Judging AI-augmented coding capabilities based on "one-shot" examples is misleading and insufficient for true evaluation. A single successful one-shot demonstration, such as creating "Minecraft," often relies heavily on the semantic weight of the keyword itself within the prompt. To properly test the AI's ability, evaluators should challenge the system to achieve the same result without using that highly specific, loaded term. Programming, even with AI augmentation, remains the intricate process of transforming ambiguity into precise, stable specificity through careful "sculpting." This AI sculpting requires finding and maintaining desired specificity while keeping the system stable across modifications, emphasizing that good Software Development Life Cycle practices and historical knowledge remain crucial. While generating established games like Mario Brothers or Space Invaders in one shot is a neat parlor trick, the resulting code will be statistically "mid" or the most generic, average version of those games. The model is essentially tasked with drawing the statistical average face of the concept from memory rather than executing unique, specific requirements. As high-level programming evolves into prompt-based prose compilation, defining clear goals and specifications for the "ambiguity loop" becomes paramount. Developers must exercise superior judgment and meticulously choose their words to ensure no single term carries an undue hidden or obvious semantic burden.