# Exa Redefines Search for the Agentic Economy

**Podcast:** AI + a16z
**Published:** 2026-06-04

## Transcript

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 like, you know, misleading in some way or straight up wrong.
And if everyone had, like, information that was accurate, most reasonable people would be reasonable.
We have a family open club, Michael Claudeburg, 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, like, you could search something and then get not just, like, 10 results or 100 results, but 1,000 results or 10,000.
How have we, a team that, you know, 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.
Well, excited to be here.
So I want to start with the origin story.
You've been interested in search for a long time.
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'd 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 willing to devote my life to this because it's everything I care about is about information.
And so, yeah, I 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.
And the idea that a startup could be better than Google at search is quite amazing.
But how do you define...
perfect search.
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 like 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 Exa writing a history book, I just got obsessed with history and I wanted to get to the bottom.
What did it feel like to live?
in every period in history going back 5,000 years.
I don't know if you 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 can automate the building of writing of books.
And I feel like that turned out to be true.
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 surface 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, a 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, 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.
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 from the ground up, basically we were like, in 2021, we could build a better search 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.
There was a thought experiment that really excited me, which is that, wait, I could totally build a better search than Google right now.
And here's how I do it.
For every query, I would take all the trillion documents on the web and I would run GB3 over it.
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 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 gone 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 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, 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, Chashpti 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 in November 2022.
Two weeks later, Chashpti came out.
Actually, to me, I was at NURBS and 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.
It was a very big learning, which was 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.
It ended up being the exact same search engine for agents.
We're 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 claw, Michael Claudeberg, 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 Convax 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 make the analogy of like agents to humans is like humans to sloths.
Imagine we had a search engine.
I'm picturing that Zootopia.
Yeah, yeah, that's what I'm thinking too.
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 is 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, right?
So you want to build a search engine for that.
So what matters for that type of...
creature?
Well, lots of things.
So first of all, you need assertions that can handle complex queries, right?
Like you do not want that feature to have to simplify its complex need for its user into simple keyword phrases because you're just losing information.
So you want something that can actually semantically handle complex queries, but also handle keywords because sometimes you just literally want, hey, like I have this like complex chemical formula, like I want that to be part of the document, right?
So you want a tool that can handle both semantic queries, keyword queries, really just like expose all the fundamental toggles to the agent.
Because the agent, by the way, has the patience to, like, you know, make a domain filter here and a keyword filter there or, like, search in this way, search in that way.
So you want to, like, have a very controllable search engine.
You know, like, with Google, like, you search something and then you're like, no, wait, that's not what I want.
And then you try to change some keywords and it's like, it's just missing it.
It's not like, it doesn't feel, like, very controllable, toggleable.
You want the opposite for an agent because the agent's just going to keep searching until it gets to its outcome.
And you don't want it to have to make, like, a thousand, like, ten thousand.
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, you don't, 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 with Exile, 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 like extreme customizability because like we're serving businesses, we're serving agents that are very different, somewhat super low latency, somewhat super high latency or low 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 kind of changed the game in search as well.
And I want to pose this thought to you that I'm sure you've heard before.
But given the fact that model intelligence is, you know, getting better, it sort of can almost make up for or do some of the heavy lifting in this user signal, right, that Google has collected over 20 years for page rank, 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 tradeoff of compute, latency, cost, right?
There's all these tradeoffs 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.
Oh, interesting.
Yeah, please.
Yes.
I know that's fucking interesting.
Okay, so why is it easier?
Well, first of all, like this whole click, you know, Google has an insane amount of human click data.
It just doesn't matter like that much for serving agents.
I remember saying this like years ago.
People thought that was crazy, but it turns out to be right.
So human click data is great for humans when you want to find results that humans click on, which is obvious, right?
So if you get a huge amount of human click data, you could train on that, and now Google can understand what you mean even when you don't even know what you mean.
However, agents like...
just don't, they don't benefit that much from clicks.
I mean, maybe a little bit in terms of like the ranking signal.
It's also, it's valuable for agents to know what humans think is valuable.
That's a very minor thing.
So it's interesting that like all that click data that Google has accumulated just doesn't really matter for agents.
And so it's a whole new ballgame.
So there's not much of an advantage there.
There's also other things like, yeah, like Google.
probably had hundreds of people working on re-ranking.
Whereas, you know, because that was a complex thing before LLMs, but now with LLMs, you could have a re-ranker that you just call an LLM and like one engineer work on it.
Now, obviously, we have more than one engineer working on re-rankers at this point because you don't want to just call an LLM.
