Prepared remarks
Ladies and gentlemen, thank you for standing by, and welcome to Kanzhun Limited First Quarter 2026 Financial Results Conference Call. 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.
Thank you, operator. Good evening, and good morning, everyone. Welcome to our first quarter 2026 earnings conference call. Joining me today are our Founder, Chairman and CEO, Mr. 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 outcomes of the company to be materially different. The company cautions you not to place undue reliance on forward-looking statements and does not undertake any obligation to update these forward-looking statements, except as required by law. During today's call, management will also discuss certain non-GAAP financial measures for comparison purposes. For a definition of non-GAAP financial measures and a reconciliation of GAAP to non-GAAP financial results, please see the earnings release issued earlier today. In addition, a webcast replay of this conference call will be available on our website at ir.zhipin.com.
Hello, everyone. Welcome to our first quarter 2026 earnings conference call. On behalf of the company's employees, management team and Board of Directors, I would like to extend our sincere gratitude to our users and our investors for their continued support. Today's presentation will cover four main parts. First, our growth in the first quarter; second, key trends among job seekers and enterprise users on our platform; third, the company's perspective on AI and strategic approach; and finally, shareholder return. Let me start with our first quarter growth. As a double-sided platform, we continue to see strong growth from both job seekers and enterprise users. Looking back to January to April this year, we acquired over 15 million newly verified users. Looking ahead, we believe that achieving over 14 million newly verified users for the full year should be achievable. Looking at paid enterprise customers' condition, number of paid enterprise customers reached 7.1 million for the 12 months ended March 31, up 10.9% year-on-year and 4.4% quarter-on-quarter. In the first quarter, the average monthly active users, or MAU, on the Zhipin app reached 16.9 million, up 5.7% year-on-year. In March, MAU exceeded 72 million, up 4.6% year-on-year. The rise in our MAU in March was more than 10 million higher than the first quarter average because the Chinese New Year occurred later this year, resulting in a later peak recruitment season. The 2026 Chinese New Year fell on February 17, while the 2025 New Year was on January 29. Hence, this year the peak recruitment season only fell within March, whereas the peak recruitment season fell within both February and March in 2025. Let me discuss our revenues. In the first quarter, the company achieved revenue of RMB 2.07 billion, up 7.6% year-on-year. Please kindly note that this figure also reflects the comparison between one month of peak season in 2026 versus two months of peak season in 2025. Looking ahead, we are confident that our revenue growth in the second quarter and for the full year will be stronger than what we delivered in the first quarter. On the profit perspective, in the first quarter, the adjusted operating income, excluding share-based compensation expenses, was over RMB 810 million, up 17.8% year-on-year. The adjusted operating margin was 39.4%, up 3.4 percentage points year-on-year. Second, key trends among enterprise users and job seekers on our platform. As discussed multiple times earlier, our platform behaves differently across tier cities and different types of industries. Overall, our continued rapid user growth has driven sustained rapid growth among lower-tier cities and small- and medium-sized enterprises on our platform. At the same time, recruitment demand from white-collar workers and large enterprises has continued to improve. From the user growth perspective, as of April 13, among the newly acquired users this year, excluding fresh graduates, nearly two-thirds were white-collar workers. From a revenue perspective, in the first quarter, white-collar revenue exceeded 40% of our total revenue for the quarter. White-collar recruitment demand has also accelerated at Chinese New Year compared to the same period last year. Looking into specific subsectors, active job postings for software engineers increased by 10.9% from January to April compared to 2025. This is consistent with recent observations from our U.S. peers. Referring to public data, their active software development job postings in the United States grew by 9.1% year-on-year from January to April 2026; we are two percentage points higher than their numbers. On our platform, we have not seen the kind of alarming large-scale reduction in programmer positions that some have feared. At the same time, revenue from AI-related roles on our platform has grown by over 100%. Simply put, development of AI has brought us more revenue and we have not yet seen a large decline in job postings. Among other industries, manufacturing, electronics, telecommunications, semiconductors, transportation and logistics, urban services and various professional services led in year-on-year growth on our platform. For large enterprises, their recruitment demand showed a notable recovery trend during this year's spring recruitment season. In the first quarter, companies with 1,000 to less than 10,000 employees recorded the fastest year-on-year revenue growth, followed by those with 500 to less than 1,000 employees. The average number of job postings per user also increased modestly. If we compare it to the last quarter, back then, we shared that the strongest growth in hiring demand was coming from small and micro enterprises. This quarter, however, large enterprises are delivering our year-on-year revenue growth. That concludes the recent trend on enterprise users and job seekers on our platform. Now we will discuss two views and four strategic