Good day ladies and gentlemen. Thank you for standing by. Welcome to Alibaba Group's June quarter 2026 results conference call. At this time all participants are on listen only mode. After management's prepared remarks there will be a Q&A session. I would now like to turn the call over to Lydia Lu, Head of Investor Relations of Alibaba Group. Please go ahead. Thank you.

Good day everyone and welcome to Alibaba Group's June Quarter 2026 Earnings Conference Call. Joining the call today are Zhou Cai, Chairman, Eddie Wu, Chief Executive Officer, Toby Xu, Chief Financial Officer, Jiang Fan, Chief Executive Officer of Alibaba eCommerce Business Group. Before we get started, I would like to remind you that today's discussion may contain forward-looking statements based on management's current expectations that are subject to risks and uncertainties. We also make reference to non-GAAP financial measures. Reconciliations between GAAP and non-GAAP measures are included in today's earnings press release and investor presentation. Our comments will be on year-over-year comparisons unless we state otherwise. A replay of the call will be available on our website later today. With that, I would like to turn the call over to Eddie.

Good evening, good morning, and welcome to Alibaba Group's earnings call for the first quarter of fiscal year 2027. Over the past quarter, Alibaba's strategic AI investments have translated into robust results, with a total group revenue growing 9% year over year. AI commercialization has also accelerated across the board. Alibaba Cloud's external revenue grew 45%, and EBITDA increased 133% year over year. continuing to deliver on our commitment to accelerate growth. Revenue from AI-related products has maintained triple-digit growth for the 12th consecutive quarter with annual revenue run rate surpassing 49.5 billion RMB, around 7.3 billion US dollars. It is the core engine of Alibaba Cloud's growth acceleration. I'll now walk you through four key areas, AI and cloud commercialization, full-stack AI capabilities, AI application ecosystem, and consumption business. First, AI and cloud commercialization accelerated across the board and is expected to sustain high growth going forward. This quarter, Alibaba Cloud's external revenue growth accelerated to 45%, a 22-quarter high, while adjusted EBITDA margin reached 11.6%. Notably, this 45% growth was broad-based, driven by compute, storage, model-as-a-service, mass, and AI applications. We proactively scaled back low-margin business, continuing to improve the quality of our growth. This quarter, Annual revenue run rate from AI-related products exceeded 49.5 billion RMB and its share of Alibaba Cloud's external revenue rose to 35%. AI-related products generate significantly higher gross margins than the average cloud portfolio. Our recurring AI-related product revenue spans multiple layers, AI compute, mass, and AI applications. This multi-layered mix of AI revenue sources and monetization models means growing customer demand at any layer converts directly into commercial opportunity for us. This structural advantage will underpin sustained rapid growth in recurring AI-related product revenue going forward. The surge in AI agents directly drives demand for tokens and GPU compute while also significantly boosting demand for our traditional cloud products across CPU compute, storage databases, and networking. Alibaba Cloud is undergoing a comprehensive upgrade to an agentic cloud. Based on the latest data, the ARR of our model and application services, including MAS, has surpassed 16 billion RMB. Based on current market feedback and our contract pipelines, compute demand will continue to outstrip supply. As we continue to ramp up our supply, our AI and cloud revenue growth will accelerate further in the coming quarters. alongside continued improvement in profitability. Second, our full-stack AI capabilities continue to strengthen, marked by the scaled commercialization of proprietary chips, faster model iteration, and a thriving open-source ecosystem. This quarter, deepening synergy between proprietary THET chips and proprietary foundation models further improved our AI commercialization efficiency. T-Head has established a full-stock proprietary silicon portfolio spanning GPU, CPU, and networking chips. As of early August, Zhenwu chips have served more than 650 customers on Alibaba Cloud. The SuperNode instance powered by T-Head's next-generation Zhenwu M890 AI processor recently launched on Alibaba Cloud at commercial scale. We expect supply to continue ramping up in the second half of the year to meet strong customer demand. Alibaba Cloud's Zhenwu M890 Supernode can efficiently run inference workload for foundation models with more than 2 trillion parameters. Both KBK3 and QN3.8 Max are already using it to provide mass services to external customers. At the data center layer, Alibaba Cloud has cut the delivery time for hyperscale AI data centers to 100 days, a world-leading pace that will significantly speed up our global compute infrastructure build-out. At the model layer, our model release cadence has intensified over the past month with major iterations across our large language, image, audio, video, and music models, all ranking among the world's top tier. Last week, we opened the model weights of QN 3.8 Max with 2.4 trillion parameters and the QN 3.8 27B model series. To date, the QN model series has been downloaded more than 3 billion times globally with more than 300,000 derivative models built on it. We believe a thriving open-source model ecosystem drives greater demand for our cloud computing services, creating a virtuous cycle. Our AI-native applications span both enterprise and consumer use cases, driving rapid growth in token consumption. On the enterprise side, we launched QNWork, a new AI productivity product built for enterprise workforce scenarios, delivering agentic capabilities at scale. We expect productivity agents to become another engine of ARR growth. On the consumer side, the QN app continued to steadily grow its user base and is expanding the range of its value-added offerings. Through close coordination between Alibaba Token Hub and Alibaba Cloud, we're running a highly efficient commercial flywheel across compute, models, tokens, applications, and monetization. Fourth, our e-commerce businesses remain solid this quarter. In quick commerce, we continue to narrow losses substantially while growing business scale by 45%, with unit economics improving quarter over quarter. Having crossed the AI commercialization inflection point last quarter, we're now seeing growth accelerate and margins expand this quarter. Our AI business's own capacity to self-fund and sustain itself is strengthening, giving us greater confidence to keep investing. Looking ahead, AI has become Alibaba's most certain growth engine. We will stay strategically disciplined and drive long-term growth through our full stack AI capabilities. I'll now hand over to Toby to walk you through our financial results. Thank you.

