Glean just crossed $300M in top-line revenue seven years in, and its founder gave up his real playbook in a five-minute hallway interview over lunch.
Arvind Jain, the 2x unicorn founder behind Rubrik and Glean and a decade-long Google search engineer, broke down who to hire first, when to raise, what investors actually bet on, and how Glean quiewetly became the layer enterprises use to cut AI costs by close to 6x. We went through the entire conversation question by question so you can steal the moves without watching a single minute.
In this guide you’ll find:
The origin story: ‘Google for your work life’ (and 20 engineers before anyone else)
Arvind’s fundraising rules: raise fast, dilute early, and know investors bet on you not the idea
We asked about Uber burning its AI budget: is enterprise AI ROI actually real?
The use cases nobody predicted, and who actually buys Glean
Openrouter getting acquired by Stripe 💰
Will AI erase all jobs as Dario Amodei says?
The OpenAI panel: a 3-layer playbook for deploying agents safely
The Enterprise AI Playbook by Glean
High-signal analysis on markets, capital and the companies shaping the next decade.
1. The origin story: ‘Google for your work life’ (and 20 engineers before anyone else)
We opened with the classics: how did you end up here, who do you hire first after raising your first round, and how do you communicate the vision to so many engineers?
The wedge was a problem Arvind lived, not one he researched. At Rubrik, the enterprise data security company he co-founded, fast growth quietly wrecked internal productivity. “Everybody would complain about not being able to find things inside the company or connect with the right people to get help,” Arvind told us. He had spent over a decade building search systems at Google, so the fix was obvious to him: a real search product for all of a company’s data and knowledge. That became Glean in early 2019. Before that, he grew up in India and moved to the States in the ‘90s for grad school, and he’s been in the tech industry — and based in San Francisco — ever since.
His answer to “who do you hire first” was one word: builders. “The first 10 people that we hired at Glean, they were all engineers. Maybe even the first 20, I don’t remember hiring anybody outside of R&D,” Arvind said. Office, devices, food, early customers, he handled all of that himself so the product team could stay heads-down until the product hit maturity.
He could hire 20 engineers around one idea because the idea fit in a sentence. “It’s a Google for you in your work life. It’s a place where you go and ask questions when you’re looking for information or answers and we’ll bring the right information back to you,” Arvind said. A pitch this simple removes the friction of explaining what you’re building. But he flags the harder half: people have to believe it will be big. “They’ve got to feel that it’s going to be something big.”
The steal: if you can’t recruit engineers around your idea in one sentence, the idea is too complicated to fund and too complicated to build. Pressure-test your mission on a non-technical friend this week. If they can repeat it back, you can hire around it.
2. Arvind’s fundraising rules: raise fast, dilute early, and know investors bet on you not the idea
Next we asked when a founder should raise at all — bootstrapping versus raising money — and whether raising was easier the first time at Rubrik or the second time with Glean.
Arvind’s capital strategy is deliberately aggressive, and it’s built to protect focus, not valuation. “I like to go big and go and go big fast. So if you can, then I would recommend that, just raise capital. What’s the downside?” he told us. His reasoning is operational: he does not want to spend every month calculating whether the company can afford one more engineer. Raising more removes that recurring distraction.
He directly rejects the most common counterargument. “Sometimes people feel that, oh, is it too early to dilute the company? And I don’t really believe in that,” Arvind said. His view is that the drag of under-capitalization costs more than the dilution.
The deeper insight came from our second question: raising was definitely easier the second time, and the reason is about what investors are actually buying. “Investors are actually never looking to invest in great ideas. They’re actually, they’re looking to invest in people,” Arvind said. His logic: any early idea gets transformed, refined, probably changed into something completely different once you talk to customers, so investors are underwriting your persistence, not your pitch deck. A track record signals you’ll “stay on the mission” and not give up.
He also reads the current market as unusually generous. “There’s a lot of capital that is actually waiting to be deployed” on AI ideas, Arvind said, so founders “should feel really, really, really comfortable” they’ll be able to raise. Note this is his read, not a hard figure.
