SAN FRANCISCO: Arvind Jain, the founder and CEO of $7.2 billion enterprise AI startup Glean, said his team receives thousands of job applications a day and still cannot find candidates with the trait he values most — and it is not the trait most applicants are optimizing for. In a Fortune profile published May 26, 2026, Jain said the filter that separates the candidates his team chases from the rest is work ethic, not degree, not skill set, not an impressive CV.
“I have a firm belief that hard work solves all the problems,” Jain told Fortune. His personal version of the yardstick: “The yardstick for me is that when I work in a group, I want to be known as the person who gives in the most.”
Who is Arvind Jain?
Jain joined Google in late 2003 and spent more than a decade there, promoted to Distinguished Engineer — a title reserved for the top 1% of the technical staff. He led engineering teams across Search, Maps, and YouTube before leaving in 2014 to co-found Rubrik, which went public via direct listing in April 2024 at a $5.6 billion valuation. Jain founded Glean in 2019 to build an enterprise layer that ingests data from a company’s existing tools — email, chat, documents, tickets — to ground large language models in company context and power AI agents that can act on behalf of employees. Glean raised a $150 million Series F led by Wellington Management in June 2025 at a $7.2 billion valuation, according to the company, and was named to CNBC’s 2026 Disruptor 50 for the third straight year.
What does Glean’s CEO look for in candidates?
Jain says his team is looking for “constant learners” — people who want to grow with the company rather than collect a title. Pedigree is not disqualifying, but it is not the signal. The signal is what a candidate did with whatever situation they were handed, and whether they were the person in the room who carried more than their share. Jain also told Business Today earlier in May that AI will not replace human workers at Glean — a position that puts him at odds with founders publicly warning of mass white-collar displacement. The two positions track together: if AI augments rather than replaces, you need more humans, and the top-of-funnel filter becomes the bottleneck.
How many applications does Glean receive?
Thousands per day, per Jain. His team reads roughly one-fifth of them. The rest, in practical terms, do not get a human eye. AI-generated cover letters, resume optimizers, and one-click apply tools have collapsed the cost of submitting an application to roughly zero. Companies on the receiving end have seen application volume explode and signal quality collapse at the same time. The natural response is to filter on something harder to fake — which is what “work ethic,” demonstrated through specific contributions or a body of work, actually represents to a CEO like Jain.
The contrarian Gen Z frame
The line that will travel furthest from this interview is the one that pushes back on the dominant Gen Z job market narrative. “Students think it’s hard to find jobs,” Jain said, “but we think it’s hard to find them.” Class of 2026 graduates are entering one of the toughest entry-level job markets on record, with layoffs at Meta, Bill.com, and Salesforce openly framed as AI-driven. Jain’s counterpoint is that the panic looks different from the hiring side: AI unicorns at his scale are hiring aggressively and still cannot fill the seats with the people they want. Both can be true. Entry-level openings have shrunk in legacy software roles. Openings at AI-native companies have expanded, but the filter has tightened.
What this means for founders and candidates
For founders building in the AI tier, Jain’s framework is a signal that the top of the funnel is broken and the screen has moved further down. Resume screens, GPA filters, and name-brand school filters were already noisy. They are now noise-saturated by AI-generated submissions. The filter that still works is evidence of past output — code committed, products shipped, problems owned — at a volume a candidate cannot fake in 30 seconds with a model. In practice that means more take-home projects, portfolio review, contribution history, and reference checks. Less weight on resume keywords.
For candidates, “work ethic” is a vague word and Jain knows it. What he is describing in concrete terms is a candidate who can point to a thing they built and whose story about it survives a second-level question. Cover letter language about being “passionate” or “hardworking” is exactly the kind of cliché the filter is designed to ignore. Lead with the work, not the credentials.
Glean crossed $100 million in ARR less than three years after launch, then doubled to $200 million nine months later, per a December 2025 company release. Hiring is the rate-limiter, not capital. That is the position more AI unicorns will be in over the next 18 months, and Jain’s “work ethic” line is the first widely quoted version of how a founder at that scale is filtering when the funnel is infinite.
