There are two possible responses. One might argue that time has moved on, things are different now, and there are serious reasons to distinguish today's belief that AI is around the corner from yesterday's belief that AI is around the corner. Wrong then, right now, because...

I'm reminded of a historical analogy from reading Artificial Addition. Think of it this way: a society that believes addition is the result of adherence to a specific process (or a process isomorphic thereto), and understands part of that process, is closer to creating "general artificial addition" than one that tries to achieve "GAA" by cleverly avoiding the need to discover this process.

We can judge our own distance to artificial general intelligence, then, by the extent to which we have identified constraints that intelligent processes must adhere to. And I think we've seen progress on this in terms of more refined understanding of e.g. how to apply Bayesian inference. For example, the work by Sebastian Thrun on how to seamlessly aggregate knowledge across sensors to create a coherent picture of the environment, which has produced tangible results (navigating the desert).