AdMarketplace’s Sam Cox On AI Search: ‘This Is A Time For Betting – And Betting Hard’

Recorded: March 27, 2026, 6 a.m.

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AdMarketplace’s Sam Cox On AI Search: ‘This Is A Time For Betting – And Betting Hard’ | AdExchanger

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Home Technology AdMarketplace’s Sam Cox On AI Search: ‘This Is A Time For Betting – And Betting Hard’

Technology
AdMarketplace’s Sam Cox On AI Search: ‘This Is A Time For Betting – And Betting Hard’ By Allison Schiff

Friday, March 27th, 2026 – 1:00 am
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An interview with
Sam Cox
Chief Product Offier

AdMarketplace is a 25-year-old search ad network that runs search ads for browsers and other partners. It’s also been a vocal critic of Google and was cited during the recent search antitrust trial as an example of an independent search ad competitor harmed by Google’s dominance.
Well, now one of adMarketplace’s key executives is a former top Googler.
Sam Cox joined adMarketplace as chief product officer in December after stints at Google, Amazon and Integral Ad Science. He was the group product manager in charge of Google’s programmatic exchange business between 2016 and early 2021, and then moved over to Amazon for a two-year stretch as director of technical product management for its DSP.

Between Amazon and IAS, where he spent two years as SVP of product management, Cox took a little time off to work on his Hudson Valley farm, tend to animals – he and his family raise sheep, goats and pheasants – and think about what AI is doing to search.
He landed on two main paths forward: using AI to squeeze more performance out of traditional programmatic media or focusing on how to monetize AI-powered search and new chatbot experiences.
Most of the industry is running down the first path. Cox picked the second.
At adMarketplace, he’s tasked with helping build “the next generation of search advertising,” to quote from the press release about his hiring. That may sound a little grandiose, but in practice just means upgrading adMarketplace’s search stack, including using AI to match queries with more relevant ads and designing ad formats specifically for AI search.
“I think this is a time for betting, and betting hard,” Cox said.
AdExchanger spoke with Cox about his take on ads in ChatGPT, zero-click search behavior and why he’s skeptical of anyone who claims to have cracked generative AI optimization.
AdExchanger: What’s on adMarketplace’s AI roadmap?
It comes down to three things: relevance, what I call “objects of decoration” and ad selection.
On relevance, we’re using AI to better interpret and normalize queries from long, messy natural language to more structured intent.
What I mean by objects of decoration is that we’re borrowing from OpenRTB. An RTB call can contain hundreds of fields, and third-party search has a lot to learn from that. We’re figuring out which fields matter, which are populated and how to layer causal signals on top of the query term without drifting into user-specific targeting, especially with privacy-centric partners like Firefox and Opera.
The third piece, ad selection, is about using AI to decide which ads, formats and copy to show in a given context.
What do you think about ads in ChatGPT?
The principles they outlined are pretty close to how we think about it. AI chat experiences need ads, but those ads should be privacy-centric and clearly marked. They also shouldn’t influence a model’s answers. The ad results have to be separate from the advice being given by a bot.
Based on what OpenAI has shared so far, that’s the direction they seem to be going in, which I think is the right instinct.
Back in June 2024, adMarketplace published a blog post with the headline “Search No More? How PMax Has Effectively Nullified Your Search Advertising Efforts.” I know that’s from well before you joined, but do you agree?
What I’ll say is that it reflects a tension we’ve seen for a long time between control and performance. When we worked on Performance+ at Amazon, the pattern was the same. Advertisers say they want granular control, but what they really want is better outcomes, and those two things often conflict.
My view is, you need to draw hard lines around what can’t happen – brand safety, for example, is nonnegotiable – and be more flexible on targeting and optimization. You also need multiple strategies at once, like a “champion” approach and a “contender” that tries to beat it. If the contender wins, you shift spend and explain to the customer what changed and why.
That’s how you preserve transparency and a sense of ownership while still letting the system automate as much as possible, whether you call it PMax or something else.
I keep getting pitched by vendors that claim they’re able to help brands control how they show up in generative AI search results. How real is that?
I don’t think anyone truly knows. There’s a lot of demand for certainly so, of course, there are people willing to claim they’ve figured it out. But we’re dealing with systems and emergent behaviors that are changing very quickly.
Even if you reverse-engineer something today, there’s no guarantee it will hold true tomorrow as the models get updated. So, I’d treat any very definitive advice with skepticism. We’re in a period of genuine experimentation, and a lot of the “rules” people are selling right now are probably false certainty.
You were deposed for the Google ad tech antitrust case. What’s your reaction to how the trial played out?
I have to be careful answering that. I was directly involved on the Google side, some of my emails are in the record and I was named as a witness, so I’m still kind of not allowed to talk about the substance or the outcome.
What I can say is that it was a surreal experience. I was deposed multiple times during COVID in a tent, which was also my office on the farm. I had a wood stove going, I was surrounded by multiple computers and I was being questioned by the US government.
It’s definitely one of the stories I’ll be telling when I’m an old man.
This interview has been lightly edited and condensed.
For more articles featuring Sam Cox, click here.

