Google is turning its demand-side platform for advertisers, Display & Video 360 (DV360), into an AI-powered media buyer backed by Gemini, the tech company’s flagship family of AI models. 

While Gemini tools have been baked into DV360 since at least last spring, they were primarily used to aid marketers in audience discovery, surface reporting insights, and offer tweaks to help optimize campaigns here and there. Now, Gemini will become the true operating layer of the DSP, moving upstream and taking on a larger role in media planning, the company explained at its NewFronts presentation in Manhattan on Monday.  

“Marketers can upload their media plan and automatically translate it into a comprehensive campaign setup,” said Bill Reardon, general manager of the enterprise platform at Google Ads, on a call with the press. 

In its presentation on Monday, the company also hyped up additional AI-powered updates for ad buyers: AI will be deployed within DV360 to help manage real-time fluctuations in live sports inventory across connected TV; and soon, marketers will be able to use Ads Advisor, an agent embedded in the Google Ads Console, to build custom reporting dashboards.

Beyond supercharging the buyer experience with automation, Google also indicated that it is investing more deeply in cross-channel measurement—specifically in connected TV attribution. The company is debuting Confidential Publisher Match, a new identity model that can link marketers’ first-party data with signals from streamers like Roku in a privacy-safe trusted execution environment.

“This shift empowers you with a cross-device conversion memory, connecting a CTV impression directly to a purchase,” Reardon said. 

The push could help Google level up its measurement capabilities on non-owned inventory environments in CTV, specifically, an area where it’s historically been weaker than rivals like The Trade Desk.

To further buoy its CTV measurement push, Google is turning to retail media data. 

The company is working with Kroger to integrate the retail giant’s first-party shopper data into YouTube and Google’s other partner inventory. The company will also roll out SKU-level reporting in DV360, “so brands can see the precise impact of their YouTube and display ads on Kroger sales,” Reardon explained.

“This really changes how brands think about YouTube and its direct impact on sales,” Christine Foster, Kroger’s group vp of commercial strategy and operations, said in a statement. “Now, advertisers can reach Kroger shoppers on the most-watched video platform in the world—and measure how their spend drives sales at the SKU level. The reach is massive, the ability to unify once-siloed efforts is profound, and the opportunity to connect with shoppers across the full customer journey has never been better.”

In fact, YouTube is the cornerstone of Google’s NewFronts pitch this year. It’s the company’s crown jewel, expected to bag nearly 12% of all CTV ad revenues this year, per Emarketer estimates.

While YouTube isn’t getting any new ad formats, Google is aggressively pushing for wider adoption of a select number of the platform’s ad offerings. Now, the company said Monday, YouTube’s pause ads and creator takeovers—which allow a brand to buy out all of a specific creator’s ad inventory for a select period—can be bought through DV360. 

Increasingly, the DSP is being designed to act as a buying environment that reinforces Google’s walled garden, encouraging buyers to transact on YouTube in particular. 

Reardon only underscored the notion that Google is more tightly binding its buy-side tool with its owned inventory and measurement capabilities. “Gemini, with [DV360] and YouTube, will be core to the success of the future of advertising,” he said.

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YouTube isn’t satisfied with serving one or two slices of brands’ creator marketing demands. It wants the whole pie. 

On Monday, ahead of YouTube’s NewFronts presentation this week, the Google-owned video site announced that it is repackaging BrandConnect, its tool for connecting YouTube creators with brands for sponsorship deals, into what will now be known as Creator Partnerships—and giving it an AI makeover. The platform will be backed by Gemini, Google’s suite of frontier AI models, to intelligently pair advertisers with creators that suit their needs. 

“It’s historically been challenging for advertisers to find the right creator to work with and then scale their efforts globally,” said Melissa Nikolic, director of product management for YouTube Creator Ads, on a call with press. Now, she said, the company is integrating Google’s AI spine into YouTube’s suite of creator tools—a change that she expects will “make it easier for brands to discover and work with creators at scale.”

Creator Partnerships will also be integrated with YouTube Studio for creators, Google Ads, and Google’s demand-side platform Display & Video 360 (DV360) into one centralized hub for advertisers.

Within Google Ads or DV360, users can generate a target list of YouTube creators through simple natural language queries.

“You can say, ‘Find me us tech creators reviewing sports gear with high Gen Z retention,’ and our AI will surface creators who match, and will include insights about their channel, their audience, sample videos, and more,” Nikolic explained. 

