Building Search for AI Agents with Exa CEO Will Bryk

Summary

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Podcast Summary: Building Search for AI Agents with Exa CEO Will Bryk

Podcast: AI + a16z
Episode: Building Search for AI Agents with Exa CEO Will Bryk
Date: June 3, 2026
Host: a16z
Guest: Will Bryk, CEO and Co-Founder of Exa

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Episode Overview

This episode explores the evolution of search in the era of AI agents, focusing on how traditional human-centric search engines like Google are being challenged—and in some domains, surpassed—by advanced agent-oriented platforms. Will Bryk shares the origin story and mission of Exa, insights into building search for agents, technical and business challenges, and his thoughts on the future of knowledge retrieval and agentic infrastructure.

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Key Discussion Points and Insights

1. Redefining Search for the AI Age

• Google’s Strengths & Limits: Google is highly effective at meeting generalized consumer needs and providing quick, surface-level answers. However, it struggles with deep, comprehensive research and complex queries, which are increasingly in demand by AI agents and specialized business users.
  • Quote: “If you want to go really deep into some topic, Google fails.” — Will Bryk (00:11)
• Agentic Search Paradigm: AI agents require vast, comprehensive, and nuanced datasets for specialized queries, unlike humans who are content with the top 10 results. This necessitates fundamentally new architectures and capabilities.
  • Quote: “Agents search differently… They need deeper context, more complete information, and the ability to navigate far more complex questions.” — a16z Host (00:48)

2. Exa’s Origin and Motivation

• Will’s lifelong mission is to perfect information access, motivated by the idea that many societal issues stem from poor knowledge retrieval.
  • Quote: “Since I was a kid, I’ve cared about finding the highest quality knowledge. If you can make search perfect, that has so many downstream positive implications for the world." — Will Bryk (01:46)
• Exa began with the ambition to build a better search engine for deep understanding, leveraging new transformer models, and transitioned into addressing agentic use cases as AI tools proliferated (2021–2023).

3. Building a Search Engine for Agents – Technical and Strategic Challenges

• From Scratch, for Agents: Exa was constructed to process a vast array of documents semantically, blending keyword and neural approaches.
  • Optimization Challenge: Ideal search with full LLM sweeps across trillions of docs was prohibitively expensive—requiring optimization at every level for efficiency.
• Agent Needs vs. Human Needs:
  • Agents demand "comprehensive" results—thousands, not tens, of answers.
  • Agents can handle complex and toggleable search parameters; they optimize rapidly for their goals and tolerance for latency is application-dependent.
  • Comparison: “Agents to humans is like humans to sloths. Imagine building a search engine for sloths, then humans come around. Agents want to do crazy things, really fast, and with high accuracy.” — Will Bryk (09:12)

4. Impact of Advancing Model Intelligence

• LLMs allow small teams to achieve what used to require scores of search engineers.
• Human user signal (e.g., Google’s click data) is less relevant for agents; the playing field for new entrants is leveled.
  • Quote: “All that click data that Google has accumulated just doesn’t really matter for agents.” — Will Bryk (13:24)
• As agents’ requirements intensify (businesses’ “billion dollar investments are on the line”), the bar for quality in search rises continuously.

5. Is Search Getting Commoditized?

• LLMs vs. Search: Will predicts LLMs will become commoditized faster than search. Much of knowledge work is retrieval-bound rather than intelligence-bound.
  • Quote: “A lot of knowledge work is actually a search problem, not only intelligence… every extra 9 of quality and search really matters.” — Will Bryk (17:54)
• Hardest Problems in Search: Areas like company/people search, recruiting, and go-to-market intelligence remain unsolved and hugely valuable.

6. Broader Societal Perspective: Search as a Foundational Problem

• Many global problems—political polarization, loneliness, even social connection—are rooted in imperfect search and knowledge discovery.
  • Memorable moment: “Loneliness is a search problem. People can’t find others to connect with. If search were perfect, this would diminish.” — Will Bryk (20:05)

7. Data Partnerships and Challenges with a Closing Web (22:15)

• The future ideal: content creators directly benefit from the agent economy, with value flowing to data sources as agents proliferate.
• There is an opportunity to construct incentive models so content providers are integral, not antagonistic, to search.

