Innovation as Search

The Concept

Innovation is navigation through a high-dimensional solution space where each point has uncertain value. Success depends less on moving efficiently toward a known destination and more on sampling broadly across unfamiliar terrain.

Why This Framing Matters

Most optimization problems assume you know what “better” looks like and can iteratively improve. Innovation doesn’t work that way. The value of an idea is revealed only after substantial investment (prototypes, market tests, full development). You’re not climbing a visible hill, you’re searching in fog.

This reframes the goal from “generate good ideas” to “generate diverse ideas.” In a rugged landscape with multiple peaks, a diverse pool of starting positions gives you better odds of having at least one position near the global optimum.

How It Changes Practice

For Brainstorming: Stop asking “is this a good idea?” until you’ve generated enough ideas to cover varied territory. Premature quality judgment narrows exploration too early.

For AI-Assisted Ideation: The model’s tendency to cluster around plausible centers is exactly wrong for this task. Techniques that increase variance (Chain-of-Thought, hybrid strategies) directly address the structural problem.

For Evaluation: The metric that matters is the quality of your best idea, not your average. A pool with higher variance and lower average quality can still produce better outcomes.

Connections to Other Domains

This is the same exploration-exploitation tradeoff that appears in:

• Reinforcement learning (balancing known rewards vs. uncertain exploration)
• Data sampling (coverage vs. precision)
• Research strategy (depth in one area vs. breadth across many)

The AI diversity deficit means current models default toward exploitation when creative tasks require exploration.

Related: 05-atom—ai-diversity-deficit, 05-molecule—prompt-engineering-for-diversity