Arvind Jain. Glean.

Arvind Jain:

AI is not capable of replacing you right now. Let's be clear, it's not ready to replace even a single human today. I actually don't believe that companies will be successful in reducing their workforce significantly. My high school kids, they can tell you very quickly what text is written by AI and which one is not. It's the pace of innovation that is actually creating that feeling of anxiety. You never feel this feeling of, hey, you have arrived, or okay, a company has been built, we have a stable business. It's completely elusive right now for us.

Nakul Mandan:

There is a war going on right now over who owns the AI assistant for knowledge workers. The one tool that sits across everything you do at work and actually knows your company. OpenAI and Anthropic want that seat. So do Microsoft, Google, Notion, and 100 startups. One company got there first. Glean. Arvind Jain founded it in 2019 after a decade building search at Google and co-founding Rubrik. Today, Glean is over $300 million in ARR and valued at $7.2 billion. In 2024, OpenAI reportedly told its own investors not to fund 5 competitors. Glean was on that list. Our conversation today with Arvind Jain is interesting for 2 reasons. One, he holds that pole position against the most powerful companies in tech. And two, He's running one of the first and most AI-native companies in the world. This is going to be a power-packed conversation for everyone in AI.

Arvind Jain:

Knuckle Up!

Nakul Mandan:

Arvind Jain, welcome to the show.

Arvind Jain:

Thank you for having me.

Nakul Mandan:

You've put yourself in this pole position which every company in the world wants to win, the AI assistant for knowledge workers. The big labs want it, Google wants it, Notion wants it, tens of startups are chasing this. But as all of them now make forays into it, and especially the big labs, let's start with them. As they get to know every knowledge worker and what's happening in every organization, what is Glean's product edge in this world?

Arvind Jain:

First of all, we started Glean in early 2019. That makes us the world's first enterprise generative AI company. And we've been focused on the knowledge worker since then, trying to understand how people work, what are their information needs, knowledge needs. It's very interesting that in the last 7 and a half years, as we've sort of brought our product to thousands of enterprises, first of all, the sheer number of challenges that you have to actually solve, which have mostly nothing to do with your actual product that you're trying to build. Like we're building an AI product, of course, right? You come, you come in Glean You ask questions and we'll bring the right answers and the right information back to you. That's how we, you know, that's how we got started. Today we are a coworker. You come to Glean, you can actually go beyond asking questions. You can give us some work to do and we'll do that work on behalf of you behind the scenes. Of course, we're connected with all of these, you know, your enterprise systems. We understand how your business works. So, so we do that, but the challenge is like to actually get this product up and running. Inside an enterprise, a lot of, a lot of that has nothing to do with AI. It has got to do with how do you actually handle data safely and securely? How do you understand what is the governance architecture of an enterprise? What information can be seen by what users? What are sort of policy and compliance requirements? If you're in a regulated industry, what are the things that you can do with AI and you can't? So say, I think one thing that we always educate our enterprise customers is that this is a complex undertaking, bringing a really good coworker to your employees while meeting all of your enterprise requirements. It requires effort and energy and that experience that we have learned over the years. So the main edge for us is that experience, the fact that we've been in this business for a long time. But then maybe I will also share a little bit more about where we spend our R&D resources. So the company now is about 1,500 employees. That makes us one of the largest pure-play enterprise AI companies in the world. But we are actually not spending a lot of that time trying to train and build models. Glean is a multimodal product. We actually partner with the best model companies, whether that's OpenAI or Anthropic or Google. We actually also incorporate open source models built on NVIDIA technology or like another open source model out there. It's really great to be in that position where we can actually leverage that innovation that the labs are bringing. And then we get to actually build things that they're not focused on yet so that together with their technology plus our technology, we can bring the best experience to our users. So that's part of our core strategy.

Nakul Mandan:

So it's not just then connectors and permissions. You're saying it's much more than just building connectors with CRM, Workday, every other system and permissioning.

Arvind Jain:

Yeah, it goes well beyond that. That's the basics. That's the nuts and bolts. Like you connect with all the enterprise systems. Even that is actually, by the way, quite hard. So, and you should understand that today the industry has taken the direction of not wanting to build those integrations. We build it, but other, you know, if you look at the strategy that the labs have taken, they are actually choosing this MCP-based architecture. Where they don't build integrations anymore. They're expecting that each individual enterprise application will put up an MCP server, which allows an AI model to interact with those systems, you know, a little bit, but that's actually quite shallow. And it doesn't actually, in that approach, you don't get to actually learn how your business works. Ultimately, like, you know, our viewpoint is this, if you're going to have AI do some work, a complex piece of work that a human today does in your enterprise, then that AI has to actually learn from that human, from that human, how they've been doing this work. You know, some of it is written down in, you know, SOPs and documentation, but some of it is in their mind and they exercise, you know, that judgment every day. So you have to, to be able to actually bring that automation in your business, you have to actually go much deeper, much deeper than connecting with the systems and understanding what data and what knowledge is where. You have to actually understand how work actually happens. And that requires you to actually even understand what work is like, what are the tasks, what are the key jobs to be done in this business? You have to observe like, who are the people who actually do that work, who are experts on it? You have to observe their activity to see these are the 10 steps, you know, that this person took to complete this particular task. And these are all the observations that we build. This is the context graph. And so, so Glean has been investing in that for many, many years. And that right now we still see. As an area which is, you know, completely open. Like we're not seeing other people actually building that yet.

