Arvind Jain

CEO and Founder of Glean, and co-founder of Rubrik

Claims

As speaker

Mentioned in

Key positions and views

The primary bottleneck in enterprise AI is not model intelligence but the retrieval of correct, up-to-date, and properly permissioned information from a multitude of fragmented systems [47].

In the rapidly evolving AI landscape, traditional technology moats are a liability; the key to survival and success is constant adaptation and leveraging external innovation rather than defending a static advantage [1, 53, 54].

AI's true business purpose is to be a force multiplier that enables a 10x increase in employee output, not a tool for reducing headcount [4].

AI will not eliminate the need for specialized software applications; instead, enterprises will use a greater number of more specialized tools, with AI acting as an intelligence layer across them [22].

The future of work involves every employee having a personal team of AI assistants, coworkers, and coaches that will proactively perform 90% of their tasks and help them upskill [31, 46].

Podcast consensus on Jain

Points of consensus

▶Arvind Jain's vision for the future of work involves every employee having a personal team of AI assistants that will proactively perform 90% of their tasks and act as a coach for upskilling, a point he reiterates across multiple discussions [31, 46].Apr 2026

▶The founding premise of Glean in early 2019 was based on the insight that transformer models could fundamentally solve the enterprise search problem, which was historically seen as a 'graveyard' for failed companies [21, 27, 44, 55].Apr 2026

▶A core technical and business challenge for Glean is managing data permissions, as an estimated 90% of enterprise knowledge is private or restricted. The platform's solution is to bake user permissions directly into its core indexing technology [23, 50].Apr 2026

▶The idea for Glean originated directly from Jain's personal experience at his previous company, Rubrik, where he could not find a commercial product to solve the internal search problem across their 300 SaaS systems [65].

Points of debate

▶There is an internal tension in Glean's model strategy. Jain states the company's strategy is to leverage external foundation models and that internal efforts to build some custom models were unsuccessful [19, 54]. However, he also details how Glean builds its own proprietary small models for semantic matching and fine-tunes others [38, 34], while also predicting they will likely stop this practice in the future [51].Apr 2026

▶Jain's perspective on AI's impact on senior engineers is nuanced. He argues for a 10x increase in work output via AI [4] and predicts the engineering role will shift to reviewing AI code [32]. Yet, he also observes that senior engineers at Glean are highly productive without heavy AI tool use and that there is no significant productivity difference between heavy users and non-users in this group [25, 68].Apr 2026

▶Jain expresses conflicting feelings about rapid company growth. He is bullish on the market, seeing it as 10x larger than a year ago and predicting Glean's headcount will double in the current year [3, 10]. Concurrently, he admits to feeling 'panic' when the company surpassed 1,000 employees, citing concerns about organizational bloat and maintaining alignment [9].Apr 2026

▶The valuation of Jain's previous company, Rubrik, is presented with some variance. A podcast host speculates that Rubrik IPO'd at a valuation over $20 billion [6], a claim marked as speculative and representing a high-end estimate that may contrast with public market data.Apr 2026

Key themes

▶The AI-Augmented EnterpriseApr 2026

Jain consistently frames AI not as a tool for headcount reduction, but as a force multiplier to achieve a 10x increase in employee output. He envisions a future where every worker is supported by a team of AI assistants that handle 90% of their tasks, create 'evergreen documentation', and act as personal coaches for upskilling.

For investors, this theme suggests that the primary ROI for enterprise AI, in Jain's view, is not cost-cutting through layoffs but radical productivity gains, shifting the value proposition from efficiency to amplification.

▶Navigating the AI Arms RaceApr 2026

Jain characterizes the AI industry as 'brutally competitive,' creating a high-pressure environment where technology built a year ago is likely obsolete. He believes traditional technology moats are a liability, prioritizing rapid adaptation and leveraging external innovation over building static, proprietary advantages.

This perspective implies that sustainable success in the AI application layer depends less on defensible code and more on strategic agility, speed of integration, and the ability to build value on top of a constantly shifting foundation model landscape.

