Added a bit more: "The research demonstrates something interesting about language models' ability to simulate search behavior in controlled conditions. But cla

Added a bit more: "The research demonstrates something interesting about language models' ability to simulate search behavior in controlled conditions. But claiming equivalence to a "real search engine" is like saying you've built a military defense system because your soldiers performed well in peacetime maneuvers. The real test isn't whether it works when nobody's trying to break it—it's whether it works when half the internet is trying to game it for profit. To illustrate, imagine a small corpus with two documents: Mr. Fox is great. Mr. Fox is not great. If the search term is "Mr. Fox," then, from the perspective of semantic relevance, the two documents are equal. Instead, to build a more useful ranking, you need some signal of consumer demand, which would include biases toward Mr. Fox (and perceptions of trustworthiness) that presumably affect consumer utility. Now, imagine I use GenAI to flood the Internet with 100,000 pages praising Mr. Fox. These aren't crude spam pages—they're well-written articles with proper grammar, coherent arguments, and seemingly legitimate citations. Each page offers minor variations on the same theme: "Mr. Fox is innovative," "Mr. Fox shows exceptional leadership," "Studies confirm Mr. Fox's approach is effective." From a pure information retrieval perspective, a language model examining this corpus would find overwhelming "evidence" that Mr. Fox is great. LLMs have no built-in mechanism to recognize that these pages are 'artificial' unless we model signals like "All 100,000 pages appeared within the same week", "None have meaningful engagement from real users", etc." And now, we can give context w/ 'solve for the equilibrium'