Can AI Agents Become the Lifeline for Web3 + AI?

AI Agent projects are mainly popular and mature in the enterprise service sector in Web2 entrepreneurship, while in the Web3 field, model training and platform aggregation projects have become mainstream due to their key role in building ecosystems.

Currently, there are not many AI Agent projects in Web3, accounting for 8%, but their market capitalization in the AI sector reaches as high as 23%, demonstrating strong market competitiveness. We expect that with the maturation of technology and an increase in market recognition, multiple projects with valuations exceeding $1 billion will emerge in the future.

For Web3 projects, introducing AI technology may become a strategic advantage for non-AI core application products. The integration method for AI Agent projects should focus on the construction of the entire ecosystem and the design of the token economic model to promote decentralization and network effects.

AI Wave: The Current Situation of Emerging Projects and Rising Valuations

Since the release of ChatGPT in November 2022, it has attracted over 100 million users in just two months. By May 2024, ChatGPT's monthly revenue reached an astonishing $20.3 million. After the launch of ChatGPT, OpenAI quickly released iterative versions such as GPT-4 and GP4-4o. In this rapid development, major traditional tech giants have recognized the importance of cutting-edge AI model applications like LLM and have launched their own AI models and applications. For example, Google released the large language model PaLM2, Meta launched Llama3, and Chinese companies introduced large models like Wenxin Yiyan and Zhipu Qingyan. Clearly, the AI field has become a battleground for competition.

The competition among major tech giants not only drives the development of commercial applications, but our investigation into open-source AI research shows that the AI Index report for 2024 reveals the number of AI-related projects on GitHub surged from 845 in 2011 to about 1.8 million in 2023. Notably, after the release of GPT in 2023, the number of projects increased by 59.3% year-on-year, reflecting the enthusiasm of the global developer community for AI research.

The enthusiasm for AI technology is directly reflected in the investment market, with the AI investment market showing strong growth, experiencing explosive growth in the second quarter of 2024. There were a total of 16 AI-related investments exceeding $150 million globally, which is double that of the first quarter. The total financing for AI startups has soared to $24 billion, more than doubling year-on-year. Among them, Musk's xAI raised $6 billion, with a valuation of $24 billion, becoming the second highest-valued AI startup after OpenAI.

The rapid development of AI technology is reshaping the landscape of the tech industry at an unprecedented pace. From the fierce competition among tech giants to the flourishing of open-source community projects, and the enthusiastic pursuit of AI concepts in the capital market, projects are emerging one after another, with investment amounts reaching new highs and valuations rising accordingly. Overall, the AI market is in a golden period of rapid growth, with significant advancements in large language models and retrieval-augmented generation technology in the field of language processing. Nevertheless, these models still face challenges in translating technological advantages into actual products, such as the uncertainty of model outputs, the risk of generating inaccurate information, and issues related to model transparency. These problems become particularly important in application scenarios that require high reliability.

In this context, we began to study AI Agents, as they emphasize the comprehensiveness of solving practical problems and interacting with the environment. This shift marks the evolution of AI technology from purely language models to intelligent systems that can truly understand, learn, and solve real-world problems. Therefore, we see hope in the development of AI Agents, as they gradually bridge the gap between AI technology and practical problem-solving. The evolution of AI technology continuously reshapes the architecture of productivity, while Web3 technology is reconstructing the production relations of the digital economy. When the three key elements of AI: data, models, and computing power, merge with the core concepts of Web3 such as decentralization, token economy, and smart contracts, we foresee the emergence of a series of innovative applications. In this promising intersection, we believe that AI Agents, with their ability to autonomously execute tasks, demonstrate great potential for large-scale applications.

To this end, we began to explore the diverse applications of AI Agents in Web3, from the infrastructure, middleware, and application layers of Web3, to data and model markets, aiming to identify and evaluate the most promising types of projects and application scenarios, in order to gain a deeper understanding of the profound integration of AI and Web3.

Concept Clarification: Introduction and Classification Overview of AI Agents

Basic Introduction

Before introducing AI Agents, in order to help readers better understand the distinction between their definition and the model itself, let's illustrate with a practical scenario: Suppose you are planning a trip. Traditional large language models provide destination information and travel suggestions. Retrieval-augmented generation technology can offer richer, more specific destination content. An AI Agent is like Jarvis from the Iron Man movies; it can understand your needs and actively search for flights and hotels based on a single sentence from you, execute booking operations, and add the itinerary to your calendar.

The current general definition of an AI Agent in the industry refers to an intelligent system that can perceive its environment and take corresponding actions. It acquires environmental information through sensors, processes it, and then affects the environment through actuators (Stuart Russell & Peter Norvig, 2020). We believe that an AI Agent is an assistant that combines LLM, RAG, memory, task planning, and tool usage capabilities. It can not only provide information but also plan, decompose tasks, and truly execute them.

According to this definition and characteristics, we can find that AI Agents have long been integrated into our lives, applied in different scenarios, such as AlphaGo, Siri, and Tesla's Level 5 and above autonomous driving, which can all be considered examples of AI Agents. The common trait of these systems is that they can perceive external user inputs and respond accordingly to influence the real-world environment.

