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 it remains largely inaccessible to most investors and manufacturers. Today, training a competitive AI model, similar to those experienced 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. So, for most investors, especially individuals, there is no direct way to own a share of the artificial intelligence sector.
This constraint is about to change. A new generation of decentralized AI networks is moving from theory to production. These networks connect GPUs of all kinds from around the world, ranging from expensive high-end hardware to consumer gaming rigs and even the M4 chip in your MacBook, into a single training structure capable of supporting large-scale processes. What matters for markets is that this infrastructure does more than coordinate computation; it also coordinates ownership by issuing tokens to participants who contribute resources, giving them a vested interest in the AI models they help create.
Decentralized training constitutes a real advance in the state of the art. Training large models on heterogeneous and unreliable hardware over the open Internet was, until recently, considered an impossibility by AI experts. However, Prime Intellect has now trained decentralized models currently in production – one with 10 billion parameters (the fast and efficient all-rounder who is fast and capable of performing everyday tasks) and another with 32 billion parameters (the deep thinker who excels at complex reasoning and delivers more nuanced and sophisticated results).
Gensyn, a decentralized machine learning protocol, has demonstrated reinforcement learning that can be verified on-chain. Pluralis has shown that training large models using commodity GPUs (the standard graphics cards found in gaming computers and consumer devices, rather than expensive specialized chips) in swarms is an increasingly viable decentralized approach for large-scale pre-training, the foundational phase where AI models learn from massive datasets before being fine-tuned for specific tasks.
To be clear, this work is not just a research project: it is already underway. In decentralized learning networks, the model does not “stay” inside a single company’s data center. Instead, it lives through the network itself. Model parameters are fragmented and distributed, meaning no participant owns the entire asset. Contributors provide GPU computing and bandwidth, and in return they 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 of the AI they create. 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, with cash flows reflecting the model’s demand. Just as OpenAI and Anthropic charge users for access to APIs, so can decentralized networks. The result is a new type of asset: tokenized intelligence.
Instead of investing in a large public company that has models, investors can gain direct exposure to the models. Networks will implement this through different strategies. Some tokens may primarily grant access rights (priority or guaranteed use of the model’s capabilities), while others may explicitly track a share of net revenue generated when users pay to run queries through the model. In both cases, tokenized markets begin to function like a stock market for models, where prices reflect expectations about a model’s quality, demand, and usefulness. For many investors, this is perhaps the most direct route to financially participating in the growth of AI.
This evolution does not occur in a vacuum. Tokenization is already moving into the financial mainstream, with platforms like Superstate and Securitize (expected to go public in 2026) bringing traditional funds and securities online. Real asset strategies are now a popular topic among regulators, asset managers and banks. Tokenized AI models naturally fall into this category: they are digitally native, accessible to anyone with an internet connection regardless of location, and their core economic activity (computing for inference, the process of running queries through a trained model to get answers) is already automated and trackable by software. Among all tokenized assets, continuously improving AI systems may be the most inherently dynamic, as models can be upgraded, retrained, and improved over time.
Decentralized AI networks are a natural extension of the thesis that blockchains enable communities to collectively finance, create, and own digital assets in ways previously impossible. First there was money, then financial contracts, then real-world assets. AI models are the next digitally native asset class to be curated, owned, and traded on-chain. Our view is that the intersection of crypto and AI will not be limited to “AI-themed tokens”; it will be anchored in the model’s actual revenues, supported by measurable calculation and usage.
It’s still early. Most decentralized training systems are under development and many token designs will fail technical, economic, or regulatory testing. But the direction is clear: decentralized AI training networks are poised to become a liquid, globally coordinated resource. AI models become shareable, owned and exchangeable via tokens. As these networks mature, markets will not only value companies that develop intelligence; they will set the price of intelligence itself.
