Grit

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

January 12, 2026

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  • The rapid evolution of the technology stack necessitates a mindset where anything built last year must be considered obsolete, demanding constant innovation to avoid stagnation. 
  • Despite increasing AI capabilities, current usage across industries is estimated to be less than 1% of what the models can actually do, indicating massive untapped potential for business value generation. 
  • In the brutally competitive AI industry, success hinges on deep focus on a smaller problem rather than trying to be everything to everyone, as agility and deep specialization create the true competitive moat. 

Segments

Startup Grind and AI Competition
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(00:00:00)
  • Key Takeaway: The AI industry is the most brutally competitive environment, characterized by relentless change that makes founders feel they cannot step off the treadmill.
  • Summary: The technology stack evolves at an unprecedented pace, demanding that built solutions from the previous year become obsolete due to a lack of imagination if not improved. Building a startup is hard work, especially in the AI sector, which requires constant effort just to keep pace with the competition. The feeling of being on a treadmill is constant, but the belief in a great destination keeps the team motivated.
Arvind Jain’s New Incubation
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(00:01:29)
  • Key Takeaway: Arvind Jain is incubating a new company focused on solving complex pricing and quoting software issues using natural language interfaces.
  • Summary: The new venture aims to address the difficulty AEs face with complex pricing models, consumption-based SKUs, and multi-year deals. The initial focus is on a Configure Price Quote (CPQ) system using natural language for AEs and administrators. This will eventually expand to cover billing and contract lifecycle management.
Glean’s Office Expansion and Size
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(00:05:48)
  • Key Takeaway: Glean opened a San Francisco office after six years, motivated by a desire to eliminate long commutes for SF-based employees, especially as the company moved to a four-day in-office week.
  • Summary: The decision to open an SF office was delayed to maintain team cohesion at the Palo Alto HQ, which was established as the primary location where the CEO would work. The CEO’s primary motivation for the expansion was preventing employees from wasting three hours daily commuting. The company recently crossed the 1,000-employee mark, which triggered panic due to the increased organizational complexity required for alignment.
Scaling Pains and Founder Enjoyment
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(00:09:53)
  • Key Takeaway: Scaling past 1,000 employees forces founders to shift focus from product to organizational structure, a necessary but unenjoyable part of leadership.
  • Summary: The CEO does not enjoy the organizational aspect of leadership, finding it hard to excel at setting processes, though recognizing their necessity to prevent redundant work. The shift from a small, aligned team to a large organization requires intentional effort in alignment and prioritization, which was previously easy. The lived experience at a successful startup like Glean is exhausting, requiring employees to find joy in the daily grind rather than waiting for a future stable outcome.
Competitive Landscape and Moats
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(00:22:25)
  • Key Takeaway: In the current AI environment, traditional moats based on static code are liabilities; agility and deep customer relationships are the new currencies for success.
  • Summary: The rate of technological change is so high that building a moat based on current technology is risky, as LLMs evolve quickly, demanding continuous product and code evolution. Agility—the speed at which a company can adapt and replace code—is crucial for leveraging the latest technology foundations. The true moat lies in deep customer relationships, acting as a core partner in AI transformation by sharing expertise and building shared roadmaps.
AI Fluency in Hiring and Talent
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(00:39:02)
  • Key Takeaway: Glean now tests all roles for ‘AI fluency,’ prioritizing curiosity and demonstrated interest in the revolution over traditional experience alone.
  • Summary: AI fluency testing assesses how inquisitive candidates are about the ongoing revolution and what they have personally done with AI tools. Younger employees often become power users naturally as they lack established traditional work habits to rely on. While senior engineers remain productive due to their skills in debugging and design, the company values those who actively adopt AI tools as a sign of an open mindset.
CEO’s Personal AI Workflow
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(00:51:24)
  • Key Takeaway: The CEO’s work habits have fundamentally shifted to using Glean as a powerful, objective colleague for deep strategic research before engaging the human team.
  • Summary: The CEO’s personal AI usage is heavily concentrated at work, transitioning from knowledge-seeking to using Glean for complex strategic work, which reduces the guilt of distracting the team. By first asking Glean for a two-page research report, the CEO enters team discussions far more informed, ensuring time spent with employees is surgical and objective. This process helps eliminate bias inherent when asking single-function experts for input.