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[ARTICLE · art-115638] src=jeroensangers.com ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

Structure is cheaper than intelligence

A well-structured knowledge base outperforms AI guessing for personal data, with roughly 98 percent of a digital assistant system being data and 2 percent AI, according to an analysis. The article argues that adding structure reduces the need for AI, making retrieval over clean schemas more effective than large models over messy data.

read2 min views4 publishedAug 30, 2026

Ask a language model something about your own life and it has to guess. It never saw your data, so it reconstructs what is probably true, and every so often it hands you something that never happened. No amount of clever phrasing fixes that. Guessing is what generation is.

A well structured knowledge base works the other way around. It looks things up and gives back what is there, with the source attached and with how certain that source is.

Underneath sits a ratio worth remembering: roughly 98 percent of a “Digital assistant” system is data, 2 percent is AI. And that 2 percent does translation, not thinking. It understands that “called Joe about the fieldlab” is a phone call, that Joe is a person and that fieldlab is a project. Nothing more is asked of it.

Every layer of structure you add makes the AI layer thinner.

What looks like intelligence usually turns out to be retrieval. Who should I call is a sort on contact strength and time. This pattern stands out is a count. What did I do last time is a lookup in history. Query, weigh, show. A simple search over a clean schema beats a brilliant model over messy data.

That reframes where the work goes. The interesting question stops being which model you use and becomes how well your own material is organised. Naming things consistently, linking them, recording the type of a thing rather than leaving it as loose text: each of those shifts work out of the model and into the schema, where it stays put and costs nothing to run.

You still need a large model while the schema is being discovered. That is the architect designing the house: which tables relate to which, where the structure has gaps, which patterns hold. Once the house stands, you can live in it without the architect. What remains is small enough to run on your own laptop, no cloud and no dependency on a vendor who might change the terms next year.

So the useful measure of a personal system is not how smart its assistant is. It is how much of your life the assistant can simply look up. Every time you tidy a corner of that, you need slightly less intelligence to make sense of it.

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