As China's AI landscape shifts towards offering low-cost, enterprise-capable models, India faces increasing risks of dependence on foreign AI technology amid the absence of its own globally competitive foundational models.
- China’s AI models offer enterprise-grade performance at significantly lower cost.
- India lacks a competitive homegrown foundational large language model.
- Dependence on foreign AI risks strategic and economic vulnerabilities.
What happened
China has pivoted its AI ambitions from purely maximizing model capabilities toward drastically lowering costs, introducing models like GLM-5.2 that provide near top-tier performance at about one-quarter the token-processing price of leading US models. This pricing advantage is accelerating adoption, as Chinese models accounted for nearly four times the token volume of American competitors recently, signaling a growing preference for affordability in AI deployment.
In contrast, India is lagging behind in developing foundational large language models, despite boasting a strong IT services sector and rapid AI application growth. Analysts warn that India’s continued reliance on foreign AI technologies might be a critical strategic weakness, especially as countries increasingly treat advanced AI as protected national assets and restrict broader access.
Why it matters
The shift in AI competition towards cost and accessibility rather than just performance reshapes global AI dynamics, potentially enabling Chinese technology providers to outpace rivals in enterprise AI adoption across emerging and established markets. For India, this dynamic threatens to leave its software industry technologically obsolete if it cannot secure stable access to advanced AI or develop its own competitive models.
Geopolitical tensions and export controls on cutting-edge AI models add urgency to developing domestic capabilities. Analysts highlight that AI is now treated on par with critical industries like semiconductors and defense, meaning India's dependency on foreign platforms may result in delayed technology deployment and increased operational costs, undermining its digital sovereignty and technological leadership potential.
What to watch next
India’s path forward may lie in building specialized, domain-specific AI models tailored to sectors such as healthcare, manufacturing, and finance, leveraging proprietary domestic data. This strategic focus on vertical AI markets could reduce dependence on global AI giants while cultivating competitive advantages in niche areas where India has strong expertise and datasets.
The evolution of AI sovereignty policies and investments in foundational model research by Indian companies and government agencies will be critical indicators. Monitoring China’s continuing innovations in cost-efficient enterprise AI and how Indian enterprises respond—either by adopting foreign models or developing indigenous alternatives—will shape the competitive landscape for the next decade.