Earnings Call
Pony AI Inc. (PONY)
Earnings Call Transcript - PONY Q3 2025
Operator, Operator
Hello, ladies and gentlemen. Thank you for standing by, and welcome to Pony AI Inc's Third Quarter twenty twenty-five Earnings Conference Call. At this time, all participants are in a listen-only mode. After the management's prepared remarks, there will be a question-and-answer session. As a reminder, today's conference call is being recorded. And a webcast replay will be available on the company's Investor Relations website at irpony.ai under the News and Events section. I will now turn the call over to your host, George Shao, Head of Capital Markets and Investor Relations at pony.ai. Please go ahead, George.
George Shao, Head of Capital Markets and Investor Relations
Thank you, operator. And hello, everyone. We appreciate you joining us today for Pony AI's third quarter twenty twenty-five earnings call. Earlier today, we issued a press release with our financial and operating results, which is available on our Investor Relations website. An earnings presentation, which we'll refer to during this conference call, can also be accessed and downloaded on our Investor Relations website. Joining me on the call today are Dr. James Tong, Chairman of the Board and Chief Executive Officer; Dr. Tianqin Luo, Chief Technology Officer; and Dr. Liu Wang, Chief Financial Officer of the company. They will provide prepared remarks followed by a Q&A session. Before we begin, please refer to the safe harbor statement in our earnings release, which applies to this call, as we'll be making forward-looking statements. Please also note that we'll discuss non-GAAP measures today, which are more thoroughly explained and reconciled to the most comparable measures reported under GAAP in our earnings release, available on our Investor Relations website and filings with the SEC and Hong Kong Stock Exchange. I will now hand it over to our Chairman and CEO, Dr. James Tong. Please go ahead.
James Tong, CEO
Thank you, George. Hello, everyone. Thank you for joining our earnings call. I'm excited to share that we have successfully completed the dual primary listing on the Hong Kong Stock Exchange under stock code 2026 on November 6, just one year after our Nasdaq listing. With strong support from both international and domestic investors, we secured the largest IPO in the global autonomous driving sector this year, raising more than 800 million US dollars. This significantly strengthens our balance sheet and provides the dry powder to accelerate mass production and the largest scale commercialization. We now expect stronger growth, surpassing our 1,000 robotaxis fleet plan by year-end and expanding to more than 3,000 vehicles for 2026. We have already seen the flywheel in action; the expanded fleet is driving higher user adoption, shorter wait times, more orders, and strong revenue growth. After launching Gen Seven Robotaxi, we have already achieved citywide unit economics breakeven. This, in turn, gives us more room to increase fleet size. The capital we raised also fuels our business development research and development, market-making strategic investments in new markets, new applications, and attracting world-class AI talent. All these are set to further propel our technology leadership and long-term growth. Our Hong Kong IPO also powers our core mission: bringing autonomous mobility to everyone around the world. We're firmly delivering on this commitment. Earlier this month, we officially launched fully driverless commercial service for Gen Seven Robotaxis across Guangzhou, Shenzhen, and Beijing. Today, our management team, including myself, actually arrived at our Shenzhen office in a fully driverless Gen Seven Robotaxi to host this earnings call. This is more than just a normal ride for us; it actually marks a giant leap in autonomous driving's advancement. We are making level four autonomy more accessible than ever to a much broader user base. I'm excited to share a critical milestone: our Gen Seven Robotaxis have reached city-level unit economics breakeven in Guangzhou, shortly after their official commercial launch. This is pivotal to validate our viable business model. It not only gives us strong confidence to further scale our fleet but also attracts more and more third-party partners enabling them to fund our fleet and support our asset-light model. The scaling up of the fleet is key to our growth, as a large-scale operational footprint drives efficiency through the economy of scale. Our Robotaxi vehicles are effectively moving billboards. In fact, many new users discover and download our Pony Pilot app after spotting our vehicles on the road during daily operation. To lead fleet expansion serves as a highly efficient self-reinforcing marketing engine, facilitating user adoption and strengthening brand recognition. This creates a powerful upward spiral: more vehicles generate greater visibility, which attracts more users and establishes network effects. The results are already evident. Building on that momentum, new