China’s gen-AI user base doubled in 6 months
China's generative AI user base doubled to 515M in six months. The strategic question is no longer who is using GenAI. It is who captures the monetization.
GenAI users reached 515M in H1 2025, doubling from December 2024 in six months.
AI smart terminal sales on Taobao and Tmall hit RMB 14.22B, up 131% YoY.
Gen Z leads adoption at 71.4%, a 34.8 percentage point gap above the overall average.
AI shopping assistants on Taobao and JD are restructuring product discovery at scale.
The monetization race has begun: platforms owning the AI interface own purchase intent.
China’s generative AI landscape reached a new phase in 2025. The term “generative AI” in China broadly encompasses large language model applications, AI-powered search and shopping tools, AI image and content generators, and the expanding category of AI-embedded smart terminals including phones, tablets, earbuds, and glasses.
CNNIC‘s Digital Consumption Development Report for 2025 tracks GenAI adoption as a discrete consumer behavior category for the first time. Its methodology captures users who have actively engaged with a generative AI product or feature in a given period, based on a 30,000-respondent telephone survey across 31 provinces.
The December 2024 baseline and H1 2025 figure together produce the sharpest adoption curve in the report. 515M users in six months is not a projection. It is a measured user count that places China’s GenAI consumer base larger than the population of most G20 economies.
For the macro picture of China’s digital consumption landscape, including the RMB 9.37T total and the services digitization thesis, see CIW’s companion analysis.
The doubling in context: why speed matters more than scale
Large user numbers in China require calibration. A country of 1.4B people with 1.123B internet users generates large denominators naturally. The 515M GenAI figure needs to be read against the pace of adoption, not just the absolute count.
The doubling from December 2024 to June 2025 occurred in a six-month window. For comparison, China’s mobile internet user base took several years to double at equivalent scale. Online food delivery, one of the fastest-adopted consumer categories in the CNNIC dataset, did not show comparable velocity at this stage of its adoption curve.
The speed signal matters for two reasons. First, it indicates that the friction threshold has been crossed. GenAI products in China are no longer in the phase where users require significant effort or technical literacy to engage. Second, the doubling rate implies that a substantial share of new adopters came from mid-tier cities and older cohorts, not just the urban youth demographic that drove early uptake.
Why it matters for investors: A user base that doubles in six months at 515M scale is structurally different from one that grows incrementally. Platforms with established AI interfaces are accumulating behavioral data at a pace that compounds their advantage. Latecomers face a widening gap across user count, model training quality, and personalization depth.
The hardware cycle: AI terminals accelerate faster than platforms
The consumer software story is well-covered. The hardware story is not. According to CNNIC, AI smart terminal sales on the Taotian platform (Alibaba’s combined Taobao and Tmall) reached RMB 14.22B in H1 2025, up 131% YoY. That growth rate on a multi-billion renminbi base is a product cycle signal.
AI smart terminals in this context include AI-enabled smartphones, wireless earbuds with real-time translation and ambient intelligence features, smart glasses, and AI-embedded home devices. The category is distinct from standard consumer electronics in that the AI capability is the primary purchase driver, not a feature add-on.
The policy dimension reinforces the hardware trajectory. The Chinese government has set explicit penetration targets: 70% smart terminal penetration by 2027, and 90% by 2030. These targets are backed by subsidy programs and procurement incentives that function as demand guarantors for the terminal hardware supply chain.
For semiconductor and component suppliers, the 131% YoY growth rate on AI terminal sales is the most actionable data point in the CNNIC report.
It maps directly to demand for edge AI chips, on-device inference memory, and low-power neural processing units. The supply chain implications extend from Chinese fabless designers through to international component makers with China exposure.
AI glasses represent the most promising sub-category for near-term growth. IDC projects the global AI glasses market at USD 300B by 2030.
Meta’s Ray-Ban smart glasses sold over 2M units through Q4 2024, validating mainstream consumer appetite for the form factor. Chinese manufacturers including Xiaomi, Huawei, and a cohort of emerging specialists are positioned to compete on price and AI integration depth.
Platform competition: how Alibaba and JD are competing for AI-mediated purchase intent
The most consequential near-term monetization arena is AI-powered product discovery. Both Alibaba and JD have deployed AI shopping assistants that intercept the consumer at the moment of purchase intent.
Taobao’s “AI Wan Neng Sou” (AI All-Capable Search) restructures the traditional search-and-browse model. Users can input natural language queries, image references, or cross-category descriptions and receive curated product recommendations rather than ranked listings.
