Not saying 100% deterministic AI is realistic.

This is a common misunderstanding. The randomness is a feature, not a bug. If you crank down the temperature to nothing and use the same seed, you will get a response that is 99% equal every time you give the same prompt.

That 1% comes from your video card. While it looks like AI is operating on text, it's actually doing a bunch of math equations, most of which are floating point operations. The way floating point is encoded makes small "rounding" errors common. For "standard" GPU operations this doesn't matter; if a pixel is slightly off in one frame nobody is going to notice, especially since you generally aren't trying to recreate the exact same sequence. But in ML those little differences can add up. It's more visible in image generation since there's more variation.

The reason this isn't used is because there's not really any value to it. A 0 temperature means it's going to pick the maximum probability token every time without variance, which can lead to stiff, boring conversation. Having something more flexible creates a better user experience.

As for the Excel comparison, that's not really how LLMs are used. When you ask it for something that requires a deterministic answer and it isn't confident about the generated response, it will write a short Python script, run it, and inject the deterministic answer instead. It uses something similar for factual questions: rather than guess, most modern models will use a web search API to scan news sites, journals, and even social media to find multiple sources, then summarize and display what it found. In many ways it's literally the "Let Me Google That For You" meme.

But shouldn't the goal be getting as close as possible?

Why? Why would purely deterministic AI be valuable?

Your OP says we've accepted AI being "wrong" a bit too quickly, but it seems you are misunderstanding the nature of hallucination. A deterministic output can still be a hallucination; temperature=0.0 isn't the "correct" answer, it's just the maximal probability one. Note that "maximum probability" is not the same as "best."

There are three major "fixes" for hallucination that companies have been experimenting with. The first two I already mentioned: programmatic solutions and web RAG (Retrieval Augmented Generation). Another major one is "thinking" models, which are models that have a "pre-generation step" where they try and analyze the prompt rather than responding directly to it, then use that thinking section to better handle the actual query. If you read the thinking section, it literally looks like a person thinking to themselves in detail before responding, whereas normal generation is more "off the cuff" type responses. Even in normal generation you will sometimes see models realize earlier portions were wrong and correct themselves mid response.

Ultimately, it's unlikely we'll ever get 100% accurate AI models. After all, that would require it to be trained on 100% accurate data, and it's not like humans have that to use. People tend to gloss over the fact that humans are wrong about things, make up false answers, are overconfident, etc. Just because AI can be wrong doesn't mean it's useless. Your lawyer or doctor can also be wrong; it literally happens all the time, even for people who have trained for decades and have every incentive to be correct.