[api]

Programming has already become this way. Opinions about different languages and architectures are taste, or sometimes even just vibes. Few try to actually ask “can I quantify whether microservices or monoliths are better in terms of either maintainability or scaling?”

A lot of this is a result of systems having long ago exceeded the complexity threshold of things people can hold in their heads. There are too many layers, subsystems, languages, APIs, all glued together. Attempts at radical simplification fail because each of those layers and subsystems has features or behaviors someone needs, and a lot of it isn’t even documented.

AI takes this to the extreme. I’ve already learned that certain models have “personalities.” Some are more likely to go with you on magical journeys into hallucination while others are more critical. Some are better at detail while others seem better at abstraction but fall over on detail. Some are better instruction followers. All their quirks are complex and the systems themselves are impossible to understand.

Computer systems are becoming organic, biological.

[dofm]

"Feeping creaturism" has always been a problem, for sure.

But those technologies are layers, and there are reliable things that sometimes bubble across the boundaries — type hints, better code patterns to trigger compiler optimisation, interesting tricks with key column selection — and someone with expertise from that layer below can explain why, and their advice will always work in situations that are sufficiently similar.

You are right about AI personalities. Obvious even with the open weights models. Gemma and Qwen write code and documentation like people from different cultures. Because I guess they are a bit like that.

[ACCount37]

They're almost literally "from different cultures" - because of how post-training does things.

All "personality traits" within an LLM are entangled. So when you mid-train or post-train on ESL texts, or run RLHF using people from a given culture, you risk bleeding some of the related cultural traits into the LLM itself. A lot of the resulting "personality" is downstream from different AI teams picking different datasets and training signals.

RLAF is more of a "funhouse mirror" distortion - it takes existing traits and twists them, sometimes amplifies them to comical extremes. Weird can become weirder. A verbal tic can become a style signature. Part of the reason why AI writing from GPT-4 era and to now has changed so dramatically.