AI Sovereignty and the National Bet

Balance national and global AI resources: use open-source models, develop in-house capabilities, and leverage foreign AI partnerships strategically.

Feb 11, 2026 7m 29s

Elizabeth Seger

Expert Insights

Elizabeth Seger emphasizes the importance of national sovereignty in the context of AI-assisted software development. She suggests that there are both immediate and broader considerations in determining the balance between reliance on foreign AI services and the cultivation of domestic AI capabilities.

Elizabeth recommends careful investment in open-source software to bolster national AI development. She maintains that this route offers opportunities for control, adaptability, and affordability. She also advises against disregarding the potential of partnerships with major AI developers, recognizing that these entities offer state-of-the-art AI technologies that may be valuable.

Here's what she explains:

Why it's significant to invest in homegrown AI capabilities instead of investing billions in foreign AI services.
How open-source tools can drive AI development in a cost-efficient manner, despite the challenges in providing proper support.
Why understanding the implications of AI model locations can play a big role in response times and data sensitivity.
The idea of 'sovereignty', even in tech, and how it consists of more than just data and where it's stored, but extends to the entire tech stack.
How the ideal approach may be to leverage both open source and relationships with global tech giants to navigate the future of AI development.

AI sovereignty is the idea of how independent you can be or self-reliant on the entire tech stack. Everything from the sourcing of data to the computing you need to run the systems, to developing the systems themselves, chip manufacturing, just everything in that stream. And right now, there is no country that is able to do that completely on its own.

Monterail Team Analysis

Here's how software development teams can navigate the balance between national and foreign AI resources:

Understand the landscape: Gain knowledge of both national and foreign AI capabilities and consider the balance between reliance and sovereignty.
Evaluate open source: Consider the potential of harnessing and developing open-source AI tools as a cost-efficient and sustainable approach.
Consider data sensitivity and responsiveness: Recognize the implications of AI model locations and how they can affect response times and data sensitivity in your applications.
Embrace diverse strategies: Leverage both open-source tools for local control and also establish relationships with global AI developers to access state-of-the-art technologies.
Invest in in-house capabilities: Teams must strategically invest in strengthening their own AI capacity for long-term sustainability and control.
Create a mixed-model approach: Consider using a combination of open source and proprietary models based on the specific needs and capacities of your team.
Prepare for contingencies: Develop a plan for scenarios where foreign AI resources might be unavailable or compromised.
Monitor the geopolitical landscape: Ensure the team stays aligned with national policies around AI development and use.