It was not 'strictly philosophical' in the way some things like the Chinese Room argument are; in Chinese Room, it's stipulated that the Room is pragmatically capable of responding like a native Chinese speaker (it is somehow implemented super-fast and can chat in Mandarin with you and pass a Mandarin Turing Test).

However, the stochastic parrot arguments (and Gary Marcus in many of his writings) made specific, unambiguous empirical predictions about how LLMs would never be able to do many things, such as 'add numbers'. For example, in the original Bender & Koller 2020 paper "Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data" (which lays out the core ideas which are the justification for the 2021 paper that actually introduced the rhetoric of 'stochastic parrot') made many clear, falsifiable statements about what LLMs would never be able to do; here's one of them: https://aclanthology.org/2020.acl-main.463.pdf#page=14

> To get a sense of how existing LMs might do at such a task, we let GPT-2 complete the simple arithmetic problem 'Three plus five equals'. The five responses below, created in the same way as above, show that this problem is beyond the current capability of GPT-2, and, we would argue, any pure LM.

One could say many things about this claim, but not that it is a 'strictly philosophical one'.