ChatGPT Ads: Ads Without Keywords. What It Means

In brief: Since late August 2026, ads in ChatGPT have been available. They are not purchased based on keywords. The system aligns them with the meaning of the entire conversation, the content of the landing page, and the description of the situation in which the customer needs assistance. Ads appear directly below the model's response, meaning their effectiveness also depends on what ChatGPT has just said about the brand. Brand Semantics research across eleven industries, from construction and manufacturing to hotels, fashion, and legal services, shows that this background is rarely neutral. This shifts the skill set required for running campaigns from managing bids and phrases to managing meaning, context, and brand representation in language models. Below, I explain how this channel operates, why it could change the rules of the game in conversion, and what is required from a company wishing to leverage it.

1. What Actually Happened

OpenAI announced ads in ChatGPT on February 9, 2026, and tested them in the United States for six months. On August 24, 2026, the channel was launched in 31 European countries. The Ads Manager is currently in beta. In Europe, the first campaigns were launched through partners, with self-service being gradually rolled out.

Here are a few facts worth noting, as they help clarify the rest:

• Ads appear below ChatGPT's response, marked as sponsored and visually separated from the model's text. They are not embedded within the response.

• One format: brand name with a favicon, a headline of up to 30 characters, a description of up to 60 characters, a square image of 256×256 px, and a link to the landing page. No video, no carousel.

• Currently, they are visible to users on free plans (Free, and in markets where available – also Go). OpenAI states that ads are not shown on Plus, Pro, or Business plans, nor to users under 18. Ads also do not appear in temporary chats or for users who are not logged in. This is important to consider when planning reach. It is also worth monitoring whether this policy changes.

• Advertisers have no influence over the model's response and receive no user data: neither the content of the conversation, nor history, nor email addresses.

• Billing: CPM (cost per thousand impressions), CPC (cost per click; OpenAI recommends rates of $3–5), and – from August – oCPC, which optimises for conversions. The previous minimum budget of $50,000 has been lifted.

• Measurement: impressions, clicks, CTR, average CPC and CPM, spend, conversions. Campaigns can be tagged with UTM parameters, and conversions can be measured with a pixel and through the Conversions API. OpenAI notes that the channel currently lacks benchmarks for effectiveness across industries.

Sources: OpenAI Help Center – Ads in ChatGPT: The Basics, OpenAI Help Center – Create ads for ChatGPT Ads, Socialpress – ChatGPT Ads Launch in Poland, StackAdapt – How to Advertise on ChatGPT.

2. How ChatGPT Selects Ads. And Why It’s Not “The New Google Ads”

In search engines, the mechanics have been known for nearly a quarter of a century: a user enters a phrase, an advertiser bids on that phrase, and the one with the best combination of bid and quality score wins. The phrase is the common denominator. It can be purchased, excluded, and measured.

In ChatGPT, that common denominator does not exist. According to OpenAI's documentation, the system selects ads based on four factors simultaneously:

1. the context and intent of the entire conversation, not just the last sentence;

2. the content of the landing page of the ad, which OpenAI reads with its own bot (OAI-AdsBot) and checks for compliance with advertising policies;

3. the title and description of the ad;

4. contextual hints provided by the advertiser – and, if the user has enabled personalisation, selected signals from their previous conversations.

Contextual hints are not keywords. OpenAI explicitly states that they do not function like exact match and do not guarantee display in a specific conversation. They describe a situation of need: who is asking, about what, at what decision-making moment. Instead of the phrase “CRM system,” the advertiser describes “a sales team comparing reporting automation tools and wanting to reduce manual work.” The final decision on ad placement is made by OpenAI's relevance model.

Additionally, there is an auction. This is a second-price auction weighted by relevance: the bid is just one of the inputs, and the relevance of the ad and landing page to the conversation jointly determines who wins and how much they pay. In other words: if the landing page clearly describes what the product is, for whom, in what situations, and how it differs from alternatives, the system has a basis for matching, and the ad wins at a lower cost.

The implication is clear: you cannot “set a campaign” on phrases in this channel. You need to understand how people actually talk to the model about a given category, at what decision-making moment this occurs, and what the model responds with before displaying the ad.

