Operator:

Ladies and gentlemen, thank you for standing by and welcome to Pony AI Inc's First Quarter 2025 Earnings Conference Call. At this time, all participants are in a listen-only mode. After the managements 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 ir.pony.ai. 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: Thank you operator and hello everyone. We appreciate you joining us today for Pony AI's first quarter 2025 earnings call. Earlier today, we issued a press release with our financial and operating results, which is available on our Investor Relations website. Joining with me today are Dr. James Peng, Chairman of the Board, Co-founder and Chief Executive Officer; Dr. Tiancheng Lou, Director, Co-founder and Chief Technology Officer; and Dr. Leo Wang, Chief Financial Officer. 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 will be making forward-looking statements. Please also note that we will 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. I will now turn the call over to our Chairman, Co-Founder, and CEO, Dr. James Peng. Please go ahead. Jun Peng: Thanks, George. This is James Peng, Founder and CEO. 2025 is a year of scaling up for Pony AI and we embraced it with strong growth momentum. Before we dive into our business development, I would like to highlight four key milestones. Firstly, revenue from our Robotaxi services doubled year-over-year for the first quarter of 2025 with fare charging revenues grew approximately eightfold. This is a landmark validating our commercial deployment readiness. Secondly, we launched our seventh-generation autonomous driving system in the Shanghai Auto Show. The Gen 7 system achieves a 70% reduction in bill of materials, basically bond costs, compared with our last generation, the Gen 6. This showcased our technological advancement to effectively drive cost and efficiency. Thirdly, we have secured the production capability and all the relevant components to grow our fleet to 1,000 vehicles by year end, which will significantly increase our fleet density across our operational network. Lastly, we reached strategic partnerships with some key partners, such as Tencent and Uber, to forge comprehensive ecosystems, both domestically and worldwide. Now let me walk you through the details of our business progress. In late April, we showcased the Gen 7 Robotaxi lineup at the Shanghai Auto Show. Gen 7 is a game-changing breakthrough in autonomous driving technology as 100% of the sensors, components, and all the add-ons are automotive-grade, which means that Gen 7 will have an extended product lifecycle, rock-solid reliability, and also next-level safety. Its modular architecture also enables rapid deployment across multiple vehicle platforms, starting with three Robotaxi models we unveiled in the Shanghai Auto Show. Through our strategic partnership with leading OEMs, including Toyota, BAIC, basically Beijing Auto, and the GAC, the Guangzhou Auto, Gen 7 Robotaxis will enter mass production and deployment, and thereby growing our fleet size to 1,000 vehicles by year end. Most importantly, I want to highlight that such technological advancement and large-scale production and deployment capabilities have driven a measurable increase in our efficiency, lowering both the capital expenditure and also operational costs. The two key underlying drivers are, first, on the vehicle economics. Our Gen 7 Robotaxi total cost per vehicle, this includes actually both the vehicle platform and also the ADK, the Autonomous Driving Kit's BOM costs, both have all been reduced, especially with the ADK's BOM costs coming down by 70%. The cost down is driven by multiple design optimizations, including an 80% reduction in autonomous driving computation and also 68% reduction in the LiDAR costs. These efficiencies demonstrate the power of our system integration as well as scalable production approach, positioning us competitively against the industry benchmarks. Second, on operational costs. In this front, we have reached a remote assistant to driver ratio of up to 20. This means that one remote assistant can effectively monitor 20 vehicles. In addition, we largely reduced our insurance cost as our commercial insurance premiums stand at approximately half of the typical cost for traditional human-operated taxis. The aforementioned operational cost reductions are the result of our proven safety track record and also years of Robotaxi operational experience. Now moving to the operational expansion. This is another cornerstone that will empower our quick growth. With strong foundation in place for the future scaling, our total commercialized operational domains across Beijing, Guangzhou, Shenzhen, and Shanghai now span over 2,000 square kilometers. This is nearly 20 times larger than the city area of San Francisco. Our vast coverage includes some high-value transportation hubs such as central business districts, airports, and high-speed train stations. Notably, we secured China's first fully driverless commercial Robotaxi license in Shenzhen's Nanshan District in late March this year, unlocking operations in the city's core economic and transportation hubs. Regarding the market adoption of our Robotaxi service, the number of registered users on our