DATE

Thursday, Aug. 20, 2026 at 7:30 a.m. ET

CALL PARTICIPANTS

• Chairman – Joe Tsai
• Chief Executive Officer – Eddie Wu
• Chief Financial Officer – Toby Xu
• Chief Executive Officer of Alibaba E-commerce Business Group – Jiang Fan
• Head of Investor Relations – Lydia Lu

TAKEAWAYS

• Revenue — RMB 269 billion (US$39.6 billion), an increase of 9% year over year driven by momentum in cloud computing and quick commerce segments.
• Adjusted EBITA — RMB 27.3 billion (US$4.0 billion), a 30% year-over-year decrease reflecting significant investments in technology infrastructure.
• GAAP Net Income — RMB 10.4 billion (US$1.5 billion), representing a 75% year-over-year decrease primarily due to reduced gains from investment disposals and mark-to-market changes.
• Non-GAAP Net Income — RMB 20.7 billion (US$3.1 billion), a decrease of 38% year over year.
• Alibaba Cloud External Revenue — Grew 45% year over year, representing a 22-quarter high for the segment.
• AI-Related Product Revenue — Reached an annual revenue run rate of RMB 49.5 billion (US$7.3 billion), maintaining triple-digit growth for 12 consecutive quarters.
• Cloud Adjusted EBITDA Margin — 11.6% to 12.0%, reflecting improved economies of scale and stronger pricing power in a supply-constrained market.
• MaaS ARR — Surpassed RMB 16 billion as of Aug. 2026, including revenue from proprietary and third-party models.
• Capital Expenditures — RMB 67.7 billion (US$10.0 billion), a 75% year-over-year increase focused on expanding AI infrastructure.
• Free Cash Flow — Outflow of RMB 44.7 billion (US$6.6 billion), compared to an RMB 18.8 billion outflow in the prior-year period due to higher cloud infrastructure spending.
• Net Cash Position — Approximately $30.7 billion as of June 30, 2026, providing liquidity for continued investment.
• Share Repurchases — $162 million used to repurchase 13.4 million ordinary shares during the quarter.
• Alibaba E-commerce Group Revenue — RMB 205.9 billion, an increase of 4% year over year.
• Customer Management Revenue — RMB 82.5 billion, a 7% year-over-year decrease, though it would have grown 1% excluding contra revenue impacts from new business programs.
• Quick Commerce Revenue — RMB 53.3 billion, a 45% increase driven by Freshippo and Taobao Instant Commerce.
• AI Labs and Applications Loss — RMB 13.9 billion, reflecting increased costs for model training and marketing for the Qwen application.
• 88VIP Membership — Approximately 64 million members, representing double-digit year-over-year growth.
• Qwen Model Adoption — Downloads exceeded 3 billion globally with more than 300,000 derivative models built on the platform.
• Zhenwu M890 Processors — Serving more than 650 customers on Alibaba Cloud for both training and inference workloads.
• MaaS ARR Target — RMB 30 billion by the end of the current fiscal year.
• Quick Commerce Profitability — Management expects the segment to reach overall profitability in fiscal year 2029.
• Diluted Earnings per ADS — RMB 3.71 (US$0.55), a 79% year-over-year decrease.

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RISKS

• Wu warned that international e-commerce growth has been pressured by tariff policies and the geopolitical environment.
• Xu stated that free cash flow was an outflow of RMB 44.7 billion, “mainly attributed to the investment in cloud infrastructure,” reflecting high upfront hardware costs.
• Wu noted that the domestic e-commerce landscape faces short-term macroeconomic challenges despite current growth initiatives.

SUMMARY

Management reported a realignment of corporate segments into four groups to integrate full-stack AI capabilities and commerce platforms. The company stated that it is increasing investments in AI infrastructure to address compute demand that currently exceeds available supply. Management indicated that AI-related revenue now represents 35% of external cloud revenue and is contributing to segment margin expansion through higher gross margins and proprietary chip substitution.

• CEO Wu indicated that the current API-based monetization for large language models is a transitional approach, stating that achieved AGI would enable a model focused on delivering specific products and operational results.
• Management reported that servers equipped with AI chips typically reach breakeven within three years and generate positive free cash flow for at least two years thereafter.
• CFO Xu noted that AliExpress achieved operating profit during the quarter, driven by logistics optimization and enhanced cost efficiencies.
• The company reported that its Zhenwu M890 supernode can support inference for foundation models with more than 2 trillion parameters.
• Management indicated that Alibaba Cloud has reduced the delivery time for hyperscale AI data centers to 100 days to accelerate global infrastructure expansion.
• Management expects the transaction volume of quick commerce for non-food categories to exceed that of food categories within the next fiscal year.

