腾讯0700.HK

Welcome to Tencent Holdings Limited 2026 second quarter results announcement webinar. I am Wendy Huang from Tencent IR team. At this time, all participants are in a listen-only mode. After management's presentation, there will be a question and answer session. For participants who dial in by phone, if you wish to ask a question, please press five on your telephone to raise your hand. If you are accessing from the Tencent Meeting or Room Meeting application, please click the raise hand button at the bottom left. Please be advised that today's webinar is being recorded. Before we start the presentation, we would like to remind you that it includes forward-looking statements, which are underlined by a number of risks and uncertainties and may not be realized in the future for various reasons. Information about general market conditions is coming from a variety of sources outside of Tencent.

This presentation also contains some unaudited non-IFRS financial measures that should be considered in addition to, but not as a substitute for, measures of the group's financial performance prepared in accordance with IFRS. For a detailed discussion of risk factors and non-IFRS measures, please refer to our disclosure documents on the IR section of our website. Let me now introduce the management team on the webinar tonight. Our Chairman and Chief Executive Officer, Pony Ma, will kick off with a short overview. President Martin Lau will provide a strategy review. Chief Strategy Officer James Mitchell will provide a business review, and Chief Financial Officer John Lo will conclude with financial discussion before we open the floor for questions. I will now pass it to Pony.

Thank you, Wendy. Good evening. Thank you everyone for joining us. As we enter into the third quarter of the year, we are making substantial progress toward building a new AI-empowered Tencent in terms of intelligence, applications, and infrastructure. At the intelligent level, Hunyuan 3's production version provides user a wide use model with strong cost performance metrics and serves as a steppingstone toward the Hunyuan family of models, attaining state-of-the-art capabilities in the future. At the application level, our WorkBuddy AI office productivity service, and CodeBuddy AI coding tool are achieving breakout user growth and are the clear leaders in the field in China today. At the infrastructure level, we substantially stacked our power procurement of compute, which will enable us to convert usage of our applications and models into revenue going forward.

At the same time, we continue to enhance our existing services with the sustained marketing service revenue growth, several successful recently released games, and rapidly increasing video views on Weixin Video Accounts. Looking at our financial numbers for the second quarter. Total revenue was RMB 205 billion , up 11% year-on-year. Gross profit was RMB 118 billion , up 13% year-on-year. Non-IFRS operating profit was RMB 76 billion , up 9% year-on-year. Excluding new AI products, non-IFRS operating profit was RMB 86 billion , up 19% year-on-year. Non-IFRS net profit attributable to equity holders was RMB 68 billion , up 9% year-on-year. Turning to our key services. For communication and social networks, combined MAU of Weixin and QQ grew year-on-year and quarter-on-quarter to 1.4 billion.

For digital content, TME's acquisition of Ximalaya strengthened our audio content and unlocked new synergies with the ecosystem IPs, including from China Literature. For games, new game, "Roco Kingdom: World," ranked first by average DAU and by gross receipts among all new games released in China industry-wide this year. For cloud, WorkBuddy recently ranked first among productivity AI service in China based on monthly interactions. I will now hand it over to Martin for the strategy review.

Thank you, Pony, and good evening and good morning to everybody. Today, we want to provide you with an update on our overall AI strategy. Tencent's existing businesses are growing solidly due to intrinsic modes and AI enablement. As discussed early this year, our modes arise from factors including network effect, depth, and value added along supply chain, IP, low tick rates, regulatory requirements, and private data. In addition to these modes, we're further deploying AI to boost returns in areas including WeChat, games, and advertising. As a result, our existing businesses provide a very strong financial support for our new AI initiatives.

Regarding our new AI initiatives, we've made significant progress in constructing a robust foundation, including a substantially improved Hunyuan 3 foundation model with leading cost performance, WorkBuddy and CodeBuddy that lead the China market in terms of AI productivity usage, and Yuanbao and Xiaowei serving as gateways to drive broader consumer AI adoption. We see increasing potential to generate attractive financial returns from franchise products with differentiated advantages, including Hunyuan, WorkBuddy, and Xiaowei overtime. We're comfortable in making significant investments in AI because not only there is a substantial upside potential, there is also clear downside protection. The AI investments we're making are mostly in AI infrastructure, and in the worst case, which we do not believe that would happen, we can choose to rent that infrastructure out at cost recovery or even better prices via Tencent Cloud if needed. Now, going on to the different components.

