It is correct that the first model that will beat ARC-AGI will only be able to handle ARC-AGI tasks. However, the idea is that the architecture of that model should be able to be repurposed to arbitrary problems. That is what makes ARC-AGI a good compass towards AGI (unlike chess).
For instance, current top models use TTT, which is a completely general-purpose technique that provides the most significant boost to DL model's generalization power in recent memory.
The other category of approach that is working well is program synthesis -- if pushed to the extent that it could solve ARC-AGI, the same system could be redeployed to solve arbitrary programming tasks, as well as tasks isomorphic to programming (such as theorem proving).
For instance, current top models use TTT, which is a completely general-purpose technique that provides the most significant boost to DL model's generalization power in recent memory.
The other category of approach that is working well is program synthesis -- if pushed to the extent that it could solve ARC-AGI, the same system could be redeployed to solve arbitrary programming tasks, as well as tasks isomorphic to programming (such as theorem proving).