Alibaba Group Holding Ltd Stock

Good day, ladies and gentlemen. Thank you for standing by. Welcome to Alibaba Group's June Quarter 2026 Results Conference Call. [Operator Instructions].

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 Joe Tsai, Chairman; Eddie Wu, Chief Executive Officer; Toby Xu, Chief Financial Officer; Jiang Fan, Chief Executive Officer of Alibaba E-commerce 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 AR rated products has maintained a triple-digit growth for the 12th consecutive quarter with annual revenue run rate surpassing RMB 49.5 billion around USD 7.3 billion. It is the core engine of Alibaba's Cloud's growth acceleration.

I'll now walk you through 4 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 RMB 49.5 billion 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 multilayered 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. 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 mass, has surpassed RMB 16 billion. 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 stock AI capabilities continue to strengthen, marked by the scaled commercialization of proprietary chips, faster model iteration and a driving open source ecosystem.

This quarter, deepening synergy between proprietary T-Head 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, the Zhenwu chips have served more than 650 customers on Alibaba Cloud. The super node 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 super node can efficiently run inference workloads for foundation models with more than 2 trillion parameters, both KBK3 and Q1 3.8 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 here, our model release cadence has intensified over the past months 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 modeled weights of Q1 3.8 MAX with 2.4 trillion parameters and the Q13.827B model series. To date, the Q1 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.

Third, our AI native applications span both enterprise and consumer use cases driving rapid growth in token consumption. On the enterprise side, we launched Q1 work, a new AI productivity product built for enterprise workforce scenarios, delivering agent capabilities at scale. We expect productivity agents to become another engine of ARR growth. On the consumer side, the Q1 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 remained solid this quarter. In quick commerce, we continued to narrow losses substantially while growing business scale by 45% with unit economics improving quarter-over-quarter. Having crossed the AI commercialization inflection point this quarter, we're now seeing growth accelerate and margins expand this quarter. Our AI businesses 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 continued 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 preparatory chips as well as our enhanced scale efficiencies and the robust pricing power in a supply-constrained market.

On consumption, Taobao instant commerce continued to improve its unit economics while maintaining market share. Overall e-commerce EBITDA remained relatively stable year-over-year. To realize synergies across our commerce platforms and strengthen our full stack AI capabilities, we have implemented strategic realignment 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 GAAP 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 and higher pricing of a broad range of chip components.

As of June 30, 2026, we held approximately USD 30.7 billion in net cash excluding debt with maturities beyond 5 years, our net cash position stands at approximately RMB 46.5 billion. This balance sheet strength gives us confidence to invest for robust growth. Our AIs cloud investment has a clear path to attractive ROIC, our service equipment with chips typically reach breakeven within 3 years. With a 5-year useful life, we expect them to get positive free cash flow, at least in the 2 years following breakeven. For the quarter ended June 30, 2026, we repurchased a series of an aggregate consideration of USD 162 billion. We remain committed to maximizing long-term shareholder returns through disciplined capital allocation across investments for AI plus cloud business growth, share buybacks and dividends. We will adjust our priorities as market conditions and the 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 comments 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 contra 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 Freshippo and Taobao instant commerce. Alibaba e-commerce Group's adjusted EBITDA remained relatively stable year-over-year at RMB 39.7 billion, underscoring our cost discipline against the backdrop of increased investments in user experiences and technology. Taobao instant commerce continued to improve its uneconomic quarter-over-quarter while maintaining market share. driven by higher average order value and enhanced fulfillment logistics efficiency. In addition, Ad Express achieved our pre-profit this quarter. We aim to maintain steady profit in our conventional e-commerce business of continuing to drive profitability improvement in our quick commerce business.

Now let's review the business updates and results of Ali Cloud -- AI Cloud and compute services, which comprises the Cloud Intelligence Group and T-Head. 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 EBITDA 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 applications comprises AI model labs Qwen Consumer Business Group and Qwen work. Its adjusted EBITDA was a loss of RMB 13.9 billion, primarily due to our increased investment in AI capabilities and higher influence costs related to Qwen APP. The loss significantly narrowed quarter-over-quarter due to the reduction in marketing expenses for Qwen ABB. We expect the segment loss to narrow over the coming quarters driven by improving efficiency in both model training and marketing spend on Qwen ABB.

We have launched our frontier language coding, video, audio, image and music models or delivering top-tier performance. $250 million have had their first AI-driven shopping experience through Qwen APP's agentic features across an expanding range of e-commerce and other services since the launch of Qwen APP. All other segment revenue remained stable at RMB 28.8 billion. All other adjusted EBITDA was a loss of RMB 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. A third-party translator will provide consecutive interpretation. In the case of any discrepancy, our management statement in the original language will prevail. Operator, please start the Q&A session. Thank you.

[Operator Instructions] Your first question comes from Alicia Yap with Citigroup.

Also congrats on your solid cloud performance. Could management please comment on the reasons and the drivers for the significant increase in the CapEx this quarter? And also, what's the expected CapEx trend for the coming quarters? And are these -- are there any updates to the existing 3-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 CapEx allocation across the different services like the training costs and all that? And then also, what is management affected return on the invested capital for these investments?

[Foreign Language] [Interpreted] 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 going forward. So indeed, last February, we announced a 3-year capital investment plan with total investment of RMB 380 billion as of the end of the June quarter this year, we had already spent RMB 190 billion with progress broadly in line with our expectations. While this quarter spending of RMB 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-centric era. 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 4 to come up with an annualized figure for the year or to expect that there'll be a steady linear progression. The build-out has been progressing at a steady pace, but that is the overall situation.

Next, let me expand on our full stock 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 upfront 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.

Next, let me explain why we see return on invested capital in AI-related as highly certain. There's 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 3 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.

Following that 3-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 and A100 purchased in 2020 or A100 purchased in 20 -- sorry, V100 purchased in 2018. Even are still running at full capacity.

Additionally, we have 3 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 enhanced gross margin on AI products themselves and continue to expand a higher-margin model as a service mass businesses, and we can adopt our product mix across IAS and across software to achieve higher gross margin on the portfolio as a whole. And 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.

A very important piece of this is our ability to deploy our own proprietary chips. As you know, our own head proprietary chip spend, 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 ships and replace commercially procured chips, we can expect to see substantially higher gross margin as well as profitability.

Third and also very importantly, we have means to monetize and get better efficiency of utilization of our own cash flow. These include, for example, co-building data centers with partners as well as pre charging and receiving prepayments for compute-based services. So these are important ways in which we can further enhance ROIC.

So through these 3 different methods, we can shorten the payback period for AI CapEx, for example, to 2.5 years or even 2 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 3-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 expansion. As our product gross margin improves and our proprietary chip substitution rate increases, our payback period will shorten to 2.5 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.

Your next question comes from Charlene Liu with HBSC (sic) [ HSBC ].

I come from HSBC. First, when we get an update on the latest developments in quick comers and 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. [Foreign Language]

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 4 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 4 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 operational 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.

[Foreign Language] [Interpreted] 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 618 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 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 Qwen Office to launch AI agents that are specifically tailored for e-commerce scenarios.

Next, on quick commerce. 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 mind share, 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 Freshippo and Tmall supermarket to develop the nonfood categories growth within the Quick Commerce business, and we'll place a particular focus on expanding our front warehouses. Over the past year, Freshippo has accelerated the development of front warehouses leading to a year-over-year increase in GMV.

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 nonfood 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 FY '29. 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.

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.

Our B2B businesses, including the 1688 and alibaba.com platforms have grown consistently over the past 2 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 genetic model will play an increasingly important role in B2B transactions. We've launched Accio Work, 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 2 years of know-how in the 2 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, over the past few years, we have completed a 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.

Your next question comes from Yang Bai with CICC.

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 USD 100 billion in external cloud revenue over the next 5 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 2 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 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?

[Foreign Language] [Interpreted] Thank you for the question. And I think I can expand on this in 3 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 9 consecutive quarters. And in this last quarter, growth has already accelerated to 5%. 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 RMB 12.4 billion in revenue this quarter. And so if we convert that into an annualized U.S. dollar figure, that works out to USD 7.3 billion in annual revenue. Looking ahead to the next quarter, our own forecast is that, that same annualized revenue for AI quarters next quarter will approach USD 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 improvement in inference efficiencies. So the ARR of our mass business has now surpassed RMB 16 billion. And actually, let me clarify. That's the latest data as of August. It's already surpassed RMB 16 billion.

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 pure-play AI companies. We are pursuing an intensive strategy across the full stock including chips, including cloud infrastructure and including models. And we maintain a leading position in the industry across all 3 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, 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.

Next, let me look ahead to what we think is going to be the most important growth driver over the next 1 to 2 years in the short term. So we've seen exponential demand for commercial insurance 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 consensus across the industry, as I mentioned, that compute will remain in a shortage of supply for some time to come.

At the same time, the higher gross margins of mass inference services have also made 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, both comprehensive multimodal model capabilities. Our models are 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 sign -- resign 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.

Next, let me talk about the scale effect 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 compute, cloud-based AI compute because all of these different workloads need to run on a full stack of AI cloud compute, including training, inferencing AI software and agents requiring GPUs, CPUs, storage, databases, 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 stock AI cloud services or the urban infrastructure, which in turn attracts more new residents and enhances the stickiness of the existing residents. So this is where you see an extremely powerful network effect and scale effect.

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 T-Head AI chips allows us to avoid the high price premiums associated with procuring expensive commercial GPUs and 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 strength, 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 RMB 100 billion in external cloud revenue by 2030. And we have good visibility into achieving gross margin of 20%.

Your next question comes from Yuan Liao with CITICS.

[Foreign Language] [Interpreted] Congratulations on the strong quarterly results and especially the progress made in the AI sector. I have a follow-up question on the mass business. As Eddie mentioned earlier, ARR as of August has exceeded RMB 16 billion in last quarter. I believe you stated that the target for year-end is to surpass RMB 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 MaaS 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 all these factors possibly affect gross margin and profitability in the MaaS business.

[Foreign Language] [Interpreted] Thank you for the question. Yes, indeed, growth in Bailian's MaaS business is very rapid. And in -- as of August, we reached RMB 16 billion or surpassed RMB 16 billion 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 RMB 30 billion ARR by the end of the year.

So on our MaaS platform, our own proprietary model still account for the majority of the revenue. But having said that, revenue from third-party models is also not small. And having said that, perhaps let me talk a little bit about how we see different model capabilities. A lot of customers tend to need to use or want to use multiple different models in 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 that we can achieve on a platform like Bailian from hosting our own proprietary models versus third-party models is actually very similar. It's highly comparable. 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 AI.

But simply from the perspective of the mouse business, the level of gross margin from those 2 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.

Your final question comes from Alex Yao with JPMorgan.

[Foreign Language] [Interpreted] I'd like to come back to Eddie's earlier remarks, he spoke at length about how Alibaba is developing a full stock AI ecosystem. My question really is in which layer of that full SAC ecosystem, do you think value will accrete and monetization will be concentrated. We saw just after it has been released for 3 months that you open sourced the weight of your flagship model, Q1 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 the 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 then 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.

[Foreign Language] [Interpreted] Thanks. That's a very professional question, and really it's 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 infrastructure.

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 segment.

