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

Markdown copy of https://realalpha.app/v/zPtwI6JaRy0/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

Arvind Jain argues AI will change knowledge work, but competition will push companies to reinvest productivity gains rather than cut headcount.

- Show: Knuckle Up with Nakul
- Hosts: [Nakul Mandan](https://realalpha.app/people/nakul-mandan/)
- Guest: [Arvind Jain](https://realalpha.app/builders/arvind-jain/) is founder and CEO of Glean.
- Published: Sep 9, 2026
- Length: 47 min

## The best minutes

- [27:54](https://realalpha.app/v/zPtwI6JaRy0/?t=1674) [AI gives Jain strategic visibility he could not get before](https://realalpha.app/s/ai-gives-jain-strategic-visibility-he-could-not-get-before/) (5 min)
- [24:02](https://realalpha.app/v/zPtwI6JaRy0/?t=1442) [AI will raise customer expectations, not shrink company teams](https://realalpha.app/s/ai-will-raise-customer-expectations-not-shrink-company-teams/) (4 min)
- [41:51](https://realalpha.app/v/zPtwI6JaRy0/?t=2511) [Fast innovation makes product moats feel temporary](https://realalpha.app/s/fast-innovation-makes-product-moats-feel-temporary/) (4 min)

## Also worth watching

- [33:11](https://realalpha.app/v/zPtwI6JaRy0/?t=1991) [Leaders at Glean must build their own priority agents](https://realalpha.app/s/leaders-at-glean-must-build-their-own-priority-agents/) (4 min)
- [38:16](https://realalpha.app/v/zPtwI6JaRy0/?t=2296) [Glean tests AI fluency and uses AI to screen hiring packets](https://realalpha.app/s/glean-tests-ai-fluency-and-uses-ai-to-screen-hiring-packets/) (2 min)
- [12:33](https://realalpha.app/v/zPtwI6JaRy0/?t=753) [Core SaaS systems will remain, while point apps get rebuilt](https://realalpha.app/s/core-saas-systems-will-remain-while-point-apps-get-rebuilt/) (2 min)
- [15:04](https://realalpha.app/v/zPtwI6JaRy0/?t=904) [Glean says its context layer cuts token use by 30 to 50 percent](https://realalpha.app/s/glean-says-its-context-layer-cuts-token-use-by-30-to-50/) (4 min)
- [3:54](https://realalpha.app/v/zPtwI6JaRy0/?t=234) [Glean's edge depends on learning how enterprise work gets done](https://realalpha.app/s/gleans-edge-depends-on-learning-how-enterprise-work-gets/) (3 min)

Skip the opening Glean overview, the roughly five-minute discussion of knowledge-worker AI adoption, and the AI SDR segment.

## Full analysis: Arvind Jain says AI cannot replace one human today

- Jain says AI cannot replace even one human today.
- His competition case is plausible, but the episode does not show whether staffing will hold.
- Measure customer outcomes before turning productivity expectations into headcount plans.

Its 6 sections, what it means for you and what to watch are on [the page](https://realalpha.app/v/zPtwI6JaRy0/#analysis), free with an account:

- [44:48](https://realalpha.app/v/zPtwI6JaRy0/?t=2688) Jain says AI cannot replace one human today
- [24:33](https://realalpha.app/v/zPtwI6JaRy0/?t=1473) Jain expects competition to absorb productivity gains
- [29:44](https://realalpha.app/v/zPtwI6JaRy0/?t=1784) Jain uses agents for strategic visibility, not just speed
- [17:53](https://realalpha.app/v/zPtwI6JaRy0/?t=1073) Glean says its context layer cuts token use
- [13:02](https://realalpha.app/v/zPtwI6JaRy0/?t=782) Jain expects most core systems to persist near term
- [42:40](https://realalpha.app/v/zPtwI6JaRy0/?t=2560) Jain says rapid innovation leaves Glean's position unsettled

## Summary

Arvind Jain is founder and CEO of Glean, which he started in 2019 after more than a decade as a distinguished engineer at Google. He previously co-founded Rubrik; the episode description says Glean is valued at $7.2 billion and generates more than $300 million in annual recurring revenue.

Jain argues AI is not ready to replace any human today and expects competition to turn productivity gains into higher customer expectations, not significant workforce cuts. He says Glean's edge lies in years of enterprise experience and a context layer that understands permissions and how work gets done, while remaining independent of any one model. He expects systems of record to persist as AI becomes more agentic, alongside more internally built applications. At Glean, he uses AI for strategic analysis and expects leaders across departments—not only engineers—to build agents; he says rapid innovation keeps the company from feeling settled.

