How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work

Majority of the work that we do today is not going to be done by us anymore in five years from now. And that applies to me, that applies to you. Like, you know, we both do very different things, but still, like, I think, you know, we are knowledge workers and I think a lot of our work is actually going to be done by these amazing AI assistants that are actually in many ways, you know, more powerful than us. Like, you know, the they, they have access to all of our company's data, our knowledge, they have all the context from all the past conversations and meetings. They don't forget anything and they can really sort of, and they have the, you know, on top of that, you know, they have the reasoning capabilities, you know, that allow them to be super helpful to you in like any task that you do. So that's sort of, you know, our core belief, like, you know, that majority of our work is actually going to be done by these AI companions or assistants. And we want Glean to be, you know, that, you know, that, that assistant, you know, in the workplace.

Joining us today is Arvind Jain, co founder and CEO of Glean. Earlier in his career, Arvind was instrumental in building Google Search and co founder and CTO of Rubrik. Glean began life as an enterprise search company and today has evolved into a general purpose work assistant. Bringing AI into an enterprise context is notoriously difficult because of the integrations, the permissions, the ranking, the parsing, all the other magic that needs to happen to make AI work on your company data. Arvind joins us today to share how Glean is solving this problem where other companies have failed and what he's learned as one of the first successful AI native application companies. Arvind, thank you so much for joining us. We have a lot of questions about Rag and agents and knowledge graphs and all of that. But before we do that, can you give us one or two minutes on what is Glean and what are you building?

Yeah. First of all, thank you for having me. Glean, think of it as the Google or ChatGPT, but inside your enterprise. It's a place where your employees go and ask questions and Glean answers all of those questions. Using your company knowledge, regardless of where that knowledge is, brings it all back to you. So that's what Glean does. Glean is also an AI platform. So if you want to actually build AI applications inside your company, you can use the Glean Rack platform to build those applications quickly.

Wonderful. And since you make the analogy of a Google for work, Google for Work I think is Something that every CIO has described as their holy grail. And we have two decades of failed attempts at building it. You were actually a star search engineer at Google before and even Google never managed to crack this category before. Maybe can you just say a word on why is this such a hard problem and how did you do it?

Well, I mean search is hard because it's actually magic in some sense. Like you can come and ask any question that you have and you expect the system to actually give you back the right answer. So expectations are always high and it's a difficult problem, especially in the enterprises because there's so much information inside the enterprises spread across so many different systems. It's both hard to actually even get hold of that information, but then even harder to actually make sense of what information is actually good, what has become out of date. There is lots and lots of challenges around building that, you know, system. And, and in the past, like, you know, I would say, I would say that like, you know, there were no good attempts made because you know, the problem was so like it was so hard, it requires so much R and D, so much investment. Like, you know, it was not really startup friendly in many ways. And, and you also like couldn't even, you know, in the pre SaaS world you couldn't even build a product because you know, just getting, connecting with all of your enterprise data meant that, that you have to spend like a year sitting with an enterprise trying to actually bring the data into your search system and then actually solve the real problem, which is make that information searchable.

Arvind, one of the things that I think is so interesting about Glean is you are probably one of the first and best examples of what an enterprise AI application company can or should look like. And we're going to focus most of this conversation on the AI aspects of Glean. However, I know there are a lot of layers to your stack. You've got the infrastructure, you've got the connectors, you've got the governance engine, you've got the knowledge graph. Can you say a couple words about all the stuff you had to build before you even got to the AI part to make the AI work?

