The Beginning of ChatGPT Ads

2026-08-05

AI Summary

This study presents the first empirical audit of advertising within large language models, investigating whether ChatGPT’s interface exhibits racially or income-based disparities in ad display. Employing a sock puppet auditing methodology, the authors created 91 simulated user accounts, leveraging IP address rotation and prompt engineering to manipulate demographic signals. Systematic data collection began in February 2026, capturing ads shown to U.S.-based users. The findings reveal that lower-income profiles received significantly more advertisements, which initially consisted predominantly of generic consumer-category promotions attributed to advertisers rather than specific products and were clearly separated from model-generated responses. The project amassed over 3,000 ads from 186 distinct advertisers, yielding the first publicly accessible, queryable dataset of LLM-integrated advertisements and demonstrating that income level substantially influences ad exposure—highlighting critical fairness concerns in algorithmic advertising systems.

Abstract

This paper presents the first empirical study of advertising content being rolled out in the user-facing online interfaces of large language models (LLMs). We systematically examine possible demographic differences in ad content shown to U.S. users of ChatGPT using a sock puppet audit methodology. We create and deploy 91 sock puppets in a 3x3 factorial design, using geolocation cues (account IP proxies and location-signaling prompts) to signal three racial/ethnic groups (Black, Hispanic, and White) and three income terciles (low, medium, and high). We conduct data collection starting in February 2026, collecting over 3,000 advertisements from 186 unique advertisers in response to 335 prompts on a range of realistic user queries. We find that accounts begin receiving ads 14 days after account creation, and that lower-income accounts, regardless of race, are more likely to receive ads. In this first phase of ChatGPT ads, the ads themselves skewed heavily towards consumer goods, directed users to a specific advertiser rather than a particular product, and were clearly separated from the LLM's response text, observations we anticipate will change as ads continue being integrated into LLM chat interfaces. We release a public, searchable archive of all collected advertisements. Finally, we discuss the implications of our findings, and conclude with methodological and theoretical recommendations for future empirical studies of LLM advertisements.

Problem

• advertising
• large language models
• demographic bias
• ChatGPT
• online interfaces

Innovation

• sock puppet audit
• LLM advertising
• demographic targeting
• ChatGPT ads
• empirical study