Artificial Intelligence (AI) has rapidly evolved from a lab novelty to a cornerstone of modern productivity, influencing healthcare, education, manufacturing, and governance. While China pledges $138 billion for its state AI venture fund and Europe commits €200 billion to AI infrastructure, America relies primarily on market forces and private-sector innovation. Companies like OpenAI, Anthropic, and Google have significantly advanced AI technologies but lack incentives to provide universal, equitable access. This hands-off approach risks leaving the U.S. behind, overlooking AI’s role as essential infrastructure for sustained national competitiveness.

AI as Essential Infrastructure

Historically, transformative technologies like electricity, telephony, and broadband began as market-driven initiatives but ultimately required government intervention to achieve universal accessibility. AI mirrors these historical precedents, though the stakes are considerably higher due to its extensive impact on economic productivity and innovation.

Today, AI literacy is as crucial as computer literacy was in the 1990s. Nations that widely adopted computing infrastructure became global leaders in innovation, while those that hesitated faced economic stagnation. Similarly, countries actively fostering widespread AI literacy today are poised to dominate future economic landscapes.

My experience developing AI systems for Fortune 500 companies, including Disney, IBM, and Amazon, has revealed a clear pattern: organizational success hinges far more on an AI-literate workforce than merely the sophistication of the technology itself. Unfortunately, many American workers currently view AI primarily as a threat to their jobs. The real concern, however, isn’t AI replacing workers, but rather a globally dispersed, AI-literate workforce replacing workers who lack AI skills.

The Market’s Limitations

Proponents of free markets often argue that competition will naturally broaden technology access. However, AI development uniquely benefits from substantial network effects, allowing dominant firms to continually strengthen their market positions. Leading AI innovators have minimal incentive to widely distribute advanced AI services, as evidenced by premium-priced services such as ChatGPT Plus or Gemini Pro. Consequently, lower-income and working-class individuals, who could most benefit from AI-driven productivity enhancements, remain the least able to access these transformative tools.

Although the U.S. government recently acknowledged AI’s strategic significance in federal operations, this awareness has yet to broadly inform policies ensuring widespread public accessibility. Without comprehensive adoption, the full economic and societal potential of AI remains unrealized, posing a genuine risk to American competitiveness.

International Lessons and Implications

Other countries have proactively recognized AI as strategic infrastructure and taken decisive action. For instance, the United Arab Emirates recently made ChatGPT Plus universally accessible, effectively categorizing AI infrastructure alongside traditional public utilities such as highways and electricity grids. This approach encourages broad-based innovation, supports widespread skill development, and accelerates economic growth.

AI fundamentally differs from traditional industries because it lacks geographical constraints. With open access coupled with comprehensive education, nations can swiftly leapfrog into positions of global innovation leadership, actively shaping technology rather than merely purchasing it. Conversely, nations failing to establish broad AI literacy and robust infrastructure risk becoming technologically dependent, transitioning from creators to mere consumers of technology.

Building America’s AI Infrastructure

To maintain future competitiveness and economic resilience, America must adopt several strategic initiatives:

• Public AI Infrastructure: Establish publicly-funded AI computational resources accessible to researchers, educators, entrepreneurs, and the broader public, similar to public libraries or utilities.
• Mandatory AI Education: Integrate comprehensive AI literacy into national educational curricula, effectively preparing students for future job markets and economic participation.
• Open-Source AI Investment: Provide federal support for open-source AI models, ensuring equitable access and mitigating monopolization risks by large corporations.
• Guaranteed AI Access Rights: Formally recognize basic AI accessibility as essential infrastructure, comparable in importance to public education and healthcare, reflecting AI’s critical economic significance.

Historically, infrastructure investments have shaped economic opportunities and global competitiveness. AI, as cognitive infrastructure, follows this established trajectory, but the window to secure leadership is quickly narrowing.

Economic and National Security Considerations

While the Pentagon allocates significant resources toward military AI applications, the primary threat to U.S. national security isn’t adversarial AI weaponry. It is the economic vulnerability posed by an unprepared workforce. Workers in other countries who successfully integrate AI into their workflows will substantially outpace American productivity. Such disparities could lead to economic irrelevance without any direct military confrontation.

The central question America faces today is not whether it can afford universal AI infrastructure, but whether it can bear the consequences of failing to provide it. Each delay exacerbates the risk of leaving millions of Americans without essential skills, eroding national competitiveness and economic stability. Other nations are already swiftly prioritizing AI infrastructure development, rapidly narrowing the technological gap America once comfortably led.

Now is the critical moment for America to decisively invest in AI as essential infrastructure, ensuring sustained economic resilience, innovation leadership, and enduring global competitiveness.

About the Author:

Dr. Javid Huseynov is a Principal AI Scientist at BuildOps, Inc and an Associate Professor of Applied Analytics at Columbia University.

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