ChatGPT users on the mobile app saw roughly twice as many ads per hour this July as they did in April, according to Business Insider. The jump coincides with a sharp expansion in the pool of advertisers buying those slots.

US data from market intelligence firm Sensor Tower estimates OpenAI has nearly tripled its advertiser count on ChatGPT, from about 300 in April to more than 820 in July. At least 160 advertisers joined in July alone.

The advertiser mix has shifted too. Tech companies including Speechify, Jotform and Natural Intelligence dominated the top ranks in April. By July they had fallen out of Sensor Tower’s top 10, replaced by Booking Holdings, Intuit, Home Depot and L.L.Bean.

Scheels, a sports clothing retailer, was the only company to sit in the top 10 in both months.

The shift extends beyond advertiser count. Retail still accounts for the largest share of ChatGPT ad spending, but its dominance is slipping as financial services, travel and tourism, and jobs and education gain ground.

Financial services has grown the fastest, rising from 2% of ad spend in April to 12% in July.

Sensor Tower identified BestMoney.com as one of ChatGPT’s top advertisers in July, saying the broader mix suggests OpenAI is diversifying its advertiser base — a potentially positive sign for monetization.

The numbers remain a fraction of OpenAI’s ambitions. The company reported $100 million in annual recurring ad revenue in May — a rounding error next to Google’s $81.6 billion and Meta’s $59.3 billion in ad revenue in their most recent quarters.

OpenAI serves ads to users on its free tier and its $8-a-month Go plan, which together account for the vast majority of a user base Sensor Tower estimated at 1 billion monthly app users in May.

OpenAI ads chief Dave Dugan told Business Insider in June that the company initially kept ad placements deliberately limited to protect the user experience. Four months in, he said, the team has grown more confident about scaling up. For now, ads appear beneath ChatGPT’s answers, while OpenAI avoids placing them alongside queries about personal health or politics.

For publishers and media buyers, the bigger story is not today’s ad revenue but where the next wave of attention is forming. A chatbot with a billion users putting ads beneath its answers creates a new pool of inventory competing with search and social for the same budgets.

OpenAI’s self-serve ad platform, launched in May, remains rudimentary compared with the mature systems run by Google and Meta. That makes the early surge in big-brand spending look more like testing than a long-term commitment.

For newsrooms already watching referral traffic decline as readers turn to chatbots instead of clicking links, the stakes are higher. The way OpenAI builds out its ad business could shape where publishers reach audiences — and how they get paid — as search behavior shifts.

OpenAI’s next steps include expanding into new markets and adding targeting tools, both designed to pull more advertising dollars into an app most people still use for free. The bigger question is whether advertisers stick around once the novelty wears off. July’s numbers suggest growing interest, but not yet lasting commitment.

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ChatGPT has begun refusing requests to write in the style of named authors, marking a notable change in how OpenAI’s chatbot handles imitation prompts. As reported by Ars Technica, the system now steers users toward describing literary traits rather than directly invoking a writer’s style.

The Media Copilot was able to replicate the result on July 28. After asking ChatGPT to write a short story in the voice of Ernest Hemingway, the chatbot replied, “I can’t write in the exact style of a specific author or closely imitate a copyrighted work’s voice. I can, however, write an original passage that draws on some broad characteristics often associated with Ernest Hemingway—plain language, understated emotion, concrete observation, and spare dialogue—without closely imitating his distinctive expression.”

The change is less about the output than the prompt. Requests that explicitly name an author may now be refused, but prompts describing the same stylistic traits—such as minimalist prose, clipped dialogue or a gothic mood—can still generate comparable writing without invoking a specific writer.

The apparent shift comes as OpenAI continues to face multiple copyright lawsuits over its AI models and outputs. While those cases primarily concern training data rather than stylistic imitation, they have intensified scrutiny over how AI systems reproduce or emulate copyrighted creative works, prompting companies to adopt more cautious safeguards around requests that closely mimic identifiable authors.

Other chatbot makers have drawn the line differently. Ars Technica cites a No Latency study that found Google’s Gemini complied consistently with author-imitation requests, while Perplexity refused and redirected them. Anthropic’s Claude and Microsoft Copilot fell between those positions, complying while adding qualifications. The uneven treatment means a refusal in one product does not prevent a writer or editor from trying the same prompt elsewhere.

