Moo Live was live · Nov 25, 2025 06:00

Pony AI Q3 2025 earnings conference call

Key Takeaways (AI-Generated)

Financial Performance:
- Pony AI Inc. reported a revenue of USD 25.4 million in Q3 2025, a growth of 72% year-over-year.
- Net loss for Q3 2025 was USD 61.6 million, compared to a net loss of USD 42.1 million the previous year.
- Gross profit margin improved significantly from 9.2% in Q3 2024 to 18.4% in Q3 2025.

Business Progress:
- Successfully completed a dual primary listing on the Hong Kong Stock Exchange, raising over USD 800 million.
- Launched Gen-7 robotaxis across multiple cities, achieving city-wide unit economics breakeven.
- Expanded the operational footprint to 8 countries and forged strategic partnerships to support fleet expansion.

Opportunities:
- Expanding the robotaxi fleet to drive higher user adoption and operational efficiency.
- Entry into new international markets and partnerships with local entities to enhance market presence.
- Leveraging technological advancements such as Gen-7 robotaxis and the new Gen-4 robotruck for logistics.

Next Quarter Guidance:
- Plans to grow the fleet to more than 3,000 vehicles by 2026.
- Aim to leverage recent IPO funds for further fleet expansion and deepening R&D investments.

Full Transcript (AI-Generated)

Operator

Hello, ladies and gentlemen. Thank you for standing by, and welcome to Pony AI, Inc.'s Third Quarter 2025 Earnings Conference Call. [Operator Instructions]

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 2025 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 Dr. James Peng, Chairman of the Board and Chief Executive Officer; Dr. Tiancheng Lou, Chief Technology Officer; and Dr. Leo Wang, Chief Financial Officer of the company. They will provide prepared remarks followed by a Q&A session.

Before we begin, please refer to the safe harbor statement in our earnings release, which applies to this call as we will be making forward-looking statements. Please also note that we will discuss non-GAAP measures today, which are more thoroughly explained and reconciled to the most comparable measures reported under GAAP in our earnings release available on our Investor Relations website and filings with the SEC and Hong Kong Stock Exchange.

I will now hand it over to our Chairman and CEO, Dr. James Peng. Please go ahead.

Jun Peng

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 1 year after our NASDAQ listing.

With strong support from both international and domestic investors, we secured the largest IPO in the global autonomous driving sector this year, raising more than USD 800 million. This significantly strengthens our balance sheet and provides the dry powder to accelerate mass production and large-scale commercialization.

We now expect stronger growth, surpassing 1,000 robotaxi 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 strong revenue growth.

After launching Gen-7 robotaxi, we have already seen a citywide unit economics breakeven. This, in turn, gives us more room to increase fleet size. The capital we raised also fuels our business development, research and development making strategic investments in new markets, new applications and attracting world-class AI talent. All these are set to further propel our technology leadership and long-term growth. Our Hong Kong IPO also powers our core mission, bringing autonomous mobility to everyone around the world. We are firmly delivering on this commitment.

Earlier this month, we officially launched fully driverless commercial service for Gen-7 robotaxis across Guangzhou, Shenzhen and Beijing. Today, our management team, including myself, actually arrived at our Shenzhen office in a fully driverless Gen-7 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 advancement. We are making Level 4 autonomy more accessible than ever to a much broader user base. I'm excited to share a critical milestone.

Our Gen-7 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 in enabling them to fund our fleet and support our asset-light model.

The scaling up of fleet is key to our growth as large-scale operational footprint drives efficiency through the economic of scale. Our robotaxi vehicles are essentially moving billboards. In fact, many new users discover and download our PonyPilot app after spotting our vehicles on the road for daily operation.

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 1 week of launching Gen-7 from late October, reflecting robust user demand and an effective go-to-market strategy.

Now let me highlight some key advances we made in recent months in executing our scale-up strategy. First, we have ramped up production at an accelerating pace since the start of production in the middle of this year. By November, more than 600 Gen-7 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 fleet size to surpass 3,000 vehicles in 2026.

