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It does seem like a problem that these tools depend on humans to create new information that they can regurgitate and that they’re creating the economic conditions that will make those people even rarer than they already are

LLMs are inherently backwards looking. Not even Amazon has solved the "we see you bought a toilet, here's a dozen more toilets you might like" problem in traditional ML models. Even if they were somehow collect new data, they'd only look where they already got data from

From a development standpoint, the toilet thing should be easy to fix. They have a huge amount of data to show that most people buy, at most, 1 toilet and thus it is very unlikely that you would buy a second one It’s actively strange if this is a real and ongoing issue with one-time purchases

My theory is it’s because of their origins. When you’re buying books, it works ok, but it’s stuck on similar items, rather than related items. (And tweaking it probably drops the media sales while not really helping on bathroom fixtures)

It's extremely category/product dependant. If you have millions of products across thousands of categories with different repeat and associated buying patterns, it's hard to validate performance across all variables. You train the model for aggregate performance which means lots of weird edge cases