AI Is a Force Multiplier
Let us be direct: the best developers we know have adopted AI coding tools. Not because they can't code — but because the tools let them operate at a higher level.
Here's our honest breakdown of how AI fits into our workflow.
What AI Is Excellent At
Boilerplate generation. Writing a Prisma schema, generating API route scaffolding, creating TypeScript interfaces from a database model — AI does all of this in seconds.
Code review. We paste code and ask "what are potential security issues?" or "how could this be more efficient?" The responses are often better than a junior reviewer.
Documentation writing. Nobody enjoys writing docs. AI drafts them; humans review and edit.
Debugging error messages. Obscure error messages that would take 20 minutes to search Stack Overflow for are resolved in seconds.
Test generation. "Write Jest tests for this function" produces a solid first draft that covers most edge cases.
What AI Is NOT Good At
System architecture. AI can suggest patterns, but designing the right architecture for your specific constraints requires experience and context.
Business logic. If you can't explain the business rules clearly yourself, AI will give you convincing-sounding wrong answers.
Security-critical code. Always have a human with security expertise review smart contracts, auth flows, and payment processing.
Our Actual Workflow
- We define the feature spec in plain English
- AI generates the scaffolding (models, routes, types)
- Developer fills in business logic manually
- AI does a first-pass review
- Developer fixes issues, adds tests
- Human code review before merge
This workflow ships features in roughly half the time compared to pre-AI. Not 10x in LOC — but 10x in time from spec to production.
