OpenAI is officially in the ad business. And not just any ads — super-premium, top-shelf, $60-CPM ads. That’s the pricing territory of live NFL broadcasts, way above Meta’s sub-$20 CPM and miles from your average programmatic placement.
The pitch? ChatGPT isn’t like Google or social media. It’s a new kind of environment, where advertisers reach users not with search results or news feed interruptions, but in the middle of a conversation. OpenAI is reportedly asking advertisers to commit nearly $1 million upfront for access, charging per view rather than per click. Ads will appear for both logged-in free users and subscribers to the new $8/month ChatGPT Go plan.
Behind this aggressive move is a practical need: compute costs. OpenAI expects to burn through at least $9 billion this year, and advertising is one way to offset that fire. But can this intent-rich, conversation-native model really deliver the return marketers expect at this price point?
The rise of the “intent economy”
To understand OpenAI’s bet, you have to understand the shift it’s proposing. In the traditional search world, advertisers bid on keywords. They hope to intercept a user’s intent in the split second it surfaces. In ChatGPT, that intent is already known.
“The fundamental difference is that ChatGPT advertising operates in a post-intent environment,” said Caroline Giegerich, VP of AI at IAB. “With search, you’re bidding on keywords to intercept intent as users express it. With conversational AI, intent has already been identified through dialogue. The AI knows not just what you’re looking for, but the context and constraints behind it.”
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“Search advertising operates in the attention economy,” added Calvin Scharffs, VP of Marketing at Orange 142. “Users type short, ambiguous queries, and advertisers infer intent from keywords, past behavior and probability models. ChatGPT operates in the intent economy. Users articulate their needs directly and completely in natural language. There is no guessing.”
That shift changes not just how ads are targeted, but also when and how they appear. “Instead of interrupting a search results page,” said Scharffs, “brands can appear at the exact moment intent is fully formed, inside a decision the user has already defined.”
Data precision vs. data opacity
If this model is so precise, why the hesitation? For one, marketers don’t yet know what kind of data they’re getting. OpenAI is likely using conversation data internally to surface relevant ads — for example, showing travel ads if a user mentions a trip. But it’s unclear what parameters are exposed to advertisers, what counts as “personalization,” or how granular targeting really is.
“Most likely, OpenAI is using conversation data internally to inform targeting (e.g., ‘this person is discussing travel, show travel ads’) without sharing raw conversation content with advertisers,” said Nicole Green, VP Analyst at Gartner. “But the vague language leaves critical questions unanswered: What targeting parameters do advertisers actually get? What does ‘personalization’ include? What performance or audience data is provided?”
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Ultimately, unless OpenAI clarifies what data and controls advertisers actually get in ChatGPT, it’s impossible to fairly compare its targeting capabilities to Search, which has had decades to build a mature system of audience insights, behavioral tracking and attribution.
And then there’s the question of attribution.
When a user sees a product recommended by the AI and also sees an ad for it, what drove the click? Was it the AI’s logic or the ad placement? That blurring of organic versus paid introduces complexity search ads don’t have.
“Clear, consistent labeling is essential,” said Scharffs. “If users can’t immediately tell what’s paid versus organic, trust erodes quickly.”
Governance, risk and the hype gap
Green sees governance as one of the biggest challenges ahead.
“We have never worked with any technology in this capacity before,” she said. “For marketing, that means we really need to elevate our risk profile. Particularly for regulated industries, it means bringing compliance and legal into the conversation much earlier.”
She also pointed to the hype gap: “The biggest impact of AI on marketing this year has been the need to manage expectations. There is so much energy around GenAI, AI agents, emotion AI, machine customers—but the executional maturity isn’t always there.”
What comes next is likely more radical. As AI agents begin transacting on behalf of humans, marketers will need to consider not just human journeys but machine ones.
Preparing for machine customers
“We predict that 15% of revenue will come from machine customers by 2030,” Green said. “Sophisticated AI agents will buy and sell much like corporate entities do today. As we look at the future of marketing, it’s going to actually get harder. We’re going to need human journeys, machine journeys and combinations of both.”
That changes how marketers approach everything from messaging to UX to data structure. Caroline Giegerich advises that brands must now treat their reputation as a dataset.
“Your product information needs to be AI-readable,” she said. “If an AI can’t easily understand what you sell, who it’s for and why it’s differentiated, you won’t get recommended… Brand reputation becomes algorithmic input. Reviews, mentions, third-party validation all become signal for whether an AI recommends you.”
And in this new environment, direct brand relationships could see a renaissance.
“When a user expresses clear intent in a conversational environment,” Scharffs said, “there’s a chance for a brand to show up directly—with its own voice, terms and experience.”
Whether OpenAI’s $60 CPM ask is justified remains to be seen. But it’s clear that AI-powered environments are challenging long-held assumptions about how advertising works, what targeting means and what a customer even is.
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