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 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.

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 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)

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…

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 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.

Then let me dive into the three criteria that put us the frontier forefront of 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 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 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…

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.

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 in local road conditions. Such as unique traffic signals configuration, and the various driving 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)

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 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.

We expect

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 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 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%

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 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

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.

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.

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 the deployment. So 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 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 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.

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

Hi, management. Thank you for taking my question. I 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.

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 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.

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

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.

Yes. I'll I'll take this question. Like you said, we 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.

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

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

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.

Thank you. This is James. I'll take this one. So 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 of commercialization. As the 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 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 and the we because of early mover,…

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.

So

at 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 the 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.

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

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.

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 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.…

Thank you. That's very helpful.

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

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.

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 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 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.

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

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?

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 quarter. To reach one to 30 by year end. Hope that can answer your question. Go back to the operator.

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

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?

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.

As there are no further questions, I'd like to turn the call back over to the company for closing remarks.

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.

This concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your line.