> that’s not what I said. you honestly seem like you just want to argue about stuff (e.g. not elaborating on the “no” when I basically repeated and agreed with what you said). and you seem to consistently miss my point

I have repeated myself many times and you decide to continue to ignore the reliability points that inherently impede LLMs in many use-cases which exclude them in areas where predictability in critical systems is required in production.

Vibe coders can use them, but the gulf between useful for prototyping and useful for production is riddled with hard obstacles as such a software like LLMs are fundamentally unpredictable hence the risks are far greater.

> I’m saying these non-deterministic neural networks are already widespread in industry with these regulations, and it’s fine.

So when a neural network scales beyond hundreds of layers and billions of parameters, equivalent to a production-grade LLM, explain exactly how is such a black-box on that scale explainable when it messes up and goes wrong?

> they can be explained despite your repeated assertions they cannot be.

With what methods exactly?

Early on, I said formal verification and testing on CPUs for explaining when they go wrong at scale. It is you that provided absolutely nothing of your own assertions with the equivalent for LLMs other than "they can be explained" without providing any evidence.

> also the entire point on CPUs which you may have noticed I dropped from my responses because you seemed distracted arguing about it). this is not productive and we’re both clearly stubborn, glhf

You did not make any point with that as it was a false equivalence, and I explained why the reliability of a CPU isn't the same as the reliability of a LLM.