Author: Azuma, Odaily
The AI Agent sector continued to accelerate its pullback today, with the majority of leading Framework/Launchpad/Agent tokens such as VIRTUAL, AI16Z, AIXBT, ARC, and other large and small tokens recording double-digit declines, apart from some meme concepts related to DeepSeek.
While the decline is certainly related to the overall market weakness, a review of market discussions reveals that some investors attribute the collective decline of the AI Agent sector to the explosive popularity of DeepSeek, believing that DeepSeek, as a new generation of more economical LLMs, will disrupt or even reshuffle the current Crypto AI landscape.
I do not personally support this view.
The core question underlying the above doubts is - Will the iterative development of LLMs disrupt the existing Agents? Putting aside the debate over whether DeepSeek's current reasoning quality has surpassed ChatGPT and Claude, and assuming that DeepSeek has more promising iterative potential from an economic sustainability perspective, the question is whether the iterated DeepSeek will recreate a new batch of Agents, making the existing Agents "yesterday's flowers"?
To answer this question, we need to first understand the relationship between LLMs and Agents. Let's try to answer this question using DeepSeek itself and another "national treasure" Kimi.
DeepSeek:
The relationship between AI Agents and AI LLMs is that LLMs can serve as one of the core components of Agents, especially in handling natural language understanding and generation tasks. AI Agents are intelligent systems that can autonomously execute tasks and interact with the environment, while LLMs provide Agents with powerful language processing capabilities, enabling them to better understand and generate human language, and thus play a role in scenarios such as dialogue, recommendation, and analysis. In short, LLMs are an important technical support for AI Agents to achieve intelligent language interaction.
Kimi:
Agents typically rely on LLMs to understand and generate natural language. LLMs provide powerful language processing capabilities, enabling Agents to engage in natural language interactions with users, understand user needs, and generate appropriate responses.
Agents also do not solely rely on the language processing capabilities of LLMs, but also combine decision-making, problem-solving, interaction with external environments, and action execution capabilities. LLMs provide 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. LLMs in this process provide the ability to understand and generate language, enabling AI Agents to better interact with these tools.
In summary, the operation of Agents requires the support of LLMs, but their expression and interaction quality do not depend entirely on LLMs. In fact, it is the capabilities outside of LLMs that determine the significant differences between different Agents.
For example, the reason why aixbt can "overwhelm" other similar Agents in terms of output is that 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 a moat, and this is the current advantage of aixbt.
After understanding this logical relationship, let's now answer the core question raised earlier: "Will the iterative development of LLMs disrupt the existing Agents?"
The answer is negative, because Agents can easily integrate the capabilities of the new generation of LLMs through API integration to achieve evolution, thereby improving interaction quality, enhancing efficiency, and expanding application scenarios... especially considering that DeepSeek itself provides API formats compatible with OpenAI.
In fact, Agents that react quickly have already completed the integration of DeepSeek. The founder of ai16z, Shaw, stated 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 other major frameworks and Agents will also quickly complete the integration of DeepSeek after ai16z's Eliza. In this case, 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 achievements brought by the first-mover advantage will be highlighted again.
Finally, let's share some comments from industry leaders on DeepSeek to recharge the faith of those who are defending the AI Agent sector.
DeGods founder Frank said yesterday: "The idea that people have about this (DeepSeek disrupting the existing market) is wrong, the current AI projects will benefit from models like DeepSeek, they just need to replace the OpenAI API calls with DeepSeek, and their output will improve 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 the DeepSeek model is low-cost and open-source, then you need to know that DeepSeek is actually very helpful in extending AI applications to millions of users at a low barrier to entry. This may be the best thing for the industry so far."
Shaw also published a long article this morning responding to the impact of DeepSeek, with the first sentence as follows: "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..."
