Cutting-edge artificial intelligence—the most advanced general artificial intelligence systems currently under development—is becoming one of the most strategically and economically important industries in the world, yet it remains inaccessible to most investors and builders. Today, training competitive AI models (similar to those often used by retail users) can cost hundreds of millions of dollars, require tens of thousands of high-end GPUs, and require a level of operational complexity that only a few companies can support. Therefore, for most investors, especially retail investors, there is no direct way to get a position in the artificial intelligence field.
This restriction is about to change. A new generation of decentralized artificial intelligence networks is moving from theory to production. These networks connect a variety of GPUs around the world—from expensive high-end hardware to consumer gaming rigs and even MacBooks’ M4 chips—into a single training fabric capable of supporting large, cutting-edge processes. Importantly for the market, this infrastructure does more than coordinate computation; it also coordinates ownership by issuing tokens to participants who contribute resources, which allows them to participate directly in the AI models they help create.
Decentralized training is a true advancement in state-of-the-art technology. Until recently, AI experts said it was impossible to train large models across untrusted, heterogeneous hardware on the open internet. However, Prime Intellect has now trained decentralized models that are currently in production – one with 10 billion parameters (a fast, efficient all-around model that is fast and able to complete everyday tasks) and another with 32 billion parameters (a deep thinker that excels at complex reasoning and delivers more nuanced, complex results).
Gensyn is a decentralized machine learning protocol that has demonstrated reinforcement learning that can be verified on-chain. Pluralis shows that training large models in swarms using commodity GPUs (the standard graphics cards found in gaming computers and consumer devices, rather than expensive specialized chips) is an increasingly feasible decentralized approach to large-scale pre-training, the fundamental stage in which AI models learn from massive data sets before being fine-tuned for specific tasks.
To be clear, this work is more than just a research project—it’s already happening. In a decentralized training network, the model does not “live” within a single company’s data center. Instead, it exists within the network itself. Model parameters are decentralized and distributed, meaning no one participant owns the entire asset. Contributors provide GPU compute and bandwidth, and in return they receive tokens that reflect their stake in the generated model. In this way, training participants not only serve as resources; They gain consistency and ownership in the AI they create. This is very different from what we see in centralized AI labs.
Here, tokenization becomes integral, giving the model economic structure and market value. Tokenized AI models are like stocks, with cash flows reflecting the demand for the model. Just like OpenAI and Anthropic charge users for API access, so do decentralized networks. The result is a new type of asset: tokenized intelligence.
Investors have direct exposure to models rather than investing in large public companies that own models. Networks will achieve this through different strategies. Some tokens may primarily grant access—priority or guaranteed use of model features—while other tokens may explicitly track a share of the net revenue generated when users pay to run queries through the model. In both cases, the token market begins to function like a model stock market, where prices reflect expectations for model quality, demand, and utility. For many investors, this may be the most direct way to participate in the development of artificial intelligence from a financial perspective.
This development did not happen in a vacuum. Tokenization has entered the financial mainstream, with platforms such as Superstate and Securitize (due in 2026) bringing funds and traditional securities on-chain. Real-world asset strategies are now a hot topic among regulators, asset managers and banks. Tokenized AI models fit naturally into this category: they are digitally native and accessible to anyone with an internet connection, regardless of location, and their core economic activity—inferential computation, the process of running queries to obtain answers by training a model—has been automated and tracked by software. Of all tokenized assets, ever-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 argument that blockchain enables communities to collectively fund, build and own digital assets in ways that were previously impossible. First there is 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 cryptocurrency and AI will not be limited to “AI-themed tokens”; it will be based on real model revenue and backed by measurable computation and usage.
It’s still early. Most decentralized training systems are under active development, and many token designs will fail technical, economic, or regulatory tests. But the direction is clear: a decentralized AI training network will become a fluid, globally coordinated resource. AI models are becoming shareable, ownable and tradable through tokens. As these networks mature, the market will not only price companies that build intelligence; They will put a price on the intelligence itself.
