

Yes although, it is probably a reasonable guess at how labs would go about implementing advertising - building partnerships and preferences into the prompt. The other option would be to fine tune models to favour particular companies which could become prohibitively expensive if your ads are highly targeted.
The scenario that isn’t accounted for in this paper is taking a general LLM and fine tuning it to exhibit more fair/consistent behaviour when prompted about ads/partnerships but we all know with non-deterministic systems you’re just increasing the odds that the model regurgitates something more sane rather than providing any strong guarantee
Edit: another possibility would be to have a gateway/proxy layer between the LLM and the user output that rewrites the vanilla model’s responses to include ads where relevant. That would prevent the need to modify the original LLM but could introduce a lot of latency though, especially if the original output is long.


Google released their new Gemini 3.5 “flash” model at I/O yesterday. For those who aren’t familiar, the “flash” model is typically marketed as the lower end and the “pro” model is the higher end for each given model generation.
The interesting thing here is that the new “flash” model is almost as expensive as the “pro” from the previous generation.
As my favourite “neutral-but-not-really” AI booster Simon Willison says:
Speed running enshittification - a process that typically only works when people are reliant on your product and have no other option than to pay the inflated price