Author: Shu Fen
Many people have noted that this round of the cryptocurrency bull market lacks innovative narratives. In fact, AI is the most innovative and enduring core narrative. By December 2024, the highest-performing projects in the entire cryptocurrency market (off-chain) will come from the AI field—Virtuals, with returns as high as 23,079%.
"The next stop for large models," "completely changing human lifestyles," "opening a new era of industrial revolution"... People do not hesitate to describe the importance of AI Agents. Regarding the current development momentum and future trends of AI Agents, both retail investors and institutions are unprepared in investment cognition. Many people around me didn't pay attention before, but after the explosion, they felt the need to understand. However, now with an overwhelming amount of information flooding the market, it can be quite confusing, causing numerous troubles. Today, I will completely sort out AI Agents. This research report will help you quickly become literate, serving as a beginner's guide to AI Agents (cryptocurrency version)!
This article will be divided into three parts: first, the current state of AI Agent development; second, screening potential AI Agent projects and analysis; third, expectations for AI Agents in the Web3 field.
1. AI Agent Literacy Basics
AI Agents first appeared in front of the public in March 2023 when a project called AutoGPT framework was released, utilizing large language models to automatically break down a large task into smaller tasks and use tools to complete them.
AutoGPT shocked the world upon its release, as it was the first to extend language processing, content creation, logical reasoning, and perceptual action technologies into application scenarios. Following that, OpenAI launched a series of GPTs, prompting many tech companies to layout their application layers, platform layers, development layers, and operational layers to increase barriers in the next wave of ecology.
What exactly is an AI agent? How does it work? The term 'agent' means a proxy in Chinese, and AI agent simply refers to a proxy powered by AI technology. Unlike traditional software, it does not passively execute instructions. Its workflow is: perception module (input acquisition) → LLM (understanding, reasoning, and planning) → tool invocation (task execution) → feedback and optimization (validation and adjustment).
OpenAI defines 'AI Agent' as a system driven by LLM as its brain, capable of autonomous understanding, perception, planning, memory, and tool usage, which can automate complex tasks. Unlike traditional artificial intelligence, AI Agents have the ability to independently think and use tools to gradually achieve given goals.
To give an example for better understanding: for instance, if you have a cold and fever, traditional software would only tell you to see a doctor and pay more attention to protection. If it's an AI agent, it can detect your temperature and other health indicators, combine online information, help you find the right medication, request payment, and deliver it to your home, while also writing your leave request for the next day. This is the magic of AI agents.
2. Analyze AI Agent Projects
According to the latest data from Cookie.fun, as of December 30, the overall market cap of AI Agents has reached $11.68 billion, with a growth rate of nearly 39.1% over the past 7 days. This growth trend indicates the rapid growth of the AI Agent ecosystem in the cryptocurrency market.
In this wave of AI Agent enthusiasm, ai16z and Virtuals Protocol are undoubtedly the two most powerful representative projects. Specifically, the ecological market cap of Virtuals has reached $5.01 billion, while ai16z stands at $1.63 billion, together occupying 56.8% of the AI Agent market share and contributing over half of the share.
From the on-chain distribution perspective, Base and Solana are the two major battlegrounds for AI Agents. Among them, the market cap of AI Agents on Base is about $5.76 billion, while on Solana it is $5.47 billion, together contributing 96.1% of the overall market, with the market cap of other on-chain projects only totaling $920 million.
This also reflects that although the AI Agent ecosystem is rapidly rising in the cryptocurrency market, attracting a lot of attention and capital, the market structure still appears singular, mainly relying on the promotion of a few leading projects. The AI Agent ecosystem is still in its early stages.
Now, combined with the current market situation, let's analyze the hot ecological projects of AI Agents, with three main criteria: 1. Long-term value of the project 2. Authentic market demand 3. Cash flow income situation. If after reading, you wonder why the XXX project is not included, please reevaluate based on these three criteria. The opinions on the following projects are for reference only and do not constitute financial advice.
1.Virtuals
Virtuals actually went online last year. The Virtuals protocol mainly establishes co-management for AI agents in the gaming and entertainment fields, allowing AI agents to be tokenized and managed through blockchain, with features including autonomous planning, goal achievement, environmental interaction, and on-chain wallet control.
The greatest innovation and difference between Virtuals and other Web3 AI agent protocols is the simplification of the complexity of AI agents, providing a plug-and-play solution similar to Shopify, allowing non-AI professionals to easily deploy AI agents in gaming and consumer applications, and obtain protocol revenue through tokenization and decentralized co-management.
