I'm pretty content to say this may be true, but may well prove quite wrong.

Why? Because humans—including the smartest of us—are continuously prone to cognitive errors, and reasoning about the non-linear behavior of complex systems is a domain we are predictably and durably terrible at, even when we try to compensate.

Personally I consider the case of self-driving cars illustrative and a go-to reminder for me of my own very human failure in this case. I was quite sure that we could not have autonomous vehicles in dynamic messy urban areas without true AGI; and that FSD would in the fashion of the failed Tesla offering, emerge first in the much more constrained space of the highway system. Which would also benefit from federal regulation and coordination.

No Waymos have eaten SF, and their driving is increasingly nuanced; and last night a friend and very early adopter relayed a series of anecdotes about some of the strikingly nuanced interactions he'd been party to recently, including being in a car that was attacked late at night, and, how one did exactly the right thing when approached head-on in a narrow neighborhood street that required backing out. Etc.

That's just one example, and IMO we are only beginning to experience the benefits of "network effects" so popular in tails of singularity take-off.

Ten years is a very, very, very long time under current conditions. I have done neural networks since the mid-90s (academically: published, presented, etc.) and I have proven terrible in anticipating how quickly "things" will improve. I have now multiple times witnessed my predictions that X or Y would take "5-8" or "8-10" years or "too far out to tell," instead arrive within 3 years.

Karpathy is smart of course but he's no smarter in this domain than any of the rest of us.

Are scaled tuned transformers with tack-ons going to give us AGI in 18 months? "No" is a safe bet. Is no approach going to give us AGI inside of 5 years? That is absolutely a bet I would never make. Not even close.