A growing pattern is emerging among engineering teams that adopted AI coding tools within the last year. While velocity often doubles in the first month, by month three, the time required to safely modify generated code begins to rise sharply. This phenomenon, known as the black box problem, occurs because AI models prioritize speed over structure. A developer opening a module generated in a single session may find hundreds of lines where relationships, assumptions, and service ordering are implied rather than documented. Consequently, any change requires deep comprehension and full review, even though the code itself functions correctly. AI-generated code tends to create unstructured systems with several distinct characteristics. First, models exhibit a strong bias toward monolithic files. Asking for a checkout page often results in a single file containing rendering, payment processing, validation, and API calls. This makes testing and modification difficult because one cannot alter a part without affecting the whole. Second, AI frequently creates circular and implicit dependencies. Service A might call Service B simply because they appeared in the same context window, leading to hidden coupling that breaks when one service changes. Third, well-engineered systems rely on explicit contracts and typed interfaces, which AI often skips. Finally, AI-generated documentation typically explains internal implementation details rather than usage patterns, leaving new developers unable to understand how to consume components or what might break if an interface changes. Consider two approaches to generating a notification system. Unstructured generation results in a single 600-line module handling templates, delivery, preferences, and analytics. Changing the email provider requires editing the entire file and mocking the whole system. In contrast, structured generation decomposes the functionality into five independent components with declared interfaces and one-directional dependencies. While both produce identical runtime behavior, the structured version allows developers to change, test, and replace individual pieces without understanding the entire system. The critical difference is composability: building systems from components with well-defined boundaries and isolated testability. Current AI workflows often fail here because the generation environment provides no structural feedback during the coding process. Unlike human developers who receive immediate signals from type errors or linting tools, AI typically produces code in a single pass without real-time architectural constraints. To solve this, generation environments must enforce structure by validating boundaries, dependencies, and tests in real time before code is committed. The true measure of AI productivity is not generation speed, but how quickly a team can move code to production and maintain it weeks later. Teams succeed when generated code is reviewable, shippable, and changeable. This requires treating every AI generation as a boundary decision. Developers should define component responsibilities, dependencies, and public interfaces before prompting. For existing systems, teams must audit for implicit coupling and mixed responsibilities, particularly in code generated in a single session. When choosing tools, organizations should evaluate whether the output supports structural reviews, explicit dependency declarations, and isolated testing. Ultimately, the black box problem is an environment issue, not an AI limitation. By ensuring that generation targets enforce explicit component boundaries, validated dependency graphs, and interface contracts, organizations can produce code that is both fast to write and easy to maintain. Fixing the environment allows AI to generate systems that teams can confidently ship and evolve.