Pony AI - Earnings Call - Q4 2024

Transcript

Operator (participant)

Ladies and gentlemen, thank you for standing by, and welcome to Pony AI Inc. Fourth Quarter and Full Year twenty twenty four 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 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 Hsiao, Head of Capital Markets and Investor Relations at Pony AI. Please go ahead, George.

George Shao (Head of Capital Markets & IR)

Thank you. This is George speaking. Hello, everyone. We appreciate you joining us today for Pony AI's fourth quarter and full year twenty twenty four earnings call. Earlier today, we issued a press release with our financial and operating results, which is available on our IR website.

Joining with me today on the call are Doctor. James Pung, Chairman of the Board, Co Founder and Chief Executive Officer Doctor. Tiancheng Lo, Director, Co Founder and Chief Technology Officer and Doctor. Liu Wang, Funding Member and Chief Financial Officer. They will provide prepared remarks followed by a Q and A session.

Please note that today's discussion will contain forward looking statements made under the safe harbor provisions of The U. S. Private Securities Litigation Reform Act of 1995. Forward looking statements are subject to risks and uncertainties that may cause actual results to differ materially from our current expectations. Further information regarding these and other risks and uncertainties is included in the relevant public filings of the company as filed with the U.

S. Securities and Exchange Commission. The company does not undertake any obligation to update any forward looking statements, except as required under applicable law. Please also note that Pony Air's earnings press release and this conference call include discussions of both unaudited GAAP information and unaudited non GAAP financial results. For a reconciliation of these non GAAP measures to the most directly comparable GAAP measures, please refer to Ponyia's disclosure document available on our IR website.

I will now turn the call over to our Chairman, Co Founder and CEO, Doctor. James Pang. Please go ahead.

Jun Peng (Chairman, Co-Founder & CEO)

Thanks, George. This is James Pang, Founder and CEO. We consider this as an exciting time for Pony dot ai as we report our first earnings results as a public company. Our NASDAQ listing marks our significant milestone and is timed perfectly with the imminent mass commercialization of our robotaxi services. With ample financial resources now available, we are well positioned to lead and capitalize on the upcoming large scale rollout of robo taxis, making this year an inflection point for the widespread adoption of autonomous transportation solutions.

We are taking a robotaxi first, China first as a Tier one cities first approach. This is our current focus is on scaling robotaxi operations in China, which not only generates sizable recurring revenue, but also offers a solid foundation for further expansion into various global markets. China's online ride hailing market is exceptional. The country's Tier one cities, Linley, Beijing, Guangzhou, Shanghai and Shenzhen offer a unique combination of demand, consumer readiness and regulatory clarity, making them ideal for large scale robotaxi deployment. We estimate each city can easily support a fleet of over tens of thousands of robotaxis.

With technology that meets regulatory standards and backed by the fully driverless fair charging licenses we have already secured. We are all ready for quick scale up. Launching large fleet in Tier one cities will enable us to validate our business model, optimize our operations and establish these markets as a benchmark and scalable framework for future growth either into other Chinese cities or extends to international markets. Next, I'll explain why we anticipate our global taxi service will soon achieve mass commercialization. First and foremost, we have achieved technological readiness for mass commercialization.

Our operational records proved that our RoboTaxi has achieved level four driverless operation 20 fourseven in all weather conditions, making it commercial ready. Our technology is empowered by virtual driver and the world model. The virtual driver is a comprehensive full stack system with proprietary software and hardware. This enables us to collaborate effectively with automakers and the transportation network companies, we call them TNCs, to create a scalable mobile taxi business model. Additionally, our generated Ponyworld model treats our virtual driver to be much safer and better than an expert human driver through advanced reinforcement learning.

Our Pony world simulates a wide range of scenarios, including extreme cases and the long tail events By employing a training method called learn by practicing, our virtual driver does not just know what to do. It actually understands the reasons behind its actions. This is quite different from imitation learning that is widely used for the typical L2 systems. Because the L2 systems imitate the driving patterns of human drivers, They can only reach human level safety. In contrast, Pony World has improved our virtual drivers' safety by 16 times, while at the same time significantly improving its comfort and driving efficiency.

