Resolution Criteria

This prediction resolves TRUE if AI-generated code accounts for >30% of commits in the top 50 GitHub repositories:

1. Repository Selection: Top 50 repositories by star count on GitHub.com as of December 31, 2026

2. AI-Generated Code Definition: Code commits that can be reasonably attributed to AI assistance through:

   • Commit messages mentioning AI tools (Copilot, Claude, ChatGPT, etc.)
   • Automated detection of AI-generated patterns
   • Author self-identification of AI assistance
   • Analysis of code style/pattern consistency with known AI outputs

3. Measurement Period: All commits made between January 1, 2024 and December 31, 2026

4. Threshold: Strictly greater than 30.0% of total commits

5. Verification Method: Combination of automated analysis tools and manual verification by independent researchers

Calculation Method:

• Count commits containing any AI-generated code (not lines of code)
• If commit contains both human and AI code, counts as AI-assisted
• Exclude purely administrative commits (version bumps, config changes, etc.)
• Include all branches that merged to main/master during the period

Edge Cases:

• If GitHub changes their starring system, use whatever equivalent "top repositories" metric exists
• Repositories that become private or are deleted are excluded from analysis
• Forks don't count separately - only original repositories

Evidence and Reasoning

Current AI Coding Adoption:

• GitHub Copilot has millions of users and is integrated directly into development workflows
• AI coding assistants becoming standard in many companies and open source projects
• Observable increase in commit patterns consistent with AI assistance
• Growing acceptance of AI tools in professional development environments

The "Junior Developer Plateau" Thesis:

• AI excels at generating boilerplate code, simple functions, and tests
• Strong performance on well-defined coding tasks with clear patterns
• Current tools struggle with complex system architecture and design decisions
• Domain-specific knowledge and debugging complex legacy code remain challenging

Supporting Factors for 30%+ Adoption:

• Productivity gains from AI tools driving widespread adoption
• Cost savings for organizations using AI-assisted development
• Open source maintainers increasingly using AI to manage contribution volume
• Educational initiatives teaching AI-assisted coding becoming mainstream

Limiting Factors (Plateau Concerns):

• Code commits may not properly credit AI assistance
• Complex architectural decisions still require human expertise
• Code review and quality control processes may limit AI code acceptance
• Performance optimization and debugging often require deep system understanding
• Security-sensitive projects may restrict AI tool usage

Timeline Feasibility:

• 2-year window allows for significant tool improvement and adoption
• Enough time for organizational policies and workflows to adapt
• Sufficient for next generation of AI coding tools to emerge and be adopted