▶Primacy of Data Retrieval and Governance

According to Jain, the biggest challenge in enterprise AI is not model hallucination but the fundamental problem of retrieving correct, up-to-date, and appropriately permissioned information from countless disparate systems. This challenge is so significant that early customers feared Glean because it exposed their internal data governance gaps, forcing Glean to evolve into a security and 'AI readiness' product.

This highlights that the 'picks and shovels' of the enterprise AI gold rush are the systems that can solve data access, indexing, and governance, suggesting that the 'intelligence layer' is where significant value will be captured.

▶Glean's Pragmatic Technical and Go-to-Market StrategyApr 2026

Jain outlines a hybrid technical approach for Glean: leveraging large, third-party models for reasoning while building smaller, proprietary models for specialized tasks like semantic matching. This technical reality, combined with the product's company-wide nature, structurally forced Glean into a top-down enterprise sales motion rather than a product-led growth model.

Jain's experience demonstrates that building a successful enterprise AI company requires a pragmatic, non-dogmatic approach to the 'build vs. buy' decision for models and a go-to-market strategy that aligns with the product's inherent deployment scope.

Source episodes

Timeline

Pre-2019

While at his prior company, Rubrik, Jain experiences firsthand the difficulty of finding information across 300 different SaaS systems, planting the seed for Glean [65].

Early 2019

Jain founds Glean, inspired by the potential of transformer models like BERT to revolutionize the enterprise search market, which was widely considered a 'graveyard' of failed companies [21, 55, 63].

Early Product Phase

Glean encounters resistance from potential customers who fear that a powerful search tool would expose their poor data governance and unsecured sensitive documents [28]. This feedback prompts Glean to evolve its product positioning to include being a security and 'AI readiness' solution [24].

Post-LLM Boom

The mainstream emergence of large language models allows Glean to evolve its product from a Google-like search tool into a conversational 'Glean Assistant' [64]. Jain assesses that this shift has expanded Glean's market opportunity by a factor of ten compared to the previous year [3].

Current Scaling Phase

As Glean surpasses 1,000 employees, Jain expresses concern about organizational bloat while simultaneously predicting the company will double in size again within the year [9, 10]. The company formalizes its culture by increasing in-office days and making 'AI fluency' a hiring criterion for all roles [58, 67].

Suggested prompts

How does Arvind Jain's philosophy of 'moats as a liability' influence Glean's product roadmap and partnership strategy with foundational model providers? &nearr;Analyze the tension between Jain's goal of 10x productivity augmentation and his observation that senior engineers see limited gains from current AI tools. What does this imply for the future of specialized knowledge work? &nearr;Given Jain's prediction that Glean may stop training its own models, what are the long-term strategic risks and benefits of relying entirely on third-party model providers for a core part of its technology stack? &nearr;Evaluate Jain's claim that AI will capture significant revenue from the services industry. What specific service sectors are most vulnerable to this shift, and what new business models might emerge? &nearr;

Key concepts

Enterprise Search 4 ep Large Language Models (LLMs) 4 ep AI Strategy & Competition 3 ep Future of Work 3 ep Model Training & Fine-Tuning 3 ep Data Governance & Security 2 ep Transformer Models 2 ep AI Agents 2 ep Productivity 2 ep Glean's Company Culture 1 ep

Notable quotes

“lately I've really transitioned to where I feel like Glean is a more powerful colleague of mine than any other colleague.”

Arvind Jain · Why We’re Only Using 1% of AI | Glean CEO Arvind Jain

“the bigger problem in the enterprise when people ask questions is, can you even get that right information from all these different systems that you have? And can you get the information that are actually up to date and actually correct”

Arvind Jain · Arvind Jain on building Glean and the future of enterprise AI

“In fact I think whatever you think of mode is probably liability in some ways because you have to evolve, you have to move fast, change your product, change your technology to adapt to the new sort of foundation as you mentioned.”

Arvind Jain · Why We’re Only Using 1% of AI | Glean CEO Arvind Jain

“it's actually going to grab a lot of revenue that actually today is in services industry which is 25 times larger than software industry so there's a lot of spend that is going to move”

Arvind Jain · AI Enterprise - Databricks & Glean | BG2 Guest Interview

Report last updated: Apr 21, 2026

Get started free

Back to Entities Intelligence Report