China EVs: AI Jobs Shift and Battery Alliances

China’s electric vehicle industry is being reshaped on two fronts at once: inside the factory and across the supply chain. In late September, CALB and Henkel opened a joint innovation center in Changzhou to accelerate next-generation battery materials and manufacturing, while a parallel debate intensified over how AI is changing automotive jobs. At the same time, Tesla’s long-delayed second-generation Roadster was pushed back again, underscoring a wider industry reality: EV competition is no longer just about headline-grabbing products, but also about who can build better batteries, smarter workflows, and more resilient organizations.

AI in the auto industry: less mass layoffs, more job restructuring

One of the most important takeaways from the latest discussion in China’s auto sector is that AI is not, at least for now, the main direct cause of large-scale layoffs. Liu Songbo, deputy dean at Renmin University of China’s School of Labor and Human Resources, argued that many companies are effectively using AI transformation as a narrative cover for broader headcount optimization driven by business pressure.

That view is supported by broader labor data:

• Gallup surveyed 23,000 U.S. workers in February 2026.
• Among 660 respondents who had lost jobs and remained unemployed, only about 1% said AI was the direct cause.
• Most pointed instead to:
  • organizational restructuring
  • cost cutting
  • weaker macroeconomic conditions

This matters for the automotive industry because profitability is under pressure. According to China’s National Bureau of Statistics, total profits in the auto manufacturing sector reached RMB 195.35 billion in the first half of 2026, down 19.5% year-on-year. When margins fall, automakers reassess product lines, R&D programs, plant utilization, and staffing.

In other words, AI and layoffs may be happening at the same time, but they are not always the same story.

Why automakers are cutting some roles while hiring others

The clearest pattern emerging in the global auto industry is not simple job elimination, but structural change in talent demand.

Recent examples from outside China illustrate the shift:

• Block announced about 4,000 layoffs, or more than 40% of staff, with management citing AI-enabled productivity gains.
• Oracle reduced headcount from roughly 162,000 to 141,000 in fiscal 2026, a drop of about 21,000 employees, while also pointing to AI adoption alongside strategic and organizational changes.
• General Motors cut about 600 IT jobs in May 2026, more than 10% of its IT workforce, even as it reportedly sought stronger AI talent.

China’s auto sector shows a similar dual-track dynamic. Some legacy operations are shrinking, while top EV players are still hiring aggressively.

Examples include:

• GAC Honda closing an older gasoline-vehicle plant in Guangzhou’s Huangpu district.
• Staff redeployment to new-energy vehicle facilities and voluntary separation packages instead of blunt public layoff figures.
• Industry-wide disposal of idle capacity as regulators push the exit of outdated production resources.
• At the same time, leading groups such as BYD and Geely continue expanding graduate recruitment and strategic talent reserves.

Talent demand is shifting toward software, intelligence, and electrification

China’s hiring data shows where the market is tightening:

According to Zhilian Zhaopin’s 2026 HR trends report, demand is rising particularly for:

• large-model AI talent
• autonomous driving engineers
• software-defined vehicle specialists
• battery, electric drive, and power electronics engineers

This suggests the real impact of AI in automotive is not a broad disappearance of work, but a reallocation of work toward higher-value technical and decision-oriented roles.

AI is changing workflows before it changes headcount

The more practical story inside Chinese automakers is how AI is being inserted into day-to-day processes.

A useful example comes from Changan Automobile. According to Li Ning, deputy general manager of the company’s quality department, an AI assistant can review supplier corrective-action reports in about 2 minutes, compared with roughly 30 minutes for manual review.

That does not necessarily eliminate the quality engineer’s role. Instead, it breaks the task into parts:

• AI handles repetitive text parsing and comparison
• the system extracts issues and flags inconsistencies
• human engineers focus on judgment, supplier communication, and final decisions

This is a crucial distinction. In most cases, AI first compresses low-value, standardized tasks rather than replacing an entire position.

From isolated AI tools to end-to-end process redesign

The next challenge for automakers is scale. Many companies can launch a few AI assistants, but far fewer can connect them into full business workflows.

Deloitte has observed that:

• more than 80% of mainstream automakers have started generative AI pilots
• only around 15% have achieved scaled deployment

That gap is the industry’s current “pilot trap.” The problem is not lack of experimentation; it is lack of integration.

If each department deploys separate AI tools, companies get isolated productivity gains but not organizational transformation. Data remains fragmented, processes stay largely unchanged, and functional silos survive.

