$7B AI founder: "AI is not capable of replacing you right now" | Arvind Jain (CEO, Glean)

First of all, we started Glean in early 2019. That makes us the world's first enterprise generated 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 seven 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 in Glean, you ask questions and we'll bring the right answers and the right information back to you. That's how we got started. Today, we are a co-worker. You come to Glean, you can actually go beyond us 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 I think one thing that we always educate our enterprise customers is that this is a complex undertaking, bringing a really good co-worker to your employees while meeting all of your enterprise requirements.

It requires effort and energy and that experience, you know, that we've learned over the years. So the main edge, the main edge for us is that experience, you know, that the fact that we've been in this business for a long time.

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 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 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 multi-modal nature of it what is the cutting 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 All right.

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 a thousand enterprises over the last one year, some of the key trends that we have actually heard, like the top concerns that enterprises today have are, number one, 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 two or three 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 allocated like token spend management and stuff You know, they've created these annual budgets and they've blown past that like, you know, in one month or two months. 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, you know, 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 know, you are ending up with a lot of mess, you know, at your hands. You have a lot of AI slop, like, you know, a lot of, you know, 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 three problems that businesses have.

So 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 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 and frontier model, we can actually use a 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. 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 for it. 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're primarily enterprise they might be more smb mid market 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 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 agentic layer, to be independent of the system of records underneath. Like, you don't want to be locked down, like, you know, 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 the 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 clean as not being a system of record. You know, if you build agents on our platform, they will work whether you use CRM1 or CRM2.

How do you see a 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 kind of AI to be used, whether your own agent or somebody else's agent on top of? What is your current view of Glean's platform?

Glean today is enterprise or people asking a question versus pure agents? And how is that ratio may be changing? 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 Cloud, 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 cloud 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, they just do it all by itself and the agent doesn't even have to look at the ticket. So that co-working has now fundamentally increased. Fast forward two, three 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 two, three years? 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 two to three years, we want to keep extending this context and intelligence platform that we're building. We believe that, you know, we have an unbounded problem to solve. So we're not going to be able to solve even the same products. Like, you know, if you think about that, you know, we allow people to search and find things. We allow them to actually do work. But we have to make them, all of these products, 10 times better, 100 times better than what they are today. So we'll continue this journey of investing in this platform. And we do expect us to become one of the de facto standards in the enterprise where a lot of other AI products use us as this underlying platform to build, you know, core AI capabilities, you know, within their own product experience.

sales 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 uh 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 five percent of people are actually doing a lot with AI. They've completely changed their workday. In fact, they've become largely a reviewer of the work that AI does for them. But the remaining 95% are still using AI, but they're using it for that use case that you first mentioned, which is knowledge, like question answering. Like, now I have a question, give me an answer. I need some information, give me that information. 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 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 say that, hey, by the way, I can do these five 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, like to be that best proactive coworker. And that's what we think is going to happen to knowledge workers.

You know, 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're going to be moving towards in the in the future and and you can imagine that like a lot of things you know that takes that feels like drudgery to us like you know If you're not excited about, our companion, our team of agents, personal team of agents is going to do that work for us.

and then how we can actually build a product together with that team. 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 GroupRick 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? Was it still keeping? How are you using AI in your daily life today? 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.

still feels though it's a very very sophisticated knowledge retrieval information center 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 well well well i do but i see 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 are 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 it replaces one human. But I think the more exciting part is doing these things which are truly strategic,

Are you guys using AISDR? We don't use it. Okay, so actually, can you double click as to what you found flawed in that dream? I think, first of all, AI lacks judgment. and you know from a str perspective we use ai heavily so it is doing 90 percent 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 uh you know this is account this is the this is the person who we're going to send the message to and i like the contents of 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 and say that no like you know i don't i think we're missing you know this 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 uh an actual we do have strs as an actual str who's actually going to be sending that message is fully their eyes have been on it and they have tweaked it to the best of their sort of

you know satisfaction and that actually creates much better results uh because otherwise you know like people like you know like my uh my high school kids you know they can tell you very quickly what what text is written by ai and which 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.

the 5% versus the 95%? I mean, maybe it's not 595, but yes. And by the way, 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 are have so much of a workload that they have absolutely no time to actually figure something out but we do see that if somebody is excellent at their job like sales but they are not ai fluent that person person can still thrive at glean in your judgment they should but actually i think you can be the problem is that in that particular example you're going to be 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 want to actually manually go and try to search in google now like 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 so you mentioned 1500 employees today the first question is really how much is the actual

output if you compare it like by like to a prior world do you actually feel that your 1500 are already 2500 people in the proper prior world 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 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 one support person could actually resolve on a daily basis, and that number has steadily been going up.

You've built a successful life for yourself. They want your life. What would you tell them of what the journey actually requires?

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 and 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're building these connections, people who have worked with you, and your impressions always carry forward. And that's the one that actually gives you those rewards. For example, when I actually went to raise funds for the very first time, why was I able to raise funds? Because I actually created a good impression with my manager at my past job and that reference check you know was was a positive one so that's that's sort of like you know my advice you know keeping it simple don't have the anxiety about whether you're learning something or not just work hard if you work hard you'll naturally also learn

Arvind this was great thank you so much for coming on thanks