AI is Making Enterprise Search Relevant, with Arvind Jain of Glean

Arvind Jain joins Sarah and Elad on this episode of No Priors. Arvind is the founder and CEO of Glean, an AI-powered enterprise search platform. He previously co-founded Rubrik and spent over a decade as an engineering leader at Google. In this episode, Arvind shares how LLMs are transforming enterprise search, why most tools in the space have failed, and the opportunity to build apps powered by internal knowledge. He discusses how much customization is still needed on top of foundation models, what made building Glean uniquely challenging compared to Arvind’s previous ventures, and what’s next for the company. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jainarvind Show Notes: 0:00 Introduction 0:58 How LLMs are changing search 2:05 Building out Glean’s platform 5:09 Why most search companies failed 8:41 Out of the box vs. bespoke models 10:26 Creating apps on top of internal knowledge 15:34 User behaviors & insights 19:11 Unique challenges of building Glean 21:51 Product-led growth vs. enterprise sales 25:00 Succeeding in traditionally bad markets 27:08 What Glean is excited to build next

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Speaker 00

  Hi listeners, welcome to No Priors. This week, we're speaking to Arvind Jain, CEO and co-founder of Glean. Glean is an AI-powered enterprise search and knowledge management platform, which allows you to not only access all the different internal documents and slacks and other things that your company may have, but also allows you to enhance workplace productivity by using different applications on top of that. Prior to Glean, Arvind had a really storied career. He co-founded Rubrik, he was early at Google, worked on search there, amongst other things. And so, we're very excited to have him here today. Arvind, welcome to No Priors.

• 0:34Speaker 01

  Thank you for having me.

• 0:35Sarah Guo?

  So, I'm really excited about this. I've known you for years and a lot has known you for maybe 15 more years than that. You're an amazing, repeat-successful founder with Rubrik and Glean. I want to start by just asking you about search. You've been a search guy, you know, since before it was cool, for a long time when it felt like not solved, but not as dynamic. How broadly has search changed because of LLMs?

• 1:00Speaker 01

  I've been working on search for almost 30 years now, a long, long time. The paradigm has completely shifted. I think I would say that search had been static for a long time. It was this keyword-based paradigm, like, you know, people ask questions, you find words and try to find them in documents and bring them up, you know, to the users. But LLMs have completely changed it. Like, it has actually, the main thing it has done for search is that it has allowed us to really deeply understand a question that a user is asking. And similarly, it allows us to very deeply understand what a document is about. And you can actually, you know, match people's questions with, you know, the right information conceptually. And that gives us so much, so much more power. It's not brittle anymore. And I think it's been a, it's been a foundational technology

• 1:45Speaker 01

  to really evolve search into these new experiences that you're seeing these days, you know, where you can go far beyond just surfacing a few links, you know, to an end user, to actually deeply understand their questions and answering them for them directly using the knowledge that you have.

• 2:02Speaker 00

  If I remember correctly, Glean got started in the more traditional search world. And then, as these foundation models and these LLMs have come to the fore, you've really kind of shifted how you think about both the capability set that you provide and how you bridge things. Could you tell us a bit more about how you started off building the systems and how that's shifted? And then, how you've kind of mapped new use cases against it? Because you're now effectively like this really interesting platform. That can be used in all sorts of ways inside of an organization around the purpose of information they have. I'd even love to hear the technology transition. Like, how did you think about that? When did it happen?

• 2:31Speaker 01

  Yeah.

• 2:31Speaker 00

  I think you really lived through it in a really meaningful way.

• 2:34Speaker 01

  We know we had good timing, I would say. So like, you know, we started thinking about building clean in late 2018. I started the company early 2019. And so interesting thing is that transformers as a technology had emerged by then. Now, the whole world was not talking about it. But in search teams like at Google, you know, we saw the power of embeddings and how it could fundamentally change search. And so we had that luxury to actually see this in action. So the version, one of our product actually already used transformers for semantic, you know, matching. Like, you know, we didn't have these terms, like, nobody used to call it vector search. You know, we didn't have that. Like, these terms had not been invented yet, or generative AI for that matter.

