This article is based on publicly available information and is intended for informational exchange only, and does not constitute any investment advice.
On August 20, Alibaba Group released its Q1 FY2027 financial report. During the analyst conference call, Alibaba CEO Eddie Wu shared a perspective: Over the past two years, industry investors have been asking what the super application of AI is. We believe the ultimate super application of AI is the AI cloud platform centered on AI computing power.
Today, everyone is focused on who has released a more powerful model or who has launched a more conversational office platform.
But if we ask a more critical question: Which link in the entire AI value chain is the most valuable?
In my view, the real industrial answer lies not in model launch events, but in hard numbers such as AI cloud revenue, growth rates, and chip shipments:
Whether AI is truly "useful," revenue is the undisputed North Star metric.
On August 20, 2026, Alibaba confirmed an ongoing industrial shift with a financial report:
AI competition is irreversibly shifting from the parameter race at the model layer to deepening into the hardware layer.
Alibaba Cloud's external commercialization revenue grew by 45% year-on-year this quarter, hitting a 22-quarter high in growth rate. The annual recurring revenue (ARR) of AI-related products, including AI computing power, models, and application layers, reached 49.5 billion RMB, becoming the core engine for Alibaba Cloud's accelerated growth.
This is a set of data compelling enough to make the market re-evaluate Alibaba's value. Last quarter, Alibaba announced the inflection point of AI commercialization; this quarter, it is accelerating its realization.
Looking at all Chinese concept stocks, only Alibaba simultaneously meets these three conditions: full-stack AI capabilities with self-developed chips, the ability to disclose AI revenue, and accelerating improvement in cloud profit margins.
01. Hard Metrics: Deconstructing Alibaba Cloud's AI Capabilities Across Three Dimensions
1. Cloud Growth: The only Chinese company to enter the global AI cloud "acceleration club."
Alibaba Cloud's 45% growth in external commercialization revenue this quarter is highly competitive in the global coordinate system.
Google Cloud ranks first globally with an 82% growth rate, followed by Alibaba Cloud at 45% in second place. Azure, at approximately 43%, was surpassed, while AWS ranks last at about 37%.
The last time Alibaba Cloud's growth rate simultaneously exceeded Azure and AWS dates back to the previous high-growth cycle of 2019-2020. However, the nature of this time is entirely different:
Back then, the growth was driven by the overall cloud migration dividend of the internet industry. Today, it is driven by structural demand brought by AI inference, model fine-tuning, and intelligent computing clusters.
The growth rate has "returned to its peak," and the underlying logic has already been upgraded.
2. AI Revenue: Alibaba is the only Chinese company that has crossed the dividing line between the "AI story" and "AI revenue."
Another noteworthy figure is Alibaba Cloud's quarterly AI-related revenue of 12.376 billion RMB, marking twelve consecutive quarters of triple-digit growth.
The standard to measure a company's AI capabilities is actually quite simple: whether it can disclose specific AI revenue figures. "Can" and "cannot" form the dividing line between AI revenue and the AI story.
Currently, only two companies globally publicly disclose AI business revenue on a comparable basis: the AI business ARR of Amazon AWS, and Alibaba today.
3. Chips and Profit Margins: Profit realization from integrated software and hardware is underway.
Delving deeper into this financial report, we have noted numerous advancements in Alibaba's chip R&D:
Today, the Zhenwu series chips have completed the closed loop from R&D to large-scale commercialization.
T-Head has built a full-stack self-developed system covering GPUs, CPUs, and network chips. Chips such as the Zhenwu M890 have been commercially deployed among over 650 external customers in more than 20 industries, including autonomous driving, the internet, and finance. The continuous expansion of chip shipments and customer coverage validates the external market demand for self-developed computing power.
At the profit margin level, the adjusted EBITA profit of the AI Cloud and Computing Power segment grew by 133% year-on-year, and the adjusted EBITA profit margin rose to 12%.
The North American benchmark for this figure is Amazon:
The ARR of Amazon's AI business, supported by its self-developed Trainium/Graviton chips, has exceeded $25 billion. Improving cloud profit margins through self-developed chips is a globally validated path, and Alibaba is replicating and accelerating this logic.
