A company known for moving slowly does not usually post its biggest market move in five years. Tencent did exactly that on June 2. Three separate pieces of AI news landed within hours of each other.

WeChat is the context that makes the move matter. The app carries roughly 1.4B monthly active users. It anchors social messaging, payments, transport, and daily services across China. Each new WeChat entry point has historically faced careful internal review.

The caution is what makes the current stance notable. Tencent spent much of the past decade weighing AI bets carefully. The June 2 announcements suggest a different stance. The company now appears willing to open WeChat to outside systems rather than guard it.

The three items, taken alone, look like routine product updates. Read together against the past year, they trace a clear strategic turn. Tencent is repositioning itself as a connector, not a model champion.

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Why Tencent refuses the scale war

The logic starts with a market Tencent has decided not to fight head-on. Rivals already lead the contest for standalone AI apps.

ByteDance’s Doubao has passed 340M monthly active users. Alibaba’s Qwen is bound tightly into Taobao and Alipay to build what the company calls a consumer agent. Both firms are escalating their fight over the consumer AI entry point.

Tencent’s own assistant, Yuanbao, has crossed 100M monthly active users. Closing the gap with Doubao or Qwen would mean a costly user-acquisition war on someone else’s terms.

Tencent chose a different surface. Instead of competing on app downloads, it is leaning on WeChat’s installed base. A super-app of 1.4B users reaches scenarios no standalone assistant can match.

The asymmetry is the strategy. Alibaba and ByteDance try to route around WeChat. Tencent’s plan is to make WeChat the thing they cannot route around.

Capex and org chart back the shift

The financial commitment confirms the shift is more than messaging. Tencent has steadily raised its AI outlay.

Full-year 2025 R&D spending reached RMB85.75B, up 21% YoY, with about RMB18B tied to AI. Capital expenditure for the year hit RMB79.2B, a record. First-quarter 2026 capex then climbed to RMB31.9B, nearly all of it directed at AI compute and model work. Leadership has said AI investment will at least double in 2026.

The organizational changes are sharper signals than the budget lines. In December 2025, Tencent created an AI Infra unit, an AI Data unit, and a data computing platform unit.

It also hired Yao Shunyu, a core researcher recruited from OpenAI, as Chief AI Scientist. Yao reports directly to President Liu Chiping. A reporting line straight to the president is unusual inside a major Chinese tech firm. Senior technical leaders normally sit within a specific business group.

Yao also leads the AI Infra unit and the large language model department. The dual role is meant to fuse algorithms and compute under one owner. Tencent calls the approach a co-design model tuned to its own business scenarios.

The clearest break came in March 2026. Tencent dissolved its AI Lab after nearly a decade. The move shifted AI work from research exploration toward commercial delivery. Folding a nearly decade-old research unit into commercial teams marks a compressed product timeline.

The Hunyuan reset points at agents

Tencent’s own model strategy looked defensive a year ago. After DeepSeek surged in early 2025, Tencent embraced it and lowered the priority of its in-house Hunyuan model. External readings at the time called it a concession in the model race.

After Yao’s arrival in December 2025, Hunyuan was rebuilt from the ground up across pretraining, reinforcement learning, and infrastructure.

The output landed in late April 2026. Tencent released and open-sourced the Hy3 preview model, carrying 295B total parameters and only 21B active parameters on a mixture-of-experts architecture.

The design choices matter more than the headline size. The roughly 7% activation rate favors inference cost and latency, the constraints that matter for tool-calling loops and multi-step agent runs. Hy3 is tuned for reasoning, long context, tool calling, and agent scenarios. The architecture choice points the model at agents, not at benchmark wins.

Adoption followed quickly. According to OpenRouter data cited in early reporting, Hy3 token volume rose to roughly 10 times the prior generation. The model topped OpenRouter’s weekly overall ranking for three consecutive weeks. Ma Huateng told shareholders that “after the new model trained, my confidence has grown stronger.”

WeChat as connector, not fortress

Tencent’s AI products resolve into a single layered system. Tencent is stacking its AI assets rather than betting on any one of them.

Hunyuan sits at the base as the model foundation. Yuanbao serves as the general consumer assistant. A planned WeChat AI agent acts as the ecosystem execution hub. WorkBuddy serves the enterprise productivity layer. Agent-to-agent links, known as A2A, form the connective channel across platforms.

The three June 2 announcements are proof of that doctrine in motion.

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China’s 618 shopping festival began in 2004 as a sales event for JD.com. It has since expanded into a month-long industry-wide promotion and is now China’s second-largest annual commerce event after Double 11.

This year, a second contest is running in parallel to the GMV race. Four major platforms have deployed AI shopping assistants in the months leading into 618, each betting that the next structural shift in Chinese e-commerce runs through conversational AI.

The timing reflects two converging forces. Foundation model capability crossed a practical threshold in late 2025. China’s Interim Measures for Generative AI Services, effective August 2023, provided enough regulatory clarity for platforms to commit to agentic transaction features at scale.

The four entrants arrived in rapid succession. Alibaba’s Qianwen integrated directly with Taobao on May 11, enabling users to browse and purchase via AI dialogue inside the Qianwen app.

ByteDance’s Doubao is expected to launch paid commerce features in late June, according to 36Kr, with Douyin Mall subsidy integration planned for Q3 and full operational rollout in Q4.

JD launched a standalone app called JD AI Gou in December 2025 and opened it to broader testing ahead of 618. Xiaohongshu, through its Dots AI division and its Wenyiwen in-search assistant, has embedded AI dialogue directly into its main app search layer, building a path from conversation to product discovery to in-app purchase.

Which platform architecture can make AI commerce work at scale is the useful question here. The decisive variable is which platforms have closed the loop between user-generated product content and transaction data. Only 2 platforms in this field qualify.

Two philosophies, not four competitors

Testing conducted by Jingzhe Research Institute ahead of 618 sent identical product queries to Doubao, Qianwen, and JD AI Gou. The results exposed a fundamental divergence in product philosophy.

Doubao and Qianwen behave as discovery agents. Both platforms probe user intent through dialogue, narrow options progressively, surface product tradeoffs explicitly, and close each response with a follow-up question designed to refine the recommendation. Doubao flags product weaknesses alongside strengths. Qianwen offers a summary comparison table. Both treat the chat interface as a reasoning environment, not a search box.

JD AI Gou operates differently. A query for earphones under RMB 500 returned 15 product recommendations across 5 categories. The categories were defined by functional attributes: noise cancellation, battery life, audio quality, sports use, and value. JD offered no follow-up question. No tradeoffs surfaced. The interface prompts users to browse and filter, which is structurally identical to JD’s existing shelf-based search experience with a chat input layer placed on top.

The design language reveals the strategic intent. Doubao and Qianwen are attempting to shift where purchasing decisions are made, from the browse-and-filter shelf to the AI conversation. JD AI Gou is attempting to route existing JD search behaviour through a new interface without disrupting the underlying shelf logic.

The distinction matters because it determines what each platform needs to win. Conversational discovery requires deep, accurate, preference-sensitive knowledge about products. Shelf search augmented by chat requires SKU breadth and fast retrieval. These are different technical and data problems. They reward different platform architectures.

Xiaohongshu, tested separately through its Wenyiwen feature, behaves closer to the Doubao-Qianwen pattern. It has a distinct architectural advantage examined below.

The accuracy problem nobody is pricing in

What JD AI Gou’s recommendations contain matters more than how many it returns. That is the significant finding from comparative testing.

In a follow-up query for earphones with strong audio quality, JD AI Gou returned a category labelled “bone conduction audio.” 2 of the top-ranked products in that category do not use bone conduction technology.

Both are Edifier earclip models. Jingzhe Research Institute verified this with JD merchant customer service. Both products use air conduction, not bone conduction. The AI attributed a feature to a product that the product does not have.

A separate category surfaced the same Edifier Comfo Clip Q model in 2 distinct recommendation buckets simultaneously. 2 different store listings for the same Moto Buds Clip model appeared across separate categories in the same response.

Both findings are structural outputs of a system that prioritises recommendation breadth over recommendation accuracy. When the query input is treated as a keyword trigger rather than a reasoning prompt, the output optimises for coverage, not correctness.

The commercial risk is concrete. China’s Law on the Protection of Consumer Rights and Interests and the 2023 Interim Measures for Generative AI Services both carry obligations around accuracy of information provided to consumers.

A platform whose AI agent misrepresents product specifications to drive purchase decisions faces exposure on both fronts. Whether regulators have appetite to test this in 2026 is an open question. That the exposure exists is not.

The deeper risk is trust.

Conversational commerce requires consumers to delegate a portion of their purchasing judgment to an AI agent. That delegation is contingent on accuracy.

One high-profile failure is sufficient to collapse consumer confidence in the agent format. A bone conduction earphone that uses air conduction. A waterproof jacket that is not. JD’s current architecture creates that risk at scale.

Why the loop matters more than the model

Foundation model capability will reach parity across all four platforms within 12 months. Alibaba’s Qwen 3 series and ByteDance’s internal model investments are both at frontier quality for Chinese-language commerce tasks.

JD licenses third-party model infrastructure. Xiaohongshu’s Dots unit, announced in April 2026, is a first-party AI development effort at significant scale. The model gap is a timing advantage, not a structural one.

The structural advantage belongs to whichever platform owns a closed content-to-commerce loop: the flywheel in which content generated by real users about real products feeds AI training data, which improves recommendation accuracy, which drives more transactions, which generates more content.

This loop requires 2 inputs that are hard to acquire independently: authentic product experience content at scale, and transaction data that links that content to actual purchase outcomes.

Only Doubao has both inputs fully connected today. By monthly active users, Douyin is China’s largest short-video platform, according to QuestMobile. Its content corpus includes hundreds of millions of product reviews, demonstrations, and purchase-experience videos.

TikTok Shop, operating under ByteDance infrastructure, provides the transaction linkage. When a Doubao user asks for a product recommendation, the underlying model can draw on content generated by users who bought the product and returned to post their own assessment. The loop is closed.

Xiaohongshu has the content input. Its UGC corpus is among the deepest product review databases in Chinese consumer goods, with particular strength in beauty, fashion, home, and outdoor categories. The transaction data to close the loop is not yet there.

Xiaohongshu’s in-app commerce function has grown. Transaction volume remains a fraction of Doubao’s Douyin Mall pipeline. Xiaohongshu is the only credible challenger, provided it can convert content depth into transaction gravity within the next 12-18 months.

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China’s 618 shopping festival, launched by JD.com in 2010, is the country’s second-largest annual e-commerce event. Every June, platforms run weeks of coupons, flash deals, merchant subsidies, and category-specific promotions. The festival tests logistics, pricing systems, recommendation engines, and consumer patience.

In 2026, 618 is testing something else: whether AI assistants can replace the search box as the first step in a purchase decision.

Alibaba and ByteDance moved first. Their answers to the same product problem look similar on the surface. Underneath, they point to very different assumptions about where value will sit in AI commerce.

Two products, two theories of value

On May 11, 2026, Alibaba linked its Qwen AI app to Taobao. Users can now search, compare, and complete orders through natural language inside Qwen. Within Taobao itself, a Qwen assistant helps with virtual try-on, discount calculations, and deal hunting. The catalog spans more than 4 billion products.

ByteDance moved in stages. Doubao’s first Douyin commerce integration began in December 2025, covering ticket bookings and selected shopping categories. In-app shopping gray-scale testing followed in March 2026. The full “Help You Choose” closed-loop feature went live in May 2026.

The two platforms enter 618 with different user bases. According to QuestMobile data, Doubao had roughly 345 million monthly active users in Q1 2026, compared with 166 million for Qwen. Qwen’s growth curve still matters: it reached 100 million MAU within two months of its November 2025 public beta launch.

Scale alone will not decide the race. Doubao’s user base was built through entertainment-driven engagement. Qwen’s was built closer to productivity, search, and shopping-adjacent intent.

618 will test which base converts when the question changes from “what should I watch?” to “what should I buy?”

The product design split is more important than the user count.

Qwen surfaces category results and keeps the Qwen frame visible. Ask for a product recommendation and Qwen returns a curated list. The user still sees Qwen as the filter and makes the final choice.

Doubao pushes harder toward a single answer. Ask the same type of question and Doubao can return a named product on a purchase page. The Doubao interface fades away as the transaction layer takes over.

Qwen is betting on assistant brand equity. The user should remember that Qwen helped make the decision.

Doubao is betting on transaction efficiency. The fewer steps users feel, the more useful the system becomes.

That split matters because AI commerce will not be judged only by recommendation quality. It will also be judged by who gets credit for the decision. If the user remembers the assistant, the AI layer gains long-term bargaining power. If the user remembers only the purchase, the commerce layer keeps the value.

China is hardly alone in this race. Amazon’s Rufus, Walmart’s Sparky, and ChatGPT’s shopping integrations all point toward agentic commerce. China’s structural difference is ownership. Alibaba and ByteDance control both the AI layer and the commerce layer, which makes the experiment faster and the conflict sharper.

Neutral recommendations collide with paid ranking

Qwen and Doubao have different product designs, but they share the same monetization problem.

AI shopping recommendations depend on trust. Users ask an assistant for help because they expect a better match than they would get from keyword search, which has been shaped for years by sponsored placement and merchant bidding.

Paid ranking works in the opposite direction. China’s e-commerce ad systems are built around auction logic. Merchants pay for visibility. The platform monetizes that visibility.

Alibaba’s customer management revenue is the clearest example. It remains the core of Alibaba’s highest-margin China e-commerce business and grew 8% on a like-for-like basis in fiscal year 2026.

The scale of that line is the problem. Customer management revenue is the largest single revenue stream inside Alibaba’s China e-commerce business. Any serious shift from keyword bidding to AI-mediated recommendations pressures one of Alibaba’s most profitable engines.