mobile app, basically the Pony Pilot App, increased by more than 20% quarter-over-quarter in the first quarter of this year. We continue to enhance the user experience through innovative operational models, product features, and in-car infotainment systems. Our user growth trajectory will be further amplified by our new strategic partnership with Tencent, which enables us to integrate our Robotaxi services into Tencent's Weixin Mobility Services and also Tencent Maps. As a result, we can tap into China's billions of user base. As we launch Gen 7 Robotaxi later this year, we will expand our capability to meet the fast-growing user demand. It sets a solid foundation to drive our future fare charging revenues through optimized fleet utilization and enhanced customer experience with faster pickup time, superior safety, and premium comfort. Now let me share with you our global expansion progress. At Pony AI, our mission has always been, since the day we were founded, has always been autonomous mobility everywhere. Our global footprint now spans across Europe, the Middle East, South Korea, Southeast Asia, and beyond. Recently, we have achieved multiple breakthroughs in these areas. First, we have forged strategic alliance with key industry partner players. We recently partnered with Uber, which will enable users to access our Robotaxi service directly through Uber's platform. The partnership is expected to first launch in a key market in the Middle East later this year with the goal of scaling deployments to additional international markets in the future. We are also collaborating with ComfortDelGro on a joint Robotaxi pilot program. ComfortDelGro is one of the largest land transport companies headquartered in Singapore and operates in 13 markets covering Europe, China, Australia, and others. Second, we are pleased to see the ongoing favorable regulatory and testing programs globally. During the first quarter, we have secured an L4 Robotaxi testing permit from Luxembourg's Ministry of Mobility. And we also started road testing in Seoul's Gangnam district of South Korea. All these developments collectively demonstrate our platform's adaptability to complex global conditions while positioning Pony AI for future commercial scaling. In summary, I think the mass production and large-scale deployment of our Gen 7 Robotaxis remain a top priority for us. Through continuous technical innovations, we are seeing accelerating production coupled with significant cost reduction. Given the structural efficiency advantages of the Gen 7 Robotaxis, we have a clear line of sight to break even and long-term profitability. With that, I will now pass it over to our CTO, Dr. Tiancheng Lou. Tiancheng Lou: Thanks, James. Hello, everyone. This is Tiancheng. So it's a great pleasure to share with you the latest advancement and progress in our technology. So when we showcased our Gen 7 auto driving system during the Shanghai Auto Show in late April, I also shared the progress of our PonyWorld and hardware system. PonyWorld, as an industry-leading AI-powered world foundation model, has built a high-fidelity training environment and evaluation system, breaking through the limitation of imitation learning. Each week, PonyWorld generates test data exceeding 10 billion kilometers. The breadth and complexity of the data accumulated have far surpassed the data that a human driver can ever collect. This generative world model trains our proprietary virtual driver, a full-stack system featuring integrated software and hardware, where the virtual driver in turn provides valuable human feedback. It fosters the virtual cycle of continuous enhancement, accelerating the improvement in safety and reliability of our auto driving technology. The virtual driver has demonstrated our proven L4 auto driving capability in the real world, successfully navigating complex urban environments across all urban cities in China, operating through rush-hour traffic and the inclement weather. By the end of the first quarter, we have accumulated over 6 million kilometers of driverless operation, a clear validation of our technology's maturity and its readiness for large-scale deployment. Next, let me elaborate how this advancement drives operational cost optimization across the board. From a mass production perspective, hardware cost reduction remains a fundamental challenge that no automaker can avoid. With respect to our Gen 7 Robotaxis, our in-house developed auto driving domain controller is world's first to achieve full-scenario Level 4 auto driving built on auto grid chips, featuring extended product lifecycle and a mileage lifespan of 600,000 kilometers. Regarding computing platform, we are the first player in the L4 industry to adopt autonomous-grade SoC. This advancement has successfully reduced the domain controller's size, weight, power consumption, and cost, each by 50% to 80%. Our proprietary where PonyWorld has also effectively improved computing efficiency by three times through AI-influenced optimization, model distillation, and other technological innovations, significantly outperforming the broader L4 industry. As a result, we are able to adopt more cost-efficient computing power with a total capacity of 1016 TOTS. This cost-efficient