INDUSTRY GLOSSARY

• MaaS (Model as a Service): A cloud-based service providing access to machine learning models without requiring users to manage the underlying infrastructure.
• ARR (Annual Revenue Run Rate): A projection of annual earnings based on current performance over a shorter period.
• CMR (Customer Management Revenue): Revenue from services provided to merchants, including advertising and commissions.
• 88VIP: Alibaba’s premium loyalty membership program for high-spending consumers.
• T-Head (Pingtouge): Alibaba’s semiconductor division focused on developing proprietary chips for AI and cloud computing.
• Qwen (Tongyi Qianwen): Alibaba’s family of large language models and AI applications.
• UE (Unit Economics): The direct revenues and costs associated with a business model expressed on a per-unit basis.
• Bailian: Alibaba Cloud’s platform for deploying and managing Model as a Service applications.

Full Conference Call Transcript

Operator: Good day, ladies and gentlemen. Thank you for standing by. Welcome to Alibaba Group’s June Quarter 2026 Results Conference Call. [Operator Instructions]. I would now like to turn the call over to Lydia Lu, Head of Investor Relations of Alibaba Group. Please go ahead.

Lydia Lu: Thank you. Good day, everyone, and welcome to Alibaba Group’s June Quarter 2026 Earnings Conference Call. Joining the call today are Joe Tsai, Chairman; Eddie Wu, Chief Executive Officer; Toby Xu, Chief Financial Officer; Jiang Fan, Chief Executive Officer of Alibaba E-commerce Business Group. Before we get started, I would like to remind you that today’s discussion may contain forward-looking statements based on management’s current expectations that are subject to risks and uncertainties. We also make reference to non-GAAP financial measures. Reconciliations between GAAP and non-GAAP measures are included in today’s earnings press release and investor presentation. Our comments will be on year-over-year comparisons unless we state otherwise.

A replay of the call will be available on our website later today. With that, I would like to turn the call over to Eddie.

Yongming Wu: 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 a triple-digit growth for the 12th consecutive quarter with annual revenue run rate surpassing RMB 49.5 billion around USD 7.3 billion. It is the core engine of Alibaba’s Cloud’s growth acceleration.

I’ll now walk you through 4 key areas: AI and cloud commercialization, full-stack AI capabilities, AI application ecosystem and consumption business. First, AI and cloud commercialization accelerated across the board and is expected to sustain high growth going forward. This quarter, Alibaba Cloud’s external revenue growth accelerated to 45%, a 22 quarter high, while adjusted EBITDA margin reached 11.6%. Notably, this 45% growth was broad-based driven by compute storage Model as a Service, MaaS, and AI applications. We proactively scaled back low-margin business continuing to improve the quality of our growth. This quarter, annual revenue run rate from AI-related products exceeded RMB 49.5 billion and its share of Alibaba Cloud’s external revenue rose to 35%.

AI-related products generate significantly higher gross margins than the average cloud portfolio. Our recurring AI-related product revenue spans multiple layers, AI compute, MaaS and AI applications. This multilayered mix of AI revenue sources and monetization models means growing customer demand at any layer converts directly into commercial opportunity for us. This structural advantage will underpin sustained rapid growth in recurring AI-related product revenue going forward. 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 MaaS, has surpassed RMB 16 billion. Based on current market feedback and our contract pipelines, compute demand will continue to outstrip supply. As we continue to ramp up our supply, our AI and cloud revenue growth will accelerate further in the coming quarters, alongside continued improvement in profitability. Second, our full 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 has established a full stack proprietary silicon portfolio, spanning GPU, CPU and networking chips. As of early August, the Zhenwu chips have served more than 650 customers on Alibaba Cloud. The 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 Kimi K3 and Qwen 3.8 Max are already using it to provide MaaS 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 months with major iterations across our large language, image, audio, video and music models, all ranking among the world’s top tier. Last week, we opened the modeled weights of Qwen 3.8 Max with 2.4 trillion parameters and the Qwen 3.8-27B model series. To date, the Qwen 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 QwenWork, a new AI productivity product built for enterprise workforce scenarios, delivering agent capabilities at scale. We expect productivity agents to become another engine of ARR growth. On the consumer side, the Qwen app continued to steadily grow its user base and is expanding the range of its value-added offerings. Through close coordination between Alibaba Token Hub and Alibaba Cloud, we’re running a highly efficient commercial flywheel across compute models, tokens applications and monetization.

