Arvind Jain’s AI Playbook: How The Glean CEO Built to $300M ARR

Let’s I'm say hiring for a finance role. You don't want to tell them that, Hey, by the way, don't use a calculator for these things or don't use Excel formulas or don't use these macros. The AI is the same way. Just think of it as a tool. It's a very powerful tool, but yeah, you use it as much as you want. So one thing that has happened is that our practical exercises that we use for testing, they've all become more complicated. So now what we ask you to build is 10 times more complex. You will not be able to succeed in that assignment unless if you used AI.

Welcome to the Main Branch podcast. This is your host, Cagla Kaymaz. Main Branch is a show where I talk to founders and early adopters about building, deploying, and scaling world-class AI startups.

My guest today is Arvind Jain, founder and CEO of Glean. So Arvind, you and I have known each other for a number of years now and I feel super lucky to have been an investor at Glean at my old firm, seeing you as an amazing leader and epic executor, taking the company to where it is today. I'm super excited to double-click and learn from you and learn from your journey. Thanks for joining me.

I'm really excited to be here. Thanks, Cagla.

So I suspect most people are already familiar with Glean, but it's always great to hear from you. What does Glean do and how do you help your customers today?

Glean is an enterprise AI company. First, think of us as the Google or ChatGPT, but inside your company. Imagine if ChatGPT knew everything about your company, your people, your customers, your business, your processes, and it can use all of that context and knowledge to help answer any questions that an employee has or do some work for them. Think hopefully in that way. So we are the enterprise version of an AI assistant and a coworker. And Glean is also an AI platform that powers other AI applications within your company. All the AI applications that you have in your business that need that broader and deeper enterprise context, data that's besides the data that they have in their own applications, you use Glean as a platform to get that context to power AI within your own products.

Amazing. I definitely want to talk about the product journey and evolution. But when you first started back in 2019, I remember you telling me you were starting as an enterprise search company and given how successful you were at Google and Rubrik, VCs told you, Hey, I want to back you, do anything but enterprise search. And I think you said, No, this is what I want to do, and you went ahead with it. Can you talk a bit about that journey and how did you build conviction to work on something a lot of people told you not to work on?

That's a great question. The reason why I wanted to work on search inside our work life was because it's a pain that I felt personally every day. No matter what job I did, whether I was at Google or at Rubrik, it was always a big problem. Whenever you're looking for information, whenever you need a quick answer to a question that you have, you struggle. Inside enterprise, you have hundreds of thousands of different systems. A lot of knowledge has become outdated. It's obsolete. So the getting answers to simple questions has always been really, really hard. And in fact, even today, like in the world of AI, it still kind of remains the same for most enterprises. So when we started Glean, we didn't have this thought of that, Hey, we're going to build a really amazing company that will succeed a lot. We'll build a huge business. We're not really starting from that mindset.

We were starting from the mindset of that here's this problem and I face it and everybody who I know faces the same problem and it's got to be solved. It's going to improve people's experience. And I think there were studies that were done which showed that one-third of all working time by knowledge workers is spent just looking for information. So we also knew that it was a big-impact problem to work on. Now, the interesting thing is that nobody actually built a successful search product inside businesses before. There were many efforts over decades, but no successful company. And so that was sort of what was the non-exciting part of it, like for any investor that I went and talked to. When you see that nobody has succeeded for many years, you start to make some assumptions based on that. And one of the assumptions is that it's probably not a problem worth solving.

And so that was what I think I heard a lot as I was planning to raise funds, but my conviction came from just my own experience. And that's actually important for any entrepreneur, if you have experienced a problem yourself, then you can stick with it.

I remember trying some of the earlier search products and they just didn't work right. So everyone is like, This experience sucks. Someone should do this, but this has been tried. And I think there was a couple big acquisitions before Glean, even a decade ago. Then after they acquired, got those companies in, the product basically died. It just didn't work. Why do you think they failed to succeed and what did you think you were going to do differently and you could make it work?

