Q3 2026 Earnings Call Transcript

Good day, ladies and gentlemen. Thank you for standing by, and welcome to Alibaba Group's December Quarter 2025 Results Conference Call. [Operator Instructions] I would now like to turn the call over to Lydia Liu, Head of Investor Relations of Alibaba Group. Please go ahead.

Thank you. Good day, everyone, and welcome to Alibaba Group's December Quarter 2025 Earnings Conference Call. Joining us today are Joe Tsai, Chairman; Eddie Wu, Chief Executive Officer; Toby Xu, Chief Financial Officer; Jiang Fan, Chief Executive Officer of Alibaba E-commerce Business Group. I would like to remind you that this call is also being webcast on our corporate website.

A replay of the call will be available on our website later today. Now I will quickly cover the safe harbor. Today's discussions may contain forward-looking statements based on current expectations and assumptions that are subject to risks and uncertainties. Actual results may differ materially.

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

And now I will turn the call over to Eddie.

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

AI models and our capabilities are rapidly being embedded into mainstream work environments across all industries with token consumption surging across sectors. Cloud and software budgets for enterprise IT services have traditionally represented only around 5% of corporate revenue as model-driven agents begin to handle mainstream work tasks across industries, our total addressable market will expand by several multiples. From AI infrastructure to the application layer, Alibaba has built a complete full stack AI capability set to support the exponential growth in AI demand. Faced with an industry transformation and strategic opportunity of this magnitude, Alibaba Group is itself entering a new phase of entrepreneurial reinvention and critical investment oriented toward the future.

Next, let me share Alibaba's AI strategic road map. We have complete full stack AI capabilities, chips and cloud computing form the AI infrastructure layer while the AI application layer is anchored by Alibaba Token Hub and comprises foundation models, MaaS in both enterprise and consumer applications. Together, these give us end-to-end coverage across the full stack from AI infrastructure to applications. Given the enormous and sustained growth momentum of the AI market, combined with Alibaba's full stack positioning across the AI value chain, the business goal of Alibaba's AI strategy is very clear.

Over the next 5 years, our goal is to surpass USD 100 billion in combined cloud and AI external revenue, including MaaS. Regarding our infrastructure, driven by sustained strong AI demand, Cloud Intelligence Group's revenue from external customers accelerated to 35% this quarter with AI-related product revenue delivering triple-digit year-over-year growth for the tenth consecutive quarter. Cloud Intelligence Group's market share has grown for 3 consecutive quarters, rising to 36% with our lead continuing to widen. Alibaba Cloud's cumulative external revenue through February for fiscal year 2026 officially surpassed RMB 100 billion.

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

In real-world business deployments through Alibaba Cloud, more than 60% of the T-Head ships serve external customers, and we've completed scaled adoption for external customer AI workloads. T-Head now supports the AI workloads of over 400 enterprise customers across industries, including Internet financial services and autonomous driving. We're confident that T-Head's compute supply capacity will continue to expand, contributing high-quality compute to our cloud infrastructure and MaaS platform, strengthening the overall competitiveness of our cloud services. Regarding our application layer, centered on the core mission of creating, delivering and applying tokens, we established the new Alibaba Token Hub Business Group, [ ATH. ] It comprises Tongyi Laboratory, the MaaS business line, the Qwen business unit, the Wukong business unit and the AI Innovation business unit.

It is the organizational foundation for executing Alibaba's AI strategy and the hub for efficient coordination across our AI businesses. During Chinese New Year, we launched our latest generation large model, Qwen3.5-Plus, which delivered outstanding performance across comprehensive benchmarks in reasoning, coding and agentic capabilities. Qwen3.5-Plus demonstrated significant improvement in inference efficiency through foundational architectural innovation. Building on Qwen3.5, we will soon release the next generation of models optimized for coding and agentic use cases.

On the consumer application side, powered by the strength of our models, Qwen's consumer-facing monthly active users have surpassed 300 million. During Chinese New Year, we deepened integration across Alibaba's ecosystem connecting Qwen app with Taobao Instant Commerce, Alipay, Fliggy, Damai, and Amap giving it unique capabilities relevant to the everyday life and becoming China's first all-in-one personal AI assistant for life, work and learning. We've also recently launched Wukong, our enterprise AI agent platform. Wukong is the world's first AI native enterprise grade agent platform, enabling AI-powered upgrades to enterprise workflows while remaining compatible with each organization's data permissions and management processes.