registered users nearly doubled within just one week of launching Gen Seven from late October, reflecting robust user demand and an effective go-to-market strategy. Now let me highlight some key advancements we made in recent months in executing our scale-up strategy. First, we have ramped up production at an accelerating pace. Since the start of production in the middle of this year, by November, more than 600 Gen Seven Robotaxis had rolled off our assembly lines, bringing the total fleet size to over 900 vehicles. Thanks to the streamlined production process, we now expect to outperform our full-year target of 1,000 vehicles, delivering ahead of schedule. This gives us increasing confidence to sustain robust momentum, driving fleet size to surpass 3,000 vehicles in 2026. Second, in Q3, our Robotaxi revenue surged by 90% year over year, with their charging revenues delivering over 200% year-over-year growth. This was fueled by rising user adoption across all four tier-one cities, improved fleet operational efficiency, and a tailored pricing strategy for diverse user segments. We have seen that the higher order density leads to lower users' average waiting time and, in turn, a higher vehicle utilization rate. This allows us to continuously optimize our pricing strategy. Third, we have continued to expand our operational footprint. For example, in Shanghai, we became the city's first company to launch fully driverless commercial global taxi operations earlier this July, covering the Jingqiao and the Huamu areas of Pudong. In Shenzhen, we extended commercial fully driverless operations to larger city areas, including the Circle and Overseas Chinese town. We're taking major steps toward our scale-up strategy. Following our collaboration with Hehu in June, we recently forged another partnership with Sunlight Mobility. This alliance reflects growing market recognition of our business model, with an increasing number of third parties wanting to fund fleet deployment. This actually enables us to speed up further fleet expansion. Now let me turn to our global expansion. We are deeply dedicated to advancing global taxi services while strategically expanding our international fleet. Now we have Robotaxi presence established in eight countries across China, the Middle East, East Asia, Europe, and the U.S. We entered a new market in the Middle East, Qatar, through a partnership with Nova Salet in Q3. Nova Salet is the country's largest transportation service provider. As part of this collaboration, our Robotaxis have recently begun testing on public roads in Doha, the capital of Qatar. We have also advanced our presence in South Korea by securing nationwide Robotaxi permits enabling operation across the country's autonomous testing and operational zones. Our collaboration with local partners continues to deepen. We're working closely with Comfort Air World, the country's largest transportation service provider, to begin road testing in Luxembourg. We plan to deploy testing vehicles based on the perjury eTraveler through our alliance with Stellantis, a European leader in light commercial vehicles. This effort will initially focus on vehicles designed for Europe's diverse mobility needs to enable a range of use cases. In addition, we have partnered with global ride-hailing platforms that also participated in our Hong Kong IPO. Those platforms include Uber and Bolt. Bolt is an Estonia-based mobility company operating in over 50 countries and 600 cities. Built upon our collaboration with Uber, we aim to leverage Uber's robust ecosystem to enter the Middle East and then scale into additional international markets. Last but not least, we recently released our fourth-generation robot truck, with production and the initial fleet deployment expected in 2026, featuring fully automotive-grade components, optimized software-hardware integration, and the transition from internal combustion engine vehicles to electric vehicles. The Gen Four Robotruck delivers a significantly more efficient cost structure and greater energy savings. The new platform fully leverages the technological foundation and operational expertise developed through our Gen Seven Robotaxi vehicles. In addition, we deepened our collaboration with SANE Group and added Liuzhou Moto as a new partner to support our further operations. To sum up, 2025 is a critical year for mass production and commercialization for Pony.ai. We take pride in the progress we have made and are steadily delivering on the promise we made to our shareholders at the time of our U.S. IPO last year. Our recent Hong Kong listing not only marks a major milestone for our company but also underscores the promising future of the industry. Moving forward, we will drive technological innovation and create lasting value by scaling fast, efficient, and comfortable autonomous mobility services toward our mission: autonomous mobility everywhere. With that, now I'll hand it over to our CTO, Dr. Tianqin Luo, to share more about our technology strategies. Tianqin, please go ahead.
