this post was submitted on 29 Apr 2026
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Because imagine spending billions on training it specifically to produce useful answers and then not even trusting it to not randomly start answering with something completely unrelated.
What matters is the outcome, not how it is achieved.
And is the outcome good? Eh, sometimes.
If that were true, then anyone with any sense would have recognized a long time ago that deterministically incorrect is a lot more valuable than nondeterministically correct occasionally, and given up on all this language model nonsense.
A deterministic system that produces wrong output can be fixed. A nondeterministic system that produces wrong output cannot be fixed in any way that can be demonstrated conclusively.
Nondeterministic software is basically worthless in any case where accuracy or reliability are required.
Non-deterministic software is fine and we've been using it for ages. It's usable when:
That rules out several applications of current LLMs, but it rules in several others.
this is the most damning fucking part of it. Oh, it's kind ok sometimes. Fucking hell.
It could be a shitload better, but that would be difficult to source accurate data instead of everything off github and stack overflow and let it fuckin rip bud. This fucking problem has existed since the LITERAL dawn of computing, garbage in, garbage out.
https://en.wikipedia.org/wiki/Garbage_in,_garbage_out#History
Pray tell, Mr Altman, if you were to feed the AI incorrect information, will the AI generate correct results?
There is no magically reliable source of data that will make everything in one LLMs consistently accurate because their underlying design requires some randomization to reflect human conversation.
Dedicated models for specific use purposes where terminology is defined and they are designed to be deterministic would make them a lot better for actual use. We have had those models for years, just without the pretending to be conversational crap and they were constantly improving and actually useful.
That's just false. Although the first step of creating an LLM from scratch is to generate a gaussian distribution, which is randomized, those matrices get overwritten multiple times throughout the process of pre-training and fine-tuning, when parametric weights are finely adjusted based on the training data.
During inferencing, tokens pass through various layers along specific embedded vectors weighted for relevance. It's not random at all. It's non-deterministic, but that's not the same thing as random.
If the training data all came from JSTOR or DevDocs or even WikiPedia, it's going to make much more accurate inferences than if it was trained on Reddit, Quora, and Yahoo Answers.
I'm not defending AI here, but lets keep our criticisms factual.
Except if you make the output token temperature too cold, it has a higher tendency to get stuck in loops and the like. A little bit of actual randomness is important.
That's just adding noise, it's not unique to AI. It's also used in audio and visual design, and even cryptography.
If the outcome burns the resources needed to power a small town in order to generate, but the outcome is good, it's still bad
It's about 10x more power intensive than a Google search. It's not trivial, but it doesn't take megawatts to power a single person's query.
Ok, but then explain why I would care about a technology that's 10 times less efficient than an existing, 25 year old technology