It serves as the unified interface for Alibaba's AI capabilities in enterprise work environments and the B2B capabilities of businesses across Alibaba's full ecosystem will be progressively integrated to support Wukong becoming the best AI work system. On Alibaba's other strategic priority, the consumption segment, we continue to advance our strategic initiatives. This quarter, our quick commerce business further expanded in scale with continued share growth, high customer retention and sequential improvement in both unit economics and average order value. At the same time, quick commerce and e-commerce demonstrated clear synergies driving Taobao app monthly active consumers to double-digit year-over-year growth.

That concludes my remarks. I'll now hand over to Toby to share the financial update.

Thank you, Eddie. . Our strategic priorities are clear: we remain focused on AI plus cloud and consumption businesses. We are seeing great momentum with gains in technology, customer adoption, market share and user engagement. On AI plus cloud, we have the full stack AI capabilities with all 3 core elements, model, cloud infrastructure and chips and leadership in each with Qwen, Alibaba Cloud and T-Head.

We also operate the most comprehensive consumer ecosystem in China that can monetize through AI. The launch of Qwen App was a major milestone, and it can bring our consumer applications together. On consumption, our quick commerce business continued to gain GMV market share in December quarter, while unit economics and AOV also continued to improve. Now let's look at the financial results.

On a consolidated basis, total revenue was RMB 284.8 billion, excluding revenue from Sun Art and Intime revenue on a like-for-like basis have grown by 9%. Total adjusted EBITA decreased by 57% primarily due to our strategic investments in technology-related innovation initiatives and the consumption front, including quick commerce business, partly offset by the improved operating results in cloud business and enhanced operating efficiencies across various businesses. Our GAAP net income was RMB 15.6 billion, a decrease of 66%. Operating cash flow was an inflow of RMB 36 billion.

Free cash flow was RMB 11.3 billion, a decrease of RMB 27.7 billion from the same quarter last year. We are reinvesting our cash flow to be a leader in AI and quick commerce. As of December 31, 2025, we held USD 42.5 billion in net cash. Excluding that with maturities beyond 5 years, our net position stands beyond the USD 60 billion.

This balance sheet strength gives us confidence to reinvest for long-term growth. Now let's look at our consumption businesses. Revenue from China e-commerce group was RMB 159.3 billion, an increase of 6%. Customer management revenue increased by 1%.

The slowdown in revenue growth was primarily due to weaker transaction activities and phase out of the impact of software service fee implementation. The Taobao App achieved a double-digit increase in [ MAC ] during the quarter, driven by the growing mind share and increasing scale of our quick commerce business. Revenue from our quick commerce business increased 56% to RMB 20.8 billion. During the quarter, we executed our plan to further grow the scale of our quick commerce business, improved user experience, improved [ UE ] and increased AOV month-over-month during the quarter.

Alibaba China E-commerce Group adjusted EBITA was RMB 34.6 billion, a decrease of 43%, primarily due to the investment in quick commerce, user experiences and technology. Going forward, this adjusted EBITA will continue to fluctuate quarter-over-quarter due to intense competition and significant investment in user experience. Revenue from AIDC grew 4% this quarter. AIDC's adjusted EBITA loss narrowed significantly year-over-year, driven by a combination of logistics optimization and investment efficiency enhancement.

The UE of the AliExpress Choice business also improved on a sequential basis. Next, let's look at the business updates and results of Cloud Intelligence Group. Our Cloud Business delivered another quarter of accelerating growth. Revenue from external customers grew 35%, up from 29% last quarter.

AI-related products continue to lead this momentum. We delivered our tenth consecutive quarter of triple-digit growth in AI revenue. Its share of external cloud revenue continues to increase. This is a clear reflection of the scale and acceleration in our AI business.

The adjusted EBITA margin remained relatively stable at 9%. We will continue to invest in customer growth and technology innovation to increase adoption of AI cloud infrastructure and strengthen our market leadership. All other segment revenue decreased by 25% to RMB 67.3 billion, mainly due to the disposal of Sun Art and Intime businesses as well as a decrease in revenue from Cainiao, partly offset by the increase in revenue from Freshippo and Alibaba Health. All others adjusted EBITA was a loss of RMB 9.8 billion, primarily due to the increased investment in technology businesses, including Qwen models and consumer-facing Qwen, partly offset by the improved results of Cainiao, Hujing DME and other businesses.

