China's AI cloud market has transitioned from its early "wild growth" phase to a new era dominated by full-stack capabilities and deep applications, now firmly under the leadership of two major players.
The closing month of October has been marked as a "strategic breakout period" for top AI providers. OpenAI has been aggressively securing computing resources through multi-billion-dollar chip procurement deals with AMD and plans to acquire hundreds of thousands of AI chips over four years. Its collaboration with NVIDIA on a $100 billion infrastructure project aims to expand OpenAI's computing power through data centers powered by millions of NVIDIA GPUs. Meanwhile, OpenAI's rival Anthropic has partnered with Google on a similarly ambitious chip procurement plan, potentially utilizing up to 1 million Google Tensor processors.
The primary goal behind this computing arms race is cost optimization. Reports indicate OpenAI generated $4.3 billion in revenue in H1 2025, a 16% YoY increase, but its cash burn surged to $2.5 billion due to AI development and ChatGPT operation costs. This financial pressure explains why OpenAI has raised more capital than any private company in history, with CEO Sam Altman calling it potentially "the most capital-intensive startup ever."
This trend highlights a critical industry shift: cloud providers can no longer rely on single-component advantages to meet AI companies' cost optimization demands. Both traditional cloud providers and AI cloud newcomers must extend their reach across the value chain to form vertically integrated solutions that deliver superior cost efficiency.
Market research from firms like Frost & Sullivan and IDC confirms this transformation. Frost & Sullivan's "China Full-Stack AI Cloud Services (Public Cloud) Market Report H1 2025" shows that Alibaba Cloud and Baidu AI Cloud collectively command over 50% of China's AI cloud market share. Notably, these two were the only Chinese partners named in Apple Intelligence's collaboration list.
The industry consensus is clear: only full-stack solutions qualify as true AI clouds. The strategic rationale for this approach includes: 1. Cost pressures: Training GPT-4 reportedly cost hundreds of millions, with inference expenses being equally staggering. As models grow exponentially, this "computing tax" threatens to drain corporate cash flows. 2. The need for end-to-end optimization: Providers must build complete "chip-computing-model-application" stacks to achieve maximum cost efficiency and attract AI companies. 3. Strengthening winner-takes-all dynamics: New entrants face dramatically higher barriers as full-stack giants consolidate their positions.
Baidu AI Cloud exemplifies this full-stack approach through its "Cloud Intelligence Integration" strategy: - Computing layer: Its self-developed Kunlun AI chips and Baige AI computing platform enable breakthroughs in hardware performance and cost optimization, including China's first self-developed 10,000-card cluster. - Model platform: The Qianfan platform integrates over 150 models with comprehensive development tools, allowing enterprises to complete model selection, fine-tuning, evaluation, and deployment in one place. - Application layer: Direct translation of technology into business solutions like digital employees and industry-specific agents ensures shortest path from R&D to value creation.
The business case for full-stack AI clouds grows stronger as enterprises move beyond AI experimentation to demand tangible results. Frost & Sullivan reports that 80% of companies face "technology cliffs" in AI adoption due to fragmented toolchains and high technical barriers. Full-stack solutions address these through: 1. Simplified development processes 2. Scalable, customizable architectures 3. Enhanced security and compliance
In practice, these capabilities are delivering measurable benefits across industries: - Finance: Baidu AI Cloud leads in both project count and contract value for AI implementations in 2025 (358 projects, up 170% YoY). Its partnership with China Merchants Bank demonstrates how self-developed chips (Kunlun P800) can enable secure, large-scale AI deployments while meeting strict financial regulations. - Manufacturing: The collaboration between China Iron & Steel Research Institute and Baidu AI Cloud achieved 50%+ production efficiency gains and 40%+ quality inspection labor savings through system-level AI infrastructure that understands specialized manufacturing processes.
The market is rewarding these full-stack leaders for their strategic foresight. Both Alibaba and Baidu made early, massive bets on AI: - Baidu has invested over $180 billion in R&D since 2017, maintaining >20% R&D intensity - Alibaba Cloud committed to $380 billion in cloud/AI infrastructure investment over three years starting 2025
These investments are now paying off: - Baidu's AI new business (including cloud) grew 36% YoY to $19.4 billion in H1 2025 - Alibaba Cloud revenue reached $63.5 billion, up 22% YoY
As the AI market matures, customers increasingly prioritize demonstrable business value over technological potential. The long-term winners will be those who treated AI as existential to their corporate strategy, making sustained, sometimes isolated investments through multiple technology cycles. With Alibaba Cloud and Baidu AI Cloud at the forefront, China's AI ecosystem appears poised to deliver both deep industry transformation and globally competitive innovation.