As the global enthusiasm for artificial intelligence (AI) begins to wane, India is confronted with a pivotal decision: whether to pursue costly Western AI strategies reliant on extensive computing power or to carve out its own path that emphasizes practical, domain-specific innovation. This choice comes in the wake of China’s introduction of effective yet affordable AI models like DeepSeek, which have challenged the notion that high expenditure is essential for creating capable AI.
Amid the United States’ fluctuating safety warnings and investment hype, as well as Europe’s stringent regulations, India has developed a more adaptable approach to AI governance. By relying on judicial precedents and sector-specific guidelines, India is poised to shape its AI landscape in a way that reflects its unique needs and market conditions, rather than simply mirroring Western frameworks.
The narrative surrounding AI has been largely shaped by the dramatic rise of large language models (LLMs) since 2020. As these technologies gained traction, U.S.-China tensions over semiconductor resources revealed a fixation on massive investments in AI. The U.S. has maintained an alarmist stance regarding AI safety, while European Union proposals, such as the AI Liability Directive, reflect a rigid regulatory approach. In contrast, India’s evolving legal framework indicates a preference for a more nuanced strategy that prioritizes meaningful innovation rather than getting swept up in global hype cycles.
The AI hype that defined recent years is now deflating, as the market matures and investors recalibrate expectations. In the U.S., venture capital surged into AI startups, fueled by big promises from tech giants about the transformative potential of LLMs. The Biden administration’s Executive Order 14110, issued in October 2023, underscored the duality of urgency and investment frenzy surrounding AI safety. However, failures and limitations in AI technologies have dampened enthusiasm, revealing the need for a more prudent and realistic approach.
China’s AI strategy has also been scrutinized, as it has focused more on domain-specific applications that align with national economic goals. By April 2025, a significant number of generative algorithmic tools had been registered under Chinese law, demonstrating a strategic approach to AI governance. However, even China has not been immune to the allure of futuristic narratives, as evidenced by the BRICS Declaration on AI, which embraced the concept of Artificial General Intelligence despite its speculative nature.
As the hype continues to deflate, a shift toward sustainable, practical AI innovation is becoming apparent. This transition favors applications tailored to regional needs, promoting collaboration between humans and AI, and supporting diverse innovation ecosystems that can thrive without vast financial resources. In this context, India has the opportunity to leverage its unique strengths by fostering local talent and focusing on applications that address its developmental needs.
The debate on AI safety in the U.S. has contributed to exaggerated expectations regarding LLMs. Critical voices, including Sriram Krishnan, Senior Policy Advisor on AI at the White House, have pointed out the distortive effects of safety narratives that often overshadow immediate challenges related to AI deployment. The disparity between ambitious forecasts and actual capabilities has prompted calls for a recalibration of both investment and regulatory approaches.
India’s regulatory landscape for AI contrasts sharply with the inconsistencies of U.S. policy and China’s legislative mandates. According to AIACT.IN, as of January 2026, India’s AI regulatory framework comprises a mix of judicial precedents and institutional guidance, underscoring a preference for adaptable regulations that can evolve with technological advancements. This contrasts with rigid regulatory frameworks that may not account for the complexities of AI technology.
As India navigates this landscape, it must avoid the pitfalls of mimicking Western strategies. Instead, the focus should be on cultivating a robust decentralized innovation ecosystem that emphasizes collaboration and domain-specific advancements. The Indian government and private sector appear to recognize the importance of strategy over speed, indicating a shift toward a more sustainable model for AI development.
In conclusion, India stands at a crossroads in its AI journey. By prioritizing homegrown talent and practical applications, the country can avoid the volatility afflicting other AI markets. While external narratives may push for a capital-intensive approach, India’s distinct regulatory framework and innovation culture position it to build resilient AI capabilities that serve its unique developmental needs.
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