Pony AI (PONY) Q3 2025 Earnings Call Transcript

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Date

Tuesday, Nov. 25, 2025 at 7:00 a.m. ET

Call participants

• Chairman and Chief Executive Officer — James Tong
• Chief Technology Officer — Tianqin Luo
• Chief Financial Officer — Liu Wang
• Head of Capital Markets and Investor Relations — George Shao

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Takeaways

• Hong Kong dual primary listing -- Completed on the Hong Kong Stock Exchange, raising over $800 million and marking the largest autonomous driving IPO globally this year.
• Fleet expansion -- Fleet size surpassed 900 vehicles by November, with expectations to exceed 1,000 robotaxis ahead of year-end and targeting over 3,000 vehicles for 2026.
• Robotaxi revenue -- Reached $6.7 million, up 89.5% year over year and 338.7% quarter over quarter; fare charging revenue rose 233.3% year over year prior to Gen 7 commercial launch.
• Robotruck revenue -- Generated $10.2 million, representing 8.7% year-over-year growth.
• Licensing and application revenue -- Achieved $8.6 million, up 354.6% year over year, mainly from autonomous domain controller demand.
• Total revenue -- $25.4 million, a 72% increase year over year, fueled by robotaxi commercialization and licensing/application growth.
• Gross margin -- Improved to 18.4% from 9.2% the previous year; gross profit recorded at $4.7 million, attributed to revenue mix optimization and higher-margin robotaxi revenue.
• Operating expenses -- Total operating expenses rose to $74.3 million, up 76.7%, with non-GAAP operating expenses at $67.7 million, up 63.7%, including a $12.7 million one-time R&D expense related to Gen 7 vehicles.
• Net loss -- Reported net loss for the third quarter was $61.6 million, compared to $42.1 million in the third quarter last year; non-GAAP net loss for the third quarter was $55.0 million, compared to $41.4 million last year.
• Cash position -- Cash and equivalents stood at $587.7 million as of September 30, 2025, down from $747.7 million at June 30, 2025, driven by capital injection into a Toyota JV and Gen 7 vehicle procurement; free cash outflow was $173.6 million for the first nine months.
• User metrics -- Registered users nearly doubled within one week of Gen 7 robotaxi launch; daily net revenue per vehicle in Guangzhou reached RMB 299 with 23 daily orders per vehicle.
• Gen 7 robotaxi milestone -- Achieved city-level unit economics breakeven in Guangzhou shortly after commercial launch.
• Asset-light model adoption -- Accelerated fleet growth and capital efficiency through partnerships with third-party operators, generating additional technology licensing and vehicle sale revenue streams.
• International expansion -- Robotaxi presence established in eight countries, with new operations launched in Qatar and ongoing regulatory licensing across East Asia, Europe, and the U.S.
• Production advances -- Over 600 Gen 7 robotaxis produced by November; Bill of materials costs reduced by 70% for Gen 7 compared to prior generation and 20% further reduction planned for 2026 production.
• Robotruck technology -- Gen 4 robotruck to launch in 2026 with fully automotive-grade components, a 70% reduction in ADK hardware cost, and planned deployment at 1,000-vehicle scale.
• Remote assistance ratio -- Improved to a target of 1 remote assistant per 30 vehicles by year-end to enhance operational efficiency.
• IPO proceeds use -- Proceeds from the Hong Kong IPO to fund fleet expansion, platform optimization, and R&D initiatives for long-term growth.

Summary

Pony AI (NASDAQ:PONY) accelerated its global strategy with a completed $800 million Hong Kong listing, directly bolstering cash reserves and enabling planned rapid fleet expansion. The company demonstrated clear traction in scaling its Gen 7 robotaxi business, evidenced by city-level breakeven in Guangzhou and targeted deployment above 3,000 vehicles in 2026. Operational metrics highlighted compelling user adoption and cost optimization, while management introduced an asset-light, partnership-driven model for fleet growth, capturing both technology licensing fees and vehicle sales revenue. The transition to automotive-grade, cost-reduced platforms extended across both robotaxi and robotruck offerings, with Gen 4 robotruck production and initial deployment expected in 2026. Increased R&D investments supported technology leadership, but drove higher operating expenses and net losses, offset by record revenue growth, improving margins, and the company's strengthened capital access.

