Arvind Jain: Why Now Is the Time to Solve Enterprise Search
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Hey everyone, welcome to Generative Now. I am Michael Magnano. I am a partner at Lightspeed. Enterprise AI is transforming how companies work and few understand this revolution better than Arvind Jain. As the founder of Glean, Arvind has been building one of the most sophisticated AI workplace assistants since years before ChatGPT became a household name. And now it is a massive player that dominates the enterprise search space. Starting his career at Microsoft and then working as an engineer in Google's early days,

Arvind co-founded Rubrik and took that company public in the high stakes world of data security. Arvind and I had a great conversation about why he loves competition, how technical innovation informs product strategy, and how Glean found product market fit. Take a listen. Hey, Arvind. Good to see you. Good to see you too. Glean has been an obvious success story in the world of AI and generative AI.

The interesting thing about Glean is it seems to have started well before the current wave of generative AI and the AI explosion that we've now been going through really since kind of the launch of ChatGPT. I figure a good place to start would be to hear the story of Glean and how it got started in 2019, you know, from your journey at Rubrik to starting this and like really what you saw before others did before the AI wave took off.

So we started in early 2019. And at that time, you know, we didn't have, you know, that much of an idea of how AI was going to take over the world like it has today. But the problem that, you know, we wanted to solve was an important one. We started with this vision of building a really powerful platform

search experience inside businesses for people in their work lives. Before Glean, I was one of the founders of Rubrik, which is an enterprise data security company.

And one of the things, you know, like at that company, like we grew very fast, we were very successful. But as the company grew, like, you know, as we sort of crossed a thousand employees, we started to see like, you know, big drop in productivity, like across all of our different teams, right?

And part of it is like, you know, you sort of like take it for granted that, yeah, like, you know, as things, you know, as companies get bigger, like, you know, like things become more complex and slower. But I felt there was more to it. Like, you know, we were actually seeing very, very steep drops in productivity

And I think part of it was caused by this issue of the company has so much knowledge, so much data, spread across so many different systems. And also as the companies grow fast, people's roles, it's not always clear who works on what, who's an expert who can help me on any given topic. And these were actually the largest complaints that we were seeing in our Pulse surveys,

People are complaining about these. People are saying that, you know, I'm not enabled. I can't actually get my work done because of, you know, these issues. And so me being a search engineer, like, you know, before Rubrik, I spent over a decade of my career building, you know, Google search.

Is it like, well, people are complaining about not being able to find things and, well, we should go and buy a search product that can connect our enterprise data that's fragmented and spread across all these different systems so that it's easy for people to find things. And also it's easy for people to find other people who can help them do their work.

So we decided, you know, that was sort of the origins of, we were trying to solve a problem internally, you know, at our company. And that's when I realized that there's no good search product that we could buy. And so we wanted to go and solve that. And one of the interesting things is that in 2018,

In the search industry, among the search engineers, we were already seeing the power of the transformer-based neural nets and these models. There were some language models. They're small, like the BERT family of models that were already out there. And in Google search, we were seeing how you could take

every component of the search technology and you could actually build a new system using these transformer-based models and they would actually perform better than these previous generation of models that were built over the last 15 years. And so we knew that

Transformers was going to completely change how search works. You can actually do search at a much more conceptual and semantic level. And so that was an opportunity to actually also leverage and build a really good product in the industry. So that's how we sort of got started. In fact, Glean became the first company, I believe, that has brought the transformer technology to the enterprise in that sense.

It's so cool. And it's such a great insight. I mean, you know, even relating to my own experience, I was building a small startup, you know, got it to a couple dozen employees and then we sold to a much bigger company and we show up at this big company and there's thousands of employees and there's all this context that we're missing. And you go and grab it in Slack or an email or in Google Drive or in any any number of these systems, Confluence, Okta,

But it's all disconnected and there's no and there's no way to sort of bring it back together and bundle it. And it sounds like that's the opportunity you saw. I mean, even though you saw that this opportunity was coming for LLMs, I imagine that you weren't necessarily leveraging them from day one in the product. So like, how were you tying all these systems together that I just mentioned? Like, what was the actual technical approach to connecting all of these endpoints? Yeah.

