Hiring and Firing a DBA (www.jmduke.com)

In a recent experiment, a team utilized a scheduled LLM (GPT 5.3 Codex High) to implement performance improvements in their database management process on PlanetScale, resulting in a notable 25% reduction in overall database reads. The LLM autonomously generated pull requests for optimization based on previous database load data, but the initiative was halted after two months when the quality of suggested changes declined. Initially, 75% of the pull requests were approved, tapering down to just 30% in the following month, as the model began to submit less useful and riskier modifications. This experiment illustrates both the potential and limitations of applying AI to database optimization. While the LLM effectively identified initial opportunities for improvement, it struggled to maintain efficacy over time, suggesting a lack of access to real production data limited its ability to generate high-quality results. The findings underscore an essential point for the AI/ML community: while leveraging AI for tasks like performance enhancement can yield significant benefits, the diminishing returns on available optimizations highlight the need for a nuanced approach to training and utilizing models in production environments. As organizations explore AI's capabilities, they must balance the pursuit of efficiency with the inherent risks and realities of machine-generated solutions.