Nakul Mandan:

So let me steelman the other, other side of the argument just for debate, right? So one is with MCP, now the onus is on every enterprise company to build their own integrators into the model, right? So that makes, makes life slightly easier. The big labs also, including Google, by the way, they all have endless resources. In Google's case, they also have the Google Docs and the Google Work ecosystem. And this is one of the most important races. So they are going to put all the resources with FDE teams and everything So everything you're talking about from connections to permissions to getting to know how they, uh, how the knowledge work actually works with FDE teams, wouldn't they want to catch up to this in a pretty aggressive way? And let's say they at least solve through MCP and the connectors and integrations thing and memory, which then adds up to how this, how this recruiter does their job and stuff. Is the edge still enterprise features? Is it the regulatory aspect you talked about? Is it the multimodal nature of it? What is the cutoff? Edge, because my guess is there's something that even you built that is getting commoditized for you in front of your eyes, but then you are continuing to create more edge.

Arvind Jain:

No, that's a very good question. And in fact, you're absolutely right that technology today changes so fast that, you know, you have to keep reinventing, of course. But coming back to this question, we do believe that, you know, we have a structural advantage over the labs, model companies. You know, talking to over 1,000 enterprises over the last 1 year, some of the key trends that we have actually heard, like the top concerns that enterprises today have are number 1, you know, there are all these different models out there, so many different AI products, and they're having a difficult time trying to figure out who they should work with, who they should bet on. Like one day you have GPT being the best model, the other day Gemini takes it over, then Claude takes it over, and then GPT comes back. And it's very hard to make that decision on who to work with. Second is cost. And this has become a real issue now. Like, you know, for the last 2 or 3 years, we were in this pure go and invest and we'll figure out like how to get value from it. But that investment became too big. Like companies have—

Nakul Mandan:

like token spend management and stuff.

Arvind Jain:

Like, you know, they've created these annual budgets and they've blown past that, like, you know, in 1 month or 2 months. So, so cost has become a real concern because, you know, business value is still elusive. And then third, there's this sprawl problem, which is that the CEOs, executives, they pushed AI so hard inside the enterprise. In fact, there was things like token maxing. Everybody was being encouraged to do more and more work with AI. And that has also resulted in now you are ending up with a lot of mess at your hands. You have a lot of AI slop, like a lot of AI-written documentation that nobody's time to read. People are building skills and agents and there's a lot of duplication and this sprawl has become a big issue. So these are the top 3 problems that businesses have. So now, now when you think about models, when they can't make a decision and when you think about cost, open source now has to become a big part of like, you know, their solution stack. So who do they work with? The working with the lab, you know, they're going to actually direct them to their models. You know, they could be probably more too, too expensive and you're not getting all the innovation. In fact, this is very interesting that a lot of this divergent set of, you know, technology that is being made available even by the labs. For example, Claude today, to the best of my knowledge, doesn't have great image generation models. For that, you know, we have to use OpenAI models. So Glean can actually use all of these models. We can go to our customers. We can say that we bring all the innovation from all the labs because it's available to us. We're their customers. We can also bring the open source models so that we can actually when, you know, a task doesn't require the top-end frontier model, we can actually use an open-source model that is equally good at that task, but costs like, you know, an order of magnitude less. So we can bring that. So that actually is a story as we being as an independent AI company, which is not, has vested interest towards one particular model, you know, actually puts us in a very advantageous position because this is the core question that enterprises are trying to actually They want to have that independence, you know, from a specific model.

Nakul Mandan:

What about then companies like Notion and some others who are platforms, and maybe Granola is coming up in a different audience that are also collecting across the board organization. They're also independent. You know, Notion has made a big foray. Actually, Notion is one of the few companies like you guys, which is a pre-AI company that has completely revamped their platform in a good way. It seems to me from a distance. What do you think of them? Or is that a different— you are primarily enterprise, they might be more SMB mid-market?

Arvind Jain:

That is true. But also one thing that's very interesting is that when you think about AI inside your enterprise, let's say you're building these agents, like, and you build these agents for your go-to-market team, an agent that actually helps you prospect, an agent that helps you run effective meetings, an agent that, you know, nurtures opportunities better for you. All of these agents, you know, these are like, you have your core intellectual property in that you've actually designed them. You've actually brought the intelligence and this intelligence compounds over time as those agents get better. And you want to make sure that these agents keep working even if you change the underlying CRM. You want that layer, the AI agent layer, to be independent of the system of records underneath. Like you don't want to be locked down, like, you know, the, as like, you know, a better CRM comes about or a better document creation system comes about, you want to have that flexibility without losing all that intelligence that you've compounded. So that's another like technical architectural sort of decision that businesses are making. That they want their AI systems to be independent of the system of records. So that's sort of, again, like, is good for us. You know, we're seen as not being a system of record. You know, if you build agents on our platform, they will work whether you use CRM 1 or CRM 2.

