I think the pace of change in this industry has caught us all off guard. AI isn't just for research anymore; it is embedded in our daily lives, shaping how we learn and how things are done.
For software engineers using AI to generate code, it's hard not to see the writing on the wall. Some roles in business are likely to disappear, and new ones will be created. But the "AI will replace people" narrative is too crude. The real question isn't about replacement; it’s about value.
Software engineering has never strictly been about typing code. At its best, this discipline is about judgment, trade-offs, and managing systems that break in weird ways.
That doesn't go away.
What has changed is the cost of raw code. As the cost of generating boilerplate drops to near zero, the value of knowing what to build and why to build it increases.
The Shift in Responsibilities
AI absolutely destroys work that follows patterns. Refactoring, test generation, and exploratory implementations are faster than they have ever been. However, LLMs still fail at architectural reasoning and domain-specific context. They cannot anticipate failure modes that may only emerge after a system has been live for six months.
Someone still has to take responsibility for those decisions. That burden cannot be automated.
For most organizations, this comes down to efficiency math. Roles focused on repetitive implementation are becoming impossible to justify. Meanwhile, roles that blend development with operations, product oversight, and architecture are becoming the standard. This shift is gradual, which makes it dangerous. You might not notice you're obsolete until it's too late.
Managing the Machine
Engineers who thrive in this environment treat AI differently. They don't use it as a substitute for thinking; they use it as a lever.
Working with AI is often less like coding and more like managing a junior engineer, one who types incredibly fast but lacks the judgment to determine if a path is truly viable. Poorly framed requests get poor results. Clear context, constraints, and iterative feedback produce gold.
We aren't seeing the end of software engineering, but we are seeing a serious redefinition. Routine coding matters less. Many small tasks can now be done in minutes rather than days.
Skepticism toward AI is reasonable, even necessary, but disengagement is professional suicide. The industry is changing, whether we like it or not. The only open question is: are you willing to change with it?