AI-Native Investing Workflow

AI-native investing workflow is the venture-capital work pattern Will Wang Tianfan / 汪天凡 describes in 「热爱一个行业15年的理由是什么？」｜对谈汪天凡：我要投真正的快乐、投最纯的愿景、投人性的光辉【公路播客】. He uses long prompt templates for pre-meeting research, product and community scans, application-store and social-platform feedback, competitor checks, pricing, channel, financing history, due diligence transcript synthesis, and portfolio tracking.

The source’s boundary is that AI improves information preparation and research coverage, but it does not replace high-level belief formation. Wang still treats investment judgment as a combination of market thesis, user-product testing, founder assessment, values, and timing.

Key Claims

• AI makes pre-meeting preparation, source gathering, and due diligence synthesis much denser.
• Prompt quality becomes a professional skill because the investor must expose anxieties, questions, information gaps, and target judgments to the model.
• Post-investment monitoring can scale when AI tracks public information across many portfolio companies.
• Better research automation does not remove the need for human judgment about non-consensus opportunities, founder quality, or valuation.

Connections

• Will Wang Tianfan / 汪天凡 — source practitioner.
• AI Investment Research — broader finance/investing AI research concept this specializes for VC.
• Human Judgment Under AI, Research Taste, and Investment Risk Management — judgment boundaries.
• Three-Non Venture Theory / 三非理论, Startup High-Beta Bet, and Founder Product Fit — investing frameworks that the workflow feeds.
• AI-First Organization — adjacent company-operation version of AI-native practice.