Machine Relations — Evidence Base

Forbes Business Council piece argues AI summaries have made PR more important than ever — 'Public relations (PR), long treated as a parallel track to Search, is suddenly having an outsized influence on the answers people see.'

“Public relations (PR), long treated as a parallel track to Search, is suddenly having an outsized influence on the answers people see. Mentions in respected outlets, expert commentary... [are what shape AI answers].”

PR now has outsized influence on AI answers — Forbes Business Council, January 2026

Forbes Agency Council piece on 'Using Public Relations to Support Your GEO Strategy' opens by stating GEO success starts with earned media and cites Muck Rack: 89% of all AI-cited links are earned media.

“Muck Rack analyzed over one million links cited by ChatGPT and found that 89% of citations originated from earned media... Companies proactively engaged in PR efforts are reaping a new advantage: visibility within generative AI engines.”

89% of AI citations from earned media — Muck Rack (1M+ citation dataset), cited in Forbes

Deloitte's GEO strategy guide for CMOs identifies earned media and thought leadership as a primary tactic: 'build brand equity through earned media and thought leadership' alongside technical GEO tactics.

“Manage reputation proactively. Marketers should update brand information in industry databases, encourage customers to evaluate products on review websites, and look for opportunities to build brand equity through earned media and thought leadership.”

Deloitte GEO strategy for CMOs: earned media is a primary AI visibility tactic

Peer-reviewed GEO research finds AI search engines show 'systematic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand-owned and Social content.' Strategic recommendation: 'dominate earned media to build AI-perceived authority.'

“AI Search exhibit a systematic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand-owned and Social content, a stark contrast to Google's more balanced mix.”

AI search shows systematic bias toward earned media over brand-owned content — Chen et al. ACM 2025

17 PR and marketing executives advising on GEO strategy unanimously center their guidance on earned media: 'success with GEO depends on creating content that resonates with both audiences and algorithms — and it starts with credibility built through earned media.'

“Success with GEO depends on creating content that resonates with both audiences and algorithms — and it starts with credibility built through earned media. Mentions in reputable outlets that are structured, quotable, and clearly attributed have a higher chance of appearing in AI snippets.”

17 PR agency executives: GEO success 'starts with credibility built through earned media' — Forbes 2025

SEO has evolved into a multi-engine visibility problem with GEO and AEO layered

SEO has evolved into a multi-engine visibility problem with GEO and AEO layered on traditional SEO, driven by AI Overviews on 88.1% of informational queries and 83% zero-click sessions.

Media Relations Are Becoming 'Machine Relations' — and Most Brands Aren't Ready

Gab Ferree — Founder, Off the Record

An independent communications professional coined 'machine relations' as the natural evolution of media relations in the AI era.

“Media relations are becoming machine relations. It's on the comms professionals to learn the patterns of AI and then take action on them.”

PR trends in 2026 center on AI, GEO, and Narrative Intelligence (Reputation Engi

PR trends in 2026 center on AI, GEO, and Narrative Intelligence (Reputation Engine Optimisation) to ensure brands are accurately represented in AI systems and search assistants.[4]

“Narrative intelligence means proactively creating how AI systems understand and describe your brand across all digital touchpoints.[4]”

The rise of LLMs has fragmented search, spawning AIO, GEO, and AEO as new pillar

The rise of LLMs has fragmented search, spawning AIO, GEO, and AEO as new pillars beyond traditional SEO, prioritizing AI citations and entity clarity over rankings[2].

“This transition has created what industry experts call “Fragmentation Anxiety.”[2]”

SEO, GEO, AEO, and AIO function as complementary optimization layers rather than

SEO, GEO, AEO, and AIO function as complementary optimization layers rather than competing strategies, with GEO specifically positioned to earn citations from generative AI tools that synthesize answers[2].

“These aren't competing strategies. They're complementary layers... GEO extends your reach. It positions your content to be cited by generative AI tools that synthesize answers[2].”