Tianqin Luo, CTO
Thanks, James. Hello, everyone. This is Tianqin. Let me first share my thoughts on our driving technology stack. From day one, we believed that full-stack integration across software, hardware, and operations was the only way to build a truly scalable autonomous mobility. That conviction has been validated again and again, especially for this critical year of scaling up. With the achievements we made, it is clear that our early technology best helps us achieve the leading position and will further accelerate our future growth. Our deep foresight into the tech stack is positioning us as leaders in the industry today. We have become one of the few companies to operate large scale fully driverless robotaxi services. As early as 2020, we recognized the importance of training through reinforcement learning in simulation. In that year, we transitioned our tech stack into a unified model, which is what we call PonyWorld today. Through years of R&D effort and real-world validation, our top driving model has evolved into a closed-loop training. We have achieved unsupervised self-improving iterations. In recent years, we are seeing the broader autonomous and robotic industry converge on one model, validating the approach we adopted today. This full-time AI tech stack has given us a meaningful head start, and we're confident that we will stay ahead for multiple years. Then let me dive into the three criteria that put us at the forefront of model development. First, the high-fidelity interactive simulation. This is far beyond the ability to just generate scenarios and render sensor data. Driving is, by nature, interactive. The robotaxi's actions directly affect how other agents behave, including vehicles and pedestrians adapting to our driving behavior. It must understand and adapt to new situations and complex physical interactions in real-time, mirroring true road interactions. This enables robotaxi operations that are safe, smooth, and socially aware. After 10 billion kilometers of test miles generated each week, more than 99% keep vehicle agent detections, while less than 1% involve static environments such as center rendering. Okay. Second, the ability to reproduce a scale and realistic corner cases. While these long-tail scenarios do not occur frequently, they are critical for safety. More importantly, every scenario must be something that could really happen in the real world, not those edge cases with no basis in reality. Third, the AI-based learning evaluator. This is the reward-based evaluation mechanism because driving is a multi-object optimization problem. What is considered good driving also changes in various driving scenarios. Within the closed-loop training environment, PonyWorld and our virtual driver are continuously evaluated on key driving metrics. This assessment does not rely on real-world data or human-labeled data or rules but uses an AI-powered model to learn what good driving looks like directly from outcomes, turning real and simulated experiences into a powerful cycle of self-improvement. A best-in-class word model must meet all three criteria to enable truly unsupervised and self-improving closed-loop training. This is critical for realizing large-scale driverless auto driving. Leveraging our full-stack technology as a core strength, I will now turn to how we drive business progress during the third quarter. First, on cost and operational efficiency. We pioneered a 100% automotive-grade autonomous driving kit for the Gen Seven Robotaxis. We've optimized the design, reducing BOM costs by 70% compared to the previous generation. The Gen Seven vehicles have been officially operating for the public in Guangzhou, Shenzhen, and Beijing, fully validating our safety standards and operational efficiency. We build on our momentum and deliver further progress driven by scaling production and enhancing R&D. We've already realized an additional 20% reduction in the autonomous driving kit costs for the Gen Seven platform designed for 2026 production, compared to the 2025 baseline. This lays a foundation for sustained cost efficiency. Our robust AI algorithms and fleet management have proven effective at driving operational efficiency. To better identify user demand in hotspot areas during rush hours, we optimize our algorithm for fleet dispatch matching and scheduling, ensuring sustained efficient robotaxi utilization. We have also improved our virtual driver to recognize more complex scenarios. This allows us to improve our remote assistant to vehicle ratio substantially, targeting a one-to-thirty ratio by year-end. Our superior service experience has become the key reason users choose Pony Robotaxi. After the launch of Gen Seven Robotaxis, we earned widespread positive feedback and generated great social media buzz from users. As we deliver a high-quality experience, users are increasingly willing to pay a premium for the enhanced reliability and safety of our autonomous journey. For ride comfort, our advanced interactive planning capabilities optimize the frequency and magnitude of acceleration, braking, and steering, delivering smooth natural motion control tailored to the electronic vehicles and the ride-sharing markets, offering a consistent comfort experience for every Pony pilot taxi ride. These enhancements reflect significant improvements for Gen Seven, such as the emergency brakes and the steering over the past few months. Additionally, our low-tech features enhance the in-cabin experience. We also pioneered the innovative smart positioning feature. With one tap, users can remotely adjust their vehicle position for more convenient pickup and drop-off. We introduced the voice-activated features called POPO voice assist, allowing users to start trips and control air conditioning, etc. We will continue to upgrade the cabin into an AI-powered mobility terminal. Together, these upgrades create a more accessible and streamlined user experience. So third, our tech stack is also built for generalization. The alpha-native tech architecture allows us to adapt quickly to new markets and platforms. In terms of cost-region generalization, our virtual driver and the show can quickly understand and adapt to diverse traffic conditions around the world. For example, leveraging our high-fidelity training environment and evaluation mechanism powered by fully driverless coverage in the Pudong District in just a few weeks. In addition, when sending to Europe, the system intelligently identified and adapted to key differences in local road conditions, such as unique traffic signal configurations and various driving patterns. Our technology boosts generation power across platforms as well. The latest generation robot truck will commence production and operation from next year, demonstrating our capability to create synergies between Robotaxi and Robotruck tech stacks. Looking ahead, we will leverage our successful Hong Kong listing to reinforce our technological leadership, increasing R&D investment, and attracting top AI talent to advance our Robotaxi, Robotruck, and new market initiatives. We will continue pushing the frontier of autonomous mobility, refining what is possible in transportation. Okay. This concludes my prepared remarks. I will now pass the call over to our CFO, Dr. Liu Wang, for a closer look at our financial results. Liu, please go ahead.