Yeah. So, like, if you think about the search stack, so number one, like, to be able to search over information, you need to have access to that information. If you look at a modern enterprise, like, most of their systems where their knowledge and data is, is a, you know, is a SaaS, like, cloud-hosted system. And typically, like, SaaS systems interoperate well with each other. They have good APIs available to them. We started to build these integrations, you know, with

products like Confluence and Jira and Google Drive, you know, SharePoint, whatnot, right? And all of these integrations were built using, you know, the already published APIs, you know, that these systems exposed to us. So you guys should connect with these systems. You could see all the data inside them, and then you can bring it together in one common search system. So that was the first step of building the technology was building these integrations.

But you have to also understand that enterprise search is very different from search on the web. On the internet, we can all access all information. Everything is available to us. In an enterprise, information is privileged. It's secure. There are certain documents you can access, but then there are many others that you can't. So when you actually build a search product, you have to actually build a safe version of search. You can't actually start to leak information to people online.

from this search experience. So you have to sort of think about the problem differently, you have to figure out how you're going to actually solve, you know, secure, you know, permission-aware access, like, you know, within the search results. So we had to sort of build a lot of security infrastructure to actually handle that. But to actually, you know, on your question about transformers, actually the transformer models were part of our first version of the product. Like it was from day one. Yeah, so from day one. That's really cool. Yeah.

Because, you know, the technology was in front of us, you know, we saw these models were so powerful and you can build a much more smarter search. You can actually come in and you can ask questions in natural language and we could actually match it much better at a conceptual and semantic level, you know,

to those documents in Confluence or Google Drive, as opposed to doing the brittle keyword-based search. So it was a great technology and it was available to us. So what we did there was we took BERT, which was a model that Google had put in open domain for startups like ours to use.

And then we would take that model and for every enterprise customer of ours, we would actually build a model, you know, customized to that company. We would actually train, we would actually train, you know, retrain these models

on the enterprise corpus on that data in a safe and secure way again um so that you know the the model starts to understand your company your lingo your code names you know acronyms you know all of that stuff um and uh and then

In our core search stack, we would use both traditional search techniques to figure out what's the best information to return given a question, but we'd also use this transformer-based semantic matching technology. So it was a hybrid search stack that we built from day one. So what was finding product market fit for that like? I think about all the startups that have launched over the past couple of years, they almost have this

truly magical experience the first time you experienced them because the AI is just so powerful, right? It's like the first time you used ChatGPT or the first time you used one of these image generators. Going back to 2019 or 2020 when you started to go to the market, the concept of a transformer must have been so new for all these enterprise companies. Did you find that same sort of

magical experience where you're like, oh, hey, enterprise, let us show you how you now can, you know, prompt semantically against your entire organization and just have it, you know, come back to them like a human being. I mean, that must've just been really, really powerful for them.

Yeah, actually, you know, it's interesting that we didn't actually talk about transformers as a cool thing. It was not a lot, not a hard thing. Like people are not talking about it. I think what they cared about was what you could do with it, which was that, well, like, you know, in Glean, you can ask questions in natural language and it will conceptually match the information with your question. You don't have to like remember the exact keywords.

And that was sort of one of the big pain points of the previous generation search systems is that, well, I know I'm looking for this email, like it's in my Outlook, but I still cannot get to it because I don't remember the exact words. So I think it appealed to people, but you actually ask a very interesting question. I don't think we had a product market fit for a long time, like in the sense that the

Somehow, like, you know, like the industry felt that search was a vitamin and not a painkiller. Like, you know, well, yeah, like, you know, everybody would like, you know, they would, they would,

that yes, we have a problem. We have lots of information. It's spread and fragmented across many different systems. And people do struggle with finding information, but well, ultimately they find it and I guess things are working for us. And there was not a product that they were actually buying. They're not buying a search product.

