China's AI price war is exposing India's biggest weakness: Why India needs its own DeepSeek

The artificial intelligence landscape is shifting dramatically, particularly as China intensifies its focus on reducing the costs of AI models. This emerging trend poses a significant challenge for India, which may find itself increasingly reliant on foreign AI solutions just as China consolidates its influence in the global AI market. A recent analysis by Jefferies highlights the launch of GLM-5.2, a new model from Hong Kong-listed Z.ai (previously Zhipu AI), which is being heralded as a potential game changer. This model offers enterprise-level performance comparable to that of leading US AI systems, yet its cost is only about a quarter of similar offerings. Such pricing could become a pivotal factor for companies deciding which AI models to adopt, as economic considerations take precedence over marginal performance improvements. The data further underscores this shift; according to OpenRouter, Chinese AI models processed a staggering 21.37 trillion tokens within a week, vastly outpacing the 5.76 trillion tokens handled by top US models. This trend suggests that as AI models become more commoditized, businesses are prioritizing cost-effectiveness, deployment flexibility, and data security over minute differences in technical specifications. However, India faces challenges that extend beyond competing with giants like OpenAI and Anthropic. Bernstein notes that India has yet to experience its own pivotal moment akin to a 'DeepSeek' event. While the country boasts a robust IT services sector and is experiencing rapid growth in AI applications, it lacks a competitive foundational large language model of its own. This reliance on foreign AI technologies could become a strategic liability, especially if geopolitical tensions or export restrictions hinder access to advanced AI systems. The increasing treatment of AI as a strategic asset, much like advanced semiconductors and defense technologies, raises concerns about India's future. Bernstein warns that if Indian enterprises and government bodies predominantly depend on foreign models, they risk falling behind their global competitors, potentially stifling the country's software industry's growth. China's approach has been to cultivate domestic internet companies that generate vast datasets, fostering AI research and engineering talent. This structural advantage has empowered Chinese firms to develop competitive AI solutions at lower costs. Nonetheless, this does not imply that India must immediately replicate existing models like those of OpenAI. Bernstein suggests that India's opportunity lies in creating domain-specific AI models tailored to sectors such as healthcare, manufacturing, and finance. By developing proprietary datasets in these areas, India could mitigate its dependence on foreign platforms and produce globally competitive AI solutions. The insights from the Bernstein and Jefferies reports suggest that the future of AI may hinge not just on who develops the most advanced model, but also on who can provide the most cost-effective solutions. As China implements a strategy of enhancing model quality alongside aggressive pricing, India must decide whether to concentrate on AI applications or to invest more substantially in developing its own foundational models. Without a strategic pivot, India risks remaining a consumer of AI technologies while China continues to emerge as a key global supplier of affordable enterprise AI.