Pony AI Q2 2026 Earnings Call - Robotaxi Revenue Surges 691% on Joint-Deployment Scale

Operator: Ladies and gentlemen, thank you for standing by, and welcome to Pony AI Inc.’s second quarter 2026 earnings conference call. At this time, all participants are in listen-only mode. After the management’s prepared remarks, there will be a question and answer session. As a reminder, today’s conference call is being recorded and a webcast replay will be available on the company’s investor relations website at ir.pony.ai. I will now turn the call over to your host, George Shao, Head of Capital Markets and Investor Relations at Pony.ai. Please go ahead, George.

George Shao, Head of Capital Markets and Investor Relations, Pony AI Inc.: Thank you, operator, and hello, everyone. We appreciate you joining us today for Pony.ai’s second quarter 2026 earnings call. Earlier today, we issued a press release with our financial and operating metrics, which is available on our IR website. An earnings presentation, which we will refer to during the conference call, can also be accessed and downloaded on our investor relations website. Joining me on today’s call 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 IR website, and filings with the SEC and the Hong Kong Stock Exchange. I will now hand it over to our chairman and CEO, Dr. James Peng. Please go ahead.

Dr. James Peng, Chairman of the Board and Chief Executive Officer, Pony AI Inc.: Thank you, George. Hello, everyone. Thank you for joining our earnings call today. We delivered another fantastic quarter, highlighted by multifold expansion across the board. First, strong top-line growth. Total revenue surged by 69% year-over-year, driven by a close to eight times jump in robotaxi revenue and over nine times surge in fare charging revenue. Second, rapid fleet scaling. Our robotaxi fleet expanded to 2,000 vehicles, putting us on track to deliver 3,500 vehicles by year-end. Third, expanded deployment. Domestically, we reinforced our leadership in tier 1 cities as we surpassed 1.5 million registered users. We also improved our network density with more deployed vehicles and operational coverage. Internationally, we unlocked demands by scaling our joint-deployment model. Currently, we have secured over 4,000 vehicle commitments with Uber and other overseas partners.

The expanded deployment in both China and overseas markets clearly shows that our dual-engine strategy is turning into robust top-line growth. Looking at our domestic operations first. The L4 industry in China is entering a new phase where higher standards are required to keep the industry on a sustainable, healthy trajectory. For any robotaxi company to enter into large-scale deployment, it now needs proven driverless capabilities, positive user satisfaction, and verified safety records. We are perfectly positioned to capitalize on this shift because we have already been operating well ahead of this curve. These rising standards will only widen our competitive moat and solidify our leadership in China. Our confidence actually is grounded in solid results from commercial robotaxi operations. We have three Gen-7 robotaxi vehicle models in our daily services, including the GAC Aion V, the BAIC Arcfox Alpha T5, and the Toyota bZ4X.

We are continuously improving user experience, which is the key driver for our organic user growth as our total registered users have surpassed 1.5 million. In Guangzhou, we extended our robotaxi services into the city center, now adding over 300 square kilometers since the beginning of this year. The operational area spans across Haizhu, Tianhe, Huangpu, and Panyu districts, covering a population of over 7 million. As a result, our driverless fleet is positioned to capture highly concentrated urban mobility demands. Shenzhen, known as China’s Silicon Valley, serves also as a great showcase of our capability to navigate highly complex traffic scenarios. Our operational resilience was rigorously validated by corner cases such as the high-demand holidays, such as the Dragon Boat Festival, the peak rush hours, and heavy rainstorms. Despite these demanding conditions, we effectively met high-frequency commuting demands.

In addition, by seamlessly integrating three major transit hubs, including Bao’an International Airport, Shenzhen Bay Port, and the Shekou Cruise Port, we further expanded our network to provide users with greater convenience and more mobility options. Now turning into our global expansion. To meet the ever-increasing demand of L4 mobility in overseas markets, we have entered more international markets with huge consumer demand and commercial potential. We are using our joint-deployment model to form global alliance, fulfilling autonomous mobility demands in these international markets and creating values for our partners. To that end, we collaborate with multiple partners to accelerate our international pipeline. Currently, we have secured over 4,000 vehicle commitments, led by over 2,000 robotaxis across five European cities with Uber, alongside commitments from some other partners. Meanwhile, we continue to deepen our operations in existing markets. In Luxembourg, our deployment with Bolt and Stellantis keeps moving forward.

