The relatedness of human language cognition and machine language performance is already evident, in that studies of machine language performance can already falsify some theories of human language cognition. The pattern goes like this.
1. Language competency requires component X. 2. Humans have language competency. 3. Therefore, humans have component X.
When we see a machine, without component X, using language as a human does, we know the theory has gone wrong at the first step. The article mentions one such instantiation of this pattern. The new data has narrowed the space of plausible theories of human language cognition. The machine learning engineers play the role of experimentalists. They collect data but may not have a theory ready to explain what results.
> You have to have a reason to believe that it would yield insights. There isn't one.
The reason is that models in general yield insights. They have yielded insights before, and so we may expect them to yield insights in the future. The explanation for this is either philosophical or psychological. Whatever the reason, humans who look into a specific system are very good at generalizing to a whole class of systems. F=ma is a model, and certainly wrong. But by probing it, we can begin to discover general principles like energy conservation. In the same way, a large language model is a model of a system that acquires language. By examining its specific behavior, we may come to understand more general principles of language acquisition.
> You have to actually have a rational process to produce insights, not just mix a bunch of numbers in a vat and "assume" that some insight is going to come from it.
Where there is already an understood rational process, you no longer need the insight. Sometimes the big leaps of scientific understanding come from looking at the data and then coming to Eureka.