First, on Hunyuan foundation model. The release of Hunyuan 3's full production version is very successful, showing a substantial step-up in performance compared to the Hunyuan 3 preview version. Leveraging the feedback loop from product teams to improve the quality and diversity of data used for post-training, and by scaling up reinforcement learning, Hunyuan 3 achieved a notable improvement in task completion rates and meaningful reduction in hallucination and error rates. The improvements in Hunyuan 3's capabilities are most evident in its agentic capabilities and product experience. The model's performance step-up across reasoning, agentic, and long contest tasks delivered clear advantages for use cases such as coding, office work, financial modeling, and front-end design.

These performance improvements drove accelerated user adoption and growing external customer demand, validating its practical utility in real-world usage, as demonstrated by the approximately 6x increase in average daily tokens usage of Hunyuan 3 compared to the preview version across all channels during the pay period. Additionally, Hunyuan 3 consistently ranks among the top 3 models globally on OpenRouter based on token usage. Hunyuan 3's production version has performed well and will serve as a stepping stone toward the Hunyuan family of models, achieving state-of-the-art capabilities in the future while providing users with the cost-performance efficiency that they need today. We have been integrating Hunyuan into our products, making great impact. For WorkBuddy, Hunyuan can facilitate complex agent workflows with higher task success rates and reduced time to completion. For Yuanbao, Hunyuan delivers leading execution quality in information retrieval, data processing, document workflows, and everyday decision-making.

In games, we are leveraging Hunyuan for AI teammate creation and code review for games, including our flagship game, Peacekeeper Elite. In Weixin, we deployed Hunyuan for powering the AI assistant in official accounts and the developer tools for mini-programs. At the same time, product integration is making Hunyuan better by continuously feeding real-world product usage and domain feedback into model training. Our model product co-design approach allows Hunyuan to validate model accuracy and identify and work on edge cases, enabling faster model iteration and sustained performance gains. Having now established a new system for fast model iteration, and with the Hunyuan 3 validating the system, we are accelerating the improvement of our model. We are scaling more powerful reinforcement learning to substantially upgrade models after pre-training is done. We are in the process of upgrading multimodal capabilities.

More importantly, we are training a larger parameter model, Hunyuan 4, which we expect to release later this year. By accelerating the technical iteration and pushing the boundaries of model intelligence, we are confident Hunyuan's capabilities will reach state-of-the-art level. We believe we will generate significant return in building a large and valuable AI-native new business for Tencent. The rationale behind investing in our own foundation model is that we can achieve better unit economics, more innovative features, and more exposure to the value of intelligence through co-design across our applications, our model, and our compute infrastructure, especially at this early stage of AI diffusion. Moving on to the application front. Our AI office productivity workspace, WorkBuddy, and coding tool, CodeBuddy, are achieving breakout success in terms of capability and user growth. They are the clear leading office productivity service in China based on monthly interactions.

WorkBuddy serves as a one-stop-shop workspace that orchestrates multiple agents to handle complex work from end to end. Users can remotely control WorkBuddy via Weixin and WeCom, as well as VPC, and access to over 70,000 skills from Tencent Cloud SkillHub. Besides the rapid user adoption of WorkBuddy, it is also achieving high retention rates and high willingness to pay among users as it directly contribute to users' productivity. It also attracts growing and more vibrant developer community by embedding skill pay and Weixin Pay inside task flows to enable payouts for developers when their skills are called. This progress supports our view that there are substantial opportunities to be unlocked in the productivity market, including coding and existing office work scenarios. We are currently focused on investing in market education and extending our market leadership position.

Over time, product economics will be attractive as enhanced premium benefits accelerate paying user growth, while we can reduce token costs through agent efficiency, inference efficiency, and model optimization. Given Tencent applications such as Weixin, WeCom, and Tencent Meeting are already widely used by enterprises, WorkBuddy provides a new way for us to monetize our enterprise relationships. On the consumer front, we recently released a prototype of Xiaowei, which delivers an embedded and context-aware agentic AI experience within Weixin, leveraging Weixin's social graph, knowledge graph, merchant reach, and payment functionality. Xiaowei is powered by the Weixin customized model, WeLM, built with a focus on user privacy, Weixin-specific use cases, and cost efficiency. Xiaowei can help users navigate and derive insights from Weixin's diverse content universe in a personalized and efficient manner.

Xiaowei can also leverage Weixin's unique mini-program ecosystem to help users discover products, make purchase decisions, and place orders, laying the groundwork for an agent-to-agent transaction loop. While the prototype can technically handle advanced agentic workflows, we currently configure Xiaowei to require user intervention and multiple step confirmations as safety measures. Xiaowei will be rolled out to broader user base in a phased approach as we work on several core initiatives to elevate the user experience. These include upgrading Xiaowei's dialogue, memory, and recommendation capabilities, expanding service and content integrations, scaling our AI infrastructure, and upgrading our harness to support a significantly larger user base. As we upgrade Weixin for the AI era, we can do it in a cost-efficient way, and we're confident that AI will over time accelerate the growth and thus the monetization of the entire Weixin ecosystem, generating attractive return for us.

