Does AI-written code look different, and can you tell?

Does AI-written code look different, and can you tell?

Often it does, and often you can. Heavy AI assistance leaves visible traces in a codebase, and a developer who reads a lot of code learns to spot them the way an editor spots machine-translated prose.

The important part comes right after that yes. The traces reveal how the work was produced. They say very little about whether the work was any good, because the same tools sit in the hands of the most careful engineers in the industry and the most hurried ones. A careful developer edits the traces away. A hurried one leaves them in. So the traces end up measuring editing habits more than talent, and this lesson walks through what they are, why they appear, and where the real signal moved.

What traces does heavy AI assistance leave?

Four traces come up again and again when developers describe reading AI-heavy code.

The first is verbose comments. A comment is a note a developer writes inside the code for other humans to read. AI tools tend to produce comments that restate what the line below them already says: a note reading "increment the counter" above a line that increments a counter. Human developers write fewer comments and spend them on the why rather than the what.

The second is uniform boilerplate. Boilerplate is standard, repetitive setup code that most files need a little of. AI tools generate it in the same recognizable shape every time, so unrelated files across a project end up with identical scaffolding, the way twenty cover letters from one template share the same bones.

The third is large uniform commits. You met commits in the release pipeline: one saved change, with a note. AI-heavy work tends to land as a few big commits with generic notes, where a person working by hand leaves a trail of small ones that tells the story of the change.

The fourth is tests that mirror the code. A good test challenges the code with awkward inputs and edge cases. A generated test often checks that the code does what the code plainly does, which passes every time and proves nothing.

Why do those traces appear?

Each trace follows from how the tools work, and knowing the mechanism makes the trace easier to read.

A coding agent produces a whole change in one pass. It writes the code, the comments, and the tests together, and hands the finished bundle back. Small commits with a narrative arc are a habit a person imposes on the work afterward, by splitting the bundle up. When the bundle lands whole, that is one large commit, and its note reads like a summary because it is one.

The comment verbosity has a similar root. The models learned from public code, including enormous amounts of tutorial code, where over-explaining is the point. The habit came along.

The mirroring tests are the most instructive trace. An agent generating tests for code it also wrote will confirm its own understanding, since it has no independent knowledge of what the code should do. Only the developer knows the intent, so tests that genuinely challenge the code need the developer's intent pushed into them. Where the developer did that pushing, the tests bite. Where they accepted the first draft, the tests mirror.

Every one of these traces is removable in minutes. That fact is the hinge of this whole lesson: presence of a trace means nobody spent the minutes.

Do the traces mean the code is bad?

No, and this is the trap worth slowing down for. The traces are evidence about process. Quality is a separate question, and it can go either way underneath the same surface.

The tools produce plausible, conventional code. Most of what they generate would pass a casual read. A strong developer working at high speed accepts the good parts, rewrites the weak parts, and ships something excellent that still carries a few generated fingerprints. A weak developer can produce trace-free code that solves the wrong problem. The vibe coding lesson from earlier in this module already gave you the real dividing line: whether the developer read and understood what they accepted, which no surface trace records directly.

The honest reading is one step subtler than "traces are fine." Traces correlate weakly with hurry. A pull request full of restating comments and mirroring tests suggests nobody made an editing pass, and an editing pass is where a lot of defects get caught. Weak evidence of hurry deserves a closer look at the substance. It never deserves a verdict on its own.

What does AI-assisted tell you on a resume?

On its own, close to nothing. By 2026 the tools reached near-universal adoption among professional developers, a fact you have from earlier in this module. A phrase that describes nearly everyone describes no one in particular. "AI-assisted development" on a resume is like "used email" on a resume in 2005: true, and empty. It dates the resume to the era, and that is the whole content.

The phrase starts carrying information only when it gets specific. A developer who describes which rung of the autonomy ladder they work at, or what their team's automated review catches before a human looks, is describing an actual workflow with actual decisions in it. The generic phrase is wallpaper. The specific sentence is a window.

Where does the judgment show?

In the output, and specifically in the decisions visible inside it. When the typing is delegated, what remains of the developer is a chain of accept-and-reject choices, and finished work records those choices whether or not anyone meant it to.

Three places hold most of the signal. First, the size of the change: whether it is the smallest change that solves the problem, or whether generated bulk came along for the ride. Second, the tests: whether they encode intent and challenge the code, or mirror it. Third, coherence: whether the codebase reads like one person made consistent decisions across it, with one naming style and one way of handling errors, or like a series of first drafts from different sessions that nobody reconciled.

This is why engineers evaluating a portfolio in the AI era read pull requests and code review comments with more interest than the code itself. A review comment records a developer pushing back on a change, and pushing back is precisely the act the tools cannot fake on someone's behalf. The developer whose fingerprints you want are the fingerprints of rejection: the moments they said no to a plausible draft.

Does using AI tools make developers worse?

Serious engineers argue both sides of this, and the honest answer in 2026 is that the argument is live.

The worry runs like this. Developers built their deep knowledge by writing code by hand and debugging their own mistakes. Debugging what you wrote is how the system's real behavior gets into your head. If a generation of developers delegates the writing, they may never build the model of the system that debugging used to force, and the industry ends up with people who can produce software they cannot fix. People who hold this view point to junior developers who ship generated code into production and stall completely when it breaks.

The counter runs like this. Every abstraction in the field's history drew the same charge. Developers who write in modern languages rarely understand the machine instructions underneath, and the field called that progress. On this view the tools moved the skill up a level: from producing code to specifying, judging, and verifying it, and the developers who look worse are the ones who skipped the judging, which was always the load-bearing part.

Notice what both sides agree on, because it is the practical takeaway. The clearest evidence of erosion involves developers who never debugged what they shipped. That is a claim about a work habit, and the habit is observable: a developer who can walk through why their change works, and what broke along the way, built the understanding regardless of who typed the code. Both camps would hire that person. The next lesson in this module takes the argument to its larger form: what the job itself becomes.