AI use often starts with simple tasks and expands to complex work involving files, calendars, and codebases. Early gains feel dramatic as timelines compress and building and shipping accelerate. Eventually the AI may return errors or incomplete solutions, leaving the user to diagnose and recover without knowing the underlying fix. Instead of only improving AI accuracy, systems should be designed so users can catch failures early and remain capable of solving problems when they occur. Faster output can reduce careful review, skip alternatives, and ship unverified results. Increased automation can also reduce practice of key skills, raising concerns about long-term skill degradation and user outcomes.
"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 first, this feels incredible. A month of work compresses into a few days. Everything starts to revolve around building and shipping faster. But 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."
"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. What's more, if the AI is doing more of the work, the user is practicing those skills less - which raises a question we don't ask often enough: how do those skills change over time, and what happens to the user in the long run?"
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