Turning to Yuanbao, we are focusing on improving its capabilities and user experience, particularly in search, speech recognition, and text-to-speech functionality. We're also improving its ability to address broader long-tail AI needs of consumers, including multimodal generation. Yuanbao plays an important role in the co-design flywheel as its conversational use cases generate valuable feedback to help improve our Hunyuan family of models. Over time, functionalities developed and honed by Yuanbao can become atomic capabilities for use in other Tencent products such as WorkBuddy, CodeBuddy, Weixin, and QQ Browser. With that, I pass on to James.

Thank you, Martin. For the quarter, total revenue was up 11%, with social networks contributing 16%, domestic games 23%, international games 9%, marketing services 21%, and fintech and businesses services 30%. Our gross profit was up 13%, within which VAS gross profit increased 14%, marketing services 21%, and fintech and business services 9%. Value-added service revenue was RMB 98 billion , up 8% year-on-year, within which the social network revenue was up 1% to RMB 32 billion , driven by increased revenue from app-based game item sales, partially offset by decreased revenue from long-form video subscriptions, where revenue decreased 6% year-on-year. However, our exclusive drama series, "The Lead," was the most-watched drama series across all video platforms in China in the second quarter. Audio subscription revenue increased 8%, driven by higher music ARPU and enriched content stemming from the inclusion of Ximalaya.

We facilitated users discovering new music by enabling one-click access from video accounts to QQ Music. in May, we completed the acquisition of Ximalaya. By bringing Ximalaya into Tencent Group, we can enhance Tencent Music's resilience, deepen the content supply relationship between China Literature and Ximalaya, and provide users with new content formats, including audiobooks and podcasts. Domestic games revenue grew 17%, primarily driven by "Delta Force," "Valorant PC," "Valorant Mobile," and "Roco Kingdom: World." International games revenue was down 1%, although up 4% in constant currency terms, as revenue growth from "Wuthering Waves" and "Valorant PC" was offset by revenue decreases from two Supercell games. For communications and social networks, video accounts total time spent grew over 20% in the second quarter, benefiting from enriched content supply, upgraded interactivity, and the introduction of a new multi-variable content ranking system.

We've added content that appeals to younger users through IP partnerships with game studios, music labels, TV shows. We've provided new revenue-sharing opportunities for creators, expanding the population of creators that generate direct revenue from within the video accounts. Mini shops GMV increased, within which GMV generated from Weixin's centralized e-commerce gateway page grew significantly. For mini shops merchants, we introduced marketing tools such as lucky draws, helping them to enhance brand awareness and drive product discovery. For mini shops consumers, we enhanced rewards for repeat shoppers to increase customer life cycles, and thus customer lifetime value to merchants. On domestic games, "Delta Force" achieved lifetime high average DAU in the second quarter, driven by the Burst Fest campaign, the game's first professional esports final, and a global 20 versus 20 tournament.

In terms of production, the Delta Force team have integrated AI across multiple workflows, including using data agents for performance analysis and the Hunyuan 3D model for asset generation. Valorant PC also achieved lifetime high average DAU in the second quarter, benefiting from the Skirmish: Ascension mode with round progressive weapons and the Summit map with droppable walls. The game expanded its reach via influencer collaborations, on-the-ground city events, and promotions in over 10,000 Internet cafes. Among new games, Roco Kingdom: World ranked fifth by average DAU and eighth by gross receipts across all mobile games released industry-wide in the second quarter, making it the highest-ranked new title released year to date. The game has maintained a rapid content delivery cadence since launch, adding 100 creatures and expanding the map with seven new regions. On July 9, we released Runaway Evolution.

This game is adapted from Rust, a survival open-world crafting game on PC that has generally ranked among the top 20 games on Steam by concurrent users for the past eight years, thanks to its unique high-risk, high-reward gameplay in which players compete to outlast the other players on their server in one-week competitive sprints. Runaway Evolution seeks to tailor this gameplay for China market preferences via availability on mobile as well as PC devices and via a sandbox safe zone for new players. Among our international games, League of Legends DAU increased year-on-year in the second quarter, primarily driven by the ARAM: Mayhem mode. We launched League of Legends Classic, a nostalgia mode that reengages longtime fans by recreating early-era gameplay with pre-reworked champions, classic runes, and the original Summoner's Rift map layout.

