> Computer Science argues for (and gets) high levels of funding by asserting economic rationales for funding with a justification that physics and maths struggle to match.
Every single grant application makes (often bogus) economic rationale for its puported benefits to society and the economy. The trope in mathematics is that everything is relevant for either cryptography or protein folding.
> theoretical CS which (tragically) effectively includes much of the database, programming language and methodology community, and much of the AI and ML community too, then this is a misallocation of capital
You may not accept it but there is a whole bunch of very theoretical mathematical work that goes on in AI and ML. There is a whole bunch of work that is more empirically grounded and less whiteboard as well. There is a whole spectrum on the whiteboard to deployed-in-the-real-world. That is why there are often Applied Physics programs, different from Physics programs, different from Engineering programs. And people in each of those have varying levels of overlaps with each other based on where they sit on the theoretical-applied spectrum.
> Don't you care about the harm that is inflicted on the people doing the development, their victims (everyone) and the reputation of the infrastructure that they create? What about voting machines?
I never said anything about not caring - this is a silly red herring. I was making a statement about the computational resources needed to solve people and project management issues in software engineering as a counter to your "just give them some whiteboards" comment. I still don't see why throwing more cloud compute resources at Software Engineering departments will make your Scrum meetings more efficient. In fact I don't know if academia is well-poised to solve such problems at all.
> arguing for grant funding on the basis of real world impact
The idea behind funding the sciences in academia is that we fund research that may have long-term impact on society. You don't get to throw a fit because every problem you have at work isn't being solved by someone sitting in a university.
> the estimates of performance based on the methodologies of testing from academia are so woeful?
Are you claiming that every experiment that comes out of a physics lab works flawlessly out in the real world? Or every paper from a life science lab goes on to successfully become a new medical treatment? I mentioned it before but there are often several fields of study dedicated to just taking highly controlled results from labs and trying to get them to work in the real world. Not everything makes it (especially in the life sciences example). AI/ML are at least better in that they often (but they should be doing it even more) give you what you need to replicate the lab experiment on the controlled, sanitized data.