Our safety record enabled our commercial insurance costs per robotaxi to be reduced to almost half that of the traditional taxis. Second, we have established strong relationship with local governments and secured the required policy approvals for large scale commercialization. We have obtained all the most advanced licenses in China's Tier one cities. For instance, in recent weeks, Pony AI launched paid robotaxi service that connects key transportation hubs such as Beijing South Railway Station, Beijing Daxing Airport and Yizhong District with plans to gradually expand to Beijing City Center. Moreover, in February, we launched paid robotaxi services in Guangzhou City Center, Guangzhou Baiyun International Airport and Guangzhou South Railway Station.

We are the first and only company approved to provide robotaxi services on these high demand routes. Moving forward, we will gradually expand our operations in these cities paving the way for future growth. Third, we have built extensive mass production partnerships to support large scale commercialization. For example, in the first half of twenty twenty four, we established a joint venture with Toyota. As part of the deal, we will roll out mass production of the robotaxis based on BZ4X, as well as build the value chain of autonomous driving operations, including maintenance, charging and other aspects.

In addition, in the second half of twenty twenty four, we respectively reached mass production partnerships with DAIC Niu Energy, that is Beijing Auto and GAC Group, that is Guangzhou Auto. Based on the BAC ARCFOX Alpha T5 models and the GAC ION models. We carried out cooperation in the mass production of auto grade autonomous driving kits, vehicle model production, redundant safety design of the chassis and some other areas. These partnerships have been reinforced through strategic equity investments from all these three OEMs. All three upcoming RoboTaxi vehicles are based on our seventh generation autonomous driving systems.

This latest generation has achieved a major breakthrough in cost efficiency, reducing unit farm cost by over 70% compared to the previous generation, with further cost reduction anticipated as we scale up. Fourth, we have fortified our operational capabilities to support the ramp up of fleet size and accommodate fast growing user demands. We have developed our own ride hailing platform, which is called Pony Pilot and forged strategic partnership with leading D and Cs, such as OnTime Mobility and Alipay to offer driverless robotaxi services. In the fourth quarter of last year, we also established a partnership with Alibaba's online mapping and ride hailing platform, AMAP, and integrated our Robo taxi service into its mobile app and the media programs, making our services more accessible to the public. In 2024, the average daily orders per vehicle reached 15 and in Q1 twenty twenty, twenty five, we continue to see the growth of daily orders per vehicle.

With significant progress have been made in all the four pillars of autonomous driving, that is technology, regulations, mass production and large scale operation. We do see that a critical inflection point for mass commercialization is right in front of us. Now, let's look at our Robotruck business, which we have also seen significant growth in 2024. We deepened our joint venture with FinalTrans, transforming it into a comprehensive autonomous driving transportation as a service platform. Together, we will continue building a smart, efficient, safe and environmental friendly logistics road transport network, while further expanding our Robotruck fleet.

A major milestone that highlights our leadership in Robotruck business is our approval as the first company in China to conduct Robotruck driver out platoning across provincial highways, linking Beijing, Tianjin and Hebei products. With only the leading truck requiring a safety operator and the following trucks to be fully driverless. Testing has already begun on the Beijing Tianjin Panghu Expressway, making a significant step towards full autonomy for all trucks in the platform, which will further reduce logistics costs and accelerate commercialization. In summary, our transition to a public company marks the beginning of exciting new chapter. We stand at a defining moment as we move towards the large scale commercialization of autonomous mobility and continue to gain momentum. Building on a solid foundation of technological advancements, regulatory support and industry partnerships. Looking ahead, our priority for this year is clear. Accelerating the mass production and the deployment of our seventh generation robotaxi fleet, further reducing the unit bond cost and expanding operation areas and density in China's Tier one cities.

With that, I will now pass it over to our CTO, Doctor. Tiancheng Luo to review our technological progress. Tian Chen, please go ahead.

Tiancheng Lou (Director, Co-founder & CTO)

Thanks, James. Hello, everyone. This is Tian Chen. So I'm delighted to have this opportunity to share with you the latest progress of our technologies. Pony's technological development is centered around enabling the mass commercialization of road taxi.

To achieve successful road taxi commercialization, a pump driving technology must meet three key criteria. First, it must attain a sufficiently high standard of safety. Overachurrence shows that a magnitude safer than a typical human driver is attainable and should be needed. Secondly, cost control is essential. Costs should be managed across various aspects, including sensors, computing hardware, daily operation and insurance.

