We're in very early stages of this AI revolution. The amount of things we're going to be doing with AI is minuscule compared to where it's going to be five years from now, ten years from now.
OpenAI at some point had stated directly publicly that you were one of their main competitors, but at the same time, you're able to leverage and partner with them.
So, I don't actually worry about competition. I've never worried about it. The dream started in early 2019, which makes us the world's first enterprise generative AI company.
I particularly remember your vision was to build like Google for enterprises. That vision changed.
One-third of all employee time is spent just trying to find things. So we wanted to solve for that. When you work with a thousand plus customers, you kind of see what usage patterns are today.
A lot of application companies with their data advantage are also coming into the modern businesses. Cursor has Composer, etc. Is that also something?
Yeah, in fact today if all of us came together, if all the model companies, if all the application layer companies like us, and became one company and we start to deliver products and services without competition to all the enterprises, we will still fall short. We still won't meet even that percent of the demand.
Hey Arvind, thank you for joining us. You're one of the technology luminaries of our time. You started many massive companies. Rubric went public and now you began Glean. I know you and Karan have a history together. He worked with you at Rubric and then this led to this partnership that we have with Glean and Composio. Can you tell me how you met Karan, how that started?
Yeah, I mean as you said, first of all, thanks for having me. Excited to be here. Karan and I have known each other since our Rubric days, where he was one of our early star engineers. So that's how we got to know each other. And when we started this company and he was building technology which kind of made sense for us to partner on, so it was a clear choice because we believe in him.
Thanks. I mean obviously, Rubric was the first company that I joined. Thanks a ton for having me there. I think that's where I essentially learned how to build systems, how to build the engineering team. So yeah, and thanks a ton for believing in us for the first time, and that has shaped our product as well.
Can you briefly explain what Glean does and its history as a company? I know you guys started as an enterprise search company, sort of vector search systems. You moved to something much broader. In a recent podcast, you described it as something like a superset of ChatGPT and Claude Code. So can you talk me through what the product is today and how you sort of got here, what the history looks like?
Yeah. So, Glean started in early 2019, which makes us the world's first enterprise generative AI company. Well, I mean, just to be clear, the term generative AI did not exist at the time. But we were the first ones to play with transformers and language models and bring these technologies to the enterprises. And we were doing it for our first product that we had built, which was search for employees within their own company. I don't know how many people know this, but the origins of transformers and language models is initially these things were built at Google with the single purpose of making Google search smarter, more intelligent. So when we started Glean and we were also building a search product for businesses, like I said, it made all the sense for us to use this technology. So that's how we became the first ones to bring transformers to the enterprises.
The reason we wanted to solve that problem was our own experience at Rubric, and in fact actually my experiences at Google before that, where I used to work. It's always been incredibly hard to actually find the information that you need to get answers to questions that you have, that allow you to then go make progress on the things that you're working on. It's deeply frustrating. It used to be at Rubric, it was our lowest scoring question in our pulse surveys. People complaining that it was so hard to find information that they needed to do their work. So that's how we got started. Now we've evolved from that. So in addition to actually, of course, having this product that looks like Google or ChatGPT inside your company, one place where people come in and ask questions or give us some work to do, and what Glean does is it uses both the best models that are out there that can actually help solve this task or answer this question, and these models come from companies like OpenAI, Anthropic, Google, and all the open source models. But more importantly, what Glean does is it's connected to all of your company's internal data and systems. It knows where every single piece of knowledge or data is inside your company. It knows who has permissions to use that information internally.
It also understands deeply what content, what data is fresh, up to date, relevant, high quality. So we sort of had that context of your business, your data, your knowledge, and how work happens inside the company, and we're able to use that context and that knowledge along with all these great AI models and technology to then start to work for you. And the way you work with Glean is you ask questions, you give us tasks. You can think of us as a superset of ChatGPT, Claude, Copilot. We will actually solve that task using all of that company's context and in fact picking the right models, the best models for each of the subtasks that we will have to execute to complete that task. So that's sort of one product, very simple, use us just like how you would. But underlying that is this platform, this platform that's connected to all of your enterprise systems where we're able to read information, we're able to take actions, and where we actually partner with you. We are able to understand the security and governance of all of that data, and we're able to build this deep context and knowledge graph of our enterprise. And all of that platform is also, just like how it's available to our own product that we deliver to our customers, that platform is also available to our customers to directly integrate into their other AI systems. So many of our customers, for example, connect their Claude Code or Claude Copilot or Cursor with the Glean MCP gateway that brings that intelligence and context of your company to those tools and make them better. So that's how you should think about Glean, an enterprise AI platform and a coworker.
So I remember, I think even in Rubric days, I particularly remember your vision was to build like Google for enterprises. And as from what I understand, you started Glean with also that vision. How has that, given that during that time language models came along the way, how has that vision changed or has it remained?
