Operator: Thank you for standing by, and welcome to Kanzhun Limited Second Quarter 2026 Financial Results Conference Call. [Operator Instructions] Today's conference is being recorded. At this time, I'd like to turn the conference over to Ms. [ Laura Zhan ], Senior Manager of Investor Relations. Please go ahead, ma'am.

Unknown Executive: Thank you, operator. Good evening, and good morning, everyone. Welcome to our second quarter 2023 earnings conference call. Joining me today are our Founder, Chairman and CEO, Ms. Jonathan Peng Zhao; and our Deputy CFO, Ms. Wenbei Wang. Before we start, we would like to remind you that today's discussion may contain forward-looking statements, which are based on management's current expectations and observations that involve known and unknown risks, uncertainties and other factors not under the company's control, which may cause actual results, performance or achievements of the company to be materially different. The company cautions you not to place undue reliance on forward-looking statements and did not undertake any obligation to update the forward-looking information, except as required by law. During today's call, management will also discuss certain non-GAAP financial measures for comparison purpose-only. For a definition of non-GAAP financial measures and reconciliation of GAAP to non-GAAP financial measures, please see earnings release issued earlier today. In addition, a podcast replay of this conference call will be available on our website at ir.kanzhun.com. With that, I will now turn the call to Jonathan, our Founder, Chairman and CEO.