02. Computing Power is the Link Closest to Money
We have actually proposed more than once:
Market narratives have long focused the spotlight on the model and application layers, ignoring a basic fact: in today's AI value chain, the links that can truly generate positive cash flow are hardware and computing power infrastructure.
Even today's leading model layer is still marching through the valley of death.
OpenAI: 2025 revenue was approximately $13 billion, with an operating loss of about $20.9 billion; in Q1 2026, it had already burned about $3.7 billion (against $5.7 billion in revenue). The projected annual loss/cash consumption is in the range of $14-27 billion (with differences between internal forecasts and external estimates). It is still far from positive FCF and is not expected to turn positive until 2029-2030. Although cash reserves are thick (post-financing), the unit economics are still burning cash.
Anthropic: ARR has surged to approximately $47 billion (mid-2026), with a trajectory significantly better than OpenAI, potentially approaching operating profit sooner (reports point to possible quarterly operating profit by mid-2026). However, there are still massive infrastructure investments, and the expectation for FCF turning positive is later (around 2028-2029). Cumulatively, it is still consuming capital.
The coexistence of high gross margins and massive losses in the model layer precisely illustrates that this is an arms race where "participation is mandatory, but profitability alone is difficult."
Peter Thiel expressed a similar view early on:
The market has placed too much attention on the model layer. The hardware and infrastructure at the deeper underlying levels are often the ones that truly create value and capture profits.
Nvidia, TSMC (Taiwan Semiconductor Manufacturing Company), and memory manufacturers have captured the vast majority of the industry's profits, while most model companies are still burning cash.
Nvidia: TTM (as of April 2026) FCF is approximately $119 billion; FY2026 (as of January 2026) is about $96.7 billion. Operating cash flow is extremely high, while CapEx is relatively very low (only in the range of $6 billion). The cash conversion rate is astonishing (FCF margin is close to 45%+). It is almost the strongest cash printing machine in the current AI industry chain.
TSMC (Taiwan Semiconductor Manufacturing Company): TTM (as of June 2026) FCF is approximately $35-36 billion; the full year of 2025 was about $32 billion. CapEx is high (expansion of advanced process nodes), but it continues to generate strong positive FCF.
SK Hynix: FY2025 FCF is approximately $19 billion (25.85 trillion KRW); TTM (mid-2026) has surged to the range of $67 billion (about 92 trillion KRW), driven by HBM-driven explosions in profits and cash flow. Analysts have even higher expectations for the full-year 2026 FCF.
Samsung Electronics (especially the DS semiconductor division): The company's overall TTM FCF has significantly increased (reaching a high level by mid-2026, with some data pointing to the range of tens to hundreds of billions of dollars). The single-quarter operating profit in Q2 2026 set a record (approximately $58 billion, almost entirely from semiconductors/memory). HBM and server DRAM contributed massively, with FCF improving synchronously.
Roughly totaling (Nvidia + TSMC + SK Hynix + Samsung Semiconductor related): The current annualized positive free cash flow easily exceeds the $200-300 billion range and is still growing rapidly. Nvidia alone accounts for the absolute majority.
In the United States, for highly utilized computing power centers, the payback period can be compressed to about two years when multi-year, high-load contracts are signed.
SpaceX disclosures show that it leases Colossus capacity to Anthropic (approximately $1.25 billion/month) and Google (approximately $920 million/month), among others.
In the most recent quarter, the AI division's adjusted EBITDA has turned positive, reaching approximately $1.1 billion, driven almost entirely by computing power leasing. The pure Grok model business itself contributes limitedly; it is the infrastructure leasing that truly improves the financials.
This is even more true for traditional hyperscale clouds (AWS, Azure, Google Cloud):
- AWS: The operating profit margin has risen to approximately 39%, with significant contributions from AI-related businesses. Google Cloud: The operating profit margin has increased to over 35%, growing extremely fast. Azure also maintains high profit margins. They treat AI computing power as a high-value-added extension of existing cloud businesses, with relatively good pricing and utilization rates. Although the overall company FCF is suppressed by massive CapEx, the unit economics of the cloud/AI computing power segment are healthy.