Let me turn next to where the ultimate commercial value will be realized from these models. 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 AEI or we're close to achieving AGI at that point, the ultimate business model will be delivering actual products, delivering actual results that clients are looking for. It will be conducting the actual R&D that delivers products and that delivers operations. So 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 to eyes on that ultimate end game where I think that the monetization level will be significantly higher, be much higher than what you see today selling the service through API calls.

In terms of hardware, I'd like to add a few thoughts regarding our head proprietary chips. I know it's a topic about which we haven't communicated a lot with investors in the past, but the last generation of T-Head chips, we've already manufactured over 500,000 of them and shipped. And then the latest generation in August has already been deployed on AI 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 tea 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 2 of chips, we are going to start developing them in the second half of this year. And we expect them to boost 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 the T-Head's future is highly certain as a very key and core component of Alibaba Cloud, and we remain highly confident in our core competitive strength in this area. I've interacted with a lot of different engineers across China. And I can tell you that these chips have a very broad audience with engineers across a wide range of different engineering domains.

So to sum up, I think that our tea head ships are definitely the best among domestic Chinese ships for supporting both training and inference across a wide range of different industries. So we really are #1 in the industry. And then I think in terms of future production capacity and deployment, we can confidently claim to be at least 1 of the top 2. But in terms of our ability to actually reach customers with AI chips, Alibaba Cloud is the largest player by market share in China's cloud and AI market. So I think we have a very strong edge when it comes to channel distribution. So from this perspective, I am highly confident in the long-term commercial value of T-Head chips.

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.

Good day, ladies and gentlemen. Thank you for standing by. Welcome to Alibaba Group's March Quarter and Full Fiscal Year 2026 Results Conference Call. [Operator Instructions] 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. Please go ahead.

Good day, everyone. Thank you for joining Alibaba Group's March Quarter and Full Fiscal Year 2026 Earnings Call. On the call with me are Joe Tsai, Chairman; Eddie Wu, Chief Executive Officer; Toby Xu, Chief Financial Officer; Jiang Fan, Chief Executive Officer of Alibaba E-commerce Business Group. As a reminder, this call is being webcast live. A replay of the call will be available on our website later today.

On this call, we may make forward-looking statements and discuss certain non-GAAP financial measures. The forward-looking statements reflect management's current expectations that are subject to risks and uncertainties. Our GAAP results and reconciliations of GAAP to non-GAAP measures is included in today's earnings press release and investor presentation. Our comments will be on year-over-year comparisons unless we state otherwise.

And with that, let me turn the call over to Eddie.

[Interpreted] Welcome to Alibaba Group's Fiscal Year 2026 Fourth Quarter Earnings Call. Over the past quarter, Alibaba's high-intensity investment in our 2 strategic priorities of AI + Cloud and consumption is rapidly translating into tangible business results with group revenue growing 11% year-over-year. This quarter, Cloud Intelligence Group's external revenue growth accelerated to 40%, and AI-related product revenue achieved triple-digit growth for the 11th consecutive quarter.

China e-commerce CMR grew 8% year-over-year on a like-for-like basis, and the quick commerce market achieved significant unit economics improvement while maintaining market share. We are at a pivotal inflection point in the evolution from conversational chatbots to autonomous AI agents, which is directly driving explosive growth across 3 core workload categories: training, inference and agent orchestration. Against this backdrop, Alibaba's AI has moved beyond the initial investment phase and progressed commercialization at scale.

Next, let me walk you through 4 areas in detail: AI commercialization, cloud infrastructure, the AI application ecosystem and our consumption business. First, the AI and cloud commercialization inflection point has arrived. This quarter, Cloud Intelligence Group's annualized AI-related product revenue has surpassed RMB 35.8 billion, continuing to maintain triple-digit growth. AI-related product revenue now accounts for 30% of Cloud Intelligence Group's external revenue. We expect that in about 1 year, AI-related product revenue will cross the 50% threshold, becoming the primary engine driving the Cloud business's revenue growth.

As a result, Cloud Intelligence Group's external revenue growth is expected to continue accelerating beyond its current 40% rate over the coming quarters. Given the certainty of long-term AI demand and our full stack technology advantages, we expect this trajectory to sustain strong growth over the medium to long term. This reflects AI's role in driving a comprehensive upgrade of Alibaba Cloud's entire business as its growth engine fully pivots from traditional compute and storage to models, AI compute and agent services.

We're also seeing exponential growth in AI model and application services revenue, a new revenue engine driven jointly by foundation model services and AI-native software. Over the past 3 months, token consumption volumes on our model services platform grew substantially quarter-over-quarter as enterprise customers accelerated their shift from simple tasks to production scale and complex workloads, driving continued growth in demand for model and application services on the Model Studio platform. We expect model and application services annualized recurring revenue, ARR, inclusive of the Model Studio platform to surpass RMB 10 billion in the June quarter and RMB 30 billion by year-end. The high margin profile of this revenue stream is becoming increasingly apparent, making it a source of healthy, high-quality growth.

Second, our AI infrastructure underpins our full technology stack and constitutes a durable moat. T-Head's proprietary GPU chips have achieved scaled MaaS production with over 60% of compute capacity already serving external customers across Internet, financial services and autonomous driving verticals. As the only AI cloud provider in China capable of delivering self-developed AI chips at scale, we've secured autonomy over our compute supply chain while providing customers with highly competitive AI inference and training services. In an environment of compute scarcity, this structural advantage is favorable to our revenue growth and gross margin improvement. At the same time, our cloud products are accelerating their AI-oriented upgrade. The surge in agent workloads has significantly elevated demand for traditional cloud products built around CPU storage and containers, and we're upgrading these into infrastructure solutions optimized for the agent era.

Third, at the application layer, we have built a complete closed loop spanning AI-native software to a full agent ecosystem. Alibaba Token Hub ATH continues to launch new products, connecting consumer and enterprise environments with breakthrough progress in AI-native software and coding agents. The Q1 model continues to iterate across reasoning, coding and agentic capabilities. On the enterprise side, we've launched a range of products spanning intelligent workplace tools, AI coding and business operations management, helping enterprises unlock greater productivity.

On the consumer side, Qwen app fully integrated Taobao and Tmall's commerce service capabilities on May 7. And with this, Qwen app is now deeply embedded across the ecosystem spanning Taobao, Alipay, Amap and Fliggy, making it China's first all-in-one personal assistant to seamlessly bridge everyday life productivity and learning.

Fourth, across our consumption business and at the group level, we're prioritizing long-term value. Beyond AI, our consumption strategy continues to progress steadily with CMR growth rebounding significantly. This quarter, CMR grew 8% year-over-year on a like-for-like basis as we continue to improve user experience and merchant operating efficiency. The quick commerce business achieved significant unit economics improvement while maintaining stable market scale.

In summary, the return on our investments in AI + Cloud and consumption are increasingly clear. AI + Cloud revenue growth is accelerating with improving margins, model and application services ARR continues to grow at pace and operating efficiency across our consumption business continues to improve. Facing the historical opportunity that AI represents, Alibaba is in a pivotal juncture where our technology investments are beginning to pay off commercially. We'll maintain our strategic results and leverage our full stack AI capabilities to support long-term growth.

That concludes my prepared remarks. Next, I'll hand over to Toby to talk you through our financial results. Thank you.

Thank you, Eddie. Our strategic priorities remain laser focused on AI + Cloud and consumption businesses. Multiple growth catalysts, including technological advancement and business innovation are aligning to create strong tailwinds. On AI + Cloud, our full stack capability span models, cloud infrastructure and applications. With established leadership in every layer, the strong growth of our AI + Cloud businesses and a clear path to monetization of our MaaS platform give us confidence to make significant investments to extend our leadership. On consumption, we achieved a strong CMR growth on a like-for-like basis during the quarter, and our quick commerce business continued to improve UE and AOV quarter-over-quarter.

Now let's look at the financial results for this quarter. On a consolidated basis, total revenue was RMB 243.4 billion. Excluding revenue from Sun Art and Intime, revenue on a like-for-like basis would have grown by 11%. Total adjusted EBITA decreased 84%, primarily due to our strategic investments in technology businesses, quick commerce and user experience, partly offset by the improved operating results supported by continued growth in consumer management service in the cloud business and enhanced operating efficiencies across various businesses. Our GAAP net income was RMB 23.5 billion, an increase of 96%, primarily attributable to the year-over-year increase in net gain from mark-to-market changes of our equity investments and disposal losses of Sun Art and Intime in the same quarter last year, partly offset by the decrease in adjusted EBITA.

Operating cash flow was an inflow of RMB 9.4 billion. Free cash flow was an outflow of RMB 17.3 billion. We are reinvesting our operating cash flow to enhance our competitive advantage in AI. As of March 31, 2026, we held approximately USD 38 billion in net cash. Excluding debt with maturities beyond 5 years, our net cash position stands at approximately USD 59 billion. This balance sheet strength gives us confidence to invest for growth.

Now let's look at our consumption businesses. Revenue from China E-commerce Group was RMB 122 billion, an increase of 6%. Customer management revenue increased by 1%. To help merchants grow their businesses and increase willingness to spend on our platform, we upgraded our business development program for select merchants during the quarter, under which the level of platform subsidies for these merchants is directly tied to their marketing spend on our platform. For accounting purpose, such subsidies previously recorded as sales and marketing expenses are now recorded as a contra revenue item to CMR. Accordingly, CMR grew 1% year-over-year during the quarter. Excluding the contra revenue impact from the program, on a like-for-like basis, CMR would have grown 8% year-over-year.

Revenue from our quick commerce business increased 57% to RMB 20 billion. The quick commerce business further improved the UE and increased the AOV quarter-over-quarter, primarily driven by order mix optimization. Alibaba China E-commerce Group adjusted EBITA was RMB 24 billion, a decrease of 40%, primarily due to the investment in quick commerce, user experience and technology, while there's positive contribution from customer management service. Excluding loss from our quick commerce business, our Alibaba China E-commerce Group EBITA would have been stable year-over-year and will fluctuate quarter-over-quarter due to significant investment in merchant retention and user experience.

Revenue from AIDC grew 6% this quarter. AIDC's adjusted EBITA loss narrowed significantly year-over-year, approaching breakeven, driven by a combination of logistics optimization and operating efficiency. The unit economics of AliExpress' Choice business continue to improve substantially on a sequential basis.

Next, let's look at the business update and results of Cloud Intelligence Group. Our cloud business delivered another quarter of accelerating growth. Revenue from external customers accelerated to grow 40%. AI-related products continued to lead this momentum. We delivered our 11th consecutive quarter of triple-digit growth in AI revenue. Its share of external cloud revenue continue to increase now account for 30%. This quarter's AI revenue is RMB 9 billion and the annual revenue run rate is RMB 36 billion or USD 5.3 billion. This is a clear reflection of the scale and acceleration in our AI business.

The adjusted EBITA margin remained relatively stable at 9.1%. All others segment revenue decreased by 21% to RMB 65.5 billion, mainly due to the disposal of Sun Art and Intime businesses as well as the decrease in revenue from Cainiao, partially offset by the increase in revenue from Freshippo and Amap. All others adjusted EBITA was a loss of RMB 21.2 billion, primarily due to the increased investment in technology businesses, including foundation models and the consumer-facing Qwen app.

As we close this fiscal year, we remain committed to delivering consistent shareholder returns. Our Board of Directors has approved an annual dividend of USD 1.05 per ADS. We will continue to invest decisively in AI and consumption businesses where we see significant long-term growth potential and our competitive advantages are compounding. We believe these investments to deliver growth and returns over time, ultimately creating greater value for our shareholders.

