One approach, proposed by Google Research, is what you might call a “token auction.” In this model, advertisers don’t buy ad slots on a page. Instead, they bid, token by token, on the actual text the model generates. Each advertiser brings their own LLM, and an auction mechanism decides whose model gets to influence the next word. The output is a weighted blend of competing interests, shaped by who’s willing to pay more.Another approach – also from Google researchers – fits the new “Search” much more precisely. It’s called “prominence allocation.” Here, when a user submits a query with commercial intent, the system runs an auction that doesn’t just decide which ads appear, but how prominently the LLM writes about each one. The auction outputs a prominence score for each advertiser, essentially telling the model: give this product 35 words, that one 20, and this one zero. The ad isn’t next to the answer. The ad is the answer. Or rather, it shapes how much space and enthusiasm each product gets within the answer.
This is terrible. I’m glad I signed up for Kagi a while back.