When AI Writes the Code, What Becomes the Model?
Picture a mobile machine during testing.
An experienced operator makes a movement and notices something immediately. It feels a little too aggressive. Perhaps it overshoots slightly before settling, or the transition does not feel as smooth or predictable as expected.
A control system engineer looks at the implementation.
Not long ago, trying another approach could require a significant amount of work: study the problem, modify the algorithm, review the change, compile it, deploy it, find another machine test slot, and ask the operator to reproduce the situation.
Now the first part of that process can look quite different. The engineer may ask an AI coding assistant to propose a modification that reduces the overshoot, smooths the transition, or takes a different structural approach. A plausible alternative can appear quickly.
That implementation is not automatically correct. It still needs to be understood, reviewed, and verified. But producing the next candidate may no longer be the slowest part of the loop. AI assistance increasingly extends beyond implementation as well. Tools can help create tests, suggest edge cases, analyze failures, and work with test results.
So the interesting change is not simply that we can write code faster. More of the engineering experiment can potentially become faster.