If you cannot find a signal that predicts recommendation, the next question is obvious. Just ask the model why it picked what it picked. We did, 29,633 times. 90% of the answers were completely unique text. The most repeated answer showed up 12 times. There is no rule in there to find. Only a new sentence, every time.
Every measurable predictor in this series has come up empty. Store quality explains 0.7% of recommendation. Public fame explains 1.2%. Knowing where a brand's website lives explains nothing about whether the model bothers to recommend it. At some point the obvious move is to stop guessing at predictors and just read what the model itself says.
Every recommendation in our dataset comes with a short reason attached, a line the model writes to justify its own pick. If that line reflects a real rule the model is applying, the same rule should produce recognizably similar language across similar picks. If it does not, the reason is not a rule. It is something written after the fact to make the pick look justified.
We took the full reason field from the recommendation dataset, all 29,633 entries, and checked how many were exact duplicates of another entry in the set. If the model applies a consistent internal rule, that rule should show up as the same handful of phrasings recurring across thousands of picks.
Of 29,633 reasons, 26,812 were completely unique. That is 90% of every reason the model ever gave. The single most repeated reason in the entire dataset showed up 12 times, out of nearly thirty thousand chances to repeat itself.
A model with a real, applied rule for why it picks what it picks would keep reaching for the same handful of justifications. Instead, out of 29,633 opportunities to repeat itself, it managed to do so meaningfully only a handful of times, and even then, never more than 12 times for any single phrasing. There is no repertoire to find. There is a new sentence, written on demand, every single time.
This is what confabulation looks like in a language model. The pick happens first, produced by whatever weighting of memory, training signal, and pattern completion actually drives the recommendation. The reason is generated afterward, as a plausible-sounding sentence that fits the pick it already made. It is not a report on the mechanism. It is a caption written for it.
That has a direct, practical consequence for anyone trying to reverse engineer why AI recommends one brand over another: asking the model why does not work. You can ask it, and it will always answer, fluently and confidently, with something new every time. But a fluent answer to a why question is not evidence that the model knows why. It is evidence that the model is good at writing justifications on request, which is a different skill entirely.