Author: Shu Fen
I've seen many people say that this round of the crypto market lacks innovative narratives, but AI is actually the most innovative and enduring core narrative. By December 2024, the projects with the highest returns 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 change the way humans live" "Open 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 their investment cognition. Many people around me didn't pay attention before, but after the explosion, they felt it necessary to understand it. However, with the current flood of various information in the market, it can be dizzying and bring a lot of confusion. Today, I will thoroughly sort out AI Agents; this research report will help you quickly get up to speed, serving as an entry guide to AI Agents (crypto version)!
This article will be divided into three parts: first, the current development status of AI Agents; second, selecting potential AI Agent projects and analyzing them; third, the application expectations of AI Agents in the Web3 field.
1. AI Agent Basic Literacy
AI Agents first came to the public's attention in March 2023 when a project called AutoGPT was released, utilizing large language models to automatically break down a big task into smaller tasks and using tools to complete them.
AutoGPT shocked the world upon its release because it was the first time language processing, content creation, logical reasoning capabilities, and perception-action technologies were expanded into application scenarios. Following that, OpenAI launched a series of GPTs, prompting many tech companies to begin laying out application, platform, development, and operational layers to increase barriers in the next wave of ecosystems.
What exactly is an AI agent? How does it work? The term 'agent' means representative in Chinese. An AI agent, simply put, is a representative powered by AI technology. Unlike traditional software that passively executes instructions, its workflow follows: 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 the brain, capable of independent understanding, perception, planning, memory, and using tools, able to automate complex tasks. Unlike traditional artificial intelligence, AI Agents have the ability to independently think and utilize tools to gradually accomplish given goals.
To give an example for better understanding: if you have a cold and fever, traditional software will only tell you to go see a doctor and take precautions. If it's an AI agent, it can check your temperature and other health indicators, combine online information, help you match the right medication, request payment, deliver it to your home, and write your leave request for the next day. This is the magic of AI agents.
2. AI Agent Project Analysis
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 seven 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 prominent representative projects. Specifically, Virtuals' ecosystem market cap 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 main battlegrounds for AI Agents. Among them, the market cap of AI Agents on Base is about $5.76 billion, while on Solana it is approximately $5.47 billion, together contributing 96.1% of the overall market, with other on-chain projects' total market cap only reaching $920 million.
This also reflects that although the AI Agent ecosystem is rapidly rising in the crypto market, attracting a lot of attention and capital, the market structure still appears singular, mainly relying on a few leading projects to drive it. The AI Agent ecosystem is still in its early stages.
Next, let's analyze the current hot ecosystem projects of AI Agents in conjunction with the current market situation. The criteria mainly include three points: 1. Long-term value of the project 2. Genuine market demand 3. Cash flow income situation. If after reading, you feel that a certain project is not included, please revisit this 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 live last year. The Virtuals protocol mainly establishes co-management for AI agents in the gaming and entertainment sectors. AI agents can be tokenized through blockchain and achieve co-management, with functionalities including self-planning, goal achievement, environmental interaction, and on-chain wallet control.
The greatest innovation and difference between Virtuals and other Web3 AI agent protocols is that it simplifies 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 income through tokenization and decentralized co-management.
Additionally, Virtuals has generated an AI virtual idol—AI-dol band, leveraging its AI technology principles, which has hundreds of thousands of fans on TikTok and is quite interesting.
The total supply of Virtuals tokens is 1 billion, and they have all been released. The token distribution is as follows: 60% held by the public, 5% for the liquidity pool, and 35% for the ecosystem treasury, with a maximum of 10% released each year over three years. Currently, the ecosystem fund holds over 30% of the tokens.
From the long-term value perspective of the project, it addresses the pain point that current non-AI professionals cannot participate in the AI boom, has a certain user base, and its token economics are relatively open and transparent, with excellent marketing. In terms of market value, as the leader of the AI Agent ecosystem, this recent surge has hardly seen any adjustments, so there is a high probability of a significant adjustment in the future, making the short-term risk for Virtuals relatively high.
2.ai16z
Although ai16z has the same name as the well-known venture capital firm a16z, this project has no connection to A16Z and has not received any investment from A16Z. The only connection is that it has attracted the attention of a16z founder Marc Andreessen.
The total token supply is 1.09 billion, and the project operates as a DAO. According to its key influencer Shaw, in the future, ai16z will launch several games based on the Eliza framework, and more effort will be directed towards creating a practical AI Agent investment tool, DeFi AI Agent. Its founder stated that ai16z's goal is not to create an AI robot that imitates a16z but to outperform it in its strongest investment field.
Let's talk about the AI16Z project, which focuses on investing models for AI agents. It seems to have no difference from previous AI bots and Telegram bots. Can AI really invest and make money? This raises a question mark. The most important core technology, Eliza OS, is based on the capabilities of OpenAI, which has done some simple development. If OpenAI releases its own AI agents, how should Shaw respond?
In summary, I believe the AI16Z project is merely riding on the coattails of AI16Z. Its long-term value lies in DeFi AI Agents, but this demand is also a pseudo-demand, reverting back to the logic of previous AI bots. Its technical development relies on OpenAI's open-source database, with limited imagination.
3.SWARMS
Swarms is a multi-agent LLM framework for AI agents, providing a large number of cluster architectures and seamless third-party integrations. It currently enables enterprises to easily build and manage collaboration between multiple AI agents, and can seamlessly coordinate under the scheduling of Swarms to complete complex business tasks. In simple terms, the users of SWARMS are B-end enterprises, providing enterprise-level AI agent applications.
Its founder, Dev, is just 20-year-old Kye Gomez (source: internet), who publicly claimed that OpenAI infringed on the team's intellectual property, stole our project name, and copied the 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, more than 45 million agents are operating in production environments, serving the world's largest financial, insurance, and healthcare institutions.
