No Priors: Artificial Intelligence | Technology | Startups - AI is Making Enterprise Search Relevant, with Arvind Jain of Glean

Episode Date: May 15, 2025

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 [e-mail] 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

Hi, listeners. Welcome to NoPriars. This week we're speaking to Arvin Jane, CEO and co-founder of Glean. Glein 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 it also allows you to enhance workplace productivity by using different applications on top of that. Prior to Glean, Arvin had a really stored 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 them here today. Garvin, welcome the no priors. Thank you for having me. So I'm really excited about this. I've known you for years and Alad's 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 since before it was cool for a long time when it felt not solved but not as dynamic. How broadly has

search changed because of LMs? I've been working on search for almost 30 years now, 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'll find words and try to find them in documents and bring them off, you know, to the users. But LMs have completely changed it.

Like, you know, 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 powers. It's not brittle anymore. And I think it's been a, it's been a foundational technology to really evolve search into

these new experiences that you've seen 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 that. and put them directly using the knowledge that you have. If I remember correctly, Gleon got started in the more traditional search world, and that as these foundation models and these LLMs have come to before, 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. I'd just even love to hear the technology. transition. Like, how did you think about that? What did it happen? I think you really lived through it in a really meaningful way. We know, we had good timing, I would say. So, like, you know, started thinking about building clean in late 2018, started the company early

2019. And so interesting thing is that transformers as a technology had emerged by them. 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 inaction. 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. And so, like, internally, we used to call it embedding search. And it was a core technology that we started out with.

So you were super early to it, actually. Yeah. And you don't look, you know, the models at the time were were not like as powerful as today. Like, you know, we started with this bird model that Google had put in open domain, which was trained on all of the, all of the internet's, 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 in an enterprise, imagine a company that has been around for a few decades. You know, there are tons and tons of information spread across many, many different systems. 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. right information sort of semantically or conceptually matches what the user is asking. Well, you've got to solve other problems, too.

We'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 documents. I 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 we started with, you know, building the product in that shape and form. It was very different product, actually. like nobody did actually search 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 Rook, 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. 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. A lot of attempts and no successes. Why do you think it didn't work? because it felt like an awful market. 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. In an enterprise, like even getting access to all the data that you want to search,

it was such a big problem. In the pre-SAS world, there was no way to sort of go into those data centers, figure out where the servers were, where the storage systems were, trying to connect with information in them. It was a big challenge. The SaaS actually solved that issue. So, like, search products, like most of them, most of those companies started in the PSAS world. They failed because you could just build a turn-key product.

But SaaS actually allowed you 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, and SaaS systems don't have versions. Like everybody, all customers have the same version. You know, they are 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. So it was the rides 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 or underlying content. You guys must remember Google Search Appliance? Yeah. The idea of like I need to slurp your data continuously into. a hardware appliance in order to actually do search as ludicrous. 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 Pulse surveys. 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 really the origins of how Glean got started as a company.

And so that was like, you know, one big issue. Like, you know, the 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. 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 Alar and I, you know, when we were working on search at Google, you know, in 2004, the entire intern 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 eight-cloud technology. And then, of course, transformers. Like, you know, that's really the big unlock, you know, that we had was that we could, actually understand enterprise information more deeply.

And it 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 up for that, you know, lack of signal from users, you know, with other techniques and transform is one of them. It sounds like you feel a combination of, I'd call it,

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? 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 think, you know, like they're anywhere close to, you know, that happening.

I'll give you an example. Let's say that models are mimicking human intelligence. intelligence. 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, 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, one million documents. And, 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 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 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 model is 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, you know, it matters, like, you know, how you provide them with the right information. 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. Can you talk a bit about that and what are some of the common use cases that you're seeing? 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, like Green Assistant, you know, it sort of looks and feels more like Chad GPD. So instead of like, you know, me going, asking questions and seeing, you know, a bunch of links coming back to me, you know, now of course you converse with Glean, you ask questions. And it works just like Char GPD. You come and ask 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 companies, 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.

So that's sort of like the first progression in terms of our product. Like, you know, we evolved from being at Google to, you know, something that looks more like chat GPD, more powerful version of chat GPD inside your company. As it built that, this Glean 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, as your sidekick, you know, 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

that, but in actually thinking about how they can transform their company with AI, how they can take specific business processes, you know, where they're spending a lot of money, How do they bring automation in that with AI? So we've been asked before agents became, you know, the talk of the, like, you know, of the day and, like, you know, everybody's a force building agents. But early last year, when agents had not yet taken off, people are asking us for that, hey, you know, 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.

