the current AI Agent has capital attention, sustained popularity in broad narratives, typical cases of wealth effects, and practical value for long-term construction.
Written by: Ice Frog
AI Agents have become popular in the crypto world, and they are on the verge of exploding in popularity. In the face of a massive wealth effect, they are being shaped into a new revolutionary variable, highly sought after and unmatched. However, the term "revolutionary" is a highly inflated concept in the crypto world, scattered across various white papers and social media. For seasoned investors, similar rhetoric has become hard to ignite passion. The rise and fall of countless tokens in the crypto world have proven that only a few new narratives can achieve a major bullish trend, while the vast majority are merely fleeting.
Thus, we must delve deeper into a classic question in the crypto space: Will this time be different? Everyone has different answers, but if we look back at the past, at least the fundamental conclusion to this question is unlikely to change significantly.
That is: the crypto world follows the rules of attention economy; how far a new narrative can go depends on how much user attention and network effects can be spread. Even Bitcoin cannot escape this fundamental law.
Naturally, to analyze this issue, we must start from the source and seek answers to the questions.
1. What is an AI Agent?
An AI Agent refers to an Artificial Intelligence Agent, and the concept that is currently relatively well-known and commonly used is: an AI Agent is an intelligent entity capable of perceiving the environment, making decisions, and executing actions. It is primarily based on LLM (Large Language Model); in other words, it is a functional carrier of large language models. The interesting point is that, conceptually, it is a specific application of LLM, but in terms of nomenclature, it is called an Agent, emphasizing the power and ability to make autonomous choices, actions, and decisions.
From the definition's distinction, although AI Agents and large models have a sequential relationship, the interaction between large models and humans requires prompts to provide specific tasks, which then yield answers. An AI Agent, on the other hand, is given a specific goal by humans, and the AI Agent will autonomously break down the execution steps based on your goal, generate prompts for itself, and thus achieve the completion of the goal.
From the perspective of human-AI collaboration, the Agent model is also a relatively advanced form of collaboration, comparable to early Siri—Microsoft Copilot—AI Agent. In this collaboration, we can basically clarify that AI Agents are essentially a digital mapping of human thinking and behavior patterns. Therefore, their structure mainly consists of a question-and-answer interface + fully automated workflow (perception, decision-making, action) + knowledge base (human hippocampus). AI completes the vast majority of the work, rather than merely assisting.
From a specific technical framework perspective, former OpenAI Chief Security Researcher Lilian Weng wrote a blog in June 2023 specifically explaining this, titled "LLM Powered Autonomous Agents," as follows:
In this article, Lilian Weng proposed that the foundational framework of AI Agents = LLM + Planning + Memory + Tool Usage, with the main role of large models being to undertake functions similar to human brain reasoning and planning.
Overall, an AI Agent is a standalone computational entity, fundamentally based on the reasoning and planning functions of large models, combined with perception of the external environment, tool usage, and actions, thereby achieving the role of AI as a human agent to complete a series of relatively complex tasks.
2. How is the AI Agent industry progressing?
Since 2023, AI Agents have entered the industry's vision, and discussions and advancements regarding Agents have accelerated. Most major companies view 2025 as a key year for the commercial explosion of AI Agents, and the industry is entering a phase of accelerated development.
From the overall perspective of the industry chain, the upstream of the entire industry chain is still dominated by computing power and hardware suppliers like NVIDIA, data suppliers, algorithm and large model development, etc. The midstream mainly consists of AI Agent integrators, while the downstream focuses on vertical applications for different industries or general Agents.
Currently, the AI Agent industry, aside from the existing upstream infrastructure, is mainly concentrated in the mid to downstream sectors, especially with downstream applications showing a flourishing state.
Additionally, from the perspective of downstream development, both C-end and B-end have different progress. The adoption of AI Agents on the C-end can significantly enhance user experience, while on the B-end, it can greatly reduce costs and increase efficiency.
From the actions of several major companies, AI Agents have begun to accelerate their launch in the second half of this year, with an overall acceleration expected next year.
Google, while releasing Gemini 2.0 this month, not only emphasized that the model is primarily for AI Agents but also launched three AI Agent products: Project Astra (general), Project Mariner (browser operation), and Jules (programming).
Microsoft launched 10 AI Agents on its Dynamics 365 platform at the end of October this year.
Amazon announced this month that it will open an artificial intelligence lab in San Francisco, focusing on the implementation of AI Agents.
OpenAI has begun a series of new product releases over 12 consecutive days this month, with Sam Altman himself declaring that next year will be the year AI Agents enter the mainstream, although OpenAI itself has not released related AI Agents, it has launched a series of tools to support the development of AI Agents.
Whether from major companies or the overall prosperity of the industry, the current AI Agents have indeed entered a period of acceleration, and their entry into Crypto is only a matter of time, but everything still belongs to the early stage.