Nakul Mandan:

What does the enterprise stack of 2027, 2028 look like in your view? What do Google and Microsoft have? What do Salesforce, Workday have? Like, do these systems of record continue and you are on top of them? Because there's also a thesis in the world that agents might just completely take away interface-based SaaS applications.

Arvind Jain:

The truth is kind of like, you know, it makes both of those things make sense to some degree. Like I personally believe that most of these systems of records will remain in the next few years. The Google productivity suite, Microsoft, you know, productivity suite, Salesforce, and many of these, you know, like your HR systems, like Workday, ITSM systems, like ServiceNow, each of them will be just fine. They will all be there. And in fact, they will all get very agentic in nature. So if they don't, then of course they will not stay there. In fact, those are the products that are going to go away, which are not advancing with AI. I think businesses will actually build more systems internally. And so the number of applications may end up reducing because if you're building, like, you know, if there was a very point use case, like, you know, one specific thing that a SaaS app did for you, you know, where And that SaaS app is, you know, really let's say UI wrapper on top of your system of record data and Salesforce, let's say. That kind of app, like we do think, you know, that there are people are going to start to build those kinds of systems, you know, on their own because it's very easy. In fact, I'm doing it myself. Like, you know, there are, like we had this asset management app and it was very, very hard to actually use. I, you know, I couldn't do any, any kind of like, you know, literally say it's an Excel spreadsheet., you know, that you're trying to view in a few different ways. And, and that system didn't allow you to do that. It didn't allow me to sort, didn't allow me to filter the way it was not convenient. And I couldn't actually ask a quick question off of that. So I just actually went in Glean and, and asked it to, you know, code up that application and just, you know, connect to the backend database. And like, it actually took me like, you know, 5 minutes. And even the first version of that app was actually pretty good. Behind the scenes, you know, we must have used, I don't know what model it used, probably must have used Claude. To actually compute that app. But that's, that's an example of where users are feeling more confident of building some systems themselves. So we will see that. We will see a lot of like internal, internally built applications.

Nakul Mandan:

How do you see Glean's product today? Is it an app layer product in your mind? Is it a completely agentic offering or is it actually the infra that enables all kinds of AI to be used, whether your own agents or somebody else's agents on top of— what is your current view of Glean's platform?

Arvind Jain:

So we are fundamentally the context and intelligence layer for your enterprise. So it's a platform. You bring it, you connect Glean with all of your enterprise systems. And now we understand, like, of course, you know, all the data, all the knowledge where it sits. We connect with structured data systems like Databricks and Snowflake. We connect with unstructured systems like your documents. And once you've connected Glean with all of your enterprise systems, We now become that gateway, an AI— think of us as an AI gateway, and it has a few different capabilities. One of the capabilities is that we can actually retrieve any information from any system given any task, any question that is given to us. Another one is that we can actually help you take an action inside those enterprise systems with the right security. Yet another one is that, you know, you can actually ask us, hey, how is this task being done today in the enterprise? And we can actually bring the right sort of, you know, we mine, like, you know, how work gets done.

Nakul Mandan:

You can do that? Like, you can ask Glean saying, hey, how does this person do again using this particular task.

Arvind Jain:

Yeah. So we'll, we will actually go and go back and share that because behind the scenes we're building task-based memory. We're building personal individual memory, like for any given person, we deeply understand who they are, what role do they play in the enterprise? How do they like to read and write? What are their preferences? What are they, what are the things that are expert on and what are the things they need to get done this week? So we know all of that. And this memory infrastructure is also another piece of infrastructure that we build as part of this platform. And so now this platform is available for all of your AI applications inside a company to use. So you may be building some agents, let's say building those agents in Vertex in Google or Bedrock in AWS. And so you'll connect it with that Glean MCP gateway, which brings all of that enterprise context and intelligence into those agents now. So when these agent builder platforms now they don't have to go and figure out how to connect with all enterprise systems. As an agent builder, like all of that connectivity is pre-established for you. So you just focus on what task you're trying to actually go and build. Similarly, we can actually serve as a backend inside your Claude, Cowork, or ChatGPT, and we just make those tools better, make them more company context aware, smarter. You get better answers and we actually also make them faster and cheaper. Because interestingly, what happens with AI today, Claude is actually super smart. You can go and give it an arbitrary task and ultimately it'll figure out how to solve that, but it does it in a brute force manner. It'll spend lots and lots of cycles first assembling just even the raw materials that it needs, the basic context to actually finish this task, and then it starts to work with and apply the model intelligence on it. So that first part of that we can actually short circuit. Claude can just instantly get all the context through Glean. Customers have reported, we've actually done studies that it actually reduces token consumption by 30 to 50%. So that's a, that's a big use case. In fact, a lot of companies actually right now investing in Glean for that reason, because cost is a real, real issue for them. So that's what, so coming back to your question, so that actually makes us the platform, right? It's a platform that makes all your other AI agents and your AI products more capable, better, faster, cheaper. But then we do come with our own first-party products. We have our own agent builder. We have our own Glean coworker, which we think is powerful, more powerful. And in fact, you can think of it as a superset of ChatGPT, Claude, Gemini, all combined into one. So that's our strategy. So build that platform, build it in an enterprise-grade manner with the right security, safety, governance capabilities. And, but then also keep investing in our end user applications.

