Get Connected to Stay Distinctive

Chris Fields’ quantum modeling applied to the Free Energy Principle suggests something quietly unsettling. An entangled quantum system keeps an information boundary that is only loosely coupled to its environment. Or, put more simply—strong internal connectivity reduces entropy, increases predictability and makes a system more distinct from everything else. Which brings us to an unexpected consequence: If ChatGPT can give satisfying answers about your business, you have a serious problem.

Why Your Knowledge Might Not Be Yours Anymore

Here’s why. If an LLM can confidently describe your strategy, your edge, your secret sauce—it means your internal connectivity is weak. It means the world already knows what you know. And worse, it means that your organization is losing its shape, slipping into the background noise of general knowledge. A weak distinction boundary allows external entropy to creep in, eroding your unique position.

Many businesses fall into this trap without realizing it. The convenience of external knowledge sources, automation, and AI-generated insights comes at a cost: a dilution of internal expertise. When the same answers are available to anyone who asks, what remains as your competitive advantage?

Strengthening Your Internal Connectivity

So what’s the way out? You must increase internal connectivity. Your processes can’t just be workflows; they need to be a tightly woven, dynamic network. Your data can’t just be stored; it has to move, transform, and mirror the very essence of your intellectual property. Your data must become an extension of your knowledge assets, not just a reflection of past transactions.

This requires a shift from static information repositories to dynamic, evolving knowledge structures. Teams must cultivate an internal knowledge ecosystem that is resilient to external absorption. The key is to transform operational processes into continuously learning systems, where data flows like a circulatory system rather than sitting in isolated silos.

The Role of Data Meshes in Retaining Organizational Intelligence

This is where Data Meshes come in—not just as analytical structures, but as living, breathing operational streams that bind value creation to data. When done right, they become the foundation for your own knowledge graph, an ontology that isn’t just a byproduct of operations but the very architecture of your intelligence.

A well-designed Data Mesh ensures that knowledge doesn’t just exist but actively evolves with the organization. It aligns data ownership with domain expertise, ensuring that insights are contextually relevant and deeply integrated into the workflow rather than being passively collected.

How AI Can Work For You, Not Against You

AI is only as useful as the structure of the knowledge it can reason upon. If your knowledge is just a loose collection of facts already absorbed by public models, AI won’t differentiate you. But if your knowledge is a structured, evolving mesh of proprietary insights, AI can amplify it—helping you reason, anticipate, and act in ways that remain uniquely yours.

The real challenge isn’t whether AI can replace knowledge, but whether organizations can create environments where knowledge grows internally rather than being outsourced to external models. This isn’t about blocking AI; it’s about ensuring that AI works in service of your unique intelligence rather than eroding it.

Keeping Your Edge in an AI-Driven World

ChatGPT might be able to answer a lot, but it should never be able to answer everything about you. Businesses that rely too heavily on external intelligence without reinforcing their own will find themselves indistinguishable from the rest. The future belongs to those who understand that intelligence isn’t just about having access to knowledge—it’s about creating and structuring it in a way that remains uniquely theirs.