Most AI tools make users faster. The best AI tools make users better.

"We've all lived a version of this story. You start small - asking AI to refine an email. Then something a little harder, like writing a function in a language you barely know. Then a whole feature. Eventually you give it access to your files, your calendar, your codebase. At first it feels like an intern. Then it feels like a colleague. At some point, it even feels like the expert in the room."

"At some point, the confident, seemingly omniscient AI hands the problem back to you. You stare at the error. It stares back. You don't actually know how to solve it either, so you type "try a different approach" or "fix this" and hit send like you're pulling the lever on a slot machine. Fingers crossed for a result that magically works - or at least looks like it works."

"When moments like this happen, the instinct is to ask: how do we make the AI better next time? But AI will never be error-free, and there will always be situations it can't handle. The more important question is: how do we design the system so the user can catch those moments before it's too late, and stay sharp enough to actually solve them when they do?"

"This makes us rethink what a good AI tool actually does. It's not just about making users work faster. Speed can mean decisions that no one carefully reviewed, alternatives that never got considered before the team moved on, and outputs that shipped without a real check. Sometimes the right thing is to might be slowing them down."

AI use often starts with simple tasks and expands to complex work involving files, calendars, and codebases. Early results feel transformative, compressing weeks into days and increasing shipping speed. Eventually the AI can return errors or incomplete solutions, leaving the user to guess and iterate without real understanding. Improving AI alone cannot guarantee error-free performance, so system design must help users catch failures early and remain capable of resolving them. Faster output can also reduce careful review, prevent consideration of alternatives, and allow unverified results to ship. When AI handles more work, users practice fewer skills, raising concerns about how those skills change over time and what happens long term.

Read at Medium