腾讯2026Q2业绩直播（即时传译）
Key Takeaways (AI-Generated)
Financial Performance
- Total revenue was 205 billion RMB, up 11% year-on-year
- Gross profit was 118 billion RMB, up 13% year-on-year
- Non-IFRS operating profit was 76 billion RMB, up 9% year-on-year
- Free cash flow was -13.8 billion RMB due to large AI infrastructure investments
Business Highlights
- Hunyuan 3 achieved 6 times increase in average daily token usage compared to preview version
- Work Buddy ranked first among productivity AI services in China by monthly interactions
- Combined MAU of Weixin and QQ grew to 1.345 billion users
- TME completed acquisition of Ximalaya to strengthen audio content ecosystem
Financial Guidance
- Cloud revenue growth accelerated from high teens in Q1 to low 20s percentage in Q2
- Expecting to release larger parameter model Hunyuan 4 later this year
- AI-related investments described as 'lump sum' for this and next year
Opportunities
- International cloud business expanding rapidly, particularly in Indonesia with telecom partnerships
- Developing state-of-the-art AI models with Hunyuan 4 expected later this year
- TME's Ximalaya acquisition creating synergies with ecosystem IPs including China Literature
- AI integration across multiple game workflows including data agents for performance analysis
Full Transcript (AI-Generated)
Operator
Good day and a good evening. Thank you for standing by. Welcome to Tencent Holdings Limited 2024 Second Quarter Results Announcement. I am Wendy Fong 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 5 on your telephone to raise your hand. If you are accessing from the Tencent Meeting or Voov Meeting application, please click the Raise hand button at the bottom left. And please be advised that today's webinar is being recorded.
Before we start a 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 CEO, 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.
Pony Ma
Thank you, Wendy. Good evening. Thank you everyone for joining us. As we entering 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 provide users of wide use model with a strong cost performance metrics and serves as stepping stone toward the Hunyuan family of models attaining state-of-the-art capabilities in the future.
At the application level, our Work Buddy AI office productivity service and Code Buddy AI coding tool achieving breakout user growth and are clear leaders in the field in China today. At the infrastructure level, we substantially stepped up our 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 substantial 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 205 billion RMB, up 11% year on year. Gross profit was 118 billion, up 13% year on year. Non IFRS operating profit was 76 billion RMB, up 9% year on year. Excluding new AI products, non IFRS operating profit was 86 billion RMB, up 19% year on year. And non IFRS net profit attributable to equity holders was 68 billion RMB, up 9% year on year.
Turning to our key services for communications and social networks combined MAU of Weixin and QQ grew year on year and quarter on quarter to 1.345 billion. For digital content, TME's acquisition of Ximalaya strengthen our audio content and unlock 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 Work Buddy recently ranked first among productivity AI services in China based on monthly interactions. I will now hand over to Martin for the strategy review.
Martin Lau
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 at Tencent. Tencent's existing businesses are growing solidly due to intrinsic moats and AI enablement. As discussed earlier this year, our moats arise from factors including network effects, depth and value added along supply chain, IP, low churn rates, regulatory requirements and private data. In addition to these moats, we're further deploying AI to boost returns in areas including Weixin 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, Work Buddy and Code Buddy that lead the China market in terms of AI productivity usage and Yuanbao and Xiao Wei serving as gateways to drive broader consumer AI adoption. We see increasing potential to generate attractive financial returns from franchise products with differential advantages including Hunyuan, Work Buddy and Xiao Wei. Over time, 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. 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 stepped up across reasoning, agentic and long context tasks delivering 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 6 times increase in average daily tokens usage of Hunyuan 3 compared to the preview version across all channels during the period.
Additionally, Hunyuan 3 consistently ranks among the top three 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 Work Buddy. 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're leveraging Hunyuan for AI teammate creation and code review for games, including our flagship game Peacekeeper Elite. For Mini Programs, 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.
