Incredible work mapping IQ thresholds to actual math performance, the IRT framework application here is really smart. I've noticed somethign similar in tutoring undergrads where there seems to be a cognitive wall around real anaylsis that no amount of effort breaks through. The lazy-as-extended-stupidity framing is kinda brutal but tracks with what we see in practice.
Your model treats intelligence as efficiency within a fixed problem space. That’s useful, but it’s not the whole thing. IQ is good at measuring how well someone moves from A to B when the route and rules are already defined. What it struggles to capture is a different cognitive move: noticing when the route itself is wrong.
Some people optimize known paths. Others pause, explore, and test odd possibilities that look like distraction or laziness under standard metrics. Most of the time, that exploration goes nowhere. Occasionally, it reveals a shortcut that reframes the entire problem. Once that shortcut is formalized, high-IQ optimizers adopt it and it looks obvious in hindsight. Our measurements are excellent at grading performance inside a model, and poor at detecting who will break or replace the model. That doesn’t make IQ meaningless, but it does make it incomplete. The mistake isn’t measuring intelligence. It’s mistaking model-optimization for intelligence itself.
Incredible work mapping IQ thresholds to actual math performance, the IRT framework application here is really smart. I've noticed somethign similar in tutoring undergrads where there seems to be a cognitive wall around real anaylsis that no amount of effort breaks through. The lazy-as-extended-stupidity framing is kinda brutal but tracks with what we see in practice.
There are no thresholds in real life. Or in item response theory terms, no item has infinite discrimination (loading =1).
Would be essential for a modern rational society.
Your model treats intelligence as efficiency within a fixed problem space. That’s useful, but it’s not the whole thing. IQ is good at measuring how well someone moves from A to B when the route and rules are already defined. What it struggles to capture is a different cognitive move: noticing when the route itself is wrong.
Some people optimize known paths. Others pause, explore, and test odd possibilities that look like distraction or laziness under standard metrics. Most of the time, that exploration goes nowhere. Occasionally, it reveals a shortcut that reframes the entire problem. Once that shortcut is formalized, high-IQ optimizers adopt it and it looks obvious in hindsight. Our measurements are excellent at grading performance inside a model, and poor at detecting who will break or replace the model. That doesn’t make IQ meaningless, but it does make it incomplete. The mistake isn’t measuring intelligence. It’s mistaking model-optimization for intelligence itself.