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AI Debt Calculator

Estimate the hidden technical debt from AI-generated code in your codebase with our interactive risk assessment tool

Why Measuring AI-Specific Debt Matters

AI coding assistants are transforming software development, but they are also introducing a new category of hidden technical debt. Unlike traditional shortcuts that developers consciously choose, AI-generated debt accumulates silently -- accepted suggestions pile up, untested patterns spread, and codebases drift from their intended architecture.

Studies show that teams accepting more than 60% of AI suggestions without modification experience 2-3x higher bug rates within six months. The problem is not the AI itself -- it is the gap between generation speed and review quality.

This calculator helps you estimate your AI debt exposure based on your team's actual practices. Answer six questions about your AI usage patterns and get an actionable risk assessment with specific recommendations.

The AI Debt Calculator

Fill in the details below based on your team's current AI coding practices. All calculations happen in your browser -- no data is sent anywhere.

Number of developers using AI coding assistants

Estimated percentage of your codebase written or heavily assisted by AI

30%

How thoroughly does your team review AI-generated code before merging?

How long has your team been actively using AI coding assistants?

What percentage of AI suggestions does your team accept without significant modification?

50%

What percentage of AI-generated code is covered by automated tests?

40%

Understanding Your Score

Low Risk (0-25)

Your team has strong AI governance practices. AI-generated code is well-reviewed, adequately tested, and unlikely to harbor significant hidden debt. Continue your current practices and monitor for drift.

Medium Risk (26-50)

Some AI debt is accumulating but it is manageable. Focus on improving code review processes for AI-generated code and increasing test coverage. Schedule quarterly audits of AI-heavy modules.

High Risk (51-75)

Significant AI debt is building up. Your team is likely experiencing more bugs, longer debug cycles, and growing maintenance costs from AI-generated code. Immediate process improvements are recommended.

Critical Risk (76-100)

Your codebase likely contains substantial hidden debt from AI-generated code. Expect escalating bug rates, security vulnerabilities, and architectural decay. A comprehensive audit and process overhaul is urgently needed.

What to Do Next

Score 0-25: Maintain & Monitor

  • Document your current AI review process as a team standard
  • Set up automated metrics to track AI code quality over time
  • Share your practices with other teams as a model
  • Recalculate quarterly to catch any process drift early

Score 26-50: Improve Processes

  • Implement mandatory code review checklists for AI-generated code
  • Increase test coverage targets for AI-heavy modules by 20%
  • Train the team on identifying common AI code antipatterns
  • Schedule monthly AI debt review sessions

Score 51-75: Take Immediate Action

  • Conduct a focused audit of the highest-churn AI-generated modules
  • Reduce AI acceptance rate targets -- aim for under 40% unmodified acceptance
  • Dedicate 15-20% of sprint capacity to AI debt remediation
  • Implement pair review for all AI-generated business logic

Score 76-100: Emergency Response

  • Pause new AI code generation until review processes are established
  • Run static analysis and security scans on all AI-generated modules
  • Dedicate 25-30% of sprint capacity to immediate debt reduction
  • Present findings to leadership with a remediation roadmap and budget request

How the Score Is Calculated

The AI Debt Score is a weighted composite of six factors. We believe in transparent methodology -- here is exactly how your score is computed so you can evaluate and adapt it to your context.

1

AI Code Volume (25% weight)

Higher percentages of AI-generated code increase the surface area for hidden debt. Scales linearly from 0% to 100% AI code.

2

Review Thoroughness (25% weight)

The most impactful factor. Minimal review scores highest risk; comprehensive review dramatically reduces debt. Values: Minimal=100, Basic=65, Thorough=30, Comprehensive=10.

3

Acceptance Rate (20% weight)

Teams that accept a higher percentage of AI suggestions without modification accumulate more unvetted code. Maps directly from the 0-100% input.

4

Test Coverage Gap (15% weight)

Inverted test coverage: lower coverage means higher risk. 0% coverage scores 100 risk; 100% coverage scores 0 risk. Untested AI code is where bugs hide longest.

5

Time Accumulation (10% weight)

Debt compounds over time. Longer AI adoption periods without process improvement increase risk. Scales up to 24 months then caps at maximum risk contribution.

6

Team Scale Factor (5% weight)

Larger teams using AI tools have more variance in review quality and coding standards. Risk increases with team size up to 50 developers, then levels off.

Note: This calculator provides a directional estimate based on industry research and common risk patterns. Every codebase is unique -- use this score as a conversation starter and prioritization tool, not as an absolute measurement.

Frequently Asked Questions

The calculator provides a directional estimate based on industry research and common risk factors. It is designed to highlight areas of concern rather than provide an exact measurement. Real-world debt depends on many additional factors like language, framework maturity, and the specific AI tools used. Use this as a starting point for deeper investigation -- not as a definitive audit result.

An AI Debt Score specifically measures risk from AI-generated code patterns that traditional tech debt metrics miss. Regular metrics like cyclomatic complexity or code coverage do not capture AI-specific issues such as hallucinated API usage, context-unaware implementations, or the "looks correct but is subtly wrong" pattern common in AI-generated code. This score fills that gap by focusing on the human-AI interaction factors that drive hidden debt.

Not necessarily, but statistically teams that accept a higher percentage of AI suggestions without modification tend to accumulate more technical debt. The key factor is the combination of acceptance rate and review thoroughness. A team with a 70% acceptance rate but comprehensive reviews may have less debt than a team with 40% acceptance rate but minimal reviews. The calculator weighs both factors together to give a more nuanced picture.

Reassess monthly or after significant changes such as onboarding new AI tools, changing review processes, adding team members, or major project milestones. Tracking the score over time reveals trends that are more valuable than any single measurement. A score that is rising month-over-month signals process erosion even if the absolute number looks acceptable.

No. This calculator identifies risk areas and provides estimates, but a thorough code audit with static analysis tools and human review is essential for accurate debt measurement. Think of this tool as triage -- it helps you decide where to focus your audit resources first. Pair the results with tools like SonarQube, CodeClimate, or manual architecture reviews for a complete picture.

Related Resources

Tech Debt Calculator

Calculate your overall technical debt score with our comprehensive assessment covering all debt categories.

Measuring Tech Debt

Learn proven frameworks for quantifying technical debt impact beyond AI-specific metrics.

AI Slop

Understand the full spectrum of AI-generated technical debt that feeds into your debt calculations.

Ready to Take Action on Your AI Debt?

Use our general Tech Debt Calculator to assess your full codebase, or explore strategies for managing AI code quality effectively.

General Tech Debt Calculator Managing AI Code Quality