Fourth, our e-commerce businesses remained solid this quarter. In quick commerce, we continued to narrow losses substantially while growing business scale by 45% with unit economics improving quarter-over-quarter. Having crossed the AI commercialization inflection point last quarter, we’re now seeing growth accelerate and margins expand this quarter. Our AI businesses own capacity to self-fund and sustain itself is strengthening giving us greater confidence to keep investing. Looking ahead, AI has become Alibaba’s most certain growth engine, we will stay strategically disciplined and drive long-term growth through our full stack AI capabilities. I’ll now hand over to Toby to walk you through our financial results. Thank you.

Toby Xu: Thank you, Eddie. Our strategic priorities in AI + Cloud and consumption businesses backed by disciplined investments delivered strong results this quarter. Cloud segment revenue growth further accelerated to 45% with its EBITDA margin sequentially rising to 12%. AI-related product revenue continued to drive this momentum marking the 12th consecutive quarter of triple-digit growth and accounting for 35% of external cloud revenue. The strong performance demonstrates growing customer adoption of our full stack AI capabilities, spanning AI agents, models, cloud infrastructure and preparatory chips as well as our enhanced scale efficiencies and the robust pricing power in a supply-constrained market. On consumption, Taobao Instant Commerce continued to improve its unit economics while maintaining market share.

Overall e-commerce EBITDA remained relatively stable year-over-year. To realize synergies across our commerce platforms and strengthen our full stack AI capabilities, we have implemented strategic realignment of certain businesses in our financial reporting. Starting from this quarter, our segment reporting will present the following: first, Alibaba E-commerce Group; second, AI Cloud and Compute Services; third, AI Labs and Applications; and number four, all others. Now let’s look at the financial results for this quarter. Total revenue increased 9% year-over-year to RMB 269 billion, driven by the strong momentum in cloud business and quick commerce.

Total adjusted EBITDA decreased 30% to RMB 27.3 billion, primarily attributable to the investment in technology, partly offset by the improved operating results in our cloud business as well as enhanced operating efficiencies across various businesses. Our GAAP net income was RMB 10.4 billion, a decrease of 75%, primarily due to the decrease in income from operations and decrease in net gains from disposal of investments and mark-to-market changes of our equity investments. Operating cash flow this quarter increased by 11% to RMB 22.9 billion compared to RMB 20.7 billion in the same quarter last year. Free cash flow was an outflow of RMB 44.7 billion compared to an outflow of RMB 18.8 billion in the same quarter last year.

The decrease was mainly attributed to the investment in cloud infrastructure. CapEx was RMB 67.7 billion this quarter, reflecting our continued investments in AI infrastructure to meet strong and growing customer demand. The significant year-over-year increase is due to several reasons, including fluctuations in procurement cycles, increase in CPU compute capacity driven by anticipated growing customer adoption of AI agents and higher pricing of a broad range of chip components. As of June 30, 2026, we held approximately USD 30.7 billion in net cash, excluding debt with maturities beyond 5 years, our net cash position stands at approximately [ USD 46.5 billion ]. This balance sheet strength gives us confidence to invest for robust growth.

Our AI + Cloud investment has a clear path to attractive ROIC. Our servers equipped with chips typically reach breakeven within 3 years. With a 5-year useful life, we expect them to get positive free cash flow, at least in the 2 years following breakeven. For the quarter ended June 30, 2026, we repurchased shares of an aggregate consideration of USD 162 billion. We remain committed to maximizing long-term shareholder returns through disciplined capital allocation across investments for AI + Cloud business growth, share buybacks and dividends. We will adjust our priorities as market conditions and the strategic needs evolve. Now let’s first look at our e-commerce businesses.

The new Alibaba e-commerce group reflects our strategic focus on unlocking significant synergies across our domestic and cross-border e-commerce businesses. Starting from this quarter, we will present Alibaba E-commerce Group’s revenue as the following: first, China e-commerce; second, China quick commerce; third, international e-commerce; and fourth, global wholesale. Revenue for Alibaba e-commerce group was RMB 205.9 billion, an increase of 4%. Customer management revenue decreased by 7%. Excluding the contra revenue impact from the new business development program, customer management revenue would have grown by 1% year-over-year. Revenue from China quick commerce business was RMB 53.3 billion, an increase of 45%, driven by Freshippo and Taobao instant commerce.

Alibaba E-commerce Group’s adjusted EBITDA remained relatively stable year-over-year at RMB 39.7 billion, underscoring our cost discipline against the backdrop of increased investments in user experiences and technology. Taobao instant commerce continued to improve its unit economics quarter-over-quarter while maintaining market share, driven by higher average order value and enhanced fulfillment logistics efficiency. In addition, AliExpress achieved our pre-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 T-Head. The year-over-year growth of total revenue and revenue from external customers both accelerated to 45%.