In Singapore, our service is now officially live for the general public on ComfortDelGro’s ride-hailing app called Zig. These international demands are a direct endorsement of our Gen-7 robotaxi operations in China’s tier 1 cities, where we have proven our superior driving capability, reliable 24/7 operations, high user satisfaction, and positive UE. I am confident that this proven model will continue to win partners with more vehicle deployment commitments and drive user adoption globally. Now let me elaborate a bit more on our joint-deployment model. As we expand our fleet across China and overseas, we leverage existing local ecosystems and our partners’ on-the-ground expertise to drive capital efficient expansion. I am very pleased to share that the model is already delivering strong, tangible commercial results. First, look at the strong monetization was validated in Q2. By broadening our partnerships, we delivered significant quarter-over-quarter growth in revenue contribution.

That’s a direct proof point of this DDM model’s financial viability. Second, the joint-deployment model is the asset light one, where our partners fund the fleet. This fundamentally enables faster scaling, lower unit costs, and superior capital efficiency for our fleet expansion. Third, with fast scaling, DDM essentially unlocks massive commercial value for years to come. For example, we’ve recently expanded our partnership with Uber to target at premium markets. This creates a highly repeatable growth engine, allowing us to attract more partners and deliver even higher growth trajectory. Now let’s move to our robotruck business. Our robotruck business delivered outstanding results in Q2, with revenue jumping more than 40% year-over-year. We actually expect this growth momentum to persist and even strengthen in the second half of this year. We continue to expand our long-haul operations with Sinotrans through our joint venture.

At the same time, our Gen-4 robotrucks have entered mass production and already begun commercial operations. Working with China Merchants Port, we launched our commercial deployment of robotrucks at Shenzhen’s Mawan Port, where our fully driverless robotrucks operate together with other human-driven trucks. This success highlights our unique cross-segment synergies. We have leveraged our rich operational experience from urban robotaxis and long-haul robotrucks to enable our trucks to seamlessly navigate traffic interactions at the ports. As we pass the midpoint of the year, our acceleration across both domestic and international markets puts us well on track to surpass our 20 city goal by year-end. In China’s tier-one cities, we will continue to deploy more vehicles to our fleet to widen our competitive moat and advance our scaling edge. At the same time, we are on track to enter multiple domestic new markets.

Internationally, the joint-deployment model will contribute top-line growth with great capital efficiency. This dual momentum gives us greater confidence in beating our original robotaxi revenue outlook, which is exceeding 3.5 times last year’s level. Looking ahead, our focus remains clear: delivering long-term value creation and driving the commercialization of autonomous driving with capital efficiency. Now, I’ll hand it over to our CTO, Tiancheng, to go over the technology progress. Tiancheng, please go ahead.

Dr. Tiancheng Lou, Chief Technology Officer, Pony AI Inc.: Thank you, James. Hello, everyone. This is Tiancheng. To start, our strong Q2 momentum is driven by our unique tech stack. This foundation allows us to scale rapidly and adopt seamlessly across both domestic and international markets. Starting with our domestic market, this is where we validate our technology in most challenging scenarios and translate this mastery into commercial value. Tier-one cities such as Guangzhou and Shenzhen are clear examples. In older urban cores and the major transit hubs, roads are narrow, residential neighborhoods are dense, and roadside parking are common. Our world model and the Virtual Driver prove to be more agile and precise in navigating these extreme conditions, ultimately delivering high commercial returns than regular scenarios. We also rapidly replicate this success to more high-premium urban market globally. Traffic rules and driving habits vary significantly across China, Europe, the Middle East, and Asia.