### Minute by minute

- [1:47](https://realalpha.app/v/zPtwI6JaRy0/?t=107) **Jain says experience gives Glean an edge.** Jain says Glean, founded in early 2019, learned through deployments in thousands of enterprises that secure data access, permissions, governance and compliance are as hard as building the AI product. He says that experience is a core edge; Glean partners with OpenAI, Anthropic and Google and also uses open-source models.
- [4:52](https://realalpha.app/v/zPtwI6JaRy0/?t=292) **Jain says MCP misses how work happens.** Jain argues that MCP servers and basic integrations expose systems but do not teach an AI how a company works. He says Glean observes tasks, experts and the steps people take—including unwritten judgment—to build a context graph that can support automation.
- [7:54](https://realalpha.app/v/zPtwI6JaRy0/?t=474) **Model independence addresses enterprise concerns.** Jain says enterprises struggle to choose between changing frontier models, manage AI costs that can exceed budgets, and control duplicated agents and AI-generated material. He argues Glean's model independence lets it combine frontier and open-source models, using a cheaper model where adequate; he says open source can cost an order of magnitude less for some tasks.
- [11:00](https://realalpha.app/v/zPtwI6JaRy0/?t=660) **AI agents should outlast systems of record.** Jain says companies want agent intelligence to remain separate from underlying systems of record, so it survives a change in CRM or document system. He expects major systems such as Google and Microsoft productivity suites, Salesforce, Workday and ServiceNow to remain and become more agentic, while businesses build some narrow applications themselves. He says he built a more usable asset-management app by connecting to its database in about five minutes.
- [14:47](https://realalpha.app/v/zPtwI6JaRy0/?t=887) **Jain positions Glean as a context layer.** Jain describes Glean as an enterprise context and intelligence platform that connects structured and unstructured systems, retrieves information, takes secure actions and stores task and personal memory. He says it can feed context to third-party agents or tools such as Claude and ChatGPT, reducing token use by 30 to 50 percent. Glean also offers its own agent builder and coworker product.
- [19:19](https://realalpha.app/v/zPtwI6JaRy0/?t=1159) **AI use is shifting from answers to tasks.** Jain says question answering remains Glean's top use case, while at least a third of its users now do deeper tasks; he points to support agents resolving customer cases before a person reviews them. He expects AI to become a proactive work companion that identifies tasks and offers or completes work, rather than waiting for employees to prompt it. He says only 5% of employees at Glean's tech-forward customers have substantially changed their workdays so far.
- [24:02](https://realalpha.app/v/zPtwI6JaRy0/?t=1442) **Competition may absorb AI productivity gains.** Jain rejects significant headcount reductions as a likely outcome, arguing that competitors will get the same tools and higher customer expectations will absorb productivity gains. He expects work to shift toward choosing problems, asking better questions and directing AI, but not a near-term move to shorter workweeks. He says AI itself could help broaden adoption beyond the current group of power users.
- [27:23](https://realalpha.app/v/zPtwI6JaRy0/?t=1643) **AI lets Jain self-serve company insight.** Jain says Glean is his first stop for questions, letting him self-serve company data rather than repeatedly ask executives. He used it to assess which engineering projects no longer fit a strategy shift, identify strong but less visible employees, and track customer churn risks through a live dashboard that recommends actions. He also uses an agent to read his inbox and draft important replies, but says strategic analysis matters more than small time savings.
- [33:11](https://realalpha.app/v/zPtwI6JaRy0/?t=1991) **Leaders must build agents themselves.** Jain says Glean's finance team has built many agents, but he is not satisfied with the company's overall adoption. He launched a program that uses Agent Finder to identify the 20 or 30 tasks consuming the most employee time, then requires leaders—not engineers or individual contributors—to build agents for them without delegating. He says interviews also assess candidates' AI fluency, while managers should still be able to focus on their core jobs.
- [36:41](https://realalpha.app/v/zPtwI6JaRy0/?t=2201) **AI handles most SDR work, but people review it.** Jain says Glean does not use an AI SDR as an autonomous replacement: AI does 90% of prospecting, but a human checks and edits messages because AI lacks judgment and customers can recognize AI-written text. Glean's engineering interviews now give candidates a task too complex to finish in two hours without AI, and an agent checks hiring packets before they reach Jain for offer approval.
- [40:58](https://realalpha.app/v/zPtwI6JaRy0/?t=2458) **Jain says rapid innovation keeps Glean unsettled.** Jain says Glean sees more code output, but engineering productivity is hard to quantify because teams also spend more time debating what to build; support staff handle more cases per day. He says execution and alignment matter as AI changes quickly, and even code can feel like a liability if it must be replaced. Jain says no current job can be replaced by AI today, though systems could get closer in future, and admits he has no answer for managing the anxiety.
- [45:27](https://realalpha.app/v/zPtwI6JaRy0/?t=2727) **Jain's advice: work hard and build trust.** Jain advises people to work hard and stay dedicated even in a job they dislike, saying the work builds skills and relationships that carry forward. He says a positive reference from a former manager helped him raise funds for his first company, and argues that effort naturally leads to learning.

