“AGI is here!” “AGI is a few years away!” “AGI is decades away…”
Lots of talk about AGI. But here’s the problem: nobody arguing about AGI timelines can tell you what the G stands for. The whole debate runs on a vibe: AGI is when the models get scary good.
But “scary good” is what we already have. I’ve been playing with Anthropic’s Fable 5 model (released yesterday), and, yes, it’s amazing. But if “generality” just means performing well across many domains, AGI happened a while ago and the debate is over.
It isn’t over, because that’s not what GENERAL intelligence is.
I wrote recently about @DavidDeutschOxf ‘s idea of explanatory knowledge: humans don’t just predict what happens next, we construct theories about why. Conjecture, refutation, better theory. Every capability we lump under “general intelligence” (creativity, judgment, taste) flows from that one capacity. It’s general precisely because explanations reach beyond the data that produced them. Newton's equations covered apples as well as comets and planets he never saw.
LLMs are prediction engines. That’s not an insult, it’s the design. They generate plausible continuations of patterns, and they’re now better at it than we (humans) are. But prediction interpolates. It doesn’t explain. Scale that up a thousandfold and you get something extraordinary that is still, categorically, not the thing.
So here’s my view: AGI arrives when an AI generates explanatory knowledge. That will likely require a different architecture entirely. Nobody knows. Which means every confident timeline you’ve heard is a prediction about something its predictor can’t define.
The people building these models are the best in the world at prediction. Maybe that’s why they keep mistaking it for the whole of intelligence.
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