Second, in Q3, our robotaxi revenue surged by 90% year-over-year with fare-charging revenues delivering over 200% year-over-year growth. This was fueled by rising user adoption across all 4 Tier 1 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 robotaxi operations earlier this July, covering the Jinqiao and Huamu areas of Pudong.

In Shenzhen, we extended commercial fully driverless operations to more and bigger city areas, including Shekou and Overseas Chinese Town. Fourth, we're taking major steps towards scalable mobility...

[Technical Difficulty]

Operator

Excuse me, I believe there has been interruption. Just one moment, please. Excuse me, I've rejoined management. Please continue. Thank you.

Jun Peng

Sure. I was talking about the scale-up strategy. So following our collaboration with Xihu in June, we recently forged another partnership with Sunlight Mobility. This alliance reflects growing market recognition of our business model with an increasing number of third parties wanting to fund fleet deployment. This actually enables us to speed up further fleet expansion.

Now let me turn to our global expansion. We are deeply dedicated to advance robotaxi services while strategically expanding our international fleet. Now we have robotaxi presence established in 8 countries across China, the Middle East, East Asia, Europe and the U.S.

We entered a new market in the Middle East, Qatar, through a partnership with Mowasalat in third quarter. Mowasalat 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 collaborating closely with ComfortDelGro, the country's largest transportation service provider to begin road testing. In Luxembourg, we plan to deploy testing vehicles based on the Peugeot e-Traveller through our alliance with Stellantis as a European leader in light commercial vehicles.

This effort will initially focus on vehicles designed for European diverse mobility needs to enable a range of use cases. In addition, we have partnered with global ride-hailing platforms that also participated in our Hong Kong IPO. Those platforms include Uber and Bolt. Bolt is an Estonia-based mobility company operating in over 50 countries and 600 cities. Built upon our collaboration with Uber, we aim to leverage Uber's robust ecosystem to enter the Middle East and then scale into additional international markets.

Last but not least, we recently released our fourth-generation robotruck with production and 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-4 Robotruck delivers a significant more efficient cost structure and greater energy saving. The new platform fully leverages the technological foundation and operational expertise developed through our Gen-7 Robotaxi vehicles. In addition, we deepened our collaboration with SANY Group and added Liuzhou Motor 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 commercialization for Pony AI. 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 U.S. IPO last year. Our recent Hong Kong listing not only marks a major milestone for our company, but also underscores the promising future of the industry. Moving forward, we will drive technological innovation and create lasting values by scaling fast, efficient and comfortable autonomous mobility services towards our mission, Autonomous Mobility Everywhere.

With that, now I'll hand it over to our CTO, Dr. Tiancheng Lou, to share more about our technology strategies. Tiancheng, please go ahead.

Tiancheng Lou

Thanks, James. Hello, everyone. This is Tiancheng. Let me first share my thoughts on autonomous driving technology stack. From day 1, we believe that full stack integration across software, hardware and operations was the only way to build a truly scalable autonomous mobility. That conviction being validated again and again, especially for this critical year of scaling up. With the achievement we made it is clear that our early technology bet helped us achieve the leading position and it will further accelerate our future growth.

Our deep foresight into tech stack is what is positioning us as a leader in the industry today as we become one of the few companies to operate large-scale full driverless robotaxi services. So as early as 2020, we recognized the importance of training closed-loop based on reinforced learning unit simulation. In that year, we transitioned our tech stack into a world model, which is what we call the PonyWorld today.

Through years of R&D effort and real-world validation of our top 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 world model, validating the approach we adopted today. This foresight in AI tech stack has given us a meaningful head start, and we are confident that we will stay ahead for multiple years.

Then let me dive into the 3 criteria that put us the frontier forefront of world model development. First, the high-fidelity interactive simulation. This is far beyond the ability to just generate scenarios and render sensor data. Driving is by nature interactive. The robotaxis action directly affect how surrounding agents behave, such as other vehicles and pedestrians need to react to overall driving behavior.