According to CIW’s analysis, Alibaba’s China E-commerce Group adjusted EBITA fell 40% YoY to RMB24 billion in the March 2026 quarter. That earnings base now supports Qwen app user acquisition, quick commerce expansion, and the broader AI push.

Alibaba is funding the AI layer from a weaker commerce core. The AI layer, if it works as promised, can reduce the value of the advertising revenue that core depends on.

ByteDance faces the same pressure from the other side. Douyin’s advertising revenue, driven by auction-based placement, is ByteDance’s primary commercial engine. Connecting Doubao to Douyin’s commerce layer places an AI decision system inside a platform that sells merchant visibility.

The tension is direct. The AI assistant earns user trust by making neutral recommendations. The ad layer earns money by selling ranking advantage.

Alibaba and ByteDance have both signaled that paid placements will stay out of AI recommendation results at launch. That buys time. It does not solve the business model.

A neutral AI assistant can reduce the return on merchant ad spending, especially for large brands that have spent years buying keyword visibility. A sponsored AI assistant risks losing the trust that made users try it in the first place.

A compromised AI shopping assistant becomes a familiar product with a new interface: search, but dressed as conversation.

That is why the platforms are quiet on monetization. The product can launch before the revenue model is settled. Trust cannot be rebuilt as easily.

GEO is moving into the black box

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Alibaba reported March quarter 2026 results on May 13. The headline revenue figure of RMB243.4B represents a 3% increase YoY. The number is misleading. Excluding divested businesses Sun Art and Intime, Alibaba grew 11% on a like-for-like basis. Reported revenue is depressed by the absence of assets Alibaba chose to shed.

The more consequential numbers sit elsewhere. Cloud Intelligence Group AI-related products generated RMB8.97B in revenue, an annualized run rate of approximately US$5.2B. The segment posted its 11th consecutive quarter of triple-digit YoY growth. At the same time, adjusted EBITA fell 84% YoY to RMB5.1B.

Free cash flow turned to a RMB17.3B outflow from a RMB3.7B inflow one year earlier. Non-GAAP net income, which strips out investment gains and non-cash items to approximate operational earnings power, reached RMB86M, effectively zero, against RMB29.8B in the prior year.

Alibaba is funding three simultaneous bets: AI model development and cloud infrastructure, quick commerce market share, and Qwen consumer app user acquisition.

The quarterly cost of that combination is now visible. The strategic question is whether one bet (AI cloud) justifies the cost of running all three at once.

Cloud is the proof point, and the scale is now globally meaningful

Cloud Intelligence Group is the only segment where Alibaba’s AI investment is generating commercial validation this quarter. Revenue reached RMB41.6B (US$6.0B), up 38% YoY. External customer revenue accelerated to 40% YoY growth, driven by AI-related product adoption within the public cloud.

AI-related product revenue reached RMB8.97B, representing approximately 30% of Cloud Intelligence Group revenue. Annualized, the AI run rate sits at approximately US$5.2B.

According to Alibaba’s earnings release, the segment posted its 11th consecutive quarter of triple-digit YoY growth in AI-related cloud products. Compounding triple-digit growth across 11 quarters implies the base level in early 2023 was negligible. The current quarterly figure is now large enough to reshape competitive dynamics.

Global calibration matters. Microsoft disclosed an AI revenue run rate of approximately US$37B in late April, growing 123% YoY, according to Microsoft’s Q3 FY2026 release. AWS reported an AI run rate exceeding US$15B with triple-digit growth.

Google Cloud crossed US$20B in quarterly revenue at 63% YoY growth, with AI as the primary driver. Alibaba’s AI cloud business at approximately US$5.2B annualized is smaller than each of these. The growth rate is comparable to the hyperscalers, and the trajectory of 11 consecutive triple-digit quarters has no peer.

Within China, Alibaba Cloud is the unambiguous leader. Huawei Cloud’s external cloud revenue actually declined 3.5% in 2025 to RMB32.2B, according to Omdia, even as the Chinese cloud infrastructure market grew 26% YoY in Q4 2025. Alibaba is capturing share in a market where the #2 player is shrinking.

Cloud Intelligence Group adjusted EBITA grew 57% YoY to RMB3.8B. The segment is growing and becoming more profitable at the same time. EBITA margin expanded meaningfully as revenue scale began absorbing fixed infrastructure costs. For fiscal year 2026, Cloud Intelligence Group revenue reached RMB158.1B, up 34% YoY, with adjusted EBITA growing 35% to RMB14.3B.

The Model Studio platform, Alibaba’s MaaS offering, saw its customer base expand 8x YoY as of March 2026. The platform hosts Qwen3.6-Plus, enterprise agents including Wukong and Meoo, and industry-specific models. The breadth of that offering is a direct response to enterprise demand for flexible deployment across model sizes and use cases.

Qwen’s positioning: reasoning, coding, and multimodal expansion

Alibaba launched Qwen3.6-Plus during the quarter. The model delivers what the company describes as state-of-the-art results across front-end web development and complex repository-level tasks.

It features a native context window of up to 1 million tokens, enhanced multimodal reasoning, and improved stability. The context length positions it competitively for long-document enterprise workflows.

Complementing the core Qwen family, Alibaba introduced two specialized models. HappyOyster is a world model enabling real-time creation and interaction. HappyHorse handles video generation.

Both are in phased commercial rollout. The multimodal expansion reflects Alibaba’s assessment that enterprise AI applications will increasingly require vision, video, and world-simulation capabilities alongside text.

T-Head silicon: a strategic hedge against domestic chip dependency

The competitive context for Alibaba’s chip strategy has shifted dramatically. Nvidia’s share of the Chinese AI accelerator market has collapsed to zero, confirmed publicly by CEO Jensen Huang and reported by Tom’s Hardware.

Huawei has filled the vacuum. Its Ascend chip business is targeting US$12B in revenue in 2026, up from US$7.5B in 2025, with major Chinese tech companies including Alibaba, Tencent, and ByteDance among the buyers.

The dependency risk is real. DeepSeek V4 was optimized specifically for Huawei’s Ascend architecture, with Huawei engineers collaborating directly on kernel-level integration.

Alibaba Cloud deployed V4 inference services within hours of release. Morgan Stanley projects Chinese suppliers will capture 86% of China’s AI chip market by 2030. Alibaba is exposed to single-vendor concentration risk on its most critical input.

T-Head, Alibaba’s proprietary chip design subsidiary, is the strategic hedge. Over 100,000 Zhenwu PPUs have been deployed on Alibaba Cloud’s public cloud platform. More than 30 automakers and autonomous driving companies are using these chips for intelligent driving R&D.

The Zhenwu chips, combined with Alibaba Cloud infrastructure and Qwen models, form a vertically integrated stack that reduces both inference costs and reliance on Huawei silicon. Proprietary silicon is a medium-term competitive advantage. The strategic value has increased materially in the past 12 months as Nvidia’s China business closed and Huawei’s domestic dominance solidified.

The profitability collapse: two bets running simultaneously

The 84% collapse in adjusted EBITA reflects two distinct investments running at scale in the same quarter. Separating them is analytically important.

Quick commerce is the first. Revenue from quick commerce reached RMB20B, up 57% YoY, driven by Taobao Instant Commerce, which launched in April 2025. The growth rate is high. So is the burn. Quick commerce requires logistics subsidies, delivery network investment, and merchant incentives. The segment is not yet profitable on a standalone basis.

Qwen consumer app user acquisition is the second. Sales and marketing expenses rose 47% YoY to RMB53.4B, reaching 21.9% of revenue from 15.3% a year ago. A significant portion of this increase was allocated to acquiring users for the consumer-facing Qwen app.

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Alibaba integrated Taobao and Tmall e-commerce directly into the Qwen app during the quarter, creating a closed-loop AI shopping experience. The consumer Qwen app is a direct response to competitors including ByteDance and Baidu who have deployed conversational AI surfaces with large user bases.

The China E-commerce Group adjusted EBITA fell 40% YoY to RMB24B. The e-commerce core is profitable and growing. Customer management revenue grew 8% on a like-for-like basis. Its earnings are being consumed to fund the two expansion bets.

The All Others segment posted an adjusted EBITA loss of RMB21.2B, compared to a RMB3.4B loss in the same quarter of 2025. The sharp widening reflects investment in technology businesses and Qwen app user acquisition, which are classified within this segment.

Cash position and the funding gap

Free cash flow turned to a -RMB17.3B outflow in the March quarter, against a +RMB3.7B inflow a year earlier. For the full fiscal year 2026, free cash flow was a -RMB46.6B outflow against +RMB73.9B in fiscal year 2025. The swing is RMB120.5B across two annual periods.

Capital expenditure for fiscal year 2026 reached RMB126.1B, up from RMB84.3B the prior year. The bulk of that increase reflects data center and cloud infrastructure investment.

Alibaba retains substantial liquidity. Cash and other liquid investments stood at RMB520.8B as of March 31, 2026. The balance sheet can absorb continued investment at current pace for multiple years without structural stress.

The board declared an annual regular cash dividend of US$1.05 per ADS for fiscal year 2026, approximately US$2.5B in aggregate. Maintaining dividends while generating negative free cash flow signals management’s confidence in the liquidity position. It also signals a desire to preserve institutional shareholder support during the investment cycle.

The strategic logic of running three bets simultaneously

A reasonable investor reading the financials might conclude Alibaba is over-extended. Three concurrent investment cycles are running at scale: AI cloud infrastructure, quick commerce expansion, and Qwen consumer user acquisition. Each could absorb full corporate attention. Running all three at once concentrates risk.

The strategic logic is defensible. Each bet defends a different flank. AI cloud is the offensive bet, where Alibaba’s category leadership translates into compounding revenue.

Quick commerce defends the China e-commerce core against Meituan, which has been eroding share through Instashopping. Qwen consumer app responds to ByteDance’s Doubao and Baidu’s Ernie Bot, which are building large consumer AI surfaces that could become alternative distribution channels for AI-mediated commerce.

The temporal logic also matters. Each investment cycle has a finite peak. Quick commerce burn rate will fall as unit economics improve. Management explicitly highlighted improving order economics and rising average order value this quarter.

Qwen consumer app user acquisition is most expensive in the land-grab phase, before competitive equilibrium. AI cloud capital expenditure follows a multi-year infrastructure cycle. Running them concurrently compresses the pain period rather than extending it.

The risk is execution complexity. Three simultaneous bets demand attention from senior management and capital from a single balance sheet. If any one underperforms, the strategic logic fragments. The investor question is whether Alibaba can sustain disciplined execution across all three until at least one generates positive cash contribution.

Risks and signals to monitor

The financial trajectory creates near-term investor risk on three fronts.

First, the quick commerce bet carries execution risk that cloud does not. Quick commerce requires physical logistics density, merchant network effects, and sustained subsidy capacity. Ele.me and Taobao Instant Commerce are competing against Meituan’s established network in a market where logistics unit economics are hard to compress quickly.

Second, Qwen’s consumer traction is self-reported and unverified by independent data. The 8x growth in Model Studio customers is a meaningful enterprise signal. The consumer app user base is not disclosed with comparable specificity.

Third, the free cash flow outflow trajectory implies Alibaba is in a period of maximum investment intensity.Management has not signalled when the investment cycle peaks. Investors who require near-term cash generation have limited visibility on the timeline to positive free cash flow resumption.

Non-GAAP net income of RMB86M, effectively zero, means Alibaba’s operational earnings power is temporarily negligible. Investment gains inflated the GAAP net income figure to RMB23.5B. Strip those out and the operational business generated almost nothing after expenses.

Cloud is what matters. The rest is noise around the central bet.

Alibaba entered fiscal year 2026 needing to prove its AI investment thesis was real, scalable, and ultimately profitable. The March quarter answers the first two questions with hard data.

AI-related cloud revenue at a US$5.2B annualized run rate makes Alibaba a globally relevant AI cloud business. The 11th consecutive quarter of triple-digit growth and 8x expansion in Model Studio customers confirm enterprise demand is converting into cloud billings.

The profitability question remains open. The answer is increasingly visible in the segment data. Cloud Intelligence Group adjusted EBITA grew 57% YoY this quarter, with margin expansion as fixed infrastructure costs absorb across a larger revenue base.

The cloud business is profitable today and becoming more profitable with scale. The group-level earnings collapse is a consequence of quick commerce and Qwen consumer spending, not of the AI cloud business.

For institutional readers, the analytical view is this: Alibaba is the only Chinese tech company with a credible path to a top-tier global AI cloud business at scale. The cost of acquiring that position is visible and painful. The position itself is becoming durable.

Investors who require near-term cash generation will avoid the name through fiscal year 2027. Investors with multi-year horizons are watching the only Chinese company where AI cloud revenue is large enough to matter on the global comparison set. The next two quarters will reveal whether quick commerce unit economics improve fast enough to ease the cash flow pressure, allowing the cloud thesis to deliver without distraction.

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Tencent reported first-quarter 2026 results on May 13. Revenue reached RMB196.5B, up 9% YoY. Gross profit rose 11% YoY to RMB111.3B. For the first time, Tencent’s financial disclosures formally carve out “new AI products” as a separate cost center.

These products span Hy (the foundation large language model, "Hunyuan"), Yuanbao (consumer AI assistant), CodeBuddy (AI coding agent), WorkBuddy (enterprise productivity agent), and QClaw. The separation matters. It makes the cost of Tencent’s AI transition visible and measurable.

Tencent entered 2025 widely seen as a second-tier player at the foundation model layer. Moonshot’s Kimi, Alibaba’s Qwen series, and Baidu’s Ernie dominated analyst coverage of China’s large language model race. By April 2026, Hy3 preview had topped OpenRouter by token usage. The path from perceived laggard to lead took 12 months and a full rebuild of the model team.