The steal: frame your raise around the person and the persistence, not the perfect idea, and if you can raise enough to stop thinking about hiring math for 18 months, take it. Run the raise as a distraction-elimination decision, not a valuation-optimization one.
3. We asked about Uber burning its AI budget: is enterprise AI ROI actually real?
We put the reported case of Uber burning its 2026 AI budget in four months without seeing ROI directly to Arvind. He didn’t dodge it. “It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now,” he said.
The clearest value, per Arvind, is basic knowledge-seeking that makes every employee “more productive, more knowledgeable,” and “that’s not in contention.” The catch: “it’s hard to measure. The fact that, you know, people can work faster and, you know, oftentimes, you know, that doesn’t translate into like your P&L statement.” The companies seeing real results are larger ones with intentional programs, good metrics, and systematic process-by-process adoption — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.” Reality, he said, is “like any other technology where you have to provide a lot of love and care and investment.”
That’s why Glean sells a long-term engagement, not a switch you flip. “We bring our platform so you immediately get those productivity savings, you get a great coworker, but then the real business value, like, you know, we spend many, many months understanding, you know, their business and helping them build those agents and get them to production level of quality,” Arvind said.
How Glean is using this to win. Arvind’s Glean:GO keynote turned the ROI gap into Glean’s entire product pitch. He named the failure mode most enterprises are living: employees now work for the AI instead of the reverse. “Today employees spend a lot of time coaching AI agents and are feeling overburdened with that. This is bot sitting. We are spending too much time working for AI as opposed to AI working for us,” he said. Botsitting — employees feeding context, supervising, and cleaning up after AI agents — has become a significant drain on worker time: Glean’s Work AI Institute, surveying 6,000 full-time digital workers, found employees spend 6.4 hours a week on it, more time than they spend using AI to actually produce work. In the keynote he laid out the three pains every enterprise names: struggling to bring the right context to agents, AI costs running higher than they can afford, and the proliferation of AI-written documents (SLOP), skills, and agents that are hard to manage and secure. Every one of those pains maps to a Glean feature.
On the OpenAI panel he was blunt about the near-term math: “AI is often becoming more expensive than the business value that it is delivering in the short order... We need to make sure companies don’t run out of money before that happens.” Positioning Glean as the platform that closes that gap — cheaper, better-contexted agents — is the commercial answer to the ROI question we asked.
The steal: stop selling (or buying) instant ROI. Set the expectation that production-quality agents take months of context-building, and instrument the specific business processes where you can actually measure cost-per-task before and after. Pick one high-cost, high-human-involvement process and commit to a multi-month build rather than spraying pilots.
4. The use cases nobody predicted, and who actually buys Glean
We asked four go-to-market questions in a row: have customers used Glean in ways you never imagined, who do you reach out to in the organization, do customers build the agents themselves or do you send in your engineers, and where are your customers?
Because Glean is horizontal, the entry point is the person who owns company-wide AI. “Typically we’ll start with the CIO. You know, the CIO typically thinks about how to bring company-wide AI technologies,” Arvind told us. From there, Glean expands to departmental leaders when the value is concentrated. “Sometimes, you know, we do work with departmental leaders too, because for example, for salespeople, for CROs, we do a lot.”
The footprint is mid-to-large enterprises, weighted to the US. “About 70% of the business is probably US and then 30% is the rest of the world right now,” Arvind said, naming Europe, Asia, Canada, and Latin America as the rest. (Those percentages are Arvind’s stated figures.) Named customers on stage at Glean:GO included General Motors (helping engineers design cars faster), McCarthy (construction management), and Navita (patient support), showing how far the horizontal platform stretches across industries.
The use cases prove the “chief of staff” positioning, and Arvind admits almost none were planned. “Almost all use cases are things that we didn’t imagine people would actually use our product for,” he said. The surprises: a performance-management agent that “writes the first drafts” of annual reviews for individuals and managers, customers “responding to RFPs” with AI, teams “reviewing legal contracts with AI,” and salespeople using AI “to figure out what messages to send to which one of their accounts.” His mental model: “since Glean is so horizontal and it connects to all enterprise systems, it sort of functions as your chief of staff.”
