Is it fair to say that alignment is just the task of getting an AI to understand your intentions? It is an error to confuse the complexity of a specification of what kind of output you want, with the complexity of the process of producing that output. Getting superintelligent AI to understand simple specifications should be a non-issue. If anything, we would assume that it could be aligned using a specification of inferior quality to what a less intelligent AI would require, assuming that the superintelligent AI is better at inferring intentions.
If a little girl with no knowledge of cooking asks her dad to cook the macaroni extra crispy, his knowledge of how to do that isn't a barrier to understanding what his daughter wants. A trained chef with even greater skills might even be able to execute her order more successfully. Superalignment is nothing less mundane than this.
Advances in AI will lead to more ambitious applications. As well as requiring more intelligent technology, these new applications may well require more detailed specifications to be inputed, but these two issues are pretty orthogonal. In traditional computing, it is already clear that simple specifications often require highly complex implementations, and that some simple computational processes lead to outputs whose properties are highly difficult to specify. Why wouldn't the same apply in ML?