Gemini analyzes billions of data points on everything from brand mentions to subscriber growth to surface these recommendations. 

“It’s designed to save time for brands who traditionally have had to manually sift through eligible creators in the YouTube Partner Program, and may have missed some really great creators. It also gives creators of all sizes more opportunities to be discovered,” Nikolic said.

When possible matches are identified, advertisers can then filter down to a shortlist of top picks and reach out en masse to multiple creators at once. Within the platform, advertisers can track and manage their creator deals.

With the overhaul, YouTube aims to eliminate the need for laborious influencer discovery that has historically required agency planning and resources paired with manual keyword- or tag-based searches. 

In the near future, the process will be even easier, Nikolic suggested: “In the coming months, advertisers will be able to simply input their campaign brief. Our AI will recommend the ideal creators to drive a marketer’s goal.”

YouTube has also embedded a unified measurement feature into Creator Partnerships. Here, paid ads and organic creator content performance data come together in one workflow, in theory making it easier for marketers to get a clean, deduplicated look into the material impact of their creator partnership investments.

The company is making Creator Partnerships available to some partners via API, enabling marketing agencies or SaaS companies to plug the discovery and matching capabilities into their own tools.

“Creator marketing has matured into a core growth channel, but the industry has lacked the depth of measurement needed to scale with confidence,” said Tim Sovay, the chief business development and partnerships officer at influencer marketing platform Creator IQ, in a statement. He said that the rollout of the Creator Partnerships API will help give advertisers “trusted, first-party insights that help unlock deep creator audience understanding within their existing workflows,” whether on YouTube or on another platform like CreatorIQ.

On the whole, however, YouTube is now positioning itself as a comprehensive operating system for creator marketing—compared to a platform that simply aids in influencer partnerships. In the process of that transformation, YouTube could threaten the businesses of third-party creator platforms. 

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X is imploring advertisers who’ve abandoned the platform to return, promising some a sweetheart deal of up to $200,000 in extra value, according to a leaked pitch deck viewed by ADWEEK. 

On one slide, presented to an ad agency with many clients that have not invested in the platform for a number of months, X suggested it would toss in 50% of added value for every dollar spent, up to $200,000 per advertiser. 

Details of what constitutes “added value”—whether discounts, free ad credits, rebates, or something else entirely—were unspecified.

The incentive was framed as a “return-to-platform incentive” in the slide.

The incentive structure is the same as a promotion X ran last year for Omnicom specifically, though the company’s pitch to the holding company also included additional financial incentives like 15% in media credits for spending on X’s inventory via auction, excluding revenue-sharing products.

The platform ad revenues were roughly halved after Elon Musk’s $44 billion takeover in 2022. Last year, it brought in about $1.25 billion compared to 2021’s $2.43 billion from ads, per Emarketer estimates. Many advertisers dialed back their investments or exited the platform altogether over brand safety concerns posed by X’s lax approach to content moderation and misinformation under Musk’s leadership. 

More than 20 slides of the leaked deck were dedicated to promoting X’s brand safety credentials, transparency practices, and built-in controls for advertisers that help them avoid “sensitive content.” 

In one slide, X explained how Grok—the AI chatbot operated by X’s parent company xAI and integrated natively into X—is being deployed to aid brand safety. “ALL (sic) posts on X are reviewed by xAI’s Frontier model Grok for Brand Suitability,” it read. It also claimed that profile ads will only show up on X profiles that have been “fully vetted by Grok, which evaluates all compliance with X’s Rules and policies and Brand Suitability.” 

The push aligns with a broader pattern for X, which is increasingly positioning Grok as a cornerstone of its brand safety practice, as reported by ADWEEK earlier this month. The push arrives just weeks after Grok users filled X with a deluge of nonconsensual sexualized deepfakes of real users—a scandal that has prompted a handful of regulatory investigations and lawsuits.

In the slides, X also pitched the platform as an ideal advertising environment for high engagement and high performance. It purported to be “#1 for ad attentiveness,” and said X users are 45% more likely to buy products they’ve seen advertised on X compared to non-X users, according to GWI figures from Q2 of 2024 through Q1 of 2025. 

The company also claimed that, per X internal data from August 2 to Dec 7, 2025, the platform observed a 140% lift in click-through rates, a 43% increase in conversion rates, and a 37% increase in sales. 

