In the intensely competitive global AI landscape of 2025, Alibaba and Google, two leading AI companies worldwide, are simultaneously pursuing full-stack AI strategies, establishing complete closed-loop ecosystems from underlying hardware to applications. They have emerged as the only two major technology companies globally possessing comprehensive full-stack AI capabilities.

The foundation of this offensive lies in their unprecedented capital expenditure commitments.

Today, Alibaba released its Q1 FY2026 financial results (Q2 2025 in calendar year), showing quarterly capital expenditure (Capex) of 38.6 billion yuan, reaching a historic high. During the earnings call, CEO Eddie Wu announced that over the past four quarters, Alibaba has cumulatively invested over 100 billion yuan in AI infrastructure and AI product development.

The AI investments are paying off, with strong performance in AI + cloud this quarter. Alibaba Cloud revenue growth continued to accelerate to 26%, with AI-related product revenue achieving triple-digit year-over-year growth for eight consecutive quarters. Additionally, Alibaba disclosed for the first time that quarterly AI revenue accounts for over 20% of external commercial revenue.

Supported by the depth and intensity of AI investments, Alibaba's large model innovation engine is operating at an astonishing pace. Within July, Alibaba successively released and open-sourced multiple heavyweight models: the Qwen3 reasoning model, topping global rankings as the strongest open-source reasoning model; the new open-source Qwen3 version becoming the world's strongest in classic foundation models; Qwen3-Coder with programming capabilities rivaling top global closed-source models; and the industry's first video generation model using MoE architecture, Wan2.2, released at the end of July.

In August, Alibaba open-sourced the new text-to-image model Qwen-Image, which reached the top of Hugging Face's model rankings on the same day. On August 22, Alibaba also released the Agentic programming platform Qoder.

This high-frequency model iteration is rapidly building an ecosystem moat through open-source strategy. Currently, the number of Qwen derivative models has exceeded 140,000, surpassing Llama to become the world's largest AI open-source model, with over 400 million global downloads.

This series of actions perfectly exemplifies the core strategy of "user-first, AI-driven" established by Wu Yongming.

Cross-Pacific Response: Google's Heavy AI Investment and Emergence of Full-Stack Model

Alibaba's AI strategic ambitions have received strong resonance across the Pacific. Google's Q2 2025 financial report clearly revealed the strategic model of "full-stack AI."

The most striking data in the financial report was Google's token usage doubling within a month through enhanced model capabilities and low-cost or free various applications, jumping dramatically from 480 trillion in May to 980 trillion.

To address the explosive growth in inference demand, Google significantly raised its FY2025 capital expenditure (Capex) guidance from $75 billion to a staggering $85 billion. This move, described by Wall Street analysts as "un-Googly," starkly contrasts with Google's historically "prudent and mature" investment style.

Although this severely compressed the company's free cash flow (FCF) in the short term, analysts generally believe this is exactly what investors have been "strongly calling for," indicating that Google is finally ready to "truly take AI seriously."

Google's "irrational" gamble is precisely aimed at building and strengthening its full-stack AI capabilities.

The core of this model lies in technology giants controlling every critical component from bottom to top to achieve optimal performance, cost, and innovation speed.

This model can be clearly divided into three levels:

Hardware/Infrastructure Layer: Including self-developed chips, server clusters, and global data center networks. Foundation Model Layer: Possessing a powerful proprietary large model serving as the "intelligent brain," such as Google's Gemini. Application/Ecosystem Layer: Relying on market-dominant applications (such as Google Search, YouTube) as primary channels for AI technology deployment, data collection, and user engagement.

Google and Alibaba's simultaneous choice of this capital-intensive, full-stack technology path is no coincidence.

This proves that the global AI competition paradigm is changing: the era of pure algorithmic competition has ended, replaced by comprehensive warfare over integrated systems, capital strength, and ecosystem control. This full-stack AI model is becoming the only path for enterprises aspiring to achieve AI leadership in global or regional markets, significantly raising industry entry barriers.

Full-Stack AI Layout: Hardware, Models, and Applications Advancing Together

Through in-depth analysis of Alibaba and Google's AI strategies, both demonstrate remarkable "strategic mirroring" across three levels of full-stack deployment. While specific tactics differ, their underlying logic is highly consistent.

1. Computing Power Foundation: Building Trillion-Scale AI Infrastructure Platforms

Controlling physical infrastructure is the starting point of full-stack strategy. This affects the performance and cost of the entire downstream ecosystem, with both Alibaba and Google viewing ownership and operation of proprietary computing power as an unshakeable foundation.