Taking ChatGPT as an example for conceptual clarification, we should clearly point out that Transformer is the technical architecture that constitutes AI models, and GPT is a series of models developed based on this architecture, with GPT-1, GPT-4, and GPT-4o representing different versions of the model at various stages of development. ChatGPT, on the other hand, is an AI Agent evolved from the GPT model.

Classification Overview

Currently, the AI Agent market has not yet formed a unified classification standard. We categorized 204 AI Agent projects in the Web2 and Web3 markets by tagging them based on their significant labels, resulting in primary and secondary classifications. The primary classifications are infrastructure, content generation, and user interaction, which are further subdivided based on their actual use cases.

Infrastructure: This type focuses on building foundational content in the Agent field, including platforms, models, data, development tools, and more mature underlying application B-end services.

• Development tools: Provide developers with auxiliary tools and frameworks for building AI Agents.
• Data processing category: Handle and analyze data in different formats, primarily used to assist decision-making and provide sources for training.
• Model Training Category: Provides AI model training services, including inference, model establishment, settings, etc.
• B-end services: Mainly aimed at enterprise users, providing enterprise service types, vertical types, and automated solutions.
• Platform aggregation type: A platform that integrates various AI Agent services and tools.

Interactive type: Similar to content generation type, the difference lies in the continuous two-way interaction. Interactive Agents not only accept and understand user needs, but also provide feedback through technologies such as Natural Language Processing (NLP) to achieve two-way interaction with users.

• Emotional companionship: AI Agents that provide emotional support and companionship.
• GPT type: AI Agent based on the GPT (Generative Pre-trained Transformer) model.
• Search type: Focused on search functionality, providing a more accurate information retrieval-oriented Agent.

Content Generation: This type of project focuses on creating content by utilizing large model technology to generate various forms of content based on user instructions, which can be divided into four categories: text generation, image generation, video generation, and audio generation.

Analysis of the Current Development Status of Web2 AI Agents

According to our statistics, the development of AI Agents in the traditional Web2 internet shows a clear trend of sector concentration. Specifically, about two-thirds of the projects are concentrated in infrastructure, mainly in B-end services and development tools. We have also conducted some analysis on this phenomenon.

Impact of Technology Maturity: The dominance of infrastructure projects is primarily due to their technology maturity. These projects are often built on time-tested technologies and frameworks, thereby reducing development difficulty and risk. They serve as the "shovel" in the AI field, providing a solid foundation for the development and application of AI Agents.

Driving Market Demand: Another key factor is market demand. Compared to the consumer market, the demand for AI technology in the enterprise market is more urgent, especially in seeking solutions to enhance operational efficiency and reduce costs. At the same time, for developers, the cash flow from enterprises is relatively stable, which is beneficial for them to develop subsequent projects.

Limitations of application scenarios: At the same time, we notice that the application scenarios of content generation AI in the B-end market are relatively limited. Due to the instability of its output, companies prefer applications that can consistently improve productivity. This has resulted in a smaller proportion of content generation AI in the project library.

This trend reflects the practical considerations of technological maturity, market demand, and application scenarios. With the continuous advancement of AI technology and the further clarification of market demand, we anticipate that this pattern may be adjusted, but infrastructure will still be a solid foundation for the development of AI agents.

Analysis of Leading Web2 AI Agent Projects

We delve into some current AI Agent projects in the Web2 market and analyze them, taking Character AI, Perplexity AI, and Midjourney as examples.

Character AI:

Product Introduction: Character.AI provides an AI-based conversation system and virtual character creation tools. Its platform allows users to create, train, and interact with virtual characters that can engage in natural language conversations and perform specific tasks.

Data Analysis: Character.AI had 277 million visits in May, with the platform having over 3.5 million daily active users, most of whom are aged between 18 and 34, indicating a youthful user demographic. Character AI has performed excellently in the capital markets, completing a $150 million financing round, with a valuation reaching $1 billion, led by a16z.

Technical Analysis: Character AI has signed a non-exclusive licensing agreement with Google's parent company Alphabet to use its large language model, indicating that Character AI is adopting self-developed technology. It is worth mentioning that the company's founders, Noam Shazeer and Daniel De Freitas, were involved in the development of Google's conversational language model Llama.

Perplexity AI:

Product Introduction: Perplexity can crawl the internet and provide detailed answers. By citing and referencing links, it ensures the reliability and accuracy of the information, while also educating and guiding users to ask follow-up questions and search for keywords, meeting users' diverse query needs.

Data Analysis: Perplexity's monthly active users have reached 10 million, with an 8.6% increase in traffic to its mobile and desktop applications in February, attracting approximately 50 million users. In the capital markets, Perplexity AI recently announced it has raised $62.7 million in funding, achieving a valuation of $1.04 billion, led by Daniel Gross, with participants including Stan Druckenmiller and NVIDIA.

Technical Analysis: The main models used by Perplexity are fine-tuned GPT-3.5 and two large models fine-tuned based on open-source large models: pplx-7b-online and pplx-70b-online. The models are suitable for professional academic research and queries in vertical fields, ensuring the authenticity and reliability of the information.

Midjourney:

Product introduction: Users can create images in various styles and themes in Midjourney through Prompts, covering everything from realistic to