AI Technology Is Universal, But Economic Gains Will Remain Unequal

Artificial intelligence (AI) is often seen as the most egalitarian technology in history, with powerful models accessible globally at similar costs and ease. This availability suggests that smaller countries could leapfrog traditional development stages and compete equally with larger powers. However, the paradox of this decade is that greater access to AI technology does not translate into economic equality. Historical patterns show that when technology becomes widespread, its value shifts from the technology itself to the surrounding ecosystem, skilled human capital, adaptive organizations, capital markets, and enabling regulation.

Examples from the past include electricity and the internet, which were widely accessible but generated concentrated economic value in a few countries and companies. The same dynamic applies to AI: the advantage lies in the complementary infrastructure rather than the AI models alone. This shift affects knowledge professions such as software developers, lawyers, analysts, and consultants, where those who build new AI-driven work models will see productivity gains, while those who merely purchase tools will see value leak to infrastructure owners.

For countries, economic sovereignty in the 21st century will depend not on market size or algorithm quality but on the ability to convert available technology into sustained advantage. Israel exemplifies this challenge: it boasts world-class innovation infrastructure, including human capital, research, entrepreneurship, and venture capital, but operates within a small domestic market. While its high-tech sector exports technology globally, other sectors like commerce, industry, services, and the public sector adopt AI slowly, resulting in consistently lower productivity compared to peers such as the United States.

Within Israeli firms, new AI-based work models are emerging, where machines handle research and analysis, freeing experts to focus on judgment, strategy, creativity, and accountability. This transformation leads to deeper, broader work closer to decision-making. The critical question for Israel in the coming decade is how to extend this high-tech productivity model across the entire economy.

The authors argue that market forces alone will not drive this change, as businesses adopt AI only where immediate returns are clear. They call for a national productivity program with measurable goals, such as halving the productivity gap with the US within ten years, expanding computing and data infrastructure, regulatory frameworks that encourage experimentation, incentives for small businesses and public sector adoption, and sustained investment in human capital, the irreplaceable component of infrastructure. Ultimately, AI models and computing power will be universally available, but economic success will depend on who builds an economy capable of leveraging them.