• 3:17Speaker 01

  And so like internally, we used to call it embedding search. And it was a core technology that we started out with.

• 3:23Speaker 00

  So you were super early to it, actually.

• 3:24Speaker 01

  Yeah. And you know, the models at the time were not like as powerful as today. Like, you know, we started with this BERT model that Google had put in Open Domain, which was trained on all of the, all of the internets, you know, data and knowledge. And we would then take those models and then for every customer of ours, we'd actually build custom embeddings, you know, on their business content. And then that would sort of power the semantic part of the search. But remember, like, you know, search as a technique, there's a lot of focus on embeddings and vector search over the last few years. But that's actually only one part of building a good search system. Because if you think about an enterprise, imagine a company that has been around for a few decades. You know, they have tons and tons of information spread across many, many different systems.

• 4:10Speaker 01

  A lot of that information has become obsolete, you know, now because it was, you know, written like, you know, many years back. And so when you build a search product, it's not just enough to say that, hey, I want to understand a people, you know, somebody's question. And I'm going to match it with the right information, sort of semantically or conceptually matches what the user is asking. Well, you've got to solve for other problems too. You've got to actually pick information that's correct today, that is up to date. That has some authority, like, you know, somebody with an expert on this topic has actually written that document. So you have to do all of those other things too, to actually truly sort of, you know, pick the right knowledge and bring it back to people. So, so we started with, you know, building the product in, in that shape and form.

• 4:51Speaker 01

  It was a very different product, actually, like nobody had actually searched enterprise search as a problem before. In fact, like, you know, the interesting thing that I remember is that, you know, even though I was coming off of a successful company, like, you know, with Rubrik, you know, we had good success. I don't think people really wanted to invest in enterprise search or me, you know, for that matter, because, you know, this problem was not exciting.

• 5:09Speaker 00

  It was traditionally a very bad problem, right? So there's all these search engines, Fast, I remember when the early Google days was sort of an enterprise search engine, I think, based in Norway, like, there's lots of attempts at this.

• 5:18Speaker 01

  Lot of attempts and no successes.

• 5:20Speaker 00

  Why do you think it didn't work? Because it, it felt like an awful market.

• 5:23Speaker 01

  It was like a graveyard, like, you know, of all these companies that tried to solve the problem and it didn't. Part of it was just that I think search is a hard problem. When an enterprise, like even getting access to all the data that you want to search, it was such a big problem. In the pre-SaaS world, the, there was no way to sort of go into those data centers, figure out where the servers were, where the storage systems were. Try to connect with information in them. It was a big, it was a big challenge. So SaaS actually solved that issue. So like search products, like most of them, most of the companies started in the pre-SaaS world, they failed. Because you could just couldn't build a turnkey product, but SaaS actually allowed you to, to actually build something, you know, which is my insight. Was that like, look, you know, the enterprise world has changed. We have these SaaS systems now.

• 6:07Speaker 01

  And SaaS systems don't have versions, like everybody, all customers have the same version. You know, they're, they're open, they're interoperable, you can actually hit them with APIs and get all the content. I felt that the biggest problem was actually solved, which was that I could actually easily go and bring all the enterprise information and data in one place. And build this unified search system on top. So that was actually a big unlock.

• 6:30Speaker 00

  So is the rise of these connectors and APIs internally. So you're using Google Docs instead of. Older school systems or using Slack or using these new tools that now provide you access to the data on your line of content.

• 6:41Sarah Guo?

  You guys must remember Google Search Appliance.

• 6:44Speaker 01

  Yeah.

• 6:44Sarah Guo?

  The idea of like, I need to slurp your data continuously into a hardware appliance in order to actually do search is ludicrous.

