I grew up in Russia and I have a very similar experience emotionally, hating every second of my school life, but somehow I couldn't remember any particular horrors. It was just all so ridiculously sad and hopeless and boring, I couldn't stand listening unpassioned teachers talking about sili memorisation tasks the whole day and didn't feel like I fit socially either (even though I wasn't bullied) so it all felt like a torture.
I think it's important to keep track of which particular wealth we create and how it gets distributed. If eg GDP grows 2x, but instead of affording 2x more food everyone can now afford 10x more/better memes and better drones for their country military, I don't consider it a win.
In other words, "growing wealth" means better access of people (on average) to some resources, but whether it's a good thing depends on the exact resources the world gains.
I'm not sure it's a question of what's "fair" or "right" or "legal". We (as a society) need to figure out what incentive we want to impose on the world.
We (through a convoluted, but in the end democratic process that is supposed to represent the will of the people) decided that piracy is illegal not because there is some divine right of authors to get compensated, but because we believe that by making piracy too common we would kill the incentive for the future authors to create.
Similary, we will need to decide on the laws around ad blocking. If we decide to keep it accessable, then the downside is that both the infrastructure work done by Google (or its competitors) and the creative work done by authors will become less profitable and thus the quality will decline. If we don't envision this happening, we should definetly get ad blockers without a second thought.
I came up with a lot of small custom algorithms for my thesis, all based on np.einsum. I had to estimate the assymptotic complexity for them, and every time I would get a different answer than the last. So at some point I built a system to which you give an einsum expression and a mapping between axis and their names and you get the assymptotic complexity (e.g. for MxN times NxM matrix multiplication 'ij,jk->ik' you would use {'i': 'M', 'j': 'N', 'k': 'M'} to get O(M^2 N) complexity). It also supports chains of einsum expressions, summing the complexities together and symbolically simplifying. And it uses opt_einsum to reorder the operations to get the best possible asymptotics for an expression.
