The Race for AI Sovereignty: Why Nations Must Build Their Own AI Infrastructure
Talk and preparation for AI sovereignty within nations is not simply about advancing technology; it is about national security. The defining technology of our time, AI, has transformed the world, necessitating that nations rapidly work toward the ability to develop, control, and deploy AI infrastructure without external dependencies. Their economic resilience and digital autonomy are at stake.
The launch of DeepSeek and the upheaval around it highlight the U.S.’s need for AI sovereignty. U.S. chip vendors have struggled to run the model efficiently, and concerns over privacy and potential data flow to China have sparked discussions about restricting these models on U.S. government-issued devices.
AI sovereignty isn’t merely about developing the best technology; it’s about who owns the infrastructure and controls the data. Those who do will dictate the future of AI innovation. Governments and enterprises seeking to maintain control over their AI capabilities cannot afford to rely solely on foreign cloud providers or centralized Big Tech ecosystems.
The primary driver toward sovereign AI is computational power. Training and deploying modern AI models requires substantial processing capabilities, which in turn place a heavy burden on energy grids worldwide. AI inference, which allows AI to make real-time decisions, requires continuous operation, further escalating energy consumption. Ongoing innovation and growth in AI infrastructure are essential to meet these demands efficiently while maintaining national control over AI capabilities.
The traditional GPU-based architectures of AI computing are proving unsustainable at a national scale. A single large-scale AI model requires hundreds, sometimes thousands, of GPUs, consuming megawatts of power, and demanding massive cooling infrastructure.
To build and secure domestic AI capabilities, it must establish efficient AI infrastructure, reduce reliance on foreign technology while maintaining scalability, and explore alternatives to inefficient GPU clusters. Achieving this requires optimizing AI accelerators for sovereign, energy-efficient, and scalable deployments, ensuring long-term technological independence.
SambaNova, Groq, and NVIDIA are leading the development of sovereign AI infrastructure, each with a distinct approach. SambaNova’s Reconfigurable Dataflow Unit (RDU) provides an on-premises alternative to GPU-based AI, enabling governments and enterprises to run massive models with significantly fewer chips while maintaining full data ownership—critical for defense, finance, and health care.
Groq specializes in AI inference, using its low-latency Language Processing Unit (LPU) to deliver real-time processing with 10 times better power efficiency than traditional accelerators. Its partnership with Saudi Aramco signals a shift in large-scale sovereign AI deployment. Meanwhile, NVIDIA’s H100 and upcoming Blackwell GPUs are poised to become the building blocks of worldwide present scalability and energy challenges. Large deployments require thousands of power-hungry GPUs, making sovereign on-premise AI costly. Additionally, NVIDIA’s reliance on cloud-based models raises concerns over data sovereignty and national security.
The rapid global adoption of AI is driving a surge in energy consumption, with some predictions suggesting that AI data centers could use more power than entire countries by 2030. Ensuring sustainability without compromising performance requires efficient solutions that reduce both costs and energy use. Specialized AI accelerators play a crucial role in optimizing power efficiency, allowing nations to build sovereign AI infrastructure without straining their energy grids. Companies like SambaNova and Groq are working toward approaches that reduce energy requirements. Their architectures allow governments and enterprises to deploy AI at scale while lowering the total ownership cost.
The global AI race is not about who develops the best model but who controls the infrastructure that powers them. As governments move aggressively to secure their own AI capabilities, prioritizing on-premise deployment, energy efficiency, and geopolitical independence, there are a few key trends we must keep track of.
First is the expansion of national AI clouds in places like Saudi Arabia, the United Arab Emirates, and Europe. How will these nations continue to invest in sovereign AI data centers, and at what pace?
Next is the rise of energy-efficient AI compute. Can Groq and SambaNova make good on their promise of AI that is both powerful and sustainable?
Then, watch the shifts in AI hardware. Can NVIDIA maintain its market lead as specialized accelerators gain traction?
Finally, what do the new regulatory frameworks look like for AI sovereignty? Data localization and AI governance laws will reshape global AI strategy. How do nations and those that build infrastructure respond and adapt?
The nations and enterprises that invest in sovereign AI today will control the AI-driven economy of the future—those who fail to act risk falling behind. AI sovereignty cannot be a theoretical discussion; it has to be a strategic necessity. The future of AI will not just be built on models alone but on who controls the infrastructure that runs them.
Navin Chaddha is managing director at Mayfield, a leading venture capital firm. He has been named to the Forbes Midas List of Top 100 Tech Investors sixteen times.
Mark Minevich is president of Going Global Ventures (GGV), a New York-based investment, technology, and strategic advisory firm. Minevich is also a strategic advisor to Mayfield, a leading VC in Silicon Valley.
The views expressed in this article are the writers’ own.