Looking at specialized neoclouds (such as CoreWeave), the situation is the same.
Every one of them is experiencing surging revenue (single quarters have exceeded the $2.5 billion level), with adjusted EBITDA profit margins often at 55-60%.
However, because they are still expanding frantically, free cash flow remains significantly negative (CapEx far exceeds operating cash flow). Once capacity is delivered and begins generating recurring revenue, profit margins will rise rapidly. The contract backlog has reached the hundred-billion-dollar range, demonstrating the degree of demand lock-in.
AI-related businesses of AWS, Azure, and Google Cloud are also continuously contributing higher operating profit margins.
This is a repeatedly validated business model:
Large-scale, high-density, and high-load computing power supply, once it achieves economies of scale and customer lock-in, can generate cash flow characteristics similar to infrastructure.
This judgment equally applies to the Chinese market.
As the penetration rate of AI applications rises along an exponential curve, and token consumption rapidly approaches the quadrillion or even ten-quadrillion level, the narrative in China inevitably points to large-scale, continuous infrastructure investment.
Inference demand has shifted from "occasional invocation" to an infrastructure-level load that is "continuous, highly concurrent, and 24/7." Training can be completed in phases, but inference happens every day, every hour, and every second.
According to Omdia's "China AI Cloud Market Share 2025" report, Alibaba Cloud firmly ranks first with an overall share of 38.1% (AI IaaS reaches 38.6%).
When you see the smartest model players like Alibaba starting to buy land, build data centers, and hire civil engineers, you know where the focus of the industry is shifting: AI revenue does not lie, and Alibaba's AI logic is accelerating its realization.
03. The Compound Interest Effect of Full-Stack Self-Development
The story of Alibaba Cloud holds true not just because of its impressive growth figures, but because it simultaneously possesses three levels of structural advantages.
Realization of self-developed chips at the chip layer. From R&D to large-scale deployment, the Zhenwu M890 has gone through the validation cycle of 650 external customers. The design concept of integrated training and inference perfectly hits the current industrial node where the focus shifts from training-dominated to an explosion in inference demand. The cost advantages and supply chain certainty brought by self-developed chips are what relying on third-party GPU manufacturers cannot provide.
Open-source ecosystem at the model layer. The Qwen series has over 3 billion global downloads, over 300,000 derivative models, and a cumulative total of over 460 open-source models. The open-source strategy is forming a positive flywheel: the expansion of the developer ecosystem brings more scenario feedback, accelerating model iteration, increasing invocation volume, boosting computing power consumption, and increasing cloud revenue.
Economies of scale in infrastructure. The fully modular design architecture CUBE 5.0 compresses the delivery cycle of large-scale AIDC data centers to 100 days, leading the world. Upon the completion of this round of global infrastructure expansion, Alibaba Cloud will cover 30 regions and 104 availability zones. The economies of scale brought by the global layout will directly dilute the unit computing power cost and enhance profit margin elasticity.
The superposition of the three forms a complete closed loop from chips to models to computing power infrastructure.
Morgan Stanley believes that Alibaba possesses large-scale AI infrastructure, high-quality cloud services, and validated Qwen model capabilities. The combination of these three capabilities can support a certain price premium. The massive capital expenditures invested upfront are expected to yield relatively attractive ROIC and profit margins.
This is not a narrative that requires external validation; the data is already on the table:
Alibaba Cloud's growth rate is 45%, AI revenue has seen triple-digit growth for 12 consecutive quarters, and the EBITA profit margin has risen from 9.1% to 12%. The simultaneous improvement of these three sets of data points to the same conclusion:
The commercialization return path of Alibaba's AI is already clearly visible.
04. Conclusion
Alibaba Cloud is accelerating its growth on the basis of holding the first market share in China. Its profit margin has moved from single digits to 12%, AI revenue has achieved triple-digit growth for twelve consecutive quarters with an increasing proportion, and the full stack—from chips to Agent infrastructure to applications—is scaling up simultaneously.
The core fact reflected by this set of data is that previous strategic investments in AI have passed the incubation period and entered a new stage of accelerated AI commercialization.
The market is witnessing the realization of Alibaba's AI business logic.