Qwen Model has become one of the most widely adopted open source model families globally, surpassing 1 billion cumulative downloads on Hugging Face by the end of this January, and the consumer facing Qwen has surpassed 300 million [ MAU ] across platforms which reinforces user engagement and expand long-term monetization potential. We have been increasing investments on these technology fronts, including the Spring Festival campaign. Building on the strong momentum and results achieved, as Eddie mentioned earlier, we will continue to invest substantially in Qwen models and Qwen App. Our unallocated adjusted EBITA was a loss of RMB 2.7 billion compared to a loss of RMB 0.2 billion in the same quarter last year, which reflected costs associated with talent retention incentive from the one-off replacement awards plan of Ele.me.

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

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

If you are unable to hear the Chinese translation, bilingual transcripts of this call will be available on our website within one week after the end of the meeting. [Foreign Language] Operator, please go ahead with the first question. Thank you.

[Operator Instructions] First question today comes from Robin Zhu at Bernstein.

Could you give us some specific examples of how Token Hub will change how the different cloud and AI businesses work together going forward from an organizational standpoint? And strategically, what changes or goals are you hoping to achieve with this new structure that improves on the previous arrangement going forward? And then if management could share hierarchy of priorities in cloud and AI, is it market share and revenue growth, such as the target you just announced versus having the best first-party model capabilities versus consumer side traction with customers using agentic AI or anything else?

[Foreign Language] [Interpreted] Great. Thank you very much for your question. I think that the goal and purpose of the establishment of the [ ATH ] Business Group, is very much connected to the era that we're now in as of the end of 2025 and going into the first few months of 2026. In terms of the development of AI, we're now in the agent-driven era of AI development. And this is different from the earlier period of AI development in the agentic AI era we need to achieve a very close integration of model with application.

In the earlier AI era, a lot of model training data was static data, but in the genic era, we need to enhance the integration between models and applications and achieve tight integration and a lot of the data is now coming from the customer side. So if you look at the different layers involved in AI deployment from application model, the AI infrastructure, through chips. I think what's most different and most important about the agentic AI era is the need to achieve this tight integration between application and model. That's the critical priority.

Next, let me address the interconnection and synergies among the different businesses in relation to ATH. If you look at the trends of where this industry is going, and we think we see these trends very clearly. The AI agents will be tightly integrated together with the application layer. And there will be a multitude of highly diverse applications.

In the consumer or 2C space, we're strongly developing the Qwen app as a personal assistant for individuals. And in the 2B space, we're positioning Wukong as a 2B assistant. In the AI application layer, there will be a multitude of different industry and vertically specialized applications to serve different industry use cases. And all of this needs to be supported by a very robust model as a service, MaaS layer.

So MaaS supports, of course, our own internal applications as well as a multitude of external and industry-specific use cases that leverage AI. So in this context, we see massive value that we can provide and a huge total addressable market or TAM. So going forward, we see the AI application layer as the main channel through which tokens will be distributed. And the stronger the model capabilities that you can offer at the MaaS layer, the more attractive and compelling all of these different offerings will be to customers.

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

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

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

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

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

And whether you have started to see any improvement in consumer sentiment?

[Foreign Language] [Interpreted] Thank you for your question. Indeed, in the December quarter, weak macro consumption, a warm winter, the later timing of the Chinese New Year challenged the growth for the December quarter. And due to the extended promotional season, our investments in consumer benefit increased compared to previous years. So as a result, the CMR and EBITA trend softened.

Going into the March quarter with the improving consumer sentiment that we've observed and momentum from our Quick Commerce strategy, our physical goods GMV and CMR trend have significantly recovered from the December quarter, and EBITA is expected to improve accordingly.

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

Your next question comes from Gary Yu at Morgan Stanley.

My question is related to quick commerce. I understand that in the past couple of months, we have achieved certain milestones in terms of [ GDV ] market share and also improvement. How should we look at the priority going forward? Are we aiming for market share or hoping to take this opportunity to improve unit economics, reduce loss?

And how should we look at the synergy between quick commerce and traditional e-commerce? And how should we see these synergies to translate into CMR better growth going forward?

[Foreign Language] [Interpreted] Certainly, while growing our market share, we have continued to significantly improve UE driven by improvement in fulfillment logistics efficiency by improvement in monetization as well as by order mix optimization, driven by those factors, we expect to further optimize UE in the coming quarters. In terms of the positive impact that quick commerce is bringing to our conventional e-commerce business and to our entire ecosystem. We saw a very significant increase in AACs on the platform in the past year. Our AAC number increased 150 million in 2025 including 100 million conventional e-commerce physical goods AAC, which is more than the previous 3 years combined.

