AI models are not objective; they have strong opinions on history, culture, and values. Because these models are becoming the primary interface for everything from education to infrastructure, the “values” baked into the code essentially become a form of digital sovereignty. If a country relies on a foreign model, they aren’t just importing software—they are importing a specific worldview and set of ethics, leading to a modern struggle where nations fight to avoid being “colonized in cyberspace.”

This shift has transformed technology from a mere business sector into the central arena of national security and geopolitical influence. Deterrence is no longer measured solely by the size of a military, but by the pace of innovation in reacting to cheap, software-built tech. Because these tools are being built by the private sector rather than governments, the gap between government policy and technological reality is widening, making deep partnerships between venture capital, founders, and international governments essential for maintaining stability and economic growth.

For entrepreneurs, the traditional playbook for international expansion—waiting until hitting hundreds of millions in revenue—is obsolete. Products now travel faster than companies can, with AI allowing for near-instant localization and global discovery. However, while the tech is frictionless, the business of scaling remains relationship-based. Success in “top-heavy” economies like Mexico, Japan, or Saudi Arabia depends on navigating local power structures and securing a few key flagship partnerships rather than attempting a broad, grassroots market entry.

Surprising Insights

• The “Dual-Use” Paradox: AI’s ability to find vulnerabilities in code is a double-edged sword; locking down these capabilities to prevent cyberattacks also prevents defenders from finding and patching their own holes, potentially leaving them more vulnerable to “jailbroken” models.
• Deterrence Evolution: National security is shifting away from “exquisite platforms” (expensive, few-and-far-between assets) toward cheaper, larger numbers of autonomous systems developed in the private sector.
• The Norway Example: The introduction of an unrealized capital gains tax in Norway is cited as a primary reason the tech ecosystem there effectively ended, illustrating how a single policy shift can kill entrepreneurial risk-taking.
• Cyber-War Evolution: The trajectory of hacking has moved linearly and rapidly from simple vandalism (PC crashes) to theft (financial crime) and finally to state-level warfare.

Practical Takeaways

• Internationalize Earlier: Don’t wait for massive revenue milestones to consider global markets. Use AI tools to localize products and enter high-opportunity regions while you are still in the growth phase.
• Target “Top-Heavy” Markets: In relationship-driven economies, focus on the top 5–10 most influential companies or government entities. One major partnership in these regions is often more valuable than a full-scale local office.
• Prioritize “Trusted” Infrastructure: When deploying AI globally, emphasize the transparency and values of the models to differentiate from authoritarian alternatives that may use censorship or backdoors.
• Audit Legacy Code with AI: Use current AI models to aggressively “hack” your own 20-year-old legacy codebases now, as the cost of fixing vulnerabilities has dropped while the cost of a breach has skyrocketed.