Over the past decade, China's automotive industry has been celebrated as a trailblazer in smart manufacturing and industrial advancement. However, since the full-scale onset of involution in 2023, this once rapidly growing and highly profitable sector has become ensnared in a vicious cycle of price wars, excessive competition, and profit erosion. More alarmingly, this "involution plague" is spilling over into other hard technology sectors through multifaceted channels such as talent, capital, and cognitive frameworks, particularly causing structural damage and distortion to the nascent robot industry.

In this commentary, we delve into three key dimensions: the alienation of entrepreneurial behavior patterns, the misalignment of investor expectations, and the misdirection of end-customer perceptions. Our core contention is that the survival strategy of "racing against time, adapting to change" cultivated in China's automotive industry has profoundly impacted the healthy development trajectory of the robot industry.

I. Entrepreneurs: The 'Involution Migration' of Mindsets and Loss of Direction

Over the past three years, an influx of engineers, product managers, and entrepreneurs from the automotive industry has joined the robot sector. Many of these individuals hold prominent positions in companies like NIO, Xpeng, Li Auto, Huawei's automotive BU, and even Tier-1 supply chains. Their backgrounds offer invaluable experience in mechatronics, supply chain management, and large-scale team collaboration. Nevertheless, this influx also poses challenges:

They bring not only expertise but also the mental inertia of 'automotive industry involution.'

The first inertia is an over-reliance on 'mass production thinking.' In the smart automotive industry, platform-based manufacturing, modular architectures, and integrated front-end delivery are paramount. However, the robot industry, particularly in the realms of service, specialty, and complex environments, demands a 'scenario-centric' and 'problem-oriented' approach, which cannot directly apply the rapid mass production logic of the automotive sector.

The second inertia is a path dependence on 'price wars.' In 2024, over 200 domestic car models underwent price reductions, and in the first four months of 2025, more than 60 models saw price cuts, with some reductions exceeding RMB 50,000. The overall industry profit has plummeted to an extremely low level of below 4%. While fighting price wars to capture market share and trading scale for financing were once tolerable survival tactics in the automotive industry, blindly 'emulating automotive tactics' in the robot sector, where hardware costs are high and project delivery cycles are long, will inevitably lead to a collapse in gross margins and even a crisis of 'overdraft before delivery.'

Consequently, we observe that the impulse for more robot startups to 'self-manufacture complete machines' is waning, and they are transforming into 'robot training platforms,' 'operating systems for the physical world,' and 'large models + control strategy providers,' bypassing products and evading delivery, focusing solely on platforms and software. While this short-term approach enhances storytelling capabilities, it undermines the end-to-end product connectivity in the long run.

II. Investors: Heightened Anxiety About Robots Following Deep Wounds from the Automotive Industry

Not only entrepreneurs but also investors have been deeply scarred by the automotive industry's involution. The once-inflated bubble of 'new forces' caused significant losses for a cohort of early investors, who experienced tumultuous fluctuations in projects such as NIO, Xpeng, Li Auto, Nezha, Leap Motor, WM Motor, and others. Some funds are still reeling from these investments.

These investors now approach the robot field with extreme caution. They generally exhibit three tendencies:

1. Only investing in 'platform-type' robots, shunning startups that 'manufacture complete machines.' The rationale is that complete machines are capital-intensive, have long cycles, and are prone to failure.
2. Favoring 'asset-light + high software proportion' teams, i.e., those focused on training systems, operating software, and deployment toolchains.
3. Placing greater emphasis on rapidly validating scenarios and business models, no longer accepting the pace of 'hardware development preceding product launch by three years.'

While this behavioral logic aims to mitigate risks, it inadvertently steers the entire early-stage robot sector into another extreme – capital no longer incentivizes deep scenario exploration, no longer supports hardware refinement, and no longer bears the waiting cost for decades-long research and development. This shortsighted 'risk avoidance' behavior essentially propels the robot industry towards another bubble of 'PPT project explosions.'

Worse still, capital begins to evaluate robots using 'automotive valuation logic': number of shipments × unit price × P/S multiple. This logic inherently demands that the robot industry enter a period of large-scale delivery to secure valuation premiums, while early-stage exploratory, software-hardware integrated teams are swiftly excluded from mainstream financing.

III. Customers: Misjudging Risks by Viewing Robots Through Automotive Procurement Lenses

We interviewed over ten industrial robot and service robot startups, and almost all teams cited a common challenge – customers' 'excessively high expectations' for delivery capabilities.

These customers often hail from traditional industrial heavyweights such as automotive manufacturers, parts enterprises, and park infrastructure providers. When selecting robot solutions, they instinctively apply the cognitive model of smart cars:

• 'Can you deliver in bulk?'
• 'Can modules be standardized for replacement?'
• 'How robust is the scenario migration ability?'
• 'Send us the BOM cost list for review.'

This logic deals a 'devastating blow' to startups in the robot industry. Startup teams simply cannot deploy a full-process delivery system akin to automotive industry suppliers. However, if they fail to convince customers to embrace the path of 'customization + pilot implementation + gradual optimization,' they easily fall into a vicious cycle of 'excessively high customer expectations – initial deployment failure – trust crisis – refusal to repurchase.'

Even more concerning, some robot customers have proposed a cooperation model akin to 'subsidies for new energy vehicles,' requiring companies to deliver the initial equipment at extremely low prices and even bear the cost of pilot deployment. This behavior of 'exploiting early-stage robot companies' further undermines the already fragile business model.

IV. Escaping the Automotive Industry Does Not Necessarily Save Robots

We often discuss 'track transformation' but seldom delve into 'thinking transformation.' Entrepreneurs, investors, and customers from the automotive industry, while pouring into the robot industry, have also brought with them their deeply ingrained 'worship of speed,' 'obsession with scale,' 'illusion of subsidies,' and 'valuation anxiety.'

These concepts are precisely the root causes of the automotive industry's current involution, profitability challenges, and prolonged money-burning.

If the robot industry fails to promptly establish its own rhythm, scenario logic, and valuation language, it will ultimately be dragged into the same abyss.

We must cease using 'delivery volume' as the sole evaluation criterion, cannot substitute real customer value with 'platform concepts,' and cannot replace the long-term path of technological productization with 'PPT presentations + large model expectations.'

Conclusion

The involution in China's automotive industry is a costly industrial trial and error. It has unveiled the darker side of rapid iteration, capital boosting, and price wars: exceedingly high failure rates, systematic losses, and the burnout of an entire generation of entrepreneurs. Today, the robot industry, positioned as the next trillion-yuan growth frontier, is confronting similar temptations and pitfalls.

Without profound introspection, robots risk repeating the 'high opening and low closing' trajectory of the automotive sector rather than forging its own path into deeper waters.

The future belongs not to those who replicate 'the next automotive myth' but to those who dare to confront industry illusions and build genuine technological and commercial closed loops.