The "Christmas Tree" Revelation of AI Agents: The Development Logic Behind Market Fluctuations
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The "Christmas Tree" Revelation of AI Agents: The Development Logic Behind Market Fluctuations
In the long term, AI Agents have significant room for development, which depends on advancements in AI technology itself and the time and cost required for training within the Web3 domain.
Navigating Web3 tides with focused insightsAuthor: DeFi Teddy
AI Agent valuations have gone through a rollercoaster—what lies ahead? As a Web3 AI entrepreneur and investor, here are some personal thoughts:
1. Why the crash
The AI Agent crash had two triggers. One was Trump-themed meme coins siphoning off market liquidity; on the day $TRUMP launched, many sold their AI tokens to gamble on it. The more important factor was the release of low-cost DeepSeek, which reduced AI's reliance on computing power, dragging down NVIDIA and spreading negative sentiment to other AI companies' stock prices and Web3 AI Agent token values—even dealing a blow to U.S.-led technological innovation, given the strong correlation between crypto markets and U.S. equities.
In the long term, however, lower underlying compute costs greatly benefit AI Agent development. For example, developers previously had to limit API calls due to ChatGPT's high cost, but with DeepSeek, this constraint is significantly reduced.
2. Valuation logic for AI Agents
Current market cap valuation for AI Agents = (fundamentals + narrative) * market sentiment.
Fundamentals refer to a project’s current state—cash flow, active users, user engagement time, Twitter热度, etc.—while narrative refers to future potential. If fundamentals are weak but the story is compelling, the market may still assign value, just like investors buying unprofitable but promising stocks. Market sentiment swings widely: recent FOMO drove up valuations, while current FUD has severely compressed them.
3. Successful applications of AI Agent
Why focus on successful AI Agent applications rather than AI infrastructure? Because successful applications inherently validate infrastructure value. Without killer apps, the business model fails.
Currently, @AIxbt_Agent has demonstrated product-market fit by rapidly building an AI-powered crypto KOL outperforming humans. In mindshare within the AI space, AIxbt_Agent consistently ranks in the top 3.
Additionally, @_kAItoAI, though not an AI Agent, represents another successful Web3 AI application. Previously, kAIto struggled to find breakout use cases in crypto data analytics, but Yaps unlocked its product-market fit. Using LLMs, it identifies high-quality KOLs and content, and supports marketing for projects. Top-tier projects such as @StoryProtocol and @monad_xyz have partnered with KAIto. By driving both projects and users to engage with Yaps, KAIto emerges as the primary beneficiary.
4. Challenges facing AI Agents
The first challenge is market sentiment, which can recover over time. Second is liquidity—if the broader crypto market faces liquidity issues, AI Agents rooted in this ecosystem will inevitably suffer. Third, many AI Agents launched via Pump.fun are short-term schemes without long-term development plans, crowding out genuine builders and creating a "bad money drives out good" effect. Fourth, while AI excels in language tasks, it lacks proven capabilities in real trading decisions and may even cause investment errors due to hallucinations.
5. Outlook
In the short term, AI Agents may remain constrained by these challenges. But in the long run, they hold immense potential—dependent on advancements in AI technology and the time and cost invested in training within the Web3 domain. @AIxbt_Agent has already proven itself as a successful crypto KOL. The next phase is DeFAI, though we're still early. Recall that DeFi was first introduced in summer 2018, but didn't truly flourish until two years later during DeFi Summer—perhaps DeFAI will follow a similar cycle.
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@DeFiTeddy2020
