How Local AI Ecosystems Are Rewriting the Global AI Assistant Race

TL;DR: Global AI leaderboards are good at showing which assistants have the largest audiences. They reveal much less about which assistants can turn a query into a booking, purchase, route, or payment. Naver, Yandex Alice AI, and China’s platform companies show how the surrounding ecosystem is becoming part of the product, while India shows that large-scale adoption does not necessarily produce one dominant local AI ecosystem.

After reading Sensor Tower’s 2026 report, we are reviving our Global AI Affairs series to examine why global AI rankings capture only part of the market. We then go deeper, exploring how Korean Naver, Russian Yandex Alice AI, and China’s platform companies use local data, services, and distribution to turn an assistant from a chatbot into an interface for action. India offers a useful counterexample: enormous adoption does not automatically produce a dominant local AI ecosystem.

Sensor Tower’s 2026 report gives us a good starting point: the global AI assistant market is still highly concentrated. In May, ChatGPT held 46% of the audience across the 25 markets included in the analysis, followed by Gemini at 28% and Claude at 10%. During the first quarter of 2026, the three largest assistant apps accounted for 89% of all time spent in the category.

At the same time, the report shows that this concentration is beginning to shift. Users move between assistants for different jobs, including coding, research, image creation, shopping, and everyday questions. Gemini’s growth has been supported by Android and its integration across Google products. Claude has expanded through coding, research, and professional workflows.

These are already ecosystem effects. Users are not choosing assistants solely by comparing the underlying models. They encounter them through devices, search engines, work tools, existing accounts, and services they already use.

Global rankings capture this dynamic best when the competitors are standalone products distributed through the same app stores and websites. The comparison becomes less straightforward when an assistant is embedded inside a larger domestic platform.

Naver’s AI assistant is connected to Korean search, maps, shopping, places, and reservations. Alice AI operates within Yandex’s ecosystem of search, transport, delivery, commerce, media, and devices. In China, assistants from Alibaba, ByteDance, Baidu, and Tencent are connected to broader ecosystems spanning commerce, payments, social platforms, content, and logistics. India presents a different pattern: its AI capabilities are distributed across startups such as Sarvam and Krutrim, public infrastructure, telecom providers, payment networks, global platforms, and regional-language services rather than concentrated within one dominant local ecosystem.

In these cases, app downloads and time spent capture only part of the experience. A user might encounter AI in search, move into maps or shopping, and complete a booking or purchase without opening a standalone assistant.

As assistants begin to support these actions, the surrounding infrastructure becomes increasingly relevant. Access to local information, services, distribution, and payment systems can shape what the assistant is able to do.

That gives us three separate layers of competition to examine.

The current discussion often slides between model quality, assistant popularity, and ecosystem strength. They measure different things.

These layers overlap, but they should not be treated as interchangeable. A strong model does not automatically produce the most useful assistant, especially when a task depends on local inventory, maps, payments, or an existing account.

Global rankings are most reliable at the assistant layer. Sensor Tower’s China figures, for example, cover iOS and Google Play but exclude third-party Android stores, so they capture only part of the domestic market. Usage embedded in search, maps, shopping, telecom services, or devices may also sit outside standalone assistant metrics.

This keeps the point, removes the repetition, and gets you to the transactional AI section faster.

“Transactional AI” has started appearing in product strategies and market reports, although the term is still used loosely.

Here, it means an assistant that can connect information to execution.

A conventional assistant can recommend a restaurant. A transactional assistant can identify an available table, show the location, make the reservation, and add the route.

It can compare products and proceed to checkout. It can find a service provider and schedule an appointment. It can prepare a trip and book the transport. It can discover an item, confirm that it is in stock, apply a discount, and arrange delivery.

The model handles language, reasoning, and planning. The ecosystem contributes the rest: merchants, inventory, maps, identity, payment rails, customer history, and permission to execute.

This direction is already visible among global platforms. Sensor Tower found that Amazon sessions involving its Rufus shopping assistant maintained conversion rates above 40% during the first quarter of 2026, compared with about 20% for sessions without Rufus. Walmart has said that users of its Sparky assistant have average order values approximately 35% higher than other shoppers.