Absolutely. So as you said, like, you know, search starts first with the data and the knowledge that you need to actually make searchable. So the first part of the Glean tech stack is these deep integrations that we built with most common enterprise systems. So think of systems like Salesforce or Confluence, Jira, Google Drive, SharePoint, ServiceNow. Like your enterprise data typically lives in all of these different systems and you have to bring it all together in one place. So that's the first part of our technology stack is these integrations. But then if you think about like enterprise data, and this is one of the most unique things about enterprise search versus, versus, if you think about Google search on the web is most of our enterprise information is actually private in nature. When you author a document in Google Drive, this document may actually be private to you or you may share it with a few other people. And so you can't build a search engine where you just dump all the company knowledge and make it accessible to everyone. You have to actually understand permissions of each content. So when you go and search, the system should understand who you are and only retrieve information that you actually have access to. So that's our governance layer. Understanding governance across all of these hundreds of different systems, which is quite complicated. And then the third part, and this is where really most products have failed in the past is search is not about just putting like a whole bunch of documents in an index. And then like, you know, when somebody comes and asks a question, take those words or take that question and just match it up semantically or with keywords with the right content. You got to actually also understand who's the person who's asking a question. I can come in and ask for an onboarding guide because I'm a new employee, but then which onboarding guide should be actually given to me. It depends on whether I'm in the marketing team or I'm in the engineering team. So sort of understanding people and understanding knowledge and relationships between them, you know, that's a big part of actually making a search or a question answering service work inside an enterprise. So we do that. So we actually build a deep knowledge graph where we look at all the employees, understand what roles do they play in the company, look at all the documents, and then we sort of try to understand what documents are meant for what departments, what documents actually are popular. Is there like what are the relationships between a particular individual and a particular document? And that is what we used as a core foundation that sort of then governs like when somebody comes and asks a question, what are the most relevant pieces of knowledge for them? So we had to do all of that work. And actually like, interestingly you mentioned like what came before AI even became relevant. You know, for us, the AI was actually part of the core search technology from day one. We were actually working with LLMs in 2019 or I mean, at least language models like, you know, these were, you Know, bird based language models that you could use. And so when glean MLMs. That's right. Is that the name for them now? But yeah, you know, in the search like, you know, engineering sort of like community like, you know, we just called them language models at the time. And so the language models were actually part of the core search experience, you know, from day one because it really allowed us to understand content at a semantic level. So that we already baked in our core search experience on day one when we were actually trying to look at a question from a user. We were never limited by the actual exact keywords that users use. We were able to actually understand the meaning behind the question and actually match it up with the right documents. But still that's all the work that you have to do before you can actually even do anything with LLMs.

Can you say a word about rankings? I think part of what makes Google work so well is I always get the answer I want at the top of the page. In the case of the public Internet, you have so much web data and links and all that to make the rankings really work. To what extent is that part of the magic for Glean and how do you guys do it?

Yeah, so that's of course the core of the product is all the effort that we put in to actually build a really good ranking system for our search. I'll give you some examples of the kind of things that go into determining what documents are the best ones to rank for a given question. So of course, if you imagine that there's a document that people in the company are constantly looking at, so that gives you obviously a signal that there must be something about it, it's actually important, people like to spend time on it. If there's a document that was actually written in the last one or two weeks and there's some engagement around it, you know that this again is information that people care about, it's not actually become obsolete yet. Then if you think about a particular document that we see is not popular. When you think look at a company level, but, but you look at one individual team inside the company, we see there's heavy usage for that document inside that particular team. So that sort of tells us more about that, hey, this document may actually be relevant for this particular set of people. Or the last thing, the last example, imagine that somebody had a question and they're not bothering to a search. They go in Slack and ask a question and then somebody else posts a link to a document as a response to that. And the person who asked the Question gave a thumbs up to it. Just imagine this interaction, what it means, it actually means that particular document was actually a really good answer for that question that the user had asked. So if you keep that association in mind, it's going to help you later when somebody else came and asked a similar question. So those are some of the signals you have to constantly look for. All these signals, they have to collect them differently in the enterprise setting than on the web. Google only has to look at all the activity that's happening right on Google itself because that's the gateway to any sort of knowledge quest. But if you look at in the enterprise, not all the things are happening through the search paradigm. So you have to go and look at all the activity around all the knowledge in different systems like your communication systems, your document systems, and just try to learn from that human behavior because ultimately that's how you learn. You learn from what people are doing inside the company. The more you can actually collect that information, the better your ranking systems are going to be.

Can we spend a minute on rag? As Pat mentioned, you were kind of in the right place at the right time. You had put together all the hard stuff so that when the LLMs got really good, you kind of had all the infrastructure in place. And I think you've been one of the experts in using RAG to make these LLMs actually useful on your corporate content. Can you explain RAG to me like I'm 5 years old and like what are the secrets to making it work? What are the things that people don't talk about? What are examples of things that you can do thanks to rag, that you can't in a generic chat interface?

