> You have no guarantee the API models won’t be tampered with to serve ads. I suspect ads (particularly on those models) will eventually be “native”: the models themselves will be subtly biased to promote advertisers’ interests, in a way that might be hard to distinguish from a genuinely helpful reply.

I admit I don't see how that will happen. What are they gonna do? Maintain a model (LoRA, maybe) for every single advertiser?

When both Pepsi and Coke pay you to advertise, you advertise both. The minute one reduces ad-spend, you need to advertise that less.

This sort of thing is computationally fast currently - ad-space is auctioned off in milliseconds. How will they do introduce ads into the content returned by an LLM while satisfying the ad-spend of the advertiser?

Retraining models every time a advertiser wins a bid on a keyword is unwieldy. Most likey solution is training the model to emit tokens represent ontological entries that are used by the Ad platform so that "<SODA>" can be bid on by PepsiCo/Coca-Cola under food > beverage > chilled > carbonated. Auction cycles have to match ad campaign durations for quicker price discovery, and more competition among bidders

you mean the API response then will contain the Ad display code?