Warframe's DAU grew year-on-year, and gross receipts achieved a lifetime high in this quarter, benefiting from a new wolf-themed Prime Warframe and a new storyline, Jade Shadow's Constellations. Arrows – Puzzle Escape, a maze-clearing game developed by Miniclip subsidiary Lessmore, was the most downloaded mobile game globally in the second quarter. Arrows' success demonstrates that the innovative capabilities of Miniclip's family of studios, supported by Miniclip's publishing expertise, can together pioneer and break out leaders in new genres of casual games. Arrows monetizes via in-app advertising, so we report its revenue in our marketing services segment rather than the international games sub-segment. Adjusted to include Arrows and other in-app advertising game revenue in the prior year and current periods, our international games year-on-year revenue growth would have been 4 percentage points faster than the disclosed figures.

For marketing services, revenue grew 22% year-on-year to RMB 44 billion , driven by higher eCPM and impressions. Most major categories increased their marketing spending with us, including e-commerce, internet services, and local services. We upgraded AI Marketing Plus end-to-end execution capabilities to better support closed-loop WeChat Minishop and Mini Drama advertisers. For example, AI Marketing Plus now enables WeChat Minishop owners to automatically select products for promotion, generate product-relevant ad creatives, and then run smart bidding to buy inventory for those creatives. We significantly scaled up the parameters of our advertising AI recommendation system to capture user interest with greater granularity and thus improve ad conversion rates. Weixin Video Accounts ad impressions grew rapidly year-on-year, driven by higher video views and ad load, although ad loads remain well below the short video industry average. Mini programs attracted increasing marketing spend from Mini Drama and Mini Game Studios.

For fintech and business services, segment revenue was RMB 60 billion , up 9%. Fintech services revenue grew year-on-year, driven by increases in commercial payment, wealth management, and consumer loan services. For commercial payment, the number of transactions grew year-on-year, while the decline in value per transaction narrowed. For wealth management, aggregated customer assets increased year-on-year, benefiting from the popularity of automated investment strategies and thematic index funds. Within business services, while we're still working through capacity constraints, our cloud revenue growth rate accelerated from high teens percentage year-on-year in the first quarter to low 20s percentage in the second quarter, benefiting from AI-related demand, international expansion, and increased usage and pricing for general cloud services. AI-related demand translated into increased revenue across GPU rental, Model-as-a-Service, and WorkBuddy and CodeBuddy token usage. Our international cloud business expanded rapidly.

Using skills developed with CodeBuddy is enabling us to conduct customer cloud migrations over to Tencent Cloud faster than we could in the past, for example, on behalf of a leading telecom company in Indonesia. I'll pass to John.

Thank you, James. For the second quarter of 2026, total revenue was RMB 204.8 billion, up 11% year-on-year. Gross profit was RMB 118.4 billion, up 13% year-on-year. Operating profit was RMB 67.3 billion, up 12% year-on-year. Interest income was RMB 4.2 billion, up 2% year-on-year. Finance costs were RMB 3 billion, compared with RMB 3.9 billion in the same period last year, reflecting favorable Forex movements and lower interest expenses due to lower average interest rates. Our share of losses of associate and joint venture was RMB 10 billion for the second quarter of 2026, primarily reflecting our share of the fair value adjustment recognized by an unlisted investee from the valuation of this issue convertible redeemable preferred shares arising from increased valuation of the investee, which was excluded from our non-IFRS profit.

On a non-IFRS basis, our share of profit of associates and joint venture for this quarter was RMB 6.4 billion, compared with share of profits of RMB 6.3 billion in the same period last year. Income tax expense increased by 3% year-on-year to RMB 11.7 billion. On non-IFRS financial figures, operating profit was RMB 75.6 billion, up 9% year-on-year. Operating profit excluding new AI products was RMB 86.1 billion, up 19% year-on-year. Net profit attributable to equity holders was RMB 68.4 billion, up 9% year-on-year. Diluted EPS was RMB 7.433, up 9% year-on-year. Moving on to gross margins for Q2. Overall gross margin was 58%, up 1 percentage point year-on-year. By segment, VAS gross margin increased by 4 percentage points year-on-year to 64%, driven by a favorable revenue mix shift towards high-margin internally developed games.

Marketing services gross margin was 57%, down 0.3 percentage points year-on-year, as higher revenue supported by enhancement to our AI-driven marketing capabilities largely offset higher costs, including depreciation and operating costs associated with expanding our AI infrastructure to improve ads and content recommendation. Fintech and business services gross margin was 52%, broadly stable year-on-year. On operating expenses, selling and marketing expenses were RMB 11.9 billion, up 26% year-on-year due to higher marketing spend to support our games business and to drive adoption of our AI-native products. R&D expenses rose by 35% year-on-year to RMB 27.2 billion, primarily reflecting higher R&D spend to support Hunyuan Model enhancements, Huaxin AI initiatives, and development of AI capabilities across our products and services. G&A excluding R&D expenses decreased by 1% year-on-year to RMB 11.5 billion.