Thank you. We will now open for Q&A.

Hi, everyone. You're welcome to ask questions in Chinese or English. A third-party translator will provide consecutive interpretation. In the case of any discrepancy, our management statement in the original language will prevail. [Foreign Language]

Operator, please start the Q&A session. Thank you.

[Operator Instructions] Your first question comes from Ronald Keung with Goldman Sachs.

Thanks for sharing the very sizable AI MaaS and applications ARR scale and the target for the first time. So I just want to ask, how much of that ARR is driven by our in-house models like Qwen versus third-party models? And given the recent token price hikes, what would be the implications to MaaS and also our Cloud margins as a result?

[Foreign Language] [Interpreted] Thank you for that question. This quarter marks the first time that we announced the latest figure for model and application service revenue. That really comprises mainly 2 things. On the one hand, it includes revenue from API calls on MaaS on our Bailian platform, and it also includes revenues from our AI software subscriptions. At present, most of the revenue is coming from the first of those 2 pieces, but this is an open platform. So we are providing access both to our proprietary models as well as third-party models, including open source models and closed models. But for the time being, most of that revenue is coming from our own proprietary models, including Qwen as well as Tmall as well as our voice and video generating models.

[Foreign Language]

Your second question was also a really important one because in the past quarter, over the past few months, we've seen a very large shift in the market where AI is shifting from functioning as a conversational chatbot to providing agentic capabilities. So these agents are increasingly capable of solving for very complex problems, meaning that they need to do a lot more inferencing than in the past. And precisely because these agents can help to solve very complex tasks, customers' acceptance for higher prices, and we have increased per token prices, is good and the demand continues to be high and growing.

In fact, our ability to supply this demand is not able to keep up with all the growth and demand. We actually have a lot of customers still waiting to access the service. Inherently, MaaS will have higher gross margin than IaaS. That's important to know. And I can also add a few important points on top of that. First is that the development of reasoning or inferencing technology still continues to advance. So every quarter, we're seeing new results in terms of optimization in reasoning, in inferencing with continuous incremental effects in terms of the token capacity of a single server and a single card. At the same time, as the capabilities of models continue to strengthen and price of the models continues to increase in the next year or 2, we see that this should be a process of continued price improvement. So I think from this point of view, the rapid growth in this business over the next few quarters will result in a very positive impact on our overall gross profit margin.

Your next question comes from Kenneth Fong with UBS.

Congrats on the very strong progress on the AI. I have a question regarding the return on invested capital on the AI investment. So while our AI investments have driven impressive 40% cloud growth, they have also created significant drag on the group free cash flow as well as our EBITA. So how do investors assess the return on this investment? And what is the management's framework for balancing the aggressive AI spending versus earnings stability?

[Foreign Language] [Interpreted] Thanks, Kenneth for that question. This is Toby, and I'm going to start by answering that first question, because I think it's important and of interest to everybody. The question is the reason for the negative free cash flow and how we are managing that. So starting there, the answer is that the negative free cash flow is primarily due to the very significant investments we've been making in AI over the past year. And we've been extremely resolute in making those investments precisely because we've seen the historic opportunity of AI.

[Foreign Language]

[Interpreted] So we've been very resolute in making those investments over the past year. And looking forward to the next 2 years, we intend to be equally resolute in continuing these investments, again, because we see this is a critical window of opportunity that will be open for that period for the next couple of years. Additionally, there's really been no big change in the way that we look at cash flow. First of all, the major contributor of operating cash flow for the group is Taobao and Tmall and that cash flow is very stable. And looking ahead over the next 2 years in terms of quick commerce, the losses will narrow very substantially. At the same time, AIDC will develop from making a loss to being profitable. So we see these developments over the next 2 years as being highly positive for our net cash flow.

[Foreign Language] [Interpreted] Another important point to be added is that our ongoing investments in cloud infrastructure will increase the revenues that we can achieve from our AI and cloud offerings. At the same time, we will increase gross margins in those very same offerings. So in those ways, we expect that we can achieve higher net cash flow from our cloud and AI business, and that cash flow can in turn be used to support the development of the relevant infrastructure.

An additional point is that we have a very strong balance sheet. As of March 31, 2026, we held approximately USD 38 billion in net cash. And if you exclude debt with maturities beyond 5 years, our net cash position stands at approximately USD 58 billion. So that balance sheet strength also gives us confidence to reinvest for growth. And beyond that, I would also add that we have a very strong capacity for pursuing financing in capital markets, and we have the capability to raise capital from the markets as we need to support our development.

So I wanted to open with that in response to your question on cash flows, and I'll pause there to see if Eddie has anything to add.

[Foreign Language] [Interpreted] Thank you. You asked about our investments in AI and the ROI on those investments going forward. So on top of what Toby has already said, I'd like to add a few notes regarding where we're heading. I think the best analogy is manufacturing. In other words, in order to be able to manufacture more and sell more in the future and achieve more revenue, what we're doing today is investing capital to build 2 factories, if you like. The first we can call the AI training factory. The second, we can call the inferencing factory. And both of those factories need to be powered by our AI data centers, and that requires the investment of cash flow today.

However, looking to the future, the pathway to achieving a solid return on investment in those factories, in those areas, is very clear. On the 2B side by monetizing our 2B offerings, including our cloud-based IaaS as well as MaaS, of course, and our AI-native apps. And I can tell you that today, there isn't a single card on our service that is idle. So we see the ROI on this investment in the next 3- to 5-year period as being extremely clear.

Your next question comes from Thomas Chong with Jefferies.

My question is on quick commerce. I've talked about the improvement in UE in the prepared remarks. I just wanted to get some more color about the drivers behind in terms of AOV, subsidies ratio, fulfillment ratio, et cetera. And on top of that, I remember last time we talked about the outlook for quick commerce over the next couple of years. Is there any update or changes in terms of how we think about the landscape or the UE in the next 3 years?

[Foreign Language] [Interpreted] Thank you. Well, first, as a result of our strong investments in quick commerce, we've achieved very rapid growth in quick commerce over the past year, marking a very fundamental shift in our market position. Compared to the same quarter last year, which was, of course, prior to all this large-scale investment, both our order volume and our market share have increased significantly. Overall order volume was 2.7x that of the same quarter last year with non-food orders at 3x.

From April onwards, while maintaining order volume, we've continued to drive substantial improvement in UE through enhanced fulfillment logistics efficiency as well as order mix optimization. So we are confident that UE will turn positive by the end of fiscal year '27.

[Foreign Language] [Interpreted] While optimizing UE, we will continue to innovate to improve the experience for both consumers and merchants, thereby sustaining our long-term competitiveness in quick commerce. We're confident that our quick commerce business will achieve overall profitability in the future at new scale and market share. This quarter, quick commerce continued to generate synergies with our conventional e-commerce business, as demonstrated in driving customer acquisition, enhancing user engagement, fulfilling diverse consumer demands, increasing transactions, improving monetization and supporting logistics infrastructure.

In terms of categories, quick commerce continued to drive sales in various categories, especially food and fresh produce and healthcare and contributed to Freshippo's and Tmall Supermarket's accelerated growth. So in our conventional e-commerce business, we saw GMV and CMR demonstrate strong growth momentum in the March quarter, and quick commerce played a vital role in driving that performance.

Your next question comes from Jialong Shi with Nomura.

[Foreign Language] [Interpreted] I have a follow-up based on your opening remarks concerning MaaS. I'm wondering, first of all, what are the major advantages that Alibaba has when compared to the other major AI platforms in China as well as Chinese AI start-ups? In the United States, we see that AI agents and especially coding are the fastest growth track in AI. I'm wondering when you think we'll see that kind of growth in China in terms of AI coding.

We also know that Chinese customers are less willing than U.S. customers to pay for SaaS. So do you think that will -- that means that the future commercialization of Chinese AI coding products might have less potential than we see with the U.S. counterparts?

[Foreign Language] [Interpreted] Thank you. Well, the way we define our MaaS platform, Bailian Model Studio, is as an open AI inferencing platform. Certainly at present, the majority of Bailian's revenue is driven by our own proprietary models. But in contrast to the AI start-ups in China, I think that we are investing at a much higher scale and across a much broader range of different model types. In contrast, those start-ups may tend to focus on a very narrow particular vertical segment and they can move rapidly ahead with that kind of focus.

And strictly in terms of our MaaS business, I think those AI start-ups really are partners rather than competitors. But in Alibaba, we particularly place emphasis on our model capabilities and developing them across all different spaces and all verticals to serve a very broad and diverse set of needs, including our coding model capabilities, including image-based models, so both on Wanxiang and HappyHorse as well as these new world models and, of course, voice models as well. So we aim to provide all different kinds of models to meet all different kinds of needs. And this is different, I think, from those start-ups. And at the same time, those start-ups are our partners.

[Foreign Language] [Interpreted] Okay. The other part of your question was about when we can see the similar kind of growth in China as is being witnessed in the U.S. around AI coding. And I would say in terms of what we're seeing, based on the trends that we ourselves see on Bailian, as well as the experience of some of these AI start-ups in China who work closely with us. I would say China is already there. Most of the growth that we're seeing in utilization from, say, November or December of last year through to May of this year has been driven by capability upgrades in terms of coding. And these models are not able to replace software engineers. Basically, they're able to solve a wide array of very complex tasks beyond just coding, per se, in any kind of digitalized productivity scenario.

So we've seen AI coding capabilities improve significantly in both the U.S. and in China, and these capabilities are capable of supporting much more than just a coding, per se. It can address a whole wide range of very complex tasks in the workplace in so far as those tasks can be digitalized. So looking ahead to the next 2 to 3 years, we see this as a very, very important growth driver.

[Foreign Language] [Interpreted] On your other comment, we have also taken note of the lower willingness in China to pay for SaaS. However, I think that is poised to change as the models become increasingly powerful and are able to truly solve for very complex tasks, very complex problems. As they are providing truly valuable intelligence. I think we can expect to see the same demand for that kind of service in China as in the U.S. In a certain sense, when the value provided by tokens exceeds the cost of those tokens, the demand for tokens will become infinite in a sense. So we see growth in AI demand as a long-term certainty.

And I can also share some numbers with you in terms of the growth that we're seeing on our own Bailian platform from November, December last year through to May of this year. It's higher than a 10x growth. In terms of our ARR, it's already over RMB 8 billion. And I think this quarter, it's highly certain that we can achieve ARR of over RMB 10 billion.

Your next question comes from Ellie Jiang with Macquarie.

Just wanted to stay on the topic of that global comparisons. So if you look at it globally, the overseas peers seems to have captured the most immediate ROIs in enterprise agentic workflows, whereas for the consumer and the monetization which remained a bit lagged. So going forward, considering that Alibaba is investing kind of in multi-fronts for infrastructure models, cloud and Qwen app, how do we evaluate the strategic priority and resources allocation between 2B and 2C initiatives? If going forward enterprise side continues to gain more traction, will we consider gradually shifting more resources away from Qwen app to Cloud and MaaS?

[Foreign Language] [Interpreted] Thanks for that question. And it's a good question. But I think, fundamentally, from the perspective of AI development, it's really all about a paradigm shift in computing and it's about leveraging this new technology to help users, whoever they are to complete tasks and solve problems. And that applies equally on the 2C side as well as on the 2B side. Now certainly, at present, we see higher willingness to pay on the 2B side because it's easier to show a business case with compelling ROI for a business. And at present, most of our infrastructure resources are therefore channeled to the 2B side.

