A brand-new website got named by Google's AI in nine days

We picked the worst possible site to test it on

AI answers do tend to quote the names everybody already quotes, so we ran the test on a site we own and made the conditions as unfair to ourselves as we could:

• A brand-new domain with no history and effectively nothing linking to it.
• Thai. It is written without spaces between words, so a machine has to work out where each word ends before it can read a sentence.
• One of the most crowded subjects online: teaching people to use AI coding tools.
• One person, part-time, no ad budget, no outreach.

If a site like that can get named, “AI only quotes big brands” describes what happens when nobody does the work.

First month: nothing wrong, and nobody found it

For the first month the site was a shop. A homepage, one thing to sell, one button. Every visitor arrived from social media. Search sent nobody. Not a few people. Nobody.

Nothing was broken. Everything a search engine needs was already working.

What the site actually was, technically

Sitemap auto-generating, robots.txt allowing the AI answer engines, canonical tags, social tags, structured data and security headers all in place, Search Console connected. Pre-rendered pages, no slow database call in front of a reader.

None of this is why the page got cited. It is listed so the record is complete, and because a site that returns errors or loads slowly never gets the chance to be cited at all.

One day of checking beat a month of guessing

We had a plan. Fifteen articles, written from instinct. Everyone we showed it to agreed it was good, and the whole site sat on one idea: teaching people to build a website using AI.

Before writing any of it, we did something cheap. We typed our phrases into Google and watched what it offered back. No expensive tools. Google tells you for free what people actually type.

The phrase the entire business was built on returned nothing. Not rarely. Never. Nine of the fifteen planned titles returned nothing at all, and two of them were already sitting on the site as drafts, waiting for an audience that does not exist.

How the checking actually worked, and three traps in it

Google’s own autocomplete, queried directly in Thai, in six rounds: base phrases, then every letter of the alphabet appended to each one, then topic probes. Raw results filtered by hand for words that mean two things.

Three traps, each of which would have quietly ruined the list:

Thai has two ways to write the same word. Google returns one form where a person types the other. Same word to a reader; the two do not match as text. Any script comparing what people search against what you wrote misses every one of them unless it normalises first.

English looked bigger, and would have been a trap. The first shortlist was 66 English phrases against 75 Thai. English has real volume, and it would have meant competing against the entire English-language internet from a standing start. Every row got a language tag and the list was rebuilt Thai-only.

Words that mean two things. “AI” on its own in Thai pulls a phone network, an insurance company, an aircraft manufacturer and a shoe brand. Filtered by hand.

The harvest produced 2,930 phrases, narrowed to 141 worth a second look, then to a 75-phrase target list.

Real demand, wrong people

This is the part that changed how we think about all of it, and no tool will show it to you.

Some phrases had plenty of demand. Real people, really searching. Just not the people we pictured.

The phrase	Who we assumed	Who is actually searching
“website not secure”	Owners wanting it fixed	Their customers, seeing a browser warning and wondering if they are about to be robbed
“website design”	People looking to hire	Mostly students hunting for exam papers and presentation templates

Chase the numbers and you write two beautiful articles for people who were never going to buy anything. The demand was real. The humans were wrong.

The AI said the site belonged to someone who does not exist

We now check one thing for every client before anything else, and this is why.

We asked an AI to read the homepage and tell us who runs it. It answered with a full name. That person does not exist. There was a video channel handle on that page, and it was the only thing on the entire homepage shaped like a human name. So the machine took it and decided that was the owner.

The fix was small: a real name on the page, with real credentials somewhere a machine can read them, and the channel labeled as a show.

A competitor with a thousand pages

The same day, we looked at who we would be competing with. One of them had over a thousand articles already published, organized into exactly the categories we were planning to use. We had six.

You cannot out-publish someone with a thousand pages. If you are running a business and writing this yourself at night, you were never going to win that race.

The good news is that you do not have to. What was left was narrower: a real person with a real name on the pages, teaching one specific group: business owners who do not write code. A smaller room, and a room you can actually win.

The two changes that did the work, and both are boring

Most pages make you work for the answer. You land, you scroll, you get the author’s life story, and somewhere near the bottom is the thing you came for.

Every page opens with the answer. Two sentences, in their own box, before anything else, and nothing is allowed to touch that box. A machine building an answer is looking for a sentence it can quote. If it has to assemble one out of paragraph seven and a table halfway down, it will not. It will find a page that already did that work.

Which means the fix is usually not writing more. It is moving one paragraph to the top of something you have already written.

Answer every version of the question. People ask the same thing five ways. The page that got cited carries eight of them.

Nine days

On 2 August the page went live. A seventeen-minute answer to a question about what a tool costs. Five days later it was sitting at position 3. Four days after that, Google stopped showing a list and answered the question itself, naming the page as one of its sources.

The page that earned it was the boring one about price, the one the data told us to write. The original argument we were proud of had no measurable demand, so it got pulled from the first wave.

That exact question was written into the page as target number six, nine days before Google answered it. The page was pointed at it on purpose. We just did not know whether it would work.

Then we left the page alone for three weeks

Once it started performing we had three improvements ready, and we shipped none of them. Not to protect the ranking. Editing a page does not by itself cost you position. We left it alone so that if the page moved, we would know what moved it.

The rule we wrote down, and the one exception in it

The reason we could afford to wait is narrower than it looks: change five things at once and the page moves, and you will never know which one did it. Then you are standing in front of a client telling them all five worked.

With one exception written into the rule so it could not be abused: corrections never wait. A wrong fact on a page about money gets fixed the day it is found. Improvements wait for the measurement window.

That is the difference between a case study and a testimonial. We wrote down, in advance, what would make our own result unreadable.

Three weeks earlier, none of these questions had an AI answer

Back in July, not one of the five questions we were targeting had an AI answer on it at all. Three weeks later, they did.

It is moving under your industry too, and it will not wait.