Liu Wang, CFO
Thank you, Tianqin. Hello, everyone. This is Liu. I will focus on year-over-year comparisons for the third quarter, unless otherwise noted. Q3 twenty twenty-five was a landmark quarter. We delivered robust revenue growth, specifically with solid progress in robotaxi large-scale commercialization. Now we expect to outperform our full-year fleet target of 1,000 vehicles. Moreover, our newly deployed Gen Seven Robotaxis fleet has reached a pivotal citywide unit economic breakeven milestone. This lays a solid foundation for further scaling up and the implementation of our asset-light business model, which will be further accelerated by our successful Hong Kong IPO capital raise. In this quarter, revenue finished at 25,400,000.0 US dollars, growing by 72%. This strong performance was primarily driven by the continuous optimization of our robotaxi services and the sustained demand in our licensing and application business. Firstly, robotaxi services revenue reached 6,700,000.0 US dollars, representing a remarkable growth of 89.5% year over year and 338.7% quarter over quarter. Specifically, fare charging revenue continued to deliver triple-digit growth, surging 233.3%. This was achieved even before the commercial rollout of our Gen Seven Robotaxis, supported by a stable commercial fleet of our Gen Five and Gen Six vehicles. The strong growth during Q2 and Q3 stemmed from growing user demand in tier-one cities in China. Our continuous effort to optimize fleet operations and the pricing strategy led to increased fleet utilization and efficiency. This is a testament to growing user recognition and brand loyalty toward Pony Pilot services. Going forward, as we follow this strong momentum toward a significant fleet expansion of over 3,000 vehicles by 2026, we expect robotaxi revenue growth to accelerate even further, driving more orders and higher operational efficiency. In Q3, another key robotaxi update is the implementation of our asset-light model for fleet expansion. As we have shown promising numbers in vehicle unit economics, we received a strong interest from third parties who are willing to purchase Gen Seven vehicles to operate as robotaxi operators. Such partners include, but are not limited to, leading ride-hailing or taxi operators. For instance, Shenzhen Shihu Group and Sunlight Mobility. The asset-light model has contributed revenues through technology licensing fees and vehicle sales while giving us further leverage and capital efficiency for fleet expansion. Aside from strong top-line growth domestically, we are also seeing fast growth of robotaxi revenues from overseas markets. Moving forward, we expect robotaxi revenues from overseas markets to continue to grow. Currently, our robotaxi footprint has expanded into multiple countries globally, serving as a promising foundation for our exploration of international opportunities. Secondly, moving to Robotruck, robotruck service revenues were 10,200,000.0 US dollars, growing by 8.7%. Moreover, as we launch our Gen Four fully auto-grade robot truck, we expect to reduce the BOM cost of its autonomous driving hardware kit by 70% and reach a thousand-unit scale of the robot truck fleet going forward. This new generation of robot trucks will powerfully accelerate the progress of robot truck commercialization at scale. Thirdly, licensing and application revenues were 8,600,000.0 US dollars, growing significantly by 354.6%. We continue to see robust and growing demand for our autonomous domain controller, primarily from robot delivery clients. Turning to gross margin, we delivered significant gross profit margin improvement from 9.2% in Q3 twenty twenty-four to 18.4% in Q3 twenty twenty-five with gross profit of 4,700,000.0 US dollars in the third quarter. This remarkable improvement was firstly driven by our strategic initiatives to optimize the revenue mix and secondly, by a greater contribution from robotaxi services, which carry a relatively higher margin. The unique economic breakeven achievement validates our focus on go-to-market execution and optimizing operational efficiency. Since the launch of Gen Seven commercial operations in Guangzhou, daily net revenue per vehicle has reached 299 RMB. The net revenue refers to the total RMB value generated from ride-hailing services after deducting discounts and refunds. Notably, daily average orders per vehicle have reached 23, fueled by robust user demand and our operational optimization. Meanwhile, we have also optimized hardware depreciation as well as operational costs, including charging, remote assistance, ground support, service, maintenance, insurance, parking, and network costs. This will further improve our margins down the road. The total operating expenses were 74,300,000.0 US dollars, up by 76.7%. Excluding share-based compensation expenses, non-GAAP operating expenses were 67,700,000.0 US dollars, up 63.7%. The increase primarily reflects the one-off R&D investment in Gen Seven vehicles and the expansion of our R&D personnel, critical to securing and extending our technological leadership. Specifically, approximately half of the increase in research and development expenses stemmed from a one-time customized development fee of 12,700,000.0 US dollars for Gen Seven vehicles. The net loss for the third