There's no point at which turning your brain off will work

In early 2025, it became apparent that people were using large language models (LLMs) to automate tasks without closely monitoring the process. These individuals would submit prompts to an LLM, assuming the model would produce the desired outcome without any further input. This approach, known as "brain-off meat-proxy development," often resulted in unsatisfactory results due to the inherent limitations of the technology at the time.

As LLMs have evolved since then, this method has become increasingly common, with some people relying on the models to generate code or troubleshoot issues. However, the effectiveness of this approach is questionable, as the final product is often far from perfect. Employing a human in this process is crucial, as the company could potentially lay off the employee if the automated system is capable of handling the work independently.

The author argues that the methodology's success hinges on the nature of the task at hand. For low-value tasks, the risk is minimal, and the failure of an LLM is acceptable. Conversely, high-value tasks demand human oversight to ensure accuracy and prevent potential disasters. The author also observes that the increased efficiency of LLMs pushes them to maintain higher standards, often resulting in more polished and thorough outputs.

However, this heightened bar may lead to a sense of self-doubt, as the human may question their own abilities and the validity of the generated solutions. Despite the potential for automation, the author's recent experience with converting codebases to Bazel, a build system, has revealed the limitations of relying solely on LLMs.

The process is time-consuming, involving numerous decision points and unknown factors that require human intervention. The author suggests that even in seemingly automated tasks, human supervision is essential to navigate the complex decision-making required to achieve a satisfactory outcome.