Additionally, Virtuals has generated an AI virtual idol—AI-dol band using its AI technology principles, amassing hundreds of thousands of followers on TikTok, which is quite interesting.
The total supply of Virtuals tokens is 1 billion, and all have been released. The token distribution is as follows: 60% held by the public, 5% for liquidity pools, and 35% for the ecosystem treasury, with a maximum annual release of 10% over three years. Currently, the ecosystem fund holds over 30% of the tokens.
From a long-term project value perspective, it addresses the pain points of current non-AI professionals who cannot participate in the AI boom, has a certain user base, and its token economy is relatively open and transparent, with excellent marketing. From a market cap perspective, as a leader in the AI Agent ecosystem, this recent surge has been nearly unadjusted, so there is a high probability of a significant adjustment in the future. Therefore, the short-term risks for Virtuals are relatively high.
2.ai16z
Although ai16z has the same name as the well-known venture capital a16z, this project has no relation to A16Z and has not received any investment from A16Z. The only connection is the attention it received from A16Z founder Marc Andreessen.
The total token supply is 1.09 billion, and the project operates as a DAO. According to Shaw, the core influencer of the project, in the future, ai16z will launch several games based on the Eliza framework and will devote more energy to creating a practical AI Agent investment tool, the DeFi AI Agent. The founder stated that ai16z's goal is not to create an AI robot that mimics a16z but to defeat it in the investment field where it excels.
Let's talk about the AI16Z project, which focuses on investment models for AI agents. It seems that there is no difference from previous AI bots or telegram bots. Can AI really invest and make money? This raises a question mark. The most important core technology, Eliza OS, is just some simple development based on the capabilities of OpenAI. If OpenAI later opens its own AI agents, how will Shaw respond?
To summarize the AI16Z project, I believe it merely rode the coattails of AI16Z. Its long-term value lies in DeFi AI Agents, but this demand is a pseudo-demand, returning to the previous logic of AI bots. Its technological development capability relies on the open-source database of OpenAI, and its imagination is average.
3.SWARMS
Swarms is a multi-agent LLM framework for AI agents, providing a large number of clustering architectures and seamless third-party integration. Currently, it enables enterprises to easily build and manage collaborations among multiple AI agents and seamlessly cooperate under Swarms' scheduling to complete complex business tasks. Simply put, SWARMS targets enterprise B-end users, providing enterprise-level AI agent applications.
Its founder, Dev, is only 20-year-old Kye Gomez (source: internet), who publicly claims that OpenAI infringed on the team's intellectual property, stole our project name, and plagiarized code structure and methods. Subsequently, Gomez provided a more detailed explanation: Swarms is a multi-agent framework that has been running for nearly three years. So far, over 45 million agents are operating in production environments, serving the world's largest financial, insurance, and healthcare institutions.
After the Swarms token was issued on December 18, it quickly surged to a market cap peak of $74.2 million on the 21st. Unfortunately, the good times didn't last long, as the market cap plummeted like a roller coaster to a nadir of about $6 million. Subsequently, it fluctuated around $13 million until the 27th, when it began to rebound, rising from a low of $12 million to $30 million, and then surged close to $70 million, nearly breaking the previous high.
Compared to the fantastical castles in the air of the AI16Z system, if Swarms is indeed created by the 20-year-old AI genius Kye Gomez as rumored, then there is no doubt that Swarms has a strong technological barrier. Its official website has already provided efficient solutions to numerous enterprises, demonstrating its strength.
As an open-source project, Swarms has sparked enthusiastic attention in the developer community, with GitHub stars surpassing 2.1K, gaining the wisdom and support of numerous developers. Thus, everything accumulated by Swarms confirms the maturity and innovation of the technology. Swarms has stronger technical capabilities, and the market demand is robust (enterprise-level), making it stand out in this competition of AI agents.
4.GRIFFAIN
Griffain is an AI agent engine based on Solana, similar to Copilot and Perplexity. It is one of the closest projects to the Agentic APP. The ultimate form of search engines in the AI era should be that users directly express needs, and AI provides results or solutions directly, rather than just providing web links. One of the catalysts for this project is its open access mechanism. As a leading agent engine, Griffain undoubtedly attracts significant market attention.
Currently, Solana is the blockchain with the most AI agents. In October, Goat, as an AI agent, conducted financing for humans through pumpfun. From a certain perspective, this is an AI singularity because the excellent liquidity of the entire chain and the well-established AI agent developer community make it not an exaggeration to call Solana the most imaginative nurturing ground for AI Agents.
