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  • Tariffs and AI’s global transformation
    Posted on May 23rd, 2025 in Exam Details (QP Included)

     The US presidential election in 2024 has led to substantial tariffs, potentially restructuring global technology supply chains that power AI development.

    • Countries like India, as the “third option” in the U.S.-China technological rivalry, are in a precarious position due to the increased costs of imported components critical to AI infrastructure.

    • The tariffs have raised the costs of electronics imports to the U.S., with data processing machine imports costing around $200 billion, sourced largely from tariff-affected countries.

    • The current tariff regime has expanded this to as high as 27% on critical AI hardware components in 2025, particularly affecting specialized AI accelerators and advanced logic chips.

    • Economic theory suggests tariff policies should stimulate domestic production through import substitution, but this approach often comes at the cost of economic efficiency and innovation.

    • The tariffs may shield domestic firms from competition, reducing their incentive to innovate, and limit access to advanced imported technologies necessary for AI advancement.

    • Rapid expansion in AI chip demand will require massive increases in data centre power capacity, which could undermine the U.S.’s competitiveness in AI.

    • India, which has positioned itself as a strategic “third option” in the U.S.-China technological competition, could create unexpected opportunities due to the tariffs and supply chain disruptions that raise costs of AI infrastructure.

    • The economic reshaping catalysed by these tariff policies has accelerated what economists call “capital substitution effects”.

    • Tariffs and supply chain disruptions that raise costs of AI infrastructure could slow down India’s global ambitions in AI.

    • Tariff structures interact with differential regulatory environments uniquely to create novel competitive dynamics.

    • Decentralised AI development has led to the development of specialized AI hardware that is designed specifically for particular applications rather than general-purpose computation.

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