Artificial intelligence needs no introduction, driving new innovation and transforming the way people work. But the adoption of AI and our increasing reliance on it also raises questions about the centralised nature of the infrastructure it runs on, and the risks that poses.
Cryptocurrencies have taught us of the importance of decentralisation, and the dangers of concentrating compute resources and data in a small handful of facilities. While such an approach may seem efficient, it also introduces critical vulnerabilities and concerns over access and governance.
Centralised AI systems are incredibly vulnerable, as the big server farms that run them represent a single point of failure that could bring hundreds of applications crashing down. Data centres that power AI models like ChatGPT pose a tempting target for hackers, too, due to the enormous amounts of data they possess.
Centralised servers also mean more headaches in terms of regulation. When an AI system is located in a single country, it falls under that nation’s governance, which can cause problems for users in other territories subject to different data sovereignty and privacy rules. Of course, centralisation also means monopolisation, and we already have plenty of evidence of this with the likes of OpenAI, Google, and Anthropic being extremely secretive about how they train their most advanced AI models. The danger is that just a handful of big corporations will end up becoming gatekeepers of a technology that becomes a vital part of modern life, restricting access to those who are willing to pay whatever price they demand.
Fortunately, there is a ready-made solution to these problems in the shape of decentralised AI. With a decentralised AI, the infrastructure that powers models can be distributed in a wide network of users, eliminating the risks associated with centralisation. Decentralisation means no single point of failure, more transparency and user co