• James Tong said, "We have already seen the flywheel. In action. Expanded fleet is driving higher user adoption. Shorter wait time, more orders, and a strong revenue growth."
• Chief Financial Officer Wang said, "our daily net revenue per vehicle has hit 299 RMB. It's based on a two week daily average figures as of November 23. Following the launch of our gen seven vehicle in Guangzhou. And this net revenue also refers to the total RMB value generated from ride hailing service after deducting discounts. And the refunds. And in terms of daily orders, from this 299 RMB number, it was average 23 orders per day. It's fueled by robust widespread of user demand." and confirmed citywide unit economics breakeven for Gen 7 robotaxis.
• Chief Technology Officer Luo reported a 70% bill of materials cost reduction for the Gen 7 autonomous driving kit versus the previous generation and an additional planned 20% reduction for the Gen 7 platform designed for 2026 production compared with the 2025 baseline.
• IPO proceeds of over $800 million are explicitly allocated for accelerating fleet expansion, platform optimization, and increased R&D spend to reinforce core technology.

Industry glossary

• Gen 7 robotaxi: The company's latest autonomous vehicle platform, incorporating substantial improvements in cost efficiency and operational scale for ride-hailing applications.
• Asset-light model: A business framework whereby Pony AI partners with third-party operators, who purchase vehicles and deploy them as robotaxis, reducing Pony AI's direct capital expenditure.
• ADK (Autonomous Driving Kit): The proprietary combination of hardware and software enabling self-driving capabilities in Pony AI's vehicles.
• Unit economics breakeven: The point at which revenue generated per vehicle covers its associated direct operational costs, indicating commercial viability at the city level.
• Pony Pilot: Pony AI's ride-hailing application platform for robotaxi users.

Full Conference Call Transcript

Operator: Hello, ladies and gentlemen. Thank you for standing by, and welcome to Pony AI Inc's Third Quarter 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 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: Thank you, operator. And hello, everyone. We appreciate you joining us today for Pony AI's third quarter 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 with me on the call today are doctor James Tong, chairman of the board and chief executive officer. Doctor Tianqin Luo, chief technology officer. And doctor Liu Wang, chief financial officer of the company. They will provide prepared remarks followed by a q and a session.

Before we begin, please refer to the safe harbor statement in our earnings press 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, Doctor. James Peng. Please go ahead.

CEO Remarks (James Tong)

James Tong: 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 the domestic investors, We secured the largest IPO in the global autonomous driving sector this year. Raising more than 800,000,000 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 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. Expanded fleet is driving higher user adoption. Shorter wait time, more orders, and a strong revenue growth. After launching Gen seven Robotaxi, we have already sync a citywide unit economics breakeven This in turn gives us more room to increase fleet size. The capital we raised also fills our business development research and development, market making strategic investments in new markets, new applications, and attracting world class AI talents. All these are set to further propel our technology leadership and the 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 robo Texas across Guangzhou, Shenzhen, and Beijing. Today, our management team, including myself, actually arrives at our Shenzhen office in a fully driverless gen seven robotaxis to host this conference 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 UE 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 attract more and more third party partners enabling them to fund our fleet. And the support. Our asset light model. The scaling up of a fleet is key to our growth. As large scale operational footprint drives efficiency through the economy of scale. Our robotaxi vehicles are moved essentially moving billboards. In fact, many new users discover and download our Pony pilot app after spotting our vehicles. On the road for 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 establish 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 effective go to market strategy. Now let me highlight some key advanced we made in recent months in executing our scale up strategy. First, we have ramped up production at a 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 be 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 speed 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 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, 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 more and bigger city areas. Including Circle and Overseas Chinese town. We're taking major steps toward scale up strategy. So following our collaboration with Hehu in June, we recently forged another partnership with Sunlight Mobility This alliance reflect growing market recognition of our business model, with 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 advance 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 US. We entered a new market in The Middle East. Qatar. Through a partnership with Nova Salet in third quarter. Nova Soleil 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 continue to deepen We're closely with Comfort Air World, the country's largest transportation fee transportation service provider. To begin road testing in Luxembourg, we plan to deploy testing vehicles based on the perjury eTraveler through our alliance with the Stellantis. It's a European leader in light commercial vehicles. This effort will initially focus on vehicles designed for Europeans diverse mobility need 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. A boat is a 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 in 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 Robotex robotruck delivers a significant more efficient cost structure and a greater energy saving. 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 have multiple vehicles to support. Our further operations. To sum up, 2025 is a critical year of mass production and the commercialization for Pony AR. We take pride in the progress we have made and are steadily delivering on the promise we have made to our shareholders at the time of our US 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 values. 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, doctor Tianten Lo, to share more about our technology strategies. Hinton, please go ahead.