Thank you, Eddie. Our strategic priorities in AI plus cloud and consumption businesses backed by disciplined investments delivered strong results this quarter. Cloud segment revenue growth further accelerated to 45%, with its EBITDA margin sequentially rising to 12%. AI-related product revenue continues to drive this momentum, marking the 12th consecutive quarter of triple-digit growth and accounting for 35% of external cloud revenue. The strong performance demonstrates growing customer adoption of our full-stack AI capabilities, spanning AI agents, models, cloud infrastructure and proprietary chips, as well as our enhanced scale efficiencies and the robust pricing power in a supply-constrained market. On consumption, Taobao Instant Commerce continue to improve its unit economics while maintaining market share. Overall e-commerce EBITDA remain relatively stable year over year. To realize synergies across our commerce platforms and strengthen our full-stack AI capabilities, we have implemented strategic alignment of certain businesses in our financial reporting. Starting from this quarter, our segment reporting will present the following. First, Alibaba e-commerce group. Second, AI cloud and computer services. Third, AI labs and applications. And number four, all others. Now let's look at the financial results for this quarter. Total revenue increased 9% year over year to RMB 269 billion, driven by the strong momentum in cloud business and quick commerce. Total adjusted EBITDA decreased 30% to RMB 27.3 billion, primarily attributable to the investment in technology, partly offset by the improved operating results in our cloud business, as well as enhanced operating efficiencies across various businesses. Our gap net income was RMB 10.4 billion, a decrease of 75%, primarily due to the decrease in income from operations, and decreasing net gains from disposal of investments and mark-to-market changes of our equity investments. Operating cash flow this quarter increased by 11% to RMB 22.9 billion compared to RMB 20.7 billion in the same quarter last year. Free cash flow was an outflow of RMB 44.7 billion compared to an outflow of RMB 18.8 billion in the same quarter last year. The decrease was mainly attributed to the investment in cloud infrastructure. CapEx was RMB 67.7 billion this quarter, reflecting our continued investments in AI infrastructure to meet strong and growing customer demand. The significant year-over-year increase is due to several reasons, including fluctuations in procurement cycles, increasing in CPU compute capacity driven by anticipated growing customer adoption of AI agents in a higher pricing of a broad range of chip components. As of June 30, 2026, we held approximately US dollar 30.7 billion in net cash, excluding debt with maturities beyond five years. Our net cash position stands at approximately 46.5 billion. This balance sheet strength gives us confidence to invest for robust growth. Our AI plus cloud investment has a clear path to attractive ROIC. Our service equipped with chips typically reach break-even within three years. With a five-year use for life, we expect them to generate positive free cash flow at least in the two years following break-even. For the quarter ended June 30, 2026, We repurchase the shares of an aggregate consideration of US$162 billion. We remain committed to maximizing long-term shareholder returns through disciplining the capital allocation across investments for AI plus cloud, business growth, share buybacks, and dividends. We will adjust our priorities as market conditions and strategic needs evolve. Now let's first look at our e-commerce businesses. The new Alibaba e-commerce group reflects our strategic focus on unlocking significant synergies across our domestic and cross-border e-commerce businesses. Starting from this quarter, we will present Alibaba e-commerce group's revenue as the following. First, China e-commerce. Second, China quick commerce. Third, international e-commerce. And fourth, global wholesale. Revenue for Alibaba e-commerce group was RMB 205.9 billion, an increase of 4%. Customer management revenue decreased by 7%. Excluding the contract revenue impact from the new business development program, customer management revenue would have grown by 1% year over year. Revenue from China quick commerce business was RMB 53.3 billion, an increase of 45%. driven by Fresh Apple and Taobao Instant Commerce. Alibaba e-commerce group's adjusted EBITDA remained relatively stable year-over-year at RMB 39.7 billion, unscoring our cost discipline against the backdrop of increased investments in user experiences and technology. Taobao Instant Commerce continued to improve its unit economics quarter-over-quarter while maintaining market share. driven by higher average order value and enhanced fulfillment logistics efficiency. In addition, Art Express achieved operating profit this quarter. We aim to maintain steady profit in our conventional e-commerce business while continuing to drive profitability improvement in our quick commerce business. Now let's review the business updates and results of AI Cloud and compute services. which comprises the Cloud Intelligence Group and THED. The year-over-year growth of total revenue and revenue from external customers both accelerated to 45%. Revenue from Alibaba Cloud also accelerated, growing 45% year-over-year. We are confident the growth rate will further accelerate in the coming quarters. This quarter's AI-related product revenue was RMB $12.4 billion, implying an annual revenue run rate of RMB 49.5 billion. It delivered a 12th consecutive quarter of triple-digit growth and accounted for 35% of external cloud revenue. The adjusted epitome margin expanded to 12%, driven by improved economies of scale and a stronger pricing power of AI-related products amid tight market supply. We expect EBITDA margin to further expand steadily in the coming quarters. By improving resource utilization, optimizing model portfolio, and innovating new scenarios, we are accelerating the growth of AI plus cloud business and driving greater benefits of scale. AI Lab and Applications comprises AI Model Labs, Queen Consumer Business Group, and QueenWorks. Its adjusted EBITDA was a loss of RMB 13.9 billion, primarily due to our increased investment in AI capabilities and higher inference costs related to Queen APP. The loss significantly narrowed quarter over quarter due to the reduction in marketing expenses for Queen APP. We expect the segment loss to narrow over the coming quarters, driven by improving efficiency in both model training in a marketing span on QueenApp. We have launched our frontier language coding, video, audio, image, and music models, all delivering top-tier performance. 250 million users have had their first AI-driven shopping experience through QueenApp's agentic features across an expanding range of e-commerce and other services since the launch of QueenApp. All other segment revenue remained stable at RMB 28.8 billion. All others adjusted at the top with a loss of 3.3 billion, primarily due to our increased investment in technology. AI has progressed from incubation to commercialization at scale. As we expand our market share, strengthen AI leadership, and improving operating efficiency, we are gaining greater strategic and financial flexibility to make disciplined and sustained investments in both full-stack AI capabilities and consumption opportunities, driving secular growth and greater value for our shareholders. Thank you. That's the end of our prepared remarks. We can open up for Q&A.

Thank you, Toby. We will now begin the Q&A session. You're welcome to ask questions in Chinese or English. If third-party translators will provide consecutive interpretation. In the case of any discrepancy, our management statements in the original language will preview. Operator, please start Q&A session. Thank you.

