I can at least say why offering a "theory of AI" in the dective sense is very difficult: AI development is an anti-inductive process. Anti-inductive processes

I can at least say why offering a "theory of AI" in the dective sense is very difficult: AI development is an anti-inductive process. Anti-inductive processes are those, like e.g., fraud, which change because you have measured them. Once a certain sort of fraud is made illegal and difficult, most move on to a different kind. AI models are at-base memorisers, with a little structured generalisation around the region of memory. They are not structure learners who happen to memorize. This makes creating a theory of an AI model kinda impossible because the "representations" they learn are so-called "entangled", another way of saying: garbage. The AI model's representation of language does not match language structure, rather each representation is a mixture of what we can evidently see as several competing structural features. But the model has no access to this structure, because its literally not a property of the data but of the data generating process. Now this seems like "a good theory" of AI in the monkish sense, but a problem arises: for each fragile boundary that benchmarking and testing shows, the AI companies collect data on these failures and retrain the model. This is, in a sense, anti-inductive fraud: companies seek to find out how they are found out, and extend the model to cover those regions. But models never actually gain the structural capabilities their behaviour is taken to imply. So, of course, this would drive any theorist insane. By the time you've figured out gpt3.5's fakery, all the openai chatbot data theyve collected has made gpt4 -- and all paper has been covered over all the edges. Until your fingers are cut next time on the now dark fragile boundary of openai's deception. I can give very good reasons, independent of any particular AI model why: the manifold hypothesis is wrong, AI models are memorizers + structure, why they dont find latent structure but combine statistical heurstics, why their representations are not "entangled" such that they can be "disentangled" but necessarily model-unknowable blends of expert-known scientific joints -- and so on. But none of this knowledge can become useful in the face of a billion-dollar training company competing against me, anti-inductively, to apply any of this to any given model. Perhaps then, I suppose, we can look at the behaviour these systems induce in their users and its downstream effects. This is what OP here does, throws hands up at a theory and says only: however this is working, it cannot be good. This is, of course, what we do with fraud, cults, and many other systems of deception. We say: we're not going to argue with the conspiracy theoriest, they are specialists at coming up with ever more elbobate adaptions to the self-deception. Instead, we observe by proxy, that they behave pathological;y -- that they are broken in otehr ways. I think that's a fair approach, and at least one that allows the author to proceed without a theory.