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Tencent vs Alibaba: Two Ways to Bet on China's AI Future
Tencent and Alibaba are both spending billions on chips and computing power. But one is defending the present, while the other is gambling on the future.
On August 12, Tencent released its latest earnings report. Quarterly capital expenditure had reached RMB 52.8 billion up 176% from a year earlier.
Tencent’s shares fell 4.46% the next trading day.
A week later, Alibaba reported an even larger number. Its quarterly capital expenditure reached RMB 67.7 billion, driven largely by AI infrastructure. Both companies saw free cash flow turn negative as they bought chips, expanded data centres and prepared for the next stage of the AI race.
Investors are now facing a difficult question: how should all this spending change the valuation of China’s two largest technology companies?
I find the comparison useful not because Tencent and Alibaba must defeat each other, but because they are approaching the same market from almost opposite directions. Tencent and Alibaba were the infrastructure builders of China’s internet era. Both companies now have to rebuild parts of their own empires around AI — a process that will inevitably create disruption, uncertainty and painful adjustments.
I call this an “earnings crossfire.” A financial report is not merely a collection of numbers. It also reveals what management wants investors to notice, which questions executives avoid and how they explain difficult strategic decisions.
After reading both reports and earnings calls, the difference is becoming clear.
Tencent is trying to integrate AI into the businesses it already owns. Alibaba is trying to build an entire AI stack, from chips and cloud infrastructure to foundation models and applications.
Tencent’s approach is safer but increasingly fragmented. Alibaba’s is more coherent, but far more dependent on the AI boom continuing.
Tencent AI’s Strategy: Model, Agent and APP
Tencent’s AI strategy currently revolves around three main products: Hunyuan is the model, WeLM is for agent, and WorkBuddy is the product.
WeLM and Xiaowei are designed to bring AI agents into WeChat. Hunyuan is Tencent’s foundation-model platform. WorkBuddy is its productivity assistant for office workers.
Hunyuan represents Tencent’s infrastructure-level AI ambition.
After former OpenAI researcher Yao Shunyu joined the company, the model’s performance improved considerably. Tencent says it wants Hunyuan to reach frontier-level multimodal capability within 12 to 18 months.
Unlike consumer AI companies chasing user numbers, Tencent expects Hunyuan to improve advertising recommendations, enterprise software, game development, AI workflows, 3D asset generation and WorkBuddy inference.
But Tencent’s greatest AI asset is not its model. It is WeChat. With more than one billion users, WeChat is one of the world’s most valuable consumer platforms. Tencent’s long-term goal is to turn it from a communication tool into something closer to an AI operating layer.
WeLM is being developed partly around this idea. Unlike a conventional cloud chatbot, it places greater emphasis on device-side processing, privacy and low latency. The approach is closer to Apple Intelligence than ChatGPT.That is a very Tencent-style strategy: use a new technology to strengthen an existing empire rather than build a separate one.
WorkBuddy is Tencent’s attempt to build a serious AI productivity product.
Unlike OpenAI’s Codex or Anthropic’s Claude Code, it did not initially target software developers. Tencent went after ordinary office workers, a much larger potential market.
The challenge is monetization. Chinese enterprise software has historically struggled to generate high margins. A large user base does not automatically become a profitable software business.
WorkBuddy’s growth looks promising. Desktop monthly visits exceeded 20 million in June, ranking first among similar products in China.
Tencent strategy chief James Mitchell said gross margins from paying WorkBuddy users and model services were already comparable with Tencent Cloud.
That sounds encouraging until you ask what Tencent Cloud’s margin actually is.
Tencent does not disclose it separately. Analysts often estimate that cloud infrastructure businesses can produce gross margins of around 40 % and EBITA margins of roughly 10 %.
More importantly, Mitchell was referring mainly to paying WorkBuddy users. Most individuals still access the product through free credits.
Good economics among paying customers do not necessarily mean the entire product is profitable.
Tencent still needs to show how many users convert to subscriptions, how much computing capacity each user consumes and whether an AI productivity product can generate sustainable margins in China.
Tencent Is Spending Like an AI Company
Two numbers disappointed Tencent investors.
Under IFRS, profit attributable to shareholders reached RMB 56 billion, below the Reuters consensus estimate of approximately RMB 61.8 billion.
More unusually, free cash flow was negative RMB 13.8 billion. It was the first negative quarter in Tencent’s reporting history.