Despite these fundamental regional differences, our robust generalization enables rapid deployment. Our proven technical track record, especially in tier 1 cities of China and Zagreb of Croatia, is exactly why top-tier partners are choosing to scale with us through our joint-deployment model. Beyond the driving capability, another key engine behind our expansion is efficiency. Let me now elaborate on how our unique technical and operational capabilities deliver these efficient benefits. As I shared in previous quarters, the key to enabling our robotaxi to seamlessly navigate diverse urban environment lies in our world model precision. This is what bridges gap of so-called sim-to-real in physical area. In autonomous driving, closing the gap comes down to modeling the probability distribution of different behaviors among traffic participants. For example, the probability of a pedestrian standing on the roadside suddenly jaywalking varies from city to city.

A high-precision world model accurately captures these dynamics, enabling the Virtual Driver to handle such scenarios with confidence. Our current upgraded PonyWorld 2.0 brings this precision alignment into loop engineering. The system automatically isolates deeply hidden issues, generates targeted solutions, and validates them for real-world deployment, reducing the need for human engineers to analyze cases one by one. This also dramatically accelerates our development timeline. The old way of entering a new city takes dozens of engineers doing manual work to review local driving issues, analyze root cause of these issues, upgrade the world model, and retrain the onboard models, and then deploy and validate the new model on the road. However, with PonyWorld 2.0, our system leverages AI to resolve these local challenges automatically. This turns cities expansion from effort that used to take dozens of engineers into a human-in-the-loop automatic process that just a few people can run.

For example, when we went to Zagreb, we noticed local drivers almost never slow down when they hold the right of way, even near blind spot. PonyWorld 2.0 caught this difference automatically, and we quickly trained a new version of Virtual Driver that fits local habit perfectly, with very few engineers involved. As a result, we can now launch in multiple cities with completely distinct driving environment all at once. This scalability ensures we efficiently achieve our target of 20 cities by the end of this year. This gives us the unique efficiency advantage to scale our footprint far more rapidly. On the operational side, we are also using technology to redefine efficiency. For example, we don’t need a closed, dedicated parking lot to charge our cars. Our robotaxis can share a normal parking lot with human drivers.

The drivers themselves to find an open charging spot without human intervention. This means a tiny ground team can easily manage charging and service for a large fleet. This optimizes personnel allocation and lower our UE cost. The vehicle to staff ratio for our ground supporters and remote assistant team has improved significantly. More importantly, it also boosts the willingness of industry partners to adopt our joint-deployment model. As James mentioned, multiple partners such as Uber are clear examples. In short, our tech-driven efficiency give us a unique operation leverage as we scale across new markets. This not only reinforce our competitive moat, but also positions our technical innovation as a core engine driving the entire industry forward. 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.

Dr. Leo Wang, Chief Financial Officer, Pony AI Inc.: Thank you, Tiancheng. Hello, everyone. This is Leo Wang. I will focus on year-over-year comparisons for the second quarter and the first half of 2026, unless otherwise noted. For detailed financials, please refer to our earnings release. This quarter, total revenues reached $36.2 million, representing a remarkable 69% increase from $21.5 million in the same quarter last year. Breaking down this strong top-line growth by business segment, most notably, our robotaxi revenue are growing 691%, and the robotruck revenue is growing 40%. Our phenomenal triple-digit robotaxi growth is a strong demonstration that our commercialization strategy is translating into good financial numbers. Looking deeper into robotaxi, we delivered a very strong growth this quarter. Robotaxi revenues reached a record high of $12.1 million, growing 691%, a further acceleration from the 395% growth compared to the first quarter.

Our fare charging revenue delivered an exceptional growth rate of 849%. These rapid growth rates show that robotaxi continues to serve as our core growth engine. This acceleration was driven by several factors. First, our fare charging fleet continued to expand across more regions and specifically into core downtown areas with high economic values. Second, our joint-deployment model gained a significant momentum, and our commercial robotaxi launched in Zagreb, Croatia, has served as a powerful showcase. As the first of its kind in the city center of a European capital, Zagreb, has proved our high-quality service in a demanding international market and enabled us to secure additional overseas contracts. Under the joint-deployment model, we are currently recognizing upfront vehicle delivery revenues, which establish a solid foundation for us to having high margin, recurring revenue-sharing income going forward as our fleet operations scale.

