AI Productivity Gains Are Real Until They Aren’t.
Vish
AI has rapidly become the most talked-about shift in software development. Tools like Cursor, GitHub Copilot and Codeium are being adopted at record pace, promising 40–50% productivity improvements. Venture capital is pouring in, Cursor alone recently raised $2.3B.
Everywhere you look, the industry is repeating the same message:
“AI is the biggest opportunity in software development.”
And early metrics do look promising.According to McKinsey and Forrester:
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- 60% of teams using AI report productivity gains
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- 45% increase in release speed
But the same surveys reveal what’s not being said out loud:
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- 91% of organizations say they aren’t prepared to implement AI
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- 85% struggle to integrate AI into existing workflows
So we have a strange duality: extraordinary hype, but limited readiness.
Cursor case: 807 Real Projects Tell a Different Story
Carnegie Mellon University published the largest causal analysis of AI coding assistance to date, studying 807 real GitHub projects before and after Cursor adoption.
The findings contradict most public claims:
Short-Term Spike (Month 1–2)
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- Commits ↑ 55%
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- Lines added ↑ 281%
Developers feel faster.
Teams look faster.
Output increases.
Then the boost disappears.
Long-Term Quality Decline Is Consistent
The same study found sustained degradation:
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- Static analysis warnings ↑ 30%
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- Code complexity ↑ 41%
Unlike productivity, these do not fade.
They compound month after month.
Technical Debt Kills Future Velocity
The data shows:
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- Doubling warnings → future velocity ↓ 50%
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- Doubling complexity → future velocity ↓ 65%
This means the initial 55% speed-up is fully erased in the long run.
From a QA Engineering perspective: AI Has Shifted the Bottleneck
Most companies evaluate AI success only through developer output:
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- More commits
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- More lines of code
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- Faster PRs
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- More features shipped
But QA teams see a very different reality.
1. QA Workload Increases Even When Dev Workload Decreases
AI generates more code and more change requests.
But QA must:
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- Validate more scenarios
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- Expand regression packs
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- Maintain more automation
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- Catch more integration issues
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- Manage higher complexity
AI accelerates creation, but not correctness. QA absorbs the imbalance.
2. AI-Generated Complexity Makes Automation Harder
When complexity increases, test automation becomes: Harder to write, Harder to maintain, More brittle, More flaky, More expensive.
Testing is tied to the structure and behavior of the codebase, so when code complexity balloons, test complexity multiplies.
3. Long-term Velocity Depends on QA Scaling With AI
Real productivity improvements with AI will come not from code generation speed, but from:
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- Stronger quality gates
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- Better code review processes
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- Fast & stable automated testing
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- Continuous refactoring
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- Good observability & monitoring
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- QA Engineering enablement
Without this, AI is like driving with a more powerful engine but worn-out brakes.
Speed becomes dangerous. And velocity eventually drops.
The Real Takeaway: AI makes QA way more important
AI can deliver long-term productivity gains. But only if QA evolves in parallel.
When teams scale:
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- Their review processes
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- Their automation maturity
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- Their test coverage goals
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- Their refactoring discipline
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- Their quality engineering capacity
… AI becomes a force multiplier.
When they don’t, the industry will keep seeing the same pattern:
Short-term speed. Long-term slowdown.
At AgileVerify, we help teams adopt AI safely, with sustainable quality and long-term velocity as the core goal.
