Social’s dilemma is the open web’s opportunity

In recent testimony during Meta’s Federal Trade Commission’s antitrust trial, Mark Zuckerberg acknowledged that social platforms are shifting from friend-based networks to algorithmic discovery engines.

In other words, social is becoming less social — and that has major implications for advertisers.

As these platforms prioritize engagement-driven feeds over community and control, they also limit how advertisers can plan, measure and manage their media buys.

What’s emerging is a dilemma, but also an opportunity: The more opaque and automated social media becomes, the more opportunity there is for brands to reevaluate the open web as a performance-driven, brand-safe alternative.

The key to smarter investment lies in a more strategic assessment of each platform’s merits using data-driven evaluation models while having a firm grasp on best practices and media governance.

The social dilemma

The ad performance engines of social media rely heavily on predictive audience modeling. These models bundle ads into multiformat, AI-driven inventory packages, limiting advertisers’ ability to exercise granular control over where and how their ads appear.

While such predictive targeting can be highly effective in reaching valuable customers, the degree of opacity can raise doubts about measurement accuracy and accountability.

Total time spent is now plateauing, so platforms need to compete for attention while driving revenue growth. The result is a high ad load on these platforms of swipeable, skippable formats.

Further, users are often peppered with promotional content, potentially leading to ad fatigue and lower engagement rates.

Issues with content verification on social platforms are returning to the fore due to the deprecation of moderation capabilities. It’s plain in 2025 that algorithms often prioritize engagement over accuracy, leading to adjacency concerns as ads appear alongside harmful or misleading content, potentially damaging consumer trust.

The open web opportunity

When done correctly, the open and premium web offers a controlled and transparent advertising environment.

With nondisruptive ad formats and curated inventory, advertisers can effectively balance reach and user experience. The open web provides unique opportunities for decision-making regarding audience targeting and identity resolution, giving advertisers the choice to refine their strategies with greater precision.

The opportunity to leverage transactional and retailer data, stitched to a persistent identifier offers a similar level of data efficacy as the logged-in environments of walled gardens.

These identity signals can be matched against a rich marketplace of programmatic vendors across the display, online video and AV landscape.

Throw in the burgeoning “bring your own custom algorithm” subcategory in ad tech, and you can enable advertisers to align in-platform efficiency gains with media effectiveness and real-world business outcomes.

The issue of poor buying practices

Despite the opportunities of the open web, digital ad spending remains weighted toward walled gardens.

The frequent headlines about poor programmatic buying practices obscure the reality of where brand-safety issues originate. It’s often overlooked that walled garden platforms can extend their reach beyond their own properties, contributing to a significant percentage of brand-safety violations.

Rather than bluntly blaming programmatic advertising for these issues, the industry needs a more nuanced understanding of where and how these violations occur.

The extension of walled garden inventory beyond owned-and-operated properties requires due diligence and scrutiny of media placement quality.

Despite enhanced programmatic capabilities around curated inventory supply and identity-led addressability, advertisers could be missing a competitive advantage due to these misconceptions.

A smarter approach to advertising investment

Signal loss is skewing the lens by which media performance can be qualified, with the open web impacted disproportionately.

Advanced measurement techniques — such as incrementality testing, media mix modeling (MMM) and regression-based attribution — can help advertisers determine the true performance of each platform.

MMM allows brands to evaluate the impact of different marketing channels by analyzing historical data and identifying which platforms drive the most meaningful business outcomes.

Similarly, regression-based attribution provides insights into the incremental value of each advertising touchpoint, helping marketers avoid overreliance on any single platform’s self-reported performance metrics.

Rather than following industry trends without consideration, advertisers can move beyond surface-level metrics like eyeballs and traffic, instead focusing on tangible business results such as incremental sales, customer retention and long-term brand equity.

This op-ed represents the views and opinions of the author and not of The Current, a division of The Trade Desk, or The Trade Desk. The appearance of the op-ed on The Current does not constitute an endorsement by The Current or The Trade Desk.