AI is an amplifier. In the hands of an experienced engineer, it can be an extraordinary productivity multiplier—accelerating implementation, exploration, debugging, documentation, and even architectural thinking.
But amplification works both ways.
Using AI to build systems you don't understand—or allowing your own engineering skills to atrophy because AI has become a crutch—is a recipe for fragile software.
AI can produce remarkably convincing solutions that are subtly wrong. Give it incomplete requirements, flawed assumptions, missing business context, or misleading data, and it can confidently build an elegant solution to the wrong problem.
This is where engineering experience matters.
A senior engineer is more likely to recognize when an abstraction is wrong, an architectural boundary doesn't make sense, a security assumption is dangerous, or a solution that works perfectly at small scale will become painful at production scale.
The danger isn't simply that AI makes mistakes. Engineers have always made mistakes. The danger is that AI can produce mistakes faster, at greater volume, and with enough polish that they look correct.
For small, isolated applications, the consequences may be minor. At scale—where reliability, security, maintainability, performance, observability, and years of accumulated complexity matter—the rules change dramatically.
The more I use AI, the more impressed I am by its capabilities—and the more convinced I become that engineering expertise and judgment are as critical as ever in the AI era.
AI doesn't eliminate the need for expertise. It increases the leverage of expertise.
Like electricity, AI is extraordinarily powerful when properly harnessed—but a short circuit can still burn the house down.