Alibaba plans to invest 380 billion yuan over the next three years, with the explicit goal of "building a globally competitive cloud computing network."

This year, Alibaba Cloud has invested in and launched 8 new AI and cloud data centers and availability zones globally, including Beijing, Shanghai, Hangzhou, as well as Thailand, South Korea, Malaysia, Dubai, and Mexico, representing initial results of this plan. In the second half of this year, Alibaba Cloud's global infrastructure layout will expand to 30 regions and 95 availability zones.

Google's $85 billion capital expenditure plan similarly focuses on this area, with approximately two-thirds allocated to servers and one-third to data center construction and network equipment, primarily aimed at meeting cloud customers' strong demand for AI computing power and supporting its massive internal AI services.

2. Model Hub: Qwen and Gemini as Ecosystem Brains

If computing power is the "body," then large models are the "brain." Both Alibaba and Google position their flagship models as core hubs for building developer ecosystems, not merely as products.

Alibaba's Qwen series chooses an open-source-led ecosystem path. With its high performance and low-cost advantages, Qwen rapidly reached the top as the world's strongest open-source model this year, with over 140,000 global derivative models and over 400 million global downloads.

Through China's largest AI open-source community "ModelScope," Alibaba has gathered over 16 million developers, jointly building a massive AI ecosystem centered on Qwen.

Google's Gemini took a different path—deep integration and large-scale application. Through its vast existing developer network, Gemini has attracted over 9 million developers for building. Its processing capability has reached an astonishing over 980 trillion tokens per month, doubling since May. This clearly demonstrates Google's powerful ability to leverage its platform advantages and deeply embed Gemini into global developer workflows.

3. Application Supremacy: From E-commerce and Search to AI Implementation Across Countless Scenarios

The ultimate value of the full-stack model is reflected in the application layer.

Both Alibaba and Google cleverly utilize their absolutely dominant C-end applications as optimal testing grounds, distribution channels, and data sources for AI technology, forming a powerful positive feedback flywheel.

Alibaba's flywheel is driven by its massive commercial ecosystem. Recently, Amap has undergone comprehensive AI transformation, DingTalk completed its latest AI upgrade, and Taobao platform is upgrading through AI search, AI advertising platforms, and other AI applications. Financial reports show that Taobao's AI tool "Quan Zhan Tui" continues to drive efficiency improvements for Taobao merchants. In June, Taobao launched the hundred-billion parameter large model RecGPT, making "guess what you like" more accurate, with tests showing user add-to-cart actions and dwell time both increasing by over 5%. Alibaba International's AI tool Marco has over 1 billion daily calls.

Google's flywheel revolves around its core search and cloud businesses. The AI Overviews feature in search has covered over 2 billion monthly active users and increased query volume for related searches by 10%. On the enterprise side, Google Cloud effectively drove 32% revenue growth by bundling Gemini functionality into the Workspace suite.

The logic of this closed-loop system is clear: the application layer (such as e-commerce, search) generates massive high-quality proprietary data and user feedback, which is used to train and optimize the model layer (Qwen, Gemini); stronger models, in turn, enhance application experiences, attract more users, and generate more data.

The entire cycle operates efficiently on proprietary, optimized infrastructure layers, forming insurmountable competitive barriers.

Alibaba's full-stack AI advantages have achieved perfect integration with a complete, end-to-end commercial ecosystem, constituting its core technological barrier—an efficiently operating "AI flywheel."

Conclusion: Beyond Technical Competition, It's a Battle of Business Ecosystems

The convergence of Alibaba and Google's AI strategies, jointly choosing capital-intensive, technically vertical full-stack models, may become a key characteristic defining the AI era landscape.

It heralds the arrival of a new era: future competition will no longer be about individual algorithms or products, but about complete ecosystem confrontations from chips and data centers to large models and killer applications. This is not only the entry ticket to top-tier competition but also the ultimate weapon determining victory.

For Alibaba, implementing the full-stack AI strategy holds particularly profound significance. On a solid AI infrastructure platform, by injecting AI capabilities into e-commerce, logistics, transportation, search, office work, finance, and thousands of industries, Alibaba is building a self-reinforcing, efficiently monetizing commercial intelligence closed loop.

Looking at the Chinese market, the endgame of this AI competition will not depend on who possesses the smartest single model, but on who can build the most powerful, comprehensive AI-driven commercial ecosystem with the strongest synergistic effects. In this ultimate showdown, Alibaba, which already possesses a complete commercial closed loop and is fully investing in full-stack AI, has undoubtedly occupied advantageous strategic high ground.