You want to train your own models and make them faster, higher quality.
But you don't need a team of hundreds.
You could do it with like a couple people.
So like, 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 like LLMs unlock, the technology unlocks like new types of techniques.
And then also like because serving agents, like you don't need all the click data and like, yeah, data that Google has been collecting.
So those are some ways why it's easier to build a search engine and why we've been able to build a fantastic search engine with a small team.
I think a small crack team.
But it's also harder.
Why is it harder?
Well, it's because like the requirements for a search engine now are getting more and more intense.
And so like.
you know, I wake up in the morning and we have like, you know, customers love us, but we still have customers being like, wait, why can't you be perfect at this search?
Or what about this search?
And like, they're constantly pushing us towards the edge because we're serving business use cases that have like deeper and deeper needs, like, you know, billion dollar investments around the line.
Like, this has to be perfect.
And that's great because it's pushing us towards perfect search.
And so like, basically like, if traditional surgeons had like 99.9% quality or reliability, like these.
these new search engines for agents need 99.99, 99.9999.
And this is a great thing because it's just pushing us towards that dream of perfect search I've always been dreaming about.
It's very similar to like LLMs, like, you know, Opus 4.6 comes out, everyone's really happy, this is working on 99.9% of use cases.
And then when the next one version comes out, everyone wants that thing, right?
Because the extra nines of quality are so important in this new agentic economy.
And so you have a similar thing for search.
So it's both easier to build a search engine fast, but really hard to build a perfect search engine.
Yeah, you know, it's interesting.
When we were doing our diligence in the space, which, you know, has taken place over the last few years now, some people came to us with the opinion that search is just getting commoditized.
And I think we look at that and, you know, if you go out there and search for information we know is out there public, et cetera, you can't find the answers to everything still.
So the fact that it's commoditized, you know, you'd have to kind of divide up, oh, what type of search is commoditized?
So maybe I'll ask you that question.
And then...
What is the type of search that's still really hard?
And what is the key to unlocking that?
Is it data partnerships?
Is it a technical breakthrough?
Like, how do you think about the edges, as you mentioned, in terms of, you know, perfecting or pushing forward the frontier of search?
Yeah, I guess what does commoditize mean?
It means like over time.
Will the thing, will it just not matter which tool you use?
Like, they're all kind of the same.
I would argue that the LLMs are going to get commoditized or are getting commoditized faster than search is.
And the reason is because you don't need to run, like, mythos over every cell in your Excel sheet when you're trying to find competitors or something.
Like, most of knowledge work does not require the smartest model.
You act like, you know, like just an open source model that's big enough, you know, and now the infrastructure for running them is very good.
It's pretty cheap.
You can just reuse open source models for most of knowledge work.
Not that the crazy smart LLMs don't.
They do have a super amount of value in terms of inventing new science and math and in certain cases you want to find any bugs in your code or something like that.
But increasingly, I would say if you think of knowledge work, all the different tasks you might do as concentric circles of difficulty, a huge amount of that service area is covered by off-the-shelf models you get right now.
Yeah, very fair.
Yeah, right.
So, but like, on the other hand, like search, like when you are trying to, you know, enrich every cell in your Excel sheet with competitors or people you're trying to recruit, then like every extra nine of quality in search really matters.
It's basically, I'd argue that a lot of knowledge work is actually a search problem, not only an intelligence problem.
And so, yeah, I mean, what are some examples where search is not good?
I do think company and people search is the most like, like easy to see and just most value to people.
Like every company in the world has to search over companies to sell to or almost every company in the world has to search for companies to sell to and people to hire.
You could ask yourself if like finding companies to sell to or finding people to hire is a solved problem.
I think every company would say no, it's not.
That's why people are constantly, you know, switching tools, like trying out new tools is because like...
We just don't have comprehensive information over all the people or companies we want.
So that's a really good example.
That's something Exo is leaning very deeply into, like go to market intelligence, because we care a lot about it.
It's very exciting.
It's also very useful to use internally.
We have companies to sell to and we have people to hire.
It's been great to dog food our own thing.
Exactly.
But yeah, that's an example.
I think you just want comprehensive, like you just want all the people that could be connected to you that are relevant.
And by the way, it's a very beautiful thing.
Yeah.
You didn't ask this, but I think one beautiful thing about search is that a lot of important problems in the world are actually search problems, like, dressed up in a different way.
Say more.
What's the example?
Okay, political polarization.
