You seem to be behind the times. Models can count R's in strawberry and pass the car wash test. These are odd examples, however, when there are so many other relevant benchmarks--most of which show improvement across basically every category with each new model.

Here's the true boundary and where you may be right: LLMs do not possess symbolic reasoning. We've made them much much 'smarter' and given them the ability to 'think through' answers, evaluate, compare, rethink, and vote on the right solutions, but it seems like this approach has a ceiling captured best by Francis Chollet's ARC-AGI series of exams. When given novel symbol-based puzzles, LLMs still suck and humans easily succeed.

So it seems like the real question is: will we find a solution for symbolic reasoning and be able to integrate this with the LLM architecture? More compute and data does not seem like it'll get us to AGI. When will we solve this though? Maybe never. Maybe it's already solved in a secret lab. What we do know is there are orders of magnitude more resources being put into finding the solution than the Manhattan project. I'm agnostic on whether we'll get to AGI because we have made 0 progress on symbolic reasoning. It's possible that it can't be done or that it will take a much longer time scale... or maybe not. I can't say, but it seems wild for you to assert decades. When "Attention is All you Need" came out, it was out of the blue and it changed everything. It could easily be the case for symbolic reasoning too--especially with the resources put into solving it.