As AI capabilities continue to gain strength and steam in brand safety, publishers and platforms are looking to generate new revenue streams. Many are testing expanded real estate in the social-scape, where audiences are at their most authentic, and most unpredictable. One example is a new focus on comment sections. Long treated as a dicey liability, they are now emerging as a valuable source of “intent-based data” given the growth of AI because they can evaluate real conversations between real people. Platforms aggregating and anonymizing this discourse are attracting advertisers because they offer greater or expanded contextual relevance at scale. Yet these environments also underscore a central tension: the same human expression that creates monetization opportunity also introduces risk that automated systems alone cannot fully anticipate or resolve.
Sports streaming and athlete-driven content have proven to be a complicated atmosphere for comments and expression. We have already highlighted the harsh fan commentary that sometimes accompanies NCAA content. Platforms monetizing live events and user-generated highlights offer brands deeply contextual, emotionally resonant placements. These offerings capitalize on reaching audiences at moments of peak engagement. But these environments are dynamic, real-time, and culturally charged, making them particularly sensitive to risky content alignment with abusive fans. No AI model, however advanced, can fully grasp the cultural nuance, timing, or reputational stakes of live sports without human guidance.
Verification firms are deploying agentic AI systems capable of also autonomously optimizing campaigns in real time, analyzing trillions of signals across video, audio, text, and imagery. These systems promise speed, precision, and scale - but are never foolproof. The critical question is, can agentic AI be sufficiently accountable when a brand’s reputation is on the line?
The future of brand safety continues to show promise in the combination of machines and human judgment. Effectiveness will be shaped by how well organizations pair agentic AI with human teams who set guardrails, interpret context, and intervene when automated decisions fall short. AI can surface risk, optimize delivery, and process scale, but humans must define values, adjudicate ambiguity, and take responsibility. In an ecosystem where monetization increasingly depends on trust, the strongest brand safety systems will be those that treat AI not as a replacement for human oversight, but as a force multiplier for it.
