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No Priors Ep. 115 | With Glean Founder and CEO Arvind Jain

Arvind Jain joins Sarah and Elad on this episode of No Priors. Arvind is the founder and CEO of Glean, an AI-powered enterprise search platform. He previously co-founded Rubrik and spent over a decade as an engineering leader at Google. In this episode, Arvind shares how LLMs are transforming enterprise search, why most tools in the space have failed, and the opportunity to build apps powered by internal knowledge. He discusses how much customization is still needed on top of foundation models, what made building Glean uniquely challenging compared to Arvind’s previous ventures, and what’s next for the company. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jainarvind Show Notes: 0:00 Introduction 0:58 How LLMs are changing search 2:05 Building out Glean’s platform 5:09 Why most search companies failed 8:41 Out of the box vs. bespoke models 10:26 Creating apps on top of internal knowledge 15:34 User behaviors & insights 19:11 Unique challenges of building Glean 21:51 Product-led growth vs. enterprise sales 25:00 Succeeding in traditionally bad markets 27:08 What Glean is excited to build next

Elad GilhostArvind JainguestSarah Guohost
May 15, 202531mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Glean’s Arvind Jain on Reinventing Enterprise Search with AI Agents

  1. Arvind Jain, founder and CEO of Glean and former Google search leader, explains how large language models and transformers have fundamentally changed enterprise search from brittle keyword matching to deep semantic understanding.
  2. He describes Glean’s evolution from a Google-like internal search engine to a ChatGPT-style assistant and agent platform that sits on top of a company’s entire knowledge corpus while strictly enforcing permissions and governance.
  3. Jain outlines the technical and go-to-market challenges of building effective enterprise AI, including data access, scale, security, and user education, and why many previous enterprise search attempts failed.
  4. He also discusses choosing an ostensibly bad market, building for top-down enterprise sales, and his vision of every worker having a personalized AI “team” of assistants, coworkers, and coaches.

IDEAS WORTH REMEMBERING

5 ideas

Modern enterprise search must combine embeddings with classic IR signals.

Vector search/embeddings are powerful but insufficient alone; high-quality enterprise search also needs relevance signals like freshness, authority, and correctness to avoid surfacing obsolete or low-trust content.

APIs and SaaS unlocked a problem that killed previous enterprise search attempts.

Pre-SaaS, simply connecting to scattered on-prem systems was prohibitive; today’s API-first SaaS ecosystem lets platforms like Glean reliably ingest and unify company-wide knowledge, making turnkey enterprise search feasible.

Enterprise AI must treat permissions and governance as foundational, not add-ons.

You cannot dump all internal data into a single model and expose it to everyone; every AI experience must mirror underlying ACLs so that users only see content they’re authorized to access, or it becomes a data-leak engine.

Good search can expose governance gaps—often becoming a security product.

Once search actually works, it reveals sensitive docs (e.g., salaries, M&A plans) that were misconfigured; customers now use Glean both to find information and to discover and fix security/governance issues to be “AI ready.”

Users are still trained on keyword search and need help learning AI capabilities.

Despite a simple chat box, most employees don’t naturally write rich prompts; companies must scaffold usage with targeted suggestions and education, aligned to users’ roles and daily tasks, to realize ROI on AI investments.

WORDS WORTH SAVING

5 quotes

LLMs have completely changed [search]… they allow us to really deeply understand a question a user is asking and similarly… what a document is about.

— Arvind Jain

You can't actually build a model inside your enterprise, dump all of your internal company's data into it, and then make that model available to everybody… because if you do that, you're leaking information.

— Arvind Jain

We had no budgets, there was no concept of buying a search product in the enterprise… it was a graveyard of all these companies that tried to solve the problem and didn't.

— Arvind Jain

We actually ended up becoming a security product. A lot of companies buy us to fix governance… and become AI ready.

— Arvind Jain

Each one of us is gonna have this amazing team of assistants, coworkers, coaches that are totally personal to you… and this team… does 90% of your work for you.

— Arvind Jain

How LLMs and transformers transformed search and semantic understandingTechnical evolution of Glean from traditional IR to AI assistant and agentsEnterprise data challenges: access, scale, freshness, authority, and governanceSecurity, permissions, and making AI safe inside organizationsUser behavior, education, and driving adoption of AI assistants at workGo-to-market strategy: top-down enterprise sales vs. PLG in infra-heavy productsFounder lessons: defying bad market priors and transitioning from engineer to CEO

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