It's kind of interesting. Now we're in the modern AI era so some of these things that were on our mind seven years back may sort of sound foolish now, but the reason why all search companies in the past failed was because search is a really hard problem to solve. And in the pre-AI era — so now forget all these great capabilities that models give us — in the pre-AI era, one of the first things that you need to build a good search product inside an enterprise is you need to be connected to all of your data systems. You need to actually know where the knowledge is, where the information is, so that you can actually then create an index and actually help people find that information. Just connecting to systems was incredibly complicated in the past because in the pre-SaaS world, enterprises had these custom applications that they built themselves or they installed applications in their data center, and connecting to those applications is completely bespoke.

So you had to spend... If you go to a large enterprise, you spend multiple years just trying to even get hold of data. And then once you get hold of data, there's not enough context that helps you understand what data is good, what is bad, what is like high quality, what's not, and what's up to date versus what has become obsolete. All of those signals are hard to ... In the past it [was] very, very hard to actually get hold of those signals, and nobody bothered to actually really work in such a deep level at those search companies in the past. So those were some of ... The complexity of the problem was higher, but as we went through some of these technology shifts like SaaS and cloud, and they were big enablers, the fact that we can build super-scalable systems in the cloud without spending too much time building systems infrastructure, that's a big enabler because search is a very intensive technology.

Similarly, the fact that businesses have standardized on these common SaaS systems, it makes it easy for us to interconnect and get hold of that data. But the biggest enabler was transformers themselves. In 2019, we already had transformers come out in the market. Nobody is talking about it, but for people in the search domain —

You're talking about like more BERT-like models?

Yeah, like BERT-like models. Those were like a step-function change in terms of how much you could understand enterprise knowledge and data and do search at a more conceptual level as opposed to BERT keyword-based search. So these were all big trends which made us feel like this was the right time to go and solve the problem.

What's really interesting about that is you were using some of those models before the mainstream caught up. And then as you were building the company and the product evolved, a lot has started to happen on the more ... Like, ChatGPT came out and has gained a lot more traction in the mainstream. And then as you mentioned earlier, now you have this super successful assistant, you have a whole product suite. Can you talk about the product evolution and what was happening in parallel from a technological shift perspective?

Yeah. So when we started, our product looked and felt like Google, but inside your work life. You come, you ask questions, we'll surface the right information back to you. You could actually ask in natural language because it was, from day one, a transformer-based search-matching system. So we could understand people's questions in natural language, could understand content also semantically. So that was one small difference, but otherwise basically the product looked and felt like Google. But as the models got better, so did our capabilities. One of the first things that happened as generative AI started to happen was that AI could actually write and synthesize answers. So we could go one step further in our product and instead of just surfacing links to information to end users, we could actually build a conversational AI interface where we would actually take all of that data ... So you come and ask a question and we build the industry's first RAG, where we would actually use our retrieval engine to pick the right information and then we'll actually make a AI model work on it to summarize, synthesize that response.

So that was the first evolution of the product where it moved from Google to a ChatGPT-like experience, but inside your company. And so we're the first ones, of course, to build that. RAG as a term was still not invented when we did that, but it became clear to enterprises after ChatGPT that, hey, you couldn't actually go and follow the same consumer architecture where you just take all the information inside your enterprise and you train a model and then you can get a ChatGPT-like experience. People tried that and nobody got that. And they realized that they had to combine a search system and retrieval system with the power of the model like the way we were doing it.

So that's when the term "RAG" got invented. And from there, the model capability has continued to evolve. And so the product has changed from a place where you go and ask questions and get answers to where you also do your work. Glean today is very much a personal coworker for you. You can actually come and do a lot of things inside of it. You can write documents, you can create artifacts, you can do data analysis. And of course, like now you can give Glean a lot more agency where it can complete long-running tasks for you. So these are all incremental developments and product capabilities. One of the main ones that I would say is that the shift from information retrieval — like a place where you come and ask questions or do data analysis, parse information, synthesize it, analyze it — from there, now Glean has also evolved into where it actually works on behalf of you.