But at the end of the day, AI ultimately is an invention that's there to be a helper and assistant to humans, to help humans with a whole range of things spanning their own daily life, their studies and their work. And what AI does really is the same across all of those scenarios. It's about solving problems. And that's true for 2B and for 2C.

So certainly, there's less willingness to pay today for 2C, but we're already seeing a business model where consumers are paying for individual usage in the U.S. And I'm confident that the same will come to be the case in China, especially as the technology improves, as it's better able to help them solve real problems in their daily lives. So we think that ultimately, this will be the same business model internationally. And I think we'll probably get there in China in the next, say, 1 to 2 years.

Our next question comes from Joyce Ju with Bank of America.

[Foreign Language] [Interpreted] I have a follow-up question also on the future growth of the cloud business. And I'd just like to understand what your view is on EBITA margins in the cloud business over the next few quarters. Do you think as this business accelerates, we can expect to see similar margins as we see in your international peers?

[Foreign Language] [Interpreted] I think when it comes to the deep penetration of AI technology across all different industries, we're still really in the early days of that long process. But our objective is clear. Our objective is to achieve growth, to drive growth, to drive growth in token consumption and to acquire larger market share. We aim to maintain growth that is faster than the market average in order to gain larger market share and firmly cement our absolute market leadership position. So those are the primary objectives, and margin is still secondary.

The other thing to be pointed out is that for the next 3 or even 5 years to come, there are physical constraints on production, production capacity for chips, for memory, for the physical things that are needed to support all this growth in demand. An advantage that we have in Alibaba Cloud is the scale of our customer base as well as the scale effect from all of the CapEx that we've put in over these years. But in this environment of market scarcity, we're already seeing that the cost for us to deploy one new server this year is double what that same server would have cost a year ago. So the cost inflation has been over 100%. So given that higher replacement cost effect, we have a certain pricing power with respect to new customers and also old customers. So I think in the long term, the asset pricing effect will be positive for our revenues going forward.

Secondly, we see very rapid growth in MaaS, as we reported to you. And inherently, as we've said, MaaS represents a much higher level of gross margin than IaaS or traditional types of IT operations. So as demand for inference continues to grow exponentially, we expect this will be very positive for gross margin. And due to the optimization of our reasoning technology, the output capacity, the productivity of a single card will continue to rise.

An additional factor is as we continue to scale up the deployment of T-Head, the T-Head chips represent the highest value for money compute power on the cloud platform, and that also will contribute to a better gross margin. But for several objective reasons that I've outlined, I think, overall in the next 2 to 3 years, we can expect to see a significantly higher gross margin for Alibaba Cloud, and we can expect to start to see that in the next 1 to 2 quarters.

Your last question comes from Gary Yu with Morgan Stanley.

I have a question regarding CapEx. So what kind of level of CapEx investment is required in order to satisfy the demand from both MaaS and also the long-term cloud revenue? And also management mentioned about T-Head opportunity. What is the current penetration of T-Head being deployed on AliCloud? And as this penetration increase margin uplift we should expect from our in-house chip?

[Foreign Language] [Interpreted] Thanks. So the first question is quite an important one. And actually in our prepared remarks delivered at the last quarter's earnings call, we set out a forecast for the coming 5 years for revenues, and it was a very target. But essentially, I think if you compare where things were in the year 2022 before this explosive growth in AI models and what we expect to need in 2033, I think we're talking about a 10x increase. So we need 10x the amount of data center infrastructure compared to what we had in 2022.

But there are different ways to get that compute capacity. Some of it can be CapEx. Part of it can also be OpEx. And we're actually now acquiring quite a bit of computing capacity using OpEx. The situation is complex today for reasons we've discussed. But I think it's likely, given that kind of investment, that we will overshoot the original CapEx figure that we had stated of RMB 380 billion. But at the same time, we can acquire some compute through OpEx, and as we have our own proprietary T-Head chips, we can actually also sell AI servers, leveraging those chips to other computing centers or we can co-build computing centers with others.

So there are different ways that we can get to where we need to get. But the bottom line is that the demand for compute infrastructure is going to be 10x of 2022.

[Foreign Language] [Interpreted] Yes. So in terms of our T-Head proprietary chips, they can be deployed across a very large part of our AI infrastructure, and not just compute chips, but we have a full stack including memory. But at present, the ratio is still relatively low, and that's because of constraints around production capacity in China, which has been limited. Of course, it's been growing. But as we deploy more and more of our own proprietary T-Head chips, the new chips will certainly contribute very, very significantly to gross margin expansion.

It's true to say that domestically produced semiconductors in China lag behind the leading overseas ones in terms of energy efficiency and production efficiency. However, if you look at globally leading AI chip vendors today, their gross margins are as high as 60% or even 80%. So as we ramp up domestic chip production and the capabilities improve, I think there's a lot of room for our chips to be providing very high value for money as compared to that 60% to 80% gross margin than other vendors are taking.

Thank you. This brings us to the end of today's earnings call. We appreciate your time and participation, and we look forward to speaking with you soon.

Thank you. You now may disconnect your lines.

[Portions of this transcript that are marked [Interpreted] were spoken by an interpreter present on the live call.]

Good day, ladies and gentlemen. Thank you for standing by, and welcome to Alibaba Group's December Quarter 2025 Results Conference Call. [Operator Instructions]

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 December Quarter 2025 Earnings Conference Call. Joining us today are Joe Tsai, Chairman; Eddie Wu, Chief Executive Officer; Toby Xu, Chief Financial Officer; Jiang Fan, Chief Executive Officer of Alibaba E-commerce Business Group. .

I would like to remind you that this call is also being webcast on our corporate website. A replay of the call will be available on our website later today. Now I will quickly cover the safe harbor. Today's discussions may contain forward-looking statements based on current expectations and assumptions that are subject to risks and uncertainties.

Actual results may differ materially. Please refer to the safe harbor statements that appear in our press release and investor presentation provided today. Please note that certain financial measures are expressed on a non-GAAP basis. Our GAAP results and reconciliations of GAAP to non-GAAP measures is included in today's earnings press release and investor presentation. Our comments will be our year-over-year comparisons unless we state otherwise. And now I will turn the call over to Eddie.

Thank you, and welcome to this quarter's earnings call. Over the past quarter, we maintained strong investment momentum in our 2 strategic priorities, AI plus cloud and consumption. Cloud Intelligence Group revenue growth accelerated to 36%, while our Quick Commerce business continued to expand in scale with ongoing improvement in economics. With the dawn of the AI agent era, the addressable market for AI infrastructure providers like Alibaba is set to grow exponentially. .

AI models and our capabilities are rapidly being embedded into mainstream work environments across all industries with token consumption surging across sectors. Cloud and software budgets for enterprise IT services have traditionally represented only around 5% of corporate revenue as model-driven agents begin to handle mainstream work tasks across industries, our total addressable market will expand by several multiples.

From AI infrastructure to the application layer, Alibaba has built a complete full stock AI capability set to support the exponential growth in AI demand. Faced with an industry transformation and strategic opportunity of this magnitude, Alibaba Group is itself entering a new phase of entrepreneurial reinvention and critical investment oriented toward the future.

Next, let me share Alibaba's AI strategic road map. We have complete full stack AI capabilities, chips and cloud computing form the AI infrastructure layer while the AI application layer is anchored by Alibaba Token Hub and comprises foundation models, mass in both enterprise and consumer applications. Together, these give us end-to-end coverage across the full stack from AI infrastructure to applications.

Given the enormous and sustained growth momentum of the AI market, combined with Alibaba's full stack positioning across the AI value chain, the business goal of Alibaba's AI strategy is very clear. Over the next 5 years, our goal is to surpass USD 100 billion in combined cloud and AI external revenue, including mass.

Regarding our infrastructure, driven by sustained strong AI demand, Cloud Intelligence Group's revenue from external customers accelerated to 35% this quarter with AI-related product revenue delivering triple-digit year-over-year growth for the tenth consecutive quarter.

Cloud Intelligence Group's market share has grown for 3 consecutive quarters, rising to 36% with our lead continuing to widen. Alibaba Cloud's cumulative external revenue through February for fiscal year 2026 officially surpassed RMB 100 billion.

Over the past 3 months, token consumption on the model studio platform has grown by 6x. We expect mass to become Cloud Intelligence Group's largest revenue product. T-Head's proprietary GPU chips have achieved scaled mass production. As of February 2026, T-Head had cumulatively shipped 470,000 AI chips.

In real-world business deployments through Alibaba Cloud, more than 60% of the T-Head ships serve external customers, and we've completed scaled adoption for external customer AI workloads. T-Head now supports the AI workloads of over 400 enterprise customers across industries, including Internet financial services and autonomous driving. We're confident that T-Head's compute supply capacity will continue to expand, contributing high-quality compute to our cloud infrastructure and mass platform, strengthening the overall competitiveness of our cloud services.

Regarding our application layer, centered on the core mission of creating, delivering and applying tokens, we established the new Alibaba Token Hub Business Group, ATH. It comprises Tongyi Laboratory, the mass business line, the QN business unit, the Wukong business unit and the AI Innovation business unit. It is the organizational foundation for executing Alibaba's AI strategy and the hub for efficient coordination across our AI businesses. .

During Chinese New Year, we launched our latest generation large model, Qwen3.5-Plus, which delivered outstanding performance across comprehensive benchmarks in reasoning, coding and Agentic capabilities. Qwen3.5-Plus demonstrated significant improvement in inference efficiency through foundational architectural innovation. Building on Qwen3.5, we will soon release the next generation of models optimized for coding and agentic use cases. On the consumer application side, powered by the strength of our models, Qwen's consumer-facing monthly active users have surpassed 300 million. During Chinese New Year, we deepened integration across Alibaba's ecosystem connecting Qwen app with e-commerce Alipay, Fliggy Amap giving it unique capabilities relevant to the everyday life and becoming China's first all-in-one personal air system for life work and learning.

We've also recently launched Wukong, our enterprise AI agent platform. Wukong is the world's first AI native enterprise grade agent platform, enabling AI-powered upgrades to enterprise workflows while remaining compatible with each organization's data permissions and management processes. It serves as the unified interface for Alibaba's AI capabilities in enterprise work environments and the B2B capabilities of businesses across Alibaba's full ecosystem will be progressively integrated to sort Wukong becoming the best AI work system.

On Alibaba's other strategic priority, the consumption segment, we continue to advance our strategic initiatives. This quarter, our Quick Commerce bills further expanded in scale with continued share growth, high customer retention and sequential improvement in both unit economics and average order value. At the same time, Quick Commerce and e-commerce demonstrated clear synergies driving Taobao app monthly active consumers to double-digit year-over-year growth. That concludes my remarks. I'll now hand over to Toby to share the financial update. .

Thank you, Eddie. Our strategic priorities are clear: we remain focused on AI plus cloud and consumption businesses. We are seeing great momentum with gains in technology, customer adoption, market share and user engagement. On AI plus cloud, we have the full stack AI capabilities with all 3 parliaments, model, cloud infrastructure and chips and leadership in each with Queen, Alibaba Cloud and T-Head. We also operate the most comprehensive consumer ecosystem in China that can monetize through AI. .