## Transcript

Knuckle Up with Nakul, hosted by Nakul Mandan, with Arvind Jain.

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[0:00](https://realalpha.app/v/zPtwI6JaRy0/?t=0) AI is not capable of replacing you right now. Let's be clear, it's not ready to replace even a single human today. I actually don't believe that companies will be successful in reducing their workforce significantly. My high school kids, they can tell you very quickly what text is written by AI and which one is not. It's the pace of innovation that is actually creating that feeling of anxiety. You never feel this feeling of, hey, you have arrived, or, okay, a company has been relatively stable business. It's completely elusive right now for us. There is a war going on right now over who owns the AI assistant for knowledge workers.

[0:33](https://realalpha.app/v/zPtwI6JaRy0/?t=33) The one tool that sits across everything you do at work and actually knows your company. OpenAI and Anthropic won that seat. So do Microsoft, Google, Notion and 100 startups. One company got there first, Glean. Arvind Jain founded it in 2019 after a decade building search at Google and co-founding Rubrik. Today, Glean is over $300 million in ARR and valued at $7.2 billion. In 2024, OpenAI reportedly told its own investors not to fund five competitors. Glean was on that list. Our conversation today with Arvind is interesting for two reasons. One, he holds that pole position against the most powerful companies in tech.

[1:20](https://realalpha.app/v/zPtwI6JaRy0/?t=80) And two, he's running one of the first and most AI-native companies in the world. This is going to be a power-packed conversation for everyone in AI. Knuckle up. Arvind, welcome to the show. Thank you for having me. You've put yourself in this pole position which every company in the world wants to win. The AI assistant for knowledge workers. The big labs want it, Google wants it, Notion wants it. Tens of startups are chasing this. But as all of them now make forays into it, and especially the big labs, let's start with them. As they get to know every knowledge worker and what's happening in every organization, what is Glean's product edge in this world?

[2:00](https://realalpha.app/v/zPtwI6JaRy0/?t=120) 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?

[2:38](https://realalpha.app/v/zPtwI6JaRy0/?t=158) 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.

[3:49](https://realalpha.app/v/zPtwI6JaRy0/?t=229) 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. But then maybe I will also share a little bit more about where we spend our R&D resources. So the company now is about 1,500 employees. That makes us one of the largest pure-play enterprise AI companies in the world. But we are actually not spending a lot of that time trying to train and build models. Glead is a multi-model product. We actually partner with the best model companies, you know, whether that's OpenAI or Enthropic or Google.

[4:26](https://realalpha.app/v/zPtwI6JaRy0/?t=266) we actually also incorporate open source models built on NVIDIA you know technology or like you know the open source model out there you know it's really great to be in that position where we can actually leverage that innovation that you know the labs are bringing and then we get to actually build things that they're not focused on yet so that together you know with their technology plus our technology we can bring the best experience to our users so that's that's part of our core strategy so it's not just then connectors and permissions is you're saying it's much more than just building connectors with crm workday every every other system and permissioning yeah it goes well beyond that that's the basics that's the nuts and bolts like you connect with all the enterprise systems even that is actually by the way quite hard so and you should understand that today the industry has taken the direction of not wanting to build those integrations we build it. But, you know, if you look at the strategy that the labs have taken, they are actually choosing this MCP-based architecture where they don't build integrations anymore. They're expecting that each individual enterprise application will put up an MCP server, which

[5:35](https://realalpha.app/v/zPtwI6JaRy0/?t=335) allows an AI model to interact with those systems, you know, a little bit. But that's actually quite shallow. And it doesn't actually, in that approach, you don't get to actually learn how your business works. Ultimately, like, you know, our viewpoint is this. If

[5:47](https://realalpha.app/v/zPtwI6JaRy0/?t=347) **Arvind Jain:** If you're going to have AI do some work, a complex piece of work that a human today does in your enterprise, then that AI has to actually learn from that human how they've been doing this work.

[6:01](https://realalpha.app/v/zPtwI6JaRy0/?t=361) You know, some of it is written down in, you know, SOPs and documentation, but some of it is in their mind and they exercise, you know, that judgment every day. So you have to be able to actually bring that automation in your business. You have to actually go much deeper, much deeper than connecting with the systems and understanding what data and what knowledge is where. You have to actually understand how work actually happens. And that requires you to actually even understand what work is, like what are the tasks, what are the key jobs to be done in this business. You have to observe like who are the people who actually do that work, who are experts on it.

[6:32](https://realalpha.app/v/zPtwI6JaRy0/?t=392) You have to observe their activity to see these are the 10 steps, you know, that this person took to complete this particular task. And these are all the observations that we built. This is the context graph. And so Glean has been investing in that for many, many years. And that right now we still see as an area which is, you know, completely open. Like we're not seeing other people actually building that yet. So let me steel man the other side of the argument just for debate, right? So one is with MCP. Now the onus is on every enterprise company to build their own integrators into the model, right?

