Generative AI search is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple web pages. However, while this paradigm enhances convenience, it disrupts the feedback-driven improvement loop that has historically powered the evolution of traditional Web search. Web search can continuously improve their ranking models by collecting large-scale, fine-grained user feedback (e.g., clicks, dwell time) at the document level. In contrast, generative AI search operates through a much longer search pipeline, spanning query decomposition, document retrieval, and answer generation, yet typically receives only coarse-grained feedback on the final answer. This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop. In this paper, we envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search. NExT-Search integrates two complementary modes: User Debug Mode, which allows engaged users to intervene at key stages; and Shadow User Mode, where a personalized user agent simulates user preferences and provides AI-assisted feedback for less interactive users. Furthermore, we envision how these feedback signals can be leveraged through online adaptation, which refines current search outputs in real-time, and offline update, which aggregates interaction logs to periodically fine-tune query decomposition, retrieval, and generation models. By restoring human control over key stages of the generative AI search pipeline, we believe NExT-Search offers a promising direction for building feedback-rich AI search systems that can evolve continuously alongside human feedback.

Generative AI search, one of MIT Technology Review's 10 Breakthrough Technologies of 2025, is ushering a new era of the internet. Hear from MIT Technology Review editor in chief Mat Honan and executive editor Niall Firth as they explore how AI will alter search.

But Google's global search dominance makes it the most important player, and the company has already rolled out AI Overviews to more than a billion people worldwide. The result is searches that feel more like conversations. Google and OpenAI both report that people interact differently with generative search--they ask longer questions and pose more follow-ups. This new application of AI has serious implications for online advertising and (gulp) media. Because these search products often summarize information from online news stories and articles in their responses, concerns abound that generative search results will leave little reason for people to click through to the original sources, depriving those websites of potential ad revenue.