quarter was 61,600,000.0 US dollars, compared to 42,100,000.0 US dollars in the same period of last year. Non-GAAP net loss was 55,000,000.0 US dollars, compared to 41,400,000.0 US dollars last year. Looking ahead, we expect to sustain disciplined investment to accelerate larger-scale commercial deployment. Turning to the balance sheet, our cash and cash equivalents, short-term investments, restricted cash, and long-term debt instruments for wealth management were 587,700,000.0 US dollars as of September 30, 2025, compared to the balance as of June 30, 2025, of 747,700,000.0 US dollars. Around half of this decrease comes from one-off cash outflow, including capital injection into Jifeng, our joint venture with Toyota, to support Gen Seven mass production and deployment. All of the capital commitment in Jifeng has been completed. The remaining cash balance reduction primarily reflects our mass production and large-scale deployment status, including ongoing operational cash outflow and capital expenditures for the procurement of Gen Seven vehicles in Q3 to support our goal of a 1,000 vehicle fleet by year-end. For the nine months ending September 30, 2025, we have accumulated free cash outflow of 173,600,000.0 US dollars. With the completion of our recent Hong Kong IPO, we have over 800,000,000 US dollars in cash newly added, providing us with substantial fuel for the next phase of growth. The IPO proceeds will help us accelerate fleet expansion into key addressable markets, further optimize our platform for scale, and deepen our R&D investments to solidify our technology model. Looking ahead, our mass production momentum continues to strengthen, and we are on track to exceed our full-year vehicle target of 1,000, achieving this milestone ahead of schedule. This acceleration reinforces our confidence in scaling rapidly, and we now anticipate to grow our fleet to more than 3,000 vehicles by 2026. In addition, we've already transitioned to an asset-light model for a meaningful portion of our new vehicles. This will enhance our capital expenditure efficiency and provide greater leverage for scalable fleet expansion. With the proven operational model and the financial runway from the recent Hong Kong IPO, we are uniquely positioned to accelerate our business plan, turning momentum into sustained profitable growth. I will now turn the call over to the operator to begin our Q&A session. Thank you.
Operator, Operator
Thank you. We will now begin the question-and-answer session. If you're using a speakerphone, please pick up your handset before pressing the keys. To withdraw your question, please press star then 2. For the benefit of all participants on today's call, please limit yourself to one question. If you have more questions, please reenter the question queue. If you ask questions in Chinese, please repeat them in English. The first question comes from Ming Shun Li with Bank of America. Please go ahead.
Ming Shun Li, Analyst
Thank you. Thank you management for giving me the opportunity to ask a question. So I just have one question. Could the management team give us some more updates on the fleet size for this year and also the outlook in 2026? For the new vehicles added, what is the full fleet deployment plan across different cities? Thank you.
James Tong, CEO
This is James. I'll take this one. As you can see, since the launch of our Gen Seven Robotaxi, we have actually seen much faster than expected production and deployment. For this year, we certainly expect to outperform our previous target of 1,000 robotaxis by year-end. We expect this strong momentum to continue into 2026 with a conservative target of over 3,000 vehicles. This is mainly because we have already seen an upward spiral with the launch of our Gen Seven vehicles. Essentially, the fleet density creates a much shorter wait time for the passengers, which leads to a better user experience. This user experience leads to much higher utilization for our vehicles, and we can actually charge a better price. This spiral really creates strong momentum for us to expand much faster. In addition, we have also started experimenting with the asset-light model by collaborating with fleet managers such as Shihu, Sunlight, and we will certainly add more partners. This asset-light model allows us to deploy a much larger fleet with less CAPEX. This is our growth plan. As for the fleet deployment plan, we'll go deeper into our existing markets and at the same time, we'll go much wider to explore some new opportunities. The citywide unit economic breakeven for Gen Seven in Guangzhou, in my view, is a pivotal milestone to validate our business model. This gives us huge confidence and allows us to deepen our collaboration and our operation in the existing markets, which are the tier one cities in China, as I mentioned. The expanded fleet size creates an upward spiral. But at the same time, we also plan to expand into more domestic cities and overseas markets, as we see those as future growth opportunities. Our go-to-market strategy for those markets is that we'll collaborate deeply with local partners and local government agencies to establish a presence and prepare for our future growth. So stay tuned; we think there will be great news ahead of us. With that, back to the operator.
