A CoinDesk opinion piece argues that decentralized AI training networks could reshape how artificial intelligence is developed and valued, turning AI models and related digital resources into tradable, investable assets rather than the proprietary tools of a few large tech companies. Instead of centralized corporations owning the most powerful systems, decentralized networks can aggregate global computing resources — from high-end GPUs to consumer devices — into unified platforms that train AI collaboratively. This approach aims to democratize access to AI development and ownership.
In decentralized AI ecosystems, participants who contribute compute power, data, or other resources can receive tokens or digital equity stakes tied to the resulting models and training outcomes. These tokens represent ownership or influence over AI models and can be traded or monetized, akin to how cryptocurrencies and tokenized assets function in blockchain economies. Projects like Prime Intellect, Gensyn, and Pluralis have shown that decentralized training is technically feasible for models with billions of parameters, suggesting the model could scale beyond niche experiments.
This tokenization of AI resources creates what the article describes as a new asset class for “digital intelligence.” Rather than only valuing AI companies’ stocks or infrastructure, investors could hold tokens representing portions of trained AI models or the networks that govern them, potentially earning returns based on adoption, usage, and further development. In this view, decentralized AI turns the underlying intelligence itself into a financial instrument, reshaping how capital flows into AI innovation.
However, the concept also raises regulatory and technical challenges. Decentralized AI asset markets would need frameworks to define what exactly is being tokenized (e.g., models, datasets, compute shares), how rights and revenues are allocated, and how governance works across borders. There are also questions about quality, security, and interoperability of models trained in decentralized environments. Still, proponents believe that by aligning incentives and broadening participation, decentralized AI training could unlock new forms of investment and accelerate innovation beyond the control of a few centralized players.
