Operator
Good day, ladies and gentlemen. Thank you for standing by. Welcome to Alibaba Group's June quarter, 2026 results conference call. At this time, all participants are on listen-only mode. After management's prepared remarks, there will be a Q&A session. I would now like to turn the call over to Lydia Liu, 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 Zhu Cai, 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. Reconcilations 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 say otherwise. A replay of the call will be available on our website later today. With that, I would like to turn the call over to Eddie.
Good evening, good morning, and welcome to Alibaba Group's earnings call for the first quarter of fiscal year 2027. Over the past quarter, Alibaba's strategic AI investments have translated into robust results, with a total group revenue growing 9% year-over-year. AI commercialization has also accelerated across the board. Alibaba Cloud's external revenue grew 45%, and EBITDA increased 133% year-over-year, continuing to deliver on our commitment to accelerate growth. Revenue from AI-related products has maintained triple-digit growth for the 12th consecutive quarter with annual revenue run rate surpassing 49.5 billion RMB, around 7.3 billion U.S. dollars. It is the core engine of Alibaba Cloud's growth acceleration. I'll now walk you through four key areas, AI and cloud commercialization, full-stack AI capabilities, AI application ecosystem, and consumption business. First, AI and cloud commercialization accelerated across the board and is expected to sustain high growth going forward. This quarter, Alibaba Cloud's external revenue growth accelerated to 45%, a 22-quarter high, while adjusted EBITDA margin reached 11.6%. Notably, this 45% growth was broad-based, driven by compute, storage, model-as-a-service, mass, and AI applications. We proactively scaled back low-margin business, continuing to improve the quality of our growth. This quarter, annual revenue run rate from AI-related products exceeded 49.5 billion RMB, and its share of Alibaba Cloud's external revenue rose to 35%. AI-related products generate significantly higher gross margins than the average cloud portfolio. Our recurring AI-related product revenue spans multiple layers, AI compute, mass, and AI applications. This multi-layered mix of AI revenue sources and monetization models means growing customer demand at any layer converts directly into commercial opportunity for us. This structural advantage will underpin sustained rapid growth in recurring AI-related product revenue going forward. The surge in AI agents directly drives demand for tokens and GPU compute, while also significantly boosting demand for our traditional cloud products across CPU compute, storage databases, and networking. Alibaba Cloud is undergoing a comprehensive upgrade to an agentic cloud. Based on the latest data, the ARR of our model and application services, including MAS, has surpassed 16 billion RMB. Based on current market feedback and our contract pipelines, compute demand will continue to outstrip supply. As we continue to ramp up our supply, our AI and cloud revenue growth will accelerate further in the coming quarters, alongside continued improvement in profitability. Second, our full-stack AI capabilities continue to strengthen, marked by the scaled commercialization of proprietary chips, faster model iteration, and a thriving open-source ecosystem. This quarter, deepening synergy between proprietary T-Head chips and proprietary foundation models further improved our AI commercialization efficiency. T-Head established a full-stock proprietary silicon portfolio spanning GPU, CPU, and networking chips. As of early August, Zhenwu chips have served more than 650 customers on Alibaba Cloud. The Supernode instance, powered by T-Head's next-generation Zhenwu M890 AI processor, recently launched on Alibaba Cloud at commercial scale. We expect supply to continue ramping up in the second half of the year to meet strong customer demand. Alibaba Cloud's Zhenwu M890 Supernode can efficiently run inference workload for foundation models with more than 2 trillion parameters. Both Kibbe K3 and QN 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 layer, our model release cadence has intensified over the past month with major iterations across our large language, image, audio, video, and music models, all ranking among the world's top tier. Last week, we opened the modeled weights of QN 3.8 Max with 2.4 trillion parameters and the QN 3.8 27B 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 virtual 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 agentic capabilities at scale. We expect productivity agents to become another engine of ARR growth. On the consumer side, the Q&A app continued to steadily grow its user base and is expanding the range of its value-added offerings. Through close coordination between Alibaba Token Hub and Alibaba Cloud, we're running a highly efficient commercial flywheel across compute models, tokens, applications, and monetization. Fourth, our e-commerce businesses remain solid this quarter. In quick commerce, we continue to narrow losses substantially while growing business scale by 45%, with unit economics improving quarter over quarter. Having crossed the AI commercialization inflection point last quarter, we're now seeing growth accelerate and margins expand this quarter. Our AI business's own capacity to self-fund and sustain itself is strengthening, giving us greater confidence to keep investing. Looking ahead, AI has become Alibaba's most certain growth engine. we will stay strategically disciplined and drive long-term growth through our full-stack AI capabilities. I'll now hand over to Toby to walk you through our financial results. Thank you.
