I think you're confusing various different things as "neurosymbolic AI". There is a NeSy symposium and I happen to have met many of the people there, and they are not GOFAI ideologues, rather they recognise the obvious limitations of neural nets (i.e. they're crap at deduction, though great at induction) and they look for ways to address them. Most of that crowd also has a predominantly statistical ML/ neural nets background, with symbolic AI as an afterthought.
I don't think I've ever heard anyone say that "ML is not real AI" and I mainly move in symbolic AI circles. I would check my sources, if I were you.
Anwyay, honestly, this is 2026, there is no sensible reason to be polarised about symbolic vs. statistical AI (or whatever distinction anyone wants to make). An analogy I like to make is as follows: a jetliner is a flying machine, a helicopter is a flying machine. We can use both for their advantages and disadvantages, but a flying machine is something too useful to give up on any one kind for ideological reasons. The practical benefits overwhelmingly make up for any ideological concerns (e.g. "jets bad" or "propellers bad").
And just to be clear, symbolic AI is still in rude health: automated theorem proving, planning and scheduling, program verification and model checking, constraint satisfaction, discrete optimisation, SAT solving, all those are fields where symbolic approaches are dominant, and where neural nets have not made significant inroads in many decades; nor are they likely to, not any more than symbolic approaches are likely to make any inroads in e.g. machine vision, or speech recognition. And that's just fine: lots of tools, lots of problems solved.