Recently, while using AI tools, I had an insight that might be the most noteworthy underlying logical shift in this wave of AI development:

I started asking myself a question—what is the smallest unit of expression of information?

My initial thought was straightforward, believing that the answer lay within the tools themselves. More powerful features, cooler interfaces, smarter assistants—seemed like they could solve the problem. But after using them, I realized that this approach was off track.

The more I delve into AI, the more I discover a pattern: all seemingly complex things ultimately revolve around the concept of structure.

Those visually flashy designs, stacked feature templates, automated workflows, and intricate knowledge systems—at first glance, they can be impressive, but the core competitive advantage isn’t there. What truly matters is how information is organized, how logic is connected, and how data is mapped.

This is somewhat similar to the discussion in blockchain—no matter how many application-layer wrappers there are, the design of the underlying protocol is the decisive factor. The same principle applies to information processing.