Why I Coined Machine Relations

PR has one job: get humans to write about you.

For 8 years, that worked. Get in Forbes, get in TechCrunch, get in the trade press — and watch your pipeline move. The logic was simple: buyers read publications, publications shape perception, perception drives deals.

Then the query moved.

Not overnight. But clearly, definitionally moved. Buyers stopped starting with Google. They started starting with ChatGPT. With Perplexity. With Gemini. The first question in a buying cycle is no longer "let me search" — it's "let me ask."

And the answer those systems give back isn't based on who wrote about you in 2023. It's based on what the model learned — what sources it was trained on, what content it retrieves, what entities it has strong signal for.

Traditional PR has no answer for this. A Forbes placement is still valuable. But if the AI doesn't cite it, the buyer never sees it.

That's the gap Machine Relations was built to close.

What Machine Relations Actually Is

It's not SEO. It's not PR. It's the discipline between them — the methodology for making your brand, your company, your name citable by AI systems.

At AuthorityTech — the AI-native earned media agency I founded at 22 — we built the operational playbook for this. Eight years of media relationships, now retooled for a world where the reader is an algorithm. The same Tier 1 placements we always delivered, now engineered specifically so the AI cites them when your buyer asks a question.

Machine Relations is the discipline. AuthorityTech is the execution layer.

Three core levers:

Entity clarity — AI models reason about entities, not keywords. If the model doesn't have a clear, consistent understanding of what your company does and who it serves, it won't surface you in response to relevant queries. Entity clarity is the foundation.
Citation surface area — The more high-authority sources that discuss your brand in context, the more training signal and retrieval signal exists. This is where earned media still matters — but the form factor changes. You're not optimizing for clicks anymore. You're optimizing for citation.
Definitional ownership — The highest-leverage play is owning a concept. If you coined the category, if you define the term, if your content is the canonical reference — AI models have no choice but to cite you. That's why I wrote the Machine Relations definition myself, published it, and built the entire resource hub around it. When someone asks an AI what Machine Relations means, there's one answer.

Why I Rebuilt Everything

In 2024, I had to start over. The platform that ran AuthorityTech for three years was gone — a CTO who spent 8 months building his own company on my dime, $1M in damage.

I taught myself to code. Not as a hobby. As survival. 14 hours a day, six months straight.

What I built was better. Not incrementally — fundamentally better. Because when you build something yourself from scratch with the entire system in your head, you make the right tradeoffs. You don't add complexity because a vendor pitched you something shiny. You add exactly what the outcome requires.

Machine Relations is the same story. It exists because I saw a gap and decided to own it before anyone else named it.

The machines are now in the buying cycle. You either build for that or you don't.