The shift from keyword search to intent interpretation changes the competitive dynamic for merchants. Visibility is no longer determined by keyword bidding alone. It is determined by how well a product’s attributes match the AI’s interpretation of user intent.
JD’s “Jing Xiao Zhi” operates on a similar logic. It adds a business-facing layer serving both consumers and merchants, providing AI-generated product listing optimization, pricing recommendations, and demand forecasting. According toCNNIC, JD’s AI-optimized merchant listings achieved 52% higher payment conversion in overseas markets.
That conversion uplift, if sustained at scale, represents a structural margin advantage for merchants who adopt AI tooling early. The platform that owns the AI interface owns the moment between search and purchase. This is not a marginal efficiency gain. It is a restructuring of where value is captured in the e-commerce stack.
The 60,000 SME signal: AI adoption moves down-market
Large platform deployments are expected. The more telling adoption signal is what is happening among smaller operators.
According to CNNIC, 60,000 small and medium enterprises are using Alibaba International’s full-process AI for cross-border commerce operations. AI-published product listings across the platform have reached 7M.
This figure matters because SME adoption of AI tooling historically lags enterprise adoption by two to four years. An SME cohort of 60,000 actively using full-process AI in H1 2025 suggests the technology has crossed the accessibility and cost threshold that previously limited deployment to larger operators.
The cross-border context adds a further dimension. SMEs using AI for international commerce are deploying it for tasks including multilingual product description generation, localised marketing content, customs documentation, and demand signal interpretation across foreign markets.
These are not single-feature applications. They are workflow integrations that, once embedded, create switching costs and compound operational advantages.
The SME adoption curve is a leading indicator for the broader enterprise AI deployment wave. The 60,000 figure will grow. The question is which platform ecosystems capture that growth. The secondary question is what revenue share accrues from licensing fees versus total sales volume gains.
Gen Z as the proof of concept
The 34.8 percentage point gap between Gen Z GenAI usage (71.4%) and the overall average (36.5%) is the sharpest cohort divide in the CNNIC dataset. It is also the most strategically important one for platform operators planning a three-to-five year horizon.
Gen Z in China (born 1995-2009) entered digital consumption as mobile-native users. They are now entering it again as AI-native users. The difference matters.
Mobile-native behavior was shaped by interface constraints: tap, swipe, scroll. AI-native behavior is shaped by expectation of personalization, contextual response, and frictionless fulfilment.
A Gen Z consumer using Taobao’s AI search is not just running a more efficient query. They are developing an expectation that the platform understands their aesthetic, their budget range, their size history, and their social context. That expectation, once formed, is difficult to satisfy through a non-AI interface.
The Gen Z adoption rate of 71.4% represents a cohort that will carry AI-native consumption habits into higher income brackets over the next decade. The platforms that shape those habits now are building a switching-cost advantage at the most valuable point in the consumer lifecycle. A full analysis of Gen Z’s emotional spending, content payment behavior, and brand strategy implications is available in CIW’s companion piece on China’s AI-native consumer cohort.
The monetization gap: users without revenue models
The risk embedded in the 515M figure is the gap between adoption and monetization. Chinese consumers have demonstrated consistent resistance to paying directly for AI features. The dominant monetization model to date has been indirect: AI as a conversion tool, AI as a retention mechanism, AI as an advertising targeting layer.
Direct subscription models for GenAI products remain limited in penetration. The consumer market has not yet produced a Chinese equivalent of ChatGPT Plus at meaningful scale. Enterprise and SME licensing is growing. It starts from a low base.
The platforms best positioned to close this gap are those that have embedded AI into transactional workflows rather than offering it as a standalone product. When AI generates a sale, the revenue model is clear. When AI provides a conversation, the revenue model requires construction.
The next twelve months will test whether China’s GenAI platforms can translate user scale into revenue density.The 515M user base is a structural asset. Its monetization trajectory is the variable that will separate durable platform value from inflated adoption metrics.
China’s GenAI consumer story in H1 2025 is real, fast, and commercially significant. The doubling of the user base reflects genuine friction reduction, not measurement artifacts.
The hardware cycle is accelerating on the back of policy support and consumer demand. The platform competition for AI-mediated purchase intent has begun in earnest.
The monetization models remain underdeveloped relative to the user base. That gap is where the next phase of competitive differentiation will be decided. The platforms that solve the monetization equation in the next two years will define the architecture of China’s AI consumer economy for the decade that follows.