There are a number of ML enthusiasts who would disagree with this point (Geoffrey Hinton probably most notably, such as in this video for example). The missing piece of the puzzle is reference frames. Geoffrey attempts to address this major gap with a concept he calls “capsules”. CNNs completely miss this point (as Geoffrey cleverly demonstrates near the beginning of that video)
Also I have seen videos where Jeff has stated (in the context of TBT) that they are not throwing out the traditional idea of hierarchy completely, but expanding on it (that there is more to it in a biological system). Rather than deep, narrow hierarchies like in current DNNs, the brain has relatively shallow, wide hierarchies, and the connections are shared across multiple levels to support scale (basically, even the fundamental wiring is completely different than in a DNN).
If you are interested, I’ve also posted on other threads (for example here), the implication of modelling happening within a region as opposed to between regions, supporting unusual hierarchical connections like level skipping and recursion. Note that the concepts of an “output layer” comes from the Columns paper, which you can arrive at by tracing through references from the Frameworks paper which introduced TBT. I’ve not seen Jeff indicate that they believe they were wrong about that – instead they’ve built on it.
In any case, the fact that TBT specifically predicts “hundreds” being the number of objects that a single CC can model, I don’t see any way around use of hierarchical levels for building abstractions. Otherwise, what would be the point of CCs which receive input from other CCs and not directly from sensors? Maybe that is a question for Jeff - just pointing it out. And probably agreeing with your original argument, if your interpretation of TBT is that it is devoid of all abstraction-building hierarchical connections (that’s just not how I interpret it)