Yeah, well, so first of all, I think since you were talking about a five year old, let's first talk about.

What ten year old? Ten year old.

So yeah, let's keep it five.

I need five.

Let's start with five. Let's start with five. Yeah. So I mean like if you think about all these amazing models, you know, in GPT and Gemini and Claude, these models are all trained on the world's public knowledge and data. And so if you were to actually go in ChatGPT and ask a question like, hey, how many days do I get off with my PTO policy? It has no idea, it can't answer that question because that's my company's private knowledge, the answer is somewhere there and the model is not trained on it. So how do you bring your private enterprise data to these models so that you can actually have AI create that magic for you that's what RAG based AI application architecture allows you to do. So the way it works is that you come and ask a question and you have a search engine or a retrieval engine, whatever you want to call it. And given the question, this retrieval engine finds potentially relevant documents that could actually answer your question. And then you're going to take those documents or those sort of content fragments and make the model work on it. You'll tell the model like GPT that hey, I have this question and I have this company knowledge that I think is relevant in terms of answering that question. Now you answer that question using this knowledge. So this is how like most AI applications today are being built in the enterprise. The only way to actually connect your private enterprise data to the power of these language models is basically a search engine that's sort of sitting, you know, you know, in the middle. So we like our given, you know, that you know, at glean also like you know, we of course built a search engine or all of your enterprise content, you know, in the last five years it actually allowed us to actually be, you know, become like, you know, one of the best RAG systems, you know, that allows you to now not only like, of course, you know, we deliver our own end user application which is a glean assistant using this RAG based application architecture, but we're also allowing companies to actually build more and more applications using rag. Now I think in terms of while this is the architecture that is emerging as a canonical architecture for building AI applications, I think it is still full of challenges. It's actually really hard to build great AI applications using RAG because one of the things, you know that how models themselves are sort of while very powerful, they're also still an emerging technology. Models hallucinate, they make things up. And what you're doing now is you're actually adding one more complex layer of technology in this application architecture. So think of it like you're chaining two things which are both not perfect. So oftentimes you will see a RAG based AI application not perform well because you asked a question and the failure actually happens at the racks, at the retrieval stage where you didn't even actually were able to find the right pieces of knowledge or maybe you found stale information that then you're actually giving to the LLM to work on. And then of course it's going to give you bad results. While it's the only way to bring knowledge together, it creates these interesting challenges for you as well.

Let me see a question and just to paraphrase a bit, what you Were saying at the start of this conversation, Act 1, enterprise search, Act 2, application platform for that, Act 1, which is enterprise search. How do the concepts of enterprise search and RAG relate? Is one a superset or a subset of the other? Are they similar but distinct? Are they the same thing? How does Enterprise search in rag? How do those concepts relate?

So I think like, you know, I think of search and RAG as being in some sense, you know, they're one and the same thing. The real core technology is taking all of your knowledge, enterprise knowledge and putting them into this search system where now you can actually ask questions and the system is able to actually give you relevant pieces of information back. So that's sort of the core technology. Now you can actually use this technology either as a standalone product. So that's what the Glean Search product is, for example, where people come in, they ask questions and we can give them the relevant documents that potentially are useful to them based on that question. Or you could actually use this as an API layer in your overall AI application. So where you know, the, the search system, the search module now is only one component of your overall, you know, AI application architecture. And the. So, so, so I think in that sense, you know, it's sort of similar but, but the industry on the other hand, like, I think like what we've seen, you know, like most of these RAG based applications in the enterprise today, they actually use a much more simpler version of a retrieval system in their RAG application, typically like a vector search based system which doesn't really have full enterprise context. I would say that's the key difference. So for us, our approach always has been that really think about how to build it a standalone search system, something that is as good that you can actually put them in front of the users as a standalone product. That's really the real test for how good the search is. And that then actually when you put it behind the scenes in a RAG based application, it's actually going to actually create better AI experiences.

So is it fair to say that the magic that you've done in terms of getting a good ranking of search results that is exactly like you've made that ranking good for people. It turns out making that ranking good for people is also what you need to make the ranking good for machines in order to get the best possible results. And that's why what you've built is very different from somebody that's just diying a data pipeline and their own little retrieval system.