CTO Remarks (Tianqin Luo)

Tianqin Luo: Thanks, James. Hello, everyone. This is Tian Cheng. Let me first share my thoughts on our home driving technology stack. From day one, we believe that full stack integration across software, hardware, and operations was the only way to build a truly scalable autonomous mobility. That conviction have been validated again and again. Especially for this critical year of scaling up. With the achievement we made, it is clear to over early technology best help us help us achieve the leading position and it will further accelerate our future growth. Our deep foresight into tech stack what is what is positioning us as a leader in the industry today.

As we become one of the few company to operate large scale 40 driverless stroke protection services. So as early as 2020, we recognize the importance of a training go through base on reinforcement learning unit simulation. In that year, we transit transitioned over tech stack into a one model. Which is what we call a pony word today. Through years of R and D effort and the real world validation, over a top driving world of the driving model have evolved into a closed loop training. We achieved unsupervised self improving iterations. In recent years, we are seeing the broader autonomous and robotic industry coverage converge on one model. Validating the approach we adopt today.

This full time in AI tech stack has given us a meaningful head start and we're confident that we will stay ahead of for multiple years.

Tianqin Luo: Then let me dive into the three criteria that put us the frontier forefront of what model development. First, the high fidelity impacted simulation. This is far beyond the ability to just generate the scenarios and render sensor data. Driving is by nature interactive. The robotaxis action directly affect how to run the agent to behave. Such as other vehicles and pedestrians need to react to over driving behavior. It must understand and adapt to new situation and the complex physical interaction in real time. Mirroring true unload interactions. It enables robotax operation that are safe, smooth, and social aware.

After 10,000,000,000 kilometer of test miles that only were generated each week, more than 99% kept vehicle agent detections, while less than 1% are still static environment such as center rendering. Okay. Second, the ability to reproduce scale and the realistic color cases. While this long tail scenario don't occur frequently, the way are they are critical to safety. In our top More importantly, every scenario must be something that could real have really happen in the real world. Not those use case useless edge cases with no basic no basic in reality. So the third, the AI based learning evaluator. This is the reward based evaluation mechanism.

Driving is a multiple object optimization problem What is considered as a good driving also changes in various driving scenarios. Within the cross loop training environment, the PonyWord and our virtual driver are continuously evaluate on key driving metrics. This assessment does not rely on real world data. Human label data, or rules. Instead, it use AI in part model to learn what good driving looks like directly from the outcomes. Turning real and assimilated experience 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 to realizing large scale driverless auto driving.

And leveraging over full stack technology as a core strengths, I will now turn to how to drive business progress during the third quarter. First, on cost and operational efficiency. We pioneer we pioneered 100% automotive grade autonomous driving kit. For gen seven robotaxis. We've optimized the design reduce reducing bomb cost by 70% compared with the previous generation. The gen seven v have been officially operating for public in Guangzhou, Shenzhen, Beijing, fully validating our safety standard and operational efficiency. We build on our momentum and deliver further progress.

Driving by scale the production and enhance R and D We've already realized an additional 20% reduction in the atomic driving kit from cost for the gen seven platform designed for 2026 production, compared with 2025 baseline. This slide foundation for sustained cost fit Our robust AI algorithm and fleet management has proven effective at driving operational efficiency. To better identify user demand in hotspot areas, during rush of hours, we will hand our algorithm for all the dispatch. Matching, scheduling. Thereby ensuring sustained different sustained efficient robotactic utilization. Have also improved our virtual driver to recognize more and more complex scenarios. This allow us to improve over remote assistant to vehicle ratio substantially.

On the track to reach one to one to 30. By year end. Our superior servers service experience have become the key reason user choose only Airover taxi. After launch of Gen seven robotaxis, we will earn the worldwide widespread positive feedback and generate great social media bot from users. As we deliver high quality experience, users are increase increasingly willing to pay a premium for the enhanced effort reliability, the safety of the of our autonomous journey. For ride comfort, over advanced interactive planning cap capability intelligence to optimize for the frequency, and the magnitude of acceleration, braking, and steering. This delivers smooth natural motion control. Tell to the electronic vehicles and the ride sharing markets.

Offering consistent comfort experience for every Polyair prover taxi ride. This enhancement have reflect the imaginable improvement for gen seven such as the emergency brakes and the steering over the past few months.