Ad performance—and ad-linked sales in particular—has been a fixation of Musk’s as the company looks to recover ad revenues. In February of 2023, Musk wrote in a post: “Almost nobody buys anything on Twitter, but almost everyone on Instagram does.”

As recently as this month, Musk posted an interactive survey on X asking users if they’d ever made a purchase based on an ad they’d seen on the platform. Of some 1.6 million responses, 88% they had not.

Have you ever bought anything based on an ad on this platform?

— Elon Musk (@elonmusk) March 9, 2026

X did not provide an on-the-record comment by press time. 

In a heated discussion with leading AI scholars Tuesday evening, Eric Schmidt, the American businessman who held the top post at Google from 2001 to 2011, argued that AI systems can develop unexpected behaviors that limit the extent to which companies like Google can implement preemptive safety and governance mechanisms into their products.

A central “problem” with regulating frontier AI models, is that, sometimes, “a new feature emerges in these systems that is not tested, testable,” Schmidt said onstage at the annual Isaac Asimov Memorial Debate, moderated by physicist Neil deGrasse Tyson in Manhattan. 

“We can stop [the emergence of new features or behaviors], and therefore stop all progress, by law, by banning larger models,” he said, “but as long as you have this new emergent power, you have deep reasoning, deep capabilities, and they will make mistakes. You have to be tolerant.”

Schmidt—who also served as chairman of Google’s parent Alphabet from 2015 and 2017 and advised Alphabet for three years after that—supported the 2014 acquisition of DeepMind, the unit that now houses Google’s most cutting-edge AI research. 

Schmidt said that AI developers, like Google and others “should be held accountable” if they’re found in violation of the law, but emphasized that AI developers frequently have to ship AI products and retroactively correct bad behaviors they didn’t predict as the models evolve. 

“I went through this when I was at Google, in earlier versions of [Google’s AI] technology, where the system would actually do something that was wrong, and we fixed it. And we fixed it as fast as we could, because we had to, because it was the right thing to do,” he said. 

Google did not respond with an on-the-record statement by press time. 

Schmidt was challenged by Latanya Sweeney, a professor of government and technology at Harvard and the former chief technology officer at the Federal Trade Commission, who cast doubt on the suggestion that AI leaders would happily comply with regulations. She said that leading tech companies have proven time and again to ignore key regulations or attempt to bend the law to their commercial interests. 

Sweeney is not off base. Just last year, U.S. federal judges found Google guilty of operating not one but two illegal monopolies—one in adtech and one in online search. The decisions came six years after Meta, then Facebook, was forced to cough up $5 billion to the FTC and modify its business practices after it was found to have mishandled reams of user data in the now infamous Cambridge Analytica scandal.

“Technology just ignores [laws] and rewrites them. That’s true in social media. It’s true in AI as well,” Sweeney said to Schmidt. “We already have laws [that] address issues of bias, consumer protection, and so forth. None of those are enforced online.”

Sweeney suggested that mitigating the risks associated with AI, including algorithmic harms and biases encoded into AI systems from their training data, requires more foundational changes rather than ex-post-facto fixes. 

“There are questions about existential harms in the future, but there are a lot of harms happening right now. And it doesn’t have to be that way….It depends on who this AI is servicing, and in particular, the design of the technology—the decisions made in that design is really determining what our values will be,” she said.

Schmidt pushed back on the implication that all harms could be preempted with better design and training, arguing that leading AI programs are not simple machinery but complex, non-linear systems that often develop unforeseen capabilities. But he agreed with Sweeney’s assertion that Silicon Valley leaders have at times “rushed” products to market, adding, “They’ve found all sorts of problems, and then they’re busy correcting them. I think that’s the cycle, and it’s very hard.”

Luckily, he said, AI developers have evaluation cards and safety testing teams in place to mitigate as much risk as possible in advance. 

But these measures are insufficient for protecting humanity, according to another panelist, Nate Soares. Soares is president of the Machine Intelligence Research Institute in Berkeley, California and co-author of If Anyone Builds It, Everyone Dies, a 2025 book on the existential risks posed by AI. 

In leading AI labs, the primary focus areas for safety and governance today are interpretability research, or “trying to figure out what’s going on inside the AIs’ heads,” and model evaluation cards, “which are trying to figure out how dangerous the AIs are,” as Soares explained.

