Alibaba Group Holding (BABA) FY 2026 earnings call transcript

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.

Reconciliation 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 total group revenue growing 9% year-over-year. AI commercialization has also accelerated across the board. Alibaba Cloud's external revenue grew 45% and EBITDA increased 133% year-over-year, continuing to deliver on our commitment to accelerate growth.

Revenue from AI-related products has maintained triple-digit growth for the 12th consecutive quarter with annual revenue run rate surpassing 49.5 billion RMB, around 7.3 billion US dollars. It is the core engine of Alibaba Cloud's growth acceleration. I'll now walk you through four key areas, AI and cloud commercialization, full-stack AI capabilities, AI application ecosystem, and consumption business.

First, AI and cloud commercialization accelerated across the board and is expected to sustain high growth going forward. This quarter, Alibaba Cloud's external revenue growth accelerated to 45%, a 22-quarter high, while adjusted EBITDA margin reached 11.6%. Notably, this 45% growth was broad-based, driven by compute, storage, model-as-a-service, mass, and AI applications. We proactively scaled back low-margin business, continuing to improve the quality of our growth. This quarter, annual revenue run rate from AI-related products exceeded 49.5 billion RMB, and its share of Alibaba Cloud's external revenue rose to 35%. AI-related products generate significantly higher gross margins than the average cloud portfolio. Our recurring AI-related product revenue spans multiple layers, AI compute, mass, and AI applications. This multi-layered mix of AI revenue sources and monetization models means growing customer demand at any layer converts directly into commercial opportunity for us.

This structural advantage will underpin sustained rapid growth in recurring AI-related product revenue going forward. The surge in AI agents directly drives demand for tokens and GPU compute, while also significantly boosting demand for our traditional cloud products across CPU compute, storage databases, and networking. Alibaba Cloud is undergoing a comprehensive upgrade to an agentic cloud. Based on the latest data, the ARR of our model and application services, including MAS, has surpassed 16 billion RMB.

Based on current market feedback and our contract pipelines, compute demand will continue to outstrip supply. As we continue to ramp up our supply, our AI and cloud revenue growth will accelerate further in the coming quarters, alongside continued improvement in profitability.

Second, our full-stack AI capabilities continue to strengthen, marked by the scaled commercialization of proprietary chips, faster model iteration, and a thriving open-source ecosystem. This quarter, deepening synergy between proprietary T-Head chips and proprietary foundation models further improved our AI commercialization efficiency. T-Heads established a full-stock proprietary silicon portfolio spanning GPU, CPU, and networking chips. As of early August, Zhenwu chips have served more than 650 customers on Alibaba Cloud.

The Supernode instance powered by T-Head's next-generation Zhenwu M890 AI processor recently launched on Alibaba Cloud at commercial scale. We expect supply to continue ramping up in the second half of the year to meet strong customer demand. Alibaba Cloud's Zhenwu M890 Supernode can efficiently run inference workload for foundation models with more than 2 trillion parameters. Both KBK3 and QN3.8 Max are already using it to provide mass services to external customers.

At the data center layer, Alibaba Cloud has cut the delivery time for hyperscale AI data centers to 100 days, a world-leading pace that will significantly speed up our global compute infrastructure build-out.

At the model layer, our model release cadence has intensified over the past month with major iterations across our large language, image, audio, video, and music models, all ranking among the world's top tier.

Last week, we opened the model weights of QN 3.8 Max with 2.4 trillion parameters and the QN 3.827B model series. To date, the QN model series has been downloaded more than 3 billion times globally with more than 300,000 derivative models built on it. We believe a thriving open-source model ecosystem drives greater demand for our cloud computing services creating a virtuous cycle.

Third, our AI-native applications span both enterprise and consumer use cases driving rapid growth in token consumption. On the enterprise side, we launched QN 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 and accounting for 35% of external cloud revenue. The strong performance demonstrates growth in 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, and 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. dollar $162 billion. We remain committed to maximizing long-term shareholder returns through disciplining capital allocation across investments for AI plus cloud, business growth, share buybacks, and dividends. We will adjust our priorities as market conditions and strategic needs evolve.

