This question depends on how you define research productivity. There is close to two hundred AI papers published every weekday. Most of them are about GenAI. Most don't seem to be all thay good. The progress in actual model improvement had mostly stalled. If you interact with the latest "raw" models they display all of the issues we've seen in GPT-3.5, just at a smaller rate. The "amazing gamechanger breakthroughs" I read about on social media every week do not seem to lead anywhere. It's all kind of boring, really.
The new "hotness" in AI is clearly building more and more elaborate harnesses. This is not at all the direction AI boosters have predicted couple years ago.
Personally, I think the "stochastic parrot" mental model is far more useful for science, because it primes people for proper testing, skepticism and researching alternatives. If you want useful AI, you want people working on it being skeptical, not credulous.
To me the real question begins only once we have a clear example of a non-trivial scientific discovery that is implicit (IE, not an obvious outcome of reading the literature and talking to the experts) and experimentally verifiable. Once that happens- especially if it is a reproducible process (IE, more discoveries) and it's significant (IE, impacts human life and mind in some profound way)- then the onus very much lies on Bender and her coauthors to explain whether we need more than a sufficiently advanced stochastic parrot.
By that standard, parrots, and it's not even close. The framing of intelligence led to an enormous number of predictions that simply haven't been realised: an end to all white collar work, UBI, a total revolution in society, a literal robot god.
People are so desperate to view 'stochastic parrots' as dismissive that they misread the original argument while quickly ignoring all the failed predictions about how AI was going to overturn, save, and destroy everything.
But the different terms imply different mental models of what LLMs are and can do. If you take two people, one who thinks of them as "artificial intelligence" and one as "stochastic parrots" (with all the implicit context and connotations of the individual words composing them), what mental model would have led to better predictions of LLMs' future circa 2020?
The "stochastic parrots" phrase is very dangerous in that frame. People read far more into what capabilities it implies are (im)possible than the narrow technical description the authors originally argued for. If all they are is spicy autocomplete or pastiche plagiarizers, there's nothing serious to worry about. And when an opposition gets stuck in a trough that mindlessly dismisses their future capabilities out of hand because of a bad mental model, it renders them ineffective at preventing the worst outcomes.