Hy3 preview leads OpenRouter, rewriting the laggard narrative

Hy3 preview accumulated 7.7 trillion tokens on OpenRouter between April 23 and May 12, according to Tencent’s Q1 2026 results presentation.

The next closest model was Kimi K2.6 at 5.0 trillion tokens. Claude Sonnet 4.6 ranked third at 4.0 trillion. Claude Opus 4.7 registered 3.4 trillion. Gemini 3 Flash Preview reached 3.0 trillion. DeepSeek V3.2 placed sixth at 2.7 trillion.

Hy3 preview’s lead over Kimi K2.6 is 54% by cumulative token count in that window. OpenRouter routes developer traffic across multiple model providers, making token usage a direct proxy for developer adoption rather than self-reported benchmarks.

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The model was built on comprehensively rebuilt infrastructure. Tencent rebuilt and re-staffed its foundation model team over the prior six months, bringing in what management described as LLM-native researchers and engineers.

Management characterised the new team as young, energetic, and cohesive. Pre-training, reinforcement learning, and evaluation systems were each rebuilt from scratch. Evaluation criteria shifted away from widely-gamed benchmarks toward real-world use cases and out-of-distribution tests, including unseen PhD qualifying exams from Tsinghua and Princeton.

Hy3 preview has been deployed across 131 internal Tencent products, including Yuanbao, QQ, WorkBuddy, and Tencent News.

The internal deployment creates a closed-loop feedback system: real-world product usage generates signal that informs iterative improvement. No external model provider can replicate that pipeline at this scale within a single company’s ecosystem.

The caveat is material. Hy3 is a smaller-parameter preview model. Tencent’s management acknowledged it is “just the first step.” The next milestone is scaling to a larger parameter model, leveraging the infrastructure and data learnings accumulated from Hy3. No public benchmark exists for the larger model.

Productivity agents are the first real test of AI monetization

Tencent’s agentic AI layer comprises three products. CodeBuddy handles AI code generation. WorkBuddy operates productivity software with higher security and controllability than general computer-use agents. Claws is an open-sourced general computer-use agent that interacts with the web and real-world infrastructure.

WorkBuddy is the most-used productivity AI agent service in China by DAU, according to Tencent. The claim is self-reported and comparable DAU data from competitors is not publicly available. The retention metrics are more informative. Among active users, CodeBuddy and WorkBuddy achieve 60%+ retention. Among paying users, that figure rises above 80%.

High retention among paying users signals genuine productivity value rather than novelty-driven engagement. Users who commit financially tend to churn unless workflows develop dependency on the tool. An 80%+ rate at this stage of the adoption cycle suggests that dependency formation is underway.

The agent monetization flywheel is beginning to operate. High-frequency agent usage enables Tencent to identify complementary services, deploy them, and deepen the user relationship. This expands agent utility for enterprise and prosumer users. As tasks grow more complex, paying user conversion increases. The downstream effect is accelerating token consumption on Tencent Cloud.

Tencent noted “rapid growth in token usage on Tencent Cloud in recent weeks” without disclosing specific figures. The agent-to-cloud revenue link is forming. Precise disclosure of token revenue contribution is a metric to watch in subsequent quarters.

The AI margin drag is quantified and contained

Q1 2026 is the first quarter investors can measure the direct cost of Tencent’s AI push. The arithmetic is clear.

Non-IFRS operating profit excluding new AI products grew 17% YoY to RMB84.4B. Non-IFRS operating margin excluding new AI products reached 43.0%, up from 39.9% a year earlier. Including new AI products, operating profit grew 9% YoY to RMB75.6B. Operating margin held stable at 38.5%.

The gap is approximately 4.5 percentage points of margin. The figure represents the current quarterly cost of Tencent’s AI product investment, expressed as forgone operating profit.

Selling and marketing expenses grew 47% YoY to RMB11.3B, driven by AI product promotion. Research and development costs rose 20% YoY to RMB22.6B. Capital expenditure reached RMB31.9B, up 16% YoY. Despite that spending, free cash flow grew 20% YoY to RMB56.7B. Net cash position rose 63% YoY to RMB146.9B.

The free cash flow figure is strategically significant. The core business generates sufficient cash to fund the AI build while maintaining share buybacks. Tencent repurchased approximately 12.7 million shares for a consideration of approximately HKD7.6B during Q1 2026.

The margin drag will likely widen before it narrows. The larger-parameter Hy model is in active development. Agent monetization is early. Infrastructure investment is ongoing. Investors should treat the current 4.5ppt drag as a floor rather than a ceiling.

Marketing services accelerated, validating AI-improved ad targeting

Marketing services revenue grew 20% YoY to RMB38.2B in Q1 2026, accelerating from 17% YoY growth in Q4 2025. The driver is a combination of improved AI-driven ad recommendation models and expansion of closed-loop commerce within the Weixin ecosystem.

AIM+, Tencent’s automated campaign management solution, powered approximately 30% of total marketing services advertiser spending in Q1 2026. The tool has gained traction specifically among mini game, mini drama, and Mini Shops advertisers, where closed-loop attribution is most direct.

Video Accounts’ total time spent grew over 20% YoY. Ad impressions grew rapidly as both total time spent and ad load increased. A new instant-play ad format for mini games embeds native gameplay inside the ad unit. This eliminates redirections and improves conversion rates for mini game developers.

Weixin Search query volume increased over 25% YoY, driven by LLM-powered ranking. The increase signals AI improvements to the search product are translating into measurable user behavior change, not just backend efficiency gains.

Marketing services at RMB38.2B is now the fastest-growing major segment. The AI-improved ad engine is subsidizing the AI build. The self-funding loop Tencent’s architecture requires is now active: better models improve ad targeting, ad revenue funds model development, and the cycle reinforces itself.

Tencent’s architecture differs from Alibaba and Baidu

Alibaba’s approach is cloud-infrastructure-first: Qwen models anchor the Alibaba Cloud value proposition and drive enterprise cloud migration. Baidu’s approach is vertical-first: Ernie is embedded into search, autonomous driving, and maps.

Tencent’s approach is distribution-layer-first: AI agents run on Weixin, QQ, WeCom, QQ Browser, and Yuanbao, with users interacting through familiar surfaces rather than new destinations.

Combined MAU of Weixin and WeChat reached 1.43B, up 2% YoY. The installed base is a structural advantage at the agent distribution layer. Users do not need to learn a new interface to access Tencent’s AI.

The Mini Programs ecosystem is a moat specific to Tencent. Over time, Mini Programs code can evolve into AI Skills: pre-built, context-aware tools deployable by agents within Weixin. Brand merchants on Mini Shops more than tripled their GMV YoY in Q1 2026. Mini Shops eCommerce transaction fees contributed to Business Services revenue growth of 20% YoY.

Tencent Cloud’s international revenue grew over 40% YoY, with global footprint spanning 65 Availability Zones. AI-related demand contributed to growth across GPU, CPU, and storage. At this growth rate, Tencent Cloud’s international operations are outpacing the domestic core. The enterprise AI agent proposition is gaining traction in markets outside China.

The distinction between the three Chinese tech giants’ AI architectures carries investment implications. Alibaba’s returns will be concentrated in cloud billings. Baidu’s will be concentrated in vertical monetization. Tencent’s will be concentrated in agent-layer activity across the existing user base. Q1 2026 results offer the first hard data on which architecture is converting capability into commercial momentum.

Risks and signals to monitor

The Hy3 preview OpenRouter lead warrants scrutiny on three fronts.

First, cumulative token usage on a developer routing platform does not directly translate to enterprise or consumer revenue. Developer adoption and commercial monetization are separate milestones.

Second, Hy3’s 54% lead over Kimi K2.6 may partly reflect novelty adoption from a new entrant. Third, the larger-parameter Hy model that Tencent is now developing has not been publicly benchmarked.

Revenue growth decelerated to 9% YoY from 13-15% across the three prior quarters. Domestic Games grew 6% YoY with Spring Festival timing distorting recognition. Fee-based VAS subscriptions declined 0.7% YoY to 266M, suggesting subscription monetization intensity within the platform is near a ceiling.

The fair value of listed investee shareholdings fell from RMB672.7B at year-end 2025 to RMB547.1B at March 31, 2026. The RMB125.6B decline in one quarter reflects broader market movements. It represents a meaningful erosion of Tencent’s investment portfolio value.

The 4.5ppt AI margin drag could widen materially if the larger Hy model requires significantly higher inference costs, or if productivity agent token consumption growth outpaces monetization conversion.

The architecture is functioning. The question is what comes next.

Tencent spent much of 2025 being assessed as a foundation model also-ran. Q1 2026 delivers three counters to that assessment: Hy3’s OpenRouter lead, WorkBuddy’s retention metrics, and 17% operating profit growth from the core business before AI costs.

None of these resolves whether the larger Hy model will hold its position. Nor does it confirm WorkBuddy’s DAU lead will survive pressure from Alibaba and ByteDance.

What Q1 2026 establishes is that the architecture is functioning. The distribution layer is distributing. The model is being used. The remaining question is whether the current trajectory sustains when the AI products are no longer excluded from the margin calculation.

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China’s mobile internet industry publishes impressive aggregate numbers each quarter. The headline figures for Q1 2026 are no exception. But the data from QuestMobile for March 2026 tells a more nuanced story. Total user growth has slowed to a near-standstill. What is expanding rapidly is the depth, demographic composition, and platform diversity of engagement.

QuestMobile is one of China’s leading third-party mobile data analytics firms. Its quarterly reports are widely used by brand advertisers, platform operators, and institutional investors to track user behaviour across China’s app ecosystem. The Q1 2026 dataset covers over 1.28B mobile internet users and tracks time-on-platform, sector-level growth, device penetration, and demographic shifts.

The platform scale era is over. What follows is a competition for time, intent, and demographic capture.

User saturation arrives, but the composition is shifting

Total mobile internet users in China reached 1.28B in March 2026, according to QuestMobile, up just 1.4% YoY. That growth rate signals structural saturation at the population level. The incremental user pool is nearly exhausted.

The demographic profile of that remaining growth is telling. Users aged 46 and above grew their share of the total user base by 2.1 percentage points YoY. This reflects an older cohort that has migrated online gradually and is now participating fully in the mobile ecosystem.

Device pricing data supports the same structural reading. Users holding devices in the RMB 2,000 to 4,999 range grew their share by 2.1 percentage points YoY. Both the premium segment (above RMB 5,000) and the low-end segment (below RMB 2,000) are losing relative share. Mid-range device ownership is becoming the baseline. It shapes which content, services, and app categories this expanded user base engages with.

The implications for product strategy are direct. Apps designed around premium-device UX or youth-skewed content assumptions are increasingly misaligned to where user growth is actually occurring.

AIGC leads sector growth by a wide margin

AI-generated content (AIGC) is the industry classification used in China for apps built natively around generative AI capabilities. It encompasses AI assistants such as Doubao (ByteDance), Yuanbao (Tencent), and Qianwen (Alibaba), as well as AI video generation tools and smartphone-embedded AI features.

AIGC user growth hit 61.7% YoY in Q1 2026, according to QuestMobile, making it the fastest-growing sector in China’s mobile internet by a considerable distance. Flying shooter games ranked second at 35.4% YoY. File transfer tools reached 25.0% YoY. Comprehensive wealth management apps grew 22.7% YoY. Car services expanded 21.0% YoY.

Critically, AIGC’s user growth is no longer concentrated in the core demographic. Male users now account for 64.6% of the AI-native app base, up 4.5 percentage points YoY, with 120M net new male users entering the category.

At the same time, users born in the 1960s (currently in their late 50s to mid-60s) grew their share of the AIGC base by 1.7 percentage points. Users from Tier 3 cities and below grew their share by 2.4 percentage points.

This demographic broadening matters for monetisation. AI apps that built initial scale on young, urban early adopters are now reaching audiences with different content preferences, different purchasing behaviour, and different retention triggers. The product strategies that drove initial growth may not be the ones that sustain the expanded base.

Stickiness, not scale, is the new growth metric

User growth is plateauing. Engagement depth is not.

Monthly per-capita usage time across China’s mobile internet reached 192.2 hours in March 2026, up 9.3% YoY, according to QuestMobile. That aggregate figure conceals extreme variation at the sector level.

The top five sectors by YoY growth in monthly per-capita time-on-platform were: comprehensive wealth management at 78.8%, AIGC at 41.4%, online video at 30.9%, audiobook platforms at 28.3%, and flying shooter games at 28.0%.

Each category reflects a distinct demand driver. Wealth management’s 78.8% time-on-platform surge is consistent with elevated trading and investor activity reported across Chinese financial platforms in early 2026.

Users are not merely checking balances. They are actively monitoring positions, executing trades, and consuming financial content. For platforms in the brokerage and fintech space, this represents a high-intent, high-frequency engagement profile.

AIGC’s 41.4% time-on-platform growth reflects utility deepening. Early AIGC adopters used these tools episodically. The data now indicates habitual use patterns forming across a broader demographic base.

Online video and audiobooks reflect content consumption as a structural need, not a cyclical preference. The 30.9% and 28.3% YoY time growth in these categories occurred despite a mature competitive landscape and high existing penetration. That is a durability signal.

Mobile video consolidates above 40% of total engagement time

Content entertainment is the single largest claimant of Chinese mobile internet time.

According to QuestMobile, mobile video apps now account for over 40.3% of total time spent across China’s mobile internet. Within that, the split between short video and long-form online video holds at approximately 8:2 by user time.

Douyin and Kuaishou maintain commanding positions in the short-video segment. iQIYI, Youku, and Tencent Video anchor the long-form segment.

AI video generation tools are introducing a new content layer that directly feeds time-on-platform growth.

Jimeng AI’s launch of Seedance 2.0 drove measurable daily active user gains. Kling AI’s monthly active users rose 53.2% MoM. These tools are compressing the cost and skill barrier for video content creation, accelerating the emergence of new formats.

