Generative Artificial Intelligence is fundamentally reshaping how users find, consume, and interact with information online. For decades, search engines functioned primarily as directories, pointing users toward external sources of authority via traditional links. Today, AI-powered search engines—such as Google AI Overviews and conversational models like ChatGPT—do something far more consequential: they synthesize answers, making real-time decisions about which sources, viewpoints, and cultural realities get surfaced while leaving others in the dark.
This shift from indexing to synthesis introduces a host of structural challenges, particularly in regions where cultural, legal, and linguistic boundaries overlap. To understand where AI search is headed, we must look at areas where these boundaries are constantly tested. Multilingual regions act as an organic stress test for AI search infrastructure. By observing how these systems process queries in environments where multiple languages share the same geographic space, we can see the cracks in current retrieval models—cracks that will eventually impact monolingual markets in different but equally destructive ways.
Catalonia, a wealthy European region with its own distinct language and culture, serves as a prime real-world case study. When the exact same queries are run in both Catalan and Spanish across modern search surfaces, the discrepancies go far beyond mere translation. They expose a deeper systemic failure in how AI models assign meaning, authority, and jurisdiction.
The Catalan Stress Test: A Microcosm of Global Retrieval Failures
To understand the depth of the issue, consider a simple linguistic anomaly. If you enter the phrase Tradicions de Sant Jordi (Catalan for “Saint George’s Traditions”) into Google Translate, the system will often identify the source language as Occitan. While Occitan and Catalan share a common Romance ancestry and are linguistically related, they are by no means interchangeable in a modern demographic or search context.
Occitan has roughly 200,000 speakers, primarily residing in southern France. Catalan, on the other hand, boasts approximately 9 million speakers and is a co-official language in Catalonia, a region where Google has maintained physical and business operations for more than two decades. Yet, even when queried from a residential IP address within Barcelona, Google’s translation engine frequently defaults to the language with a fraction of the speaker base, subsequently translating the proper noun Sant Jordi into the Spanish San Jorge—an unnecessary castilianization of a deeply regional cultural figure.
This minor quirk points to a much larger, systemic problem within Google’s core architecture. The language-identification layers beneath the search and translation pipelines have suffered from structural instability for years. In fact, Google has publicly acknowledged it. In January 2023, the search giant’s official Search Liaison account responded to mounting complaints from Catalan users who noticed their preferred language results being systematically downgraded in favor of Spanish alternatives. Google deemed the issue “a priority” and released updates later that year that temporarily restored Catalan visibility in traditional organic Search Engine Result Pages (SERPs).
However, the underlying structural layer was never fully repaired. When Google introduced AI Overviews, the generative synthesis layer inherited the same flawed pipeline. When a Catalan speaker today queries Google’s AI Overview in Catalan and receives a response in Spanish, it is not a new bug. It is a legacy infrastructure failure propagated and amplified by a newer, more complex generative layer.
When AI search engines treat the language of a query as unreliable, the retrieval pipeline begins to flatten regional nuance. This is highly visible in Catalonia, but the same mechanics apply to other complex search environments. As documented in studies on how AI search collapses Hispanic markets, search engines frequently treat over 20 Spanish-speaking nations as a single, homogenized statistical demographic. While that collapse is geoloculturally broad, Catalonia presents an even tighter challenge: the geography remains identical, but the choice of language triggers two entirely different versions of reality.
The Methodology: Deconstructing the AI Retrieval Experiment
To demonstrate these structural patterns, a series of simple, reproducible tests were conducted from a residential IP address in the Barcelona metropolitan area. The setup was designed to eliminate personalization and search history biases:
- ChatGPT: Tested using a logged-out, fresh session in incognito mode with no user history or personalization enabled.
- Google Search: Tested in incognito mode, enabling Google’s AI Overviews where the engine chose to generate them.
These paired queries were executed twice, roughly a week apart, to ensure the findings represented stable, algorithmic patterns rather than temporary session anomalies. Five specific search intents were analyzed, each representing a unique layer of the information retrieval stack:
- A Politically Charged Factual Query: Focusing on Catalan independence arguments, modeled after Walker and Timoneda’s 2025 study on language-conditioned LLM outputs, published by Cambridge University Press.
- A Transactional Commercial Query: Seeking local accounting services (gestorías) for freelancers in Barcelona, illustrating the day-to-day commercial SEO landscape.
