Key takeaways:
- The AI sector in crypto is no longer limited to the largest projects. Data networks, decentralized GPUs, private inference and autonomous agents are becoming increasingly important.
- Smaller AI tokens can respond faster to market trends, but carry higher risks: low liquidity, high volatility, token unlocks, and narrative dependence.
The artificial intelligence token market is once again attracting investor attention. According to CoinGecko data, the capitalization of the AI category currently exceeds USD 26 billion, and the AI Agents category itself is worth approximately USD 3.7 billion. This is still a small part of the overall cryptocurrency market, but large enough to show a clear division into several segments: data for AI, computing power, agents, private inference and consumer applications.
This is not an investment ranking or a “best tokens to buy” list. This is an overview of projects that are in important AI x crypto market narratives and have lower or average capitalization compared to the largest players such as NEAR or Bittensor. In practice, this means greater sensitivity to good news, but also a greater risk of sudden declines.
Why are AI tokens one of the most important topics in crypto?
Artificial intelligence needs three things: data, computing power and tools to operate. Blockchain adds a layer of ownership, settlement and coordination between users, creators and infrastructure providers.
This is where DePIN, or decentralized physical infrastructure networks, comes into play. In simple words: instead of building one large, central cloud, DePIN projects try to combine the resources of many users, for example internet bandwidth, disks, servers or graphics cards. In return, users can receive AI tokens.
The second important trend is AI agents. An AI agent is a program that not only answers questions, but can independently perform tasks, communicate with other systems, manage processes or operate in applications. In crypto, there is also the tokenization of such agents, i.e. an attempt to create markets, ownership models and financing mechanisms around them.
1. Grass (GRASS), data for artificial intelligence
This is important because data is one of the biggest bottlenecks of artificial intelligence. AI models need large, up-to-date and well-described data sets. Grass is trying to build an alternative to centralized data providers.
According to CoinGecko data, GRASS’s capitalization is currently approximately USD 314 million, and the token has recorded strong growth in the last seven days. This shows that the market is reacting to the AI data layer narrative, but at the same time it increases the risk of entering after a strong price movement.
Grass’s biggest opportunity lies in the real demand for data for AI. The biggest risk is whether the project will be able to prove sustainable revenue and not just fuel activity with token incentives.
2. Virtuals Protocol (VIRTUAL), an economy of autonomous agents
It sounds abstract, but the idea is simple: if AI agents start performing real services, the market will need a way to finance, account for and co-own them. Virtuals tries to occupy this layer.
VIRTUAL’s capitalization is currently approximately USD 500 million, which places the project in the group of medium-sized AI tokens, and not very small speculative assets.
However, the risk is high. The AI agent segment is heavily dependent on narrative. Many projects promise automation, but only a fraction of them will show lasting use beyond the short-term interest of traders.
3. Venice Token (VVV), private AI inference
Inference is the moment when an AI model actually generates a response, text, image, code or analysis. If training a model can be compared to learning, inference is its day job. As the number of AI applications increases, so does the need for low-cost and private inference.
VVV is currently capitalized around USD 840 million, so it is larger than most of the projects on this list. According to CoinGecko, the token increased significantly on a 7-day basis, but also recorded significant declines within 24 hours, which clearly shows the volatility of this segment.
Venice’s greatest advantage is the combination of two narratives: AI and privacy. The biggest risk remains the question of whether the token will capture value from real use of the platform, and not only from the popularity of the product itself.
4. io.net (IO), a decentralized GPU cloud
This is one of the most understandable use cases for crypto in AI. If companies need computing power and classic clouds are expensive or difficult to access, a decentralized GPU network can serve as an alternative marketplace.
The capitalization of IO, according to historical data from CoinGecko from May 25, 2026, was approximately USD 48 million. This is much less than VIRTUAL or VVV, but that is why the risk is higher.
In the case of io.net, investors should especially look at real network utilization, revenue, the number of active GPU vendors, and competition from large clouds and other DePIN projects.
5. MyShell (SHELL), AI for creators and users
This distinguishes MyShell from typical infrastructure projects. Instead of selling computing power or data, MyShell is trying to build a consumer layer, i.e. AI products used by creators, communities and ordinary users.
SHELL’s capitalization according to CoinGecko is currently approximately USD 8.7 million, which means a very small scale compared to sector leaders.
Such capitalization may attract the attention of people looking for projects at an earlier stage, but it also carries specific risks: lower liquidity, greater price fluctuations and greater susceptibility to outflow of interest if the platform does not maintain user growth.
6. Freysa AI (FAI), experimental agent on Base
Recently, information about Coinbase appeared on the roadmap. It is worth clarifying: Coinbase added Freys to the roadmap in March 2025, so it is not a fresh impulse from 2026. At that time, the information caused a strong price movement, but today it should be treated as a historical factor, not current news.
FAI’s capitalization is currently approximately USD 22 million. This is a small project, so its price may strongly react to individual information, listings, community activity or changes in moods around AI agents.
Freys’ greatest potential lies in the experimental nature of the project. The biggest risk is the same: the experiment may interest the market for a short time, but it does not have to translate into permanent use of the token.
7. AIOZ Network (AIOZ), AI, streaming and storage in one network
This is not a pure AI agents project or a pure GPU cloud. AIOZ is trying to build a broader infrastructure for Web3 and AI applications. Thanks to this, he can use several narratives at the same time, but it is more difficult to clearly assess which segment will be the most important for him.
AIOZ’s capitalization is currently approximately EUR 87 million according to CoinGecko.
The greatest advantage of AIOZ is the diversification of applications. The biggest risk may be distraction. Infrastructure projects must clearly show where real demand arises and how it translates into the value of the network.
What to look for when evaluating AI tokens?
When analyzing small and medium-sized AI tokens, narrative alone is not enough. There are a few things worth checking.
- First, market capitalization. This is the value of the tokens currently in circulation. Low capitalization may mean greater growth potential, but also a greater risk of sudden declines.
- Secondly, FDV, i.e. fully diluted valuation. This is a hypothetical valuation of the project if all tokens were already in circulation. If the FDV is much higher than the current capitalization, the market must absorb new AI tokens in the future, often from unlocks for the team, investors or the ecosystem.
- Third, real use. In AI x crypto, data revenues, the number of active users, paid model queries, GPU usage, agent activity and the number of applications built on a given protocol are particularly important.
- Fourth, liquidity. The token may look attractive on the chart, but if volume is low, larger trades can move the price significantly. This is a particularly important problem for small projects.
The AI sector in crypto has a strong narrative because it touches on a real technological trend. However, this does not mean that every AI token will be a long-term winner. The market will quickly distinguish projects that have users and revenue from those that have mainly marketing.
This article is for informational and educational purposes. It does not constitute investment advice, a recommendation to buy or sell any digital asset. The cryptocurrency market is volatile, and investing in small- and mid-cap tokens carries a high risk of losing capital. Before making financial decisions, it is worth verifying the data yourself, checking the project’s tokenomics and consulting a licensed advisor.