I would argue that's a search problem because there are people out there who want to, everyone wants to understand the world, or most people want to understand the world, but they're getting fed information that's just, like, you know, misleading in some way or straight up wrong.
And if everyone had, like, information that was accurate and like controllable and like comprehensive, I think most reasonable people would be reasonable.
And I think because our information environment is so chaotic, it's so polarized, it's causing reasonable people to be unreasonable.
By the way, I'm part of this.
Like I'm sure I have incorrect beliefs on all sorts of political things because my information is not perfect.
It's something I really want to solve.
Loneliness is a search problem.
Weird.
No one thinks it's lonely.
Same war, yeah.
A lot of people are feeling lonely in modern society.
Well, it's because they're not finding people to hang out with or to be in relationships with, right?
Yeah.
And so like, yeah, loneliness is a search problem.
And especially in a city life, like it's hard to find other people with similar interests or who you might bond with.
And this is like, you know, with a perfect search engine, with whatever information people are willing to share, it would help.
to find other people.
So for example, I have a lot of crazy ideas about flying cars.
I really want flying cars.
I hate cars on the road.
I would love to go to a group of people talking about flying cars.
I'm sure I would become great friends with those people.
I can't just be like, find me all the flying car enthusiasts in San Francisco.
I would love to at some point be able to do that.
Okay, and this gets, in your earlier part of your question, is like, what allows for this differentiation?
So, this is a long answer to how to search not commodity.
You can start to see that search is like a bigger thing that people don't, people think of search as like, in 1998, you see a text box, you type in a few keywords, you get a few things.
Like, that was search.
No, like, search is way broader.
Search is coordinating the human species around anything we're trying to do.
How does search become less and less commodity?
Well, it's always about really good retrieval and really good data.
So if you do perfectly on both those things, you have all the data and you have all the best possible retrieval, that is perfect search.
And so it is like both accumulating all the web's data, you know, accumulating data that's not on the web, and then training extremely powerful models to search over it.
Those have always been the two, like, index and retrieval have always been the two pushes at Exa on the engineering side since five years ago.
Can you say more about the data element?
And, you know, obviously...
No need to share any secret sauce there, but it does feel like the web is getting increasingly closed to some extent, parts of the web, right?
But there's a lot of fear out there from data providers on, oh, we don't want to be stack overflowed, right?
Especially if your business model is around impressions you serve to humans visiting your site.
I think the fear is the greatest among those business models.
How do you think about just sort of that interplay with data providers and making sure you can get...
to the path of perfect search, but, you know, work with these data providers that are maybe becoming more closed.
Yeah, so I want to get to the ideal world where you have perfect search and then everyone's intensivized to create amazing content, even more so than before.
I actually think there's an opportunity here, and I'll talk about how, to create a system where, like, content providers are making more revenue because they are participating in this massive agentic economy.
Yeah.
Right?
So, like, I like to think for first principles, you know, the first principles idea here is that the agentic economy is going to be huge.
Like, basically, like, we're all going to have agents.
So those agents are going to be participating.
You can imagine it's like agentic economy and cyberspace where basically they're doing like commerce.
They're like reading information.
It's like everything we do on the internet, they're doing, but like a thousand times bigger, right?
So there's a massive amount of value in this agent economy, meaning money.
And so if there's so much value, like instead of like, you know, hundreds of billions of dollars going to one company, what if, you know, $50 billion a year went to one company and the other $150 billion went to...
all the content providers, right?
Like there's ways to distribute the value in this new agenda economy that are more favorable towards providers of content.
And like this is kind of how I, and by the way, it won't be 200, but it'd be a trillion dollars a year of value.
So there's, if we could figure this out well, like there's an opportunity for everyone to just like do amazingly.
I loved all the use cases you talked about.
There's go to market, right?
There's also the, I'll put it in the loneliness bucket, but finding people you can connect with.
One that you didn't talk about that I just want to pause on really quickly is coding.
Can you share more about why web search makes coding agents more powerful for this use case in particular and why Exa is such a perfect fit for the coding use case?
Yeah, for sure.
So every agent at some point, agents are like humans.
At any point when you're coding, when humans used to code, you would have to look up information, right?
Because you want the most, recent technical documentation, or you might look at a blog for inspiration.
And so agents are very similar.
In particular, they really want the freshest information so that every line of code they write does not have some critical error.
And, you know, especially with coding agents, like the stakes are so high that, again, every extra nine of quality matters.
So these coding agents are very intelligent right now.
But in terms of retrieval quality, they've been in the dark ages or the dark ages of like the early 2000s in terms of search quality.