- A Cultural Heritage Query: Inquiring about the traditions of Sant Jordi, an event with high regional authority and low political sensitivity.
- A Highly Localized Regulatory Query: Researching regional rental subsidies managed by the local government (Generalitat de Catalunya).
- A Language-Identification Stress Test: Using a mix of casual, highly colloquial, and formal Catalan phrases to see if the search engine could identify the input correctly.
The results of these tests revealed four distinct algorithmic patterns that explain how AI search engines handle, and often fail to handle, multilingual and multi-jurisdictional queries.
Divergence 1: Vocabulary, Frame of Reference, and Source Plurality
When asking both ChatGPT and Google’s AI Overviews about the core arguments surrounding Catalan independence, the language of the query radically altered the historical and legal framing of the answer.
When queried in Spanish, both platforms produced a heavily legalistic frame. The synthesized answers centered on the Spanish Constitution of 1978 and the illegality of the 2017 referendum. The tone was formal, focusing on state-level constitutional boundaries.
However, when queried in Catalan, the exact same engines pivoted their vocabulary and conceptual framework. The outputs prominently featured terms like dret a decidir (the right to decide) and autodeterminació (self-determination) as primary conceptual pillars. It also surfaced deeper historical context, pointing back to the loss of Catalan institutions following the Decrees of Nova Planta in the early 18th century.
Crucially, the Catalan output was not simply a pro-independence echo chamber; it actually provided a more comprehensive overview by retaining the anti-independence legal arguments while presenting a broader historical timeline. The divergence lay in the source citations. The Spanish AI Overview drew its information from national and international sources, including the BBC, Wikipedia (ES), France 24, and the Fundación Espacio Público. The Catalan AI Overview pulled from regional publications like El Punt Avui, VilaWeb, the r/catalunya subreddit, and Wikipedia (CA), alongside the BBC and El País.
Despite originating from the exact same physical location, the language of the query acted as a hard filter on the retrieval corpus, leading to two completely different sets of reference documents and historical timelines.
Divergence 2: The Commercial Divide and the Silent Ad Market
To test how these systems handle commercial local intent, the transactional query Millors gestories per a autònoms a Barcelona (Catalan) and its Spanish equivalent Mejores gestorías para autónomos en Barcelona were run.
While ChatGPT recommended similar brick-and-mortar firms in both sessions, its digital-first recommendations diverged. The Catalan response surfaced regional digital providers like Openges and Gestasor, while the Spanish response recommended national platforms such as Gestoría Online and Gestorum.
In Google’s organic search results, the divide was even more stark. The Catalan query elevated highly localized, bilingual websites (including Gremicat, Calders Assessors, and Gestumm). The Spanish query, by contrast, bypassed these local businesses in favor of large national directories and aggregators like Legify and Zaask.
Two critical secondary signals emerged from this commercial test:
First, Google’s search engine openly doubted the validity of the Catalan commercial query. Directly above the search results, Google displayed an autocorrect prompt: “Quizás quisiste decir: Millors gelateries per a autònoms a Barcelona” (“Did you mean: Best ice cream shops for freelancers in Barcelona?”). Despite the user sitting on a Barcelona IP, the engine assumed a commercial accounting query in Catalan was highly unlikely and suggested a phonetically similar but contextually absurd alternative.
Second, the ad landscape was entirely asymmetrical. The Spanish-language search results were crowded with paid search ads from prominent fintech and accounting brands like Talenom, Declarando, and Horus Firm. The Catalan-language results featured zero ads.
Because the search engine marketing (SEM) market largely treats Catalan as a dead zone for bidding, the search engine receives no commercial ad-click signals for these terms. Over time, machine learning models trained on click and engagement data interpret this lack of ad activity as a sign that the language itself lacks transactional value. This creates a self-reinforcing loop: lower ad spend leads to fewer commercial signals, which causes the search engine to deprioritize minority-language variations for high-value transactional search terms. For more insight into this dynamic, see how AI search defines market relevance beyond hreflang.
Divergence 3: Cultural Ownership and the Reassignment of Authority
The cultural test centered on the celebration of Sant Jordi, a highly celebrated holiday in Catalonia. The results demonstrated how AI search engines reassign cultural custody and authority based entirely on the language of the user’s prompt.