Thank you. If you wish to ask a question, please press star one on your telephone and wait for your name to be announced. If you wish to cancel your request, please press two. If you're on a speakerphone, please pick up the handset to ask a question. To give more people the opportunity to ask questions, please keep yourself to know more than one question at a time. Your first question comes from Alicia Yap with Citigroup. Please go ahead. Thank you. Good evening, management. Thanks for taking my questions and also congrats on your solid cloud performance. Management, please comment on the reasons and the drivers for the significant increase in the CapEx this quarter. And also, what is the expected CapEx trend for the coming quarters? And are there any updates to the existing three-year CapEx budget that you have of this $380 billion that you mentioned before? And also, we would appreciate if management can also provide a breakdown of the CapEx allocation across the different services like the training calls and all that. And then also, what is management expected return on the invested capital for this investment? Thank you.

感谢管理层接受我的问题。 大家晚上好,也祝贺你们取得云方面的非常扎实的业绩表现。 我想问的问题主要是有关于本季度CAPEX大幅增加背后的原因和驱动力。 I also want to understand the overall trend of CapEx in the next few seasons. How do you see it? And for the previously announced budget of $3.8 billion invested in three years, is there any new updates? I also want to understand the current situation. Thank you for your question. I think this question is very important. I would like to use this question to explain in detail the whole problem of AI.

We have announced 3.8 billion investment plans in February last year. By the end of June this year, we have invested 1.9 billion yuan. The progress is still in line with the expectations. The 6.7 billion yuan of this quarter is indeed a little higher, but because of hardware delivery, it has a cycle that is not uniform in each quarter, so it is more of a fluctuation of equipment delivery. . . . . . . . . . . .

Thank you very much for the question. It's an important question and I'd like to take the opportunity perhaps to explain generally what our business model is for AI and our expectations around CapEx. So indeed last February we announced a three-year capital investment plan with total investment of 380 billion RMB. As of the end of the June quarter this year, we had already spent 190 billion RMB with progress broadly in line with our expectations. While this quarter's spending of 67.1 billion is somewhat higher, Hardware deliveries follow different procurement cycles. There can be fluctuations in the cadence and pace of hardware deliveries. So it's not evenly distributed across different quarters. So the increase primarily reflects volatility in those equipment delivery schedules. At the same time, we increased procurement of CPUs this quarter as we are witnessing a substantial surge in demand driven by the agent demand. of course rising prices for semiconductor components have also contributed to this trend. So I don't think we should take the spending for this quarter and multiply it by four to come up with an annualized figure for the year or to expect there will be a steady linear progression. The build-out has been progressing at a steady pace but that is the overall situation.

I would like to take this opportunity to explain the commercial model of full-time AI platform service. It is essentially a commercial model of heavy assets. We now see all the AI transformation methods, whether it is AI software subscription, or big model API reasoning, or GPO S service, and AI-related training or reasoning software service. Basically, all these transformations all need to be based on the AI computing center. The construction of the AI computing center allows us to achieve a high market share growth. So this is a very clear business model of heavy assets. The premise of achieving high growth in the heavy asset business model needs to be preempted on the investment of CapEx. So we will see that we have entered a very positive hardware and so on. From this perspective, if we want to achieve a high-speed growth, we have to invest more in the construction of an AI-based center in the first few years. So this is what we decided in the business model. We have to start with CAPEX in order to achieve the growth of the business.

Next, let me expand on our full-stack AI business model. This is an asset-heavy business model. If you think about all of the different ways that AI is monetized and can be monetized, be it through software subscriptions, be it through API calls, through models as a service, through training, inference, In all of these different respects, you need compute centers to run and to monetize. So it's only possible to monetize when you have that compute capacity in place. So what that means is that we need to be investing up front in order to be able to grow this business model and monetize across all of those different areas. So that's why beginning in 2025, we began a heavy investment cycle in hardware. And this is really a function of that asset-heavy business model, as I explained. In order to be able to capture that future growth, we first need to make these CapEx investments to build out the necessary compute capacity.

Okay, let me explain why at this stage, The return of AI-based capital investment is very high. The industry consensus is that before 2030, we did not see a significant change in the accuracy of AI-based capital investment. So in the context of this industry, we now see that the return of AI-based capital investment is very high. According to the data, according to the average profit level of our current AI products, Next, let me explain why we see return on invested capital in AI-related CapEx is highly certain.

The consensus across the industry that the current shortage in AI compute will not be resolved until at least 2030. So industry-wide then, it makes sense that there should be high certainty in our investments in AI compute. Based on average gross margins today, roughly, We can break even on AI-related CapEx in three years. And of course, average gross margin continues to rise, and we expect to be able to shorten that payback period, say, to 2.5 years.

According to our current investment return cycle, our AI algorithm will be able to calculate will continue to contribute to the positive cash flow for a very long time. From the actual situation we are seeing now, a most direct example is in our data center. The A100 purchased in 2020, the V100 purchased in 2018, have been used by customers for almost a full year. The actual use of AI calculated assets is far longer than the theoretical saving cycle.

Following that three-year payback period then, these AI assets that we've invested in can achieve very positive and robust cash flow. So to give you some direct examples, an A100 purchased in 2020 or A100 purchased in 20, sorry, V100 purchased in 2018, even are still running at full capacity.

At the same time, we have three important methods to increase the profitability of AI products and the return on investment. The first is that we can continue to increase the profitability of AI products, including our continuous investment in promoting our short-term model construction and expanding our mass business with high profitability. At the same time, we can also use our better product combination to increase the profitability of our AIaaS products or AI-related software products Additionally, we have three means that we can leverage to further enhance gross margin and return on invested capital.

First is we can continue to develop state-of-the-art models and enhance growth margin on AI products themselves and continue to expand a higher margin model as a service mass businesses and we can adapt our product mix across IaaS and across software to achieve higher gross margin on the portfolio as a whole. You know, as a result of improving gross margin, you've already seen an overall increase of 4.4 percentage points in Alibaba Cloud's overall segment profitability, bringing it this quarter to 11.6%. So that represents initial validation of that thesis.

The second important measure is our self-taught chip. It can also be said to be the most important method in this process. Our flat-header This is the most expensive cost in the AI data center, which is chips and storage. Chips and chips are our long-term important direction. As we cut chips and chips, the future productivity will continue to improve. The proportion of chips and chips in our data center A very important piece of this is our ability to deploy our own proprietary chips

As you know, our own T-head proprietary chips span GPUs, CPUs, and networking chips, which are the critical chipsets for AI. And in AI data centers, the most expensive components are, of course, chips and storage. So we have a very significant advantage in being able to deploy our own proprietary chips. As we ramp up deployment of our own proprietary chips in our data centers as they account for an increasing proportion of total chips and replace commercially procured chips, we can expect to see substantially higher gross margin as well as profitability.

