Arvind Jain on building Glean and the future of enterprise AI
Summary
Glean’s founding thesis was enterprise search, but its early transformer bet made the 2019 product unusually well positioned for generative AI. Arvind Jain began with a universal pain point—research suggested employees spend one-third of their working time finding information—and used BERT-based models to match concepts rather than keywords. As generation and reasoning improved, Glean evolved from “a Google for you in your work life” into “ChatGPT for work life.”
Glean uses frontier models where broad capabilities already exist while building enterprise-specific retrieval. It trains small models on a customer’s corpus for custom embeddings, and separately fine-tunes small open-domain models for narrow search tasks such as spell-checking, synonym handling, and acronym expansion. It uses GPT, Gemini, or Claude for synthesis and multi-step reasoning. Jain’s rule is blunt: “Do not reinvent things that have been invented already.”
Permissions and data freshness—not merely model quality—are the central enterprise-AI constraints. Glean imports governance from systems including Google Drive, Slack, and Salesforce, bakes permissions into its index, and retrieves only documents the signed-in user may access. Because embeddings themselves might leak restricted information, customer-specific models are trained only on subsets Glean judges safe.
Jain’s repeat-founder pattern is a contrarian bet on universal problems in markets others have abandoned. Lukas Biewald pressed him on whether Rubrik and Glean reflected exceptional execution rather than novel ideas; Jain agreed enterprise search had produced “only failures” and become a “dead area” where investors did not want to invest. His conviction came from two changes: SaaS made fragmented data much worse but also more accessible, while transformers made semantic understanding technically viable. His Google-derived operating model is to put innovation first, hire smart engineers, and largely let them build.
A concrete ROI example comes from reasoning across the 95% of enterprise data that is unstructured. A non-engineer in Glean’s finance team asked an agent to combine Salesforce customer lists, shared Slack sentiment, and product usage into green/yellow/red account-risk profiles after churn appeared. Jain says the result was better than a conventional dashboard because it could incorporate subjective textual evidence.
Glean evaluates the complete answer pipeline, while conceding that enterprise AI cannot eliminate errors. It derives “golden” question-answer sets from real interactions such as well-received Slack replies, tests retrieval and model changes against them, and uses LLMs as judges. For hallucinations, it checks answers “line by line” against supplied source material and may suppress unsupported claims or abstain—but Jain says stale, missing, or poorly retrieved knowledge causes more failures than fabrication alone.
Jain rejects labor reduction as the most valuable AI strategy and instead wants every employee surrounded by a scalable “dream team.” He imagines assistants, coworkers, and coaches helping each person do up to 90% of the work they need to do, while companies retain and even expand teams whose members can do “ten times more work.” The transformation may feel incremental—one task at a time—until workers discover they are fundamentally different from two years earlier.
Deep dive
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