docs: 💡 Add insight on LLM verification asymmetry

Artificial Intelligence Models.md

@@ -5,6 +5,7 @@
 - Classic ML system where humans are designing how the information is organized (feature engineering, linking, graph building) scale poorly ([the bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html)). LLMs are able to learn how to organize the information from the data itself.
 - [LLMs may not yet have human-level depth, but they already have vastly superhuman breadth](https://news.ycombinator.com/item?id=42625851).
 - Learning to prompt is similar to learning to search in a search engine (you have to develop a sense of how and what to search for).
+- [LLMs are useful when exploiting the asymmetry between coming up with an answer and verifying the answer](https://vitalik.eth.limo/general/2025/02/28/aihumans.html) (similar to how a sudoku is difficult to solve, but it's easy to verify that a solution is correct).
 
 ## Prompting