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Key Takeaways
- A few years ago, I redefined what trust signals mean for brand builders. A few weeks ago, I Googled the term and got my exact definition back — no author, no source.
- The AI knew the history. It just didn’t lead with it. The concept got adopted widely, and once it did, it became “standard industry knowledge” rather than a specific author’s contribution.
- When you’ve done the work, you feel like the position is established. But in the AI era, you have to actively defend your position by continuously publishing substantive work on the topics you want to own.
For decades, “trust signals” were a conversion optimization concept — the SSL badges, BBB seals and security icons that gave skittish online shoppers enough confidence to hand over a credit card number. Useful, tactical and almost entirely confined to your website.
Then, in 2020, it hit me: Those badges and seals were pointing to something much bigger. Before any purchase, every buyer is really asking the same question — whether they can trust the brand they’re considering. I organized the full body of evidence buyers use to answer that question into three categories: website trust signals, inbound trust signals and SEO trust signals. I published the argument, built a site around it and eventually wrote a book.
A few weeks ago, I typed “trust signals” into Google to see how the term is understood today. The answer came back using my three categories with my exact labels. No author. No source. Just my definition, presented as received industry wisdom.
So I followed up, asking Google’s AI why the definition hadn’t been attributed to anyone. The response was worth reading carefully because it got something right that the original answer got wrong.
Google acknowledged that while the term “trust signals” had existed before I expanded the definition, the specific three-category structure was mine — and it explained, with some precision, why I wasn’t credited in the first place. The concept was logical enough that it got adopted widely, and once it did, it became “standard industry knowledge” rather than a specific author’s contribution.
Concept generalization, the AI called it.
What AI knows vs. what it shares
The AI knew the history. It just didn’t lead with it.
That’s the thing about AI and attribution. The problem isn’t that AI gets the facts wrong. The problem is that AI absorbed the idea and served it back without the source. It learned what the definition was without retaining where it came from, the way most of us know that an apple fell on Newton’s head without being able to name who first wrote it down. The idea became common knowledge. The author became optional.
ChatGPT launched 43 days after my book came out. What happened to my definition wasn’t a failure of planning. It was a preview of a problem the whole industry is now catching up to.
The chain that AI breaks
Before large language models, visibility was largely self-reinforcing. A brand that earned authority in the right places got found, got cited and earned more authority. The record stayed intact because humans doing research followed trails — bylines, citations, links — that kept the original source visible.
AI doesn’t follow the trail. It reads everything, learns from it and returns an answer that often has no trail at all. An idea gets absorbed, generalized and comes back as if it were always true and always known. The content makes the journey. The source doesn’t always come with it.
This is the specific thing the AI era does that the pre-AI world did not. And it changes something fundamental about how brands need to think about the ideas they originate.
IP is something you defend by advancing
Here is the part that took me a while to fully understand. The instinct when you’ve done the work — written the book, done the interviews, built the record — is to feel like the position is established. You earned it. It’s yours. But in the AI era, a position you’re not actively defending is a position you’re quietly losing.
This isn’t really about AI specifically. AI just made it visible faster. The deeper truth is that intellectual authority in any domain works the same way a market position does. The moment you stop advancing, someone or something fills the space. Before AI, that process was slow enough that you might not notice it happening. Now it happens at the speed of training data.
The brands that own topics in AI-generated answers aren’t the ones that established a position once. They’re the ones that kept producing — new research, new frameworks, new arguments — so that their name stayed attached to the idea as it evolved. Attribution follows activity. The moment you stop leading on a topic, the topic starts to belong to whoever is still talking about it.
Idea Grove and TrustSignals.com show up well in AI answers overall. But this experience revealed something specific: Showing up and getting credited for originating an idea are two different things. The definition I created is now common knowledge. Common knowledge doesn’t have an author. The only way to stay ahead of that is to keep making the next argument before the current one gets absorbed.
What this means in practice
For any brand that invests in original thinking — research, methodologies, coined terms — the implications are straightforward even if the execution isn’t.
You cannot establish a position and then maintain it. You can only keep earning it. That means continuously publishing substantive, attributed work on the topics you want to own. It means going deeper and further than anyone else is willing to go, so that the leading edge of the conversation still has your name on it even as the earlier arguments become assumed knowledge. It means treating thought leadership not as a campaign with a start and end date but as the permanent condition of being the most credible voice in your space.
The practical goal isn’t to prevent your ideas from spreading. Spread is good. Spread means the idea worked. The goal is to always be a few steps ahead of where the spread has reached — so that when AI or a buyer or a journalist goes looking for who owns this territory, the answer is still you, because you’re the one still breaking new ground on it.
I didn’t just observe this dynamic. I’ve lived it. My name came off the definition while the definition kept going. The only answer I’ve found is to stay ahead of it — new arguments, new research, new ground. In the AI era, that’s not optional. It’s the whole game.
Key Takeaways
- A few years ago, I redefined what trust signals mean for brand builders. A few weeks ago, I Googled the term and got my exact definition back — no author, no source.
- The AI knew the history. It just didn’t lead with it. The concept got adopted widely, and once it did, it became “standard industry knowledge” rather than a specific author’s contribution.
- When you’ve done the work, you feel like the position is established. But in the AI era, you have to actively defend your position by continuously publishing substantive work on the topics you want to own.
For decades, “trust signals” were a conversion optimization concept — the SSL badges, BBB seals and security icons that gave skittish online shoppers enough confidence to hand over a credit card number. Useful, tactical and almost entirely confined to your website.
Then, in 2020, it hit me: Those badges and seals were pointing to something much bigger. Before any purchase, every buyer is really asking the same question — whether they can trust the brand they’re considering. I organized the full body of evidence buyers use to answer that question into three categories: website trust signals, inbound trust signals and SEO trust signals. I published the argument, built a site around it and eventually wrote a book.
A few weeks ago, I typed “trust signals” into Google to see how the term is understood today. The answer came back using my three categories with my exact labels. No author. No source. Just my definition, presented as received industry wisdom.
This story originally appeared on Entrepreneur
