Original | Odaily ([@OdailyChina](https://x.com/OdailyChina))
Author | Azuma ([@azuma_eth](https://x.com/azuma_eth))
The AI Agent sector continued to accelerate its pullback today, with the exception of some meme tokens related to the DeepSeek concept. Top Framework/Launchpad/Agent tokens such as VIRTUAL, AI16Z, AIXBT, ARC, as well as other large and small tokens, recorded double-digit declines.
Odaily Note: See《Deepseek Tops AppStore, What Are the Related AI Concept Tokens?》.
The decline is certainly related to the overall market weakness, but looking at the market discussion, some investors also attribute the collective decline of this round of AI Agents to the explosion of DeepSeek, believing that DeepSeek as a new generation of more economical LLM will disrupt or even reshuffle the current Crypto AI landscape.
I personally do not support this view.
The core point of the above doubts is actually - Will the iterative development of LLM disrupt the existing Agents? Putting aside the current reasoning quality of DeepSeek whether it has surpassed ChatGPT and Claude, and assuming that DeepSeek has more promising iterative potential from the perspective of economic sustainability, will the iterated DeepSeek recreate a new batch of Agents, making the existing ones "yesterday's flowers"?
To answer this question, we need to first understand the relationship between LLM and Agent. Let's try to answer this question using DeepSeek itself and another "national treasure" Kimi (to be honest, Kimi's quality seems better in this regard).
DeepSeek:
The relationship between AI Agent and AI LMM is that LMM can be one of the core components of Agent, especially in handling natural language understanding and generation tasks. AI Agent is an intelligent system that can autonomously execute tasks and interact with the environment, while LMM provides Agent with powerful language processing capabilities, enabling it to better understand and generate human language, and thus play a role in scenarios such as dialogue, recommendation, and analysis. In short, LMM is an important technical support for AI Agent to achieve intelligent language interaction.
Agents usually rely on LLM to understand and generate natural language. LLM provides powerful language processing capabilities, enabling Agents to interact with users in natural language, understand user needs, and generate corresponding responses.
Agents also do not rely solely on LLM's language processing capabilities, but also combine decision-making, problem-solving, interaction with external environments, and action execution capabilities. LLM provides Agents with the foundation for understanding and generating language, while Agents plan and execute tasks based on this foundation.
Agents can also combine other tools and APIs to expand their functionality. For example, Agents can call search engines, database query tools, file read/write tools, etc. to complete more complex tasks. LLM provides the ability to understand and generate language in this process, enabling AI Agents to better interact with these tools.
In summary, the operation of Agents requires the support of LLM, but their expression and interaction quality do not depend entirely on LLM. In fact, what determines the obvious differences between different Agents is the capabilities other than LLM.
For example, the reason why aixbt can "crush" other similar Agents in terms of output is essentially because it has done better in prompt design, post-processing mechanisms, context management, fine-tuning strategies, randomness control, external tool integration, and user feedback mechanisms - you can call it first-mover advantage or moat, and this is the current advantage of aixbt.
After understanding this logical relationship, let's now answer the core question in the previous text: "Will the iterative development of LLM disrupt the existing Agents?"
The answer is negative, because Agents can easily inherit the capabilities of the new generation of LLM through API integration to achieve evolution, thereby improving interaction quality, improving efficiency, and expanding application scenarios... Especially considering that DeepSeek itself provides API formats compatible with OpenAI.
In fact, Agents who react quickly have already completed the integration of DeepSeek. Shaw, the founder of ai16z, mentioned this morning that the AI Agent construction framework Eliza developed by the ai16z DAO has completed support for DeepSeek two weeks ago.
In the current trend, we can rationally assume that after Eliza of ai16z, other major frameworks and Agents will also quickly complete the integration of DeepSeek. In this way, even if there will be some impact from the new generation of DeepSeek Agents in the short term, in the long run, the competition between Agents will still depend on the external capabilities mentioned earlier, and the accumulated development results brought by the first-mover advantage will be highlighted again.
Finally, let's share some comments from big shots on DeepSeek to recharge the faith of the defenders of the AI Agent sector.
DeGods founder Frank said yesterday: "The idea that people have about this (DeepSeek iterating the old market) is wrong, the current AI projects will benefit from models like DeepSeek, they just need to replace the OpenAI API call with DeepSeek, and their output will be improved overnight. New models will not disrupt Agents, but will accelerate their development."
Trader Daniele, who focuses on the AI sector, said: "If you are selling AI tokens because DeepSeek has a low-cost and open-source model, then you need to know that DeepSeek is actually very helpful in extending AI applications to millions of users at a low threshold. This may be the best thing for the industry so far."
Shaw also released a long article this morning to respond to the impact of DeepSeek, and the first sentence of the opening reads: "Stronger models are always good for Agents. For years, various AI labs have been surpassing each other. Sometimes Google is ahead, sometimes it's OpenAI, sometimes it's Claude, and today it's DeepSeek..."