that we would say that, hey, look, you know, we have a better one than what you buy. There was no concept of them buying technology like this. So we had to create a market. And it took us time. We had to do a lot of evangelism. We had to talk about why it is important. Like, you know, it reduces frustration. It makes your employees happier. You know, they spend a third of their time just looking for information. So you will save a lot of time. And so it took us some time, you know, before, like, you know,

we started to gain that traction and gain that word of mouth, like, you know, from our initial customers. - And what was the moment that it all flipped? Was it just, you know, but you reached some moment that you had enough people talking about it in the market that it just sort of snowballed from there? Or was it a key customer or a key sort of product unlock? Like what really did it? - Yeah, I would say there were two, like I would say two distinct moments. Like one is where we actually had a,

like about 30 or so companies, and we were first focused on the tech sector. So we got most like, you know, the most iconic tech companies, you know, they started to use our product at an at scale, and the word of mouth started to happen from there, which sort of created like, you know, that further momentum and inbound like, you know, to us in terms of more interest in the company. And then

And then there was another big moment, which was ChatGPT. In some ways, you can think of Glean as the enterprise version of ChatGPT. It does everything that ChatGPT does, but it does it with that knowledge and context of your company. When the world saw ChatGPT and the power that it has, as an enterprise leader, you were thinking about, well,

Well, what if, you know, I had something like this inside my company, something that knew everything about my, you know, my employees, you know, my, you know, internal data, and it could also answer questions for me using all of that, that would be fascinating, right? So, so that actually, you know, started to create that, you know, organic, you know, demand, you know, it was a second wave of like, you know, massive momentum for us.

Yeah, that's really, really interesting. It's almost like ChatGPT kind of did a lot of the external marketing for you. That's right. But all the things you had previously been working on, like security and, you know, safety and like you mentioned, permissioning between different users, obviously like so, so important to bring that technology into an enterprise. So that's fascinating. You mentioned early on that, you know, you started out the first transformer based model you're using was Bird.

Since then, like how is your like, how do you manage the sort of model component of the product? Do you train your own models now? Are you, you know, you hot swapping things under the hood between, you know, open AI, anthropic, et cetera. Like, how's that all work?

Yeah, more of the latter. So let me explain the architecture of this. We're not a foundation model company. We don't train these super large models. When you think about like the product, like how does Glean actually work? So it looks and feels like ChatGPT, right? You come in, you ask questions.

And Glean will actually use either all of the world's knowledge and context to answer those questions for you, or it's going to actually use your internal company's data and knowledge to answer those questions for you. Now, the way the models themselves, when you think about GPD or Cloud or Gemini or X or

deep seek and whatnot, none of those models are trained on your enterprise information. So they don't know anything about how work happens inside your company. And so how do you actually answer questions, make these models answer questions

know using data and knowledge that's inside your company you know the the architecture for that is you know drag right where you actually you know you know it's a two-step process like you know given any task or any question that a user comes and asks you know first we use our code retrieval and search technology to assemble like some relevant pieces of information from inside our company and then we give all of that to the model so that the model can actually reason over it and

and actually generate the appropriate answers or responses back to you. So that's the architecture. That's typical. Most enterprise agents or applications are built in this rack style architecture where search is a first leg. I mean, not search or retrieval, whatever you want to call it. And then the model reasoning and inferencing is the second part.

Now, our architecture is, so we do build models and they're still built on like BERT or modern BERT, like these small, I guess these are SLMs, is what the industry calls them now. So we actually still train models on your enterprise corpus, but that is done mostly to actually do semantic matching of people's tasks with your enterprise knowledge. So it's used for that, you know,

retrieval task, but then for reasoning and generation, we use the foundation models. Our architecture is not tied to one particular model. What we're seeing in the industry is that there are many model providers. They're all in different models and getting better at different things. For example, these days you'll hear a lot of people say that for code generation, like Cloud is actually doing better than any other model for reasoning, GPT is

doing better than others. And so they're all like all these models are getting better at different things. And our goal, you know, at Glean is to make sure that for our enterprise customers, like we can bring all the amazing innovation that's happening in the industry, you know, to our customers, they don't, they should not have to choose, you know, what model to use. And so like, so that's, that's how, that's how the architecture is that behind, under, underneath, you know, we have

access to all these different models and given, you know, given the task given, you know, we can actually, you know, help an enterprise choose what's the right model to use for that. Yeah. Yeah. And what's really cool about that is as these models keep getting better and, you know, you know, and I'm sure everyone in the audience knows, like, it feels like every week these things are getting better.