The third point is also very important. We have many commercialized methods to improve our Third, and also very importantly, we have means to monetize

Through these three paths,

We can continue to push the return cycle of AI CapEx to a shorter time, such as 2.5 years, or even close to 2 years. I think I can give you a simple framework. If we follow our current AI product profit level, under the conditions of CapEx return in 3 years, theoretically, we can achieve positive cash flow by controlling our value within 33%. But this is not our strategic choice now. Since AI is still in the early stages of the entire industry, for our strategic choices, we will firmly invest in the positive construction of capacity to promote the rapid growth of business. And later, with the increase in product efficiency, the increase in the replacement ratio of self-propelled chips, our return period will be 2.5 years or even shorter. We can see that while pursuing more than 40% of the true number, we can still maintain the positive trend. This is our long-term direction.

So through these three different methods, we can shorten the payback period for AI CapEx, for example, to two and a half years or even two years. And we can apply a simple framework to understand this. At our current level, of gross margin for AI products and under the assumption of a three-year payback period on CapEx. Theoretically, keeping our growth rate below 33% would already enable positive cash flow. However, that is not our strategic choice at this time. Given that AI remains in a very early stage, we're committed to aggressively investing in CapEx and proactively scaling up to drive our rapid business expansions. As our product gross margin improves and our proprietary chip substitution rate increases, our payback period will shorten to two and a half years or even less. And so under those circumstances, while pursuing growth of over 40%, we'll also be able to maintain positive cash flow. So that is our long-term strategic direction. Paul?

Next question please.

Thank you. Your next question comes from Charlene Lu with HBSC. Please go ahead.

I'm from HSBC, but thank you very much for this opportunity. Thank you for taking my question. First, can we get an update on the latest developments in QuickCommerce? under the reclassification of multiple business lines which are regrouped under the Alibaba e-commerce group. Can you talk about the future strategic focuses of these lines of businesses? Let me quickly translate the question myself. 非常感谢给到我这个提问的一个机会。 我想问一个跟电商相关的一个问题。 Thank you. Thank you for your translation. Let me introduce you.

In the new year, we have re-organized the e-commerce market. In the future, we will focus on China e-commerce, real-time sales, international e-commerce, and global B2B, the four core markets, to share with you our progress. Today, I would like to briefly share the progress and thoughts of these four markets. First, China e-commerce. In the short term, domestic e-commerce is facing challenges. In the long term, we focus on core supply, Okay, thank you very much for the question as well as for the translation.

In the new fiscal year, indeed we've realigned our e-commerce business segments and moving forward we'll be updating progress on four core areas, China e-commerce, quick commerce, international e-commerce, and global B2B, global wholesale. Let me then briefly share the strategic priorities and key considerations for each of these four segments in the period ahead. So starting with China e-commerce, While the domestic e-commerce landscape faces short-term macroeconomic challenges, our long-term strategy centers on strengthening core supply capabilities and at the same time we aim to leverage AI to enhance the overall shopping experience and improve operation efficiency across the board. So first, regarding supply, since last year Taobao and Tmall have focused on supporting Original merchants, including branded sellers, while simultaneously unlocking the potential of high-quality white-label suppliers from key industrial clusters.

We will continue to strengthen the cooperation between the head brand and the head brand, helping brand customers achieve stable and sustainable business development. TMAO is still the most core business location for brand and many original customers. At the same time, we will also dive deeper into the industrial era, dig up the original good goods, support more manufacturing, factories, and directly open and manage on the platform, and also use the platform AI ability to help more white-collar merchants carry out simpler and more efficient decommissioning and management. The decommissioning and management model of the industrial era is continuously increasing in the number of transactions on the platform. In the just-passed 618, although the macro environment faces certain challenges, it meets our expectations from the results.

We will continue to strengthen our partnerships with leading brand merchants helping them achieve stable and sustainable business growth. Tmall remains the most critical operational hub for both major brands and many original merchants. At the same time, we are diving deeper into industrial clusters to source high quality products directly from their origins. We are supporting more manufacturing factories and operating directly on our platform and leveraging our platform AI capabilities to enable white label merchants to adopt a simpler and more efficient managed operation model. and the share of transactions being generated through that industrial cluster managed model continues to rise steadily. In the past quarter, during the recent 6-18 shopping festival, despite certain macroeconomic challenges, the outcomes were aligned with our expectations and notably core merchants achieved solid growth.

At the same time, we also see the opportunity of AI in e-commerce. We will continue to launch more new experiences and new scenarios based on AI capabilities on the user side, such as multi-mode search, AI test and so on. On the other hand, we will use AI technology to improve the experience and efficiency of existing shopping scenarios. For example, we see that AI has brought a very significant improvement in our product recommendations. On the business side, we see that business has been using AI very commonly in business. We are trying to use AI to help businesses improve their capabilities in various areas of business, especially data analysis, advertising, marketing, customer service, etc. Businesses can obviously benefit from it. Later, we will also sign with office cooperation to introduce AI intelligence that is more suitable for e-commerce scenarios.

At the same time, we see significant opportunities for AI across both the supply and demand sides of e-commerce. On the consumer side, we will continue to launch new experiences and scenarios powered by AI, such as multimodal search and virtual try-ons. Our goal is twofold. First, to use AI technology to enhance the experience and efficiency of existing shopping scenarios, and we've already observed that AI has driven significant efficiency gains in our product recommendations and secondly to drive new kinds of AI-driven interaction. On the merchant side, we observed that merchants are already widely adopting AI in their operations. We're exploring ways to leverage AI across various operational links to boost merchant capabilities, particularly in data analytics, advertising and marketing, and Customer Service, where merchants can derive clear benefits. And going forward, we'll also collaborate with Quen Office to launch AI agents that are specifically tailored for e-commerce scenarios.