It must understand and adapt to new situation and complex physical interaction in real time during true on-road interactions. It enables robotaxi operations that are safe, smooth and social aware. After 10 billion kilometer of test miles that only were to generate each week, more than 99% capture vehicle agent interactions, while less than 1% are static environment such as center rendering.

Okay. Second, the ability to reproduce scale and realistic corner cases. While this long-tail scenario don't occur frequently, they are critical to safety in autonomous driving. More importantly, every scenario must be something that could really happen in the real world, not those useless [ age ] cases with no basis in reality.

So the third, the AI-based learning evaluator. This is the reward-based evaluation mechanism. Driving the multiple objects of addition problem, what is considered as a good driving also changes in various driving scenarios. Within the closed-loop training environment, pointing word and our virtual driver are continuously evaluating on key driving metrics.

This assessment does not rely on real-world data, human label data or rules. Instead, it used AI-empowered model to learn what good driving looks like directly from outcomes, turning real and simulated experience into a powerful cycle of self-improvement. A best-in-class world model must meet all 3 criteria to enable truly unsupervised and self-improving closed-loop training. This is critical to realizing large-scale driverless autonomous driving.

And leveraging our full stack technology as a core strength, I will now turn to how to drive business progress during the third quarter. First, on cost and operational efficiency. We pioneered 100% automotive-grade autonomous driving kit for Gen-7 robotaxis with optimized design reducing BOM cost by 70% compared with the previous generation. The Gen-7 vehicles have been officially operating for public in Guangzhou, Shenzhen and Beijing, fully validating our safety standard and operational efficiency.

We built on our momentum and delivered further progress driving by scaled production and enhanced R&D. We have already realized an additional 20% reduction in the autonomous driving kit BOM cost for the Gen-7 platform designed for 2026 production compared with 2025 baseline. This laid foundation for sustained cost saving.

Our robust AI algorithm and fleet management exercise has proven effective at driving operational efficiency to better identify user demand in hotspot areas during rush hours, and we enhanced our algorithm for order dispatch, matching and scheduling, thereby ensuring sustained and efficient robotaxi utilization. We have also improved our virtual driver to recognize more and more complex scenarios. This allow us to improve our remote-assistance-to-vehicle ratio substantially on the track to reach 1:30 by year-end.

Our superior service experience has become the key reason users choose Pony AI robotaxi. After the launch of Gen-7 robotaxis, we have earned a world widespread positive feedback and generated great social media buzz from users. As we deliver high-quality experience, users are increasingly willing to pay a premium for the enhanced effort, reliability and safety of our autonomous journey.

For ride comfort, our advanced interactive planning capability intelligently optimized for the frequency and the magnitude of acceleration, braking and steering. This delivers smooth natural motion control tailored to the electronic vehicles and the ridesharing market, offering consistent comfort experience for every Pony AI robotaxi ride. This enhancement have reflected in a measurable improvement for Gen-7 such as emergency brakes and steering over the past few months.

Additionally, our robotaxi features a superior in-cabin experience. We also pioneered the innovative Smart Repositioning feature. With one tap, users can remotely adjust their vehicle position for more convenient pickup and drop-off. We introduced voice-activated features, we call the Popo Voice Assist, allow users to start trips and control the air conditioning, et cetera. We will continue to upgrade the cabin into an AI-powered mobility terminal. Together, this upgrade creates a more accessible and streamlined user experience.

So third, our tech stack is also built for generalization. The L4-native tech architecture allow us to adapt quickly to new markets and platforms. In terms of cross-region generalization, our virtual driver assurance can quickly understand and adapt to diverse traffic conditions around the world. For example, leveraging our high fidelity training environment and variation mechanism powered by PonyWorld. We extend our fully driverless coverage in Pudong District in just a few weeks.

In addition, when expanding to Europe, the system intelligently identified and adapted key difference in local road conditions, such as unique traffic signals configuration and various driving patterns. Our technology boost generalization power across platform as well. The latest generation robotruck will commence production and operations from next year. This demonstrates our capability to create synergy between robotaxi and robotruck tech stack.