pillars on AI that we are all concerned about. Since the ChatGPT 4.0 launch in March 2023, this AI paradigm has given us 38 transformative months. Meanwhile, recent generative AI breakthroughs have put our generation in unprecedented territory. Our perspectives are broadening. Our convictions are being reshaped. Civilization is facing new challenges, which is unavoidable. Many things that once were familiar are now becoming uncertain, including how we think about our companies and how you think about investment and competition. As one of the leaders in the worldwide and Chinese recruitment industry and as an entrepreneur myself, I have spent the past three years navigating between two main sides, recognizing that new technologies could solve major problems while at the same time worrying about technology's potential disruptive impact. I am informed, yet hopeful, and continuously exploring all within the full scope of my responsibilities. Our shareholders and employees have also gone through ups and downs—sometimes concerned, sometimes excited; we have all been on a roller coaster ride. Looking back on the past three years, we believe it is time for the company to provide a progress report to our shareholders, the public and our employees. This will be organized around two key observations and four main strategies. Our first observation is that to date, for the company's business model and industry position, the opportunities brought by AI technology have outweighed the risks. First, based on reliable public data, credible data released by listed companies and our own channel data, we would conclude that over the past three years, the company's market-leading position has been further strengthened. Market shares have continued to increase, and we have maintained steady growth despite a challenging environment. Second, lower token costs have accelerated the proliferation of AI applications in both our internal operations and user services. In the past quarter, stability for our platform—the ability for over 10 million users on our platform to access AI services—has been made possible by this reduction in cost. Third, extensive exploration in large model pretraining and application has accelerated the growth of our younger employees, including their sense of pride and standing with the company. The more widely AI technologies are used, and the more convincingly they solve real problems, the faster young talent will rise and the smoother the process of rejuvenation of our leadership team will become. In the long term, this helps slow down corporate entropy. The growth of leaders within our organization is a core result of individual aspirations and progress aligning with the company's shared vision. Young talent can more quickly gain recognition from the organization's shared systems and standards. This also explains why in every wave of technological and cognitive revolution, young people tend to emerge as leaders. Add to that, technologically oriented companies have benefited from the rise of young people and the reduction of corporate entropy. Our second observation is that to date, either enterprise-type or job-seeker standalone agents have not been capable of replacing the company's current business model. On the contrary, once agents are embedded within our double-sided network ecosystem, the company's accumulated user base and data play a positive and constructive role. Recruitment and job seeking is always a many-to-many game. Whether a laborer or contractor should be hired or not, or whether each day the matching ultimately succeeds, it comes down to a management game between two large groups of people. The double-sided network that has been built over the past 12 years and our understanding of users over the past 12 years has always been designed to reflect the real dynamics of job seeking and recruitment in China. In essence, embedding AI agents into our double-sided network serves as a driving force that enhances information collection, processing and dissemination. Looking at our first quarter data. First, the application of AI agents has improved the time and efficiency from an initial contact to successful mutual-consent conversion rate by 50%. Second, the large-scale application of AI may enable user retention to reach its highest level since the pandemic in 2020. Third, the average revenue per enterprise user increased by a double-digit percentage. Overall study of data and series tells us that AI is our friend instead of the enemy. Now let me walk you through our four strategic considerations on AI. First, investing in AI to advance the cross-business model. We firmly believe that in the recruitment industry, the important thing is that we deliver the placement of candidates for enterprise users. Whether we do surveys or not, every enterprise user is willing to pay for the delivery of candidates instead of traffic or clicks. We firmly believe that a results-oriented business model is achievable on our platform. It is a very important part of our company, and we will continue to allocate resources to this effort. While protecting the experience of high-end job seekers, we are also open to leveraging new technologies and new operating systems to collaborate with external companies that specialize in closed-loop recruiting. I will share three data points. First, within the company's in-house recruiting team for consultants who recently used AI, 20% of the candidate recommendations they deliver already come from AI-driven operations. Second, in another post-pilot project, the combined productivity of humans plus agents increased fourfold in the first quarter, already exceeding the average productivity level of recruiters in the industry. Third, for the AI agent campus recruitment service, the company's external revenue grew by more than 50% year-on-year in the first quarter. Therefore, the AI-powered closed-loop service is one of our core strategies. Second, we are