A direct beneficiary is the animated short drama format, known in China as manga drama.

These series blend stylised animation with short-episode narrative structures. They have attracted an audience that traditional drama formats did not reach. According to QuestMobile, Hongguo Free Manga Drama’s monthly active user base exceeded 25M. Huolong Manga Drama surpassed 5M users in its second month after launch.

The viewer profile is notable.

Manga drama users skew toward older male audiences and middle-aged cohorts who previously showed low engagement with conventional drama content. This is a net-new engagement pool.

For advertisers and content investors, it signals a viable route into a demographic that was previously resistant to long-form streaming. It also illustrates how AI-assisted content production unlocks audience segments that established formats had left behind.

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Smart TVs and NEVs open new traffic surfaces

China’s mobile internet has always been defined by the smartphone. That framing is now incomplete.

According to QuestMobile, smart TV monthly active devices reached 300M in February 2026 and held at 293M in March 2026, representing a net YoY increase of over 10M active devices.

Chinese New Year drove a sharp spike in large-screen engagement. Elevated levels then persisted through March. This suggests a durable behavioural shift rather than a seasonal event.

New energy vehicle in-car systems showed comparable momentum. Monthly active NEV units reached 42.12M in March 2026, a net YoY gain of 11.21M vehicles. As NEV penetration in China continues to rise, the in-car screen is becoming a meaningful content and commerce surface.

Platforms with a presence on both smartphone and NEV or large-screen surfaces hold a structural reach advantage that pure-mobile players cannot replicate. Media planning built exclusively around smartphone inventory now misses a growing and measurable audience segment.

The structural opportunity for allocators and operators

China’s mobile internet is not growing in the way it grew between 2015 and 2022. The user acquisition era is over. What the QuestMobile data describes for Q1 2026 is a market reorganising around three structural dynamics.

First, demographic extension. The 46-plus cohort and lower-tier city users are growing their share of total internet participation. Products calibrated for this audience gain access to incremental users that others cannot reach.

Second, engagement concentration. AIGC, wealth management, and content entertainment are capturing a disproportionate share of available user time. Sectors outside this cluster face a structurally tightening attention environment.

Third, terminal diversification. Smart TVs and NEV dashboards are material surfaces. At 293M and 42.12M monthly active units respectively, they represent real reach. Platforms secured on both mobile and large-screen or in-vehicle surfaces hold a structural advantage in audience assembly.

The competition for user time has replaced the competition for user count as the defining metric of China’s mobile internet.

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16 data charts to see the rising mobile apps

AI has become the clearest growth engine. AIGC was the fastest-growing mobile internet sub-sector by MAU in March 2026, reaching about 446 million users with 61.7% YoY growth. AI-native apps also expanded rapidly, with average MAU rising from about 224 million in Q1 2025 to 413 million in Q1 2026, up 84.1%.

Within AI apps, a few leaders are pulling far ahead. Doubao, Qwen, DeepSeek, and Yuanbao dominate the AI-native app rankings, while Qwen stands out as an extreme growth outlier among large-scale apps, with more than 4,000% YoY growth. This suggests China’s consumer AI market has moved from curiosity to mass adoption.

The “Claw Agent” charts show that AI agents are still early but strategically important. Desktop agent products such as WorkBuddy, QoderWork, AutoClaw, JVSClaw, and QwenPaw have much smaller user bases than AI apps, but they reflect a shift from chat-based AI toward task execution, workflow automation, and enterprise productivity.

Short video remains the strongest attention engine. The mobile video app industry reached about 1.223 billion MAU, with short-form video accounting for 79.1% of mobile video time spent. Douyin alone captured 44.3% of total mobile video app time spent, while Douyin Lite and Hongguo Free Short Drama showed notable gains.

Entertainment growth is broadening beyond short video. Online music showed strong pockets of growth, especially Ximalaya Music, NetEase Cloud Music, and Bilibili Music, while the mobile video category showed rising engagement from short drama and lighter video formats.

Social networking is mature but still massive. WeChat, QQ, Weibo, Xiaohongshu, and Momo remain the top social platforms by MAU, but the growth picture is mixed. Mature platforms show low or negative growth, while smaller community or interest-based platforms show more volatility.

Travel services show a multi-channel traffic structure. Apps remain important for platforms such as Ctrip, Qunar, Fliggy, and Tuniu, but WeChat mini programs dominate traffic for some players, especially Tongcheng Travel. This suggests travel platforms rely heavily on ecosystem distribution, not only standalone app usage.

The internet advertising market remains resilient but not explosive. China’s internet advertising market reached about RMB 168.4 billion in Q1 2026, with 5.8% YoY growth. Q4 remains the seasonal peak, but growth appears moderate, suggesting advertisers are still spending while becoming more performance-driven.

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$60B in three months. That is what Chinese drug developers signed in outbound licensing deals during Q1 2026. The figure approaches half of 2025’s full-year total.

These are signed contracts, not projections.

The individual deals illustrate the scale. In January, CSPC and AstraZeneca signed a licensing agreement for obesity and weight management assets worth a potential $18.5B. AstraZeneca paid $1.2B upfront. RemeGen and AbbVie reached a $5.6B licensing agreement the same month.

In February, Innovent and Eli Lilly completed their seventh collaboration, with milestones reaching up to $8.5B. In March, Sino Biopharm out-licensed rovadicitinib to Sanofi for $1.53B, a record for Chinese pharma in the transplant indication. Rovadicitinib is a first-in-class oral JAK/ROCK inhibitor.

These transactions follow a five-year trend. According to Nature, China’s share of global licensing deal value rose from roughly 3% in 2020 to over 30% in 2024.

In absolute terms, that is a move from approximately $5B to over $50B in annual deal flow. A tenfold rise in five years.

Three structural forces explain the trajectory.

The patent cliff forces the hand

The most quantifiable pressure on global pharma is also the most urgent. According to Morgan Stanley, multinational drugmakers face $115B in revenue erosion by 2035 as blockbuster patents expire. The gap requiring coverage before 2030 reaches $40B.

The assets at risk are not obscure. Merck’s pembrolizumab and Bristol Myers Squibb’s nivolumab are among the most exposed. These are the revenue pillars that fund the rest of each company’s pipeline.

The structural problem runs deeper than individual products. According to Nature, the top 20 pharmaceutical companies approved 168 new drugs between 2011 and 2020. Just 36 of those accounted for 70% of total new-drug sales.

The top 7 contributed 28% of all new-drug income. Losing two or three core assets creates a disproportionate revenue hole.

External licensing is the fastest route to replacement. Internal R&D cycles run 10 to 15 years. Licensing a Phase II or Phase III asset compresses that to five years or fewer.

For companies facing cliff dates in 2028 or 2029, licensing is faster and cheaper than building internally.

China became the logical destination. According to Nature, 11 top multinational companies deployed over $150B across Asia between 2020 and 2025. 65% of that went to China.

Over 50 clinical-stage or applicable assets were licensed in from the region over the period. The majority originated in China.

Why China, not India or Korea

The patent cliff created demand. China’s specific position in next-generation drug technology created the match.

The platforms reshaping oncology today include ADC, bispecific antibodies, T-cell engagers, and CAR-T therapies. They are displacing traditional small-molecule drugs as the primary source of new approvals and revenue.

China entered these platforms without legacy drag. Western incumbents built decades of infrastructure, expertise, and organizational identity around small-molecule chemistry. Pivoting toward biologics and next-generation platforms requires writing down those assets.

The larger the legacy franchise, the heavier the transition cost. Chinese developers started from a different baseline. With no equivalent infrastructure to protect, capital went directly into ADC, bispecific, and CAR-T programs.

The result is a pipeline that aligns precisely with where global demand is concentrated now.

The scale of China’s position is measurable. Over 50% of global clinical-stage ADC programs are run by Chinese developers. In first-in-class approvals, Chinese teams produced 4 of 11 globally novel mechanisms approved in 2025. A decade ago, that category barely existed in China.

The clinical evidence is specific:

• First global Trop2 ADC for lung cancer: Kelun-Biotech’s sacituzumab tirumotecan. Western Trop2 programs had focused on breast cancer.

• First PD-1/CTLA-4 bispecific approved: Akeso’s cadonilimab, with Phase III advantages over standard PD-1 combinations in cervical and gastric cancer

• First Chinese-origin CAR-T approved in the US: Legend Biotech’s ciltacabtagene autoleucel, reaching nearly $2B in global sales in 2025

• First bispecific T-cell engager with published Lancet data: Baili-Tianheng’s BL-B01D1, targeting two antigens simultaneously to address single-target resistance

Alternatives do not compare. India’s pharmaceutical sector is built around generic manufacturing with limited innovative pipeline. Korea has competitive contract manufacturing capability but a narrower innovation base. Its domestic market cannot sustain large-scale novel drug R&D at the required pace. Southeast Asia relies on imported active ingredients and core technology across the board.

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The global cloud infrastructure market has crossed $222B, and the growth engine is no longer enterprise digitalisation. According to Gartner’s 2025 IaaS market share data, Alibaba Cloud extended its lead in China, accelerated in Asia-Pacific, and gained ground globally. Revenue grew at roughly 35% in a market expanding at 25%.

The pattern signals a phase transition in how cloud market share is won. Scale and full-stack AI capability are compounding faster than at any prior point in the cycle.

China market: a structural lead extends

Alibaba Cloud’s China IaaS share reached 32.8% in 2025, up from 30.1% in 2024. The 2.7 percentage point gain widens the lead over the next tier of Chinese cloud providers.

Revenue growth of 34.4% YoY placed Alibaba Cloud more than 10 percentage points above the Chinese IaaS market’s 23.4% overall expansion.

The competitive context is real. Huawei Cloud, Tencent Cloud, and Baidu AI Cloud all compete at scale. Huawei in particular is investing heavily in AI infrastructure tied to its own chip and software stack.

The advantage sits in AI-native infrastructure: GPU clusters, model serving capacity, AI-optimised networking, and integrated tooling. Alibaba Cloud’s Tongyi model family and DAMO Academy research arm support a vertically integrated stack that pure-play infrastructure vendors cannot match.

Asia-Pacific: a $75.2B market with a clear leader

The Asia-Pacific IaaS market reached approximately $75.2B in 2025, growing 25.3% YoY. Alibaba Cloud’s regional share rose from 20.8% to 22.5%, implying roughly $16.9B in APAC IaaS revenue.

Alibaba Cloud’s Southeast Asia infrastructure, anchored by data centres in Singapore, Indonesia, and Malaysia, positions it as a credible local alternative to US hyperscalers. AI demand is amplifying the advantage. Regional enterprises often prefer providers with proximity and local compliance familiarity.

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The Stanford AI Index 2026, released in April, is among the most widely cited annual benchmarks of AI progress. Its 423 pages document a consistent pattern. China and the United States are converging on capability metrics. They are diverging on structural indicators that predict long-term leadership. For investors tracking China, the report separates signal from noise across five dimensions.

The model performance gap has effectively closed

As of March 2026, the top U.S. model leads the top Chinese model by just 2.7% on the Arena benchmark. Anthropic’s Claude Opus 4.6 scores 1,503. China’s Dola-Seed-2.0 Preview scores 1,464. The gap was far wider in 2023.

In February 2025, DeepSeek-R1 briefly closed within 0.4% of the leading U.S. model. Over the past year, the performance gap has fluctuated between near parity and low single digits.

This convergence has emerged from structurally different innovation environments, the report notes.

The U.S. still produced more top-tier models in 2025: 50 notable models to China’s 30. The absolute count matters less than performance at the frontier. The Arena leaderboard shows that China’s top models are competitive with all but the very best U.S. systems.

China dominates volume; the U.S. retains influence

The patent and publication data reveal a structural split likely to shape the next decade.

China holds 74.2% of global AI patents granted in 2024, up from under 20% a decade earlier. The United States accounts for 12.1%. China holds 97,990 AI patents, against 15,920 for the U.S.

On a per capita basis, South Korea leads at 14.3 patents per 100,000 people. China comes third at 7.0, ahead of the United States at 4.7.

In research publications, China accounted for 17.8% of AI papers in 2024, against 7.3% from the United States. Chinese AI publications also represented 20.6% of all AI citations globally, compared with 12.6% from the U.S. China’s share of the top 100 most-cited AI papers grew from 33 in 2021 to 41 in 2024.

The influence asymmetry cuts the other way. The U.S. accounts for more than 50% of all AI patent forward citations.Later inventions build more heavily on American foundational work than on Chinese patents. Chinese patents are frequently cited in U.S. filings. U.S. patents appear far less often in Chinese ones.

On the publication side, China’s government institutions contributed 25.1% of research output. In the U.S., industry leads at 24.6%. This reflects the different structural roles of the state in each country’s R&D pipeline.

Chinese AI research, while prolific, is not yet setting the reference standards that define technological direction globally.

The investment gap is large but understated

U.S. private AI investment reached $285.9B in 2025, more than 23 times China’s $12.4B in private funding. In generative AI specifically, U.S. investment exceeded the combined total of China and Europe.

The AI Index is explicit that this comparison understates China’s actual spending. Chinese government guidance funds have deployed an estimated $184B into AI firms between 2000 and 2023. These vehicles do not appear in private investment figures. The total AI capital deployment comparison between the two countries is therefore considerably closer than the private-funding gap suggests.

The structural difference matters. U.S. capital flows primarily through venture and growth equity, concentrating on frontier model companies and AI infrastructure. Chinese state capital operates through different vehicles with different timelines and incentives.

The U.S. created 1,953 newly funded AI companies in 2025, more than 10 times any other country. China’s entrepreneurial formation rate is not disclosed in the report. Organizational AI adoption data does show a different pattern. China and Europe posted the highest year-over-year increases in 2025 for generative AI use across business functions.

