thesis cycle + 100m fund
Z / Runner ππ»ββοΈ
ππ»ββοΈ thesis recycle Google's trash bin β $10B
https://www.calicolabs.com/- tech.https://verily.com/- tech.
ππ»ββοΈ chatbot & mental health: headspace.
ππ»ββοΈ Limitless voice journaling + ChatGPT entries made headspace less useful. BUT, i need alt from Limitless, Meta acquisition means all my daily conversation will be egressed to FB.
ππ»ββοΈ Venture funds focusing on longevity & healthspan.ππ»ββοΈ Yes on DTC, we need to solve DISTRIBUTION.$4.15b A16z(Levels, FX health), Khosla (circulate health), General Catalyst (Commure, Olive)ππ»ββοΈ Category 1: No FDA Approval Needed ("General Wellness") Category 2: Deployed in Mass Market (2025 Standard), Category 3: Projected Next-Gen (2026 & Beyond)ππ»ββοΈ 10 AI Supernova startups we surveyed reached ~$40M ARR in their first year of commercialization and ~$125M ARR in the second year of revenue generation. β¦ On average, these AI Supernovas have only 25% gross margins, often trading distribution for profit in the short term. Despite these low margins, these AI Supernovas seem to demonstrate an incredible $1.13M ARR/FTE, which is 4-5x above a typical SaaS benchmark."ππ»ββοΈ Shooting Stars reach the ~$3M ARR range within their first year of revenue while quadrupling in YoY growth with ~60% gross margins, and ~$164K ARR / FTE (full-time employees) in their first years.ππ»ββοΈ we need ecosystems like FastMCP from Prefect (that make it much easier to build MCP servers) and tools like Arcade and Keycard (that facilitate agentic authorization and permissioning.).ππ»ββοΈ In 2025, large context windows and retrieval-augmented generation (RAG) have enabled more coherent single-session interactions, but truly persistent, cross-session memory remains an open challenge. While the foundational model companies are working on memory, so too are startups like mem0, Zep, SuperMemory, and LangMem by Langchain.ππ»ββοΈ combine the following: Short-term memory via expanded context windows (128k to 1M+ tokens, depending on model and architecture) Long-term memory via vector DBs, memory OSes (e.g., MemOS), and MCP-style orchestration Semantic memory via hybrid RAG and emerging episodic modules, designed for context-rich recall.
Stephen / Basketball π
π aging dieases.π precision health, harmonize biomedical data.π how much do you still use it (now that you run 150 miles a week)?
π $100m health + ai fund: maximize fitness performance for 10b people. whitepaper "body intelligence for optimal self".
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continuous contexts: tiny wearables, streaming biomarkers. -
persistent agents: composable memories, deductive advice. -
blue socials: connected humans, atomic lifestyle. -
π more importantly, what our fund is not: healthcare (insurance, providers, treatments). hence, the words "fitness" and "performance" β for direct-to-consumers (DTC) and better-than-preventive. "if you have a body, you are an athlete." -
π via high-school athletes + startup founders. -
Healthspan Capital: Focuses on early-stage longevity biotech (LongBio) and regenerative medicine, targeting fundamental aging drivers, LongVC: biotech, early stage, Apollo Health Ventures, life science, aging, Maximon, studio, company building, Continuumhealth VC.
π "rank 100 biosensors by cost, energy, weigth - for healthspan wearables and fitness ai". for "wearables with no screen or button, but open source and development".
π how (often) do you use mcp / agents?π yes, but show me your (good) session runs β i can hack db together to test different memory strategies / implementations.π OpenEvidence automates medical literature review and delivers instant answers at point of care.
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