They probably take into account day-specific trends , such as if the data shows sales are usually lower on a Monday than a Tuesday, they would take that into account in the forecast. This is as far as I understand.

So, assuming they are doing this, the time scale does matter. What I am trying to say is that these solutions (like prophet) are opinionated and that is why they can get accurate, as they are taking into account these time-scale specific trends.

But being opinionated means that they are assuming stuff about your data. For example saying that the number of sales you make in a day is a function of or correlated to the day of the week is probably a reasonable statement. However if you move away from sales and marketing, and try to forecast say the number of seismic events in a day, nature doesn't care if it's a Monday or Tuesday or holiday. So any such correlation the program is able to find out and use in forecasting would be incorrect. Like maybe there are more earthquakes on Monday than any other day in a particular dataset, but that would just be incidental and doesn't mean earthquakes are more likely to occur in future on Mondays. It's not a good example but there could be other such cases where such assumptions could be wrong.