Do ChatGPT Ads Get You Recommended? Analysis of 3,602 Placements

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Key takeaways<br>Across 3,602 ChatGPT ad placements collected in spring 2026, paid advertisers appeared in the answer text only 8.0% of the time.<br>After controlling for the prompt (same question, same brand, ad on vs ad off, across 91 pairs), the average lift from paying was −0.3 percentage points.<br>Half of all ad placements (49.8%) landed on cooking, how-to, and health topics where advertisers were named zero times in the answer.<br>Zoom paid for zero placements and was named 101 times. Mercari paid for 66 placements and was named zero times. Of 32 brands that paid inside purchasable_products, 15 never appeared in a single answer.<br>The ad slot and the recommendation slot are separate systems. Ads buy visibility next to the conversation. Recommendation goes to the brand whose structured product data lets the model answer what it is, who it is for, and how it compares.<br>Data is a Q1 2026 baseline (March 8–April 12). ChatGPT Ads has expanded internationally and evolved formats since; treat the numbers as a baseline the field can be measured against, not a current snapshot.

Paying for ChatGPT Ads gets you next to the answer. It does not get you into it.

I analyzed 3,602 ChatGPT ad placements from a Penn/Haverford research dataset, and paid advertisers appeared in the actual answer text 8% of the time. Once I controlled for the question being asked, the average lift from paying was −0.3 percentage points. In this data, paying to advertise did not causally improve a brand's chance of being named in the AI's recommendation.

What follows is the methodology, four findings from the data, and what this means for anyone spending on ChatGPT Ads today.

The question I asked, and the one the original researchers did not

Lurie, Encarnación, Friedler, and Metaxa at the University of Pennsylvania and Haverford College released a public dataset of 3,602 ChatGPT ad placements they collected in the spring of 2026. Their paper (The Beginning of ChatGPT Ads, forthcoming at AAAI/ACM AIES 2026) asked who gets shown ads, and how placement varied across the race and income signals in their sock-puppet accounts.

I asked something adjacent on the same data: when a brand pays for a ChatGPT ad, does that brand actually appear in the answer ChatGPT gives?

The two questions matter for different reasons. Theirs is about the fairness of ad distribution. Mine is about whether "paying for a ChatGPT ad" and "getting recommended by ChatGPT" are the same event. For a merchant deciding where to spend an advertising budget, that distinction is the whole game.

This analysis uses their public dataset (CC BY-NC-SA 4.0). The paid-vs-named framing, the alias handling, and the same-prompt controlled comparison are mine. The research team did not do this comparison in their paper.

Method

The data. The Lurie et al. dataset covers 3,602 ad placements from 191 unique advertisers, spanning 139 unique prompts, collected via 91 sock-puppet accounts in a 3×3 factorial design (three race signals × three income buckets) between March 8 and April 12, 2026. Every observation records the ad_advertiser shown, the full response_text ChatGPT returned, an OpenAI-assigned topic tag, and the user context (race, income, ZIP). A 272-observation control set has no ad served.

The match. For each record, I checked whether the ad_advertiser string appeared inside its own response_text. Word-boundary matching, so "Target" doesn't match "targeting."

Alias normalization. A few advertisers use multiple names. Universal Technical Institute is matched both fully and as "UTI." Top10.com matches with and without the .com. Suffixes like "Inc." and "®" are stripped before matching.

Ambiguous advertisers. Eight advertisers have brand names that also appear as common English words: Target, Shop, Factor, Whoop, Spectrum, Pure, Method, and The General. For these, raw string matching cannot distinguish "Target" the retailer from "target audience" in a response, or "Shop" the Shopify surface from the verb "shop for." I excluded these eight from the main cohort as a false-positive safeguard, and report them separately: 256 placements at a 1.2% match rate. Including them shifts the main result from 8.0% to 7.5%, and changes the direction of no finding below.

The critical control. Raw match rates conflate two effects. Nike ads run on shoe questions, and shoe answers name Nike anyway. To separate paying from category, I paired every (brand, prompt) combination and compared the naming rate when that brand was advertising against the naming rate on the same prompt when it was not. Same question. Same brand. Ad on vs ad off. 91 pairs had enough data on both sides to compare.

Baseline caveat. This dataset predates the...