What is particularly encouraging is that this acceleration is broad-based, not concentrated in a single market. In China in this quarter, we continue to strengthen our leading position in tier 1 cities with fast-growing scale and a strong user base. Overseas, we are building an alliance that accelerates our global footprint. For example, we have secured over 4,000 initial vehicle deployment commitments with Uber and other overseas partners. Our continuous expansion in China and overseas will translate into a rapidly increasing base of recurring robotaxi revenues. Turning into robotruck, the revenue grew 40% year-over-year to $13.3 million this quarter. This growth was driven by increased logistic transportation revenues. Robotruck growth is more than just about volume. It reflects the cross-segment synergies within our ecosystem from robotaxi to robotruck.

As James Peng highlighted, the Mawan Port demonstrates our ability to apply the technology and operational capabilities polished in robotaxi urban environments and robotruck long-haul routes to a new vertical. Our intelligent solution segment delivered a revenue of $10.8 million this quarter, a 4% year-over-year increase, with the growth rate moderating due to the delivery fluctuation from domain controllers. For the first half of 2026, the intelligent solutions revenue reached approximately $26.3 million. The same quarter last year, total GAAP operating expenses were $72.1 million this quarter, and the non-GAAP operating expenses were $63 million, representing a modest 9.6% increase. The expense increase is significantly lower than our revenue growth rate of 68.8%.

As Tiancheng mentioned, our leading PonyWorld 2.0 model, an AI-powered closed-loop R&D framework, allows the same engineering team to handle far more work across different cities and a complex corner case analysis. The R&D efficiency is directly visible in our financial numbers. We are scaling globally without proportionally scaling our cost base. We continue to see our operating loss margin narrowing and operating leverage beginning to materialize as revenue scale. The loss from operation was $65.7 million, a modest 7.3% increase. The operating margin narrowed dramatically from negative 285.6% in Q2 2025 to negative 181.5% this quarter, an improvement of over 100 percentage points. On a non-GAAP basis, loss from operation was $56.7 million, increased by less than 5% year over year. Net loss narrowed significantly to $45.4 million, a 14.9% year-over-year decrease compared to Q2 2025.

The net loss margin narrowed from negative 248.3% to negative 125.2%, an improvement of more than 100 percentage points. From a broader perspective, our revenue growth rate significantly outpaced our non-GAAP operating expense growth rate, clearly demonstrating economics of scale and operating leverage. Turning to our balance sheet, cash and cash equivalents, short-term investments, restricted cash, and long-term wealth management instruments stood at $1.39 billion as of June 30th, 2026, compared to $1.44 billion as of March 31st, 2026. We continue to maintain a prudent cadence in cash management and maintain a robust financial position. Net cash used in operating activities was $44 million this quarter, compared to $25.4 million in the second quarter of 2025.

The increase was due to normal working capital fluctuation, especially the settlement of accounts payable during the current quarter, coupled with strategic investment in inventory, and prepares to support our fleet expansion in the second half this year. Capital expenditures were $32.2 million this quarter, bringing first-half CapEx to $44.3 million. This was mainly driven by the fleet and the autonomous driving kit CapEx, as we see robotaxi acceleration in both domestic and overseas markets, as well as increasing spending in data centers to support our greater scale deployment and continuous R&D. As we scale up our fleet, we expect to maintain capital discipline, supported by our partners’ co-investment under the joint-deployment model framework. Our capital allocation strategy is designed to balance disciplined investment with scalable growth.

Specifically, we invest in our core technology and owned fleet in key domestic markets, while partners contribute fleet capital and the local operating capability through the joint-deployment model. This allows us to expand our revenue-generating footprint across China and the international markets without a proportional increase in capital intensity. Together with approximately 2,000 vehicles produced, operating footprint across the world, more than 1.5 million registered domestic users, and $1.39 billion cash reserve, we have the operating momentum, global opportunities, and financial resources to execute our full year’s target and support sustainable growth beyond 2026. Meanwhile, with our recent inclusion in Hong Kong listings Stock Connect, we are excited to welcome onshore investors and maintain committed to transparent market engagement and long-term shareholder value creation. I will now turn the call over to the operator to begin our Q&A session. Thank you.