also maintaining a proportion of our resources in AI research, currently focused primarily on the training and development of small models. There are five reasons for this. First, small models are less expensive for us to use internally. Second, continuous in-house R&D helps us enhance business accountability and enable the development of long-term technical capabilities tailored to vertical recruitment scenarios. Third, small models are increasingly gaining attention across the industry, which helps ensure a sense of pride and recognition for our science team. Fourth, our in-house model has also been actively applied to our search and recommendation functions, demonstrating advantages in both efficiency and accuracy compared to large models. Last, under the current paradigm, large models are too costly for us to deploy widely. Our third strategy on the AI stack is that we will continue to invest heavily in AI applications. Our primary evaluation criteria is how AI helps our double-sided job-seeking and recruitment network ecosystem. Over the past 12 months of investigation, we have formed some logical principles and common-sense rules for these efforts. We believe we will treat AI technology as a value-added tool for identifying and solving problems. Regarding rewards and bonuses, teams that use AI to discover and resolve issues will generally be rewarded by us. So far, we believe that our exploration and investments in AI will be based on our double-sided ecosystem theoretical framework. Our fourth strategy is that we believe AI-driven revenue growth is a natural process and a natural result. As AI technology improves platform efficiency, it will lead to higher user engagement, better satisfaction and stronger brand reputation among users. This in turn will drive sustained revenue growth. We see this as smoother, lower-risk and more sustainable growth. Investors who are familiar with us understand that we have an original self-developed model; we don't sell advertising or clicks. Our business model is based on delivering placements through our double-sided network ecosystem. So whether to grow by 50% this year or 15% over the next five years, we choose the latter, and we believe the combination of our technology and our organization supports this choice. After three years of exploration, the two observations and four strategies I just talked about have been verified. This is not a new idea; our product teams have been working on this for a long time. For investors who care about us, and for our internal employees, I want to say that the strategies we just discussed are based on real exploration and contributions from employees who work on the front line of our AI development. All the team leaders of our core AI teams have contributed to these points. Finally, on shareholder returns, we have remained fully committed to delivering on our shareholder return promises. Since the start of this year, we have repurchased over USD 200 million in shares, or around 3% of our total outstanding shares. In aggregate, since 2022, we have bought close to 10% of our total shares. As a reminder, last quarter we announced a shareholder return plan committing that over the next three years, the annual amount we allocate to buybacks and dividends will be no less than 50% of the prior year's adjusted net income, and we are following through on that commitment. With that, I will now turn to our Deputy CFO, Wenbei Wang, to review our financials. Thank you.
Thanks, Jonathan. Now let me go through the details of our financial results for the first quarter. We are delighted to report a solid start to this year characterized by continued expansion in our user base and engagement alongside sustained revenue growth. Despite a later Chinese New Year, which meant a shorter window of the peak recruitment season within this quarter, our revenue reached RMB 2.1 billion, up 8% year-on-year. We are witnessing accelerated revenue as well as cash traction growth post Chinese New Year supported by robust demand. Our paid enterprise customers grew by 11% year-on-year to 7.1 million in the trailing 12 months ended March 31, 2026, primarily driven by the increase in the ratio of active enterprises that pay on a sequential basis. Growth from key accounts and large customers continued to show better trends compared to the same period last year, resulting in a more balanced customer structure. As a result, ARPU in the first quarter increased 2% year-on-year. Moving to the cost side, our total operating cost and expenses decreased by 3% year-on-year to RMB 1.4 billion this quarter. Total share-based compensation expenses dropped by 24% year-on-year and 11% quarter-on-quarter to RMB 181 million. As a percentage of revenue, share-based compensation accounted for 9.2% of total revenue for this quarter, down by 3.9 percentage points year-on-year and 1.1 percentage points quarter-on-quarter. We expect share-based compensation expenses as a percentage of revenue to stay around 9% in the near term. Excluding share-based compensation expenses, our adjusted operating costs and expenses were RMB 1.3 billion, remaining relatively stable year-on-year. Our adjusted operating income was RMB 815 million, up 18% year-on-year, representing an adjusted operating margin of 39.4%, up 3.4 percentage points year-on-year. Despite the first quarter normally having the lowest margin due to seasonality, we believe there remains substantial room for further margin improvement in our core business segment due to the robust operating leverage of our business model. But considering our continued investment in AI, significant spending for long-term brand initiatives and investment in new businesses along with the corresponding margin dilution for those new businesses, we can still expect a modest margin expansion in the coming years. Looking into each segment, cost of revenue decreased by 4% to RMB 298 million this quarter. This decrease was primarily