8. Use Cases: Coding Agents, Token Consumption, and the 'Token Apocalypse'

• Coding agents benefit uniquely from comprehensive, up-to-date web retrieval—agents like Devin (Cognition) are more robust when powered by Exa.
  • Quote: “Every agent, at some point, is like humans… needing to look up the latest technical documentation, SDKs, etc. Every extra 9 of quality matters.” — Will Bryk (23:54)
• Retrieval enables smaller models to act intelligently and more efficiently, reducing token (compute) consumption—key in an era of spiraling AI cloud costs ("token apocalypse").
  • Quote: “Retrieval helps small models act like big models in a cheap way… smaller models using retrieval is much more efficient.” — Will Bryk (26:12)

9. Exa as a Research Lab: RL for Search and Cutting Edge Methods (29:34)

• Many LLM training ideas (pre-training, post-training, RL) also work for retrieval. Exa experiments with RL on their search engine itself, leading to more efficient and precise agentic behavior.
  • Quote: “We do RL on — like, search tools. A lot of these things that are working for LLMs work in retrieval too.” — Will Bryk (29:34)
• Customer needs drive rapid, diverse research directions beyond consumer search.

10. On Benchmarks and Evaluation (32:45)

• Retrieval benchmarks are limited, often “benchmaxed,” and fail to capture agentic requirements; true assessment comes from customer AB testing in real-world use cases.

11. The Explosion of Agentic Search – Market Outlook

• Prediction: By the early 2030s, agentic search (search done by AI agents) will eclipse even the scale and business scope of Google Search:
  • “At some point it’s going to be like the world is filled with search, in a way it’s filled with electricity. Fundamental infrastructure that powers everything.” — Will Bryk (34:12)
• This conviction is based on the multiply-accelerating number of searches per user/agent in the new ecosystem.

12. Bottlenecks and the Road Ahead (36:18)

• Near-term bottlenecks will be infrastructure—handling massive query loads, high-throughput vector databases, etc.
• Data will be critical: not all valuable data is even recorded or on the web; the challenge moves from intelligence to data acquisition to ultra-efficient, high-quality retrieval at scale.

13. Leadership, Culture, and Recruitment at Exa

• Will draws inspiration from Elon Musk’s attention to detail and memetic mission framing (“make humanity interplanetary” for SpaceX; “perfect search” for Exa).
• Office culture emphasizes fun, excitement, and self-driven project selection; the alignment between individual enthusiasm and business needs is deliberate and valued.
  • Quote: “People need to be super excited about what they’re doing and have a ton of fun… Everyone can work on whatever they want. What people want to work on and what we need somehow perfectly aligns.” — Will Bryk (45:36)
• Exa hires for “passion”—the most “agentic” candidates with fire in their eyes to build the future, leveraging the meritocratic possibilities of the AI era.

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Notable Quotes & Moments

• On AI agent requirements:
  • “Agents to humans is like humans to sloths.” — Will Bryk (09:12)
• On perfect search and societal problems:
  • “Political polarization? I’d argue that’s a search problem.” — Will Bryk (19:23)
  • “Loneliness is a search problem.” — Will Bryk (20:05)
• On commoditization:
  • “I would argue LLMs are getting commoditized faster than search is.” — Will Bryk (16:51)
• On passion in hiring:
  • “I look for passion. 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.” — Will Bryk (48:10)

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Timestamps for Key Segments

| Timestamp | Segment | |---------------|---------------------------------------------------| | 00:00-01:28 | Intro, main theme, origin of Will’s mission | | 03:31-05:40 | Old vs. new Google, deep research limitations | | 06:12-09:12 | Building Exa, technical approach, agent use cases | | 09:39-13:20 | Agent-centric search architecture | | 16:06-18:34 | Search commoditization, hardest search problems | | 19:23-21:37 | Societal impacts of search quality | | 22:15-23:54 | Data, partnerships, web closing, incentives | | 23:54-26:12 | Coding use case, token apocalypse, cost saving | | 29:34-31:47 | RL for search, research lab, innovation culture | | 32:45-34:12 | Benchmarks, evaluating ground truth | | 34:12-36:18 | Agentic search vs. Google scale, future trends | | 36:18-38:48 | Bottlenecks (infra, data, ultra-scale retrieval) | | 39:24-44:01 | Leadership style, symbolic branding, naming | | 45:36-47:31 | Building a high-energy, aligned company culture | | 47:31-48:52 | Passion in hiring, final reflections |

