It's not like AI that's going to replace people. I think it's going to be AI-enabled people who are going to replace people who are actually AI laggards. I think we'll be able to do 10 times as much work. in the future as we can do today. That doesn't mean that we have to work less. I think the history points more to that we figure out new things. We all have this amazing team of agents around us.
that are going to actually not only take care of most of our work, they understand who we are, what we do. but they also could actually help us you know like you know get better take a ceo of a company They've got an amazing team of people around them. You can come right out of the school and you'll have that luxury. All right, welcome everyone. My guest today is Arvind, the CEO of Gleam. Arvind believes that every employee should have their own team of AI agents to help them get work done.
And Glean, I believe, recently passed 100 million AR. So super excited to talk to Arvind about the future of work and his top product lessons from building Glean. Welcome, Arvind. Thank you so much for having me.
So, Arvind, you've had a really long career. You've done multiple startups. You've worked at Google for a long time. You know, in big tech, let's start with a provocative question, right? So in big tech... you know you have all these layers of management and then every person has like a very specific role on the other team and do you think all this stuff will change fundamentally with ai or ai agents or how will the work environment evolve
Well, AI is certainly going to change how everyone works, whether you are in a startup or a small company or in a large enterprise. I think one thing that's also going to be the case for the future is the work that we do today. That kind of work, I think we'll be able to do 10 times as much work in the future as we can do today.
It's the same thing as, well, like, you know, if you had to sort of add and subtract numbers, like, you know, with the calculator, we can go much faster than we can do it manually. Same thing is going to happen with most of our work today. All these manual things that you work on, AI will be able to actually handle those for us.
That doesn't mean that we had to work less. I think the history points more to that. We figured out new things to do. We figured out how to build more advanced technologies and products. But certainly, there's a big change coming. And now going back to your question, in startup, you tend to have broader roles. Like, you know, for example, you know, at my companies, you know, that I've started.
You know, when we start, like, you know, I'm actually, I play a lot of different roles. I play, you know, I'm the IT person, I'm the HR person, I'm also a developer, I'm also a salesperson, I've been a BDR. And so there's less specialization in... you know, in startups, you know, more in large company. And what does AI do to that? I don't have a direct answer to that. But one thing that I do feel is that a lot of, like, you know, a lot of the tasks
are something that AI will be able to do. So in that sense, you can imagine a person who has a primary role of, let's say, being a developer, he has a lot of mileage through AI to do a bunch of other things, which you had to have other people to do in the past. It's almost like everyone will become a manager of... AI agents? Is that fair to say? Absolutely. I think it's the concept that I'm really excited about. I'll give you a real-world example. Take a CEO of a company.
They've got an amazing team of people around them. They've got their chief of staff. They've got maybe one or two assistants. They've got this executive team.
you know that is super capable and and then also a coach like you know is constantly guiding you know you to be a great ceo and all of that help around you like obviously allows you to actually make a much much bigger impact on the company then like you know if you're an individual contributor in some ways because you know you have these multiplier forces coming and helping you
Similarly, go into a non-work scenario, take a tennis player, Roger Federer. He goes and plays with a racket and a ball on the court and I do the same. But he has a team of 50 people around him. you know who are making sure that you know he's well practiced you know that he's like coaching to figure out how to actually play this match properly and he has a world-class performance and i don't right and is you know is that concept like you know like you know we when you come back into ai
You know, everybody is going to have a different experience in the future. We're going to all have this amazing team of agents around us. that are going to actually not only take care of most of our work, they understand who we are, what we do, but they're also going to actually help us get better. They will act as our coaches. And so that's the future we're looking at. You can come right out of the school and you'll have that luxury. You don't have to wait for many years for that to happen.
This episode is brought to you by Merge. Product leaders cringe when they hear the word integration. They're not fun for you to build, launch, or maintain, and they probably aren't what lets you do product work in the first place. Luckily, the folks at Merge are obsessed with integrations. They built a single API that helps SaaS companies launch over 200 integrations in weeks, not quarters. Think of Merge like Plaid, but for B2B SaaS.
companies like Ramp, Drata, and Electric use Merge to access their customers' accounting data for bill reconciliation, file storage data for searchable databases, and HRIS data for auto-provisioning access. for their customers' employees. If you need AI-ready data for your SaaS product then merge is also the fastest way to get it.