Revenue from Alibaba Cloud also accelerated growing 45% year-over-year. We are confident the growth rate will further accelerate in the coming quarters. This quarter’s AI-related product revenue was RMB 12.4 billion, implying an annual — revenue run rate of RMB 49.5 billion. It delivered a 12th consecutive quarter of triple-digit growth and accounted for 35% of external cloud revenue. The adjusted EBITDA margin expanded to 12%, driven by improved economies of scale and a stronger pricing power of AI-related products amid tight market supply.

We expect EBITDA margin to further expand steadily in the coming quarters by improving resource utilization, optimizing model portfolio and innovating new scenarios, we are accelerating the growth of AI + Cloud business and driving greater benefits of scale. AI Lab and Applications comprises AI model labs Qwen Consumer Business Group and QwenWork. 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 Qwen app. The loss significantly narrowed quarter-over-quarter due to the reduction in marketing expenses for Qwen app. We expect the segment loss to narrow over the coming quarters driven by improving efficiency in both model training and marketing spend on Qwen app.

We have launched our frontier language coding, video, audio, image and music models or delivering top-tier performance. 250 million have had their first AI-driven shopping experience through Qwen app’s agentic features across an expanding range of e-commerce and other services since the launch of Qwen app. All other segment revenue remained stable at RMB 28.8 billion. All other adjusted EBITDA was a loss of RMB 3.3 billion primarily due to our increased investment in technology.

AI has progressed from incubation to commercialization at scale as we expand our market share, strengthen AI leadership and improving operating efficiency, we are gaining greater strategic and financial flexibility to make disciplined and sustained investments in both full stack AI capabilities and consumption opportunities driving secular growth and greater value for our shareholders. Thank you. That’s the end of our prepared remarks. We can open up for Q&A.

Lydia Lu: 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 prevail. Operator, please start the Q&A session. Thank you.

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

Alicis a Yap: Also congrats on your solid cloud performance. Could management please comment on the reasons and the drivers for the significant increase in the CapEx this quarter? And also, what is the expected CapEx trend for the coming quarters? And are these — are there any updates to the existing 3-year CapEx budget that you have of this RMB 380 billion that you mentioned before? And also, we would appreciate if management can also provide a breakdown of CapEx allocation across the different services like the training costs and all that? And then also, what is management expected return on the invested capital for these investments?

Unknown Executive: [Interpreted] Thank you very much for the question. It’s an important question, and I’d like to take the opportunity perhaps to explain generally what our business model is for AI and our expectations around CapEx going forward. So indeed, last February, we announced a 3-year capital investment plan with total investment of RMB 380 billion as of the end of the June quarter this year, we had already spent RMB 190 billion with progress broadly in line with our expectations. While this quarter spending of RMB 67.1 billion is somewhat higher, hardware deliveries follow different procurement cycles. There can be fluctuations in the cadence and pace of hardware deliveries. So it’s not evenly distributed across different quarters.

So the increase primarily reflects volatility in those equipment delivery schedules. At the same time, we increased procurement of CPUs this quarter as we are witnessing a substantial surge in demand driven by the agent-centric era. Of course, rising prices for semiconductor components have also contributed to this trend. So I don’t think we should take the spending for this quarter and multiply it by 4 to come up with an annualized figure for the year or to expect that there’ll be a steady linear progression. The build-out has been progressing at a steady pace, but that is the overall situation. [Interpreted] Next, let me expand on our full stack AI business model. This is an asset-heavy business model.

If you think about all of the different ways that AI is monetized and can be monetized, be it through software subscriptions, be it through API calls, through Models as a Service, through training, inference. In all of these different respects, you need compute centers to run and to monetize. So it’s only possible to monetize when you have that compute capacity in place. So what that means is that we need to be investing 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. [Interpreted] Next, let me explain why we see return on invested capital in AI-related CapEx as highly certain. There’s consensus across the industry that the current shortage in AI compute will not be resolved until at least 2030. So industry-wide then, it makes sense that there should be high certainty in our investments in AI compute. Based on average gross margins today, roughly, we can breakeven on AI-related CapEx in 3 years.

And of course, average gross margin continues to rise, and we expect to be able to shorten that payback period, say, to 2.5 years. [Interpreted] Following that 3-year payback period, 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 today are still running at full capacity. [Interpreted] Additionally, we have 3 means that we can leverage to further enhance gross margin and return on invested capital.

First is we can continue to develop state-of-the-art models and enhanced gross margin on AI products themselves and continue to expand a higher-margin Model as a Service, MaaS, businesses, and we can adopt our product mix across IaaS and across software to achieve higher gross margin on the portfolio as a whole. And as a result of improving gross margin, you’ve already seen an overall increase of 4.4 percentage points in Alibaba Cloud’s overall segment profitability, bringing it this quarter to 11.6%. So that represents initial validation of that thesis. [Interpreted] A very important piece of this is our ability to deploy our own proprietary chips.