approach enables us to meaningfully reduce overall costs and reach break-even as we scale up our fleet in the future. In terms of LiDARs, we have also made significant improvements to software algorithms to adapt to cost-efficient LiDARs. For Gen 7 autonomous driving kits, we opt for highly cost-efficient semi-solid-state LiDARs. While this choice may involve many trade-offs in individual sensor performance, we have compensated through advanced software algorithms that reduce the noise point by up to 30 times, ultimately resulting in enhanced overall system performance and a 68% cost reduction compared with the previous generation. For sensors, more broadly, we have achieved significant performance improvements through upgrading not just the hardware but also the algorithms. A key component of overall cost is related to remote assistance. Unlike remote control, overall system features request-based remote assistance providing suggestions to autonomous driving vehicles rather than direct or indirectly controlled. Over virtual technology allows us to achieve industry-leading remote assistance-to-vehicle ratio of 1 to 20, compared with 1 to 3 for the same period of last year. We expect this trend to continue and improve as our technology advances. Over-virtual driving enables smarter navigation and advanced monitoring capability. For instance, in scenarios like traffic police gesture recognition, no additional human support is required. Similarly, during passenger pick-up and drop-off, there is no need to manually confirm passenger status and inspect the vehicle's cabin. This allows us to operate Robotaxi services with a high-efficiency remote assistant to vehicle ratio, significantly reducing operational costs. This metric also shows how far we have advanced beyond industry standards. Moreover, our technical innovations have significantly enhanced the safety of our Robotaxi fleet, leading to a more favorable insurance economics. As James mentioned, over-commercial insurance premiums are approximately 50% of typical costs of traditional government-operated taxes. This reduction is the result of thorough accurate assessment by insurance providers who have verified our assessment log incident and claim rate. This continued decrease in insurance costs is a direct outcome of over-safety performance and a testament to the reliability of our over-autonomous driving system. Moving forward, we still have significant room to further solidify and expand our technological leadership and thereby scale up for future commercialization deployment. Before I conclude, I want to recap the tremendous effort we have made over the past few years to prepare for mass production, both on software and hardware fronts. Our Pony World platform leads the industry in areas such as technology and model training. On the hardware side, we have optimized the key components required for Robotaxi operations, including sensors, LiDARs, and domain controllers. Not only through traditional cost reduction measures, but also through parallel software driven enhancement that significantly boost our overall system performance. This concludes my prepared remarks. I will now pass the call over to our CFO, Dr. Leo Wang, for a closer look at our financial results. Leo, please go ahead. Haojun Wang: Thank you, Tiancheng. Hello, everyone. This is Leo. Before reviewing our first quarter financial results, I would like to reiterate that as we enter this pivotal year for scaling up the Gen 7 Robotaxi fleet, we remain fully committed to disciplined investment in mass production and deployment. We will ensure strong operational momentum while maintaining solid financial resilience, all to create long-term value to our shareholders. Moving to our financial performance for the first quarter of 2025, we started this year with a strong note, demonstrating solid execution of our go-to-market strategy. Revenues totaled at U.S. $14 million, up 11.6% year-over-year, mainly driven by rapid growth in Robotaxi services. The quarter-over-quarter volatility was primarily due to the variation in revenue recognition of project-based engineering solution services and product sales, a trend consistent with historical pattern. In the first quarter, Robotaxi's service revenue were U.S. $1.7 million, growing significantly at 200.3% year-over-year. This growth was driven by both fare charging and the project-based engineering solution services, with fare charging revenues achieving faster growth rate, increasing by roughly 800% year-over-year. The strong growth rate was attributed to the expansion of our public-facing fare charging Robotaxi operations in Tier 1 cities in China, as well as our optimizing operations for diverse user groups. Robotaxi services revenue grew by 4.2% year-over-year, to U.S. $7.8 million for the first quarter, primarily driven by contributions from new clients. Licensing and application revenues were flattish year-over-year at U.S. $4.5 million. We saw increasing orders and delivery for Autonomous Domain Controller, ADC sales primarily driven by new robo delivery clients. The total cost of revenue was U.S. $11.7 million, up 17.9% year-over-year, in line with revenue trends. Our gross profit reached U.S. $2.3 million, resulting a