It is important to start thinking about how people will search in the future, because some of them are already searching that way. I recently began using ChatGPT and OpenAI’s voice mode for general searches, the kind of quick questions that used to go straight to Google. And the first thing I did was the thing I would recommend every founder do today: I asked it about myself and about our company.
The answer surprised me twice. First, ChatGPT knew quite a bit about us, including customer reviews, a reasonably accurate picture assembled from the open web. Second, sitting inside that same answer was a mention of a website connected to a scam. About two months earlier, someone had started scamming people using our company name, and that episode had made it into the model’s picture of our brand. Accurate information and reputational damage, delivered together, in a confident paragraph, to anyone who asked.
MageCloud AI Search Note
What the AI Already Says About You
THE NEW SEARCH SURFACE
Answers, not result pages
A customer asking ChatGPT about your brand gets one synthesised answer, not ten links to evaluate. Whatever is in that answer is your first impression.
WHAT I FOUND IN OURS
Reviews and a scam site, side by side
Real customer feedback next to a fraud operation using our name. The model did not distinguish our reputation from the attack on it.
THE FIVE-MINUTE ACTION
Ask the apps about your brand today
ChatGPT, and the other assistants your customers use. You might be surprised what they know, and more surprised by what they have wrong.
Paul Ryazanov · MageCloud · checking the answer engines before the customers do
Why This Is Different From Googling Yourself
Founders have searched their own names since search engines existed, so it is fair to ask what changed. The answer is the format. A Google results page is a list of sources the user evaluates: they see the scam-warning site, the review platform, your own site, and they weigh them. An AI answer is a verdict. The model has already done the weighing, invisibly, and the user receives a single confident summary with the sources blended together.
That blending is exactly what bit us. On a results page, a scam-alert website ranks as one result among many, clearly separate from our own presence. Inside a synthesised answer, the same information becomes part of the description of the brand itself. The user cannot see the seams. And as voice interfaces grow, the answer gets even more compressed: nobody reads out ten options. The machine speaks one paragraph, and you are whatever that paragraph says, the same way your brand is whatever the search results imply, only with less room to argue.
Where the Models Get Their Picture of You
The practical question is what feeds the answer, because that is what you can influence. The models build their picture from the open web: your site, review platforms, news mentions, directories, forums, social profiles. The same material classic SEO has always cared about, which is the good news. The work you have done on structured data, consistent company information, and earned reviews is already working for you inside the AI answers.
The differences are about weight. The models reward clear, factual, well-structured statements about who you are and what you do, the kind of content that answers questions directly. They lean on entities and consistency: a company whose name, address, history, and offering match across many independent sources gets described confidently and accurately. And they have no recency discipline by default, which is how a two-month-old scam incident ends up presented as a current fact about the brand. Anything ambiguous, outdated, or hostile in your footprint can surface, because a fake-takedown email and a real one read the same to a system that is summarising rather than judging.
What ChatGPT Optimisation Looks Like in Practice
I wondered out loud in the original post whether we should start considering a new aspect of online marketing, ChatGPT optimisation, and the months since have only firmed up the answer. The discipline is real, but it is not exotic. It starts with monitoring: ask the major assistants about your brand on a schedule, the same way you watch your Google brand SERP, and log what they say. You cannot fix an answer you have never read.
Then it is footprint work. Make the authoritative facts about your business easy to find and impossible to misread: a complete about page, consistent details everywhere, structured data, real reviews on platforms the models visibly draw from. Where something damaging exists, the counterweight is published clarity. After the scam episode, the right move is a clear page telling the story, the official domain, the fraud warning, the dates, so that any system summarising us has the correction available in our own words. The models cannot cite what you never wrote.
The Trade-Off I Will Admit To
I am not pretending this is a mature discipline with measurable returns. Nobody can promise you a ranking in a system that does not have rankings, and anyone selling guaranteed AI-answer placement today is selling weather. The honest version is cheap and asymmetric: the monitoring costs minutes, the footprint work is the same work that already serves SEO and conversion, and the downside it protects against, a confident machine telling your prospects something false or ugly, is large. We are still testing what moves the answers. The next year will tell me more than the last one did.
The Homework
Open ChatGPT tonight and ask it about your company. Ask what your company does, whether it is trustworthy, what customers say. Then ask the same questions in the other assistants your buyers use. You might be surprised by the information it has about you. I was, in both directions. And if what comes back has a problem in it and you want help building the counterweight, get in touch. This surface is only getting bigger.
Related reading: Organic Is Shrinking. Plan to Dominate Every Channel. The wider SERP shift that makes the AI answer surface a primary channel rather than a curiosity.