• 6:52Speaker 01

  It was a challenge. The, you know, search as a, and by the way, the origins of Glean is, so at Rubrik, you know, we had this problem, like, you know, we grew fast, we had a lot of information across 300 different SaaS systems and nobody could find anything in the company. And people were complaining about it in our poll surveys and I, and I was, you know, I always run ID in my startups and so there's a complaint that, you know, it came to me, like I had to solve it. So I tried to buy a search product and I realized there's nothing to buy. I mean, that's, that's really the origins of how, how Glean got started as a company. And so that was like, you know, one big issue, like, you know, the search SaaS made it easy for, to actually connect, you know, your enterprise data and knowledge to a search system. So that actually made it possible for us to, for the very first time, build a turnkey product. But there are a lot of other advances as well.

• 7:37Speaker 01

  You know, one is, you know, like, look, you know, businesses have so much information and data. One interesting, you know, facts are, one of our largest customers, they have more than one billion documents inside their company. Now, here's this, you know, when Elad and I, you know, when we were working on search at Google, you know, in 2004, the entire internet was actually one billion documents. You know, there's a massive explosion of content like inside businesses. So you have to build scalable systems and you couldn't build like a system like that before in the pre-cloud era. I would spend all my time just trying to build that scalable distributed system, which, you know, we don't have to anymore because of thanks to, you know, all the big cloud technology. And then of course transformers, like, you know, that's really the real, the big unlock, you know, that we had was that we could actually understand enterprise information more deeply

• 8:20Speaker 01

  and was very necessary in the enterprise compared to on the web. On the web, even if you don't have good semantic understanding, there is so much that you can learn from people's behavior because, you know, you have a billion people, you know, coming and using your product. In the enterprise, you don't have that luxury. So you have to sort of like, you know, make, make up for that, you know, lack of signals from users, you know, with other techniques and transformers one of them.

• 8:42Sarah Guo?

  It sounds like you feel a combination of, I'd call it like more traditional IR and search techniques and embeddings is relevant. Do you think that persists? Like where would you want bespoke infra or, you know, signals like freshness and authority? Or like, how much do models just do in the end?

• 8:58Speaker 01

  Yeah, I mean, I think there's always this thought of that, like, you know, the models will have near infinite context windows and you can just give them everything and they can figure things out automatically. But I don't, I don't think, you know, like they're anywhere close to, you know, that happening. I'll, I'll give you an example. Let's say that models are mimicking human intelligence, right? So they're actually getting more and more capable of like, you know, how we work like humans. But as a human, like, you know, imagine like, you know, if I were to actually give you, like, let's say I give you a question and then I say that, well, here's, you know, here's everything, like, you know, in a completely non-organized fashion, I give you like a whole bunch of like 1 million documents and, and let's imagine you have, you know, the memory powers and speed, but it still just feels like, you know, a very complicated

• 9:42Speaker 01

  thing, like it's very hard to make sense of information that is, for example, being given to you out of order. Like, can I give you one document that is something from today, something from four months back, something from three years, then something again from two days back? If I give you like, you know, information in a, in a, in a manner where, you know, where it's sort of not organized in any shape or form, then as a human, you're going to have a lot of difficulty reasoning over it. So do we think about the models the same way? There is a good amount of work that you have to do and present the information to the model in some, you know, in some organized fashion. That's when they're going to actually do a much better job reading that information, reasoning over it and giving the answers. And sure, like, you know, you can actually give them more and more over time, but still,

• 10:25Speaker 01

  you know, it matters, like, you know, how, how you provide them with the right information.

• 10:28Speaker 00

  Now that you have this sort of corpus of information, right? You basically aggregated all the internal documents of a company, which in itself is incredibly useful just for search. But you've also got down the route of like enabling applications to be built on top of it in different ways. Could you talk a bit about that and what are some of the common use cases that you're seeing?