Low cost ensure that robotic service remains economically sustainable. Finally, robotic service should cover large enough geographical areas to enable large scale operations. According to our operational and safety record, Pony's technology have matured to a level that can support mass commercialization, focusing on safety's cost effective and intensive service coverage. Through years of effort, we have been commercially operating fully wireless robotizing for over two years. During this time, safety has already surpassed typical human driver by an order of magnitude.

As we progress, costs are expected to decrease by 70% in Mac generation, which will be mass produced in the second half of the year. Moreover, our service coverage has received regulatory approval and licenses in all Tier one cities in China, which are capable of operating tens of thousands of lower taxis. Moving forward, our technical goal will remain focused on enhancing cost efficiency and operational capability without compromising safety. In the competitive landscape of the global taxi services, only companies that can run driverless commercial operations with a significant fleet hold our position at the forefront. Years of innovation and diligence have given us a strong competitive edge.

It took us four years to progress from initial garbage dumpsterization to fully launching commercial robotaxi service in China Taiwan cities. So you may wonder why it took companies like Weibo and Pony almost five years to get there from demo to commercial operation. The reason that we had to move from simply matching human driving capability to significantly exceeding them. This means we had to rebuild our core algorithms as older ones were designed in a way subject to human limitations. Now let me further explain why the technology evolution that allows us to bridge the gap and launch over fully driverless services.

The key is moving from imitation learning to reinforcement learning, a trend that is the key driver brought us a seat at the forefront. With imitation learning, which is still widely used by most of the L2 systems, So AI drivers learn by copying human behavior using data from the real world driving. By mimicking human driving patterns, imitation learning cannot understand the reasoning behind the driving behavior. As a result, this solution is not general enough to handle ever changing traffic scenarios. Reinforcement learning, on the other hand, uses a generative virtual environment called our word model or pointy word at our kindergarten point, where our virtual driver teaches itself through billing of even trillions of generative acts of trial.

This allows our virtual driver to understand why by analyzing the outcome of every action, equipping them to make smarter decisions in complicated scenarios. Through repeated reinforcement learning, our virtual driver gradually learned to adapt to new situations, unexpected challenges and the qualifications, preparing them to operate safely in the real world. Over time, our virtual driver trained under Ponyworld developed the advanced skills needed for complex tasks, such as multi navigating business streets handling unpredictable traffic scenarios or safely operating for tens of thousands hours without any incident. There are three key components making our Pony World approach possible: the ability to generate realistic scenarios and sensor data, a high fidelity simulation system and a comprehensive set of evaluation metrics. Together, these elements allow our Pony World to effectively coach our virtual driver to handle real world challenges.

I would like to highlight our high fidelity simulation engine here, which leverages the latest technology to create an environment that precisely replicate real world conditions in both subtle details and dynamic responses. Unlike traditional systems that rely on human driving data, our simulation engine generates its own driving scenarios and challenging situations for autonomous vehicles to understand, adapt to and make decisions. The traffic participants in our simulation engine are designed to behave like real humans, interacting with autonomous vehicles in a natural and human like way. This makes our Pony World a powerful tool for coaching our virtual drivers. Finally, let me share the latest progress we will make in advancing our technology for mass production and commercialization.

Largely, probability in certain cases with hardware that has lower performance. To address this challenge, we continue to innovate our Pony Works. Here's how it works. We have trained an Oracle AI driver in our Pony Works, a virtual environment that that time can be rewatched. This oracle learns to predict future outcomes and then acts as a coach to train other AI drivers, helping them anticipate and respond to future events.

Using similar methods, we will be able to maintain safety standards for mass produced and auto grid lidar domain controllers and the larger robotaxis fleet. Tony Ward has improved our virtual driver's safety record by 16 times, while significantly improving its comfort and driving efficiency. This advancement has reduced the commercial insurance cost per robot taxi to almost half of that of traditional taxi, as this is a clear objective measure by the insurance of safety of our technology. Before I conclude, I would like to highlight the creation of Pony World took years to dedicate its research and development, driven by a team of exceptional talented engineers who evolved and thrived together with us over time. This journey was fueled by the belief of that our Pony world offer a greater potential and is critical for achieving driverless domestic commercialization.

Those years we spent were the toughest for our company and for me personally. I deeply grateful for the trust and the support of our investors and colleagues along the way. This concludes my prepared remarks. I will now pass the call over to our CFO, Doctor. Leo Wang, for a closer look at our financial results. Leo, please go ahead.

