Written by: Kamil Ponicki, Director of Talent Acquisition, Digital Colliers
Everyone measures throughput. Lines written, features shipped, velocity. The problem is that none of those numbers tell you whether your augmented team is going to work. They are lagging indicators that hide problems until they are expensive to fix.
In month one, you need leading indicators. Signals that tell you whether the system you are building will scale or collapse under its own weight. Three numbers matter more than any output metric.
The review burden tells you if you will scale
When you add AI coding assistants to a team, experienced developers end up reviewing 6.5% more code. That sounds manageable until you realize their own original code productivity drops by 19%. The math does not work. You are trading productive engineering time for review time.
In month one, track how much time your senior engineers spend reviewing AI-generated code versus writing their own. If the ratio is climbing above 2:1, you have a problem. The system is consuming your most expensive resource to babysit output that may or may not be saving you time.
This is not about being anti-AI. It is about being honest about the cost structure. More than 80% of AI projects fail, and a large part of that failure rate comes from teams that never modeled the review burden. They measured velocity and assumed the rest would work itself out.
The survival rate tells you if quality is real
More than 15% of commits from AI coding assistants introduce at least one issue. Some assistants run as high as 29%. In month one, you need to know what percentage of AI-generated code makes it through review unchanged.
If your survival rate is below 60%, you are not augmenting. You are creating a second job for your engineers. They write the prompt, the AI writes the code, and then they rewrite half of it to make it actually work.
Track this weekly. If the survival rate is not climbing, the AI is not learning your codebase or your standards. You are stuck in a loop where throughput looks good on paper but the actual output is a tax on your team.
The pattern I keep seeing is that teams measure velocity in week one, celebrate the speed, and never look back. By month three, they are drowning in technical debt and wondering why morale is in the floor.
The debt velocity tells you if this compounds
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 is not a typo. The debt compounds.
In month one, track how many AI-introduced issues you are deferring. If you are closing tickets as "acceptable" or "fix later", you are building a liability. The majority of operators I talk to do not realize they have a debt problem until they are sitting on thousands of issues and no clean way to triage them.
Debt velocity is simple to measure. Count the number of AI-related issues you defer each week. If that number is not zero or close to it, you are in trouble. The failure rate of AI projects is roughly twice the failure rate of conventional IT projects, and a large part of that gap is unmanaged debt.
The winning move is to treat month one as discovery. You are not trying to maximize output. You are trying to figure out whether the system can work at all. If your review burden is climbing, your survival rate is stuck, and your debt velocity is positive, you have your answer. Throttle back before the cost gets worse.
Throughput will lie to you. These three numbers will not.