So if you want to solve your company's integration dilemma once and for all, book a meeting and receive a $50 Amazon gift card when you attend. That's merch.dev slash Peter Yang. Now back to the episode. Yeah, I have a Claude Prot project where... I've given it way too much personal information about myself. And then I just check in with it every three months, you know, and it gives me like specific advice.
on what I should do. It's like a very nice personal coach, I think. So, you know, a lot of people listening to this are like product managers and, you know, like product managers, they have to do a lot of internal alignment. They have to make a bunch of documents or artifacts. Do you think this role is like... well positioned for the future like do you think like even in your own company right for the pms like how do you get them to upscale for this ai agent future
Yeah. Yeah. That's a great question. Actually, first, in our own company, the product managers are actually very heavy users of AI. And, you know, in Glean, you can think about Glean like just for context.
You can think of us as a... a more like enterprise or more powerful version of chat gpd inside your company like you know it's able to actually help you with all the knowledge of the world it has, you know, through models and web search, but it's also able to actually help you with all the internal knowledge that it's connected to, to answer questions for you, help you with whatever tasks that you're working on.
But then we also have this agent platform where you can actually go and build specific agents for specific day-to-day processes that you have. So, so that agents, you know, so people build agents in Gleam. You can build agents for yourself. You can build agents for your department. And what I was saying was that when we look at like, you know, how many agents,
a product manager is using in our company that's the highest like you know across all the different functions like you know take engineers product managers sales people support people they're doing the most and why because i think you know their job is i think it's constantly about working with information, working with data.
that's being generated by different people within your organization, from your customers, from your product usage. And you have to actually make data-driven decisions, knowledge-driven decisions all the time.
so a simple example i'll give you you know how do you decide on what features to prioritize this is a process that in our company you know it has gotten reinvented you know with asian And so before, in an enterprise software company, the way you decide how to prioritize some of the new work and features is that you'll work with some of your most important customers. You would sort of learn from them what are the key things they're looking at.
over data across 10 or 15 meetings that you would do and then build a roadmap based on that. But now what our team does is that they have access to all the customer conversations that have ever happened in the company. And we have thousands and thousands of them. And they have built this agent, which actually goes. and listens to all of those called recordings. And the agent is instructed to basically listen for like any... requirements, any sort of new features that the customer is asking for.
and then basically collect that. After that, the agent is instructed to classify those requests into different themes. You come up with maybe six or seven different themes. And they put those feature requests in that. And then you sort of create this, you know, big, huge, you know, spreadsheet with all the feedback from all the calls. And then you ask the agent to also summarize, synthesize and say like, you know, hey, pick the top themes to pick.
And so it does all of that work for you. And this is something that you couldn't do before, right? Before you had to work with limited set of... information, you could only meet a few number of people. And now they're able to leverage from this collective knowledge that's sitting in the organization, which was completely untapped before AI, but now you can tap into it.
So that's how our team is now doing roadmap planning. They feel a lot more confident. Yeah, because then you can spend that time thinking instead of just trying to gather all the information. exactly yeah I do something pretty similar like when I write a PRD like I maybe I got too lazy or something but like you know I have this customer community that I've talked to and I just ask them some questions like hey you know what kind of problem you have what kind of solutions you want
And then I have a template to just draft the PRD. And I should draft from scratch. Yeah. But yeah, it's definitely helped a lot there. You know what? I think Roblox actually has a green, but I don't think I've ever used the Asian thing before. I think I've been missing out. So you can actually create these agents that just like automatically every day just like summarize information for you. Yeah, so you can, yeah, so agents can... The way you build agents in Glean is
First, it's trivial to build an agent. You don't need to be actually a developer or a product manager. You could be a salesperson. and you can just describe a piece of work that you do like you know something that you are bothered by like you know you feel it's too tedious like you know and so just go in and in natural language you describe to glean that hey this is the work that i do and can you do it for me from now on like that's basically you know as simple as that
And then we will build that agent. It's already connected with your enterprise systems and data. So it's actually able to provide the right data and access to that agent, use the power of LLMs. And then also like, you know, take actions into those different systems, you know, to sort of complete the piece of work. And now when you look at these agents, there are two types of them. Like, you know, one of them is...