As you know, our own T-Head proprietary chip 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. [Interpreted] 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 pre-payments for compute-based services. So these are important ways in which we can further enhance ROIC. [Interpreted] So through these 3 different methods, we can shorten the payback period for AI CapEx, for example, to 2.5 years or even 2 years. And we can apply a simple framework to understand this. At our current level of gross margin for AI products and under the assumption of a 3-year payback period on CapEx. Theoretically, keeping our growth rate below 33% would already enable positive cash flow. However, that is not our strategic choice at this time.

Given that AI remains in a very early stage, we’re committed to aggressively investing in CapEx and proactively scaling up to drive our rapid business expansion. As our product gross margin improves and our proprietary chip substitution rate increases, our payback period will shorten to 2.5 years or even less. And so under those circumstances, while pursuing growth of over 40%, we’ll also be able to maintain positive cash flow. So that is our long-term strategic direction.

Operator: Your next question comes from Charlene Liu with HSBC.

Charlene Liu: I come from HSBC. First, when 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. [Foreign Language]

Unknown Executive: [Interpreted] Okay. Thank you very much for the question as well as for the translation. In the new fiscal year, indeed, we’ve realigned our e-commerce business segments. And moving forward, we’ll be updating progress on 4 core areas: China e-commerce, quick commerce, international e-commerce and global — B2B global wholesale. Let me then briefly share the strategic priorities and key considerations for each of these 4 segments in the period ahead. So starting with China e-commerce. While the domestic e-commerce landscape faces short-term macroeconomic challenges, our long-term strategy centers on strengthening core supply capabilities and at the same time, we aim to leverage AI to enhance the overall shopping experience and improve operational efficiency across the board.

So first, regarding supply, since last year, Taobao and Tmall have focused on supporting original merchants, including branded sellers, while simultaneously unlocking the potential of high-quality white label suppliers from key industrial clusters.

Unknown Executive: [Interpreted] We will continue to strengthen our partnerships with leading brand merchants, helping them achieve stable and sustainable business growth. Tmall remains the most critical operational hub for both major brands and many original merchants. At the same time, we are diving deeper into industrial clusters to source high-quality products directly from their origins. We are supporting more manufacturing factories and operating directly on our platform and leveraging our platform AI capabilities to enable white-label merchants to adopt a simpler and more efficient managed operation model. And the share of transactions being generated through that industrial cluster managed model continues to rise steadily.

In the past quarter, during the recent 618 shopping festival despite certain macroeconomic challenges, the outcomes were aligned with our expectations and notably, core merchants achieved solid growth. [Interpreted] At the same time, we see significant opportunities for AI across both the supply and demand sides of e-commerce. On the consumer side, we will continue to launch new experiences and scenarios powered by AI, such as multimodal search and virtual try-ons. Our goal is twofold: first, to use AI technology to enhance the experience and efficiency of existing shopping scenarios, and we’ve already observed that AI has driven significant efficiency gains in our product recommendations; and secondly, to drive new kinds of AI-driven interaction.

On the merchant side, we observed that merchants are already widely adopting AI in their operations. We’re exploring ways to leverage AI across various operational links to boost merchant capabilities, particularly in data analytics, advertising and marketing and customer service where merchants can derive clear benefits. And going forward, we’ll also collaborate with Qwen Office to launch AI agents that are specifically tailored for e-commerce scenarios. [Interpreted] Next, on quick commerce. After more than a year of investment and development, Taobao Instant Commerce has undergone substantial changes in scale and in market share with significant improvements across user mind share, supply diversity, logistics experience and order volume.

Last quarter, while maintaining growth in both users and orders unit economics, UE, substantially improved and losses significantly reduced. On that basis, we will accelerate the integration of businesses such as Freshippo and Tmall supermarket to develop the nonfood categories growth within the Quick Commerce business, and we’ll place a particular focus on expanding our front warehouses. Over the past year, Freshippo has accelerated the development of front warehouses leading to a year-over-year increase in GMV. [Interpreted] Meanwhile, Quick Commerce will continue to expand its category coverage and innovate in key areas to enhance the consumer experience.

We expect the transaction volume of quick commerce for nonfood categories to surpass that of food categories within the next fiscal year, driving growth in many different physical goods categories across the overall e-commerce business. The Quick Commerce business is expected to achieve overall profitability in FY ’29. In the long term, we believe it has the potential to contribute 30% of the platform’s total GMV, becoming the second growth curve for our e-commerce business. [Interpreted] 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. [Interpreted] Fourth is global B2B. Our B2B businesses, including the 1688 and alibaba.com platforms have grown consistently over the past 2 decades and we see that AI technology will bring profound changes to our B2B platforms and may even fundamentally reshape existing business models.

