ok so i've spent years thinking about what it takes for execs to AI-pill their teams. a few thoughts on where i think leaders should start:
đź’» Technical Readiness
1. Stop chasing incremental velocity; you can 2-3x PRs if you try (h/t
@darraghcurran)
2. Make it easy for jr engineers to onboard. your agents will thank you.
3. Increase investment in platform teams (devx ftw)
4. Focus on verification loops, not prompt engineering - we love a /goal
5. Speed up core feedback loops (lints, tests, etc.)
6. Figure out how to bypass human code review for some PRs
7. Do extreme eng experiments (delete your IDE! h/t
@chintanturakhia)
8. Ensure your team can experiment w models, harnesses, etc.
🤝 Operating Model
9. Put AI workflows into buckets: automate / augment / ignore / kill
10. Turn your best AI user's flows into a system- i think
@JJEnglert is good at this
11. Give agents actual jobs, not tasks
12. Get non-engineers committing code to production - use
@DevinAI if you need a place to start
13. Measure tokens (it works, sorry not sorry)
14. But have a controlling quality measure
15. Question every process built to protect scarce engineering time
16. No pure managers
17. Create new team topologies (v small! very large! v flat!)
@GammaApp and
@thisisgrantlee @thatsjonsense do this well
🥳 Culture
18. Require leaders to build something with AI and demo it publicly
19. empathaize: your team fears looking incompetent without AI AND replaceable with it
20. Measure AI fluency quantitatively (I have a survey for this)
21. Name AI champions and reward them like
@doshkim
22. Learn how to hire for AI proficiency
23. Don't be a CEO that delegates understanding AI to someone else
24. Create informal and formal reward systems
25. Have fun (my mantra)
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