• 10:43Speaker 01

  So we started with, you know, this vision of building a Google in your work life. But then as models got better, developed like these reasoning and generation capabilities. So first, like, you know, it changed our product and like our new product, Green Assistant, you know, it sort of looks and feels more like ChatGPT. So instead of like, you know, me going asking questions and seeing a bunch of links going back to me. You know, now of course you converse with Green, you ask questions and it works just like ChatGPT. You come and ask a question is going to actually take all of the world's knowledge. And also additionally, you know, it's going to take all of your internal company's, you know, data and knowledge. And use that in a safe and secure manner, like knowing who you are and what information you can really use within the company to answer questions back for you.

• 11:28Speaker 01

  So that's sort of like the first first progression in terms of our product, like we evolved from being a Google to to, you know, something that looks more like ChatGPT, a more powerful version of ChatGPT inside your company. As you build that, this Green Assistant, actually, you can think of it more like a personal assistant that you're actually giving to every employee in your company. It's a tool, you know, it's your sidekick and it's always available to help you with whatever questions or tasks you have. It's going to use all of your company's context and data to help you with, you know, with your work. But, you know, businesses are actually a lot more, but more interested in not in that, but in actually thinking about how they can transform their company with AI.

• 12:10Speaker 01

  Or they can take specific business processes, you know, where they're spending a lot of money. And how do they bring automation in that with AI? So we've been, we've been asked like before agents became, you know, the talk of the like, you know, of the day and like, you know, everybody is a force building agents. But early last year, when agents had not yet taken off, people were asking us for that, hey, you know, we need, we need to build more curated applications, you know, using this data platform that you have. So as an example, HR teams, you know, would come to us and say that, look, we love Green Assistant, people come in there, ask questions. About, you know, benefits and, you know, PTO and vacation policy and whatnot.

• 12:51Speaker 01

  And and it works, it works great, but sometimes it uses, you know, content that's not authorized or blessed by us. And if somebody's coming and asking questions on people related topics, we want Glean to only use, you know, the curated content that, you know, our people team has created. Uh, and we wanted to behave in a particular way, with a particular tone and all of that. So so that's a, that was a request that we started to get last year that like, you know, can we create create more specific curated experiences, you know, function by function for, you know, for different use cases. So we started to build that and we're not calling them agents, we're calling them apps. Uh, now of course, like, you know, the people think of, you know, them as more as agents because it's no longer just, you know, asking questions and getting answers, but you want, you know, these these specific functional experiences to

• 13:36Speaker 01

  to actually replace the business process, which also involves doing some work for, um, you know, not just answering questions, but actually, you know, doing some work in those systems.

• 13:44Sarah Guo?

  Arvind, when you talked about, you know, access to the the right data with the right authority and also like, it really begs the question of like access control. Right? In a a platform like Glean, when you have all of this unstructured data, um, uh, this seems much more complicated. What is like your overall stance or how how you think this is going to work in the future?

• 14:05Speaker 01

  Yeah. Well, so look, enterprise information, in some sense, you know, it's governed and it's protected. You like most of the knowledge, actually, you say like ninety percent of the knowledge inside the company is private in some shape or form inside, then they're in your company. Like you'll have a document that maybe is private to you or or you share with a few other people. But that's the nature of, uh, you know, enterprise knowledge, uh, that's that's the fundamental sort of, uh, way like it works. And you can't take, you can't actually build, like, for example, a model inside your enterprise and dump all of your internal company's data and knowledge into it and then make that model available to everybody in the company. Because if you do that, you're leaking information, like, you know, inside your company, you're letting somebody in the engineering team,

• 14:47Speaker 01

  you know, see sensitive stuff, you know, which probably only HR team should be able to see as an example. So any AI experiences that you build inside the company has, it has to. Think you know about security and governance and permissions like you know at a fundamental level. And that's what we do in Glean. So when we connect with all these different systems. Um, you know, inside your enterprise, we, you know, if we index, you know, a particular document from Google Drive or a conversation from Slack. We also keep track of, you know, who are the users can actually access that information. And this is fundamental, like any access to data that's going to happen through our platform is going to actually match, like, you know, the users have to be signed in. And we will actually only let them use information that they have permissions for.