Operator: Thank you. We will now begin the question and answer session. To ask a question, you may press star then one on your telephone keypad. If you are using a speakerphone, please pick up your handset before pressing the keys. If at any time your question has been addressed and you would like to withdraw your question, please press star then two. If you ask questions in Chinese, please repeat them in English. At this time, we will pause momentarily to assemble our roster. The first question today comes from Ming Hsun Lee with Bank of America. Please go ahead.

Ming Hsun Lee, Analyst, Bank of America: Hi, James and Tiancheng Lou and Leo. Congrats for the good results. I only have one question. Given that Uber partners with several autonomous driving companies worldwide, what are the main reasons that made Uber choose Pony.ai in its European rollout? Thank you.

Dr. James Peng, Chairman of the Board and Chief Executive Officer, Pony AI Inc.: Thanks, Ming Hsun. This is James, and I will take this one. As you can see, I am actually quite pleased that we have signed commercial agreement with Uber to deepen our collaboration. I think the reasons Uber decided to work closely with us are actually quite straightforward. Uber always looks for autonomous driving partners whose technology is reliable at scale, and also whose cost structure brings the attractive economics. That is exactly the two reasons that we can offer on the table. We actually worked with Uber back in early 2025. At that time, our Gen-7 Robotaxis just started the deployment in China. At that time, there were some doubts whether our autonomous driving capabilities can handle the European cities, especially the big ones, where the infrastructure and road condition are typically mixed with old and new.

But after a year, now look at, I think the question has been answered with resounding real-world evidences. We have already launched large-scale robotaxi commercial operations in all tier 1 cities in China. The unit economics turned positive in Guangzhou and Shenzhen. In addition, we also rolled out Europe’s first commercial robotaxi service in Zagreb, Croatia, with Uber and Verne. All this evidence shows that our robotaxis can cover the most complex, highest demanding scenarios. Also, what we have found out is the more places a vehicle can operate, the higher utilization becomes. On the cost side, we also can offer is even more compelling, right? Compelling with the hardware and also the operational costs. Our total cost per mile is the most competitive in the industry.

I think another important reason is that the culture alignment has also been a hallmark of our collaboration between Pony and Uber. Both sides are impressed by one another’s professionalism and dedication. The mutual appreciation and the mutual commitment really lead to right now what we have seen, the expanded collaboration. For both of our companies, the strategy is to begin with the most socially and economically meaningful markets, and then we’ll even extend our mobility services to additional geographies. What we announced about the 2,000 vehicles is under the current contract. With this contract, we become Uber’s largest autonomous driving partner in Europe. Going forward, as the performance and also the economics continue to validate at scale, we’ll see substantial room to expand the fleet size even further. Back to the operator.

Operator: The next question comes from Tim Shio with Morgan Stanley. Please go ahead.

Tim Shio, Analyst, Morgan Stanley: Thank you, management, and congratulations on the strong quarterly results. Could you please elaborate on your strategy going forward for the joint-deployment model? Can you share more color on how the commercialization model works and operate under asset-light model?

Dr. James Peng, Chairman of the Board and Chief Executive Officer, Pony AI Inc.: Thanks for the question. This is James again. Probably let me begin with a high level, and I’ll probably regarding the details, I’ll hand over to Leo. The joint-deployment model will actually accelerate our fleet expansion with high capital efficiency, both domestically and internationally. You can think of this as, in this model, we are building a win-win model across the value chain. The success of our Gen-7 Robotaxi operations across the tier 1 cities, it’s really a showcase. It proves that our superior safety record and operational efficiency, and then ultimately turn positive UE margins, by delivering these top-tier driving capabilities and user experience, and at the same time, at very low hardware and operational costs. We can achieve high margins than our peers.

