Thorn said Bitcoin mining, which initially began as a decentralised activity carried out on personal computers, has evolved into a capital-intensive industry dominated by specialised ASIC machines and large-scale mining farms. This shift, driven by high infrastructure and energy costs, has concentrated mining power in fewer hands, raising concerns about the long-term sustainability of decentralisation within the Bitcoin network.
In contrast, Artificial Intelligence is expected to become more distributed over time. Thorn explained that AI development started within highly centralised systems, relying on large data centres and cloud-based platforms. However, emerging constraints such as limited data availability and memory challenges are pushing the industry toward more open-source and decentralised models.
A key driver of this shift is the rise of edge computing, which allows AI models to run directly on user devices without relying on central servers. This approach not only improves efficiency and real-time processing but also enhances data privacy by reducing dependence on cloud infrastructure.
Market projections support this trend. According to Grand View Research, the global edge AI market is expected to grow from $25 billion in 2025 to $119 billion by 2033, fueled by the rapid expansion of Internet of Things (IoT) devices and increasing demand for instant data processing.
Despite the centralisation trend, Bitcoin mining is still experiencing geographic redistribution. Rising energy costs in the United States have prompted some mining operations to relocate to countries such as Paraguay and Ethiopia, reflecting ongoing shifts in the global mining landscape.
The contrasting trajectories of Bitcoin and AI highlight a broader technological shift, where one of the earliest decentralised systems is becoming more concentrated, while a traditionally centralised technology is gradually moving toward a more distributed future.
