No, humans make less mistakes. Less. That's the key here, statistical models are trained on human data so by pure logic can never, ever, under any circuimstance, reach 100% accuracy. With current understanding of LLMs with a focus on AI Scaling Laws, and more importantly of natural human language adaptation, they will never reach 94% accuracy with infinite power and infinite training. That's what the curve shows us in OpenAI's 2020 research paper on AI Scaling Laws and later Deepmind's paper correcting their math, that the diminishing returns will hit a limit far before convergence.

In addition to that, the AI also cannot detect subtle changes to established problems or any new unaccounted for variables, because they're a statistical model and not capable of actual thought. They also lack any sense of responsibility for their actions for the same reason.

You fucking sloppers always try to say "HuMAnS mAkE misTAKeS, TOO!" Yeah and the fucking slopbots are trained on those mistakes and make them again but worse.

But you're forgetting the key difference that makes it so much worse - we can fix human mistakes especially if we can talk to the human to figure out how. With an llm we have no external reference, only poorly designed code where the comments are there to guide the writing, not describe what was written. So it's much harder to debug an output, and the llm cannot be trusted to clean it up either.