3. Why Conversion May Look Different Than in Search Engines

To be fair: OpenAI does not publish benchmarks, and the market is just beginning to gather initial data. However, there are three independent premises that help understand why this channel has a conversion potential different from traditional media.

First, the moment. A ChatGPT user is not “searching”; they are already conversing. They asked a question, received an answer, and often clarified further. The ad appears at the moment when the need has already been articulated, and the selection criteria discussed. This is the closest to the decision moment that advertising has ever been.

Second, the quality of traffic. Shopify reported that in Q1 2026, traffic from GenAI assistants converted 49% better on product pages than traffic from search engines, and visits from this source increased eightfold year-on-year. Criteo, in a sample of 500 sellers, measured that traffic referred by language models converts about 1.5 times better than other referral channels. This data pertains to organic traffic, not paid, but it shows what type of user we are dealing with: someone who has already gone through the comparison stage in conversation.

Third, initial paid tests. A two-week test by SE Ranking in English-speaking markets yielded a 1.30% CTR on over 97,000 impressions. Another test, published by a Canadian agency, yielded a 0.65% CTR at a cost per click of approximately 4.4–4.9 CAD. The variance is significant and should be treated as a signal that the channel works, rather than a benchmark.

There is also a downside that needs to be clearly stated. Ads currently reach users on free plans, not Plus, Pro, or Business subscribers. Two published B2B tests suggest that business decision-makers use paid plans more frequently than the average user, meaning that ads are currently not reaching them. The cost per click is $3–5. Inventory is limited, there is only one format, and optimisation tools are still in their infancy. Every misalignment in this channel is costly. This is precisely why contextual precision ceases to be a matter of aesthetics and becomes a condition for profitability.

Sources: Brand Semantics – Shopify Q1 2026 Data on Homepage, Choice OMG – ChatGPT Ads 2026 Field Guide, Socialpress.

4. One Window, Two Layers: What the Model Says Above Your Ad

Here we arrive at something not covered in any guide to ChatGPT Ads, which – in my opinion – determines the outcome of the campaign.

The ad appears below the model's response. The user first reads what ChatGPT thinks about their problem, what solutions it recommends, which brands it mentions, and how it describes them. Only then do they see the sponsored card. This means that there are two layers of brand messaging in one window: the organic representation of the brand in the model's response and the paid card. The advertiser controls only the latter.

Let’s consider three typical situations we observe in model response studies conducted with our research tool Semantio:

Situation A: the model recommends a competitor. The user asks for a solution in a category, the model lists three brands and recommends one of them. Below appears an ad for a fourth brand – yours. The paid card competes with the recommendation the user has just read and trusted. A click is possible, but the ad starts from a position the model has already lost for it.

Situation B: the model provides incorrect information about the brand. Your brand appears in the response, but with an incorrect price, a non-existent certificate, a wrong location, or a feature attributed to a competitor. The ad below leads to a page that states otherwise. The user receives conflicting messages and does not know whom to trust.

Situation C: the model does not know the brand. The response is about a category, but your brand does not appear in it. The ad is the first contact – and the only argument. This is the most expensive scenario, as all persuasive work relies on 30 characters of the headline and 60 characters of the description.

There is also Situation D: the model recommends your brand, and the ad confirms it. In this case, the two layers work together: the organic response builds trust, and the paid card provides the shortest path to action. This is the only arrangement where the cost per click has a chance to yield a return.

What We See in Research: Eleven Industries, the Same Mechanism

The examples below come from brand representation studies we conducted in 2026 at Semantio for Brand Semantics clients and as part of a study by the AI Business Center Foundation using Semantio data. We provide the industries and the scale of the study because they demonstrate that the mechanism is independent of the sector. Each result is a dated snapshot from one day of research – models change their responses over time, which is why the measurement is repeated.

Three things from this table have direct implications for ChatGPT Ads.

First, “lack of brand in category questions” is the norm, not the exception. In eight out of eleven studies, the brand is nearly absent when the client does not yet know its name, precisely in those conversations where the ad would need to enter. In such a context, the ad operates in situation C: it is the only argument and pays the full price for every click.

