The Evolution and Future Trends of On-Chain Liquidity

Concentrated Liquidity (CLMM) and Capital Efficiency Enhancement

Traditional AMM models allow liquidity to be evenly distributed across the entire price range, but in reality, most trades are concentrated within certain ranges, leaving large amounts of capital idle. Concentrated Liquidity Market Maker (CLMM) was created to address the issue of low capital utilization.

With the CLMM mechanism, LPs can independently choose the price range where their funds are active, concentrating liquidity in the most active trading zones and significantly improving capital efficiency. Protocols like Uniswap V3 are typical examples, allowing LPs to manage price ranges similarly to traditional market makers.

The core changes brought by CLMM include:

• Funds are no longer evenly distributed but focused on high-volume price ranges
• The same amount of capital can provide deeper market depth
• LPs have higher yield potential but need to actively manage positions
• Market liquidity structure becomes more dynamic

However, this model also raises the participation threshold, as LPs must understand price ranges, rebalancing strategies, and market volatility.

Liquidity Aggregators and Cross-Protocol Routing Mechanisms

With the rapid growth of DeFi protocols, liquidity is no longer concentrated on a single platform but is dispersed across multiple AMMs and order book protocols. Liquidity aggregators enable trades to find the best price path across multiple pools.

When a user initiates a trade, the aggregator automatically scans for liquidity across different protocols and intelligently splits orders based on slippage, fees, and price conditions. For example, a large trade might be divided across several pools to reduce overall costs.

From a user experience perspective, the advantages of aggregators include:

• Automatically finding the optimal execution path
• Reducing time spent manually comparing different DEXs
• Improving execution efficiency for large trades
• Lowering slippage caused by insufficient liquidity in a single pool

This cross-protocol routing mechanism gives the DeFi market an increasingly unified liquidity layer feel and brings users closer to seamless trading experiences.

Intent-Based Trading and RFQ Models

Traditional on-chain trading usually requires users to manually specify trading paths and parameters such as slippage limits, trading pairs, and execution methods. However, as DeFi grows more complex, a new trading model has emerged for intent-based trading.

In this model, users only need to express their desired outcome, such as swapping one asset for another at the best price, and the system automatically finds the execution path. This process is typically carried out by solvers or market makers who provide quotes or execution plans based on market conditions.

The RFQ (Request for Quote) model is another important trend. It allows users to request quotes directly from market makers and then choose the best offer. Compared to traditional AMMs, RFQ is better suited for large trades because prices can be customized based on trade size.

The emergence of intent-based trading means users are shifting from operating trading tools to expressing trading goals, while liquidity providers compete behind the scenes for execution opportunities.

The Development Path of Market-Making Protocolization (PMM / DPMM)

As the on-chain trading ecosystem matures, some protocols are embedding market-making strategies directly into smart contracts, realizing what is called market-making protocolization. Models such as PMM (Proactive Market Maker) and DPMM (Dynamic PMM) aim to make price curves more closely reflect real market demand.

Unlike traditional AMMs with fixed curves, these models can dynamically adjust quotes based on market data and external price sources. For instance, when market demand increases, the price curve can automatically become smoother to reduce slippage; when volatility rises, it can increase quote flexibility to lower risk.

Changes brought by this model include:

• Market-making strategies shift from manual decision-making to protocol rules
• Price mechanisms better reflect real market supply and demand
• LPs are no longer just passive capital providers, they participate in strategy design
• Market-making logic is gradually becoming composable DeFi modules

Market-making protocolization transforms liquidity from single capital pools into programmable financial components and lays the foundation for future automated market structures.

AI Market-Making and Automated On-Chain Liquidity Management

As the scale and complexity of on-chain data continue to grow, AI is increasingly used in liquidity management and market-making strategies. Compared with traditional algorithms, AI can analyze more complex data patterns, such as on-chain behavior, market sentiment, and cross-market price relationships, to dynamically adjust market-making strategies.

Applications of AI market-making include:

• Automatically adjusting liquidity ranges and rebalancing strategies
• Predicting changes in trading volume and deploying funds in advance
• Real-time monitoring of risk exposure and executing hedging operations
• Optimizing capital allocation across chains and protocols

In the future, on-chain liquidity management may become more like autonomous driving systems, LPs will no longer need frequent manual operations but will manage assets through intelligent agents. However, this also means markets will become more reliant on algorithms and models; if a strategy fails, risks may escalate rapidly.