The launch of Qwen APP was a major milestone, and it can bring our consumer applications together. On consumption, a quick commerce business continued to gain GMV market share in December quarter, while economics and AOV also continued to improve.

Now let's look at the financial results. On a consolidated basis, total revenue was RMB 284.8 billion, excluding revenue from Sun Art and Intime revenue on a like-for-like basis have grown by 9%. Total adjusted EBITDA decreased by 57% primarily due to our strategic investments in technology-related innovation initiatives and the consumption front, including quick commerce business, partly offset by the improved operating results in cloud business and enhanced operating efficiencies across various businesses.

Our GAAP net income was RMB 15.6 billion, a decrease of 66%. Operating cash flow was an inflow of RMB 36 billion. Free cash flow was RMB 11.3 billion, a decrease of RMB 27.7 billion from the same quarter last year. We are reinvesting our cash flow to be a leader in AI and quick commerce.

As of December 31, 2025, we held USD 42.5 billion in net cash. Excluding that with maturities beyond 5 years, our net position stands beyond the USD 60 billion. This balance sheet strength gives us confidence to reinvest for long-term growth.

Now let's look at our consumption businesses. Revenue from China e-commerce group was RMB 159.3 billion, an increase of 6%. Customer management revenue increased by 1%. The slowdown in revenue growth was primarily due to weaker transaction activities and phase out of the impact of software service fee implementation.

The Taobao App achieved a double-digit increase in MAC during the quarter, driven by the growing mind share and increasing scale of our quick commerce business. Revenue from our quick commerce business increased 56% to RMB 20.8 billion. During the quarter, we executed our plan to further grow the scale of our quick commerce business. improved user experience, improved UE and increased AOV month-over-month during the quarter.

Alibaba China E-commerce Group adjusted EBITDA was RMB 34.6 billion, a decrease of 43%, primarily due to the investment in quick commerce, user experiences and technology. Going forward, this adjusted EBITDA will continue to fluctuate quarter-over-quarter due to intense competition and significant investment in user experience.

Revenue from AIDC grew 4% this quarter. AIDC's adjusted EBITDA loss narrowed significantly year-over-year, driven by a combination of logistics optimization and investment efficiency enhancement the UE of the Alibaba Express Choice business also improved on a sequential basis.

Next, let's look at the business updates and results of Cloud Intelligence Group. Our Cloud Business delivered another quarter of accelerating growth. Revenue from external customers grew 35%, up from 29% last quarter. AI-related products continue to lead this momentum.

We delivered our tenth consecutive quarter of triple-digit growth in AI revenue. Its sheer of external cloud revenue continue to increase. This is a clear reflection of the scale and acceleration in our AI business. The adjusted EBITA margin remained relatively stable at 9%. We will continue to invest in customer growth and technology innovation to increase adoption of AI cloud infrastructure and strengthen our market leadership.

All Other segment revenue decreased by 25% to RMB 67.3 billion, mainly due to the disposal of Sun Art and Intime businesses as well as a decrease in revenue from China, partly offset by the increase in revenue from Freshippo and Alibaba Health. All others adjusted EBITA was a loss of RMB 9.8 billion, primarily due to the increased investment in technology businesses, including Quick models and consumer-facing Qwen, partly offset by the improved results of TainiHujin DME and other businesses.

Qwen Model has become one of the most widely adopted open source model families globally, surpassing 1 billion cumulative downloads on hacking phase by the end of this January, and the consumer facing Qwen has surpassed 300 million MAU across platforms which reinforces user engagement and expand long-term monetization potential. We have been increasing investments on these technology fronts, including the Spring Festival campaign.

Building on the strong momentum and results achieved, as Eddie mentioned earlier, we will continue to invest substantially in Qwen models and Qwen APP. Our unallocated adjusted EBITDA was a loss of RMB 2.7 billion compared to a loss of RMB 0.2 billion in the same quarter last year, which reflected costs associated with talent retention incentive from the one-off replacement awards plan of. Thank you. We will now open for Q&A.

Hi, everyone. You're welcome to ask questions in Chinese or English. A third-party translator will provide consecutive interpretation. In the case of any discrepancy, our management's statement in the original language will prevail.

If you are unable to hear the Chinese translation, bilingual transcripts of this call will be available on our website within 1 week after the end of the meeting. [Foreign Language]

Operator, please go ahead with the first question. Thank you.

[Operator Instructions]

First question today comes from Robin Zhu at Bernstein.

Could you give us some specific examples of how Token Hub will change how the different cloud and AI businesses work together going forward from an organizational standpoint? And strategically, what changes or goals are you hoping to achieve with this new structure that improves on the previous arrangement going forward?

And then if management could share hierarchy of priorities in cloud and AI, is it market share and revenue growth, such as the target you just announced versus having the best first-party model capabilities versus consumer side traction with customers using Agenetic AI or anything else?

[Foreign Language] [Interpreted]

Great. Thank you very much for your question. I think that the goal and purpose of the establishment of the ATH Business Group, is very much connected to the era that we're now in as of the end of 2025 and going into the first few months of 2026.

In terms of the development of AI, we're now in the agent-driven era of AI development. And this is different from the earlier period of AI development in the Agentic AI era we need to achieve a very close integration of model with application. In the earlier AI era, a lot of model training data was static data, but in the genic era, we need to enhance the integration between models and applications and achieve tight integration and a lot of the data is now coming from the customer side.

So if you look at the different layers involved in AI deployment from application model, the AI infrastructure, through chips. I think what's most different and most important about the Agentic AI era is the need to achieve this tight integration between application and model. That's the critical priority.

Next, let me address the interconnection and synergies among the different businesses in relation to ATH. If you look at the trends of where this industry is going, and we think we see these trends very clearly. The AI agents will be tightly integrated together with the application layer. And there will be a multitude of highly diverse applications. In the consumer or 2C space, we're strongly developing the Qwen app as a personal assistant for individuals. And in the 2B space, we're positioning Wukong has a 2B. .

In the AI application layer, there will be a multitude of different industry and vertically specialized applications to serve different industry use cases. And all of this needs to be supported by a very robust model as a service, mass layer. So mass supports, of course, our own internal applications as well as a multitude of external and industry-specific use cases that leverage AI. So in this context, we see massive value that we can provide and a huge total addressable market. or TAP. So going forward, we see the AI application layer as the main channel through which tokens will be distributed. And the stronger the model capabilities that you can offer at the mass layer, the more attractive and compelling all of these different offerings will be to customers. That is the business logic that we have laid out within this new business unit. .

So from the perspective of both the model and the application layers, our top priority absolutely is to develop the most intelligent models. And I really need to emphasize that only when you have the most powerful models, can you truly drive the deployment of AI applications across all kinds of different industries. Only with the strongest models, can you attract applications from across diverse industries to adopt our mass offering. .

However, in order to build the most robust models, you need to have very close collaboration with various industries and with our own 2C and 2B applications to connect with our mass 2 applications across all kinds of different industries and use cases. So we need to get more users to leverage and make use of our models in order to gradually be able to leverage the data flywheel effect. Only in that way, can we continuously enhance the capabilities of our models. So that's one of the reasons why we have established the ATH business unit at this time.

So to summarize, I would say our top priority is definitely to enhance model capabilities. However, to enhance model capabilities requires concerted efforts across the entire model pipeline as well on the application and infrastructure side in order to achieve sustained improvements over the long term.

Your next question comes from Joyce Ju at Bank of America.

Congrats on the solid progress you've made in cloud and AI. My question is we see CMR growth slowing notably in the December quarter, given the macro pressures. We have seen China's online retail sales only up 2% year-over-year in the fourth quarter '25. But more recently, MBS data point to a reacceleration in January and February. Could you share your latest view on the CMR trends heading into the March quarter? And whether you have started to see any improvement in element?

[Foreign Language] [Interpreted]

Thank you for your question. Indeed, in the December quarter, weak macro consumption, a warm winter, the later timing of the Chinese New Year challenged the growth for the December quarter. And due to the extended promotional season, our investments in consumer benefit increased compared to previous years. So as a result, the CMR and EBITDA trend softened. Going into the March quarter with the improving consumer sentiment that we've observed and momentum from our Quick Commerce strategy, our physical goods GMV and CMR trend have significantly recovered from the December quarter, and EBITDA is expected to improve accordingly.

All right. Let's move on to the next question.

Your next question comes from Gary Yu at Morgan Stanley.

My question is related to Quick Commerce. I understand that in the past couple of months, we have achieved certain milestones in terms of GDV market share and also GDV improvement. How should we look at the priority going forward? Are we aiming for market share or hoping to take this opportunity to improve unit economics, reduce loss? And how should we look at the synergy between Quick Commerce and traditional e-commerce? And how should we see these synergies to translate into CMR better growth going forward? .

[Foreign Language] [Interpreted]

Certainly, while growing our market share, we have continued to significantly improve UE driven by improvement in fulfillment logistics efficiency by improvement in monetization as well as by order mix optimization, driven by those factors, we expect to further optimize in the coming quarters.

In terms of the positive impact that Quick Commerce is bringing to our conventional e-commerce business and to our entire ecosystem. We saw a very significant increase in AACs on the platform in the past year. Our AAC number increased 150 million in 2025 and including 10 million conventional e-commerce physical goods AAC, which is more than the previous 3 years combined. .

Now new consumers ARPU and purchase frequency are lower than that of existing users. So we aim to continually increase their ARPU and purchase frequency, which will serve as a new growth engine for our platform in the coming years. Quick Commerce is clearly driving sales in various categories such as food and fresh produce and health care and is contributing to Freshippo and Tmall supermarkets accelerated growth.

In terms of the outlook, we maintain our target of achieving over RMB 1 trillion in Quick Commerce GMV by FY '28. We expect to generate positive cash flow when the GMV target is achieved, and we expect the Quick Commerce business to be profitable in FY '29.

Quick Commerce has become a cornerstone of our e-commerce business, playing a strategically vital role in the AI era by driving customer acquisition, enhancing user engagement fulfilling diverse consumer demand, increasing transactions and improving monetization and supporting logistics infrastructure. We are committed to investing in Quick commerce in the next 2 years towards achieving the RMB 1 trillion GMV target as a market leader.

Operator, let's move on to the next question. .

Your next question comes from Alicia Yap at Citigroup. .

I have some questions regarding your chip business,. So there have been reports that Alibaba plans to spin off the that unit as a separate listing. Can management provide any information of this? And if so, what is the expected time frame for this to occur? And in the meantime, can you share more operating metrics? So in addition to the 470,000 chips that you mentioned you shipped to external customers, how we reconcile that number, the shipments to the revenue side? And also what is the expected growth rate for your chip business in the coming year? And I think you mentioned currently it's 60% of these from external customers. So maybe can you also share with us, are these chips for external customer mainly used for inferencing? And then for internal, is it used for model training and also infere ring? And then lastly, how do the chips or TS chips are compared to other domestic chips? If management can share some detail would be great.

[Foreign Language] [Interpreted]

Okay. Thank you very much for this question. And I'd like to take the opportunity to expand on this a bit because T-Head is a very important component of Alibaba's company-wide AI strategy. So in the context of China's domestic AI chip ecosystem, we firmly believe that T-Head is ranked in the top tier of the domestic AI chip ecosystem in terms of the technology capabilities and product capabilities. Our products cover the entire AI workflow from model training and fine-tuning through to inference. And our T-Head AI chips are already in extensive large-scale use via Alibaba Cloud, both for training workloads and for by inferencing use cases.