Tencent generated RMB 52.7 billion in operating cash flow. That was more than offset by RMB 59.3 billion in capital-expenditure payments, RMB 5 billion in media-content payments and RMB 2.2 billion in lease payments.
A large proportion of the additional spending came from commitments for AI computing resources.
The effect also appeared in operating profit. Tencent reported non-IFRS operating profit of RMB 75.6 billion. Without Hunyuan, Yuanbao, WorkBuddy, CodeBuddy, Xiaowei and its other new AI initiatives, the figure would have been approximately RMB 86.1 billion.
In other words, Tencent’s AI businesses reduced quarterly operating profit by around RMB 10.5 billion.
That is a meaningful change for a company known for producing stable profits from games, advertising, payments and social networking. Tencent is becoming more capital intensive.
The question is whether that transformation produces enough growth.
Over the next six to 12 months, Hunyuan needs to become more competitive with Qwen and Kimi. WorkBuddy needs more paying customers. Xiaowei needs to prove that agents inside WeChat can create measurable economic value.
Until then, Tencent is spending like an AI infrastructure company while continuing to operate like a traditional internet company.
What Exactly Is Tencent Building?
Every AI company needs computing capacity. The question is whether owning that capacity becomes a business in itself.
Tencent President Martin Lau described three possible uses for the company’s computing resources: training internal models, supporting AI applications and renting capacity to outside cloud customers.
James Mitchell added that previously purchased hardware could itself generate returns because computing resources ordered earlier may now be worth considerably more.
The logic is reasonable. The disclosure is not.
Investors still cannot answer a basic question: what exactly is Tencent’s AI infrastructure for?
Is Tencent trying to become a larger cloud provider? Is it building Hunyuan into a frontier model? Or is it mainly buying capacity to support WorkBuddy, WeChat agents and other internal applications?
Tencent Cloud does not provide enough financial detail to answer these questions. After spending tens of billions of yuan, Tencent continues to present AI as one combined story.
Investors cannot clearly separate cloud revenue, model-development costs, application revenue or the economics of renting computing capacity.
Without that distinction, it is difficult to know whether Tencent is building a new business or merely increasing the cost of its existing one.
Alibaba Is Building the Entire Stack
Alibaba is taking almost the opposite approach.
While Tencent is adding AI to its existing ecosystem, Alibaba is reorganizing itself around AI infrastructure.
In its latest results, Alibaba placed Qwen and Qwen Office under “AI Labs and Applications,” moved semiconductor subsidiary T-Head into “AI Cloud and Computing Services,” and integrated instant retail more closely with e-commerce.
Compared with the company’s previous “Cloud Intelligence” structure, the new organization sends a much clearer message.
Alibaba does not see AI as merely a model business. It sees AI as an infrastructure business.
The strategy increasingly resembles Amazon’s approach to AWS. Amazon does not need to dominate every application. It wants to provide the computing infrastructure on which everyone else builds.
Alibaba wants to occupy that position in China. It has one additional advantage: it controls both the infrastructure and one of the country’s leading foundation models, Qwen.
Spend First, Profit Later
Alibaba Cloud generated quarterly revenue of RMB 48.4 billion, up 45 % from a year earlier.
AI-related product revenue reached RMB 12.4 billion, up 38 %. Annual recurring revenue reached RMB 49.6 billion.
For the first time, Alibaba also revealed part of the financial burden created by Qwen and Qwen Office. Its AI businesses produced approximately RMB 13.9 billion in quarterly losses, mainly because of inference costs and infrastructure investment.
CEO Eddie Wu remained confident.
Alibaba has committed RMB 380 billion to AI infrastructure over three years. Wu described the current spending as a temporary investment cycle rather than a permanent increase in costs.
His argument is counterintuitive: the more Alibaba spends today, the lower its future AI costs may become.
The assumption is that demand for computing capacity will continue to exceed supply before 2030.
If an accelerator has a depreciation period of five years but earns back its cost within three, Alibaba can continue generating returns from the hardware for another two years.
Older Nvidia V100 and A100 chips remain heavily utilized even after newer generations arrive. Alibaba therefore believes that today’s infrastructure can continue producing value for years.
It is an attractive argument, but it depends on one critical assumption: AI demand must remain strong.
If computing demand keeps growing, early investment creates an advantage. If demand slows, the same infrastructure becomes a very large depreciation burden.
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