• 15:28Speaker 01

  And and this is, this is important uh as a problem to solve, like, you know, unless if you have infrastructure like that, you cannot overload AI safely inside your device.

• 15:36Sarah Guo?

  I learn a lot from people who work on search, especially like search with any sort of scale, because you get all sorts of weird user behavior. Um, and so related to your idea of, you know, us with our personal assistant team. What are some behaviors you see from end users in terms of how they're using Glean or AI in general that you think we should just do more of? Right? Like, I, you know, I I'm always very surprised when I learn from Google people about just like the behaviors around navigational search and how many are one word queries or what the popular queries are and those sorts of patterns. And so I'm sure you you see like Glean and AI super users.

• 16:10Speaker 01

  One of the biggest surprises for me. Um, I always felt that, you know, we're building such an intuitive product, you know, the it's like it's little, there's no UI, you know, there's one box and you ask question, you put in the search and and and what's the big deal? Like, why do you have to learn how to use this? Um, and and we realized that as we added more and more of these natural language capabilities and you know, the ability for you to actually ask a really long question, like, you know, paragraph long, you know, uh, set of instructions that are given to us. Um, and we realized that people won't do it. Like, you know, I think everybody has been trained over the last twenty years

• 16:48Speaker 01

  to actually type in, you know, one or two keywords, like Google has sort of taught us, you know, you know, on what search can do. So with search, we never had a problem, like, you know, launch a product. They're like immediate high usage. Uh, nobody was confused like how to use the product. With with assistant, people didn't know what to do with it. Some people with, you know. More curiosity and they will they will ask all kinds of, you know, questions that we couldn't actually answer. Um. For example, somebody says, hey, what should I do in my life? So we, so I think, but anyways, coming back to this, that that was one of the key learnings is that. AI is is actually very unintuitive. For most people, you have to actually really ex- expose to them these capabilities in a in a sort of a.

• 17:30Speaker 01

  Incremental fashion, you know, like some things, you know, which sort of are more meaningful to their day to day work. For example, if I'm an engineer, um, like, you know, prompt the user sometimes that like, look, you could actually learn about a new piece of technology. Like I can actually give, you know, create a two page tutorial for you right now. Um, and and you sort of have to understand, like, you know, what people's, you know, people, like, you know, what their core work is. And and then you have to actually give them these, you know, sort of prompts, like prompts for them to sort of. Start experimenting and get excited about like trying something out with AI. One thing in fact, which I would also add here is. Make a lot of time, you know, with AI, businesses are excited, like, you know, they have a lot of dollars to spend on AI. And, uh, but they're all like also asking for ROI.

• 18:14Speaker 01

  Um, that well, like, you know, to make all this, you know, investment, what are the returns? Like, what are the efficiency gains, you know, that I'm I'm going to be getting or what are the top line? You know, rumors that I can make to my business. Uh, there's a lot of focus on that. And any one thing that often gets overlooked is education. Cause you know, the world is changing. Imagine like, you know, three years from now, you wake up, you're the CEO of a large enterprise. What are you going to see in your workforce? You you actually want to see people like who are trained and are AI first. Like, you know, they're experts, they know how to leverage the strengths of AI. Cause this is a difficult technology, like, you know, it's not perfect, it's not easy. Uh, it makes mistakes, it hallucinates, but yet it's powerful. And if you become an expert, um, you can get a lot done with it.

• 18:58Speaker 01

  That has to be like the objective today is like, you know, with like as leaders think about AI. How do you sort of build people tools, uh, that?

• 19:08Speaker 01

  Sort of motivate and motivate them to bring AI in their day to day work.

• 19:13Speaker 00

  You had amazing career between being early at Google, starting Rubrik, now starting Glean and running it. What was unexpected about doing Glean? Cause you'd gotten to so much scale, you'd done such amazing things in the context of Rubrik. What was hard or unexpected or just very different about Glean that you didn't anticipate?