A 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're accelerating the improvement of our model. We're scaling more powerful reinforcement learning to substantially upgrade models after pre-training is done and we're in the process of upgrading multimodal capabilities. More importantly, we're 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're 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.
And moving on to the application front, our AI office productivity workspace Work Buddy and coding tool Code Buddy are achieving breakout success in terms of capability and user growth that are clear leading office productivity service in China. Based on monthly interactions, Work Buddy serves as a one stop shop workspace that orchestrates multiple agents to handle complex work from end to end. Users can remotely control Work Buddy via Weixin and WeCom as well as PC and access to over 70,000 skills from Tencent Cloud Skill Hub.
Besides the rapid user adoption of Work Buddy, 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 Vision Pay inside task flows to enable payouts for developers when their skills are called. Progress supports our view that there's substantial opportunities to be unlocked in the productivity market including coding and existing office work scenarios. We're 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, Work Buddy provides a new way for us to monetize our enterprise relationships. On the consumer front, we recently released a prototype of Xiao Wei, which delivers an embedded and context aware agentic AI experience within Weixin, leveraging Weixin's social graph knowledge graph, merchant reach and payment functionality.
Xiao Wei is powered by the Weixin customized model WLM built with a focus on user privacy, Weixin specific use cases and cost efficiency. Xiao Wei can help users navigate and derive insights from Weixin's diverse content universe in a personalized and efficient manner. Xiao Wei can also leverage Weixin's unique mini program ecosystem to help users discover products, make purchase decisions and place orders linking the groundwork for an agent to agent transaction loop. While the prototype can technically handle advanced agentic workflows, we currently configure Xiao Wei to require user intervention and multiple step confirmations as safety measures.
Xiao Wei 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 Xiao Wei dialogue memory and recommendation capabilities, expanding service and content integrations, scaling our AI infrastructure and upgrading our harness to support a significantly largest 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 overtime 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 Work Buddy, Code Buddy, Weixin and QQ browser. And with that, I pass on to James.
James Mitchell
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 business 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 98 billion renminbi, up 8% year on year, within which the social network revenue was up 1% to 32 billion RMB 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 audio books 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 Weathering Waves and Valorant PC was offset by revenue decreases from 2 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 and 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 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. And for Mini Shops consumers, we enhance 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 E sports 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 by influencer collaborations on the ground, city events and promotions in over 10,000 Internet cafes.
Among new games, Roco Kingdom World ranked 5th by Average DAU and 8th 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 7 new regions. On July the 9th, we released Runaway Evolution. This game is adapted from Rust, the 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 by 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 Classic and Nostalgia mode that reengages long time fans by recreating early era gameplay with pre reworked Champions Classic items and the original Summoners Rift map layout. Warframe's DAU grew year on year and gross receipts achieved a lifetime high in this quarter benefiting from New Wolf, famed Prime Warframe and a new storyline Jade Shadows Constellations.
Arrow's Puzzle Escape, a maze clearing game developed by Miniclip subsidiary Lessmore, was the most downloaded mobile game globally in the second quarter. Arrow's success demonstrates that the innovative capabilities of Miniclip's family of studios supported by mini clips publishing expertise can together pioneer and break out leaders in new genres of casual games. Arrows monetizes by in app advertising, so we report its revenue in our marketing services segment rather than the international game 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 four percentage points faster than the disclosed figures.
For marketing services, revenue grew 22% year on year to 44 billion renminbi 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 mini shop and mini drama advertisers. For example, AI Marketing Plus now enables mini shop 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 to improve ad conversion rates. 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. The Fintech and Business Services segment revenue was 60 billion renminbi, 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 Work Buddy and Code Buddy token usage. Our international cloud business expanded rapidly using skills developed with Code Buddy is enabling us to conduct customer cloud migrations over to Tencent cloud faster than we could in the past, for example, on behalf of the leading telecom company in Indonesia. And now I'll pass to John.