In particular, the agentic model will play an increasingly important role in B2B transactions. We’ve launched Accio Work, which is an AI agent for cross-border merchants and it had already attracted over 50,000 paying merchants shortly after its launch. AI is comprehensively transforming the way that B2B merchants do business, especially cross-border merchants. We believe that building on our 2 years of know-how in this — 2 decades of know-how in this field, we have the opportunity to create entirely new business models and commercial opportunities in B2B and in cross-border trade in the AI era. [Interpreted] 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: Your next question comes from Yang Bai with CICC.

Yang Bai: [Interpreted] My question is about the cloud and AI business. We’ve seen that Alibaba Cloud’s revenue growth has been accelerating quarter-by-quarter reaching 45% this quarter. We know the company has previously set a long-term goal of exceeding USD 100 billion in external cloud revenue over the next 5 years. And you’ve also now indicated that growth will remain on an accelerated trajectory in the quarters ahead. So I’d like to ask 2 questions. First, looking ahead to the coming quarters, what do you anticipate being the pace of growth in the cloud business. What are the core drivers underpinning the continued acceleration of cloud computing growth?

And then secondly, as you mentioned, the industry is now in a phase of relatively tight capacity in terms of supply of compute. And you just mentioned that, that supply demand dynamic may shift around 2030. So I’d like to ask from an even longer-term perspective, what are the fundamental growth drivers for the cloud business? And do they differ from those in the short term?

Unknown Executive: [Interpreted] Thank you for the question. And I think I can expand on this in 3 different areas. I can start by looking at our current business and the relevant data. Secondly, I can discuss the drivers for growth. And then thirdly, I can share with you our long-term perspective based on that analysis. So let me begin with the first part, covering our current business and the key metrics. So as you’ve seen, external revenue for the AI and Cloud segment has been accelerating now for 9 consecutive quarters. And in this last quarter, growth has already accelerated to 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 RMB 12.4 billion in revenue this quarter. And so if we convert that into an annualized U.S. dollar figure, that works out to USD 7.3 billion in annual revenue. Looking ahead to the next quarter, our own forecast is that, that same annualized revenue for AI quarters — next quarter will approach USD 10 billion. So our growth rate remains exceptionally strong.

At the same time, we also expect our EBITDA margin to improve quarter by quarter sequentially over the next few quarters. Additionally, something very important in respect to the cloud business is growth in demand for MaaS. We’ve seen very significant growth in demand for MaaS this quarter, coupled with ongoing improvement in inference efficiencies. So the ARR of our MaaS business has now surpassed RMB 16 billion. And actually, let me clarify. That’s the latest data as of August, it’s already surpassed RMB 16 billion. [Interpreted] Next, let me expand on the growth drivers within our business model. So it’s important to understand that Alibaba’s investment model for AI is fundamentally different from that pure-play AI companies.

We are pursuing an intensive strategy across the full stack including chips, including AI cloud infrastructure and including models. And we maintain a leading position in the industry across all 3 of those most critical domains. Moreover, we believe that the development of AI and technology across the industry is still in its early stages. Looking forward, different stages of technological development, the core commercial value within the AI industry may shift across different layers, including chips, cloud computing models and applications.

Our full stack investments ensure that we can deliver optimal service capabilities and the best value for money positioning us favorably in the industry going forward and ensuring that within each stage of technological development, it’s possible for us to maintain competitiveness and sustained growth momentum. [Interpreted] Next, let me look ahead to what we think is going to be the most important growth driver over the next 1 to 2 years in the short term. So we’ve seen exponential demand for commercial insurance services as of the end of 2025. This exponential growth in demand for inference has marked a fundamental shift in the model, whereby compute has now become the core asset driving AI revenue.

And today, all AI-related revenue models are centered on AI compute. And at the same time, there’s a consensus across the industry, as I mentioned, that compute will remain in a shortage of supply for some time to come. At the same time, the higher gross margins of MaaS inference services have also made a major difference if compute was once a cost center, traditionally, compute has now been transformed into a core productive asset whose value generation is positively correlated with revenue. So high-priced computing power remains in short supply across the industry precisely at a time where you have widespread adoption of GPUs across diverse use cases.

So pricing models are tending to converge on the most high margin, the most margin generative monetization approaches. So this is driving the pricing models for nearly all GPU-related products. Moreover, Alibaba, both comprehensive multimodal model capabilities. Our models are state-of-the-art level within the industry, giving us a distinct advantage in realizing the value of that compute power and providing a robust anchor for our pricing strategy. When it comes time to price for new customers or to sign — reassign 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.