PR strategies in 2026 must prioritize algorithmic credibility by training AI sys

PR strategies in 2026 must prioritize algorithmic credibility by training AI systems through communications to influence what machines recommend to humans.[8]

“In 2026, the PR edge belongs to “algorithmic credibility.” Your comms strategy isn’t just talking to humans anymore—it’s training the machines that will tell humans what to believe.[8]”

The proliferation of acronyms like GEO, AEO, AIO, SXO, and LLMO creates chaos an

The proliferation of acronyms like GEO, AEO, AIO, SXO, and LLMO creates chaos and confusion in SEO evolution, driven by budget anxiety and rebranding of existing processes[6].

“GEO, AEO, SXO, LLMO: every week, a new acronym pops up promising to be the “new SEO.””

AEO and GEO are emerging acronyms often used interchangeably for AI-optimized se

AEO and GEO are emerging acronyms often used interchangeably for AI-optimized search strategies that remain fundamentally rooted in SEO principles[2].

“While here I’m trying to make a clear distinction between AEO and GEO, in practice they are often used interchangeably[2].”

Academic literature explores the evolving dynamics of **human-machine relations*

Academic literature explores the evolving dynamics of **human-machine relations** as a distributed agency phenomenon[4].

“This article delves into the intricate and evolving dynamics of human–machine relations[4].”

Relational autonomy can incorporate human-machine relations through decision-the

Relational autonomy can incorporate human-machine relations through decision-theoretic frameworks, enhancing human decision-making while setting boundaries to preserve autonomy[2].

“"relational autonomy account and describe how it amounts to a distinct way of understanding the decision-making complexities of embedded social beings."[2]”

The evolution from automation to autonomy in AI is reshaping human-machine relat

The evolution from automation to autonomy in AI is reshaping human-machine relations in diverse contexts[4].

Machine Behavior is proposed as a new academic discipline focused on the scienti

Machine Behavior is proposed as a new academic discipline focused on the scientific study of intelligent machines' behavior as actors, distinct from computer science[2].

“We advocate the need for a new, distinct scientific discipline of Machine Behavior: the scientific study of behavior exhibited by intelligent machines[2].”

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

MR relevance: Validates the Machine Relations thesis.

Stacker-distributed stories achieved a median 239% lift in AI citations compared

Stacker-distributed stories achieved a median 239% lift in AI citations compared to owned content, with 97% earning at least one citation versus 82% for owned[3].

MR relevance: This recent study reinforces Machine Relations by demonstrating earned media distribution as a proven tactic for tripling AI visibility and citations.

Earned media distribution produces a median 239% lift in AI search visibility, w

Earned media distribution produces a median 239% lift in AI search visibility, with 97% of distributed stories earning at least one AI citation compared to 82% for owned content[2]

MR relevance: Directly validates that earned media is the primary driver of AI engine citations and brand discovery, demonstrating measurable lift in GEO (Generative Engine Optimization)[2]

AI Media Citations and the Role of Credible Journalism

Angela Dwyer · IPRRC Academic Conference

47% of all AI citations in responses came from journalistic sources. 89%+ of cited links were earned media. 95% were unpaid.

MR relevance: Direct empirical validation of Layer 1 (Earned Authority) as the foundation of Machine Relations. Academic conference presentation gives peer-reviewed status.

The AI Citation Economy: 1M+ Data Points

OtterlyAI Research · OtterlyAI

73% of sites have technical barriers blocking AI crawler access. Community platforms and brand domains capture outsized citation share. Wikipedia maintains strong performance across ChatGPT.

MR relevance: Validates Layers 2-3 (Entity Clarity + Citation Architecture) — technical accessibility is a prerequisite for citation, independent of content quality.

How Press Release Distribution Drives LLM Citations

Signal Genesys Research · Signal Genesys

179.5 million citation records, 6.1 million unique domains, 6 LLM platforms. 88.4% domain citation coverage. Perplexity drives largest citation volume of tested platforms.

MR relevance: Direct measurement of earned media distribution → AI citation rates. Validates the Layer 1 → Layer 4 connection in the MR stack.

AI Citation Refresh — January 2026

Yext Research · Yext Research

17.2 million distinct AI citations analyzed across Q4 2025. No single optimization strategy works across ChatGPT, Gemini, Perplexity, Claude. Model-specific citation behavior requires multi-surface approach.

MR relevance: Validates Layer 4 (Distribution Across Answer Surfaces) — different AI engines cite different sources, requiring a systematic cross-platform approach.