Yeah, that's correct. I think it's really hard to build These systems yourself and build them in matter of weeks. I think, I think you can build a great AI demo in one day, maybe in two hours now. But I think to actually build where it's sort of, it's robust, it's stable, it actually adds value to your, within your enterprise, it's a hard problem.

So we've talked a little bit about how you've built what you've built and we know that it's working. We know that the company is quadrupling year over year and we use it here internally. And there are a lot of happy people out there who are customers of yours. The real measure of success in some ways is how your product is changing the lives of its customers. And so I'm curious to hear from you. When you look at your customers and sort of how they operate day to day pre Glean versus post Glean, what are some of the changes you notice? How does this help people do their jobs?

So Glean is actually a product that is used quite heavily by people. The, like, there's, there's like, you know, many, many different types of things. You know, like we're often surprised by what, what people are using Glean for, but I would give you a few examples. So for engineering teams, you know, they find Glean super useful like in terms of like troubleshooting whenever you run into any kind of a roadblock or an issue, like sometimes an error, like, you know, your, you know, programs are not working properly. And so Glean serves as a really good troubleshooting tool for them. Like, you know, it's a place where you go and debug because, you know, you post the issue more, more often than not. You're, you're not the first one like who's going to experience an issue like somebody else has experienced those issues before. So just getting the context from all of those, you know, like all the other people and like how they solved that problem before, like, you know, sort of helps you move, you know, like solve that issue for yourself. So that's a big use case for engineering for some roles, like for support their life. Day in, day out is about resolving, answering people's questions. And I think a tool like Glean actually fundamentally just changed how they work now because by default now they don't think about giving a question, like trying to go and look for answers in different knowledge bases and whatnot. Instead the first reaction that they have now is that there's a question that's coming in from a customer and then Glean on the side is actually Already answering those questions for them. So they're sort of model of working changes from trying to find things to actually trying to validate like you know, what AI is telling them as is the right answer and then just share that back with users. Some teams are actually really changed their behavior. Like salespeople, for example, they use Glean as a way to prep for meetings. So before like customer call is coming up, they will just ask glean like you know, they can be lazy. They can ask glean like, you know, help me prep for this meeting. And glean is actually going to bring that 360 view of all of the data from that customer, like what happened in the last meeting, who's like what opportunities are open with them and things like that. So it sort of really helps them prep for a meeting, then actually run a meeting. Well, because customers always have lots of questions and so the salespeople feel more confident running that meeting because if somebody throws a curveball at them, you know, they can just like, you know, just ask clean like right in the meeting, you know, get the answer and quickly sort of, you know, get the responses back. So in fact like you know, you know, in our company of like, you know, we don't allow salespeople to actually bring in sales engineers in the call. Like they have to answer those questions themselves. So that's like, you know, one change in behavior that we drive like you know, in the first few calls. Yeah, those are some of the things. But overall the use cases are unbounded. Like the one that I think is the one that's universal across everybody inside the company is finding other people who can help you. That is one of the things that glean makes it really easy for people. We help you connect with the right subject matter experts based on what questions you have. So that's one thing that we see everybody in the company make use of a lot.

Is there a North Star metric you track? These are wonderful stories of customer impact, I guess. How do you benchmark yourselves objectively?

Yeah. So our key metric is how many questions people ask on a daily basis and actually get successful. Like you know, we were successful in answering those questions correctly for them.

So similar to like Google's like search sat metric then like okay, yeah. Can you share anything about those numbers or you prefer to keep it private?

Well, we have, so yeah, so we have this technical metric. I don't know how much sense it's going to make, but we tend to actually keep that number at 80%. So I think it's a proxy for that, the 80% of the sessions that users had with us, they were actually successful in getting what they needed.

And do you measure that success? Is that explicitly they thumbs up, this was good. Or is that implicitly they take action on the basis of the results that you served and you can see that action taking, how do you actually measure the success?

It's actually implicit. So we will track their actions. For example, in search, when you come and ask a question and then you click on one of the top two or three results and go to the destination and then stay there for a long time. So that sort of gives us an indication that you are happy you didn't come back and ask another question quickly or refine your search. So that's sort of how we track whether somebody is successful or not.

Got it. What are some of the top things that are not in the product yet that you think will make people more successful?