At quarter end, we had approximately 116,000 employees, up 4% year-on-year and 1% quarter-on-quarter, mainly driven by headcount additions to our games and our technology platform, including AI-related headcount. Our second quarter non-IFRS operating margin was 36.9%, down 0.6 percentage point year-on-year. Non-IFRS operating margin excluding new AI products was 42%, up 2.8 percentage points year-on-year. To conclude, I will highlight some key cash flow and balance sheet metrics. Operating CapEx was RMB 51.8 billion, up 190% year-on-year or 66% quarter-on-quarter as we accelerated investments in AI infrastructure to support Hunyuan Model enhancements with PAPI and coPAPI inference needs, Huaxin AI initiatives, and development of AI capabilities across our products and services, as well as to meet growing external demand for our cloud services. Non-operating CapEx was RMB 1 billion.

Free cash flow was -RMB 13.8 billion, reflecting large AI infrastructure CapEx and AI-related prepayments, as well as seasonally lower games gross receipts. Excluding the prepayments for compute procurement, free cash flow would have been RMB 37.6 billion. Net cash position was RMB 58.2 billion, compared with RMB 146.9 billion as at March 31st, 2026, reflecting capital expenditure payments of RMB 59.3 billion and 2025 dividend payments of RMB 41.6 billion made during the quarter. Thank you.

Thank you, John. We shall now open the floor for questions. If you are dialing in by phone, please press five to raise a question and then press six to unmute yourself. If you are accessing from the Tencent Meeting or Blue Meeting application, please click the raise hand button at the bottom. We will take one main question, up to one follow-up question each time. The first question comes from Robin Zhu from Bernstein. Robin, your line is open.

Thank you for the question, Robin. Given the surge in demand and therefore rental pricing for compute, we could recover the depreciation almost immediately by renting the compute out to third parties as many neo cloud businesses are doing. We would then achieve a decent return in an immediate timeframe. However, in reality we are playing a different game or executing a larger strategy in that we are allocating a very substantial proportion of the new compute to building our own models to state-of-the-art status, and also to deploying, popularizing, and bringing our own AI applications to market leadership in China. Our belief is that by providing the superior intelligence that we can achieve through state-of-the-art models, through market-leading AI applications, that superior intelligence, we can then convert into superior economic returns over the longer term, for example, by selling tokens through the WorkBuddy application.

That's the path we've chosen.

Just to elaborate a little bit more on that. I think at the time being, you can actually look at the Tencent businesses and break it into two business. One is actually the existing core franchises, which actually generates solid growth and also with quite a bit of operating leverage. That's the high-quality growth track that we have been building and we continue with that. Then there's another new AI native business that we're actually building, and the new AI native business would involve, as James Mitchell said, our own model as well as new applications that we're building and also a corresponding compute infrastructure. The financials that you should look at there is the revenue and profit in relation to our core existing business, and we do separately disclose the investments in our AI native business as an operating line.

When you look at the CapEx, I would say the CapEx will be divided into two parts too, right? One part is really in relation to our existing business, which you can just like in the past, right, you can just say, oh, this is the free cash flow in which we generate operating cash flow, and there is a CapEx in relation to that. That part of the business still very cash flow generative. Then there is another set of CapEx which is related to the new AI native business, which is essentially a lump sum that we need to invest in order to get our compute for model training, in order to prepare for inference needs, and in order to also order some more for building our AI compute and AI Cloud business. That's essentially what it is.

The reason we are actually investing in all these compute is that we need that in order to essentially get the business kick-started. At the same time, when we make the investment, there's clear upside that we're seeing because our model is doing well, our new applications is doing well, and we also have a lot of demand for compute. Today, if we can actually allocate the compute toward leasing on the Tencent Cloud would actually generate a lot more revenue and would generate significant return from the CapEx. As a matter of fact, for the prepayment and for some of the compute orders that we had made just a couple of months ago, today, we can actually sell that at more than 30% profit compared to the price that we paid just a few months ago.

But we believe if we use this compute for building our own model and building our application and then allocating the compute for rental in that order, over time, we'll build a very significant AI native business, and that will be hugely profitable, as well as highly cached, as well as return generated for Tencent. So that's the way we think about the business right now.

