From Entropy to Intelligence: Redefining Boundaries with Knowledge Graphs

The universe tends towards disorder. That’s the second law of thermodynamics in a nutshell. And yet, life exists. Stars form. Galaxies coalesce. How does breathtaking order emerge from chaos?

This isn’t just a philosophical question. It points to an important truth. The second law applies to closed systems, but reality is a web of open systems, all exchanging energy with their surroundings.

Physicist Jeremy England proposed a fascinating extension to this idea called dissipation-driven adaptation. The theory suggests that any system pushed by an external energy source (like the sun) will spontaneously reorganise itself to get better at dissipating that energy. Over time, matter stumbles upon configurations that are more stable, more structured, and more "alive" precisely because they are better at this energy-shedding process. Order isn't a miracle; it's a predictable outcome of physics under non-equilibrium conditions.

We can relate this to information processing. A neural network uses energy for the computation needed to search a parameter space. We can see the process of "learning" as the network settling into a configuration that most efficiently processes noisy input into low-error output.

But the most powerful insight from this lies in the concept of boundaries - the lines we draw to separate one system from another.

As a system dissipates energy, order doesn't appear uniformly. Pockets of high organisation emerge, creating their own boundaries and becoming distinct subsystems (think of a cell membrane forming). These subsystems work to minimise their internal disorder/entropy, but they do so by exporting disorder/entropy to the larger system they inhabit.

This is precisely what has happened in our organisations.

We drew our boundaries around applications and databases. We incentivised teams to achieve local optimisation. Each application became a master of its tiny universe - its own perfect codebase, its own clean data model. But in doing so, it exported chaos to the rest of the organisation.

The result? Data silos, brittle integrations, and immense "organisational entropy" that cripples our ability to adapt.

To become AI-native, we need to weaken the boundaries around individual applications and strengthen the boundary around the organisation as a whole. This is a profound technological, political, and philosophical shift away from local optimisation and towards collective intelligence.

The good news is that we have a technological pattern designed for this: The Knowledge Graph.

By creating a shared connective tissue of links and ontologies, a Knowledge Graph allows us to dissolve the artificial walls between systems. It provides the foundation to build a coherent, intelligent whole, rather than a collection of warring, entropic parts.

This isn't just another data project. It's about redefining the boundaries of your organisation to prepare it for a future where adaptability is the ultimate competitive advantage.