• 19:31Speaker 01

  From a product side, the, one of the most interesting things for me was like, how hard was it to actually roll the product out to our customers? We had a very different journey, like in, you know, Rubik compared to Glean. Again, Rubik were an established market, like there were budgets, there were dollars. And you had to actually replace an old technology with a new technology. Here, we were in a market where we had no budgets. There was no concept of buying a search product in the enterprise. And, and everybody thought that, yeah, like this is an important problem, but I'd like, you know, it's not a line item in my business priorities. You know, it's, it's a, it's a vitamin, it's a painkiller, people are living without it. Well, yeah, that's, that's true.

• 20:12Speaker 01

  I mean, you live without something you don't have, like, you know, that's by definition, you know, you know, true. So we, so we had a lot of challenges, like we had to do a lot of evangelism to actually get the right, like, you know, folks like, you know, who wanted to be the innovators, like for them to actually make that bold call and, and actually buy a product that's, you know, they're not used to buying. So that's, that's sort of first part of it, like, you know, you have to create the market for this, which actually was difficult. And second, which was actually very interesting one is, you know, we, we, our product was actually working well, like, you know, it was, it was doing good search or they're, you know, letting people find things. But then we started to hear from businesses that, oh, I'm scared of good search. And what, I don't want a good search product in my company because I have all these governance gaps.

• 20:56Speaker 01

  I have like, you know, sensitive information all over the place and. You know, now people are discovering these things. So we launched like, you know, for example, you know, people found like salaries of other people, you know, there was like in one of our customers, somebody found a sensitive M and A dog that was, you know, or something that was, you know, not yet happened. Uh, and you start like, so people like you actually were very, very scared of actually having good search. So we had to actually like, that was, that was interesting challenge. We did some good work. We're doing it safely and securely. But you know, you don't have good governance. And now like, you know, we don't, we can't sell because the product is so good.

• 21:30Speaker 00

  It seems like LLM should be able to help with that, right? In terms of classifying documents and surfacing, hey, this one may be sensitive. Do you want to secure it, et cetera?

• 21:37Speaker 01

  Yeah. So in fact, that is exactly right. Like, you know, so we actually were forced to build that. We were forced to actually go and above and beyond respecting permissions in individual systems to knowing who you are, what you're asking, like, you should have the right to even ask the question or like, you know, when the information comes back, like, does it, you know, feel, you know, safe enough for us to show it to you? So we actually, in fact, like, you know, in that sense, you know, we actually ended up, ended up becoming a security product. A lot of companies actually buy us to fix governance in their sort of, you know, data and systems and become AI ready, like AI ready for the clean search product, uh, the clean assistant, but also for all the other AI products that you can buy inside the enterprise. So that was, so that was actually a very interesting journey. But then personally, you know, for me, um, you know, at,

• 22:21Speaker 01

  at Rubrik, you know, I, I didn't actually, I was in this, I was the CEO, uh, I ran R and D as one of the founders of the company. And here I'd actually learn how to become a CEO. And I don't think I've learned it yet. And like, you know, that's it. That's a constant, you know, challenge and like, you know, learning that I go through because fundamentally, like, you know, I'm still an engineer, everything I do, like, you know, like, that's the mindset that I have. So, so growing, growing, you know, out of that into like, you know, being able to run a large business, you know, that's a, that's a personal transformation that I'm going through.

• 22:52Speaker 00

  One thing that I think is striking is that, um, from a good emerging perspective, you all have really focused on big enterprises, right? And you mentioned some of these enterprise data needs. A lot of people always just want to do PLG. And you've really done sort of the top down sale. It's been incredibly successful. You've done it twice now, right? Because Rubrik was always doing that as well. Could you talk a little bit more about when it makes sense to do? Big direct enterprise deals versus PLG motion? And how you think about that as you build businesses? Because I think it's very differentiated and most people just can't pull that off. So I'm curious about how you think about when to do it and then how to do it.