During the first session, Google’s Spanish-language AI Overview cited commercial hospitality websites—specifically Casa Llimona Hotel Boutique and Sumus Hotels—as its primary authorities on the traditions of the day. The Catalan version, however, cited the Ajuntament de Barcelona (the city council), which has officially organized and preserved the festival’s local traditions for centuries.
In the second session a week later, the citations shifted again. The Spanish version cited Spain.info (the state-run national tourism portal aimed at foreign visitors) alongside municipal links. Meanwhile, the Catalan AI Overview elevated its institutional authority, citing the Generalitat de Catalunya (the regional government) and linking directly to the official state guide for the holiday (Guia Oficial de la Diada de la Generalitat de Catalunya).
This reveals a consistent structural pattern: Catalan-language queries consistently lead to primary, native institutional custodians (municipal and regional governments). Spanish-language queries, even when executed from the same local IP, redirect authority toward commercial entities, tourism boards, and national platforms that frame the culture from an outside perspective.
Conversational AI reinforces this framing in its prose. ChatGPT’s Spanish output describes the holiday as an exotic, external phenomenon, using phrases like “Día del amor a la catalana” (“Love, Catalan style”) and calling it an “oportunidad para conocer el patrimonio cultural catalán” (“an opportunity to discover Catalan cultural heritage”). The Catalan output, conversely, discusses the holiday from an internal, native perspective, using precise regional terminology without any distancing language.
Divergence 4: The Core Failure of Language Identification
The most fundamental issue underpinning all these retrieval discrepancies is that search engines struggle to identify minority languages reliably in the first place.
This is highly evident when conducting standard informational searches. For example, querying receptes de calçots (recipes for calçots—a regional green onion native to Catalonia) on Google Search triggers a prominent warning banner: “Sugerencia: Mostrar resultados en español. También puedes consultar más información sobre cómo filtrar por idioma” (“Suggestion: Show results in Spanish. You can also find more information on how to filter by language”).
Even though calçots are an exclusively Catalan culinary phenomenon and the query was written in perfect Catalan, the engine suggested filtering the native language out entirely. Furthermore, the search engine refused to generate an AI Overview for the Catalan query, whereas Spanish-language culinary queries of similar intent regularly trigger synthesized summaries.
Worse still, this behavior is highly erratic. In some test sessions, searching for Tradicions de Sant Jordi in Catalan yielded an AI Overview written entirely in Spanish that cited national tourism sites. In other sessions, the engine correctly identified the language and generated a Catalan response. This lack of consistency is a major hurdle for search engine optimization (SEO) professionals and site owners, as it is nearly impossible to diagnose or optimize for an algorithm that behaves unpredictably from session to session.
While formal or highly literary queries (e.g., festivitats de Catalunya or poetes catalans contemporanis) are generally recognized and answered in Catalan, everyday commercial and lifestyle queries are frequently misidentified. Because commercial search terms are where businesses stand to lose the most visibility, this identification failure directly impacts the economic viability of minority-language content online.
The AI “Doom Loop” and Semantic Degradation in Minority Languages
This problem is compounded by a secondary, slower algorithmic mechanism that is beginning to corrupt the web’s linguistic ecosystem: the generative feedback loop, or “slop loop.”
As Large Language Models (LLMs) are deployed at scale, they are used to generate massive amounts of programmatic content. Much of this content is translated or spun into minority languages using automated tools to capture long-tail search traffic. This low-quality, AI-generated content is subsequently indexed by search engines, crawled, and fed back into the next generation of LLM training datasets. When a model that lacks a strong understanding of a language produces content in that language, it essentially poisons the well for future models.
According to a 2024 Princeton University study by Brooks, Eggert, and Peskoff (available on arXiv), over 5% of newly created English Wikipedia articles show clear statistical signatures of being AI-generated, with similar trends occurring in French, German, and Italian editions. For minority and under-resourced languages with smaller volunteer editorial bases, the impact of this synthetic content is disproportionately high.
This linguistic degradation was documented in detail by the MIT Technology Review in September 2025, which highlighted a linguistic “doom loop” threatening vulnerable languages on Wikipedia:
- Volunteers overseeing four African-language Wikipedia editions estimated that 40% to 60% of all published articles were uncorrected, low-quality machine translations.
- The Inuktitut-language edition of Wikipedia was found to contain machine-translated text across more than two-thirds of its substantive pages.