And 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 and we wanted to behave in a particular way, a particular tone and all of that. So that was a request that we started to get last year that, like, you know, can be 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 were not calling them agents. We were calling them

apps. 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 specific functional experiences to actually replace a business process, which also involves doing some work for, you know, not just answering questions, but actually, you know, brings on work in both systems. Arvin, when you talked about, you know, access to the right data with the right authority and also, like, it really begs the question of like access control, right, in a platform like Lean, when you have all of this unstructured data, this seems much more complicated.

What is like your overall stance or how you think this is going to work in the future? 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 would say like 90% of the knowledge inside the company is private in some shape or form inside within 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 you know enterprise knowledge that's the fundamental sort of 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 knowledge into it and then that model available to everybody in the company because if you do that, you're leaking information. Like, you know, inside a company, you're letting somebody in an engineering team, you know, see some sort of the stuff, you know, which probably only HR team should be able to see 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, a fundamental level. And that's what we do in clean.

So when we connect with all these different systems, you know, inside our enterprise, we, you know, if we index, you know, a particular document from Google Drive or a conversation from Slack, you 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. And this is, this is important as a problem to solve, like, you know, unless if you have infrastructure like that, you cannot roll out AI safely inside your enterprise.

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. 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'm always very surprised when I learned 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 see like Glean and AI super users. One of the biggest surprises for me, 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 a search and, and what's the big deal? Like, why do you have to learn how to use this? 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 a paragraph long set of instructions that are giving to us

and we realized that people won't do it like you know I think everybody has been trained over the last 20 years to actually type in one or two keywords like Google has sort of taught us

you know on what search can do so with search you never had a problem like you launched a product they'd like immediate high usage nobody was confused like how to use the product with assistant people didn't know what to do with it some people with, you know, more curiosity

and they will ask all kinds of, you know, questions that we couldn't actually answer. 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 actually very unintuitive.

For most people, you have to actually really expose to them these capabilities in a sort of a incremental fashion. You know, like some things, you know, it sort of are more meaningful to their day-to-day work, For example, if I'm an engineer, like, you know, prompt the user sometimes that, like, look, you can actually learn about a new piece of technology.

Like, I can actually give, you know, create a two-page tutorial for you right now. 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 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, you know, 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 but they're all like also asking for ROI that, well, like, you know, I'm going to make all this, you know, investment. What are the returns? Like, what are the efficiency gains, you know, that I'm going to be getting or what are the top line, you know, improvements that I can make to my business?

There's a lot of focus on that. And any, one thing that often gets overlooked is, education because 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 do you want to see in your workforce 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 because 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 that has to be like

the objective today is, like, you know, as leaders think about AI, how do you sort of build people tools that sort of motivate and motivate them to bring AI in the day-to-day work? You've had an amazing career between being early at Google, starting Rubrik, now starting glean and running it. What was unexpected about doing glean? Because you'd gotten to so much scale. We've done such amazing things in the context of rubric. What was hard or unexpected or just very different about Glean that you didn't anticipate. From a product side, 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, Rupick compared to Glean. Like, in Rubik, we're an established market, like there were several 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, like, you know, it's not a line item in my business priorities. You know, it's a, it's a vitamin. It's a painkiller. People are living without it. Well, yeah, that's just true. I mean, you live without something you don't have.

Like, you know, that's by definition, you know, you know, true. So we had a lot of challenge. Like, we have 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 actually buy a product that's, you know, they're not used to buying. So that's sort of the first part of it. Like, you know, you have to create the market for this, which actually was difficult. And second, which is very interesting one is, you know, we, our product was actually working well.

Like, you know, it was doing good search or letting people find things. But then we started to hear from businesses that, oh, I'm scared of good search. And I don't want a good search product in my company because I have all these governance gaps. I have, like, you know, sensitive information all over the place. And, you know, now people are discovering these things. And we launched, like, you know, for example, you know, people found, like, salaries of other people, you know, those, like in one of our customers, somebody found a sensitive M&A dog that was, you know, or something that was, you know, not yet happened.

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 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. It seems like LLM should be able to help with that, right, in terms of classifying

documents and servicing, hey, this one may be sensitive, do you want to secure it, etc. 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 you have the right to even ask the question or, like, you know, when the information comes back, like, does it do, and, you know, feel, you know, safe enough for us to show it to you.

