The Hard Side Is Still Human
Chen's central argument in The Cold Start Problem is that every network product has a hard side: the participants whose presence creates value for everyone else. Agents do not dissolve this problem. The hard side for an AI-native product is often the humans who supply domain knowledge, judgment, and accountability — the reviewers, the experts, the customers who make the system trustworthy. Recruit them by hand before you automate anything.
One Atomic Network Before Anything Else
Chen shows that growth fails when founders try to seed too large a surface before a small unit sustains itself. This maps directly to distribution: if anyone can build your product in a weekend, your moat is the specific group of people who trust you enough to use it first. Hand-onboard the first fifty. The unscalable work is market research disguised as customer service, and it defines which atomic network is actually viable.
Escape Velocity Is Engineered, Not Wished For
Chen's escape velocity phase requires that each new network cluster pulls in the next. For founders shipping with agents, this means designing feedback loops — evals, corrections, human sign-offs — that compound trust over time. A product whose agents improve visibly with each customer cohort builds the same reinforcing dynamic Chen describes. The growth mechanism is intentional structure, not momentum alone.