For newsrooms and publishers, ChatGPT’s new author-style restrictions underscore the need for AI policies that govern editorial practices rather than any single technology provider. Internal guidelines should define whether staff may request imitation of living authors, deceased writers, competing publications or an organization’s own house style, while requiring disclosure and human review before AI-generated text is incorporated into published work.

OpenAI has not, based on the material cited here, announced a formal written-text policy that matches the chatbot’s current behavior. Until it does, publishers should expect the boundary to be defined largely by the product itself: prompts that explicitly name authors may be declined, while requests describing the same stylistic qualities in broader terms can still generate a response.

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The Delhi High Court handed OpenAI a major legal victory Friday, ruling that the company’s use of news agency ANI’s articles to train ChatGPT does not infringe copyright because it qualifies as fair reporting for research purposes under India’s Copyright Act, Reuters reported.

It is the first substantive court finding in India on whether AI companies can train large language models on copyrighted news content without a license. That question is still open in courtrooms in the United States and Canada, where OpenAI faces similar suits.

ANI first sued OpenAI in November 2024, alleging the company trained ChatGPT on its copyrighted news reports without permission and that the chatbot falsely attributed fabricated stories to the agency. Presiding over the ANI v. OpenAI lawsuit, Delhi High Court Justice Amit Bansal rejected the copyright claim, finding that ANI failed to prove ChatGPT had memorized or reproduced its articles in response to users.

The judge also rejected ANI’s claim over OpenAI’s storage of its articles, ruling that retaining the content for AI training is protected as research under India’s Copyright Act of 1957 and does not infringe the agency’s copyright.

The ruling contrasts with ongoing litigation in the United States, where The New York Times, the Center for Investigative Reporting and a growing number of publishers argue that OpenAI’s use of copyrighted material for AI training violates U.S. copyright law. Unlike India’s fair-dealing framework, which lists specific permitted uses, U.S. courts evaluate fair use case by case. In Canada, a coalition of Canadian news organizations, including CBC and The Globe and Mail, is testing a separate copyright framework in its own lawsuit against OpenAI.

The decision could reshape negotiations between AI developers and publishers in India, where the threat of copyright litigation may carry less weight after the ruling, It could reduce pressure on OpenAI and other AI companies to pursue licensing agreements with Indian news organizations, even as similar disputes continue elsewhere.

The ruling is not the final word in the case. It addresses ANI’s claims at this stage of the proceedings, and appeals remain possible. The judge’s finding on reproduction was also based on the evidence presented by ANI, not a broader conclusion that AI systems can reproduce copyrighted material.

Even so, India has become the first major jurisdiction to find that training AI models on copyrighted news content can qualify as protected research. As courts in the United States and Canada continue to weigh similar claims under different copyright laws, the divergence in legal approaches is becoming increasingly difficult for AI companies and publishers to ignore.

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OpenAI’s first piece of consumer hardware is reportedly a screenless, mobile AI companion that syncs with ChatGPT and is being pitched internally as a humanlike AI companion that lives in the home.

That description comes from a Bloomberg report published Tuesday, which TechCrunch summarized the same day. The device is still under development, and its sources describe it as something well outside the standard smart-speaker mold.

The speaker reportedly has a personality and can learn about its owner over time to deliver more personalized responses. It would tap into a user’s digital life, pulling from sources like email. And it apparently includes mechanical elements that can move on their own, designed to feel like a companion and act as a physical manifestation of ChatGPT.

OpenAI announced prototypes for hardware in November 2025, with earlier rumors pointing to a phone that would compete directly with Apple. The company also acquired io, the hardware startup co-founded by former Apple design chief Jony Ive, in a deal valued at roughly $6.5 billion. Bloomberg reports the speaker was built with help from ex-Apple engineers who worked on the iPhone and Mac.

That pedigree is now a legal liability. Apple sued OpenAI last week, accusing it of stealing trade secrets and calling the allegations “the tip of the iceberg.” OpenAI denies wrongdoing. Its sources told Bloomberg the new product veers significantly from anything Apple sells today.

OpenAI is not alone in chasing this category. Hark, the AI lab founded by Brett Adcock, raised an oversubscribed $700 million Series A in May at a $6 billion valuation to build proprietary models paired with custom hardware it calls a universal interface between humans and machines. Hark has yet to disclose the device’s design, highlighting the surge of investment flowing into AI hardware long before products ship.

For newsrooms and publishers, a screen-free device that pulls from email and learns a user’s habits raises the same questions as any voice-first platform. If people increasingly ask a companion device for news instead of opening an app or a browser, distribution shifts again, and this time to a surface with no visible headlines, no scannable feed and no obvious place for a byline.