like you know something that's you know a person uses like you know they like i go to an agent and ask it to do some work for me and there could be another type of agent you know which just runs in the background autonomously like maybe based on some triggers right like for example Like every time there's a new lead.
you know, that comes, you know, you know, into our, into your marketing system. I'll do a whole bunch of work and like, you know, like enhance that lead with some information or whatnot. Right. So, so you can like, yeah, you can have agents of both types, you know, in Glean. Okay. so the latter one is kind of like a Zapier for like internal you know company stuff right it's kind of like Workflows. JPN is actually a very extensive set of actions library.
But an agent is a little bit more than actions library. There's a lot of logic and... thinking. So when you think about an agent and how you represent an agent, there are some parts of that agent process which are about reading information from enterprise systems. Some of it is about
like, you know, taking actions into those systems. But then a lot of it is about thinking and doing analysis and synthesis. And that's where the AI shines. And then that's the new thing. Like, you know, when you think about automation in the enterprise, like now you're able to automate things. which would definitely narrate humans in the past because of the requirement for intelligence.
So let's talk a little bit about enterprise adoption of AI. So I recently tweeted that it went viral, but the tweet was like, startups have an advantage over big tech just because they're allowed to use all the AI tools available. And why do you think some companies have been so slow to adopt this stuff? Is it security or it just takes a while? Yeah. So large enterprises,
I think it takes time, longer time for them to adopt some of the AI technologies. Even for us, as a company that serves large enterprises. we had to be very careful in terms of what technologies we use inside our company because ultimately, if something bad happens, we are risking.
like, you know, our customers in some ways, right? So I think there is a natural friction, like, you know, like security is definitely like one of those key points of friction, like, you know, in terms of how do you make sure that your AI is safe? when you build these agents that you are actually respecting you know the governance
the security and the permissions architecture of your enterprise. I don't expose you know information even internally like of course not externally but even internally don't expose information to your employees that they should not be you know like reading you know that they should not have access to right a lot of enterprise data is private in nature So you have to solve that. So that's definitely one factor. And as AI is taking off, there are new types of security attacks for AI.
you can be susceptible to prompt injection attacks. And so you have to like, you know, when you build these platforms, you need to make sure that like everything is done in a, like, you know, you know, in a button down manner with the right security.
controls but that's only one of the challenges like there are other challenges like one of them is one of them i would say is inertia And, you know, you like in a large enterprise, like business processes are more solidified, like you have certain way of doing things. Let's say that I have a busy work schedule. I've come in and I have to finish 10 different things in the day. I don't have the time to actually think about how I'm going to do this thing differently with AI.
like i just don't like i'm always just you know it's like you know you're on the treadmill you just you're gonna fall behind if you start to think You also have to battle that. When you're starting a new company, you're in the reverse situation. You don't have people, you don't have processes. and well like you need to actually figure out how to get things done and you know you use the modern tools because they're better than like you know and this you know you know have the promise of
like helping you with, you know, your business processes without requiring, you know, people or like, you know, so, so, so I think that's, so that's a divide. So, you know, that that's a big factor too, like, you know, which is often underestimated. You have to like, you know, when you think about AI as a large enterprise. like first thing you have to do is you have to educate people like you have to actually like you know ai today feels inaccessible like as a if i'm a hr person
and you ask me that, hey, use an AI tech or build an agent, they'll say, what are you talking about? I've never built anything like that. That seems like technology. right and so you have to sort of get to that like you know like train your workforce give them some basic fundamental AI tools. like let them use chat gpt or let them use glean which is a more you know, like more enterprise focused. product of the same nature. Once they start to get comfortable, once they know how to use AI,
then they will actually both drive that demand for more products and actually adopt and embrace them. So anyway, those are some of the things that we see as roadblocks. And then finally, of course, Like people are confused. Like, you know, there are as if you're a CIO of a large enterprise.
you know you're being bombarded and pitched by like thousands of you know startups and large companies because every company a company has been around for 50 years you are an ai company today you have ai products and there is a lot like you know to sort of choose from and the it's not you know these decisions are not easy like you know like people don't know like enterprises you know they have to
They also have to be careful to think long term. One of the things that I hear a lot is they don't know what LLM providers are going to win. Who's going to be winner in the AI space. They don't know what startups are even going to be around in the year or two.