Nakul Mandan:

So other, other people, you will continue to encourage other agents to be built on top of Glean?

Arvind Jain:

100%. Yeah. In fact, like, you know, we have our R&D team is divided into two. There's a core platform team, which delivers this context and intelligence platform to the enterprise. And then there is the Glean co-work team, which is building the first-party product experience on top of this platform, but they get no extra rights. To use this platform than what Claude and ChatGPT gets. In fact, that's the working model. The platform team doesn't even test Glean coworker. They actually only work with Claude and ChatGPT as the primary test platforms.

Nakul Mandan:

How much of the use of Glean today is enterprise or people asking a question versus pure agents? And how is that ratio maybe changing?

Arvind Jain:

Getting answers to questions, seeking knowledge, it's the top use case for Glean, but it's also the top use case for AI in the enterprise in general. So even in ChatGPT or Claude, that's the number one use case. Coworking and doing tasks is emerging as the new category, which is growing at a much faster pace. And so we see now, like, I think at least a third of our users going into very, very deep and complex tasks where they're no longer actually just asking questions. They're actually fundamentally like doing a lot of their work with it. Like all engineers, of course, you know, they are at this point, like, you know, Nobody's writing code, you know, from the first principles anymore. Like, you know, AI is doing the first version of code writing. So, so, so in general, like, you know, the co-working has fundamentally increased. So like for us, we're not the platform that people use to write code and as much, although they can, like, you know, it's again, like, you know, it's the same model. So you can actually do everything that you can do with Claude Code, but that's not the use case that people actually use Glean for more. But think about a support engineer as an example. As a support person, all your cases, they're already using Glean as the first person to actually go and understand that ticket from the customer and resolve that case. And in fact, most of the times it'll just do it all by, by itself and the agent doesn't even have to look at the ticket. So, so their coworking has now fundamentally increased.

Nakul Mandan:

Fast forward 2, 3 years, what does Glean look like in that world? And maybe an extension of that question is what does a knowledge worker's life look like in 2, 3 years?

Arvind Jain:

We want to remain the leading enterprise AI company in the world. Our mission always has been to help people do extraordinary work and expand their human potential. The way we think about our journey, like next 2 to 3 years, we want to keep extending this context and intelligence platform that we're building. We believe that we have an unbounded problem to solve. So we're not going to be able to solve even the same products. Like if you think about that, you know, we allow people to search and find things. We allow them to actually do work, but we can, we can, we have to make them all of these products 10 times better, 100 times better than what they are today. So, so we'll continue this journey of investing in this platform. And we do expect us to become one of the, one of the de facto standards in the enterprise where a lot of other AI products use us as that, as this underlying platform to build, you know, core AI capabilities, you know, within their own product experiences. In terms of knowledge workers and like, you know, how our lives are going to change right now, people are already doing that, by the way, in most, most of our customers. We have some of the most tech-forward companies as our customers today. And you would expect that everybody in those companies are basically becoming AI-first and using AI, you know, in a maximal possible way, but it's not happening. We see like this power law where 5% of people are actually doing a lot with AI. They've completely changed, you know, their workday. In fact, they've become largely a reviewer of the work that AI does for them. But the remaining 95% are using, they're still using AI, but they're using it for that use case that you first mentioned, which is knowledge, like question answering, like, you know, I have a question, give me answer, or I need some information, give me that information. So, so first we have to actually fundamentally change that about AI. The reason why it is only 5%. Of the people doing so much and 95% not because people don't have time. They don't have, and not everybody has the same level of curiosity. AI today is very reactive. It only helps you when you actually ask for help. And so that's one thing that we're changing with Glean is we want to be that proactive work companion for every knowledge worker. Before you even think about like, hey, should I do this task with AI? AI has already figured out that, you know, you had to do these 10 things. And has come to you and said that, hey, by the way, I can do these 5 things for you, or I've actually already done it for you, and here's my work. And so that is sort of going to fundamentally change, first of all, making this AI a truly broadly adopted technology when it becomes proactive. So that's our vision, to be that best proactive coworker. And that's what we think is going to happen to knowledge workers. Each of us will have this personal companion that knows everything about us in our work life and it proactively helps us. And the burden is no longer on me to learn how to use AI. So that's the world that we are going to be moving towards in the future. And you can imagine that like a lot of things that takes, that feels like drudgery to us, like tasks which we are not excited about, our companion, our team of agents, personal team of agents is going to do that work for us.

