AI tools help teams ship 2-3x more code. But are reviews keeping pace?
Start Free TrialTeams ship 2-3x more PRs, but reviews take much longer. The bottleneck shifted from writing to verification.
AI tools accelerate code generation, but review capacity hasn't scaled. High-adoption teams merge 2-3x as many PRs while review times increase significantly. The bottleneck shifted from writing to reviewing.
AI-generated PRs are 2-3x larger on average. Reviewers skim 500-line changes instead of reading deeply. Bugs hide in the noise. What should be 5 separate PRs becomes one unmaintainable monster.
Here's the scary part: developers approve AI code they couldn't write themselves. One engineer reviewed his own AI PR three days later and had no idea how it worked. This invisible debt surfaces as maintenance nightmares.
Many teams approve AI PRs without proper verification. One architectural mistake propagates across 10 PRs before anyone catches it. By then, you've built a house on sand.
AI code looks correct but makes wrong architectural assumptions. Conceptual errors (not syntax bugs) require multiple revision cycles. Many developers say reviewing AI code takes more effort than human code.
The metrics that worked pre-AI now mislead managers. AI inflates volume metrics while hiding quality problems.
Doesn't distinguish AI-assisted vs manual work. Penalizes developers doing complex, AI-resistant tasks.
Fast shipping doesn't equal good code. May indicate rubber-stamping AI-generated code without review.
These metrics reveal AI's true impact on your team:
Most teams don't review AI code thoroughly. Track what % of your PRs get proper human oversight, not just quick approvals.
AI-generated PRs are 2-3x larger on average. Big PRs slow reviews, hide bugs, and create comprehension debt. Track size to keep PRs reviewable.
High-AI teams see significantly longer review times. Track hours-to-first-review to catch the verification bottleneck before it chokes delivery.
Many developers struggle with "almost right" AI solutions. Track how many PRs need multiple revision cycles. This reveals conceptual errors, not just syntax bugs.
AI speeds up delivery, but are you introducing more bugs? Track bugs vs features to catch quality decay before customers do.
Track the metrics that reveal AI's hidden costs: review bottlenecks, PR bloat, quality decay, and comprehension debt.
Most teams don't review AI code thoroughly. Track what % of PRs get proper human oversight to prevent rubber-stamping.
PRs are 2-3x larger with AI. Track files touched and lines changed to keep PRs reviewable and catch bloat early.
Reviews take much longer with AI. Track time-to-first-review and total review cycles to spot the verification bottleneck.
Many teams struggle with "almost right" AI code. Track bug ratio and rework rate to catch architectural mistakes early.
Beyond proxy metrics. We're building direct integrations with Claude, Copilot, and Cursor APIs to measure token usage, calculate ROI, and detect assumption propagation.
Connect to Claude Code and GitHub Copilot APIs to track token usage, measure AI adoption, and calculate ROI.
Track token usage and costs from Claude API. Calculate ROI: developer time saved vs AI tool spend.
See velocity, quality, and review metrics broken down by AI usage. Understand which archetypes benefit most from AI.
Compare Claude Code vs GitHub Copilot vs Cursor ROI. Get recommendations on which tool fits which developer type.
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View Full RoadmapStop guessing if AI is helping. Track review bottlenecks, PR bloat, and quality decay. Know if your team is shipping faster or just generating more code.
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