Back to the issue of time-saving sales, after more than a year of development and investment, the scale and market share of Taobao Shopping have already undergone substantial changes. Whether it's the new users, the supply and demand, the logistics experience, or the order size, In the previous quarter, we achieved a significant improvement in terms of the size of the users and orders, and the loss was significantly reduced. Based on today's basis, we will accelerate the development of non-food products in related sections such as He Ma and Tianmao Supermarket, especially accelerating the development of front-end warehouses. He Ma has accelerated the layout of front-end warehouses in the past year, which has led to the same acceleration of the entire transaction volume.

Next on QuickCommerce, after more than a year of investment and development, Taobao Instant Commerce has undergone substantial changes in scale and in market share with significant improvements across user mindshare, supply diversity, logistics experience, and order volume. Last quarter, while maintaining growth in both users and orders, unit economics, UE, substantially improved and losses significantly reduced. On that basis, we will accelerate the integration of businesses such as Fresh Depot and Tmall Supermarket to develop the non-food categories growth within the quick commerce business. And we'll place a particular focus on expanding our front warehouses. Over the past year, Fresh Depot has accelerated the development of front warehouses leading to year-over-year increase in GMV.

At the same time, retail will continue to expand more categories and cover some key categories to continue to innovate and provide consumers with experience. We believe that the scale of in-store retail transactions of non-food categories will exceed food categories in the next fiscal year and will drive many food categories in e-commerce. The in-store version is expected to achieve overall profit in the second fiscal year. In the long term, we believe that in-store retail is expected to contribute 30% of the overall trading volume of the platform as the second curve of e-commerce.

Meanwhile, quick commerce will continue to expand its category coverage and innovate in key areas to enhance the consumer experience. We expect the transaction volume of quick commerce for non-food categories to surpass that of food categories within the next fiscal year, driving growth in many different physical goods categories across the overall e-commerce business. The quick commerce business is expected to achieve overall profitability in FY29. In the long term, we believe it has the potential to contribute 30% of the platform's total GMV, becoming the second growth curve for our e-commerce business.

We are facing such a pressure. But we also see that even though the market environment is very complicated, cross-border e-commerce still maintains an increase in the scale of trade, and the level of profit is also significantly improved. Whether it is the scale of trade or the ability of profit, we all think that cross-border business has a long-term potential. In addition, our local e-commerce platform in Turkey, the Middle East and other areas is also developing rapidly. The efficiency of the market in Southeast Asia is also continuously improving.

Third is international e-commerce. In the short term, our international e-commerce business has indeed been affected by tariff policies and the geopolitical environment pressuring growth. That said, despite the complex market environment, our cross-border business has delivered significant improvement in profitability while maintaining growth in transaction volume. In terms of both transaction scale and profitability, we believe the cross-border business holds long-term growth potential. In addition, our local e-commerce platforms in international markets such as Turkey and the Middle East are growing rapidly, and operating efficiency in markets such as Southeast Asia continues to improve.

Finally, about the global B2B business. B2B business includes our 1688 platform and alibaba.com. In the past 20 years, this business has been continuously developing. We have seen that AI technology will bring great changes to B2B trading platforms. It may even change the business model fundamentally, especially the agent model. It will play an increasingly important role in B2B trading. Our face-to-face cross-border e-commerce business is launched by AI intelligent XU Work. It will soon have 50,000 paid merchants using it. AI is completely changing B2B merchants, especially the way cross-border merchants do business. We believe that And fourth is global B2B. Our B2B businesses including the 1688 and Alibaba.com platforms have grown consistently over the past two decades.

and we see that AI technology will bring profound changes to our B2B platforms and may even fundamentally reshape existing business models. In particular, the agentic model will play an increasingly important role in B2B transactions. We've launched AxioWork, which is an AI agent for cross-border merchants and it had already attracted over 50,000 paying merchants shortly after its launch. AI is comprehensively transforming the way that B2B merchants do business, especially cross-border merchants. We believe that building on our two decades of know-how in this field, we have the opportunity to create entirely new business models and commercial opportunities in B2B and in cross-border trade in the AI era.

Overall, in the past few years, we have completed the step-by-step layout of several areas in the e-commerce platform. Next, we hope to continue to play our advantage from co-op to AI technology, let each business release more potential in the AI era, and at the same time realize a more diversified income-benefit structure to promote a more stable development of the entire platform.

Overall, over the past few years we have completed new strategic positioning for our e-commerce businesses across several key areas and going forward we aim to continue leveraging our strengths from supply chain synergies to AI technology to unlock greater growth potential for the e-commerce segment in the AI era while building a more diversified revenue and profit structure to drive steadier development of the overall segment.

Operator, let's go to the next question. Thank you. Your next question comes from Yang Bai with CICC.

Please go ahead. Thank you. Hello, Director. My question is about Yun's AI business. We observed that the income increase of AliYun has increased by 45% in this quarter. The company has also mentioned that in the next five years, Yuen Jen Yao But I also want to ask, if we look at it from a longer perspective, what are the core drivers of cloud business long-term growth, and will they be different in the short term? Thank you.

Thank you. My question is about the cloud and AI business.

We've seen that Alibaba Cloud's revenue growth has been accelerating quarter by quarter, reaching 45% this quarter. We know the company has previously set a long-term goal of exceeding US$100 billion in external cloud revenue over the next five years. And you've also now indicated that growth will remain on an accelerated trajectory in the quarters ahead. So I'd like to ask two questions. First, looking ahead to the coming quarters, what do you anticipate being the pace of growth in the cloud business? What are the core drivers underpinning the continued acceleration of cloud computing growth? And then secondly, as you've mentioned, the industry is now in a phase of relatively tight capacity in terms of supply of compute. And you just mentioned that that supply demand dynamic may shift around 2030. So I'd like to ask from an even longer term perspective, what are the fundamental growth drivers for the cloud business? And do they differ from those in the short term? Thank you.