Who builds the agents once Glean is in? The customers do, with Glean enabling them. When we asked whether Glean sends in its own engineers, Arvind said customers build the agents themselves, backed by Glean’s “training and enablement programs,” and “sometimes we actually bring our partners to help build agents for our customers.”
The steal: for a horizontal platform, sell the platform to the CIO and the sharpest single-department win (CRO/sales) in parallel, then let unpredicted use cases pull expansion. Ship analytics that surface which unplanned workflows customers invent, because those are your next feature roadmap and your next case study.
5. Is Glean just a model router? The context moat and the 81% cost cut
Our spiciest question: OpenRouter — reportedly being acquired by Stripe — what do you think of that? It got the most strategically important answer of the whole interview.
Arvind said he’s very familiar with them: “They were one of the first model routing companies, but more sort of model access company than routing.” Glean does some of the same things OpenRouter did, but mostly in the enterprise context for large enterprises, and the business is very different: model access “is actually only one part of our overall solution.” That’s why Arvind resists being called a model router even though Glean does model access. “Largely, like, you know, we think of Glean as the context and intelligence company,” he told us, describing the goal as “bringing the collective human intelligence and the know-how, the knowledge and experience of your enterprise in one place and then make it available to AI systems.”
How Glean is using this to win. The “context company” framing isn’t marketing — it’s the entire thesis, and it rests on one number: structured data is a rounding error of what a company actually knows. “Structured data and data lakes capture only 5 to 10% of all enterprise knowledge,” Arvind said in his Glean:GO keynote, and documents like playbooks and SOPs only go so far. “Much of the real know-how of how work happens lives in people’s heads.” (The 5-10% figure comes from his talk; treat it as his framing.) The good news, per Arvind, is that the other 90-95% “does surface in emails, messages, meeting notes, exception approvals” as people do their regular work.
Capturing that is the moat, and Arvind draws a hard line between Glean’s approach and the connector-first competition. Glean’s context engine “combines advanced retrieval with a powerful enterprise graph” that maps the people, customers, teams, projects, and the relationships between them. “That deeper understanding of your business, how it operates, is what Glean sets itself apart from the industry’s typical approach of delivering shallow context via MCP,” Arvind said, adding: “Glean is the only context and intelligence company that truly understands your enterprise.” Glean CPO Emrecan Dogan sharpened the same point in the company’s Glean:GO press release: “Models are getting better and more interchangeable. Understanding the enterprise is not. We designed Glean around that reality in 2019.”
And context is exactly how Glean wins the cost war. The headline number from Glean:GO: “Glean saves 81% on token costs compared to Claude, with Glean response preferred 78% of the time,” Arvind said. (Percentages are Arvind’s own from the keynote.) Independent observers noticed the token gap too; Rohan Paul flagged the 1.3M vs 4.4M split on X.
As we put it in the interview, “the cost is even like 6 times or close to 6 times less than other options like Claude Cowork.”
The waste Glean attacks is architectural, not technical. “Most systems still point the most expensive models at the simplest of the tasks. That’s not really a limitation of AI, it’s an architecture choice,” Arvind said. Glean’s Pareto frontier analysis makes the spread concrete: across 1,000 enterprise tasks and 37 model/reasoning configurations, the cost spread between the cheapest and most capable options is dramatic for modest quality differences. There is no single best model, so routing is the point. Auto-routing uses a small specialist model to pick the right reasoning effort per task.
The second half of the savings comes back to context. “Quality context stops AI from having to reconstruct it at runtime, which burns tokens and hands back answers it can’t use,” Arvind said. Better context means fewer tokens and better answers at the same time. The delivery layer is the AI Gateway, which pairs model choice with governance: closed and open models routed through one place with Glean Protect controls, so customers can pick models that meet their data retention and regulatory requirements.
The steal: the durable asset in enterprise AI is the years-long graph of how work actually happens, not a thinner MCP connector to the same systems. Then audit which model is handling your simplest, highest-volume tasks — if a frontier model is answering questions a mid-tier model could answer, you’re paying the 36x spread for a 22% gain you don’t need. Build (or buy) a routing layer that matches task difficulty to model cost, and measure it in dollars per task, not tokens.