Now new consumers ARPU and purchase frequency are lower than that of existing users. So we aim to continually increase their ARPU and purchase frequency, which will serve as a new growth engine for our platform in the coming years. Quick commerce is clearly driving sales in various categories such as food and fresh produce and health care and is contributing to Freshippo and Tmall supermarkets accelerated growth. In terms of the outlook, we maintain our target of achieving over RMB 1 trillion in Quick Commerce GMV by FY '28. We expect to generate positive cash flow when the GMV target is achieved, and we expect the quick commerce business to be profitable in FY '29. Quick Commerce has become a cornerstone of our e-commerce business, playing a strategically vital role in the AI era by driving customer acquisition, enhancing user engagement, fulfilling diverse consumer demand, increasing transactions and improving monetization and supporting logistics infrastructure.

We are committed to investing in quick commerce in the next two years towards achieving the RMB 1 trillion GMV target as a market leader.

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

Your next question comes from Alicia Yap at Citigroup.

I have some questions regarding your chip business, T-Head [indiscernible]. So there have been reports that Alibaba plans to spin off the T-Head unit as a separate listing. Can management provide any information of this? And if so, what is the expected time frame for this to occur?

And in the meantime, can you share more operating metrics? So in addition to the 470,000 chips that you mentioned you shipped to external customers, how we reconcile that number, the shipments to the revenue side? And also what is the expected growth rate for your chip business in the coming year? And I think you mentioned currently it's 60% of these from external customers.

So maybe can you also share with us, are these chips for external customer mainly used for inferencing? And then for internal, is it used for model training and also inferencing? And then lastly, how do the [indiscernible] chips or T-Head chips are compare to other domestic chips? If management can share some detail would be great.

[Foreign Language] [Interpreted] Okay. Thank you very much for this question. And I'd like to take the opportunity to expand on this a bit because T-Head is a very important component of Alibaba's company-wide AI strategy. So in the context of China's domestic AI chip ecosystem, we firmly believe that T-Head is ranked in the top tier of the domestic AI chip ecosystem in terms of the technology capabilities and product capabilities.

Our products cover the entire AI workflow from model training and fine-tuning through to inference. And our T-Head AI chips are already in extensive large-scale use via Alibaba Cloud, both for training workloads and for by inferencing use cases. At the same time, over 60% of T-Head chips are being used by external commercial customers across Alibaba Cloud's public and hybrid cloud offerings. The external commercial clients span multiple industries, including Internet finance, autonomous driving and intelligent manufacturing.

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

But given that our chips still lag behind foreign counterparts and performance in various respects, we aspire to engage in more profound co-design with Alibaba's cloud infrastructure and the Qwen model to provide improved cost effectiveness. So this is one key differentiator and how we approach chip design at T-Head that sets us apart from other chip companies. Our primary goal is to create AI capabilities that offer superior value for money. This will make it a key product for the [indiscernible] platform, allowing us to reduce inference costs going forward.

Beyond generally improving our AI efficiency and reducing costs, there's another factor at play namely the unique circumstances currently facing the AI industry in China. In that context, one significant benefit for us is the guaranteed supply of AI computing power. Because I believe that over the next 3 to 5 years, global AI computing power will be an extremely short supply, especially in the Chinese market, as the only cloud compute company in the Chinese market with proprietary chip development capabilities. T-Head is of paramount importance, therefore, to the Alibaba Group, increasing the supply of AI computing power will help our cloud and AI businesses, including our MaaS business to achieve stronger growth momentum.

At T-Head, over the past 2 years, we've successfully commercialized and launched chips with total volume exceeding 470,000 units with annual revenue reaching the RMB 10 billion level. Looking ahead to '27 well, through 2026, this year through '27. Next year, we expect T-Head's production capacity for high-quality AI chips to continue to expand. This will provide robust computing power support for our group's AI business and serve as a powerful growth driver for our overall AI initiatives. We also believe that future improvements in profitability will be achieved further enhancing profit levels, which will also be very beneficial.

Overall, T-Head's value to Alibaba goes beyond cost optimization. It primarily serves to ensure supply chain resilience and in an era of scarce computing power. I see this as crucial to Alibaba's AI strategy. So it is possible, and we don't rule out T-Head considering an IPO in the future, although we currently do not have any definitive time line.