But like search can be way better over coding, you know, We're talking technical documentation.
We're talking SDKs.
Just like perfect.
Perfect retrieval of those things.
That was our goal.
And so we're not perfect, but we're extremely good at search over any sort of coding material.
And so, yeah, so when a bunch of coding agents try us, like Cognition, for example, we've talked about, tried us, like we power Devin now, and they've just found when they tested it that it just makes Devin way better, way more accurate, make way fewer mistakes, which really matters.
And this will just continue.
By the way, you can also think about coding agents as just like, like every agent is going to want to do everything.
And so it's not just searching over code.
It's also at some point searching over the world's information, just being up to date with the news because like if the coding agent will become your agent.
It's like I think coding agents and just general agents are going to merge.
And it's an interesting trend.
But yeah.
Right.
No, I mean, it's clearly Codex is not for developers, right?
It's sort of like the everything app to that point.
I want to bring in mostly because it's in the it's all over Twitter right now.
And I think it might be an interesting tie to Exa.
And that's sort of this topic of token maxing, tokenomics, just the fact that, you know, Uber is talking about how they're spending too much.
ServiceNow hit their budget for the year already.
You know, I think Microsoft talked about pulling their cloud code licenses.
Tokenpocalypse.
Tokenpocalypse.
Yeah, okay, there we go.
Yeah, exactly.
Better word for it.
But we've talked about this before, but in terms of how you think about search actually making token consumption more effective and efficient, can you share more perspective on that?
And to the extent you can, like, what are results that you're seeing on that front?
Yeah, sure.
So, like, retrieval can help solve the tokenpocalypse.
because we should not be using gigantic models for every test.
We should be using, and people are starting to realize this, you should use a family of models of different sizes.
The big model decides what to do, and it dishes out commands to the small models.
And those small models can be way more accurate and reliable if they're using retrieval.
So retrieval helps small models act like big models in a cheap way.
And so we do save our customers a huge amount of tokens because they can use smaller models and use retrieval.
They could also, we have also, we care a lot about this.
So we have like, we've put a lot of research effort into how to, you know, extract only the most relevant information from documents so that these models can just like not have to consume too much tokens because like a lot of, you know, any sort of input tokens can dramatically increase spend.
So we could like, we could save like 20x on cost for customers compared to other providers by like being very efficient in like what information does the, from the web does the agent actually see.
But yeah, in general.
smaller models using retrieval is much more efficient.
And like Andre Carpathy had a tweet about, I keep mentioning Andre Carpathy on Twitter.
I know we all do.
Yeah, it's great.
He had a tweet about this a couple, I think a couple of years ago where it was like the trend is towards like smaller raw intelligence modules using tools.
And that trend will, like that's an important trend because like you have, you know, you have a limited, like the cost of the model is determined by the number of weights that, you know, determines the cost of the inference.
And if those weights are, if you're wasting those weights on like all sorts of information about the world, like the capital of France or, you know, this random blog that you read, like you're just wasting tokens.
Sorry, you're wasting weights.
Those weights should be focused only on like intelligent processing.
And you could probably get to models that are like 1 billion, even less than a billion parameters that are extremely hyper-intelligent and completely.
unknowledgeable.
It's like Einstein who never saw the world.
That's kind of like the way I think about it.
And then it uses tools that are very cheap and efficient.
And that's a much more efficient world that will help solve this compute shortage that is affecting everybody.
Do you think, this is more of like a hot take moment, but do you think that reality will start to...
take place second half of 2026?
Because right now we're in this phase where everyone's playing with the biggest best model that just arrived.
And to your point, probably overspending.
So when do you think this reality kind of sets in?
I mean, the reality is definitely starting.
I don't have the exact, it's like trends are everywhere.
Yeah, yeah.
When does it become noticeable?
Yeah, I would say by end of 2026, it's very noticeable.
Oh, okay.
Kind of a hot take, actually.
So you talked about doing Just the research that you're doing.
And, you know, I think EGSA, you sort of famously structured as more of a research lab, honestly, than, you know, what people are calling application layer companies, infrastructure companies, right?
You know, this is sort of a research lab focused on search.
And coming out of that, one of the things that we were most excited about, frankly, is just the exciting cutting edge work that you guys are doing.
One of those things was actually.
search as it pertains to RL, and there's a lot of efficiencies there, et cetera.
I wanted to just flag that because it was an interesting finding.
I think you used Tinker, so shout out to Thinking Machines.
But say more about what you guys are finding there and also just sort of what research directions you guys think about as being important.
