According to a report from PPC Land this week, OpenAI is giving ChatGPT advertisers a new way to prove their campaigns work, and the clock is ticking to get ready.

As reported by PPC Land, the company announced that conversion-optimized campaigns will begin rolling out in early June, with early access opening June 5 for advertisers who have conversion tracking in place. The shift moves ChatGPT ads from a reach-and-traffic play into a direct performance channel, one where the platform optimizes delivery toward actual purchases and leads rather than just clicks.

The change is structural. Until now, ChatGPT’s Ads Manager offered two objectives: Reach and Clicks. Conversion optimization adds a third option, allowing the system to use post-click behavioral data to shift spending toward users statistically more likely to complete a defined action. That signal has to come from somewhere, which brings us to the infrastructure.

The measurement loop

OpenAI provides two tools to close the attribution loop: a JavaScript Pixel for browser-side tracking and a server-side Conversions API for direct backend-to-platform transmission.

The JavaScript Pixel captures a “privacy-preserving identifier” from the landing page URL and stores it in a first-party cookie. The Conversions API sends events server-to-server, which means browser-level ad blockers and cookie restrictions don’t interrupt the data flow. Running both simultaneously—a dual-tracking approach that mirrors how Meta’s Conversions API is commonly deployed—requires deduplication to avoid counting the same conversion twice.

For teams without dedicated engineering resources, a community GTM template published by marketing analytics professional Utku Gulden on May 7 offers the fastest path to pixel deployment through standard tag management workflows.

The performance question

Before June 5, comparing ChatGPT to other ad platforms meant looking at CPM rates and cost-per-click benchmarks. The conversion objective changes the comparison set. The relevant question becomes cost per conversion, which is the metric performance buyers use when deciding where to move budgets.

Early data is suggestive but unverified. Criteo, one of several technology partners operating inside the ChatGPT ad pilot alongside Kargo, Adobe, Pacvue, and StackAdapt, reported on May 5 that AI-referred conversion rates approached twice those of traditional search in several retail categories: consumer electronics, lifestyle and wellbeing, and home and garden. Those figures come from Criteo’s own client base. Independent third-party verification has not been published.

The platform crossed $100 million in annualized revenue within six weeks of launching, according to Reuters. With 700 million weekly active users, the audience scale has never been the question. Conversion optimization is what makes that audience actionable in the terms performance marketing actually uses.

The legal framework arrived before the technical one. OpenAI updated its US privacy policy on April 30, disclosing for the first time in binding language that it receives purchase data from advertisers and their partners to measure ad effectiveness, shares personal data with marketing partners for third-party targeting, and uses personal data to promote its own products. Five days later, the technical infrastructure the measurement model depends on was available at scale.

June 5 is the date the platform stops being a branding experiment and starts being a performance channel. Whether the conversion rates justify the move is a question each performance team will have to answer with their own data.

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Google is not a company anyone expected to root for in the AI era. The early Bard demos were rough, Perplexity and ChatGPT were peeling away curious users, and antitrust regulators were closing in. The narrative a year ago was that the search giant had finally been outmaneuvered.

Now that looks dead wrong. Google is in a much stronger position today. Not because of it’s just coming off a prolific I/O developer conference, and not because it suddenly has the best model or the most capable AI ecosystem. Those titles get passed around the big labs every few months in any case. The reason is simpler: the money is still coming in.

The business is adapting

The Q1 numbers tell the story. Alphabet’s Google Services revenue was up 16% to $89.6 billion. Google Search and “Other” revenue was up 19%. The data is a strong indicator that the supposed AI disruption to its search products hasn’t dented the ad machine. If anything, it has fed it.

That confidence showed at I/O. Google announced many new AI products, but one of the most notable ones to the media industry was a set of new ad formats. Conversational Discovery ads are built on the fly to fit naturally into the answer to the person’s query, appearing as a “sponsored” section. Highlighted Ads and AI-powered Shopping Ads work similarly inside general product category queries. And then there are Business Agents for Leads, tailored versions of Gemini that live inside the ad itself.

These formats are still in testing, but the direction is obvious. Google is getting more sophisticated about how it monetizes AI experiences. A few months ago, the company stated it had no plans to sell ads in Gemini, a line executives floated in response to ChatGPT ads. Technically that line is still operative; Google can still say that the Gemini chatbot is not becoming an ad product. But that distinction feels less meaningful now that so many Gemini-powered AI experiences across Search are being commercialized.

