Google has significantly accelerated the global rollout of AI Mode in Search, expanding the feature to over 200 countries and territories with support for 40 new regions and 35 additional languages. The expansion brings AI-powered search summaries to users in Arabic, Chinese, Croatian, Czech, Dutch, German, Greek, French, Malay, Russian, Thai, Vietnamese, and numerous other languages.
The rapid deployment comes as Google leverages its custom Gemini model’s advanced reasoning and multimodal understanding capabilities to handle linguistic subtleties across diverse languages. However, the expansion occurs against a backdrop of growing concerns from online publishers who attribute declining web traffic to AI-generated search summaries that reduce user click-through rates.
Custom Gemini Model Enables Multilingual Understanding
Google’s implementation of AI Mode relies on a specialized Gemini model developed specifically for Search applications. According to the company, this custom model’s advanced reasoning capabilities and multimodal understanding allow it to grasp the subtleties of local languages, preventing misunderstandings of user inquiries and avoiding stilted or unnatural responses.
The linguistic adaptation represents a significant technical challenge, as AI language models often struggle with idiomatic expressions, cultural references, and grammatical structures that vary substantially across languages. Google’s claim of effective localization suggests substantial training on language-specific datasets and careful tuning to maintain response quality across diverse linguistic contexts.
The multimodal understanding component enables AI Mode to process not just text queries but also visual inputs, allowing users to incorporate images into their search inquiries. A recent update enhanced these visual comprehension capabilities, expanding the types of queries the system can effectively handle.
Phased Rollout Follows US Launch and Initial Language Expansion
Google began testing AI Mode with users in its Labs program in early March 2025, using the controlled environment to gather feedback and refine the feature before broader deployment. In May, approximately two months after initial testing commenced, the company rolled out AI Mode to all users in the United States.
At that time, Google indicated plans to “graduate many features and capabilities from AI Mode right into the core search experience in AI Overviews” based on accumulated user feedback. This integration strategy suggests the company views AI Mode as both a standalone feature and a testing ground for improvements to its broader search product.
In early September, Google expanded language support to include Hindi, Indonesian, Japanese, Korean, and Brazilian Portuguese. The current announcement extends this foundation substantially, with users in newly supported regions expected to see AI Mode responses appear gradually over the coming weeks as the rollout progresses.
The phased approach allows Google to monitor system performance, identify potential issues, and make adjustments before reaching maximum scale. This methodology has become standard practice for major platform updates affecting billions of users, as it reduces risk of widespread problems while enabling iterative improvement.
Publisher Traffic Concerns Shadow Expansion
While AI Mode provides users with convenient summary information directly in search results, online publishers have raised concerns about the feature’s impact on website traffic. Publishers attribute declining click-through rates to AI-generated summaries that answer user queries without requiring visits to source websites.
Research from the Pew Research Center found that users are less likely to click on website links and more likely to end their browsing sessions when they encounter an AI Mode summary at the top of their results page. This behavioral change has significant implications for publishers who depend on search traffic to generate advertising revenue and build audience relationships.
The tension between user convenience and publisher sustainability represents one of the most significant challenges facing AI-powered search features. While users benefit from immediate answers without clicking through multiple websites, publishers lose both traffic and the opportunity to monetize that traffic through advertising or subscription conversions.
Industry observers note that this dynamic mirrors earlier disruptions in the publishing ecosystem, such as when Google began displaying featured snippets that answered queries directly in search results. However, the comprehensive nature of AI-generated summaries potentially amplifies the traffic diversion effect.
Strategic Implications for Search Market Dynamics
The aggressive expansion of AI Mode reflects Google’s efforts to maintain competitive positioning as artificial intelligence reshapes search behavior and user expectations. Microsoft’s integration of OpenAI technology into Bing demonstrated the potential for AI-enhanced search to differentiate products and attract users, prompting Google to accelerate its own AI feature deployment.
By making AI Mode available in over 200 countries and territories with support for dozens of languages, Google aims to establish its approach as the standard for AI-powered search before competitors can achieve similar global reach. The company’s existing search dominance provides advantages in data collection, infrastructure, and user access that smaller competitors struggle to match.
However, the rapid expansion also carries risks. Quality issues in AI-generated responses can damage user trust, particularly in markets where Google faces regulatory scrutiny or local competition. Cultural insensitivity or factual errors resulting from inadequate language model training could generate criticism and potentially regulatory intervention.
Technical Architecture and Capability Evolution
Google’s custom Gemini model for Search represents a specialized implementation distinct from the general-purpose Gemini models available through other Google products. This customization allows optimization for search-specific requirements, including response latency, factual accuracy, and integration with Google’s existing knowledge graph infrastructure.
The multimodal capabilities recently enhanced in AI Mode extend beyond simple image recognition to understand context, relationships, and user intent when visual elements accompany text queries. This functionality addresses increasingly common search patterns where users combine different modalities to express complex information needs.
Google has indicated plans to continue expanding AI Mode capabilities, though the company has not disclosed specific feature roadmaps. Potential enhancements could include improved handling of multi-step queries, better integration with specialized search verticals like shopping or local results, and more sophisticated reasoning capabilities for complex questions.
Industry Response and Market Adaptation
Publishers and content creators are adapting to the AI-powered search landscape through various strategies. Some focus on creating content that AI summaries cannot easily replicate, such as original reporting, in-depth analysis, or interactive experiences. Others are exploring direct relationships with audiences through newsletters and social media to reduce dependence on search traffic.
Google has attempted to address publisher concerns through various mechanisms, including providing more prominent source attribution in AI Mode responses and maintaining links to original sources. However, these measures may not fully compensate for reduced click-through rates if users find AI summaries sufficient for their needs.
The situation presents a complex policy challenge with no obvious resolution. Regulators in various jurisdictions are examining the relationship between AI-powered search features and their impact on the broader information ecosystem, potentially leading to new frameworks governing how these technologies operate.
The expansion of AI Mode to over 200 countries and territories demonstrates Google’s commitment to AI-enhanced search as a core product direction, even as it navigates the tension between user convenience and publisher sustainability. Whether this approach succeeds in both satisfying users and maintaining a healthy content ecosystem will depend on ongoing technical improvements, business model adaptations, and potentially regulatory interventions that shape how AI-powered search features balance competing stakeholder interests.