• 23:22Speaker 01

  Just to be candid, clean, the, when we started, my dream was to do PLG. I'm an engineer and I wanted the company to have engineers and then products should sell itself, you know, on the web. Who doesn't want that? It was, it was something that, you know, was a desire for us. But the problem is like, you know, with our product, the, it is by definition, a, a company-wide product. Like it's not like, you know, we cannot offer the product to one individual inside a company. Even one person, you know, their, their search needs require us to actually search over all the entire company's information for them. So it's, it's, it's expensive. You have to actually index, you know, all of a company's data and knowledge. And so we never had that concept of that, you know, we could make it available to one or two or 10 people inside the company.

• 24:04Speaker 01

  So we're sort of forced just structurally to actually build in that fashion where it is, it is, you know, like enterprise. It is, um, like, you know, we roll the product out company-wide, you know, every employee. Um, that, that's what makes it cost-effective. But like, you know, coming back to your question, the standard approach, I think that companies prefer now is that like they think of PLG as basically lead gen as a funnel. You sort of nurture and expand using, you know, enterprise sales motion. So the right recipe for me, like, you know, if I had a choice, I would, I would actually start both the motions simultaneously. Like I would actually say that, look, you know, for the first three years, I'm, I would actually focus, you know, uh, on just being PLG and then bring enterprise sales later. Because you're actually leaving a lot, you're leaving a lot on the table. Uh, timing, timing matters always.

• 24:48Speaker 01

  And, and so you have to sort of like start the, start, start the motions at the same time.

• 24:53Sarah Guo?

  Arvind, one thing that we have talked about that I feel like must have been, um, I mean, hard. The priors on this market were not great, right? And we talked a little bit through the rationale of like, you know, you feeling like you, you really saw the problem internally anyway. And understand that there are these, uh, sort of architectural foundational things that had changed in terms of movement to SaaS and API-based integrations and such. But still, I, I think it's a really good question of advice for founders or maybe people joining startups. Like, when should you agree with the priors on like something is a bad market? Or how should you think about that question?

• 25:30Speaker 01

  So I'll share a few things on this. Number one, I think as engineers, like, you know, there are, first of all, there are always doubts. Like, you know, the more you look at priors, the more you're going to actually likely you're going to actually ultimately kill your own idea. There is a lot, like, you know, sometimes.

• 25:45Speaker 00

  Everything's been tried, yeah.

• 25:47Speaker 01

  Yeah, everything's been tried, like, so, you know, a lot of things have failed and, and I think there are, like, for any given idea, like, you know, there are ten reasons why it won't work. Um, like, as you start to go into details, sometimes, like, a more simpler approach is helpful, you know, which is, uh, well, there's a problem, like, you know, you talk to people, they have and they feel this pain, and which clearly means that nobody is actually yet solving, you know, that because the pain exists. And so they don't want to do details anymore, just do it, things will just get figured out over time. So, like, at least, you know, for me, like, it was actually unusual for me, like, I'm, I'm engineered by training myself and I'm, I'm, like, you know, I'm naturally trained to question,

• 26:25Speaker 01

  and, like, there's a lot of self-doubt in my mind, so I don't know what happened to me when we started Lean, because, you know, there were all, all these people saying, like, no, not do it, and, and somehow they couldn't, like, you know, they couldn't actually discourage me. Like, you know, I just felt that this was an exciting problem, um, like, you know, I was, I was, I knew everybody in the world, you know, has this issue, like, you know, even at Google, like, it was a big joke, you know, always we had internally, like, you know, all of us were spending all of our time making it easy for people to find things, but not us internally at Google, uh, it's super hard to find anything inside the company. So, so I think I, I somehow, like, found that conviction. I was sort of being lazy, not willing to go into the details and, like, look at all those priors and just, like, just, just do it, just solve it.

