# 🟩D2: Need to Prescribe Quality Assuming Commitments The prescription approach takes the opposite tack—make confident assumptions about stakeholder commitments and optimize quality accordingly, like a newsvendor deciding how many papers to stock based on expected demand. This "if we build it, they will come" mentality emphasizes decisive action over prolonged analysis. For Tesla, a pure prescription strategy would mean assuming luxury car buyers would definitely want supercar performance and battery partners could definitely deliver the required technology, then engineering the Roadster to those exact specifications without extensive market validation. The prescription model's strength lies in its speed and decisiveness—it generates clear quality targets (q*) based on cost parameters and assumed response curves, enabling rapid market entry. Given our parameters (Cu=1, Co=V=2), prescription would immediately suggest q*≈0.5 under linear assumptions or q*≈0.405 under symmetric sigmoid assumptions, providing actionable targets for engineering teams. Tesla could launch quickly, establishing market presence and brand identity while competitors remained in analysis mode. However, prescription without prediction proves too brittle when assumptions meet reality. If luxury buyers care more about charging infrastructure than raw performance, or if battery partners cannot achieve the assumed energy density safely, the entire venture risks catastrophic misalignment. Tesla's prescribed quality level might perfectly optimize for a market that doesn't exist—building an incredibly fast sports car when customers actually wanted a practical daily driver, or designing around battery capabilities that prove unachievable at scale. The newsvendor parallel illuminates the limitation: while a newspaper vendor faces relatively stable, predictable demand patterns, entrepreneurs operate in markets where the fundamental parameters themselves are unknown and shifting. Prescription provides a decision rule but lacks adaptive mechanisms to correct course when initial assumptions prove wrong, potentially locking ventures into trajectories that lead away from actual market needs. In environments where commitment windows are perishable, being decisively wrong can be just as fatal as being analytically paralyzed.