Your product just keeps getting better and better as a result, right? Anything these models can do, you get to now offer to your customers, your enterprise customers. What must that be like? Because I have to imagine that as these models improve in capabilities, they probably unlock like completely new feature sets for you. You know, like one thing I keep thinking about with Glean is like, as these models become truly agentic and they move from, you know, they move from individual sort of retrieval of information to more

sequential task oriented, things like that. Like that's gotta open up a huge service area for Glean and your customers. Is that how you think about it? - Absolutely. I mean, I think the, and these features are actually coming

every day, like, you know, like, you know, like, you know, you saw in the last month, you know, with, you know, operators, like, you know, is a new thing, you know, which actually, like, really unlocks, you know, our ability to work with many, many more enterprise applications. One of the important things, like the way I think about AI and its impact in the enterprise,

and enterprise is that like if I'm a business user and I have a question or I have a task which requires me to play with or work with some data inside my enterprise, like AI is supposed to make it super seamless for me. Like, you know, like I don't want, like, you know, this, we don't, we're not going to be in a world anymore where we know that the data exists, but accessing it is hard. Like, you know, AI is going to make it super easy. Like, you know, a lot of it, you know, we do through our integrations.

But a lot of it we can also do through these new LLM capabilities like computer use, operators where you don't even need those systems to have APIs. You can still go and drive a browser and get that data that you need to work on.

for your agents. So the innovation is going to keep happening at the fast pace. Our strategy at Green is always very clear. Everything that the model providers, that the cloud hyperscalers, all the technology that they're building, we basically don't compete with them. We leverage that technology and

And we do the remaining part, which is like, you know, how do you make it easy to access that, like, you know, all that technology and make it work on your data in your enterprise. The third thing that you can do with Glean, like, so we have a Google-like search, we have a chat GPT-like AI assistant, but then we also have an agent building platform. So you can actually build all kinds of agents to transform your business processes using AI. And in fact, I think the best thing with AI is that

It's actually bringing that power to every business user. You don't have to be a developer anymore to be able to build something complex or interesting. You don't need developers for everything anymore. There are a lot of agents that you can actually just build by expressing a business process in natural language.

do AI and then AI sort of like, you know, like does, you know, things like what you mentioned, like, you know, you don't, you don't do the reasoning is like, she take the business process, it will create a multi-step workflow that you can then go and review and execute. So, so I think, yeah, we are definitely, you know, going, you know, towards a world where a lot of like, you know, how, like just how work happens is going to fundamentally change. And yeah.

Yeah, so this agent builder, which you mentioned, what does that require from the models to work? Does that require models that are specifically capable of reasoning or being able to browse the web like operator or computer use?

Or is Glean really the product in the system that enables to take these LLMs and sort of piece them in a way together that they're actually executing on tasks on behalf of your enterprise? Yeah, it's a combination of that. Like today, if you just work with an LLM, that's not enough for you to actually build an agent and automate a business process with it.

You know, you do need, you know, some more work, you know, that needs to happen on top of it. Like, for example, you need to figure out how to actually bring the right data to these models that's in your enterprise. And like over time, like, you know, there's going to be more and more features that you will get from the LLM providers. Like, you know, computer use, for example, is huge.

is an example of that. This is a feature. This is not like really the model, but it's a feature that the LLM companies are actually providing to you. It's a mechanism for you to sort of bring more data into the models, into the LLM reasoning. The way things will evolve is today, I think you still need

agent building frameworks and platforms that will actually sit on top of LLMs. The way we are helping our customers build agents is you get this agent builder UI where you can just talk in natural language, converse with clean to build an agent together. Behind the scenes, we're using both our API based integrations to enterprise data sources, but like that the overall orchestration is being done by us.