[Interpreted] Next, let me talk about the scale effects and networking effects, which are very important long-term growth drivers in AI cloud. For the past couple of years, a lot of people have asked what is the super app for AI. And the answer to that is that the real super application is compute, cloud-based AI compute because all of these different workloads need to run on a full stack of AI cloud compute, including training, inferencing, AI software and agents requiring GPUs, CPUs, storage, databases, virtualization as well as harness tools among others.

So AI cloud is like a super city in which workload is the residents and continually iterating full stack AI cloud services or the urban infrastructure, which in turn attracts more new residents and enhances the stickiness of the existing residents. So this is where you see an extremely powerful network effect and scale effect. [Interpreted] 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 compute resources. So looking ahead from the perspective of industry development trends and our own product strength, the long-term revenue growth trend and margin expansion trend are exceptionally strong. And as a result, we’re highly confident in our ability to achieve our goal of RMB 100 billion in external cloud revenue by 2030. And we have good visibility into achieving gross margin of 20%.

Operator: Your next question comes from Yuan Liao with CITICS.

Yuan Liao: [Interpreted] Congratulations on the strong quarterly results and especially the progress made in the AI sector. So I have a follow-up question on the MaaS business. As Eddie mentioned earlier, ARR as of August has exceeded RMB 16 billion in last quarter, I believe you stated that the target for year-end is to surpass RMB 30 billion in MaaS ARR. So I’m wondering, given the progress to date, do you anticipate making any adjustments to that year-end goal? And then additionally, within the MaaS business, what are the respective shares of our own proprietary models versus third-party models.

And as model-related competition intensifies and more open source models emerge how all these factors possibly affect gross margin and profitability in the MaaS business?

Unknown Executive: [Interpreted] Thank you for the question. Yes, indeed, growth in Bailian’s MaaS business is very rapid. And in — as of August, we reached RMB 16 billion or surpassed RMB 16 billion in ARR. So given the current growth momentum as well as the pipeline of new models slated for launch, we remain confident that we will achieve our year-end target of RMB 30 billion ARR by the end of the year. [Interpreted] So on our MaaS platform, our own proprietary model still account for the majority of the revenue. But having said that, revenue from third-party models is also not small. And having said that, perhaps let me talk a little bit about how we see different model capabilities.

A lot of customers tend to need to use or want to use multiple different models in their own AI applications because those different models they can draw and have different characteristics or different capabilities. So having more open source models on platforms like ours like Bailian to provide inferencing is a good thing for us and for Bailian. When it comes to gross margin, the level of gross margin that we can achieve on a platform like Bailian from hosting our own proprietary models versus third-party models is actually very similar. It’s highly comparable.

We’re really developing those proprietary models on the one hand in order to keep creating higher levels of model intelligence and also as part of our ultimate drive to achieve AGI. But simply from the perspective of the MaaS business, the level of gross margin from those 2 kinds of models is actually very comparable. But overall, having a prosperous and flourishing open ecosystem with many of these open source models on it is highly favorable for a cloud provider like Alibaba Cloud.

Operator: Your final question comes from Alex Yao with JPMorgan.

Alex Yao: [Interpreted] I’d like to come back to Eddie’s earlier remarks, he spoke at length about how Alibaba is developing a full stack AI ecosystem. My question really is in which layer of that full stack ecosystem, do you think value will accrete and monetization will be concentrated. We saw just after it has been released for 3 months that you open sourced the weight of your flagship model, Qwen 3.8 Max. At the same time, your proprietary chips are also proving successful, now serving over 600 external customers.

I’m wondering if this means that the future value will accrete mainly in the compute layer or perhaps in the orchestration layer and not necessarily in the model layer or do you think that the value will accrete to different layers in different stages of development of the industry. And in the long term, if you think that value and monetization will largely be concentrated in the hardware and compute layers then how should we think about competition going forward, given that it will be a government-led process for allocating a lot of that hardware and compute capacity?

Unknown Executive: [Interpreted] Thanks. That’s a very professional question, and really it’s a matter of long-term judgment. So I think it’s inherently associated with a high level of uncertainty. But what I can say is that we are investing in the full stack. And what that means is that whichever layer represents the greatest value and no matter how that may shift across layers in different periods of time. All of those layers are part of our ecosystem. I guess I can share with you my own short-term view namely in the short-term perspective, I think that most of the value will be in chips and in AI cloud infrastructure.

It’s a pattern that we can see not just in China but globally across a lot of different companies when a technology is in its early stages and especially when there’s a shortage of supply. Lots of the value tends to be concentrated in the infrastructure and in the core hardware. In this case, chips and storage. So in Alibaba’s case, we’ve integrated our compute power, our cloud infrastructure and our AI inference into one core business segment. [Interpreted] Let me turn next to where the ultimate commercial value will be realized from these AI models. It’s a question around which there’s a lot of debate within the industry and indeed, there are different views even inside our own company.