Second, high recommendation does not guarantee a safe background. In the lighting manufacturer case, 16 out of 18 errors appeared in responses where the model recommended the brand. In the ventilation manufacturer case, models recommended the company while simultaneously attributing it to a foreign capital group. An ad under such a response reinforces both the recommendation and the error.

Third, the same question in different systems yields different backgrounds. For the regional sausage manufacturer, the recommendation score (on a scale of 0–100) varied from 16 to 51 depending on the system; for the ventilation manufacturer, four systems saw the brand in 20 out of 20 questions, while a fifth saw it in 13 out of 20. A campaign in ChatGPT pertains to one system, but the client compares across several – and the organic background must be understood in each of them.

The difference between situation A and D is not a matter of bid or creative. It is a matter of how the model represents the brand before the campaign even starts. And this is not visible in any advertising panel. To see this, one must ask the models the same questions clients ask – multiple times, in various forms, across several systems – and measure the responses. This is precisely the subject of Generative Engine Optimization (GEO) and the focus of measurement in Semantio.pro.

5. Four Competencies Required by This Channel

Since ChatGPT does not match ads to phrases but to meanings, the question is: who can work with meanings? From our experience, four competencies are needed. It is worth checking which of these your campaign team – whether internal or external – possesses.

5.1. Entity Map and Assertion Map – to Ensure the Landing Page is Understandable to the Model

The OpenAI system reads the landing page and assesses the ad's relevance to the conversation based on it. If the page describes the company as an “innovative technology partner,” the model has no basis to infer in which conversations the ad makes sense. If the page clearly states: what product, for whom, for what problem, in what category, with what evidence, and how it differs from alternatives – the model has a basis for matching.

Two examples from our audits show how often this condition is not met. The local construction centre's page rendered all content only in the client's browser – the bots that read the internet saw a “blank page”; the only thing they could read was two words. Such a page as a landing page for ads in ChatGPT has nothing to win the relevance auction, and the OAI-AdsBot has nothing to check. Meanwhile, a Polish industrial manufacturer operating in an international group had 699 out of 729 addresses in its site map leading to the foreign parent company's domain; models cited the Polish domain in 7% of responses and attributed references and certificates to another company. An ad for such a brand would lead to a page that the model does not associate with the entity it speaks about in the response above.

We call this semantic clarity. In practice, it means separating two things: entity map (what objects create the brand's world: products, recipients, problems, competitors, evidence) and assertion map (what statements about these objects should be true, source-verified, and retrievable by the model). A well-prepared landing page is simultaneously a better page in search engines, a better source for language models, and a cheaper landing in a relevance-weighted auction. This is one job with three effects.

5.2. Intention Scenarios – to Ensure Contextual Hints Are Data-Driven, Not Brainstormed

A contextual hint is a description of a need situation: who, what need, what context, what decision moment. To write it well, one must know how customers actually ask models about a given category – at the discovery stage (“how to solve problem X”), comparison stage (“how does A differ from B”), and decision stage (“where to buy, whom to commission”).

At Semantio, we work precisely on such units: scenarios, which are full questions embedded in a specific persona, product, and decision stage, directed to selected systems. A company that has been studying purchasing scenarios in the client's category for months writes contextual hints from measurement: it knows which situations end in recommendations, in which the model mentions competitors, and in which the brand is absent. A team that lacks such measurement writes hints from intuition – and only tests them with the budget.

5.3. Measurement of Brand Representation – Before, During, and After the Campaign

This is a competency that distinguishes running a campaign in ChatGPT from running a campaign in any other medium. Since there is an organic response from the model above the ad, one needs to know:

• before launch – in what percentage of purchasing scenarios the model mentions the brand, how often it recommends it, what its Share of Voice is against competitors, what tone it uses to describe it, and whether it provides any false information about it;

• during – whether the organic response supports the ad (situation D), or fights against it (A, B, C); whether changes on the page and in sources shift the model's responses; what sources the model cites when discussing the category;

• after the campaign – what exactly the model said about the brand on the day of airing, in a format that can be shown to management or clients.