I think the. Like, I refer to this, you know, when we started that building a product like ChatGPT or Glean, it's sort of like magic. Like, you know, the expectations are infinite and because, you know, like supposed to basically not just answer any questions that people have, but also like, you know, perform like any task that they actually ask you to do. And so for us, it's not so much about what features are missing. The big thing that we have to actually keep working on is actually be successful at this core feature, which is answer people's questions correctly and answer questions of higher and higher complexity correctly for them over time. So we feel like us or anybody else out there today, we're all very, very far away from that true vision for our product, which is that we want Glean to be that AI assistant that can actually answer any questions that you have using your company knowledge, that can actually do half of your work for you in the future. And so I think I would say we're maybe 2% of the way there. AI, for all said and done, we're still in very, very early stages of making that impact.

So we're only 2% of the way there. I'd love to ask you actually about agentic reasoning. It's something that's been on our mind a lot as a partnership at Sequoia. And I know it's been on your mind as well as a founder. And one of the results that I was really impressed by in the coding space was that with RAG, I think these coding agents can get to 3, 4% completion rates, but if you give them more agentic reasoning capabilities, they can get to 14, 15%. So, like a multifold improvement. And, you know, it's as simple as, you know, go reflect on what you just said or best event or, you know, what are the techniques are. I'd love to, I'd love to understand how you guys are thinking about incorporating more agentic reasoning into your products and anything else to kind of get from that 2% where you said we are today to what you hope to build one day.

Yeah, and I want to clarify the 2% is something that I was making up. Like, as, you know, it's not a measured number. I just wanted to sort of, sort of, you know, you know, express like, you know, like how, how early, you know, things are today and, and, and how much, how much amazing things we're going to see in the future. Like, I was just basically, you know, trying to, you know, talk more about that. But in terms of like, you know, agentic sort of like, behavior, one of the things that we are doing on that front is first try to actually get a lot of input from our users. So we have a concept of building a workflow inside Glean to solve, to actually answer a complicated question. And today we actually seek a lot of help from our users in sort of completing that workflow. We'll actually say, for example, if you come and ask a question, help me write a weekly status report of all the work that my team did. So this is your question. Now, if you think about this question, it's complicated. There are a few things you need to do to actually go and really figure out the answer to it. The first thing is you have to actually understand what do you mean by your team? Like, who's your team? You have to maybe go in your HR system, try to figure out who are the people who report to you. Then we are talking about work. So where does work happen for each one of these team members? You have to sort of build an understanding of that and then go pull a bunch of knowledge from all these different systems. So I think right now what we're doing is we're actually trying to actually get help from our users and we will sort of create a plan for a complicated question, try to actually get the user to actually input and tell us, like, are we getting it right? Sometimes users can actually explicitly, like, they can completely ignore what we do and just build a workflow on their own. And I think that's going to be essential for us to build that fully agentic behavior for the future. I think you can build agentic behavior for specific narrow set of problems. But in Glean, since our footprint is so wide, the range of questions that people can have, the range of tasks that they want to perform, is so broad that we feel like first we have to learn, we have to learn from workflows that people are going to actually create manually and then build these models which can then take complicated questions in the future and automatically build those, convert them into these sort of like agentic loop or a complicated workflow. So that's the approach that we are taking on it.

So you're saying since you have such a broad surface area, you can't build agentic reasoning for every single possible task and so instead you're exposing a workflow engine for your users to individually be able to build different automations and different agents.

Yeah, and then you learn from it. And then you learn from it. So once you see people building these workflows, that sort of then fits as goes into a training data set to allow you to actually automatically build new workflows based on complicated questions that people have. So those agentic capabilities are coming. But again, I would say when it is hard for you to answer simple questions, then if you want to do complicated tasks, it's equally hard because you're going to make a mistake. And imagine an agent that actually breaks down a complex task into a series of 10 individual tasks, then your error rate is going to compound like each step is 90% accurate. It is incredible. But I think it is still something that we are. I feel like the human assist is actually critical in building these complicated workflows.

Arvind, it might be worth saying a word. Maybe this is obvious to people who are listening, but just to say it explicitly, how Act 1, which is the enterprise search business, gives you the moral authority or the unfair advantage to get into Act 2, which is the application platform or the platform for agentic behavior. It may not be totally obvious to people how Act 1 leads to Act 2. Can you just say a couple words about that?