So we actually, in fact, you know, in that sense, you know, we actually ended up becoming a security product. Like, 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, the clean assistant, but also for all the other AI products that you can buy inside the enterprise. So that was actually a very interesting journey. But then for personally on, you know, for me, you know, at Rubrik, you know,

I didn't actually, I wasn't this, I wasn't the CEO, I ran R&D as one of the founders of the company. And here I had to actually learn how to become a CEO. And I don't think I've learned it yet. And like, you know, 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, 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. One thing that I think is striking is that from a go-to-market 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 already that as well. Could you talk a little bit more about when it makes sense to do big direct enterprise deals

versus the 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. Just to be candid, clean, when we started, I mean, 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 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 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 search needs require us to actually search over all. entire company's information for them. So it's expensive. You have to actually index, you know, all of your company's data and knowledge. And so we never had that concept or that, you know, we could make it available to one or two or ten people inside the companies. We're sort of forced just structurally to actually build in that fashion where it is, it is, you know, like enterprise. It is like, you know, we roll the product power company-wide, you know, every employee. 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, They think of PLG as basically Leachan as a funnel. You sort of nurture and expand using, you know, Enterprise States motion. So the right recipe for me, like, you know, if I had a choice, I would actually start both the motions simultaneously. Like, I won't actually say that, look, you know, for the first three years, I'm, I want to actually focus, you know, on just being PLG and then bring Enterprise States later. Because you're actually leaving a lot, you're leaving a lot on the table. Timing, timing matters always. And so you have to sort of like start the, start the motion.

at the same time. Arvin, one thing that we have talked about that I feel like must have been, 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 really saw the problem internally anyway and understand that there were these sort of architectural, foundational things

that had changed in terms of movement to SaaS and API-based integrations and such. But still, I think it's a really big 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? 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 will actually ultimately kill your own idea. There is a lot, like, you know, sometimes. Everything's been tried. Yeah. Yeah, everything has been tried. Like, so, you know, a lot of things have failed.

and I think there are, like for any given idea, like, you know, there are 10 reasons why it won't work. Like, I just start to go into details. Sometimes, like, a more simpler approach is helpful, you know, which is, 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 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. I'm engineered by training myself, and I'm, like, you know, and naturally trained to question and, like,

there's a lot of self-doubt in my mind. So I don't know what happened to me when we started clean because, you know, there were all these people saying, like, no, not do it. 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. Why, you know, 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. It's just super hard to find anything inside the company. So I think I somehow found that conviction. I was sort of being lazy, not willing to go into the details and look at all those priors and just like this word just followed it.

I mean, that's what I think like worked for us in this particular case. I feel like Gleon had like three big components to it that all came together that you mentioned earlier, right? There was the need that you identify it just as somebody running IT for your own company into your point, it goes back to Google that this was a need, and every company that I talked to has always wanted to build search and directories and all the 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, these embeddings, et cetera. Given the latter two, are there other big opportunities that Gleet isn't going to work on that you've kind of identified as really interesting areas that suddenly are tactable again? I think for us right now, the focus remains on the two core products that we have. So we, you know, the way we think for our company is that we have this really powerful end user, uh, AI, you know, assistant that helps every person like, you know, work differently in the future.

And then we have this Asian 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 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. Green will use all of the world's knowledge and all of your internal company's data and knowledge in a safe and secure way and answer those questions for you or complete those tasks. But actually, I just promise to you that Glean does everything. You don't have to work anymore. We've had long, long ways from actually

even solving, you know, that, you know, the pitch that I just mentioned to you, like, you know, I mean, we have to understand knowledge properly. Like, you know, pick, you know, the right, you know, the correct information, throw away the old information. There are so many challenges there. There are 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, 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. And so there are, like, a lot of challenges. And I think 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, big, big success. So we're going to stay focused on these two, you know, these two products. But then they're 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. to change everything. Yeah, it's going to change how people work. AI are going to actually change how business is actually even look and feel,

you know, what kind of, you know, what force you have in the future. And one thing that's going to fundamentally happen is that each one of us is going to have this amazing team of, you know, call it assistants, co-workers, 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 it's proactively helps you, does 90% of your work for you,

and also, like, you know, help you get better, like, you know, at your, you know, like, upskill you, be a coach. And this, and that's the world that we want to be living in. And, 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 assistance, your chief of thousands, and an exact 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 senior we are, You know, maybe new crack joining the work course. 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 at an Xer. And that's just a natural extension of like just keep evolving our clean assistant product, make it better and better over time. Yeah, Arvin, thanks so much for joining us today.

Yeah, that's excellent. Yeah, fun questions. It's always nice to see you. Yeah, like all. Find us on Twitter at No Pryor's Pod. Subscribe to our YouTube channel if you want to see our faces, follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no dash priors.com.