These numbers do not prove that the assistants caused the difference. People with a strong intention to buy may simply use shopping assistants more frequently. They do show why platform companies are connecting AI directly to commercial activity.

The assistant becomes another interface for the platform underneath it.

That is where local ecosystems may have an unusual advantage.

South Korea provides one of the clearest current examples. Naver remains the country’s leading search platform. InternetTrend estimated its share of South Korean web search at 63.8% in March 2026, compared with 28.7% for Google. Search-share estimates vary by measurement service, so the exact percentage should not be treated as universal. The larger point is stable: Naver has retained a major domestic position while global AI assistants have grown.

That position is supported by more than search.

Naver has spent years accumulating Korean-language blogs, community posts, shopping reviews, place information, maps, reservations, payments, and merchant relationships. Much of this information reflects local activity that global models cannot reproduce through general pretraining alone.

Naver is now connecting those assets through AI Tab, its conversational search product.

The beta version passed four million cumulative users in about two months. During that period, Naver said click-through rates for both product and place cards exceeded 20%. Users who visited AI Tab at least 11 times produced 2.7 times more product clicks and twice as many place clicks as those who used it once. By July 15, Naver said the product had passed ten million users, 18 days after its general release.

Again, these are company-reported figures. Frequent users are likely to differ from occasional users in several ways, and the comparison does not establish that AI Tab caused the additional clicks.

The product design is more revealing than the growth number.

AI Tab can show information from Naver Maps and display reservation availability directly in its answers. A user looking for a restaurant can move from asking a question to viewing local information and booking a table within the same flow. Naver plans to add real-estate and health agents and integrate AI Tab into its Whale browser.

The company calls the underlying system a “product-native LLM.” It is optimized for Naver’s service environment, which receives around 50 million daily visitors, and combines HyperCLOVA X with the company’s vertical data, user feedback, and tool-calling infrastructure. Naver has described its assets as including roughly ten billion data records and API tools.

This is a specific form of local advantage.

The assistant does not simply know more Korean. It operates inside a platform that already knows which businesses exist, where they are, what users say about them, which products are available, and whether a reservation can be completed.

Google is also expanding its access to South Korean mapping data, which could narrow some of that gap. For now, Naver’s position comes from its combination of local information, distribution, established user habits, and connected services.

Alice AI presents a similar analytical problem in Russia. 

In global assistant rankings, Alice AI has a smaller audience than ChatGPT or Gemini. Its position looks different when considered as part of Yandex.

Yandex already operates Russian-language search, maps and local business information, transportation, food delivery, grocery delivery, e-commerce, media, browsers, and smart devices. Alice AI can potentially become a common interface across those services. Paid Sensor Tower exports reviewed for this article show that sessions per user increased 2.8 times over roughly 18 months, compared with 1.5 times for ChatGPT over the same period. This points to faster growth in repeat usage, although it does not show which tasks users completed or how successfully.

The ecosystem argument becomes more interesting when we examine what Yandex is building around those sessions.

Yandex introduced a new Alice AI experience in October 2025 with multimodal answers containing images, video, links, and information from Yandex Maps. It also outlined agents intended to find products, compare prices, book restaurants, fill out forms, and conduct research. Within Yandex Go, the company described plans for Alice AI to route requests to services for taxis, food, grocery delivery, and courier delivery.

Yandex has also positioned Alice AI across its browser, smart speakers, and wearable devices. The company describes these surfaces as one connected experience, with shared memory and agents capable of placing orders, booking services, sending messages, and completing forms.

Some of these features were announced as future capabilities, so they should be read as product direction rather than evidence of large-scale use.

Still, Alice AI shows why the ecosystem is the correct analytical unit.

Alice AI without Yandex is one assistant among many. Alice AI with Yandex has access to Russian-language information, local places, transportation, delivery, commerce, and existing consumer accounts.

Its opportunity is unlikely to come from defeating every global model across general benchmarks. It comes from making those connected services easier to use through one conversational interface.

This also raises the standard for evaluating future comparisons between Alice AI Search and Google AI Overviews.

A headline saying that one system “beat” another would be insufficient. The result would need to explain the query set, language distribution, evaluator, answer coverage, freshness, citation quality, local-query composition, and whether the two products had equivalent access to relevant information.