Nakul Mandan:

Yeah. Do you feel like a almost like a revolution is coming where companies quickly identify that 80% of the work is done by 5% people because they've supercharged themselves.

Arvind Jain:

I actually don't believe that companies will be successful in reducing their workforce significantly while still having the same level of market success or the share of the market. Because if you think about it, take an industry and if you are one of the companies in that, and there are 4 competitors, all 5 of you have the same tools. All of you have all the great AI capabilities. If you can actually do the work that needs to get done with half as many employees, so could they. In reality, what's going to happen is that your customer expectations are going to double. You have to sort of build better products, stronger products, and you're still going to need that same staffing to do that. In fact, like you're ultimately the business success is indeed proportional to, you know, the human sort of capital that you have inside the company. So that's my belief. I would like take a different stance, like, you know, where I do believe that this power law will change. Take calculator. Does everybody know how to use it today? Equally well, right? And I think that's what's going to happen with AI. So it won't be that 5% like who have figured it out and the 95% haven't because AI itself will be that great teacher.

Nakul Mandan:

The thought that is expressed, I think Elon Musk has expressed this, Scott Wu recently expressed this in some interview, that we have gone, we are going as a human race from survival mode where we were all doing work to creativity mode where it's an age of abundance because agents do all the work. It sounds like you're saying, correct me if I'm wrong, that that's not very imminent. It might be some futuristic vision, but in the near future, everybody will be just more productive, more work will be expected, and competition will take care that everybody will still need their people.

Arvind Jain:

I think so. I mean, like it is true that, you know, as such, you know, we are moving in the world of abundance. There will be enough food for everybody to feed and all of that. But yes, like, you know, as a business, the competition is going to, you know, increase. Like I really sort of don't see this world where somebody can build a very flourishing, great business where their people only work 2 days a week and the competition actually works, you know, 5 days a week. I don't actually know how. The workday comes down from 5 to 4 to 3.5 that everybody's talking about, because companies are going to immediately see that as a competitive edge. I would want our team to be fully motivated and energetic and actually keep working the schedule that they are today.

Nakul Mandan:

But does the human work evolve from doing to reviewing mostly?

Arvind Jain:

Human work completely changes across different functions. A lot of knowledge work, is going to be done by AI for you. You don't need to, for example, do information synthesis. You can actually get AI to do all of that work. So I think our work will shift more to asking the right questions, actually thinking about what are the things we should be working on. And then as AI acts as a very powerful companion to us, it's sort of like, I would say, think of it as now Instead of being an individual contributor, I have, I have like, you know, 10 really, really good people, you know, available to me. And then now we can actually build a product together with that team.

Nakul Mandan:

Let's shift towards how Glean is run as an AI-native company. So let's start with you actually. I mean, you were a co-founder of Rubrik and then you were at Google. So you've been an exec for a long enough time. How has your day changed as a CEO with AI? What are decisions that you are now actually fully giving to an AI system? versus still keeping? Yeah. How are you using AI in your daily life today?

Arvind Jain:

Yeah. Well, I mean, our primary tool to use is Glean because it's meant for this work, but I also use ChatGPT quite a bit. Partly I always compare these tools and see like, you know, how we make Glean better as a result. The things that I do with AI, number one, this, like, I'm a very curious person. I have questions all the time.

Nakul Mandan:

I mean, that's why you started Glean as an answering machine in the first place.

Arvind Jain:

That's right. Like I was truly passionate about that, you know, and the, yeah, cause you know, I felt that. That would be a product that I would like so much for my work. So I have these questions all the time. And before, my way of getting these answers was that I would ask my executive team to give me a report on this or give me data on that. And I think I was probably very annoying to them with the frequency and the volume of questions that I had for them. And now I self-serve myself for all of those things. I have actual system access to those systems, you know, where I'm looking for answers in and Glean is my always the first stop. Sometimes, you know, I will verify. So if I did some analysis, I got some data back and I'll ask my CTO, hey, does this look right or not? So that's, that's one thing, like, you know, just self-serving myself with whatever information needs, whatever questions that I have has become so easy. And in fact, there are certain things, you know, that you just simply couldn't do before. I'll give you an example. And this is like how I'm actually doing strategic work with AI. We made a shift in our product direction earlier this year and Context and Intelligence platform became a big part of our story. We always had the platform, but we said that like this year we won't actually have half of our users not coming from our first-party products, but from these other products like Claude and ChatGPT And that required a change in, you know, company directions. I communicated, I talked to the team. But then, like for the first few months, I would always run a deep analysis in Glean. And I would say that, hey, look, you know, look at every individual, look at every engineering project and tell me, you know, which projects are actually no longer aligned, you know, with our new strategy, something that we should deprioritize. And this analysis, you know, before, like, you know, you could do this analysis once a year. It's a full trickle down from like, and I would ask my, you know, the head of engineering, they would ask their managers and And then you get these reports, but now you can get these kind of analysis done super quickly. And that allows me to sort of like now move much, much faster as an organization, align our projects better with our strategy. And there are a lot of other topics like those. I'll give you two more examples. One of my favorite ones is that I'm always trying to look for who are the best people in our company and the ones, you know, who are somewhere there hidden in the organization. They do great work, but they're not that vocal and, or maybe they're like a little bit too far down in the organization for us to have visibility. So I have this agent that actually helps find the top 10 people for me and explains to me why they are.