He likened these efforts to a comically inadequate attempt to curtail nuclear disasters. “If someone was making a nuclear power plant in your hometown, and you went to them and you said, ‘Hey, I hear that this uranium stuff can have lots of energy benefits, but also can melt down when things go badly. What have you guys got that makes you think you’re going to get the benefits and not the pitfalls?’ If the engineers say—‘Oh yeah, we’ve got two crack teams working on this; the first team is trying to figure out what the heck is going on inside, and the second team is trying to measure whether it’s currently exploding’—that’s not a good sign.” 

Soares expressed urgent concern about the future of AI for the same reason Schmidt found the technology to be too difficult to govern: because frontier AI models tend to act in ways their makers didn’t explicitly design or even anticipate.

Schmidt argued that the benefits of AI will ultimately outweigh the risks, disagreeing with arguments made by Sweeney, Soares, and other academics onstage. 

“The companies that are doing this work are well aware of the dangers, and I know this because I work with them on this, and we spend an awful lot of time talking about them,” Schmidt said. “We can talk about some of them, but that is not to take away from the enormous benefit of these technologies.”

Schmidt, deGrasse, Sweeney, and Soares were joined onstage by Kate Crawford, a professor of AI at the University of Southern California, Chris Callison-Burch, a University of Pennsylvania computer science professor, and Cindy Rush, a statistician who teaches at Columbia University. 

X will temporarily bar creators from monetizing their content through its Creator Revenue Sharing program if they share AI-generated videos of an armed conflict without clearly disclosing that it was made with AI. The move comes three days after the U.S. and Israel launched strikes against Iran, sparking a chain of violent reactions.

X’s head of product Nikita Bier made the announcement in a post on X Tuesday morning, writing: “During times of war, it is critical that people have access to authentic information on the ground.” He added that AI platforms have lowered the bar to making “content that can mislead people.” 

Today we are revising our Creator Revenue Sharing policies to maintain authenticity of content on Timeline and prevent manipulation of the program.

During times of war, it is critical that people have access to authentic information on the ground. With today’s AI technologies,…

— Nikita Bier (@nikitabier) March 3, 2026

Creators who post AI-made videos of armed conflict without adequate disclosure, Bier said, will be suspended from Creator Revenue Sharing for 90 days. Additional violations will lead to permanent suspension from the program.

Replying to one user, Bier explained that creators would need to click on the menu and select ‘Add Content Disclosures’ where they will find a ‘Made with AI’ label option.

X will be able to check whether videos are AI-generated using available metadata imbued by AI systems in combination with Community Notes, X’s crowd-sourced fact-checking tool, Bier added. 

X debuted its revenue-sharing initiative in mid-2023. Through the program, X Premium-subscribed creators or verified organizations with at least 5 million organic impressions in the past three months who have at least 500 verified followers are eligible to cash out on their content. These users to earn based on engagement—a 2024 departure from previous rules that determined payouts based on ad impressions. 

The policy update comes two days after a Wired investigation found X drowning in misinformation about the conflict in Iran. Various months- or years-old videos were presented as new, some posts included outright disinformation about specific attacks, and  AI-generated imagery and videos spread like wildfire, shared even by the Iranian newspaper Tehran Times.

X has been recently plagued with its own endemic issues related to AI-generated content, too. In late December and early January, the site was overwhelmed with nonconsensual sexualized deepfakes of real users after users prompted Grok AI, embedded within X, to produce the images. Though the platform eventually tweaked its rules, the changes were far from comprehensive, as users can still use various Grok interfaces to produce such content. 

Meanwhile, the company has also been signaling its willingness to crack down on content that might deter advertisers from spending on X. Last week, X held a webinar for advertisers—the presentation for which was obtained by ADWEEK—promoting its brand safety capabilities. X’s ad revenues are about half of what they were before billionaire Tesla boss Elon Musk acquired the platform for $44 billion in late 2022, according to data from Emarketer.

X did not comment by press time. 

In a new 44-slide pitch deck, obtained exclusively by ADWEEK, X is once again pitching itself as a highly safe environment for advertisers. The slides promote various tools for transparency, campaign measurement, and brand safety that debuted between 2022 and 2025. X presented the deck to brand- and agency-side advertisers last week, according to a source familiar with the matter. 

These include keyword controls, blocklists, as well as partnerships with leading media quality firms DoubleVerify, Integral Ad Science (IAS), and Trustworthy Accountability Group. 