In practice, this measurement changes campaign decisions. For the gate and door manufacturer, the organic background at the decision phase was neutral (0% direct recommendations with a sentiment of 8.1/10) – the ad only makes sense there together with evidence on the landing page that allows the model to justify a higher price. For the shoe brand, the background at the decision phase was directly unfavourable (mentions of complaints) – a campaign without prior work on sources would pay for clicks from customers who just read a warning. For the premium hotel, the background was excellent in wellness scenarios and poor in conference ones – contextual hints and landing pages must differ for each of these segments.

Semantio measures these dimensions on live responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overview, Microsoft Copilot, Grok, and DeepSeek: Visibility Rate, Share of Voice, recommendation level, sentiment, source impact, and detection of statements contradictory to verified facts about the brand. Each result is a dated measurement from a specific study, not a forecast from signals on the page. This means that a campaign in ChatGPT is not run in the dark: we know what organic background the ad is displayed against and whether that background works for or against it.

5.4. Evidence – to Ensure the Results Can Be Defended

In a channel without benchmarks and with a 24–48-hour reporting delay for conversions, it is particularly important that diagnoses and conclusions are auditable. At Semantio, every model response is logged along with metadata, classification, and cited sources, and selected responses can be preserved as a verifiably independent, tamper-proof record of what a given system said on a given day.

6. How We Run Campaigns in ChatGPT

At Brand Semantics, we manage ChatGPT Ads campaigns from diagnosis to reporting – with direct access to the Ads Manager and our own measurement tool. The process looks like this:

1. Diagnosis of brand representation in models. Study in Semantio: real purchasing scenarios in the client's category, in the market language, across several systems. Result: where the brand is recommended, where it loses to competitors, where models are mistaken, and what sources shape the response.

2. Entity map and preparation of the landing page. Organising what the model needs to understand about the brand and translating it onto the landing page: clear, source-based, allowing OpenAI's bots (OAI-AdsBot and OAI-SearchBot – blocking either means rejection of the ad or disappearance from organic responses).

3. Scenarios → contextual hints and creatives. From the measurement arise need situations where the ad makes sense, and variants of headlines and descriptions tailored to the decision stage.

4. Campaign in Ads Manager. Objective (reach, clicks, or conversions), geography, platform, daily budget, conversion measurement via pixel and through Conversions API configured before launch.

5. Monitoring two layers. Concurrently: campaign results from Ads Manager and periodic measurement of organic model responses in Semantio. If the organic background works against the ad – GEO actions on sources and content, not just on bids.

6. Report with evidence. What the model said, what the ad did, what changed between measurements – with logs and verifiable records of responses.

7. Who Should Enter Now, and Who Should Wait

It is worth testing now if the brand operates in a category where the customer compares, asks, and returns with further questions: e-commerce with considered products, electronics, personal finance (within OpenAI's advertising policy), telecommunications, automotive, travel, education, services chosen after research, as well as home products, construction and finishing products, or children's items. It is also worthwhile if the company wants to gain experience in the channel before competitors do – in a young channel, the cost of learning is lower than the cost of delay.

It is worth waiting if:

• customers are mainly B2B decision-makers using paid ChatGPT plans – ads will not reach them for now; in this case, the organic representation of the brand in responses, which all users see regardless of plan, is more important. In the ventilation manufacturer study, we termed this “a tender lost before the tender”: the designer asks the model what to include in the specification, receives a list without the brand – and the brand only enters as a substitute at the execution stage, competing on price. A sponsored card will not fix this, only presence in the response itself;

• the product is an FMCG or local service with a low basket value – with CPC at $3–5, the economics do not add up;

• the industry is subject to advertising policy restrictions (alcohol, gambling, political content is prohibited; health and financial services are limited, and mostly not allowed outside the USA);

• the company does not have a landing page that the model could understand – in this case, the first step is not a campaign, but organising brand representation.

Regardless of whether the company enters paid advertising, the organic layer always operates: a user on a paid plan will not see the ad, but will see what the model says about the brand. For many B2B companies, this is now a more important channel than any sponsored card.