Looking ahead, we will leverage our success Hong Kong listing to reinforce our technology core leadership, increasing R&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 and refining what is possible in the transportation.

Okay. This concludes my prepared remarks. I will now pass the call over to our CFO, Dr. Leo Wang, for a closer look at our financial results. Leo, please go ahead.

Haojun Wang

Thank you, Tiancheng. Hello, everyone. This is Leo. I will focus on year-over-year comparisons for the third quarter, unless otherwise noted. Q3 2025 was a landmark quarter. We delivered 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-7 robotaxi fleet have reached a pivotal citywide unit economic breakeven milestone. This lay out a solid foundation for further scaling up and the implementation of asset-light business model, which will be further accelerated by our successful Hong Kong IPO capital raise.

In this quarter, revenue finished at USD 25.4 million, growing by 72%. This strong performance was primarily driven by the continuous optimization of our robotaxi services and the sustained demand in our licensing and application business. Firstly, robotaxi services revenue reached USD 6.7 million, representing a remarkable growth of 89.5% year-over-year and 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-7 robotaxis.

Supported by a stable commercial fleet of our Gen-5 and Gen-6 vehicles, the strong growth during Q2 and Q3 stemmed from growing user demand in Tier 1 cities in China. Our continuous effort to optimize fleet operation and pricing strategy, altogether leading to increased fleet utilization and efficiency. This is a testament to growing user recognition and the brand royalty to PonyPilot 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 higher operational efficiency.

In Q3, another key robotaxi update is the implementation of our asset-light model for fleet expansion. As we have shown promising numbers in vehicle unit economics, we received a strong interest from third parties who are willing to purchase Gen-7 vehicle to run as robotaxi operators. Such partners include but are not limited to leading ride-hailing or taxi operators, for instance, Shenzhen Xihu Group and Sunlight Mobility.

The asset-light model has contributed revenues through technology licensing fee and 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 robotaxi 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 8 countries globally, serving as a promising foundation in our exploration of the international opportunities.

Secondly, moving to robotruck. Robotruck service revenues were USD 10.2 million, growing by 8.7%. Moreover, as we launch our Gen-4 fully auto-grade robot truck, we expect to reduce the BOM cost of its ADK, autonomous driving hardware kit, by 70% and reach 1,000 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 USD 8.6 million, growing significantly by 354.6%. We continue to see robust and growing demand of our autonomous domain controller, primarily from robot delivery clients.

Turning to gross margin. We delivered a significant gross profit margin improvement from 9.2% in Q3 2024 to 18.4% in Q3 2025, with gross profit of USD 4.7 million in the third quarter. This remarkable improvement was firstly driven by our strategic initiatives to optimize the revenue mix and, secondly, by a greater contribution from robotaxi services, which carry a relatively higher margin. The unit economic breakeven achievement validates our dual focus on go-to-market execution and optimized operational efficiency.

Since the launch of Gen-7 commercial operations in Guangzhou, the daily net revenue per vehicle has reached RMB 299. The net revenue refers to the total RMB value generated from ride-hailing services after deducting discounts and refunds. Notably, daily average orders per vehicle have reached 23 fueled by a robust widespread user demand and our operational optimization. Meanwhile, we have also optimized hardware depreciation as well as operational costs, including charging, remote assistant, ground support, service and maintenance, insurance, parking and network costs. This will further improve our margin down the road.

The total operating expenses were USD 74.3 million, up by 76.7%. Excluding share-based compensation expenses, non-GAAP operating expenses were USD 67.7 million, up 63.7%. The increase primarily reflects the one-off R&D investment in Gen-7 vehicles and the expansion of our R&D personnel, critical to securing and extending our technological leadership. Specifically, approximately half of the increase in research and development expenses stemmed from onetime customized development fee of USD 12.7 million for Gen-7 vehicles.