So in that sort of dynamic industry like you know how do you make big decisions and you know like replace you know like transform your business processes you know with products that may not be around next year right so these are the practical considerations you know which makes enterprises go slow Yeah. And there's also like, for example, like something as simple as using AI to like take notes during meetings.
like you know for like a random employee like me like it sounds like a great idea why don't we have it but like there could be certain meetings that are like very sensitive like maybe like employees getting laid off or something like you know what
All that stuff's recorded, necessarily. So maybe, like, as someone building products for enterprises, you want to build the right controls, right? That, like, maybe consumer products don't think about. And in terms of, like, getting enterprise adoption, like, you know... Like OpenAI and all these other companies are also trying to get enterprise adoption, right? So like what has been Glean's secret sauce? Is it just like everything you just talked about or is it...
Is there something that... Yeah. Well, there are two parts to it. One is just the product itself. We are focused on the enterprise and we've been around for a long time. Our product is... three times as much older as any other product out there like you know we've been building this for the last six years And part of our product has been connecting with...
all of our enterprise systems, data, knowledge, and it's the largest application of AI today anyway. Like when you think about AI, what are people trying to do? Number one, they're trying to use it as a system, you know, that can actually help you with knowledge. Like they can actually answer questions for you.
So that's a product that we built over a long time. So we fit right into the most successful domain for AI right now in the enterprise. But also, the other thing that we believe... is necessary for adoption in the enterprise is that this is not a technology that's easy to use. And you can't just build products and just ship them, throw them over the fence to your customers and expect them to drive full success with it. You have to build a collaborative go-to-market motion.
And so we do that. Like, you know, we have a large enterprise team, go-to-market team, and, you know, a lot of... you know, solution specialist, you know, AI outcomes. you know, engineers that we have, you know, that we work closely with, you know, our customers.
understand their key business problems, build a roadmap with them on AI for the next one year, and then execute on it together. So a lot of it also just set focus again on the enterprise and thinking of this as a people business, not a... well like you know i'm only a software and technology business and i build something and like now you have to figure out how to use it like you know that's not the approach we take
Yeah, because a chat is just a very horizontal tool, right? Who knows what we're going to put in the chat box? A lot depends on. Interesting. You made an interesting point there, actually, by the way. Yeah. A search or a chat product is easy, right? Like, you know, in the sense that, well, it's a blank box. Go ask any question. But actually, it turns out that it's actually super hard. Like a lot of people actually have no context or orientation of what AI can actually do for you.
And so we used to actually take pride in giving you a very clean interface where there's nothing on the page besides a box. And you can just type into that box. And we realized that people are not using it. They're not figuring out what can be done. And so since then, we've taken a lot more proactive approach.
And, you know, like, you know, AI education has become like, you know, the key theme for us. Like, you know, we understand like, you know, user when they come to our product, we know who they are. And like now based on their role, based on like how much, you know, they have. you know, used, you know, the product and AI, we start to now sort of try their education for them. You know, we prompt them, we suggest, you know, things, you know, for them to try out.
And that has proven to be actually very, very powerful. You know, I think at every company, there are these like AI evangelists or people who are power users of the stuff. And, you know, like speaking to them, Like, how can they actually try to drive? Let's say they're not a CEO. They're just like an employee.
So how can you try to drive the rest of the company to actually use this stuff? Maybe your strategy is to partner with them to get adoption of Glean or these other tools, right? Yeah. I mean, we absolutely need those. Every company will have a small...
fraction of people who are those AI enthusiasts. They want to be on the frontier. They're the ones making very interesting discoveries. One thing that I want to make clear I don't think anybody in the world really understands what the current models Because you can only figure it out as you put them to task, as you sort of ask them to do things. And that's where discoveries happen, that the model is actually pretty good at doing certain type of tasks for you.