Got it. That makes a lot of sense. You know, speaking of computer use and, and, and again, these models getting more agentic and the fact that it does seem like a lot of the, the big sort of model providers, the open AIs, the anthropics, et cetera, like they are leaning more and more over time into the enterprise offering, you know, more and more ways to connect different parts of your, of your, of your business. How do you think about the landscape of competition when, you know, I'm sure many of your competitors are sort of building kind of like

at the application layer on top of the models. And now there's also this, I don't know, new sort of a front from the model layer where they're coming at it as well. How do you keep the product sort of competitive when you have this competition coming from all different angles? - Yeah, it's true. From a technology stack perspective, like an overall agent architecture, you're going to have

the LLMs and model capabilities that are at the base level. You're going to have a data connectivity layer and a retrieval and search system. And then you have basically like, you know, the overall like agent building and

you know, like the reasoning flows, you know, that you're going to build on top of them. First, I'll make this assertion. A lot of things are going to change about work in five to 10 years from now. Majority of the work that we do today, we won't do anymore. It's going to be done by, you know, AI, you know, assistants and agents, you know, that are going to be helping us, you know, perform those tasks. Our work is not going to go away. Like, you know, it's just going to change a little bit. Like, you know, it's going to become maybe more creative, more,

more thinking oriented and the base level working with data, doing some research analysis on it. The models can actually do a lot of that work for us. So majority of our work is going to change the way the organizations are built, the business processes, like most of these business processes are going to actually also get automated with AI. So one thing that I feel is that the body of work that needs to get done

versus the companies that are actually doing that work. There's a big delta between that. You know, the white space is huge. And the, like, if all of us, you know, work super hard, like, you know, we're still only getting 1% of, you know, what needs to happen done in the next one year. This is not the time where there are too many players and, you know, and not enough, like, you know, problems to solve, like, you know, within the opposite, you know,

It's probably the opposite. Yeah. And so therefore, we don't feel like, you know, any sort of concern, like, you know, with competition. You know, our strategy will remain, you know, what it is today, which is we will continue to partner with all of these, you know, players. We'll continue to partner with the language model companies, with the cloud hyperscalers, and we'll, you know, fully leverage, you know, all the innovation that they are doing.

making because well, like they won't be able to cover more than 10% of what needs to get done. And, and so we have enough, you know, for us to work on. And so we can't like so basically think of us as you know, sitting on top of, you know, like the core technology from these players, as you know, as they build more, like we also rise and like, you know, we start to build like, you know, the other things, you know, that we haven't built yet.

So we think of all of these companies more as our partners, not as our competitors. But now, practically, if you think about as a customer, you're being pitched agents from 300 different vendors. And who do you pick? Who do you choose?

I think Glean has a very unique architecture and a position in the marketplace where we're telling our vendors that we're not an LLM company. So when you actually think about your AI stack, you have to work with the language model companies anyway, and you can work with them through us or directly. We're fine with both those options.

But what we are doing is, you know, we're giving, you know, our customers a horizontal AI platform. A platform that's, you know, connected, you know, to, you know, all of your enterprise information and data knowledge. So you can actually use this as a way to consolidate your AI work, like as opposed to sort of buying AI products.

agents like, you know, function by function, product by product. And like, if you use 1000 SaaS applications, you can probably use, you know, you're going to have 10,000 agents, you know, in your company in the future. And so like one model, you know, is that you actually go very, you know, functional, and you get a lot of these vertical agents, other other approaches, you pick us as the horizontal AI layer, and and then use us as a central platform to build like many of these agents.

So I think right now we definitely are unique compared to any other approach that you see from other vendors in the enterprise. And it remains to be seen how many of these agents will be built on a horizontal platform like us versus fully dedicated, verticalized agent solutions.

Yeah, it's kind of like that classic saying that the only money to be made in software is sort of bundling and unbundling. You're taking both the unbundled SaaS products that exist and now all the different models, and you're bundling it up into one experience, which makes a ton of sense. Speaking of competition, you really like competition. Talk to us a little bit about that. How does that factor into sort of your management, your leadership of the team, this love that you have for competition?

Well, as an engineer, the...

I know that a lot of inspiration as an engineer comes to you from amazing technologies that you see outside. You want to actually go and match those technologies, you want to actually go and beat those and build better products. I think competition actually always serves a really strong purpose in driving the R&D engine at any enterprise.

