Frontier AI, the most advanced general-purpose AI systems currently in development, is becoming one of the most strategically and economically important industries in the world, but remains largely inaccessible to most investors and builders. Training a competitive AI model today, similar to those frequented by retail users, can cost hundreds of millions of dollars, require tens of thousands of high-end GPUs, and require a level of operational sophistication that only a handful of companies can support. Therefore, for most investors, especially retail investors, there is no direct way to own a piece of the AI sector.
That restriction is about to change. A new generation of decentralized AI networks is moving from theory to production. These networks connect GPUs of all types from around the world, from expensive high-end hardware to consumer gaming platforms and even the M4 chip in your MacBook, into a single training fabric capable of supporting large-scale processes. What matters for markets is that this infrastructure does more than coordinate computing; It also coordinates ownership by issuing tokens to participants who contribute resources, giving them a direct stake in the AI models they help create.
Decentralized training is a true advance in the state of the art. Until recently, AI experts said that training large models across heterogeneous and untrusted hardware on the open Internet was impossible. However, Prime Intellect has now trained decentralized models currently in production: one with 10 billion parameters (the fast, efficient all-rounder that is fast and capable for everyday tasks) and another with 32 billion parameters (the deep thinker that excels at complex reasoning and delivers more sophisticated and nuanced results).
Gensyn, a decentralized machine learning protocol, has demonstrated on-chain verifiable reinforcement learning. Pluralis has shown that training large models using commercial GPUs (the standard graphics cards found in gaming computers and consumer devices, rather than expensive specialized chips) in a swarm is an increasingly viable decentralized approach for large-scale pre-training, the fundamental phase where AI models learn from massive data sets before being fine-tuned for specific tasks.
To be clear, this work is not just a research project: it is already happening. In decentralized training networks, the model does not “sit” within the data center of a single company. Instead, it lives through the network itself. The model parameters are fragmented and distributed, meaning that no one participant owns the entire asset. Contributors provide compute and GPU bandwidth and, in return, receive tokens that reflect their participation in the resulting model. In this way, training participants not only serve as resources; they gain alignment and ownership in the AI they are creating. This is a very different alignment than what we see in centralized AI labs.
Here, tokenization becomes integral, giving the model an economic structure and market value. A tokenized AI model acts like a stock, and cash flows reflect demand for the model. Just as OpenAI and Anthropic charge users for API access, so can decentralized networks. The result is a new type of asset: tokenized intelligence.
Instead of investing in a large public company that owns models, investors can gain exposure to the models directly. Networks will implement this through different strategies. Some tokens may primarily confer access rights (priority or guaranteed use of the model’s capabilities), while others may explicitly track a portion of the net revenue generated when users pay to run queries through the model. In both cases, token markets begin to function like a stock market for models, where prices reflect expectations about the quality, demand, and usefulness of a model. For many investors, this may be the most direct path to financially participating in the growth of AI.
This development does not occur in a vacuum. Tokenization is already entering the financial mainstream, with platforms like Superstate and Securitize (due to go public in 2026) bringing traditional funds and securities onto the chain. Real-world asset strategies are now a popular topic among regulators, asset managers and banks. Tokenized AI models fit naturally into this category: they are digitally native, accessible to anyone with an Internet connection regardless of location, and their core economic activity (computation for inference, the process of running queries through a trained model to obtain answers) is already automated and traceable by software. Among all tokenized assets, continuously improving AI systems may be the most inherently dynamic, as models can be updated, retrained, and improved over time.
Decentralized AI networks are a natural extension of the thesis that blockchains enable communities to collectively finance, build and own digital assets in ways that were previously impossible. First it was money, then financial contracts, and then real-world assets. AI models are the next class of digitally native assets to be organized, owned and traded on-chain. Our view is that the intersection of cryptocurrencies and AI will not be limited to “AI-themed tokens”; will be anchored in real revenue from the model, supported by measurable compute and usage.
It’s still early. Most decentralized training systems are in active development and many token designs will not pass technical, economic or regulatory testing. But the direction is clear: decentralized AI training networks will become a liquid, globally coordinated resource. AI models are becoming shareable, owned and tradable through tokens. As these networks mature, markets will not only price companies that develop intelligence; They will put a price on intelligence itself.
