Arvind Jain
Showing 1–6 of 6 transcripts.
- RAISE Summit42 min
2035 Decoded: Navigating the Decade Ahead | Dust, Glean & More | RAISE Summit 2026
Gabriel Hubert, Eve Bodnia, Arvind Jain, Nico Laqua, Raphaelle D'Ornano, Chris Blundell
Projected as a 10-to-15-year strategic journey, the panel outlines how enterprises must prioritize context graphs and data ownership over specific models to navigate the divergence between the 125 projected S&P 500 winners and the majority of firms unable to transition to agentic workflows. While Large Language Models dominate creative tasks, the future architecture will integrate Energy-Based Models for critical reasoning, demanding talent with advanced systems thinking to manage non-deterministic risks in sectors like finance and robotics. Long-term competitive advantage will ultimately accrue to organizations that retain control over system specifications and leverage human oversight to compound value through defensible data moats.
- RAISE Summit21 min
From AI Adoption to AI ROI | Glean & Altimeter Capital | RAISE Summit 2026
Arvind Jain, Apoorv Agrawal, Vikas Khannabiswamy, Shyam Gollakota, Vikram Bajaj
Glean, co-founded by former Google and Rubrik executive Arvind Krishnan, differentiates itself in the enterprise AI market by building a proprietary context graph and retrieval layer rather than developing foundation models. The company is strategically pivoting from personal productivity tools to measurable departmental use cases like customer service and legal review to demonstrate clear return on investment amid rising infrastructure costs. By maintaining neutrality across major model providers and utilizing limited forward-deployed engineering, Glean aims to solve the critical gap between raw AI capabilities and secure, accurate enterprise implementation.
SemiAnalysis, Altimeter, Nebius, Glean.. 12 Hot Takes From Biggest Names in AI
Dylan Patel, Qasar Younis, Apoorv Agrawal, Arvind Jain, Ariel Cohen, CJ Desai, Gil Feig, Nikhil Benesch, Barak Kaufman, Max Junestrand, Marc Boroditsky, Laura Diorio, Kasser, Mark
RAISE Paris marked a decisive industry shift from speculative hype to enterprise-grade cost reconciliation, as buyers now demand clear ROI and physical AI adoption outpaces volatile large language model growth. Key figures including Applied Intuition's Kasser and analysts from Altimeter Research warned of an impending market bust driven by unsustainable spending, while companies like Navan and TurboPuffer demonstrated new economic models focused on profitability and reduced inference costs. The event concluded with a consensus that success requires resilient multi-model strategies, robust data layers, and a global expansion mindset to navigate rising hardware prices and geopolitical energy constraints.
Why OpenAI and Anthropic Won't Win the App Layer | Glean Founder
Industry leaders project that enterprise AI will rapidly commoditize around open-source models, with Arvind Jain forecasting a three-year shift where most workloads run on such alternatives despite ongoing geopolitical concerns over Chinese capabilities. While organizations struggle to quantify ROI due to currently absurd token economics and inefficient inference costs, companies like Glean are betting on aggressive hiring and composite roles to build 10x superior products as the market matures. This strategy involves leveraging frontier model providers as assets rather than competitors, prioritizing specific problem-solving, and preparing for a rigorous audit of AI spending by 2026 to ensure genuine returns on investment.
- Goldman Sachs27 min
Transforming Work Productivity with AI: Glean CEO Arvind Jain
Founded by Arvind Krishna to address enterprise productivity crises caused by fragmented knowledge, Glean has evolved from an internal search engine into an agentic AI platform that proactively executes business tasks while maintaining strict data security through permission-based access. The company is on track to facilitate one billion agent actions this year by leveraging a four-year head start in deep integration and specialized "thinking models" that minimize hallucinations. Krishna positions this technology as an augmentation tool designed to expand human potential and strategic capacity rather than replace teams, predicting that the majority of knowledge work will shift to proactive AI systems within five years.
- Sequoia Capital45 min
How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work
Arvind Jain, Sonya Huang, Pat Grady
Glean CEO Arvind Jain's company has evolved from an enterprise search provider into an AI application platform, leveraging a five-year vision to automate 80% of knowledge worker tasks through a unique RAG architecture that grounds responses in private data. The platform differentiates itself by prioritizing data governance, semantic knowledge graphs, and fine-grained access controls before layering on Large Language Models, which has enabled year-over-year revenue quadrupling. By abstracting complex infrastructure for developers and focusing on agentic workflows, Glean aims to shift the market from reactive querying to proactive, autonomous assistance that doubles productivity for engineering, sales, and support teams.