GoKawiil2026-09-03 The Discovery Problem
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.
Alan Kay has a metaphor for this. Imagine you’re an ant at the bottom of the Grand Canyon. You look up, and your entire notion of the sky is a thin sliver of blue between two canyon walls. Someone standing on the rim sees the whole blue plane. Same sky, completely different sense of what exists. It’s not that the ant is less capable. It just can’t see the axis of possibility from where it’s standing.
Watch Alan Kay explain it
That’s the gap between a skilled AI user and everyone else. Take a non-technical marketing person and someone fluent in agents and tool use. The agent-fluent person can watch the marketer work for an hour and immediately see a dozen things to automate, delegate, or reinvent, including things the marketer hasn’t even tried yet. But put the most intelligent tool in the world in front of the marketer, and they’re staring at a blank prompt, unsure what to type. All that intelligence, and no way to see it.
This is the strange state we’re in: the system could do almost anything, but it requires the user to already know what to ask for. Too much of the work of discovering what’s possible falls on the person, when it should fall on the system. You’d expect something this advanced to reveal its own capabilities — gradually, contextually, in ways that match your actual work. We’re not there yet.
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