Yeah, at a high level, like a lot of the big...
ideas in training LLMs apply equally well to training search models.
So for example, like we do pre-training of embedding models, we do post-training of embedding models, we do RL on like search tools, right?
So like a lot of these things that are working in LLMs work in retrieval too, which is kind of interesting.
And you don't hear a lot of people talking about.
So yeah, in that RL blog post, we just basically try, like a lot of people RL on a search tool, but we haven't seen as many studies like testing different search tools that you RL on.
And so we simply RL'd on SERP.
So like Google Wrapping versus XA and found that, you know, RLing on XA does way better.
Like it both like uses fewer calls, so it's more efficient and then it's like higher performance.
And this makes sense because again, like XA was designed for agents to use.
And so like it just allows agents to make more complex queries.
Like it's really like capture what they actually want as opposed to having like to compress what they want into like shorter phrases that are more for traditional search engines.
So that was a cool blog post to explore, and I think it was really helpful for that.
In general, our research is like the bitter lesson to just like scaling laws in lots of different directions.
Some of the ones I mentioned, post-training, pre-training, RL.
We've been pretty under the radar.
Like, I don't think people don't realize how much research we're doing.
We don't publish all of it, obviously.
We don't publish much of it.
But there is a lot to go and search, and I don't think people realize that.
And I think we realize it because we've just been obsessed with it, one.
crazily enough, like that's just what's required.
But then also, I think the biggest thing is actually we're, because of our business model and who we serve, we've just been pushed in all these crazy directions because we're not serving 2 billion consumers who are kind of all the same, like 2 billion humans.
We're serving, you know, now, you know, over 5,000 businesses that are pushing us in all these crazy directions.
Like, just like every day, it's like, why can't this be higher quality over companies or people?
Like, why can't this be faster?
Like, why can't...
Why can't the information extraction be even higher quality?
And so we're being pushed in all these crazy ways.
And that's why we're exploring all these research directions.
Like research always follows need.
So we have insane amounts of needs at EXA to do better.
And so that's why we do all this crazy research.
Yeah.
No, that makes sense.
So I guess kind of tied to this is I wanted to ask you about how you think about benchmarks and hill climbing.
And I'll say this sort of tongue-in-cheek, but we've noticed that, especially maybe among folks in your space, but also in other spaces, right, there seems to be, like, benchmark maxing or whatever you want to call it.
And, you know, of course, to no surprise, everyone is always at the top of their own benchmarks.
Yeah, that's how you know.
Something's wrong.
That's how you know.
Exactly.
And obviously, you have this relentless...
pursuit of ground truth and also, you know, self-improvement, right?
I think like you're the first to admit, hey, here are the areas we could be better on.
We're trying to improve on a continuous basis.
But how do you internally think about what benchmarks matter?
What is ground truth for you guys in terms of like, hey, we're actually better on this front, but worse on this front?
Yeah, yeah.
No, 100% the evals have been...
Benchmaxed in retrieval.
There aren't too many evals in retrieval.
So that's one problem.
There aren't that many standard third-party retrieval evals, and they've been like benchmaxed.
And they're not really actually good representations of agentic search, like what agents actually need.
And so it is a problem in the industry where like customers can't really know what is true, which is sad.
It also demonstrates the need for a really good search engine to distinguish what is true.
It's just another example.
But yeah, I mean, the ground truth for customers is their own A-B test.
And so when we do a, like, right, like, literally they are testing us versus other providers on their use case.
And if they have enough data to do A-B tests, that's the best.
If they have, often they make their own evals.
So sophisticated customers will make their own evals.
Super sophisticated customers will run A-B tests.
And then, like, customers who just want something might just, like, look at evals that are published online.
But certainly for the sophisticated customers when they test us.
it becomes a lot clearer who's on the top.
But yeah, we want to improve this ecosystem.
We want to be the research lab that helps improve the ecosystem and publishes things.
And even if we're not at the top, we want to show.
And so you'll see more coming out there.
So you predicted that Agentex Search will be a bigger business than Google Search by the 2030s.
Say more about that.
What trends are you seeing that lead you to believe this?
Yeah, I mean, just...
You could get this from basically like estimating the number of searches.
So the number of LLM calls and the percentage of those LLM calls that require search, then the cost of the search, and then you just like play it out.
And the trend has actually been pretty clear.
And we've had, you know, pitch decks from like years ago where we kind of predict where things will go.
You know, maybe we're off by a quarter here or there, but it's like pretty, you know, it follows a trend.
