Does He Xiaopeng Burn 300 Million Per Month? Can He Really Make Autonomous Driving Accessible to Ordinary People?

The turning point is coming.

On the evening of March 16, He Xiaopeng rarely revealed his bottom line.

Xpeng’s second-generation VLA, defined by He Xiaopeng as “the first version built for L4 capability,” centers on solving problems entirely with AI, not rules. During the Two Sessions, delegates also called for accelerating the revision of the Road Traffic Safety Law.

To provide enough computing power and data for the second-generation VLA, Xpeng invests 300 million yuan monthly, continuously for over ten months.

After merging the autonomous driving and intelligent cockpit business lines, Liu Xianming’s General Intelligent Center has a clear mission: turn this investment into user trust. The traffic flow of test drives at terminal stores, driven by interest in intelligent driving, is indeed warming up.

But at this moment, a video of “four children lying across the road” sparked online discussion, pushing the boundaries of autonomous driving capabilities into the spotlight. The system recognized the anomaly and slowed down to avoid, but ultimately, manual intervention was needed to fully stop the vehicle.

The focus of the autonomous driving race has shifted: it’s no longer about parameter dominance, but about helping the public understand where the safety bottom line of machines lies and when humans should take over.

Reactions and boundaries of autonomous driving

During the live broadcast, a recent viral video was discussed: several children lying in the middle of the road. A Xpeng vehicle with autonomous driving recognized the anomaly and slowed down, then the driver intervened to brake.

Liu Xianming retrieved backend data and provided analysis. The data showed the vehicle did detect the obstacle and triggered deceleration, but he also pointed out that the deceleration was insufficient to fully stop the vehicle.

He Xiaopeng added that the driver was an internal Xpeng employee testing the system, who noticed the vehicle’s abnormal deceleration and then took action to brake.

The system detected the problem, but ultimately, a human had to make the decision. This reflects the current state of autonomous driving: it can provide perception and warnings, but extreme scenarios still require human judgment.

Different technical approaches have different strategies—some focus on algorithm generalization, others on sensor redundancy—but all face challenges with long-tail scenarios.

To make ordinary people willing to drive, trust is key. This case is precisely about building that trust.

In the live broadcast, He Xiaopeng again proposed the concept of “National Autonomous Driving,” defining it as “autonomous driving that even moms love,” emphasizing that autonomous systems should return to safety and ease of use. He mentioned that after inviting employees without technical backgrounds to experience the system, acceptance significantly improved.

Data is more intuitive: the second-generation VLA reduced hard braking by 99% and sudden acceleration by 98%. No more abrupt stops or sudden accelerations, greatly reducing passenger discomfort—this is a crucial step for autonomous driving from “usable” to “willing to use.”

Without relying on high-precision maps, the second-generation VLA can cover more unstructured roads. However, Liu Xianming admitted that the current version occasionally does not follow navigation perfectly.

He explained this as a necessary phase in shifting from rule-based to reasoning-based systems—the system is trying to understand the environment and make autonomous decisions, but users need to give it more time to adapt in some scenarios.

He Xiaopeng also set a boundary: in extreme weather, or situations with no roads, where even human drivers struggle, autonomous driving is not recommended.

This is also a consensus in the industry: the system is an assistive tool, not a substitute.

The competition in autonomous driving is shifting from parameter optimization to trust.

In the past, the focus was on feature coverage; now, it’s about who can truly make users dare and want to use.

Invest 300 million yuan monthly, and the returns are coming

“Invest 300 million yuan every month to gamble on this, for over ten months. By then, I’d be pretty nervous,” He Xiaopeng admitted rarely during the live broadcast. Where is this “bet” heading? Liu Xianming’s answer: from chips and compilers to software architecture and data loops, full-stack self-research. “You also have to dare to bet.”

Why dare to gamble like this? He Xiaopeng made a highly controversial judgment—China’s autonomous driving should leap directly from L2 to L4. Staying at L3 would make it easy to lose in global competition.

The core of this leap is responsibility division. Liu Xianming explained that L4 requires the system to solve all problems without passing difficult issues to the user. He Xiaopeng revealed that the second-generation VLA is the first version designed for L4 capability, with the core logic of solving problems entirely with AI, not rules.

The market is also giving positive feedback.

He Xiaopeng disclosed that since March 11, when 732 Xpeng stores nationwide opened test drives of the second-generation VLA, the test drive rate doubled, with many users coming specifically to experience this system. The more direct impact is on order structure: the sales proportion of Ultra versions equipped with this system has significantly increased.

Regarding the rollout schedule, starting March 19, the system will be gradually pushed, prioritizing the Xpeng P7 Ultra, followed by G7 and X9 Ultra. Customers of these models will receive updates within this month. More models will be pushed out gradually in April.

On the issue of version differences, He Xiaopeng clarified: the Ultra version is built for L4-level capabilities, supporting full-scenario driving; the Max version mainly covers high-frequency scenarios like highways and main urban roads.

Beyond immediate business returns, Xpeng’s goal is global. By 2025, Xpeng’s autonomous driving team has set a target to compete with Tesla.

Recently, media comparisons between the second-generation VLA and Tesla FSD V13 have been frequent. He Xiaopeng shared his view.

“From V13, we are clearly ahead. But I think it’s because Xpeng is in China, with data in China, and more familiar with Chinese road conditions.” He emphasized that the second-generation VLA handles “car-human interactions” better, such as delivery workers, pedestrians, narrow roads. “This is not just a Chinese feature; Europe has many narrow roads, Southeast Asia too. As we expand into more countries, Xpeng may have an advantage.”

Liu Xianming was more cautious: “Honestly, we don’t know what Tesla is doing. It’s more like feeling our way across the river, stumbling over many pits, wasting a lot of money. But we believe the ultimate solutions may converge.”

He Xiaopeng believes that China and the US are both in the first tier of autonomous driving, but Chinese roads are ten times more complex—beyond highways and urban roads, there are rural paths outside third- and fourth-tier cities, where cows, sheep, and chickens can suddenly appear.

“Autonomous driving is a comprehensive contest of hardware, software, engineering capability, and scale. Currently, both China and the US are in the top tier. But China’s roads are more complex. Only by overcoming these challenges and improving AI large model generalization can the second-generation VLA truly go global,” He Xiaopeng said.

Any technology must be tested by users. The industry now believes that the inflection point for autonomous driving has arrived, but the real test is whether users are willing to recommend it to others.

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