AI Agent Ultimate Investment Research: Analysis of the Innovative Core and Future Trends Behind the Bull Market

Author: shu fen

Many people say that this round of the cryptocurrency bull market lacks innovative narratives, but in fact, AI is the most innovative and enduring core narrative. As of December 2024, the highest yielding projects in the entire cryptocurrency market (off-chain) come from the AI field—Virtuals, with returns as high as 23079%.

"The next stop for large models," "completely changing the way humans live," "opening a new industrial revolution era"…… People spare no effort in describing 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 understanding. Many people around me did not pay attention before, and only felt the need to understand after the explosion. However, the overwhelming amount of information in the market is dazzling and brings a lot of confusion. Today, I will completely sort out AI Agents; this research report will help you quickly get up to speed, serving as an entry guide to AI Agents (cryptocurrency version)!

This article will be divided into three parts: first, an overview of the current development status of AI Agents; second, screening potential AI Agent projects and analysis; third, expectations for the application of AI Agents in the Web3 field.

1. Basics of AI Agents

AI Agents first appeared in front of the world in March 2023, when a project called AutoGPT was released. The project utilizes large language models to automatically break down a large task into smaller tasks and complete them using tools.

AutoGPT shocked the world upon its release because it was the first time that language processing, content creation, logical reasoning capabilities, and perceptual action technology were extended into application scenarios. Soon after, OpenAI launched a series of GPTs, and many tech companies began to layout their application, platform, development, and operational layers based on their capabilities to increase barriers in the next wave of the ecosystem.

What exactly is an AI Agent? How does it work? The term "agent" means a representative in Chinese. Simply put, an AI Agent is a representative empowered by AI technology; it does not passively execute instructions like traditional software. Its workflow is: perception module (input acquisition) → LLM (understanding, reasoning, and planning) → tool invocation (task execution) → feedback and optimization (verification 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, and can automate the execution of complex tasks. Unlike traditional artificial intelligence, AI Agents have the ability to independently think and call tools to gradually achieve given goals.

To give an example for better understanding: if you have a cold and fever, traditional software would only tell you to go to the hospital and pay more attention to protection. If it were an AI Agent, it could detect your temperature and other health indicators, combine that with online information, help you match 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. Analysis of AI Agent Projects

According to the latest data from Cookie.fun, as of December 30, the overall market capitalization of AI Agents has reached $11.68 billion, with a nearly 39.1% increase 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, the market capitalization of Virtuals has reached $5.01 billion, while ai16z stands at $1.63 billion, together accounting for 56.8% of the AI Agent market share, contributing more than half of the total.

From the on-chain distribution perspective, Base and Solana are the two main battlegrounds for AI Agents. Among them, the market capitalization of AI Agents on Base is approximately $5.76 billion, while on Solana it is $5.47 billion, together contributing 96.1% of the overall market, with other on-chain projects accumulating only $920 million.

This also reflects that although the AI Agent ecosystem has rapidly risen in the cryptocurrency market, attracting significant attention and capital, the market structure remains singular, primarily relying on a few leading projects for momentum. The AI Agent ecosystem is still in its early budding stage.

Next, I will analyze the current hot AI Agent projects based on the current market situation, 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 a certain project is not included, please revisit these three criteria for a review. The following project opinions are for reference only and do not constitute financial advice.

1. Virtuals

Virtuals actually launched 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 co-managed through blockchain, with functionalities including autonomous planning, goal achievement, environmental interaction, and on-chain wallet control.

The biggest innovation and difference of Virtuals compared to other Web3 AI agent protocols is that it simplifies the complexity of AI agents, providing a plug-and-play solution similar to Shopify, enabling non-AI professionals to easily deploy AI agents in gaming and consumer applications, and earn protocol revenue through tokenization and decentralized co-management.

Additionally, Virtuals has generated AI virtual idols—AI-dol bands—using its AI technology principles, which have garnered hundreds of thousands of followers on TikTok, making it quite interesting.

The total supply of Virtuals tokens is 1 billion, which has already been fully 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, over 30% of the tokens are in the ecosystem fund.

From the perspective of long-term value, it addresses the pain point of non-AI professionals being unable to participate in the AI boom, has a certain user base, and its token economy is relatively open and transparent. The marketing is also very well done. In terms of market capitalization, as the leader of the AI Agent ecosystem, this surge has seen almost no adjustments, so there is a high probability of a significant adjustment in the future. Therefore, the short-term risk for Virtuals is relatively high.

2. ai16z

Although ai16z has a name similar to the well-known venture capital firm a16z, this project has no relation to A16Z and has not received investment from A16Z. The only connection is that it has gained 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 core influencer Shaw, ai16z will launch several games based on the Eliza framework in the future and will focus more on creating a practical AI Agent investment tool, DeFi AI Agent. The founder stated that ai16z's goal is not to create an AI robot that mimics a16z, but to outperform it in its area of expertise in investment.

In other words, the ai16z project focuses on investment model AI agents, which seem no different from previous AI bots or Telegram bots. Can AI really make money in investments? This raises a question mark. The core technology, Eliza OS, is merely based on the capabilities of OpenAI with some simple developments. If OpenAI opens its own AI agents in the future, how will Shaw respond?

In summary, I believe ai16z is merely riding on the coattails of AI16Z. Its long-term value lies in DeFi AI Agents, but this demand is a pseudo-demand, reverting back to the logic of previous AI bots. Its technical development capabilities rely on OpenAI's open-source database, and its imagination is average.