- Native speakers flagged roughly 35% of the vocabulary in certain Hawaiian-language entries as entirely incomprehensible.
- The Greenlandic edition of Wikipedia faced a formal recommendation for closure in 2025 after automated AI translation tools flooded the platform with nonsensical articles that actively misrepresented the grammar and vocabulary of the language.
Because Wikipedia serves as a primary gold-standard dataset for training AI models in low-resource languages, these automated errors do not remain isolated. They are ingested by future models, permanently locking in grammatical errors, translation hallucinations, and cultural misrepresentations. When bad language identification leads to poor search retrieval, and poor retrieval feeds automated content generators, the entire linguistic corpus begins to degrade.
How Curated Platforms Are Responding
The clearest sign that synthetic content threatens information integrity comes from Wikipedia itself. In March 2026, the English Wikipedia community voted overwhelmingly to ban the use of LLMs for generating article content across its 7.1 million entries. While editors are still permitted to use AI for basic copyediting or as an aid during manual translation, the wholesale generation of text using LLMs is now strictly prohibited.
This decision followed years of mounting frustration among Wikipedia’s volunteer editors, who were spending thousands of hours cleaning up fluent but factually incorrect articles, fake citations, and text that still contained AI system prompts (such as “as an AI language model…”).
If a highly curated, non-commercial platform like Wikipedia has concluded that AI-generated text poses a direct threat to the integrity of its knowledge base, commercial search engines and SEO professionals must take note. The downstream retrieval models that rely on these public data sources will inevitably inherit this degradation, leading to a decline in search quality unless robust mitigation strategies are put in place.
In response, regional organizations and governments are taking matters into their own hands. Projects like the Aina Project in Catalonia and the Latxa model initiative in the Basque Country are actively building and training sovereign, region-specific LLMs. These initiatives aim to bypass the limitations of global foundation models, which are heavily biased toward dominant global languages, by training models on highly curated, culturally accurate local data. For SEOs, this is a clear sign that relying on generic global search optimization strategies is no longer sufficient.
The Engineering Behind the Collapse: Semantic Collapse and Geo-Identification Drift
From an engineering perspective, these search failures are driven by two primary concepts: geo-identification drift and semantic collapse.
As documented by international search expert Motoko Hunt (whose work on global search can be explored on her author profile), search engines often suffer from geo-identification drift, a phenomenon where algorithms treat language as a direct proxy for physical location or market.
When two distinct languages exist within the same physical territory, a search engine cannot use geography to resolve search ambiguity. Instead, the system defaults to the dominant linguistic corpus—typically the one with the largest dataset, the most active users, or the highest commercial ad spend.
This is where semantic collapse occurs. As detailed in Stanford AI research on Retrieval-Augmented Generation (RAG) systems, when vector embeddings cannot clearly distinguish sub-national or regional context, the retrieval model flattens the query’s vector toward the most statistically dominant cluster. In a monolingual country, this default is typically national. In a bilingual region like Catalonia, the model pulls Catalan queries toward a dominant Spanish-language default unless explicit signals force it to do otherwise.
This challenge is not unique to Catalonia. It is highly visible in several major global markets:
- Quebec: French-speaking users in Quebec routinely receive search results and AI summaries optimized for Parisian French, which often cite French national regulatory bodies rather than Quebec’s unique provincial laws and civil code.
- Belgium: Users frequently receive a mix of French and Dutch jurisdictional defaults, even though Belgium’s three regions operate under highly distinct legal, educational, and administrative frameworks.
- Switzerland: Swiss queries are regularly pulled toward German or French national search corpuses, bypassing Switzerland’s unique cultural, legal, and linguistic nuances.
Whenever a retrieval system collapses regional signals, it prioritizes the largest, most commercially lucrative dataset, leaving minority populations with highly inaccurate search experiences.
The Canary in the Coal Mine: Why Monolingual Markets Are Next
It is tempting for search marketers working exclusively in monolingual English markets to view these multilingual issues as niche regional problems. This is a mistake. Multilingual regions are the canary in the coal mine for the future of all search engines.
The same retrieval flaws that cause search engines to conflate Catalan and Spanish will manifest in monolingual markets as AI search tries to handle complex, sub-national jurisdictions. While monolingual search benefits from stronger localization signals (such as GPS, browser locale, and IP addresses), the shift toward generative search means that AI engines must synthesize a single, cohesive answer rather than presenting a list of localized links.