So it can, for example, send an email message to somebody. It can go and update your CRM with some latest information after a sales meeting. So it starts to now feel a little bit more like a coworker, not just somebody who helps you get context but somebody who actually can take half of your tasks and do it for you behind the scenes. So that's been the evolution on the product side. But an interesting thing for Glean as a company is that we had to build a lot of technology underneath to power these experiences, connecting with all the enterprise systems, understanding the security and governance architecture of all the data information, building this deep knowledge graph, understanding how your business works, how work happens inside a company. And our customers started to say that, Hey, you've done all this work. I've connected all the systems to Glean, and I want to build my own AI applications and I need an API from you so I can retrieve the right content or take some actions inside an enterprise system. So our customers sort of pushed us in this direction of us becoming a platform.

So today, think of it as a horizontal AI platform that can bring the right context that any AI application needs and also the right actions capability to do work inside of enterprise systems in a safe and secure way. That's what, right now, today, our customers are doing with Glean. They connect Glean with most of their AI applications, they can connect Glean with Claude or Cursor or any other enterprise applications like Zoom or Miro or Sigma. All of these applications now use Glean as that context layer. We are on this video conversation right now. Zoom has this feature where ... Let's say we're having this conversation and you asked me a question [and] I didn't know the answer for it right off the bat. Zoom AI Companion can actually show that answer to me, and the way it shows it is by pulling that context and that information from Glean behind the scenes.

So we've also evolved into being a platform that powers almost all AI experiences inside a company.

Yeah. I remember it was a huge unlock when everyone was trying to use ChatGPT at work, and at the large enterprise, you obviously can't do that. And with Glean, having the security and governance and like RBAC and all these controls really help people adopt it and they could actually do what they want to do at a more controlled setting. What is next for Glean? And also there's this ongoing concern about the labs moving up to the application layer, you have like Claude Cowork and the likes, you have the hyperscalers who also have access to a bunch of your data. How do you think about Glean's next stage and how do you ensure you stay market leader?

Well, first I will start with the state of the AI industry. Today, we are in very, very early innings of this AI revolution. And you hear this from people and sometimes you feel like, okay, somebody has drank the Kool-Aid and is talking big. But I think the AI revolution is bigger than some of the fundamental sort of shifts in our human existence, things like the Industrial Revolution or things like [the] internet. I think AI is much bigger because it's fundamentally changing the composition of the world in some sense. For example, when you work, you don't have to work alone anymore. If you're a new grad just joining the workforce, you still kind of have like a team of 50 people helping you from day one. You don't have to be the apprentice, you don't have to be the intern. You start with a lot of knowledge skills that come from these AI agents that are at your disposal.

So how work happens is all going to fundamentally change in the future. So we are really, really at the beginning of it. There's hardly any impact that has been made so far in the industry with AI. Right now we're in this learning phase, people are learning how to use AI. So when I think about competition, or when I think about like, hey, how do we survive here? So many people are coming into this space. My first reaction to all of that is that even if we all came together, we became one company, we don't overlap on any products, we all build unique technology, we are still 100 times short compared to the demand and what businesses want from us. There's so much work that needs to get done, and technology companies simply don't have the capacity to deliver. And I'm not talking about hardware capacity, I'm talking about intellectual capacity and the ability for us to deliver the right products and experiences.

So we're in this super early journey, but at the same time, I think there are companies that have to pick what their domain is. You don't want to actually be just repeating what somebody else is doing. So at Glean, we are very particular about where we innovate and we want to innovate in areas where others are not. And so when you come, for example, when it comes to building these large foundational models, we don't have the resources or the capability to build that, but we don't need to because we get to partner with these awesome model companies. We get to use all the technology that they are building and embed that in our product experiences that we create for our customers. Our own swim lane in this AI architecture is enterprise context.