Haojun Wang (CFO)

Thank you, Tian Chen, and hello, everyone. I'm pleased to present Pony AI's financial results on our inaugural earnings call. Looking back on 2024, we kicked off our seventh generation autonomous driving system development with three OEM partners, which is critical to execute our Robo Taxi First, China First and Tier one City First strategy. We also deepened the partnership with industry leaders, creating a robust ecosystem that accelerates the adoption of these technologies. During our IPO late last year, we raised over US400 million dollars which provided us with ample fab power to drive our strategy. Looking forward, we'll concentrate and accelerate our seventh generation autonomous driving system development and deployment in China's Tier one cities, hence to solidify Pony AI's position for sustainable growth. Moving to our financial performance.

Please note, as we navigate the early stages of commercialization, we are experiencing volatility in our quarterly revenue and margins, which is expected to continue in the near term. But we are focused on executing our go to market strategy and achieving key milestones laid out by James and his remarks, which we expect to reduce variability in our financial performance in the future. Now let's take a closer look at our financial results for 2024. For additional quarterly results, please refer to our earnings release, which is posted online. Our full year revenue totaled USD 75,000,000, an increase of 4.3% year over year.

Robo taxi services revenue was USD 7,300,000.0, down 5.3% year over year. The decrease was primarily driven by reduced service fee from providing autonomous vehicle engineering solutions based on our project progression schedule. Our RoboTactic Services revenue also include passenger fares, which saw significant year over year increase driven by the expansion of our public facing fare charging road taxi operations in Tier one cities. We expect this part of growth will continue and even accelerate as we deploy the seventh generation of top driving vehicles. Normal truck services grew strongly, delivering US40.4 million dollars in revenue, up 61.3% year over year.

This robust growth was driven by the expansion of our fleet into new regions, where new demand can be fulfilled by our robot truck fleet. Licensing and applications revenue was USD 27,400,000.0, down 30.1 year over year, influenced by recognition schedule of project based revenue. Total cost of revenue was USD 63,600,000.0, up 15.6% year over year, in line with revenue trend and revenue mix. We achieved gross profit of USD 11,400,000.0, resulting a gross margin of 15.2%, a decrease from 23% in 2023. The year over year decrease was mainly due to services with relatively low gross margin contributed increasingly to our revenues.

Moving forward, we expect gross margins to improve as we further scale and optimize operation over time. Total operating expenses were USD 296,900,000.0, an increase of 85.4% year over year. Excluding share based compensation, non GAAP operating expenses were US169.9 million dollars up 8.7% year over year. The increase was mostly driven by accelerated R and D investment to support the launch of our seventh generation RoboTexas vehicles in collaboration with our OEM partners. Loss from operations was US285.5 million dollars compared to US143.2 million dollars in 2023.

Non GAAP loss from operations was US158.5 million dollars compared to US139.5 million dollars in 2023. Net loss was US275 million dollars compared to US125.3 million dollars in 2023. Non GAAP net loss was US153.6 million dollars compared to US118.5 million dollars in 2023. Turning to our balance sheet. Our combined cash and cash equivalents, restricted cash, short term investments and long term debt instruments for wealth management was US825.1 million dollars at the end of twenty twenty four.

And lastly, for our business outlook. As mentioned earlier, we expect continued fluctuation in our quarterly revenue as well as margin since we are at the nascent stage of commercialization. While we are not given formal guidance at this time, we are confident in our ability to scale up commercialization, drive sustainable growth and deliver value to our shareholders. I will now turn the call over to the operator to begin our Q and A session. Thank you.

Operator (participant)

Thank you. We will now begin the question and answer session. The first question today comes from Verena Jang with Goldman Sachs. Please go ahead.

Speaker 5

Thank you, management team. I have two questions. My first question is about the business strategy. So what's the strategic rationale behind your Robotaxi First, China First and also the Tier one cities first approach? If you could share more color behind this will be appreciated. Thank you.

Jun Peng (Chairman, Co-Founder & CEO)

I'm Jack Huang. I'll take the first question regarding our three:one strategy. Actually, from day one that Tony was founded, Autonomous Mobility Everywhere has always been our company model. This model actually reflects our vision to bring autonomous transportation to all global markets and across all types of vehicles. We certainly have the ambition for other markets down the road.

The fundamental reason behind our China First, RoboTaxi First and Tier one cities first strategy, rising our confidence in an imminent opportunity from mass commercialization. China has the largest ride hailing market with around 40% of the global market measured by the number of orders. This is roughly twice the size of The U. S. Market.