So here, I’m just sharing my own personal opinion. But in my personal view, I think that the current monetization model for large language models through APIs is just a short-term approach, a short-term transitional approach and is certainly not the ultimate business model. Our company has invested a tremendous amount of compute across our entire platform, but the objective is not simply to be able to generate that kind of short-term API revenue. I think when we get to the stage where we’ve accomplished 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 will be conducting the actual R&D that delivers products and that delivers operations. So the reason that all these different AI model companies are investing so heavily and engaging in an arms race today is not simply to be able to compete to provide that API-based service. It’s because they have to eyes on that ultimate end game, where I think that the monetization level will be significantly higher, be much higher than what you see today selling the service through API calls. [Interpreted] In terms of hardware, 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 AI — Alibaba’s AI cloud as supernodes. 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 the T-Head’s future is highly certain as a very key and core component of Alibaba Cloud, and we remain highly confident in our core competitive strength in this area. I’ve interacted with a lot of different engineers across China. And I can tell you that these chips have a very broad audience with engineers across a wide range of different engineering domains. [Interpreted] So to sum up, I think that our T-Head ships 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 #1 in the industry. And then I think in terms of future production capacity and deployment, we can confidently claim to be at least 1 of the top 2. But in terms of our ability to actually reach customers with AI chips, Alibaba Cloud is the largest player by market share in China’s cloud and AI market. So I think we have a very strong edge when it comes to channel distribution. So from this perspective, I am highly confident in the long-term commercial value of T-Head chips.

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

That’s the idea behind Anthropic new Model Hardware Standard (MHS), which the company released as a research preview on Thursday. MHS, which marks Anthropic’s first foray into so-called physical AI, is essentially a framework for connecting advanced large language models (LLMs) like Anthropic’s Claude with physical objects, from manufacturing equipment to microscopes.

With MHS, companies can integrate AI into their equipment in “hours or minutes,” Anthropic said. Typically, this process would take “weeks, if not months,” and require specialists to do a custom build. Expanded access to advanced AI tools will pave the way for “autonomous, round-the-clock experiments and workflows,” the company said. Scientific research and advanced manufacturing applications are among the primary uses.

The MHS can also help connect multiple devices to one another, enabling them to communicate through a set of commands, such as “read.” Any hardware device can understand these commands and act on them.

MHS is model-agnostic, meaning it works with any LLM—not just Claude—including models built by other companies such as OpenAI or open-source models. It’s built on the Model Context Protocol (MCP), a universal, open standard for connecting data sources that Anthropic debuted in 2024. The MCP is “kind of like the USB for AI to software connection,” Alek Kemeny, a member of the technical staff at Anthropic, tells Fortune.

Not all existing equipment can connect to MHS out of the box, as not all have a programming interface, Kemeny said. As part of this project, Anthropic is working with “a lot of device manufacturers” to build new products with the necessary interface, and are pre-loaded with the MHS. The company is also helping manufacturers to add MHS connections to existing products.

“That’s the future we imagine and are moving into,” Kemeny said. “In the future, scientists can buy these devices and out of the box it works. That’s just the process of adopting a standard.”

Jonah Cool, head of partnerships and deployment of science at Anthropic, added that often scientific equipment “suffers from proprietary solutions that are very brittle and often don’t meet the need of scientists.” MHS offers a standardized, easily programmable interface that aims to help them connect any model to their equipment. “We want to avoid vendor lock-in for scientists,” Cool said.

The MHS research preview comes amid increasing interest in the potential of combining AI and robotics. Hugging Face also debuted its first physical AI product today, a robotic duck, although it is not powered by MHS, Anthropic said. Nvidia, which is set to purchase Hugging Face for $13 billion, has also long championed physical AI. In March, Nvidia CEO Jensen Huang predicted that in the future “every industrial company will become a robotics company.”

Anthropic developed MHS in partnership with the HHMI Janelia Research Campus, a biomedical research center in Virginia. A “handful” of labs and hardware manufacturers received early access during development, in fields such as biotech, robotics, and quantum computing.

Some partners include Genentech, Carnegie Mellon university, quantum computing company QuEra, Universal Robots, Amazon Web Services, Doosan Robotics, Danaher, and Hugging Face.

Hugging Face also debuted its first physical AI product today, a robotic duck, although it is not powered by MHS, Anthropic said. Nvidia, which is set to purchase Hugging Face for $13 billion, has also long championed physical AI. In March, Nvidia CEO Jensen Huang predicted that in the future “every industrial company will become a robotics company.”