🤖 AI Didn’t Eliminate the Work — It Just Moved It
“AI makes coding faster.” Sure. But are we measuring the right thing?
AI-assisted tools like Copilot, Cody, and TabNine have undeniably sped up how developers write code. But in reality, they’ve shifted a major burden from writing to reviewing. The bottleneck didn’t disappear — it just changed shape.
💻 From Code Writing to Code Reviewing
Before AI coding assistants, devs wrote code deliberately and reviewers ensured alignment.
Now:
- Devs generate code quickly, often without full understanding.
- Reviewers are tasked with validating correctness, context, and quality.
This has introduced a new kind of review fatigue, where clean-looking AI code sails through PRs without sufficient scrutiny — and the costs show up later in bug reports or confusing refactors.
🧠 Code That Looks Smart Can Still Be Wrong
AI code is often:
- Stylistically correct
- Syntactically valid
- Plausible (but not always correct)
But unless your reviewers actively simulate, test, or model the behavior mentally, subtle logic bugs or architectural misfits can slip by.
🛠 What Engineering Teams Can Do
Here’s how to adapt your workflows to this new reality:
1. Treat AI Code Like Intern Code
Assume it needs supervision.
Even if it’s “clean,” every AI suggestion should be reviewed with the same level of rigor you’d apply to code from a junior developer.
2. Use AI to Assist Review, Too
Fight AI with AI.
Incorporate tools like:
- Static analysis with AI hints (e.g., SonarQube)
- PR auto-summarizers to spot diffs quickly
This frees up reviewer time to focus on architecture and correctness.
3. Write Tests Before Prompting the AI
Reverse the loop.
Define expected behavior via tests first. Then use AI to fill in the implementation. Now you’re validating outcomes, not guessing at logic.
4. Promote “Trusted Snippets” to Shared Libraries
Reuse, don’t regenerate.
When AI produces reliable utilities or helpers, wrap them and document their use. This reduces repeated review cycles for similar code.
5. Redefine “Productivity”
Output ≠ Quality
Encourage developers to take ownership of AI code. Speed is only useful if the result is maintainable, testable, and clear.
📌 Final Thought
AI tools have made it easier to produce code — but code isn’t the goal. Software is a team sport. As AI enters our workflows, it’s our engineering culture, review processes, and expectations that need to evolve.
In the era of AI coding, reviewing is the new engineering
👉 Want strategies for keeping AI-generated noise under control? Read our 'AI Hygiene' guide: AI Hygiene. superpower.
Tags: AI coding, software engineering, code review, team dynamics, productivity