gross margin of 16.6%, down from 21% in the same period last year. This decrease was mainly due to the change in revenue mix on increased ADC sales for new robo-delivery clients in the first quarter. That being said, we are actively working on initiatives to reduce gross margin variability for the coming quarters. Total operating expenses were U.S. $58.4 million, an increase of 56.3% year-over-year. Excluding share-based compensation expenses, non-GAAP operating expenses were U.S. $49.3 million, up 35% year-over-year. The increase was primarily due to investment in the mass production for Gen 7 and one-time expenses associated with share awards settled upon the completion of IPO. Additionally, we increased employee expenses in the first quarter to strengthen our R&D capacity for concurrently developing three Gen 7 vehicle models. Reflecting the investment preparing for our upcoming production of Gen 7, net loss was U.S. $37.9 million, compared to U.S. $20.8 million in the first quarter of 2024. Non-GAAP net loss was U.S. $28.4 million, compared to U.S. $25.7 million in the first quarter of 2024. Turning to our balance sheet, our combined cash and cash equivalents, restricted cash, short-term investments, and long-term debt instruments for wealth management was U.S. $738.5 million as of March 31, 2025, compared to U.S. $825.8 million at the end of 2024. The cash outflow was primarily driven by our Gen 7 R&D effort and supply chain preparation, in which the procurement of some key components kicked off ahead of the mass production. With the imminent scaling up and commercial deployment, we believe our current cash reserve is sufficient for our future growth and will continue to explore more opportunities to ensure sustained support. Looking ahead, we are thrilled to embark on a chapter in our journey towards mass production and deployment, aiming at scalable commercialization. With our core technological advancements as the foundation, we will continue making disciplined investments to strengthen mass production capability and drive long-term cost and operational efficiencies. Our robust go-to-market strategy will also allow us to gradually reduce financial volatility and build a more predictable path to growth. I will now turn the call over to the operator to begin our Q&A session. Thank you. Operator: Thank you. [Operator Instructions]. Today's first question comes from Ming Hsun Lee with BofA. Please go ahead. Ming Hsun Lee: Thank you, James, Tiancheng, and Leo. Congrats for the first quarter results and also your launch of Gen 7 Robotaxi product. So, I have one question. As you mentioned, 2025 is a year of scaling up. How should we understand your progress throughout this year, is there any color or pipeline for 2026? Jun Peng: I'll take this question. This is James. We actually have a very clear pipeline for the Gen 7 Robotaxi mass production. This will, as I mentioned, this remains to be our main focus for this year. We expect Gen 7 will enter mass production for the second quarter and thereby bring in the total number of our fleet size up to 1,000 vehicles by year end. In addition, the large-scale deployment will ramp up gradually throughout the second half of the year. We are especially working on the following three areas to ensure a quick ramp up. First, we are closely -- working very closely with the OEM partners such as Toyota, GAC, and BAIC for the mass production across the component sourcing pre-installment and the final assembly, thereby ensuring that each Robotaxi meets the highest industrial standards of quality and safety. Second thing we're working on is our agile and flexible approach to sourcing the key components allows us to rapidly adapt to the changing demand, ensuring our stable supply chains and supporting the efficient execution of our mass production plans. Lastly, our years of collaborations with the central and local governments have established a proven track record of our superior safety level and operational capability. This enhances our credibility and positions us to secure the required licenses. Paving the way for commercial deployment of our Gen 7 Robotaxis. With the aforementioned three things that we are working on, we will focus on reinforcing these critical foundations to realize robust growth momentum of our fleet size, ensuring a scalable and sustainable expansion. As for year 2026, I think our scale up will be even more accelerated. We will produce more autonomous driving vehicles and then deploy them in China and also the international markets. With this, back to the operator. Operator: Thank you. And our next question today comes from Ting Chang [ph] at Goldman Sachs. Please go ahead. Unidentified Analyst : Thank you. Congratulations on the results. I have two questions. And the first one is, while you emphasize China's first strategy last quarter, we have now seen some progress on global markets this time. So could you elaborate more on your evolving global strategy and to what extent does the China market remain a core focus at this stage? Thank you. Jun Peng: Sure. This is James again. I think I'll take this one. As I mentioned in my prepared remarks, Pony AI's mission has always been Autonomous Mobility everywhere. While we currently