Yeah. Well, I think it is indeed a new platform. It's a very flexible workspace for agentic AI. The core purpose is actually it will solve all the productivity needs of office workers and of all kinds of people who engage in their own businesses, right? One-person companies and the like. And below that, there will be a harness which actually helps the users to make use of the capability of different models to solve the agentic problems of the users. And over time, there will be many models serving the users through WorkBuddy. There will be many skills developed over time by all kinds of different developers. And the purpose is actually solving productivity problems, and then the platform itself would make use of all kinds of different tools and models available to do that. Then, of course, we are the orchestrator, right?

So we can actually choose the right model and choose the right skills to help users solve the problems. And we choose that to make sure that the work is done perfectly, but at the same time, it will be done also very economically, right? And Hunyuan will be one of the models that will be provided by WorkBuddy. But at the same time, if you can actually solve a lot of the user problems, right, and it's quite effective, then Hunyuan would actually be one of the main models within WorkBuddy, but it would not be the only model.

Thanks, Robin. We will take the next question from Kenneth Fong from UBS.

Well, I think all the risks that you said would not be relevant because we believe when AI enable the Weixin ecosystem to be more intelligent and it can actually help users to execute transactions, explore content, and manage their daily life with a lot of AI, then the Weixin ecosystem, which is already very rich and powerful, will become even more useful to the users. If you imagine the time when QQ was a communication and social tool in the PC stage, then when we get into the mobile age, Weixin appears and Weixin essentially, the ecosystem magnified QQ's value by more than 10x, because it is enabled in the mobile age and it becomes mobile first.

When we look at AI, we believe there is another huge opportunity for the Weixin ecosystem to be first enabled by AI, and over time, it will be AI first application and ecosystem. When that happens, users would have a lot of great experiences. Like right now, you actually have to type and you have to navigate through clicks. In the future, if you just tell Xiaowei one instruction, then Xiaowei can go off and help you execute a transaction and execute your instruction, and that would be an incredible experience for the users. It would also be an incredible empowerment for the entire ecosystem.

We believe if we can deliver that experience, if we can control the cost of that delivery, and if you look at the design of WeLM, it is actually for privacy, for cost efficiency, and for making sure that it can execute within the Weixin environment all the needs of the users, right? If we can do that, then we can really empower Weixin for the AI age under controllable cost. When that happens, Weixin's ecosystem would expand, and that would translate into a lot of value just based on the current monetization mechanisms within Weixin. I think that is the future that we are seeing. With the launch of the prototype, we grow more and more confident about that will be happening.

It is true that domestic token prices are low, but the domestic token manufacturing costs are also extremely low. I think much lower than widely perceived or externally estimated. The token business, it can be positive gross margin at these low token prices because the cost is low. If you look at the gross margin for the paying users of WorkBuddy, or you look at the gross margin for our models of service, then the gross margins today are already comparable to the gross margins for Tencent Cloud overall. Of course, WorkBuddy in aggregate has a lower gross margin because there is a proportion of free users whom we are subsidizing to drive market share and market growth. On the paying users, we are generating a pretty good gross margin right now. On your broader concern, it is true also that the China cloud market is price competitive.

But that environment has changed a great deal in the last several months as the input costs, particularly for memory, have gone up. We have been increasing the prices we charge to our customers. We increased prices across the board in May for Tencent Cloud, and beyond those headline price increases, we have also been more substantially reducing discounts. The overall pricing environment in cloud in China is not as difficult as it has been in the past. Thank you.

Thank you, Kenneth. We will take the next question from Ronald Keung from Goldman Sachs.

If you look at the Hunyuan 3, Hunyuan 3 is a very small model, even in today's terms, but it's actually very widely used. I think there are a number of characteristics of Hunyuan 3, which is, it actually has the capability of matching or beating much larger models. That's one. Two is it's actually focused on use cases rather than just benchmark beating. As a result, in real life, it has become much more useful than a lot of models of the same size or even bigger size. We believe that's a principle that we will be applying to Hunyuan 4 as well. When Hunyuan 4 comes out, it will be a bigger model, and it will be able to beat models of bigger size, and it would also be extremely useful and more useful than Hunyuan 3.

We believe that would actually take us into the next stage of being able to provide much better intelligence to a lot of our users. Bear in mind that we also have products which are co-designing with a model. When Hunyuan 4 comes around, the products that would be using Hunyuan 4 would actually become even more powerful and even more useful than what they are today, and that would actually provide a very significant lift for the products that it's powering. I think that's the path, and Hunyuan 4 is only another stop. Then we'll be upgrading to Hunyuan 5. As we continue to progress, we will be approaching SOTA, and at some point in time, we'll definitely be able to reach SOTA.

Once we are there, we would also have a lot of models of different sizes that will be able to solve different kinds of user problems at the different level of model and cost efficiency. At the same time, we have multiple models that can be used for co-design with our different products, and that would help us to make the products feature-rich and help to make the products powerful as well as the speed of execution will be fast. I think that's what we envision Hunyuan 4 and then subsequently Hunyuan 5 to be like.

