> predicting the future is hard

As I mentioned in another comment, this can also be rephrased as "predicting data with values outside the range you trained on typically doesn't go well". If you tried to predict some health metric based on weight and height but you only had people under 4' 10" and less than 120lbs you wouldn't be shocked at all if it worked terribly when applied to American football players.

Time-series forecasting is hard because you are always going to be predicting based on data outside of your observed range ("forecasting" does go much better when you're trying to fill-in-the-blanks of things that happened in the past).

The unreasonable difficulty of predicting (actual) future effects based on past effects with no regards to their cause.

Yes. Or even to go a step further -- once we exclude all the things that aren't hard to predict, we're left with the things that are.

Well, predictions that I'll go to work and things will be as usual aren't as uncertain as Bitcoin pricing or as certain as planetary motion.

However, the aim of math in these situations is often to give explanations for intuitive impressions like "predicting the future is hard". The concept of NP-completeness gives one (very partial) explanation why certain computing problems are "hard", for example. So that theory doesn't "boil down to saying programming is hard". Unfortunately, I don't think the text really gives strong explanation in this case.