And then for that, you need, you know, those folks like who are the enthusiasts and who are going to try things out. And, you know, they're going to see, you know, very bad response.
you know from ai and they're going to tweak it and they're going to work it and ultimately they get to a good place where they've prompted the model in a particular way and it has done something magical for them and when that happens That's the time when you want to make sure that the product that they're using makes it really easy for them to share that discovery with the rest of the workflow.
So, you know, in Green, we've taken this approach of We have a prompt library and an agent library, and everybody can sort of tinker, build agents, build prompt. When something cool happens, they get to share it with other people in the company. And then we figure out how we take that big library that the enthusiasts are building. evangelists are building and make sure that in an effective way, we are bringing it to people who are going to benefit from that work.
Yeah, I love that. Like I was using Gemini the other day and they have these gems they can make, right? With my, you know, advanced prompts, but they forgot to do the features. Let me share my prompts with my coworkers. So it was like, yeah. What's the point? That's a critical feature to have. As you said, data is king when it comes to AI products. If you have shitty data, your AI product is not going to be good.
the company like internal data like a lot of times it's kind of bad man like some of the documents are out of date like you know there's like half complete stuff how do you even know what's good you know yeah yeah that's a big problem That's a big problem. And actually, it's not as if internal data is worse. The data on the internet is also of the same nature. A lot of it is obsolete information.
And you have to, like, you know, that's the thing that you have to actually understand and work with. Like, we've seen two approaches. Some enterprises saying that, oh, like, I'm not ready for AI today. And I'm going to actually go and first clean my... knowledge because they tried all these POCs with different AI products and they didn't work well. And many of those products came back and told the customers that, well, your data is bad and therefore AI is bad.
But that's not the right approach. I don't think you can ever fix your data. In the sense that data gets produced over a long period of time, it's naturally going to become out of date. You know, and some data is going to be of high quality because it's written by a good writer or a subject matter expert. Some of it is not like you have to actually take all of it. Like, you know, the good, the bad, the fresh, the out of date.
the popular or the non-popular content then you have to understand that and figure out like you know you know what's the right information what's the best information for giving any task and for that you have to build a really good search system you have to connect you know, your search system.
all of this data and you have to start understanding these signals. You have to start figuring out what data to use in any given scenario for any given task. When we think about our own technology at Glean, a big chunk of it like majority of it is actually spent just trying to understand the enterprise.
just trying to understand, you know, this issue of data quality and make sure that, you know, we are for like any given task that you're going to do with AI, that we can bring the right information to the model.
and then lend them reason over it to, you know, to, you know, produce, you know, like, you know, great work. Yeah. So it's like the RAG or is it some system that's separate from the LOM, right? It's like, yeah, given the right context. Our approach, yeah, so like RAG is... one way to it's a very specific you know construct in that sense right yes like given any task in the enterprise
You do have to retrieve the right information from within the enterprise because your models are not trained with that knowledge. And then you have to make that information available to the models. But the overall sequence of how this works is that this is a collaborative process, right? You may pick some information.
then you may ask the model to say that hey this is the task this is what i've pulled picked up so far you want me to actually pick some more data like you can sort of have this you know engagement they're doing multiple retrieval passes you know in conjunction with you know the intelligence that the model has to ultimately then like assemble all the information that you need and then work on it and you know produce artifacts that we're trying to produce
Is it kind of like a deep research or like the channel thought stuff? It has the information and they ask for more information and they're like, Yeah. So we do that. And I think like you also have to, you cannot always be doing deep research because most of the times your users exactly have one second, you know, you know, to get to like, you know, what they were trying to get to. Right.
And so you cannot be sitting there being over smart and take 20 seconds. So you have to also understand the context. And sometimes you do a real simple, quick path. Sometimes you don't even, it's a question that doesn't even require retrieval because, you know, it's a general question and the models, you know, core intelligence can answer that question. Sometimes you don't even use a model.
Because, well, your question was so factual and I can just use my retrieval and it already knows the answer to it. So you have to pick and choose based on what you're trying to do. Got it. Okay. So let's talk about building problems at Glean, like improving Glean itself. There's always the obvious stuff, like you've got to talk to customers, you've got to think about the problems.