Notably, the deck positions Grok—the AI chatbot operated by X’s parent company xAI and integrated natively into X—as a cornerstone of the app’s brand safety and suitability mission. One slide claims Grok provides “enhanced contextual understanding of posts”; the ability to “identify sensitive topics based off of cultural trends on X”; and “superior text recognition across content formats including images with oddly-sized or distorted text.” 

But throughout the past year, Grok has not only failed to ensure brand safety but in some instances disseminated the very content that brands deem most unsafe. In January, users prompted Grok en masse to flood X with sexually explicit nonconsensual deepfake images of real users. Around 6,700 such images appeared hourly during one 24-hour observational period, tallied by deepfake researcher Genevieve Oh. Now, that incident is the subject of various regulatory investigations across the globe. Six months earlier, Grok had spewed violent and antisemitic content on the social platform, producing rape fantasies and calling itself ‘MechaHitler.’ 

The company says in the document that it is “deeply committed to safety for all.” 

In the new pitch deck, X claims Grok “exceeds industry benchmarks” for brand safety, with an “average brand safety score” above 99.99% according to IAS and DoubleVerify, though details about these methodologies were not shared. In 2024, DoubleVerify publicly said that X’s brand safety rate is 99.99%—a figure that referred not to the share of content that is brand-safe on X, but to the rate of effectiveness of X’s proprietary brand safety systems at the time. However, the rate was never presented as a representation of Grok’s capabilities.

“This webinar session and deck were part of our ongoing proactive communication with clients. Transparency and control are the core tenants,” said a spokesperson for X.

DoubleVerify declined to comment. IAS declined to share specifics about its methodology but directed readers to learn more about its work with X in a blog post on its website. That post includes no mention of Grok.

The deck spotlights various other products and tools designed to give advertisers greater control over their placements and to assure them that content on X is being effectively moderated.

A significant portion is dedicated to highlighting Community Notes, X’s crowd-sourced fact-checking feature, which allows users to add relevant context to posts that may include misinformation. Users are 80% more likely to delete a post if it receives a Community Note, according to research from Sorbonne Université Paris, Cornell University, and University of Washington. Now, X says it is piloting a new program that allows AI tools to propose Community Notes called the AI Note Writers API.

An appendix to the document also makes specific recommendations for the controls that advertisers should consider applying to different kinds of ad buys on X. For example, the company suggests applying pre-bid adjacency controls and “limited” sensitivity settings for timeline, profile, reply, search, and vertical video ads.

X’s advertising business has suffered substantive losses since Elon Musk’s $44 billion acquisition of the platform in late 2022, in large part due to brand safety concerns. Upon his takeover, Musk aggressively cut headcount, flipped the blue check-based identity verification system on its head, and loosened the app’s content moderation policies. The upheaval spurred a mass exodus of advertisers, many of whom did not want to risk their messages appearing next to controversial content. 

Though many major advertisers, including IBM, Disney, and Apple, have since returned to the platform, X’s total 2025 ad revenues, according to estimates from Emarketer, reached just $1.25 billion—roughly half the $2.43 billion it tallied in 2021 before Musk’s takeover.

In January, X elevated Monique Pintarelli to global head of advertising at xAI, part of its push to recover these lost revenues. In conversation with ADWEEK earlier this year, Pintarelli said X is focused on “how we can create user experiences on the platform that can also be beneficial for the advertising community.”

See the full pitch deck below. 

An Android-based mobile fraud operation designed to milk ad revenues from AI-generated shell websites has been shut down by Google and ad verification firm Integral Ad Science (IAS). The scheme was executed through more than 25 million consumer devices.

Throughout late 2025, the scheme, dubbed Genisys, used over 115 distinct mobile apps to propagate fraudulent ad activity. When users downloaded an affected app—most of which were basic utility apps like QR code scanners, PDF readers, or WiFi detectors—secret in-app browsers covertly pushed traffic to nearly 500 AI-generated domains in order to monetize ad engagement.

Affected apps were generally basic single-function tools like flashlights, PDF converters, or wallpaper providers. The websites generally looked like blogs, news publishers, or informational sites. 

Many of the AI-generated domains were designed to look like generic, informative sites.

Not only did the operation hijack users’ devices without their knowledge it also led to wasted investment for many advertisers whose messages were not shown to real people. IAS Threat Lab was unable to quantify the total ad spend wasted by the scheme, but determined that its traffic generated millions of bid requests, leading advertisers to serve ads against bot traffic.  