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Summary for Listeners

This episode offers an inside look at the shifting paradigm of search, as AI agents—not humans—become the primary users of web information. Will Bryk emphasizes that perfect search is not just a tool for efficiency, but a foundational substrate for solving large-scale human challenges, from recruitment to political division. The conversation traces the journey of Exa from a scrappy startup to a central infrastructure player, laying out both technical hurdles and societal ambitions. Fans of AI, search technology, or anyone interested in how information shapes the world will find a mix of inspiration and insight here.

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Transcript

[Will Brick]

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.

[a16z Host]

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.

[Will Brick]

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.

[a16z Host]

We have a family, openclaw, Michael Clodberg, and we wanted to give it web access. And he was like, I recommend exa.

[Will Brick]

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 like 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?

[a16z Outro Host]

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 to 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.

[a16z Host]

Welcome, Will. Thank you for being here.

[Will Brick]

Well, excited to be here.

[a16z Host]

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?

[Will Brick]

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. Cause 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 like a really important opportunity because search is the gateway to the world's information. If you could. If you could improve search, if you can make it perfect, then 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, 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.

[a16z Host]

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 technopolies 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 at, you know, I.O. 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.

[Will Brick]

Yeah. I mean, Google was amazing and it 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 moun 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 Exo 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 you ever talked

[a16z Host]

about it, but I want to read this book. It's probably on hold right now.

[Will Brick]

I just be really good. I would have finished it around now, so. 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 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's greatest 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, I've seen many Google iOS now at this point at EXA, every Google I o, 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 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.

[a16z Host]

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?

[Will Brick]

Building 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. That 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. I would say, does this document match the query? Does this document match 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 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 Andrej Karpathy retweeted it. It's 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, ChatGPT 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 exo November 2022. Two weeks later, ChatGPT came out actually to. To me. I was at nerfs 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, yeah, 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 or very similar.

[a16z Host]

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, openclaw, Michael Clodberg, and we wanted to give it web access. And he was like, I recommend exa. Before we invested, I was 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 Exit 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.

[Will Brick]

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. Imagine we had a search engine.

[a16z Host]

I'm picturing that. Zootopia D. Yeah, that's what I'm thinking too.

[Will Brick]

It's like, imagine we had a great surgeon for sloths and then humans came around. Like, they're not going to want to use that same surgeon. And so you should think of agents as these like crazy creatures that have like infinite, like time is meaningless for them. They just want to like make complex queries very fast and like 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 how do you, what do you. 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 could actually semantically handle complex, 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 us, 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, add a domain filter here and a keyword filter there or like searching this way, searching 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. Yes, 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, 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, you don't want to have any fomo, like any, like you're missing some critical startup that exists that actually like 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 Exile, like you could search something and then get not just like 10 results or 100 results, but a thousand results or 10,000. And increasingly agents are wanting this. You also want like lower latency because like 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. Some want super low latency, some want super high latency or lady 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.

[a16z Host]

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 PageRank, et cetera, 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 to your point, you said it's like a big optimization exercise. 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.

[Will Brick]

It's easier and harder to build A search engine for AI agents. And so I'll tell.

[a16z Host]

Oh, interesting. Yeah, please.

[Will Brick]

Yes, I knew that would spark your interest. Okay, so why is it easier? Well, first of all, like, this, this whole click, you know, Google has an insane amount of human click data. 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. Like human click data is great for humans when you want to find results that humans click on, which is obvious. Right? Like, so if you get a huge amount of human click data, you could train on that. And now, you know, 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 click. 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 qlik data that Google has accumulated just doesn't really matter for agents. And so it's a whole new ball game. So there's not much of an advantage there. There are also other things like, yeah, like Google probably had a, you know, 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 you have 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, you know, faster, higher quality. But you don't need a team of hundreds. You could do it with like a couple people. So like, like, how are, 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 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 are on 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 search engines had like 99.9% quality or reliability like these, these new search ins for agents need 99.999. 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 out 99.9% of use cases. And then when, when the next one version comes out, everyone wants that thing. Right. Because the extra nines of quality are so important in this new agent 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.

