The clue was buried in an employee survey. In 2018, Rubrik had the outward signs of a company moving quickly: nearly a thousand employees, hundreds of software products and teams adding people at speed. Yet output had stopped rising in step with headcount. Arvind Jain, the company's technical co-founder, sat with hundreds of responses and looked for the drag.
It was not one dramatic failure. Engineers were spending too much time outside code. Account managers could not locate the latest research or sales deck. New hires took too long to understand what the organization already knew. Documents lived in shared drives, decisions in chat threads, customer facts in business systems and useful context in the memories of people who might be asleep, busy or gone.
Rubrik used roughly 300 SaaS products. Each solved something. Together they created a geography without a reliable map.
For Jain, the irony was personal. He had spent 11 years at Google helping people find information across the public web. Google could retrieve a restaurant, a video or an obscure page in an instant. Inside a company filled with talented engineers, finding the current version of a document could consume an afternoon.
Even when I was at Google, we could never find anything internally.Arvind Jain
He asked other chief executives what they used. Their answers amounted to a request: if you find something, tell us. The complaint was not specific to Rubrik. It was a common tax on growing organizations, paid in interrupted work, repeated questions and decisions made without the context that already existed somewhere else.
A childhood in systems
Jain grew up in Jaipur, in Rajasthan. He has described himself as introspective and not naturally extroverted. His father encouraged him to look people in the eye and speak with confidence. His mother modeled something quieter: calm in conflict, patient listening and no urge to say “I told you so.” Years later, he would draw on both temperaments while learning to run a company.
Computers arrived through his older brother, who brought a PC loaded with Pac-Man into the family home. The game held Jain's attention, but the more durable fascination was that code could create something visible and useful. He entered an advanced computer science track in school, earned a place at the Indian Institute of Technology Delhi and completed a BTech in computer science in 1996. A master's degree at the University of Washington followed in 1997.
His first jobs took him through Microsoft, Akamai and Riverbed. Then, in 2003, Google hired him into a contest with the changing web. Pages were multiplying and their formats were becoming less predictable. Jain helped lead a redesign of Google's crawling system so it could index many kinds of files and scale with the web. The system launched in 2004. He went on to work on YouTube's video serving and recommendation systems and on search for Google Maps.
Maps was the messiest of the three. Roads, photographs, business submissions, land records and public data arrived in inconsistent forms and changed continually. Jain enjoyed the engineering difficulty, but what stayed with him was watching people use the result. A couple finding a restaurant or a family walking to a relative's home made the infrastructure tangible. So did work to document small towns in rural India that had not previously appeared on a map.
Waiting for the problem
In 2013, Bipul Sinha asked Jain to become the technical co-founder of a cloud data company. Jain's first reaction was blunt: “I'm not an entrepreneur.” He was happy at Google and skeptical of founding a company as an identity exercise. His standard was more demanding. A person should start a company after meeting a problem that keeps nagging them, one that others either cannot or will not solve.
Sinha proposed a clean division: he would build the business and Jain would build the product. Jain agreed. Rubrik launched in 2014 and grew into a cloud data management and security company. Four years later, its own growing pains finally supplied Jain with the kind of problem he had been waiting for.
He recruited T.R. Vishwanath, Piyush Prahladka and Tony Gentilcore to build Glean in 2019. Several early engineers had worked with him at Google. The company's first public product, launched in 2021, resembled a familiar search box. Behind it sat the unfamiliar work: connectors reaching into workplace applications, an enterprise knowledge graph, relevance systems and permission checks personalized to the employee making the request.
Data should not be the reason why you fail. It should be the reason you succeed.Arvind Jain
The answer beneath the answer
Enterprise search has a constraint the public web rarely presents in the same form. Two colleagues can type the same words and be entitled to different answers. A recruiter, an engineer and a chief executive do not have identical access across chat, documents, customer records and personnel systems. A useful system has to understand not only what a piece of information means, but who may see it.
That foundation mattered when generative AI changed the software market. Glean added generative features in 2023, but it did not have to invent its understanding of company context overnight. Search could retrieve relevant material; the newer models could summarize it, combine it and use it to perform tasks. The visible interface moved from a list of results toward an assistant, then toward agents. The underlying bargain remained: useful answers must be grounded in current company knowledge and constrained by access.
Jain is candid about the remaining limits. In late 2024, he called enterprise AI error-prone, unpredictable and difficult to make useful inside a company. This is less a contradiction than an engineer's description of the work. A demo can be fluent. A dependable workplace system must connect data, preserve controls, earn adoption and produce enough value to justify its cost.
Glean's commercial trajectory gave the thesis room to expand. In June 2025, the company raised $150 million in Series F financing led by Wellington Management at a $7.2 billion valuation. By December, Glean said it had passed $200 million in annual recurring revenue. Jain increasingly described an open platform in which companies could use multiple models and build agents on top of shared enterprise context.
The reluctant founder learns to sell
The technical challenge came naturally. The CEO role did not. Jain had to move from designing systems to explaining a category, recruiting customers and making decisions through other people. He has connected that adjustment to his parents: his father's outward confidence and his mother's habit of listening through disagreement.
The shift also changed how he thought about engineering. At Google, recognition as a Distinguished Engineer did not alter the basic job in his telling; he was still an engineer building systems. At a startup, a correct system that nobody buys remains an unfinished product. Founder-led selling became another form of debugging: listen carefully, locate the objection and determine whether the product or the explanation is wrong.
A work double, with boundaries
Jain's longer ambition is a “work double” for every employee: a digital assistant able to understand projects, communication and company knowledge, then take on tasks. The phrase sounds futuristic, but his route to it is deliberately infrastructural. First connect the information. Then resolve identity and permissions. Retrieve the right context. Only after that should a model answer or act.
He also frames AI as augmentation. In 2026, amid arguments about job displacement, Jain said he expected the technology to function as a co-pilot rather than remove whole roles. Inside Glean, the nearer-term test is whether AI can take repetitive work off a person's desk and create more room for judgment and creativity.
There is a useful tension in his story. The boy drawn to a machine because code could produce real-world effects became an engineer of very large systems. The self-described introvert became a chief executive. The Google veteran who made public information easier to reach founded a company because private information remained stubbornly scattered.
And the product's origin was not a dazzling model release. It was a set of colleagues admitting, in the plain language of an employee survey, that they could not find what they needed. Jain read the comments closely enough to see one system hiding beneath many frustrations. Search started the story. Attention did.