Hairball of Hell

There’s a moment every graph nerd hits quite early in their career. A rite of passage. A baptism by hairball.

You load your shiny new dataset into a graph database - nodes, edges, relationships - it’s beautiful. Naturally, you visualise it. That’s the whole point of putting your data in a graph, right? To see the structure unfold like a constellation of insight. Even better - why not see it in 3D!?

At first, it’s great. The small graph renders elegantly, revealing hidden patterns, semantic symmetry, and emergent order.

Then you add more data.

Then more.

And then… boom. You’re staring at 'it'. A writhing, tangled, homogeneous mess. A ball of digital spaghetti. Welcome to what a friend of mine affectionately calls the Hairball of Hell.

What just happened?

You crossed a cognitive boundary. The human brain can only track around 50 nodes before it taps out - and honestly, we’re squinting at anything over five. Graphs don’t care. Graphs can hold millions of nodes, trillions of edges, all perfectly valid and structurally rich. But your visual cortex? It’s toast.

This isn’t just a rendering problem. It’s entropy. That’s why all hairballs look the same, regardless of their underlying semantics. Past a certain point, every naïve graph visualisation collapses into noise - complexity without pattern, information without interpretation. We built the most expressive data structure in computer science… and then crashed into the limits of human perception.

But here’s where it gets interesting.

AI doesn’t suffer the same fate. The systems we’re building - vector-hungry, edge-loving, path-finding neural networks - thrive in complexity. They can ingest, embed, and reason over graphs that would melt your mind. They’re already crawling social networks, supply chains, biomedical pathways, and legal corpora. The future of data is graph-shaped, and AI is ready for it.

We are not.

Unless ... unless we change the interface.

There are some great graph visualisation tools out there that do a good job of helping us explore complexity (please feel free to post your favourites in the comments below), but I’d like to focus on something deeper - a way of organising the hairball at the level of the data itself...

⚡ Ontology ⚡

Ontology isn’t just metadata. It’s a philosophical commitment. It’s a scaffold for meaning. It’s how you take a chaotic graph and say: this part matters. It groups, abstracts, simplifies, and, most importantly, communicates. To humans and to machines.

Think of it as the semantic bridge between us and our AI co-pilots. Without it, they churn through raw complexity with no compass. With it, they navigate according to our values, our distinctions, our sense of what matters.

And remember: in a world where intelligence becomes ubiquitous and cheap, it’s meaning that will matter most. With an ontology, we can encode that meaning into our machines - let’s do it before they drift too far beyond our grasp!