SODP Dispatch - 18 December 2025

18 Best Ad Networks for Publishers in 2026

By Vahe Arabian & Samuel Fatola

Ad revenue represents one of the three monetization pillars publishers have access to—with the others being subscriptions and affiliate marketing. As such, those publishers that have prioritized ad revenue need to ensure they pick the right ad network.

The global digital advertising market is projected to hit around $1.3 trillion by 2027, driven by factors such as the growing adoption of smartphones and the ongoing rollout of the Internet of Things (IoT).

The role of digital advertising remains pivotal to brand strategies, with research showing that around 50% of online users search for a product video before making a purchase.

The growth of digital advertising and how mobile ad networks work offers immense monetization opportunities for publishers that are in a position to capitalize. A key element of that positioning is the ad networks they choose.

When choosing an ad network, it's important to consider the ad formats available, the targeting options, the optimization tools and the revenue share.

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📊 The Most Engaging Stories of 2025 | Chartbeat

Chartbeat analyzed more than 41 million pieces of content representing 187 billion minutes of engaged time. The top story: Fox News' reporting into Gene Hackman and his wife's deaths. Second: The Atlantic's "The Trump Administration Accidentally Texted Me Its War Plans." Third: The New York Times' "She Was Ready to Have Her 15th Child. Then Came the Felony Charges." Political stories dominated with more than 182 million minutes of engaged time. Crime coverage had two stories in the top 10. In Memoriam stories resulted in more than 44 million minutes of engaged time. BBC had the most entries on the list. This marks Chartbeat's 10th year celebrating customers' most engaging stories.

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By Vahe Arabian

🤖 Tips on how to leverage Agentic AI for 2026

As we look toward 2026, we're witnessing a pivotal moment in enterprise AI adoption. Agentic AI systems that can act autonomously to achieve goals is transitioning from experimental pilots to production-scale deployments. This shift represents more than just technological maturation; it's a fundamental change in how organisations operate.

Here's what leaders need to know about this transformation and how to prepare for scaled agentic AI deployment:

1) Infrastructure Readiness: The Foundation for Scale

Moving agentic AI from pilot to production requires robust infrastructure that most organizations don't yet have. Unlike traditional AI models that simply predict or classify, agentic systems need to interact with multiple tools, databases, and APIs while maintaining security and compliance.

👉 Actionable Tip: Audit your current infrastructure for API connectivity, authentication systems, and observability tools. Invest in orchestration platforms that can manage multi-step agent workflows and provide proper logging for compliance and debugging.

2) Trust and Governance Frameworks Become Critical

In pilot phases, companies can tolerate occasional errors. At scale, agentic AI systems making autonomous decisions require comprehensive governance frameworks. Organizations must define clear boundaries for agent autonomy, establish approval thresholds, and create rollback mechanisms.

👉 Actionable Tip: Develop a tiered autonomy model where agents have different permission levels based on risk. Implement human-in-the-loop checkpoints for high-stakes decisions and create transparent audit trails for all agent actions.

3) From Single-Purpose to Multi-Agent Orchestration

Early pilots typically focus on single-use cases—a customer service agent or a data analysis assistant. Production-scale deployment means coordinating multiple specialized agents that work together, each handling distinct tasks while sharing context and goals.

 👉 Actionable Tip: Design your agentic systems with modularity in mind. Create specialized agents for distinct functions (research, analysis, execution) that can collaborate through standardized protocols. This approach enables easier scaling and troubleshooting.

4) ROI Measurement Shifts from Proof-of-Concept to Business Impact

Pilot success is often measured by technical feasibility—"Does it work?" Production requires demonstrable business value: cost savings, revenue generation, or productivity gains that justify the investment.

👉 Actionable Tip: Establish clear KPIs before scaling. Track not just agent performance metrics (accuracy, response time) but business outcomes (customer satisfaction scores, operational cost reduction, time-to-resolution). Build attribution models that connect agent actions to business results.

5) Data Quality and Access Become the Bottleneck

Agentic AI systems are only as effective as the data they can access. At scale, data fragmentation, inconsistent formats, and access restrictions become major obstacles. Organizations discover that their biggest challenge isn't the AI, it's the data infrastructure.

👉 Actionable Tip: Prioritize data integration and standardization efforts. Implement semantic layers that give agents consistent access to enterprise data regardless of source. Consider investing in knowledge graph technologies that help agents understand relationships between data points.