Now let's first look at our e-commerce businesses. The new Alibaba e-commerce group reflects our strategic focus on unlocking significant synergies across our domestic and cross-border e-commerce businesses. Starting from this quarter, we will present Alibaba e-commerce group's revenue as the following.

First, China e-commerce. Second, China quick commerce. Third, international e-commerce. And fourth, global wholesale. Revenue for Alibaba e-commerce group was R&B $205.9 billion, an increase of 4%. Customer management 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's 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 the EBITDA remained relatively stable year-over-year at R&B 39.7 billion, unscoring our cost discipline against the backdrop of increased investments in user experiences and technology. Taobao Instant Commerce continues to improve its unique economics 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 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 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 the tar 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.

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 And then the management of Cappex is if we can review this Cappex its return to the capital revenue Thank you for your question This question is very important I want to get this question to explain our AI-based business and the entire business of Cappex and the overall business of Cappex We went to 2nd year 3,800 million of the investment plan That's the year of the year, the year of the year We already have a 1,900 million The year is still符合 the expected The year of the year of the year, the year is higher But because of the year of the year, it has a week It's not a week that is not a week that is a lot of the spread of the spread And since the year of the year, we also increased the CPU The Cpu is also a huge increase in the future of the CPU.

or I'm still in a more積極 attitude. billion RMB. As of the end of the June quarter this year, we had already spent 190 billion RMB with progress broadly in line with our expectations. While this quarter's spending of 67.1 billion is somewhat higher, hardware deliveries follow different procurement cycles. There can be fluctuations in the cadence and pace of hardware deliveries. So it's not evenly distributed across different quarters. So the increase primarily reflects volatility in those equipment delivery schedules.

At the same time, we increased procurement of CPUs this quarter, as we are witnessing a substantial surge in demand driven by the agent-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 buildout has been progressing at a steady pace, but that is the overall situation.

I also want to give you a few people to understand the AI platform. The business model is a large-scale business model. We can see all the AI platform, whether it's AI software, or the API, or GPU, or AI-based training or training, So we need to build a new model for AI 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 进入了一个非常积极的一个硬件方面的一个 投资建设走起 那从这个角度上来说 我们要获得一个高速增长 就必须在前面几年去投入更多的一个 AI算力中心的建设 所以这是我们一个商业模式上决定的 就必须是CAPEX先行 才能获得后面的业务的增长 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 up front in order to be able to grow this business model and monetize across all of those different areas. So that's why beginning in 2025, we began a heavy investment cycle in hardware.

And this is really a function of that asset-heavy business model, as I explained. In order to be able to capture that future growth, we first need to make these CapEx investments to build out the necessary compute capacity. 好,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,比如说往2.5年回本的方向去推进。 next let me explain why we see return on invested capital in AI related capex is 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 到现在还是近乎满载的被客户使用 AI算力资产的实际使用寿命周期 远远长于理论上的择救周期 Following that three-year payback period that these AI assets that we've invested in can achieve very positive and robust cash flow so to give you some direct examples A100 purchased in 2020 or A100 purchased in 20 sorry V100 purchased in 2018 even are still running at full capacity 同时我们在提升 AI产品的毛利率水平上 和投资回报率上 有三个重要的手段 第一是我们可以持续提升 AI产品的毛利率 包括我们现在持续投入 推动我们的SATA模型建设 拓展高毛利的MAS业务 同时还可以使用我们更完善的产品组合 来提升我们的AI20产品 或者AI相关的软件产品的竞争力 从而提升毛利率水平 我们看到AI产品的毛利率还在持续提升 本机动已经带动了阿里云整体板块的利润率 提升4.4个百分点来到11.6% 这是初步的验证 Additionally, we have three means that we can leverage to further enhance gross margin and return on invested capital First is we can continue to develop state-of-the-art models and enhance growth margin on AI products themselves and continue to expand a higher margin model as a service mass businesses and we can adapt our product mix across IaaS and across software to achieve higher growth margin on the portfolio as a whole.