Earned media accounts for 82% of generative AI citations, with press releases ex

Earned media accounts for 82% of generative AI citations, with press releases experiencing a 5x increase since July 2025 due to higher rates in ChatGPT and Gemini.[1]

MR relevance: This validates Machine Relations by showing earned media dominates AI citations, making it essential for brands to optimize for AI engine recommendations and visibility.

Earned media distribution across third-party news outlets increases AI citations

Earned media distribution across third-party news outlets increases AI citations by up to 325%, from 8% to 34% across multiple LLMs.[3]

MR relevance: Demonstrates how earned media distribution amplifies brand discovery in AI responses, core to Machine Relations strategies for entity optimization and AI SEO.

91% of PR professionals use generative AI in workflows, with 73% for idea genera

91% of PR professionals use generative AI in workflows, with 73% for idea generation and 68% for content refinement, signaling AI's central role in adaptive communications.

MR relevance: This demonstrates PR's shift toward AI-enabled strategies, aligning with Machine Relations' focus on AI SEO and measurement for earning engine citations.

LLMs cite nonpaid mentions and earned media in over 95% of links, with 27% direc

LLMs cite nonpaid mentions and earned media in over 95% of links, with 27% directly from earned media, positioning PR ahead of SEO for AEO and reputation in AI search[6].

MR relevance: This reinforces Machine Relations by highlighting earned media's dominance in AI citations and the need for AEO strategies to drive brand discovery over traditional SEO.

A Fractl survey reveals GEO as the most recognized (84%) and preferred (42%) ter

A Fractl survey reveals GEO as the most recognized (84%) and preferred (42%) term among marketers for AI-era brand visibility, outpacing AEO (61% recognition, 14% preference) and AISEO (60% recognition, 16% preference), highlighting ongoing naming fragmentation[3].

MR relevance: This survey validates the Machine Relations thesis by demonstrating widespread recognition of GEO within a fragmented naming landscape, underscoring the need for a unifying discipline focused on earning AI citations and entity optimization.

The 2026 AEO/GEO Benchmarks Report provides industry benchmarks for AI visibilit

The 2026 AEO/GEO Benchmarks Report provides industry benchmarks for AI visibility, urging measurement of citations and alignment of AEO/GEO with SEO strategies[3].

MR relevance: Establishes empirical benchmarks for AI citations as KPIs, directly supporting Machine Relations' focus on earning and measuring AI engine recommendations[3].

Analysis of 680 million AI citations across ChatGPT, Google AI Overviews, and Pe

Analysis of 680 million AI citations across ChatGPT, Google AI Overviews, and Perplexity reveals that different AI platforms cite fundamentally different sources, with no universal top source for citations—only patterns shaped by intent, platform, industry vertical, and time.

MR relevance: Validates Machine Relations thesis that different models cite different sources, requiring platform-specific citation strategies rather than monolithic AI search optimization.

Earned Media Still Drives Generative AI Citations as Press Release Visibility Grows

Muck Rack Research Team · GlobeNewswire

82% of all AI-cited links are earned media. 95% unpaid. Press releases grew 5x but still represent only 1% of total AI citations.

MR relevance: Core data source for MR Layer 1. 1M+ citations analyzed across GPT, Gemini, Claude.

94% of AI citations originate from non-paid earned media sources, with press rel

94% of AI citations originate from non-paid earned media sources, with press release citations growing 5x between July and December 2025 across ChatGPT and Gemini[3].

MR relevance: This supports the Machine Relations thesis that earned media dominates AI recommendations, driving brand visibility through non-paid third-party placements over owned or paid channels.

Analysis of over one million AI citations found that 82% came from earned media

Analysis of over one million AI citations found that 82% came from earned media sources and 94% from non-paid sources, with AI models relying more heavily on earned media and journalism for brand discovery questions.[2]

MR relevance: Demonstrates at scale that AI engines systematically prioritize earned media citations over owned content, particularly for brand discovery scenarios where users seek category leaders.

Generative Engine Optimization: How to Dominate AI Search

Mahe Chen, Xiaoxuan Wang, Kaiwen Chen, Nick Koudas · arXiv / ACM 2025

AI search engines show 'systematic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand-owned and Social content.' Researchers conclude the primary GEO strategy is to 'dominate earned media to build AI-perceived authority.'