Net loss for the third quarter was USD 61.6 million compared to USD 42.1 million in the same period of last year. Non-GAAP net loss was USD 55 million compared to USD 41.4 million last year. Looking ahead, we expect to sustain disciplined investment to accelerate large-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 USD 587.7 million as of September 30, 2025, compared to the balance as of June 30, 2025, of USD 747.7 million. Around half of this decrease comes from one-off cash outflow, including capital injection to Zhuifeng, our joint venture with Toyota to support the Gen-7 mass production and deployment. All of the capital commitment in Zhuifeng has been completed.

The remaining cash balance reduction primarily reflects our mass production and large-scale deployment status, including, firstly, ongoing operational cash outflow; and secondly, capital expenditure for the procurement of Gen-7 vehicle in Q3 to support our goal of 1,000 vehicle fleet by year-end. For the 9 months ending September 30, 2025, we have an accumulative free cash outflow of USD 173.6 million. With the completion of our recent Hong Kong IPO, we have over USD 800 million cash newly added, providing us with substantial fuel for the next phase of growth.

The IPO proceeds will help us accelerate fleet expansion into key addressable markets, further optimize our platform for scale and deepen our R&D investments to further solidify our technology moat. 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 an asset-light model for a meaningful portion of our new vehicles. This will enhance our capital expenditure efficiency and provide greater leverage for scalable fleet expansion. With the proven operational model and the financial runway from the recent Hong Kong IPO, we are uniquely positioned to accelerate our business plan, turning momentum into sustained profitable growth.

I will now turn the call over to the operator to begin our Q&A session. Thank you.

Operator

[Operator Instructions] And the first question comes from Ming-Hsun Lee with Bank of America.

Ming-Hsun Lee

So I just have one question. So could the management team give us more update on the fleet size for this year and also the outlook in 2026. For the new vehicles added, what is the fleet deployment plan across different cities?

Jun Peng

This is James. I'll take this one. So as you can see that since the launch of our Gen-7 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 a conservative target of over 3,000 vehicles. This is mainly because we have already seen an upward spiral with the launch of our Gen-7 vehicles. Essentially, the fleet density creates a much shorter wait time for the passengers.

And then that creates a better user experience and 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 Xihu, Sunlight and, certainly, we'll add more partners. This asset-light model allows us to deploy 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 city-wide UE breakeven for the Gen-7 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 1 cities in China.

This is because, as I already mentioned, expanded fleet size creates an upward spiral. But at the same time, we'll 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

The next question comes from Bin Wang with Deutsche Bank.

Bin Wang

I just have one question, which is about the fare-charging. I'd like to know fare-charging revenues have made another growth in 3Q '25. So what is the outlook for fare-charging revenues as we deploy more vehicles?

Haojun Wang

Yes. This is Leo. I'll take this question. Yes, in Q3, our fare-charging revenue actually surged even faster. It was growing about 233%. Though at that time, our fleet were still with the Gen-5 and Gen-6 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 riding experience and also the user experience. So with this effort, we've seen robust and organic user demand in Tier 1 cities.

This is also a signal of a strong consumer adoption of our robotaxi 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 order fulfillment, as Tiancheng already mentioned in his remarks. So for example, we enhanced our fleet dispatching and 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 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-7 vehicles are into our service.

First of all, we expect that our fleet has been growing exponentially from 270 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, et cetera, et cetera. With all these being done, I think we can boost the average order value per trip.

Okay. Now I'll get back to the operator.

Operator

The next question comes from Kailin Wu with Citi Research.

Kailin Wu

This is Kailin from Citi Research. And congratulations on achieving the milestone of Citywide UE breakeven. Could you elaborate more about the assumption behind the UE breakeven, including daily order pricing, daily operating hours and the ratio of remote assistance?

Jun Peng

Yes, I'll take this question. Like you said, we all believe the citywide unit economic breakeven is a pivotal milestone for the company and also for the industry. First of all, we achieved this pivotal milestone in Guangzhou city since our Gen-7 vehicle has been put into commercial service. And we always believe China is the largest market of global ride-hailing market. And for the Tier 1 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 a commercial perspective.

