The most important takeaway here is that the conversation is shifting from "Which model should we use?" to "What controls exist around the model?" That's a much healthier way to think about production AI.

We've seen the same pattern at IT Path Solutions teams rarely struggle because the model is incapable. They struggle because there are weak approval flows, limited observability, unclear permissions, or no way to audit and recover from mistakes. Those system-level decisions end up having a much bigger impact on reliability than the model choice itself.

I also like the emphasis on architecture-first workflows. Reviewing requirements and system design before generating thousands of lines of code catches issues when they're still cheap to fix. AI may accelerate implementation, but good engineering still starts with good architecture.