MR relevance: A GEO research paper independently concludes that earned media dominance is the mechanism for AI citation — which is Machine Relations Layer 1 (Earned Authority). GEO researchers and Machine Relations practitioners converge on the same truth from different starting points.

Systematic testing of eight generative search tools found that only 49% of answe

Klaudia Jaźwińska · Columbia Journalism Review Tow Center

Systematic testing of eight generative search tools found that only 49% of answers contained any citation at all, and just 31% of those links pointed to the original publisher, with premium chatbots providing more confidently incorrect answers than free counterparts.

MR relevance: Demonstrates critical gap in AI citation reliability and attribution accuracy, underscoring the need for deliberate Machine Relations strategies to ensure brand visibility and correct attribution in AI-generated answers.

Optimizing AI-Generated Content for Generative Engine Optimization

Aggarwal et al. · SIGKDD 2024

Adding verifiable statistics to content improves AI citation rates by 30-40%. Citing credible sources increases probability of being cited. Structure matters more than volume.

MR relevance: Validates Citation Architecture (Layer 3) and the foundational role of credible third-party sources (Layer 1 — Earned Authority) in AI citation behavior.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Press release syndicated to Yahoo Finance, Business Insider Markets, Benzinga, Barchart, and GlobeNewswire defining Machine Relations as the discipline where GEO, AEO, SEO, and PR converge in AI search. Appeared in Google AI Overview within hours of publication.

Why it matters: First major press wire syndication of the Machine Relations category definition to high-DA financial media. 5 syndication nodes (DA 62-95) plus Medium article (DA 96) create the densest third-party entity corroboration cluster for the MR category to date. Yahoo Finance placement appearing in Google AI Mode same-day is direct proof of the earned media → AI citation pipeline AT sells.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

Why it matters: Validates the Machine Relations thesis.

AI-driven discovery restores PR's strategic role. PR outputs — media coverage, expert commentary, institutional validation — are exactly what AI systems prioritize.

Why it matters: Independent industry executives reaching the MR thesis independently: earned media is the infrastructure of AI visibility. Gartner prediction: PR spend doubles by 2027.

Academic paper published on arXiv exploring machine relations era dynamics in AI brand discovery.

Why it matters: Academic research independently exploring the Machine Relations frame validates the scholarly legitimacy of the category.

Earned media's role in AI search results is growing dramatically, with 85% of GEO results from earned sources and the figure increasing.

Why it matters: Reinforces Machine Relations thesis that earned media is essential for AI citations and brand authority in 2026 search ecosystems.

Forbes Business Council piece argues AI summaries have made PR more important than ever — 'Public relations (PR), long treated as a parallel track to Search, is suddenly having an outsized influence on the answers people see.'

Why it matters: A PR industry CEO writing in Forbes frames the shift identically to Machine Relations: PR has become the infrastructure of AI answers. 'Outsized influence on the answers people see' is what Machine Relations is designed to systematize. Another external voice arriving at the MR thesis independently, in a Forbes piece that itself becomes an AI-cited source.

The rise of AI search engines is reordering communications priorities, elevating media relations as the primary gateway for brand discovery since 82% of AI-generated answer citations come from earned media.

Why it matters: This validates Machine Relations by quantifying earned media's dominance in AI visibility, underscoring the need for entity optimization and earned AI citations over owned assets.

44% of AI-powered search users now consider AI their primary source of insight, and Google AI Overviews appear in over 60% of search queries, establishing AEO and GEO as essential operational disciplines alongside traditional SEO[5].

Why it matters: Demonstrates market adoption shift toward AI-driven discovery channels, validating the business case for Machine Relations as a distinct discipline separate from traditional SEO.

89% of links cited by AI systems come from earned media sources, with 95% of AI citations originating from non-paid media, indicating a fundamental shift in how LLMs source brand information[6].

Why it matters: Confirms that earned media dominates AI training data and citation patterns, establishing earned media strategy as the primary lever for machine relations and AI-driven brand visibility in 2026.

