2/18/2026 Cato Miller reporting

Artificial intelligence will likely be used in economics moving forward. It will not be a single dramatic shift, but a gradual restructuring of how value is created, distributed, and governed. AI will increasingly automate both white and blue-collar labor. It will handle tasks in logistics, finance, design, medicine, law, engineering, and even creative fields. Rather than replacing all human work, AI will often augment workers. Over time, the share of economic output produced by autonomous systems could grow substantially. Barring major greedflation, it should lower costs of products and services.

In such an economy, productivity could rise sharply. AI systems could operate continuously, optimize supply chains in real time, and reduce waste. This could lower prices and increase abundance in sectors like energy, food production, healthcare diagnostics, and transportation. However, the key question would not be whether AI creates wealth , but who captures that wealth?

A utopian model could involve broader ownership. For example, public AI utilities, cooperative AI platforms, or national AI funds that distribute dividends to citizens. Some economists have proposed ideas such as universal basic income (UBI) or “AI dividends” funded by productivity gains from automation. However, in a more dystopian model, corporations could own AI infrastructure, data, and compute resources, and profits concentrate among shareholders and highly skilled AI engineers. This will almost certainly lead to increased inequality.

What else could be expected? Labor markets would also change. Routine jobs may decline, while new roles emerge in system design, ethics, maintenance, human-centered services, and AI oversight. Education systems would need to pivot toward adaptability, critical thinking, and interdisciplinary skills rather than narrow task specialization. Human work might shift more toward areas that emphasize empathy, judgment, creativity, and complex social interaction.

Infrastructure would become a central economic pillar. Data centers, advanced chips, renewable energy systems, and high-speed networks would function like the industrial factories of the 20th century. Regions with abundant clean energy and cool climates could become hubs for AI computation. Governments might treat AI infrastructure as strategically important, similar to ports or railroads in earlier eras.

There would also be governance challenges. Policymakers would need to address competition, monopolization of AI capabilities, labor displacement, cybersecurity risks, and ethical misuse. International coordination could become critical, especially if AI systems influence military strategy, financial stability, or public information sources.

A fully mature AI economy might eventually resemble a “post-scarcity” system in certain domains. Goods and services could be tied to information and become extremely inexpensive. But physical resources, land, and energy would still impose constraints. The long-term stability of such an economy would depend on whether institutions adapt quickly enough to distribute benefits broadly.

The future AI economy is less about machines replacing humans and more about redefining the relationship between intelligence, labor, capital, and ownership. The biggest variable is not technical capability, but it’s how societies choose to structure incentives, rights, and access around increasingly autonomous systems.

Cato Miller

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