As a result of improving growth margin, 你已经看到总结的4.4%的距离 在Alibaba Cloud's总结的利益 引起来到11.6% 所以这代表现初评计的组织 第二个重要措施是我们的自研芯片 也可以说是在这个过程当中最重要的一个方案 我们平头哥的自研芯片路径 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 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 他们的毛利率就很高 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 非常重要的,是我们的能力 使用我们的产品 产品,我们的产品 产品,GPU,CPU,和线路线 是我们的重要性质量 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 growth margin as well as profitability 我们还有 第三点也很重要 我们还有多种商业化的手段 来提升我们的 现金流的使用效率 比如我们和合作伙伴 共建AI的算力中心 比如我们现在有一些的 AI的算力合同 已经开始采取预付的方式 所以这些的 通过商业上的方式也可以大幅提升我们的投资回报率 compute-based services. So these are important ways in which we can further enhance ROIC.

把我们的帧数控制在33%以内 就可以实现正向的现金流 但这现在不是我们的战略选择 由于AI还处于整个行业的非常早期阶段 对我们的战略选择 我们是会坚定的投入 开派式的积极的建设 去推动业务的高速增长 而后面随着我们的产品毛利率的提升 自研芯片的替代比例的提高 我们的回本周期将进入2.5年 甚至更短的时候 我们可以看到在追求40%以上帧数的同时 就能够维持正向的现金流 这是我们的长期方向 所以通过这三个方法 我们可以短缺了贷款的时间 for AI CapEx, for example, to two and a half years or even two years. And we can apply a simple framework to understand this. At our current level of gross margin for AI products and under the assumption of a three-year payback period on CapEx, theoretically, keeping our growth rate below 33% would already enable positive cash flow. However, that is not our strategic choice at this time, given that AI remains in a very early stage we're committed to aggressively investing in CapEx and proactively scaling up to drive our rapid business 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% will also be able to maintain positive cash flow.

So that is our long-term strategic direction. Cool.

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. 我便便分享了一些重要的项目 和重要的项目 在这些项目的期间 开始了 中国的市场 在中国的市场 对短期的市场 对短期的抗产业的抗产业 我们的 long-term strategy 是在增加的 额抗产业的侧业 在同时,我们将来 利用 AI 来修行的购买 购买购买 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.

实现稳定且可持续性的生意发展 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 尤其是核心商家的这个经营结果 还是取得了不错的成绩 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.

同时我们也看到AI在电商供需两侧的机会, 那么用户侧我们会持续推出更多基于AI能力的新体验新场景 例如多摩泰搜索AI试衣等 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的使用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 后面我们也会跟签问办公合作 推出更加适配电商场景的AI智能体 同时我们看到AI的产品有大大机的资源 在产品与购买和供应的方面 在产品方面 我们将继续发展新的经济和选择 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. 我们在探索方法去批准AI 通过各种优势的连接 来增加商品的功能 特别是在数据专业 专业、商品、商品、 专业和客户服务 在商品上可以领导的利益 然后我们将来专业业业业 专业业业业业业业 专业业业业业业业业业业业 专业业业业业业业业业业业业业业业业 回到既视零售的这个问题 那么经过一年多的发展与投入 淘宝产购的规模跟市场份额 都已经发生了实际性的变化 无论是用户心智 供给丰富性 物流体验还是订单规模 都取得了巨大的提升 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 在今天这样一个基础上 接下来我们会加速 整合荷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 荷马过去一年加快前置仓的布局 带动了整个交易额的同比加速 Next on quick commerce After more than a year of investment and development Tabao Instant Commerce has undergone substantial changes 在下方的情况下,和市场比较大量的增加, 优优独播,质量量,质量量,质量量,质量量,质量量,质量量。 在下方的情况下,在质量在质量中, 在质量中,的质量中,并质量,坚持有关于质量。 在这方面,我们将继续续增加的质量, of businesses such as Fresh Shippo and Tmall Supermarket to develop the non-food categories growth within the quick commerce business and will place a particular focus on expanding our front warehouses. Over the past year, Fresh Shippo has accelerated the development of front warehouses leading to year-over-year increase in GMV.

