Agreed. My pet theory is that it boils down to how
superficially accessible AI is. A conversation about AI is kind of like Bikeshedding, except no one realizes it's
actually a conversation about the Nuclear Power Plant. Everyone's got practical, direct experience with sentience, with thinking, with speaking a language, with seeing things. Everyone can introspect for a little bit and make genuine claims to having contemplated the fundamental problems of AI. And since so little genuine progress on the really fundamental parts of the problem (the Hard Problem, as Chalmers would say, versus the somewhat more tractable 'parsing problems' that Deep Learning has made such great gains on), the most frustrating part is that it's really hard to prove some amateur thinker's conclusions wrong. You can bury them with the technical jargon of three thousand years of philosophers, but no one's been able to build a formally verifiable model for the whole thing so far, and so amateurs and experts are in some sense separated only by how impressively technical they sound when talking about the matter. This problem is amplified by the technical jargon being painfully close to the technical jargon for philosophy of the mind, and to straight lay terminology. So a person with technical knowledge can talk about, say, 'training', and a person with no technical knowledge can think they understand, because the word is so ill-separated from their own internal, non-technical definition.
It's so easy to have an opinion about AI, the fundamental ideas are so prevalent in day-to-day discourse, and it's so hard to prove any given opinion wrong.