Hours Actual
Hours Manual Est.
ROI Multiplier
Net Savings
The Breakdown by Session
| Session | Date | Actual | Manual Est. | Saved | ROI |
|---|---|---|---|---|---|
| 1 | Nov 24 | 2.0h | 8.0h | 6.0h | 4.0x |
| 2 | Nov 25 | 2.0h | 10.0h | 8.0h | 5.0x |
| 3 | Nov 26 | 5.5h | 25.0h | 19.5h | 4.5x |
| 4 | Nov 26 | 1.0h | 14.0h | 13.0h | 14.0x |
| 5 | Nov 29 | 3.0h | 15.0h | 12.0h | 5.0x |
| 6 | Nov 30 | 2.0h | 8.0h | 6.0h | 4.0x |
| 7 | Dec 1 | 4.0h | 20.0h | 16.0h | 5.0x |
| Total | 19.5h | 100.0h | 80.5h | 5.1x |
Visual: Time Savings by Session
2.0h / 8.0h
2.0h / 10.0h
5.5h / 25.0h
1.0h / 14.0h (14x!)
3.0h / 15.0h
2.0h / 8.0h
4.0h / 20.0h
Financial Analysis
Cost Calculation
| Hourly Rate | $150 |
| Actual Hours | 19.5h |
| Actual Cost | $2,925 |
| Manual Hours | 100h |
| Manual Cost | $15,000 |
Savings
| Hours Saved | 80.5h |
| Dollar Savings | $12,075 |
| Claude Code Cost | ~$200 |
| Net Savings | $11,875 |
What Made It Work
Success Factors
-
Clear Requirements: We knew what we wanted before each session.
No exploratory design during execution. -
Pattern-Based Architecture: Consistent service/route/types patterns
meant AI could replicate structure across features. -
Type Safety: TypeScript caught errors at compile time.
Less debugging, faster iteration. -
Throwaway Mindset: Sessions 1-2 (Laravel) weren’t wasted – they
clarified requirements. Pivoting early was cheaper than pivoting later. -
AI Code Review: Security issues found in minutes, not hours.
Would have been easy to ship vulnerable code without it.
What the Numbers Don’t Show
The 5x ROI is impressive, but it understates some benefits:
- Decision Fatigue: AI made hundreds of micro-decisions (naming, structure, patterns) that would have slowed solo development.
- Context Switching: Sessions could be resumed from notes. No “where was I?” delays.
- Documentation: Code was documented as it was written, not as an afterthought.
- Security: The 9 issues found might have taken weeks to discover in production.
Honest Limitations
AI-assisted development isn’t magic. Here’s what didn’t work perfectly:
Challenges
- Context Window Limits: Long sessions required re-explaining context. Session notes helped but added overhead.
- Debugging Complex Issues: The Prisma date/timezone bug took multiple iterations to diagnose correctly.
- Architectural Decisions: AI can implement patterns but humans need to choose the right patterns.
- Domain Knowledge: TimOS concepts (Feels Layer, Slips vs Outs) required human input – AI couldn’t invent the product.
Output Summary
Lines of Code
Files Created
API Endpoints
Bottom Line
19.5 hours of focused work with AI produced what would have taken
100+ hours manually. The savings funded the entire project
infrastructure for the first year.
Key Takeaways
- AI-assisted development delivers 4-14x ROI depending on task type
- Repetitive patterns (CRUD, services, routes) see highest gains
- Clear requirements before sessions maximize efficiency
- Security review alone justifies the AI investment
- Humans still drive architecture and domain decisions