AI Crypto Has a Data Problem: How Much of the Sector Is Actually Decentralized?

The decentralized nature of many proposed decentralized AI implementations relies mainly on the distribution of graphics processing units. However, distributed datasets, which neural networks need, may still be subject to centralized access control.

Why Decentralized AI Still Depends on Centralized Data AI Models Need More Than Decentralized Compute Training such models requires GPUs, and datasets and software to prepare and access data.

A licensing market is developing for training AI models, including curated and higher-quality datasets, according to the U.S. Copyright Office.

Decentralized AI compute mainly decentralizes compute used in training rather than data: i.e., separate computers conducting the training, but one developer/rights holder controlling the corpus used to train the model. Read More: What Is GMGN AI?