CODE TUNER INSIGHTS
AI Is Making Code Faster to Create. Can Your Visibility Keep Up?
AI-assisted development can increase code velocity faster than teams can review and understand the resulting system. Learn why codebase visibility matters.
AI can make producing an initial implementation much faster. Developers can scaffold features, generate tests and explore alternatives with less manual drafting. The next challenge is understanding the system those changes create.
As code output increases, review, integration and maintenance still need to keep pace. The question for engineering leaders is whether visibility is growing alongside development capability.
The bottleneck can move downstream
Faster code generation does not remove the need to test behaviour, review changes, understand dependencies or make architectural decisions. It can shift pressure towards those activities.
McKinsey’s research on agentic product development highlights this gap. Survey respondents reported greater time savings in development activities than reductions in rework. The article argues for verification and measurement that can keep pace with faster creation.
The implication is practical: assess the whole delivery process when judging the value of AI. Code produced is an input; reliable, maintainable software is the outcome.
What the evidence does and does not say
AI-generated code is not automatically poor code. Human-written code also contains defects and technical debt, and AI can support testing, improvement and refactoring.
A 2026 empirical preprint, revised in April, examined approximately 302,600 verified AI-authored commits across 6,299 public GitHub repositories. More than 15% of commits from each assistant studied introduced at least one static-analysis issue; 22.7% of tracked AI-introduced issues remained at the latest revision examined.
Static-analysis findings are not all production defects. The study also does not establish that every AI-assisted team performs worse than a human-only team. It supports attention to quality assurance and persistence of issues in the repositories studied.
Use external findings to frame questions for your own environment. Your languages, tools, review practices and product risks will shape the answers.
Review the change and watch the system
Pull-request review asks whether an individual change is acceptable. Codebase-level analysis asks how the overall system is evolving as many changes accumulate.
Both matter. Individually reasonable changes can gradually increase coupling, complexity or the effort required to understand a component. A team can approve each contribution while losing sight of that broader pattern.
Combine the two views instead of expecting one to answer every question. Normal review, tests and security checks remain essential alongside trends in codebase condition.
Six questions for an AI-assisted team
- Is code volume growing while maintainability indicators worsen?
- Where are complexity hotspots emerging?
- Which AI-assisted areas are changed most frequently?
- Do recently generated areas need repeated correction?
- Is review or integration becoming a capacity constraint?
- Can engineers explain and safely modify the code the team now owns?
Where authorship information is available, treat it carefully. A change may combine generated and manually written work; provenance alone does not establish its quality or business priority.
Understanding is part of the maintenance model
A study of undergraduate software projects describes “comprehension debt”: a gap between the code teams own and what they understand well enough to maintain. It also identifies uses of AI that helped students understand code. This is early, educational evidence, not a measurement of commercial engineering teams.
The concept still offers a useful prompt. Ask whether documentation, review conversations and shared knowledge are keeping pace with the volume of accepted changes.
Scale confidence alongside output
Greater development capability is valuable when the organisation retains the ability to verify, maintain and change what it builds. Review the effect of AI across coding, testing, rework and ongoing understanding.
The leadership question becomes: how confidently can this team understand and govern a codebase changing at this speed? That is a more complete measure of progress than the amount of code generated.
Make the next engineering decision with better evidence.
Code Tuner analyses technical debt, complexity and codebase health to support engineering judgement and investment decisions. Explore how Code Tuner can help your team.