6. Will model companies eat every startup idea (and what happens to jobs)?
We closed the interview with the two questions on every founder’s mind: Dario Amodei’s much-quoted prediction that AI will erase 50% of white-collar jobs in the next 5 years, and what Arvind would say to founders starting a company right now.
On jobs, he’s a clear skeptic of the doomsday number. “It’s a very bold statement and obviously I don’t think there’s any kind of deep analysis or research that was done to come up with the number 50. The fact that the number is 50 means it’s more figurative... But I kind of disagree with that regardless. I don’t think people are going to be replaced with AI in the sense that there are no jobs for us anymore. I think we’ll all have jobs and I think the nature of our jobs is going to change” — the same way his own work has continuously evolved in tech.
On founders, Arvind spends real time reassuring people who fear the model companies will eat everything. He hears it constantly: “Every idea they come up with, you know, it feels like, you know, you can just use an AI model or the model companies, you know, just come and do that.” His worry is that founders “start to give up too soon” and never build.
His counter is that change is the entrepreneur’s raw material. “AI changes, you know, if anything, you know, that AI changes, it actually creates more opportunity for entrepreneurs. Because it brings change, and whenever there is change, that is great for entrepreneurs,” Arvind said. And he doesn’t think the model layer can cover the surface area: “Model companies won’t be able to meet even 10% of all the needs, you know, that the market is going to have.”
The OpenAI panel added the sharpest version of the upside case. Alexander predicted a step change in what one person can do: “We are now going to enter a situation where the most productive individuals could be running unicorn companies, and folks who are less productive are, well, where they are today.” His prescription: hire high-agency people and structure teams to unlock their creativity, then scale it. Alexander framed it as an old pattern, not a new one; since the history of our species, someone who is good at using tools is faster and more productive than someone who is not.
The steal: pick a specific workflow the model companies will never prioritize (your 10%+) and build the context and product around it. Then staff for leverage, hire high-agency people who can each drive AI across multiple functions, because the ceiling on individual output just jumped.
7. The OpenAI panel: a 3-layer playbook for deploying agents safely
The panel with OpenAI’s Alexander produced the cleanest framework in all three sources for deploying agents without spooking the security team. It starts with a simple rule: an agent acting for you inherits exactly your permissions, no more. “If they’re running on behalf of me, these agents should not be given access to enterprise data and information that I did not have rights to see or use,” Arvind said, adding that they “should not have the ability to make any changes into the system that I could not make.” Then you scope further, because an agent is built for a specific set of tasks and shouldn’t wield all the powers you personally hold.
The third case is the new one: independent agents with their own identity. “Take your customer support team. Maybe you have 10 people in the team today and now this agent is the 11th member of that team,” Arvind said. Once an agent has its own identity rather than impersonating a person, you need a distinct permissions and guardrails model so it behaves like a good teammate, “adding positive value and not actually escalating and, you know, fighting with the humans inside.”
Alexander distilled the whole thing into three layers. “One is the control of what the agent can do. The second is the observability of that. And then lastly, starting to shift away from agents always impersonating us as people and having more agents that have a specific role with exactly the right permission set for that workflow, and that’s an independent agent.” His rollout advice was to start narrow and expand: connect agents to the most important context first, then add more incrementally. “Your security team will be very happy if you add one and then you add the other one. You can actually move really fast if you do it incrementally.”
This connects straight back to the ROI question from our interview: the enterprises that actually reach production-quality agents are the ones whose security teams never got a reason to say no. Glean’s three-layer model is how it sells “safe to deploy” alongside “cheaper to run.”
The steal: deploy agents in exactly this order. Mirror the user’s permissions, scope down to the task, then graduate the most valuable workflows to independent agents with their own identity, observability, and guardrails. Give your security team an incremental rollout they can approve one connection at a time, and you’ll ship faster than a big-bang launch that gets blocked.