Therefore, partners in our ecosystems, whether it is a mobility platform or a fleet operator, they can share the most economic value per deployed vehicle. On the highlight, we can think of the joint-deployment model. They give partners are naturally incentivized to commit a large portion of their fleet shares to Pony because, in this model, they can maximize their total value generated together with us. Regarding the details of this business model, I will now hand over to Leo.

Dr. Leo Wang, Chief Financial Officer, Pony AI Inc.: Yeah. Thanks, James. This is Leo. Yes, Tim, you mentioned it is correct. This is an asset-light model for Pony to expand our fleet. In most cases, there are three parties, and each plays a different role. For Pony, we supply our Gen-7 Robotaxi with our Virtual Driver capability. That is an AI driver. A mobility platform that can introduce user demands, and an operating company who can deal with fleet management and maintenance. We, of course, acknowledge in different markets, the consumer can have the choice on mobility platforms, and there are existing operating companies. We do not want to disrupt this ecosystem, in these markets. Instead, our joint-deployment business model is trying to bring values and form a win-win alliance.

For example, we leverage Uber and Bolt as mobility platforms to attract demands, and we are also partner with Verne in Croatia and ComfortDelGro in Singapore as local fleet operators. From a financial perspective, this model could generate sharing-based revenue or technology licensing fee for Pony. This is not only broaden our revenue base, but also introduce higher margin recurring income across the entire Robotaxi operating life cycle. As we expand our footprint into higher premium international markets, for example, in Europe, in Middle East, and in other parts of Asia, we definitely think this could lift our long-term financial outlook. Just to be clear, these 4,000 vehicle commitment from Uber and other partners will serve as a multi-year growth catalyst for 2026 and beyond. I will now turn the call back to the operator.

Operator: The next question comes from Paul Gong with UBS. Please go ahead.

Paul Gong, Analyst, UBS: Hi. Thanks for taking my question. I have one question regarding PonyWorld 2.0. I think Tiancheng has mentioned about its self-evolution and the loop engineering. Can you please provide more color on what makes self-evolution different in autonomous driving, and how does it improve your R&D efficiency? If we think in the future, if someone open source a world model, would your modes be affected? Thank you.

Dr. Tiancheng Lou, Chief Technology Officer, Pony AI Inc.: Thank you. This is Tiancheng. I will take this one. To start, I will say autonomous driving is a physical AI. Training the onboard model or improving the world model are both built on real-world feedback. A general purpose to open source world model is basically just a 3D video generator. It can generate the data, but that’s nowhere near enough to train our autonomous driving system. We use world model to train the onboard model through reinforcement learning. To do this right, it is not just simulating what people do, it’s about how often they do it. Take a pedestrian suddenly jaywalking as example. The chance isn’t 99%. It’s not 1% either. Precision means matching the exact real-world probability. That level of statistical accuracy is what we mean by precision of the world model.

Dr. Leo Wang, Chief Financial Officer, Pony AI Inc.: The probability distribution of traffic participants varies from city to city. Although our model generalized capability is strong enough to handle extreme scenarios worldwide, we still need to fine-tune it for local driving styles. For example, in both China and Croatia, there are drivers who change lanes without checking behind them. It happens with different probability in different places. That’s where PonyWorld 2.0 comes in. It is a self-evolving system that continues in improving the world model precision. In the past, our workflow was human-led. When we enter a new city, we collect data from that region that engineers will determine which scenario the current world model lacks precision. Now, AI drives the whole process. Humans are still involved, but mostly for verification and validation. As a result, we significantly reduced engineering resources to enter a new city.

In other words, without adding R&D resources, we can enter many new markets at the same time, quickly achieving safe and smooth L4 autonomous driving.

Dr. Tiancheng Lou, Chief Technology Officer, Pony AI Inc.: This ability to scale is a very large moat. I do not think it will be affected by any open source generative model. With this, back to the operator.

Operator: The next question comes from Jeff Chung with Citi. Please go ahead.

Jeff Chung, Analyst, Citi: Hi, this is Jeff. Thank you for the opportunity. My question is about the domestic market. How should we think about Pony’s new outlook for the domestic market heading into the second half of the year? Thank you.

