My claim is that intelligence is more than just statistical associations and abstract symbol shuffling. It's impressive what large statistical models can do but they still can not solve sudoku so something is clearly missing here because neural networks do not have feedback loops and backtracking. It's like saying all we need to do is continue building bigger and bigger abaci and stacking them in just the right way as to emulate the statistical properties of the real world. Dall-e is dazzling but it is still a statistical model with no symbolic understanding (it's still a giant abacus). It's obvious that people have symbolic understanding (e.g. written language, mathematics, solving sudoku, writing code/software, etc.). So if people are the benchmark of intelligence (dubious but let's assume for the sake of argument) then at what point do you suppose there will be statistical models with symbolic understanding? Furthermore, what reason is there to believe that larger and larger statistical models are going to get us closer to non-human intelligent systems that do more than generate stimuli adapted to our senses?

There is also a meta-problem that no one seems to address when discussing AI. All the systems we have built rely on compositional symbolic systems (mathematics) for expressing statistical associations and human interpretation of their inputs/outputs. Clearly there is something people can do that no existing AI system can which is to generate a symbolic description of statistical models that can be adapted to various data sets.

I could say more here but none of what I'm saying is anything new. Others much more capable of describing the issues and shortcoming of the existing approaches to AI have written books exploring the issues in much more detail, e.g. Gary Marcus, Melanie Mitchell, Douglas Hofstadter, Gian-Carlo Rota, etc.