China’s robotics lead is widening

Industrial robotics is the dimension where China’s lead is most pronounced and accelerating fastest.

China accounted for 54% of global industrial robot installations in 2024, up from 51.1% in 2023. Global year-over-year growth was flat. The United States, Germany, and Italy all saw declines. China installed more industrial robots than the rest of the world combined. The margin is growing.

In humanoid robotics, Chinese vendors are scaling production at price points and unit volumes that resemble consumer hardware. Unitree’s R1 is priced from $4,900 and its G1 from $13,500.

AgiBot has manufactured approximately 10,000 units. Around 100 of its teleoperated humanoids run up to 17 hours per day. UBTECH’s Walker platforms integrate LLM-based planning with autonomous battery swapping for industrial deployment. Fourier Intelligence and DeepRobotics are also active in the segment.

By contrast, U.S. humanoid programs are operating in pilot mode. Figure AI’s Figure 02 spent 11 months on a BMW production line in South Carolina. It logged over 1,250 runtime hours and loaded more than 90,000 parts.

The scale and cost trajectory in China suggests Chinese hardware economics will define the next phase of industrial automation.

China surges in responsible AI research

China overtook the United States in responsible AI (RAI) research papers at major AI conferences in 2025.

China produced 812 accepted RAI papers at select conferences in 2025, compared with 394 from the United States. This reversed the 2024 picture, when the U.S. led with 788 papers to China’s 322. The AI Index notes that the reversal is consistent with China’s broader lead in AI publication volume. The scale and speed of the shift are nonetheless notable.

Over the full 2019-2025 cumulative period, the United States still holds the largest total of accepted RAI papers. China’s jump in 2025 is best understood as a structural shift in research focus. Chinese institutions are increasingly publishing on alignment, bias, interpretability, and governance.

What the data tells institutional investors

The AI Index 2026 maps a consistent picture across five dimensions.

China leads on volume: patents, publications, citations, and robot installations. The U.S. leads on influence: patent forward citations, notable model count, and private capital formation.

The frontier model performance gap is narrow but persistent. China is closing on capability, with model performance effectively at parity at the top of the leaderboard.

The U.S. retains a lead on talent attraction, though the number of AI researchers moving to the U.S. has dropped 89% since 2017. On governance, both countries remain outside formal international readiness assessments. Global trust in both trails trust in the EU.

For investors, the practical implications are sector-specific. In foundation models, the gap is narrow enough that China’s top-tier labs warrant tracking as direct competitors to U.S. frontier systems.

In industrial AI and robotics, China’s deployment scale and cost structure form a first-mover position hard to close. In responsible AI and governance, China’s increasing research presence signals intent to shape the emerging regulatory architecture. That has direct implications for standards-setting across Asian and developing-market jurisdictions.

The U.S.-China AI competition is no longer a story of one leader and one follower. It is a story of two systems strong on different dimensions, on different timelines, with different capital structures.

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The China Internet Network Information Center (CNNIC) publishes its Statistical Report on Internet Development twice a year.

The 57th edition, released in February 2026, covers data through December 2025. It is the closest Chinese equivalent to a Pew Research baseline on digital adoption — the authoritative census of who is online, what they use, and how Chinese internet infrastructure is evolving.

Its industrial AI and capital allocation data is drawn from IDC enterprise research and Ministry of Industry and Information Technology (MIIT) reporting. Most international coverage of the 57th report anchored on the consumer headline. The more consequential data sat two chapters deeper.

CIW Premium subscribers can download a more comprehensive report (April issue) here.

China’s generative AI user base: real scale, familiar shape

As of December 2025, 602M Chinese had used a generative AI product in the prior six months. That is up 353M from December 2024, a 141.7% increase. Population penetration reached 42.8%, a 25.2 percentage-point gain in twelve months.

The user count is real. It is also the wrong anchor for competitive analysis.

The St. Louis Federal Reserve’s November 2025 survey put US generative AI adoption at 54.6% of working-age adults, up roughly 10 percentage points YoY. China is behind on penetration, not ahead. GenAI adoption has outpaced both PC and internet diffusion curves globally — PC adoption sat at 19.7% three years after its mass-market launch; internet adoption at 30.1%. The speed is a worldwide phenomenon.

Use patterns are nearly identical across both markets. CNNIC reports 76% of Chinese GenAI users treat it primarily as a question-answering tool. 47.8% generate or process images and video. Just 10.8% generate code. OpenAI’s May 2025 analysis of 1.5M ChatGPT conversations found the same shape: 49% asking questions, 40% getting work done, 11% exploring ideas. Both markets remain in the shallow-tooling phase.

Consumer GenAI in China is additive tooling, not category-redefining behavior. The same is true in the West. The divergence begins one layer down.

Where China’s adoption curve actually diverged

Chinese industrial enterprise AI and agent penetration reached 47.5% in 2025, up from 9.6% in 2024. The figure originates from IDC enterprise research. NDRC cited it in its 29 August 2025 policy briefing. CNNIC reproduces it in the 57th report’s chapter on generative AI industry development.

A nearly fivefold increase in one year warrants scrutiny. McKinsey’s State of AI 2025 survey, based on 1,993 respondents across 105 countries, provides the benchmark: 88% of organizations now use AI in at least one function, up from 78% in 2024. But only 23% are scaling AI agents. Just 6% qualify as high performers attributing more than 5% of EBIT to AI.

The methodologies differ. McKinsey surveys enterprise respondents globally across functions. The IDC figure focuses on Chinese industrial enterprise deployment specifically. A clean comparison is not possible. The directional signal is clear. Global enterprise AI deployment remains stuck in the pilot loop McKinsey describes. Chinese industrial enterprises are moving from pilot to production at a materially faster pace.

The CNNIC report breaks down deployment across three functional buckets: research and design (33%), production manufacturing (24%), and management and operations (32%). The R&D-heavy distribution suggests Chinese industrial AI is concentrated in functions where productivity gains compound — not in the customer-facing use cases that dominate Western enterprise GenAI.

MIIT’s lighthouse factory program is the proof point. At designated lighthouse sites in 2025, AI penetrated more than 70% of business scenarios. Over 6,000 vertical-domain industrial models are deployed across the program. More than 1,700 smart-manufacturing equipment and software integrations have reached production scale.

If the penetration figure is directionally correct, Chinese manufacturing is compounding an AI productivity premium. Western industrial competitors cannot match it at current compute cost structures.

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Two months after going public at a $6.5B valuation built on open-source credibility, MiniMax has locked its latest model behind a paywall. M2.7, released March 18, is the company’s first proprietary model.

It matches top US systems in software engineering at 1/50th the cost. And it helped build itself.

MiniMax is one of China’s “AI Tigers,” a cohort of foundation model startups racing to match US frontier labs. Yan Junjie, former vice president of SenseTime, founded the Shanghai-based company in 2022.

Its products span text, video, speech, and music AI, reaching 27.6M monthly active users across more than 200 countries. Over 70% of revenue comes from outside China, with the US accounting for roughly 20%.

MiniMax listed on the Hong Kong Stock Exchange on January 9, 2026, raising approximately $620M at HK$165 per share, according to Bloomberg. Shares doubled on debut, backed by cornerstone investors including Alibaba and Abu Dhabi’s sovereign wealth fund. Rival Zhipu AI had debuted one day earlier.

The company built its developer reputation on open-source models. M2, released in October 2025 under an MIT license, ranked first among all open-source systems on the Artificial Analysis Intelligence Index. M2.5 followed in February 2026, also open-source.

M2.7 breaks that pattern.

The shift carries implications for developers, investors, and the competitive landscape of Chinese AI.

Open-source to proprietary, a strategic U-turn

Before M2.7, MiniMax was a standard-bearer for open-source AI in China. M2 and M2.5 both shipped under permissive open-source licenses. Any developer could download, modify, and self-host the models.

Now, MiniMax has kept M2.7 closed. Developers can access the model through the MiniMax API and third-party model providers. They cannot download, fine-tune, or self-host it.

MiniMax is not the only Chinese lab making this shift. Zhipu AI (z.ai) released GLM-5 Turbo as a proprietary model in recent months. According to VentureBeat, Alibaba’s Qwen team is also reportedly moving toward proprietary development following the departure of senior leadership.

DeepSeek remains a notable holdout, keeping its V3 and R1 models open-source. The pivot is selective, not universal.

The pattern reflects a maturing business logic. Open-source was an acquisition strategy. When Chinese labs needed developer mindshare, freely available models attracted users who might otherwise default to OpenAI or Anthropic.

The strategy worked. MiniMax’s M2 became the top-ranked open-source model globally within weeks of release.

Proprietary is a monetization strategy. MiniMax is now publicly traded with a market capitalization of approximately HK$294B (roughly $38B). Shareholders expect defensible revenue streams. Giving away frontier models undercuts the API business that generates that revenue.

ZhenFund founding partner Huang Mingming invested in 6 consecutive MiniMax funding rounds. He framed the company’s appeal in terms of the “impossible triangle” of high performance, low cost, and commercial scalability, according to TechNode. Closing model access is one way to hold that triangle together while protecting margins.

At the IPO, Yan Junjie pledged to “ensure cutting-edge AI truly serves everyone.” M2.7 tests how far that mission stretches when shareholders expect returns.

For the past year, a useful shorthand described the global AI landscape. Chinese labs were open. US labs were closed.

The framing no longer holds. Global buyers now face proprietary options on both sides. Selection criteria increasingly center on capability, cost, ecosystem fit, and jurisdictional risk.

For enterprises that built production workflows on M2 and M2.5, the switch raises a practical question. Open-source models allowed self-hosting. M2.7 requires API dependency on Chinese infrastructure.

Teams that adopted MiniMax for its openness must now re-evaluate their vendor risk.

The model that helped build itself

M2.7’s most significant technical claim is not a benchmark score. It is the model’s role in its own creation.

MiniMax used earlier M2 versions to build an automated research system. The system manages data pipelines, training environments, and evaluation infrastructure. According to MiniMax, M2.7 then ran entirely autonomously through an iterative improvement loop.

The model analyzed its own failure patterns, planned code changes, and modified its training infrastructure. It ran evaluations, compared results, and decided whether to keep or revert each change. It executed more than 100 rounds of this cycle without human intervention.

During the process, M2.7 discovered optimizations that human engineers had not prioritized. These included parameter tuning, automated bug-pattern detection across files, and loop prevention in the training workflow.

The result, according to MiniMax, was a 30% performance improvement on internal evaluation sets. The company claims the system handled 30-50% of the operational work that would normally require human ML engineers.

The innovation is structural, not incremental. Most AI models are passive artifacts. Humans write the training code, tune the parameters, and fix the failures.

M2.7 inverts part of that process. The model actively manages portions of its own development pipeline. It identifies issues and applies fixes faster than a human team could cycle through them.

These claims deserve scrutiny. The 30-50% figure refers to operational tasks such as pipeline monitoring and evaluation reruns. It does not mean the model performed half of its own fundamental research.

Independent benchmarks also show mixed signals. On at least one coding test, according to VentureBeat, M2.7 actually scored lower than its predecessor M2.5. Self-evolution does not guarantee improvement on every axis.

MiniMax is not alone in exploring this approach. OpenAI recently described a similar process for GPT-5.3 Codex, according to The Decoder. The Codex team used early model versions to find bugs, manage deployment, and evaluate results during training.

Why it matters for enterprise leaders: If self-evolving training loops deliver consistent gains, the economics of AI research change. Labs with effective self-improvement systems could reduce the headcount needed per model generation. They could also compress development timelines.

The competitive moat would shift from model quality to iteration speed. For organizations evaluating AI vendors, the key question becomes whether a provider’s R&D engine can sustain compounding improvements.

Matching US frontier models at 1/50th the cost

The cost structure is the sharper competitive edge. M2.7 is priced at $0.30 per million input tokens and $1.20 per million output tokens. According to WaveSpeed AI, M2.7 costs roughly 1/50th as much as Claude Opus 4.6 on input tokens. On output, it costs roughly 1/60th as much.

In practical terms, an agent workflow that costs $100 to run on Opus would cost roughly $2 on M2.7. With automatic cache optimization, the effective blended cost drops to approximately $0.06 per million tokens.

M2.7’s efficient architecture activates only 10B parameters per query, keeping compute costs low while delivering high reasoning capability. The model runs at approximately 100 tokens per second, about 3 times faster than Opus.

The benchmark results support the pricing story. On SWE-Pro, a benchmark for real-world software engineering, M2.7 scored 56.22%, according to MiniMax. The score matches GPT-5.3-Codex.

On SWE-bench Verified, the model scored 78%, outperforming Claude Opus 4.6’s 55%.

For office tasks such as spreadsheet and document editing, M2.7 achieved the highest ELO score (1,495) on GDPval-AA among open-API models. In one demo, M2.7 independently read TSMC annual reports, built a sales forecast, and generated a presentation.

M2.7 integrates with major AI coding tools, including Cursor and Claude Code. Developers can configure existing environments to point at MiniMax’s API endpoint. The switching cost is low.

The ease of integration cuts both ways. It lowers the barrier to adoption. It also means developers can leave just as easily if a competitor matches the price.

The post-IPO valuation test

Shares opened at HK$235.40 on January 9, 2026, a 42.67% premium over the IPO price. They closed the first day at HK$345, up 109%, according to CNBC. The IPO was oversubscribed 1,837 times by retail investors.

As of early April 2026, the stock trades around HK$949.50, according to Investing.com consensus estimates. All 6 covering analysts rate it a buy, with an average 12-month target of HK$1,092.

The fundamentals tell a more complicated story. According to PitchBook, trailing 12-month revenue as of December 2025 was $79M. Revenue in the first 9 months of 2025 grew 170% YoY, with gross margins of 69.4%, according to TechNode.

Net losses in the same period reached $512M.

