The article argues that AI’s first automation shock is likely to hit richer economies harder because their labor markets contain more clerical, professional and digital work. Developing economies face lower immediate exposure, but that is not pure protection. It often reflects weaker electricity, connectivity, institutions and managerial capacity, which limit their ability to turn AI adoption into productivity gains.

Using the proposed Google data center hub in Visakhapatnam as a test case, the piece warns that hosting compute is not the same as building technological sovereignty. Data centers can bring infrastructure, jobs and cloud access, but they also consume scarce power, water and public concessions. The article argues developing economies should prioritize adoption and adaptation before frontier imitation, approving large AI infrastructure only when it leaves behind durable capability, local value capture and broad economic benefit.

The article argues that the AI boom has physical public costs that should not be quietly shifted onto households and municipalities. Data centers require electricity, transmission, substations, water, land, and cooling infrastructure, while a small group of firms captures much of the upside. The case is not to stop AI investment, but to make developers pay for attributable grid, water, and local infrastructure costs in exchange for faster, clearer approvals.

It also proposes a modest, independently governed sovereign wealth fund funded by AI-linked public revenues or infrastructure-use levies. The fund would diversify public exposure, preserve fiscal capacity, and support underprovided capabilities like public-interest compute, cybersecurity and worker-transition pathways. The broader bargain is to trade a little short-term speed for better cost allocation, stronger workplace safeguards, and a public balance sheet that lets households share in AI-driven wealth.