A local system may perform better because it understands the language and market more deeply. It may also benefit from an evaluation set built around information contained inside its own ecosystem. Both are meaningful findings, but they support different conclusions.

China takes the same pattern to a larger scale.

The market is often represented internationally through DeepSeek because DeepSeek’s models have had the greatest global impact. Chinese consumers, however, use a much broader collection of assistants and AI-powered platforms.

Sensor Tower identifies ByteDance’s Doubao as the leading assistant app by monthly active users in mainland China. Separate QuestMobile data has also placed Doubao ahead of DeepSeek, with Alibaba’s Qwen rapidly gaining users. These estimates use different definitions and panels, so they should not be merged into one ranking. Together, they show that China’s consumer AI market cannot be described through DeepSeek alone. Or any other single model.

The Chinese competition also demonstrates how quickly an AI product can connect to commercial infrastructure.

During the 2026 Lunar New Year campaign, Alibaba spent 3 billion yuan on incentives that users could redeem for food and drinks through the Qwen app. QuestMobile data cited by Reuters showed Qwen’s daily users jumping from 7 million to 58 million during the promotion.

The campaign was heavily subsidized, so it offers limited evidence of durable retention. It still shows what Alibaba can assemble: a model, an assistant, merchants, payments, discounts, and delivery inside one coordinated product.

ByteDance is taking a somewhat different route. The company positioned Doubao 2.0 for an “agent era” in which models execute multi-step real-world tasks. Doubao also benefits from ByteDance’s expertise in consumer distribution and its connections to content platforms and devices.

Baidu brings search and maps. Tencent brings messaging, payments, gaming, and a large service network. Alibaba brings commerce, cloud infrastructure, merchants, logistics, and payments. ByteDance brings content distribution, advertising systems, and consumer-product reach.

This is ecosystem-versus-ecosystem competition inside one country.

The underlying models remain important, but the market position of each assistant also depends on which part of digital life its parent company already controls.

India offers a useful contrast. 

Sensor Tower identified it as the largest generative AI web market in the first quarter of 2026, with more than 13 billion visits, compared with over 8 billion in the United States. Yet the report does not identify an Indian consumer assistant with a domestic ecosystem position comparable to Naver in South Korea or Yandex in Russia.

This does not mean that India lacks local AI development. Sarvam AI has developed models and voice systems designed for Indian languages, while the Indian government has supported work on locally trained models, public-service applications, and AI infrastructure. Sarvam’s systems target 22 Indian languages and emphasize voice, documents, and citizen-facing services.

Krutrim has pursued another version of the ecosystem strategy through its relationship with Ola, cloud infrastructure, and Indian-language models. These efforts remain fragmented, and none has yet emerged in Sensor Tower’s global consumer data as a dominant domestic assistant.

India shows that scale and adoption do not necessarily lead to a single dominant local AI ecosystem. The relevant capabilities are spread across telecom providers, payment networks, public infrastructure, startups, global platforms, and regional-language services.

The market may develop through shared infrastructure and several specialized interfaces rather than one dominant platform.

The competition should not be reduced to local assistants versus global assistants.

Google is an ecosystem company. Gemini benefits from Android, Search, Chrome, Maps, YouTube, Gmail, Workspace, and Google’s existing identity and payment systems. Sensor Tower specifically identifies Android integration and distribution across Google services as contributors to Gemini’s growth.

Amazon and Walmart are local in a different sense. They operate enormous commercial ecosystems in the United States and other markets, with direct access to products, inventory, customer accounts, logistics, and payments.

OpenAI has less owned consumer infrastructure, but it can connect to external services through partnerships, APIs, browser control, and agents. Anthropic can take a similar route through enterprise systems and tool integrations.

The distinction is therefore better understood as embedded ecosystems versus assistants that must assemble access through partnerships.

Local platforms may have an advantage where language, regulation, merchant relationships, maps, payments, or user habits are difficult to transfer. Global companies may have better models, larger engineering budgets, broader developer ecosystems, and the ability to improve quickly across many countries.

The balance will differ by task and market.

The emergence of transactional assistants creates a measurement problem.

Model benchmarks show how systems handle controlled questions, while app analytics track usage and retention. Neither shows whether an action was completed correctly.