Thank you, Eddie. Our strategic priorities in AI plus cloud and consumption businesses, backed by disciplined investments, delivered strong results this quarter. Cloud segment revenue growth further accelerated to 45%, with its EBITDA margin sequentially rising to 12%. AI-related product revenue continues to drive this momentum, marking the 12th consecutive quarter of triple-digit growth in 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 the proprietary 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 alignment of certain businesses in our financial reporting. Starting from this quarter, our segment reporting will present the following. First, Alibaba e-commerce group. Second, AI cloud and computer services. Third, AI labs and applications. And number four, all others. Now, let's look at the financial results for this quarter. Total revenue increased 9% year-over-year to RMB $269 billion, driven by the strong momentum in cloud business and quick commerce. Total adjusted EBITDA decreased 30% to RMB $27.3 billion, primarily attributable to the investment in technology, partly offset by the improved operating results in our cloud business, as well as enhanced operating efficiencies across various businesses. Our gap net income was R&B $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 R&B $22.9 billion compared to R&B $20.7 billion in the same quarter last year. Free cash flow was an outflow of R&B $44.7 billion compared to an outflow of R&B $18.8 billion in the same quarter last year. The decrease was mainly attributed to the investment in cloud infrastructure. CapEx was R&B $67.7 billion this quarter, reflecting our continued investments in AI infrastructure to meet strong and growing customer demand. The significant year-over-year increase is due to several reasons, including fluctuations in procurement cycles, increasing in CPU compute capacity driven by anticipated growing customer adoption of AI agents in a higher pricing of a broad range of chip components. As of June 30, 2026, we held approximately U.S. dollar $30.7 billion in net cash. Excluding debt with maturities beyond five years, our net cash position stands at approximately $46.5 billion. This balance sheet strength gives us confidence to invest for robust growth. Our AI plus cloud investment has a clear path to attractive ROIC. our service, equipped with chips, typically reach break-even within three years. With a five-year useful life, we expect them to generate positive free cash flow at least in the two years following break-even. For the quarter ended June 30, 2026, we repurchased a series of an aggregate consideration of U.S. $162 billion. We remain committed to maximizing long-term shareholder returns through disciplining the capital allocation across investments for AI plus cloud, business growth, share buybacks, and dividends. We'll 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 commerce. Third, international e-commerce. And fourth, global wholesale. Revenue for Alibaba e-commerce group was R&B $205.9 billion, an increase of 4%. Customer revenue decreased by 7%. Excluding the contrary 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 R&B $53.3 billion, an increase of 45%, driven by Freshable and Taobao instant commerce. Alibaba e-commerce groups adjusted EBITDA remained relatively stable year-over-year at R&B $39.7 billion, unscoring our cost discipline against the backdrop of increasing investments in user experiences and technology. Taobao Instant Commerce continues to improve its unieconomics quarter-over-quarter while maintaining market share, driven by higher average order value and enhanced fulfillment logistics efficiency. In addition, Aliexpress achieved operating profit this quarter. We aim to maintain steady profit in our conventional e-commerce business while continuing to drive profitability improvement in our quick commerce business. Now let's review the business updates and results of AI Cloud and Compute Services, which comprises the Cloud Intelligence Group and THAT. 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 R&B $12.4 billion, implying an annual revenue run rate of R&B $49.5 billion. It delivered the 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 and Applications comprises AI Model Labs, Queen Consumer Business Group, and Queen Work. Its adjusted EBITDA was a loss of RMB $13.9 billion, primarily due to our increased investment in AI capabilities and higher inference costs related to Queen APP. The loss significantly narrowed quarter over quarter due to the reduction in marketing expenses for Queen APP. We expect the segment loss to narrow over the coming quarters, driven by improving efficiency in both model training and marketing spend on Queen APP. We have launched our frontier language coding, video, audio, image, and music models, all delivering top-tier performance. 250 million users have had their first AI-driven shopping experience through Queen APP's agentic features across an expanding range of e-commerce and other services since the launch of Queen APP. All other segment revenue remained stable at RMB $28.8 billion. All others adjusted EBITDA with a loss of $3.3 billion, primarily due to our increased investment in technology. AI has progressed from incubation to commercialization at scale. As we expand our market share, strengthen AI leadership, and improving operating efficiency, we are gaining greater strategic and financial flexibility to make disciplined and sustained investments in both full-stack AI capabilities and consumption opportunities, driving secular growth and greater value for our shareholders. Thank you. That's the end of our prepared remarks. We can open up for Q&A.
Thank you, Toby. We will now begin the Q&A session. You're welcome to ask questions in Chinese or English. A third-party translator will provide consecutive interpretation. In the case of any discrepancy, our management statements in the original language will preview. Operator, please start Q&A session. Thank you.