Operator, Operator
Thank you. The next question comes from Bin Wang with Deutsche Bank. Please go ahead.
Bin Wang, Analyst
Hi, management. Thank you for taking my question. I just have one question, which is about the charging. I'd like to know what fare charging revenue delivered in Q3 2025. So what is the outlook for fare charging revenues as we deploy more vehicles? Thank you.
Liu Wang, CFO
Yes, this is Liu. I'll take this question. In Q3, our fare charging revenue actually surged even faster, growing about 233%. However, at that time, our fleet comprised still of Gen Five and Gen Six vehicles. We believe such growth was driven by both the demand and operational sides. On the demand side, we have been continuously improving the overall riding and user experience. With our efforts, we've seen robust and organic user demand in tier one cities, which signals strong consumer adoption of our robotaxi service. For example, the total registered users more than doubled year over year in Q3. On the operational side, we have also optimized our fleet operation to improve vehicle utilization and order fulfillment. As Tianqin mentioned in his remarks, for example, we enhanced fleet dispatching and deployment, consistently reducing our wait times by approximately 50% compared to the same period in 2024. We also continue to expand our pickup and drop-off points to create a smoother user experience. For example, in Shenzhen, we now have more than 10,000 pickup and drop-off points, which is a more than 300% increase since the end of June this year. With all these demand-side and operational-side improvements, I believe we could see sustained strong growth momentum through continuous fleet expansion with more Gen Seven vehicles coming into service. We expect that our fleet has been growing exponentially from 270 last year to be more than 1,000 this year, with a target of more than 3,000 next year. This scaling up would also create a better network effect, shortening wait times and improving vehicle utilization and user adoption. We will also progressively expand our service areas in cities such as Shanghai and Shenzhen. We have already been doing so today, increasing our population coverage and expanding to more drivable miles. With all these being done, I think we can boost the average order value per trip. Okay, I'll pass it back to the operator.
Operator, Operator
Thank you, sir. The next question comes from Kyle Wu with Citi Research. Please go ahead.
Kyle Wu, Analyst
Thanks for taking my questions. This is Kyle from Citi Research. Congratulations on achieving the milestone of citywide unit economic breakeven. Could you elaborate more on the assumptions behind the delivery per vehicle, including daily orders, pricing, daily operating hours, and a ratio of remote assistance? Thank you.
Liu Wang, CFO
Yes, I'll take this question. Like you said, we all believe the citywide unit economic breakeven is a pivotal milestone for the company and also for the industry. We achieved this pivotal milestone in Guangzhou City since our Gen Seven vehicle has been put into commercial service. We always believe China is the largest market in the global ride-hailing market, and for tier-one cities, the total TAM accounts for a huge percent of the ride-hailing market in China. So achieving this milestone in this market is far more meaningful from a commercial perspective. The revenue side has two major components: revenue and cost. On the revenue side, our daily net revenue per vehicle has hit 299 RMB based on a two-week daily average as of November 23, following the launch of our Gen Seven vehicle in Guangzhou. This net revenue refers to the total RMB value generated from ride-hailing services after deducting discounts and refunds. For daily orders, from this 299 RMB number, it averaged 23 orders per day, fueled by robust user demand. Now let's look at the cost side. The cost side has two major components: first, it’s the hardware depreciation, and for Gen Seven vehicles, the annual vehicle depreciation is based on a six-year useful life. The second major component on the cost side is the operational cost, including charging, remote assistance, ground supporting staff, vehicle service and maintenance, insurance, parking, and internet network costs. Regarding the remote assistant, we are on track to achieve well over a 30-vehicle ratio. This milestone gives us confidence to capture the huge TAM in China, while also establishing a strategic foundation for further scaling up. This, not only gives us strong confidence to further scale our fleet but also sees more and more third-party companies enable to fund their fleet and help us transition into an asset-light model. So all these together, we believe will drive our top-line growth while also optimizing costs. Okay, I'll pass it back to the operator.
Operator, Operator
Thank you. The next question comes from Purdy Ho with Huatai Securities. Please go ahead.
Purdy Ho, Analyst
Hello, James, Dr. Luo, and Liu. Thank you for taking my question, and congratulations on the results. We've observed a surge in diverse players attempting to enter the robotaxi operation. Particularly the legacy makers. Right? So what's your take on these new entrants in the level four autonomous driving space? Additionally, could you elaborate on the main technical and operational challenges such as tackling corner cases and fleet management for digital commerce?