John Lo
Thank you, James, for the second quarter of 2024. Total revenue was 204.8 billion renminbi, up 11% year on year. Gross profit was 118.4 billion renminbi up 13% year on year. Operating profit was 67.3 billion renminbi, up 12% year on year. Interest income was 4.2 billion renminbi, up 2% year on year. Finance cost were 3 billion renminbi compared with 3.9 billion renminbi in the same period last year, reflecting favourable forex movements and lower interest expenses due to lower interest rates.
Our share of losses of associates and joint ventures was 10 billion renminbi for the second quarter of 2024. Primarily reflecting our share of the fair value adjustment recognized by an investee from the revaluation of this investee's convertible redeemable preferred shares arising from increased valuation of the investee which was excluded from our non IFRS profit. On non IFRS basis a share profit of associates and joint venture for this quarter was 6.4 billion renminbi compared with share profits of 6.3 billion renminbi in the same period last year. Income tax expense increased by 3% year on year to 11.7 billion renminbi.
On non IFRS financial figures. Operating profit was 75.6 billion RMB, up 9% year on year. Operating profit excluding new AI products was 86.1 billion RMB, up 19% year on year. Net profit attributable to equity holders was 68.4 billion RMB, up 9% year on year. Diluted EPS was 7.433 RMB up 9% year on year. Moving on to gross margins. For Q2, overall gross margin was 58 percent, up one percentage points 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 50.7%, 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, the improved ads and content recommendation. Fintech and business Services gross margin was 52%, broadly stable year on year.
On operating expenses, selling marketing expenses were 11.9 billion renminbi up 26% year on year due to higher marketing spend to support our games business and to drive adoption of our AI native product. R&D expenses rose by 35% year on year to 20.7 billion renminbi, primarily reflecting higher R&D spend to support Hunyuan model enhancements. AI initiatives and development of AI capabilities across our products and services. G&A excluding R&D expenses decreased by 1.2% year on year to 11.5 billion RMB.
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 games and our technology platform including AI related headcount. 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 51.8 billion renminbi up 190% year on year or 66% quarter to quarter as we accelerated investments in AI infrastructure to support Hunyuan enhancements Work Buddy and Code Buddy inference needs. Weixin 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 1 billion renminbi. Free cash flow was -13.8 billion renminbi reflecting large AI infrastructure cutbacks and AI related prepayments as well as seasonally lower games gross receipts.
Excluding the prepayments for compute procurement, free cash flow would have been 27.6 billion renminbi. Net cash position was 58.2 billion renminbi compared with 146.9 billion renminbi as at 31st of March 2024, reflecting capital expenditure payments of 59.3 billion renminbi and 2023 dividend payments of 41.6 billion renminbi during the quarter. Thank you.
Wendy Fong
Thank you, John. We shall now open the floor for questions. If you are dialing by phone, please press 5 to raise a question and then press 6 to unmute yourself. If you are accessing from the Tencent meeting or Voov meeting application, please click the raise hand button at the bottom. We will take one main question up to 1 follow up question each time. The first question comes from Robin Zhu from Bernstein. Robin, your line is open.
Robin Zhu
Thanks, Wendy. Thanks management for the opportunity to ask a question. I guess if we look at your latest quarters, CapEx 53 billion, it's a step up from the previous quarter annualizes over 200 billion. If we just multiply by 4, how should we think about the DNA costs, the results from this? To what extent do you think this will be paid off from incremental revenues that comes as a result of your investments in AI or is this essentially eating into earnings into the next few quarters? And would love to hear your thoughts on the time lags involved when it comes to the payback cycle, especially if we include some of the R&D costs incurred as well. Thank you.
James Mitchell
Thank you for the question, Robin. So given the surge in demand and therefore rental pricing for compute, we could recover the depreciation almost immediately by renting the computer out to third parties, as many cloud businesses are doing. And we would then achieve a decent return in a immediate time frame. However, in reality, we're playing a different game or executing a larger strategy in that we're 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.