It's like building the deepest, most fundamental, and broadest understanding of how your business works today, aggregating that human intelligence inside your enterprise, the know-how, how work happens today, bringing it all in one place. That's what we focus on, that's something that we see others are not focused on. And it's a combination of that plus the amazing model technology that actually then gives enterprises a solution that they can bring to modernize their enterprises, to modernize their business processes. Because remember now, like when you have that enterprise understanding from Glean plus the models, you're not actually having AI start in grade one kind of a thing. It's not starting not knowing anything. It actually is starting at the base layer, which is the current human intelligence of the company.

It's interesting or great to hear you talk about this like rising tide is raising all boats. There's so much work to be done and you're just getting started. I think a lot of founders would die to be in your position where you're the leader, you have had a head start, you've been working on this for some time, you have this enterprise context you're bringing in. If you were just getting started today in 2026, how would you think about finding an area to get started where there seems to be a lot of competition from startups, hyperscalers, growth-stage companies? If you put yourself in the shoes of a founder getting started today, how would you think about it then?

There is a lot of opportunity in every area. It's hard for me to pick even a single thing where I would say that, oh, this is a problem that now is being addressed by a lot of other people so I don't need to work on it. I just don't think that. It doesn't matter whether you think about AI for finance or use cases, for legal use cases, for HR, for engineering. We are still super, super early. And as a founder, you need to remember that we are in early innings. If you say that, hey, this market is crowded and I don't want to be in, it's kind of like, well, I mean, see, there's a race that's going to happen and you said that there's too many people in the world and therefore there's no point in me participating in this race.

Well, you're going to win the race if you work hard for it, if you train for it, and not everybody else is going to do that. So that's fundamentally my mindset, it's not about being smart about picking the right areas. I think the best ideas actually come from your own personal experiences. So you work every day, you know, see what challenges you face, and if those are generic enough like the other people do, then just go and build a company on that. I think nothing is solved at the moment. It's more important to ... I think ultimately what drives success is, number one, long-standing belief and conviction because oftentimes as an entrepreneur, you give up, you give up because you lose confidence in your idea. You forget why you had started in the first place and you start to worry too much about competition.

There's also a lot of large numbers, especially with startups. Imagine there are four startups already in this space that you want to be in. So now there's five of you, but if it's a broad enough problem and there are a million customers, potential customers that you all have, it doesn't matter if there are five companies, like even if you throw darts, your probability of actually having to compete with one of them at a client is minuscule. So it's irrelevant. It doesn't matter whether there are four other companies doing something. What wins is whoever builds a better product, who's more persistent. So that's my take right now of the AI industry is that everything is super early and you pick an area, you work hard, you keep at it, and you will win.

I 100% agree. As a seed investor, I am betting on these people getting started today because you're just getting started, right? The opportunity is huge. I'm curious, one of the things you had mentioned to me again a while ago was there's this conventional wisdom of even if you have conviction, you don't want to wait too long to like put your product out there, you want to do this early, iterate, get feedback, versus I think your approach was, you already know this is a problem. I'm just going to go build this in stealth. I don't even need a single design partner initially because I know what the problem is and I know what to build. Was that the right approach for you and how did you, again, build conviction on this is the idea, and even if you're not working with any customers, I can go and work on this for a couple of years.

It is true that we didn't care about making revenue in the first two years of our company existence because we had a few things that was true for our business. Number one, the problem itself is understood. People struggle to find information, making it easy is going to help them. So it was an understood problem and we knew what technology needs to get built to solve it. We also knew that we were going through this phase where businesses didn't have discretionary spend and this was not a budget line item. Nobody was used to buying this. So we knew that in this journey we'll have to evangelize, we'll have to create a market, and so we had to have patience. But you always need design partners. Even if you know what you're building, you cannot build in vacuum, you cannot go and just be in the room and end up building something without getting any feedback.

So I actually spent a lot of time getting feedback. I used to send about 100 messages on LinkedIn every day to people just telling them that, hey, I'm working on this idea. I'm not selling you anything, but I want to actually know if it makes sense, if it's going to benefit you individually or your company, and is there something that I should ... I'm not thinking about the problem that we should be solving. So that was a key part of making sure that you are on track, and those conversations help because you are doing a lot of ... First of all, it's going to help you build the right product, but you're also now building this product for these people who you talked with and they feel that obligation to actually try it out once you've actually built it. And so yeah, so you absolutely have to be aggressive about connecting with people.