Thank you for your question. I think this question can be divided into three parts. First, let's talk about the current status and data of our business. Then, let's talk about what drives the growth of our AI-related business. Finally, let's talk about the long-term outlook from our analysis. First, let's talk about the current status and data of our business. We see that the external income of AI plus cloud has continued to increase by 9 seasons. This season has continued to increase by 45%. We see that the demand of customers is strong, and our supply has a very strong advantage compared to other cloud manufacturers. So in the next few seasons, we judge that the income increase will continue to increase. We see that the products related to AI this season We can see that our real value is still very high. At the same time, we also judge that in the next few seasons, our EBITDA profit rate will gradually There will be some gradual improvements in each quarter. Also, our mass business, which is very important to our cloud business, is growing in terms of AI needs in this quarter, and we are promoting the improvement of our reasoning efficiency. The latest mass business of ARR in this quarter has already surpassed RMB16 billion. I would like to add that the latest data in August have already reached 1.6 billion yuan.

Thank you for the question. I think I can expand on this in three different areas. I can start by looking at our current business and the relevant data. Secondly, I can discuss the drivers for growth. And then thirdly, I can share with you our long-term perspective. based on that analysis. So let me begin with the first part covering our current business and the key metrics. So as you've seen, external revenue for the AI and cloud segment has been accelerating now for nine consecutive quarters. And in this last quarter, growth has already accelerated to 45%. We're seeing very strong customer demand and our offerings boast a distinct competitive advantage compared to those of other cloud providers. As a result, we expect revenue growth to continue accelerating over the coming quarters. We've observed that AI-related products generated 12.4 billion RMB in revenue this quarter and so if we convert that and many more. Looking ahead to the next quarter, our own forecast is that that same annualized revenue for AI quarters next quarter will approach $10 billion, so our growth rate remains exceptionally strong. At the same time, we also expect our EBITDA margin to improve quarter by quarter sequentially over the next few quarters. Additionally, something very important in respect to the cloud business is growth in demand for mass. We've seen very significant growth in demand for mass this quarter, coupled with ongoing So the ARR of our mass business has now surpassed 16 billion RMB. And actually, let me clarify, that's the latest data as of August. It's already surpassed 16 billion RMB.

Okay, let me explain the growth of our business model. I think we should first talk about Alibaba's growth in AI. and many more. We have the biggest difference between AI companies and single AI companies. We invest in AI full-time technology, especially in chip, AI cloud infrastructure and model testing. We guarantee that we are at the forefront of the industry in these three most important technical levels. And we judge that the technology development of the AI industry is still early. In different technical development stages, AI industry's core commercial value will flow between chips, clouds, models, and applications at different levels. And our full-time investment can ensure that we have the best service capacity and the best cost-effectiveness, which also allows us to maintain competitiveness and long-term growth at different stages of technology development in the future industry.

Next, let me expand on the growth drivers within our business model. So it's important to understand that Alibaba's investment model for AI is fundamentally different from that of pure play AI companies. We are pursuing an intensive strategy across the full stack including chips, including AI cloud infrastructure, and including models. And we maintain a leading position in the industry across all three of those most critical domains. Moreover, we believe that the development of AI and technology across the industry is still in its early stages. Looking forward at Different stages of technological development, the core commercial value within the AI industry may shift across different layers, including chips, cloud computing, models and applications. Our full stack investments ensure that we can deliver optimal service capabilities and the best value for money, positioning us favorably in the industry going forward. and ensuring that within each stage of technological development it's possible for us to maintain competitiveness and sustained growth momentum.

后面我再来讲一下我们觉得在最近一两年的一个短期的最重要的增长动能 我觉得从2025年底开始爆发的大规模的商业化推理服务 已经彻底改变了AI云的商业模式 刚才我已经提到了 are the core assets of AI revenue. Now, all AI revenue models are based on AI profit. And the lack of profit is now the middle ground of the entire industry. At the same time, due to the high labor force of mass service providers, AI profit is no longer a cost center in the original sense, but a production data related to income. Therefore, high-end profit in the industry is not required, are all using GPUs in different scenarios. But in different scenarios, their pricing models are becoming more and more similar to the industry's highest-performance transformation methods, and they are promoting pricing models for almost all GPU-related products. Alibaba also has full-model model capabilities. Most of our model capabilities are at the top of the industry, which makes us have an advantage in computing value implementation. so that we have a very strong milestone in our profit-making pricing, and we have a differentiated pricing ability in Aliyun's profit-making. So we see that the abundant demand and profit-making commercial value is being re-priced by the market, and we can also sign new contracts at a healthier price, and the renewals of customer old contracts are all with the change of pricing model with the new market demand.

Next, let me look ahead to what we think is going to be the most important growth driver over the next one to two years in the short term. So we've seen exponential demand for commercial Inference Services. As of the end of 2025, this exponential growth in demand for inference has marked a fundamental shift in the model whereby compute has now become the core asset driving AI revenue. And today, all AI-related revenue models are centered on AI compute. And at the same time, there's a and many more. A major difference. If compute was once a cost center, traditionally compute has now been transformed into a core productive asset whose value generation is positively correlated with revenue. So high-priced computing power remains in short supply across the industry precisely at a time where you have widespread adoption of GPUs across diverse use cases. So pricing models are tending to converge on the most high margin, the most margin generative monetization approaches. So this is driving the pricing models for nearly all GPU-related products. Moreover, Alibaba boasts comprehensive multimodal model capabilities. Our models are at the state-of-the-art level within the industry, giving us a distinct advantage in realizing the value of that compute power and providing a robust anchor for our pricing strategy. When it comes time to price for new customers or to re-sign contracts with existing customers as they renew, we can adopt more healthy pricing models. And so we expect to see this as a very positive short-term driver for improving margin in the coming year plus.

Let me talk about the long-term scale and network effect of the business model of AI cloud. It is actually a long-term growth function. In fact, two years ago, industry investors have been asking what the super application of AI is. But in fact, we think that the biggest super application of AI is the AI cloud platform with AI algorithm as the core. We have seen that for a long time, the core AI work load of most companies and many more. For example, large-scale training, large-scale reasoning, customized reasoning software, corporate agent development and operation, all of these need to be provided by a full-time cloud platform to cover GPU, CPU storage, database and virtualization, including HANIS tools and other comprehensive cloud-based infrastructure services. So AI cloud is like a super-model. Training, reasoning, and AI Aging, just like the residents of this city, continue to improve the full-time AI cloud service, just like the basic facilities of the city, continuously attracting more new residents, and increasing the flow rate of old residents. So we look at the size effect and network effect of the AI cloud platform for the long term, which can promote our AI business for the long term.

