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The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale

Sinan Aral, Haiwen Li, Rui Zuo

TL;DR

The Rise of AI Search investigates how AI-powered search reshapes information markets and human judgment at scale. By running 24,000 searches across 243 countries in 2024 and 2025, the study isolates platform policy effects from user behavior to map global AI search exposure, including a dramatic Covid-query policy shift. The findings reveal rapid geographic expansion of AI search, reduced response variety, lower source credibility, and a shift toward right-leaning and centrist domains, with significant implications for the economics of information and decision making. The authors advocate for governance and measurement reforms across platforms, regulators, scientists, and consumers, and commit to open data and replication to spur global policy debate.

Abstract

We executed 24,000 search queries in 243 countries, generating 2.8 million AI and traditional search results in 2024 and 2025. We found a rapid global expansion of AI search and key trends that reflect important, previously hidden, policy decisions by AI companies that impact human exposure to AI search worldwide. From 2024 to 2025, overall exposure to Google AI Overviews (AIO) expanded from 7 to 229 countries, with surprising exclusions like France, Turkey, China and Cuba, which do not receive AI search results, even today. While only 1% of Covid search queries were answered by AI in 2024, over 66% of Covid queries were answered by AI in 2025 -- a 5600% increase signaling a clear policy shift on this critical health topic. Our results also show AI search surfaces significantly fewer long tail information sources, lower response variety, and significantly more low credibility and right- and center-leaning information sources, compared to traditional search, impacting the economic incentives to produce new information, market concentration in information production, and human judgment and decision-making at scale. The social and economic implications of these rapid changes in our information ecosystem necessitate a global debate about corporate and governmental policy related to AI search.

The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale

TL;DR

The Rise of AI Search investigates how AI-powered search reshapes information markets and human judgment at scale. By running 24,000 searches across 243 countries in 2024 and 2025, the study isolates platform policy effects from user behavior to map global AI search exposure, including a dramatic Covid-query policy shift. The findings reveal rapid geographic expansion of AI search, reduced response variety, lower source credibility, and a shift toward right-leaning and centrist domains, with significant implications for the economics of information and decision making. The authors advocate for governance and measurement reforms across platforms, regulators, scientists, and consumers, and commit to open data and replication to spur global policy debate.

Abstract

We executed 24,000 search queries in 243 countries, generating 2.8 million AI and traditional search results in 2024 and 2025. We found a rapid global expansion of AI search and key trends that reflect important, previously hidden, policy decisions by AI companies that impact human exposure to AI search worldwide. From 2024 to 2025, overall exposure to Google AI Overviews (AIO) expanded from 7 to 229 countries, with surprising exclusions like France, Turkey, China and Cuba, which do not receive AI search results, even today. While only 1% of Covid search queries were answered by AI in 2024, over 66% of Covid queries were answered by AI in 2025 -- a 5600% increase signaling a clear policy shift on this critical health topic. Our results also show AI search surfaces significantly fewer long tail information sources, lower response variety, and significantly more low credibility and right- and center-leaning information sources, compared to traditional search, impacting the economic incentives to produce new information, market concentration in information production, and human judgment and decision-making at scale. The social and economic implications of these rapid changes in our information ecosystem necessitate a global debate about corporate and governmental policy related to AI search.
Paper Structure (4 sections, 1 equation, 5 figures, 2 tables)

This paper contains 4 sections, 1 equation, 5 figures, 2 tables.

Table of Contents

  1. Unit of Analysis
  2. Calculating Response Variety
  3. Domain Credibility
  4. Domain Political Leaning

Figures (5)

  • Figure 1:
  • Figure 2: Global Exposure to AI Search Results. This figure shows heatmaps of the fractions of queries that returned AI Search Overviews (AIO) across the world, in 2024 (A) and 2025 (B); the fraction of queries returning AI search results in 2024 and 2025, in the 7 countries exposed to AI search in 2024 (C) (with relative change shown; a “+40%” indicates a 40% increase in 2025 compared to the 2024 baseline); in 7 countries exposed to AI search starting in 2025 (D); and in 7 countries with limited to no exposure to AI search in 2024 or 2025 (E) (a full description of these results across all countries is provided in the SOM). (F)shows the fraction of queries returning AI search results across topics in 2024 and 2025 in the 7 countries exposed to AI search in 2024; (G) shows the fraction of queries returning AI search results across topics in 2024 and 2025 in countries exposed to AI search in 2025; (G - Inset) shows the fraction of AI search responses with references across Covid and non-Covid queries; (H) shows the fraction of queries returning AI search results across query style in 2024 and 2025 in countries exposed to AI search in 2025; (H - Inset) shows the fraction of queries returning AI search results across query style in 2024 and 2025 in the 7 countries exposed to AI search in 2024 (with relative changes shown; a “+40%” indicates a 40% increase in 2025 compared to the 2024 baseline).
  • Figure 3: Predictive Features and Information Content of AI Search Results. This figure displays (A) feature importance comparisons, as odds ratios, from a logistic regression trained on all country, topic and style features, in 2024 and 2025; (B) logistic regression prediction performance comparisons across four feature sets (country, topic, style, all) in 2024 and 2025; (C) the average response variety, measured by aggregated information uniqueness, across traditional and AI search results, by category and for all queries; (D) the average share of domain traffic rankings, measured by cite visits, across traditional and AI search results; (E) the average share of source credibility, measured using the Media Bias / Fact Check database, across traditional and AI search results; and (F) the average share of source political leaning, measured using the Media Bias / Fact Check database, across traditional and AI search results. Additional details of all measurements are provided in the SOM.
  • Figure S1: Traditional Results Unit of Analysis (in Light Blue Boxes)
  • Figure S2: AI Overview Unit of Analysis (in Dark Blue Boxes)