Peng Zhao: [Foreign Language] [Interpreted] Hello, everyone. Welcome to the company's second quarter 2026 earnings call. On behalf of all our employees, management and Board of Directors, I would like to express our sincere gratitude to our users and investors. Today, I will mainly focus on three areas: second quarter results, changes in company's growth strategy, and shareholder returns. In second quarter, the company generated revenue of RMB 1.44 billion, up 14% year-on-year. In terms of profitability, adjusted income from operations, excluding share-based compensation expenses, was RMB 1.05 billion, up 19% year-on-year. Our adjusted operating margin was 43.8%, 1.9 percentage points year-on-year. As of June 30th, the total paying enterprise customers in the past 12 months reached 7.2 million, up 11% year-on-year. Several key operating metrics reached record highs in this quarter. Average monthly active users, or MAU, are at exceeding 17 million in the second quarter. The average number of matches per job seekers increased both year-on-year and quarter-on-quarter. Once again, data has also proved that user outcomes also improved. Next, I would like to expand how the company's growth strategy differs from higher tier and lower tier cities. The second quarter of this year marks the fifth anniversary of the company's IPO. Investors who are familiar with us will remember that throughout the past 5 years, we have constantly maintained that the core driver of the company's growth is user growth. This is determined by the size of the market. China has nearly 500 million people in its urban workforce and more than 40 million active businesses. Based on this, BOSS Zhipin has cumulatively served approximately 300 million users and approximately 22 million employers. Even from where we stand today, there is still considerable room to grow. Second, this is determined by our model. BOSS Zhipin pioneered the mobile recommendation and direct chat model, and as a whole, this model substantially lowered the cost of communication between recruiters and job seekers. This low-cost model enables tens of millions of companies to shift from traditional recruitment to mobile Internet recruitment, thereby digitalizing and mobilizing recruitment on a large scale. For the vast majority of our enterprise users, the first time they used our services was also the first time they used online recruitment. Third, this is determined by our strength and user needs. Double-sided network effects give the company strong vitality. The larger the user base on both sides, the greater the variety of users, the more users express themselves, and the more users interact, the better we can serve them. The process of driving user growth is also the process of continuously producing digital oil for the recommendation engine. Over the past several years, we have consistently seen that as monthly active users on both sides have increased, user outcomes created have also improved. With the engine supported by AI, we saw not only that AI improves the engine's efficiency, but also that the engine helps AI quickly establish its data flywheel. Over the next five years, we will adopt different growth strategies for Tier 3, Tier 4, and Tier 5 cities and for Tier 1 and Tier 2 cities. In Tier 3, Tier 4, and Tier 5 cities, the core driver of growth will continue to be user growth, and our most important objective will remain user penetration. In Tier 1 and Tier 2 cities, while continuing to grow our user base, we will add reasonable price increases as a growth factor. With regard to the pricing of our services, let me first take a look at the actual situation in the second quarter. Revenue in the second quarter was RMB 2.4 billion. That is a 10-billion number. It looks good, but here in Beijing, for many jobs, the price of a one-month job post is just the price of two cups of coffee. The value of many, many mutual matches that happen in every month combined is only enough to buy one bottle of mineral water. What do we mean by mutual match? For those who are less familiar with us, let me explain again. A mutual match on our platform is equivalent to a job seeker submitting an application to a specific recruiter on another recruitment platform, and that recruiter also confirming the acceptance of the application. That is what we call a mutual match. It is a sad combination. In Beijing, in Shanghai, in Shenzhen, in Guangzhou, in Hangzhou, in Chengdu, in many cities, one such match is worth only one bottle of mineral water at 7-Eleven stores. To make this easier to understand, let's start data from the leading recruitment platform in a mature market. According to publicly available information, one click on that platform costs approximately $0.25 to $1, while generating one application for a basic role costs approximately $5 to $10. I do not have data on how many applications for such a basic role result in one match. If I assume, based on a high efficiency case, that a recruiter will accept one out of every five applications, that would translate into $25 to $50 per match. This, my friends, gives you an intuitive sense of two things. First, compared with developed countries, as importance placed on talent increases, the human resources services industry grows. There is considerable room for Chinese companies to increase what they pay for such services. Of course, this will take time. Time is a powerful tool. One example is that today, the salary of a very good software engineer in China is roughly at the same salary as in Silicon Valley. Second, compared with one aspect of the enterprise expense in Beijing, I have seen that many enterprises have achieved a unit price, which is about 1/10 of a mineral water. The total monthly employment cost of junior human resources personnel could buy 1,000 unit of matches. Therefore, we can see that compared with Beijing, the service price in our field also has some potential to be improved. Put differently, if we do not reform this, the human resources service industry is destined not to be valued by companies. It is destined not to receive high quality resources, and it might shrink. Therefore, at the beginning of the second 5 years, the company's growth strategy has changed, which is based on the first-tier market and some second-tier cities to improve the user experience while gradually increase the amount of customer payment in mature markets, including a reasonable increase in payment rates. This process has been sustained for a while. The result growth that we have seen, part of the reason is that because of that. In fact, this also that in the last quarter, they would predict that growth and profit growth in second quarter will be better. That is part of the reason. This is the right time and right place to change the growth model. Everyone application has played a critical role, which is mainly reflected in three elements. First, the large scale application of AI increased the platform efficiency. Secondly, some big customers in the white collar or blue collar factories agree very much that they believe that the AI powered interview, AI assisted resume screening, and other competitive solutions will also help to them. The combination of with our platform business is actually consistent with the pursuit of job seekers on the platform and within the recruiter. That is to achieve not the goal to do the recruitment, but to do a successful hire. This brings us to our closed-loop business. The closer our services get to the actual hiring stage, and the closer we get to charging based on the successful hire, the more this model approaches a closed loop. The company will continue to invest in exploring this area. One point worth mentioning is that the revenue we received from our AI-enabled closed-loop business grew rapidly quarter over quarter in Q2. Let me discuss shareholder returns. The board today passed a resolution approving the distribution of annual dividends of $230 million. Since the beginning of this year, the company has repurchased approximately $300 million worth of shares, representing more than 4.7% of its total share capital. In 2026, the company's total shareholder returns through share repurchase and dividends amounted to $530 million, exceeding 100% of last year's adjusted net income, and also exceeding the 50% we previously committed to. We share the benefits of the company's growth with shareholders. That concludes my remarks. Next, our Deputy CFO Wenbei Wang will walk you through the financials in detail.

