AI research, much like evolution, is strongly in the camp that anthropomorphizing is rational; that human culture often fails to recognize this has more to do with a common intellectual pit that pop psychology and philosophy fall into: when something is clearly in error, it does not follow that it is in error. People often think they can safely critique general methods with specific examples, because the nature of the algorithm that both evolution and AI research condones is to do just that. The thing is, this doesn't reject the algorithm itself, it is what the algorithm does, not a refutation of the algorithm. If you want to actually reject anthromorphizing what you actually need to reject is that in multi-agent decision problems the complexity of the correct solution grows combinatorially with respect to the complexity of the problem such that there are not enough atoms in the universe and not enough time to tractably compute the correct answers such that it makes sense to start with a solution that has error and then improve it in specific situations. As an agent living in that reality, what you see is the constant failure, which you can critique, because it helps you improve, but it is an error to think the tendency itself is in erorr - the error isn't actually irrational, it is more like the speed of light, a physical inescapable law. That is why you see something analogous to anthropomorphizing in the superhuman AI we have made: it shows up in poker AI, in self-driving car AI, in chess AI, in Go AI; actually DeepMind found that if you remove this specific component from the superhuman AI we currently have, they stop being superhuman.
I can link an interesting talk on this subject if you are interested in hearing more.