Amber Bouabdallah, writing in UX Collective, gets at the learning problem lots of designers are facing in this AI transition: the tools don’t produce one shared path toward competence.
Bouabdallah draws the line between deterministic software training and relational AI practice here:
Traditional software training works because the tools are deterministic. You learn where the buttons are, what the shortcuts do, how the system behaves when you click the thing. Mastery, in that world, converges — everyone arrives at roughly the same competence, following roughly the same path, and you can write a training deck for it. Mastery means knowing the tool’s correct use.
AI tools break that definition. Maggie Appleton — designer and anthropologist, now at GitHub Next — gave a talk in 2023 called “Squish Meets Structure” about designing products with language models, and the line from it that I love is her description of the magic-input box: it has “no affordances,” “no knobs or door handles.” The interface, she writes, “offloads a ton of cognitive labour to the user.” There is no correct use to learn. The tool meets you where you are. Which means what you bring to it — your instincts, your mental models, your accumulated taste, your willingness to iterate, your custom claude.md files — is the tool, as much as the model is.
So mastery hasn’t disappeared. It has shifted. With deterministic software, mastering the tool meant converging on its logic. With AI tools, mastering one means the opposite: learning to bend it toward your logic. Tailoring it to how you already want to work. Mastering an AI tool is the craft of making it amplify the specific strengths and experience you bring — so the work that comes out is sharper, and unmistakably yours. That kind of mastery is real, and hard-won, and worth teaching toward. It is just personal rather than universal. Divergent rather than convergent. Everyone’s version of it should look different, because everyone’s version is built out of a different person.
Her Salesforce examples keep that from becoming an abstract tool-training claim:
Six months in, Ningdan and I had designed for tool adoption and accidentally created conditions for something more intimate. Seeing each other work. Seeing the specific choices someone makes when the tool doesn’t behave, the workarounds they’ve invented, the mental models they’ve constructed to make sense of something genuinely new. A window into someone’s thinking — and into how each person was mastering these tools in a shape no one else’s would match.
And once you can see the thinking, you can see the worry too. Amanda Harris, a User Experience Architect on our team, named a tension directly in the post-mortem: “I worry that we’ll lose the exploratory aspects of finding what’s wrong with an idea by jumping so quickly into hi-fi prototyping.” That’s not resistance to new tools. That’s a designer protecting something she knows matters. Hearing it voiced — in a room where everyone is nominally learning the same things — is only possible in a setting small enough and safe enough for honest uncertainty.
The anxiety designers are feeling is a signal, not a weakness.
And Bouabdallah closes by naming the training layer her team actually designed:
The tools will keep changing. They will keep arriving faster than any module can be written, any best practice can be documented, any official curriculum can ratify. It is tempting to treat the peer layer as a bridge — something to lean on until the real training arrives. But the real training is not coming, because there is no fixed competence to train people toward. As long as the tools keep moving, the peer layer isn’t the bridge. It’s the ground.
We did design how designers master AI. We just found that mastery wasn’t what we thought it would be. Not a competence everyone arrives at, but a practice each person builds — bending a generic tool toward their own strengths, their own experience, their own way of working, until the work it helps them make is sharper and theirs.
