# 🟩D2': Bet q then Learn β (Push) The push strategy inverts traditional sequencing—commit to quality first, then learn from market response. Tesla exemplified this approach: announcing ambitious Roadster specifications, taking deposits based on renderings of a car that didn't fully exist, pushing out bold performance targets that stretched technical feasibility. Rather than extensive pre-launch research, they bet that luxury early adopters would pay premium prices for supercar performance in an electric vehicle, then used actual customer deposits and supplier negotiations to refine their understanding of true market parameters. This effectuation-style approach recognizes that in many contexts, action generates more valuable information than analysis. Real customers writing $100,000 checks reveal preferences more accurately than survey responses; battery partners facing actual production deadlines surface constraints that negotiations might hide. Tesla's push generated immediate market feedback: which customers actually converted from interest to deposits, what performance specs generated the most excitement, which battery configurations partners could realistically deliver. The push model's advantages align perfectly with perishable commitment dynamics: immediate market presence, cash flow from deposits, learning from actual rather than hypothetical decisions. Tesla captured early adopters who might have bought competitors' offerings, established themselves as the electric performance leader, and generated buzz that attracted both more customers and better suppliers. The strategy transforms the venture from abstract concept to concrete reality that stakeholders can evaluate and commit to. However, push strategies risk significant misallocation when initial bets prove wrong. If Tesla had guessed incorrectly—building a practical commuter car when luxury buyers wanted performance, or promising specifications that proved technically impossible—the venture could have failed spectacularly. The approach works best when adjustment costs remain manageable, feedback loops are rapid, and some market presence beats perfect optimization. Tesla's modular architecture and staged deposit structure created flexibility to adjust based on learning, preventing complete lock-in to initial assumptions. The push model's deeper insight: in fast-moving markets with perishable commitment, being approximately right and in the market beats being precisely wrong and still planning. Tesla's willingness to bet on quality, then rapidly iterate based on real stakeholder responses, enabled them to capture a market window that pure analysis would have missed entirely.