A $38B market cap on $79M in trailing revenue translates to a revenue multiple above 480 times. Even adjusting for growth, that pricing requires sustained rapid growth. No Chinese foundation model company has yet demonstrated a clear path to profitability.

The bull case rests on three pillars. First, MiniMax generates over 70% of revenue outside China, reducing single-market risk. Second, its API pricing undercuts every major competitor by an order of magnitude.

Third, self-evolving model development could compress R&D cycles and lower the cost of each successive model release.

The bear case is equally clear. The company allocated 90% of IPO proceeds to R&D, signaling years of heavy spending ahead. The AI model market is commoditizing rapidly. Multiple open-source alternatives exist at the same capability tier.

The Disney, Universal, and Warner Bros. Discovery copyright lawsuit against Hailuo AI, filed in September 2025, adds legal risk.

Geopolitical exposure adds a structural concern. Over 70% of MiniMax’s revenue comes from outside China. The US alone accounts for roughly 20%, according to eWeek.

With M2.7 now proprietary and API-only, enterprise customers have no option to self-host. If US export controls or data governance restrictions expand to cover Chinese model API access, MiniMax’s fastest-growing revenue stream faces regulatory risk.

For enterprises in regulated or government-facing industries, a proprietary Chinese API with no self-hosting fallback raises vendor risk. Expect risk committees to scrutinize these dependencies closely.

From model weights to training flywheels

M2.7 is a test case for whether AI labs can escape the commoditization trap by building models that improve themselves. If self-evolving training systems scale, the competitive moat shifts from model quality to training infrastructure.

For investors, the measurable proxy is release cadence. MiniMax shipped M2 in October 2025, M2.5 in February 2026, and M2.7 in March 2026. The gap between releases is shrinking.

Whether that pace holds will indicate whether self-evolution is a real structural advantage or a one-cycle narrative. A sustained acceleration would validate the thesis. A plateau would suggest the gains are front-loaded.

The question for strategists watching China’s AI sector has changed. The question is no longer who can build the best model. It is who can build the model that builds the next model.

China’s largest publicly listed tech companies released their Q4 and full year 2025 earnings between late February and mid March 2026. Baidu reported on February 26. Alibaba and Tencent both reported on March 18.

The results arrived at a moment when the global AI race is entering its most capital intensive phase. For institutional investors and corporate strategists, these earnings offer the sharpest comparison yet of three divergent AI strategies operating at scale in the same market.

The central question is not whether AI investment pays off. It is which architecture for investing pays off fastest: vertical integration across the full stack, incremental embedding into proven profit pools, or wholesale replacement of a legacy business.

PDD Holdings and Meituan are excluded. PDD posted no material AI narrative. Meituan reports on March 26.

Alibaba goes full stack on AI, absorbs the margin hit

No Chinese tech company is building across as many layers of the AI stack as Alibaba. Proprietary chips from T-Head. Foundation models through Qwen. Cloud infrastructure. Consumer applications with agentic transaction capabilities. The Q4 earnings show both the momentum this strategy is generating and the margin it is consuming.

Cloud Intelligence Group revenue reached RMB 43.3B, a 36% increase YoY. External customer revenue grew 35%, accelerating from prior quarters. AI related product revenue delivered triple digit YoY growth for the 10th consecutive quarter. Total company revenue was RMB 284.8B (+2% YoY, or +9% excluding the disposed Sun Art and Intime businesses).

On the consumer side, the Qwen app surpassed 300M monthly active users across all platforms. Alibaba integrated the app with Taobao, Tmall, Taobao Instant Commerce, Amap, Fliggy, and Alipay in January 2026.

Following a Chinese New Year promotional campaign launched on February 6, approximately 140M users had their first AI driven shopping experience through Qwen’s agentic features by the end of the month.

Alibaba’s foundation model suite, Qwen, surpassed 1B cumulative downloads on Hugging Face as of January 21, 2026. The company launched Qwen3.5 in February with improved multimodal reasoning, coding, and agentic task performance. Qwen is now the most widely used open source model family globally, according to Alibaba, based on download metrics.

T-Head, Alibaba’s chip design subsidiary, has brought its proprietary GPU into production at scale. The chip supports end to end AI workloads from training through inference. It is compatible with mainstream AI frameworks.

By pairing T-Head hardware with Qwen models and Alibaba Cloud infrastructure, Alibaba is assembling a vertically integrated AI offering that spans the entire value chain. That integration is the strategic moat. The cost is visible in the earnings.

Adjusted EBITA fell 57% YoY to RMB 23.4B. Net income dropped 66% to RMB 15.6B. Free cash flow fell 71% to RMB 11.3B.

The compression came from heavy investment in quick commerce, user experience, and AI infrastructure. The company has committed to spending at least RMB 380B on AI and cloud over 3 years. CEO Eddie Wu noted that figure may prove to be too small.

The question for investors is whether the margin pain is temporary or structural.

Tencent embeds AI into profit centers, avoids building a standalone AI business

Tencent took the opposite approach to Alibaba. Rather than building AI as a distinct business line, it wired AI into the products that already generate profit: advertising, gaming, cloud services, and the WeChat ecosystem. The result is the only company among the three that grew both revenue and profit in the period.

Full year 2025 revenue reached RMB 751.8B, up 14% YoY. Net profit rose 16% to RMB 224.8B. Free cash flow grew 18% to RMB 182.6B.

The AI impact shows up most clearly in two areas.

Business services revenue accelerated to 22% growth in Q4, driven by higher cloud services revenue including AI related workloads. Tencent Cloud achieved profitability at scale in 2025. The company attributed the acceleration to enterprise demand across domestic and international markets. Higher e-commerce technology fees tied to WeChat Mini Shops also contributed.

Marketing Services revenue reached a record RMB 145B for the full year, up 19% YoY. AI powered ad targeting, the automated campaign solution AIM+, and higher engagement across Video Accounts and Mini Programs contributed directly to this growth. AI also enhanced game engagement, content recommendation, and user retention across Tencent’s portfolio.

Capital expenditure hit a record RMB 79.2B. R&D spending reached RMB 85.8B. The company spent RMB 18B on AI products specifically in 2025, according to CNBC. It plans to double that in 2026.

President Martin Lau noted on the earnings call that 2025 capex came in below target due to GPU supply constraints. The company will expand computing power acquisition through both direct purchase and flexible leasing in 2026.

On the model front, Tencent is preparing to launch Hunyuan 3.0, its next generation large language model, in April 2026. The model is in internal testing.

The company also announced plans for an advanced AI agent within WeChat, branded as QClaw. The agent integrates as a mini program and handles tasks including file management, commerce, bookings, and PC control. This positions WeChat as a platform for agentic AI rather than just messaging.

Tencent also restructured its Hunyuan development teams into dedicated LLM and multimodal divisions, signaling a more focused approach to model development.

Tencent’s strategy treats AI as an operating lever rather than a separate bet. Chairman Pony Ma stated in the earnings release that AI capabilities improved ad targeting, supported game engagement, and delivered improving cloud revenue growth. He did not describe a standalone AI business. That framing is deliberate.

By embedding AI into existing profit pools, Tencent avoids the margin compression that Alibaba is absorbing. The trade off is that Tencent may be moving more slowly on frontier model capabilities and consumer AI products.

Baidu’s AI pivot hits an inflection point, but the legacy business keeps shrinking

Baidu faces the most structurally complex situation of the three. It is not supplementing an existing growth engine with AI. It is replacing one. The Q4 results show AI revenue growing fast enough to matter, while legacy search declines fast enough to offset the gains at the consolidated level.

Baidu’s AI powered business crossed RMB 11.3B in Q4, representing 43% of what the company now calls Baidu General Business (formerly Baidu Core) revenue. A year earlier, that share was 26%. For the full year, AI powered business revenue reached RMB 40B, up 48% YoY.

Within that category, the numbers tell different stories.

AI Cloud Infra revenue was RMB 5.8B in Q4, with subscription based revenue from AI accelerator infrastructure growing 143% YoY. Full year AI Cloud Infra revenue reached approximately RMB 20B, up 34% YoY. This segment is Baidu’s fastest growing and highest conviction bet.

AI native Marketing Services surged 110% YoY in Q4 to RMB 2.7B. Full year revenue of RMB 9.8B still represents a small share of total revenue. AI Applications contributed RMB 2.7B in Q4. ERNIE Assistant reached 202M MAU in December 2025.

Apollo Go, Baidu’s autonomous ride hailing service, delivered 3.4M fully driverless rides in Q4 with weekly rides peaking above 300,000. Total rides grew over 200% YoY. Cumulative rides surpassed 20M by February 2026.

The fleet has logged over 300M autonomous kilometers, including over 190M fully driverless kilometers. Apollo Go expanded to 26 cities globally, including partnerships with Uber and Lyft in London and Dubai. The international expansion builds regulatory and operational moats that are difficult for later entrants to replicate.

Baidu released an updated ERNIE 5.0 foundation model in January 2026. The company also announced the proposed spin off and separate listing of Kunlunxin, its AI chip subsidiary. Separately, Baidu unveiled a new $5B share repurchase program and its first ever dividend policy.

The challenge is on the other side of the ledger.

Total Baidu revenue fell 3% YoY for full year 2025 to RMB 129.1B. Legacy Business, which consists primarily of traditional search advertising, generated RMB 12.3B in Q4, declining as AI powered alternatives cannibalize the core. A RMB 16.2B impairment charge on a core asset group pushed full year operating results into a loss of RMB 5.8B. Excluding the impairment, operating income was RMB 10.4B.

Non-GAAP operating income for the full year was RMB 15B at a 12% margin. Operating cash flow turned positive in the second half of 2025, generating RMB 3.9B combined after negative cash flow in the first half.

The impairment charge underscores the shrinking value of the traditional business. Baidu’s ability to sustain AI investment depends on whether AI Cloud Infra and Apollo Go can scale revenue faster than legacy search contracts.

Three strategies, one shared problem

The Q4 earnings reveal three distinct approaches to AI among China’s tech leaders.

Alibaba is building full stack: models, cloud infrastructure, proprietary chips, and consumer applications. It accepts severe near term margin compression in exchange for the broadest possible AI moat.

The Qwen ecosystem and T-Head chip advantage give Alibaba vertical integration that no Chinese peer currently matches. But the steep declines across every profitability metric test investor patience.

Tencent is embedding AI into existing profit centers. It uses AI to improve advertising yield, game engagement, cloud monetization, and platform stickiness. Profitability and cash flow remain strong.

The risk is that this defensive approach leaves Tencent behind on frontier models and consumer AI products. The planned Hunyuan 3.0 launch and WeChat AI agent suggest the company recognizes that risk.

Baidu is replacing its core business with AI. Its AI powered revenue is growing faster than either peer. Apollo Go’s global expansion gives Baidu a unique asset in autonomous mobility. But the declining legacy business and significant impairment charge create a narrower margin for error.

One theme connects all three: AI investment is no longer discretionary. Alibaba plans to spend over RMB 380B on AI and cloud over 3 years. Tencent plans to double its AI spending in 2026. Baidu is channeling resources into AI Cloud Infra and autonomous driving as it phases out legacy search. The capex cycle is accelerating regardless of near term profit pressure.

For global investors and corporate strategists, the takeaway is structural. The AI transition for China’s tech giants is no longer a strategic option. It is the business.

The question is no longer whether these companies will spend on AI. It is which company’s AI revenue will be first to self-fund its own capex cycle.

Three metrics will tell the story over the next two quarters. For Alibaba: whether Cloud external revenue growth holds above 30% as capex continues to accelerate.

For Tencent: whether AI related revenue is broken out as a separate line item, signaling confidence that AI is a growth driver rather than an embedded feature. For Baidu: whether AI Cloud Infra growth can outpace the legacy search decline rate, narrowing the gap at the consolidated revenue level.

On February 26, Hefei-based Origin Quantum Computing Technology Co. released its quantum computer operating system, Origin Pilot, for public download. The announcement was reported by Xinhua and Global Times.

No other company has made a full quantum operating system available for local installation. The UK-based Riverlane released Deltaflow.OS in 2020 under more restrictive terms. Origin Pilot is the first quantum OS freely downloadable for local deployment.

A quantum operating system manages the relationship between software and quantum hardware. It schedules tasks, calibrates qubits, and provides the driver layer that lets developers interact with physical quantum chips.

In classical computing terms, it is the equivalent of Windows or Linux. In quantum computing, the challenge is greater. The processor’s basic units of computation are unstable, noisy, and require constant recalibration.

IBM, Google, and Microsoft all operate sophisticated internal quantum operating systems to power their cloud-accessible quantum hardware. None offers that underlying OS as a downloadable package. Origin Pilot occupies a space the Western quantum industry has left empty.

What Origin Pilot gives you

The strategic value of Origin Pilot is that it is hardware-agnostic, locally deployable, and free. It supports three major qubit types: superconducting, trapped ion, and neutral atom.

It integrates quantum processors with classical CPUs, GPUs, and AI acceleration in a unified workflow. And it ships in two tiers: a free Community Edition focused on noise mitigation and hybrid compilation, and an Enterprise Edition with post-quantum cryptography tools for industrial use, according to Quantum Computing Report.

The OS is now in its 4th major version.

First described in a 2021 research paper, it has undergone multiple upgrade cycles. According to Origin Quantum, it handles resource scheduling, parallel quantum task processing, automatic qubit calibration, and software-hardware coordination. The company calls it a “quantum-classical-intelligent” operating system.

For anyone building a quantum computer from components, this is the layer that makes the parts work together.No Western company currently offers that layer as a download.

Origin Wukong: the proof point

Origin Pilot is not theoretical software. It manages a live quantum computer that has processed real workloads from users across the globe.

The system powers Origin Wukong, China’s third-generation superconducting quantum computer. The Wukong chip contains 198 physical qubits: 72 working qubits and 126 coupler qubits that manage interactions between them. This tunable-coupler design is similar to approaches used by Google on its Sycamore and Willow processors.

