AI in a developer's daily work: where it truly speeds things up and where it creates silent debt

The hype and the real use

There are two narratives about generative AI applied to code, and both exaggerate. One says it will replace programmers. The other says it is just a fancy autocomplete, with no real value. The practical experience of people who use it every day sits in the middle: AI changes the pace of specific parts of the work, without changing the nature of the work itself — which is still understanding problems and making decisions.

It is worth separating where it truly helps from where it only looks like it helps.

Where AI truly speeds things up

• Boilerplate and repetitive code. Initial setup, CRUDs, adapting an existing pattern to a new case — all of this is exactly the kind of task AI handles quickly and well, because the solution already exists somewhere and just needs adapting.
• Exploring unfamiliar libraries and APIs. Asking "how do I do X in this lib" and getting a contextual answer saves the time of digging through incomplete documentation.
• Tests for code already written. Generating test cases from an existing function is usually faster — and more thorough — than writing them by hand, because the AI remembers edge cases a tired developer at 6pm forgets.
• A first draft of something you will review anyway. A migration draft, a data script, a utility function — where the value is having a starting point, not having the final version.

Where it creates silent debt

• Code that solves the problem but nobody understands why. The biggest risk is not the AI generating something wrong — it is generating something that works, but whose logic nobody on the team can explain six months later. That is pure technical debt, just without the relief of knowing it was a conscious shortcut.
• Shallow review of a large pull request. As the volume of generated code grows, the temptation is to skim the review. A two-hundred-line generated PR reviewed in five minutes is an invitation for a subtle bug.
• Architecture decisions. AI is great at filling in the details of a decision already made. It is bad at making the decision itself — because architecture decisions depend on business context, team constraints, and a future that is not in the prompt.
• Trust in the name of speed. Accepting a suggestion because it "looks right" while the deadline is tight is the classic recipe for the bug that only shows up in production, under load, three weeks later.

A simple rule: if you cannot explain it, do not merge it

The most practical rule for this is old and has nothing AI-specific about it: if you cannot explain why that code works, it is not ready to merge — no matter who or what wrote it. This is not distrust of the tool. It is the same rule that should already apply to code copied from Stack Overflow, from a colleague, or from any external source.

The real gain is in the cycle, not the line of code

The mistake in measuring AI's value is counting lines of generated code per minute. The real gain shows up across the whole cycle: less time lost on mechanical tasks frees up time for the part AI still does not do well — understanding the right problem, designing the right solution, and deciding what not to build. Teams that use AI well do not code faster. They think faster, because they outsourced the part that did not require thinking.