Building Search for AI Agents with Exa CEO Will Bryk

13:20Sarah Wang: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. Like 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, you know, Google can understand what you mean even when you don't even know what you mean.

13:54Sarah Wang: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, you know, hundreds of people working on re-ranking. 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 you have one engineer working on it.

14:26Sarah Wang: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 a couple people. So 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 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.

14:56Sarah Wang: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.

15:27Sarah Wang: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 new surgeons 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.

16:00Sarah Wang: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.

16:06Will Bryk: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, So 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, etc., 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?

16:36Will Bryk: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?

16:51Sarah Wang: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 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 just an open source model that's big enough, and now the infrastructure for running them is very good. It's pretty cheap.

17:23Sarah Wang: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 won't 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.

17:53Will Bryk:Yeah, very fair.

17:54Sarah Wang: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 and search really matters. 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 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.

18:34Sarah Wang:You could ask yourself if 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 switching tools, like trying out new tools. It's because 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-toot our own thing.

18:59Sarah Wang:It gives us some advantage. 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.

19:22Will Bryk:Say more. What's the example?

19:23Sarah Wang: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, it's so polarized, it's causing reasonable people to be unreasonable. By the way, I'm part of this.

19:55Sarah Wang: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. Say more, 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.

20:29Sarah Wang: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'm 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 go to do that. Okay, and this gets in your earlier part of your question. It's like, what allows for this differentiation? So it's a long answer to how to search not a commodity. You start to see that search is like a bigger thing that people don't. Yeah, exactly.

20:56People think of search as like, in 1998, you see a text box.

21:00Sarah Wang:You type in a few keywords, you get a few things. That was search. No, 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, 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.

21:37Will Bryk: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 work with these data providers that are maybe becoming more closed?

22:15Sarah Wang: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. Those agents are going to be participating.

22:41Sarah Wang: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 agenda 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.

23:17Sarah Wang: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 is opportunity for everyone to just like do amazingly.

23:29Will Bryk:I loved all the use cases you talked about. There's go to market. Right. There's also the, you know, 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. 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?

23:54Sarah Wang:Yeah, for sure. So every agent at some point, agents are like humans, like at any point when you're coding, or when humans use 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 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 over those things.

24:38Sarah Wang: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, has 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, 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.

25:07Sarah Wang: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. Yeah. And it's an interesting trend. But yeah.

25:19Will Bryk: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 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?

26:09Will Bryk:And to the extent you can, like what are results that you're seeing on that front?

26:12Sarah Wang:Yeah, sure. So like retrieval can help solve the tokenpocalypse because like 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 care a lot about this.

26:47Sarah Wang:So we've put a lot of research effort into how to 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.

27:18Will Bryk:I know, we all do.

27:20Sarah Wang:Yeah, it's great. he had a tweet about this a couple 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 intelligent processing.

27:55Sarah Wang: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.

28:19Will Bryk: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?

28:36Sarah Wang:I mean, the reality is definitely starting. I don't have the exact. It's like trends are everywhere.

28:40Will Bryk:Yeah, yeah.

28:41Sarah Wang:When does it become noticeable? Yeah, I would say by the end of 2026, it's very noticeable.

28:44Will Bryk:Wow, OK. 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.

29:21Will Bryk: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.

29:34Sarah Wang: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 LMs 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 many studies, like, testing different search tools that you RL on.

30:04Sarah Wang:And so we simply RL'd on SERP, so, like, Google Wrapping versus EXA, and found that, you know, RLing 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 allows agents to make more complex queries, like just 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.

30:37Sarah Wang: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 in, because of our business model and who we serve.

31:10Sarah Wang: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 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.

31:41Sarah Wang: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.

31:49Will Bryk: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.

32:16Sarah Wang:Yeah, that's how you know. Something's wrong. That's how you know.

32:19Will Bryk: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?

32:45Sarah Wang:Yeah, yeah. No, 100 % the evals have been bench maxed 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 bench maxed. 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 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.

33:21Sarah Wang:And so when we do A-B, 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 this, often they make their own evals. So sophisticated customers will make their own evals. super sophisticated customers will run A-B tests. And then customers who just want something might just 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.

33:56Sarah Wang:And even if we're not at the top, we want to show. And so you'll see more coming out there.

34:00Will Bryk:So you predicted that agentic 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?

34:12Sarah Wang: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. We've had, you know, pitch decks from like years ago where we kind of predict where things will go. So, 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.

34:42Sarah Wang: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 groffable to people, but really it's going to be millions at some point. It's just going to be like the world's being filled with search in a way that the world is filled with electricity.

35:13Sarah Wang:It's like, it's a fundamental infrastructure that powers everything. Like information, 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 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. 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.