The most important thing Sol has done is to revitalize the ecosystem with Griffain. To truly bring about an 'Agentic app szn', it requires not only AI but also infrastructure channels. Although GriffAIn has not yet clarified the specific application scenarios of its token, in the future, GriffAIn will connect the demand side with the Solana ecosystem, and as long as it exists within the scope of Solana's existing technological framework, it can meet basic requirements, whether in Pumpfun targeting specific qualifying tokens or creating tokens. This vision has gained Toly's recognition and adds considerable imagination to its GriffAIn prospects.
5.AIXBT
Aixbt is one of the agents created based on the Base platform's Virtuals. It monitors Crypto Twitter and market trends through intelligent analysis tools, providing users with valuable market insights. Some analysis content will be shared on Twitter, while the rest is accessible only to token holders, who can communicate directly with the agent through their exclusive terminal.
Aixbt's analysis has a certain accuracy in predicting price trends, demonstrating how AI interprets blockchain data and helps traders make wiser decisions across multiple platforms and fields.
I went to take a look at the related content published by Aixbt, and my intuitive feeling is that the content is very rich, covering almost all tracks, and easily accessing various data. Additionally, there may be some potential investment opportunities in the short-term cryptocurrency splits; for example, finding that vapor on hype is relatively undervalued compared to similar AI launchpads. Data shows that out of the 210 tokens recommended, 183 achieved profit after being recommended by aixbt, with a profit ratio of up to 83%.
However, there are some shortcomings, such as being unable to fully split complex items. The analysis and data are still shallow and cannot indicate investment opportunity risks. However, I think it is much stronger than some current cryptocurrency KOLs.
From the perspective of long-term project value, Aixbt has segmented market demand, and users are motivated to hold tokens to unlock more information data and price analysis. As Aixbt continues to evolve through data feeding, I believe Aixbt will be the absolute king of market prediction AI agents.
In summary, I have analyzed five highly popular AI Agents in the current market. Based on the aforementioned three points, I believe the ranking of these five projects, in terms of market cap potential from high to low, is: SWARMS, GRIFFAIN, Virtuals, AIXBT, Ai16z.
3. Future Development Trends of AI Agents in the Web3 Field
Regarding the applications of AI Agents in the Web3 field, there are currently several noteworthy directions that also represent future trends. One is privacy security, where AI must be designed from the outset with respect and protection for users and society as fundamental principles. However, as AI becomes more aware of us, privacy will become increasingly blurred and fragile. Every interaction with smart devices and every input of personal information will serve as food for AI evolution.
Privacy issues are crucial because privacy concerns are inextricably linked to security issues. Systems that store and process personal data, once targeted by hackers, can lead to information leaks, identity theft, and asset loss. So is there an environment that can leverage AI's powerful capabilities while protecting personal privacy? Clearly, in the Web3 field, compared to traditional methods, it can provide users with higher levels of data protection, perfectly balancing AI development capabilities and privacy protection.
Therefore, we see that many large model data storage has begun to attempt on the blockchain. The perfect AI soil of Web3 has attracted many AI developers to ensure data security and privacy in specific industries with high privacy requirements, such as healthcare and finance, through blockchain technology.
Another important direction is computing power and data. AI Agents, especially Multi-Agent collaboration, face cost issues in development, training, and operation. For enterprises, training an AI Agent requires massive computing power, often hundreds or even thousands of high-performance GPUs or specialized hardware like TPUs. The cost of acquiring and using these computing resources is already high. For example, Stanford's virtual town includes 25 agents for multi-agent research. However, after the town framework was open-sourced, testing one agent costs about $20,000 a day in data sources.
In Web3, it is possible to reallocate idle computing power or personal datasets through reasonable token economics and user incentive programs, further reducing computing and data costs, and allowing more individual users to participate in the construction of the AI industry. For example, some data platforms allow users to monetize their own data, providing low-cost data sources for AI Agents.
Finally, I believe AI Agents can serve as a new infrastructure for Web3, deeply integrating with other core elements to spawn entirely new application models, rather than simply being AI Memecoins. Currently, in the Web3 field, AI Agents can help users lower barriers and enhance experiences, even if it's just simplifying parts of the asset issuance process, it is meaningful.
However, from a macro Web3 perspective, AI Agents, as off-chain products, currently serve only as tools for assisting smart contracts, so there is no need to overhype their capabilities. Due to the lack of significant wealth effect narratives apart from MeMe in the second half of this year, it is normal for AI Agents to gain popularity around MeMe.
However, relying solely on MeMe cannot sustain long-term value. Therefore, if AI Agents can bring more innovative gameplay to the trading process and provide tangible ground value, they may develop into a common infra tool.