[7:02](https://realalpha.app/v/zPtwI6JaRy0/?t=422) 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.

[8:00](https://realalpha.app/v/zPtwI6JaRy0/?t=480) 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.

[8:40](https://realalpha.app/v/zPtwI6JaRy0/?t=520) 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.

[9:15](https://realalpha.app/v/zPtwI6JaRy0/?t=555) 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.

[9:48](https://realalpha.app/v/zPtwI6JaRy0/?t=588) 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.

[10:18](https://realalpha.app/v/zPtwI6JaRy0/?t=618) 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.

[10:55](https://realalpha.app/v/zPtwI6JaRy0/?t=655) 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

[11:56](https://realalpha.app/v/zPtwI6JaRy0/?t=716) 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.

[12:27](https://realalpha.app/v/zPtwI6JaRy0/?t=747) 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. What does the enterprise stack of 2027, 2028 look like in your view? What do Google and Microsoft have? What do Salesforce Workday have? Like, do these systems of record continue and you are on top of them? Because there's also a thesis in the world that agents might just completely take away interface-based SaaS applications. The truth is kind of like, you know, it makes both of those things make sense to some degree.

[13:02](https://realalpha.app/v/zPtwI6JaRy0/?t=782) Like I personally believe that most of these systems of records will remain in the next few years. The Google Productivity Suite, Microsoft, you know, Productivity Suite, Salesforce, and many of these, you know, like your HR systems like Workday, ITSM systems like ServiceNow, each of them will be just fine. They will all be there. And in fact, they will all get very Asian-tic in nature. So if they don't, then of course they will not stay there. In fact, those are the products that are going to go away, which are not advancing with AI. I think businesses will actually build more systems internally.

[13:34](https://realalpha.app/v/zPtwI6JaRy0/?t=814) And the number of applications may end up reducing because if you're building like, if there was a very point use case, like, you know, one specific thing that a SaaS app did for you, you know, where, and that SaaS app is, you know, really let's say UI wrapper on top of your system of record data and Salesforce, let's say, that kind of app, like we do think, that people are going to start to build those kind of systems on their own. It was very easy. In fact, I'm doing it myself. Like we had this asset management app and it was very, very hard to actually use. I couldn't do any kind of like, literally it's an Excel spreadsheet that you're trying to view in a few different ways.

[14:14](https://realalpha.app/v/zPtwI6JaRy0/?t=854) And that system didn't allow you to do that. It didn't allow me to sort, it didn't allow me to filter the way it was not convenient and I couldn't actually ask a quick question off of that.

[14:22](https://realalpha.app/v/zPtwI6JaRy0/?t=862) **Arvind Jain:** So I just actually went and gleaned and asked it to, you know, code up that application and just, you know, connect to the backend database.

[14:31](https://realalpha.app/v/zPtwI6JaRy0/?t=871) And like, it actually took me like, you know, five minutes. And even the first version of that app was actually pretty good. Behind the scenes, you know, we must have used, I don't know what model it used, probably we must have used Cloud to actually, you know, compute that app. But that's an example of where users are feeling more confident of building some systems themselves. So we will see that. We will see a lot of like internally built applications. 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?

[15:09](https://realalpha.app/v/zPtwI6JaRy0/?t=909) So we are fundamentally the context and intelligence layer for your enterprise. So it's a platform. You bring it, you connect Glean with all of your enterprise systems. And now we understand, like, of course, you know, all the data, all the knowledge where it sits. We connect with structured data systems like Databricks and Snowflake. We connect with unstructured systems like your documents. And once you've connected Glean with all of your enterprise systems, we now become that gateway. And AI, think of us as an AI gateway. And it has a few different capabilities. One of the capabilities is that we can actually retrieve any information from any system, given any task, any question that is given to us. Another one is that we can actually help you take an action inside those enterprise systems with the right security. Yet another one is that, you know, you can actually ask us, hey, how is this task being done today in the enterprise? And we can actually bring the right sort of.

[16:00](https://realalpha.app/v/zPtwI6JaRy0/?t=960) you know, with mind, like, you know, how work gets done. Actually, you can do that? Like, you can ask Lean saying, hey, how does this person do, again, using this group? Is this part of a task? Yes. Yeah, so we'll actually go back and share that because behind the scenes, we're building task-based memory. We're building personal, individual memory. Like, for any given person, we deeply understand who they are, what role do they play in the enterprise, how do they like to read and write, what are their preferences, what are they, what are the things that are expert on, and what are the things they need to get done this week.

