AI-generated code has outperformed traditional neural networks in industrial fluid dynamics control, according to recent findings. Researcher Paul Garnier utilized OpenAI's Codex 5.5 to write Python scripts for fluid simulation analysis, bypassing neural networks entirely. This approach led to superior performance in over half of more than ten physical tests, including automotive drag reduction and pipe turbulence stabilization. The AI-generated rules, such as delaying jetting based on local curvature, replaced complex neural network models with straightforward, executable code. This method eliminated the need for costly retraining when hardware changes occurred, offering a flexible and cost-effective solution. The entire strategy was executed with 21.25 million tokens, costing under $14, demonstrating a significant advancement in AI application for industrial processes.