Can AI Agents Become the Lifeline for Web3+AI?

The AI Agent project demonstrates strong market competitiveness in the Web3+AI sector. Currently, the number of AI Agent projects in Web3 is relatively small, accounting for 8%, but their market capitalization in the AI sector reaches as high as 23%. It is expected that with the maturity of technology and increasing market recognition, multiple projects with valuations exceeding 1 billion dollars 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 building a complete ecosystem and designing a token economic model to promote decentralization and network effects.

AI Wave: Current Status of Project Emergence and Valuation Increase

Since the launch 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 release of ChatGPT, OpenAI quickly launched iterative versions such as GPT-4 and GP4-4o. This rapid development has made major traditional tech giants realize the importance of cutting-edge AI models like LLMs, prompting them to release their own AI models and applications. For instance, Google released the large language model PaLM2, Meta launched Llama3, while Chinese companies introduced large models such as Wenxin Yiyan and Zhipu Qingyan. It is evident that the AI field has become a battleground for major players.

The competition among major tech giants has not only driven the development of commercial applications, but our investigation into open-source AI research reveals that the 2024 AI Index report shows the number of AI-related projects on GitHub surged from 845 in 2011 to approximately 1.8 million in 2023. Notably, after the release of GPT in 2023, the number of projects grew 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 and 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 soared to $24 billion, more than doubling year-on-year. Among them, Elon Musk's xAI raised $6 billion, with a valuation of $24 billion, making it 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 speed. From the intense competition among tech giants to the vigorous growth of open-source community projects, and the enthusiastic pursuit of AI concepts in the capital markets. Projects are emerging one after another, investment amounts are reaching new highs, and valuations are rising accordingly. Overall, the AI market is in a period of rapid growth, with large language models and retrieval-augmented generation technologies making significant progress in the field of language processing. Nevertheless, these models still face challenges in converting technological advantages into actual products, such as the uncertainty of model outputs, the hallucination risk of generating inaccurate information, and issues of model transparency. These problems become particularly important in application scenarios that require extremely high reliability.

In this context, we began research on AI Agents, as AI Agents emphasize the comprehensiveness of solving practical problems and interaction 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, which are gradually bridging the gap between AI technology and practical problem-solving. The evolution of AI technology is continuously reshaping the architecture of productivity, while Web3 technology is reconstructing the production relationships of the digital economy. When the three core elements of AI: data, models, and computing power, merge with the key concepts of Web3 such as decentralization, token economies, 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 immense potential for large-scale applications.

To this end, we began to delve into the diversified applications of AI Agents in Web3, from the infrastructure, middleware, application layer of Web3, to data and model markets, among other dimensions, aiming to identify and evaluate the most promising types of projects and application scenarios to gain a deeper understanding of the deep integration of AI and Web3.

Clarification of Concepts: Introduction and Classification Overview of AI Agents

Basic Introduction

Before introducing the AI Agent, in order to help readers better understand the difference between its 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 advice. Retrieval-augmented generation technology can offer richer and more specific destination content. The AI Agent is like JARVIS from the Iron Man movies; it can understand your needs and proactively search for flights and hotels based on a single sentence, execute booking operations, and add the itinerary to your calendar.

The current industry definition of an AI Agent is generally regarded as an intelligent system capable of perceiving the environment and taking corresponding actions. It acquires environmental information through sensors, processes it, and then influences 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, all of which can be regarded as 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 environment.

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

Category Overview

Currently, the AI Agent market has not yet formed a unified classification standard. We have labeled 204 AI Agent projects in the Web2 + Web3 market separately and classified them into primary and secondary categories based on their significant labels. The primary categories are infrastructure, content generation, and user interaction, which are further subdivided based on their actual use cases.

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

• Development Tools: Provide developers with auxiliary tools and frameworks for building AI Agents.

• Data processing category: processing and analyzing data in different formats, mainly used to assist decision-making and provide sources for training.

• Model Training Category: Provides model training services for AI, including inference, model establishment, settings, etc.

• B-end services: Mainly aimed at corporate users, providing enterprise services, vertical solutions, and automation solutions.

• Platform Aggregation Type: A platform that integrates various AI Agent services and tools.

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

• Emotional companionship: AI Agent that provides 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 main Agent for more accurate information retrieval.

Content Generation: This type of project focuses on creating content, utilizing large model technology to generate various forms of content based on user instructions, 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 the infrastructure category, mainly consisting of 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 technological maturity. These projects are typically built on time-tested technologies and frameworks, which reduces development difficulty and risk. They serve as the "shovels" in the AI field, providing a solid foundation for the development and application of AI Agents.

Market demand push: 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 improve operational efficiency and reduce costs. At the same time, for developers, 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 noticed that the application scenarios of content generation AI in the B-end market are relatively limited. Due to the instability of its output, enterprises tend to 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 undergo adjustments, 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 AI-based conversation systems 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 boasting 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 well in the capital market, 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 and provide detailed answers from the internet. It ensures the reliability and accuracy of information by citing and referencing links, while also educating and guiding users to ask follow-up questions and search for keywords, thus meeting the diverse query needs of users.

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

Technical Analysis: The main model used by Perplexity is a fine-tuned GPT-3.5, along with 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 of various styles and themes in Midjourney through Prompts, covering a wide range of creative needs from realistic to abstract. The platform also offers image blending and editing, allowing users to overlay images and perform style transfers. The platform's real-time generation feature ensures that users receive results in seconds to a few.