At the same time, over 60% of T-Head ships are being used by external commercial customers across Alibaba Cloud's public and hybrid cloud offerings. The external commercial clients span multiple industries, including Internet finance, autonomous driving and intelligent manufacturing. And these are external commercial customers are utilizing T-Head chips in both their training and inferencing workloads.

Moreover, on the T-Head software stack, we have excellent compatibility with the Linux ecosystem. So customers can migrate their systems easily without spending a lot of time on the migration. Another point I would make is that in my view, T-Head's significance to Alibaba lies not only in our aspiration to close the gap between domestically produced chips and foreign counterparts, foreign-produced chips in terms of manufacturing processes and overall performance across various dimensions.

But given that our chips still lag behind foreign counterparts and performance in various respects, we aspire to engage in more profound co-design with Alibaba's cloud infrastructure and the Qwen model to provide improved cost effectiveness. So this is one key differentiator and how we approach chip design at T-Head that sets us apart from other chip companies. Our primary goal is to create AI capabilities that offer superior value for money. This will make it a key product for the platform, allowing us to reduce inference costs going forward. Beyond generally improving our AI efficiency and reducing costs, there's another factor at play namely the unique circumstances currently facing the AI industry in China. In that context, one significant benefit for us is the guaranteed supply of AI computing power.

Because I believe that over the next 3 to 5 years, global AI computing power will be an extremely short supply, especially in the Chinese market, as the only cloud compute company in the Chinese market with proprietary chip development capabilities. T-Head is of paramount importance, therefore, to the Alibaba Group, increasing the supply of AI computing power will help our cloud and AI businesses, including our mass business to achieve stronger growth momentum.

At T-Head, over the past 2 years, we've successfully commercialized and launched chips with total volume exceeding 470,000 units with annual revenue reaching the 10 billion on level. Looking ahead to '27 well, through 2026, this year through '27. Next year, we expect T-Head's production capacity for high-quality AI chips to continue to expand. This will provide robust computing power support for our group's AI business and serve as a powerful growth driver for our overall AI initiatives. We also believe that future improvements in profitability will be achieved further enhancing profit levels, which will also be very beneficial. Overall, T-Head's value to Alibaba goes beyond cost optimization. It primarily serves to ensure supply chain resilience and in an era of scarce computing power. I see this as crucial to Alibaba's AI strategy. So it is possible, and we don't rule out the T-Head of considering an IPO in the future, although we currently do not have any definitive time line.

Next question, please.

The next question comes from Yuan Liao at Citic.

[Foreign Language] [Interpreted]

My question is about the business objectives for your AI strategy that you just mentioned. Revenue for the next 5 years is expected to exceed 100 billion. Could you provide more details on this target? For example, if the next 5 years is through to 2031, what kind of CAGR would that correspond to in this 5-year period? And could you also break out what will be driving that growth and how we should understand those drivers? Given this scalable growth in revenue, when can we expect to see sustained improvement in Alibaba Cloud's margins?

[Foreign Language] [Interpreted]

Thank you for your question. So yes, we certainly believe that within 5 years, revenues from our AI and cloud-related business will exceed $100 billion. We think that, that is very clear. If you look at the market growth that we're seeing today are the strength of our product portfolio and the road map to get there.

I think that the major driver underlying all this really is continued breakthroughs in the capabilities of large AI models. And we've certainly seen a riskier trend over the past couple of months, 2 months of 2026, whereby large models have now gained the capability to execute complex B2B workflows. More and more enterprises are deploying agents powered by large models to handle end-to-end business tasks. And that marks a fundamental transformation in the way that the market looks at IT budgets, IT budgets traditionally allocated to AI and cloud services. The shift really is that many enterprises now when consuming tokens don't treat token consumption as part of their IT budget any longer. Instead, they see tokens as part of their overall operational or R&D costs. Tokens are a key component of their production inputs, not just a part of their IT budget. So this is the most fundamental long-term factor that we see driving future AI growth. .

I believe that the largest drivers of growth will come from 3 areas. First is the mass-driven business, which really is the core growth engine. And the growth of our mass business will be supported by a variety of different use cases, including our own applications as well as a diverse array of AI application scenarios from across our customer base and across various different industries, including AI application software. And we believe that the growth driven by mass initiatives will be a key driver of future revenue for both AI and cloud services.

But secondly, for AI and cloud computing, there's another very important growth opportunity. Of course, we believe that public mass will be a substantial market in the future. But in a considerable number of large -- medium and large-sized enterprises, there'll also be a demand for enterprise level, internal inference and training, a new marketplace. And that market will continue to exist in the long term. It's not one that will disappear simply because each enterprise makes decisions based on its own business model and the security requirements of its specific use case or the particularity of an application scenario. So for some application scenarios, enterprises will opt to use public mass API services, while many others will be based on privately deployed solutions within the enterprise. So those kinds of application scenarios represent a large incremental growth opportunity for Alibaba Cloud's AI infrastructure.

Third, there's another important driver, an important opportunity that I think tends to get ignored a lot of the time. And I'm talking about CPU-centric cloud computing, the traditional cloud computing, which has significant room for expansion in this AI-enabled era. So traditional cloud computing is designed for IT engineers, which in China may number a few million, say, perhaps no more than 10 million potentially traditional IT engineers. And those have been the traditional cloud computing customers.

However, in the future, there could be billions of agents that are created by large AI models and their operating environment. The operating environment of these agents will also require substantial support from traditional CPU-centric cloud computing. They need these traditional CPUs as well as databases, storage and large amounts of memory to support their long-term problem solving and sustained operations. So the challenge lies in transforming the traditional cloud computing market shifting from a cloud platform designed for human users, those IT engineers to one that's optimized for agent-based implication. So I believe there's tremendous room for growth there. So a key challenge for us this year is transforming traditional cloud computing into a platform that is better suited for Agentic use. And that's a key focus of Alibaba Cloud's upgrade.

As the revenue from this business continues to grow, our AI business will undergo transformation and upgrading, shifting from selling resources to selling intelligence, selling intelligent capabilities. And I think that represents a massive upgrade to the business model. At the same time, by integrating our proprietary T-Head chips we are achieving and will achieve cost reduction and efficiency gains, we believe that as our AI and cloud business continues to grow in revenue scale, cloud profitability should become increasingly visible and we see it is on a steady path of improvement. However, the process of continued improvement is not a linear one. It's possible that there could be a scale effect breakthrough, the achievement of an economy of scale or the scaling up of our T-Head chips, and there could be a massive leap forward, but I think that's a function of the product as well and those kinds of economies of scale.

But it will not unfold in a linear fashion. So you asked about the CAGR compound annual growth rate from 2026 through to 2031. I think you can plug that into your calculator and figure out what it would be assuming it were to be linear, but I don't think that it will be linear. Our R&D investment and growth in the market will not be linear and some of the investments we're making today may not yield significant growth until 1 or even 2 years from now. However, regarding that overall 5-year goal, we are highly confident in our ability to achieve it. .

Thank you. Peter, let's take the last question.

The last question comes from Alex Yao with JPMorgan.

[Foreign Language] [Interpreted]

I'd like to shift the topic a little bit and ask a question about E-commerce. You previously said that we were in a 3-year investment cycle for E-commerce. I'm wondering if that is now being adjusted or being driven by the new opportunities that have arisen in Instant Commerce and in agentic commerce or if we're still thinking of it in terms of the original 3-year plan, which would put us now in the middle really of that 3-year period where I guess we would start to be reaping the returns on a stable basis from those investments. So if you could speak to us about the overall direction of E-commerce in the context of that 3-year investment cycle that you'd told us before and also share with us how you're thinking about being positioned and your strategies on this e-commerce track.

[Foreign Language] [Interpreted]

Thank you. So as I just mentioned, we are making a very significant investment in the instant retail business this year, the Quick Commerce business this year. And at this point in time, we're seeing a highly definitive opportunity in this space. So again, as I just mentioned, we will continue to invest heavily over the next 2 years in order to achieve our goal of surpassing RMB 1 trillion in Quick Commerce sales. We also believe that in 2 years' time, our investments in Quick Commerce will generate positive economic returns for our E-commerce business as a whole. .

[Foreign Language] [Interpreted]

But I'd like to add to that by bringing in the dimension of AI because Eddie has talked a lot about AI. I believe that AI will also have a very, very significant impact on e-commerce. However, 3 years is too long a time to talk about when it comes to AI because AI today is evolving at a pace that's measured in weeks or in months. But that's precisely why we're making significant investments on the AI front and we are leveraging AI to roll out new experiences for consumers and for merchants as well as upgrading merchants' business models with AI.

We believe that AI will allow us to make huge upgrades in e-commerce across different parts of the e-commerce business. It's beneficial for our B2B business, where we see tremendous opportunities for its deployment and we will actively seize on all of these new opportunities. .

Okay. That wraps up the Q&A session of today's earnings call. Thank you very much for joining us today, and we look forward to speaking with you soon.

Thank you. That concludes the call for today. Thank you for participating. You may now disconnect your lines.

[Portions of this transcript that are marked [Interpreted] were spoken by an interpreter present on the live call.]

Good day, ladies and gentlemen. Thank you for standing by. Welcome to Alibaba Group's September Quarter 2025 Results Conference Call.

[Operator Instructions] 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. Welcome to our September quarter 2025 earnings conference call. With me today from Alibaba are Joe Tsai, Chairman; Eddie Wu, Chief Executive Officer; Toby Xu, Chief Financial Officer; Jiang Fan, Chief Executive Officer of Alibaba E-commerce business group.

I would like to remind you that this call is also being webcast on our copy website. A replay of the call will be available on our website later today. Just a few forward-looking statements before we begin today. Today's discussions may contain forward-looking statements, particularly statements about our business and financial results that are subject to risks and uncertainties, which could cause actual results to differ materially from those contained in the forward-looking statements.

Please refer to the safe harbor statements that appear in our press release and investor presentation provided today. Please note that certain financial measures that we use on this call are expressed on a non-GAAP basis. Our GAAP results and reconciliations of GAAP to non-GAAP measures can be found in our earnings press release. With that, I'm going to turn the call over to Eddie.

[Interpreted] Welcome to Alibaba Group's quarterly earnings call. Over the past quarter, Alibaba delivered steady and healthy growth. Our total revenue increased 15% year-over-year, excluding Sun Art and in time. Our continued investment in core businesses is yielding results with China e-commerce CMR growing 10% and Cloud Intelligence revenue rising 34%.

Let me walk you through the latest developments across our AI + Cloud and consumption businesses. Sustained strong demand for AI and rising usage of public cloud drove Alibaba's Cloud 34% revenue growth this quarter, while revenue from external customers accelerated by 29%. And AI-related products continued to post triple-digit year-over-year growth for the 9th consecutive quarter. In the cloud computing market, 2 major trends are becoming increasingly apparent. First is AI applications scale, more developers and the enterprise customers are choosing vendors with full stock AI technology portfolios.

Second, Customers are deepening and broadening their use of AI, which is significantly increasing demand for compute, storage and other traditional cloud services. Together, these forces are accelerating revenue growth driven by external customer demand. This quarter, we continued to strengthen our full stack AI capabilities, spanning high-performance AI infrastructure, foundation models and AI development frameworks. Our flagship model, Qwen3-Max ranks among the global leaders in benchmarks for real-world coding tasks, agent tool use capabilities and other specialized valuations.