due to lower employee-related expenses resulting from enhanced operating efficiency and partially offset by higher network bandwidth cost. As a result, our gross margin went up by 1.8 percentage points to 85.6%. Additionally, the introduction of store commission fees starting in March also contributed to gross margin improvement. Sales and marketing expenses increased by 2% year-on-year to RMB 502 million this quarter, primarily due to an increase in advertising and marketing expenses, partially offset by a decrease in sales employee-related compensation as a result of our continued efforts to improve sales efficiency. R&D expenses were RMB 424 million this quarter, remaining relatively stable year-on-year. Excluding share-based compensation expenses, our adjusted R&D expenses increased by 5% year-on-year to RMB 351 million, primarily due to higher cost of services fees and server depreciation expenses relating to AI infrastructure. Our G&A expenses decreased by 15% year-on-year to RMB 282 million this quarter, primarily driven by lower share-based compensation expenses. Our interest and investment income was RMB 781 million in the quarter, up 422% year-on-year. This increase was primarily driven by investment income of RMB 640 million arising from fair value changes of one of our invested companies, which went public in January 2026. Income tax expenses were RMB 299 million this quarter, up 293% year-on-year. This increase was primarily due to the tax impact from the aforementioned investment income of RMB 154 million as well as the provision of RMB 60 million top-up tax under the Pillar 2 tax rules and an additional tax of RMB 8 million, as well as higher income from operations. Our net income reached RMB 1.1 billion this quarter. Excluding share-based compensation and net gains from the aforementioned investment, our adjusted net income increased by 12% to RMB 856 million. Net margin improved to 54.4%, while adjusted net margin increased to 41.4%, up 1.7 percentage points year-on-year. Net cash provided by operating activities reached RMB 1.2 billion this quarter, up 19% year-on-year. Our cash position, including cash, cash equivalents, short-term deposits and short-term investments excluding investments in marketable securities, stood at RMB 19.8 billion as of March 31, 2026. Our strong cash position and cash-generating capability will support us to continue to deliver on our commitment to shareholder returns. As Jonathan just mentioned, we have purchased a total of over USD 200 million worth of shares, representing approximately 3% of our total outstanding shares. We will continue to maintain substantial shareholder return efforts, including share buybacks and dividends based on specific market and operating conditions. And now for our business outlook. For the second quarter of 2026, we expect our total revenues to be between RMB 2.38 billion and RMB 2.42 billion, a year-on-year increase of 13.2% to 15.1%. That concludes our prepared remarks, and now we would like to take questions. Operator, please go ahead.
Questions and answers
Operator Instructions We will now proceed to take our first question. And the question comes from the line of Wei Xiong of UBS.
I have two questions. First, you mentioned the business impact from a delayed Chinese New Year this year. May I ask if we only consider the days in the first quarter post CNY as well as the second quarter based on our expectation, do we see an improvement in the year-over-year growth rate and which industries or job postings have seen more notable improvement? Second, regarding the AI disruption, have we seen any impact on job postings on our platform considering we already have a very mature white-collar business, but the AI disruption to blue-collar roles is theoretically smaller—will we accelerate the blue-collar business going forward? Also, regarding the AI-powered closed-loop services, could management give an update on the latest progress as well as the feedback from industry participants such as agencies and partners?
Second question first, about the closed-loop service. We have discussed this before and will give you several numbers. The first number is among all of our different businesses in the first quarter, the AI-supported closed-loop service was the fastest-growing among different experimental groups. Some pilot groups had growth rates over 100%, some around 50%. But the overall revenue scale is still relatively small. For the first quarter, our AI-facilitated closed-loop services had total revenue of around RMB 50 million. About your first question regarding the peak recruitment season, our MAU for the first quarter averaged around 60 million. However, MAU for March was over 70 million and MAU for April was also close to the number in March. I think that's a representative number showing that the overall industry has been rapidly robust after the peak season. The ratio between supply and demand, between enterprise users and recruiters, is healthy. Our newly added registered users increased by 10% year-on-year. More practically, for the first half and overall this year, I think the growth rate of our core business will be accelerating compared to last year. For the full year, we are looking at at least a double-digit year-on-year growth. Regarding which jobs will be impacted by AI or even replaced by AI, I want to emphasize that we have been dedicating serious resources to study this matter and want to make a careful and scientific conclusion. Once we have our report, we will open it to the public. But up to today, our observation is that software development engineers have not experienced any decline; on the contrary, the active number of job postings for software roles increased by 10.9%. I will continue to follow this topic and our team wants to publish some papers, but I want to take more time—perhaps two quarters—and then we may provide a clearer answer to this question. Those are some of our views.
