AI makes producing software easier, but good software still requires human judgment to frame problems, set constraints, and review output. The shift is from writing code to thinking clearly about what to build.
The story
Can anyone write good software now that AI can crank out code like a caffeinated intern with infinite fingers?
Yes… and also, not exactly.
Typing is cheap. Thinking is premium.
Modern AI makes it easier for non-experts to *produce* software. But “reliably good” software still lives in human territory: problem-framing, architecture, constraints, and review. The center of gravity moved from “write code” to “think clearly.”
If I don’t frame the problem, the agents will sprint in the wrong direction, fast and loud, like a marching band with no plan.
If I do frame it, their output becomes reviewable, testable, and oddly calm, like a marching band with a plan.
What helps me most is running AI like a team, not a genie.
The ClearSay approach describes a customizable agentic coding team: specialized agents for architecture, coding, tests, docs, refactoring, dev-ops, coordinated by a “manager” agent that plans, delegates, and integrates.
The trick is resisting “vibe coding.”
Instead, I can set goals and constraints up front, performance, security posture, stack, testing thresholds. Then I split work into modules, assign agents, even use different models for different roles. Cheaper model for boilerplate, stronger reasoning model for architecture, plus audit loops where testing or security agents critique the main coder.
So is dev experience still necessary?
Less for syntax. More for judgment: knowing what to ask for, how to define boundaries, what to verify, and when not to trust the model.
What do you want to build, and what constraints do you want it to respect? That’s where making the most of AI starts.
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