long time.
And yeah, I think the world of agents searching is just completely different from human searching. I guess you can make the analogy of, like, agents to humans is like humans to sloths. Okay? I'm picturing that Zootopia DMV scene. It's like, imagine we had a great search engine for sloths, and then humans came around. They're not going to want to use that same search engine.
And so you should think of agents as these crazy creatures that have infinite time. It's meaningless for them. They just want to make complex queries very fast and analyze it really fast. And they want perfect output for their human users. So you want to build a search engine for that. So what matters for that type of... creature, well, lots of different things.
So first of all, you need a search engine that can handle complex queries, right? Like, you do not want that creature to have to simplify its complex need for its user into simple keyword phrases, because you're just losing information.
So you want somebody that can actually semantically handle complex queries, but also handle keywords, because sometimes you just literally want, hey, like, I have this, uh,
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Chapter 8: What is the future of search in the context of AI advancements?
And you don't want it to have to make like a thousand, like 10,000 keyword queries and still never get to its comprehensive information. You want it to like make a few queries and get comprehensive information. Anyway, so complex queries, toggleable, also like comprehensive results. So this is a thing where it's like, an agent doesn't just want 10 results or 10 pieces of information.
It wants everything. Because imagine you're an investor. You don't have to imagine that. If you're an investor and you're looking at biotech companies, you want complete information because you're making very important monetary decisions and you don't want to miss anything. You don't want to have any FOMO.
You're missing some critical startup that exists that actually reflects well or badly on the current one you're thinking about. And so you want your agent to have complete information about every topic. So like, With Excel, you could search something and then get not just 10 results or 100 results, but 1,000 results or 10,000. And increasingly, agents are wanting this.
You also want lower latency because agents search faster than humans. But at the same time, you want higher latency because certain applications don't care about latency at all. So I think another big thing with serving agents is extreme customizability because we're serving businesses, we're serving agents that are very different.
Some want super low latency, some want super high latency, or latency doesn't matter to them. And so it's just a whole, it's like hard to express how different, I have like a list of like 20 different ways like humans and agents are different. And when you just build it from scratch for agents, you just make fundamentally different architectural decisions.
So maybe just to go back to this point you made on model intelligence improving and how that's changed the game in search as well. I want to pose this thought to you that I'm sure you've heard before, but given the fact that model intelligence is getting better, it can almost make up for or do some of the heavy lifting in this user signal that Google has collected over 20 years for PageRank, etc.
It can actually help get over that hump and do a pretty good job. And so my question to you, I guess, in that is, how do you think about this trade-off of compute, latency, cost, right? There's all these trade-offs that have to happen in terms of what you're actually using to, and, you know, to your point, you said it's like a big optimization exercise.
Like, how do you think about what to optimize? And I know you have different products, right? And so maybe your answer is like, well, depends on the product offering we're doing, but bring that into it as well.
It's easier and harder to build a search engine for AI agents. And so I'll explain why.
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