GoKawiilEssay argues AI's core barrier is discoverability, not capability
A new essay contends that the main obstacle to wider AI adoption isn't the technology's power but users' inability to know what to ask for. It compares this to Alan Kay's ant-in-the-canyon metaphor: skilled users see a full landscape of possible automations, while everyday users face a blank prompt with no sense of what's achievable.
GoKawiil's interpretation of the reporting above, not reported fact.
Even as AI models grow more capable, adoption stalls when interfaces demand users already know what they want, leaving most value undiscovered by non-experts. This reframes the product challenge for AI companies from raw capability to designing better discovery mechanisms, such as templates, context-awareness, or observational agents.
- The main AI adoption barrier is discoverability, not raw capability.
- Templates and personalized context only partially solve the 'you don't know what you don't know' problem.
- Skilled AI users can see automation opportunities invisible to everyday users staring at a blank prompt.
Co-Intelligence: Living and Working with AI" by Ethan Mollick — If the article's "blank text box" problem resonates, this book is basically a guided tour of what to actually ask an AI for. Mollick walks through concrete ways to use these tools at work and in daily life, which is exactly the discovery gap the piece describes. Think of it as a step up to the canyon rim instead of squinting at the sliver of sky.
See Co-Intelligence: Living and Working with AI" by Ethan Mollick on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: mhacevedo.com, 2026-09-09
Published there as: “AI Has a Discovery Problem”
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