AI Has a Discovery Problem
This article explores the 'discovery problem' in AI, where users struggle to identify how to effectively utilize powerful tools hidden behind blank text prompts. It argues that systems should be designed to proactively suggest relevant capabilities rather than relying on the user to know what to ask for.
Why it matters
It highlights a critical UX barrier in AI adoption that prevents non-technical users from leveraging the full potential of generative models.
The biggest bottleneck to AI adoption is a simple question: what can this do for me?
The hard part is that you don’t know what you don’t know. You don’t know what a button does until you press it. Press me
You don’t know what a prompt can produce until you write it, hit go, and watch it run.
As long as capabilities stay locked behind a blank text box, the possibilities stay invisible. That’s a discovery problem, and it’s the one we’re still stuck on.
There are partial fixes. Templates give people something to run without needing to invent the request themselves. But then the question becomes relevance. Do these templates actually match your work? Do you care? Context helps too: a system that knows about you can suggest things that matter to you instead of things that matter in general. Both help. Neither solves it.
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