This is a pretty wild comparison in my opinion, it counts almost everything as gambling which means it has almost no use as a definition.
The most obvious issue is it’d class working with humans as gambling. Fine if you want to make that as your definition but it seems unhelpful to the discussion.
How does it 'count almost everything as gambling'? They just said 'non-deterministic' output is gambling-like, that is not 'almost everything'. Most computation that you use on a day-to-day basis (depending on how much you use AI now I suppose) is in all ways deterministic. Using probabilistic algorithms is not new, but it your point is not clicking...
You seem to have a fundamental issue understanding what the term deterministic even means.
If you give the same trivial task to the same human five times in a row, let's say wash the dishes, your dishes are either gonna be equally clean or equally not clean enough every time. Hell, it might even get better over time by giving them feedback at the end of the task that it can learn from.
If you run the same script five times in a row while changing some input variables, you're gonna get the same, predictable output that you can understand, look at the code, and fix.
If you ask the same question to the same LLM model five times in a row, are you getting the same result every time? Is it kind of random? Can the quality be vastly different if you reject all of its changes, start a new conversation, and tell it to do the same thing again using the exact same prompt? Congrats, that's gambling. It's no different than spinning a slot machine in a sense that you pass it an input and hope for the best as the output. It is different than a slot machine in a sense that you can influence those odds by asking "better", but that does not make it not gambling.