Then if we talk about the unit economic, there is 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 RMB 299. It's based on a 2-week daily average figures as of November 23, following the launch of our Gen-7 vehicle in Guangzhou. And this net revenue also refers to the total RMB value generated from ride-hailing service after deducting discounts and refunds. And in terms of daily orders from this RMB 299 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 unit economic basically has 2 major components. First of all, it is the hardware depreciation. For Gen-7 vehicle, the annual vehicle depreciation is based on a 6-year useful life. The other major component on the cost side is the operational cost, which includes 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 1:30 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 scaling up domestically and internationally. This not only gives 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 an asset-light model. So all these together, we believe will drive our top line growth and also the cost optimization.

Okay. I'll get back to the operator.

Operator

The next question comes from Purdy Ho with Huatai Securities.

Purdy Ho

Congratulations on the results. We've observed a surge in diverse players attempting to enter into the robotaxi operations, particularly the EV makers, right? So what's your take on these new entrants in the Level 4 autonomous driving space? And also specifically, could you elaborate on the main technical and operational challenges such as tackling corner cases and fleet management for digital commerce?

Jun Peng

This is James. I'll take this one. So first and foremost, I think it's definitely, as we see more and more companies announcing that they're going to enter into robotaxi industry, I think itself is actually a great thing because it indicates increasing recognition and confidence in robotaxi imminent potential for the large-scale of commercialization. As the awareness increase, more resources, 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 being OEM maker or being a ride-hailing platforms, none of them have fully driverless vehicles deployed on the open road. So it's a clear evidence this is not an easy industry to be entered. I think there are certainly 3 huge hurdles for 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 robotaxi, as you see, it's not just about L4 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 L4 fleet on the road, we generated better brand awareness. We have optimized the cost on every aspect of the business, as Leo already mentioned in his answer to the last question. And we -- because of early mover, we also have secured more partners.

I think all those are important and creates a big hurdle for any new entrants. The second hurdle that I want to mention is on the regulatory front because L4 or robotaxi needs a very high safety requirements. 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 step 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 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 turn to Tiancheng to elaborate.

Tiancheng Lou

Yes, sure. So I'm Tiancheng. So let me continue from a technology perspective. So as I said in my prepared remarks, we are now seeing the broader industry starting to using world 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 reinforced learning for autonomous driving 5 years ago. This gives us an early mover advantage, making us one of the most experienced company in the world model.

We believe that we will continue to stay ahead as more peers follow the same path. So once the world model mature, there was a human feedback and the real-world data are no longer used for further iterations. So at the stage of training closed-loop, the world model and the virtual driver co-evolve into a dual spiral cycle. This means the world model is training the virtual driver. And at the same time, the world model improves sales through feedback of the virtual driver. This sharply reduced reliance on the real world data.

The question will touch on the technical challenges for meeting the corner cases. Maybe an example here that when the virtual driver need some corner cases, so this can fuel feedback to the world model and the world model will improve its distribution of the corner cases, then the next generation -- next version of world model will be able to create or generate and testing and also improving the capability of the virtual driver to handle the corner cases.

Okay. So looking ahead, our real advantage lies in ability to validate new technologies quickly and deploy that at scale. So based on our proven track record of scaling robotaxi operations, so we believe we can quickly capture the next wave of innovation. Also last but not least, our current Hong Kong IPO will further accelerate R&D and education cycles, reinforcing our technical leadership and widening our competitive moat.

With that, back to the operator.

Operator

The next question comes from Xiaoyi Lei with Jefferies.

Xiaoyi Lei

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 a technical perspective, are you using large language models? And if so, how are they helping push L4 autonomy forward?

Tiancheng Lou

Thank you. This is Tiancheng. I will continue to answer this question. I think your question consists of 2 parts. Let me answer your question on generalization first, then I will address the other one on large language model later. For generalization, I would say, technically, our tech stack is by nature built for generalization. So a good example is that our operational area expansion into new areas in Shanghai, Pudong and Shenzhen Nanshan District in the third quarter. In both cases, it only took us only a few weeks from verifying the city to truly realizing fully serviced operation to the public. There was no need for additional model training.