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 among multiple AI agents, and under the scheduling of Swarms, they can seamlessly cooperate to complete complex business tasks. In simple terms, SWARMS' users are B-end enterprises, providing enterprise-level AI agent applications.

Its founder, Dev, is a 20-year-old Kye Gomez (source: internet), who publicly claimed that OpenAI infringed on the team's intellectual property, stole their project name, and copied their 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 launched on December 18, it quickly peaked at a market capitalization of $74.2 million on the 21st. Unfortunately, the good times did not last long, as the market cap plummeted like a roller coaster to a low of about $6 million. It then oscillated around $13 million until the 27th, when it began to rebound, rising from a low of $12 million to $30 million, and then surged nearly threefold to close to $70 million, almost breaking the previous high.

Compared to the fantastical and unrealistic nature of the ai16z project, if Swarms is indeed created by the 20-year-old AI genius Kye Gomez, then undoubtedly Swarms has a strong technical barrier. Its official website has already provided efficient solutions for numerous enterprises, and its strength is evident.

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 many developers. Thus, everything accumulated by Swarms confirms the maturity and innovation of its technology. Swarms has stronger technical capabilities and robust market demand (enterprise-level), and it is likely to stand out in the competition of AI agents.

4. GRIFFAIN

Griffain is a Solana-based project—an artificial intelligence agent engine, similar to Copilot and Perplexity. It is one of the closest projects to an Agentic APP. The ultimate form of a search engine in the AI era should be that users directly state 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. As a leading agent engine, Griffain undoubtedly attracts a lot of market heat.

Currently, Solana is the blockchain with the most AI agents. In October, Goat, as an AI agent, raised funds from humans through pumpfun. From a certain perspective, this is an AI singularity, due to the excellent liquidity of the entire chain and the well-established AI agent developer community. It is not an exaggeration to call Solana the most imaginative breeding ground for AI Agents in the blockchain space.

The most important thing Solana has done is to revitalize the ecosystem with Griffain. Because to bring about a true "Agentic app szn," what is needed, besides AI, is the infrastructure channel. Although Griffain has not yet clarified the specific application scenarios for its tokens, in the future, Griffain will connect the demand side with the Solana ecosystem. As long as it exists within the existing technical framework of Solana, it can meet basic needs, whether it is targeting certain compliant tokens on Pumpfun or creating tokens. This vision has been recognized by Toly, adding a lot of imagination to Griffain's prospects.

5. AIXBT

Aixbt is one of the agents created based on the Virtuals platform on Base. 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 directly interact with the agent through their exclusive terminal.

Aixbt's analysis has a certain accuracy in predicting price trends, demonstrating how AI can analyze blockchain data and help traders make more informed decisions across multiple platforms and fields.

I checked the relevant content published by Aixbt, and my intuitive feeling is that the content is rich, covering almost all sectors, and it can easily handle various data. Additionally, there are potential investment opportunities in the short-term cryptocurrency splits; for example, it identifies that vapor on hype is relatively undervalued compared to similar AI launchpads. Data shows that among the 210 tokens recommended, 183 achieved profitability after being recommended by Aixbt, with a profit ratio of 83%.

However, there are also some shortcomings, such as the inability to fully break down complex items, and the analysis and data are still superficial, unable to indicate the risks of investment opportunities. Nevertheless, I believe it is much stronger than some current cryptocurrency KOLs.

From the perspective of long-term value, Aixbt has a demand in niche areas, 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 become 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 rank these five projects in terms of market capitalization potential from high to low as follows: SWARMS, GRIFFAIN, Virtuals, AIXBT, ai16z.

3. Future Development Trends of AI Agents in the Web3 Field

Regarding the application of AI Agents in the Web3 field, there are currently several directions worth paying attention to, which also represent future trends. One is privacy and security; AI should be designed from the outset with respect and protection for users and society as fundamental principles. However, as AI learns more about us, privacy becomes increasingly blurred and fragile. Every interaction with smart devices and every input of personal information becomes food for AI evolution.

The importance of privacy issues is closely linked to security issues. Systems that store and process personal data, once targeted by hackers, can lead to information leaks, identity theft, asset loss, and other problems. Is there an environment that can leverage AI's powerful capabilities while also protecting personal privacy? Clearly, in the Web3 field, compared to traditional methods, it can provide users with a higher level of data protection, perfectly balancing the contradiction between AI development capabilities and privacy protection.

Therefore, we see that many large models are beginning to experiment with data storage on the blockchain. The perfect AI soil of Web3 has also attracted many AI developers in specific industries with high privacy needs, such as healthcare and finance, to ensure data security and privacy 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 AI Agents requires a large 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 was open-sourced, testing one agent required $20,000 worth of data sources per day.

In Web3, it is possible to reduce computing power and data costs through reasonable token economics and user incentive schemes, allowing idle computing power or personal data sets to be redistributed, and enabling 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 that AI Agents can serve as new infrastructure for Web3 in the future, deeply integrating with other core elements to give rise to 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 is just simplifying part of the asset issuance process, it is still meaningful.

However, from a macro Web3 perspective, AI Agents, as off-chain products, currently only serve as auxiliary tools for smart contracts, so there is no need to overstate their capabilities. Due to the lack of significant wealth effect narratives in the second half of this year, apart from MeMe, 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 landing value, they may develop into a universal infra tool.