The company reports that the Wukong cloud platform has attracted over 30M visits from users in more than 120 countries since January 2024, with over 339,000 quantum computing jobs executed across finance, biomedicine, and materials science. These are company-reported figures, according to Origin Quantum. Its 4th-generation control system, Tianji 4.0, deployed in 2025, supports over 500 qubits.

The company behind these systems is Origin Quantum, founded in 2017 as a spinout from the University of Science and Technology of China (USTC). Professor Guo Guoping and Academician Guo Guangcan lead the company.

According to PostQuantum, it is the only quantum computing company that builds the entire stack in-house: quantum processor chips, dilution refrigerators, control electronics, an operating system, a programming framework, and a cloud platform.

The company has raised approximately $150-165M in funding, entirely from domestic Chinese investors, according to PitchBook. Key backers include the China Internet Investment Fund (under the Cyberspace Administration of China), Guoxin Fund (State Council), Shenzhen Capital Group, and CITIC Securities. The deliberate exclusion of foreign capital reflects its positioning as a national security asset.

Why this is not just a framework update

The distinction between a quantum SDK and a quantum OS matters. IBM’s Qiskit and Google’s Cirq are powerful programming frameworks. They let developers write quantum circuits in Python and submit them to cloud-hosted hardware. They are not downloadable operating systems that manage the full hardware stack locally.

According to PostQuantum, the release connects directly to the Quantum Open Architecture movement. Institutions and governments worldwide are seeking to build sovereign quantum computing capabilities from modular, multi-vendor components. That effort requires an integration layer. It requires an OS.

The Western landscape offers few alternatives. A university or national lab assembling a quantum computer from parts has three choices: build an OS from scratch, contract with an emerging quantum systems integrator, or download Origin Pilot for free from China.

The strategic parallel to DeepSeek is direct. DeepSeek demonstrated that open-sourcing a capable AI model forces competitors into a response. It attracts a global developer community. It establishes conventions before others can.

But the quantum version of this play is more potent. In AI, DeepSeek entered a market crowded with open-source alternatives. In quantum operating systems, Origin Pilot enters a market with none.

Whoever provides the OS defines the driver standards, API conventions, and hardware abstraction patterns that others build against. Guo Guoping told Global Times that making Origin Pilot globally available marks a deliberate shift from “closed-door tech innovation” toward open-source ecosystem development. The goal is not just adoption. It is standard-setting.

The 15th Five-Year Plan puts quantum at the center

Origin Pilot’s release arrives at a moment of elevated policy support. China’s 15th Five-Year Plan (2026-2030), unveiled on March 5, identifies quantum technology as the first among seven “future industries” positioned to become new economic growth engines, according to China Briefing.

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China’s silver economy describes the commercial ecosystem built around consumers aged 60 and above. The term entered mainstream business vocabulary in China around 2015, coinciding with the government’s first formal policy acknowledgment that population aging would reshape domestic consumption patterns over the following decades.

By H1 2025, the demographic reality is concrete. CNNIC‘s Digital Consumption Development Report for 2025 counts 117M consumers aged 60 and above actively participating in digital commerce.

That figure represents 52% internet penetration within the age group, a rate that has risen steadily. Mobile-first platforms have lowered the technical barrier to entry. The cohort itself has aged upward from a generation with higher baseline digital literacy.

The silver economy is frequently analyzed through a deficit lens: lower incomes, lower digital literacy, lower spend per transaction. The CNNIC data does not support this framing uniformly. It reveals a cohort with strong habitual purchase behavior, above-average healthcare spend, and documented vulnerability to specific commercial tactics.

Understanding all three dimensions simultaneously is the prerequisite for any credible analysis of this segment’s commercial value and regulatory risk.

72.7% habitual shoppers: the behavioral foundation

The first corrective to the low-value assumption is the habitual shopping rate. 72.7% of silver economy digital consumers are classified as habitual online shoppers. They make regular, recurring purchases through digital channels rather than treating online shopping as an occasional or experimental behavior.

This figure sits above the overall digital consumer average. It indicates that for the 117M who have adopted digital shopping, the behavior has become embedded in daily routine rather than remaining a supplementary channel. The implication for platforms is that silver economy users, once acquired, show strong retention characteristics.

59.4% read product reviews before purchasing. This is a further corrective to the assumption that older consumers are less discerning or more easily manipulated than younger ones. A cohort that systematically consults peer reviews before transacting is exhibiting informed purchase behavior. It is not the behavior of a passive or vulnerable consumer across the board.

51.2% prioritize quality within their budget. The dominant purchase logic among silver economy consumers is value-seeking rather than price-seeking. They are not buying the cheapest option available. They are buying the best option within a defined spending threshold. This distinction matters for brand strategy. Premium positioning with clear quality evidence performs better with this cohort than pure price competition.

Why it matters for platforms: The habitual purchase rate, review-reading behavior, and quality-within-budget logic collectively describe a consumer segment with above-average loyalty potential and below-average price sensitivity.

Platforms that have invested in trust signals, review infrastructure, and quality certification for products targeting older consumers are positioned to capture a disproportionate share of this segment’s spending.

Healthcare: the high-value spend concentration

The most commercially significant finding in the silver economy data is the healthcare spend profile. 26.1% of silver economy digital consumers spend RMB 300 or more per online healthcare transaction, exceeding the overall user average by 5.3 percentage points.

Online healthcare in China encompasses medical consultation platforms, prescription drug delivery services, health supplement e-commerce, and medical device purchasing. The category is growing rapidly as digital health infrastructure matures. The 60-and-above cohort is also developing confidence in online medical channels.

The 26.1% figure at RMB 300 or more per transaction is notable for two reasons. First, it contradicts the assumption that elderly consumers concentrate their digital spending in low-ticket categories. The healthcare spend concentration shows above-average transaction values in a high-frequency, high-need category.

Second, the figure implies significant total market value. 117M users with 26.1% spending RMB 300 or more per healthcare transaction represents a substantial addressable market even before accounting for transaction frequency.

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China’s rural digital economy is one of the most consequential and least precisely analyzed segments in global consumer research. The standard framing treats rural China as a lagging market. Lower income, lower digital penetration, lower average spend. That framing was accurate in 2015. It is increasingly inadequate in 2025.

CNNIC‘s Digital Consumption Development Report for 2025 provides the most granular publicly available dataset on rural digital consumption behavior. It is based on a 30,000-respondent telephone survey covering all 31 provinces, with rural respondents defined as residents of townships and villages outside designated urban administrative boundaries.

The H1 2025 data describes a rural digital consumer base that has largely completed the adoption phase of online shopping and is entering the depth phase.

The gap metrics, the income data, and the forward-looking spending expectations all point in the same direction. Rural China is not catching up to urban consumption patterns. It is developing its own consumption profile. Its category priorities and platform preferences require separate analysis from the urban market.

83.2% penetration: the adoption phase is largely complete

The headline rural digital consumption figure is the online shopping penetration rate. 83.2% of rural netizens shopped online in H1 2025, according to CNNIC. The urban equivalent is 88.4%. The 5.2 percentage point gap has narrowed by 4.9 percentage points since the end of 2024.

At 83.2%, rural online shopping penetration is not a developing market metric. It is a near-mature market metric. The comparable figure for several developed economies in the same period sits below 80%.

Rural China’s e-commerce penetration rate is not a sign of catching up. It is a sign of structural completion.

The remaining 5.2 percentage point gap is not uniformly convertible. It is concentrated among the oldest rural residents, those with the lowest digital literacy, and those in remote areas with the least reliable logistics infrastructure.

Converting these remaining non-shoppers to online purchasers is a long-cycle project requiring infrastructure investment rather than platform marketing.

The strategic implication is the same one that applies to the overall digital consumer base documented in CIW’s analysis of China’s 46.5% digital consumption threshold: the rural market has shifted from an acquisition story to a spend-depth story.

The relevant competitive question is no longer how to get rural consumers online. It is how to capture a larger share of what they are already spending online.

Income growth: rural outpaces urban by 1.5 percentage points

The income data is the most structurally significant finding in the rural consumption section of the CNNIC report. Rural disposable income reached RMB 11,936 in H1 2025, growing at 6.2% YoY. Urban disposable income grew at 4.7% YoY over the same period.

The 1.5 percentage point rural income growth premium over urban is not a temporary distortion. It reflects several converging structural factors.

Rural labor migration patterns have shifted as urban manufacturing employment has consolidated. This has driven higher remittance flows back to rural households. Agricultural commodity prices have been supported by domestic food security policy. Rural producers have benefited directly. Government transfer payments to rural households have increased as part of the domestic consumption stimulus framework.

The income growth differential compounds over time. A rural household growing income at 6.2% YoY closes the absolute income gap by a measurable increment each year. An urban household growing at 4.7% YoY does not. The rural consumer in 2030 will have meaningfully higher purchasing power relative to urban peers than the rural consumer in 2025.

A consumer whose income is growing faster than average requires different positioning than one at a stable income level. The rural consumer is the marginal income growth story in China’s household sector.

The geographic dimension of the income data adds nuance. Income growth is not uniform across rural China. Eastern coastal rural areas, with stronger integration into manufacturing supply chains and proximity to urban labor markets, show higher growth rates than western interior rural areas.

Investment strategies calibrated to “rural China” as a monolith will systematically misallocate against the actual income distribution.

Trade-in subsidies: policy transmission into rural demand

The government’s trade-in subsidy programs, which drove RMB 2.9T in overall sales in H1 2025, showed strong rural penetration. 13.5% of rural netizens participated in subsidy programs, with 43.4% of rural subsidy participants buying appliances and 30.5% buying 3C products (consumer electronics).

The rural subsidy participation data matters beyond the aggregate number. Trade-in programs were structured to route purchases through digital channels.

For many rural consumers, a subsidized appliance purchase is their first high-ticket online transaction. The behavioral consequence is a first digital purchase in a high-ticket category.

First digital purchases of major categories tend to anchor subsequent behavior. A rural consumer who successfully purchased an appliance online through a subsidy program has demonstrated to themselves that the channel works for high-value transactions. The probability of their next appliance purchase also being digital is substantially higher than it was before the subsidy transaction.

The 43.4% appliance share among rural subsidy participants reflects genuine pent-up demand in the rural market. Rural appliance ownership rates for certain categories including dishwashers, dryers, and certain premium kitchen appliances remain significantly below urban rates. The subsidy programs have functioned as a demand-release mechanism for categories that rural consumers wanted but had not yet purchased.

30.5% buying 3C products through subsidy programs reflects a different dynamic. Consumer electronics in rural areas are increasingly driven by the same preference for AI-enabled smart terminals documented in CIW’s analysis of China’s GenAI consumer surge.

Rural Gen Z consumers show nearly identical smart product purchasing preferences to their urban counterparts. The price sensitivity differs. The product preference does not.

Platform preference: online leads offline decisively

The platform preference data in the rural subsidy context reveals an important structural shift. 66.3% of rural subsidy program participants preferred online platforms over offline stores (55.3%) for their subsidy-related purchases.

This figure is significant because it inverts the common assumption that rural consumers default to offline channels for major purchases due to familiarity and trust. For subsidy-mediated transactions, online platforms are the preferred channel.

The explanation lies in program architecture. Online platforms provided simpler subsidy verification, broader product selection, and faster fulfillment than local offline retailers in many rural areas.

The preference for online over offline in rural major-purchase contexts is a behavioral shift that will persist beyond the subsidy programs. Consumers who have completed a high-ticket online purchase successfully do not revert to offline channels for subsequent similar purchases.

The subsidy programs have functionally expanded the category range of rural online purchasing beyond the low-ticket goods that historically dominated rural e-commerce.

Rural platform preference among the broader shopping population shows Pinduoduo as the dominant operator, consistent with its performance in the silver economy segment.

The platform’s price-competitive positioning, social sharing mechanics, and group-buying infrastructure align with rural consumer behavior. Premium-positioned platforms do not replicate this alignment.

The services gap: where rural digital penetration lags most

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Tencent Cloud engineers set up installation booths outside the company’s Shenzhen headquarters. The queue stretched across the north plaza. Attendees carried NAS devices, MacBooks, and mini PCs.

Some had flown in from Hangzhou the night before. A retired aviation engineer waited alongside a 9-year-old primary school student. They were all there to install OpenClaw.

The open-source AI agent framework, nicknamed “lobster” for its claw-shaped icon, has become the fastest-growing project in GitHub history. It surpassed 247,000 stars in just a few months, overtaking Linux and React. According to CGTN, nearly 1,000 people attended the Shenzhen event.

OpenClaw was created by Austrian developer Peter Steinberger in November 2025. It is an autonomous AI agent that connects large language models to messaging platforms. It can browse the web, execute shell commands, manage files, write code, and operate across applications.

Unlike chatbots that respond to prompts, OpenClaw runs tasks continuously and autonomously. For China’s cloud vendors, model companies, and device makers, this is not a community event. It is a land-grab.

Why China adopted OpenClaw faster than anywhere else

OpenClaw’s appeal in China runs deeper than novelty. It addresses a structural bottleneck that has stalled enterprise AI adoption for years.

China’s large enterprises average more than 150 independent IT systems, according to TMTPost. Roughly 60% are legacy platforms with no API documentation, no maintenance, and no open interfaces.

Traditional agent deployment requires mapping APIs, restructuring permissions, coordinating vendors, and rebuilding data pipelines. This “heavy integration” path is slow, expensive, and fragile.

OpenClaw bypasses it entirely. The framework operates at the screen level through visual recognition. It identifies buttons, text fields, and input boxes.

It then completes tasks through simulated mouse clicks and keyboard inputs. No API access required.