I'll tell you, like, just from our own company's perspective, when we started with our core search and before everybody decided to actually come into the search and the AI, you know, assistant market, we were alone for a long time. We were the first company to sort of actually start

really take transformers and start to sort of, you know, work on these search and assistant products like, you know, six years back. So for a long time, like, you know, we were by ourselves. And when you're by yourself, like, you know, it's very hard to pace. It's very hard to actually figure out are you moving fast enough or not? Like, you know, because there's no comparison, there's no benchmark, you don't see anything outside. And so you have to be really just like, you know, you're just charging ahead with no clear indication of that, like, you know,

do you need to move faster or not? Sometimes, you know, actually get, you know, complacent, like, because, you know, you are the best product out there. And, and, and, and I think so that, and that's, that's the, that's, that's what competition does to you is that not only does it actually, you know, create that urgency, you know,

you know, in your team, because now you know that like, you know, well, if you don't innovate fast, you know, and if you don't stay ahead, then you're going to be dead. And so it sort of creates, you know, that, you know, that sort of urgency and velocity. But also like, you know, competition is good because no one company has,

like the monopoly and all the ideas like, you know, we like, we can't think of all the great things like, you know, we learned so much from like what other people like, you know, come up with, like, you know, the ideas that know that, that, you know, that, that, you know, like they have, they have new ways of thinking new things that come out and then we take inspiration from them and like, you know, build our own R&D roadmap based on that. So,

So I think it is essential, you know, in any industry, the more people that work on an idea, like it's actually is better for all of us. And like ultimately, like, you know, it also creates, you know, like the pie gets just bigger, like, you know, and all of us benefit from it.

Yeah, it makes total sense. I mean, it's a driver of urgency, which you need as a startup. And like you said, I mean, it sort of shows you where to focus. And it seems like it's working. You know, I know this past year has been massive for you. Just announced recently that you just crossed over 100 million ARR. Super, super impressive number. And I believe the business has tripled dramatically.

over the past year, which is just incredible. What would you attribute this to? Obviously, there's a lot of excitement and demand for the product, but were there key drivers that enabled you to drive that kind of growth over the past year? I mean, it's really phenomenal.

Yeah, I think so. So first of all, I think it's all about timing. So we feel fortunate, like, you know, that all the work, you know, that we did over the last six years, you know, has allowed us to actually create a product that is ahead of the market, you know, in a very significant way. And so when people are now looking for this product, like, you know, they like our, they like what they see, you know, from Glean and they're embracing and adopting it. One other thing that I want to actually also, the market itself, like, I think,

when you think about AI, you will also hear stories on that, well, I see all these great demos, but when I try to actually deploy AI in production, it doesn't actually work. I feel like I'm just experimenting and

And I don't know, like, where the value and the ROI is coming from. And one of the things, you know, that it changed in the mindset that I've seen last year is that people are also realizing that, look, you know, AI technology is

is very powerful. There's no doubt about it. It's going to change our world. There's no doubt about that either. But it's also very difficult to use. It's a machine, like a machine that nobody has ever seen before. As a human, when you use a machine, you know a machine is predictable. It does one task and it does it precisely the same way. That's the definition of the word machine.

And AI has changed that. It doesn't feel like that. We ask the same question to it four times, it's gonna answer it four different ways. And so sometimes people get very confused with a technology like this and they give up. And a lot of people actually gave up after they asked some questions in ChatGPT and made some things up. And they said, "You know what? I don't believe it. I don't trust it." And so you have this conundrum. You have this conundrum where,

As a leader, you know that AI is going to transform my business. I need to be ready. I need to get ready for it. But at the same time, this is a hard technology. So how do I make sure that I don't fall behind because my employees give up? And you have to drive that education inside the company. And so that's a big thing on the CEO's minds these days is that, well, look, yes, I want ROI from AI, but I also want education to happen.

I want like, you know, every employee in my company to become an AI first employee. I want them to be fully comfortable, you know, figure out like, you know, how to actually get the most out of AI. Lean is actually growing at a rapid pace. It's because companies feel that, you know, we are the most accessible tool that you can actually give to every employee.

and it can actually help them with their day-to-day work and sort of get them familiar with how to use AI and make it part of their work lives. So that's been a big catalyst for us. - How do you do that with your own team? I mean, is it through dogfooding? Is it through constant communication? Is it through certain OKRs? How do you drive a culture like that?

