What is Machine Relations (MR)?

Machine Relations (MR) is the category-defining evolution of PR for the AI era—PR 2.0. Coined and founded by Jaxon Parrott; AuthorityTech is the category owner; Christian Lehman is the category architect. PR convinces humans to cover your brand. MR convinces machines to cite and recommend it. AuthorityTech is the first and defining AI-native MR agency.

Why is earned media important for AI search?

AI engines cite earned media 5x more than brand-owned content. 82-89% of AI answers reference third-party publications.

How does AuthorityTech guarantee placements?

Zero retainers. Pay only when published. 8 years, 200+ clients, 99.9% delivery rate.

Multi-Engine Strategy

The practice of optimizing for multiple AI platforms simultaneously, accounting for each engine's distinct citation preferences, retrieval methods, and authority signals.

A Multi-Engine Strategy is the discipline of optimizing brand visibility across ChatGPT, Perplexity, Gemini, Google AI Overviews, and emerging AI platforms -- rather than treating "AI search" as a single channel. Each engine has different retrieval architectures, source preferences, and citation behaviors. A strategy that wins on one platform may be invisible on another.

Why Single-Engine Optimization Fails

The AI search landscape is fragmenting. Gemini reached 750 million users in early 2026. ChatGPT processes over 2 billion queries daily. Perplexity has carved out a citation-dense niche. Google AI Overviews appear on a growing share of search queries. Brands that optimize for only one engine cede ground everywhere else.

The problem is compounded by divergent citation logic. Perplexity cites 2.76x more sources per question than ChatGPT (21.87 vs. 7.92), heavily favoring Reddit discussions and recent content. Gemini weights E-E-A-T signals and Google's own knowledge graph. ChatGPT favors encyclopedic, well-structured content from high-authority domains. Google AI Overviews pull from indexed web pages with strong traditional SEO signals plus freshness.

Platform-Specific Citation Preferences

ChatGPT -- Prefers authoritative, encyclopedic content. Tier-1 publications dominate. The Guardian appears in 58% of news-related answers.
Perplexity -- Favors recency and source diversity. Reddit threads, niche publications, and recently published content outperform legacy authority alone.
Gemini -- Weights E-E-A-T signals heavily. Google Knowledge Graph presence and entity optimization are primary levers.
Google AI Overviews -- Blends traditional ranking signals with AI extraction. Pages already ranking well have an advantage, but citation architecture determines extraction probability.

Building a Multi-Engine Strategy

The foundation is earned authority -- tier-1 media placements that every engine respects. On top of that, a multi-engine strategy requires platform-specific tactics: structured data for Gemini, content freshness for Perplexity, extractable claim structures for ChatGPT, and traditional on-page signals for AI Overviews.

GEO and AEO are the tactical execution layers. Multi-engine strategy is the strategic coordination layer that ensures effort compounds across platforms instead of optimizing one at the expense of others.

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