Frontier AI – the most advanced general purpose AI systems currently in development – is becoming one of the world’s most strategically and economically important industries, yet remains largely inaccessible to most investors and developers. Training a competitive AI model today, similar to the ones retail users frequent, 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. Thus, for most investors, especially retail traders, there is no direct way to own a piece of the AI sector.
That limitation is about to change. A new generation of decentralized AI networks is moving from theory to production. These networks connect all kinds of GPUs from around the world, from expensive high-end hardware to consumer gaming rigs and even your MacBook’s M4 chip, to a single training fabric capable of supporting large, cross-border processes. What matters to the markets is that this infrastructure does more than coordinate data processing; 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 education is a true advancement in the latest technology. Training large models across unreliable, heterogeneous hardware on the open internet was until recently said to be an impossibility by AI experts. But Prime Intellect has now trained decentralized models that are currently in production—one with 10 billion parameters (the fast, efficient all-rounder that’s quick and capable of everyday tasks) and another with 32 billion parameters (the deep thinker that excels at complex reasoning and delivers more nuanced, sophisticated results).
Gensyn, a decentralized machine learning protocol, has demonstrated reinforcement learning that can be verified onchain. 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 a swarm is an increasingly viable decentralized approach to large-scale pretraining, the foundational stage 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 happening. In decentralized education networks, the model does not “sit” inside a single company’s data center. Instead, it lives across the network itself. Model parameters are fragmented and distributed, meaning that no single participant owns the entire asset. Contributors provide GPU computation and bandwidth, and in return receive tokens that reflect their stake in the resulting model. In this way, training participants not only serve as resources; they gain customization and ownership in the AI they create. This is a very different adaptation than we see in centralized AI labs.
Here, tokenization is integrated, which gives 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 API access, so too can decentralized networks. The result is a new kind of asset: tokenized intelligence.
Instead of investing in a large public company that owns models, investors can gain exposure to models directly. Networks will implement this through various 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 the net revenue generated when users pay to run queries through the model. In both cases, the token markets begin to function as a stock market for models, with prices reflecting expectations about a model’s quality, demand, and utility. For many investors, this may be the most direct route to participate financially in AI’s growth.
This development does not happen in a vacuum. Tokenization is already moving into the financial mainstream with platforms like Superstate and Securitize (set to go public in 2026) bringing funds and traditional securities onchain. Real-world active strategies are now a hot 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 get answers – is already automated and trackable by software. Among all tokenized assets, continuous improvement of 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, build, and own digital assets in ways previously impossible. First was money, then financial contracts, then real-world assets. AI models are the next digitally native asset class to be organized, 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 rooted in actual model revenue, supported by measurable calculation and usage.
It’s still early days. Most decentralized training systems are in active development, and many token designs will fail technical, financial or regulatory tests. But the direction is clear: the decentralized AI training networks are ready to become a fluid, globally coordinated resource. AI models become shareable, can be owned and traded through tokens. As these networks mature, markets will not only price companies that build intelligence; they will price the intelligence itself.