The full interview Q&A
Every question we asked Arvind in the 20-minute interview, one by one, with his answers (lightly cleaned of filler words; the sections above expand on each theme).
Q1. How did you end up here? What’s your personal story as a founder?
Glean started in early 2019 while he was still co-founder at Rubrik, where fast growth had quietly wrecked internal productivity: “Everybody would complain about not being able to find things inside the company or connect with the right people to get help.” Before Rubrik he spent over a decade at Google building search systems, so the fix was obvious to him: “We should actually build a really good search product for all of our enterprise data and knowledge. And that’s how Glean got started.” Personally: grew up in India, moved to the States in the ‘90s for grad school, and has been in tech — and based in San Francisco — ever since.
Q2. You just raised your first round — who do you hire first?
“Hire software engineers, people who are going to build the product, because right now that is all we need.” Everything else — office, devices, food, finding early customers — he handled himself: “The first 10 people that we hired at Glean, they were all engineers. Maybe even the first 20, I don’t remember hiring anybody outside of R&D,” until the product reached a certain level of maturity.
Q3. How do you communicate the vision of the product to so many engineers?
“Simple ideas are easy to communicate. So if you think about Glean’s idea, it was actually very, very straightforward. It’s a Google for you in your work life. It’s a place where you go and ask questions when you’re looking for information or answers and we’ll bring the right information back to you.” The second half is belief: “They’ve got to feel that it’s going to be something big.”
Q4. Bootstrapping versus raising money — when should a founder raise?
“I personally have chosen the path of raising capital quickly... I want to focus all of my attention and energy into building the product. I don’t want to continuously think every month about, okay, well, how much can we invest? Can we really hire one more engineer or not? I like to go big and go big fast. So if you can, then I would recommend that, just raise capital. What’s the downside?” And on the classic objection: “Sometimes people feel that, oh, is it too early to dilute the company? And I don’t really believe in that.”
Q5. Was raising money easier the first time or with Glean?
Definitely easier the second time: “Investors are actually never looking to invest in great ideas. They’re actually looking to invest in people.” Ideas get transformed once you talk to customers, so investors underwrite persistence — “do you have the persistence to stay on that journey?” A track record answers that. He also notes today’s market is unusually founder-friendly: “There’s a lot of capital that is actually waiting to be deployed on those ideas.”
Q6. Dario Amodei says 50% of white-collar jobs could be erased in 5 years. What do you think?
“It’s a very bold statement and obviously I don’t think there’s any kind of deep analysis or research that was done to come up with the number 50. The fact that the number is 50 means it’s more figurative... But I kind of disagree with that regardless. I don’t think people are going to be replaced with AI in the sense that there are no jobs for us anymore. I think we’ll all have jobs and I think the nature of our jobs is going to change” — the same way his own work has continuously evolved in tech.
Q7. Uber reportedly burned its 2026 AI budget in four months without ROI — do enterprises actually see ROI from AI?
“It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now.” The clearest value is basic knowledge-seeking — “every employee in the company becoming more productive, more knowledgeable” — “but it’s hard to measure. The fact that people can work faster oftentimes doesn’t translate into your P&L statement.” Larger companies with intentional programs, good metrics, and systematic process-by-process adoption are seeing results — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.” Glean’s model is built for that reality: immediate productivity savings up front, then “many, many months understanding their business and helping them build those agents and get them to production level of quality.”
Q8. Have you found any use case you didn’t think of when you started?
“Many. Actually, almost all use cases are things that we didn’t imagine people would actually use our product for.” His list: a performance-management agent that “writes the first drafts” of annual reviews, customers “responding to RFPs” with AI, teams “reviewing legal contracts with AI,” and salespeople using AI “to figure out what messages to send to which one of their accounts.” Why it works: “Since Glean is so horizontal and it connects to all enterprise systems, it sort of functions as your chief of staff.”
Q9. Who do you reach out to to sell Glean in the organization?
“Because we’re horizontal, typically we’ll start with the CIO. The CIO typically thinks about how to bring company-wide AI technologies. And sometimes we do work with departmental leaders too, because for example, for salespeople, for CROs, we do a lot — there’s a lot in Glean that can help you truly improve the productivity of your sales team.”