The referral traffic problem publishers already face with chatbots gets sharper when the interface is spoken. The reported device would extend a broader shift in news discovery, making AI assistants an even more prominent intermediary between publishers and their audiences.

OpenAI has not announced the product, its price or when it will ship. Still, the reported device points to the company’s broader strategy of extending ChatGPT beyond screens and into the home.

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Nic Newman stood in front of a slide that read “AI can do what journalists do quicker and better.” It was a deliberately provocative framing, and the Reuters Institute senior research associate used it to make a sharper point about where the threat to publishers is actually heading.

Speaking at the Media Voices Publisher Summit in London on 10 July, Newman warned that AI models’ ability to serve up what he calls “liquid content” in whatever format a reader wants is a really serious challenge for news media, as reported by Press Gazette.

He demonstrated with a live example. Asking Anthropic’s Claude about the recent U.K. heatwaves, Newman got answers in a Q&A format, filterable graphs comparing air conditioning uptake across countries and Met Office warnings pulled together on the fly. The same information base used by news outlets, adapted to a user’s specific needs and context.

“Content is becoming much more fluid, whether we like it or not,” Newman said. “It’s going to be remixed in many different ways, and audiences will expect and like that level of personalisation.”

The worry is not just today’s chatbots. Newman pointed to agentic tools that complete tasks without being asked, such as ChatGPT Pulse, which digests a user’s chat history and connected apps like Google Calendar to deliver a morning brief unprompted. He expects AI to increasingly turn publisher newsletters into convenient audio digests too.

“People not having to put words into a search box, but the AI agents knowing what you’re interested in and bringing it to you automatically” is the shift publishers should fear most, he said.

Newman’s 2026 trends and predictions report, based on responses from 264 news leaders, found publishers see novel content as their way forward. Original investigations and reporting from the ground ranked highest, followed by contextual analysis and community-building through events. General news for everyone, Newman argued, is exactly what AI will commoditise.

He also urged publishers to fold AI into their own products rather than cede the ground entirely. On-site chatbots, like Ask The News, a product The Washington Post has been developing, can pair human curation with AI’s ability to answer specific reader questions.

The second disruption he flagged is the rise of personality-led and creator-style journalism. Citing Financial Times analysis by John Burn-Murdoch, Newman noted social platforms have become less social and more about following individuals. Joe Rogan now reaches roughly a fifth of American adults weekly. Audiences describe creator-led media as more trustworthy and relatable, even as they rate it less impartial overall.

For newsrooms, the takeaway is uncomfortable but clear. The funnel model, built around using newsletters, podcasts and social traffic to bring users back to a website, remains relevant but faces long-term pressure as audiences consume news differently. Younger audiences are moving elsewhere: 52% of 18-to-24-year-olds now name social, video and AI platforms as their main source of news, up from 40% five years ago, according to the latest Digital News Report covered on The Media Copilot.

Newman sees the future in show- and talent-led models built around personalities and niche audiences. Goalhanger, a U.K. podcast producer known for series including The Rest Is History and The Rest Is Politics, is adding written content to their model, while The Guardian’s Guardian Studios is expanding the publisher’s work across branded storytelling and commercial partnerships. The growth opportunity, Newman said, is building habit and trust around personalities and brands, then finding ways to turn that relationship into a lasting business.

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The New York Times and a group of other publishers are asking a federal court to sanction OpenAI, accusing the company of withholding or destroying evidence in a high-stakes copyright case over how ChatGPT was trained and used.

In a motion filed Thursday in federal court in Manhattan, the publishers alleged that OpenAI misrepresented its ability to search training datasets and ChatGPT output logs for copyrighted news material. According to Reuters, the publishers said OpenAI told the court it could not search its large language models for their work while allegedly concealing that it had already done so “even before the first News Plaintiff filed suit.”

The motion is the latest escalation in the copyright fight between major news organizations and AI companies. It also moves the dispute deeper into discovery, where the question is not just whether AI companies can use journalism to train models, but whether they can preserve, search and produce the records needed to prove what happened.

The plaintiffs include The Times, the New York Daily News and other media organizations, including Ziff Davis and the Center for Investigative Reporting, according to The Associated Press and Variety. The original New York Times article reported that the publishers are seeking legal sanctions against OpenAI, including monetary penalties and other remedies.