Operator
Thank you. If you wish to ask a question, please press star 1 on the telephone and wait your name to be announced. If you wish to cancel your request, please press. If you're on a speaker phone, please pick up the handset to ask a question. To give more people the opportunity to ask questions, please keep yourself to no more than one question at a time. Your first question comes from Alicia Yap with Citigroup. Please go ahead. Thank you. Good evening, management. Thanks for taking my questions and also congrats on your solid cloud performance. Management, please comment on the reasons and the drivers for the significant increase in the capex this quarter and also what is the expected capex trend for the coming quarters and are these you know are there any updates to the existing three-year capex budget that you have of these 380 billion that you mentioned before and also we would appreciate if management can also provide a breakdown of the capex allocation across the different you know services like the training calls and all that and then also what is management expected return on the investor capital for this investment thank
和驱动力 还想了解一下 接下来几个季度 你们对于CAPEX的总体趋势 怎么看 以及对于之前公布的 就是三年投入 3800亿的这样一个预算 是不是有任何新的更新 另外想要了解当前这些CAPEX 它的分配情况 多少用于训练等等 各方面的一个分解
好 谢谢您的问题 这个问题我觉得挺重要的 就是我也想借这个问题 详细解释一下 我们的AI方面的整个的商业模型 以及对未来的CAPEX投入的一个预期 我们去年2月份 公布了三年3800亿的投资计划 那到今年六月季度末 已经累计投入了1900亿 进度还是符合预期的 本季度的670亿 却是高了一些 但是因为硬件交付 它有一个周期 并不是每个季度均匀的 所以更多的是一个设备交付的波动性 从此本季度我们还增加了 对于CPU的采购 因为aging的时代 我们看到对CPU的需求 在大幅的增长 当然也有芯片组件价格的上涨的因素构成 我们总体来看 我们不应该强调 就是本年度的CAPEX 是677乘以4 这样的一个匀速投入的建设 但总体来说 总体来说 我们是希望我们的今年的投入建设 还是维持在一个更积极的 积极态度上
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 three-year capital investment plan with total investment of 380 billion RMB. As of the end of the June quarter this year, we had already spent 190 billion RMB with progress broadly in line with our expectations. While this quarter's spending of $67.1 billion is somewhat higher, hardware deliveries follow the 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 four to come up with an annualized figure for the year or to expect there'll be a steady linear progression.
The build-out has been progressing at a steady pace, but that is the overall. 训练或者推理的软件服务,本质上所有的这些变现都需要在AI的算力中心这个基础上,而AI算力中心的建设,我们才能够获得市场份额的高速增长,所以这是一个非常明确的一个重资产的商业模式,而重资产商业模式的要获得高速的增长的前提需要在CAPEX投入上进行前置,所以我们会看到我们从2025年开始进入了一个 It's a very strong investment in the market market market. From this perspective, if we want to get a high-speed increase, we need to invest in a more AI management center in the past few years. So this is what we have decided on a business model. It must be CAPEX first, so we can get a growth growth in the future.
Next, let me expand on our full-stack AI business model. This is an asset-heavy business model. If you think about all of the different ways that AI is monetized and can be monetized, be it through software subscriptions, be it through API calls, through models as a service, through training, inference, you know, 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 后面我再来解释一下为什么在现在这个阶段进行AI算力相关的CAPEX投资的回报确定性非常高
行业共识是在2030年之前我们看不到AI算力紧缺的这个情况有所会有非常大的改变 所以在这个行业背景下 我们现在看到我们的AI算力的CAPEX投资回报确定性非常高 从数据上来看 按照我们现在的AI产品的平均毛利水平 我们的CAPEX可以实现在三年之内回本 而我们AI的产品的毛利率水平还在处于持续的提升过程当中 这个回报周期未来还会持续的缩短 Next, let me explain why we see return on invested capital in AI-related capex as highly
certain. There's a consensus across the industry that the current shortage in AI compute will not be resolved until at least 2030. So industry-wide then, it makes sense that there should be high certainty in our investments in AI compute. Based on average gross margins today, roughly we can break even on AI-related CapEx in three years.
And of course, average gross margin continues to rise and we expect to be able to shorten that payback period, say, to 2.5 years. 按照我们现在的投资回报周期,我们的AI算力在投入三年之后,回本之后,还会非常长时间的持续不断的贡献,正向的现金流。 从我们现在看到的实际情况来看,一个最直接的例子,在我们的数据中心里面,2020年购买的A100,2018年购买的V100,到现在还是近乎蛮载的被客户使用。 AI算力资产的实际使用寿命周期 远远长于理论上的折旧周期
purchased in 2018, even today are still running at full capacity. Additionally, we have three means that we can leverage to further enhance gross margin and return on invested capital. First is we can continue to develop state-of-the-art models and enhance gross margin on AI products themselves and continue to expand a higher margin model as a service mass businesses and we can adapt our product mix across IaaS and across software to achieve higher gross margin on the portfolio as a whole. 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...
The second important tool is our digital smartphone. It's also the most important tool in this process. 最重要的一个方案 那我们平头割的自研芯片路径覆盖GPU CPU和网络芯片 这是新一代AI数据当中最核心的芯片组合 在AI数据中心里面最昂贵的成本就是芯片以及存储 那芯片的自研芯片是我们长期重要的方向 随着我们平头割自研芯片未来的产能的持续提升 在我们的数据中心的这种 自研芯片的比例还会持续提升 替代我们更多的商业化采购的芯片 我们知道在现在这个算力紧缺的时代 商业化芯片的本身它们的毛利率就很高 所以自研芯片的比例大幅提升 会大幅度提升我们的产品竞争力 以及我们的毛利水平 A very important piece of this is our ability to deploy our own proprietary chips.