Tianqin Luo: Additionally, our low tech features are super in cabin experience. We also pioneered the innovative smart positioning feature with one tap, user can remotely adjust their vehicle position for more convenient pickup and drop off. Introduced the voice active features call it POPO voice assist. Allow users to do star trips, and the country air condition, etcetera. We will continue to upgrade to the cabin into an AI powered mobility terminal. Together, this upgrade create a more accessible and streamlined user experience.

Tianqin Luo: So third, over text stack is also built for generalization. The alpha native tech architecture allow us to adapt quickly to new markets and platforms. In terms of cost region generalization, all virtual drive and the show is can quickly understand and adapt to diverse traffic conditions around the world. For example, leveraging over high fidelity training environment and evaluation mechanism powered by 40 jobless coverage in Pudong District in just a few weeks. In addition, when sending to Europe, the system intelligently identified and adapted key difference in local road conditions. Such as unique traffic signals configuration, and the various driving patterns. Our technology boost generation power across platform as well.

The latest generation robot truck will commence production and operation from next year. This demonstrate our capability to create synergy between Robotexi and Robotrex tech stack. Looking ahead, we will leverage our success Hong Kong listing to reinforce our technology core leadership. Increasing r and d investment, and attract top AI talent to advance our robotaxi, robotruck, and new market initiatives. We will continue pushing the frontier of the autonomous mobility refining what is possible in the transportation. Okay. This concludes my prepared remarks. I will now pass the call over to our CFO, doctor Liu Wang. For a closer look at our financial results. Liu, please go ahead.

CFO Remarks (Liu Wang)

Liu Wang: Thank you, Tien Tsin. Hello, everyone. This is Leo. I will focus on year over year comparisons for the third quarter. Unless otherwise noted. Q3 twenty five was a landmark quarter. We delivered a robust revenue growth specifically with solid progress in robotaxi large scale commercialization. And now we expect to outperform our full year fleet target of 1,000 vehicles. Moreover, our newly deployed Gen seven robotaxis fleet have reached a pivotal citywide unit economic breakeven milestone. This layout a solid foundation for further scaling up. And the implementation of ASA Live business model. Well which will be further accelerated by our success 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 robotaxis 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 the 338.7% quarter over quarter. Specifically, fare charging revenue continued to deliver a 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 Gens five and Gens 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 operation and the pricing strategy, altogether leading to increased fleet utilization and efficiency. This is a testament to growing user recognition and the brand royalty to Pony Pilot service Going forward, as we follow this strong momentum towards a significant fleet expansion, of over 3,000 vehicles by 2026.

Tianqin Luo: We expect

Liu Wang: robotaxi revenue growth to accelerate even further driving more orders and a higher operational efficiency. In Q3, another key robotaxi update is the implementation of our ASA Lido 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 vehicle. To run 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 fee and the vehicle sales. While giving us further leverage and capital efficiency for further fleet expansion.

Aside from strong top line growth domestically, we are also seeing fast growth of robotaxis revenues from overseas market. Moving forward, we expect robotaxi revenues from overseas market to continue to grow. Currently, our robotaxi footprint have already expanded into a country globally. Serving as a promising foundation in our exploration of the 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 bound cost of its ADK autonomous driving hardware kit. By 70% and the reach a thousand unit scale of Robotruck fleet going forward.

This new generation of Robotruck will powerfully accelerate the progress of Robotruck 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 of our autonomous domain controller. Primary from robot delivery clients. Turning to gross margin. We delivered a significant gross profit margin improvement from 9.2% in Q3 twenty four to 18.4% in Q3 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 robotaxis services. Which carry a relatively higher margin.

The UE, the unique economic breakeven achievement validates our due focus on go to market execution. And optimize the 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 service after deducting discounts and the refunds. Notably, daily average orders per vehicle have reached 23. Fueled by a robust widespread user demand and our operational optimization. Meanwhile, we have also optimized the hardware depreciation as well as operational cost. Including charging remote assistant, ground support, service, maintenance. Insurance, parking, and network costs. This will further improve our margin down the road.

The total operating expenses were 74,300,000.0 US dollars up by 76.7%

Tianqin Luo: Excluding share based compensation expenses,

Liu Wang: non GAAP operating expenses, were 67,700,000.0 US dollars. Up 63.7%.