And 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 their Work Buddy application. So that's the path we've chosen.
Martin Lau
So just to elaborate a little bit more on that right now. So 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 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. And then there's another new AI native business that we're actually building.
And the new AI native business would involve, as James said, our own model as well as new applications that we're building and also new corresponding compute infrastructure. And the financials you should look at there is the revenue and profit in relation to our core existing business and we do separate disclose the investments in our AI native business as an operating line. And then 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 an CapEx in relation to that and that part of the business still very, very cash flow generative. And 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.
So that's essentially what it is. And the reason we're actually investing in all these compute is that we need that in order to essentially get the business kick started. And 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 have made just a couple months ago, today we can actually sell it at more than 30% profit compared to the price that we paid just a few months ago. But we would 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 a highly cash as well as return generator for Tencent. So that's the way we think about the business right now.
Robin Zhu
Got it, Thank you. And if I may have a follow up just on Work Buddy love to hear your thoughts on every AI lab is essentially incentivized to develop their own harness app of some kind. And your thoughts on how the market breaks down between first and third party harness apps. How you would like to set up Work Buddy to compete against these first party harnesses. And whether Work Buddy in your minds is a piece of enterprise software that sits next to Tencent meeting docs. Or is this a new platform play that essentially becomes a marketplace for AI in the future? Thanks.
Martin Lau
Yeah, well, I think it is indeed a new platform that it's a very flexible workspace for agentic AI. The core purpose is actually something 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 to helps the users to make use of the capability of different models to solve the agentic problems of the users and overtime there will be many models serving the users through Work Buddy there will be many skills developed overtime 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. And 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 Work Buddy. But at the same time, if you can actually solve a lot of the user problems, right? Then, and it's quite effective then. Hunyuan would actually be one of the main models within that Work Buddy, but you will not be the only model.
Robin Zhu
Thank you very much.
Wendy Fong
Thanks, Robin. We will take the next question from Kenneth Fong from UBS.
Kenneth Fong
Hi, good evening management and thanks for taking my question. I have a question regarding the Xiao Wei development. So could management share any preliminary feedback or challenges from the testing phase of Xiao Wei? And from a commercial standpoint, how should we evaluate the net monetization potential? Specifically, as agents simplify the transaction path, we worry that it may just be shifting the existing volume away from just traditional user self perform transaction in mini programme over to the agents which carry a higher computing cost without a meaningfully higher net new GMV. And furthermore, would the shorten user transaction journey in Xiao Wei risk also lowering the high margin ad impression inventory as well? Thank you very much.
Martin Lau
Well, I think all the risk that you set 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, right? Then the AI the Weixin ecosystem, which is already very rich and powerful, will become even more useful to the users, right? So if you imagine, right, the time when QQ was a communication and social tool in the PC stage. And then when we get into the mobile age, then Weixin appears and Weixin essentially magnified QQ's value by more than 10X, right? Because it's enabled in the mobile age and it becomes mobile first.
So when we look at AI, we believe there's another huge opportunity for the Weixin ecosystem to be first enabled by AI. And over time, it will be AI first application and ecosystem. And 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 Xiao Wei one instruction. And that way it can go off and help you execute a transaction and execute the your instruction. And that would be an incredible experience for the users. It would also be an incredible empowerment for the entire ecosystem.
So 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 WLM 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 now. And if we can do that, then we can really empower Weixin for the AI era under controllable cost. And when that happens, Weixin's ecosystem would expand and that would translate into a lot of value just based on the current monetization mechanisms with innovation. And I think that's the future that we're seeing and with the launch of the prototype with, we grow more and more confident. About that will be happening.
Kenneth Fong
Thank you. Martin. Another follow up question on the AI cloud with domestic API token prices very rapid commoditization so and also China cloud market remains structurally price sensitive. So how do we think about the margin profile of Tencent AI Cloud currently compared to say past offering? And as this gradually scaled up as AI adoption scale, so how should we also think about the margin progression going forward? Thank you.