As you know, our own T-head proprietary chips span GPUs, CPUs, and networking chips, which are the critical chipsets for AI. And in AI data centers, the most expensive components are, of course, chips and storage. So we have a very significant advantage in being able to deploy our own proprietary chips. As we ramp up deployment of our own proprietary chips in our data centers as they account for an increasing proportion of total chips and replace commercially procured chips, we can expect to see substantially higher gross margin as well as profitability. 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.
通过这三条路径 我们可以持续地把 AI CAPEX的回本周期 推向更短的时间 比如推向2.5年 甚至接近2年 我觉得可以给大家 算一个简单的框架 如果按我们现在的 AI产品毛利水平 CAPEX三年回本的条件下 理论上 把我们的增速 控制在33%以内 就可以实现正向的现金流 但这现在不是
This is our strategy. 这是我们的长期方向。 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 two and a half years or even less. And so under those circumstances, while pursuing growth of over 40%, we'll also be able to maintain positive cash flow. So that is our long-term strategic direction.
Operator
Thank you. Your next question comes from Charlene Liu with HBSC. Please go ahead.
I'm from HSBC. But thank you very much for this opportunity. Thank you for taking my question. First, can we get an update on the latest developments in quick commerce 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. 非常感谢给到我这个提问的一个机会 我想问一个跟电商相关的一个问题 首先我想就是问一下闪购的一个就是进展的一个情况 另外呢公司把很多个业务进行了梳理 组成了阿里巴巴电商集团 那这些业务未来的一些各自的一些战略重点是什么呢 谢谢您 Thank you Thank you. 环境还是面临挑战,放眼长期,我们聚焦核心供给,同时希望通过AI提升电商的体验跟整体经营效率。
首先在供给方面,从去年开始,淘宝天猫就聚焦对平台,对包括品牌商家在内的原创商家进行扶持,同时挖掘优质产业代的白盆供给的潜力。 Okay, thank you very much for the question as well as for the translation.
In the new fiscal year, indeed, we've realigned our e-commerce business segments, and moving forward, we'll be updating progress on four core areas, China e-commerce, quick commerce, international e-commerce, and global B2B, global wholesale. Let me then briefly share the strategic priorities and key considerations for each of these four segments in the period ahead so starting with China e-commerce while the domestic e-commerce landscape faces short-term macroeconomic challenges our long-term strategy centers on strengthening core supply capabilities and at the same time we aim to leverage AI to enhance the overall shopping experience and improve operation efficiency across the board So first, regarding supply, since last year, Taobao and Tmall have focused on supporting original merchants, including branded sellers, while simultaneously unlocking the potential of high-quality white-label suppliers from key industrial clusters.
天猫依然是品牌商跟很多原创商家最核心的经营阵地,同时我们也会深入产业带挖掘源头好货,支持更多制造业工厂职业的平台上开店经营,也利用平台AI能力帮助更多白牌商家进行更加简单更加高效的托管经营,产业带托管经营模式在平台的交易比例在持续提升。 在刚过去的618,尽管宏观环境面临一定挑战,从结果上看符合我们的预期,尤其是核心商家的经营结果,还是取得了不错的成绩。 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.
AI-11等 另一方面利用AI技术提升现有 购物场景下的体验跟效率 例如我们看到AI对我们的商品推荐 带来了非常显著的提升 商家侧 我们看到商家已经在经营中 非常普遍地使用AI 我们在经营的各个环节 尝试通过AI帮助商家提升能力 尤其是数据分析 广告营销客服等环节 商家可以明显受益 后面我们也会跟签文办公合作 退出更加适配
AI智能体 AI has driven significant efficiency gains in our product recommendations, and secondly, to drive new kinds of AI-driven interaction. On the merchant side, we observed that merchants are already widely adopting AI in their operations. We're exploring ways to leverage AI across various operational links to boost merchant capabilities, particularly in data analytics, advertising and marketing, and customer service, where merchants can derive clear benefits.
And going forward, we'll also collaborate with Quen Office to launch AI agents that are specifically tailored for e-commerce scenarios. 上个季度我们在用户跟订单规模都保持上涨的前提下,我们实现了优异的大幅度优化,亏损规模显著速效。 在今天这样一个基础上,接下来我们会加速整合合马天王超市等相关板块,发展非餐饮品的即时零售,尤其是加速发展前置仓。
Next, on QuickCommerce, after more than a year of investment and development, Taobao Instant Commerce has undergone substantial changes in scale and in market share, with significant improvements across user mindshare, supply diversity, logistics experience, and order volume. Last quarter, while maintaining growth in both users and orders, unit economics, UE, substantially improved and losses significantly reduced. On that basis, we will accelerate the integration of businesses such as FreshHippo and Tmall Supermarket to develop the non-food categories growth within the quick commerce business and we'll place a particular focus on expanding our front warehouses. Over the past year, Freshippo has accelerated the development of front warehouses, leading to year-over-year increase in GMB. 实现整体盈利。长期看,我们认为既是零售有望贡献平台整体交易额的30%成为电商板块的第二曲线。 Meanwhile, QuickCommerce will continue to expand its category coverage and innovate in key areas to enhance the consumer experience. We expect the transaction volume of QuickCommerce for non-food categories to surpass that of food categories within the next fiscal year. driving growth in many different physical goods categories across the overall e-commerce business. The quick commerce business is expected to achieve overall profitability in FY29.
