At the 2026 Lean Digital Innovation Conference held at the National Convention Center in Tianjin from September 5-7, Ni Jun, Co-President of Engineering, Manufacturing and R&D Systems and Chief Manufacturing Officer at CATL, shared his insights on the intersection of battery production and artificial intelligence. Ni, who also holds the Wu Zhaoming Professorship in Manufacturing Science at the University of Michigan, emphasized that electrification replacing fossil fuels is humanity's only path to solving climate change, noting that lithium battery production represents a typical case of extreme manufacturing.

Having spent over four decades in manufacturing research across semiconductors, automotive, and aerospace industries, Ni stated that batteries are the most challenging products he has ever encountered. The industry has entered the terawatt-hour era, with cell production speeds reaching remarkable velocities. The copper foil for electrode sheets is merely 4.5 micrometers thick, less than one-twentieth the diameter of a human hair, yet slurry coating must be applied at speeds of 100 meters per minute. Battery manufacturing involves a strongly coupled multi-physics system spanning extreme scales, and since batteries are safety-critical systems that begin generating their own "lifespan" during production, they require assurance of stability over ten to twenty years.

These complex manufacturing conditions have forced the industry to fully embrace AI across all dimensions, including AI for Science, AI for Materials, AI for R&D, and AI for Engineering. Ni pointed out that the industry is now witnessing a new paradigm of AI Native operations. CATL has established cross-regional specialized teams, deploying AI for Science in Shanghai, AI for Materials in Hong Kong, and AI for Engineering at its Ningde headquarters, ensuring organizational support for AI-native implementation.

However, Ni observed that genuinely achieving AI Native status remains rare. His research shows an interesting trend: undergraduates tend to be more receptive to AI than master's students, who in turn are more receptive than doctoral students. Many engineering professionals still treat AI agents merely as auxiliary tools rather than restructuring their existing workflows, thus failing to unlock AI's full potential. Ni stressed that achieving extreme manufacturing requires more than simple AI application—it demands full integration across perception, understanding, reasoning, and decision-making execution, ensuring the credibility, timeliness, and accuracy of industrial AI throughout the entire process.

Ni concluded by reiterating that "AI plus zero-carbon represents both an opportunity and a constraint for modern enterprises." Companies must harness the productivity transformation brought by AI while also managing their carbon footprint effectively to maintain competitiveness in global markets.