James Mitchell
Well, it is true that domestic token prices are low, but the domestic token manufacturing costs are also extremely low and I think much lower than widely perceived or externally estimated. So the token business it can be positive gross margin at these low token prices because the cost is low. And if you look at the gross margin for the paying users of Work Buddy 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 Work Buddy and aggregate has a lower gross margin because there's a proportion of free users whom we're subsidizing to drive market share and market growth. But on the paying users we're generating a pretty good gross margin right now. And on your broader concern, it is true also that the China cloud market is price competitive, but that environment has changed great deal in the last several months as the input costs, particularly for memory have gone up. So we've been increasing the prices we charge to our customers. We increase prices across the board in May for Tencent cloud. And beyond those headline price increases, we've also been more substantially reducing discounts. So the overall pricing environment in cloud in China is not as difficult as it's been in the past.
Kenneth Fong
Thank you.
Wendy Fong
Thank you, Kenneth. We will take the next question from Robert Lin from Goldman Sachs.
Robert Lin
Thanks Pony, Martin, James, John and Wendy. So 2 questions. I think first on the Hunyuan model, just want to hear after the progress of Hunyuan 3 which is on cost efficiency, I would say in very good in agents, then where will Hunyuan 4 differentiate itself? As we look into a, let's say 3 trillion parameter size class is looking more crowded in the next few months. So which category are we looking or which segment or differentiation are we thinking for Hunyuan 4? And then a second question is on the CapEx and the focus on our AI initiatives. But looking at some of the US peers where there has been shifting strategy on the hyperscale of business, does only hear what stage or timeline that we think we may focus more on cloud as a potential high ROI business that is worth prioritizing more CapEx on and some similarities and differences that we see for Tencent cloud versus what help US peers have shifted that focus more from applications to cloud for some of our peers. So 2 questions. Thank you.
Martin Lau
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, right? So I think there are a number of characteristics of Hunyuan 3, which is it actually has the capability of beating the matching or beating much larger models. That's one. And two is it's actually focused on use cases rather than just benchmark beating. And as a result, Hunyuan it 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 would will be applying to Hunyuan 4 as well. So 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 and 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. And bear in mind that we also have products which are co-designing with the model.
So 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 so I think that's the path and Hunyuan 4 is another stop right now and then we'll be upgrading to Hunyuan 5. So as we continue to progress there, we will be approaching SOTA and at some point in time would definitely be able to reach SOTA and 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 curve still at the frontier curve, right, and that would actually help us to make the products feature rich and help to make the models, the products powerful as well as the speed of execution will be fast. So I think that's what we envision Hunyuan 4 and then subsequently to be like.
James Mitchell
And in terms of your second question about allocating CapEx between different use cases including Tencent cloud. So 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 Work Buddy. And so the intention of that Work Buddy 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.
Now from an accounting perspective, the majority of the Work Buddy 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.
And then toward the end of the year and into next year, we'll 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 Work Buddy. We think that it is a token production for Work Buddy that carries the most enduring economic value to us and that's why we're prioritizing it today.
Robert Lin
Thank you.
Wendy Fong
Thanks, Robert and James. Thank you. We will take the next question from Alicia Yap from Citigroup.
Alicia Yap
Hi, good evening management. Thanks for taking my questions on. Congrats on the solid results. First questions is on the Xiao Wei. Could management elaborate on your comment on the agent to agent transaction loop? Will this concept lead to the long term visions for a fully autonomous agents ecosystem within the Weixin And then management also highlight that you will explore the on device inference for Xiao Wei so. What are the challenges and the benefits of this approach and then is this on device inference approach and other reasons why the proprietary WLM models is more suitable in powering the Xiao Wei rather than the external model. And then a quick follow up is on your marketing service revenue. So these quarters the growth rate accelerated to 22%. Should we expect this ongoing upgrade of the attack and also this automated campaign to further support this growth momentum? So any further future benefit that you would anticipate from the deeper integrations into your Hunyuan 3 model? Thank you.