Hadi Shiravi, a senior engineering manager at IAS Threat Lab, warned that AI is being used to quickly set up and aggressively expand these fraud schemes. “These are scales we haven’t seen before,” Shiravi said. “It’s very easy for them to spin up these domains, scale them and at a very low cost,” Shiravi added.

Plus, technical advancements are making it easier for AI-generated content to bypass standard monitoring techniques employed by ad exchanges and supply-side platforms.

To further obscure the activity and avoid detection, fraudsters misrepresented bundle IDs, unique codes used to identify specific apps. Instead of displaying their real bundle IDs, the fraudulent apps showed the IDs of a wide range of real, popular apps—like Netflix and Instagram—to suggest that the traffic going to the shell websites was legitimate. Some of the domains showed upwards of 300 app bundle IDs generating traffic. 

After detecting the fraud operation in September of last year during a regular monitoring process, IAS Threat Lab followed its proliferation before it reached its peak in December. Google instigated its own investigation into the apps, and eventually, it pulled the fraudulent apps from the Play Store. Plus, a security tool called Google Play Protect will automatically disable Genisys-affiliated apps—even if they come from outside the Play Store—and alert users in the future if they interact with such an app. The organizations claim they’ve reduced Genisys-related bid requests by over 95%.

“Bad actors are constantly changing tactics, which is why we collaborate with IAS and others across the industry to disrupt fraud networks,” a Google spokesperson told ADWEEK. The spokesperson added that the removal of the affected apps is intended to help “protect people and businesses from abuse,” but declined to comment further on the record.

Including Genisys, Google pulled over 870 Android apps associated with at least three large-scale ad fraud schemes within the last year.

The AI-generated domains at the heart of the Genisys operation signal a sea change; next-generation ad fraud schemes will “absolutely” be enabled by AI, Shiravi said. It’s a summons the media industry will have to answer. 

The IAS Threat Lab, for its part, claims to be actively preparing, according to Vidhya Rohini Raman, the division’s senior director of data science and engineering. “We are simultaneously developing product to detect low-quality generative AI content and integrating it into our core product ecosystem,” she said, “so our advertisers will be able to benefit from improved detection and avoidance capabilities, and that, in turn, will help them focus on high-performing media.”

Outfront Media, the company behind approximately 500,000 billboards and commercial out-of-home (OOH) displays across the U.S., is leveling up its sales operations through a new partnership with AdQuick, a digital marketplace for buying OOH inventory. In exchange for access to AdQuick’s platform, Outfront has agreed to invest up to $20 million in the company. 

Through the deal, Outfront will get access to AdQuick’s sales cloud platform for at least three years, including a limited exclusivity period. With this integration, advertisers will be able to plan and secure Outfront displays through one consolidated, standardized system, rather than dealing in spreadsheets, emails, and phone exchanges. They’ll also gain access to clearer performance data to measure the impact of their campaigns. 

For Outfront, the integration means a more seamless selling process and enhanced, data-driven workflows that simplify how the company packages, offers, and transacts inventory. 

“We expect that advertisers and agencies will gain a more efficient, technology-enabled way to plan and buy premium OOH with clearer workflows, faster proposal turnaround, and better visibility into options and outcomes,” said Outfront’s chief technology officer Premesh Purayil in a statement shared with ADWEEK. He added that the tie-in will “[help] teams on both sides spend less time on manual steps and more time on planning and performance.” 

As part of the agreement, Outfront will invest in AdQuick at set “milestones,” which may include adoption benchmarks or key outcomes, per AdQuick. Contractually, no minimum investment is required from Outfront, but AdQuick is confident that the company will commit the full $20 million, an AdQuick spokesperson said.

“Beyond the strategic investment, the integration with Outfront gives us a scaled deployment partner to prove the platform at the highest levels of the industry,” said Chris Gadek, AdQuick’s CEO. “Outfront works with many of the largest advertisers in the country, and the reality is that a lot of that media is still being planned and bought using legacy tools—or no technology at all. This partnership puts AdQuick’s platform in front of those buyers and gives them a meaningfully better way to transact.”

As a result of the overhaul, Outfront may be better poised to compete with the tech-savvy OOH players of today, like Clear Channel and Lamar Advertising—and the swath of programmatic software companies like Broadsign and Vistar whose tech undergirds the OOH ecosystem. 

