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Glean

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Glean

One-line summary: Enterprise AI search / "personal companion" platform. Crossed $200M revenue run rate by Q4 2025, signing $10M deals. Tracked here for arvind-jain's framing on what proactive enterprise AI products require, and for Glean's failed-fine-tuning evidence supporting the LLM-as-commodity thesis.

What they are

Enterprise AI platform that indexes a company's internal data (Slack, email, docs, CRM, code, etc.) behind a unified search + agent interface. Goes to market as both app (employee-facing) and platform (developer-extensible). Founded by arvind-jain (former Distinguished Engineer at Google, co-founder of Rubrik).

Why they matter to this thread

  1. Vision for proactive AI. Glean's vision is the cleanest articulation in the wiki of what "proactive AI products" looks like operationally — a "personal companion" that knows your week, goals, ambitions, and acts on your behalf before you ask. Aligns directly with chatgpt-super-assistant-vision's proactivity framing from a different (enterprise) vantage point.
  2. Failed-fine-tuning evidence. Arvind's first-person evidence is one of the cleanest data points the wiki has for the commodity thesis: Glean tried building product-specific fine-tuned models, abandoned the effort, fell back to foundation models. Cross-link llm-as-commodity-thesis.
  3. Daily-Prep agent as concrete operator workflow. Arvind describes using Glean's Daily Prep Agent as his executive routine — every morning it tells him what his day will be, what to read, what to prepare. Operator-grade datapoint on what working AI deployment looks like at the CEO level.

Evidence

Revenue scale (Dec 2025)

The "personal companion" vision

  • arvind-jain in 2025-12-23-bg2-databricks-glean-enterprise-ai: "We want Glean to be this very personal companion for every person in every company in the world. This companion with which you have a very confidential relationship... this companion knows everything about you and your work life. It knows your day, it knows your week, it knows who are you going to meet in the day to day, it knows your weekly goals, it knows what things you're not good at or what your career ambitions are."
  • arvind-jain in 2025-12-23-bg2-databricks-glean-enterprise-ai on the proactivity transition: "You have to come to Glean to get most of that work done in the future. We want Glean to actually come to you and do that work." Parallel to chatgpt-super-assistant-vision's "model prompts the user, not the other way around" framing.

Failed fine-tuning, fell back to foundation models

  • arvind-jain in 2025-12-23-bg2-databricks-glean-enterprise-ai: "Some of our fine tuning work, building models for a specific use case within our product didn't really pan out for us. And ultimately the choice was that we can go with already built models, whether they are small open source models hosted on databricks or one of the large foundation models." — Direct evidence for llm-as-commodity-thesis.

Daily-Prep agent (operator routine)

  • arvind-jain in 2025-12-23-bg2-databricks-glean-enterprise-ai: "One of our agents is Daily Prep Agent, which I really love because every morning it tells me what my day is going to be, what I need to read, what I need to prepare. Like most of the meetings, I will not have context. It actually brings the plan for those meetings for me."
  • Operator-side change-of-instinct anecdote: "Whenever I have a small question, curiosity, just go and ask somebody and they're going to put 30 people on the task to actually get that answer for me. And this is going to have a [cost]... I changed that... my instinct is to, whenever I have curiosity, whenever I have questions, when I need to do data analysis, when I need to write something, you know, my letter to the company every month, all of those things, I use AI." — Evidence of how senior operators are recomposing their workflow around AI access.

Related

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