PR experts predict 2026 as the year communicators balance human authenticity with machine intelligence, emphasizing Generative Engine Optimization (GEO) to shape brand visibility in AI-generated answers.[1]

Why it matters: This validates Machine Relations by highlighting PR's shift toward optimizing for AI citations and recommendations through GEO, core to earning machine visibility for brands.

The industry lacks consensus on terminology with ongoing debate between SEO, AEO, GEO, AIO, LEO, SXO, and Search Everywhere Optimization, as no single term has emerged dominant[1].

Why it matters: The persistent naming debate supports Machine Relations as the overarching discipline that integrates GEO, AEO, AI SEO, and earned media to achieve comprehensive AI engine citations without reliance on a single fragmented term.

A major 2026 PR trend is 'Zero-Click PR,' measuring success by brand presence in AI-generated answers using specific Machine-Readability KPIs.

Why it matters: This reinforces Machine Relations by emphasizing measurement of machine-readable visibility and AI citations as key PR outcomes.

Press releases are positioned as machine-readable formats ideal for AI models, enhancing brand visibility through Generative Engine Optimization in 2026.[4]

Why it matters: Reinforces Machine Relations thesis by showing earned media like press releases optimized for AI consumption, boosting entity authority and AI recommendations.[4]

Fragmentation across search platforms demands cross-channel optimization that unifies SEO with AI discovery and emerging answer surfaces.

Why it matters: Identifies the naming fragmentation and discipline debate as a response to platform fragmentation, validating the need for unified Machine Relations strategy across GEO, AEO, and traditional SEO.

Forbes Agency Council piece on 'Using Public Relations to Support Your GEO Strategy' opens by stating GEO success starts with earned media and cites Muck Rack: 89% of all AI-cited links are earned media.

Why it matters: A PR agency CEO writing about GEO leads with earned media as the foundation — not schema markup, not technical optimization, but earned authority in trusted publications. The article is titled 'Using Public Relations to Support Your GEO Strategy' — but could be retitled 'Machine Relations: How Earned Media Drives AI Citation.' The author is describing Machine Relations without having the name for it.

Deloitte's GEO strategy guide for CMOs identifies earned media and thought leadership as a primary tactic: 'build brand equity through earned media and thought leadership' alongside technical GEO tactics.

Why it matters: Deloitte — advising Fortune 500 CMOs on GEO strategy in the Wall Street Journal — identifies earned media as a pillar of AI visibility, not an afterthought. The world's largest professional services firm, advising the world's largest companies, arrives at Machine Relations Pillar 1 when describing what GEO requires. Their authority is our validation.

Peer-reviewed GEO research finds AI search engines show 'systematic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand-owned and Social content.' Strategic recommendation: 'dominate earned media to build AI-perceived authority.'

Why it matters: A GEO paper — by definition about how to optimize for AI engines — concludes that earned media dominance is the primary mechanism. That is Machine Relations Layer 1 (Earned Authority), described independently by academic researchers who named it GEO. The tactic and the parent discipline converge at the same truth: publications are how AI engines decide what to cite.

17 PR and marketing executives advising on GEO strategy unanimously center their guidance on earned media: 'success with GEO depends on creating content that resonates with both audiences and algorithms — and it starts with credibility built through earned media.'

Why it matters: 17 independent PR and marketing agency executives, asked how to win GEO, all converge on earned media as the foundation. Not one says 'add schema markup' or 'optimize entity signals' as the starting point. They say: earn credibility in reputable publications. That is Machine Relations Pillar 1 described by an industry panel — one more proof point that the discipline exists and that its name is Machine Relations.

World's largest PR firm launched a dedicated AI PR practice.

Why it matters: Edelman reorganizing around AI-mediated discovery validates that PR's role has fundamentally shifted. They sense the shift Machine Relations names but lack the systematic architecture to describe it.

Edelman US revenue declined 8.1% in 2025 — third consecutive year of decline.

Why it matters: Traditional PR's retainer model is structurally unable to serve the AI era. The same year AI-mediated discovery accelerated, traditional PR revenue fell. The category Machine Relations describes is not theoretical — it is the commercial disruption already underway.

25% decline in traditional search volume projected by 2026 as AI search grows.

Why it matters: The world's most trusted tech analyst firm quantifying the shift Machine Relations names. When traditional search declines, AI-mediated discovery fills the gap — and earned media drives AI citation.