#数字资产生态回暖 ## AI Agents in the Crypto Space: Is It Hype or Real Deal?

In the past two years, new "AI concept coins" have emerged every few months, promising to revolutionize trading with machine learning and to take over your wallet with intelligent algorithms. Now? Most early project tokens have plummeted—drop-offs exceeding 90% are common—and many investors are starting to reflect—Is this a true revolution or just another feast for the early birds?

**The bubble has indeed burst, but don't rush to conclusions**

Interestingly, while concept coins are collapsing, real money is actually pouring in. According to public data, the funding scale in this field surpassed $1.39 billion in 2025. Investors are clearly not fools—they are making choices, shifting from projects that could raise funds just by chatting to those with teams capable of solving real problems. This shift itself indicates the answer: the industry is moving toward authenticity.

**The reality is in front of us: three hurdles that can’t be bypassed**

To upgrade from "fancy concepts" to "practical tools," you must navigate these challenges.

The first is **accuracy**. General large models often hallucinate, and their data is always lagging. Ask it for the current long/short ratio of $BTC, and it might give you data from half an hour ago or just make up a number. But in crypto markets, information equals money—delaying by a minute could mean a loss. Therefore, these AI agents must access real-time prices, track on-chain data, and provide verifiable analysis. Simple idea? Actually extremely difficult.

The second is **whether they can really act**. Many projects boast about "auto-trading," but really they’re just chatting and offering suggestions. Actual execution capability—within your authorized scope, executing trades, adjusting DeFi positions, managing risks—that’s the real difference. The threshold jumps multiple levels.

The third is **sustainability**. How do these projects survive? Token issuance, fundraising, high-level investor exits? Those routines are now transparent. Reliable projects need to design genuine tokenomics—making tokens have real utility, not just speculative instruments. This requires products with genuine users, real demand, and actual revenue logic.

**The few that are truly working**

Although not many projects can be named, some are indeed serious.

Some focus on **deep research and analysis**—not just routine market summaries, but professional tools that trace information sources and provide verifiable analysis. This is very attractive to institutional investors and serious traders.

Others are working on **democratizing trading management**. Ordinary users entering crypto need to monitor markets, analyze opportunities, and manage risks—a complex process. If AI agents can understand user intent via natural language (e.g., "Monitor $ETH dropping below 2000") and then automatically execute, it greatly lowers the entry barrier.

Some are developing **on-chain automation strategies**. 24/7, adjusting DeFi strategies based on market conditions, harvesting arbitrage opportunities, optimizing liquidity allocation. These tasks are too complex for most people, but letting AI take over could be safer and more efficient.

Additionally, there are **decentralized AI collaboration networks**—using tokens to incentivize global participants to contribute computing power and intelligence, building a distributed AI service infrastructure. Combining AI with blockchain characteristics, this is an intriguing direction.

**Final thoughts: where to draw the line between pseudo-need and real application**

It’s simple—does it solve genuine pain points in the crypto ecosystem?

Projects that exist only for fundraising or concept hype die out as soon as the token hype fades—that’s pseudo-demand.

Real prospects lie in specialized, actionable, deeply integrated AI agents within specific products—they help you research markets smarter, trade more safely, or automate complex operations. The core is: **evolving from general chatbots to professional service tools**.

By 2025, the differentiation in this field will become more obvious. The bubble fragments will sink, and truly valuable applications will surface.