Written by: Kamil Ponicki, Director of Talent Acquisition, Digital Colliers
The pattern repeats across teams I talk to. You roll out AI coding assistants. Junior developers ship faster. But your seniors start working longer hours, and nobody can explain why.
The numbers tell the story. After adopting AI coding assistants, experienced developers review 6.5% more code. Their own productivity drops 19%. The work didn't vanish. It moved.
The review queue becomes the bottleneck
AI makes it easier to write code. That's the sales pitch, and it's true. But every line still needs human judgment before it ships.
Junior developers can now produce at intermediate pace. Intermediate developers can match senior output volume. But reviewing code is different from writing it. It requires pattern recognition that only comes with years of mistakes.
You probably hired your seniors to build, not to review. Now they spend their days in pull requests. The team velocity looks great on the sprint chart. But the architecture starts to drift because nobody has time to think.
The defect rate you're not tracking
More than 15% of commits from every AI coding assistant introduce at least one issue. GitHub Copilot sits at 17.4%. Gemini hits 29.1%.
These aren't syntax errors the linter catches. They're logic problems, security gaps, and design decisions that make sense in isolation but break the system six months later.
Your seniors catch some of it in review. Most of it, probably. But code review is a sampling exercise. You can't audit every line at production depth. The rest accumulates as technical debt.
Unresolved technical debt from AI-generated code climbed from a few hundred surviving issues in early 2025 to over 100,000 by February 2026. That's across measured systems, not one codebase. But the trajectory is what matters.
What happens when you don't plan for it
Most teams don't redesign their delivery process when they add AI tools. They assume developers will figure it out. Then they wonder why their best people are updating their LinkedIn profiles.
Senior engineers leave when the work stops being interesting. Reviewing code all day isn't interesting. Building systems is interesting. Solving hard problems is interesting. But AI moved the building work downstream and left the review work at the senior level.
The cost shows up in your P&L eventually. 95% of enterprise GenAI pilots deliver zero measurable P&L impact. That's not because the tools don't work. It's because the work shifted inside the team and nobody redesigned the team for it.
What works instead
The teams that make this work add review capacity before they roll out AI tools. They hire senior engineers who want to be technical leads, not individual contributors. They build review into the sprint budget as first-class work, not leftover time.
They also change what review means. It's not just checking for bugs. It's teaching the team what good code looks like. It's building shared patterns. It's documenting the decisions that AI can't make because they require business context.
Some teams rotate review duty. Others split the role permanently. The tactic matters less than the recognition that the work is real and someone has to do it.
If you're planning to add AI coding assistants this year, plan for the review load first. If you already rolled them out and your seniors are complaining about too many pull requests, you're not too late. But you need to move before they do.