Tianqin Luo: The increase primarily reflects

Liu Wang: the one off r and d investment in gen seven vehicles and the expansion of our r and d personnel. Critical to securing and extending our technological leadership. Specifically, approximately half of the increase in research and development expenses stemmed from onetime customized development fee of 12,700,000.0 US dollars for gen seven vehicles. 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 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 instrument for wealth management were 587,700,000.0 US dollars as of 09/30/2025. Compared to the balance as of 06/30/2025 of 747,700,000.0 US dollars. Around half of this decrease comes from one off cash outflow, including capital injection to Jifeng our joint venture with Toyota, to support a 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 the large scale deployment status including firstly, ongoing operational cash outflow, and secondly, capital expenditure for the Procurement of gen seven vehicle in Q3.

To support our goal of 1,000 vehicle fleet by year end. For the nine months ending 09/30/2025, we have a 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 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 and D investments. To further solidify our technology mode. 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 reinforce our confidence in scaling rapidly. And we now anticipate to grow our fleet to be more than 3,000 vehicles by 2026. In addition, we've already transitioned to a asset light model for a meaningful portion of our new vehicles. This will enhance our capital expenditure efficiency. And provide a 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 and a session. Thank you.

Question & Answer Session

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. And the first question comes from Ming Shun Li with Bank of America. Please go ahead.

Ming Shun Li: Thank you. Thank you management to give the opportunity for me to ask a question. So I just have one question. So could the management team give us some more update on the flea size for this year and also outlook in 2026. For the new vehicles added, what is the full fleet deployment plan across different city? Thank you.

James Tong: This is James. I'll take this one. So as you can see that since the launch of our gen seven robotaxi, we actually have seen a much faster than expected production and the deployment. So for this year, we certainly expect to outperform our previous target of 1,000 robotaxis by the year end. We certainly expect this strong momentum to continue into 2026. Now with conservative target of over 3,000 vehicles, This is mainly because we have already seen upward spiral with the launch of our gen seven vehicles. Essentially, the fleet density creates a much shorter wait time for the passengers. And then that creates a better user experience.

And then the user experience leads to much higher utilization for our vehicles. And, then we can actually then charge a better pricing So this spiral really created a strong momentum for us to expand much faster. In addition, we also started experimenting with the asset light model. By collaborating with fleet managers such as, Shihu, Sunlight, and certainly we'll add more partners This asset light model allows us to deploy at a much larger fleet with, less CapEx. So this is our growth plan. Then in terms of the fleet deployment plan, we'll go deeper on our existing markets and at the same time, we'll go much wider to explore some new opportunities.

The citywide UE breakeven for the gen seven in Guangzhou In my view, it's a pivotal milestone to validate our business model. This gives us a huge confidence and allow us to deepen our collaboration and our operation in the existing markets, which are the tier one cities in China. This is because as I already mentioned, expand expanded fleet size creates a upward spiral. But at the same time, we also expand into many more domestic cities and also the overseas markets.

We see those for our future growth, Our go to market strategy on those markets is that we'll collaborate deeply with the local partners and the local government agencies to establish presence and prepare for our future growth. So stay tuned. I think we'll have, great news ahead of us. With that, back to the operator.

Operator: Thank you. The next question comes from Bin Wang with Deutsche Bank. Please go ahead.

Ming Shun Li: Hi, management. Thank you for taking my question. I just have one question. Which is about the charging. I'd like to know fair charging revenue delivered another growth in 03/2025. So what is the outlook for fair charging revenues as we deploy more vehicles? Thank you.

Liu Wang: Yeah. This is Leo. I'll take this question. Yes. In Q3, our fair charging revenue actually surged even faster. It was growing about two hundred and thirty three percent. Though at that time, our fleet were still with the gen five and gen six, gen six vehicles. So we believe such growth was driven by both the demand side as well as the operational side. On the demand side, we have been continuously to do our effort to improve the whole writing experience and also the user experience. So with this effort, we've seen, robust and organic user demand in tier one cities. This is also a signal of a strong consumer adoption of our robotaxis service.

Giving you an example that the total registered user Was more than doubled, year over year in Q3. And on the operational side, we have also been optimizing the fleet operation to improve our vehicle utilization and the order fulfillment as Tianqin already mentioned in his remarks. So for example, we enhanced our fleet dispatching and the deployment This has consistently reduced our wait time. It's approximately 50% shorter compared to the same period. In 2024. And we also continue to expand our pickup and drop off points to create a much more smooth user experience. For example, in Shenzhen, now we have more than 10,000 such points. More than 300% increase since the end of June this year.