It has expanded so much. So I think the most exciting thing for me personally was number one to build a product for myself, and that was actually the driving factor. And for everybody who I know, I knew that everybody struggled with finding information. So we were actually quite happy first just actually solving that use case, which is that every employee, whenever you need some information, we're going to make it super fast for you. One-third of all employee time is spent just trying to find things. So we wanted to solve for that. The AI of the day was kind of capable of doing that. It was capable of helping us understand data, knowledge, content. It was capable of helping us understand what questions people are asking and do a good matching between them. But AI in that time was not capable of writing on its own or doing deep reasoning or thinking. But it was still a step change. When we started Glean, we used transformers and our search was already what later on, two or three years later, the industry started to call it semantic search or vector search. We built them well before these terms were coined by the industry. We used to call it embedding search internally. That was the name that we chose for this concept that we had built.
And remember that sometimes people think of Google as one type of experience and ChatGPT as another, where Google is, I ask a question and you surface 10 links to me. ChatGPT is I ask a question and you give me an answer. But that dichotomy is actually not true. Google for decades have actually also tried to, when you come and ask questions, try to give an answer. And it didn't need generative AI models for a long time to do that. So we used to do the same when we started Glean. Often times when people came and asked questions, we would surface an answer back to that user, but that answer was extracted from your corpus, not generated by AI. And then as AI got better, we were obviously able to now start to use those generative capabilities to in fact answer questions for people. So over time, our sort of goal and mission remains the same, which is we want to make people do extraordinary work. We want to help them untap their potential to do extraordinary work. That's our mission statement. And for that, graduating from helping them find things, to answering their questions, to actually now doing some work for them, to actually becoming that holistic platform at an enterprise level that allows the company to actually operate with a new level of intelligence, we feel like it's just an evolution and expansion of our mission, which this great technology has allowed us to actually enable.
I guess it's a question about a directory choice, right? You guys started going deep on what you called embedding search, within the industry later called semantic search or vector search, and then later we sort of see this, both in terms of what you're describing as the ability for agents to do work, but also you're seeing agentic search sort of calling the tools and as an agent in a sandbox or a loop generally seems to be much more successful, and you're able to do much more with it, or it sort of democratizes this vector search product in some way. How did you guys think about that inside Glean, and how do you think about that transition? Because you guys had a very successful business in this, you transitioned to this other thing also extremely successfully.
Well, I mean I think internally for us, sometimes we get a lot of credit for pivoting the company from A to B to C, and internally we think about that, I don't think we're getting more credit than we deserve. I think we were simply taking advantage of the technology that was being built around us, and it's been in some sense a very natural progression of starting with search as the first problem that we solve for people, to answering questions for them, to actually doing work for them, because it's all correlated with the advancements in the AI models and technology.
I guess there's always sort of this trade-off curve that you're making when you're building products about focusing on what's working, focusing on the new thing. I think that's where the credit is sort of coming, or the question about was there an innovator's dilemma or sort of a push and pull within the company, like, hey, this thing is working super well and we have to go down this new bet.
I think that was obvious, that was obvious for us. The world, and I think there's a good parallel in the consumer world, and we didn't have to actually wait for that, was all obvious to us too. If I have a question, I don't want to actually go read a long document to get an answer for that. I do prefer a ChatGPT-like experience. So it was clear to us we also have to deliver it. Yes, ChatGPT was initially launched only for consumers, but in an enterprise, of course, it's the same people who use that product in their personal lives. So I don't think we had a choice. In that sense, we had to advance our product, and it actually made all the sense, and people were excited. And I mean that's how our R&D team works. They are always looking at what are the new things we could be doing within the company. Also, we have this concept that we learned from Google, this concept of 70/30, where 70% of our team is focused on short and medium-term deliverables, thinking about the current product roadmap, how to evolve it, and then we have another 30% who are working on moonshot bets, trying to think about new things that we could be building. So that's been the journey for us.
So I know for this sort of agentic search and actions transition, and maybe it's not a pivot, natural roadmap, I know we played a huge part as Composio in helping in that. We'd love to talk about where we were helpful, where we sort of came into the picture, how that partnership came about.
Yeah. So look, part of it is also users who you serve. So as we started to actually launch these agentic capabilities, as people could do more and more advanced analysis, deep research with Glean, the natural question from all of our customers was, well, I mean, I think yes, it's great to bring information to people, it's great to actually give them these tools to analyze data, to get them to write some content for you, but ultimately, everybody started to see the potential of that. Now AI was ready to actually do some work, do like complete the cycle, not just do the first part of any given task, which is research and data collection and analysis, but now you can actually complete the work if you're going to save that work in some of your systems. So that required us to start to think about actions.
We actually built actions also probably roughly the same time that you started to build it. But for us, our model at Glean has always been we want to actually work on things that others are not, and we are very much a partner-focused company. And so if we see great innovation that is happening elsewhere, and if we have the ability to actually embrace that innovation, then we'll always make that choice. And so when you think about Composio, it's very much like the partnership that we have with you is kind of similar to the partnership we have with OpenAI or Google or Anthropic. There's great tech that they've built and they make it available as a platform, so when they do, we want to actually leverage it as opposed to sort of having to reinvent that. And the same motivation we had was with you. You were deeply focused on that one specific area of actions, and we felt that you would go fast, you would actually go deep. Actions, as you know, it's been a long journey, and while it's easy to build a quick sort of system that can actually make a REST API call and execute a REST API call, but doing it the right way with the right parameters and sort of taking a task which requires you to make like four different API calls, ultimately you start to run into lots and lots of complications. And for a long time, LLMs were not good at it. And so we knew that for a company that's focused on making that happen, they'll do a good job, and we would like to partner with them.
