A HackerNoon article argues that data sovereignty, model portability, and verifiable failover matter more than choosing between centralized and decentralized AI, framing operational sovereignty—the ability to control sensitive data, evaluate substitute models, and change infrastructure without rebuilding the entire product—as the stronger goal. It describes concentrated dependency as a key architectural risk, citing Samsung employees reported to have uploaded sensitive company information, including source code, into ChatGPT in 2023; Italy’s data-protection authority announcing a €15 million penalty against OpenAI in December 2024, with a Rome court ruling published on March 18, 2026 upholding a challenge against the authority’s decision; and operational dependency from model retirement, service interruption, changing usage policies, and unpredictable demand. The article says centralized inference has advantages including mature infrastructure, managed scaling, integrated tooling, and potentially strong service commitments, while decentralized compute can offer alternative sourcing, distributed capacity, and greater flexibility in infrastructure selection, but neither approach is automatically superior. It notes a decentralized GPU marketplace introduces challenges including variable hardware, network performance, provider trust, workload confidentiality, and uncertain service guarantees, and says the objective should be to reduce concentrated dependency without introducing unnecessary operational complexity.