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OP just described ridiculously bad behavior that consistently changes over time, not randomly random results. That's more a case of bad engineering (ie light sensor interfering with microphone data feed) or some stupid management decision then 'AI is complicated but we don't have anything better'

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I think it was management that chose to go with a cheaper model, not the engineers. They got to think about profits.

You do realise that a neural net, given the same input, will always give the same output, if you set your random seeds of course. It doesn't depend on time of day unless you degrade the service when it is overloaded - say, you use a different model in the evening that is 10x faster but 2x worse.

Good luck always getting the same output from a microphone. Maybe the neural net works perfectly fine on crisp samples, but falls apart when some noise is introduced. Maybe it works fine with most kinds of noise, but not a specific type of noise generated in the evening.

Not sure about the cheaper model part, because one cannot transform trivially a big SOTA langage modeling models into a digital assistant. So maybe they are not using a cheap ML model after all ... Also, for a digital assistant the input is not only the spoken commands, it can also be: time of the day, weather, location, etc

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