Q10. OpenRouter is reportedly being acquired by Stripe — what do you think of that?
“I’m very familiar with them. They were one of the first model routing companies, but more sort of model access company than routing. And Glean does some of the same things that OpenRouter did, but we do it mostly in the enterprise context for large enterprises. But our business is actually very different. Model access is actually only one part of our overall solution. Largely, we think of Glean as the context and intelligence company” — the goal being to bring “the collective human intelligence and the know-how, the knowledge and experience of your enterprise in one place and then make it available to AI systems.”
Q11. For your agent builder — do customers build the agents themselves, or do you send in your engineers?
Customers build them, with Glean enabling rather than staffing: “We also have training and enablement programs. And in fact, sometimes we actually bring our partners to help build agents for our customers.”
Q12. What’s the main location of your customers — your go-to-market, roughly?
“We have customers worldwide. About 70% of the business is probably US and then 30% is the rest of the world right now. So we are in Europe, we are in Asia, in Canada, Latin America. And typically we work with mid-sized to large enterprises.”
Q13. To close — anything you’d like to share with founders starting a company right now?
He hears the same fear constantly: founders feel “every idea they come up with, you can just use an AI model or the model companies just come and do that... Is there any value to ideas left? What I fear and what I worry is, will people start to give up too soon and not become entrepreneurs?” His answer: “AI changes, if anything, actually creates more opportunity for entrepreneurs. Because it brings change, and whenever there is change, that is great for entrepreneurs. And model companies won’t be able to meet even 10% of all the needs that the market is going to have.”
What to steal
Recruit and fund around a one-sentence mission. If you can’t explain your product in the length of “Google for your work life,” it’s too complex to hire engineers around. Test yours on a non-technical friend this week; if they can repeat it, it’s tight enough.
Raise to eliminate distraction, not to optimize valuation. Arvind’s rule is to raise enough that you never spend a month calculating whether you can afford one more engineer. Frame your pitch around your persistence, because investors bet on people, not ideas.
Sell (and expect) months, not minutes. The “botsitting” tax is 6.4 hours a week per worker (Glean’s Work AI Institute) because vendors oversold instant magic. Commit to one high-cost process, build production-quality agents over months, and instrument the before/after so value actually reaches the P&L.
Sell horizontal platforms to the CIO, expand through surprise. Start with the company-wide AI owner, win one sharp department (CRO/sales) in parallel, then let the use cases customers invent — performance reviews, RFPs, contract review — pull your expansion and write your roadmap.
Own context, not the model, and route by task difficulty. Structured data is 5-10% of what a company knows; the moat is the years-long graph of how work actually happens, surfaced from emails, messages, and meeting notes. Then match model cost to task difficulty: the 36x spread for a 22% quality gain is pure waste, and Glean’s benchmark shows routing plus context can cut token costs 81% while winning 78% of comparisons.
Deploy agents in three layers so security says yes. Mirror the user’s permissions, scope to the task, then graduate the best workflows to independent agents with their own identity and observability. Incremental rollouts ship faster than big-bang launches that get blocked — and they free high-agency people to run what used to take whole teams.
Hope this was valuable!
Cheers,
Guillermo
Really interesting interview. The part I would add a warning label to is raise fast and dilute early.
For a second-time unicorn founder with a track record, access to capital and unusually high investor trust, more money can genuinely buy focus. For a first-time founder who is still uncertain about the problem, segment, willingness to pay or core value, more money can do something much less useful: make an untested assumption much cheaper to execute at scale.
I see this surprisingly often with startups. The problem is not under-capitalisation, but investment getting ahead of evidence. Suddenly there are more engineers, more features, more marketing and more activity, while the original market assumption remains largely untouched.
So I would probably add one question before how much should we raise: what exactly has become true about the market that justifies accelerating now?
I also loved the point about enterprise AI ROI taking months rather than appearing magically after deployment. It is almost the same underlying problem: technology increases execution capacity much faster than it increases certainty that we are executing the right thing.