[16:27](https://realalpha.app/v/zPtwI6JaRy0/?t=987) So we know all of that. And this memory infrastructure is also another piece of infrastructure that we build as part of this platform. And so now this platform is available for all of your AI applications inside a company to use. So you may be building some agents, let's say building those agents in Vertex in Google or Bedrock in AWS. And so you'll connect it with that clean MCP gateway, which brings all of that enterprise context and intelligence into those agents now. So when these agent builder platforms now, they don't have to go and figure out how to connect with all enterprise systems. As an agent builder, like all of that connectivity is pre-established for you.

[17:04](https://realalpha.app/v/zPtwI6JaRy0/?t=1024) So you just focus on what task you're trying to actually go and build. Similarly, we can actually serve as a backend inside your cloud co-work or chat GPT. And we just make those tools better, make them more company context aware, smarter, you get better answers. and we actually also make them faster and cheaper. Because interestingly, what happens with AI today, Cloud is actually super smart. You can go and give it an arbitrary task and ultimately it'll figure out how to solve that. But it does it in a brute force manner. It'll spend lots and lots of cycles, first assembling just even the raw materials that it needs, the basic context to actually finish this task and then starts to work with and apply the model intelligence on it.

[17:45](https://realalpha.app/v/zPtwI6JaRy0/?t=1065) So that first part of that, we can actually short circuit. cloud can just instantly get all the context through clean you know our customers have reported

[17:53](https://realalpha.app/v/zPtwI6JaRy0/?t=1073) **Arvind Jain:** we have actually done studies that it actually reduces token consumption by 30 to 50 percent

[17:57](https://realalpha.app/v/zPtwI6JaRy0/?t=1077) so that's a that's a big use case in fact a lot of companies actually right now investing in glean for that reason because cost is a real real issue for them so that's so coming back to your question so that actually makes us the platform right you know it's a platform that makes all your other AI agents and your AI products more capable, better, faster, cheaper. But then we do come with our own first-party products. We have our own agent builder. We have our own Glean co-worker, which we think is powerful, more powerful. And in fact, you can think of it as a super set of ChatGPD, Claude, Gemini all combined into one. So that's our strategy. So build that platform, build it in an enterprise-grade manner with the right securities, safety, governance, capabilities but then also keep investing in our end-user applications.

[18:49](https://realalpha.app/v/zPtwI6JaRy0/?t=1129) So you will continue to encourage other agents to be built on top of Glean? 100%. In fact, our R&D team is divided into two. There's a core platform team which delivers this context and intelligence platform to the enterprise and then there is the the Glean co-work team, which is building the first-party product experience on top of this platform, but they get no extra rights to use this platform than what Cloud and ChatGPD gets. In fact, that's the working model. The platform team doesn't even test Glean co-workers. They actually only work with Cloud and ChatGPD as the primary test platforms. How much of the use of 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

[20:04](https://realalpha.app/v/zPtwI6JaRy0/?t=1204) 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.

[20:35](https://realalpha.app/v/zPtwI6JaRy0/?t=1235) 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.

[21:14](https://realalpha.app/v/zPtwI6JaRy0/?t=1274) 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.

[21:57](https://realalpha.app/v/zPtwI6JaRy0/?t=1317) 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

[23:05](https://realalpha.app/v/zPtwI6JaRy0/?t=1385) 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.

[23:37](https://realalpha.app/v/zPtwI6JaRy0/?t=1417) 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. Yeah. Do you feel like almost like a revolution is coming where companies quickly identify that 80% of the work is done by 5% people because they've supercharged themselves?

[24:17](https://realalpha.app/v/zPtwI6JaRy0/?t=1457) **Arvind Jain:** I actually don't believe that companies will be successful in reducing their workforce significantly while still having the same level of market success or the share of the market.

[24:33](https://realalpha.app/v/zPtwI6JaRy0/?t=1473) Because if you think about it, take an industry and if you are one of the companies in that, and there are four competitors, all five of you have the same tools. All of you have all the great AI capabilities. if you can actually do the work that needs to get done with half as many employees so could they in reality what's going to happen is that your customer expectation expectations are going to double you have to sort of build better products stronger products and you're still going to need that same staffing to do that in fact like your ultimately the business success is indeed proportional to you know the human sort of capital that you have inside the company so that's that's that's my beliefs. I would like take a different stance, like, you know, where I do believe that this power law will change. Take, take calculator. Does everybody know how to use it today equally well? Right. And I think that's what's going to happen with AI. So it won't be that 5%, like who have figured it out and the 95% haven't because AI itself will be that great teacher. The thought that is expressed, I think Elon Musk has expressed this, Scott, who recently expressed this in some interview that we have gone we are going as a human race from survival mode where we're all

[25:42](https://realalpha.app/v/zPtwI6JaRy0/?t=1542) doing work to creativity mode where it's an age of abundance because agents do all the work it sounds like you're saying correct me if I'm wrong that that's not very imminent it might be some futuristic vision but in the near future everybody will be just more productive more work will be expected and competition will take care that everybody will still need their people I think so. I mean, it is true that, you know, as such, you know, we are moving in the world of abundance. There will be enough food for everybody to feed and all of that. But yes, like, you know, as a business, the competition is going to, you know, increase.

