Can you define 'near' for me?
I think AGI is likely closer to the present than 1987 was -- that is, I'd bet on having AGI by 2047. (Note: this is distinct from superhuman AGI.) Do you not agree?
I think a lot of people underestimate NNs because they think of NNs in terms of the semantics of their history instead of all possible semantics that can be fit to tensor networks. We know [P] that NNs are a sufficient abstraction to model human intelligence if we had arbitrary compute -- the questions that remain are all about making the hardware faster enough and the estimators efficient enough (which may require moving off tensor networks, but it's still only a refinement of the mathematics used).
Of course, one could argue that humans are caught in a "tensor trap", in that too much of our intellectual effort is now relying on estimators built out of networks of tensors. (I do.) But even then, AGI is likely to appear out of similar methods with new mathematical objects.
[P] Proof NNs can compute human intelligence with arbitrary compute:
You can embed the standard model as a NN by changing how you view the network of tensor equations. Human intelligence is (arguably) embeded in the standard model by modern science. So we can embed a model of human intelligence in a (large enough) NN.
This isn't immediately computationally useful, but it shows that there's not a fundamental flaw in using an estimator built out of a DAG of calculations to model intelligence if we can find an appropriate estimator for our computational needs.