But like, what kind of new skills do people have to build AI products? I guess like comfort of ambiguity or kind of skills you have. Yeah. Great question. So first, I think like the biggest skill or one of the key skills that our team had to learn when the industry suddenly started to move very rapidly was You know, you rely on a certain set of technologies underneath and you're building your systems based on that. And then that foundation is super unstable and it's constantly changing.
like it's a very hard thing to build you know the house on top of it right and so our team actually felt that like you know stress like you know i would say in 2023 quite a bit you know where we were struggling to figure out well how do you even develop in this new world
like nothing is a constant nothing is true like you know like you know what's happening this week you know the other week looks completely different from that so thankfully like you know we're not there like you know i guess in some ways it's a bad thing that innovation pace may have been a little bit like maybe a little bit more mature or slow
But I think you have to learn that. You have to fundamentally change your mindset of how you build systems. First, you have to become more iterative. You can't actually...
Like, you know, take, you know, two months to design a system and then build it over the next four months. You have to actually necessarily go, you know, in that MVP mode, right? In that MVP mode, like, you know, build something fast and quick. You don't know whether, you know, it's going to last, you know, like more than a month.
right and second thing is you know having that mindset of like like not getting attached to what you build and constantly reinvent and take you know the new capabilities that you get and like throw what you build like you know you should be ready to throw you know something that you built two months back because well like you know the market caught up and now you can actually get that you know for free in the market like all from the lm's capabilities have increased
So those are the main, I would say, the key skills which will drive success now is that agile mindset that you need as a developer, as a product manager, and change quickly and fast. And then besides that, like, you know, like a force AI as a technology is fuzzy, like it's actually non-deterministic and you have to learn with like how to work with, you know, systems.
you know, which are not precise, right? You have to figure out. And this is like, I would say that search engineers always had that training. Like when we're building search at Google, you know, many years back, you always have to deal with imperfect knowledge, data information. Like there's no real.
formula for like giving a question what information should be shown on the top it was by nature a exercise of judgment right and so i think like you know that skill translates well like i feel like you know folks who actually you know worked on search are going to be generally successful with ai as well
Oh, this is like, it's like more like probability based as opposed to, you know, this is going to happen for sure. Right. Yeah. Yeah. Yeah. Interesting. Now, you know, now you're on your second company, right? That you started. Is it second or third? Second. Yeah. Do you have any, what are your values? What kind of principles or values do you have for the company? I think that's really important. Yeah. Yeah. So I think first, I would say that a company is not much more than the people.
you know, the idea, the code base, like those are all i think not the minor part of what makes a company is the people and you have to like when you build a startup like you to always focus on like you know you know what's that core like you know who are the people who really care about
building this product or this technology or solving this business problem that we are solving. And you have to have that because this is going to be a long journey. There's going to be lots of ups and downs. There's going to be competition.
you know economic like you know you know turns like you know that that won't happen so you have to have that like you know like i feel like you know a big part of like you know building a company you know culture is about having that alignment you know with your mission But in terms of our values, our values are probably the same as what any company would aspire to. We are customer obsessed. We want to make sure that we're making them successful because that's what makes us successful.
We like to move fast. We want that bias to action. We want to create a culture of... like teamwork and and i think like for me that's the thing that i really care about like you know all our cultural values are obvious like you know you you'll say yeah of course yeah i've heard about it like why not like you know the you know you want people to work hard you want dedication you want
alignment to the mission all of that is true the one thing that i feel is very important which i feel like maybe i put a little bit more weight is on the value of you know trust and respect i think you build a great company when you have that positive mindset and you respect your colleagues for one good thing that they do. And you accept the fact that they're to learn the other things.