[26:15](https://realalpha.app/v/zPtwI6JaRy0/?t=1575) Like, I really sort of don't see this world where somebody can build a very flourishing, great business where their people only work two days a week and the competition actually works, you know, five days a week. I don't actually know how the workday comes down from five to four to three and a half that everybody's talking about because, you know, like companies are going to immediately see that as a competitive edge. I would want our team to be fully, you know, motivated and energetic and actually keep working, you know, the schedule that they are today. But does the human work evolve from doing to reviewing mostly?

[26:49](https://realalpha.app/v/zPtwI6JaRy0/?t=1609) Human work completely changes across different functions. A lot of knowledge work is going to be done by AI for you. You don't need to sort of, for example, do information synthesis. Like, you know, you can actually get AI to do all of that work. So I think like, you know, our work will shift more to asking the right questions, actually, you know, thinking about what are the things, you know, we should be working on. And then as AI, you know, acts as a very powerful companion to us, it's sort of like, I would say, think of it as, you know, now, instead of being an individual contributor, I have, I have like, you know, 10 really, really good people, you know, available to me.

[27:28](https://realalpha.app/v/zPtwI6JaRy0/?t=1648) 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. partly and always compare these tools and see like you know how how we make lean better as a result the things that i do with ai number one this like i i'm a very curious person i have i have questions all the time i mean that's why you started lean as a answering machine in the first place that's right like i was really passionate about that you know and the yeah because you know i felt that that would be a product that you know i would i would like so much you know for my uh for my work so um so i have i have these questions uh all the time and before my way of getting these answers was that i would ask my executive team to give me like you

[28:34](https://realalpha.app/v/zPtwI6JaRy0/?t=1714) know give me a report on this or give me data on that and and i think it was i was probably very annoying to them like you know with the frequency and the volume of questions you know that i had for them and now i self-serve myself you know for all of those things i have actual system access to those systems you know where i'm looking for answers and then glean is my always the first stop. Sometimes, you know, I will verify. So if I did some analysis, I got some data back and I'll ask my CTO, hey, does this look right or not? So that's one thing, like, you know, just self-serving myself with whatever information needs, whatever questions that I have has become so easy. And in fact, there are certain things, you know, that you just simply couldn't do before. I'll give you an example. And this is like how I'm actually doing strategic work with AI. We made a shift in our product direction earlier this year and context and intelligence platform became a big part of our story we always had the platform but we said like like this year we won't actually have half of our users not coming from our first party products but from these other products like cloud and chat gpt and that required a change in you know company directions i communicated i talked

[29:37](https://realalpha.app/v/zPtwI6JaRy0/?t=1777) to the team but then like for the for the first few months i would always run a deep analysis in glean and i would say that hey look you know look at every individual look at every engineering project and tell me you know which projects are actually no longer aligned you know with with our new strategy something that we should deprioritize and this analysis you know before like you know you could do this analysis once a year it's a full tickle down from like and i would ask my you know the head of engineering they would ask their managers and and then you get these reports but

[30:07](https://realalpha.app/v/zPtwI6JaRy0/?t=1807) **Arvind Jain:** now you can get these kind of analysis done super quickly and that allows me to sort of like now move much much faster as an organization align our projects better with our strategy and there

[30:18](https://realalpha.app/v/zPtwI6JaRy0/?t=1818) are a lot of other topics like those I'll give you two more examples one of my favorite ones is that I'm always trying to look for who are the best people in our company and the ones you know who are somewhere there hidden in the organization they do great work but they're not that vocal and or maybe they're like a little bit too far down in the organization for us to have visibility so so i have this agent that actually you know helps you know find you know that the top 10 people uh for me and and explains to me like you know why they are actually that's a great example because in the history prior world that would always be gatekept by politics and whose favorite exactly exec's favorite person yeah and so these are like self-service examples as a ceo that you know i have and then finally the third example i'll give you is uh is customer management so one of the big things that enterprise, you know, sales company is managing churn, like making sure customers are happy and doing the right things for them before those customers, you know, choose to go away from you.

[31:14](https://realalpha.app/v/zPtwI6JaRy0/?t=1874) And, and so like companies have always built these, you know, customer success dashboards and, and oftentimes, you know, there's never, you know, truth in those. They are not up to date. Like people don't, actually, people are not as motivated to actually keep, enter the latest information. And a lot of context is actually lost in customer conversations that you're having. uh with these customers uh which is very hard to sort of then summarize and and organize but now it has all become very easy so we have in glean we have this system called prism where so now i have this you know live dashboard where for each customer it actually gives us like the full view on what is happening with them and and what's the risk and it actually scores itself like you know what are the top customers you know that are churned risk for us And in fact, it goes beyond and actually also says that these are the three things you can do to actually fix these customers.

