cruel, complex, concentrated optimism FOUNDERS SHOULDN'T MAXIMIZE VENTURE'S SUCCESS PROBABILITY [[🎹scale(약속설계)]] ![[🪓잔낙모약🤙(약속설계) 2025-09-11-12.svg]] %%[[🪓잔낙모약🤙(약속설계) 2025-09-11-12.md|🖋 Edit in Excalidraw]]%% Founders must architect a promise for a reality that does not yet exist. This task forces them to navigate a treacherous paradox we term **the cruel optimism of the entrepreneurial promise**: **the very concentration—the bold, specific claims—that mobilizes immense resources can become the rigid trap that destroys the venture.** The divergent fates of two electric vehicle pioneers, Tesla and Better Place, offer a stark illustration of this dilemma. Better Place’s hyper-precise promises of "3-minute battery swaps" attracted $850 million but created a strategic prison from which they could not escape when reality diverged. In contrast, Tesla’s initially ambiguous "200 miles or more" promise preserved the flexibility to learn, adapt, and ultimately triumph. This paradox manifests in two fundamental tensions. The first is market persuasion versus operational execution; the second is the immediate credibility granted by precision versus the long-term need for adaptation. Prior literature offers limited guidance, largely because it treats success probability as an external variable to be discovered or maximized, not an internal one to be designed. To solve this, our paper develops a formal theory built on the strategic wisdom of **negative capability**—the capacity to remain in uncertainty without chasing premature facts. We make this abstract concept operational through a novel methodology we call **prediction-based prescription**. For the first time, we formalize the entrepreneurial promise as a designed **"artifact"**: a statistical prior distribution that the founder architects. The optimal promise is then _prescribed_ by maximizing the expected utility, which is calculated based on a _prediction_ of the venture’s sellability and deliverability given that chosen prior. This merges the rigor of statistical simulation with the decision-focus of operations science, providing a new engine for entrepreneurial strategy. To build this theory, we construct a sequence of four models, visually summarized in **Figure 1**, that represents a journey of increasing founder agency. We begin with naive baselines (**M1, M2**) before modeling the promise as a belief distribution to reveal the "learning traps" of fixed precision (**M3**). Finally, we empower the founder as a true architect who jointly optimizes both boldness (aspiration) and flexibility (precision), achieving a state of adaptive success (**M4**). This progression transforms the founder from a player reacting to fixed odds into a designer who sets the terms of their own game. Theoretically, we extend entrepreneurship's design literature by formalizing the "artifact" of promise. Practically, we provide a logic for navigating the cruel optimism of entrepreneurship, prescribing not just what to promise, but _how_ to design the very structure of belief and learning for the journey ahead.