By building the search product, which immediately adds value to our customers, to our users, we are able to actually solve a bunch of complicated problems that you will typically run into an enterprise. The first part of that is security. So if you think about like the Glean product, we are actually telling our customers that, hey, give us all of your data and we're going to hopefully do something useful for you after you give that data to us. And that's a big demand. It's not easy for companies to actually trust a new product company, a startup, and with all of their data when they're actually not getting any immediate value from it. So that is one of the things that we've seen to be super helpful to us because we actually have this search product that people understand that people want and they want to deploy. So it's already deployed now and so Glean is running and it's already connected with all of your enterprise data inside the company. And so then helping us, going to our customers and then saying them that, look, use that as your core, core AI data platform is a much easier sell because we don't, we don't actually have to convince them again to, to actually like, you know, to, you know, give all of their data to us. It's already there.

I say this, this might not be a perfect analogy, but hopefully it's not a terrible analogy that, you know, Tesla had an advantage in self driving because they're already selling cars. You guys have an advantage in delivering AI agents because you're already selling a data platform that organizes all the enterprise information, makes it accessible, makes it secure, makes kind of puts people in a position where they're already asking questions of it and it's kind of a logical next step to ask it to start taking actions.

Absolutely.

I think you also announced a set of APIs that let developers build on Glean, maybe say more than that. Like I think that was in response to customer demands. But what makes developers want to build on Glean versus directly access, access their own data? I think it's probably a similar effect to what you just talked about.

Yeah, so like a lot of like AI applications that our customers are wanting to build, they need to actually tap into data that lives in multiple different cloud based SaaS systems. I think it's quite tedious for them to first go and actually bring that data in one place, build a search or retrieval layer using that. The integrations are hard. Understanding permissions and governance is really, really hard on that. And I think when people as these models actually became accessible and developers started to actually develop AI applications, they realized that they were really excited about building these new cool AI apps. But basically they realized that building an app, 90% of the work was actually this sort of boring infrastructure work that they didn't want to do, like bringing data from all these different systems, running these ETL and data pipelines and then sort of building a good search over it. So you'll spend so much time before you actually even get to play with AI. And so that's the thing that they find very useful with Glean because we're actually Solving all the problems around etl, building a great search, properly obeying governance within your company. All of this stuff is done for you. You just have a search API and you can focus all of your attention on the business problem that you're working on and how AI can help you sort of achieve that automation that you're looking for.

In some ways, all the hard work you've done to ETL and put all the data together with data governance reminds me a lot of Snowflake. And you're really doing it with text data and unstructured data, but kind of just that central data platform that companies can build around, build apps on top of reminds me a lot of the Snowflake story.

Yeah.

Arvind, can we ask you a question about Future State? If you allow us to dream for a few minutes five or ten years from now, how do you think Glean is showing up inside of a business? And maybe more importantly, if you're the typical knowledge worker five or ten years from now and you are equipped with gleaning, what is your life like?

Then that's a great question. I think let's keep it five years instead of 10. And I think the, well, I mean, like, you know, one belief that I have is that majority of the work that we do today is not going to be done by us anymore in five years from now. And that applies to me, that applies to you. Like, you know, we both do very different things, but still, like, I think, you know, we are knowledge workers and I think a lot of our work is actually going to be done by these amazing AI assistants that are actually in many ways more powerful than us. They have access to all of our company's data, our knowledge, they have all the context from all the past conversations and meetings. They don't forget anything and they can really sort of. And on top of that, they have the reasoning capabilities that allow them to be super helpful to you in any task that you do. So that's sort of our core belief that majority of our work is actually going to be done by these AI companions or assistants. And we want Glean to be that assistant in the workplace. We want glean to be the place where most of your work happens. One of the things that we also think is going to change is today a lot of AI is about, is about like you go and seek help from these AI agents, For example, you go and ask questions, you get answers back. But the future is sort of future is where this assistant is going to be proactive. It's sort of like if you think about, if you have an executive assistant, you know, they actually help you a lot. A lot of their help is, you know, when you go and ask them for help, but a lot of their help is actually proactive in nature. They tell you what to do next, you know, they manage your day, they know everything about your work life and they guide you like, you know, to be effective, you know, throughout the day. And I think like AI is going to actually allow that luxury, you know, you know, like regardless of, you know, who you are like in today, like, you know, some executives in the company have that luxury, but in the future everybody's going to have these really powerful AI based assistants that are going to actually help them do their work. So we're really excited about bringing that change to the workplace and hope that Glean can be the world's most successful AI assistant.

