The TreeInterface

Linear chat is a relic. You type, the AI responds, you scroll. It mirrors a conversation, but decisions aren't conversations. They're structures. Trees. Branches that fork and merge and lead somewhere.

Why Trees Work

Hierarchical structures map directly to how decisions unfold. Every choice opens new possibilities, and every possibility has consequences. A tree makes all of this visible at once, no scrolling required.

Research in spatial cognition shows that people navigate information faster when it's laid out in two dimensions. A chat log is one-dimensional. A tree is a map.

You don't read a map top to bottom. You orient, you scan, you choose a direction. That's how decisions should work.

Decision Architecture

Architecture isn't a metaphor here. When you structure a decision as a tree, you're literally building the architecture of a choice. Each node is a room. Each edge is a hallway. You can see the entire building before you walk through it.

• Root node: the question you're asking
• Branch nodes: the options available to you
• Leaf nodes: the outcomes of each path
• Edges: the logical connections between choices

Beyond Flat Interfaces

Most AI interfaces treat every response as equal. A paragraph is a paragraph. But in a tree, hierarchy is explicit. The most important information sits at the top. Details live deeper. You choose your depth.

This is the interface AI deserves. Not a chat window with a typing indicator, but a spatial canvas where decisions come alive.