A more useful evaluation would examine several separate outcomes:

Even factual accuracy requires more care than a single score.

A 2026 study of Chinese-language factual search compared nine systems across traditional search, standalone LLMs, and AI-generated search summaries. Conditional accuracy was fairly close, ranging from 73.2% to 78.9%. The systems differed much more in how frequently they provided a definitive answer. Search engines answered most queries, while some LLMs abstained frequently. All systems also performed worse when the correct answer was “No.”

The paper did not compare Chinese systems with Google, despite being cited in some discussions as evidence that local Chinese AI had surpassed it.

That is precisely the problem with compressed competitive claims. “Better” can refer to accuracy, coverage, speed, local relevance, transactions, or willingness to answer. Each produces a different ranking.

Transactional AI adds still more variables. An assistant may complete more purchases because its owner controls the checkout flow. That says something important about product utility and market power. It does not prove that the assistant recommended the best product.

A tightly integrated assistant can make tasks easier, but it may also influence which options users see.

These trade-offs are not specific to local AI ecosystems. Any assistant connected to a broader digital platform may prioritize services, products, or partners within that ecosystem, making the ranking process less transparent.

Data concentration is another concern. More personalized and context-aware assistance requires access to searches, locations, purchases, calendars, communications, payments, and previous behavior. Local storage or domestic ownership does not remove the privacy question. It changes who holds the data and which rules apply.

There is also a competition issue. A local platform may succeed because its AI is useful, because existing distribution makes alternatives harder to reach, or because of both.

For users, a useful ecosystem should complete the intended task while preserving meaningful choice, transparency, and control.

Global assistants will continue to expand. Frontier models will keep improving at reasoning, multimodal understanding, tool use, and long-horizon execution.

Local platforms will continue connecting those capabilities to domestic data and services.

The likely market is hybrid.

Some platforms will develop their own models. Others will use open models, licensed models, or several providers underneath one interface. The user may continue interacting with the same local assistant even as its underlying model changes.

Global model companies will build partnerships to reach local services. Local ecosystem companies will use global or open technology when it is cheaper or more capable. The boundary between the two groups will remain porous.

This suggests a different way to read the AI assistant race.

ChatGPT, Gemini, and Claude remain important because of their scale and capability. Naver, Yandex, Alibaba, ByteDance, Baidu, and other domestic platforms matter because they control parts of the environment where users want something done.

India shows that demand alone does not guarantee that these pieces will consolidate inside one company. South Korea shows what happens when they already have. China shows several large ecosystems competing simultaneously. Alice AI shows why a local assistant cannot be understood separately from the platform surrounding it.

The model race continues.

It just no longer describes the whole product.

As AI moves from answers to actions, the surrounding ecosystem becomes part of what the assistant can do.

If any of those thoughts resonate with you – share them across your social networks. Let’s keep the conversation going.

📹 In this episode of Attention Span, we follow what happened when OpenAI’s agent hacked Hugging Face and what it might mean for the openness of AI. Check it out →

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What is transactional AI?

Transactional AI describes assistants that can connect an answer to an action, such as buying a product, making a reservation, arranging transportation, paying for a service, or scheduling an appointment. It depends on models plus access to tools, merchants, payments, maps, identity, and live service data.

Why do local AI ecosystems have an advantage?

Local platforms may have better access to domestic language data, businesses, maps, payments, regulations, and established user accounts. This can help an assistant complete local tasks even when its underlying model is not the strongest globally.

Can local AI assistants compete with ChatGPT and Gemini?

They can compete strongly in specific markets and workflows. Their advantage is most visible when a task requires local services or information. ChatGPT and Gemini retain advantages in global distribution, model capability, developer adoption, and investment.

Is Yandex Alice AI a standalone competitor to ChatGPT?

Alice AI is better understood as part of Yandex. Its competitive position depends on how effectively it connects Yandex Search, Maps, Browser, transportation, delivery, commerce, media, and devices through one assistant interface.

Why are global AI rankings incomplete?

Global rankings usually measure apps, web traffic, users, or time spent across a comparable set of markets. They may miss third-party app stores, embedded AI features, domestic platforms, and actions completed inside larger service ecosystems.

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