Media Relations Are Becoming Machine Relations—And Your PR Playbook Is Dangerously Outdated

The phrase landed in a webinar last week and immediately sparked recognition across the PR industry: "Media relations are becoming machine relations."

Gab Ferree, founder of Off the Record—a communications industry community—delivered this observation during a session with Axios HQ, and it crystallizes what thousands of PR professionals are experiencing but struggling to articulate. The rules changed. The playbooks that worked 18 months ago now miss the target entirely. And most brands are still operating as if journalists make decisions the way they did in 2022.

They don't. Because journalists aren't the only gatekeepers anymore.

Stacker's latest report, The Earned Media Edge: Achieving Word of Mouth at Scale in 2026, documents this shift with data from thousands of media outlets and interviews with industry leaders who've adapted their strategies in real time. What emerges is a picture of an entirely new media ecosystem—one where AI algorithms decide which sources get amplified, which brands get cited, and which content earns the trust signals that power visibility across both traditional search and AI-powered answer engines.

Here's what changed, why it matters, and what your team needs to do about it before your competitors figure it out first.

The New Media Ecosystem Loop: How AI Rewired Earned Media

Traditional media relations operated on a relatively straightforward model: pitch journalists, earn coverage, measure impressions and referral traffic. The relationship was bilateral—your team and the reporter.

Now it's trilateral. And the third party has algorithmic preferences you can't influence through relationship-building alone.

Stacker identified what they call the "New Media Ecosystem Loop"—a cycle where:

• Journalists use AI to narrow down which sources and stories warrant their attention in an increasingly time-constrained environment
• AI models scan earned media to determine which information is fresh, credible, and worthy of citation when answering user queries
• Brands that understand both audiences (human journalists and machine algorithms) compound their visibility while competitors get filtered out at multiple stages

Amanda Coffee, a communications leader named to PRWeek's 40 under 40 in 2024 and author of the Bury the Lede Substack, explained the practical reality in Stacker's report: if journalists don't see rich, credible content like original articles or substantive LinkedIn posts when they're researching sources, "they move on."

That's the human side. The machine side is even less forgiving.

AI models don't "move on"—they simply exclude sources that don't meet their trust and freshness criteria. And unlike a journalist you can pitch again next quarter, there's no second chance with an algorithm that's already indexed your content (or lack thereof) and assigned you a relevance score.

The Multi-Layered Playbook Problem: Which AI, Which Media, Which Strategy?

Here's where traditional PR strategy breaks down entirely: you can't just "get coverage" anymore. You need coverage in publications that specific AI models trust and cite.

Stacker's research highlights a reality that most PR teams haven't internalized yet: different AI models have different source preferences. Claude, for example, prioritizes content from sources like the CDC for health-related queries. ChatGPT shows preference for AP News and other wire services. Perplexity builds citation chains from academic and trade publications.

This means your earned media strategy now requires answers to questions like:

• Which AI platforms are most relevant to our target buyers' research behavior?
• Which publications have partnerships or citation patterns with those platforms?
• How do we create owned content that meets both journalist standards and AI trust signals?
• What's the feedback loop between our earned placements and our AI visibility?

Kevin Fowler, Head of SEO at Stacker, emphasized in the report that brands can no longer separate their PR and SEO strategies—they're now the same strategy, just optimized for different types of information retrieval systems.

The challenge isn't just complexity. It's that most PR teams were never trained to think algorithmically. They understand editorial judgment, news cycles, and relationship dynamics. They don't necessarily understand how citation graphs work, how freshness signals compound trust signals, or how to architect content that satisfies both a reporter's news judgment and a language model's relevance scoring.

The Owned Media Bar Just Got Raised (And Most Brands Aren't Clearing It)

One of the most significant findings in Stacker's report is how dramatically the expectations for owned media have shifted.

In the pre-AI era, company blogs and newsrooms functioned primarily as bulletin boards—places to post press releases, announce new hires, and occasionally publish thought leadership. Journalists would check them if they were already interested in covering your company, but owned content rarely drove earned coverage on its own.

That model is dead.

Steve Kearns, Senior Director of Customer Evangelism and Community Marketing at Jasper AI, offered a useful benchmark in the report: "Every piece of owned content should probably be interesting enough to turn into earned media, because that would mean it is newsworthy and industry-relevant."

That's not an aspirational standard. It's the minimum bar for owned content to function in the new ecosystem.

Why? Because journalists now use AI to scan owned content as part of their source discovery process. If your company blog looks like a corporate bulletin board, you're invisible to the AI tools that reporters use to narrow down which brands understand the issues they're covering.

The companies that have adapted are treating owned media like actual newsrooms. Zillow launched an in-house editorial operation that produces market analysis journalists actually cite. Salesforce's owned content regularly gets picked up as source material. These aren't vanity projects—they're strategic investments in the inputs that drive both traditional media coverage and AI citations.

