The author conflates AI and statistical, in particular, causal inference.
A ML model and most of what we currently call AI, is a statistical model. It predicts things, and in the process of "training" this model we can also learn about the world it interacts with.
Anyone who has ever spend any amount of time on the issue knows that the idea of "generalizing" or "adapting"in ML corresponds directly to causal inference. This is not some secret to be uncovered by every other pundit, it's a direct consequence of ML models being.. statistical models. If our AI can understand when statistical relationships hold and when they do not, then it is inferring causality from data. Currently, we are dealing with ML algorithms adapting or generalizing: learning which regularities to "trust" and which to "discard" when the "DGP" changes. This sounds nebulous and difficult, but that's only the case because the models are complex.
Nevertheless, the underlying statistical problem is old. Ancient. Talked to death in every scientific field that tries to do an experiment, to infer some causal parameter from observational data, or construct counterfactuals to guide policy. Similarly, the divide between quantitative and qualitative approaches, are decades (if not centuries) old.
These problems are so well understood that we can state very precisely what needs to happen to make our inference causal. The catch is, that first it depends on the DGP and our model thereof, and further that none of these things can be proven to be true in the same framework. Whether the DGP is as we need it to be, or whether the model identifies what we want, is something we can be confident about given a set of assumptions, but it is not something we can know to be true. And guess what, if the model is complex, then so is thinking about its inferential capabilities.
The discussion seems tired, because it is. It's not a deep philosophical issue. It's a practical one. That doesn't imply there is a good solution to it either, but the basic issue hasn't changed for a long time. Maybe we are not trying to predict a treatment's effect on some randomly selected population, maybe we are instead trying to land a plane under conditions we can barely foresee. But what needs to happen for these two predictions to be unbiased, consistent, low variance, whatever... has not changed.
Researchers are frustrated with these article, because pundit after pundit claims to uncover some general problem with AI or ML in face of reality. But it is not about AI. It's about statistics, and we all want to say: "Yes, we know. Now what?"