Here is the part publishers should sit with. All those AI-powered ads appear within or next to an answer. That answer is built, in large part, from the work of media publishers. In the old system, Google sold ads next to results, and those ads benefitted from the close proximity to links from trusted media sources. Search the best SUVs and you may see ads for Toyota or Hyundai before you see a link to Car and Driver.

Now the information, built in part from the publisher’s content, is right there on the result. The user gets the info, the AI-powered ad provides a path to transact, and everything is handled without any need for them to ever leave Google. The shift is fundamental. Instead of monetizing the path to information, Google is now monetizing the information experience itself.

Publishers, of course, get cut out of that bargain. In many cases, their content was the raw material that informed the answer. Early in the AI search era, Google’s pitch to publishers was that AI-referred traffic was higher quality, more likely to engage and transact. That was, broadly, true. But why would users engage on a publisher site when Google is providing the means to do that before they ever arrive? The new ad formats are an acceleration of a trend that was already bad for publishers.

Trust is the variable everyone is missing

And yet. Users don’t care about business models. Whether they have an inclination to buy something or engage depends not just on the content of the answer but on how much they trust it. That is where the calculus gets interesting for publishers. A study published in Nature described trust in AI as dynamic and context-dependent. In other words, it changes depending on the nature of the AI experience and over time. A separate study by the Reuters Institute found users had moderate trust in AI answers, but they also value their speed and aggregation. Translation. Utility is high. Trust is conditional.

One of the most important assets any media brand has is the trust it cultivates over time. Imagine two AI answers about the same product. One built from social posts, blogs, Reddit threads, and online forums. The other built from articles on Consumer Reports, the Wirecutter, Time, and CNET. The user doesn’t need to know the methodology to feel the difference. Which one sounds more trustworthy?

Citations, in other words, are not decoration. People will be more inclined to trust answers created from brands that they’re familiar with. Hard data on AI ad performance is thin, but the entire media ad model is founded on this idea. An ad doesn’t just benefit from being present on a platform. It benefits from being associated with that platform’s brand. Ads inherit context. They always have.

Google has not, to date, been especially responsive to what publishers want. But Google does need advertisers to believe AI search ads work. That need is the leverage. If advertisers see better performance when ads appear beside credible, well-sourced answers, they will care about the quality of those answers. Once advertisers care, Google has to care. That could create pressure on Google to maintain a healthier source ecosystem.

What that pressure looks like is the open question. It may not look like simple licensing deals. It could involve clearer traffic paths, richer citation treatment, new publisher products, commercial partnerships, or advertiser demand for premium source environments inside AI search results. Each of those is a different commercial conversation publishers should be having now, not later.

The click fades but the value doesn’t

Review sites are the clearest example because the transaction path is obvious. If someone asks for the best dishwasher, the AI answer can cite reviews and then push the user toward purchase. But the same logic extends well past commerce. A health answer, a travel plan, or even a summary of a political issue all depend on source trust. Even when there’s no immediate checkout, the user’s confidence in the answer shapes what they believe and what they do next.

For publishers, the warning is straightforward. Google’s new push into AI ad experiences could further weaken traditional publisher revenue streams, especially traffic-based display, affiliate, and search-driven monetization. For practitioners trying to think a step ahead, there is another side to the equation. If AI answers need credibility to be useful, then credible media still has value. That value will not always show up on a referral chart. But it will still shape whether users trust the answer enough to act on it.

A version of this column appears in Fast Company.

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NewsGuard has identified more than 3,000 AI content farms—more than double what it could find a year ago using manual techniques—and it’s now partnering with AI detection startup Pangram Labs to scale that tracking as the problem accelerates.

AdWeek reports the new detection tool, announced Thursday, uses Pangram’s proprietary models to scan not just individual pages but entire domains for signs of AI-generated content at scale. When Pangram flags a site, NewsGuard analysts review it manually before applying a formal “AI content farm” designation. Sites qualify when a substantial share of content appears AI-generated, there’s no disclosure to readers, and the site’s presentation could convincingly pass as human-produced journalism.

The scale of the problem is striking. Between 300 and 500 new AI content farm sites are emerging every month, according to Pangram. Many operate under generic news-adjacent names (e.g. Times Business News, Business Post) and publish misinformation about real brands, politicians, and public health. In one case, a site called Citizen Watch Report falsely claimed two U.S. senators spent $814,000 on hotels in Ukraine; the story was amplified by Russian state media before being debunked.

Another site falsely claimed Coca-Cola threatened to pull its Super Bowl sponsorship over a halftime show for which Coca-Cola wasn’t even a sponsor. Both sites ran ads from major brands.

