Jagged intelligence is the AI equivalent of a gifted kid who can recite Shakespeare but forgets how to tie their shoes. A polite way to say: “today’s AIs are wildly overqualified in certain areas and completely useless in others.”

The idea that intelligence right now is jagged is pretty accurate. And the shift toward continuous intelligence, where capabilities aren’t just isolated peaks but more like a high, even plateau, is a real milestone. That’s when you stop getting weird gaps in performance. Like, if you ask Artificial Super Intelligence to help you build a spaceship, it doesn’t just say, “Sure, I’ve written the design… now what’s a screwdriver?”

But let’s not pretend this will happen smoothly. We’re trying to get a machine to think across all cognitive terrains, with context-switching, reasoning, self-monitoring, and emotional modeling. That’s not just a patch upgrade away. We haven’t even figured out how our own brains work without accidentally assigning consciousness to a thermostat.

The five-to-ten-year prophecy window is classic tech optimism. The CEO talks to keep investors starry-eyed and regulators slightly confused. You can always believe it because it’s just far enough away that no one can prove you wrong yet.

But let’s pull on this thread: could we smooth out jagged intelligence into something general and continuous in a decade? Maybe. If breakthroughs keep happening, if we figure out how to align large models better, if we get more than just larger data piles and start adding actual reasoning and memory structure. But those are a lot of “ifs,” and AI progress is anything but linear.

The truth is, current systems still don’t have persistent memory, real-world grounding, or the ability to transfer learning in a truly general way. They’re just very, very good guessers with shiny syntax. It’s like duct-taping a cape to an octopus and calling it Superman.