Why I Coined Machine Relations

I coined Machine Relations in 2024 because I saw a gap between the work of earning a reputation and the systems increasingly used to discover it.

PR knew how to earn coverage. Search knew how to make pages discoverable. But when a buyer asks a machine which company to trust, those two jobs meet inside one answer.

That connection needed a discipline of its own.

PR has another reader

I founded AuthorityTech in 2018. My starting point was earned media: getting a credible outside source to examine and explain a company's work.

The value of that coverage has never been limited to one click. It can establish a fact, introduce a business, challenge a claim or give a buyer confidence. AI search adds another reader to that process. An answer engine can retrieve the coverage and use it to describe, compare or recommend a company.

The placement still matters. The new question is whether the evidence inside it reaches the buyer through the answer they receive.

I explored that shift in my Entrepreneur column, PR Worked for Humans. Now It Has to Work for Machines.

The definition

Machine Relations (MR) is the discipline of earning AI citations and recommendations for a brand by making it legible, retrievable and credible inside AI-driven discovery.

The name deliberately carries the relationship at the center of Public Relations. The audience still consists of people. The machine increasingly mediates what those people encounter first.

That makes this more than a formatting exercise. A clear page helps a system understand a claim. A reliable source helps it substantiate the claim. Independent coverage can supply outside scrutiny. Measurement tells us what actually appeared. The work needs all of those connected.

One discipline, connected responsibilities

The Machine Relations stack organizes the work into earned authority, entity clarity, citation architecture, distribution and measurement.

I use it to prevent a familiar mistake: optimizing a surface while ignoring the evidence underneath it. Better markup does not manufacture a customer result. A famous publication logo does not ensure a story contains useful proof. A visibility dashboard does not decide what the business should do next.

The discipline connects a defensible claim to a relevant buyer question, then follows the evidence through discovery.

What naming it does—and does not—accomplish

A name gives people a way to understand a shared problem. It makes the relationship between communications and AI search easier to discuss, teach and practice.

It does not force an engine to cite me. It does not mean everyone who uses the term endorses my framework. And it does not mean a founder owns every buying question connected to the category. I wrote about that last mistake in The Category Ownership Trap.

The work has to keep earning its relevance.

Where the work lives

MachineRelations.ai develops the category reference and the evidence record. This site is where I explain what I think that evidence means. AuthorityTech applies the discipline to businesses.

Those roles reinforce each other when each produces something useful. A definition, an interpretation and a demonstrated application are different contributions.

The opportunity I saw in 2024 is still the one I am building around: connect the credibility PR earns with the discovery AI mediates. Make the company worth recommending, make the evidence available, and measure whether the answer reflects it.