Title is bad, it's the first line of the abstract instead of the paper title. Speculative decoding for LLM inference was published in 2022: https://arxiv.org/abs/2211.17192
This paper seems to be an improvement to speculative decoding but I haven't read it yet.
I agree with your first paragraph, but not your second. Models can still hallucinate when temperature is set to zero (aka when we always choose the highest probability token from the model's output token distribution).
In my mind, hallucination is when some aspect of the model's response should be consistent with reality but is not, and the reality-inconsistent information is not directly attributable or deducible from (mis)information in the pre-training corpus.
While hallucination can be triggered by setting the temperature high, it can also be the result of many possible deficiencies in model pre- and post- training that result in the model outputting bad token probability distributions.
Money is only a bookkeeping tool for complex societies. The aim of the owner class in a worker-less world would be accumulation of important resources to improve their lives and to trade with other owners (money would likely still be used for bookkeeping here). A wealthy resource-owner might strive to maintain a large zone of land, defended by AI weaponry, that contains various industrial/agricultural facilities producing goods and services via AI.
They would use some of the goods/services produced themselves, and also trade with other owners to live happy lives with everything they need, no workers involved.
Non-owners may let the jobless working class inhabit unwanted land, until they change their minds.