AI Tokens Are the Missing Rail for Decentralized Inference – Here’s the Data

The convergence of crypto and artificial intelligence (AI) is powering a number of real-world use ￰1￱ of the most recent examples of this is the rise of decentralized networks to train AI ￰2￱ such as Bittensor, Gensyn, SingularityNET, and others are currently proving how decentralized GPU compute power can be used for inference ￰3￱ is what powers applications like chatbots, agents, or code ￰4￱ is also known as the stage where an AI model puts its “learned knowledge” into ￰5￱ has become tremendously important as AI models gain ￰6￱ to recent data , the AI inference market is experiencing rapid growth, with some reports estimating a market value of $76.25 billion this ￰7￱ market is projected to reach $349.49 billion by 2032.) October 7, 2025 Karia Samaroo, CEO of publicly traded digital asset company xTAO, told Cryptonews that xTAO seeks to accelerate the growth of Bittensor by holding and staking ￰8￱ to Samaroo, xTAO is one of the network’s leading validators.

“Bittensor is building an open marketplace for machine intelligence, or a network where anyone can contribute models and be rewarded directly in TAO for the value they provide,” Samaroo ￰9￱ further believes that TAO functions as the economic engine of the entire Bittensor system, as it measures, incentivizes, and secures intelligence across the Bittensor ￰10￱ instance, Samaroo explained that TAO coordinates open computation and intelligence across thousands of independent nodes without a central authority. “Traditional AI depends on closed data centers owned by a few large ￰11￱ flips that model, as TAO creates an open, global market where anyone can contribute compute, models, or data and be compensated directly based on ￰12￱ decentralizes intelligence itself, creating an incentive layer that keeps AI open, distributed, and censorship-resistant,” he ￰13￱ Decentralized Inference Models Using AI Tokens Gensyn is yet another protocol supporting decentralized machine ￰14￱ was early to the sector, publishing its first litepaper laying out a framework for decentralized training in February 2022.

Today, Gensyn connects data, compute, and capital into a single verifiable ￰15￱ allows users to build powerful AI systems across a global substrate of ￰16￱ is currently running its ￰17￱ Amico, COO of Gensyn, told Cryptonews that the network will soon use a native token to coordinate resources, enhance security, and align incentives among participants. “Well-designed tokens help coordinate value, trust, and verification in a decentralized machine learning network,” Amico said. “They are a common unit of exchange among participants who don’t know or trust one another, but want to transact.” In addition, Akash Network is providing decentralized cloud computing that can be used to deploy and run AI inference ￰18￱ majority of AI applications deployed on Akash leverage GPUs for inference.

Specifically, apps like Venice. ai, a privacy-first alternative to ChatGPT, utilize Akash for hosting advanced AI models. “AKT” is the native token for the Akash ￰19￱ of Akash pay in AKT to use the network, while providers get paid in ￰20￱ Osuri, founder of Akash Network, told Cryptonews that AKT secures the Akash blockchain via the proof-of-stake consensus. “This means without the token, there is no blockchain and hence no network,” Osuri ￰21￱ added that AKT provides payment rails and incentives to bootstrap the compute on Akash.

“As you know, GPUs are in high demand, and to build an alternative network to competitors like Amazon, it’ll be impossible without the token incentives.” Challenges Associated With AI Tokens Although decentralized training models have shifted from an interesting concept to functioning networks, many of these projects are far from ￰22￱ this is due to a number of reasons, Galaxy’s “Decentralized AI Training” report notes that “incentives and verification lag technical innovations.” According to the document, only a handful of networks currently deliver real-time token rewards ￰23￱ also added that challenges include reliability and latency; tokenomics balance; verification and security; and regulatory issues .

“For instance, if incentives lean too heavily toward speculation or over-rewarding supply, the network risks volatility,” he said. “ASI’s approach ties token demand directly to ￰24￱ example, pay-per-token inference; grounding value in compute consumption rather than yield farming.” Gniwecki further mentioned that ensuring honest computation remains a core challenge for decentralized inference. Additionally, he said that AI tokens interacting with fiat and enterprise budgets can create challenges. “ASI solves this via dual payment rails using crypto and ￰25￱ stabilizes access for mainstream users while retaining decentralized settlement for crypto-native ones,” Gniwecki ￰26￱ Tokens Will Advance Challenges aside, decentralized inference training models will continue to advance .

“Over the next few years, AI will shift from closed, centralized platforms to open protocols that coordinate key resources such as compute, data, and capital,” Amico ￰27￱ shared that Gensyn is particularly focused on driving this transition through applications like “RL Swarm,” which is a peer-to-peer reinforcement learning training system, along with BlockAssist, which is an assistant training framework. 2/ BlockAssist Play Minecraft and passively train an assistant model to help you in-game, with no visibility of your goals or intentions. ￰0￱ — gensyn (@gensynai) October 7, 2025 Echoing this, Gniwecki shared that over the next year ASI:Cloud will evolve from decentralized access to programmable AI infrastructure.

“These developments will turn the ASI token into more than a payment method, but rather as a coordination tool for AI collaboration, model sharing, and autonomous agent ￰28￱ the platform scales, inference usage is expected to surpass 3 billion processed tokens in its first 100 days, with future staking incentives tied to verified compute throughput.”