In terms of your second question about allocating CapEx between different use cases, including Tencent Cloud. The immediate primary use case for the CapEx is for training bigger and better Hunyuan models in the coming months, as Martin discussed. But an important secondary use case is providing inference for the use of Hunyuan models as well as DeepSeek and other models behind WorkBuddy. The intention of that WorkBuddy initiative is primarily to drive adoption of what we think is a strategically important application with critical feedback loops back to our model and our broader ecosystem. But it also has the happy effect of generating revenue upfront. From an accounting perspective, the majority of the WorkBuddy spending by users is on subscriptions.

And so similar to games and some of our other businesses, there's a lengthy time lag between the cash receipts coming to us from the users and those cash receipts translating into reported revenue. But we are seeing a substantial ramp in the cash receipts today, and that will translate into reported revenue growth for Tencent Cloud as we move through the year. Then toward the end of the year and into next year, we will also have sufficient GPU ASIC capacity to step up in terms of Tencent Cloud renting out bare metal GPU or providing Model-as-a-Service. But within those opportunities, renting out GPU Model-as-a-Service and then token production for WorkBuddy, we think that it is token production for WorkBuddy that carries the most enduring economic value to us, and that's why we're prioritizing it today. Thank you.

Thank you. We will take the next question from Alicia Yap from Citi.

Yeah. So, on the agent-to-agent transaction loop, I think we are envisioning a future in which a lot of users would be executing their instructions and over time transactions via Xiaowei and via agents. In the past, if you think about the Weixin ecosystem is users interacting with content, interacting with mini programs themselves. In the future, if they can actually send a complex instruction to an agent, then an agent can actually start helping the user to execute transactions. A lot of the mini programs, a lot of the merchants would actually also have agents, which over time can interact with the agent of the users. Longer term, there will be even user. Each user has got an agent, and they can actually interact with each other to execute transactions. So I think that essentially is what's possible for the future.

We are building the architecture for making that possible on a step-by-step basis. In terms of on-device inference, I think it would, number one, be happening maybe step by step and it will be only over the long run that most of the inference will be happening on device. But I think at some point in time, it is not hard to imagine some kind of inference will be actually happening on device, and some inference will be happening in the cloud. Over time, as the on-device compute becomes more and more powerful and as the model becomes more and more efficient, you will have more inference happening on people's devices. I think that would be going back to the normal state of the computer industry.

If you think about the computer industry as well as the smartphone industry, most of the compute, which is CPU, actually happens on device. The cloud actually only is responsible for a small part of the compute. In this initial phase of AI infrastructure, most of the compute, because it has to be very powerful. The problem of getting enough compute on device, getting it cheap enough, and also getting it power-efficient enough has not happened yet. So that is why everything happens on the cloud. There will be a time in which more and more GPU capability will be put into everybody's phone and computer. When that happens, then more and more inference will be happening on the device, and there will be going back to the time when it is actually the software, it is actually the model that becomes much more important.

The return for running models and the return on running applications will be higher because the compute CapEx will be not just borne by the model company, but it will be borne across the ecosystem. I think that would definitely happen at some point in time and we are building and preparing for that.

On your marketing services question, our advertising revenue growth has ticked up and ticked down in the past, and it will continue to tick up and tick down in the future. I would not straight-line extrapolate anything. There is a number of reasons. One is that the sort of obverse of the comments I made about in-app advertising games being a drag on the international game segment revenue growth versus where it would otherwise have been, is that they did contribute about 2 percentage points to the advertising segment revenue growth this quarter. These in-app advertising games are a new product for Tencent, to some extent, a new product for the world. So we do not have the same degree of clarity on what the growth trajectory will be for the in-app advertising game contribution as for our conventional marketing services revenue.

In addition, the China consumer, and therefore advertising market, remains choppy and there are some economic or consumption headwinds that may have an impact on advertising trends. That said, we have been outperforming the overall China advertising market, and we are confident we will continue to do so by a substantial margin given the upside to us from deploying AI ad targeting. Given the fact that engagement, especially for our key Weixin Video Accounts inventory is increasing at a good rate, and given we are early in the evolution toward more closed-loop advertising that drives much higher ad pricing. Thank you.

Thank you. We will take the next question from Alex Liu from Bank of America.

I know that our capital allocation will be dynamic and reflective of the environment that we see. If we identify that there is superior returns from capital expenditure from increasing our compute and then using that compute to build the model, renting out that compute for work by the tokens, renting out that compute for Model-as-a-Service, then we will steer more cash toward the capital expenditures than we had in the past, and therefore, potentially less cash toward buybacks. It will be a dynamic situation.