James Tong, CEO
Thank you. This is James. I'll take this one. First and foremost, I think it's definitely a great thing to see more and more companies announcing their entry into the robotaxi industry. This indicates increasing recognition and confidence in the robotaxi's potential for large-scale commercialization. As the awareness increases, more resources will flow into this industry, helping to accelerate its development. Overall, I view this as a positive development. However, the robotaxi industry is not one any new player can easily enter. Currently, none of the new entrants are major OEM makers or ride-hailing platforms; none have fully driverless vehicles deployed on the road, which clearly indicates how difficult it is to enter this industry. I see three significant hurdles for any new players: business, regulatory, and technical challenges. Let’s look at the business challenges first. The robotaxi model is not just about driving; it encompasses many aspects, including user acquisition, vehicle production, fleet dispatching, maintenance such as cleaning and charging, and everything else. As a leader and first mover in this industry, we enjoy significant early-mover advantages; we have a much bigger Level 4 fleet on the road, generating better brand awareness and optimizing costs across all aspects of the business. I think all these create a big challenge for any new entrants. The second challenge is on the regulatory front since Level 4 robotaxis must meet very high safety requirements. Global policymakers will require much higher safety standards for robotaxis compared with traditional taxis. Therefore, a new player in any city needs to prove its safety credentials step by step before it can expand even into a fully driverless fleet. Typically, a new player will start by testing with just a few dozen vehicles, and once those vehicles prove to be safe, they can incrementally add more vehicles and expand operational areas after accumulating safety records. Along the way, they also need to acquire all required licenses and permits, which is itself a lengthy process. Overall, the entire process is time-consuming and cannot be easily accelerated. The third challenge is certainly technical. Let me elaborate on this.
Tianqin Luo, CTO
Sure. So, from a technology perspective, as I said in my remarks, we see a broader industry now converging on a unified model. This shows the robotaxi players and automakers are all moving toward using reinforcement learning based on simulation training environments. First, we started developing reinforcement learning for autonomous driving five years ago, giving us an early-mover advantage. We're now among the most experienced companies in world model technology. I believe we will continue to stay ahead as more peers follow the same path. With models becoming more mature, the human feedback and real-world results are key and no longer used for further iterations.
Purdy Ho, Analyst
So?
Tianqin Luo, CTO
At the stage of training in a closed loop, the word model and virtual driver coexist, constantly enhancing each other's capabilities. This co-evolution means the word model gains through feedback from the virtual driver, significantly reducing reliance on real-world data. An example is handling corner cases; the virtual driver will provide feedback to the word model. The next generation of our model will improve its capacity to generate tests, thereby enhancing the virtual driver's ability to manage corner cases. Looking ahead, our real advantage lies in validating new technologies safely and deploying them at scale. Given our proven track record in scaling robotaxi operations, we believe we can quickly capture the next wave of innovation. The recent Hong Kong IPO will further accelerate our R&D and talent acquisition cycles, reinforcing our technological leadership and widening our competitive edge. Yeah. With that said, I'll turn it back to the operator.
Operator, Operator
The next question comes from Xia Li with Jefferies. Please go ahead.
Xia Li, Analyst
Thanks for taking my question. I have one as well. My question is about what do you see as the main factors behind the faster expansion of your operational areas. Beyond technology, what else do you think really matters? And from a technical perspective, are you using large language models? If so, how are they helping push for autonomy forward? Thank you.
Tianqin Luo, CTO
Thank you. This is Tianqin. Your question consists of two parts. Let me address the question on generalization first. Technically, our tech stack is built for generalization. A good example is the operational area expansion into new regions like Shanghai, Pudong and Shenzhen, Nanshan District, in the third quarter. In both cases, it took us only a few weeks to achieve fully driverless operations for the public, without the need for additional model training. The key to reason is that our native architecture can handle corner cases and edge cases very efficiently; while these cases are often consistent across different regions, they include things like small obstacles, pedestrians crossing, and vehicles unexpectedly changing lanes. So it's just about the probabilities of each of these events happening. The key for new area expansion is the number of robotaxi vehicles. If we expand to too many areas without adding more cars, it will dilute density. Therefore, the speed of operational area expansion cannot significantly outpace fleet growth. On the topic of large language models, I will say there are two non-negotiable requirements for Level 4 onboard language models: uncompromising safety and low latency. Those long language models do not meet these requirements and are not optimized for Level 4 driving. For safety, last we noted that long language models can generate issues with model reliability, which is unacceptable for Level 4. In terms of latency, large language models are designed for throughput, while Level 4 requires optimized low latency for fully autonomous driving capabilities given typical low-power and cost efficiency requirements. Furthermore, large language models are primarily dependent on human data, which limits their usefulness to existing human knowledge. This can lead to picking up human errors, bad habits from human drivers. However, we do utilize language models in our R&D efforts for aspects such as AI-human machine interaction, coding productivity tools, documentation, and analysis of rider feedback for ongoing improvement. But due to the reasons mentioned, large language models are inherently unsuitable for driving models onboard. So, with that said, I will turn it back to the operator. Thank you.