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AdMarketplace’s Sam Cox, a former Google executive with a background spanning Amazon and Integral Ad Science, presents a decidedly pragmatic and somewhat skeptical outlook on the evolving landscape of search advertising, particularly in the context of generative AI. Cox’s core argument, articulated as “betting hard,” reflects a belief that the industry is currently overly focused on leveraging AI for incremental performance gains within existing programmatic frameworks, while neglecting the significant opportunity presented by monetizing AI-powered search and chatbot experiences. He views the current approach as a misallocation of resources and a potential blind spot for advertisers.

Cox identifies three key areas of focus for AdMarketplace’s AI strategy – relevance, “objects of decoration,” and ad selection. The prioritization of relevance centers on utilizing AI to normalize complex, natural language queries into structured intent, effectively bridging the gap between user expression and automated ad delivery. Simultaneously, Cox recognizes the utility of leveraging data from the Real-Time Bidding (RTB) ecosystem, specifically the vast array of fields contained within RTB calls, to enrich search matching without venturing into user-specific targeting—a concern regarding data privacy. This approach, he suggests, represents a calculated step towards leveraging the data-rich environment of programmatic advertising.

The concept of “objects of decoration” signals a recognition of the shifting user experience and the growing prevalence of conversational interfaces. Cox acknowledges the need to adapt ad formats and copy to suit the immersive nature of AI-powered search and chatbot interactions, drawing inspiration from the evolving landscape of the RTB ecosystem. This indicates an awareness of the fundamental change in how users engage with information and, consequently, the need for ads that seamlessly integrate into these new search paradigms.

Cox expresses significant skepticism regarding vendors claiming to provide definitive control over ad results within generative AI search environments. He posits that the rapidly evolving nature of AI models renders any long-term guarantees of effectiveness illusory. He contends that a period of experimentation is paramount, recognizing that established “rules” surrounding AI optimization are likely to become obsolete quickly. This highlights the inherent volatility of the technology and the caution that should be exercised when accepting claims of absolute control.

His deposition experience during the Google ad tech antitrust trial underscores the cautious, somewhat wary, approach he brings to the field. The surreal nature of the experience—a deposition conducted in a tent during the COVID-19 pandemic—suggests a detachment from overly aggressive assertions and highlights a preference for grounded observation. This experience likely contributes to his skepticism and his emphasis on strategic flexibility.

Cox’s views on the tension between control and performance echo long-standing debates within the advertising industry. He advocates for establishing hard lines around non-negotiable standards like brand safety, while permitting greater flexibility in targeting and optimization strategies. Importantly, he emphasizes a “champion” versus “contender” approach to campaign management, promoting a dynamic interplay of strategies that prioritizes adaptation based on performance data, rather than rigid adherence to pre-defined parameters.

Regarding the emergence of ads within ChatGPT, Cox aligns with OpenAI’s stated principles, advocating for privacy-centric approaches and clear labeling of advertising content within chatbot interactions. This reflects a conservative yet pragmatic stance, prioritizing user trust and transparency—key considerations in the evolving landscape of AI-driven search.

Ultimately, Cox’s perspective is characterized by a measured, cautious optimism. He believes that engaging in bold experimentation with AI search is essential, but that it must be tempered with a realistic understanding of the technology's limitations and the importance of safeguarding brand integrity and user privacy.