Yeah, that's a great question. I think, you know, like, you know, humans are creatures of habit. And even in a Gen AI native company that we are, like, you know, we have found that, like, you know, people will not automatically, like,

in, you know, bring AI into their day to day work. Like, you know, you have a particular way of doing things and you keep doing it that way. So, so one of the things that we've done, like, you know, I was, I was frustrated personally about it. Like, you know, it was sort of frankly embarrassing that like, you know, we are going out there to our market and talking evangelizing AI when we can't get our own employees to actually fully embrace it. Like, you know, of course they all embrace clean.

but we're not using many more other AI tools. We're not actually also seeing an interest from our team to actually try and see what's out there. So you actually do have to have some top-down initiatives. And one simple thing that I did was because everybody's busy with their current work and all that, so I said that, look, I want every executive leader to actually come up with one use case

in a quarter and could be small use case i don't care how big or small it is but one use case you know that you'll actually start to use ai uh and llms and so i think that's that's one thing that we also recommend to our all of our customers as well as like you know some these kind of like you know top-down initiatives to to force a change because otherwise humans are like you know creatures of habit as i said yeah it's a really really cool idea maybe on that note like

This is your second startup that you founded. Obviously, we talked very, very briefly about Rubrik. How has this experience different differed from that one? You know what? Maybe what things have you done differently or what did you learn at Rubrik that you've been able to take to building Glean?

Yeah, it couldn't be more different because of just the nature of the product and the domain. And also the role, like, you know, for me personally, a lot of learnings for me, for sure. The most important thing you could do to build a successful startup is to build a great team of engineers. And it's obvious, everybody would say it, like, but you've got to bring the best people, the best engineers, best salespeople, best marketers, and let them be. Like, people do best.

when they feel the autonomy, the agency. In the first one year of Glean, or the first two years, the primary role that I felt I had as the CEO of the company was being the recruiter. Like, you know, just bring, you know, build, assemble the best team. And that's the culture that, you know, we followed, like, you know, like we used to follow Google, then now at Rupert and now at Glean. And I think that has served us well. Like, you know, we have an amazing team and the amazing team, you know, obviously we're able to

build an amazing product for us and an amazing business. In terms of like, you know, differences. So one of the things, you know, I was mentioning this before, like Rubrik was in a very established market. People were buying a product like Rubrik. We went, we built a better product and we could go to our customers and say that like, look, you're spending a million dollars on data protection, like buying this old technology, which is not kept up with the times, you know, buy a modern one from us. So it's established market.

And you don't have to actually do category creation. Versus like in Glean, we came, we built a product and we knew it from day one that we're building a product for which there are no budgets. And so it's going to be a different kind of emotion. There's no competition, but there's also no budget. And it'll be a long slog. You have to actually sort of figure out ways to create those budgets. And then the second big difference between Rubrik and Glean was

that we are an end user product that every single person in the company uses. In Rubrik, you know, it's a product that like, you know, very few people used and you actually had relationships with them. So like, you know, in Rubrik, it allowed us to actually, you know,

accelerate the go-to-market and start to sell the product before it actually really worked properly. Because all the people that you were selling the product to were people you had relationships with, your touch point, and they were willing to build the product with you. Here, you don't get to do that because we don't know all the people who use Glean at a company because it's everyone. And so you have to sort of...

you know, change the go to market motion, you have to actually really build, you know, bring the product to a level of quality and, you know, and make sure that it feels amazing to people before you could actually unleash, you know, the go to market motion. And so like, you know, we had a very different journey in terms of like how much R&D we had to do first before we actually really, you know, scale the business.

You mentioned that, you know, you had been at Google and my understanding is you were a distinguished engineer there. You seem like the type of founder CEO that's very technically minded, very technically oriented.

That seems to be increasingly common for CEOs of startups, I would say, especially in this AI wave. Like, how do you think that differs from sort of the CEO? Maybe we saw like the last wave, it was very like product oriented or very business or sales oriented. And maybe like, how does that work to glean strength?

