ARPA surged 43% in the past 24 hours, with trading volume skyrocketing over 4000 times, and market capitalization breaking through the $91 million mark. Behind this rapid rise, is the market feverishly chasing a new technological story, or is it a typical capital pump and dump?

Let's first look at what the data shows. The driving force behind this rally is clear: ARPA's official announcement that in 2026, they will launch a Layer 1 blockchain focused on AI and DeFi privacy needs. This signal is very clever — it hits two of the hottest sectors right now (AI infrastructure + privacy computing) in one stroke, providing investors with a clear long-term imagination space.

The technical side is also cooperating. The price has strongly broken through a key level like the 200-day moving average, with a single-day trading volume of $378 million. This doesn't look like retail investor enthusiasm alone; it's genuine capital pushing the price. Meanwhile, the project team has locked about 4 million ARPA tokens through staking plans, effectively reducing the immediate circulating supply and creating technical conditions for this rally.

However, while everyone is celebrating, some risk signals have also appeared. The RSI indicator has soared to 74.28, firmly in the overbought zone, indicating market sentiment is a bit crowded. More realistically, even though this surge is impressive, ARPA's current price is still far below its historical high, with old trapped positions stacked above, which could become potential selling pressure.

The ARPA example actually reflects a common phenomenon in the current crypto market: small-cap projects that can articulate a clear technical story and precisely position in new sectors tend to attract rapid capital inflows when liquidity is ample. The question is — can this short-term narrative hype truly translate into long-term ecosystem development and real application value? That’s the real test.

While the market is paying a premium for an avant-garde "Privacy AI Chain" vision, there is actually another completely different path quietly advancing. Not addressing AI privacy issues, but solving a more fundamental problem: how to make knowledge flow freely like data across geographical and economic barriers. This path doesn't have complex privacy computing protocols as backing, but is supported by transparent action records; it doesn't compete for high ground like AI computing power, but instead solidifies the foundation of educational inclusiveness. Two paths, two logics, both awaiting the test of market and time.