“`json
[
{
“Analyst name and firm name”: “Ming Sun Li at BMA”,
“The question”: “How should we address your progress throughout 2025 during this year of scaling up? Is there any color or pipeline for 2026?”,
“The response”: “James Peng outlined a clear pipeline for Gen Seven robotaxi mass production, with expectations for mass production to start in Q2 and fleet size to reach 1,000 vehicles by year-end. He mentioned collaborations with OEM partners and adaptability to supply demands are crucial for meeting these targets. For 2026, PonyAI aims to accelerate production and deployment of autonomous vehicles in China and international markets.”
},
{
“Analyst name and firm name”: “Ting Song at Goldman Sachs”,
“The question”: “Could you elaborate more on your evolving global strategy? Does the China market remain a core focus at this stage?”,
“The response”: “James Peng expressed that while China remains the priority due to its mature regulatory environment, PonyAI aims for markets with strong mobility demand and advanced infrastructure. Strategic partnerships with Uber and Comfort Aero, among others, are part of the global strategy, with regulatory progress seen in Luxembourg and South Korea.”
},
{
“Analyst name and firm name”: “Ting Song at Goldman Sachs”,
“The question”: “What factors are driving high revenue growth in Robo Taxi this quarter, and is this sustainable?”,
“The response”: “Liu Wang highlighted that growth was driven by both fare charging and project-based services, with fare charging revenue seeing 800% year-over-year growth. While revenue from project-based services can fluctuate, partnerships with platforms such as ride-hailing services are critical for long-term revenue streams. Sustainable growth is expected as operations optimize.”
},
{
“Analyst name and firm name”: “Bin Wong at Deutsche Bank”,
“The question”: “Did you need to upgrade your software to fulfill the ADK cost reduction? What improvements are there in computing power?”,
“The response”: “Tianqin Luo explained that PonyAI optimized Pony World and engineering capabilities, improving inference performance with cost-effective hardware and software systems. The company’s AI model and inference computing efficiencies allowed adoption of cost-efficient computing power, significantly outperforming L4 industry standards, leading to cost-effective, scalable L4 driving systems.”
},
{
“Analyst name and firm name”: “Chuyang Yi at Haitai Securities”,
“The question”: “Could the recent regulatory requirements from China’s Ministry of Industry and Information Technology regarding driver assist have an impact on PonyAI?”,
“The response”: “Tianqin Luo clarified that the regulations distinguishing L2 from L4 would benefit PonyAI as it emphasizes L4 capabilities. He elaborated on the technological distinctions, specifically reinforcement learning for L4 vs. imitation learning for L2, with PonyAI’s virtual driver gaining an edge through learning in complex scenarios, thus not being impacted by L2 regulations.”
},
{
“Analyst name and firm name”: “Xiaoyu Lei at Jefferies”,
“The question”: “Does the US-China tariff issue have any potential negative impact on operations? How many materials are sourced from overseas?”,
“The response”: “Liu Wang stated that the impact of tariffs is minimal as most of PonyAI’s supply chain is domestically sourced. Enhancements in supply chain resilience and reflecting such uncertainties in planning for Gen Seven mass production keep the company on course for its annual target of deploying a 1,000 vehicle fleet size.”
}
]
“`