A more advanced model is beginning to emerge:

• AI reads supplier reports
• issues are automatically categorized
• corrective actions are tracked through the same digital flow
• quality data is fed back into analytics systems
• engineers intervene where judgment matters most

At that point, AI is no longer just a tool. It becomes part of a redesigned operating process.

Geely is one of the stronger examples cited in the source material:

• more than 160 AI job assistants deployed
• over 600 AI capabilities in use
• more than 130 million tasks executed cumulatively
• estimated annual direct and indirect economic benefits of over RMB 1 billion

That is significant because it shows what scale looks like in practice: not one chatbot, but AI embedded across multiple workflows.

Battery technology is becoming a strategic partnership game

While automakers retool their internal workflows, battery makers are reshaping the external innovation landscape.

On September 29, CALB and Henkel opened a joint innovation center at CALB’s Changzhou base in Jiangsu and signed a strategic cooperation memorandum. The partnership is designed to combine CALB’s battery manufacturing expertise with Henkel’s advanced materials know-how.

The strategic importance is clear:

• the relationship moves beyond simple buyer-supplier cooperation
• R&D becomes longer-term and institutionalized
• the goal is to speed the conversion of lab-stage ideas into scalable products
• the output is intended not only for China, but also for Asia-Pacific and global markets

For the battery sector, this reflects a larger trend. Competitive advantage is no longer just about cell capacity or pack cost. It increasingly depends on how quickly companies can industrialize new materials, production methods, adhesives, thermal management solutions, and manufacturing processes.

What the CALB-Henkel tie-up could influence

Potential areas of impact include:

• next-generation battery materials
• cell and pack manufacturing efficiency
• process reliability and yield improvement
• safety, sealing, bonding, and thermal solutions
• faster commercialization for global customers

Tesla Roadster’s latest delay shows the industry’s split priorities

In contrast to China’s very operational focus on manufacturing and process efficiency, Tesla’s second-generation Roadster remains a symbol of high-concept EV ambition—and delay.

The launch event planned for October 1 at SpaceX’s McGregor, Texas test site was postponed to October 15 due to bad weather, according to the company. The outdoor-only requirement is tied to the vehicle’s much-discussed SpaceX cold-gas thruster package, a system using high-pressure inert gas rather than combustion.

Tesla has suggested this package could:

• enhance acceleration
• improve braking performance
• potentially enable short-duration hovering behavior

The Roadster was first unveiled in 2017 with an original target of 2020 deliveries. It is now roughly 9 years into development delays.

Tesla’s originally advertised figures remain eye-catching:

Yet key unknowns remain unresolved:

• final production timeline
• confirmed pricing
• mass-production feasibility
• actual delivery schedule

For Chinese EV observers, the Roadster story is interesting less as a direct market rival and more as a contrast. While Tesla is still trying to turn a halo product into reality, Chinese EV and battery players are increasingly focused on process execution, supply-chain collaboration, and industrial-scale competitiveness.

Comparison: three signals shaping the EV industry now

Why This Matters Globally

For global EV watchers, these developments highlight three bigger truths about the Chinese EV market and the wider auto industry.

First, AI in automotive is becoming operational, not theoretical. The winners will be companies that connect AI to actual engineering, quality, procurement, manufacturing, and aftersales workflows.

Second, battery innovation is becoming more collaborative and more industrialized. Joint centers like the CALB-Henkel facility show how Chinese battery makers are pairing domestic scale with international materials expertise.

Third, EV competition is maturing. The market is moving beyond flashy launch promises toward measurable execution: cost control, production yield, software capability, and talent structure.

That has implications far beyond China. Global automakers, suppliers, and technology partners will need to compete on organizational speed and supply-chain depth, not just on vehicle specs.

The road ahead

The next phase of China’s EV story will likely be defined by selective hiring rather than blanket expansion, process redesign rather than simple digitization, and cross-border technology partnerships rather than isolated R&D efforts.

Expect several trends to accelerate over the next 12-24 months:

• more AI deployment in back-office and engineering workflows
• stronger demand for smart driving, software, and battery talent
• additional joint battery and materials innovation platforms
• continued pressure on weaker capacity and legacy operations
• greater scrutiny of which EV companies can turn pilot programs into scalable productivity gains

In that sense, the most important battle in the EV market may not be the next headline model launch. It may be the less visible race to redesign how cars, batteries, and automotive organizations are built.