This is how AI May Finally Beat Humans at Creativity on the Path to AGI.

Large language models (LLMs) have revolutionized how we access and process information, but they fall short when it comes to true innovation. While these models excel at retrieving facts and generating logical conclusions, they struggle with breaking new ground in creative thinking. Why? Because real creativity doesn’t come from simply knowing more—it comes from unexpected connections, misinterpretations, and forced re-explanations.

This is where human conversations shine. If you've ever discussed an idea with someone who didn’t fully understand it, you’ve probably experienced the moment when their questions or misconceptions actually helped you clarify your own thinking. Often, it’s the act of explaining an idea—not the feedback itself—that unlocks new insight. This is something AI doesn't currently replicate well.

"This Reminds Me Of..."—A Missing Link in AI Creativity

At the core of human creativity is the ability to relate ideas across domains. When someone says, "This reminds me of…", they’re linking a concept to something from an entirely different field, life experience, or perspective. These unexpected connections are where real innovation happens.

Imagine discussing AI algorithms, and someone compares them to farming techniques—suddenly, new insights emerge because you’re forced to think about optimization, cycles, and resource management in a completely different way. This isn't just brainstorming; it's a structured mechanism for generating novel thought.

The Insight Engine: AI as a Provocateur, Not Just a Responder

Instead of simply answering questions, AI could be designed to provoke new insights by:

1. Forcing Re-Explanation: Asking the user to articulate their idea to different "audiences," such as a child, an investor, or a scientist.
2. Introducing External Narratives: Randomly pulling in unrelated industries, disciplines, or historical contexts to challenge assumptions.
3. Tracking Iteration Depth: Measuring how many steps away from the original idea an insight emerges, refining the process over time.

By formalizing this process, we could create an AI-powered friction-based creativity loop, where the user isn’t just given an answer—they’re pushed to iterate and refine their thinking until they hit a breakthrough insight.

Why This Matters for the Future of AI

Current AI models prioritize efficiency, but creativity thrives in friction. The process of misinterpretation, forced articulation, and domain shifts is what makes human conversations so valuable for idea generation. By embedding this into AI interactions, we can shift from a model that retrieves knowledge to one that actively stimulates new ideas.

This could be a game-changer for: - Entrepreneurs refining business ideas - Writers looking for narrative breakthroughs - Engineers solving complex technical problems - Anyone stuck in a creative rut

Instead of AI acting as a passive assistant, it would become an active collaborator in the creative process—not just providing answers, but helping users find better questions.

The Next Step: Testing the Model

If we were to prototype this system, key questions would include: - How much should AI control the “this reminds me of…” process? Should it be fully automated, or should users guide the connections? - How can we measure when a “breakthrough” insight happens? Can we detect moments where someone actually changes how they think? - Would this work better for personal creativity or collaborative idea generation?

By exploring these questions, we move toward a future where AI doesn’t just replicate human intelligence but augments human creativity in ways we haven’t yet imagined.

This is an ongoing exploration as part of my AI and innovation portfolio. If you're interested in these ideas or want to collaborate, reach out—I’d love to hear how you think AI can better serve the creative process.