8. ChatGPT Ads Readiness Checklist

Before launching a campaign, it is worth answering ten questions:

1. Do we know what percentage of real purchasing questions in our category ChatGPT mentions our brand at all?

2. Do we know whom it recommends instead of us – and why (what sources)?

3. Does the model provide false information about us that the ad would reinforce instead of correcting?

4. Does the landing page clearly describe the product, recipient, problem, and evidence – in a language the model can grasp?

5. Does the page allow OAI-AdsBot and OAI-SearchBot?

6. Do we have intention scenarios for three decision stages, from which contextual hints arise?

7. Do we have several variants of the headline and description, each showing a different benefit, rather than the same message?

8. Is conversion measurement (pixel, Conversions API, UTM) configured before launch, not after?

9. Do our clients use the free ChatGPT plan?

10. Do we have a way to show management what the model said about the brand on the day of airing – in a verifiable format?

If the answers to questions 1–3 are “we don’t know,” the campaign will launch in the dark – regardless of who runs it.

9. Glossary of Terms

ChatGPT Ads – OpenAI's advertising channel where sponsored cards appear below ChatGPT responses to users on free plans; matching is based on the context of the conversation, the landing page, the creative, and the advertiser's contextual hints, not on keywords.

Contextual Hints – descriptions of need situations (who, what need, what context, what decision moment) provided by the advertiser in Ads Manager; they are not exact matches and do not guarantee display.

Generative Engine Optimization (GEO) – a discipline following SEO: instead of ranking on a list of links, the goal is how language models describe, compare, and recommend the brand in a finished response. It includes organising entities and assertions about the brand, building sources that models cite, and measuring responses.

Brand Representation in Language Model Responses – the observable way in which a brand is present, described, compared, recommended, and sourced in responses from GenAI systems in a specific study. Visibility is just one of its dimensions.

Share of Voice (SoV) – the brand's share of “airtime” in the model's responses compared to named competitors in a given set of scenarios.

Brand Hallucination – a model's statement about a brand that contradicts verified facts: incorrect price, non-existent certificate, wrong location, feature attributed to a competitor.

Entity Map / Assertion Map – tools for organising brand representation: a list of objects that create the brand's world (products, recipients, problems, competitors, evidence) and a list of statements about these objects that should be true, source-verified, and retrievable by the model.

Semantio – Brand Semantics' research platform for analysing and monitoring brand representation in responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overview, Microsoft Copilot, Grok, and DeepSeek, based on live responses, in market language, with logs and verifiable evidence.

10. Frequently Asked Questions

Can advertising in ChatGPT change what the model says about the brand? No. OpenAI separates the advertising system from the model. The advertiser cannot influence the content, order, or tone of the responses. Changing the organic representation of the brand is only possible through work on sources, content, and information structure – that is, through GEO.

Can I buy an ad for the phrase “best X in Warsaw”? No. There are no keywords. You can describe a need situation in which the ad makes sense and ensure that the landing page and creative are relevant to it. The decision on placement is made by OpenAI's relevance model.

How much does a click cost? OpenAI recommends CPC rates of $3–5. The first published foreign tests fall within this range. There are no Polish benchmarks yet.

Will my B2B clients see this? If they are on a free plan – yes. There are currently no ads on Plus, Pro, and Business plans; however, their users will see the organic model responses about the brand, which determine whether the brand makes it to the shortlist.

Where to start? With measurement: what ChatGPT and other models currently say about the brand in real purchasing questions. Without this, it is unclear whether the ad will reinforce a recommendation or compete against it.

Grzegorz Miłkowski – CEO of Brand Semantics P.S.A., a consulting and technology company focused on brand representation in language models and creator of the research platform Semantio.

Sources: OpenAI Help Center – Ads in ChatGPT: The Basics · OpenAI Help Center – Create ads for ChatGPT Ads · Socialpress – ChatGPT Ads Launch in Poland · StackAdapt – How to Advertise on ChatGPT · Choice OMG – ChatGPT Ads 2026 Field Guide · Adsmurai – Hints in ChatGPT Ads · Brand Semantics – SEO/GEO Report 2026