Our full stack AI capabilities are now a defining competitive advantage. Alibaba Cloud is gaining market share across multiple segments. In the hybrid cloud market, Alibaba Cloud has become a key player, growing more than 20% year-over-year, outpacing the industry and steadily expanding market share. Our financial cloud business is also growing faster than the market with market share continuing to rise. In China's AI cloud market, we are also the clear leader with a market share larger than the combined total of the second to fourth largest providers. Recently, businesses such as the NBA, China UnionPay and Bosch have partnered with Alibaba Cloud on AI initiatives.

Last week, we officially launched the Qwen app, which aims to be the most advanced personal AIS system powered by our latest models. In the first week of its public data, the Qwen app has already surpassed 10 million in new downloads. The launch of the Qwen app marks Alibaba's commitment to both AI for enterprise and AI for consumer. In enterprise-focused AI, our goal is to build a world-leading full-stock AI provider serving businesses across all industries. For consumers, we aim to build native AI-first applications by leveraging our best-in-class models and Alibaba's extensive ecosystem.

On the one hand, Qwen3-Max's intelligence and world-class tool use capabilities combined with Alibaba's rich consumer and lifestyle use cases contributed to exceptional user retention in the Qwen apps beta release. We believe this is the right moment to scale our consumer AI efforts. On the other hand, the synergy between AI and the broader Alibaba ecosystem is a powerful multiplier. Alibaba is the only company in China with both a leading large model and extensive lifestyle and commerce use cases. will gradually integrate e-commerce map navigation local services and more becoming an AI-powered entry point for everyday life.

With AI innovation and ecosystem collaboration reinforcing each other, we're confident in our ability to deliver substantial user value. In consumption, we continue to deepen collaboration across businesses and the benefits of our large integrated platform are becoming increasingly evident. This quarter, China e-commerce Mark grew 10%. Our Quick Commerce business saw a significant improvement in unit economics with creator fulfillment efficiencies, stronger user retention, higher average order value and expanding scale.

The growth of quick commerce business contributed to rapid growth in Taobao app's monthly active consumers and supported CMR expansion. Brands on Tmall are also accelerating their adoption of on-demand retail as of October 31, approximately 3,500 brands on Tmall onboarded their online stores to our quick commerc business. Going forward, we will further enhance synergy between quick commerce and the broader Alibaba ecosystem, continue improving unit economics and meet consumers' fast-growing demand for immediate access to diverse products and services.

On October 1, Amap's daily active users reached a historical high of 360 million. In September, we launched the Amap Street Stars feature has significantly boosted user engagement. In October, Amap Street stars averaged more than 70 million daily active users with average daily user reviews more than triple the amount of the same period last year, indicating strong future growth potential. Amap Street Stars has built a trust-based rating system for local offline services using user consented metrics such as the users credit rating.

We believe that enhancing consumer sustainable strengthening consumer confidence, enabling merchants to focus on operations while giving consumers greater peace of mind, supporting the healthy and sustainable growth of the local off-line services sector. Looking ahead, we'll continue investing decisively in our 2 core strategic pillars: AI plus cloud consumption. We will advance both enterprise and consumer-focused AI unlock deeper synergies across Alibaba's businesses and use these entrants to drive Alibaba's long-term growth and carry the company to the next level. Thank you. I will now hand over to Toby.

Thank you, Eddie. We are continuing our focus and discipline on AI plus cloud and consumption and we see strong momentum from these strategies with gains in technology, market share, consumers and user engagements. Now let's look at the financial results. On a consolidated basis, total revenue was RMB 247.8 billion. Excluding revenue from Sun Art and Intime, revenue on a like-for-like basis would have grown by 15% year-over-year. Total adjusted EBITDA decreased 78%, primarily due to our strategic investments in quick commerce business to grow its user base and transaction volume, partly offset by double-digit revenue growth in China e-commerce group and Cloud Intelligence Group and improved operating efficiencies across various businesses including AIDC and Wujin DME. Our GAAP net income was RMB 20.6 billion, a decrease of 53%, primarily attributable to the decrease in income from operations.

Operating cash flow was RMB 10.1 billion, a decrease of RMB 21.3 billion compared to the same quarter last year. The year-over-year decrease was mainly attributed to our increased strategic investments in quick commerce business. Free cash flow was an outflow of RMB 21.8 billion which reflected our significant investments in quick commerce business and AI plus cloud infrastructure, we are reinvesting our free cash flow to create a winning quick commerce business and to be a leader in AI.

Our strong balance sheet backed by USD 41 billion in net cash gives us confidence for this reinvestment strategy. Revenue from Alibaba China e-commerce group was RMB 132.6 billion, an increase of 16%. Customer management revenue increased 10% primarily due to the improvement of take rate, which benefited from the increasing penetration of [indiscernible] and the addition of software service fees. Revenue from our Quick commerce business increased 60%. During the quarter, we executed our plan to grow the scale of our quick commerce business, improve user experience and narrow UE loss.

The adjusted EBITDA from Alibaba China e-commerce group was RMB 10.5 billion. Excluding loss from our Quick commerce business, our Ali Baba China e-commerce group EBITDA would have grown at mid-single-digit year-over-year for the quarter. Going forward, this adjusted EBITDA may fluctuate quarter-over-quarter due to intense competition and a significant investment in user experience. Revenue from AIDC grew 10%. AliExpress, in particular, has developed AliExpress direct model that leverages local inventories in over 30 countries. Art Express has also enhanced the range of our product offerings by launching the Brands program, providing go-to-market solutions to Chinese brands going overseas.

A combination of logistics optimization and investment efficiency enhancement resulted in AIDC's adjusted EBITDA profit of RMB 162 million this quarter. Looking ahead, while we continue to enhance operating efficiency, AIDC adjusted EBITDA may fluctuate quarter-over-quarter due to tactical investments in select markets. Our cloud business delivered another quarter of accelerated growth as both growth of cloud segment revenue and revenue from external customers accelerated to 34% and 29%, respectively.

This momentum was primarily driven by public cloud revenue growth, including the increasing adoption of AI-related products, AI-related product revenue continue to grow at triple-digit pace. AI-related product revenue this quarter accounted for over 20% of revenue from external customers with its contribution continue to increase. We are seeing accelerated adoption of our AI products across a broader range of enterprise customers with a growing focus on value-added applications, including coating assistance.

The adjusted EBITA margin remained relatively stable at 9%. We will continue to invest in customer growth and technology innovation to increase adoption of AI infrastructure cloud and strengthen our market leadership. All Other segment revenue was a decrease by 25% and mainly due to the disposal of Sun Art and Intime businesses. All other adjusted EBITDA was a loss of RMB 3.4 billion, primarily due to the increased investment in technology businesses, partly offset by the improving operating results of other businesses. Hujing Dme has achieved profitability for 3 consecutive quarters. All Other segment comprises a set of innovative initiatives, including several strategic AI-driven technology infrastructure and businesses, including our foundation model and AI apps. We are excited to continue investing in these initiatives for future growth. Thank you, that's the end of our prepared remarks, we can open up for Q&A.

Thank you, Toby. Hi, everyone. You're welcome to ask questions in Chinese or English. A third-party translator will provide consecutive interpretation for the Q&A session, the translation is for convenience purpose only. In the case of any discrempancy, our management statement in the original language will prevail. If you are unable to hear the Chinese translation, bilingual transcripts of this call will be available on our website within 1 week after the end of the meeting. [Foreign Language]. Operator, please go ahead with Q&A session. Thank you.

[Operator Instructions] Your first question today comes from Gary Yu at Morgan Stanley.

Congratulations on a strong set of results. My question is related to cloud business. How should we look at the growth outlook going forward? Should we continue to expand growth to accelerate? And on the demand side, given we don't have a big AI company like in the U.S., how should we look at the key drivers driving the external revenue growth going forward?

[Foreign Language]

[Interpreted]

Thank you for those questions. Let me start with the first one. Certainly, we see that customer demand for AI is -- remains very strong. In fact, we're not even able to keep pace with the growth in customer demand as in orders in terms of the pace at which we can deploy new servers. So we certainly do see the demand for is accelerating. In terms of where that demand is coming from, it's really coming from all aspects of enterprise operations as AI adoption continues to not only accelerate but deepen with applications across product development through our manufacturing processes and also in terms of supporting the enterprises and customers use their products. So when all of those places AI adoption continues to deepen.

And of course, all of this activity around model training and inference requires the use of compute as well. So essentially, we're talking about a huge potential and continually growing demand among real customers engaged in real world use cases. Therefore, our conviction in future AI demand growth is strong.

Your next question comes from Kenneth Fong at UBS.

Congrats on the strong performance in our quick commerce initiative. Can management share some key progress for quick commerce and synergy to our core e-commerce so far. Given the synergy, what's the outlook for December quarter CMO and EBITDA for our core e-commerce?

[Foreign Language] [Interpreted] Thank you for your question. Over the past few months, we've focused on optimizing our unit economics in quick commerce, while maintaining our market share. And we believe we've made significant progress on this front, the order mix has improved and the economies of scale from growing order volume has driven clear reductions in logistics costs. Since November, the per order UE loss for quick commerce has been cut by 50% compared to July, August.

So on this basis, quick commerce has maintained stable order share with GMV share holding steady and trending upward. And we're also seeing uplift in related physical e-commerce categories. Let me expand a bit further on those points. First, in terms of order mix optimization. Over the past 2 months, the share of higher average order value, higher AOV orders has increased. According to the latest data, non-beverage orders now account for over 75% of total orders.

Most recently, AOV for quick commerce has grown by double digits compared to August which has contributed to an increase in quick commerce's overall GMV share. On the second point about logistics as the order volume scales, quick commerce is realized in very clear economies of scale in fulfillment and logistics.

Delivery speed is now faster than the same period last year. while average logistics cost per order has declined significantly. In fact, the average cost per order is now lower than it was before we started making large-scale investments in quick harness. .

So these 2 factors together have enabled us to achieve our near-term target, namely cutting by half the per order loss versus July, August. And importantly, during this phase of narrowing UE losses, both user retention and purchase frequency have outperformed management expectations.

Beyond food delivery, we're also seeing rapid growth in retail categories via quick commerce, clearly driving growth across related categories and businesses, especially groceries, health care products and the supermarket segment within physical e-commerce. For example, [ Frico ] and Tmall supermarket quick commerce orders are up 30% from August. Over recent months, we've also actively onboarded merchants and brands onto Taobao instant commerce, and we will further accelerate integration and synergy between key retail categories and quick commerce model going forward.

So in summary, we firmly believe that the quick commerce model holds immense potential for synergy with the broader Alibaba ecosystem. In Phase 1, we successfully achieved rapid scale expansion. In Phase II, UE optimization is progressing in line with our expectations, laying a solid foundation for the long-term sustainability of the quick commerce business and reinforcing our confidence in sustained long-term investment in quick commerce.

In the next phase, we will continue to refine the user experience through operational upgrading with a focus on serving high-value users and to focus on expanding retail categories. Quick commerce is a core strategic pillar in the Taobao Tmall Group's platform upgrade. Our goal is to generate RMB 1 trillion in GMV for the platform within 3 years, thereby driving market share gains across the related categories.

[Interpreted] This is Toby. Let me take the second part of your question about CMR and EBITDA.

