How decentralized AI training will create a new asset class for digital intelligence

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 it remains largely inaccessible to most investors and builders. Training a competitive AI model today, similar to the ones retail users frequent, can cost hundreds of millions of dollars, demand 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 ones, there is no direct way to own a piece of the artificial intelligence sector.

That 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 your MacBook’s M4 chip, into a single training fabric capable of supporting large, frontier‑scale processes. What matters for markets is that this infrastructure does more than coordinate compute; it also coordinates ownership by issuing tokens to participants who contribute resources, which gives them a direct stake in the AI models they help create.

Decentralized training is a genuine advance in the state of the art. Training large models across untrusted, heterogeneous hardware on the open internet was, until recently, said to be an impossibility by AI experts. However, Prime Intellect has now trained decentralized models currently in production — one with 10 billion parameters (the quick, efficient all-rounder that’s fast and capable for 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 for large-scale pretraining, 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 some research project—it’s already happening. In decentralized training 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 no single participant owns the entire asset. Contributors supply GPU compute and bandwidth, and in return, they receive tokens that reflect their stake in the resulting model. This way, training participants don’t just serve as resources; they earn alignment and ownership in the AI they are creating. This is a very different alignment from 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 like OpenAI and Anthropic charge users for API access, so 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 different strategies. Some tokens may primarily confer access rights — priority or guaranteed usage 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, the token 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 may be the most direct path to participate financially in AI’s growth.

This development does not occur in a vacuum. Tokenization is already moving into the financial mainstream, with platforms like Superstate and Securitize (set to go public in 2026) that are bringing funds and traditional securities onchain. Real‑world asset strategies are now a popular topic among regulators, asset managers and banks. Tokenized AI models naturally fit 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, 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 fund, 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 onchain. Our view is that the intersection of crypto and AI will not be limited to “AI‑themed tokens”; it will be anchored in actual model revenue, backed by measurable compute and usage.

It is still early. Most decentralized training systems are in active development, and many token designs will fail technical, economic or regulatory tests. But the direction is clear: the decentralized AI training networks are set to become a liquid, globally coordinated resource. AI models are becoming shareable, ownable and tradable through tokens. As these networks mature, markets will not just price companies that build intelligence; they will price intelligence itself.