Wenbei Wang: Thanks, Jonathan. Hello, everyone. Now let me walk through the details of financial results of the second quarter of 2026. We continue to deliver a high-quality set of financial results this quarter, marked by solid revenue growth and further improved profitability. Our revenue achieved accelerated trend, reaching RMB 2.4 billion, representing 14% year-on-year growth. Recruitment demand in the second quarter remained broadly stable. We drove revenue and profit growth through user base expansion and improved monetization from higher-value services. The number of paid enterprise customers increased by 11% year-on-year to 7.2 million over the trailing 12 months ended June 30, 2026. Importantly, the paying ratio among active enterprise users improved for the fourth consecutive quarter, reflecting our sustained progress in monetization. ARPPU for the quarter increased 7% year-on-year, driven by more efficient and valuable services, including an expanded suite of AI-powered features, which encouraged higher customer spending. Revenue growth was broadly balanced across different account sizes this quarter with both key accounts and small-sized accounts showing healthy momentum. Moving to the cost side. Our total operating costs and expenses increased by 6% year-on-year to RMB 1.5 billion this quarter. Total share-based compensation expenses dropped by 19% year-on-year to RMB 186 million. As a percentage of revenue, share-based compensation expenses continued to continue this downward trend to 7.8% this quarter, down 3.1 percentage points year-on-year. We expect share-based compensation expenses as a percentage of revenue to remain at a high single-digit level for the full year of 2026. In the second quarter, we sponsored the FIFA World Cup and increased our investment in AI-related cloud services. Meanwhile, our headcount grew sequentially, driven by stable growth in recruitment demand. Despite these investments, our profitability continued to improve. Excluding share-based compensation expenses, our adjusted operating margin expanded by 1.9 percentage points year-on-year to a record high of 43.8%. This was primarily driven by our strong operating leverages, disciplined execution and ongoing efforts to enhance operating efficiencies through AI applications. Looking into each segment, cost of revenues increased by 2% year-on-year to RMB 312 million this quarter. This increase was mainly due to higher server and bandwidth costs, partially offset by lower app store commission fees and improved operating efficiency as we widely leverage AI in our daily operations, verification and customer services. As a result, our gross margin went up by 1.6 percentage points year-on-year to 87%. Sales and marketing expenses increased by 38% year-on-year to RMB 581 million this quarter, mainly due to the marketing campaign of 2026 FIFA World Cup as well as an increase in sales employee-related expenses related to higher cash revenues. Our R&D expenses were RMB 431 million this quarter, up 3% year-on-year. Excluding share-based compensation expenses, our adjusted R&D expenses increased by 7% year-on-year to RMB 361 million, mainly due to higher cloud service fees and server depreciation expenses related to AI infrastructure investment. Our G&A expenses decreased by 30% year-on-year to RMB 219 million this quarter, mainly due to lower employee-related expenses. Interest and investment income reached RMB 1.6 billion this quarter compared to RMB 157 million for the same quarter last year. This increase was mainly driven by investment income of around RMB 1.5 billion arising from the fair value changes of one of our invested company, which went public in January 2026. Income tax expenses were RMB 550 million this quarter compared to RMB 97 million in the same quarter last year. This increase was also mainly due to the RMB 367 million tax impact from the aforementioned investment income, withholding tax of RMB 20 million as well as RMB 10 million provision for the top-up tax under the OECD Pillar 2 rules and higher income from operations. Our net income reached RMB 1.9 billion this quarter, up 173% year-on-year. Excluding share-based compensation and net gains from the aforementioned investments, our adjusted net income increased by 9% to RMB 1.03 billion. Net cash provided by operating activities was RMB 945 million this quarter, down 10% year-on-year. This decrease was mainly due to higher advertising and marketing expenditures and tax payment as well as lower interest and investment income received, partially offset by increased cash collection from customers. As of June 30, 2026, our cash position, including cash, cash equivalents, short-term time deposits and short-term investments, excluding investment in securities stood at RMB 18.8 billion. Our strong cash position and cash-generating capability enable us to sustainably deliver our shareholder return commitments. As Jonathan just mentioned, the Board declared an annual cash dividend of approximately USD 230 million, combined with over USD 300 million in share repurchase, we have completed year-to-date, which represents roughly 4.6% of our total outstanding shares. Our total shareholder return so far this year exceeds USD 530 million, representing an over 100% shareholder return ratio compared to the adjusted net income last year. Cumulatively, we have now bought back over 10% of our total shares outstanding. And now for our business outlook. For the third quarter of 2026, we expect our total revenues to be between RMB 2.41 billion and RMB 2.5 billion, a year-on-year increase of 11.4% to 15.6%. That concludes our prepared remarks. Now we would like to take questions. Operator, please go ahead.

Operator: [Operator Instructions] We will now proceed to our first question, and the question comes from the line of Timothy Zhao of Goldman Sachs.

Timothy Zhao: [Foreign Language] [Interpreted] My first question is regarding your AI monetization. Could management share more color on the latest progress of your AI products? For the closed-loop services that you just mentioned, could you share any color on the overall revenue scale? And how do you think about the overall AI impact on the matching efficiency? And if there's any quantity metric that you can share, that would be great. Secondly is on your Nambe large language model. I recently -- I noticed that you recently launched Nbeigo4.23D model. Just wondering what is the improvement versus the last generation? And how do you compare the latest model versus the top large model in the market? And what is your different competitive position?