同时闪购也会持续拓展更多品类覆盖 在一些重点品类上持续创新 提升消费者体验 我们认为非餐饮品类的即时零售交易额 规模将在下个财年内超过餐饮品类 会带动电商大盘很多食物品类 即时零售板块预计在29财年实现整体盈利 长期看我们认为 即时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 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 不管是交易规模还是盈利能力 我们都认为 跨境业务具备长期潜力 另外我们在土耳其东东等区域的 本地电商平台也发展很快 东南亚等市场的经营效率 也在持续改善 第三是, international e-commerce 短期的短期,我們的 e-commerce business 並且受到達了財政政策的方法 和的環境環境影響力 那說,雖然,財經經濟的 complex環境 我們的 cross-border business 帶來增進進進化 進入財產量的增進化 增進入財產量 在与其中的交易和货币的交易, growth potential in addition our local e platforms in international markets such as TurkeyA and the Middle East are growing rapidly and operating efficiency in markets such as Southeast Asia continues to improve 最后关于全球的 B2B 业务 B2B 业务包括我们的 1688 平台 跟 Alibaba. com 那么过去20年 这个业务一直在 持续的发展 我们看到 AI 技术 会给 B2B 交易平台 带来非常大的变化 甚至可能从根本上改变原有的商业模式 尤其是agent代理模式 会在B2B交易中扮演越来越重要的角色 我们面向跨境电商商家推出的AI智能体AXIO Work 上线后很快就拥有了5万的付费商家在使用 AI正在全面改变B2B商家 尤其是跨境商家做生意的方式 我们认为基于过去20多年在这个领域的积累 我们有机会在AI时代的B2B跟跨界贸易领域 创造出全新的业务模式跟商业机会 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 has 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 years of know-how, 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.

协同到AI技术的优势 让各个业务在AI时代释放更多的潜力 同时也实现更加多元化的收入利润结构 推动整个板块更加稳健的发展 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.

Okay, so let's go to the next question.

好,谢谢您的问题。 我觉得这个问题我可以稍微展开一点, 分三个部分来讲吧。 第一讲一下就是我们的现在的业务的现状和数据 然后在后面再来讲一下 我们觉得驱动我们的AI相关的业务的增长的动能 最后再来讲一下 从我们的现在的分析来看 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 我们可以换算的约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 所以可以看到我们的增速还是非常之高的 同时我们也判断未来几个季度 我们的Ebitda利润率会逐渐进行 会有一个每个季度都会有一些逐步的提升 那所以还有我们 对我们的云业务相当重要的 我们的MAS业务 在本季度的AI需求的增长 以及我们的推理效率的提升 共同推动下 那我们本季度的 最新的MAS业务的AR 现在也已经突破了 160亿元人民币 不对我稍微补充一下 应该是8月份最新的这个数据 已经突破了160亿元人民币 谢谢你 我可以在三个方面的问题 我可以开始在我们的现象 和 relevant data 第二个我可以说的驾驶 第三个我可以说我们的长期观看 在那些设计 我开始在第一个部分 covering our current business and the key metrics.

So as you've seen, external revenue for the AI and cloud segment has been accelerating now for nine consecutive quarters. And in this last quarter, growth has already accelerated to 45%. We're seeing very strong customer demand, and our offerings boast a distinct competitive advantage compared to those of other cloud providers. As a result, we expect revenue growth to continue accelerating over the coming quarters.