You don't have to be super aggressive about charging them. It's more important to get people to succeed. That was my opinion. So we didn't generate revenue, but we were definitely working with people.

Okay, that makes sense. So you mentioned just now you reached out to a ton of people, just called out, reached on LinkedIn. How did you get your first one, three, five customers, and when was the first sales hire you brought on?

The first few customers that we got, those were the design partners. So we never signed them up initially as customers. We didn't actually propose a deal. They were working with us for like six months or a year already. We had this product deployed as a beta for them and they were using it, they were giving us feedback, we're improving it. And then we let them use the product for free for a long, long time. And at some point we said, okay, now —

You did not charge for design partnerships.

No, we didn't charge them. Look, every company is different. It also depends on how much money you've raised, and there is always ... There are two schools of theory there. One of them is that like, look, you have to charge your beta partners also something because if there's no skin in the game, it's not going to be serious. And if you got like even $3,000 from somebody, you know that it's somebody who has the ability to actually even sign, make a decision, you're working with the right person, and they can get some budget for you. So there's a lot of merit in terms of asking for some money. In our case, we didn't because I think the, again, as I described, we were in this deep category creation mode and that was not the time when people had discretion. The answer is very different today.

Every company is very eager to actually play with AI and they're all ready to pay. So yes, I think today it is probably the right thing to actually ask for some sort of commitment from them. But yeah, those are our first few customers and the journey of finding them was basically for us it was via LinkedIn where I would connect. And in fact, I would not connect with people who I knew because I was more interested in ... I've been working for a long time, we have a network and somebody will just buy a product from me just because of a relationship. And then that won't tell me if it was really an important problem to solve for them. And so I wanted to avoid that. And so I would do cold outreach. I was going through this journey, which was also telling me what our SDRs and salespeople are going to go through, because I wanted to have that direct experience because they won't have those relationships that I had.

So that's how we started. And I had one more colleague from our team, from our product team, so it was the two of us who would actually go and try to build this startup business organically. And then I think we did get about 20 to 30 customers after ... And remember, we are an enterprise sales company so every customer is typically a large deal, like thousands of people using the product within that company. So I think once we reached that, I think we hired our first salesperson about two and a half years into the company. Yeah. And then I think after that, then we started to grow. But to answer your question, should founders do the selling to begin with? I think that is important. That makes sense. And you have to make a trade-off. I think I would not say that, hey, up until 1 million, you don't hire a salesperson.

Kind of like 1 million is a small number. Sometimes you can even say that you do the first $5 million in sales, it all depends on what your business is. But it's good to be in the fight yourself and do that selling because that will help you effectively then go manage and build the sales organization. If you never did it yourself, then it can actually also be risky.

You went from zero to 100 million of ARR in like three years and then you doubled it to 200. These are public numbers end of last year. So obviously you have successfully replicated your sales process. What does it look like today and how do you manage it?

Now we're a large enterprise sales force. I think we published last year, we went from 100 to 250-plus in terms of ARR. I mean, so we are a sales-led business. So enterprise sales teams, we are selling internationally, US still being the primary market but we sell in Canada, in LatAm, Europe, APEC. We follow the typical journey of building an enterprise sales organization so we've segmented these teams regionally and by segment and we sell to strategic enterprise. I guess in some sense, we're doing the building like a mature enterprise sales machine at this point, but there's some interesting things like how we recruit, how we hire, what kind of skills you look for in your new sellers, and how do you run the actual sales process, like how much AI plays the role in it? That's a very interesting topic right now.

You have to make sure that you're leveraging AI to the maximum possible level because it can double, triple, 10X the productivity per rep.

I'm assuming you're dog-fooding Glean excessively internally, but what are other ways you guys are leveraging AI, whether it's with the sales team or other orgs within Glean?