Nakul Mandan:

Actually, that's a great example because in the prior world, that would always be gatekept by politics and who's favorite, who's the exec's favorite person.

Arvind Jain:

Yeah. And so these are like self-service examples as a CEO that I have. And then finally, the third example I'll give you is is customer management. So one of the big things for enterprise, you know, sales company is managing churn, like making sure customers are happy and doing the right things for them before those customers, you know, choose to go away from you. And so like companies have always built these, you know, customer success dashboards and oftentimes, you know, there's never, you know, truth in those. They are not up to date. Like people don't actually, people are not motivated to actually keep enter the latest information and a lot of context is actually lost in customer conversations that you're having with these customers, which is very hard to sort of then summarize and organize. But now it has all become very easy. So we have in Glean, we have this system called Prism where, so now I have this, you know, live dashboard where for each customer it actually gives us like the full view on what is happening with them and what's the risk and it actually scores itself like, you know, what are the top customers, you know, that are churn risk for us. And in fact, like it goes beyond and actually also says that these are the 3 things you can do to actually fix these customers. It's actually a very actionable system that we've built, which was never possible with AI, but this is, that is actually another one. And this is what we call enterprise level intelligence. So this is no longer like, how do I improve one person moving faster? This is about how do we do these things that we simply could not, you know, in the, in the pre-AI world.

Nakul Mandan:

Still feels though it's a very, very sophisticated knowledge retrieval, information synthesis, summarization insight, or are you also using it for doing like, have you almost replaced a chief of staff or like, are you using it for doing things for you?

Arvind Jain:

Well, I do, but I think you also have to remember that as a CEO, like, you know, most of my job is understanding and I think these are actually harder problems, frankly, like, you know, like having a, I do have an agent that actually reads my inbox and actually creates drafts automatically for important messages. And inbox and actually also like stars messages you know, which are actually, you know, require my attention. And so those kinds of things are there, but like, you know, they bring, you know, like sure, they may bring half an hour of savings or one hour of savings, maybe replaces one human. But I think the more exciting part is doing these things which are truly strategic, you know, which we just could not you know, get done.

Nakul Mandan:

At this point, is every department at Glean pretty AI native? Like engineering is obvious, but like finance and back office, HR.

Arvind Jain:

So we are like, since we are one of the first enterprise AI products, so we've been using it heavily inside. And so finance team has, you know, tons and tons of agents. Like they've actually one of the most AI-fied department for us. To be honest, you know, we are, I'm not satisfied with how much AI-fication we have done, you know, we could, we could do better. Last quarter I started a program. So we have this, this product inside Glean called Agent Finder. And Agent Finder is, it uses that same sort of knowledge graph and understanding of like, you know, what work is getting done and who's doing it. And it actually, it actually prioritizes and figures out that these are the top 20 or 30 agents that you should actually build because this is where most of your human time is actually getting consumed today. And it's rudimentary in the sense that it is basically basing it on where time is being spent. We're not actually saying that, hey, like, but this is too hard and therefore this should not be, like, it's just giving us where the time is being spent, right? And so based on that, we have a list of things that are not yet agentified. What we're going to do is we're going to ask, like, not engineers and not individual contributors, but we're going to actually ask our leaders to actually go and build all of those agents. And they can build it in Glean or they can build it in any other agent platform. It doesn't matter. And so that's, that's the program that we are running right now. So I was trying to achieve two things with that. One is, one is of course, you know, building, bringing more, building more automation, but also a big part of like, you know, how that's going to be achieved is when you truly make your workforce AI native, right? And so by forcing our managers to do it. And they cannot delegate it. That makes us get to a place where our leaders are fully AI-fied.

Nakul Mandan:

Do you feel your non-engineering leaders are at this point AI-fied enough where they are getting into Claude Code Because Claude Code allows you— is no longer— is literally writing the code itself. So you don't even need— as long as you can be a product manager, you could probably build agents yourself. I know that's outside of Glean, but do you feel you've kind of through the brute force of last couple of years made that a mandate? If you're not AI native as a manager, can you survive at Glean? Let me ask you this way.

