The author's initial observation after the holiday break was that large language models had reached a point of reliably producing useful and affordable code. This prompted a reconsideration of software development economics, where code production is no longer the primary constraint. The author published these thoughts, suggesting that machines would increasingly build software under human direction, necessitating architectural changes. GitLab subsequently showcased architectural components for machine-scale concurrency and lifecycle context. Anthropic's recent publication, "The AI-Native SDLC Playbook," reinforced this shift by stating that code is no longer the bottleneck. This shift means planning becomes machine-readable, handoffs automated, verification embedded, and human judgment focused on key decision points. The author is interested in what becomes scarce and what enterprise architecture is needed when people, agents, and models operate at machine speed. Experiences from companies like Stripe, Spotify, and Amplitude have solidified the conviction that the fundamental change is not just faster code generation, but the economic and architectural shifts that occur when implementation becomes cheaper. The constraint moves from producing code to trusting it, with trust depending on the environment around the model, such as context, verification, and governance. For sixty years, software engineering has been organized around the preciousness and expense of code, influencing everything from legacy system preservation to developer productivity optimization and extensive pre-release ceremonies. However, this constraint is breaking, and as with previous technological abstractions, a new problem is exposed. Code production is becoming abundant, but good software remains scarce. The gap between fast implementation and correctness, security, and adherence to business intent is the new challenge. Cheap iteration fundamentally changes strategy, shifting focus from eliminating uncertainty upfront to faster learning and adaptation. Organizational knowledge, previously residing in human minds, can increasingly become executable code, transforming production failures into regression tests and security incidents into policies. The author proposes that the key economic unit is cost per accepted change, not cost per line of code, as AI dramatically reduces the generation cost, making other aspects proportionally more important. Companies like Stripe, Amplitude, and Spotify are already navigating these changes, with their experiences revealing that AI agents expose, rather than eliminate, existing engineering constraints. The transition to AI in enterprise development is likely to occur in three coexisting modes: human-controlled legacy, agentically accelerated development, and autonomous development.