Glean

Inside Abridge: The AI Listening to 100 Million Doctor Visits — Abridge's Janie Lee & Chai Asawa

May 14, 2026 · 1:06:38

Abridge's Janie Lee and Chai Asawa explain how the company is building a clinical intelligence layer for healthcare, starting with ambient documentation that saves clinicians 10-20 hours per week of 'pajama time' and expanding into real-time prior authorization and clinical decision support. They argue that context is everything—integrating EHR data, payer policies, and medical literature to make AI proactive rather than reactive, exemplified by guiding a doctor to ask two extra questions during a visit to guarantee an MRI approval before the patient leaves. The hardest AI problem is delivering high-quality, low-latency, low-cost real-time guidance in a high-stakes setting, which Abridge tackles using a constellation of models, efficient post-training on its proprietary dataset of over 100 million medical conversations, and progressive rollout with rigorous specialty-specific evaluations. They emphasize personalization at three levels—individual style, specialty (e.g., cardiology vs. dermatology), and health system guidelines—and see clinicians embedded as 'clinician scientists' on engineering teams as a key competitive advantage. Looking ahead, they envision the same conversation…

⚡️Context Graphs: according to the authors — Jaya Gupta, Ashu Garg, Foundation Capital

Feb 4, 2026 · 33:44

Jaya Gupta and Ashu Garg of Foundation Capital introduce Context Graphs—the institutional memory of the 'why' behind business decisions, captured through decision traces of exceptions, overrides, and precedents that AI models often miss. They argue these graphs will become the defensible moat for the next generation of applied AI companies and Systems of Agents, citing early implementations at Player Zero, Olive, and Tesserae. The hosts distinguish Context Graphs from mere metadata or data meshes, emphasizing they emerge organically from automating specific workflows rather than as universal enterprise schemas. They predict that by 2026, hundreds of Context Graphs will be in production, a standard enabling stack will emerge, and the industry will shift from debating definitions to extracting value from the captured decision traces.

AI to AE's: Grit, Glean, and Kleiner Perkins' next Enterprise AI hit — Joubin Mirzadegan, Roadrunner

Dec 12, 2025 · 1:09:44

Joubin Mirzadegan, a Kleiner Perkins partner who incubated Glean and now leads its next bet, Roadrunner, argues that AI-native CPQ can fix the broken quoting workflows that plague enterprise sales. Starting the CRO-only podcast Grit as a hiring wedge, he built relationships that later helped Glean and Windsurf scale. He explains why technical founders need sales leaders with a chip on their shoulder, not just fancy logos, and why Windsurf succeeded by marrying Google-class product with Salesforce-class distribution, hitting $100M ARR in months. Roadrunner rebuilds CPQ's data model from scratch, co-develops with four hairiest design partners, and aims to use LLMs to recommend deal structures, automating the Slack-channel chaos of deal desk.

Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures

Nov 14, 2025 · 1:26:59

Deedy Das of Menlo Ventures returns to explain how Anthropic became the fastest-growing software company ever (zero to billions in revenue) and why Glean’s boring enterprise search moat of hard integrations and ranking problems is harder than competitors think. He reveals the $100M Anthology Fund’s strategy: backing OpenRouter, Goodfire, Prime Intellect, and Whisper—companies that solve thorny infrastructure or research problems rather than chasing apps. Das argues that model-layer companies will capture most value because building great models is harder than building apps, and that Anthropic’s product innovations like Claude Code emerge from a culture that lets researchers experiment freely. He also warns that vibe coding is becoming a cognitive crutch for engineers, eroding deep problem-solving skills, and discusses how enterprise AI market share has shifted dramatically: OpenAI went from 50% to 20%, while Anthropic rose from 12% to 32% of enterprise LLM API spend.

The State of AI Startups in 2024 [LS Live @ NeurIPS]

Dec 21, 2024 · 26:35

Sarah Guo and Pranav Reddy argue that 2024 has become a far friendlier ecosystem for AI startups, with the model landscape shifting from OpenAI's near-monopoly to a competitive field where Google's Gemini now leads LMSys Arena and open-source models like Llama 8B score ten points higher on MMLU than Mistral 7B a year ago. They note that OpenAI's API cost has dropped 80-85% in 18 months, and total OpenAI API share fell from ~90% to ~60% as customers switch. The funding environment is rational, not a bubble, with foundation-model labs raising $30-40B but most startups seeing sane valuations; one portfolio company grew from zero to twenty million in PLG-style spending. Key startup themes include first-wave service automation (Sierra, Decagon, Harvey, EvenUp), better search and new friends (Perplexity, Glean, Character, Replica), and democratized creativity (Midjourney, HeyGen). They argue the 'GPT wrapper' narrative is false—applications capture value—and that incumbents face innovator's dilemma because AI changes business models (outcomes-based pricing) and data needs (reasoning traces rarely saved). The speed of change and new markets (legal, healthcare, defense) structurally favor…