Well, since Glean is a horizontal AI platform, it's connected to all of our enterprise systems. So first, our core product, which is an AI coworker or assistant, and it's a superset of ChatGPT, Claude, Gemini, because you can just go ... In our product, you can pick what model you want to work on for any given task, and otherwise we'll smartly pick one for you. So that product everybody uses, it's a day-to-day product for them. It's like whenever somebody has any questions, whenever somebody needs to create some artifacts, like they're using Glean in their day-to-day work. But what's more interesting is that those are all individual productivity use cases. Everybody figures out how to use Glean for their day-to-day work, but there are business processes which are running at scale. For example, enablement, like how do we train our salespeople? Now we have hundreds of these salespeople and we want to make sure that the product velocity is so high.

Yesterday we had our company kick-off and our head of product was sharing that they were shipping one new product feature every other day. There were more than 100 product feature launches in the last one year. So there's an incredible pace of how this technology is evolving, how the product is evolving. And so how do you actually keep your salespeople educated, informed, enabled on all of that? AI is changing everything. You cannot actually follow traditional forms of where there'll be an enablement team that'll try to absorb this, wait for documentation to arrive, and then enable the field on it. It has to be continuous. This bite-size enablement that happens every day. In fact, the vision ... So Glean allows you to do that. Glean allows you to construct quick enablement materials automatically. The way sales teams run, they have agents for all the core parts of their process.

For example, there's a prospecting agent that helps them figure out who are the people that they should be reaching out to this week and what messages to send to them. There are agents that help them prepare for a meeting before it happens, agents that actually help them do meeting follow-ups. So all these day-to-day processes, and it actually is true across functions. I gave you some examples for sales, but think about other teams like legal teams. One of the key activities is reviewing contracts and NDAs. And so just doing that automatically with agents. For solutions teams, like filling RFPs. All of these time-consuming business processes which are happening at scale. There we don't ask individuals to just prompt Glean assistant and ask them to do that work. Those things, we'd actually take care of it proactively through agents that have been built.

How do you think about hiring when you want, I'm assuming, every employee to be super users of AI. Has that changed? Has how you hire changed in the last couple of years on what you look for?

We've been constantly changing it. Our teams have been doing it also. AI as a skill is part of the job requirements. And when I say AI as a skill, it's not like that you can go and build and train models. It's obviously, it's just about, can you use AI effectively and automate some of your workflows? So that's in our interview process. In fact, I'm a blocker. I'm the approver. It's supposed to be an agent that automatically reviews a hiring packet and sees me hiring somebody who's interested in AI, who wants to actually use AI to actually make their jobs easier. That's a skill, and this agent automatically detects that based on the questions that were asked that, Hey, did we do that assessment or not? And we won't hire a person if ... And right now, I'm actually doing it manually for some roles where I look for that AI aptitude.

And by the way, just to be clear, there's nothing special about AI.This is a skill which is curiosity and having that mentality to use the great new tools to actually do things differently, like thinking outside the box. Those are the skills we're testing for. It's not like we're actually testing for, specifically, can you use ChatGPT or not? But the new technology is AI, therefore these evaluations are all about how you're using AI effectively to change your work.

So one thing I've heard from a couple of people is actually, especially during video interviews, people using AI assist to a level that is borderline potentially cheating. Have you experienced that and how do you guys filter for it?

We actually ask people to use AI in the interview.

Okay.

Yeah. We're going to use all the tools. We're still able to test because we know what you'll do with AI. And actually if you know better how to use AI to solve complex problems than us, then even if you cheated, we want you because we'll learn from you how to use AI even more. So that's not considered cheating anymore. It's a tool.

That's the same for every role? Even if you're hiring an engineer versus a salesperson.

It's a tool that's available to everyone and we use it. For example, in an interview ... I'm going to say a very simple thing: If you go in an interview and you ask somebody, like, let's I'm say hiring for a finance role and you want them to develop some models and all of that, you don't want to tell them that, Hey, by the way, don't use a calculator for these things or don't use Excel formulas or don't use these macros. You want them to use it, you want them to know all of that stuff. And so the AI is the same way. Just think of it as a tool. It's a very powerful tool, but yeah, you use it as much as you want. There is still work to be done on top of that strong capability. So one thing that has happened is that our practical exercises that we use for testing, they've all become more complicated.