In the long term, we believe it has the potential to contribute 30% of the platform's total GMV, becoming the second growth curve for our e-commerce business. 然后关于国际电商板块 短期来看 我们的海外电商确实受到了国际税收政策和地缘环境的影响 面临一定的这样的一个压力 但我们也看到尽管市场环境很复杂 跨境电商依然保持交易规模上涨的同时 盈利水平也有显著提升 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. especially the agent leader mode, will perform more important role in B2B trade.
We have developed the AI technology, Axiowork, which is soon to have $5,000. AI is constantly changing the B2B trade, especially in the business of B2B trade. We believe that in the past 20 years in this area, we have the opportunity to create a new business and business model.
And fourth is global B2B. Our B2B businesses, including the 1688 and Alibaba.com platforms, have grown consistently over the past two decades, and we see that AI technology will bring profound changes to our B2B platforms and may even fundamentally reshape existing business models. In particular, the agentic model will play an increasingly important role in B2B transactions. We've launched AxioWork, which is an AI agent for cross-border merchants, and it had already attracted over 50,000 paying merchants shortly after its launch. AI is comprehensively transforming the way that B2B merchants do business, especially cross-border merchants. We believe that building on our two decades of know-how in this field, we have the opportunity to create entirely new business models and commercial opportunities in B2B and in cross-border trade in the AI era.
Let all of the businesses in the AIS time raise more attention to the AIS time, and also create a more multi-organized income and develop a more stable development.
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.
Operator, let's go to the next question.
Operator
Thank you. Your next question comes from Yang Bai with CICC.
Thank you. My question is regarding the AI business. 啊 我们观察到阿里云的收入增速逐渐提升 本季度已经来到了15% 那么公司此前其实也提出过未来五年外部云收入突破千亿美元的长期目标 这次也提出了未来几个季度人会加速 那么我想请教两点 第一呢就是如果展望未来几个季度云业务的增长节奏 支撑云计算进一步加速增长的核心驱动因素是什么 第二就是当前其实行业还是处于一个算力供给推进的阶段 虽然管理层刚才也提到可能在2030年前这个供需格局都必会有变化 但也想请教一下如果是站在一个更长期维度去看 业务长期增长的核心驱动 其实对有哪些是否会和短期有所不同 谢谢 Thank you.
My question is about the cloud and AI business.
We've seen that Alibaba Cloud's revenue growth has been accelerating quarter by quarter, reaching 45% this quarter. We know the company has previously set a long-term goal of exceeding 100 billion U.S. dollars in external cloud revenue over the next five years. And you've also now indicated that growth will remain on an accelerated trajectory in the quarters ahead. So I'd like to ask two questions. First, looking ahead to the coming quarters, what do you anticipate? being the pace of growth in the cloud business. What are the core drivers underpinning the continued acceleration of cloud computing growth? And then secondly, as you've mentioned, the industry is now in a phase of relatively tight capacity in terms of supply of compute. And you just mentioned that that supply demand dynamic may shift around 2030. So I'd like to ask from an even longer term perspective, what are the fundamental growth drivers for the cloud business, and do they differ from those in the short term?
Thank you. 现在的分析来看长期的一个远期展望吧 那第一部分我先来讲一下就是我们的现在的现状和数据 我们看到就是AI加云的外部收入已经持续九个季度加速增长 那本季度的已经持续加速到45% 我们看到客户的需求强劲 而我们的供给相对别的云厂商来说也具备非常强的优势 所以未来几个季度 我们判断收入还会 收入增速还会持续加速 我们看到本季度AI相关的产品 本季度的收入来到124亿元人民币 对应年化 我们可以换算到约73亿美元 那在我们现在的 数据的业务预测情况下 我们在下个季度的 AI产品的年化收入 将接近100亿美元 So we can see our average rate is very high We also have to decide In the next few weeks Our EBITDA revenue will be Will have a Every week Will have a lot of increase So We have to do our Yuen business We are important to We are MASS business In the last week of AI 需求的增长 And our 推理效率的提升 共同推动下 Now, our last mass year of ARR has reached 160 million USD. I'll add a little bit, it's the latest data in the 8th of May. It's already reached 160 million USD.
