AI Has Not One, Not Two, but Many Centralization Problems

Too Long; Didn't Read

A couple of months ago, I wrote a three-part series of the decentralization of artificial intelligence(AI). In that essay, I tried to cover the main elements that justify the movement of decentralized AI ranging from <a href="https://medium.com/datadriveninvestor/why-decentralized-ai-matters-part-i-economics-and-enablers-5576aeeb43d1" target="_blank">economic factors</a> to <a href="https://medium.com/datadriveninvestor/why-decentralized-ai-matters-part-ii-technological-enablers-a67e3115312e" target="_blank">technology enablers</a> as well as <a href="https://medium.com/datadriveninvestor/why-decentralized-ai-matters-part-iii-technologies-930c3c9d10d" target="_blank">the first generation of technologies that are developing decentralized AI platforms</a>. The arguments made in my essay were fundamentally theoretical because, as we all know, the fact remains that AI today is completely centralized. However, as I work more in real world AI problems, I am starting to realize that centralization is an aspect that is constantly hindering the progress of AI solutions. Furthermore, we should start seeing centralization in AI as a single problem but as many different challenges that surface at different stages of the lifecycle of an AI solution. Today, I would like to explore that idea in more detail.