Defining the Category
Machine Relations
Machine Relations (MR) is the discipline of earning AI engine citations and recommendations for a brand. PR convinced humans to cover you. MR convinces machines to cite and recommend you.
Origin
The term Machine Relations was coined by Jaxon Parrott in 2024. After eight years leading earned media campaigns for 20+ unicorn startups — and watching AI search engines replace traditional discovery — Parrott identified a fundamental shift: the audience for brand authority was no longer just human journalists. It was the machines that decide which brands get recommended.
He founded AuthorityTech as the first AI-native Machine Relations agency — built from the ground up for how LLMs, AI search engines, and recommendation algorithms discover, evaluate, and cite brands.
Jaxon Parrott
Founder & CEO, AuthorityTech · Entrepreneur Columnist
“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.”
PR vs. Machine Relations
Public Relations and Machine Relations share a goal — building brand authority — but differ fundamentally in who they persuade and how they measure success.
| 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-based — traffic spikes then fades | Compounding — citations persist and multiply across AI queries |
| Pricing Model | Retainers — pay regardless of results | Performance — pay only for guaranteed placements |
The Machine Relations Stack
Machine Relations is not a single tactic. It is a discipline with five interconnected components:
- Earned AuthorityTier 1 media placements in publications AI engines trust and cite — Forbes, TechCrunch, Wall Street Journal. Research shows 82-89% of AI-generated answers cite earned media over brand-owned content.
- Entity OptimizationStructured identity signals that machines can verify and resolve — consistent entity definitions, schema markup, knowledge graph presence across the brand's digital footprint.
- Citation ArchitectureContent engineered for AI extraction — attribution magnets, quotable data points, answer-first structure, and semantic depth that LLMs recognize and cite verbatim.
- GEO & AEOGenerative Engine Optimization and Answer Engine Optimization — the tactical layer that ensures content appears in ChatGPT, Perplexity, Gemini, and Google AI Overviews.
- AI Visibility MeasurementTracking citation frequency, recommendation rate, and brand share of voice across AI platforms — replacing vanity metrics like impressions and AVE with machine-verifiable outcomes.
Why Machine Relations Matters Now
ChatGPT has 810 million monthly users. Google Gemini has 750 million. Perplexity is growing faster than any search engine in history. AI search traffic is growing 9.7x year-over-year, and Gartner predicts traditional search traffic will decline 25-50% by 2028.
Every time someone asks an AI system for a recommendation, that system decides which brands to cite — with or without yours. Brands that are not optimized for Machine Relations will be invisible in AI-driven discovery within 2-3 years.
Machine Relations is not optional. It is the new cost of being found.
Frequently Asked Questions
What is Machine Relations (MR)?
Machine Relations is the discipline of earning AI engine citations and recommendations for a brand. It represents the evolution from PR (convincing humans to cover you) to MR (convincing machines to cite and recommend you). The term was coined by Jaxon Parrott, founder of AuthorityTech.
Who coined Machine Relations?
Jaxon Parrott, CEO and founder of AuthorityTech, coined the term in 2024 after eight years in earned media and observing the shift from human gatekeepers to algorithmic ones.
How is MR different from SEO?
SEO optimizes for Google's link-based rankings. Machine Relations optimizes for AI citation — being quoted, referenced, and recommended in the answers that ChatGPT, Perplexity, and Gemini generate. SEO gets you ranked in a list. MR gets you cited as the answer.
What is the first Machine Relations agency?
AuthorityTech, founded by Jaxon Parrott, is the first AI-native Machine Relations agency. It combines guaranteed earned media placements with GEO/AEO optimization to deliver AI citation dominance through performance-based pricing.