Next question, please.

The next question comes from Yuan Liao at Citic.

[Foreign Language] [Interpreted] My question is about the business objectives for your AI strategy that you just mentioned. Revenue for the next 5 years is expected to exceed 100 billion. Could you provide more details on this target? For example, if the next 5 years is through to 2031, what kind of CAGR would that correspond to in this 5-year period?

And could you also break out what will be driving that growth and how we should understand those drivers? Given this scalable growth in revenue, when can we expect to see sustained improvement in Alibaba Cloud's margins?

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

I think that the major driver underlying all this really is continued breakthroughs in the capabilities of large AI models. And we've certainly seen a very clear trend over the past couple of months, the two months of 2026, whereby large models have now gained the capability to execute complex B2B workflows. More and more enterprises are deploying agents powered by large models to handle end-to-end business tasks. And that marks a fundamental transformation in the way that the market looks at IT budgets, IT budgets traditionally allocated to AI and cloud services.

The shift really is that many enterprises now when consuming tokens don't treat token consumption as part of their IT budget any longer. Instead, they see tokens as part of their overall operational or R&D costs. Tokens are a key component of their production inputs, not just a part of their IT budget. So this is the most fundamental long-term factor that we see driving future AI growth.

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

But secondly, for AI and cloud computing, there's another very important growth opportunity. Of course, we believe that public MaaS will be a substantial market in the future. But in a considerable number of large -- medium and large-sized enterprises, there'll also be a demand for enterprise level, internal inference and training, a new marketplace. And that market will continue to exist in the long term.

It's not one that will disappear simply because each enterprise makes decisions based on its own business model and the security requirements of its specific use case or the particularity of an application scenario. So for some application scenarios, enterprises will opt to use public MaaS API services, while many others will be based on privately deployed solutions within the enterprise. So those kinds of application scenarios represent a large incremental growth opportunity for Alibaba Cloud's AI infrastructure. Third, there's another important driver, an important opportunity that I think tends to get ignored a lot of the time.

And I'm talking about CPU-centric cloud computing, the traditional cloud computing, which still has significant room for expansion in this AI-enabled era. So traditional cloud computing is designed for IT engineers, which in China may number a few million, say, perhaps no more than 10 million potentially traditional IT engineers. And those have been the traditional cloud computing customers. However, in the future, there could be billions of agents that are created by large AI models and their operating environment.

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

And that's a key focus of Alibaba Cloud's upgrade. As the revenue from this business continues to grow, our AI business will undergo transformation and upgrading, shifting from selling resources to selling intelligence, selling intelligent capabilities. And I think that represents a massive upgrade to the business model. At the same time, by integrating our proprietary T-Head chips we are achieving and will achieve cost reduction and efficiency gains, we believe that as our AI and cloud business continues to grow in revenue scale, cloud profitability should become increasingly visible and we see it is on a steady path of improvement; however, the process of continued improvement is not a linear one.

It's possible that there could be a scale effect breakthrough, the achievement of an economy of scale or the scaling up of our T-Head chips, and there could be a massive leap forward, but I think that's a function of the product as well and those kinds of economies of scale. But it will not unfold in a linear fashion. So you asked about the CAGR compound annual growth rate from 2026 through 2031. I think you can plug that into your calculator and figure out what it would be assuming it were to be linear, but I don't think that it will be linear. Our R&D investment and growth in the market will not be linear and some of the investments we're making today may not yield significant growth until 1 or even 2 years from now; however, regarding that overall 5-year goal, we are highly confident in our ability to achieve it.

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

The last question comes from Alex Yao with JPMorgan.

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

[Foreign Language] [Interpreted] Thank you. So as I just mentioned, we are making a very significant investment in the instant retail business this year, the Quick Commerce business this year. And at this point in time, we're seeing a highly definitive opportunity in this space. So again, as I just mentioned, we will continue to invest heavily over the next two years in order to achieve our goal of surpassing RMB 1 trillion in quick commerce sales.

We also believe that in two years' time, our investments in quick commerce will generate positive economic returns for our E-commerce business as a whole.

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

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

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

Thank you. That concludes the call for today. Thank you for participating. You may now disconnect your lines. [Portions of this transcript that are marked [Interpreted] were spoken by an interpreter present on the live call.]