u/amoorthy

https://preview.redd.it/78dt1orhe5kh1.png?width=1125&format=png&auto=webp&s=e5559010d79cf4323e58eb463726d42bfd0795ce

I have generally thought most AEO efforts are rubbish but the train doesn't seem to stop so I decided I should run some experiments.

First finding: ChatGPT and Google AI Mode mostly agree on which brands to recommend, but use almost entirely different sources to justify those recommendations.

I opened both in incognito windows and asked the same vague question:

"What are the best solutions for hosted database platforms?"

ChatGPT recommended Neon, Supabase, AWS Aurora PostgreSQL, PlanetScale, MongoDB Atlas, and CockroachDB Cloud.

Google AI Mode recommended Supabase, MongoDB Atlas, PlanetScale, Neon, Amazon Aurora/RDS, Amazon DynamoDB, Google Cloud, Azure SQL, Snowflake, and BigQuery.

Brand overlap was pretty reasonable: 66% of ChatGPT's picks appeared in Google's answer, and 40% of Google's appeared in ChatGPT's.

Then I looked at what each engine actually cited.

ChatGPT cited 56 sources. Google cited 30. Only 6 appeared in both: Reddit, YouTube, LinkedIn, Medium, GeeksforGeeks, and Northflank. (That's 11% of ChatGPT's citations and 20% of Google's — which I honestly didn't expect to be that low given how similar the brand recommendations were.)

I ran it again with a much more specific query — small company, Node/PostgreSQL/AWS Lambda stack, nascent user base. Brand overlap went way up. Every brand Google recommended also showed up in ChatGPT's list.

Citation overlap barely moved. Still around 11% and 19%.

So specificity pulled the recommendations together but had almost no effect on the underlying sources.

My experiment is just a couple prompts but a larger Semrush data set says the same thing. Across 126M prompts, ChatGPT and Google AI Mode had roughly 69% brand overlap but only 57% source overlap. (And we're not even talking about citation drift over time).

Then there's the ghost citation issue where AI will cite your source but not mention your brand. What is the value of a citation even then? (Semrush found that 62% of AI citations use a page as a source without naming the brand in the answer. e.g. Zapier is apparently the #1 most-cited source in digital tech, but only #44 in brand mentions).

I think all this says trying to reverse engineer how to get cited in AI answers is pointless?

Curious if anyone here has run similar tests, especially in B2B or dev tool categories. Would be interested to see if the citation-vs-recommendation gap holds up across different query types.

I owned the trademark for our name and still got a cease-and-desist 48 hours after launching my startup.

I named my new startup Xpertly. We owned the trademark, had the domain, and I felt weirdly confident because I’d already renamed my first startup, The Factual, twice.

Apparently that was not enough scar tissue.

48 hours after launch, the cease-and-desist landed. Eight frantic weeks, 30 founder-hours, and $3k in legal fees later, Xpertly became Rocksalt.

Three lessons I wish I’d known before:

• The domain is not the hard part. For software, the real constraint is trademark class 42 in the USPTO and UK IPO databases. A similar-sounding name in your category can still make life painful, even if you technically own your mark.
• “Fanciful” names are stronger trademark candidates than logical ones. Our lawyer used Dove chocolates as the example. Doves have nothing to do with chocolate, which makes the mark easier to defend. “Yummie” chocolates would be much harder, because everyone in the category can describe chocolate as yummy.
• Clever spelling is not as smart as simple spelling. Xpertly made sense to us because we help experts become more visible online. Then every call became: “Xpertly, without the e. I mean the first e.” Stoopid.

Our lawyer told us not to fight for Xpertly because even winning would leave us with a weak mark. Next time, trademark search comes before domain search.