This is not what the bitter lesson is about. It's not "don't develop better methods, just scale", it's that those methods which scale best win. LeCun's work is fundamentally about devising a method that scales better with data than LLMs. Agree with him or not about the feasibility of it, but this is fundamentally still a bitter lesson-pilled mindset.
However "world models" have been a thing for the entire history of computers. They have changed names over time: "rules engines", "expert systems", "semantic web", and so on and so forth.
And they have failed every single time.
The bitter lesson essay was written precisely to dismiss that approach, which used to dominate conferences and scientific publications of the era. A general learning system, given sufficient computation power, will always outperform specialized crafted systems in the long run.
Think of it like this: if a world model is a useful abstraction, the general learning system will create it by itself during its training, without us needing to implement it by hand. This is the bitter lesson. And it comes for us all.