Thank you for the question. And I think I can expand on this in three different areas. I can start by looking at our current business and the relevant data. Secondly, I can discuss the drivers for growth. And then thirdly, I can share with you our long-term perspective based on that analysis. So let me begin with the first part, covering our current business and the key metrics. So as you've seen, external revenue for the AI and cloud segment has been accelerating now for nine consecutive quarters. and in this last quarter, growth has already accelerated to 45%. We're seeing very strong customer demand and our offerings boast a distinct competitive advantage compared to those. As a result, we expect revenue growth to continue accelerating over the coming quarters. We've observed that AI-related products generated 12.4 billion RMB in revenue this quarter. And so if we convert that into an annualized U.S. dollar figure, that works out to 7.3 billion U.S. dollars. In looking ahead to the next quarter, annualized revenue for AI quarters next quarter will approach 10 billion. growth rate remain at the same time we also expect our ebitda margin to improve additionally something very important in respect to the cloud business coupled with of our mass business has now surpassed clarify that's the latest data 好后面我来解释一下我们的商业模式的增长动能
我觉得首先还是要说一下阿里巴巴在AI上面的这个 后面我来解释一下我们的商业模式的增长动能 我觉得首先还是要说一下阿里巴巴在AI上面的一个 三一的AI公司有最大的一个区别 我们是在AI的全占技术上进行饱和式的投入 尤其是在芯片 AI云基础设施和模型车的饱和投入 保证了我们在这个三个最重要的技术层面都处于行业的领先地位 而且我们判断AI行业的技术发展还在早期 在不同的技术发展阶段 AI行业的核心商业价值会在芯片 云 模型和应用等不同层次之间流动
而我们全站式的投入可以保障我们具备最佳的服务能力和最好的性价比 也使得我们在未来行业技术发展的不同阶段都能够保持竞争力和长期的增长动能 Next, let me expand on the growth drivers within our business model. So it's important to understand that Alibaba's investment model for AI is fundamentally different from that of pure play AI companies. we are pursuing an intensive strategy across the full stack including chips including AI cloud infrastructure and including models and we maintain a leading position in the industry across all three of those most critical domains moreover we believe that the development of AI and technology across the industry is still in its early stages. Looking forward at different stages of technological development, the core commercial value within the AI industry may shift across different layers, including chips, cloud computing, models, and applications. Our full-stack investments ensure that we can deliver optimal service capabilities and the best value for money, positioning us favorably in the industry going forward, and ensuring that within each stage of technological development, it's possible for us to maintain competitiveness and sustained growth momentum. 刚才我已经提到了算力才是AI营收能力的核心资产
现在所有的AI营收模式都是围绕着AI算力所展开 而算力紧缺现在又是全行业的中期共识 同时由于MARS推理服务厂商的高毛利 使得AI算力不再是一个原来意义上的成本中心 而是成为与收入正相关的生产资料 所以行业内高定价的算力供不应求 推动了各场景下,就不同的场景下,他们都需要使用GPU,但是不同场景下,他们的定价模型就开始越来越趋近于行业最高毛利的变现方式,共同推动了几乎所有GPU相关产品的定价模型。 而阿里巴巴同时具备全模态的模型能力,我们的大部分模型能力都处于行业前沿的一线水平,也使得我们在算力的价值实现上具备优势,使得我们在我们的算力定价上有一个非常强的锚点,在阿里云具备算力的差异化定价能力。
Next, let me look ahead to what we think is going to be the most important growth driver over the next one to two years in the short term so we've seen exponential demand for commercial inference services as of the end of 2025 this exponential growth and 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 compute. And at the same time, there's a consensus across the industry, as I mentioned, that compute will remain in shortage of supply for some time to come. At the same time, the higher gross margins of mass inference services, a major difference. If compute was once a cost center, traditionally compute has now been transformed into productive asset whose value generation is positively Carlo. High-priced computing power remains in short supply across the industry precisely at a time where you have widespread adoption of GPU use cases. So pricing models are tending to converge. So this is driving the pricing models for nearly all GPU-related products. Moreover, Alibaba boasts a little within the industry, giving us a distinct advantage in realizing the value of that compute strategy. Comes time to price for new customers or to sign, re-sign contracts with existing customers as they renew, we can adopt more healthy margin. 但其实我们认为AI最大的超级应用就是以AI算力为核心的AI云平台
我们看到长期来看大部分企业的核心的AI工作负载都需要全站的AI云来提供服务 比如大规模的训练 大规模的推理 定制化的推理软件 企业agent的开发与运营 这些都需要在一个全站式的云平台来提供覆盖GPU CPU存储数据库和虚拟化 包括Harness工具等全面的云端的基础基础服务 所以AI云就像是一个超级城市 训练推理 企业系统以及AI aging就像是这个城市的居民 持续迭代提升全站的AI云服务就像城市的基础设施源源不断地吸引更多的新居民并在提升老居民的流程率
所以我们长期来看AI云平台的规模效应和网络效应可以长期地推动我们的AI业务长期地增长 Next, let me talk about the scale effects and networking effects, which are very important long-term growth drivers in AI cloud. you know for the past couple of years a lot of people have asked what is the the super app for AI and the answer to that is that the 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 residence and continually iterating full stack AI cloud services are 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. 因此我们从行业的发展趋势以及我们自身的产品优势上来看 AI加云的长期的营收增长和利润率的增长动能非常强劲 所以对于加速实现2030年前云外部收入1000亿美元的目标 So given that we operate the largest number of data centers across any Asian cloud provider, we benefit from the strongest economies of scale. At the same time, the large-scale deployment of our proprietary T-Head AI chips allows us to avoid the high price premiums associated with procuring expensive commercial GPUs thus avoiding erosion of our gross margins and with our state-of-the-art performance in our proprietary models we possess strong pricing power for our resources so looking ahead from the perspective of industry development trends and our own product strengths. The long-term revenue growth trend and margin expansion trend are exceptionally strong, and as a result, we're highly confident in our ability to achieve our goal of $100 billion in external cloud revenue by 2030, and we have good visibility into achieving gross margin.