Dr. James Peng, Chairman of the Board and Chief Executive Officer, Pony AI Inc.: Thanks, Jeff. Hey, this is James. I will take this call. As you can see that China is our home base. I believe that domestic fleet expansion remains a significant part of our vehicle roll-out. China itself represents a massive mobility market with over 10 million taxis and ride-hailing vehicles. The reality is that also the mobility demand is highly concentrated in Tier 1 cities and Tier 2 cities. As a result, our strategy remains the same. We will start our focus from the highest valued markets and then expanding into other cities and regions. The Tier 1 cities alone account for a significant share of the national ride-hailing demand. These cities are also the ones that offer the most mature regulatory framework to support autonomous driving. Today, our scale and the commercial model in these Tier 1 cities remains industry-leading.

In our larger operational hubs, such as Guangzhou and Shenzhen, we are already seeing strong growth momentum. Expanding the fleet size in these markets shortens users’ wait time and boosts user retention. As a result, it directly translates into higher daily revenue per vehicle, even as we scale up our fleet size. This virtuous cycle not only drives paid order growth and margins, but also reinforces our regulatory trust and the brand recognition. At the same time, scaling allows us to amortize the operational costs, driving down our daily per-vehicle costs. What we have seen is really a continuous improvement of our UE margins. Therefore, we will proceed with deploying more and more fleets in the Tier 1 cities to widen our competitive moats. Meanwhile, of course, second-tier and even third-tier markets are strategically vital.

This year, we plan to enter key cities, such as Changsha, Hangzhou, and many of the additional Greater Bay Area cities, and potentially some other cities and regions. This will establish the foundation for these markets, essentially become a new growth engine for us to go forward. With this, back to the operator.

Operator: The next question comes from Xiaoyi Lei with Jefferies. Please go ahead.

Xiaoyi Lei, Analyst, Jefferies: Thanks for taking my question. This is Xiaoyi Lei from Jefferies. My question is on robotaxi operations. You’ve mentioned that operational efficiency is crucial for running the fleet at scale. Could you maybe give us more color on how is that actually being achieved? For example, on the remote assistant side, vehicle utilization or charging and maintenance perspective, and how those efficiency gains are helping you accelerate deployment, both in terms of expanding existing cities and entering new ones. Thank you.

Dr. Tiancheng Lou, Chief Technology Officer, Pony AI Inc.: This is Tiancheng. Thanks for the question. Regarding the operational efficiency, I will start saying, based on our experience across the Tier 1 cities, we now have developed a deep understanding of the complexity of operating the fully driverless fleet. It is a completely different game from managing traditional taxis. At the end of the day, efficiency comes down to one thing, the fleet-to-staff ratio. With traditional taxis, it is always one to one. 100 cars need 100 drivers to handle everything from cleaning, charging, to daily maintenance. For us, it is not just about managing people better, but even critically on whether technology can minimize need to human involvement. For example, when all of our robotaxis return to a depot, they require zero human assistance. Autonomous navigating, locating available chargers, and executing self-parking, even in a very tight space.

Because of that, we need three people for every 100 robotaxis to keep daily operations running smoothly. That is true whether we run them by ourselves or work with partners. This is directly translated into significantly lower operating costs per vehicle and advanced unit economics. Therefore, without inflating management overhead and cost, we can still expand into new cities and deploy more vehicles rapidly. We have developed this know-how into standardized operating procedures and automation tools. That is why more and more partners are joining us to adopt our joint-deployment model, making Pony’s robotaxi the most efficient and profitable choice available. With this, back to the operator.

Operator: The next question comes from Jiong Shao with CICC. Please go ahead.

Jiong Shao, Analyst, CICC: Thank you, management, and congratulations for the quarter. Could you give us updates on your new business initiatives, specifically the progress with your L4 light truck business? Thank you.