prioritize the China market, giving its relatively mature regulatory environment, we believe our established ecosystem, technological advancement, and the scaled operation in China have empowered us to enter new markets with proven capabilities, experiences, and proven business models. At this stage, we are aiming for markets with strong mobility demand, advanced infrastructure, and welcoming regulations. While the commercialization of these international markets is still at the early stage, we relentlessly work hand-in-hand with our global partners to showcase the technology readiness, move forward local commercial driverless regulations, build momentum for public acceptance, and generate revenues along the course. This approach actually mirrors our achievements that we have already established in the Tier One cities in China over the recent years, which we believe our successful track record in China will also help foster greater confidence for these new markets. Recently, we have formed strategic partnerships with key global players one of them is Uber, with plans to launch our Robotaxi service on their platform, starting in a key market in the Middle East this year and expanding to other international markets. Another partner is ComfortDelGro, one of Singapore's largest transport companies operating across 13 countries, including Europe, China, and Australia. We also continue to make regulatory and testing progress globally, having secured an L4 Robotaxi testing permit from Luxembourg, and also initiated road testing in Seoul's Gangnam district in South Korea. These successes have provided us with valuable experience as we explore future opportunities beyond the China market. With this, back to the operator. [Multiple Speakers] Unidentified Analyst : My second question is, you deliver very impressive revenue growth in Robotaxi. What factors are driving behind this quarter, and do you believe this is sustainable in the upcoming quarters? Thank you. Haojun Wang: Yes, this is Leo, and I'll take this question. So the revenue growth in Robotaxi segment was driven by both fare charging and project-based engineering solution services. With fare charging revenues achieving at a much faster growth rate, increasing by roughly 800% year-over-year. The strong growth rate was attributed to the expansion of our public-facing fare charging Robotaxi operations in Tier One cities in China. We also optimized our operations to cater to diverse user groups, such as interactive reward features. I would also like to take this opportunity to explain our Robotaxi revenue structure. Our revenue are currently generated from two main streams. The first stream consists of engineering solution services, which are recognized upon the achievement of project milestones. Hence, this is project-based and could fluctuate among quarters. The second stream is recurring revenue, primarily from our virtual driver operations, such as our Robotaxi fare charging services. While the project-based revenues currently make a larger portion in our total Robotaxi revenue, we believe the non-recurring revenues we are generating from partners, such as ride-hailing platforms, OEMs, and other parties are very critical to enhance and advance our recurring revenue stream. These collaborations also further pave the way for a robust long-term monetization model. As a result, we anticipate some natural volatility in revenues from-to-quarter in this segment. That being said, we will gradually reduce financial fluctuations and are very confident to deliver a strong growth trajectory in the long term. I'll get back to the operator. Operator: Thank you. And our next question comes from Bin Wang with Deutsche Bank. Please go ahead. Bin Wang: Thank you for taking my question. My small part of technology perspective, you mentioned that in the ADK pricing was quite dramatic. Did you need to upgrade your software to fulfill this ADK cost reduction, in particular, what's the improvements you're doing for computing power, you also mentioned that you actually would decline 48% of the cost for computing power? Thank you. Tiancheng Lou: Thank you. I will take this question. So this is Tiancheng. Before I answer your question, I would like to say that we believe in the field of front-driving technology. Pony AI is poised to represent China's leading companies in embracing the deep-thick moment. So by optimizing our PonyWorld and enhancing engineering capabilities, we have designed a cost-effective hardware and software system. This enables us to significantly improve inference performance while reducing associated cost, even with auto grid SoC and the lower-precision LiDAR sensors. So for instance, the old PonyWorld had effectively improved computing power efficiency by three times through AI inference optimization, auto-distillation, and other innovations. Significantly outperformed the broader L4 industry. As a result, we're able to adopt more cost-efficient computing power with a total capacity of 1,016 TOTs [ph], compared with industry pairs typically range from 2,000 to 5,000 TOTs. In terms of the inference computing, we have implemented numerous optimizations, such as optimizing operators in the AI model, increasing computational parallelism, and