AI Model Dominance: Not A Guarantee for Geopolitical Power

Minister Vaishnaw highlights risks in massive AI investments; geopolitical power relies on deployment capability, not just large models.

Gopi

•January 22, 2026•5 mins read

AI Power Lies in Deployment, Not Model Size: Vaishnaw

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1. Context: AI Investment, Geopolitics, and the Global Narrative

Artificial Intelligence (AI), particularly Large Language Models (LLMs), has increasingly been framed as a determinant of geopolitical power, similar to nuclear technology or semiconductors. Several countries and firms are investing heavily in building extremely large models, assuming strategic dominance will follow.

At the World Economic Forum (WEF) in Davos (January 2025), India’s Information Technology Minister Ashwini Vaishnaw challenged this narrative. He argued that merely producing massive AI models does not automatically confer geopolitical advantage, especially if such models are not widely deployable or economically sustainable.

This intervention is significant for governance and development as it reorients AI discourse from prestige-driven capacity building to outcome-driven deployment. If ignored, countries may misallocate public and private capital toward high-cost technologies with limited real-world utility.

The underlying governance logic is that strategic power flows from widespread, affordable, and scalable use of technology, not from its symbolic ownership; ignoring this risks technological overreach without developmental returns.

"So does creating a large model give you geopolitical power? I don’t think so." — Ashwini Vaishnaw

2. Issue: Limits of Large Language Models as Instruments of Geopolitical Power

The article highlights a core argument that extremely large LLMs are neither indispensable nor irreplaceable for most governance and economic functions. According to the Minister, India can meet nearly 95% of its AI requirements using a “bouquet of models” rather than a single, massive system.

He further questioned the idea of AI as a coercive geopolitical tool, noting that unlike energy or rare earths, AI models can be replicated, downsized, or substituted. Even if a country were to “switch off” a large model, others could continue functioning using smaller, efficient alternatives.

This challenges techno-nationalism and underscores the fragility of AI monopolies. If this reality is ignored, states may overestimate strategic leverage while underinvesting in domestic adaptability and skills.

The development logic is that resilience comes from decentralised capability and technological diversity; overlooking this creates false security and strategic complacency.

Key technical contrasts:

50 billion parameter models can be deployed using one GPU
30 billion parameter models are sufficient for ~80% of use cases and may not require GPUs
CPUs and custom silicon are widely distributed across countries, reducing dependency risks

3. Economic Implications: Sustainability of the AI Investment Boom

Mr. Vaishnaw warned that the global rush to build ever-larger AI models could produce economic distortions. Massive capital expenditure, high energy costs, and uncertain monetisation may push some AI firms toward financial instability or bankruptcy.

This observation reframes AI not just as a strategic asset but as an economic activity subject to market discipline. Countries that subsidise large-scale model creation without ensuring profitable deployment may face fiscal and industrial stress.

For public policy, this highlights the importance of cost-benefit analysis and long-term viability. Ignoring economic sustainability can result in stranded assets and reduced innovation capacity.

The governance reasoning is that technological leadership must align with economic fundamentals; neglecting this balance can weaken both public finances and private innovation ecosystems.

"It might actually be causing certain conditions where the people who are creating those large models might go bankrupt in the coming years." — Ashwini Vaishnaw

4. India’s Comparative Advantage: Deployment and Integration Capability

India’s strategic positioning lies not in building the largest models but in deploying AI across sectors and geographies. With strengths in software services, systems integration, and digital public infrastructure, India can help global firms operationalise AI effectively.

The Minister emphasised India’s role in assisting enterprises worldwide to integrate AI into existing workflows, thereby capturing value at the deployment layer. This aligns with India’s broader development model of “technology as a service” rather than technology as a symbol.

Failure to focus on deployment would mean missing opportunities for employment generation, MSME productivity, and global service exports.

The development logic is that value creation in AI increasingly occurs at the application and integration stage; ignoring this would limit inclusive growth outcomes.

5. Institutional and Policy Signals from Davos Engagements

The article notes multiple high-level meetings between the Minister and global technology leaders, including Meta, IBM, and Google. These engagements focused not only on innovation but also on governance challenges such as deepfakes and AI-generated misinformation.

Such discussions indicate India’s intent to shape global AI norms around user protection, ethical deployment, and regulatory cooperation. This has implications for GS2 (governance and regulation) and international cooperation frameworks.

If institutional engagement is weak, technological diffusion may outpace regulatory capacity, leading to social and democratic risks.

6. Way Forward: India’s “UPI-like” AI Repository Initiative

India plans to launch a “UPI-like” repository of AI tools at the upcoming AI Impact Summit in New Delhi. This platform will compile successful AI use cases that can be easily replicated by other countries.

The initiative reflects India’s experience with digital public goods such as UPI and Aadhaar, extending the model to AI. It positions India as a norm-setter in affordable, replicable, and inclusive AI solutions.

Ignoring such platform-based approaches would limit South-South cooperation and reduce India’s leadership in responsible technology diffusion.

The governance logic is that shared digital public goods lower entry barriers and democratise innovation; failure to institutionalise them entrenches global digital inequality.

Conclusion

The article underscores a strategic shift from model-size competition to deployment-led value creation in AI. India’s approach prioritises economic sustainability, technological resilience, and inclusive governance over symbolic technological dominance. In the long term, this framework strengthens development outcomes, reduces systemic risk, and enhances India’s credibility as a global digital public goods provider.