Peng Zhao: [Foreign Language] [Interpreted] Okay. Thank you for your question. And about AI talent sourcing that we are trying to use, there is something slightly different. It is well known that when some of our customers started to use our recommendation system and we begin to know them, he used some of the search function to help himself. But the search function, as we all know, the problem is the query is relatively short. And the large language model just gave us a possibility that you can use a very long query and also you can use multiple rounds of conversations to make it look like long. But actually, the system is just coming back to understanding what you really want. So that's the fundamental capability that large language model has. So a very long text and multiple rounds of communication that which can come out to understanding of our customer demand and better to serve some clients who have high requirement, who have the requirement for high professionalism and which is better traditional recommendation search model cannot serve. So that just bring our search to our next level. So based on that that fundamental we just discussed about, it's quite easy to understand the new value AI sourcing has brought to us, which we have already served cumulatively more than 300 million users. But our monthly active users last month is just 17 million. As we further penetrate to new users, the 17 million monthly active users versus 300 million total users might turn into like 100 million versus 400 million. This creates ability that we can expand our service to a lot of new users that not within the monthly active users scope. interesting part is who has the most needs to contact this kind of silent customers. This kind of customers or this kind of job seekers actually is more senior, more professional, and more likely to be liked by the headhunters. This is more and more getting close to our pursuit of closed loop service. My understanding of the closed loop service is more like just one stage to another stage, the process interlocking. We are using AI to help our customers sourcing candidates. We are using AI function to help them to screen resumes. Our AI interview functions are now working on more than 10,000 interviews every day. Stage by stage, we are getting more and more close to our onboarding, and AI is just helping us to accelerating this process and achieving our goal. For Nanbeige4.2-3B, it is quite a coincidence that early today, a very well-known testing institution. A very well-known testing institution, Artificial Analysis, combined with Liquid AI to do a drawing testing on small sized model on both iPhone 17 Pro and Samsung Galaxy Nanbeige4.2-3B has achieved number one in five areas, including following the true transforming orders, scientific illusion, scientific interference and mathematics theoretical errors. Nanbeige has achieved number one in all those five areas. Actually, our previous model 4.1-3B also achieved quite nice results. This model has been quite good in inference writing, fundamental advanced truth reasoning. On the contrary, 4.2-3B, this small size model, is better to handle more complicated agent truth and functions, including coding intelligence entity and maybe office working intelligence entity, et cetera. We believe on the road to pursue AGI, there is one way, mega size models, super consuming of electronic powers, investing a lot of money. That a lot of big companies are doing. There is another way that maybe a smaller size model can help solving some specific problems and also create its own value. For example, the application in smartphones, on mobile vehicles, on intelligent robots, et cetera. In those areas, those smaller size model, we are in the leading position and have proven our value. That is our answers to the first two questions. Operator, let us proceed to the next one.

Operator: And our next question comes from the line of Eddy Wang of Morgan Stanley.

Eddy Wang: [Foreign Language] [Interpreted] My first question is related to the macro impact. What is your view on the macro impact on our company, especially for the second half of this year? As most internet company that has reported second quarter results have mentioned that the macro overhangs and the weak consumption. To what extent will BOSS be affected under such a macro backdrop? How much of this macro-driven pressure can be offset through our operation improvement? The second question is related to the AI development, AI service and the product we have launched and probably will launch. Do you expect they will have different cost structure and will this affect our overall margin? In addition, do we have plan to materially ramp up the CapEx as we have seen with some of the other Internet companies?

Peng Zhao: So thank you for your question regarding the macro situation. I actually respect your professional observation and I will not talk too much about it. I have been starting our business for more than 12 years and we have experienced a lot, whether you have experience, or not experience, we have all gone through that. We have always maintained to be a very stable and maybe trustworthy business, and we will continue to maintain this very stable operation. And have a big opportunity here with our potential market size. We have served over 300 million customers and more than 22 million enterprises. It is well known that the average life cycle, according to the People's Bank of China's enterprise, is less than three years. Within all those 22 million companies we have served, a lot of them are not active anymore. They have turned into new elements and beginning new companies. For ease of observation, even we consider those 40 million enterprises as a fixed situation, we have more than double of our market to grow. On top of a lot of new companies are emerging every year. Our actual market size is even bigger. The second opportunity is in the paying ratio. Our actual annual served number of enterprises is more than 10 million, and over 50% of them are using our service for free. From that perspective, this is our second driver or second growth opportunities.