With all this, you know, demand side and operational side improvement, I believe we could see sustained strong growth momentum through the continuous fleet expansion with more and more gen seven vehicle are into our service. First of all, we expect that our fleet has been growing exponentially from 270 next last year and to be more than 1,000 this year. And a target of more than 3,000 next year. This scaling up would also create a better network effect. Which means shorter wait time and higher vehicle utilization and higher user adoption. We would also progressively expanding our service area. In cities such as Shanghai, Shenzhen, we've already been doing so today.

We would increase the population coverage and expanding to more drivable mileages. Etcetera, etcetera. With all these being done, I think we can boost the average order value per chip. Okay. I'll get back to the operator.

Operator: Thank you, sir. The next question comes from Kyle Wu with Citi Research. Please go ahead.

Unknown Executive: Thanks for taking my questions. This is Kyle from Citi Research. And congratulations on achieving the milestone of Citi wide UEFA even. Could you elaborate more about the assumption behind the delivery per event? Including daily order, pricing, daily operating hours, and a ratio of remote assistance. Thank you.

Liu Wang: Yes. I'll I'll take this question. Like you said, we all believe the citywide u unique economic breakeven is a pivotal milestone for the company and also for the industry. First of all, we you know, achieved this pivotal milestone, in Guangzhou City, since our gen seven vehicle. Has been put into commercial service. And we always believe China is the largest market of global ride hitting market. And for the tier one cities, the total TAM accounts for a huge percent of ride hailing market in China. So achieving this milestone in this market is far more meaningful. From commercial perspective. Then if we talk about the unique economic, there's the revenue side. There's always the cost side.

On the revenue side, first of all, on the daily net revenue per vehicle, As I mentioned, our daily net revenue per vehicle has hit 299 RMB. It's based on a two week daily average figures as of November 23. Following the launch of our gen seven vehicle in Guangzhou. And this net revenue also refers to the total RMB value generated from ride hailing service after deducting discounts. And the refunds. And in terms of daily orders, from this 299 RMB number, it was average 23 orders per day. It's fueled by robust widespread of user demand. Now let's look into the cost side. So the cost side of the unique economic basically, has two major component.

First of all, it's the hardware depreciation. For gen seven vehicle, the annual vehicle depreciation is based on a six year useful life. The other major component on the cost side is the operational cost. Which include the charging remote assistant, and the ground supporting staff. Vehicle service and maintenance, insurance, parking, Internet network cost, So regarding the remote assistant, we are on track to achieve our well over 30 vehicles. And from this milestone that we achieved, we are very confident to capture the China huge TAM. Meanwhile, it also established a strategic foundation for further scale scaling up. Domestically and internationally.

This not only give us strong confidence to further scale our fleet, But we also see more and more third party companies are enabled to fund their fleet and helping us to transition into a satellite model. So all these together we believe will drive our top line growth and also the call cost optimization. Okay. I'll go get back to the operator.

Operator: Thank you. The next question comes from Purdy Ho with Huatai Securities. Please go ahead.

Unknown Executive: Hello, James, doctor Law, and Liu. Thank you for taking my question, and congratulations on the results.

Purdy Ho: We've observed a surge in diverge players attempting to attempt into the robotaxi operation. Particularly the easy makers. Right? So what's your take on these new entry entrants in the l in the level four autonomous driving space? And not so specifically, could you elaborate on the main technical and operational challenges such as tackling corner cases and fleet management for digital commerce.

James Tong: Thank you. This is James. I'll take this one. So first and the foremost, I think it's definitely as we see more and more companies announcing that they're gonna enter into robotaxi industry, I think itself, is actually a great thing because it indicates increasing recognition and the confidence in robo taxi imminent potential for the large scale of commercialization. As the awareness increase more resource, More companies come in. More resources will pour into this robotaxi industry. To actually accelerate its development. So overall, I view this as a good thing. But on the flip side, the robotaxi industry is actually not a one that any new player can easily enter.

Because as you can see, the fact is that currently none of the new entrants are being OEM maker or being a ride hailing platforms? None of them have fully driverless vehicles deployed on the road to road. So it's clear evidence this is not easy industry to be entered. I think there certainly three huge hurdles for the any new players. And those hurdles are business side, regulatory side, and also technical challenges. Let's probably look at the business challenges first. Because Volvo Taxi, as you see, it's not just about airfoil driving itself. It also has many more aspects such as user acquisition vehicle production, fleet dispatching, fleet maintenance, such as the cleaning, charging, and everything else.

