Principal at Craft Ventures
Check size: No Zao-specific check range is public. His attributed deals span Series A through late growth, while Craft operates separate early-stage and growth funds. Founders should confirm current vehicle, ownership target and sizing through inquiries@craftventures.com rather than infer a range from individual announced rounds.
Zao invests in software with a particular focus on data and AI infrastructure and developer tools. His work favors open, interoperable or community-led platforms that become default infrastructure: technically excellent products with fast time to value, authentic developer adoption, a clear activation event and a path from grassroots use to enterprise-scale workloads. He pairs growth investing with embedded operating work on finance, business operations and product-led growth.
Use inquiries@craftventures.com and reference the exact /in/zaochen profile linked by Craft. State round, current traction and why the product should become default infrastructure. For developer or open-source products, include stars/contributors and active usage, the single activation event, time-to-value, retention and community-to-paid conversion without mistaking attention for adoption. For data/AI infrastructure, show workload economics, scale limits, interoperability, deployment control, proprietary workflow or data advantage, and credible enterprise usage. Explain how AI changes demand structurally and how the company remains valuable as models improve.
Technical founders with deep domain insight; products developers adopt organically and love; open-source ecosystems where contributors create distribution and defensibility; infrastructure that becomes a standard or source of truth; clear time-to-value and an activation event that predicts deeper use; architectures that stay open and interoperable while scaling; credible enterprise references; and a growth engine that turns community trust into activation, retention and monetization without sacrificing authenticity.
No personal pass list is public. His attributed work suggests caution around generic AI wrappers, one-click creative tools that remove professional control, closed data systems that increase lock-in, developer products with sales-heavy but inauthentic community tactics, AI adoption layered over poor code review or documentation, and products without a crisp activation event, measurable time-to-value or scalable architecture. These are inferred diligence concerns, not published exclusions.
Ask whether the product can become the standard layer that users build on repeatedly, and whether its relevance persists as models, interfaces and downstream applications change.
Identify the single action that unlocks all downstream product value, orient onboarding and lifecycle work around it, and use it as the sharpest leading indicator of durable adoption.
Treat authentic open-source participation, education and developer relations as a distribution and credibility system, then connect it carefully to activation, retention and paid expansion.
Prefer infrastructure that lets customers control storage, query engines or workflows and avoids forced lock-in; openness can drive adoption while technical execution and ecosystem depth remain defensible.
In creative AI, distinguish casual one-click generation from production systems that give experts modular, precise, reusable and scalable workflow control.
AI multiplies the quality of an existing engineering system: clean code, small reviews, testing and documentation improve faster, while weak foundations produce more slop and friction.
AI-driven software creation expands demand for a fast, developer-loved backend; Supabase's community and new scalable Postgres architecture can carry users from prototypes to enormous production workloads.
Production generative media requires modular, controllable and reusable workflows; Comfy's open-source node ecosystem can become the persistent orchestration layer beneath changing models and interfaces.
Developer infrastructure that packages enterprise requirements can shorten sales cycles and prevent software teams from diverting months of engineering effort away from differentiated product work.
Domain-native AI agents can turn fragmented public procurement records into actionable sales intelligence, creating a data flywheel while delivering clear customer ROI.
Developer platforms compound when authentic community trust, one pivotal activation event and segmented lifecycle journeys turn product love into durable adoption and monetization.
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