[a16z Host]

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, etcetera, 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, as you mentioned, in terms of perfecting or pushing forward the frontier of search?

[Will Brick]

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 searches. And the reason is because you don't need to run like Mythos over every cell on your Excel sheet when you're trying to find competitors or something. 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. Like it's pretty cheap. Like you could just use 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 like inventing new science and math and in certain cases you want like, you know, find any bugs in your code or something like that. But like increasingly like I would say, like if you think of knowledge work, like all the different tasks you might do as like concentric circles of difficulty, a huge amount of that service area is covered by like off the shelf models you get right now.

[a16z Host]

Yeah, very fair.

[Will Brick]

Yeah. Right. So but like on the other hand, like search like when you are trying to, you know, enrich every cell on your Excel sheet with competitors or people you're trying to recruit, then like every extra 9 of quality and search really matters. It's basically, I'd argue that a lot of knowledge work is actually search problem, not only intelligence. Um, and, and so yeah, I mean, what are some examples where search is not good? I do think company and people search is, is the most like, like easy to see. Yeah. And just most value to people. Like every company in the world has to search over companies to sell to or you know, almost every company in the world has to search for companies to sell to and people to hire.

[a16z Host]

Right.

[Will Brick]

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. So it's been great to dog food our own things, give us some advantage. But yeah, that's an example. I think you just want comprehensive like you, you just want all, all the, all the people that are, that could be connected to you, that are, that are relevant and that by the way, it's A very beautiful thing. Like, 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.

[a16z Host]

Say more. What's the example?

[Will Brick]

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. 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, is 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 loneliness.

[a16z Host]

Say more.

[Will Brick]

Yeah, a lot of people are feeling lonely in modern society. Well, it's because they're not finding. They're not finding people to hang out with or to be in relationships with. Right?

[a16z Host]

Yeah.

[Will Brick]

And so, like, yeah, loneliness is a search problem. And it's. Especially in a city life, like, it's hard to find other people with similar interests or 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'd be great friends of 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. I was like, what allows for this differentiation? So there's a long answer to how to. 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.

[a16z Host]

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.

[Will Brick]

Yeah. So I want to get to the ideal world where you have perfect search and then every. Everyone's incentivized 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 agency economy.

[a16z Host]

Yeah.

[Will Brick]

Right. So like, I like to think from first principles. You know, the first principles idea here is that the agent economy is going to be huge. Like basically like we're all going to have agents and those agents are going to be participating. You can imagine it's like agent 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, 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 billion, it'll be a trillion dollars a year of value. So there's totally if we could figure this out. Well, like, there's an opportunity for everyone to just, like, do amazingly.

[a16z Host]

I loved all the use cases you talked about. There's go to market, right. There's also the, you know, 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?

[Will Brick]

Yeah, for sure. So every agent, at some point, agents are like humans. Like, at any point when you're coding or when humans used to code, you would have to look up information, right? Because you want, like, 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, every line of code they write does not have some critical error. And, you know, especially with coding agents, like the. The stakes are so high that again, every. 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 it. You know, we're talking technical documentation, we're talking SDKs, just like, as like perfect, perfect retrieval over the sinks. That was our goal. And so we're not yet perfect, but we're extremely good at search over any sort of coding material. And so, yeah, so, you know, when, you know a bunch of coding agents try us like cognition, for example, we've talked about try to 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, like, 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. Will become your agent, it's like, I think coding agents and just general agents are going to like, merge.

[a16z Host]

Yeah.

[Will Brick]

And that's an interesting trend, but yeah, right.

[a16z Host]

No, I mean, it's clearly Codex is not for. 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 service now, hit their budget for the year already. You know, I think Microsoft talked about pulling their cloud code licenses.

[Will Brick]

Token apocalypse.

[a16z Host]

Token apocalypse. Yeah. Okay, there we go. Yeah, yeah, exactly. A 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?

[Will Brick]

Yeah, sure. So, like, retrieval can help solve the token apocalypse because, like, we should not be using gigantic models for every test. We should be using like. And people are starting to realize this. Like, 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 care a lot about this. So 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 Karpathy had a tweet about. I keep mentioning Andre Karpathy on Twitter.