We've observed that AI-related products generated 12.4 billion RMB in revenue this quarter, and so if we convert that into an annualized US dollar figure, 那是7.3 billion US dollars 在年轻人的收入 看着到年轻人的期待 我们的预期是 那同时的收入收入 AIQorters next quarter 会将到10 billion US dollars 所以我们的收入收入 稳定的收入 同时我们也许多 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 efficiency. 所以我们的大量的大量商业 现在超过16亿RMD 其实让我来说明 这是在秋农年最新的数据 已经超过16亿RMD 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上面的投入模式 和单一的AI公司有最大的一个区别 我们是在AI的全站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 我们将将领域的推动进行业的进行业 在它的早期期中 在不同的程度上的技术 的開放 主要的核商 在AI的技术 可能會在不同的領域 包括晶層 和機構的機構 和使用 我們的投資金 使用我們能夠 提供應得到 最好的利用 財產品 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.

商业模式 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 而是成为于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 他们都要需要使用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 所以我们看到 在旺盛的需求 以及算力的商业价值 正在在市场重新定价 而我们也能够以 更健康的价格 去签新的合同 以及包括客户老合同的续约 都是随着新的市场需求 在发生定价模式的变化 所以我们看到阿里云的盈利能力在今年开始都在处于一个显著的提升过程当中 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 所以我们看到肯定的 demand 贯定的贯定 贯定的贯定 在这个期末的期末 这肯定的贯定 贯定的贯定 贯定的贯定 在这种程度的 为了贯定的贯定 贯定的贯定 贯定的贯定 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 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 boasts comprehensive multimodal model capabilities. Our models are at the state-of-the-art level within the industry, giving us a distinct advantage in realizing the value of that compute power and providing a robust anchor for our pricing strategy.

So when it comes time to price for new customers or to re-sign contracts with existing customers as they renew, we can adopt more healthy pricing models and so we expect to see this as a very positive short-term driver for improving margin in the coming year plus 我再来讲一下就是AI云的商业模式 长期的规模效应和网络效应 其实是一个会带动是一个长期的增长功能 其实前两年行业投资人一直在问 AI的超级应用是什么 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 并再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 effect and networking effect 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 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 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.

对毛利的侵蚀 同时由于我们自研模型的行业收场的能力 具备极高的算力定价能力 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 我们具备极强的信心 也认为对AI云业务实现20%以上的利润率 有很强的可见性 所以既然我们在大量的数据中心中 在任何Asian cloud provider 我们利用最强的经济的经济 在同时 我们的大量的执行 我们的执行业的T-Ed AI Chips 使用了我们的高价值运动 所谓的高价值 购买的高价值 并且防止 大量的高价值 和我们的 有点的表现 我们的公司 我们有強烈価值 的力量 我们的 股票 所以 看着 从来的 经历的 和利物多企業的額項消息 是高随着市場的額項增幅額 是非常強的 而且我們是很高興的 我們的能力 我們能夠達成金額項增幅 在額項目的額項目 2030年 我們能夠達成金額項增幅 20% 好,谢谢 感谢管理层接受我的提问 也恭喜公司这个季度在 运气酸和AI的领域的积极进展 那我有一个follow up的问题 关于我们的MAS的业务 刚才Eddie也讲到我们 8月份的AR超过160亿人民币 那我想问 我们上个季度也有讲到 就是年底的一个目标 是要突扣300亿的MAS AR2 那基于现在的一个进展 我们年底的目标 会不会有一些调整 另外就是在MAS的业务当中 我们公民的模型 和三方的一个模型 那个占比分别是怎么样的 那未来模型的竞争家具 以及也有更多的 开源模型的一个涌现 那这些将怎么影响到我们MAS的一个毛利率以及盈利的一个能力 谢谢 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 question on the MAS 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 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?

We think it will be more confident. Thank you for the question. Yes indeed, growth in Bylien'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.