The filing does not ask for sanctions against Microsoft, which is also a defendant in The Times’ broader copyright case, according to The Times’ summary of the motion. Microsoft has invested heavily in OpenAI and integrated OpenAI technology into products including Copilot.

“The evidence is in OpenAI’s training data sets and ChatGPT output logs,” the publishers said in the motion, according to The Times. “But instead of just producing that evidence at the start of the case and focusing on the merits of its fair use defense, OpenAI chose obstruction.”

OpenAI rejected the allegations. “As the Times’ case weakens and they’ve been forced to drop claims against us, they’re persisting with their efforts to invade the privacy of people who have nothing to do with this case, including by making these blatantly false allegations,” OpenAI spokesperson Drew Pusateri told Reuters. “We’ll continue defending our users’ privacy and the long-established principles of fair use.”

The publishers allege that OpenAI deleted billions of relevant ChatGPT conversations or made them unsearchable. They also argue that an OpenAI employee later testified that the company had performed multiple searches for news publishers’ content, contradicting earlier representations about the company’s technical limitations.

A sanctions memorandum posted by Ars Technica says the publishers want the court to bar OpenAI from relying on a disputed 20 million-log ChatGPT sample, find that ChatGPT’s output logs include or would have shown substantial use of the publishers’ copyrighted material, instruct the jury on those findings and award fees and costs tied to the discovery fight.

Those remedies would matter because discovery disputes can shape the trial record. If the court finds OpenAI failed to preserve or produce relevant evidence, the ruling could affect what arguments OpenAI can make later and what conclusions a jury may be allowed to draw from missing or incomplete records.

The Times sued OpenAI and Microsoft in 2023, alleging that millions of Times articles were used without permission to train AI systems that now compete with publishers as sources of information. OpenAI and other AI companies have argued that training models on large bodies of text is protected by fair use, a theory now being tested across lawsuits from authors, artists, music labels and news organizations.

For publishers, the issue goes beyond training data. They argue that AI chatbots and AI search summaries can answer readers’ questions using journalism without sending traffic, licensing revenue or subscribers back to the organizations that reported the information. Media Copilot has been tracking the same pressure point in coverage of Google’s AI accuracy problem and The Times’ warnings about AI companies using journalism without permission.

At the same time, publishers are taking different approaches to the AI economy. Some are suing. Others have signed licensing deals with AI companies. The Associated Press announced a deal with OpenAI in 2023, while other media companies have made agreements with OpenAI, Google, Meta and Amazon.

The sanctions motion could increase pressure on both sides. A ruling against OpenAI would give publishers leverage in court and in licensing talks. A ruling for OpenAI would strengthen the company’s argument that publishers are using discovery to intrude into user privacy and commercially sensitive systems.

Either way, the case shows that AI copyright fights are becoming data-governance fights. The central questions are no longer only what AI systems were trained on. They are whether companies can prove it, search it, preserve it and explain it in court.

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OpenAI said that it plans to give a select group of government-approved partners early access to GPT-5.6 Sol, its most powerful AI model to date, before releasing the product more broadly. The limited rollout follows a request from the Trump administration, which asked the company to adopt a phased launch strategy for its next-generation AI system while security reviews are conducted. 

Last week’s request came from the White House’s Office of the National Cyber Director and Office of Science and Technology Policy, which have pushed AI developers to give federal agencies early access to frontier models so officials can evaluate their capabilities and potential security risks before wider deployment,

OpenAI CEO Sam Altman told employees in a memo that GPT 5.6-Sol will initially be available to 20 approved partners, including Amazon’s Bedrock platform. According to the memo, access is being granted on a “customer-by-customer” basis while the review process is underway. 

“We’ve made clear to the U.S. government that this is not our preferred long term model, and will work with them and others in industry to achieve a more sustainable approach for future releases,” Altman said in the memo. Altman said he hopes to release GPT-5.6 to the public a “couple of weeks later.”

The decision reflects growing concern over what the Trump administration says are legitimate national security implications of increasingly capable AI systems. 

OpenAI says GPT-5.6 Sol is its most advanced model to date, with improvements in reasoning, autonomous task execution, software engineering and cybersecurity-related capabilities. It released benchmarks that said its performance was broadly comparable to Anthropic’s Mythos 5, which was withdrawn on June 12 following a directive from the Commerce Department expressing concerns that its advanced capabilities could create new cybersecurity threats. 

At the time, Anthropic said the concerns were over “a small number of previously known, minor vulnerabilities” and that “other publicly-available models are able to discover them as well without requiring a bypass.” 