Thanks for the opportunity to ask a question and congratulations on the strong quarterly results and especially the progress made in the AI sector. So I have a follow-up question on the mass business. As Eddie mentioned earlier, ARR as of August has exceeded 16 billion RMB. And last quarter, I believe you stated that the target for year-end is to surpass 30 billion in mass ARR. So I'm wondering, given the progress to date, do you anticipate making any adjustments to that year-end goal? And then additionally, within the mass business, what are the respective shares of our own proprietary models versus third-party models? And as model-related competition intensifies and more open-source models emerge, how will these factors possibly affect gross margin and profitability in the mass business? 谢谢你的问题啊就是我们现在的百链平台的MAS业务的增速确实非常迅速 8月份的我们的AR已经突破了160亿人民币 那从现在的增速以及我们现在后面持续还会上线的更多的新的模型来看 我们在年底突破300亿的AR的目标我们觉得还是会比较确定性的实现
Thank you for the question.
Yes, indeed. Growth in Bylian's mass business is very rapid. And as of August, we reached 16 billion RMB or surpassed 16 billion RMB in ARR. So given the current growth momentum as well as the pipeline of new models slated for launch, we remain confident that we will achieve our year-end target of 30 billion ARR by the end of the year.
Thank you very much. 这样的云场上的推理平台 因为实际上在我们的毛利水平当中 托管开源的免费的三方模型和 托管我们的自研模型 从毛利水平上来说其实是相差不大的 我们的自研模型其实更多的是 更多的是为了长期的去追求 我们在模型的智能水平上的提升和 AGI方面的这个突破 但是在短期的MaaS服务上面 实际上整个的毛利水平和托管开源模型是毛利水平是差不多的。
所以更加竞争或者说更加开放繁荣的开源生态其实是有利于阿里云这样的云厂商的。 So, on our mass platform, our own proprietary models 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 AGI but simply from the perspective of the mass business the level of gross margin from those two kinds of models is actually very comparable but overall While having a prosperous and flourishing open ecosystem with many of these models on it is…
Let's take the last question.
Operator
Thank you. Your final question comes from Alex Yao with JP Morgan. Please go on.
Thank you for the question. Queen 3.8 Max的权重 同时咱们自然芯片的商业化开展也挺成功的 现在落地到了650多家的外部客户 这是不是意味着管理层的判断 价值将会最终沉浸在算力与调度编排层 而不是模型层 还是说管理层觉得AI的在不同的发展阶段 同时咱们自研芯片的商业化开展也挺成功的 现在落地到了650多家的外部客户 这是不是意味着管理层的判断 价值将会最终呈见在算力与调度编排层 而不是模型层 还是说管理层觉得AI在不同的发展阶段 它的这个权战式生态的价值可能会沉淀在不同的环节 那如果是咱们是觉得它会长期的价值沉淀在这个硬件或者是算力这个层面的话 咱们怎么考虑当政府主导的算力供给 或者是同样拥有核规芯片的竞争对手扩大供应以后 Thank you for the opportunity to ask the final question.
I'd like to come back to Eddie's earlier remarks. You spoke at length about how Alibaba is developing a full-stack AI ecosystem. My question really is, in which layer of that full-stack ecosystem Do you think value will accrete and monetization will be concentrated? You know, we saw just after it had been released for three months that you open sourced the weights of your flagship model, QN 3.8 max. At the same time, your proprietary chips are also proving successful, now serving over 600 external customers. I'm wondering if this means that the future value will accrete mainly in the compute layer or perhaps in the orchestration layer and not necessarily in the model layer, or do you think that 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?
You asked this question very carefully. It also涉及到 a long-term decision. With a long-term decision, I think there are some unconfident. 但是总体来说我们觉得在因为我们的由于全战式的AI投入所以无论这个价值在哪一层在不同阶段在哪一层的多还是少我们都有机会补货在我们这个我们这个生态当中那我说一下我自己个人的短期的判断吧就现在短期的判断上来说我们觉得大部分的价值还存在于芯片和云基础设施这一层 这是我们从国内外的更多公司当中也看到的一个情况,就是在当一个技术的早期,尤其在一个这个技术的供给稀缺的情况下,那大部分情况确实会是这样,就大量的价值会存在于提供基础设施以及最核心的硬件,包括我们的芯片啊,存储啊,这样的供应商当中。
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, you know, 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.
