This time feels different. Not because another regulation is looming or a platform is redrawing the technical map, but because the context around the data itself has shifted. 

Or at least that’s the direction events over the past week suggest. 

TikTok’s U.S. privacy policy was updated last week and now makes clear that the app may collect precise location data if users grant it permission. On its own, that is unremarkable. Granular behavioral signals are the currency of the modern platform economy, after all. But context matters. TikTok in the U.S. now operates under a new ownership structure that includes Oracle, whose co-founder Larry Ellison has spoken about AI-enabled surveillance as a tool to keep citizens “on their best behavior.” Against that rhetoric, routine data practices take on a different weight.

At the same time, the U.S. Immigration and Customs Enforcement agency is seeking information from ad execs on how “big data” and “ad tech” could be used to support investigations. In other words, tools built to predict who might buy something could potentially be used to investigate them. 

Viewed together, these developments reframe advertising’s role in the data ecosystem. The industry helped normalize continuous collection of behavioral signals at scale. For years, those signals were treated as benign inputs for relevance and measurement. Aggregated and modeled, however, they form detailed persisted records of daily life — assets whose value now extends beyond marketing.

And that’s before AI is factored into the equation. Systems can now infer attributes, risks and future behaviors with statistical confidence, even when those attributes were never explicitly shared. The unintended consequences are the concern. The opportunity for misuse — in fraud, discrimination, manipulation or state surveillance — expands rapidly once those predictive capabilities exist. 

“We’re talking about the realization of the unintended social consequences of algorithms knowing more about us than we know about ourselves,” said Jamie Barnard, CEO of Compliant, a start-up helping companies better ensure compliance with privacy laws. “Just look at how the line between consumerism and state surveillance has blurred in the most alarming and terrifying ways in China as a warning of where this ends.”

For marketers that creates a dilemma that isn’t easily solved by compliance checklists. No brand team set out to enable surveillance. But years of pushing for sharper targeting, richer measurement and persistent identity helped create the data environment AI now feeds on. The same interoperability that makes advertising efficient makes data portable in ways  companies cannot fully control.

Ad tech firms face a similar bind. Their value lies in making signals at scale. That linkage is what advertisers pay for. It is also what makes the data powerful in other contexts. The risk, as Barnard, frames it, is not simply that companies hold data. It is that aggregate datasets become accessible, directly or indirectly, to actors with very different incentives. 

If that sounds alarmist, look at how location data has surfaced in criminal investigations, how social data has been used to assess creditworthiness and instance risk or how predictive analytics underpin everything from fraud detection to border enforcement. 

“We’re entering a new paradigm where advertising infrastructure further intersects with society and democracy,” said Alex Tait, founder of digital consultancy Entropy and a former media director at Unilever. “The question isn’t awareness. It’s leadership. In markets like the U.K., where new electoral and online safety legislation is imminent, the industry has a real opportunity to share its expertise and help shape how these systems are governed. That opportunity is emerging internationally too, as governments increasingly treat advertising infrastructure as democratically consequential. 

Where this ultimately leads is still unclear. What is clearer is the industry is moving into a phase where the secondary effect of its data practices can no longer be treated as distant hypotheticals. The systems built for performance are now part of a broader information ecosystem shaped by AI, geopolitics and state power.

“The test for the next generation of industry leaders is whether they step up and apply that expertise to building solutions, or continue to treat some of these issues as a hot potato,” said Tait. 

There’s a lot to admit about this industry but integrity has never really been its calling card. Moments like now make that painfully clear. Which is why I’m always thankful for the reminders — big or small — of the relationships that actually hold this business together.

That feeling hit during a conversation with industry expert Gina Cavallo, who’s stepped into the chief revenue officer role at French measurement firm XPLN.AI along with Michael Lampert, senior director of global generative AI adoption and consumer data strategy at Mondelez International. Not trying to float off into sentimentality but moments like that land as a necessary palate cleanser to the nonstop parade of chest-puffing, insecurity masquerading as expertise and pure self-interest that so often gets dressed up as thought leadership around here.

Which made what happened next stand out even more. The discussion was meant to be about product — attention metrics, creative effectiveness, CTV — yet the subtext kept pulling focus. Before XPLN.AI’s methodology got air time, Lampert was offering something closer to a character reference, framing Cavallo as a trusted advisor in a market where that plays second fiddle to whatever shiny capability is in the slide deck that week. He voiced a truth that marketers rarely say plainly: “There’s only five or 10 people that I really trust enough, especially in a space that’s so now, that I would even let in the building, because it’s about trust.”

In emerging areas like attention and AI-fueled measurement, where no one has decades of proof, trust becomes the through line.

That perspective reframes Cavallo’s move. With stops spanning Experian (via Audigent), MiQ, Playground XYZ and LiveRamp, she had options. Instead, she chose to build the U.S. business for a European attention measurement firm, taking on the harder task of growing a startup than leveraging platform scale. The remit is structural: turning seconds of attention into something marketers can use to adjust media in flight, linking creative effectiveness with media efficiency at a moment when brands are under renewed pressure to prove their investments are driving real outcomes, not just faster output.

Still, if the conversation was any guide, XPLN.AI’s early edge in the U.S. may be relational before it is technical. As she put it, “relationships open doors” but the product has to justify staying in the room. In a hype cycle full of noise, that sequence — trust first, tech second — looks like the more durable sign.

Numbers to know

71%: Percentage of kids in Germany whose most popular VoD platform is Netflix

21%: Percentage that Reels took up in Instagram’s total ad impressions during Q2 2025

20%: Percentage increase in time spent on Meta’s Threads during Q4 2025

50%: Percentage increase in capacity of OpenAI models powering Microsoft Copilot workloads

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