improving model memory efficiency to enhance inference performance. So all these efforts help us to improve cost efficiency without giving up performance, proving that we're able to realize the cost-effective and the scale of L4 driverless auto driving. Thank you. I will give back to the operator. Operator: Thank you. And our next question today comes from Qiu Yangyi with Huatai Securities. Please go ahead. Unidentified Analyst : Thank you for taking my question. So congrats on your expansion in Robotaxi services. So we noticed that the Ministry of Industry and Information Technology of China has recently issued some regulatory requirements regarding driver assist. So I just wonder that could this potentially have an impact on Pony AI? Thank you. Tiancheng Lou: Yeah, this is Tiancheng. I will take this one. So I think a lot of people mistakenly equate L2 driver assist with L4 auto driving. Recently, the Ministry of Industry and Information Technology, MRIT, issued a notice clearly states that L2 is not equal to L4. The key requirement from MRIT includes, first, the manufacturers or solution providers must avoid using misleading terms, such as auto driving, intelligent driving, when promoting L2 driver assist system. Second, manufacturer solution providers are required to clearly define the capabilities and safety measures of driver assist system. Terms like zero takeover or hands-off must not be used. And the responsibility of the driver for continuous monitoring must be emphasized. So we believe this is a clear beneficial for Pony AI as it helps foster a comprehensive and clear understanding of distinctions between L2 and L4 for the public. That's also the reason why we consistently emphasize that L2 and L4 are fundamentally different in value add to the customer. Being more specifically, only L4 can truly fulfill user's need in the situation where they are looking for relaxation or even wish to take a nap while auto driving system is on. So I would like to go into more detail about the technological difference between L2 and L4 systems. So L2 system widely uses imitation learning. The AI drivers learn by copying human behavior from real-world driving data. The limitation of imitation learning is that AI driver cannot understand the reasoning behind the driving behavior. So as a result, it is not safe enough to handle ever-changing traffic scenarios. For L4 system, we use the reinforcement learning and over generative PonyWorld. So under PonyWorld, our virtual driver teach itself through a large amount of generative data. This allows our virtual driver to understand why by analyzing the outcome of every action, teaching them to make smarter decisions in different scenarios and eventually surpass the safety of human drivers. So over time, our virtual driver trained under PonyWorld developed advanced skills needed for complex tasks such as multi-navigating urban areas, handling unpredictable traffic scenarios, or safely operating for 500,000 hours without any human intervention. More importantly, the key compatible edge differs significantly between the two approaches. For imitation learning requires large amount of data, while reinforcement learning relies on heavily on AI motor type abilities. These underlying distinction creates a considerable barrier, making it challenging to transition from one to another. It basically requires companies to start over and build a whole team from [indiscernible] which means it cannot be simply acceleration through prior experience. Yeah, thank you. I will go back to the operator. Operator: Thank you. and our next question comes from Sha Ulay [ph] with Jefferies. Please go ahead. Unidentified Analyst : Hi. Thanks for taking my question. My question is regarding the U.S.-China tariff issue, which appears to be easing at the moment. But I'm still wondering, well, it has to have any potential negative impact on the operations, how many materials are you sourced from the overseas market? Haojun Wang: Thank you and this is Leo. I'll take this question. We believe the potential impact from the tariffs issue will be very minimal to our operation. First, the majority of our supply chain is domestically sourced. Second, over the past few quarters, we have enhanced our supply chain resilience in response to the evolving geopolitical landscape. This includes diversifying suppliers and also increasing inventories when necessary. As a result, we are well prepared to manage this risk. In addition, I would like to highlight that our Gen 7 mass production plant has also been reflected with these assumptions and also uncertainties. Therefore, we are confident that our full-year target of deploying 1,000 unit fleet size is on track and will not be affected by the changing trade environment. I will now get back to the operator. Operator: Thank you. This concludes our question-and-answer session. I'd like to turn the conference back over to management for closing remarks. Jun Peng: Thank you once again for joining us today. If you have any further questions, please feel free to contact our IR team. We look forward to speaking with you in the next quarter. Operator: Thank you. This concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines and have a wonderful day.