[32:06](https://realalpha.app/v/zPtwI6JaRy0/?t=1926) There's a very actionable system that we've built, which was never possible with AI. But that is actually another one. And this is what we call enterprise level intelligence. So this is no longer like, how do I improve one person moving faster? This is about how do we do these things that we simply could not, you know, in the pre-AI world. 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.

[32:56](https://realalpha.app/v/zPtwI6JaRy0/?t=1976) 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, you know, which we just could not, you know, get done. At this point, is every department at Glean pretty AI natives? Like engineering is obvious, but like finance and back office, HR. So we are like, since we are one of the first enterprise AI products, so we've been using it heavily inside.

[33:34](https://realalpha.app/v/zPtwI6JaRy0/?t=2014) And so finance team has, you know, tons and tons of agents. Like they've actually one of the most AI-fied department for us. To be honest, you know, we are, I'm not satisfied with how much AI-ification we have done. you know we could we could do better last quarter i started a program so we have this this product inside lean called agent finder and agent finder is it uses that same sort of knowledge graph and understanding of like you know what work is getting done and who's doing it and actually it actually prioritizes and figures out that these are the the top 20 or 30 agents that you should actually build because this is where most of your human time is actually getting consumed today and it's rudimentary in the sense that it is basically basing it on where time is being spent we're not actually saying that hey like but this is too hard and therefore this should not be like it's just giving us where the time is being spent right and so based on that we have a list of things that are not yet agentified what we're going to do is we're going to ask like not engineers and not individual contributors

[34:35](https://realalpha.app/v/zPtwI6JaRy0/?t=2075) **Arvind Jain:** but we're going to actually ask our leaders to actually go and build all of those agents

[34:39](https://realalpha.app/v/zPtwI6JaRy0/?t=2079) and they can build it in Glean or they can build it in any other agent platform it doesn't matter And so that's the program that we're running right now. So I was trying to achieve two things with that. One is, of course, building more automation. But also a big part of how that's going to be achieved is when you truly make a workforce AI native, right? And so by forcing our managers to do it, and they cannot delegate it, that gets to a place where our leaders are fully AI-fied. Do you feel your non-engineering leaders are at this point AI-fied enough where they are getting into cloud code. Because cloud code is literally writing the code itself.

[35:18](https://realalpha.app/v/zPtwI6JaRy0/?t=2118) So you don't even need, as long as you can be a product manager, you could probably build agents yourself. I know that's outside of Glean, but do you feel you've kind of, through the brute force of last couple of years, made that a mandate? If you're not AI native as a manager, can you survive at Glean? Let me ask you this way. We have a process, actually. Even when we recruit people, there is the AI native check that happens. if today like you know after all like ai is already very you know uh quite spread you know well spread right so in our interview process we we actually test for how much you've been using cloud and chat gpt and gemini and what work have you done with it in the past most of our managers You don't build agents in Cloud Code.

[36:03](https://realalpha.app/v/zPtwI6JaRy0/?t=2163) Our engineers, of course, use Cloud Code all the time to write code because, you know, like Glean is simpler and it's much easier to actually go and build these agents in Glean. And so our, and that's the interesting thing, like, you know, if you think about it, while, you know, AI is a very powerful technology and everybody should know how to use it, but it should not feel like a complex technology that people have to learn. So I would still want our sales leader to be primarily very, very good at sales leadership. And yes, they should be aware of the power of AI, but we do believe that the tools will be quite easy for them to just use.

[36:40](https://realalpha.app/v/zPtwI6JaRy0/?t=2200) Like they don't have to actually become hackers or builders. There's been this dream of an AISDR. 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

[37:54](https://realalpha.app/v/zPtwI6JaRy0/?t=2274) 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. You talked about recruiting a little bit. Yeah, what has changed in your recruiting process beyond the thing you just mentioned? Is there a part of every interview has an AI and a development to it?

[38:32](https://realalpha.app/v/zPtwI6JaRy0/?t=2312) Is there tasks you give? Certainly, like, you know, for example, for when we recruit engineers, so we used to have this programming exercise before. It was a two-hour session, and there was a program that we thought could be done if you write code yourself in two hours. So we've changed that. Like now, we don't actually tell people and prompt them that you use AI to do it, but we give them a task which is so, so much more in complexity that there's no way for you to finish that in time in two hours, unless if you use AI, right? So that's sort of like, that's an example of how we change the interview process there.