Dr. James Peng, Chairman of the Board and Chief Executive Officer, Pony AI Inc.: Thanks, Kai. This is James, and I’ll take this one. The new business initiatives, especially the L4 light truck, I think fits very well with our vision and ambition, which is autonomous mobility everywhere. The L4 light truck has a great synergy among our current product offerings. Think about it can leverage the Robotaxi’s driving capabilities and the cost-efficient hardware. At the same time, the light truck also shares the same customer base with our robo-truck. The light truck almost shares 100% of our Robotaxi’s technology and operational infrastructure. Essentially, the development and operation can slash our costs. The light truck extends the logistic portfolio from long haul into urban delivery. It essentially unlocks a new TAM. In China alone, the active light truck fleet on the road exceeds 8 million vehicles. Also, look at the current, already on the ground, the low-speed robovan.

Compared with that, our light truck offers 3 to 4 times the cargo capacity, and also the speed is 2 times faster. As a result, it can open up heavier loaded commercial applications across the full urban supply chain. If you think about typical usage, those from distribution hubs to the shopping malls, to the supermarkets, and also the convenience stores. As you recall that we actually unveiled the L4 light truck in the Beijing Auto Show. Since then, it has been 4 months, and in that 4 months, we have already built a strong commercial ecosystem. The vehicle itself are jointly developed with CATL. The vehicle is the world’s first automotive-grade, fully redundant light truck, purposely built for L4 autonomous driving. Currently, we also have secured partnerships with SF Express and China Post Technology, two leading logistic operators in China.

With the orders and the deployment schedules already in place, this partnership can create a strong pipeline for the autonomous urban delivery. Looking at the remaining of this year, I believe that the collaboration pipelines with even more OEMs and the fleet operators will still in the pipeline to drive scaling up. We’ll also integrate with urban logistic network platforms to capture even further demand. I’m actually very excited about this new initiative. With this, back to the operator.

Operator: The next question comes from Anne Ni with Everbright Securities. Please go ahead.

Anne Ni, Analyst, Everbright Securities: Hi there, management. Thank you so much for taking my question. We know that Waymo’s management recently said that a demo is only 1% of the work. Could Pony’s management share your views on this comment, please? Thank you.

Dr. Tiancheng Lou, Chief Technology Officer, Pony AI Inc.: Thank you. This is Tiancheng Lou. I will take this one. First I would say this is an interesting framing, and I think it captures something real. Building an impressive demo and scaling are two entirely different games. Autonomous driving is really a probability problem. If you get into one accident every 1,000 kilometers, sure, you can do a demo, because a demo only covers a few kilometers. But at scale, this accident rate is a deal breaker. A typical ridesharing vehicle drives about 300 kilometers a day. If you have a fleet of 100 cars in one city, that is tens of thousands of kilometers every day. The fleet will see 10 accidents every single day, then no regulators will tolerate this, and the public definitely won’t. Because autonomous driving is a probability problem, risk evolves differently at scale.

Proving safety takes time and mileage, and you cannot just shortcut by dumping thousands of cars on the street overnight. Fleet size and time are not interchangeable. This is also why regulators everywhere take exactly the same approach. They go step by step. A small fleet first, proof of safety at that scale, then to the next level. Technically, going from a demo to full scaling takes multiple 10x jumps in performance, and every jump is harder than the last. It’s not just about fixing the remaining 10% of problems, but also systematically resolving 90% of the issues without creating new ones. For example, hard braking to avoid a collision may solve a problem, but it may create more rear-ended collisions. If the underlying technical approach is wrong, safety has a hard ceiling. Therefore, proving safety to regulators is just only one of the bar.

From a technical standpoint, new players have to prove they can iterate very fast, because the leaders are already miles ahead by several order of magnitude of safety. Long story short, if all you have today is a demo, you still need to prove that you can achieve multiple 10x performance jumps. On top of that, you need time to build trust with regulators before you can scale. For Pony, we will already check both of these boxes. That’s why our focus for today is on expanding into more cities and deploying larger fleets. With that, back to the operator. Thank you.

Operator: As there are no further questions now, I would like to turn the call back over to the host for closing remarks.

George Shao, Head of Capital Markets and Investor Relations, Pony AI Inc.: Thank you once again for joining us today. If you have any further questions, please feel free to contact our IR team. We look forward to speaking with you in the next quarter.

Operator: This concludes today’s conference call. You may now disconnect your line. Thank you.