China's artificial intelligence sector is undergoing a significant transformation, shifting its focus from merely developing powerful models to making them dramatically more affordable. This strategic pivot, often described as an 'AI price war,' is creating a new challenge for India: not just competing with leading global AI developers, but reducing its increasing dependence on foreign AI models.
Reports from financial research firms Bernstein and Jefferies underscore that the next phase of AI competition will be driven by both technological innovation and strategic autonomy. For India, this means confronting a potential strategic vulnerability if it continues to rely heavily on AI technologies developed elsewhere.
China's Low-Cost AI Dominance
The launch of models like GLM-5.2 by Z.ai (formerly Zhipu AI), a Hong Kong-listed company, is being hailed as another 'DeepSeek moment.' According to Jefferies, GLM-5.2 offers enterprise-grade performance comparable to top-tier systems like Anthropic's, but at approximately one-quarter of the cost per token. This significant price advantage is quickly becoming a decisive factor for businesses when selecting AI models, often outweighing marginal performance differences.
Data from AI platform OpenRouter reveals the impact of this price advantage: Chinese AI models processed 21.37 trillion tokens during the week ending June 21, significantly more than the 5.76 trillion tokens processed by leading US models. As large language models become increasingly commoditized, enterprises are prioritizing affordability, deployment flexibility, and data security over minor benchmark variations.
India's Foundational AI Challenge
For India, the challenge extends beyond merely keeping pace with advanced models like ChatGPT. Bernstein highlights that India has yet to experience its own 'DeepSeek moment,' lacking a globally competitive foundational large language model. Despite a thriving IT services industry and rapid growth in AI applications, this dependence on foreign AI platforms poses a strategic risk.
Artificial intelligence is increasingly viewed as a national strategic asset, akin to semiconductors or defense technologies. Recent restrictions on access to frontier AI models for non-US users illustrate this trend. Bernstein likens advanced AI models to 'fighter jets,' suggesting that nations are becoming less willing to freely share their most sophisticated capabilities, making reliance on external sources a precarious position.
The Risks of Strategic Dependence
A critical question for India emerges: What happens if enterprises, government agencies, or even defense applications primarily depend on foreign AI models, and access is delayed, restricted, or becomes prohibitively expensive? Bernstein warns that India could lag one or two generations behind global competitors, diminishing the competitiveness of its software industry despite its vast engineering talent pool.
While India's technology ecosystem historically focused on IT services and application development, China's domestic internet giants fostered vast proprietary datasets, robust AI research, and engineering talent. These structural advantages now enable Chinese companies to develop competitive AI models at lower costs.
A Roadmap for India's AI Future
Developing sovereign AI capabilities does not necessarily mean India must immediately replicate OpenAI or DeepSeek. Bernstein suggests India's greatest opportunity lies in building domain-specific AI models, leveraging proprietary datasets across key sectors such as healthcare, manufacturing, financial services, and industrial automation. This approach could reduce reliance on foreign platforms while simultaneously creating globally competitive products tailored to India's unique needs.
The insights from both Bernstein and Jefferies indicate that the future of AI competition will not be solely determined by who builds the smartest model. China's strategy of combining improving model quality with aggressive pricing is already reshaping the economics of enterprise AI. For India, the imperative is clear: a shift from primarily focusing on AI applications to investing more aggressively in developing its own sovereign foundation models is crucial to avoid remaining a consumer of AI technologies developed elsewhere.