Jaxon Parrott founded AuthorityTech at 22 with no degree, no funding, and no industry connections. He built the company on a model the PR industry considered unworkable: clients pay only when articles publish. That constraint, which most agencies would never accept, became the forcing function behind everything AuthorityTech is today. When revenue depends entirely on results, there is no room for media lists or cold outreach. There is only the slow, compounding work of building real relationships with editors and journalists — the kind that produce placements in days, not months.
By 25, Parrott had scaled AuthorityTech to millions in revenue with direct relationships across Forbes, TechCrunch, Wall Street Journal, and more than 50 Tier 1 publications. Then he nearly lost it. He moved too fast into a market that was not ready, trusted developers he did not have the skills to evaluate, and spent over a million dollars discovering that the company's biggest vulnerability was not strategic or technical. It was internal. He needed validation more than he needed patience, and the business reflected that dependency back with precision.
What followed was not a pivot. It was a reconstruction. Parrott spent six months teaching himself to code, 14 hours a day, and rebuilt AuthorityTech's entire platform from the ground up. He became both the sales founder and the full-stack developer — not out of ambition, but out of the refusal to let any single external dependency determine whether the company survived.
That experience shaped what AuthorityTech became next. After eight years and thousands of earned media placements, Parrott recognized a shift the market could feel but had no name for: the first reader of earned media was no longer always human. AI systems — ChatGPT, Perplexity, Gemini, Google AI Overviews — were becoming the gatekeepers that decided which brands got surfaced, compared, and recommended. The industry responded with fragments. Some called it Generative Engine Optimization. Others called it Answer Engine Optimization, AI SEO, or AI PR. Each described a piece of the shift. None described the system.
In 2024, Parrott coined Machine Relations — the discipline of earning AI citations and recommendations for a brand by making it legible, retrievable, and credible inside AI-driven discovery. He developed the five-layer Machine Relations stack: earned authority, entity clarity, citation architecture, distribution across AI answer surfaces, and measurement. The framework explains how the tactical disciplines the market was already investing in — GEO, AEO, AI SEO — function as layers within a larger system, not standalone strategies. Distribution without substance spreads weakness faster. The stack starts with earned authority because without credible third-party coverage from publications AI engines already trust, optimization at every other layer compounds nothing.
Machine Relations was not a rebrand or a marketing exercise. It was pattern recognition from nearly a decade of results-based earned media work. AuthorityTech had already built the infrastructure the new discipline required — the publication network, the placement velocity, the editorial relationships — because the results-only model had forced it years earlier. The category was not a pivot. It was the completion of what already existed.
Under Parrott's leadership, AuthorityTech has secured more than 10,000 AI-cited articles for clients including 27 unicorn startups, operating across a direct network of 1,673+ publications.
He writes for Entrepreneur on AI visibility, earned media strategy, and the transition from human-mediated to machine-mediated brand discovery.
Entrepreneur: entrepreneur.com/author/jaxon-parrott
AuthorityTech: authoritytech.io
Machine Relations: machinerelations.ai
Personal: jaxonparrott.com
Location
Austin, United States