The author experimented with an AI-driven development process for a personal project. While AI agents handled coding tasks rapidly, the developer spent the majority of time on non-coding challenges, such as defining system behavior, managing error handling, and establishing user-facing logic. The project was eventually abandoned due to lack of marketing efforts, but it served as a case study for the changing nature of software engineering.
Modern development often relies on assembling third-party services and APIs. While this allows for rapid prototyping, it creates systems lacking clear documentation or internal logic, making them difficult to maintain long-term. The author notes that while AI-assisted coding is effective for validating hypotheses, it is not a substitute for the rigorous engineering required to build and scale stable production systems.
The shift toward AI-generated code mirrors historical transitions in software development, such as the move to high-level languages or visual development tools. However, unlike previous abstractions, AI generation is probabilistic rather than deterministic. This introduces a new cost: the need for constant verification. The author emphasizes that the primary challenge is no longer writing code, but safely replacing it.
Drawing on concepts from Chad Fowler’s book 'Regenerative Software', the author argues for a 'spec-driven' approach. Since code production has become inexpensive, the focus must shift to maintaining specifications and evaluation criteria. These act as the repository for system knowledge, ensuring that business logic and design decisions are preserved even when the underlying implementation is discarded or regenerated.
A critical aspect of this methodology is the feedback loop between code and specifications. If a bug fix or a new requirement is implemented, the knowledge must be codified back into the specification rather than left solely in the code. Without this discipline, specifications quickly become obsolete, and the system loses the institutional knowledge required for future development and maintenance.