Politico reported that the initial vulnerability was brought directly to the White House by Amazon CEO Andy Jassy. (Amazon is an investor in Anthropic.)

“We have reviewed a report that we believe is the basis of the government’s directive and validated that the level of capability displayed there is widely available from other models (including OpenAI’s GPT-5.5), and is used every day by the defenders who keep systems safe,” Anthropic said in its statement.

Anthropic and the Trump administration have been sparring for months since a dispute over how Anthropic’s models would be used by the Pentagon. Reports that the company’s models were used in U.S. operations in Venezuela in January, in violation of its licensing terms, which prohibit using Claude models to commit violence, led to a near complete rupture. 

Adding further layers to the drama, just hours after OpenAI announced the limited rollout of GPT-5.6, Anthropic disclosed that the Trump administration had approved a limited release of Mythos 5, reversing the Commerce Department restriction. 

The restrictions on GPT-5.6 Sol and Mythos 5 follow a recent call from the Five Eyes alliance for closer coordination on advanced AI development and security. A White House official said the administration continues “to collaborate with frontier AI labs to develop shared approaches for addressing the challenges of scaling this technology.” 

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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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OpenAI today released GPT-5.5, what it says is its “smartest and most intuitive to use model yet, and the next step toward a new way of getting work done on a computer.”

The company says the model understands what users are trying to do faster, can carry more of the workload itself, and excels at writing and debugging code, researching online, analyzing data, creating documents, operating software, and moving across tools until a task is finished.

The company published performance numbers from Terminal-Bench 2.0, which tests complex command-line workflows requiring planning, iteration, and tool coordination.

OpenAI said GPT-5.5 outperformed its predecessor on every major coding and agent benchmark the company tested, while using fewer tokens and running at the same speed as the older model. On one third-party coding index, it matched leading rivals at about half the cost.

Keeping a larger model that fast required rebuilding inference as a single system rather than a patchwork of tweaks, the company said. GPT-5.5 was designed, trained and served on NVIDIA’s latest hardware, and OpenAI credited its own Codex tool and GPT-5.5 itself with helping hit the efficiency targets.

Early testers told the company the model seems to grasp how a codebase fits together — why something is failing, where the fix belongs and what else the change will touch.

Dan Shipper, Founder and CEO of Every, called GPT-5.5 “the first coding model I’ve used that has serious conceptual clarity.” After launching an app, he spent days debugging a post-launch issue before bringing in one of his best engineers to rewrite part of the system. To test GPT-5.5, he effectively rewound the clock: could the model look at the broken state and produce the same kind of rewrite the engineer eventually decided on? GPT-5.4 could not. GPT-5.5 could.

Pietro Schirano, CEO of MagicPath, saw a similar step change when GPT-5.5 merged a branch with hundreds of frontend and refactor changes into a main branch that had also changed substantially — resolving the work in one shot in about 20 minutes.

One engineer at NVIDIA with early access went as far as to say: “Losing access to GPT-5.5 feels like I’ve had a limb amputated.”

OpenAI is already running the model internally at scale. More than 85% of the company uses Codex every week across functions including software engineering, finance, communications, marketing, data science, and product management. The finance team used GPT-5.5 in Codex to review 24,771 K-1 tax forms totaling 71,637 pages, accelerating the task by two weeks compared to the prior year.

The model also shows gains on scientific and technical research workflows. On GeneBench, a new eval focusing on multi-stage scientific data analysis in genetics and quantitative biology, GPT-5.5 outperforms GPT-5.4 on problems that often correspond to multi-day projects for scientific experts. On BixBench, a benchmark designed around real-world bioinformatics and data analysis, it achieved leading performance among models with published scores.

In a notable example, an internal version of GPT-5.5 with a custom harness helped discover a new proof about Ramsey numbers — one of the central objects in combinatorics — later verified in the Lean proof assistant. The result is a concrete example of GPT-5.5 contributing not just code or explanation, but a novel mathematical argument in a core research area.

OpenAI says GPT-5.5 was released with its strongest safeguards to date, including tighter controls around cybersecurity workflows and repeated misuse patterns. The model was evaluated across the company’s full safety and preparedness frameworks, with input from nearly 200 trusted early-access partners before launch.

GPT-5.5 is available today in ChatGPT and Codex for Plus, Pro, Business, and Enterprise users. GPT-5.5 Pro is rolling out to Pro, Business, and Enterprise tiers. API access, which requires different safeguards, is coming “very soon,” OpenAI said.

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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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