AI Fatigue: Why the "Test Only, Zero Code Review" Methodology Is Flawed (fastfilelink.com)

A recent article titled "AI Fatigue is Real" highlights the growing exhaustion developers feel in the AI era, where the coding process has shifted from creation to intensive code reviewing. With AI generating code rapidly and inconsistently, engineers find themselves overwhelmed by the need to extensively review outputs and tests, leading to decision fatigue and a fragmented work process. This raises concerns about a proposed methodology gaining traction in the community, which suggests bypassing code reviews entirely and focusing solely on test outputs. Critics argue that this approach is fundamentally flawed due to the probabilistic nature of large language models (LLMs), which can produce unreliable test cases that may pass even while containing significant errors. The context window limitations of LLMs mean they lack the capability to understand a project's full architecture, resulting in redundant and buggy code. As technical debt accumulates, maintenance costs can spiral, ultimately benefiting AI tool vendors rather than developers. The article emphasizes that while the idea of minimal code review might seem appealing, it merely shifts the burden of review onto test code, maintaining the reality that effective oversight is indispensable in software development.