• 27:07Speaker 01

  I mean, I think that's, that's what I think, like, worked for us in this particular case.

• 27:11Speaker 00

  I feel like Lean had, like, three big components to it that all came together that you mentioned earlier, right? There was the need that you identified, just as, uh, somebody running IT for your own company, and to your point, it goes back to Google that this was a need, and every company that I've talked to has always wanted to build search and directories and all this stuff. The second thing is this rise of connectors and APIs in the context of existing enterprise software that everybody's using, so you can extract the data more easily. And the third thing was the big shift in terms of the underlying technology, right? The shift in terms of what is capable of search, these foundation models, embeddings, et cetera. Given, um, the latter two. Are there other big opportunities that Glean isn't going to work on that you've kind of identified as really interesting areas that suddenly are tractable again?

• 27:52Speaker 01

  I think for us right now, the focus remains on the two core products that we have. So we, you know, our way we think about our company is that we have this really powerful end user AI, you know, assistant that helps every person like, you know, work differently in the future. And then we have this agent platform that you can use to actually bring, you know, AI, inject AI into, you know, every one of your business process, make them better, make them more efficient. And, and I think we have been making big promises on both to our customers. The way I describe and pitch our product to our customers is the following, you know, come to Glean, ask any questions or give it any task, Glean will use all of the world's knowledge and all of your internal company's data and knowledge

• 28:36Speaker 01

  in a safe and secure way and answer those questions for you or complete those tasks. But actually, I just promised to you that Glean does everything. You don't have to work anymore. We're long, long ways from actually even solving, you know, that, you know, the pitch that I just mentioned to you, like, you know, I think we have to understand knowledge properly, we have to like, you know, pick, you know, the right, you know, the correct information, throw away the old information. We have, you know, there's so many challenges there, there's so many issues. People talk about hallucinations as a big problem with AI models, you know, we feel like, you know, a bigger problem for us is not even hallucinations. It's about like, you know, most of the times you can't even, you know, find the right information. Sometimes it's not there. People are asking questions, but nobody wrote it down. Sometimes, you know, we are not able to actually do the needle in the haystack, you know, we picked the wrong thing.

• 29:20Speaker 01

  And so there are like a lot of challenges and I think we, we will be working on this problem for a long, long time. And I don't see us having any need, by the way, like, you know, wanting to do something different, like, you know, like just solving this one problem itself is a big, is a big, big success. So, so we're going to stay focused on these two, you know, these two products. But then there are also like, you know, talk to, talk to you about a little bit about the vision for the future. So I think the way we all work is sort of well accepted that AI is going to change everything. AI is going to change how people work. AI are going to actually change how businesses actually even look and feel, you know, what kind of, you know, workforce you have in the future. And one thing that's going to fundamentally happen is that each one of us is going to have

• 30:02Speaker 01

  this amazing team of, you know, call it assistants, coworkers, coaches that are truly personal to you. And you know, you're always surrounded by that team. And this team knows everything about you, your work life, what you need to do today. And this proactively helps you, does ninety percent of your work for you, and also like, you know, help you get better, like, you know, at your, at your, you know, like, upskill you, be a coach. And this, and that's the world that we want to be living in. Like today, you know, there are some people who have, who already live in that world. Like, you know, for example, being a CEO, you get the luxury to actually have all of that. You have assistants, you have a staff, you have an exec team, you have a coach. But in the future, that's going to be something that all of us are going to have. Like, you know, regardless of how

• 30:46Speaker 01

  senior we are, you know, maybe a new grad joining the work, joining the workforce. That's what we are trying to actually go and solve for. We're trying to actually build that amazing person team around every individual. That's going to make us all a ten X R. And that's just a natural extension of, like, just keep evolving our green assistant product, make it better and better over time. Yeah, Arvind, thanks so much for joining us today. Yeah, that's excellent. Yeah. Fun, fun questions. It's always nice to see you. Yeah, likewise.

• 31:15Sarah Guo?

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