Within Chinese sales, Tier one cities represents the largest share. Backed by supportive regulatory environment and growing users demand. In 2024 and 2025, we expanded our operations of paid robotaxi to more railway stations, international airports and the city centers in Beijing, Guangzhou and Shenzhen. We also observe that China has established the regulatory framework for global taxes in a swifter and more transparent manner compared to many other regions. As a result, we believe the Tier one cities in China are ripe and ideal for mass deployment of Robotaxi.

Not only is Robotaxi representing the largest market, it is also representing the most difficult technical and deployment challenges. The safety requirements in handling the bad weather conditions such as rain and snow and other unpredictable corner cases are very challenging. We have proven our capability to handle such challenges by successfully operating fully driverless global taxis in the last two years. It is from a commercialization perspective that we are currently more focused on global taxi in China's Tier one cities. But certainly, our know how can enable us to transfer to other transportation modes and also the global markets in the future. So thank you. Now back to the second question.

Speaker 5

Thank you, James. My second question is about a business model. Could you differentiate your business model against OEM ride hailing company and also the taxi company? And any collaboration with these companies? Thank you.

Jun Peng (Chairman, Co-Founder & CEO)

For this question, I think our CFO, Leo, is the right one for answering.

Haojun Wang (CFO)

Thank you, James. I'll take this question. So for our RoboTeci air charging service, actually it's focusing on providing a virtual driver who takes charge of the driving in the transportation service. And if you look at the traditional transportation service, that's actually provided by a human driver to take charge of the driving. And we charge our passenger based on the distance driven by our virtual driver.

During the ride, actually, we provide a more private and safer experience to the passengers. So if you look at this business model, you can regard that as like upgrade to the current ride hailing business model, not a disruption. From a ride hailing platform company perspective, its business still will be matching passenger demand with driver resources, in which you can consider our virtual driver to be part of the driver pool. Automakers or OEMs, on the other hand, they get revenues from selling purposely built vehicles that are co developed with Pony. And these vehicles will be sold to robotaxi operators, for example, Pony itself.

In a nutshell, actually, each party in the value chain in ride hailing business will still play its role in the transportation mobility service sector. We consider this will be a win win concept. And because this concept not only supported by us but also supported by our partner, you can see that we have secured mass production plans with OEM partners such as Toyota, Beijing Auto and Guangzhou Auto. We have also integrated into different traffic net companies such as AMAP, Alipay, OnTime and etcetera. So this is my answer to your question. Now I will turn back to the operator.

Operator (participant)

The next question comes from Ming Sun Li with Bank of America. Please go ahead.

Ming Hsun Lee (Analyst)

Thank you for giving me the opportunity to ask some questions. So my first question, do you foresee any challenges before mass commercialization? Maybe we can elaborate more in terms of the user acceptance, technology maturity and the regulation. Thank you.

Jun Peng (Chairman, Co-Founder & CEO)

I'm James Bong. I'll take this one first. Thanks, Vincent. As I described in my opening remarks, I'm very confident that the four key pillars for the mass production of global taxi, namely the technology, regulation, mass production and the large scale deployment, are actually all in place for Pony. I particularly want to emphasize that our technology has advanced the safety of our robotaxis to a level that actually allow us for the large scale commercialization of robotaxis.

We do not foresee any insurmountable challenges that prevent us from achieving as commercial. Thirdly, we work hand in hand with OEMs and the supply chains to launch a new generation of cost effective robotaxis, successfully reducing our unit cost by 70%. Along with continued improvements in operational efficiency, we're now on the right track to achieve breakeven at the individual vehicle level. In other words, we will have a positive contribution margin from the seventh generation global taxis. In general, we have seen supportive regulatory environment from both the central and the local governments.

We take pride in being among the first companies in China to secure licenses for operating fully driverless robotaxi across all four Tier one cities. Furthermore, we are the only autonomous driving technology company that has obtained all the necessary regulatory permits required to offer commercial public facing robotaxi services in Tier one cities. Moving forward, our main priority will be expanding our fleet size, operational areas and vehicle density to scale up revenue and enhance our profitability. So that's the answer to your first question.

Ming Hsun Lee (Analyst)

Thank you, James. So my second question, what are the key technological milestones that need to be achieved to enable your mass production of robot taxi service in 2025? Thank you.