Love it.

Arvind, can we change gears a little bit? And I'd love to step back and hear your advice for other founders. You are one of the most successful application level AI companies, I think probably number two behind Copilot and Scale. And you did it as a startup, as an independent startup. I think you've also had to navigate some unique challenges, right? Like OpenAI, for example, is one of your providers and also one of your competitors, one of your top competitors. Maybe just tell us what that dynamic is like.

Well, I mean, first of all, from a point of view of building a startup, in fact, I've been actually coding you guys in many places. Pat, I remember the slide where you talk about the overall software market being $600 billion, but then AI sort of is expanding that market to 15 trillion or 12 trillion, something massive. And that's actually the reality of where we are today, which is that everything that we do is going to actually change, fundamentally change. AI is going to be a key component that's going to drive that change. So first thing that I, as a founder, I don't actually worry about what other people are actually working on because even if all of us are working on a lot of great things, it'll still not be enough. It'll still not be enough to actually solve all the problems that need to get solved. And so that's the first mindset. So like, you know, like I think from a advice to other founders, that's the thing I want to tell them, like if you found a problem, you know, just go, you know, work on it and don't worry about like if somebody else is solving it, because the chances are that other people are not. And they won't solve it the same way as you will. But for us, like, you know, coming back to like, glean specifically like the dynamics for us, you know, we felt the same way, like, you know, for, for the first four years of our existence, we were working on a problem, you know, where we had no competition. You know, nobody, nobody was actually interested in solving the problem that we were solving because it was a dead market and, and we had to create a category. We'd actually generate interest, you know, be evangelical and. But, but we knew that we were working on a, an important problem. But then suddenly ChatGPT happened and searches become hot. And now, in fact, every company that you go and talk to wants to build a product like Lean. And so is that good news for us? Is that bad news for us? How do you think about it from our perspective? It doesn't matter. The way we feel is that it's actually great news for us. Now everybody's interested, everybody wants to buy our product. And yes, we have to compete with many, many other vendors, but that's the place where we think we'll win, because we will. We have the desire to actually solve this problem and stay focused on this problem, keep working on it, and there's no reason for us to not do a better job than others.

Part of what I heard in there was that building an AI company is just building a company find an important problem and solve it in a compelling way. I'm curious, particularly because this is not your first rodeo. Rubrik was obviously wildly successful and of course you were pretty core some of the early days of Google. How much of building an AI company is just building a company versus things that are AI specific in some way?

It's a great question. I think of AI mostly as a tool in your arsenal and one of the tools. I don't think you suddenly become a different company because you are, because you're doing something with AI. In reality, I think there's going to be no new company that is not going to use AI technologies in some shape or form. So my point of view is that, look, you have to actually find a business problem that you're planning to solve, and hopefully. You can actually solve that problem in a much better way because of the technology that AI is actually providing to you now. So I don't think it actually changes. I don't think it actually feels different. Like, you know, whether you are like. We don't think of ourselves as an AI company for that matter either.

Would you ever train your own models? I guess, maybe more broadly, how do you think about where Glean's core competencies start and stop? And, you know, if you had 100 R&D chips, where do you want to place them?

We don't have plans to train super large models, but at the same time, you know, we've actually, you know, we do train models which are smaller in size, you know, for every, you know, individual customer of ours. The, you know, these language models that we train for an individual customer, it sort of goes through all of their own enterprise corpus and sort of starts to understand like, you know, sort of the lingo, the speak, you know, the acronyms, the code names, all of that stuff within their corpus. So model training is actually a core part of the clean core technology, but not in the sense of training a model like GPT4. We don't do that. We don't have plans for it. We plan to partner with a lot of other great companies that build models of that scale.

Wonderful. Arvind, thank you so much for joining us today. This was a wonderful conversation. We really appreciate it.

Thank you for having me.