35:43Sarah Wang: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. So yeah.

35:54Will Bryk: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?

36:18Sarah Wang:yeah I mean well initially the bottleneck is going to be actually the infrastructure which kind of interesting no one realizes but like if you if you actually get for example 10x 100x 1000x more surges on google the infrastructure to handle that is insanely large it just hasn't been built yet uh so we're really excited to explore all sorts of cool new vector databases that have like super high throughput for example uh things like that yeah so that that's like an interesting bottle like in the same way there's compute bottlenecks there'll be like infrastructure for bottlenecks? I mean, it'll be solved at some point.

36:46Sarah Wang:Yes, and then other bottlenecks are 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, 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. The history of humanity is the world's been, you know, when we were hunters and gatherers, there were all sorts of information, then they started writing things down. And like the amount of information in the world has been like skyrocketing.

37:18Sarah Wang: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 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 in the world's soup of information that we could search over. Yeah. And like to fully understand the world, fully understand how crops, 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?

37:51Sarah Wang: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. Imagine, as we expand as a species, we're thinking very far in the future. Think about, or in the solar system, 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. They're interesting fundamental questions here.

38:21Sarah Wang:Right now, the web is, let's call it a trillion pages that matter. What happens if the web were a thousand times bigger, like a quadrillion pages, meaning 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.

38:48Will Bryk: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. um, Elon. And, uh, you know, I, uh, listened to your, when you, you went on the Latent Space podcast last year, one of my favorite podcasts, and, um, you talked about actually working at SpaceX. 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? Yeah.

39:17Sarah Wang:Well, first of all, the internship was magical. So, uh, like, for example, I saw the first landing on the barge. Wow. Yeah. And like, like just outside mission control or like I'm getting, uh, tingles just thinking about it, So, 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, for example, like he's very detail oriented, like he gets into details of everything.

39:47Sarah Wang:And like, for better or worse, like I do that too at Exa, like 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.

40:00Will Bryk:Yeah.

40:01Sarah Wang: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 ego flying above the company. And then when I see some detail that I think it should be important to fix, because 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?

40:35Sarah Wang: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 orbit. Yeah, like, oh, make rocket travel really good. 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 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 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.

41:26Sarah Wang:And like the name, like really like grounds the mission of that project. Yeah, absolutely. This is really important. Like I could think of like a day of just about a name.

41:36Will Bryk: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.

41:55Sarah Wang: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 many people were walking by, we were 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 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.

42:23Sarah Wang: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.

42:29Will Bryk:Yeah, I love that goat.

42:30Sarah Wang: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. Like, 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 it. So it's actually very beautiful. We're actually like an important stop for kids on their walk to school. Like, you know, when you were a kid, you had like those stops. So you would like move to the ice cream store. Like the goat is a stop. And they're always taking pictures.

42:55Sarah Wang:Very cute.

42:56Will Bryk:Starting the recruiting funnel early. That's good.

42:58Sarah Wang:But it's interesting how much those things matter.

43:00Will Bryk:Yeah, absolutely.

43:01Sarah Wang: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 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 organized and you get the highest quality knowledge and here it's like 10th to the 18th you're extracting the most important information it's still a very large amount of information but it's not overwhelming that was one of the ideas

43:40Will Bryk: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 an article recently called Grind Slop. 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.

44:28Will Bryk: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.

45:11Will Bryk:And like, what do you think the important things to balance are in culture?

45:14Sarah Wang: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. We laugh a lot at Exa and it's almost too loud sometimes and it's like distracting. So we 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.

45:45Sarah Wang: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.

46:10Sarah Wang: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. AI tools will enable you to be productive in parallel. And then what we're doing is just super cool. It's hard to find maybe these days really exciting, humongous projects and missions that other people aren't doing.

46:43Sarah Wang:And this one happens to be organizing the world's information, making perfect search. That's very exciting and important for the world, but also 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 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.

47:10Will Bryk: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.

47:49Sarah Wang:Yeah. I mean, people might not like this word, but I look for passion. 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 uh because why is that important it's 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 uh because like you can now do anything like you could be whatever you want like I you know um 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 um and then like your judgment and everything uh and so like I look for the fire in the eye because if you have that fire, you could literally do anything.

48:26Sarah Wang:It's actually crazy how meritocratic the world is becoming because of these agentic tools filling the gaps.

48:31Will Bryk:Yeah, absolutely. Well, thank you so much, Will. This was great. Such a fun conversation. Appreciate you and very excited to be partners.

48:38Sarah Wang:Awesome. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. It should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund.

49:20Sarah Wang:Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com forward slash disclosures.