Jun Peng (Chairman, Co-Founder & CEO)

Thank you, Vincent. I think this one is related to technology. So I'll hand over to Kiencian to answer.

Tiancheng Lou (Director, Co-founder & CTO)

Yes, sure. This is Kiencheng. So as I described in my remarks, Sophoni's technological development is centered around enabling mass competition of local taxi. To achieve successful RoboTaxi commercialization, our top driving technology must meet the three key criterias. They are safety, cost effectiveness and intensive service coverage.

So through years of effort, we have been commercially operating fully drive this robotaxi for over two years. During this time, safety has already surpassed tip to human driver by an order of magnitude. And cost wise, as we progress, costs are expected to decrease by 70% in the next generation, which will be mass produced in the second half of this year. Moreover, our service coverage has received regulatory approval and licenses in all Tier one cities in China, which are capable of operating tens of thousands of robotaxis. So according to our operational and safety record, we believe Pony's technology has matured to a level that can support mass commercialization.

And moving forward, our technical goal will remain focused on enhancing cost efficiency and operational capability without compromising safety. Yes, thank you. And back to the operator.

Operator (participant)

The next question comes from Bin Wong with Deutsche Bank. Please go ahead.

Bin Wang (VP - Investment Banking)

Thank you. I just have one question about technology. How do you achieve a very high safety level compared to human driver? And why you believe the level for a cost driving technology depends on more you would generate high quality data rather than the massive data you get from the street? Thank you.

Jun Peng (Chairman, Co-Founder & CEO)

Tien Tsin, this one is still yours.

Tiancheng Lou (Director, Co-founder & CTO)

Sure. Yes. So this is Tien Tsin. So yes, good point. Let me reemphasize that the Aelful AI driver is trained using reinforcement learning in a virtual world where data is generated.

So as a result, reinforcement learning does not require huge amount of real world data. Let me further elaborate on why using real world driving data to mimic human driving behavior cannot meet L4 safety requirements. The fundamental reason lies in the double standard applied to human drivers versus AI drivers. Sociality holds AI to a much higher standard than human drivers. People are far less tolerant to AI mistakes, where AI is perceived as a machine except to expect it to eliminate human shortcomings.

This creates a paradox. L4 systems must meet safety expectations far beyond that human driver can achieve. Imitation learning by its nature is limited by the ceiling of human performance and the cells cannot satisfy with safety requirements. So although the amount of data used for imitation learning driving is extremely large, it still cannot ensure that the driving capability can surpass that type of humans. Another important factor is that leveraging real world data cannot understand the reasoning behind driving behavior, because it only mimics the driving path of human drivers.

There is a common saying that describes this phenomena one knows what state is, but doesn't know why it is strong. Merely mimicking the action of human drivers does not guarantee our understanding of the reasoning behind this action. So in summary, compared to most L2 systems, L4 systems use a different data solution where generated data is the key, not the real world data. Thank you. Back to the operator.

Bin Wang (VP - Investment Banking)

Thank you.

Operator (participant)

The next question comes from Purdy Ho from Huay Thai Securities. Please go ahead.

Purdy Ho (Chief Analyst for Overseas Technology)

Good morning, management. This is Purdy Ho from Huay Thai. My questions are from The U. S. I guess, because most of them are on cost and revenues. So would you mind giving some colors on why your 2024 costs and expenses were higher year over year? And also any guidance on costs going forward that you can provide?

Haojun Wang (CFO)

I'll take this question. So as I mentioned in my earlier remarks, actually excluding share based compensation, our non GAAP R and D expenses were USD137.8 million. It represents an increase of 14% compared to USD 120,900,000.0 in 2023. That's mostly because since the second half of twenty twenty four, we have been working on three vehicle models of our seventh generation autonomous driving system. This incurred the corresponding R and D expenses growth.

We consider this ongoing development is very critical to implement our Robo Techty First, China First and Tier one City First strategy. And much of this development work will be accomplished in this year. So on the other hand, during our IPO late last year, we raised over US400 million dollars So now we have a strong balance sheet with a total of US825.1 million dollars combined with cash, cash equivalents, restricted cash, short term investments and long term debt instruments for wealth management as of 12/31/2024. This provides ample firepower to execute our strategy. But also as a start up, we still need to carefully manage our resource allocation and investment to seek for the best efficiency and the returns.

