The AI development trap that wastes your time (suchdevblog.com)

AI coding agents can trap you in a costly loop: you prompt, the model fumbles, you refine prompts, burn tokens and time, and keep hoping the next prompt will fix it. The core insight is that AI’s real value isn’t raw speed but cognitive load reduction — it frees mental bandwidth so you can do more — and that value disappears when you haven’t invested the initial thinking required. Overreliance or asking the model to operate at too high an abstraction level for a given task leads to the sunk-cost fallacy: you keep prompting because you’ve already spent time and tokens, not because it’s productive. This is significant because it flips AI from a productivity multiplier into a time sink, and highlights the limits of current models at complex, high-level planning or context-heavy debugging. Avoid the trap by spending human cognitive effort up front: clarify specs, reproduce the bug, and design an implementation plan before letting the agent code. Use atomic git commits and decide the correct abstraction level for prompts — if the model can’t handle high-level direction, step down to more granular instructions. Embrace test-driven workflows: generate failing tests first (explicitly instruct the agent to write tests but not solutions), use the AI to explore code or brainstorm but not to implement until tests exist, and reset agent context when stuck. In short: stay in the command chair, guide the model with precise scaffolding, and use AI to reduce brain work, not replace it.