AI-Assisted Development: Amplifying Velocity and Technical Debt in Enterprise Applications

When Leon Revill set out to build Under The Hedge - an enterprise-scale wildlife observation platform comparable to Instagram/Strava for nature enthusiasts - he aimed to test AI's real-world impact on software development. The results expose critical tradeoffs in the AI-assisted development era.

The Enterprise-Scale Test Case

Under The Hedge incorporates complex capabilities that mirror commercial platforms:

• AI-Powered Analysis: Gemini identifies species and behaviors in user-uploaded photos
• Geospatial Processing: Location tracking via EXIF data and geohashing
• High-Performance Data Layer: AWS DynamoDB single-table design
• Scalable Media Infrastructure: Global content delivery via CloudFront
• Social Features: User/species/location following systems

Industry Data Reveals Systemic Patterns

Recent studies contextualize Revill's findings:

1. Throughput vs. Stability (2025 DORA Report):
   • 62% faster feature delivery
   • 28% increase in production incidents
2. Quality Decline (GitClear Analysis of 211M LOC):
   • Copy/pasted code (12.3%) exceeds refactored code (9.5%)
   • 8x increase in duplicated blocks
3. Security Gaps (Veracode 2025):
   • 45% of AI-generated samples contained vulnerabilities
   • Java security pass rates as low as 29%

The Developer's Firsthand Experience

Revill's month-long development process revealed AI's dual nature:

Accelerators

• Complex UI components built in minutes instead of days
• Continuous progress maintained developer motivation
• Effective learning tool for new technologies like Next.js

The Stability Tax

• Defaulted to fastest solutions, not optimal ones
• Systematic duplication instead of DRY principles
• Backward-compatibility workarounds bloating code
• Required explicit security prompting

Mitigation Strategies for Engineering Teams

Based on his experiment, Revill recommends:

1. Specification-First Development: Use tools like Gemini's deep research feature for detailed architecture planning before coding
2. Explicit Quality Gates: Prompt AI for security audits, performance optimizations, and duplication checks
3. AI-Assisted QA: Automate code analysis for refactoring opportunities
4. Full SDLC Integration: Generate test plans alongside feature code

The New Developer Paradigm

"We're transitioning from bricklayers to site foremen," Revill observes. AI handles syntax, but human value now resides in:

• Semantic understanding
• Architectural oversight
• Quality enforcement
• Security validation

The experiment confirms AI's role as an amplifier rather than a replacement. Teams that approach it as a "brilliant but reckless junior intern" - with rigorous specifications and review - stand to gain the most from this transformative technology without drowning in technical debt.