Most People Pick AI by Brand, Not Quality —
Gemini's App Store #1 Is a Warning Sign

The day Gemini officially launched in Hong Kong, it shot straight to the top of the App Store free chart. People celebrated. News articles ran. "Hong Kong finally has AI," someone wrote.

But App Store rankings measure downloads, not accuracy. A free app backed by a recognisable brand will reach #1 regardless of how often it gets things wrong. Gemini being #1 tells you nothing about whether it's the best AI for you.

How Most People Choose AI Tools

The typical selection process goes: heard of it, seen it around, a friend uses it, it ranked well on the App Store. Not: ran a side-by-side comparison with Claude. Not: checked independent hallucination rate studies. Just brand familiarity — Google is big, Gemini has a name, therefore it must be reliable.

This isn't the user's fault. There simply isn't enough accessible information to judge by. Almost nobody explains, in plain language, how large the accuracy gap between AI models actually is — or how that gap shows up in real everyday use.

Gemini's Hallucination Problem: A Documented Industry Issue

In the AI industry, "hallucination" refers to a model generating content that sounds plausible but is factually wrong — fabricated data, non-existent citations, incorrect factual claims stated as fact.

Gemini's hallucination rate is a documented issue across multiple independent evaluations. Specific patterns include: citing academic papers that don't exist, delivering incorrect information with complete confidence, and systematic errors on tasks that require precise multi-step reasoning.

If you use AI for research, fact-checking, or any work where accuracy matters, this isn't a minor issue — it's a fundamental one.

What People Are Celebrating Is Access, Not Quality

The excitement around Gemini in Hong Kong is genuinely about access: finally, an AI that works without a VPN or a foreign credit card. That's a real barrier being removed, and it's worth acknowledging.

But celebrating access is not the same as endorsing quality. A city that finally gets a new restaurant chain isn't celebrating that chain specifically — it's celebrating having more options. Those are different things.

Fluency Is Not Accuracy

Without reference points, most users judge AI quality by feel. The response flows well, the tone is confident, the paragraphs look structured — so it must be reliable.

Fluency is a style of output. It says nothing about correctness.

In use cases that require accuracy — medical information, legal text, financial data, academic research — this isn't a cosmetic concern. It's the core concern.

Claude vs Gemini: The Actual Performance Gap by Task Type

The gap between AI models on different task types is larger than most people expect. Complex reasoning, long-document comprehension, multi-step logic, and fact-checking are areas where Claude consistently outperforms Gemini in independent benchmark research.

Gemini, in turn, has genuine strengths: Google Workspace integration, real-time web search, and multimedia handling.

Why HKSoka Uses Claude

HKSoka chose Claude as its underlying model not because it's the most famous, but because it has a consistent, measurable track record on tasks requiring accuracy and reasoning.

Combined with HKSoka's two-layer memory system — Layer 1 stores context you actively provide, Layer 2 learns your habits and preferences from ongoing conversations — Claude actually remembers who you are across sessions. No re-introductions every time.

(Want to understand how the two-layer memory system works? Read this post.)

This Isn't an Argument Against Using Gemini

It's an argument for knowing what you're using. Every AI hallucinates — the rates differ, the patterns differ, and the frequency varies by task type. Understand first, then trust.

App Store #1 means a lot of people downloaded it. It doesn't mean it's the one you need. Choosing your tools is your call. Making that choice with real information is what makes it an actual choice.