Operator, Operator
The next question comes from Jin Yu Fang with UBS. Please go ahead.
Jin Yu Fang, Analyst
Hi, thank you, management, for taking my questions. I have one question here. Currently, we only cooperate with multiple OEMs for robotaxi manufacturing, including BAIC, GAC, and Toyota. Does management see potential for improving operating leverage through working with only one OEM team?
James Tong, CEO
This is James. I'll take this one. The reality is that in the global taxi industry, local governments and local residents have strong preferences for local branded taxi vehicles. So that's a reality. Typically, when a robotaxi fleet is small, branding does not matter much. However, if we aim to deploy significant fleet sizes, requirements change. Local branded OEMs are far more preferred. Consequently, it is necessary for us to cooperate with multiple local OEMs in different regions, which can help us expand into different markets much more quickly. That’s why we are now collaborating with three OEMs to produce our Gen Seven Robotaxis. While it is true that fitting our autonomous driving kit into different vehicles does pose a technical challenge, at the same time, the ability to standardize our technology across various vehicles demonstrates our technological generalization. In the long run, this will create a significant competitive edge, allowing us to add new models faster and accelerate our expansion into new regions. For example, in Europe, we recently partnered with Stellantis. Back to you, operator.
Operator, Operator
The next question comes from Tung Zhujia with Guosun. Please go ahead. Thanks for taking my question.
Tung Zhujia, Analyst
I have one question. Why can Pony use remote assistance on robotaxis when the car meets difficulty instead of remote control human take up? And what is the technology difference behind that?
Tianqin Luo, CTO
This is Tianqin. Let me elaborate on one of the previous questions regarding remote assistance for robotaxis. First and foremost, I'd say that the remote assistance does not control the vehicle through the steering wheel or pedals. Instead, it provides remote support and suggestions by responding to service requests. For all the time, the vehicle can independently drive and make decisions without remote assistance. The remote assistance only initiates when the vehicle requests it, rather than through remote driving. After the vehicle receives assistance, the onboard driving system will still make decisions based on the actual situation, ensuring operation remains safe without dependence on network latency. One typical example of remote assistance involves handling temporary traffic control. In such cases, the system may request remote assistance, allowing it to provide high-level suggestions to confirm the vehicle's decisions while navigating through a scenario. However, as I've mentioned, we continue to improve AI algorithms and leverage our general AI capabilities to recognize more complex context. This allows us to improve remote assist-to-vehicle ratios significantly, targeting a one-to-thirty ratio by year-end. Hope that answers your question. I'll pass it back to the operator.
Operator, Operator
The next question comes from Serena Li with China Securities. Please go ahead.
Serena Li, Analyst
Okay. Thank you for taking my question. This is Serena Li from China Security. As far as we know, some countries in the Middle East have issued fully driverless robotaxi licenses recently. What's our view on that? What trends overseas are we observing?
James Tong, CEO
Sure. This is James again. Our company's mission has always been autonomous mobility everywhere, and we certainly have global ambition to utilize our technology for local societies worldwide. Currently, our global efforts are focused on high-growth potential markets—those with strong mobility demand, well-developed infrastructure, and supportive regulatory environment. When we evaluate potential markets for entry, we consider three core factors: 1) the addressable market size; 2) the openness and execution by the local government to support and issue permits for fully driverless commercial operations; and 3) the strength of local partners for their operational capacities and resources. Our current global expansion status shows that we have already entered eight countries with our robotaxi fleet. For example, in Q3, we added Qatar as a new market by collaborating with Nova Salet. We have also seen rapid revenue growth, especially in our robotaxi services in our overseas markets. We certainly expect this momentum to continue, which means moving forward, we will enter other global markets when we identify growth opportunities. This summarizes our overseas strategy. With that, back to the operator.
Operator, Operator
As there are no further questions, I'd like to turn the call back over to the company for closing remarks.
Tianqin Luo, CTO
Thank you, operator. This is George again. If anyone has any more questions, feel free to contact the IR team. We will conclude our call today. Thank you, everyone.
Operator, Operator
This concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your line.