AI will not replace human engineers unless it can do a better job than they can. Simply being faster is not enough, because engineering is not just about producing code. It is about solving problems, anticipating failure, designing for scale, and making trade-offs that affect real people and businesses.
In expansive systems, human engineers bring something AI cannot replicate: the ability to anticipate problems before they occur. That ability is not magic. It comes from years of experience across many contexts, seeing where things have broken in the past, and recognizing subtle signals of risk. A human can zoom out to see the full system and zoom in to identify a single faulty assumption. That combination of depth and breadth is what turns programming into engineering.
From my own experience using AI as a coding partner, I have found it helpful for small, isolated tasks. It can produce snippets, boilerplate, or quick scaffolding. But as models grow larger and supposedly more advanced, their practical coding skills do not show the same trajectory. It feels like we have hit a plateau.
Too often, I find myself correcting the model. I end up teaching it about syntax, approach, or even the very concepts it is meant to apply. Without my intervention, the task would stall. In other words, if I were not in the loop, the work would not get done. The AI is only useful because I am present to steer, guide, and validate.
This is why the casual claim that “AI will replace engineers, it’s just a matter of time” falls flat. Such statements reduce engineering to code generation, as if software were nothing more than lines of syntax strung together. They also reveal that the speaker probably is not using AI to write real, production-quality code. Anyone working with it daily sees both its promise and its limits.
Engineering is not simply about writing correct code. It is about weighing competing requirements, designing for maintainability, balancing performance with cost, and coordinating teams that bring a project to life. It is about debugging ambiguous failures and making judgment calls where no amount of pattern matching can substitute for lived experience.
AI will not replace human engineers unless it can perform across this full spectrum. That means more than passing coding tests or solving toy problems. It means the ability to reason through systems, handle ambiguity, and carry responsibility for outcomes. Until AI can do that better than humans, engineers will remain not only relevant but indispensable.