6) Change Management: Preparing Teams for AI Collaboration

The shift to production-scale agentic AI fundamentally changes how teams work. Employees need to learn when to delegate to agents, how to verify agent outputs, and how to collaborate effectively with autonomous systems.

👉  Actionable Tip: Develop comprehensive training programs that go beyond tool usage to teach AI collaboration skills. Create clear escalation paths when agents encounter problems, and establish feedback loops where human users can improve agent performance over time.

7) Cost Management and Resource Optimization

Pilots run on limited budgets with predictable costs. Production-scale agentic AI can consume significant computational resources, especially when agents make multiple API calls or process large datasets autonomously. Runaway costs become a real risk.

👉  Actionable Tip: Implement cost guardrails and resource quotas for agent operations. Use caching strategies to reduce redundant API calls, and establish monitoring systems that alert teams to unusual resource consumption patterns before they impact budgets.

Organizations that succeed in scaling agentic AI will be those who:

• Build a robust infrastructure with proper security and observability

• Establish clear governance frameworks with appropriate autonomy boundaries

• Design for multi-agent collaboration rather than siloed solutions

• Focus on measurable business outcomes, not just technical capabilities

• Prioritize data quality and access as foundational requirements

• Invest in change management and team readiness

• Implement proactive cost management and resource optimization

The transition from pilot to production isn't just about technical scaling; it's about organizational readiness. Companies that approach this shift strategically, with attention to infrastructure, governance, and human factors, will unlock the transformative potential of agentic AI.

The question isn't whether agentic AI will move to production at scale; it's whether your organization will be ready when it does.

AI Moves to Center of Tech and Media Stack | Digital Content Next

Deloitte's 2026 TMT predictions place AI at the center of nearly every major trend. More than half of predictions link directly to AI, which matters because TMT contributes close to 50% of global market capitalization. Daily use of search engines with integrated AI summaries will reach 29% in 2026, while standalone AI chat tools will reach 10%. Embedded summaries reshape discovery, creating pressure on publishers to show clear value beyond AI-generated content. Global spending on AI chips will surpass $50 billion, with cloud providers responding with price increases reflecting limited capacity.

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Google's Spam Problem Getting Worse | Search Engine Journal

Google is losing the war against spam with unprecedented scale. Over 50% of internet content is AI slop, with Ahrefs finding 74% of analyzed pages contain AI content. Journalist Jean-Marc Manach found over 8,300 AI-generated news websites in French and over 300 in English. Expired domain abuse is back—black hat SEOs purchase expired domains with strong backlink history and create Private Blog Networks to manipulate rankings. Google Discover has been hit hard with fake AI content reaching millions of page views. Google's anti-spam activity dropped significantly: four updates lasting 70 days in 2025 versus seven updates lasting 130 days in 2024.

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Reuters Institute: How 2025 Shaped Journalism | Reuters Institute

The Reuters Institute published findings across eight reports. Key findings: Social media dominance in the US now resembles Global South countries. Across six countries, 54% saw AI-generated answers in search, with 33% always or often clicking links. News creators have significant impact in Brazil, Mexico, Indonesia, Philippines, Thailand, and the US. Only 27% of top editors across 240 brands are women, up from 24% in 2024. Climate news use is declining in Europe, Japan, and the US. Among UK journalists, 56% use AI weekly, but 62% see it as a threat versus 15% as an opportunity.

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Jimmy Wales: Wikipedia Under Attack But Transparency Survives | The Verge

Wikipedia cofounder Jimmy Wales published The Seven Rules of Trust as the encyclopedia turns 25. Wales attributes continued trust to transparency—the open system where anyone can see decisions and participate. He recently argued Wikipedia shouldn't use "genocide" in "wiki voice" for Israel-Gaza despite editor debate. On Elon Musk's Grokipedia, Wales noted large language models "really aren't good enough to write an encyclopedia" due to hallucinations. Wales sees both threat and benefit in AI: crawling bots strain servers funded by small donors, but AI tools could help editors verify sources.

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VR and AR Weren't Quite Right for News | INMA

Immersive media hasn't delivered on its storytelling promise for news. Despite early excitement 11 years ago, headsets remain too clunky, heavy, and isolating. Apple's Vision Pro feels far from mainstream adoption. Meta's AR glasses add complexity that feels more like distraction—Instagram and emails floating in vision can be done better through audio commands. There's a social problem: talking to someone half-looking through you, eyes fixed on something only they can see. The tech is stunning, but day-to-day utility isn't there. Matterport found stronger markets in real estate than news.

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