So I guess that sort of leads us, thank you for trusting us, betting on us so early, leads us into a next question that I've been thinking a lot about. You sort of mentioned partnering with OpenAI, Anthropic, these people. How do you think about competition in the space? Because when I see Glean's product, clearly you guys are doing exceptionally well. The product is growing really well. Like I've heard speculative revenue numbers that I don't necessarily want you to comment on, but you guys are growing really fast, doing really well, but very much in the product roadmap of something like an Anthropic or OpenAI the way they stated. I think OpenAI at some point had stated directly publicly that you were one of their main competitors, but at the same time you're able to leverage and partner with them. And if you look at your contemporaries in coding, like Cursor, Cognition, are doing exceptionally well, they have all maybe even more fierce competition. So how do you as an entrepreneur think about navigating this, and how would you suggest we navigate this in terms of thinking about building products?
I think we first need to have this awareness that we're in very early stages of this AI revolution. The amount of things that we're going to be doing with AI right now is minimal. I mean, it's a lot, I guess in some sense AI has made a lot of progress over the last few years, but it's actually minuscule compared to where it's going to be five years from now, ten years from now. So there's a very big opportunity for all companies that are leveraging AI to build valuable products for customers. And I actually find it kind of ridiculous to think in a way that one model company or another model company will basically make it unnecessary for all other companies to even exist. In fact, today if all of us came together, if all the model companies, if all the application layer companies like us, platform companies like yours, if all of us came together and became one company and we start to deliver products and services without competition to all the enterprises, we will still fall short. We still won't meet even 10% of the demand that enterprises have, what they want to do with AI. That's the first realization that every startup needs to actually have. Don't focus too much on competition and what other players are going to do. Your biggest competition or your biggest challenge is going to be just yourself. Can you execute? Can you build a high quality product? Can you actually talk about it? Can you take it to the market? Can you earn the trust of your customers? Can you do well for them? Those are the real challenges, not whether somebody else is also doing what you're doing. Because it's not as if there are only 10 companies that we're all selling the products to. The customer set is actually almost infinite. So that's my take.
So I don't actually worry about competition. I've never worried about it. A lot of people talk about us being directly, like you mentioned, like OpenAI has mentioned it, and the market does recognize us as the pioneers in enterprise AI and search. And whenever another large company, and this is quite often, when they come and build a product like Glean, now we get the recognition that they're building a product like ours. So that is there. There are a lot of companies that are doing things that we are doing. And the only real reasonable response as an entrepreneur that I could have is, well, we will be focused on our customers, we'll be focused on our product, and we're just going to do better, and we're going to do it with our eyes wide open. And if there are other players in the market and they want to compete, well, I think first let's see whether we can try to partner with them, and that's what we do with OpenAI and others. They build great technology which they as a platform company make available to companies like us. So that's sort of our strategy. By and large, you can't actually of course partner with everybody. You cannot also say that if somebody else is building something that I won't build it. But in general, it makes sense to have that strategy where you try to do things which are uniquely suited to your strengths. So for us, that is search, that is building these deep knowledge graphs, context graphs, building an understanding of how your business works. And we still, that particular area is something that we are the best in. We focus on that, other people are not focused on that, and everything surrounding that is where we will have a partner for strategy. If we can get something to the market, we'll use it, and that allows us to compete.
So there is kind of a thing where a lot of application companies with their data advantage and other things are also coming into the model business as well to a certain extent. Like Cursor has Composer, etc. Is that also something?
Yeah, I think for Glean too that's an important one. I think before even talking about companies like us, for enterprises, we talk to a lot of them every day. One of the biggest concerns that they have today is overdependence on model providers. And this independence that you need as an enterprise, like you want to make sure that model is a technology that you can use, but the context of how your business works, your proprietary business processes, your sort of unique differentiators, all of those things, you remain in full control of yourself. There's this concept that over time AI is going to do more and more work in the enterprise, but how is AI getting better? Those learnings are compounding, and who owns those learnings? Enterprises need to be in full control of that. So even at an individual customer level, there's a lot of focus on how to actually not fully depend on model providers for applied AI companies like ours. It's the same thing. We cannot have a huge dependency on any one provider for our technology stack. Open source models are actually fantastic. So Glean, being multimodal, we actually work with all the different models that are out there, including open source.
I guess this sort of transitions very nicely into, if you had to say, what percentage of tokens on Glean are on a frontier American model versus some of these Chinese open source models.
Yeah. Well, today it's dominantly on the American models, but the inflection point actually just recently happened.
