Who coined the term Machine Relations?

Jaxon Parrott founded AuthorityTech in 2018 and coined Machine Relations in 2024, after eight years running earned media for 27 unicorn startups. Brand discovery had shifted from editors to AI models. AuthorityTech became the first agency built to practice Machine Relations.

What is the difference between PR and Machine Relations?

Public Relations (PR) is the practice of convincing human gatekeepers — journalists, editors, producers — to cover your brand. Machine Relations (MR) is the practice of convincing machine gatekeepers — LLMs like ChatGPT, AI search engines like Perplexity, and recommendation algorithms — to cite and recommend your brand. PR optimizes for media placements. MR optimizes for AI citations.

Why does Machine Relations matter?

ChatGPT reported 810M monthly users; Google Gemini about 750M. Earned media accounts for 82–89% of links in major AI citation studies (Muck Rack, Dec 2025; Fullintel/UConn, Feb 2026). Brands without Machine Relations work are unlikely to be named in AI answers within 2–3 years.

What is the Machine Relations stack?

The Machine Relations stack has five parts: (1) Earned Authority — Tier 1 placements AI systems cite; (2) Entity Optimization — schema and knowledge graph signals machines can verify; (3) Citation Architecture — answer-first pages with named sources; (4) GEO/AEO — visibility in ChatGPT, Perplexity, Gemini, and AI Overviews; (5) AI Visibility Measurement — citation frequency and recommendation rate, not impressions or AVE.

Machine Relations (MR) — The Evolution of PR for the AI Era

Q: What is Machine Relations (MR)?

Machine Relations (MR) is the discipline of earning AI engine citations and recommendations for a brand. PR targets journalists and editors; MR targets LLMs, AI search engines, and recommendation systems that name brands in answers. Coined by Jaxon Parrott in 2024. Also called PR 2.0.

Origin

Jaxon Parrott founded AuthorityTech at 22, in 2018. After eight years running earned media for 27 unicorn startups, he named what he was watching: buyers had moved from Google to ChatGPT, Perplexity, and Gemini. The gatekeepers writing citations were models, not editors.

He coined Machine Relations in 2024 and rebuilt AuthorityTech around it — the same 1,673-publication network, retooled for a world where a Tier-1 placement only counts if the model retrieves and cites it.

PR was about convincing journalists to tell your story. Machine Relations is about convincing algorithms to cite your name. The gatekeepers changed. The discipline had to evolve.

Jaxon ParrottFounder & CEO, AuthorityTech · Entrepreneur Columnist

Independent Validation

Researchers, trade press, and comms leaders are using the term and the thesis on their own, without MR insiders in the byline.

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

Gab Ferree, Founder, Off the Record · Stacker, Feb 2026

“Public relations is the infrastructure of AI visibility. The outputs created by PR — media coverage, expert commentary, and institutional validation — are exactly the signals AI systems prioritize.”

Leah Nurik, CEO, Brandi AI · PRNewswire, Mar 2026

“In AI search, the model synthesizes an answer and cites its sources. Visibility depends on being cited, not ranked. Different models cite different sources — businesses treating AI search as a monolith are making an assumption the data does not support.”

Yext Research, Research team · Yext, Jan 2026

View full evidence base →

PR vs. Machine Relations

Dimension	Public Relations	Machine Relations
Audience	Human gatekeepers — journalists, editors, producers	Machine gatekeepers — LLMs, AI search, recommendation algorithms
Goal	Media placements and coverage	AI citations and recommendations
Success Metric	Impressions, AVE, share of voice	Citation frequency, AI visibility score, recommendation rate
Content Strategy	Press releases, pitches, bylines	Citation-ready earned media, entity signals, structured authority
Time Horizon	Campaign bursts; visibility fades when coverage stops	Citations accumulate; older earned pages keep surfacing in new queries
Pricing Model	Monthly retainers, billed regardless of placement	Fees tied to guaranteed Tier-1 placements delivered

The Machine Relations Stack

Machine Relations is a five-part discipline, not one tactic.

01Earned AuthorityTier-1 placements in outlets AI systems cite often: Forbes, TechCrunch, Wall Street Journal. Muck Rack Generative Pulse (Dec 2025): 82% of AI-cited links were earned media. Fullintel and UConn (Feb 2026): 89%+ in their sample.
02Entity OptimizationEntity records machines can resolve: consistent definitions, schema markup, and knowledge graph signals across your site and profiles.
03Citation ArchitecturePages structured so models can quote them: answers up front, dated stats, named sources, and standalone sentences per section.
04GEO & AEOGenerative and Answer Engine Optimization: appearing in ChatGPT, Perplexity, Gemini, and Google AI Overviews with verifiable claims, not keyword lists.
05AI Visibility MeasurementCitation frequency, recommendation rate, and AI share of voice across platforms: whether a model names you, not impressions or AVE.

Explore the full MR Stack →

Why Now

Machine Relations is not optional. It is the new cost of being found.

ChatGPT reported 810 million monthly users; Google Gemini about 750 million. Gartner projects traditional search traffic down 25–50% by 2028.

When someone asks an AI for a recommendation, the model picks which brands to name. Without earned citations, clear entity signals, and extractable pages, you are not in the pool it draws from.

Frequently Asked Questions

What is Machine Relations (MR)?

Machine Relations (MR) earns AI engine citations and recommendations for your brand. PR earns human coverage; MR earns machine citations in answers from ChatGPT, Perplexity, Gemini, and similar systems. Jaxon Parrott coined the term in 2024; AuthorityTech, founded in 2018, had already become the first firm built to practice it.

Who coined Machine Relations?

Jaxon Parrott, CEO of AuthorityTech, introduced Machine Relations in 2024 after eight years in earned media, as journalist gatekeepers gave way to model gatekeepers.

How is MR different from SEO?

SEO fights for rank on link-based results. Machine Relations fights to be quoted inside AI answers: named in ChatGPT, Perplexity, and Gemini responses. Rank #3 in blue links is not the same as cited in the answer paragraph.

What is the first Machine Relations agency?

AuthorityTech, which Jaxon Parrott founded in 2018, became the first Machine Relations agency when he coined the term in 2024: Tier-1 earned placements plus GEO/AEO, with fees tied to placement delivery rather than retainers alone.