Allen Bargi draws a clean analogy that sticks: working with AI is frustrating when you treat it like a compiler, and productive when you treat it like collaboration. The skills that help — sharing context, explaining the desired outcome, setting boundaries, responding to what comes back — are leadership skills, not programming skills.
- Code gave certainty. Same input, same output. AI does not — the same prompt can produce different answers, useful connections, or obvious misses
- Good leaders do more than issue instructions. The same habits (context, intent, boundaries, feedback loops) improve AI work
- A good prompt helps, but a shared working context (examples, corrections, reusable instructions) helps more by reducing misunderstandings over time
- The investment is not in pretending AI is human — it is in becoming better at expressing intent
The line that earns the analogy: “We spent years learning how to tell computers exactly what to do. Now we also need to explain why the work matters, what a good result looks like, and where judgment is needed.” This lands because it sidesteps the usual anthropomorphism trap — Bargi explicitly says AI has no lived experience, accountability, or human judgment — and focuses instead on how the interaction pattern has changed. The technology is new. The leadership skills are not.