What 3,602 Early ChatGPT Ads Reveal About Delivery

The study measured delivery, not advertiser intent

The Beginning of ChatGPT Ads is a sock-puppet audit by Emma Lurie, Ro Encarnación, Sorelle A. Friedler and Danaé Metaxa. The researchers created controlled free ChatGPT accounts, varied location signals associated with race and household-income groups, sent a standardized prompt set, and recorded ads identified by a visible Sponsored label in the page HTML.

That design can detect differences in observed delivery across the simulated accounts. It cannot show which advertiser selected a person, whether ChatGPT knew a real user’s income or race, or which internal signal caused an individual ad. ZIP-code characteristics are group-level proxies, not personal attributes.

What the audit actually ran

Completion was imperfect. The paper reports 65% prompt completion and 59% complete account-days. The authors also suspect the sharp drop in collection after March 31 may reflect accounts being flagged as inauthentic. That possibility limits any claim about stable delivery over time.

Why the paper contains three ad counts

The abstract says the team collected more than 3,000 ads from 186 advertisers. The contribution section and findings report 3,602 ads from 191 advertisers. A sector figure refers to 3,303 confirmed impressions. These values should not be treated as interchangeable.

The paper does not provide a single sentence reconciling every difference. SearchEngineAnswer therefore uses 3,602 and 191 for the full-study result, 3,573 and 63,657 for the main-window rate, and 3,303 only when discussing the sector figure.

Ads appeared in 5.61% of the principal-window conversations

The main window contained 63,657 interactions and 3,573 ads. Dividing those values produces 5.61%. After March 31, the researchers collected 29 more ads, bringing the full total to 3,602.

That 5.61% is not a population-wide ChatGPT ad rate. The daily schedule included extra prompts that had previously elicited an ad; the accounts were simulated; only free accounts were included; the study was U.S.-based; and only responses with the detectable sponsored treatment counted as ads.

Among 53 accounts that ever received an ad, the median delay from activation to first exposure was 14 days. No exposed account received an ad in its first seven days. For the 48 accounts exposed during March 8-31, the median rate after first exposure was 24%. “Five-point-six percent of conversations,” “ever exposed,” and “24% after first exposure” answer different questions.

The income result is an association with a ZIP-code proxy

The researchers report an odds ratio of 0.98 for each $1,000 increase in ZIP-code median household income, with a 95% confidence interval of 0.96-1.00 p = 0.0438. In this sample, accounts assigned to lower-income ZIP codes were more likely to receive at least one ad.

The result does not show that advertisers targeted low-income people, that ChatGPT inferred an individual user’s salary, or that income caused the delivery difference. Location, account history, availability, prompt response, advertiser supply, and unobserved rollout rules may all matter. The study did not find a statistically significant race association, but only 16-19 exposed accounts were available per racial group, so the null result is underpowered for small effects.

Product and how-to prompts produced many visible ads

The study reports that retail and information advertisers accounted for 57% of the 3,303 sector-classified impressions. Retail contributed 1,057 and information 843. The result describes this early sample, not the future composition of ChatGPT advertising.

Several high-rate prompts involved an immediate product or service decision. Examples in the paper include replacing a broken headlight, fixing a dishwasher that will not drain, choosing a streaming service and evaluating a new iPhone. Sensitive categories such as politics, mental health and financial services were included in the prompt corpus; their treatment should be audited separately rather than inferred from the aggregate.

OpenAI documents the selection inputs at a higher level

OpenAI’s ads announcement says ads are clearly labeled and separated from answers. It says selection may use the topic of the current conversation, past chats when ad personalization is enabled, and prior ad interactions. OpenAI also says advertisers receive aggregate performance information rather than users’ conversations, memories or personal details.

Those are product-policy statements, not a disclosure of the ranking or auction system used for each impression. They do not explain the study’s income association. The independent audit and OpenAI’s documentation belong in the same record, but one does not validate every claim made by the other.

What publishers and advertisers should test

1. Separate ad eligibility, an eligible response, a rendered sponsored unit, a click and a downstream conversion.
2. Freeze the prompt panel, account type, plan, geography, interface and observation window before comparing rates.
3. Report all responses, then show ad-producing subsets separately so enrichment does not become a hidden denominator.
4. Keep sensitive-topic prompts in their own stratum and record zero-ad responses.
5. Preserve screenshots or HTML evidence without publishing account credentials, personal data or residential proxy details.
6. Record advertiser, landing page, label, placement and answer separation; do not infer targeting intent from delivery alone.

For paid-versus-organic analysis, pair this with the ChatGPT product-feed and organic-shopping distinction. For context-hint testing, use the conversation-matching protocol and keep its fixture results separate from live delivery.

Download the conversational-ad audit ledger

Download the CSV audit ledger. It records the unit of analysis, prompt family, account state, observable ad event, denominator inclusion, evidence path, limitation and review decision. Every included row is marked EXAMPLE-REMOVE; replace or delete those rows before a real study.

The template is a reproducibility aid, not a benchmark and not a copy of the researchers’ dataset. Do not store account passwords, tokens, personal prompts, raw proxy identifiers or user-level demographic data in the public file.