So we will continue and even accelerate our seventh generation system development as our top priority and deploy these vehicles in Tier one cities from hundreds to thousands. Therefore, we expect the corresponding expenditure will continue to grow this year. So I'll get back to you.

Purdy Ho (Chief Analyst for Overseas Technology)

Okay. Sure. Yes, I got a follow-up. So yes, we also noticed that revenues in the entire year 2024 was up, but for the quarter, the fourth quarter, revenue was down. So any comments on that and any guidance going forward? Thank you.

Haojun Wang (CFO)

So I'll continue to take this question. So if you look at our current revenue, it consists of recurring revenue such as we provide a robotaxi fare charging service to the public. We also provide robot truck logistics service to our business partners. And we also have so called project based revenue, for example, launching a proof of concept robotaxi fleet with our partner for a certain amount of time in certain markets. Given we have a portion of revenue that is tied to milestone based projects, so that revenue will be recognized upon delivery of contractual obligations, Revenue recognition naturally would fluctuate across different corporates.

This is very common for this type of revenues. But look, we are focusing on our seventh generation autonomous vehicle development and the deployment. And these new and more cost effective vehicle will be put into global taxi fair charging operations later this year, starting from hundreds to thousands. So as such, I think it will increase the recurring revenue portion and gradually to change our revenue mix. Hence, we think we can mitigate the fluctuation in the revenue stream in the future.

And we also expect our overall revenue will gradually to grow in the near term, following the revenue trajectory in the recent years. So this is my answer to your question. Get back to the operator.

Operator (participant)

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

Speaker 5

Thanks for taking my question. I have two questions. My first question is from the technology perspective. Given recent emergence of disruptive technologies like DeepSeq, how do you see these play out in the development of for autonomy from the industry level? Will they have a positive or negative impact on your technology roadmap? And how also will they impact Pony's timeline for the mass deployment of robotaxi? This is my first question.

Tiancheng Lou (Director, Co-founder & CTO)

Yes, thank you. This is Tiancheng. I can take this one. First, I would say let's broaden the topic a little bit. In the past few years, many disruptive technologies have emerged, including end to end architecture, transformer and also other technologies using the dipstick.

They all are giving companies like Pony a greater advantage. For example, the integration of the N2N technology has significantly enhanced Pony's service coverage. More importantly, the successful commoditization of Robotech involves multiple factors. The disruptive technology can only impact welfare. For instance, the factors include number one, driving capability such as safety, comfort and efficiency number two, cost such as sensors, computing, operations and then lastly, partnerships such as OEM suppliers, T and Cs.

The key to success through a successful commoditization of Robotaxi is ensuring that all these factors meet certain standards. Disruptive technology can only affect one aspect and any singular breakthrough only provides marginal help for the entire autonomous driving system. Yes, thank you and back to you.

Speaker 5

Thank you. That's very helpful. And my second question is regarding your cooperation with OEMs. Could you please share more details about the current progress? How does such partnership help you to achieve your mass production goals?

Jun Peng (Chairman, Co-Founder & CEO)

This is regarding partnership, so I'll take it. I'm James. So our deep collaboration with OEM is one of the keys to actually ensure ensuring our global taxi commercialization at scale. We work closely with OEMs to co develop and produce autonomous vehicles across various vehicle platforms. Most importantly, as I mentioned earlier, scale will be instrumental to enabling us to achieve positive unit economics at a quicker pace.

Our collaboration with OEMs are set to significantly reduce unit costs, slashing the bomb by 70% compared to our sixth generation robotaxi. In 2024, we have reached agreements with three OEMs, Toyota, Beijing Auto and Guangzhou Auto to produce three new vehicle models. Our collaboration with BAIC and the GAC will also endow us with more robust government support in our key markets. So in summary, I think our partnership with OEMs actually also goes beyond manufacturing. For example, the joint venture we established with Toyota last year is actually have a more comprehensive partnership.

It will provide capitals for vehicles, operate as a fleet company to burden the CapEx and also utilize the existing Toyota dealer network for the vehicle maintenance. So that's the answer to the question. Now back to the operator.

Operator (participant)

There are no further questions. Now I'd like to turn the call back over to management for closing remarks.

George Shao (Head of Capital Markets & IR)

This is George Chow again. Thank you everyone 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 (participant)

This concludes the conference call. You may now disconnect your lines. Thank you and have a great day.