And so, yeah, if you follow that trend, even conservatively, you get to a massive TAM for agentic search.
in the 20s, I mean, even before, like, late 2020s and then early 2030s.
Basically, like, the number, it's hard to express, like, how many searches will come from agents, right?
Like, humans on average make, you know, a couple searches a day.
But agents, when everyone has a personal assistant and every single software tool you use is going to be checking its work with retrieval, like, the number of searches is going to be...
We say thousands because that is, like, understandable and grottful to people, but really it's going to be millions at some point.
It's just going to be, like, the world's going to be filled with search in a way that the world is filled with electricity.
It's, like, it's a fundamental infrastructure that powers everything.
Like, I think less of search is, like, perfect information.
It's a world to be filled with, like, the highest quality information.
And so, yeah, I mean, there is a lot of, when the world is filled with something, it's usually a large...
And yeah, if you just play out the numbers, we think it will be bigger than Google Ads in 2030.
Not that ads won't be a huge part of the world, too.
Like, ads are important for commerce, and that might be a percentage of the agentic search economy, too.
But yeah, it's just the numbers here are insane.
And it really, it comes down to a belief, like, do you think LLMs will eat the world, will eat all software?
And like, we have always believed that.
It seems like very true.
It seems even more true every month.
What do you think, and there's some debate on this on the LLM side, on the training side too, but what do you think is the bottleneck today versus, let's say, three years from now?
I feel like five years is too far to predict.
Is it, I think you've said in the past it's no longer intelligence in terms of bottlenecking search.
Like, is it data, accessible data?
How do you think about how that evolves?
Yeah, I mean, well.
Initially, the bottleneck is going to be actually the infrastructure, which is kind of interesting.
No one realizes, but if you actually get, for example, 10x, 100x, 1,000x more surges on Google, the infrastructure to handle that is insanely large.
It just hasn't been built yet.
So we're really excited to explore all sorts of cool new vector databases that have super high throughput, for example.
Things like that.
So that's an interesting bottleneck.
In the same way there's compute bottlenecks, there'll be infrastructure bottlenecks.
I mean, it'll be solved at some point.
Yes, and then other bottlenecks are data bottlenecks.
Agents are increasingly going to want to ask questions about the world and that data might not be on the web.
It might not even be recorded anywhere.
And so like, you know, there will be this trend towards how can we like accumulate all the world's data, like literally unearth the world's data.
I'm really excited about this because the world is filled with information and it's not all recorded.
You know, the history of humanity, the history of humanity is the world's been.
you know, when we were hunters and gatherers, there was also some information, then they started writing things down.
And like the amount of information in the world has been like skyrocketing.
There's still so much information that's not recorded.
You know, you go from all the way from the first clay tablet, or really the first like paintings on caves would be arguably the first time things were written down.
And then like, and then, you know, clay tablets and, you know, now newspapers.
And then obviously now we have like the digital age, but there's so much information in your head, like satellite images that are not just like.
Totally.
In the world's soup of information that we could search over.
Yeah.
And, like, to fully understand the world, fully understand how crop yields are going to, you know, affect, you know, some company, or, like, what are people thinking about the world, or how do we unite the world?
Like, that requires, like, understanding the world at a deeper level.
So I think the bottleneck will be data in a lot of ways.
And then once you have...
The problem is once you have all this data, now the bottleneck will be retrieval.
Right, right, right.
Imagine, it's just a crazy idea, like imagine, you know, as we expand as a species, we're thinking like very far in the future, like think about, or in the solar system, like there's so many things going on in the world, there's so much data being accumulated, the retrieval over that is very expensive.
And so like, they're actually really fundamental, they're interesting fundamental questions here.
Like right now the web is like, let's call it a trillion pages that are matter.
What happens if the web were a thousand times bigger, like a quadrillion pages?
Meaning like everyone started uploading data.
Well, then like any search algorithm that works over a trillion pages, no longer, like it might work over a quadrillion pages, but it might be a thousand times more expensive.
And that's not practical because if anything, we want search to get cheaper, not more expensive.
So what kind of search algorithms could work over a quadrillion pages?
These are fascinating questions that like, I don't know if I've ever heard anyone else talk about.
Well, actually, I was going to say the, at least the, Thinking about when we're living on Mars or whatnot, there's probably one other founder that has thought about that extensively, Elon.
And I listened to your, when you went on the Latent Space podcast last year, one of my favorite podcasts, and you talked about actually working at SpaceX.
How was that experience?
And are there elements of Elon's leadership style that you've taken as CEO of your own company?