Next, let me talk about the scale effects and networking effects, which are very important long-term growth drivers in AI cloud. For the past couple of years, a lot of people have asked, what is the super app for AI? And the answer to that is that the real super application is AI. Thank you very much. virtualization as well as harness tools among others. So AI cloud is like a super city in which workload is the residents and continually iterating full-stack AI cloud services are the urban infrastructure which in turn attracts more new residents and enhances the So this is where you see an extremely powerful network effect and scale effect.

因此鉴于我们拥有亚洲云厂商里最多的数据中心具备最强的规模效应 同时我们自研的屏图哥AI芯片的规模化部署扩大 避免了外购高端的商业化GPU的高额溢价对毛利的侵蚀 At the same time, due to the ability of our self-proclaimed industry SOTA model, it has extremely high computing and pricing capabilities. Therefore, from the development trend of the industry and our own product advantages, the long-term revenue growth and profit growth of AI plus cloud is very strong. Therefore, for the goal of accelerating the realization of the $100 billion in external income of cloud in 2030, we have very strong confidence.

So given that we operate the largest number of data centers across any Asian cloud provider, we benefit from the strongest economies of scale. At the same time, the large-scale deployment of our proprietary THET AI chips allows us to avoid the high price premiums associated with procuring expensive commercial GPUs, thus avoiding erosion of our gross margins. And with our state-of-the-art performance in our proprietary models, we possess strong pricing power for our compute resources. So looking ahead from the perspective of industry development trends and our own product strengths, the long-term revenue growth trend and margin expansion trend are exceptionally strong. And as a result, we're highly confident in our ability to achieve our goal of $100 billion in external cloud revenue by 2030. and we have good visibility into achieving gross margin of 20%.

好,謝謝。 Next question, please.

Thank you. Your next question comes from Yuan Liao with CITICS. Please go ahead.

感謝管理層接受我的提問, 也恭喜公司這個季度在營業狀和AI這個領域的積極進展。

I have a follow-up question about our master business. Just now, Eddie also talked about our ARR of over 160 billion yuan in August. I want to ask, we talked about our goal at the end of the year, which is to break through 3 billion of the master ARR. Based on the current progress, will our goal at the end of the year be adjusted? In addition, in the master business, the ratio of our specific model and the three-sided model Thanks for the opportunity to ask a question and congratulations on the strong quarterly results and especially the progress made in the AI sector.

So I have a follow-up question on the mass business. As Eddie mentioned earlier, ARR, as of August, has exceeded 16 billion RMB. And last quarter, I believe you stated that the target for year-end is to surpass 30 billion in mass ARR. So I'm wondering, given the progress to date, do you anticipate making any adjustments to that year-end goal? And then additionally within the mass business, what are the respective shares of our own proprietary models versus third-party models? And as model-related competition intensifies and more open-source models emerge, how will these factors possibly affect gross margin and profitability in the mass business?

Thank you for your question.

The growth rate of our mass business is very fast. In August, our ARR has reached 1.6 billion RMB. From the current growth rate and the new models that will appear in the future, we believe that the goal of reaching 3 billion ARR at the end of the year will be more certain. At the same time,

Thank you for the question. Yes indeed, growth in Bailian's mass business is very rapid and as of August we reached 16 billion RMB or surpassed 16 billion RMB in ARR. So given the current growth momentum as well as the pipeline of new models slated for launch, We remain confident that we will achieve our year-end target of 30 billion ARR by the end of the year.

在我们现在的MARS业务中,我们的自研模型还是占大部分的一个比例。 但是我们三方的开源模型的比例,其实营收也是不小。 这个还是要说一下我们对于模型竞争的长期看法。 From what we see now, on the one hand, customers have stronger needs. In an AI application, it may use multiple models. Even because each model has its own ability characteristics. At the same time, we will also see more open source models of future models, more swimming lines. So from this point of view, it is actually more conducive to Aliyun, Bailian, such a cloud platform. In fact, in terms of our profit margin, the free three-way model and the self-reliance model are actually not much different in terms of profit margin. Our self-reliance model is more about long-term pursuit of improvement in the level of our model's intelligence and breakthrough in AGI. However, in terms of short-term MaaS service, in fact, the entire profit margin and the profit margin model So on our mass platform, our own proprietary models still account for the

Thank you very much. Their own AI applications because those different models they can draw and have different characteristics or different capabilities. So having more open source models on platforms like ours, like Bailian, to provide inferencing is a good thing for us and for Bailian. When it comes to gross margin, the level of gross margin and many more. We're really developing those proprietary models on the one hand in order to keep creating higher levels of model intelligence and also as part of our ultimate drive to achieve AGI. But simply from the perspective of the mass business, level of gross margin from those two kinds of models is actually very comparable but overall having a prosperous and flourishing open ecosystem with many of these open source models on it is highly favorable for a cloud provider like Alibaba Cloud.

Let's take the last question. Thank you. Your final question comes from Alex Yao with J.P. Morgan. Please go on.

Okay, thank you for giving me the last chance to ask this question. Just now, Eddie also spent a lot of time talking about our full-time AI ecosystem. My question is about our full-time AI ecosystem. The value of this is ultimately which layer to fill or to transform. Because what we see is that the company opened the whole of Queen 3.8 Max within three months of the launch of the flagship model. At the same time, the commercialization of our natural chip is also quite successful. Now it has landed more than 650 external customers. Does this mean that the management layer is I think the value of AI will eventually be in the algorithmic and calligraphy programming layer rather than the model layer. In other words, the management layer thinks that the value of AI in different stages of development, in its full-scale ecosystem, may be in different stages. If we think that it will have long-term value Thank you for the opportunity to ask the final question.

I'd like to come back to Eddie's earlier remarks. He spoke at length about how Alibaba is developing a full-stack AI ecosystem. My question really is in which layer of that full-stack ecosystem do you think value will accrete and monetization will be concentrated? We saw just after it had been released for three months that you open sourced the weight of your flagship model, QN 3.8 Max. At the same time, your proprietary chips are also proving successful, now serving over 600 external customers. I'm wondering if this means that The future value will accrete mainly in the compute layer or perhaps in the orchestration layer and not necessarily in the model layer? Or do you think that value will accrete to different layers in different stages of development of the industry? And in the long term, if you think that value and monetization will largely be concentrated in the hardware and compute layers, How should we think about competition going forward given that it will be a government-led process for allocating a lot of that hardware and compute capacity?

