AI-Powered Publishing Explained

Sponsored by Mula

Publishers are entering one of the most consequential periods in the history of the open web. Traffic is declining as audiences shift toward walled gardens and AI-driven search experiences that summarize content rather than send users to it.

Operating costs continue to rise, and the long-standing assumption that “more content equals more revenue” has stopped holding true. As AI accelerates, so do the fears: shrinking visibility, unpredictable referral patterns, and a growing sense that the tools available to publishers lag far behind those used by platforms extracting the most attention.

But there’s another way to look at the moment. The same technology that is destabilizing the open web can also be the catalyst that helps publishers reclaim value. If embraced deliberately, AI can unlock more revenue per session, strengthen editorial impact, and modernize the broken growth model that has held the industry back.

The Ad Tech Explained team developed this explainer, in partnership with Mula, to shed light on the ways in which publishers can be using AI to fight AI.

Let’s start with the basics. What exactly do we mean by “AI-powered publishing”?

AI-powered publishing refers to a shift from manual, reactive optimization to an operating-system-level approach that uses machine intelligence to elevate every page. Historically, only walled gardens had access to systems capable of predicting behavior, improving UX in real time, and optimizing for revenue across millions of surfaces.

In an AI-powered publishing model, the technology acts as a co-pilot: analyzing content, guiding decisions, improving discovery, and ensuring monetization strategies align with actual reader intent. Crucially, this approach doesn’t replace editorial judgment. It amplifies it, bringing goal-based intelligence to workflows that have long relied on guesswork and limited resources.

What does AI-powered publishing look like on the content side?

On the content side, AI evaluates every article through a semantic lens to understand intent, relevance, and the behavioral patterns likely to follow. Systems can identify where readers might want deeper context, where commerce opportunities naturally fit, and where additional content should be injected to create a smoother, more engaging journey.

Modern publisher OS platforms, like Mula, use modular AI agents trained to ingest content, analyze metadata, understand tone and relevance, and generate editorial-safe recommendations. This enables publishers to extend the life of articles, surface the right products and links, and deliver richer, more personalized experiences without adding work for already overstretched editorial teams.

Content is the fun part. But ultimately, publishers need to stay in business. So, what does AI-powered publishing look like on the monetization side?

The monetization layer is where AI can drive some of the most immediate and measurable gains. AI-powered systems dynamically evaluate whether a page should surface affiliate products, programmatic placements, next-page modules, commerce blocks, or incremental scroll units, and in what order.

Instead of setting static rules, agents can continuously test variations, optimize placements, and target toward specific business goals like higher RPS, more affiliate clicks, or deeper scroll depth. Measurement agents track performance in real time, right down to widget views, product clicks, scroll patterns, and revenue contribution. This helps publishers to turn every article into a living asset that improves over time.

Speaking of agents, how does AI-powered publishing intersect with AdCP?

The Ad Context Protocol (AdCP) provides a structured way for publishers to communicate detailed context, goals, and constraints to buyers and AI systems. As agentic advertising grows, these signals become essential: AI systems need richer metadata to understand what content represents, how users are engaging with it, and what outcomes matter for that particular impression.

AI-powered publishing expands the value of AdCP by generating high-quality contextual data in real time and packaging it in a way that programmatic buyers (and future buy-side agents) can act upon. The Mula team is actively exploring and contributing to AdCP’s development, ensuring publishers can translate this emerging standard into better monetization and a stronger competitive position.

Let’s get more specific. Where does Mula fit into the broader shift toward AI-powered publishing?

Mula is an AI-powered operating system designed specifically for publishers. It deploys in minutes via a single tag, requires zero engineering lift, and adapts to existing editorial workflows. Once live, Mula’s modular agents (named after surfing legends) analyze content, generate contextually aligned UX modules, inject commerce or ad placements, and measure performance continuously.

• Agent Hunter evaluates content and identifies optimal injection points.

• Agent Nique generates editorial-safe recommendations and related products.

• Agent Occy optimizes monetization decisions (whether to show ads, products, or next-page links) based on real-time analytics.

• Agent Taka handles dynamic distribution through scroll extensions and widget deployment.

• Agent Andy tracks performance and pushes natural-language summaries i.e. reporting back to publisher teams.

Collectively, these agents give publishers access to the kind of intelligent, goal-based optimization that walled gardens have benefited from for years.

What real-world results are publishers seeing with Mula?

Publishers using Mula are seeing meaningful, measurable improvements within days. ON3, for example, was experiencing declining CPMs on long-form articles. By deploying Mula’s SmartScroll widget and a custom affiliate feed, ON3 unlocked a double-digit lift in revenue per session and more than $3 eCPM on commerce modules, while also increasing scroll depth. All of this was achieved with no dev time and no added editorial work.

Brit + Co saw similarly compelling results. Mula delivered a 200 percent increase in affiliate clicks without requiring any changes to editorial workflow. These outcomes reflect Mula’s ability to find untapped value inside existing content that would otherwise remain dormant.

What should publishers be doing right now to prepare for an AI-powered future?

Publishers should begin by ensuring that pages are tagged and structured in ways that AI systems can interpret, making it easier to extract meaning and optimize for outcomes. They should also evaluate how their existing affiliate, commerce, and content workflows align with AI-powered recommendations, particularly where automation can remove manual bottlenecks.

Also, think like a marketer. Understand more about your users, their value (LTV), and their journeys across your site. Buying traffic will become more important, but you need to know the value of your users’ sessions in real time in order to manage profitability. Be thoughtful with push notifications and partner with companies like Pushly to understand what content should be pushed to which cohort and when. Publishing newsletters (and working with companies like Zeta to monetize them) becomes more important, and this also becomes a vehicle for retention.

Most importantly, publishers should treat AI not as a threat, but as a performance partner. Systems like Mula allow teams to drive more value from the content they’re already producing, modernize monetization without adding operational strain, and remain competitive as walled gardens and AI search reshape the ecosystem. Preparing now positions publishers to benefit from the next evolution of the open web rather than be displaced by it.

About Mula

Mula is headquartered in Mill Valley, CA, and backed by Offline Ventures. It provides an agentic OS that brings scale and efficiency to digital publisher monetization. Mula is a system of AI agents that analyzes content on a particular URL and user behavior to inject contextually relevant products and a publisher’s own content into widgets on that page. Much like today’s social media platforms, the goal is to keep users dwelling, scrolling, and transacting. By embedding into tools publishers already use, and extending this approach to platforms like Microsoft Teams, Mula lowers barriers to adoption so it can be a natural extension of a publisher’s workflow, not another system to learn.