3. Where does the combination of AI Agents and Crypto land?
Rewinding the clock a few months, it is likely that Andy Ayrey, who created the Truth of Terminal model, never imagined that an experimental AI Agent model would create a shocking wealth miracle in the crypto world. The derived GOATSE concept became a crypto hotspot, with the MEME token GOAT skyrocketing to 20 million dollars in half a day, approaching a market value of 300 million dollars within four days, and exceeding 1 billion dollars in market value within a month, a miraculous thousandfold increase reappearing. AI Agents entered the crypto world in a very crypto-like manner, igniting a tremendous wave of excitement.
Subsequently, Ai16Z (a venture capital fund driven by AI Agents) quickly gained popularity with the concept of "AI-driven DAO," and with the support of Marc Andreessen, the founder of the traditional A16z, it surged more than tenfold within a few days. Another AI project, ACT, launched on Binance, further pushed AI Agents to a new peak, not only achieving unprecedented market volume but also allowing a large number of crypto users to deeply understand the wealth effect of this track.
The first two chapters of this article have spent a considerable amount of space explaining what AI Agents are and the development of the AI Agent industry. The important significance lies in the fact that the source of AI does not come from the crypto world; more broadly, crypto is not the main battlefield for AI. However, if the development of AI Agents is poor in a broad sense, the attention on AI Agents will quickly dissipate, and the narrative of AI Agents will, like most narratives, become fleeting.
However, this time, within a visible range, it is indeed different. The main reason is:
The broad AI world has not entered a bubble denial phase. Whether it's NVIDIA, Microsoft, Google, or other major companies, they have all increased their capital expenditures for 2025 in their third-quarter reports this year. The top four companies alone will invest over $170 billion next year, with the sole goal being AI.
From the development of AI Agents, although there is currently no market-exploding phenomenon like ChatGPT, both the actions of major companies and the industry's development show a steep upward trend in momentum and financial bets. There is a certain probability that a market-exploding AI Agent will emerge in 2025.
From these two points, it can be seen that from a broad technological perspective, the topics and attention surrounding AI and AI Agents will inevitably remain among the hottest in the market. As mentioned at the beginning, attention in the crypto world is everything.
From the technical framework of AI Agent implementation, its combination with Crypto will give birth to a key turning point breakthrough similar to the birth of Ethereum smart contracts. This is not just a technical enhancement; it has the potential for a transformative leap in economic paradigms, as it will fundamentally change the creation model of attention economy.
The biggest challenge currently affecting mass adoption of blockchain may be its complex operations and entry barriers. Aside from compliance pain points related to fiat currency, such as deposits and withdrawals, the difficulty and complexity of on-chain operations and wallets are at least several times that of Web2. If a natural language model using AI Agents is adopted, a simple command can manage wallets, filter the best DeFi investments, execute cross-chain transactions, and automatically execute trading plans based on external market conditions. This not only greatly simplifies operational difficulty but also significantly reduces the learning costs for new users.
Moreover, whether it's creator economy, market sentiment monitoring, smart contract auditing, governance voting, AI autonomous DAOs, or even MEME issuance, Agents can participate. Under certain conditions, they may be more serious and fair than most people, and more capable of eliminating emotional influences.
From a narrative logic perspective, AI + Crypto brings: AI can make trustworthy blockchains smarter, and blockchain can make intelligent AI more trustworthy.
The characteristic of blockchain is the immutability of data, while a major drawback of AI lies in data quality. If the constructed AI Agent can train on-chain data and utilize its computing power, it is likely to change the current incentive model.
Looking further ahead, perhaps in the not-so-distant future, every crypto user will have a digital avatar that will help manage their token assets, social interactions, and more. Each project team will have several AI Agents to assist in operations, from asset issuance, marketing, code building, contract auditing, media operations, to even airdrop design and distribution, all of which can be accomplished with the help of AI.
These long-term changes will alter the creation model of attention, transforming larger communities and humanity into AI Agents.
Of course, the above ideas are merely long-term visions. Returning to reality, the current AI Agents in blockchain are still in an early, wild era. Aside from the explosive popularity of AI MEMEs, AI Agents remain in a phase where speculation outweighs building. Currently, there is no truly blockchain-based AI Agent framework in the market, and even the pioneering ELIZA is only at the conversational level and has not yet entered the core of the entire blockchain world.
From the three levels of AI Agents—perception, decision-making, and execution—there is a need to rebuild a more systematic and foundational infrastructure relying on decentralized features and smart contract characteristics. Not to mention the various tools, data privacy, transaction security, and so on. The encouraging thing is that whether it's the attention of top funds like A16z towards AI Agents, the mainstream narrative of AI Agents globally, or the astonishing wealth effects triggered by AI MEMEs, all provide a solid foundation for the development of AI Agents in the crypto world.
In the attention economy of crypto, the current AI Agents have capital attention, sustained popularity in broad narratives, typical cases of wealth effects, and practical value for long-term construction.
Perhaps we can be a bit bolder and say, this time, it really is different!
Passion!
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