Smarter AI, Not Just Larger: Developing India’s Strategy Amid the Global Scaling Debate

Ayndri
Ayndri
Research Analyst - Policy & Advocacy, CyberPeace
PUBLISHED ON
Aug 25, 2025
10

Introduction

The ongoing debate on whether AI scaling has hit a wall has been rehashed by the underwhelming response to OpenAI’s ChatGPT v5. AI scaling laws, which describe that machine learning models perform better with increased training data, model parameters and computational resources, have guided the rapid progress of Large Language Models (LLMs) so far. But many AI researchers suggest that further improvements in LLMs will have to be effected through large computational costs by orders of magnitude, which does not justify the returns.  The question, then, is whether scaling remains a viable path or whether the field must explore new approaches. This is not just a tech issue but a profound innovation challenge for countries like India, charting their own AI course. 

The Scaling Wall: Gaps and Innovation Opportunities

Escalating costs, data scarcity, and diminishing gains mean that simply building larger AI models may no longer guarantee breakthroughs. In such a scenario, LLM developers will have to refine new approaches to training these models, for example, by diversifying data types and redefining training techniques.  

This global challenge has a bearing on India’s AI ambitions. For India, where compute and data resources are relatively scarce, this scaling slowdown poses both a challenge and an opportunity. While the India AI Mission embodies smart priorities such as democratising compute resources and developing local datasets, looming scaling challenges could prove a roadblock.  Realising these ambitions requires strong input from research and academia, and improved coordination between policymakers and startups. The scaling wall highlights systemic innovation gaps where sustained support is needed, not only in hardware but also in talent development, safety research, and efficient model design.

Way Forward

To truly harness AI’s transformative power, India must prioritise policy actions and ecosystem shifts that support smarter, safer, and context-rich research through the following measures: 

  • Driving Efficiency and Compute Innovation: Instead of relying on brute-force scaling, India should invest in research and startups working on efficient architectures, energy-conscious training methods, and compute optimisation.
  • Investing in Multimodal and Diverse Data: While indigenous datasets are being developed under the India AI Mission through AI Kosha, they must be ethically sourced from speech, images, video, sensor data, and regional content, apart from text, to enable context-rich AI models truly tailored to Indian needs. 
  • Addressing Core Problems for Trustworthy AI:  LLMs offered by all major companies, like OpenAI, Grok, and Deepseek, have the problem of unreliability, hallucinations, and biases, since they are primarily built on scaling large datasets and parameters, which have inherent limitations. India should invest in capabilities to solve these issues and design more trustworthy LLMs.
  • Supporting Talent Development and Training: Despite its substantial AI talent pool, India faces an impending demand-supply gap. It will need to launch national programs and incentives to upskill engineers, researchers, and students in advanced AI skills such as model efficiency, safety, interpretability, and new training paradigms

Conclusion

The AI scaling wall debate is a reminder that the future of LLMs will depend not on ever-larger models but on smarter, safer, and more sustainable innovation. A new generation of AI is approaching us, and India can help shape its future. The country’s AI Mission and startup ecosystem are well-positioned to lead this shift by focusing on localised needs, efficient technologies, and inclusive growth, if implemented effectively. How India approaches this new set of challenges and translates its ambitions into action, however, remains to be seen.

References

PUBLISHED ON
Aug 25, 2025
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