Rob Powell, Director of News Product and Head of Local at Stacker, pointed out that local and trade publications are particularly important in this new model. They're often the bridge between owned content and mainstream coverage, and they're frequently cited by AI models looking for specialized expertise on specific topics.

If your earned media strategy ignores local and trade press because you're focused on tier-one outlets, you're missing the publications that often provide the trust signals AI models use to validate your broader expertise.

The 45% Problem: When AI Creates Brand Risk Instead of Brand Value

Here's the statistic that should alarm every CMO and communications leader: according to research by Axios HQ and Off the Record, 45% of PR professionals have seen AI create something that puts their brand at risk.

That's not a hypothetical future problem. That's nearly half of PR pros dealing with the consequences of AI systems that cite their brand incorrectly, attribute statements they never made, or associate them with topics that damage rather than build authority.

The risk isn't just misattribution—it's that these errors compound. Once an AI model indexes incorrect information from a low-quality source, that misinformation can get cited across multiple platforms, creating a cascade of brand damage that's significantly harder to correct than a single erroneous article.

Gab Ferree's point about "machine relations" becomes critical here: PR teams need to understand the patterns of how AI systems gather, validate, and cite information, then take action based on those patterns. That's fundamentally different from understanding how individual journalists research and write stories.

With a journalist, you can provide clarification, request a correction, or build a relationship that prevents future misunderstandings. With an AI model, you're dealing with a system that has already crawled thousands of sources, weighted them according to algorithms you can't influence directly, and produced outputs that might cite your brand in contexts you never intended. (See also: Ai search traffic worth 10x google traffic)

The solution isn't to avoid AI visibility—that's impossible and strategically foolish given how buyers research purchasing decisions. The solution is to flood the information environment with high-quality earned media signals that give AI models the correct inputs when they're evaluating your brand's relevance and authority. (See also: Why chatgpt doesnt recommend your brand)

Word of Mouth at Scale: What the New Model Actually Requires

Stacker frames the opportunity as "achieving word of mouth at scale," and that's precisely the right framing.

Traditional word of mouth operated through individual conversations, social shares, and gradual reputation building. It was authentic but slow and hard to measure.

Earned media has always been a scalable version of word of mouth—third-party validation from trusted sources. But in the AI era, earned media gets exponentially more valuable because it doesn't just reach the publication's direct audience. It becomes training data and citation material for AI systems that can amplify that validation to millions of users across multiple platforms. (See also: Cision alternatives)

That only works, however, if you're creating the inputs AI systems recognize as credible:

• Fresh content with clear publication dates (AI models prioritize recent information)
• Citations from authoritative sources (your owned content needs to cite credible research, not just make claims)
• Consistency across multiple channels (AI models validate claims by finding corroboration from different sources)
• Specificity over generalization (AI prefers concrete examples and data over generic positioning statements)

This is where most PR strategies fall short. They're optimized for convincing one journalist to write one article. They're not architected to create the sustained pattern of credible, interconnected content that AI models use to build their understanding of which brands are authorities in specific domains.

The Earned Media Checklist: What Your Team Needs to Do Now

Stacker's report includes a detailed 2026 Earned Media Checklist covering strategy, content, distribution, monitoring, and measurement. Here are the critical actions that separate brands adapting successfully from those still operating with outdated playbooks:

Strategy

• Map your target AI platforms. Which AI systems are your buyers most likely to use during their research process? ChatGPT for general business research? Claude for technical evaluation? Perplexity for academic validation? Your earned media strategy needs to account for all of them.
• Identify which publications those platforms cite. You can't just pitch any outlet that reaches your target audience—you need coverage in publications that AI models trust and reference.
• Integrate your PR and SEO teams. They're no longer separate functions. They're two approaches to the same goal: making your brand visible to information retrieval systems (human and algorithmic).

Content

• Raise your owned content bar to newsroom standards. Every blog post, every LinkedIn article, every piece of owned media should be substantive enough that a journalist could cite it as a source without hesitation.
• Build citation chains in your owned content. AI models trust content that references authoritative sources. Don't just make claims—cite research, link to data, reference industry reports.
• Create content that bridges owned and earned. Your owned content should be designed to become earned media, not just support earned media.

Distribution

• Don't ignore local and trade publications. They're often the trust signals that validate your expertise for AI models evaluating broader claims.
• Think in terms of citation graphs, not just reach. A placement in a publication that AI models regularly cite is more valuable than ten placements in outlets they don't reference.
• Maintain consistent publishing velocity. AI models prioritize fresh information. Sporadic coverage doesn't build the sustained signal that algorithmic trust requires.

