A few days ago, I ran a small LLM test. I’m in Chile, and I asked ChatGPT in Spanish for something completely ordinary:

Apartments for rent.

It’s the kind of search that any real user might make, and exactly the kind of intent that brands depend on for visibility, demand, and trust.

Google would normally return hyper-local results: nearby listings, local marketplaces, and region-specific options.

But this time I asked an LLM.

And the answer?

• Listings from Peru
  
• Recommendations for Zillow
  
• And guidance clearly written for a non-Chilean audience.

Zillow doesn’t operate in LATAM. I don’t live in Peru. My query wasn’t in English. And I wasn’t in the U.S.

LLMs are powerful, but they fundamentally struggle with the geographic, cultural, and market-specific nuances that Google Search understands effortlessly.

For brands that operate internationally, especially brands that scale across markets, languages, and regulations, this isn’t a minor difference.

Key Takeaways

1. LLMs don’t reliably understand regional context

Even when queries are in another language or contain local slang, LLMs can misinterpret the user’s market. They guess location based on patterns, so their answers can drift into regions where your brand doesn’t operate or not be mentioned at all in a region where it does operate. 

2. Google’s accuracy comes from its ad engine

Google must understand real commercial intent, because its entire advertising ecosystem depends on it. That’s why it has deep local data, market boundaries, and real-time understanding that LLMs simply don’t have (yet).

3. LLM outputs are skewed toward English and U.S. market patterns

Due to the way models are trained, they often default to U.S. brands, platforms, and behaviors, even in fully non-U.S. contexts. For international brands, that means LLMs may overlook or misrepresent your market entirely.

4. Winning internationally requires a dual strategy

Google tells users what exists. LLMs tell users what it means. Brands that optimize for both ecosystems, search engines, and AI models, will dominate global visibility. Those who don’t will gradually lose presence across markets.

LLMs Don’t Automatically Understand Where Your Customers Are

Google has spent decades building real-time, region-specific intelligence:

• Localized crawling
  
• Country-level indices
  
• Local business data
  
• Language detection
  
• Regional search intent modeling
  

It knows when a user in Barcelona searches in Spanish versus Catalan. It understands Swiss-German vs. German-German intent. It even adjusts as users cross borders or switch languages mid-session.

LLMs, however, work very differently.

They can sometimes infer your location, primarily when the prompt includes clear regional cues, such as language, place names, currency, or slang. But this is not because the model “knows where you are.” It’s because it recognizes patterns associated with specific regions.

Technically, here’s what’s happening:

1. LLMs use contextual clues + optional IP context, not geo-indexing

Even when an LLM recognizes your IP region, its response still depends on the text of the prompt, rather than a geo-ranked index like Google’s.

The model is using:

• Language
  
• Vocabulary
  
• Currency
  
• Place names
  
• IP region (when available)
  

…to guess where the user might be.

But it does not:

• Fetch region-specific databases
  
• Know local business availability
  
• Calculate proximity
  
• Factor in regional market share
  
• Reference local economic behavior
  

Although the output may feel localized, the underlying system does not function like a traditional search engine.

2. LLMs don’t have “market boundaries”, only linguistic and contextual ones

Google distinguishes:

• Madrid vs. Barcelona
  
• Mexico City vs. Monterrey
  

LLMs don’t have this market-level granularity.  Language tokens guide them more than real geography.

So “departamentos” might push it toward LATAM, while “piso” pushes it toward Spain, regardless of where the user actually is.

3. When signals conflict, LLMs fall back to the “statistically safe” answer

If the cues aren’t clear, the model predicts the scenario that most commonly fits the tokens.

That might be:

• The U.S.
  
• Spain
  
• Mexico
  
• Or any region that frequently appears with similar wording
  

They are making a probability decision, not a geolocation one. 

For brands trying to reach customers across Europe, LATAM, or APAC, this creates a simple challenge:

LLMs sometimes understand your user’s region, sometimes they don’t. And your brand cannot rely on “sometimes” when customers expect relevance.

Google Understands Commercial Intent. LLMs Understand Language.

Search queries like:

• “dentist near me”
  
• “best broadband deals”
  
• “delivery open now”
  
• “departamentos en arriendo”
  

They’re economic signals: indicators of real demand, real intent, and real purchasing behavior. And Google has spent years perfecting how it interprets that intent.

Why?

Because Google is not only a search engine but it’s actually the largest advertising engine in the world, and that engine depends on correctly understanding commercial intent.

For Google, misinterpreting a local search is a revenue problem.So it has built extremely fine-tuned systems to understand:

• What’s available in each market
  
• Real business options
  
• Proximity and operating hours
  
• Which brands have local presence
  
• Regional pricing norms and regulations
  
• Neighborhood-level context
  

This is the infrastructure behind:

• Local ads
  
• Restaurant and delivery listings
  
• Local service providers
  
• Product inventory feeds
  
• Travel verticals
  
• Marketplace integrations
  

Google must understand real-world supply and demand, accurately and locally, because advertisers demand it.

LLMs don’t have this incentive. At least not yet.

They can describe how a market theoretically works, but they don’t retrieve live inventory, can’t validate availability, and don’t personalize results based on commercial certainty.

LLM Training Data Is Not Global, And That Limits Your Reach

This is the part most marketing teams underestimate.

Most LLM training data is:

• Disproportionately English
  
• Heavily U.S.-centric
  
• Culturally biased
  
• Geographically unbalanced
  

So when an LLM is unsure, it defaults to:

• U.S. companies
  
• U.S. pricing
  
• U.S. regulatory frameworks
  
• U.S. platforms
  
• U.S. service providers
  

This bias can surface even when the user is nowhere near the U.S., even when the query is written entirely in another language, and even when the region has its own dominant platforms and brands.

In some cases, the model may recommend a service that doesn’t operate in that market at all, simply because it appears more often in its internal patterns than the local alternative.

For brands, this poses two challenges:

1. LLMs may misrepresent your market
2. LLMs may not surface your brand at all unless your content is structured in a way models can interpret correctly

In short, international brands can’t rely on LLMs “figuring it out.” They have to intentionally guide them.

How Global Brands Should Prepare for LLM-Driven Search

As LLMs continue redefining how users find and interpret information, international brands need to prepare for a future where visibility depends on both search engines and AI models. 

That requires:

• Clear regional signals that prevent LLMs from defaulting to U.S. assumptions
  
• Localized, multilingual content clusters that models can cite and trust
  
• Structured information that reinforces market boundaries
  
• Authority signals that increase inclusion in AI-generated answers
  
• Alignment between traditional SEO and emerging AI ecosystems
  

This is the new foundation of global visibility: a content system that makes sense to humans, Google, and LLMs, across markets, languages, and cultures.

For teams navigating that shift, partners who understand both the old rules and the new mechanics of AI will be essential in the years ahead.

At Previsible, this is the work we’re focused on every day: helping brands build the clarity, authority, and regional precision needed to stay visible in a world where search and AI are converging.