Arvind Jain:

We have a process actually, even when we recruit people, there is the AI native check that happens. If today, like, you know, after all, like AI is already very, you know, quite spread, you know, well spread, right? So in our interview process, we actually test for how much you've been using Claude and ChatGPT and Gemini. And what work have you done with it in the past? Most of our managers actually don't build agents in Claude Code. Our engineers, of course, use Claude Code all the time to write code because, you know, like Glean is simpler and it's much easier to actually go and build these agents in Glean. And so our, and that's the interesting thing, like, you know, if you think about it, while, you know, AI is a very powerful technology and everybody should know how to use it. but it should not feel like a complex technology that people have to learn. So I would still want our sales leader to be primarily very, very good at sales leadership. And yes, they should be aware of the power of AI, but we do believe that the tools will be quite easy for them to just use. Like they don't have to actually become hackers or builders.

Nakul Mandan:

There's been this dream of an AI SDR. Are you guys using AI SDR?

Arvind Jain:

We don't use it.

Nakul Mandan:

Okay. So actually, can you double-click as to what you found flawed in that dream?

Arvind Jain:

I think first of all, AI lacks judgment and, you know, from an SDR perspective, we use AI heavily, so it is doing 90% of the work for us in the prospecting workflow. But I think when you have that, the final judgment from the human, sometimes the human may say that, hey, look, yes, you know, this is account. This is the person who we're going to send the message to. And I like the contents on this message and they can approve it. Like, you know, let me just give a thumbs up or they can just speak to this agent and say that, no, like, you know, I don't, I think we're missing, you know, this particular piece, you know, which based on the context that you're sharing with me. So let's, you know, make it more heavily weighted towards that. So you sort of like when you tweak it a little bit, like, you know, now this is an actual, we do have SDRs. As an actual SDR who's actually going to be sending that message is fully, their eyes have been on it and they've tweaked it to the best of their sort of satisfaction. And that actually creates much better results because otherwise, people like my high school kids, they can tell you very quickly what text is written by AI and which one is not. And so customers also can tell. So I think We are very firmly of the belief that AI should be used as a tool, not something that, you know, replaces the entire sort of human task. It can replace 90% of that task.

Nakul Mandan:

You talked about recruiting a little bit. Yeah. What has changed in your recruiting process beyond the thing you just mentioned? Is there a part of every interview has an AI-native element to it? Is there tasks you give?

Arvind Jain:

Yeah, certainly. Like, you know, for example, for when we recruit engineers, so we used to have this programming exercise before. It was a 2-hour session and there was a program that we thought could be done if you write code yourself in 2 hours. So we've changed that. Like now we don't actually tell people and prompt them that you can use AI to do it, but we give them a task, which is so, so much more in complexity that there's no way for you to finish that in time in 2 hours, unless if you use AI, right? So that's sort of like, that's an example of how we changed the interview process there. We've done that across departments. AI fluency is a key check, but within the recruiting process, there are other things that we do. One of the things that, you know, I was very frustrated with was we use Greenhouse as our application tracking system. I still approve offers for employees, for every employee that joins the company. And I would get these packets and I was always, I always felt that it was incomplete. Like it was missing, like somebody puts in a feedback that, oh, I love this person, he's great, let's hire him. And that's not a good enough feedback for me. I would actually see what happened in the interview, what questions were asked. And so there's a lot of back and forth before, and it was like my time, time would get spent, like, you know, and so now I actually said everything that I want to see in the packet is written in a document. And now there's an agent that verifies every packet and they don't get routed to me until the agent actually clears it. you know, within the system. So this is an example of where AI is not replacing anyone, but it's just improving the quality of our work.

Nakul Mandan:

Are you seeing even within your organization in every department, maybe it's slightly different, the 5% versus the 95%?

Arvind Jain:

I mean, like it's, it's maybe it's not 5-95, but yes. And by the way, it's not about, that's not a judgment on who's better and who's worse. It's actually. Some people are naturally more curious and some people actually figure out they reserve some time for learning. Others have so much of a workload that they have absolutely no time to actually figure something out. But we do see that.

Nakul Mandan:

If somebody is excellent at their job, like sales, but they are not AI fluent, that person can still thrive at Glean in your judgment?

Arvind Jain:

They should, but actually I think you can be— the problem is that in that particular example, you're going to be a much better seller if you're using the right AI tools at your disposal, because there is no way to, no way to sort of, for example, get the latest context on a customer if you're going to actually manually go and try to search in Google now, right? You know, there's better ways to do it. So, so I think it's actually becoming, AI is becoming a must as a, as a tool to use.

Nakul Mandan:

So you mentioned 1,500 employees today. The first question is really how much is the actual output? If you compare it like by like to your prior world, do you actually feel there are your 1,500 are already 2,500 people in the prior world?