As search engines rely more on AI Overviews to answer queries directly, the system must decide which corpus of information represents “the truth.” In monolingual markets, this will lead to severe jurisdictional flattening, particularly for queries involving highly localized laws and regulations. For deeper insights into how Google handles these international differences, see this guide on improving international SEO with Google and LLM insights.
Consider the following scenarios where monolingual search is highly vulnerable to semantic collapse:
- State-Level Data Privacy: The California Consumer Privacy Act (CCPA) and Texas’s data privacy laws are both written in English but represent different legal frameworks. Because California’s privacy literature is far larger and more established online, a generic generative search query about data privacy rights is highly likely to default to California’s regulations, even when queried by a user in Texas.
- Highly Fragmented Local Regulations: Real estate disclosures, liquor licensing, contractor certifications, alimony calculations, and school zoning laws vary wildly by state and municipality. Because national directories and federal-level discussions dominate the English-language web, generative search engines will naturally default to these larger datasets, presenting national generalizations as local facts.
When an AI retrieval system collapses signals, it always favors the larger, more powerful corpus. This rule applies whether the boundaries are linguistic, geographic, or legal.
Strategic Playbook: How to Build Multi-Jurisdictional Resiliency
To survive the transition to generative search, brands must adapt their optimization strategies to account for both linguistic and jurisdictional fragmentation. Relying on simple international targeting like hreflang is no longer enough. Marketers must apply a comprehensive cultural SEO framework to protect their visibility across different regions and languages.
1. Isolate and Validate Sub-National Jurisdictions
If your business operates in highly regulated industries across multiple states or regions, do not rely on a single, national parent page to rank for local queries. Create distinct, highly localized landing pages that are explicitly optimized for each jurisdiction. Ensure that each page canonicalizes to itself to prevent search engines from consolidating your regional pages into a single, generic national default.
2. Deploy Explicit, Granular Schema Markup
Generative AI search engines rely heavily on structured data to parse entity relationships. Go beyond basic business schema by implementing Schema.org’s areaServed property. Use it to define your exact geographic limits down to the state, county, or municipality level. Pair this with clear on-page copy that references local regulatory bodies, regional tax codes, and localized terminology. Giving the algorithm explicit, machine-readable hooks minimizes the risk of semantic collapse.
3. Anchor Entities in Public Knowledge Graphs
AI search models do not just read website content; they constantly query major knowledge bases like Wikidata. Ensure your brand is modeled accurately within these public graphs. Use specific Wikidata properties, such as jurisdiction (P1001) and official language (P2936), to establish clear, immutable boundaries for your brand entity. By anchoring your business in the same knowledge graphs the AI models train on, you make it much easier for RAG systems to associate your brand with the correct regional queries.
4. Conduct regular Sub-National Search Audits
Do not assume your local SEO is working because your national keyword tracking looks healthy. Run routine, localized search audits using incognito sessions or proxy tools to simulate searches from different states or regional hubs. Specifically, check if the AI Overviews generated for your target keywords are conflating your local services with national competitors or incorrect regional laws. If you find the AI model is hallucinating or applying the wrong state’s regulations, you have a content fragmentation issue that must be addressed on-page.
5. Monitor and Optimize for Secondary Search Signals
Pay close attention to local search engagement and ad spend. If a specific regional product or service category lacks paid ad activity, the algorithm may eventually classify those search terms as low-value, causing organic visibility to decline. Ensure you are supporting your high-value regional SEO pages with local search ads, localized reviews, and regional citation building. These active commercial signals show the AI search engine that your local pages are highly relevant to users in that specific geography.
Adapting to the New Reality of Generative Retrieval
The failure of Google’s AI Overview to consistently answer a Catalan cultural query in Catalan is not an isolated technical glitch. It is a symptom of a foundational design flaw: current AI search engines are built on legacy retrieval pipelines that conflate language, market, and geography, prioritizing corpus volume over accuracy.
As curated platforms like Wikipedia implement strict rules against AI-generated text and regional governments build their own LLMs, the battle over information integrity is intensifying. For search marketers, this shifting landscape requires a fundamental change in strategy. It is no longer enough to publish localized content; you must actively prove to the search engine’s retrieval models that your content is the absolute, authoritative source for that specific language and jurisdiction.