That last detail is the commercial mechanism. Most of these sites are made-for-advertising (MFA) operations—cheap content churned out to capture programmatic ad spend. In a two-month period, NewsGuard found 141 blue-chip brands advertising on AI content farm sites. The slop economy runs on their budgets.

“If we can’t detect AI content, then every communication space is going to be flooded with inauthentic content that’s cheap to produce and difficult to impossible to differentiate [from] something authentic,” Max Spero, Pangram’s CEO, told AdWeek’s Kendra Barnett.

NewsGuard’s detection data will be available for advertisers to license directly or through their agencies, with a pre-built integration into The Trade Desk for pre-bid blocking. A consumer-facing browser extension integration is also under consideration. Pangram, founded in 2023 by a former Google engineer and an ex-Tesla scientist, gained independent validation when a Nature report last September found it highly capable of flagging AI-generated academic papers.

The detection arms race is worth watching. Early AI content farms were easy to spot — sites would publish articles containing ChatGPT error messages verbatim. Today’s operations are more sophisticated. The tools to catch them are getting sharper too, but the math still favors the farms: generating slop is cheaper and faster than reviewing it.

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OpenAI began testing ads in ChatGPT on Monday for users on Free and Go subscription tiers, marking a major shift for the world’s most popular AI chatbot. The move came hours after rival Anthropic ran Super Bowl commercials ridiculing the idea of ads in AI responses.

The timing underscores an escalating feud between the two AI companies over business models, safety practices and the future of artificial intelligence.

Anthropic’s Super Bowl ads showed glassy-eyed actors playing AI chatbots delivering advice alongside poorly targeted advertisements. Each commercial ended with “Ads are coming to AI. But not to Claude.” The spots directly targeted OpenAI’s January announcement that ChatGPT would include advertising.

OpenAI CEO Sam Altman responded on Twitter last week, calling the ads “clearly dishonest” and labeling Anthropic an “authoritarian company.” He defended the ad business as necessary to make free ChatGPT financially sustainable while covering development costs.

“More Texans use ChatGPT for free than total people use Claude in the US, so we have a differently-shaped problem than they do,” Altman wrote. He accused Anthropic of serving “an expensive product to rich people” and wanting “to control what people do with AI.”

The ads began rolling out Monday to U.S. users logged into Free or Go accounts. The Go plan costs $8 per month and launched globally in mid-January. Paid subscribers to Plus, Pro, Business, Enterprise and Education tiers will not see ads.

OpenAI promises ads will not influence ChatGPT’s answers and that user conversations remain private from advertisers. In a blog post, the company says ads will be “clearly labeled as sponsored and visually separated” from responses, with targeting based on conversation topics, past chats and previous ad interactions.

Users researching recipes might see ads for grocery delivery or meal kits, OpenAI said. The company claims advertisers receive only aggregate performance data like views and clicks, not individual user information.

Ads will not appear for users under 18 or near sensitive topics including health, politics or mental health. Users can dismiss ads, view why they were shown, and manage personalization settings.

For newsrooms evaluating AI tools, the ad rollout raises questions about trust and influence. While OpenAI insists ads will not affect responses, the company needs revenue to sustain operations. Anthropic argues ads create incentives to optimize for engagement over helpfulness.

“The most useful AI interaction might be a short one, or one that resolves the user’s request without prompting further conversation,” Anthropic wrote in a press release last week.

The shift marks a reversal for Altman, who once called “ads-plus-AI” a “last resort” and “sort of uniquely unsettling.” OpenAI tested app suggestions that looked like ads in December, drawing backlash before announcing the formal ad program in January.

The OpenAI-Anthropic rivalry extends beyond business models. Anthropic co-founders Dario and Daniela Amodei are former OpenAI employees who frequently critique their former employer. Dario Amodei evangelizes about AI superintelligence risks, while Altman takes a more optimistic view. Employees from both companies reportedly back opposing super PACs on AI regulation.

The rivalry between the two companies has played out publicly before. When Anthropic released Claude Opus 4.6 with 1M token context earlier this month, it positioned the model as focusing on helpfulness over engagement metrics — a subtle dig at competitors pursuing ad-supported models.

Anthropic’s research has also criticized certain AI behaviors. The company studied 1.5 million conversations and found its chatbot exhibits “disempowerment” — being too agreeable and not pushing back when users make poor decisions. That research implicitly questions whether ad-supported models might amplify such behaviors to maximize engagement.