This year, U.S. investment in OOH and DOOH is expected to swell; DOOH spend will reach nearly $3.6 billion, a 6.1% lift from 2025, according to recent figures from Emarketer. That spike is driven in part by programmatic and AI advancements that are making it easier for advertisers to plan, buy, and measure their campaigns in these environments.

Shares of Outfront, which trades on the New York Stock Exchange under the ticker $OUT, are up 44% year over year. The company will report fourth-quarter earnings after the bell today.

Ads from a handful of major brands—including Expedia, Qualcomm, Best Buy, and Enterprise Mobility—have begun appearing inside ChatGPT responses, a spokesperson for OpenAI told ADWEEK.

Online wedding vendor The Knot Worldwide is also running ads inside ChatGPT, said a spokesperson for the company. Best Buy and Enterprise Mobility also independently confirmed to ADWEEK that they are participating in early tests. Expedia and Qualcomm did not respond to requests for comment.

“We believe ads play an important role in continuing to support broad access to AI,” said Asad Awan, ads and monetization lead at OpenAI told ADWEEK in a statement. “By working closely with partners in this pilot, we’re able to thoughtfully test new ad experiences and learn together to ensure ads are separate and clearly distinct, relevant, and useful while maintaining the trust people place in ChatGPT.”

The early experiments offer a clearer picture of how OpenAI is beginning to build an advertising business inside its flagship chatbot—starting with a small group of brands, limited inventory, and tightly controlled placements. The AI company previously confirmed to ADWEEK that it wants advertisers to pay at least $200,000 for its early ad test.

OpenAI began rolling out ads inside ChatGPT for free and Go users on Feb. 9 as it looks to diversify its revenue beyond subscriptions. Holding companies including Dentsu, Omnicom, and WPP have lined up to test placements in the chatbot, bringing brands such as Adobe, Audemars Piguet, Audible, Ford, Mazda, and Mrs. Meyer’s into early experiments, ADWEEK previously reported.

ChatGPT’s ad business may be underway, but early signals suggest OpenAI is prioritizing restraint over scale, limiting placements to a small pool of advertisers and a fraction of user queries.

Adthena analyzed more than 500 prompts on ChatGPT and found ads appeared in roughly 0.8% of responses. These ads were from Expedia, Qualcomm, Best Buy, and Enterprise Mobility, the search intelligence platform  told ADWEEK.

The findings suggest that, while OpenAI is officially running ads, it is starting with a curated, and conservative approach to ad frequency, according to Adthena. 

That scarcity could also signal how OpenAI is positioning early access to advertisers, working with a curated mix of enterprise brands and select partners as it shapes its ad offering.

Google wants to make it easier for marketers to tap into the insights of its Marketing Mix Model (MMM), Meridian. The company today launched a new code-free interface that helps non-technical users explore Meridian data to understand ad campaign performance better. They can also use the tool to stress test various scenarios for campaign planning and optimization.

MMM is a framework for tracking marketing efforts across channels over time—against the backdrop of other factors like seasonal events and economic shifts—to attribute sales to specific factors.

Due to its technical nature, MMM has historically been the remit of data scientists. But the new interface, Scenario Planning, is designed to make the insights produced by Meridian more accessible. Users can simply query the platform with natural-language prompts to interact with models or test different budgets to project returns on investment.

“It transforms the conversation from a look back at what happened to a collaborative plan for what’s next, regardless of technical expertise,” Google’s senior director of data science and engineering, Harikesh Nair, wrote in a blog post.

Asos and Shopify are among Meridian’s enterprise clients.

Meridian was introduced in early 2024, at a moment when the future of third-party cookies was under threat, and marketers were seeking out privacy-safe methods of cross-channel media measurement. But even with Google pulling the rug out on its years-long plans to deprecate cookies in Chrome last year, marketers have continued to pour resources into MMM, which can prove effective for identifying what actually drives sales and can weather the winds of increasingly stringent privacy regulation and non-cookie-related signal loss on the open web.

Today, MMM is gaining more investment from marketers than any other measurement methodology, outpacing incrementality testing, multi-touch attribution, and other approaches, according to recent research from Emarketer.

At the same time, the technical barrier to entry for MMM has historically been high; with Scenario Planning, Google hopes that marketers and data teams can work in tandem to better understand campaign performance and improve future outcomes.