Martin Lau
Yeah, so on the agent to agent transaction loop like, I think we are envisioning of yeah, a future in which a lot of users would be executing their instructions and overtime transactions via Xiao Wei and via agents, right? And in the past if you think about the Weixin ecosystem is users interacting with content interacting with mini programs themselves and 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 and a lot of the mini programs, a lot of the merchants, what actually said it also have agents which over time can interact with the agent of the users and 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. And we're building the architecture for making that possible. On a step by step basis and in terms of on device inference, I think it would #1 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, right. But I think at some point of time, it's not hard to imagine some kind of inference will be actually happening on device and some inference will be happening in the cloud and overtime, as the on device compute becomes more and more powerful and as the model becomes more and more efficient and you'll have more inference happening on people's devices.
And I think that would be going back to the normal state of the computer industry, right? If you think about the computer industry as well as the smartphone industry, right? Most of the compute, right? Which is CPU actually happens on device. And the cloud actually only is responsible for a small part of the compute. But in this initial phase of AI infrastructure, most of the compute because it has to be very powerful, right? And the problem of getting enough compute on device, getting it cheap enough and also getting it power efficient enough, it has not happened yet.
So that's why everything happens on the cloud. But there will be a time in which more and more GPU capability will be put into everybody's phone and computer and 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's actually the software, it's actually the model that becomes much more important. And 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'll be born across the ecosystem. And I think that would definitely happen at some point of time. We're building and preparing for that.
James Mitchell
And 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 wouldn't sort of straight line extrapolate anything. And yeah, there's 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 two percentage points to the advertising segment revenue growth this quarter.
And these in app advertising games are sort of new product for for Tencent to some extent a new product for the world. And so we don't have the same degree of clarity on what the growth trajectory will be for the in app advertising game contribution As for our sort of conventional marketing services revenue. In addition, the China consumer and therefore advertising market remains in a 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're confident we'll continue to do so by a substantial margin in a given the upside to us from deploying AI ad targeting, given the fact that engagements especially for our key video accounts inventory is increasing at a good rate and given we're early in the evolution toward more closed loop advertising that drives much higher ad pricing.
Alicia Yap
Thank you.
Wendy Fong
We will take the next question from Alex Liu from Bank of America.
Alex Liu
Thank you for taking my questions. I have only one questions. So we noted that Tencent has recently increased the buy back. The buy back activity started from May, while at the same time the CapEx has been accelerated meaningfully as well. So we understand it's still in the relatively early stage in AI investment cycle. But with that in mind, how should investor think about Tencent's capital allocation priority into the next 12 to 24 months? Thank you.
James Mitchell
I think that or I know that our capital allocation will be dynamic and reflective of the environment that we see. And so if we identify that there's superior returns from the capital expenditure from increasing our compute. And then using that compute to build the model, renting out that compute for for Work Buddy tokens renting out that compute for model as a service, then we'll steer more cash toward the capital expenditures than than we had in the past and therefore potentially less cash toward buybacks. But it will be a dynamic situation.
Martin Lau
And the other thing you 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 lump sum that we're going to be investing this year and next year. And then I think one should not assume that it will be sort of new every year because sort of the model building part is more of a fixed cost that you actually have to get enough compute. But it would not be sort of, oh, every year you have to invest more and 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, right?
And but we will only keep on investing if it generates a great return, right? If not, then this is actually the amount that we're going to be investing. And then the additional investment in CapEx would actually be tied to what kind of returns that 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 new 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.
Alex Liu
Thanks, Martin for your supplement on CapEx and return consideration.
Wendy Fong
We will move on to the next question from XL from JP Morgan.