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OpenAI is taking the “New Year, new you” approach when it comes to its business strategy. To start 2026, the company said it would soon be introducing ads ChatGPT, which is a bit of a shock considering CEO Sam Altman had previously called advertising a “last resort” for the business.

Whether this is truly the end-of-the-road option is tough to judge without a peek at OpenAI’s balance sheet, but it’s not hard to see why they’re feeling pressure. After Google released Gemini 3 in the fall—which led to strong leaderboard results, increased market share, and plenty of AI-world praise—Altman reportedly declared a “code red” to keep ChatGPT best-in-class. And while OpenAI’s fundraising has been impressive, Google is a $4 trillion company. OpenAI needs all the resources it can get.

So ChatGPT users are getting ads. That’s a gamble, because plenty of signals suggest people don’t want advertising blended into AI answers. A report from Attest, a consumer research company, found that 41% of consumers trust AI search results more than paid search results, suggesting that AI users like that they don’t have to worry about ads in AI summaries, even if their accuracy may sometimes be questionable. Hallucinating is apparently less of an offense than selling out.

Still, the broader direction is hard to ignore: ads inside AI products are starting to feel less like a possibility and more like gravity. People don’t love commercials on TV or streaming, either, but they’re baked into the media economy. Google is already placing ads in AI Overviews and AI Mode, and it may eventually do something similar in Gemini—even as executives insist there are no plans. Whatever happens with the Gemini chatbot specifically, Google looks committed to threading advertising through its AI experiences, which is about as surprising as rain.

The ad pie gets sliced thinner

For publishers, none of this sounds like a party. If OpenAI becomes an ad platform, it’s another Big Tech competitor fighting for the same finite pool of digital ad dollars—right alongside Google, Meta, and Amazon. Meanwhile, the traffic equation keeps getting uglier: chatbots summarize the web, and that often removes the reason to click. There’s a reason web traffic to publishers dropped by a third last year.

But there’s a twist: Advertising tied directly to an AI answer might provide the cleanest argument publishers have had for getting paid. When a publisher’s work helps shape an answer, the revenue link has always been fuzzy; users typically subscribed long before they asked anything, and plenty of AI products run on free tiers. However, if your reporting powers an answer, and that answer generates money for the AI company through impressions or transactions, the chain from content to dollars suddenly looks a lot less abstract.

It’s also easier to measure than the old world ever was. Classic SEO involved a lot of educated guesswork based on keywords and clicks. AI queries are often longer and more specific, and the tooling is better at teasing out intent. That makes it much more feasible to identify which answers—and which underlying sources—most reliably push people toward a purchase.

OpenAI tried to tamp down commercialization anxiety by laying out its advertising first principles, including a promise that ads won’t change the substance of ChatGPT’s answers. In theory, if Coca-Cola buys a campaign, ChatGPT shouldn’t become any more (or less) likely to mention Coke than it was the day before. But it’s fair to wonder whether the system could still nudge users toward a transaction in general—say, buying a soft drink—while the ad sits nearby as the convenient button to press.

The new SEO: make it persuasive

Even if OpenAI keeps its side pristine, it can’t control how brands—and the publishers who want to be seen—adapt their own behavior. How effective those tactics will be is anyone’s guess, but it’s a safe bet they’ll be attempted. The emerging field of GEO (generative engine optimization) feels poised to grow a new limb: not only shaping whether you appear in an AI answer, but how strongly that answer prompts someone to act. You’re not just optimizing for presence, but also persuadability.

Right now, this is all mostly theory—and it’s entirely possible that Google, OpenAI, and everyone else will capture the ad upside for themselves. But the moment AI answers become meaningful revenue machines, marketers will obsess over which answers convert best, and what ingredients those answers contain. If publishers can demonstrate they supply the secret sauce, they’ll be in a stronger position to demand their slice.

Of course, “demonstrate” is doing a lot of work in that sentence. Real bargaining power requires proof, and proof starts with measurement—which won’t happen by accident. Figuring out how content appears in, influences, and performs inside AI answers is brand-new science, but it is science: testing, iterating, and using whatever levers exist—snippets, bot blocking, and dedicated GEO platforms among them.

Over the past 25 years, Silicon Valley slowly built tremendous platforms that ended up consuming the vast majority of advertising revenue, locking out the media in the process. And yes, AI could easily extend that same arc. But there’s an irony in monetizing AI answers with ads: It may end up creating the best opportunity for publishers to define exactly how much value they bring to them.

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