Yeah, well, first of all, the internship was magical.
So like, for example, I saw the first landing on the barge.
Wow.
Yeah, and like just outside mission control.
Like I'm getting tingles just thinking about it.
But yeah, like just seeing that, like people coming together to do something magical.
Like that was very inspiring and made a big impact on me.
And yeah, in terms of my leadership style, yeah, I like to think that I've incorporated some of what I think are the best aspects of Elon.
So, for example, like he's very detail oriented.
Like he gets into the details of everything.
And like for better or worse, like I do that too at EXA.
everything from like the algorithms, like the VectorDB algorithm to like the office space and making sure like every part of the office is like, like really just like inspires and like excites.
Yeah.
And like, like shows our passion.
And that goes across everything from our marketing to the engineering to, to go to market.
And so that's exciting.
Obviously it doesn't scale.
So one thing I've been learning as we scale the company is like, what details can I choose to go into?
And so it's, I like the ego metaphor of like.
You know, I'm an eagle flying above the company.
And then when I see some detail that I think should be important to fix, I go dive down and go into it and then come back up.
So I think Elon has some of those properties.
Obviously, Elon is like all in every day for, what is it, like decades?
I've only been doing it for five years, but I intend to do it for decades.
One last thing that he does really well, which is like he's very good at memetic names and like inspiring through like memetic things.
Like, you know, like you realize that SpaceX's mission is not like, improve rockets to get to, or yeah, like, like, oh, make, make rocket travel really fat, like good.
It's like, it's like make humanity interplanetary.
Like that's a really good memetic thing.
So I think a lot about the names of like projects and like when I, when I, I, you know, I do a, a, a team standup every Monday in front of the whole company.
Now we have like this like double floor office where people now are surrounding on the top floor.
It's really cool.
It's like a stadium and I give like a speech and I, and I like to like, uh, simplify what we're doing into like.
what is the core, what is the memetic core, and, like, come up with a cool name.
And that inspires.
By the way, a name is really important because it's, when you have a company of a certain size, people are constantly communicating in ways you're not, you're not part of those conversations.
And, like, the name, like, really, like, grounds the mission of that project.
Yeah, absolutely.
This is really important.
Like, I could think for, like, a day of just about a name.
Well, so I just, totally divergent, but on the topic of names, one, I love GOAT.
Talk a little bit about the symbolism of that.
And then, I mean, besides, you know, greatest of all time, obviously.
But and then also Exa.
You shared before what Exa means, but talk more about why you named the company Exa.
Yeah, sure.
OK, so the goat thing is basically there was a coffee shop that opened up next door and so many people.
So like the mayor of SF kept posting about it.
And so a lot of people go.
It's a really cool coffee shop, Hedge Coffee.
And so many people are walking by.
We're like, we got to like.
have them stop and look at what Exa is.
So we have some posters about Exa on the entrance to our office, but we were like, how do we get them to stop?
And so I was like, just put a goat.
I don't know why I thought this, but just put a goat there.
And I actually, some part of me wanted a real goat.
I don't know how we'd maintain the goat.
I think the ROI would still be valuable.
But then instead we bought like a nice like fake goat.
Yeah, I love that goat.
And we put like the swag on him.
So it's like, it's a beautiful, yeah.
And people stop by.
So like, we're actually like.
Tons of people stop by.
Wow.
Even on Saturday and Sunday, which is when the coffee shop is very popular, people come by and I go and I say hi to them.
I talk to them.
I tell them about it.
It's actually very beautiful.
We're actually an important stop for kids on their walk to school.
You know when you were a kid, you had those stops.
The goat is a stop.
And they're always taking pictures.
Very cute.
Starting the recruiting funnel early.
That's good.
But it's interesting how much those things matter.
Yeah, absolutely.
We've hired people because of the goat.
Anyway.
Exa, I think, is a great name.
I love Exa.
One value of Exa is, like, it's a great prefix.
So, like, Exa anything.
Exa data, Exa this, Exa that.
But Exa means 10th to the 18th, which is in contrast to Google, which is 10th to the 100th.
And the idea there was, like, you know, we're kind of overwhelmed with information.
Even though you could technically find lots of things on the web on Google, that doesn't mean it's, like, organized and you get the highest quality knowledge.
And here it's like 10 to the 18th is like you're extracting the most important information.
It's still a very large amount of information, 10 to the 18th, but it's not like overwhelming.
That was one of the ideas.
Great name.
We love the name as well.
And the goats.
My kids love the goats.