Arvind Jain:

It's so hard to measure. Like, yes, we're writing more code. Like, if you look at just the number of lines of code written, there is more. But I think, you know, we're now spending more time debating and discussing and, you know, things that we should be working on. And so we see productivity improvements, but I can't quantify it actually, especially in software engineering, it's hard to quantify. But in other functions we can, like for example, in our support team, we had a number of cases that, you know, one support person could actually resolve on a daily basis. And that number has steadily been going up.

Nakul Mandan:

What keeps you up at night about Glean today, product or organization?

Arvind Jain:

I mean, we are in a very fast moving industry. In fact, you can say that there is no real foundation, like the foundation itself keeps changing every day. So I think for us, like the biggest things on my mind always is how do we communicate our vision, our strategy effectively within the company? How do we make sure that everybody is aligned and moving in the same direction? So it's, it's the execution, you know, that keeps me worried because, and we have to be very, very fast, very, very agile because it's a very high, you know, it's a very competitive space too. You don't get that luxury. Like, you know, one of the things I was sharing in uh, in a previous discussion with somebody is that you never feel this, this feeling of, hey, you have arrived, or okay, a company has been built, we have a stable business, is completely elusive right now for us.

Nakul Mandan:

Do you think this is a new thing in Silicon Valley or this has always been true? Because I do sense a heightened sense of anxiety, success or otherwise, everybody's feeling more anxious. Feels like a now or never moment in Silicon Valley. But do you feel it's always been true?

Arvind Jain:

No, it's not always been true. I think we used to feel more stability before. I've felt it personally, like, you know, I've built a company before, I've been like, you know, and multiple times. And so I think we had this feeling that once, you know, in the past, you would get a customer and you had that relationship and, you know, it'll be a long lasting thing. But today, like, you know, it's very hard. Like, you know, if you build a product, if you are like 20% slower, somebody else will have a much, much better product than you. And there's no way for a customer to justify your product anymore compared to them. Because it's the pace of innovation that is actually making, creating that feeling of anxiety. When the world is innovating so quickly, in fact, everything that you build instead of, you know, it feeling like, you know, your moat or IP, sometimes it feels like liability because you have to actually quickly get rid of it. So like in our company, we have this, I always talk about that, hey, if somebody deletes lines of code, that's actually even more valuable. Than writing lines of code. That's, you know, that's the proof of that we are actually moving with times.

Nakul Mandan:

Do you think this is a permanent change in our world now? Like there will be just because the pace is not going to slow down ever. And so is it that we all will get used to this uncertainty around defensibility for every product that is even loved? But because that's what you're saying, the Glean is loved, but you don't feel as assured as in the prior world with Rubrik. So do you think this is a permanent part of our world in Silicon Valley?

Arvind Jain:

Like in the AI space, in the AI companies, it feels like that to me that I don't see when it's going to actually change because I think our ceiling for what AI will be able to do for us, like people talk about AGI, people talk about that, hey, this is already so good, it can actually do replace any human. But let's be clear, it's not ready to replace even a single human today, no matter what job they do today. Even if you're an EA, even if you're chief of staff, AI is not capable of replacing you right now. But in the future, we're going to get close to that. So there's a lot, like, you know, that, you know, AI needs to get significantly better. It needs to get significantly cheaper, more efficient. And so the pace of innovation is like, is going to continue, which means, you know, like, you know, companies will kind of feel that way.

Nakul Mandan:

How do we deal with this anxiety going forward?

Arvind Jain:

How do you deal with this anxiety? I need answers for this. I don't, I wish I actually had an answer. But yeah, like I'm trying to figure it out. I think ultimately, I think what saves you is the fact that there are 10 things that need to get done. And so like, as you, through your day, as you're actually getting those things done, like, you know, it takes your mind off of, you know, this more, you know, higher level question and that anxiety.

Nakul Mandan:

Last question for you. Somebody watching this, somebody young watching this wants your life. You've built a successful life for yourself. They want your life. What would you tell them of what the journey actually requires?

Arvind Jain:

Well, if you're asking about how to become an entrepreneur or grow in your career, the most important thing in my opinion is hard work and dedication and just, you know, being honest, you know, about that. Like I fundamentally believe that anybody can learn anything if they put their mind and effort into it. So hard work basically pays off. As long as you stay, you may be in a job that you absolutely hate. Like you don't like it, you know, you're bored. You don't think that's your long-term thing. And I would actually say that even if that's the case, in that job, work very, very hard until the very last day, because you know, you'll be building these connections, people who have worked with you and your impressions, you know, always carry forward. And that's the one that actually, you know, gives you those rewards. For example, when I actually went to raise funds for the very first time. Why did I, why I was able to raise funds? Because I had actually created a good impression, you know, with my manager at my past job and that reference check, you know, was, was a positive one. So that's, so that's sort of like, you know, my advice, you know, keeping it simple. Don't have anxiety about whether you're learning something or not. Just work hard. If you work hard, you'll naturally also learn.

Nakul Mandan:

Arvind Jain, this was great. Thank you so much for coming on.

Arvind Jain:

Thanks.