After the Swarms token was launched on December 18, it quickly surged to its market cap peak of $74.2 million on the 21st. Unfortunately, this good scenario did not last long, as the market cap dropped like a roller coaster to a low of around $6 million. Subsequently, it oscillated around $13 million until the 27th, when it began to recover, pushing from a low of $12 million all the way up to $30 million, and then surged nearly threefold to close to $70 million, almost breaking the previous high.
Compared to the fantastical and lofty aspirations of the ai16z system, if Swarms is indeed created by the 20-year-old AI genius Kye Gomez as rumored, then Swarms undoubtedly has a strong technical barrier. Its official website has already provided efficient solutions for many enterprises, and its strength is evident.
As an open-source project, Swarms has sparked enthusiastic interest in the developer community, with GitHub stars exceeding 2.1K, gaining the wisdom and support of numerous developers. Therefore, all that Swarms has accumulated confirms the maturity and innovation of technology. Swarms has stronger technical capabilities and robust market demand (enterprise-level), making it likely to stand out in the AI agent competition.
4.GRIFFAIN
Griffain is an AI agent engine based on Solana, similar to Copilot and Perplexity. It is one of the closest projects to an Agentic APP. The final form of the search engine in the AI era should be that users directly express their needs, and AI directly provides results or solutions, rather than just offering web links. One of the catalysts for this project is its open access mechanism. Additionally, as a leading agent engine, Griffain undoubtedly attracts a lot of market attention.
Currently, Solana has the most AI agent blockchains. In October, Goat, as an AI agent, raised funds from humans through pumpfun. From a certain perspective, this is an AI singularity, because the excellent liquidity and complete AI agent developer community of the entire chain make it not an exaggeration to call Solana the most imaginative breeding ground for AI Agents.
The most important thing that Sol does is to revitalize the ecosystem with Griffain. Because to bring about a genuine 'Agentic app szn', it requires not only AI but also infrastructure channels. Although GriffAIn has not yet clarified the specific application scenarios for its token, in the future, GriffAIn will connect demand with the Solana ecosystem, and as long as it exists within the scope of Solana's existing technology framework, it can meet the requirements, whether it is targeting certain qualifying tokens in Pumpfun or creating tokens. This vision has been recognized by Toly, adding a lot of imagination to its GriffAIn prospects.
5.AIXBT
Aixbt is one of the agents created based on the Virtuals of the Base platform. It monitors Crypto Twitter and market trends using smart analytical tools to provide users with valuable market insights. Some analysis content will be shared on Twitter, while the rest is only accessible to token holders, who can directly interact 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 check out the 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 potential investment opportunities in the short-term cryptocurrency breakdown; for example, discovering that vapor on hype is undervalued compared to similar AI launchpads. Data shows that among the 210 tokens recommended, 183 achieved profitability after being recommended by aixbt, with a profitability rate of 83%.
However, there are also some shortcomings, such as the inability to completely break down complex items, with analysis and data still at a superficial level, failing to indicate the risks of investment opportunities. However, I believe it is much stronger than some current crypto KOLs.
From the perspective of long-term project value, Aixbt addresses the segmented demand, and users are motivated to hold tokens to unlock more information and price analysis. As Aixbt continues to evolve through data feeding, I believe Aixbt will be the absolute leader in market prediction AI agents.
In summary, I have analyzed five currently popular AI Agents in the market. According to the three points mentioned earlier, I rank these five projects based on their market cap's potential from high to low as follows: SWARMS, GRIFFAIN, Virtuals, AIXBT, Ai16z.
3. Future Development Trends of AI Agents in the Web3 Field
For the application of AI Agents in the Web3 field, there are several directions worth paying attention to, which also represent future trends. One is privacy and security; AI must be designed with respect and protection for users and society as fundamental principles from the outset. However, as AI becomes increasingly familiar with us, privacy will become more ambiguous and vulnerable. Every interaction with smart devices and every piece of personal information input will become food for AI evolution.
Privacy issues are crucial because privacy and security issues are closely intertwined. Systems that store and process personal data, once targeted by hackers, can lead to information leaks, identity theft, and asset loss. Is there an environment that can harness AI's powerful capabilities while protecting personal privacy? Clearly, in the Web3 realm, compared to traditional methods, it can offer users a higher level of data protection while perfectly balancing AI development capabilities and privacy protection.
Thus, we see that many large models are beginning to explore data storage on the blockchain. The perfect AI soil of Web3 has attracted many AI developers in specific industries with high privacy requirements, such as healthcare and finance, to use blockchain technology to ensure data security and privacy.
Another important direction is computing power and data. AI Agents, especially Multi-Agents, face cost issues in development, training, and operations. For enterprises, training AI Agents requires a significant amount of 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 instance, Stanford's virtual town includes 25 agents in multi-agent research. However, after the town framework became open-source, testing one agent costs $20,000 a day in data sources.
In Web3, it is possible to redistribute idle computing power or personal datasets through reasonable token economics and user incentive programs, further lowering computing and data costs and allowing more individual users to participate in building the AI industry. For example, some data platforms enable users to monetize their own data, providing a low-cost data source for AI Agents.
Finally, I believe that AI Agents can become the new infrastructure of Web3 in the future, deeply integrating with other core elements to give rise to entirely new application models, rather than merely being AI Memecoins. Currently, in the Web3 field, AI Agents can help users lower entry barriers and enhance experiences, even if it is just simplifying part of the asset issuance process, it is meaningful.
From a macro Web3 perspective, AI Agents, as off-chain products, currently only serve as tools for auxiliary smart contracts, so there is no need to overly boast about 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 thrive 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 landing value, they may develop into a universal infra tool.
