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A recent interesting phenomenon has emerged in the DeFi space: the top platforms Metis, Jupiter, and Uniswap all surpassed $100 million in fee revenue in 2025. What does this data reflect?
**Has the era of trading volume truly arrived?**
In the past, users mainly chose DeFi platforms based on APY returns. Now, what are they turning to? The answer is trading experience. Platforms that offer fast speeds, low slippage, and strong aggregation depth are capturing an increasingly larger share. Take Jupiter on the Solana chain as an example; by integrating multiple liquidity sources, it allows traders to execute large orders more conveniently, leading to rising fee income.
**A new dimension in liquidity competition**
Previously, L2s competed over TVL (Total Value Locked). Now? Leading projects are beginning to compete on trading throughput and optimized trading experiences. Those who can enable users to complete trades at lower costs and higher efficiency will benefit from fee distribution. This shift indicates that protocol design is also evolving — no longer just stacking liquidity pools, but building trading infrastructure.
**Risks to be cautious of**
It should be noted that one year's data can indicate a trend but cannot guarantee the future. Several variables may emerge in 2026: First, centralized exchanges are also optimizing their fee systems, which could intensify competition; second, if new L2 solutions achieve breakthroughs in trading speed, they could reshape the landscape; third, if market sentiment reverses, trading volume itself could face pressure.
**Key points for future focus**
It is recommended to closely monitor projects that are upgrading Layer 2 trading capabilities, optimizing aggregator depth, or innovating fee distribution mechanisms. These are the critical indicators of whether a project can maintain competitiveness in the next phase. Simply chasing hot trends often leads to becoming a target for 'fee harvesting' — the smart approach is to understand the direction of product iteration rather than just following past data.