Thank you, Alex.

The other thing I still want to stress is that when we look at the CapEx that we allocate for building the AI native business, it is more of a sort of a lump sum that we are going to be investing this year and next year. I think one should not assume that it will be sort of new every year, because the model-building part is more of a fixed cost that you actually have to get enough compute, but it will not be every year you have to invest more. In terms of the inference compute, yes, we need to have enough so that we can generate the tokens and we can build a compute business. We will only keep on investing if it generates a great return.

If not, then this is actually the amount that we are going to be investing, then the additional investment in CapEx would actually be tied to what kind of returns that we will be generating from that business. And so in order to pay for this lump sum, then it should not be just measured against our operating cash flow. It should be measured against how much cash we have on our balance sheet, how much investment portfolio we have on our balance sheet, and then the operating cash flow, and then a prudent level of debt capacity. So, all these would come into play in terms of paying for this initial part of compute investment.

Thanks, Martin, for your supplement on CapEx and return consideration. We will move on to the next question from Alex Yao from JP Morgan.

Okay. Well, let us be very clear. So Weixin's model and the strategy, the position is very different, right? Weixin's agent doesn't really require or depend on Hunyuan's new reach and SOTA status. Weixin's design, as we have said a few times, is actually centered around user privacy and focusing on solving all the necessary interactions an agent needs within the Weixin environment and also for cost efficiency. So that is its positioning.

Now, the SOTA status would actually allow us to be able to build a very significant token business. At the same time, it will also empower WorkBuddy to be able to complete even more challenging and more value-added services and operations for the users. And one of the things that we actually focus on WorkBuddy is actually not just saying, "Oh, it is an enterprise software," and it would just do all the things that people can do today.

It is actually constantly looking for value-added use cases so that we can really deliver additional value, and return for the users. In some cases, even help the users to make more money, right? If we can do that, there will be a lot of business models that we can unlock, right? I think that is what we can also achieve with a SOTA model. At the same time, once we reach SOTA, we can actually start creating a lot of the other models which can perform specific tasks for users at different levels of cost efficiency curve, still at the frontier curve, right? That would actually help us to cater to the many different needs of intelligence for users.

At each level, the cost will be different, but we will be able to generate a margin because we control the model, we control the inference cost, we control the compute. That is, I think, what we envision for our future generations of models to be able to achieve.

Well, at this stage, it is actually very dynamic. I think we would be investing prudently until the point that we actually see breakout opportunity, then we may step up the investment. I think that is essentially the way we look at it, right? It will be a certain percentage of our profit, but if clearly we see that if we step up the pedal, it would actually generate a lot of returns, then we may step the pedal. Overall, we do believe it is a business that has to run for a long time. So we will be investing for the long run. Over time, we believe the economics would actually start coming in and, at some point in time, it would actually be able to turn into profit.

If we just switch the model to just renting out compute, it would be actually not loss-making, it would be profitable. We always have that fallback option. That is something that is why we feel comfortable.

It is also the case that we dynamically reprioritize the spend within the budget or within the envelope. If you look at where the RMB 8 billion in the first quarter flowed in terms of user acquisition spending and so forth, and which products it supported, versus where the RMB 10.5 billion in the second quarter flowed, there was actually a very big change because we identified that WorkBuddy was breaking out. Therefore, we aggressively prioritized WorkBuddy while deprioritizing some of the other products in that new AI product portfolio.

Yeah, you can assume there is an envelope at the back of our mind.

Thank you. We will take the last question from Gary, from Morgan Stanley.

I think for Xiaowei, the envelope of investment on the cost side would be less than what we actually invested in Yuanbao on an ongoing basis in the past let us say year. I think that is the way we think about it. The cost would be quite manageable, but as the experience keep getting better and better, the return would actually start flowing in, and it would actually sort of outweigh that investment pretty quickly. In terms of guidance, I do not think we are in the business of actually providing that specific guidance right now. I think we have talked a lot about how we think about the business and how we think about there is an envelope of investment that we will be adhering to. It will be kind of disciplined in the same way as Tencent has always managed our business.

If we clearly see great opportunities to build a very significant and profitable business for the future, then we would actually make the investment. We also have the comfort that if we actually just move more compute into the compute right now, we can generate revenue, profit, and return very quickly. That actually sort of is a fallback position anytime that we choose to do that.

Thank you. We are now concluding the webinar. Thank you all for joining our results today. If you wish to check out our press release and other financial information, please visit the IR section of our company website at www.tencent.com. The replay of this webinar will also be available soon. Thank you, and see you next quarter.