XL
Thank you management for taking my question. My first question is on Hunyuan flagship strategy. The Hunyuan 3 competes on cost efficiency rather than raw capability. If you succeeded in building a truly frontier level model, which should be larger and more expensive to run, what specific business value would that create that the current Hunyuan 3 cannot deliver today, whether a more capable Weixin agent or stronger advertising performance or enterprise customers. And what does that opportunity justify a major increase in training spend over the next 12 months.
Martin Lau
OK, well now let's be very clear. So Weixin model and the strategy that the positioning is a very right. Weixin agent doesn't really require or depend on on the Hunyuan's SOTA regions, so that, so the 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 and agentic needs within the Weixin environment and also for cost efficiency. So that's it's positioning.
Now, the SOTA status would actually allow us to be able to build a very significant token business and at the same time, it will also empower Work Buddy 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 Work Buddy is actually not just saying, oh, it's an enterprise software and it would just do all the things that people can do today.
It's actually constantly looking for value added use cases so that. We can really deliver additional value and return for the users and in some cases like even help the users to make more money, right, and if we can do that then and there will be a lot of business models that we can unlock, right? So I think that's what we can also achieve with a SOTA model. And 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, and that would actually help us to cater to the many different needs of intelligence.
For users and 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 and that's I think what we envisioned for our future generations and models to be able to achieve.
XL
Thank you, Martin. My follow up question is AI products economics. The new AI product drag rose from roughly 8.8 billion RMB in first quarter to about 10.5 billion this quarter. Can you walk us through how you manage that investment? Do you run these products to a spending envelope or to a return thresholds or to a strategic position? And what signals whether usage, revenue, traction or unique economics would cause you to step investment up further or begin shifting a product from investment mode to harvesting mode?
Martin Lau
Well, at this stage is actually very dynamic and I think we would be investing prudently until the point that we actually see breakout opportunity then we may step up the investment. So I think that is essentially the way we look at that, right? So it will be a certain percentage of our profit, but if clearly we see that if it's the pedal, it would actually generate. A lot of returns then we may step the pedal but the over we do believe, right, it is a business that has to run for a long time so.
We'll be investing for the long run and 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. And I think more importantly is that today, if we just switch the model to just renting out compute, it will be actually not loss making, it will be a profitable. So I think we always have that fall back option, right? So I think that's something that that's why we feel comfortable.
James Mitchell
It's also the case that we dynamically reprioritize the spend within the budget or within the envelope. And so if you look at where the 8 billion in the first quarter flowed in terms of user acquisition spending and so forth and which products it's supported versus where the 10.5 billion in the second quarter flowed, There's actually a very big change because we identified that Work Buddy was breaking out. And therefore we aggressively prioritized Work Buddy while de prioritizing some of the other products in in that new AI product portfolio.
Martin Lau
Yeah. And you can't assume there is an envelope at the back of our mind.
XL
Thank you.
Wendy Fong
We will take the last question from Gary from Morgan Stanley.
Gary
Hi, thank you for the opportunity to ask question. I have one follow up on the AI investment. I understand the priority on model training Work Buddy then maybe cloud where does Xiao Wei fits in terms of inferencing capacity require to support Xiao Wei when it's launched? So that's my first question. My second question is on management field about timing and visibility of monetization and ROI for these new AI initiatives. And particularly, should we expect close to net attribute earnings growth in the near term? And then when should we expect the operating profit including AI investment to grow even faster than excluding the AI investment? Thank you.
Martin Lau
I think for Xiao Wei Randy, the envelope of investment on the cost side would be less than what we actually invested in Yuanbao on a on an ongoing basis in the past, that's a year. So I think that is the way we think about it. So that 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 new weight that investment pretty quickly and in terms of guidance, I don't think so we are in the business of actually providing sort of that specific guidance right now I think we have.
Talk 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 some kind of disciplined in the same way as Tencent has always managed our business. But then if we clearly see great opportunities to build a very significant and profitable business for the future, then we would actually make the investment and 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. So that actually is a fall back position anytime that we choose to do that.
Gary
Thank you.
Wendy Fong
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.
Details at TENCENT IR
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