You asked a very professional question. It also involves a long-term judgment. In terms of long-term judgment, I think there is still some uncertainty. But overall, we think that because of our full-time AI investment, regardless of the value at which level, at different stages, at which level, more or less, we all have the opportunity to make up for it in our ecosystem. Let me talk about my own short-term judgment. Now, in terms of short-term judgment, we think that most of the value still exists in This is a situation that we also see in many domestic and foreign companies. In the early stages of a technology, especially in the absence of a technology supply, most of the situations will be like this. There will be a lot of value in providing basic facilities and the most core hardware, including our chips, storage, and such suppliers. In Alibaba's design, Thanks.

That's a very professional question and really is a matter of long-term judgment. So I think it's inherently associated with a high level of uncertainty. But what I can say is that we are investing in the full stack. And what that means is that whichever layer represents the greatest value, and no matter how that may shift across layers in different periods of time, all of those layers are part of our ecosystem. I guess I can share with you my own short-term view, namely in the short-term perspective, I think that most of the value will be in chips and in AI cloud It's a pattern that we can see not just in China but globally across a lot of different companies when a technology is in its early stages and especially when there's a shortage of supply. Lots of the value tends to be concentrated in the infrastructure and in the core hardware, in this case chips and storage. So in Alibaba's case, we've integrated our compute power, our cloud infrastructure, and our AI inference into one core business cycle.

好,關於您剛才說的模型的這一層的商業價值如何體現, 那這一層我覺得在行業內的爭論也很多, 我覺得在我們公司內部也是大家各有不同的看法, 我只是說一下我的個人看法。 In my personal opinion, the current business model of AI large model is a short-term business model for large models. It is definitely not the ultimate business model. In fact, our company has invested so much in our full-time business model. The goal is not for the short-term API revenue, because we think that in the future, when AI big models can achieve AGI or get close to AGI, in fact, the final business model should be to directly create products or directly create the results needed by customers, especially in and so on. So from that point of view, I think AI big models Let me turn next to where the ultimate commercial value will be realized from these AI technologies.

It's a question around which there's a lot of debate within the industry and indeed there are different views even inside our own company. So here I'm just sharing my own personal opinion. But in my personal view, I think that the current monetization model for large language models through APIs is just a short-term approach, a short-term transitional approach and is certainly not the ultimate business model. Our company has invested a tremendous amount of compute across our entire platform, but the objective is not simply to be able to generate that kind of short-term API revenue. I think when we get to the stage where we've accomplished AGI or we're close to achieving AGI, at that point the ultimate business model will be delivering actual products, delivering actual results. The reason that all these different AI model companies are investing so heavily and engaging in an arms race today is not simply to be able to compete to provide that API based service. It's because they have their eyes on that ultimate and Game, where I think that the monetization level will be significantly higher, will be much higher than what you see today selling the service through API calls.

好,关于您说的在硬件算力层面, 我觉得你这个问题我也想比较重点的回答一下, 因为实际上对于我们的自研芯片, 平头哥的芯片的布局, 其实我们在以前的, I think we are the second in the Chinese market Thank you for joining us. These are the core technical structures. Under these technical structures, we can also support the work load of training and reasoning. So among the hundreds of customers we support, we are both helping them with reasoning and modeling training, especially like many giant smart companies and many autonomous driving companies, including many large modeling companies. So from this perspective, we may be the only one in China with large-scale training and reasoning to commercialize such a chip. Then another one, I also want to talk about our expectation for Pinduoguo's second generation of domestic chips. Our expectation for Pinduoguo's second generation of domestic chips is that it will gradually flow and produce in the second half of this year. In this generation of chips, we will have very strong computing power and very strong Internet bandwidth. We think it can completely replace large-scale model training. From this point of view, I think our chip technology in China is very unique. So I don't think there is a so-called government-led computing power supply that can have a very strong competitive chip. So from this point of view, we think that for Pinduoguo to become the core competitiveness of Aliyun in the future, we think it is still very confident, especially because Aliyun now has hundreds of medium-sized customers who have acquired a better use value on Pinduoguo's chip. In China, I think from In terms of hardware, I'd like to add a few thoughts regarding our T-Head technology.

I know it's a topic about which we haven't communicated a lot with investors in the past, but the last generation of THED chips, we've already manufactured over 500,000 of them and shipped. And then the latest generation in August has already been deployed on Alibaba's AI Cloud as super nodes. And I think we're one of the only companies that's able to deploy such proprietary chips, domestic chips, at scale. One thing that's really unique about our T-Head chips, domestically manufactured chips, is that they are designed with GPU architecture as their core technical foundation and they can very well support both training and inference workloads. So there are now already several hundreds of companies that are leveraging these chips via Alibaba Cloud for both inference as well as for model training and these span companies across embodied AI, autonomous driving, as well as large model companies. So in terms of our generation two of chips, we are going to start developing them in the second half of this year. and we expect them to boast exceptionally high compute power as well as extremely robust interconnection bandwidth, making them fully capable of serving as a direct replacement for existing chips. So I think we're in a really, really unique position in the chip sector, especially when it comes to large scale model training. So I don't think that there's any government-led compute supply allocation scheme that could produce chips with such truly strong competitiveness. So I think that T-Head's future is highly certain as a very key and core component of Alibaba Cloud and we remain highly confident in our Okay, so to sum up, I think our Pinduoduo chip is a domestic chip in terms of diversity of training and reasoning that supports multi-industry training.

In terms of the future production capacity and supply chain layout of our industry, we can say that it is a dear existence. In terms of the customer channel capabilities of future AI chips, since Aliyun has been the largest market share manufacturer in China's AI cloud for a long time, So to sum up, I think that our T-Head chips are definitely the best among domestic Chinese chips for supporting

both training and inference across a wide range of different industries. So we really are number one in the industry. And then I think in terms of future production capacity and deployment, we can confidently claim to be at least one of the top two. But in terms of our ability to actually reach customers with AI chips, Alibaba Cloud is the largest player by market share Thank you very much. We appreciate your support and we look forward to updating you on our progress next quarter. Thank you.

Thank you. That does conclude our conference for today. Thank you for participating. You may now disconnect.