A simple bubble tea experiment completely changed how I think about AI recommendations.
Last week, I asked ChatGPT a question that seemed almost impossible to get wrong.
"What are the best bubble tea brands in my city?"
Surprisingly，some of the recommended brands were companies I had never heard of before. A few weren't even available in the cities I had lived in.
After asking the same question to Claude, Gemini, and DeepSeek, I noticed something interesting that many of the same brands which I'm not familiar kept appearing.
AI Isn't Judging Your Brand
Humans recommend products because they have experiences.
But AI does none of those things.
It doesn't know whether one brand actually tastes better than another.
Instead, it tries to generate the most statistically reliable answer based on the information it can understand.
So, AI doesn't recommend the best brand. It recommends the brand it understands best.
The Experiment That Changed My Perspective
Once I realized this, I started paying closer attention.
I didn't only test bubble tea but also the restaurants, beauty brands, consumer electronics, and ravel recommendations.
Again and again, I noticed a pattern.
Brands that consistently appeared in AI recommendations usually had several characteristics:
Clear product descriptions
Well-structured websites
Consistent public information
Plenty of third-party coverage
Easy-to-understand positioning
Meanwhile, some excellent brands barely appeared at all, because AI had much less reliable information to work with.
That's when I stopped thinking about AI recommendations as opinions.
They're much closer to information retrieval problems than human preferences.
Consumers Are Already Changing Their Habits
This matters because people are beginning to use AI differently from traditional search engines.
Instead of searching "Best bubble tea near me", many people (especially the youth) now ask AI to recommend a healthy milk tea brand."
The AI becomes the decision maker before the customer ever visits Google.
And this shift is happening surprisingly fast.
Adobe reported that traffic from generative AI tools to U.S. retail websites increased dramatically during 2025, showing that consumers are increasingly using AI assistants as part of their shopping journey.
PayPal's 2025 holiday shopping research found that 61% of Gen Z shoppers had already used AI tools to help make purchasing decisions.
Even more interesting, Adobe found that shoppers arriving from AI assistants often show stronger purchase intent than traditional search visitors.
In other words: AI is replacing the discovery stage.
Top comments (5)
This matches what I've been seeing too. Products with clear docs, consistent messaging, and real third-party mentions get surfaced far more often than products that are simply "better" but poorly explained.
I completely agree. Of the various LLMs compared in this article, there are some brands that have a consistent narrative between the different language models. Thus, it would seem that discoverability may be just as important as the quality of the products offered by those brands. I'm curious whether we'll eventually see "AI visibility" become a metric that companies actively optimize for.
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I noticed that too. I am using the AI now only to differentiate between technical products, comparing technical features to what I actually need. If it is about user experience I ask about products that have probably hundreds of reviews online available. I wonder if I ask a very specific question i.e which restaurants or cafes in a certain category are most frequented, whether Google will use its collected user movement profile for that. Thanks You for your thoroughly researched Post.
That's a really interesting point. I think there are probably two different recommendation signals at play. For technical products, AI can rely heavily on structured documentation and specifications. But for local businesses like restaurants or cafés, signals such as reviews, location data, and real-world popularity may become much more important, especially as AI assistants integrate with search and maps. Some apps like Uber Eats could use this kind of AI assistants. It will be interesting to see how those sources influence future recommendations.