In our current mass business, our own-end model is a large percentage. But our three-way-end model is not small. 这个还是要说一下我们对于模型竞争的长期看法 从我们现在看起来一方面客户有比较强的需求 在一个AI应用当中他可能会使用多方的模型 甚至在因为他的整个的每个模型都会有自己的能力特点 同时我们也会看到未来模型的更多的开源模型 更会更多的涌现 所以从这个角度上来说 其实是有利于阿里云 百链这样的云厂商的推理平台 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 and AGI and the growth of a mass service and the whole of the single-state is equal to so more open and open and open and open a kind of with the other of like the open and large of large of large and large large of large so 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 Bai Lian, to provide inferencing is a good thing for us and for Bai Lian. When it comes to gross margin, the level of gross margin that we can achieve on a platform like Bailean 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, having a prosperous and flourishing open ecosystem with many of these open source models on it is highly favorable for a cloud provider like Alibaba Cloud.

Let's take the last question.

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 There are a lot of value in providing the basic design and the most key features, including our computer and storage.

In Alibaba's design, we put our plan and AI design and AI design in one module. Thanks. You know, that's a very professional question. and really is a matter of long-term judgment. So I think it's inherently associated with a high level of uncertainty. But what I can say is that we are investing in the full stack, and what that means is that whichever layer represents the greatest value, and no matter how that may shift across layers in different periods of time, all of those layers are part of our ecosystem.

I guess I can share with you my own short-term view, namely in the short-term perspective, I think that most of the value will be in chips and in AI cloud 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 cycle.

I think that's a big deal. 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 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 So that stage, the AI companies or the company of the business or the company of the business model should not be provided to API. The stage of the business value is that it is that all the world's so many companies in the current stage are still in the same way. The beginning of this stage is the same as the company of the business model.

So from this perspective, I think the AI of the business value is that the business value is now far. 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. 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.

Well, regarding your question in the operating system, I think you have a question for this question. I also want to answer a bit more important. Because in fact, we have a lot of the investment in our digital smartphone, and the smartphone smartphone, actually, we have said in the previous investment meeting.

I will open it up a little bit. We have the previous one-time smartphone. We have the same number of 50 million. As we said earlier, the first smartphone smartphone has been in the 8月. It has been in the same way in the Arrayon. It has already started to be in the same way. This smartphone is a single-time smartphone.

We have the same company for the same-time smartphone. So we have the same smartphone smartphone in the customer's attention. and in the high-level market market level, we're very strong. Because I want to say the different characteristics of the AI-in-the-art in the world of the AI-in-the-art different features. The AI-in-the-art is, I see now, is the world-in-the-art in the world-in-the-art in the GPP GPU構 as the key to the core of the technology. So in the technology構造, we can also be able to 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头哥的第二代的国产芯片的一个期待 我们对平头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 We have very strong and high-reported and very strong and high-reported We can completely replace large-scale model From this perspective I think we have in the world in the software and technology itself is very unique So I don't think the government-led of the high-reported will be able to have a very strong and competitive So from this perspective We think that in the future of PINTOGAS, the main key to the competition is that we are very confident.

Especially in the future, there are a few hundred large customers in PINTOGAS of the software. We have a better quality of the use of the cost. In the world, from the company's perspective, our software is also a better quality. The network of the company's network is very wide.

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 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 boast exceptionally high compute power as well as extremely robust interconnection bandwidth, making them fully capable of serving as a direct replacement for existing chips. So I think we're in a really, really unique position in the chip sector, especially when it comes to large-scale model training. So I don't think that there's any government-led compute supply allocation scheme that could produce chips with such truly strong competitiveness. So I think that T-Head's future is highly certain as a very key and core component of Alibaba Cloud, and we remain highly confident in our core competitive strengths 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.

这个产能和供应链的布局上我们可以说是潜爱的存在 从我们对于未来AI芯片的对客户的渠道能力上 由于阿里云的长期的在中国的AI云上面是最大的最大市场 份额的厂商我们在未来的渠道能力上我觉得会是非常强的 存在所以从这个角度上我觉得我们评头各的芯片长期的商业 I think we are very confident. 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, you know, 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 THz chips. Thank you very much. We appreciate your support and we look forward to updating you on our progress next quarter.