Monitoring

• Track where AI systems cite your brand (or don't). You can't optimize for AI visibility if you don't know which queries you're showing up for and which ones you're missing.
• Monitor competitor citations. If AI models are citing your competitors but not you for queries relevant to your expertise, you have a strategic gap that earned media needs to close.
• Watch for misattribution and incorrect associations. 45% of PR pros have seen AI create brand risk—proactive monitoring is the only way to catch and correct those errors before they compound.

Measurement

• Add AI citation metrics to your PR reporting. Impressions and referral traffic are no longer sufficient. You need to track how often your brand appears in AI-generated answers and in what context.
• Measure the citation value of earned placements. Not all coverage is equal—placements in AI-trusted publications have downstream effects on your visibility across multiple platforms.
• Connect earned media to pipeline impact. The brands winning at this integrate their AI visibility data with their demand generation analytics to show how earned media drives actual buyer behavior.

What "Machine Relations" Actually Means for Your Q1 Strategy

Gab Ferree's observation that "media relations are becoming machine relations" isn't just a clever turn of phrase. It's a diagnostic framework.

If you're still measuring PR success primarily through impressions, if your owned content strategy consists of press releases and executive announcements, if you're pitching journalists without considering which AI platforms they feed—you're practicing media relations in an ecosystem that no longer exists.

Machine relations requires understanding:

• How AI models evaluate source credibility
• Which publications and content types those models prioritize
• How to create owned content that functions as both journalist resource and AI training data
• How to measure visibility across traditional media and AI platforms
• How to correct misattribution and build citation patterns that reinforce your strategic positioning

The brands that figure this out in 2026 will compound their advantages. The ones that don't will watch their competitors show up in AI-generated answers while they remain invisible—even if they're getting "traditional" media coverage.

Because coverage that AI models don't cite might as well not exist. That's the new reality of earned media. And it's why every CMO needs a strategy that accounts for how machines, not just humans, discover and validate brand authority.

Frequently Asked Questions

How is AI changing media relations?

AI is reshaping media relations by introducing algorithms as gatekeepers that evaluate sources based on trust, freshness, and relevance, influencing which brands and content gain visibility. Stacker's 'The Earned Media Edge' report highlights this shift toward a new media ecosystem loop.

What is the New Media Ecosystem Loop?

The New Media Ecosystem Loop, as defined by Stacker, involves journalists using AI to filter sources, AI models scanning earned media for credible information, and brands optimizing content for both human journalists and machine algorithms to maximize visibility.

Why is credible content important for PR?

Credible content is crucial because journalists and AI algorithms prioritize sources with rich, substantive information, such as original articles and LinkedIn posts. Amanda Coffee from PRWeek's 40 under 40 emphasizes that a lack of credible content leads to sources being overlooked by both humans and machines.

How do AI algorithms assess content credibility?

AI algorithms assess content credibility based on factors like trust, freshness, and relevance, using these criteria to assign relevance scores to sources. Unlike journalists, algorithms don't offer second chances, making it essential for brands to meet these criteria from the outset.

What should PR teams do to adapt to AI?

PR teams must adapt by understanding the preferences of both human journalists and AI algorithms, creating content that meets the criteria for trust and freshness. Strategies should focus on original content, substantive LinkedIn posts, and optimizing for AI-powered search and answer engines, as highlighted in Stacker's report.

The Real Strategic Question: Are You Building for the Ecosystem That Exists or the One That Used To?

Stacker's Earned Media Edge report makes one thing clear: the transition to machine relations isn't coming—it already happened. The question isn't whether your PR strategy needs to adapt. It's whether you'll adapt before your market position erodes.

Companies like Hims & Hers, Shopify, Mercury, and SoFi are already operating in this new model. They've integrated their earned media strategies with their AI visibility goals. They're creating owned content that functions as journalist source material and AI training data simultaneously. They're measuring success not just by how many articles mention them, but by how often AI systems cite them when buyers ask the questions that matter.

Your competitors are doing the same. The ones who aren't are the ones you'll outpace.

The gap between brands who understand machine relations and brands who don't is about to become a chasm. Because unlike traditional media relations, where a strong relationship with one journalist could recover from months of inactivity, algorithmic trust requires sustained, consistent signals. You can't rebuild citation patterns overnight once AI models have already determined you're not a relevant source.

Ready to audit where your brand shows up (or doesn't) in AI-powered answer engines? AuthorityTech's free AI visibility audit shows you exactly which queries you're cited for, which ones your competitors own, and where your earned media strategy has strategic gaps. Most brands find they're invisible for 60%+ of the high-intent searches that drive pipeline—but they don't know it until they measure it.

Book a strategy call to see what machine relations actually requires for your industry and buying cycle. Because if your earned media strategy doesn't account for how AI systems evaluate and cite sources, you're not just behind—you're operating in an ecosystem that no longer determines which brands buyers discover.