{"ID":5937685,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T12:38:41.542637154Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04282","arxiv_id":"2607.04282","title":"The New Shape of Search: How Conversational AI Recomposes Information Seeking","abstract":"Classic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens when a conversational assistant is inserted into that episode. Linking real conversations with major assistants to the same users' searches and browsing in an opt-in cross-surface panel, and reconstructing the full episode rather than a single query, we find conversational AI changes the shape of information seeking, not merely its volume. AI episodes do not uniformly collapse; they bifurcate. Most terminate in place, with no onward search or content step in the observed trace, while roughly a third scaffold into longer multi-step journeys. Which shape occurs is governed less by task type than by articulation: collapse is statistically indistinguishable across lookup, learning, and comparison episodes, yet falls monotonically with opening-ask length, from 72% at one-to-three words to 48% beyond twenty. Roughly two-fifths of assistant episodes are workbench use--drafting, coding, editing--not information seeking at all, and these collapse most. Conversational AI also does not displace search: search remains woven through roughly three-quarters of within-episode transitions, after reading a page users return to the search box over the assistant 70/30, and within-user search share does not fall. Verification is rare: searches with explicit verification language follow roughly 1% of episodes, and citation-forward interfaces do not measurably increase checking. All of this is episode structure, a compositional object identifiable without a demand counterfactual. Conversational AI recomposes the seeking episode: it answers brief asks in place and anchors invested asks in longer journeys, adding a layer rather than replacing search.","short_abstract":"Classic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens when a conversational assistant is inserted into that episode. Linking real conversations with major assistants to the same users' searches and brow...","url_abs":"https://arxiv.org/abs/2607.04282","url_pdf":"https://arxiv.org/pdf/2607.04282v1","authors":"[\"Michael Iannelli\",\"Alan Ai\"]","published":"2026-07-05T12:48:17Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.CY\",\"cs.IR\"]","methods":"[]","has_code":false}