So as a leader, and first mover in this industry, we certainly enjoyed the early mover advantages. As we have a much bigger l four fleet on the road. We generated a better brand awareness We have optimized the cost on every aspects of the business as Leo already mentioned in his answer to the last question. And the we because of early mover, we also have secured more partners. I think all those are important and it creates big hurdle for any new entrants. The second hurdle that I wanna mention is on the regulatory front. Because l four, a robotaxis needs very high safety requirement.

All the policymakers worldwide have fundamentally will require a much, much higher safety requirements for the robotaxis compared with the traditional taxi That means in any city, a new player needs to prove its safety stepped by step. Before they can expand. Even into a fully driverless fleet. Typically, a new player will start with a testing with just a few dozen or maybe even less vehicles. And then once those vehicles prove to be safe, they add more vehicles and then expand operational areas. After they can accumulate the safety records. And along the way, they also need to acquire all the required licenses and permits And this is in itself is actually a lengthy process.

So overall, the whole process takes time. And this code starting process cannot be easily accelerated. So that's the second challenge. The third challenge is certainly in my view, is on the technical side. And probably for this one, I'll tend to tend to elaborate.

Tianqin Luo: Yeah. Sure. So I'm Kenton. So let me continue from a technology perspective. So as I as I said in my prepared remarks, we are now seeing the broader industry starting to using one model. Such as robotaxi players and automakers. Essentially, they are all about using reinforcement learning based on simulation training environments. First and foremost, I would say we started developing reinforcement learning for account driving five years ago. This give us a early mover advantage. They have one of the most experienced company in the world model. We believe that we'll continue to stay ahead as more peers follow the same path.

So once the word more mature now, the human feedback and the real word, they no longer used for further iterations.

Purdy Ho: So

Tianqin Luo: at the stage of training cost loop, the word model and the virtual driver co evolve into a dual spiral cycle. This means the word model and training the virtual driver And at the same time, the word model improves sales through feedback of the virtual driver. This sharply reduce reliance on the real world data. Question will touch on the technical challenge before the meeting of corner cases. Maybe example here that why the virtual driving some corner cases. So this is gonna give feedback to the word model. And the word model will improve its distribution of the corner cases.

Then the next generation next version of our model will be able to create a generator testing and also improving the capability of the virtual battery handle the chronic cases. Okay. So looking ahead, our real advantage lies in ability to validate new technology safely and then deploy that scale. So based on our proven track record of scaling Robotech's operations, so we believe can quickly capture the next wave of innovation. Also, last but not least, our current Hong Kong IPO will further accelerate IND and the attrition cycles. Reinforcing our technical leadership at a widening over competitive mode. Yeah. With that, I'll back to the operator.

Operator: The next question comes from Xia Li with Jefferies. Please go ahead.

Purdy Ho: 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. And beyond technology, what else do you think really matters? And from the technical perspective, are you using large language models? And if so, how are they helping push for autonomy fall forward? Thank you.

Tianqin Luo: Thank you. This is Kim Chubb. Will continue to answer this question. I think your question consists of two parts. Let me answer your question on generalization first. Then we address the other one on large language model later. Generalization, would say tech technically, over text side, it's by nature built for generalization. So a good example is that over operational area expansion into new areas in Shanghai, Pudong and Shenzhen, Nan Shan District, the third quarter. In both cases, it only took us only a few weeks for our verifying the city to truly realizing fully drivers operation to the public. There was no need for additional model training.

Quick the key reading that and, also, native architecture is a beautiful handling corner cases and to June cases. While these cases are actually very consistent across different regions, They are really nothing more than things like small obstacles, boxes on the road. Pedestrians that they are crossing. And suddenly, they change from other cars without looking at the vehicle behind. Etcetera. So it's just about the likelihood and the probabilities of each what happening. So hope that can help understand why the awful tech stack by nature built for generalization. So at this moment, I will say, the key to over new area extension, the number of v number of robotaxi vehicles.

If we extend to too many areas without adding more cars, it will instead dilute the density. So that is the reason why the speed of operational error extension cannot significantly faster than that of three five. Yeah. So then let me share my thought on the second part. That's a land large language model. First, I will say first and foremost, there are two non negotiable requirement for l four onboard value model. Uncompromising safety. And also low latency. There are the lot longer more than chatbot don't need and that are not designed to meet as well. So for safety, last we went not long model generally have issue like model health and nation.