The quick key reason that an overall native architecture is built for handling corner cases there and [indiscernible] 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 suddenly are crossing and suddenly lane-change from other cars without looking at the vehicle behind, et cetera. So it's just about the likelihood and the probabilities of each one happening. So I hope that can help understand why the L4 tech stack is by nature built for generalization.

So at this moment, I will say the key to our new area expansion is number of robotaxi vehicles. If we expand to too many areas without adding more cars, it will instead dilute the density. So that is the reason why the speed of operational area expansion cannot significantly be faster than that of fleet size.

So then let me share my thoughts on the second part that is large language model. First, I will say, first and foremost, there are 2 nonnegotiable requirements for L4 onboard driving model, uncompromising safety and also low latency. There, the large language model and chatbot don't meet and they are not designed to meet as well. So for safety, large language models generally have issues like [indiscernible] which is unacceptable for L4 in terms of safety.

And for latency, large language models are optimized for throughput like tokens per second. In contrast, L4 is optimized for low latency and the ability to run fully driverless robotaxies on chips that have both low power consumption and are cost efficient. Moreover, large language model overly run human data, which fundamentally limits them to the boundary of the existing human knowledge as it inevitably makes them pick up human errors and bad habits from human driver.

So we also extensively used large language model in the R&D efforts such as AI-enhanced human machine interaction, engineering productivity tools for coding and documentation and analysis for the rider feedback for experience improvement. But however, due to the multiple reasons mentioned above, large language model is by nature not good for driving model on board.

So with that, so back to the operator. Thank you.

Operator

The next question comes from Xinyu Fang with UBS.

Xinyu Fang

I have one question here. It is currently that Pony cooperate with multiple OEMs for robotaxi manufacturing, including BAIC, JAC and Toyota. Does management see potential for improving operating leverage through working with only 1 OEM instead?

Jun Peng

This is James. 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 for the local branded taxi vehicles. So that's the reality. Typically, when robotaxi fleet is relatively small, the brand 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 collaborating with 3 OEMs to produce our Gen-7 robotaxis. It is true that fitting our autonomous driving kit into different vehicles actually post 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 fit 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 Europe, we currently added a partnership with Stellantis.

So with that, back to the operator.

Operator

The next question comes from with Tang Xuxia with Guosen.

Xuxia Tang

I have one question. Why Pony can't use remote assistant on robotaxi when the car meets difficulty, instead of remote control or human takeover? And what is the technology difference behind that?

Tiancheng Lou

This is Tiancheng. I will take this one. I think one of the previous questions also touched on the remote assistance for robotaxi. So let me elaborate on that in a little more detail. First and foremost, I'll say, our remote assist never control the vehicle through the steering wheel or pedal. Instead, they provide remote support and suggestions by responding to service requests. For all the time, the vehicle can independently drive -- independently make decisions without remote assistance. Assistance only initiates when a vehicle request it rather than through the remote driving.

So when vehicle receives the assistant response, the onboard driving system will still make timely decision based on the actual situation because the vehicle never wait for remote command to act. So it remains safe 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 decision navigating through a scenario.

But also, as I mentioned, we have continuously improved the AI algorithm and also leveraged the general AI capability to recognize more and more complex context. This allows us to improve remote-assist-to-vehicle ratio in the third quarter to reach 1:30 by year-end. I hope that can answer your question.

So back to the operator.

Operator

The next question comes from [ Serena Lee ] with China Securities.

Unknown Analyst

This is Serena Lee from China Securities. 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's Pony's overseas strategy?

Jun Peng

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 founding to actually utilize our technology to benefit the local societies worldwide. Currently, our global efforts are focused on the markets with hyper growth potential. So 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, 3 factors we will consider. One is the addressable market size, which is 10. Second is the openness and the execution of the local government to support and issue permits 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 current global expansion status is that we have already entered 8 countries for our robotaxi.

And we also -- for example, in Q3, we added Qatar as a new market by collaborating with Mowasalat. In Q3, we have also saw a rapid revenue growth, especially for the robotaxi for our -- 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.

George Shao

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.

Details at Pony AI IR

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