We will now take our next question from the line of Eddy Wang of Morgan Stanley.
My question is also related to AI. We have noticed that a peer company mentioned in its latest earnings that AI has greatly contributed to an increase in revenue, especially average revenue per job. Can AI enhance revenue for job listings on our platform as well? How much can it be enhanced and how can this be achieved? A follow-up question is that in the Chinese recruitment market, could recruitment platforms with lower matching efficiency benefit more from AI? How might this change future industry competition patterns?
Thank you for your question. We also noticed that our peer has made some public statements like you said, and we are pleased with their progress. From our perspective, to understand how AI can help us achieve higher revenue growth domestically, I will divide it into several parts. First, we need to provide better value before we increase price. Second, in the China domestic market there are over 40 million enterprises, more than half of which have never used online recruitment services before. So there is a long process from underpenetrated markets to fully cultivated markets. Third, our industry has a deep tension or controversy between selling exposure versus selling actual onboarding and placements. Moving from selling exposure to selling placements is a long-term process with many steps. We are in that process, moving from selling advertising exposure to selling an outcome—helping customers hire and paying based on that outcome. For all of the 7.1 million paying enterprise customers we had at the end of this quarter, our revenue is around RMB 8 billion per year, so on average each paying customer pays us a relatively modest amount. There is huge potential here to raise prices per customer, but first we prioritize growing the number of paying customers. In the future, we could have many more paying customers, which would give us room to increase pricing later if appropriate. Regarding whether peers with weaker matching efficiency might be able to leapfrog us with AI, I am not overly worried. Based on my experience, while AI is an important factor, execution, product-market fit and the accumulated network effects of a double-sided platform are critical. Today, I am practical and focused on solving real customer problems, and I am not worried that weaker peers will quickly exceed or disrupt us. That concludes our answer to the question.
Operator: We will now move to our final question from Timothy Zhao of Goldman Sachs.
My first question is regarding operating expense trends, specifically marketing expenses and R&D expenses related to AI. Could you share more color on your outlook for this year's overall profit margin? I also note that you have been increasing buybacks—any updates on your shareholder return plans for this year and the medium term? Secondly, regarding your overseas expansion: we note significant growth in the Hong Kong market. Could you share some color on the latest user base and monetization update in Hong Kong and any investments you made in Hong Kong in the first quarter? Do you have plans to expand into other overseas markets?
Thank you for your question. Regarding marketing and R&D expenses and AI investment, we will continue to invest. One major change this year is that for some critical roles, companies are willing to pay strong compensation for top talent, and we need to recruit and retain appropriate talent to maintain our AI-related development capabilities. In addition, computing power costs, including rentals and related costs, are increasing, so we are investing there as well. For margin perspective, we are not expecting a very large margin expansion this year—our adjusted operating margin will see a smaller increase compared to previous years—because of continued investments in AI, sponsorships and new businesses. Regarding shareholder returns and buybacks, I want to reemphasize that year-to-date we have spent more than USD 200 million to buy back shares, acquiring over 3% of total shares outstanding. There are two reasons we are doing this. First, we believe our valuation is attractive, so buybacks are an efficient use of capital. Second, we want to provide confidence to employees and investors; we have confidence in our company and want to reflect that. On overseas expansion and Hong Kong specifically, we have developed quite well in Hong Kong. Looking at market adoption, our daily active users there are approximately 60,000. To put it in context, among roughly 3 million Hong Kong workers, one out of 50 are using our product every day. We are providing enterprise customers with a large pool of active, effective candidates and suppliers for immediate communication. That is why recruiters find our model efficient. There are two reasons we are developing our Hong Kong business: first, to verify our double-sided ecosystem business model in a market close to the mainland; and second, to cultivate a core international team to support future expansion. Revenue is not our top priority there at this stage; we are investing a reasonable amount to build product-market fit and a local team. We remain confident we can become a leading local recruitment platform in Hong Kong for job seekers and recruiters, and we will continue to invest and make more efforts. Thank you for your questions.
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.
Thank you once again for joining us today. If you have any further questions, please contact our IR team. Thank you.
Thank you for your participation in today's conference. This does conclude the program. You may now disconnect your lines.