In part three, we look at both sides of the AI contribution debate. A working patch, real demand, never submitted, rejected because AI was involved. But maintainers are unpaid volunteers, and AI halved the cost of contributing without touching the cost of review. Both sides have a point.
We smile at the 18th-century crowd for being swept up by a box with a man inside, yet today it's easy to hand ChatGPT a vague idea and treat the PRD it returns as gospel. Generative AI is genuinely powerful. We get the best from it when we bring both enthusiasm and a critical eye.
Developers don't skip standards because they're careless, they skip them because there are fifteen things to remember and the code was the hard part. The real question isn't which tasks your LLM handles well. It's what's still slipping through ungated.
The AI wave is here, and the industry is already splitting into two: those adapting fast and those falling behind. The gap is widening quickly.
The machines aren't replacing developers, they're promoting them. You're no longer just writing code; you're managing agents, reviewing output, and setting standards. Three Claudes walk into a codebase, and suddenly you're a manager.
Managing multiple Claude Code accounts across machines gets messy fast. Jean-Claude keeps the useful parts in sync, separates account-specific config, and makes switching between personal, team, and client setups far less painful.
Last week, we deleted 16,000 lines of code, rewrote 11,500, migrated frameworks, ripped out React, and swapped our entire CSS setup with AI. It took less than 6 hours.
Technical debt used to justify meetings, trade-offs, and dedicated sprints. AI has changed that. Cleanup is now fast, cheap, and continuous. Teams that stop debating and start fixing unlock faster delivery and better outcomes.
Conductor by Melty Labs makes parallel agent workflows practical by running multiple agents with separate tasks simultaneously. The trade-offs are real but manageable, and this is where development is heading.