For example, for a software engineer, if you're going to ask them to build a working system that implements some things, now we know that you can actually just ask a CoreGen tool to build that for you quickly. So now what we ask you to build is 10 times more complex. And in fact, you will not be able to succeed in that assignment unless if you used AI.

Yeah. I think if you're good at assessing other people's abilities, you did need to evolve how you asked these questions on what kind of assessments we give, because you can't just use questions from five years ago and then people are answering them with AI assist. That would never work. That sounds like you guys did a great job evolving your process. I want to ask you to the extent you're comfortable sharing externally, and a lot of people think Glean has always been super successful, the growth is amazing, everything seems up and to the right, were there any times in company history that you thought, oh no, this might not make it and you went through any difficulty?

We go through a lot of difficulties every day. So I think there's no startup that has everything laid out for them. Even OpenAI, you hear from their leaders, there's always competition, there's always internal execution challenges that you have. So I mean, we have had our fair share. At a macro level, yes. For us, the journey has been good. The product that we've built, every year more and more people want those products. And we've been very fortunate with timing in the sense that all the new developments are directly relevant to us. It makes our product more relevant. It actually makes our product more powerful. So overall, we have grown in a healthy fashion, but challenges ... Look, you actually asked the question earlier that with all these other players wanting to be in this space, what does it do for us?

So we have to compete, we have to find our swim lane. So yeah, it's always a top-of-mind concern for me. One useful thing that I could say for other entrepreneurs is that the ... I mean, building a company, first of all, is very hard and there are always challenges and you will feel like I think we can't survive. You lose a customer, you can't get engagement, and you feel like I can't do this anymore, we're going to fail. But the good thing is that ultimately, I think, you have to remember that's just part of the journey. It's going to be like that. There's going to be ups and downs. But if you persist, if you keep at it, then I think ultimately you see success. So for us, we are in that zone today. It's pretty easy for us to give up as well.

We can say Google has everything more than us, and the strong brand, they have all the relationships, and they want to be in this space and therefore we should just assume defeat and we can go home. So yeah, so we do go through that and people feel that, internally. We had a fundamental shift in the market. We were in a market as the only player, the king of the market, which was pretty small. Most people didn't really care about that market. So now we have a huge market, every enterprise wants this technology and then now we have to compete. And so, we're going through that transition ourselves. And so from an up and down, there's no shortage.

What is keeping you going and what are you most excited about when you think about the next, say, one or two years? Because it's hard to tell what's going to happen in five, but maybe in the next couple of years, what are you most excited about going forward with Glean and how you're building the company?

We are really excited about our mission. Our mission is to expand human potential to do extraordinary work. We think AI is so incredible and Glean is building this personal companion, this coworker for every person in their work lives. Our product, the way to think about it is your personal companion, it's with you all times of the day. It listens to every word that you speak. It listens to every word that you hear. It goes with you to the meetings. It knows your career ambitions. It knows your OKRs for the next quarter. It knows what tasks you need to get done this week. It knows who are all the people that you're going to be meeting today. And with all that deep knowledge about you as an individual and knowing how you like to work, now it's ready to help you, and it basically does the majority of work for you — and it does it proactively.

That's going to fundamentally change, as I was saying before, the whole definition of work is changing and we feel like we're in the center seat to actually make that transformation happen. So that's the most exciting part for us. It's just that most of the times, I like to actually forget about the business and the numbers and like that, hey, we need to actually get to $2 billion, to $10 billion in revenue. Once I start thinking about that, then all the stress starts to come in. But when you think about the great product that you're building, that sort of thing is what drives you, that drives us forward, keeps us going. So we're really excited about being one of the key players in this huge transformation that the world is going through.

Great. That's super powerful. Thank you so much for joining me today, Arvind. Great conversation.

Thanks, Cagla. This was really fun.