So I wanted to, this topic feels a little bit over talked about, but it's been in the news more recently.
So I'm going to bring it up, which is, I don't know if you saw, Will Manitas wrote a...
an article recently called Grindslop.
And it's almost this backlash of, and in fact, I pulled a quote from it.
He said something about, we're witnessing a phenomenon that masquerades as discipline, but represents perhaps the most extravagant squandering of economic surplus in our civilization's history.
So, extreme words, right?
And he talked about...
Sort of all of these articles that are glorifying the grind over what they're doing, you know, all of these things that, you know, maybe are more relevant to work-life balance, etc.
So I guess I'll start with saying one of the things that I was actually most impressed and excited by when I visited Exa at, God, I think it was like 8 p.m., was that there were a ton of people in the office.
And it wasn't just that they were there for FaceTime, because obviously that's not what you and Jeff care about.
But they were excited about what they were working on.
And I think really importantly, they were excited by who they were doing it with.
And so just say more about like how you think about that culture of going all in and going after what you guys are, you know, sort of the mission that you just talked about.
And like, what do you think the important things to balance are in culture?
Yeah, yeah.
I mean, this is critical to me and it should be critical to any company that wants to do great things.
Like people need to be super excited about what they're doing and have a ton of fun, right?
So like you might have also heard at 8 p.m.
like people are just laughing.
Yeah, oh yeah, absolutely.
We laugh a lot at Exa and it's almost too loud sometimes and it's like distracting.
So you have to have like headphones for everybody.
But yeah, like people are, it's very important people are having fun and that's necessary for doing the best work of your life.
And I also, I would say like having fun just like.
just good vibes.
But also, it's very important that people work on the projects that are most exciting to them at any point.
And so like people, especially now with like, you know, all these AI tools, like anyone, especially on the engineering side or even on the good market side or ops side, everyone can work on whatever they want.
So for example, like we had someone who built the vector database who was like, hey, I want to go train models.
I was like, okay, just go do it.
And like, he didn't really have experience in training models, but I was like, you're really smart.
You'll learn it in like two weeks.
And then you'll just do amazing work.
And he's done amazing work.
So right.
So like, I make sure everyone's.
working on exactly what they want to work on.
And luckily in search, it's like, at least in our space, like in any direction we improve things, it will be good for the business.
So it's like, okay.
And it actually happens to turn out somehow that like what people want to work on and what we need like perfectly aligns.
I don't, it's like a magical thing.
I don't know how that's possible.
But yeah, so everyone's working on exactly what they want to work on.
They're also working on multiple projects because like AI tools will enable you to be like productive in parallel.
And so it's just, and then what we're doing is just super cool.
Like, you know, there aren't.
Like, it's hard to find maybe these days, like, really exciting, like, humongous projects and missions that other people aren't doing.
And this one happens to be, like, organizing the world's information, making perfect search.
That's very exciting and important for the world, but also, like, a really hard problem.
And engineers are really excited about that.
And on the go-to-market side, it's like, we're then selling this search to the whole world, and we're selling to some of the coolest companies.
And it's very exciting to them.
They get to talk to, like...
all these like hot companies and give them and give them search that really helps their products.
And so it's just an exciting space.
And yeah, but it's very important to me.
So I hope we maintain that forever.
Yeah.
No, absolutely.
Well, maybe just to end on one last question.
And I'll say, first of all, EXA is hiring.
And you told me once that you actually still interview every single person at EXA or who comes to interview for EXA.
And so I'm curious what you've attracted some of the most incredible talent, junior, senior, everything in between, very high slope, very experienced.
And I'm curious what it is that you look for when you do that last.
final interview and, you know, how you've been able to get these incredible people to come to EXA.
Yeah.
I mean, people might not like this word, but I look for passion.
It's like someone's just like a fire in their eye.
Like, do they really care about what we're doing or some aspect of doing?
I really want to scale this thing or I really want to sell this to everyone because why is that important?
It was important five years ago, but I think it's especially important now because like...
The world goes to who is most passionate, who's most agentic, because like you can now do anything.
Like you could be whatever you want.
Like, you know, like with agentic tools, you could literally like do anything.
And what matters most is like how much do you care about the end result?
Yeah.
And then like your judgment and everything.
And so like I look for the fire in the eye, because if you have that fire, you could literally do anything.
It's actually crazy how meritocratic the world is becoming because of these agentic tools filling the gaps.
Yeah, absolutely.
Well, thank you so much, Will.
This was great.
Such a fun conversation.
Appreciate you and very excited to be partners.
Awesome.
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