Wenbei Wang: I will use one first-tier city as an example. We just reported that we intend to increase monetization for certain first-tier cities. In this particular city, including both paid and free service, the average cost per mutual match our customers can get for this city, for example, is like X RMB. If we turn those free customers into our lowest level of paying customers, then those costs will grow by at least 15%. rest assured both our investors, clients or public, that actually this is a very minimal changes. I just explained that for a lot of our customers, the average cost to achieve a mutual matching is only the price of one mineral water at 7-Eleven. Either one bottle of mineral water or 1.15 bottle of mineral water is a very minimal cost to every enterprises. Eddy, just as we go through all these years, I am confident we are not only surviving, we should and we will be better and better. About the second question, thank you Eddy for asking me that. Actually all those large companies who have invest a lot of CapEx for like arm race or things like that, I think they have their ambitions, they have their beliefs, but most importantly, they have that financial capabilities. For a company like us, we chose the path of following all the tail light strategy, and we prioritize AI applications in the smaller type companies. That is our approach, our strategy to facing this AI maybe disruption or AI impact what we can do. Thank you for your trust, but I believe our investment in the AI will not impact our overall cost structure and impact our operation capability and financial markets. We will maintain current level of investment. As you know, we have good profitability, so we will maintain around like 20% to 25% of R&D expenses, and we will spend incremental money on the AI and to give more support. But I will not sacrifice our safety on our cash flow. I won't do that. Just don't worry. That's our answer to those two questions.

Operator: And the next question comes from Wei Xiong of UBS.

Wei Xiong: [Foreign Language] [Interpreted] First, it is encouraging to see our margins have been maintaining at a very healthy level. Could we quantify the benefits from AI in our internal use to drive better efficiency and lower costs? How much room of further improvement do we see? Also, after the investment in FIFA World Cup, how should we think about the investment plans, the expenses, and the margin trends in the second half? Second, could we please get an update on your overseas business, including OfferToday? How should we think about if there is any plan to expand into other markets?

Wenbei Wang: I will take the first question on margin. So actually we have been leveraging AI in all aspects of our daily operations, including security, notifications, sales and marketing and operating earning and everywhere. But to quantify it, maybe it may be more easier in the cost line. Since 2023, we have been witnessing that alongside with our user growth, our overall headcount of operating employees maintain stable. As a result, the employee-related cost as a percentage of revenue continue to go down and help to contribute around 2 percentage points of our gross margin. You can see our gross margin now stayed at a very healthy high 80s level. We believe we at least maintain this very high gross margin level. For our outlook for the second half, yes, we have sponsored the FIFA World Cup, but the cost will be evenly distributed or recognized within Q2 and Q3. Apart from that, we will maintain our current investment level of the cloud service rental cost for our AI model training. We are expecting maybe in Q3, the margin level should be similar to Q2, and for the full year as we expected at the beginning of this year, our overall adjusted operating margin can still slightly increase.

Peng Zhao: [Foreign Language] [Interpreted] Thank you for your concern about OfferToday. Our current goal for OfferToday is in maybe five years from today, it can bring the company with $100 million to $115 million of revenue, that is around about the market size of Hong Kong. We call it maybe a middle dish, not too fast, but not too low. The lessons we learned from OfferToday is that it took around 2 to 3 years for our new business like OfferToday to enter into a market. Then next additional five years to grow to achieve $100 million to $115 million of revenue. We consider this kind of place or this kind of city worth investing. Of course, those cities are in Asia and Europe. Of course, we need to avoid those high geopolitical risk areas. To sum up, 2 to 3 years of adaption and mature, five years of development, there are still a lot of cities of this size and worth investing. Also we have some markets we call it slow dish, maybe take a longer term around 10-15 years, which can also achieve a revenue like $100 million to $115 million. The profile of this kind of market is maybe generally younger in the average age of the citizens and is a developing country, but it is developing quite orderly. Its total population is around slightly less than 100 million. Some place like Vietnam, Argentina or Brazil. In 10 to 15 years, we are hoping this kind of city can accept the new models like we have created and bring about nice profit or revenue to company by them. This topic is about OfferToday and what lessons it can give us to developing our overseas business. That is our answers to all the questions today. Thank you.

Operator: Due to time constraints, that concludes today's question-and-answer session. At this time, I'll turn the conference back to Laura for any additional or closing remarks.

Unknown Executive: Thank you once again for joining us today. If you have any further questions, please contact our IR team directly. Thank you.

Operator: Thank you for your participation in today's conference. This does conclude the program. You may now disconnect.