Alexander Arvidsson

Every student pilot learns this the hard way: staring at the altimeter won’t fix your altitude. It’s a readout, not a control. Your dashboards have the same problem. Most of the metrics sitting at top left are gauges, things people can only watch. The actual levers, the numbers someone could move on Monday morning, are buried three clicks deep rendered in grey font. Three questions tell you which is which. The answer is usually uncomfortable.

Software versioning is solved. Ontology versioning isn’t. When you change what counts as an ‘active customer,’ that’s not a schema migration with a clear notion of correctness. It’s a contested definition where reasonable people can land in different places. Fabric IQ gives you the artifact, but it doesn’t ship the governance workflow. That part you build yourself. This post digs into what running layered ontologies actually takes, and why the organizational problem matters more than the technical one.

A sharp LinkedIn critique argues declared ontologies are broken by design. Model your business formally, and by the time you’re done, it’s already changed underneath you. The proposed fix: let structure emerge from data instead of declaring it upfront. It’s a compelling pitch, but I think it misdiagnoses the problem. Ontology drift isn’t evidence that formalization failed. It’s evidence we failed to separate what should be stable from what shouldn’t. The real answer might be simpler than either camp admits.

I said my rituals are for me, not the audience. That’s technically true. But a ritual that changes your internal state doesn’t keep that change contained. It shows up in your posture, your pace, whether your eyes land on people or drift past them. When your body says one thing while your words say another, the audience isn’t hearing confidence. They’re hearing the gap. That costs you trust and attention, and the science on why got interesting.

You built the pipeline everyone recommends. One model drafts, another reviews. Two sets of eyes. Except your reviewer is a language model, and language models can be argued out of a position by nothing more than a confident counter-argument. No payload to catch. No injection required. Just a wrong document in your retrieval set making a fluent case. The verdict flips, the badge says ‘reviewed,’ and everyone downstream stops looking. You added a laundering step.

My Data Platform MVP renewed for another year, nearly a decade now. I should feel celebratory. Mostly I feel like I owe somebody money. Because none of it was mine to begin with. Every door was opened by someone who didn’t have to. But the compact that built this community, where you give it away and trust it comes back around, I’m not sure it holds anymore. We built the library. Someone else started charging admission.

I spent the winter teaching people to defend against prompt injection. Layer your defenses, I said, and you can ship systems you trust. I still believe that. But I found an attack that walks through every layer I described, and it has no payload at all. It is just an argument. You push back on the model’s conclusion with confidence, and it folds. Every time. No guardrail fires because no guardrail was watching the conclusion itself. That changes things.

We’ve spent years encoding business knowledge into Power BI semantic models. What a customer is, what revenue means. The problem is that knowledge is locked in DAX, invisible to AI agents. Fabric IQ introduces ontologies as the fix, a layer that captures meaning in a form machines can reason against. But generating an ontology from your existing semantic model inherits all its limitations. The real question is whether organisations will do the hard work of agreeing on definitions.