[a16z Host]

I know, we all do.

[Will Brick]

Yeah, it's great. He had a tweet about this a couple, I think a couple years ago where it was like, the trend is towards smaller raw intelligence modules using tools. And that trend will. That's an important trend because you have a limited. The cost of the model is determined by the number of weights that determines the costly inference. And if you're wasting those weights on 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, 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. Like they. Yeah, it's like Einstein, like who never saw the world. That's kind of like the way to 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 like this compute shortage that is affecting everybody.

[a16z Host]

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 or. 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?

[Will Brick]

I mean, the reality is definitely starting. I don't have the exact. It's like trends are everywhere.

[a16z Host]

Yeah, yeah.

[Will Brick]

When does it become noticeable? Yeah, I would say by end of 2026. It's very noticeable. Wow. Okay.

[a16z Host]

Kind of a hot take actually. So you talked about doing just the research that you're doing. And you know, I think exa. You sort of famously structured as more of a research lab, honestly, than, you know, what people are calling app 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, etc. I wanted to just flag that because it was an interesting finding. I think you use 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.

[Will Brick]

Yeah, it's at a high level. Like a lot of the, 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. 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, we just basically try like a lot of people RL on a search tool, but we haven't seen as much many studies like testing different search tools that you rl on.

[a16z Host]

Right.

[Will Brick]

And so we simply rl'd on Serpent. So like Google wrapping versus exa and found that, you know, rl' ing on EXA 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, EXA was designed for agents to use. And so like, it just, it just allows agents to make more complex queries, like, 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, that was a cool blog post to explore and think it was really helpful for that. In general, our research is like the bitter lesson. So 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 kiss. But then also I think the biggest thing is actually we're in 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 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, like, just 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.

[a16z Host]

Yeah.

[Will Brick]

So we have insane amounts of needs at EXA to do better. And so that's why we do all this crazy research.

[a16z Host]

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.

[Will Brick]

That's how you know it's not something's wrong. That's how you know.

[a16z Host]

Exactly. So 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?

[Will Brick]

Yeah, yeah, no, 100%. The Evals have been benchmaxed in retrieval. There aren't too many evals in retrievals. That's one problem. There's 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 tests. And so when we do A like, right, like literally they are testing us for other providers on their use case and if they have enough data to do a B test, 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.

[a16z Host]

So you predicted that Agentix search will be a bigger business than Google search by the2030s. Say more about that. What trends are you seeing that lead you to believe this?

[Will Brick]

Yeah, I mean just you could get this from basically like estimating the number of searches. So the number of LLM calls and the number 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 have you know, pitch decks from like years ago where we kind of predict where things would 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 twenties. I mean even before like late 2000 and twenties and then early 2000 and thirties. Basically the number. It's hard to express how many searches will come from agents. Right. Humans on average make 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. The number of searches is going to be, we say thousands because that is understandable and grockable 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 a fundamental infrastructure that powers everything. I think less of search is perfect information. So the world will be filled with 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 tam. 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. 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 or eat all software? And like we have always believed that. Yeah, it seems like very true. It seems even more true every month.

[a16z Host]

So yeah, what do you think? And there's some debate on this on the LLM side and 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? Yeah, 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?

[Will Brick]

Yeah, I mean, well, initially the bottleneck is going to be actually the infrastructure which is kind of interesting no one realizes. But like if you, if you actually get e.g. 10 x 100 x 1000 x more searches than 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 like an interesting bottleneck. In the same way there's compute bottlenecks, there'll be like infrastructure bottlenecks. I mean, it'll be solved at some point. Yes. And then other bottlenecks are like data bottlenecks. So 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 there will be this trend towards how can we 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. Even where our hunters are gathered, there's results for 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 a clay. 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 in newspapers. And then obviously now we have like the digital age. But there's so much information in your head, like satellite images, like, they're not just like totally in the world's soup of information that we could search over.

[a16z Host]

Yeah.