A late-2025 survey by Anthropic and Material found that nearly half (46%) of tech leaders cite system integration as the primary AI deployment barrier. A tool that skips integration altogether changes the calculus.

The tradeoffs are significant. OpenClaw takes 15 to 30 seconds per step, compared with 1 to 3 seconds for API-integrated agents.

A “log in and export a report” task might take an integrated agent 5 seconds. OpenClaw can take 3 to 5 minutes. But for enterprises trapped behind closed interfaces, a slow workaround beats no workaround.

Cloud vendors sell the OpenClaw shovels

China’s major cloud providers moved with striking speed.

5 providers launched one-click deployment within weeks. Tencent Cloud, Alibaba Cloud, JD Cloud, Volcano Engine (ByteDance), and Baidu Intelligent Cloud all rolled out OpenClaw services.

According to PANews, Alibaba Cloud created a dedicated “Coding Plan” API subscription for OpenClaw users. Tencent Cloud crossed 100,000 OpenClaw users on its Lighthouse lightweight servers.

A cottage industry emerged in parallel. On Taobao and Xianyu, paid installation services appeared overnight, charging 100 to 500 RMB per remote setup. The top-selling Taobao store logged over 1,000 orders, according to TMTPost.

The business logic is straightforward. Installation is free. Cloud server rental, bandwidth, and API calls are not.

Tencent’s official documentation states it plainly: “OpenClaw comes from the open-source community. The cloud app is free.” The next line adds: “Cloud servers and APIs are billed based on actual usage.”

The strategy mirrors China Telecom’s PHS playbook from the early 2000s, according to TMTPost. Attract users with low entry barriers. Retain them with recurring service fees.

PHS thrived not on product superiority. The carrier needed a user-acquisition vehicle.

Chinese model companies reap the OpenClaw windfall

OpenClaw is a “token black hole.” Each task involves sustained, multi-step API calls. Context windows balloon rapidly.

A single active session can exceed 200,000 tokens. Daily consumption for a power user can reach 50M tokens, according to TMTPost.

This created a sharp demand spike for model APIs. The winners were Chinese companies offering the best price-to-performance ratio.

On February 24, the top 10 models on OpenRouter consumed roughly 8.7T tokens. Chinese models accounted for 5.3T, or 61%, according to TMTPost. Four of the top 5 by call volume were Chinese: MiniMax M2.5, Kimi K2.5, Zhipu GLM-5, and DeepSeek V3.2.

By early March, Step 3.5 Flash from Stepfun had climbed to the global top spot.

The commercial impact was immediate. Moonshot AI’s Kimi K2.5 became the default free model for OpenClaw after version 1.30. OpenClaw subsidized compute costs.

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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.

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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.

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Read the rest of the series on China’s digital economies in 2026:

• Gen Z drives China’s premium digital spend via AI

• Services drive China’s next digital consumption wave

• How AI and Elderly Demographics Created China’s Most Profitable Micro-Drama Tier

• China’s elderly digital consumers defy the low-value assumption

• Rural China’s digital consumers narrow the urban gap

Alibaba’s instant retail bet signals AI agents’ next commerce frontier

During Spring Festival 2025, Alibaba’s AI model Qwen (Tongyi Qianwen) did not launch a red-packet campaign. It launched an infrastructure experiment.

Qwen deployed a RMB 3B subsidy program it called the “Spring Festival Treat Plan,” connecting its AI assistant for the first time to Alibaba’s full consumer ecosystem: fresh grocery platform Hema, travel booking app Fliggy, ticketing platform Damai, on-demand delivery service Taobao Instant Purchase, and Tmall Supermarket. The integration spanned food, drink, entertainment, and daily necessities.

The move signals a strategic calculation. Qwen’s architects selected instant retail, not traditional e-commerce search, as the primary deployment surface for consumer AI. That choice is not accidental. It reflects a structural argument about where AI agents deliver maximum operational impact.

Instant retail refers to the on-demand delivery of physical goods, typically within 30 minutes, sourced from local stores or forward warehouses within 1 to 3 kilometers of the consumer. The model has evolved from a convenience option into a daily necessity for tens of millions of Chinese households.

According to the Ministry of Commerce’s research institute, China’s instant retail market is projected to reach RMB 971.4B in 2025 and surpass RMB 1T in 2026. The sector now encompasses groceries, pharmaceuticals, household supplies, and prepared food, operating across major cities and smaller-tier markets alike.

Why Qwen chose instant retail, not search

Traditional e-commerce already has AI. Recommendation engines, personalized feeds, and AI-generated product descriptions are standard across Taobao, JD.com, and Pinduoduo. The marginal value of adding another AI layer to a search-and-scroll interface is limited.

Instant retail presents a different operating environment. The decision cycles are shorter. The product types are more standardized, the inventory is geographically dispersed, and the fulfillment window is measured in minutes rather than days. Each of these characteristics amplifies AI’s capability to add value.

According to retail e-commerce analyst Zhuang Shuai, founder of Bailian Consulting, AI agents can intervene most effectively when demand is spontaneous, supply is local, and fulfillment requires real-time optimization across multiple simultaneous variables. Instant retail satisfies all three conditions. Traditional e-commerce satisfies none.

The commercial implication is significant. Qwen’s Spring Festival experiment was not a marketing campaign. It was a test of whether an AI assistant could become the operating system for a consumer’s daily consumption, replacing keyword searches, manual cart additions, and price comparisons with conversational intent.

The four structural advantages of instant retail for AI

First: decision speed. Instant retail purchases are low-deliberation decisions. Consumers buying bottled water, fever medicine, or late-night snacks decide in under 10 seconds. They do not comparison-shop. They do not read reviews.

This makes AI intervention highly effective. A consumer says “I’m thirsty” or “my stomach hurts,” and an AI agent can match that intent to available inventory, confirm a substitution if the preferred product is out of stock, and complete the order.

According to Bailian Consulting’s analysis, intervention success rates are significantly higher in instant retail than in categories requiring extended deliberation, such as electronics or apparel.

Traditional e-commerce purchase cycles for consumer electronics or clothing can span hours or days. AI tools in that environment can suggest options. They cannot close the decision. In instant retail, AI closes the decision.

Second: product standardization.

The core product categories in instant retail are highly standardized. Bottled water, milk, diapers, and painkillers have fixed SKUs, uniform specifications, and predictable consumption rates. AI systems can recommend, substitute, and automate replenishment with minimal error.

This contrasts sharply with apparel, furniture, or electronics, where size variation, color deviation, and model differences introduce high uncertainty into AI recommendations. Standardization is the prerequisite for reliable AI-driven purchasing. Instant retail delivers it consistently.

Third: distributed inventory.

Instant retail inventory is held in thousands of local stores and forward warehouses scattered across urban neighborhoods. This creates a data management problem that human operators and legacy systems cannot solve efficiently: real-time monitoring of stock levels across thousands of locations, responding to demand shifts driven by weather, time of day, and local events.

AI solves this structurally. According to Zhuang Shuai’s framework, an AI agent can scan nearby inventory nodes in milliseconds after a consumer signals intent, select the closest stocked location, and route the order.

When a product is unavailable, AI can recommend a functional substitute and redirect fulfillment automatically. Beyond order-level decisions, AI can generate hourly replenishment forecasts for individual stores by integrating sales history, weather data, and local consumption patterns, reducing both stockouts and perishable waste.

Traditional e-commerce uses centralized regional warehouses with inventory measured in bulk quantities and replenishment cycles measured in weeks. The scale and stability of that model makes AI optimization useful but not essential. Distributed instant retail inventory, by contrast, cannot be managed efficiently without AI.

Fourth: minute-level fulfillment.

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China’s Gen Z cohort was born between 1995 and 2009. They entered the internet as mobile-native users. They are now entering the AI era as the most digitally fluent demographic in the country’s consumer market.

At 261M active digital consumers, they represent 27.2% of China’s entire digital consumption base, according to CNNIC’s Digital Consumption Development Report for 2025.

The CNNIC report is based on a 30,000-respondent telephone survey across 31 provinces. It provides the most granular publicly available dataset on Chinese consumer digital behavior.

Its Gen Z findings are notable for their consistency across categories. Across GenAI adoption, emotional spending, smart product purchasing, and content payment rates, Gen Z systematically outperforms the overall average.

The pattern describes a consumer cohort that has upgraded not just the channels through which it spends, but the reasons and logic by which it spends.

The AI adoption gap that defines the cohort

The single most strategically significant number in the CNNIC Gen Z data is the GenAI adoption gap. 71.4% of Gen Z consumers use generative AI, against a 36.5% overall average. The 34.8 percentage point difference is the largest cohort divide in the report.

This gap is not simply a function of age or technology access. Older millennials in China have comparable smartphone penetration and internet access. The difference is behavioral.

Gen Z has adopted GenAI as a functional layer in daily decision-making, including product research, content discovery, and purchase decisions.

Before AI-powered search, product discovery on Chinese e-commerce platforms relied on keyword input, sponsored rankings, and social referrals. Gen Z consumers using Taobao’s AI Wan Neng Sou or Xiaohongshu’s AI recommendation layer interact with platforms through intent expression rather than keyword entry. They describe what they want in natural language, upload images for visual search, and receive curated outputs shaped by their behavioral history.

The implication for platform operators is direct. A user cohort that has adopted AI-mediated discovery at 71.4% penetration will find non-AI interfaces increasingly inadequate.

The barrier is not financial. It is experiential. A Gen Z consumer who has used an AI-powered interface for six months has already calibrated their expectations upward. Returning to keyword search feels like a regression.

Smart products and the hardware preference signal

Gen Z’s technology spending extends beyond software. 59.3% of Gen Z consumers bought smart products online in H1 2025, compared to 45% across all age groups. The 14.3 percentage point gap reflects a cohort that treats AI-enabled hardware as a default rather than a premium.

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China’s digital economy is typically framed as a technology story. The CNNIC Digital Consumption Development Report for 2025 is based on a 30,000-respondent survey across 31 provinces. It reframes China’s internet economy as a structural consumption story.

CNNIC has tracked China’s internet economy since 1997. Its annual reports are among the most comprehensive primary datasets on Chinese digital behavior available to international researchers and investors. The H1 2025 edition is notable for one number above all others: 46.5%.

That is the share of total resident consumption now flowing through digital channels. At RMB 9.37T, it is not a rounding error or a pandemic-era distortion. It is a durable structural threshold that changes the baseline assumptions for China consumer analysis.

Read the rest of the series on China’s digital economies in 2026:

• China’s gen-AI user base doubled in 6 months

• Gen Z drives China’s premium digital spend via AI

• Services drive China’s next digital consumption wave

• How AI and Elderly Demographics Created China’s Most Profitable Micro-Drama Tier

• China’s elderly digital consumers defy the low-value assumption

• Rural China’s digital consumers narrow the urban gap

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Is 46.5% a floor or ceiling?

The 46.5% figure deserves decomposition before interpretation. The RMB 9.37T total breaks into three categories: physical goods bought online (66.3% of digital consumption), digital services (29.2%), and digital content (4.5%).

Physical goods online penetration is mature. The growth rate is decelerating toward its natural ceiling as offline retail stabilizes at its residual share. That category’s trajectory is largely set.

The services share is the more important signal. At 29.2% of digital consumption, digital services remain structurally underpenetrated relative to their physical goods counterpart. Food delivery, travel booking, healthcare, education, and home services are all digitizing at different speeds.

The policy architecture in 2025 is explicitly designed to accelerate this transition. The 46.5% threshold will rise. The increment will come almost entirely from services, not goods. Analysts modeling China consumer exposure using physical retail metrics are measuring the wrong variable.

Services crossed RMB 1T. Twice.

The clearest evidence of the services shift sits in two category-level numbers. Online food delivery reached RMB 1.09Tin consumption value, serving 569M users. Online travel and transportation reached RMB 1.08T, serving 514M users.

Both categories crossed the trillion-renminbi threshold in a single reporting period. Neither number was widely anticipated at the scale or speed at which it materialized.

Food delivery is the more structurally interesting case. The RMB 1.09T figure captures more than transaction volume. It reflects the digitization of daily household expenditure.

When food delivery crosses a trillion renminbi in semi-annual consumption, it signals that digital channels have captured a category previously considered offline by default.

Travel digitization at RMB 1.08T reflects a different dynamic: the recovery of deferred consumption combined with platform consolidation. Trip.com Group,

Meituan’s travel vertical, and Fliggy (Alibaba) have collectively built infrastructure that now intermediates the majority of domestic travel booking. The trillion-renminbi figure is as much a platform concentration story as a consumption story.

Together, these two categories account for over RMB 2T of digital services consumption in a single half-year. That is larger than the entire digital consumption base of most economies.

The user base is near-saturated. Spend depth is the new metric.

958M digital consumers is the headline user figure from the CNNIC report.

That is 68.1% of China’s population engaging in digital consumption. Total internet users reached 1.123B. The gap between internet penetration and active digital consumption participation stands at approximately 165M.

Those 165M are disproportionately elderly, rural, and lower-income. Converting them to digital consumers is both a policy objective and a commercial opportunity. It is a long-cycle project. The near-term growth story is not acquisition. It is spend depth.

Three metrics now drive platform revenue growth and consumer brand margins: average spend per digital consumer, category breadth per household, and services penetration among existing users. The 958M user base is mature enough that marginal user acquisition has diminishing returns in most categories.

Why it matters for investors: Consumer sector equity positions in China priced on user growth assumptions are measuring the wrong variable. The correct metrics are average revenue per user, category cross-sell rates, and services attach rates on existing physical goods transactions.

Policy as accelerant: trade-in programs and infrastructure build-out

The RMB 2.9T in sales driven by government trade-in subsidy programs in H1 2025 is the most direct evidence of policy transmission into digital consumption. The programs, covering appliances, consumer electronics (3C), and vehicles, were structured to route purchases through digital channels.

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