[39:05](https://realalpha.app/v/zPtwI6JaRy0/?t=2345) We've done that across departments. AI fluency is a key check. But within the recruiting process, there are other things that we do. One of the things that, you know, I was very frustrated with was we use Greenhouse as our application technical system I still approve offers for employees, for every employee that joins the company and I would get these packets and I always felt that it was incomplete like it was missing like somebody puts in a feedback that oh I love this person, it's great, let's hire him and that's not a good enough feedback for me I would actually see what happened in the interview what questions were asked and so there was a lot of back and forth before and it was like my time would get spent and so now i actually said everything that i want to see in the packet is written in a document and now

[39:50](https://realalpha.app/v/zPtwI6JaRy0/?t=2390) **Arvind Jain:** there's an agent that verifies every packet and they don't get routed to me until the agent actually clears it you know within the system so this is an example of where

[40:00](https://realalpha.app/v/zPtwI6JaRy0/?t=2400) AI is not replacing anyone, but it's just improving the quality of our work. Are you seeing, even within your organization, in every department, maybe it's slightly different, 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

[41:15](https://realalpha.app/v/zPtwI6JaRy0/?t=2475) 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.

[41:57](https://realalpha.app/v/zPtwI6JaRy0/?t=2517) What keeps you up at night about Glean today, product or organization? I mean, we are in a very fast-moving industry. In fact, you can say that there is no real foundation. The foundation itself keeps changing every day. so i think for us like the biggest things on my mind always is how do we communicate our vision our strategy effectively within the company how do we make sure that everybody is aligned and moving in the same direction so it's the execution you know that keeps me worried because and we have to be very very fast very very agile because it's a very high you know it's a very competitive space too you don't get that luxury like you know one of the things i was sharing in uh in a previous discussion with somebody is that you never feel this this feeling of hey you have arrived or okay a company has been built we have a stable business is completely elusive right now for us do you think this is a new thing in silicon valley or this has always been true because i do sense a heightened sense of anxiety success or otherwise everybody is feeling more anxious feels like a now or never moment in silicon valley but do you feel it's always been true no it's not always been true I think I think we used to feel more stability

[43:04](https://realalpha.app/v/zPtwI6JaRy0/?t=2584) before I've felt it personally like you know I built a company before I've been like you know and multiple times and so so I think we we had this feeling that once you know in the past you will get a customer and you had that relationship and you know it'll be a long

[43:18](https://realalpha.app/v/zPtwI6JaRy0/?t=2598) **Arvind Jain:** lasting thing but today like you know it's very hard like you know if you build a product if you are like 20 percent 20 percent slower somebody else will have a much much better product than you and there's no way for a customer to justify your product anymore compared to them because

[43:32](https://realalpha.app/v/zPtwI6JaRy0/?t=2612) it's the pace of innovation that is actually making creating that feeling of anxiety when the world is innovating so quickly uh in fact everything that you build instead of you know it feeling like you know your moat or ip sometimes it feels like liability because you know you have to you have to actually quickly get rid of it so like in our company we have this i always talk about that hey if somebody deletes lines of code that's actually even more valuable than writing lines of code But that's, you know, that's the proof of that we are actually moving with times. Do you think this is a permanent change in our world now?

[44:05](https://realalpha.app/v/zPtwI6JaRy0/?t=2645) Like there will be just because the pace is not going to slow down ever. And so is it that we all will get used to this uncertainty around defensibility for every product that is even loved? Because that's what you're saying. The glean is loved, but you don't feel as assured as in the prior world with Rubrico. So do you think this is a permanent part of our world in Silicon Valley? Like in the AI space, in AI companies, it feels like that to me that I don't see when it's going to actually change. Because I think our ceiling for what AI will be able to do for us, like, you know, people talk about AGI, people talk about that, hey, this is already so good, it can actually do, replace any human.

[44:46](https://realalpha.app/v/zPtwI6JaRy0/?t=2686) But let's be clear, it's not ready to replace even a single human today, no matter what job they do today. Even if you're in EA, even if you're chief of staff, AI is not capable of replacing you right now. but in the future, we're going to get close to that. So there's a lot like, you know, that, you know, AI needs to get significantly better. It needs to get significantly cheaper, more efficient. And so the pace of innovation is like, is going to continue, which means, you know, like, you know, companies will kind of feel that way. How do we deal with this anxiety going forward? How do you deal with this anxiety?

[45:17](https://realalpha.app/v/zPtwI6JaRy0/?t=2717) I need answers for this. I wish I actually had an answer. But yeah, like I'm trying to figure it out. I think ultimately, I think what saves you is the fact that there are 10 things that need to get done. And so, like, as you, through your day, as you're actually getting those things done, like, you know, takes your mind off of, you know, this more, you know, higher level question and that anxiety. Last question for you. Somebody watching this, somebody young watching this wants your life. You've built a successful life for yourself. They want your life. What would you tell them of what the journey actually requires?

[45:50](https://realalpha.app/v/zPtwI6JaRy0/?t=2750) 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

[46:57](https://realalpha.app/v/zPtwI6JaRy0/?t=2817) Arvind this was great thank you so much for coming on thanks