Using AI’s naivety can lead to surprisingly creative solutions as well. A bit like going down the rabbit hole with an autistic co-founder.
584 karma · joined April 5, 2022
When you're cutting trees, sharpening the saw looks like you're not working. When you're doing software or organizational work, figuring out what actually matters can also look like you're not working.
The hard part is distinguishing between thoughtful idleness and ordinary procrastination. [1] https://www.franklincovey.com/books/the-7-habits-of-highly-e...
In practice, most of the complexity comes exactly from what’s described here: every system has a rich internal model, but the moment data crosses a boundary, everything degrades into strings, schemas, and implicit contracts.
You end up rebuilding semantics over and over again (validation, mapping, enrichment), and a lot of failures only show up at runtime.
I’m skeptical about “one model to rule them all”, but I strongly agree that losing semantics at system boundaries is the core problem.
Yes, staying informed can help in some cases. But there are also many situations where it wouldn’t have made a difference. The amount of noise you have to sift through to “stay updated” is huge. At some point, it becomes a trade-off: consume a constant stream of news to maybe avoid a rare edge case — or tune it out and accept that very occasionally you might get unlucky.
On the surface, that sounds like a path toward richer models: less elite-written text, more everyday language, more non-academic thinking, more embodied culture. But it also raises a deeper question: whose reality would actually be learned?
Because even if the data were global, the selection, labeling, weighting, and training objectives would still be controlled somewhere.
And then there’s preference. Would people eventually choose their models the way they choose media ecosystems today? A Californian-progressive LLM. A post-socialist Eastern European LLM. A Palestinian LLM for discussing geopolitics. A deeply conservative, tradition-preserving LLM that treats modernity itself as suspect.
If that happens, AI wouldn’t homogenize thought — it would solidify worldviews into software as it is done in media today. Dialogue might actually become harder, not easier.
So the think may not be “AI Californication” alone, but AI Balkanization, ...
The open question is whether we can build models that don’t just represent cultures, but can genuinely inhabit multiple, conflicting ontologies without collapsing them into a single moral frame. That may be the hardest problem of all — and one that current LLMs, trained mostly on English-speaking upper layers of the internet, are nowhere near solving yet.