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What is the economic cost of not using AI?
·6 min read
Every significant technology wave in economic history has produced two groups of people: early adopters who captured the productivity gains, and late movers who competed against those gains for years after. The divide between the two groups is usually measured in hindsight, and in the case of AI it is already being measured. The cost of not adopting AI is leading to structural disadvantage between those who adopt and those who take the backseat. The earliest estimates suggest that such cost runs into trillions of dollars at the national level and into hefty margin compression at the firms level.
## The historical precedent
When electrification arrived in American factories, adoption was neither instant nor uniform. Economic historians have documented how firms that converted from steam power to electric motors in the 1890s often saw only modest productivity improvements at first. The reason was that they retained factory layouts designed around a central steam shaft. The larger gains emerged over the following decades, particularly during the 1910s and 1920s, as factories were redesigned around the flexibility of electric power. Firms that postponed those organisational changes fell further behind, competing against businesses whose operations had already been rebuilt around a more productive model.
Paul David, the Stanford economic historian, made this case in his 1990 paper on the dynamo and the computer. His argument was that general-purpose technologies take time to diffuse, and that the diffusion gap itself creates lasting productivity divergence between early and late adopters. AI fits the general-purpose technology template well: it is applicable across sectors, it improves over time, and its gains compound through complementary investments in workflow, training, and data infrastructure.
The other obvious comparison is the internet. Firms that built e-commerce infrastructure in the mid-1990s, when it was operationally difficult and strategically uncertain, entered the 2000s with customer datasets, logistics systems, and institutional knowledge that newer entrants spent years trying to replicate. Amazon's dominance in 2025 is partly a function of decisions made in 1995. The lesson is not that first movers always win. The lesson is that first movers in general-purpose technologies often build structural advantages that persist for much longer than the initial technology gap.
## What the numbers say at the national level
The IMF's January 2024 analysis estimated that AI will affect 40 percent of jobs globally and 60 percent of jobs in advanced economies, with the effects running to both displacement and augmentation. The analytical implication for national competitiveness is direct: countries that build the workforce infrastructure and regulatory conditions for rapid AI adoption will capture the augmentation gains. Countries that move slowly will absorb the displacement effects without the corresponding productivity uplift.