So as Jiang Fan has just shared with you, a quick commerce is having a very significant effect in terms of enhancing user engagement as well as driving transactions in relevant categories. So that, of course, has a positive impact in CMR. So the main thing that we need to do in this next phase is to better integrate and achieve synergies across conventional e-commerce and quick commerce so as to more fully realize that impact.

However, we are in an investment phase right now. So this is relevant to EBITA. I think likely, the September quarter we will see the quarter during which the scale of those investments are the highest. And as efficiency improves on -- improves and the scale of this business stabilizes we can expect to see, I think, by next quarter a significant sizing down in the scale of those investments. Of course, having said that, we will dynamically adjust the pace and size of our investments in line with market competition.

When it comes to new CMR and the e-commerce business, there will be an impact from the base effect in respect of the payment processing fee as well as the rollout of QCT. We started charging the payment process in September of last year. And so starting from next quarter, we did expect to see a slowdown in growth due to that base effect but as we've consistently emphasized, our primary informal objective is to secure market share for the medium and long term.

And during this process, we will continue to decisively invest in consumers and merchants, and we will resolutely move ahead with business model upgrading of our e-commerce platform. And during that process, you can therefore expect that there will be short-term fluctuations in CMR and in EBITDA.

Your next question comes from Alex Yao at JPMorgan.

[Foreign Language] [Interpreted] Thank you very much for the opportunity. So as Jiang Fan just said, we've now completed the first phase of these investments we're now in the second phase where we are enhancing efficiency. So my question is, as the efficiency is optimized and we obtain cost savings what are we going to do with those cost savings? How will the benefit of those cost savings be allocated or distributed across the value chain among the different key stakeholders, say -- assume, for example, that we're going to continue to maintain the same level of intensity with respect to subsidies to consumers this ongoing incremental improvement in the financial performance of the business then what will that mean in terms of subsidies for merchants?

The cost savings will need to be allocated or distributed somehow across the key stakeholders, the consumer merchant and platform. And then so if we don't decrease those subsidies to consumers and we continue to follow the same path that we're on now and rely on optimization of user mix as well as try to increase in order share and driving higher basket sizes, what does that mean for you? And how much scope is there going forward for UE growth?

[Foreign Language] [Interpreted] Yes. This is Jiang Fan. Let me take this question. And it's actually related to, in part, some of the things that I was sharing with you a bit earlier. So what we've been doing in this period of time is enhancing user experience and at the same time, increasing the average order value. So that means that the revenues attributable to each order will increase, because our revenues are proportionate to average order value. I also spoke earlier about how we've optimized logistics, fulfillment, logistics efficiency, and we'll continue to drive improvement with scale.

So I think going forward, there is still considerable scope there on the one hand in respect of consumers because over the past few months, it's really been primarily new consumers in this business. And what we're doing is converting those users into users with a higher level of stickiness across the platform as a whole. And through that process, we'll continue to increase average order size, average order value and to modify the ways in which we provide subsidies. Also, if you look at traffic on the Taobao app over the past few months, including on the quick commerce channel, which has rapidly increased to the point where has over 100 million daily users on the channel. I think it speaks to the fact that there's considerable potential for monetization -- further monetization and I think that, that's an opportunity also to improve in the future.

Having said that, again, the market is a highly competitive market. So we will be looking at those opportunities, but adjusting our approach dynamically in line with market dynamics.

Your next question comes from Ronald Keung at Goldman Sachs.

[Foreign Language]

[Interpreted] So I'd like to ask about CapEx over the next 3 years. And I'm wondering what your thinking is as you sit here today regarding the $380 billion figure, I think you previously mentioned, in particular, because over the past 4 quarters, I believe, $120 billion has already been spent. So how should we be thinking about CapEx going forward and the incremental revenue being driven by that CapEx and how to evaluate the correlation between CapEx and the expected incremental revenue?

[Interpreted] Thank you for the question. So the $380 billion CapEx figure that we had previously mentioned was a planned figure for a 3-year period. But based on what we're seeing now, and as I just mentioned, the pace at which we can add new servers is insufficient to keep up with the growth in customer orders. So looking at the CapEx situation from where we're at today and of course, there are also supply chain issues to consider as well the pace in which we can build out IDCs and launch new service is also part of that consideration.

But essentially, we're working as fast as we can to be able to satisfy all of that customer demand. In that context, if we're not able to satisfy all of our customer demand especially well with the current pace of investment, then we wouldn't rule out further scaling up that CapEx. But again, that is somewhat dependent on supply chain and the capability. But in overall terms, certainly, we will be investing in AI infrastructure aggressively in order to meet that [indiscernible]. So in big picture terms, I would say that the $380 billion figure we had mentioned previously, might be on the small side, certainly in terms of the customer demand that we're currently seeing.

[Interpreted] Thank you. The second part of the question had to do with the incremental revenue being driven by these capital expenditures. And if there's some kind of ratio that we can calculate between x amount of CapEx investment and x amount of incremental revenue. And I don't think it's really possible to make that kind of estimate at least for the time being because overall, the AI sector is still in the early phases of its development. And if you look at the different ways that our AI infrastructure is currently being used, that's in flux and spend in several different areas, for example, we have servers that are directly granted to customers for training. We have servers that are directly rented to customers for inference.

And we're also, of course, using service ourselves by for inference. as well as for internal applications within the Alibaba Group, like Amap, like Cainiao, like Qwen and core and transforming these allocations into member services or membership-based products for our users. So overall, our AI products and AI infrastructure being used in all these different ways, different kinds of allocations, resulting in different revenues and different gross margin levels.

So I think in terms of that kind of ratio, you were asking about whatever it is, it certainly wouldn't be stable at this point. I think in the long term, though, what we care about more is that our infrastructure is serving high-quality tokens and providing good cost effectiveness.

Your next question comes from Ellie Jiang at Macquarie.

[Interpreted] This is more of a follow-up question. The company is a full-stack AI service provider and obviously is currently in an important investment cycle. And we can see that the investments you're making cover a number of different segments in the value chain. So considering the instability in the supply chains that are ongoing at present. I'm wondering how you consider the allocation of our resources because you have the model as a service, the mass layer, we also continue to build up underlying capabilities, fundamental capabilities.

And on the user-facing side, we have apps, including Q1 and AMP products like that, that we're iterating rapidly and scaling up to users. So I'm just wondering in the present macro environment, how should we think about -- how should we evaluate the return on invested capital, ROIC, in respect of AI and including both training and inferencing?

[Interpreted] Let me take that question. Indeed, as a full stack AI service provider, we are currently in a very important investment cycle for AI and investing in our products as well as in our infrastructure. So there are several different places where we're investing and we do have some internal thinking about how we prioritize them, and I can share, I think, some of those considerations with you.

First of all, I would say the most critical priorities. The first thing that we need to ensure is that we are able to continually train our own foundation models. Because in the AI space overall, the ability of our AI infrastructure to be able to acquire more customers or to be able to acquire more high-value use cases relies on our ability to continually iterate and upgrade our foundation models. We need to be doing that in order to be able to unlock new demand and to acquire new customers by unlocking risk cases.

After that, after unlocking new higher-value use cases, then the next thing is to look at the token consumption as well as token quality as well as the willingness of customers to pay for those tokens and that willingness is going to continue to strengthen gradually. So I would say that, that is one of the highest priorities when it comes to allocating those investments. Another priority is around inference, I'm thinking primarily of inference on -- as a service on Bailian. That is also a relatively high priority area for us. because we've created the Bailian platform in order to be able to serve customers all around the world. We want to ensure that those AI resources are available 24 hours a day and are being utilized 24/7 with high efficiency.

So the key there is to ensure that one AI server can run at full capacity 24 hours around the clock and thereby to generate more tokens. So Bailian is a very critical resource pool for us, and it's a relatively high priority. Separately, of course, we have internal use cases for AI inferencing. And indeed, we also have external customers who are leveraging our inferencing services to their demand. So that's also part of the picture. But when it comes to these external customers, we also had some criteria for prioritizing different external customers.

If an external customer is utilizing all of our services across cloud all of the cloud services spanning storage, spending big data and all of these other things, then, of course, not customer would be accorded to a higher level of priority. If you have a customer that's merely renting a GPU to move some very simple inferencing needs than the demands of those customers would accordingly be given a slightly lower level of priority.

Moving on to the second question, which I thought was a really good one. I think there are 2 pieces to this issue. The first is the supply side. Second is -- first is the demand side. Second is the supply side. So if we look at the foundation models and this could be video generation models. They could be omni-model models going forward, the capabilities continue to increase and be enhanced. And we're not yet seeing any issues in terms of scaling one -- nobody's hit the wall yet, so to speak, in the industry. We continue to make a lot of progress on the very important breakthroughs in terms of the capabilities. .

As the models become more powerful than the AI models will be able to do more things in the world of being able to serve larger variety of different use cases. And that will result in these models serving a lot of tasks, as the capabilities increase, they become stronger as these tasks become more deeply embedded across all industries, all aspects of business operations. So with those 2 drivers, we see in the next 3-year period, highly definitive trend of demand for AI.

And with all of this rapid growth in demand, we also need to be thinking about the supply side. I'm sure that you, as analysts have also been looking at the supply side. Starting in the second half of this year, I think we've seen worldwide. If you look at fabs, if you look at DRAM vendors, storage companies, CPU manufacturers across all of those different links in the value chain that go to making AI servers. There is a situation of undersupply, supply is unable to keep up with demand for all of these components globally. .

And I think that you can expect that to continue throughout this scaling up an investment cycle driven by real demand for AI, we know that the supply side is going to be a relatively large bottleneck. So I think that it could be at least a period of 2 or 3 years for those different suppliers, those different venues to be able to ramp up their production capacity. So in this period of 2 to 3 years, we can expect to continue to see a rapid increase in demand and not to be driving the supply side.

So I think in the next 3 years to come, AI resources will continue to be undersupplied with demand out on the supply. And what we can see internally in the industry, and if we look at the hyperscalers in the U.S., all of the latest GPUs that are running at full capacity and not just them, the last generation GPUs, even GPUs from 3 to 5 years, so also several generations back. those GPUs are to this day still running at full capacity. So looking ahead to the next, say, 3 years, we don't really see much of an issue in terms of a so-called AI bubble.

Your last question comes from Jialong Shi from Nomura.

[Interpreted] So in the last earnings call, management shared that Alibaba intends to grow its market share in the consumption market. in China. And we've seen that over the past few months, your investments in quick commerce have indeed resulted in an increase in market share. So I'd like to know apart from quick comments, apart from his e-commerce, what are the other subsectors in the consumption market that you see as good opportunities for investment where you will consider scaling up your investments?

[Interpreted] Thank you. This is Jiang Fan. Let me take this question. Alibaba has been investing strategically in consumption market over many years, and we've entered a huge number of different categories and some verticals. So apart from quick commerce, which we've been investing in heavily, we've talked a lot about it. We also, of course, have freshable, we have off-line, the offline O2O model as well as Fliggy as well as Amap and of course, local services. So that's our landscape or matrix of businesses that we've been investing in.

And I think what we need to be doing now really is working to integrate, connect those businesses and to drive more synergies across those existing businesses. And in that way, we can achieve a further increase in our market share in that larger consumption market.

Thank you. Thank you, everyone, for joining us today. We look forward to speaking with you again on our December quarter earnings call.

[Portions of this transcript that are marked [Interpreted] were spoken by an interpreter present on the live call.]