And I think that's a big part of, in my opinion, building a company with the right culture and sustain it over the long period of time. You need that mutual respect and trust in your workforce.
yeah because you people you know you can go far together you can go fast alone but go far together yeah and nobody like people have to realize this like sometimes you know if you're really good like you know maybe you know you could at like like you know engineering and like maybe you are you're a superstar and you're good at like 10 things And there may be another person who's only good at two things.
and you may not respect them then anymore and that creates a bad environment but like the reality is that well you know even with you know somebody being you know good at like two two two things you know they can actually add a lot of value
And over time, you know, they will add the third one and the fourth one. And so I think that that mindset is important. Like, you know, it's very hard. Like, you know, people don't realize it. I think in my opinion, like, you know, there's something that comes. over time you know this like this concept of well like you know
Like, you know, I respect you for what you are. Yeah, and it also makes work more fun, right? Because you spend like, you know, eight hours a day with people around you. Yeah, exactly. Yeah. Okay, cool. Well, I mean, I want to give that just one more random question. So like I've talked to AI founders who, you know, like you said, because the models are changing all the time, the ground is shifting behind their feet.
they don't really have a roadmap anymore maybe they have like a quarterly plan of what they want to do but they don't have like a five-year plan and like but like since you work in the enterprise space maybe you have like how do you balance this iterative thing with actually having a plan
Yeah. So in my previous company, we achieved success. It's a large publicly traded company now. And we did have a five-year plan. But that five-year plan was actually completely useless in the sense that neither did we hold true to that. but it never actually had an impact when we're actually doing our work. So we learned there itself, this is pre-AI. that like you know plan for a startup plan a year and don't think beyond that maybe at the most you may
have a rough idea of what the next year could be, like a very rough idea, but mostly focus your energy on what you're going to do this year. So that's whether AI, pre-AI, it doesn't matter. As a startup, that's the right time horizon. Here, we do that. We do come up with a point of view on what we should do this year. We do an annual planning, but we're not wedded to it because we know that things are going to constantly change. And so the way we execute is we actually create a monthly plan.
And the monthly plans, you know, take inspiration from the annual plan, but it allows us to actually be agile and like, you know, we can sort of adjust the annual plan like along the way as things change. But like all for all practical purposes, what we're doing, you know, we are operating at a monthly granularity. But you do have a, in terms of long-term, you do have a vision, right? Like a long-term vision for, yeah.
And maybe like real quick, what is the one sentence summary of that division? Yeah. Yeah. So our vision is, you know, is to, you know, build an amazing AI team, you know, around every individual who works.
you know and this team is going to help them become a 10xer you know that's what we you know we're trying to build towards and so yeah so everything that we do is guided by that like you know builds the best you know, AI, you know, the AI team or like, you know, which is like the team consists of your, you know, assistants and coworkers and coaches, everything, you know, that helps you sort of.
achieve more, grow more. And yeah, so that's the North Star, like, you know, that person drives the annual planning process as well as our monthly milestones. I love that. I feel like I've 10x my productivity with all these different tools. Not because of my own talent, but just because I can outsource a bunch of work that I don't want to do. That's fantastic if you're already there. I feel like we have a lot more work to bring people there, but that's incredible. I do hear it occasionally.
Especially from developers, you hear that with the new core gen tools that they're just moving so much faster than before. So any closing thoughts or any words of advice for people to... get all this track to like 10x yeah i i think my main advice is like you know just use ai like just use it more and i think it's important like you know like you know one is of course for entrepreneurs or
for developers what you want like what like you know i think the like thinking about you know thinking ai first is going to be helpful but i think like i'm talking about everybody in the world like you know there are so many jobs that are going to get displaced with AI. But I also think that it's not like AI that's going to replace people. I think it's going to be...
AI-enabled people who are going to replace people who are actually AI laggards. Because this is a good technology, and I think we just need to learn that. you know, move forward with it. And that's what I would say. Yeah, that's my core advice. Yeah, I totally agree. I think when people listen to this,
It takes a little bit longer to learn how to do stuff with AI, but once you actually do it with AI, then you save so much time afterwards. So it's worth making an initial investment. Awesome, Marvin. So where can people find you or where can people find Glean? Yeah. I mean, like, you know, we are on glean.com and my email is my first name, arvind.glean.com. Like, you know, we'd love to, you know, connect with, you know, whoever like wants to learn more about us.
Yeah. All right. Well, thanks so much for building an awesome enterprise AI tool. Really appreciate it. Thank you.