Quick Q&A

Everything you need to know

Significance of LLMs: Large language models (LLMs) represent advanced AI systems trained on massive datasets to understand, generate, and interact in human language. Their development is often seen as a marker of technological prowess and innovation capacity.

Key points:

LLMs enable automation of complex tasks, including translation, summarization, coding, and decision-making support.
They serve as a platform for AI-powered products and services, which can improve efficiency across sectors like healthcare, finance, and education.
While widely regarded as technologically advanced, their production alone does not automatically confer geopolitical power; effective deployment and integration are critical for real-world impact.

Example: A 50-billion-parameter model may demonstrate computational sophistication but requires significant hardware and expertise for deployment. Conversely, smaller models tailored for specific tasks can achieve 80–95% of practical work without heavy infrastructure, highlighting that utility, not size, determines strategic advantage.

Rationale behind the argument: Minister Vaishnaw emphasizes that geopolitical influence stems not from the mere possession of technology but from its strategic deployment and productive use.

Key points:

Countries with massive LLMs may face economic risks if investment costs outweigh practical utility, potentially leading to financial losses for firms.
Geopolitical leverage requires integration of AI into societal, economic, and industrial processes, rather than just demonstrating computational capability.
India’s approach, focusing on deploying AI models efficiently for diverse applications, ensures that even smaller models can generate substantial value without dependency on high-cost, centralized infrastructure.

Example: A country may develop a highly sophisticated model but fail to operationalize it across sectors. India, with a 'bouquet' of models addressing 95% of practical work, illustrates that deployment capability, accessibility, and adaptability define influence more than model size.

India's AI strategy: India aims to focus on practical deployment and replication of AI solutions rather than purely competing in the scale of model size.

Key elements:

Integration: Assisting domestic and global firms to incorporate AI into workflows and processes.
Accessible infrastructure: Ensuring smaller, efficient models can perform 80–95% of real-world tasks without requiring massive computational resources.
Knowledge repository: Launching a 'UPI-like' repository for AI tools and successful use cases, enabling easy replication and adoption across industries and countries.

Implication: By prioritizing deployment efficiency and scalability, India positions itself as a hub for AI application and integration rather than just AI production, creating economic value and global influence without incurring the risks associated with over-investment in massive models.

Risks of over-investment: Excessive investment in extremely large AI models can create financial and operational vulnerabilities.

Key risks:

Economic loss: Massive computational models require substantial hardware, energy, and maintenance costs. If they cannot be deployed effectively, firms may go bankrupt, as highlighted by Minister Vaishnaw.
Limited utility: Oversized models may not significantly outperform smaller, task-specific models, resulting in diminishing returns on investment.
Dependency and inequity: Countries relying on central supercomputing infrastructure risk strategic dependency, whereas distributed, smaller models provide broader access and adaptability.

Example: A 50-billion-parameter AI model may be theoretically impressive but requires GPUs and custom silicon. In contrast, a 30-billion-parameter model performing most practical tasks efficiently can be deployed widely, demonstrating that scale without strategy can be counterproductive.

Analysis: Minister Vaishnaw’s assertion challenges the perception that technological supremacy equates to geopolitical leverage.

Pros:

Focuses on practical utility, ensuring investments translate into real-world benefits.
Encourages innovation in integration, governance, and ethical AI deployment rather than just computational achievement.
Reduces dependency on massive hardware and centralization, democratizing AI access and capabilities.

Cons / counterpoints:

Large models can provide unique research advantages, enabling breakthroughs not achievable with smaller models.
Scale may still be a factor in defense, intelligence, and scientific simulations, where raw computational capacity matters.

Conclusion: While scale can provide niche advantages, geopolitical influence primarily depends on AI's deployment, accessibility, and integration into economic and societal frameworks. India’s emphasis on deployment highlights a balanced, risk-aware strategy.

Case Study: AI Impact Summit & 'UPI-like' AI Repository
India plans to launch a repository of AI tools and use cases similar to the UPI model, promoting widespread replication and adoption. This initiative demonstrates a deployment-focused strategy rather than competing solely on model scale.

Highlights:

The repository collects successful AI applications, enabling other countries and firms to adopt solutions without building large models from scratch.
Focuses on interoperability, accessibility, and practical implementation across industries such as finance, healthcare, and governance.
Promotes collaboration with global tech companies, including Meta, IBM, and Google, reinforcing India’s role as a hub for AI deployment and ethical usage standards.

Implication: By prioritizing utility, scalability, and knowledge sharing, India mitigates the risks of over-investment in large models while maximizing economic and strategic impact.

Examples of efficient AI deployment:

Chatbots for customer service: Medium-sized models can handle 80–90% of queries effectively without the computational load of massive LLMs.
Healthcare triage: Smaller AI models trained on medical datasets can assist in diagnosis and patient monitoring, enabling faster deployment in hospitals with limited infrastructure.
Language translation for regional languages: Tailored models for specific languages provide accurate, low-latency translations without requiring enormous computational resources.

Implication: These examples illustrate that practical utility and deployment efficiency, rather than sheer scale, define AI’s strategic value. India’s focus on such models ensures cost-effectiveness, accessibility, and immediate socio-economic benefits while mitigating the financial risks of developing ultra-large models.