As a software developer who works on object-level automation every day, I’m intimidated by the difficulty of attempting to definitively quantify ‘profit from automated tasks’ in a useful way.
For example, how do we define ‘automation’? “A task that formerly needed to be done by a human that now doesn’t need to be”? A printing press is automation by some interpretations of that insufficient definition.
Some changes in efficiency also have similar effects on productivity without being ‘automation’ (although much less scalable), for example a user that becomes highly proficient in the hotkeys of a complex platform may see massive improvements in their productivity, and subsequently eliminate jobs that would have been needed if they hadn’t become more productive.
I suspect if additional taxes were levied on ‘job automation’ it would merely create large incentives for companies to skirt around whatever the legal definition of automation was, and potentially hide it in things like the above example.
In the case where there was no ‘automation tax’ created, I would anticipate a NIT to be reasonable but not sustainable because I expect automation to continue to remove jobs at an accelerating rate in years to come. I do not expect tax revenue to increase at the same rate because my current understanding is that the most wealthy tend to also be those most proficient at exploiting loopholes in the tax system to evade as much as possible.
My takes here are almost entirely conjecture and I’d appreciate someone more informed to correct and/or clarify.
If you want to be generally skilled at the type of challenges D&D Sci provides, putting some points into the data science and statistics proficiencies would be a good way to start.
In particular, some related skills:
SQL—Easy to pick up for someone with good technical skills. Challenging to master. Before going too deep on relational databases I also recommend learning good theory and practices behind it like the different design forms and why they’re important.
R programming language
Familiarization with various statistical analysis methods and what use cases they are intended for