AI大模型的这一层 现在通过API来变现的商业模式 我觉得是对于大模型来说 是一个短期阶段性的商业模式 肯定不是最终极的商业模式 如果我们最 其实我们公司投入这么大的算力投入 去在我们的全站式的 就是全站的模型上面的这个投入 其实目标肯定不是为了 现在短期的API的这部分收入 因为我们觉得未来当AI大模型能够实现AGI或者接近AGI的能力的时候 其实最终的商业模式应该是会直接创造产品或者直接创造客户所需要的结果 尤其是在商业的产品研发或者产品创造上面或者商业的直接的运营上面 所以到了那个阶段 其实AI模型公司或者AI模型的商业模式 不应该是提供API服务 那个阶段的商业价值 这也是支撑全世界这么多模型公司 在现阶段持续不断的军备竞赛投入的原因所在 所以从这个角度上来看 我觉得AI大模型的商业价值
现在还远远没有看到 Let me turn next to where the ultimate commercial value will be realized from these AI models. You know, 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. You know, our company has invested a tremendous amount of compute across our entire platform, but the objective is not simply to be able to generate that kind of short-term API revenue. I think when we get to the stage where we've accomplished AGI or we're close to achieving AGI, at that point, the ultimate business model will be delivering actual products, delivering actual results that clients are looking for. It'll be conducting the actual R&D that delivers products and that delivers operations. So, you know, the reason that all these different AI model companies are investing so heavily and engaging in an arms race today is not simply to be able to compete to provide that API-based service. It's because they have their eyes on that ultimate endgame where I think that the monetization level will be significantly higher, will be much higher than what you see today, selling the service through API calls.
关于您说的在硬件 算力层面, 我觉得你这个问题我也想 比较重点的回答一下, 因为实际上对于我们的 自研芯片,平头哥的芯片的 布局,其实我们在以前的 我今天稍微展开 一下,就是 我们的平头哥的上一代芯片已经出货了50万片以上 而我们刚才所说的全国产化的第一代芯片已经在8月份开始 以超节点的形式在阿里云上面已经开始全面上架 而这一代的芯片我觉得我们是中国市场为二的 可以放量进行对超节点上架的一个公司 所以我们对这一代国产芯片 在客户的接受度上 以及大规模的商业化程度上 我们的信心非常强 因为这里面我要说一下 我们平头哥芯片的 在所有国产芯片当中的 一个不同的特点 因为平头哥的AI芯片 是就我现在看到 是在国内芯片当中 比较独特的 以GPGPU架构 作为一个核心的 一个技术架构 那么在这个技术架构下 我们同时能够非常好的 支持训练和推理的工作负载 所以我们在支持的 众多的几百个 中大型的客户当中 既在帮他们做推理 也在帮他们做模型的训练 尤其是像众多的 聚生智能公司和众多的 自动驾驶公司 包括很多的大的模型公司 所以从这个角度上 我们可能是在中国 呃,唯一具备就是大规模的训练和推理商业化客户的这样一款芯片。 然后另外一个我也想说一下我们对平投哥的第二代的国产芯片的一个期待, 我们对平投哥的第二代国产芯片因为今年, 今年下半年会开始, 开始逐渐的逐渐的流片和产出, 比如我们这一代的芯片会有非常强的算力以及非常强的互联带宽,我们觉得完全可以替代大规模的模型训练,从这个角度上我觉得我们在国内的芯片上的技术上的身位是非常独特的,所以我不觉得有一个所谓政府主导的算力供给,会能够有一个非常强的竞争力的芯片。 So from this perspective, we think that in PINTO哥, the future of the main competition, we think that we are very信心. Especially in PINTO哥, there are a lot of customers in PINTO哥 in PINTO哥 in PINTO哥, which has a better quality.
In terms of hardware, you know, I'd like to add a few thoughts regarding our T-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 Alibaba's AI cloud as super nodes. And I think we're one of the only companies that's able to deploy such proprietary chips, domestic chips, at scale. One thing that's really unique about our T-Head chips, domestically manufactured chips, is that they are designed with GPU architecture as their core technical foundation, and they can very well support both training and inference workloads. So there are now already several hundreds of companies that are leveraging these chips via Alibaba Cloud training, and these SPAN companies, AI, are developing them in the second listing chips. So I think we're in a really, really unique position. So I don't think that there's any government-led. T-Head's future is highly certain 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 the world So, to say, sum up, I think that our T-head chips are definitely the best among domestic Chinese chips for supporting both training and inference across a wide range of different industries. So we really are number one in the industry. And then I think in terms of future production capacity and deployment, we can confidently claim to be at least one of the top two. But in terms of our ability to actually reach customers with AI chips. Alibaba Cloud is the largest player by market share 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.
Operator
Thank you. That does conclude our conference for today. Thank you for participating. You may now disconnect.