[Will Brick]

And like, to fully understand the world, fully understand how crops, crop yields are going to, you know, affect, you know, some company like, or, like, what are people thinking about the world? Or how do we unite the world? Like, 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. Imagine. Just a crazy idea. Like, imagine, you know, we're, as we expand as a species, like we're thinking like very far, far in the future. Like, think about, like 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 they're actually really fundamental. There are 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 can work over a quadrillion pages? These are fascinating questions that, like, I don't know if I've ever heard anyone else talk about.

[a16z Host]

Well, actually, I was going to say 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. And I'm kind of curious how, just given we're on the. Almost on the eve of the SpaceX IPO, how was that experience? And like, are there elements of Elon's leadership style that you've taken as, you know, CEO of your own company?

[Will Brick]

Yeah, well, first of all, the internship was magical. So, like, for example, I saw the first landing on the barge.

[a16z Host]

Wow.

[Will Brick]

Yeah. And like. Like, just outside mission control, like, I'm getting tingles just thinking about it. Like, oh, no.

[a16z Host]

You know, the first time I watched the starship launch, I teared up. Like, I was like, I'm proud to be American.

[Will Brick]

You saw it on a video?

[a16z Host]

No, no, in person.

[Will Brick]

You saw it in person?

[a16z Host]

Yeah. Okay.

[Will Brick]

I've never seen that. That's crazy. That's amazing. But, yeah, like, like, just, just seeing that, like, people coming together to do something magical for, like, that was very inspiring and made a big impact on me. And yeah, I mean, 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 I know, for example, like, he's very detail oriented. Like, he gets into details of everything. And like, for. For better or worse, like, I do that, like, everything from, like, the algorithms, like the Vector DB 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.

[a16z Host]

Yeah.

[Will Brick]

And like, like shows our passion and that, that goes across everything from our marketing to the engineering to. 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 eagle metaphor of like, you know, I'm an ego flying above the company. And then when I see some detail that I think it should be important to fix, like 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 mimetic, like 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, oh, make rocket travel really fast. 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, you know, I do a, A team stand up 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, and I like to like 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, this is really important. Like I could think of for like a day just about a name.

[a16z Host]

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.

[Will Brick]

Yeah, yeah, sure. Okay. 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 so many people are walking by, we're like, we gotta 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, 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.

[a16z Host]

Yeah, I love that goat.

[Will Brick]

And we put like the swag on him. So it's like, it's a beautiful. Yeah. And, and people stop by. So like we're actually like tons of people stop by. Like, wow, it's so beautiful. Even on Saturday and Sunday, which is like when the coffee shop is very popular. Like people come by and I go and I say hi to them, I talk to them, I tell them about X. It's actually very beautiful. We're actually like an important stop for kids on their walk to school. Like, you know when you're a kid you had like those stops. So you like ice cream store. Like the goat is a stop and they're always taking pictures. Very cute.

[a16z Host]

Starting the recruiting funnel early. That's good.

[Will Brick]

But it's interesting how much those things matter.

[a16z Host]

Yeah, absolutely.

[Will Brick]

We already hired, 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, but exa means ten to the eighteenth, which is in contrast to Google which is ten to the hundredth. 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. So like and here it's like 10 to the like, you know, 10 to the 18th is like you're extracting the most important information. It's still very large amount of information. 10th to the 18th. But it's not like overwhelming. That was one of the ideas.

[a16z Host]

Great name. We love the name as well. And the goats. My kids love the goats. So I wanted to. This, 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 Mendes wrote an article recently called Grind Slop and it's almost this backlash of. And in fact I, there's a. 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 8pm 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?

[Will Brick]

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 8pm, like, people are just laughing. Yeah, 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. But yeah, like, people are. It's very important, people 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 Go 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 the 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, important for the world, but also like 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.

[a16z Host]

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?

[Will Brick]

Yeah, I mean people might not like this word, but I look for passion. It's like someone just like a fire in their eye. Like do they really care about what we're doing or some aspect of doing? Like 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 the world goes to who is most passionate, who's most agentic because you can now do anything. You could be whatever you want with agentic tools, you could literally do anything. And what matters most is how much do you care about the end result and then your judgment and everything. And so 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 in the gaps.

[a16z Host]

Yeah, absolutely. Well, thank you so much, Will. This was great. Such a fun conversation. Appreciate you and very excited to be partners.

[Will Brick]

Awesome.

[a16z Outro Host]

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