Which is which is unacceptable for l four in terms of safety. And for latency, large language models are optimized for throughput like tokens per second, In contrast, l four, the optimized for low latency and the ability to run fully driverless over textile chips. That are both low power consumption and the cost efficient. Moreover, large language model overly run human data. Fundamentally limits them to the boundary of the existing human knowledge. Add anything ever inevitably makes them pick up human errors. Bad habit from human driver.

So we also extensively use l a lot of language model in the IND effort such as AI has human machine interaction, engineering productivity tools for coding and documentation, and analysis for the rider feedback for extended improvement. But, however, due to the multiple reasons mentioned above, large language model is by nature not good for driving model onboard. So with that, so back to the operator. Thank you.

Unknown Executive: Thank you. That's very helpful.

Operator: The next question comes from Jin Yu Fang with UBS. Please go ahead.

Unknown Executive: Hi. Thank you, management, for taking my questions. I have one question here. It is currently that 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 staff? Thank you.

James Tong: This is Jets. I'll take this one. So the matter of the reality is that in the whole global taxi industry, local governments and the local residents actually have a strong preference preferences for the local branded taxi vehicles. So that's a reality. Typically, when, robotaxi fleet is relatively small. The brand that doesn't really matter much. But if we need to deploy a significant fleet size, the requirements certainly is no longer true. And the local branded OEMs is much more preferred.

So it is necessary for us to cooperate with multiple local OEMs in different regions it actually can help us to expand into different markets much quickly And that's why we are now collaborate with three OEMs to produce our gen seven robotaxis. It is true that feeding our autonomous driving kit into a different vehicles actually posts a huge technical challenge But on the if you look at from the other side, the mere fact that we were able to standardize our technology and being able to treat our setup into different vehicles. That shows our technical generalization And down the road, it actually can create a huge competitive edge.

So as a result, we can add new models much faster to accelerate our expansion into new regions. For example, in the Europe, we currently added the partnership with Stellantis. So with that, back to the operator.

Operator: The next question comes from Tung Zhujia with Guosun. Please go ahead. Thanks for taking my question.

Purdy Ho: I have one question. Why Pony can use remote assistant on robotaxi when the car meets difficulty? Instead of remote control human take up. Over? And what is the technology difference behind that?

Tianqin Luo: This is Kim Chen. I will take this one. I think one of the previous question also touched on the remote assistant for robotaxi. So let me elaborate on that at least more detail. First and foremost, I'd say over remote assist never control the vehicle. Through the thin wheel or pedal. Instead, they provide remote support and suggestions by responding to service request. For all the time, the vehicle can independently drive from this independently make decisions without remote assistance. Assistance only initiates one of vehicle requested. Rather than through the remote driving. So one vehicle received the assistance response. The onboard driving system will still make time decision based on the actual situation.

Because the vehicle never waits for remote command to react to act. So it will remain safe, operates operation without any dependence on network latency. So one typical example of remote assistance is the situation of a temporary traffic control. In such cases, the system may request remote assist which can provide high level suggestion to confirm the car's decision navigating through a scenario. But also, as I mentioned, we have to continue to improve the AI algorithm, and also leverage our general AI capability to recognize more and more complex contact context This allows us to improve remote assist to vehicle ratio in a third quarter. To reach one to 30 by year end.

Hope that can answer your question. Go back to the operator.

Operator: The next question comes from Serena Li with China Securities. Please go ahead.

Purdy Ho: 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 license recently. What's our view on that? What town is overseas? To stretch it?

James Tong: Sure. This is James again. Let me take this one. Our company's mission has always been autonomous mobility everywhere. So we certainly have the global ambition since our funding to actually utilize our technology to benefit the local societies worldwide. Currently, our global efforts are focused on the markets with hyper growth potential. Those are the markets with typically strong mobility demand well developed infrastructure, and a supportive regulatory environment. When we evaluate a potential market to enter on a high level three factors, we'll consider. One is the, adjustable market size, which is 10. Second is the openness and the execution of the local government. To support. And issue permit for the fully driverless commercial operation.

Third is how strong is the local partner for their on the ground resources. And operational capacities. So as you can see, our globe current global expansion status is that we have already entered eight countries for our robotaxi. And we also for example, in Q3, we added Qatar as a new market by collaborating with Movasaleh. In Q3, we have also saw a rapid revenue growth especially for the robotaxi for our overseas from our overseas markets. And we certainly expect this momentum to continue. So going forward, we will enter other global markets if we see, there's a good growth opportunities. So this is our overseas strategy. With this, back to the 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: 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: This concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your line.

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