It's a good question. I think like, you know, at startups, like often, oftentimes, you know, companies, engineers, you know, are the ones who start, you know, companies, you know, because they like, and that's because, you know, they have that capability to build something, you know, and, and, and, and, and, and so, so I think the is like always like, you know, I have felt like, you know, like, you know, even in the last 20 years, like, you know, the first startup CEO always tends to be a technologist, you

in some ways. And like, you know, like very quickly they realized that, well, like the role of the CEO is a lot more complicated than building a product. And so therefore you sort of make your way, you know, to somebody like who actually knows how to build a business. And I don't know, like, you know, how much of it has, it is actually changing. I mean, I know they're like, you know, founders are like increasingly like staying longer in those roles, but ultimately building a business, you know,

you know, is, you know, is complicated. And, you know, it does require you to sort of, like, have the ability to sort of think about, you know, competition, you know, customers, you know, you know, strategy, and, and sometimes, you know, they may not align with, like, an engineer mindset, you know, which is about

well, I can go and build a product. But me personally, for example, I constantly had that doubt on as the company keeps scaling, can I handle the complexity? Yes, I want to learn and hopefully I can keep scaling, but I would disagree with the notion of that

that like engineers are better CEOs. I don't think so. - Yeah, got it. Well, it sounds like you're very humble and it also it's been working well. So I'm sure you can handle each additional scale. I know you're on a tight schedule 'cause you've got a lot going on today. We're gonna do a very quick lightning round. So I'm just gonna hit you with a quick question. Feel free to just answer when the first thing comes to your mind. So maybe what's one of your favorite productivity hacks using Glean? Like what's a really interesting and fun way that you use Glean to be more productive?

I mean, like the, I ask every, every question that I have these days, like the, I don't actually ping somebody, like, you know, the first thing I do is actually ask lean. And I do it for two reasons, like not because it'll be not only because it's going to be faster for me to get an answer, because obviously, like, I like to keep testing our product, and I want to make sure it actually is working.

But the favorite thing for me is that when I used to go do demos with customers, and they're always asking me all these questions about, "Do you do this? Does Glean do that?" And I realized that I don't actually answer those questions anymore myself. I actually want to put them right in front of them in Glean and do a live demo. And so that's the thing I really love about our product.

we were able to show the power of it so easily to our customers. That's really cool. It feels like every day there's a new tech trend or a new breakthrough. What's one thing that's sort of bubbling under the surface that you feel is the most underrated technology or AI trend? What is underrated is...

you know, the fact that these models are so powerful, you can do so much engineering on top of like, you know, the current state of where the model technology is. Like we're always focused on that, like, hey, like the models are not like smart enough. They're not, you know, they can't fully reason. And, you know, we're looking for AGI or the next advances. But what I'm saying is, what I feel is that there's an amazing technology at our disposal right now. And we've not even used 1% of the

power that the current models actually provide. Not just the frontier ones, but even all the other smaller models in open domain. There's a lot of work that we will see over time. When you combine engineering and effort with the power of these models, so much more can be done. Yeah. What's a technology from a movie or science fiction that you wish existed?

Well, so I love this concept of, you know, there's a thought that comes to my mind, you know, like, or a question that came to my mind. Before I even had to actually say or speak, we fed the answer back to it. Like, you know, that's the thing. And that's going to happen. Brain interface. I love this concept of, like, zero friction. Like, a lot of times, you know, I'm curious, I have these questions, but, like, I don't have the energy to answer.

to actually like, you know, say it or speak it. Like, but I, you know, just, I would love to have a system where like, you know, thoughts like convert into answers, like, you know, just automatically. That's awesome. Maybe and hopefully it'll be powered by Glean. Arvind, thank you so much. This has been amazing. Really, really appreciate your time. Thank you for having me. This was a lot of fun. Yeah, likewise. See ya.

Thank you so much for listening to Generative Now. If you liked what you heard, please rate and review the podcast. That really does help. And of course, subscribe to the podcast so you get notified every time we publish a new episode. If you want to learn more, follow Lightspeed at LightspeedVP on YouTube, X, or LinkedIn. You can follow me at McNano, M-I-G-N-A-N-O on all the same places. And if you want to learn more,

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