“Build something 100 people love, not something 1 million people kind of like.”
Brian Chesky’s startup advice captures an early Airbnb lesson: intense value for a small group can be more informative than mild interest at scale. Broad reach may produce flattering traffic while concealing that no one would miss the product. Love is shorthand for repeated use, advocacy, tolerance during imperfection, and evidence that the service solves a meaningful problem. A concentrated group also lets founders observe behavior closely and improve the complete experience before distance and volume make every weakness expensive.
The approach begins with a narrow customer and a specific occasion. Founders can recruit early users directly, watch the journey end to end, respond personally, and ask what would make the product indispensable. Manual work is acceptable when it reveals what should later be designed or automated. Retention, frequency, referral, and willingness to pay are stronger signals than impressions alone. The goal is not to remain small, but to discover the core value and trust mechanisms that can survive when the organization no longer knows every customer by name.
Customer love can become misleading if it comes from a niche that cannot support the economics or if it depends on founder heroics that will not scale. Passionate users may also be unrepresentative of people exposed to safety, accessibility, labor, or neighborhood effects. Teams should examine unit economics, operational load, and external consequences alongside enthusiasm. Scaling requires standardization, governance, and sometimes changes that early users dislike. Devotion is evidence of value, not a complete license to expand.
AI products often reverse the sequence by launching to millions before the team understands the use. Chesky’s principle suggests a tighter path: select a bounded workflow, work with a small group, study errors and trust, and earn repeated reliance before broad deployment. Teams can document which manual safeguards and expert judgments must become part of the scaled system. The quote offers a discipline against vanity growth. First create a relationship strong enough to teach the business what excellence means; then build the operations capable of extending it responsibly. Before scaling, teams can write a “love-to-reliability” map. It identifies which early gestures create value, which depend on founder presence, which must become a system, and which should remain intentionally human. Cost and risk are attached to each transition. The map prevents automation from removing the very interaction customers valued while also preventing bespoke service from destroying the economics. Scale succeeds when it preserves the reason for affection without pretending intimacy can remain unchanged.
Chesky has used versions of this advice when describing Airbnb’s early focus and Y Combinator lessons.
The idea favors intense value and close learning with a small group over shallow attention at mass scale.
Direct observation, manual service, retention, referral, and willingness to pay reveal what makes an offering indispensable.
Passionate users do not settle economics, scalability, safety, labor, accessibility, or effects on surrounding communities.
AI products can reach huge audiences before teams understand their errors, trust requirements, or durable use.
A bounded workflow and deeply engaged users expose the safeguards and expertise that responsible scaling must preserve.
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