Dune Research Confirms 85% of Concentrated DeFi Liquidity Unused... $150 Million in Annual Fee Losses

On July 16, 2026, a Dune Research report commissioned by 1inch was released, raising new questions about capital efficiency in the decentralized finance (DeFi) sector. The report numerically proved that the concentrated liquidity model, championed by next-generation automated market makers (AMMs), is actually experiencing significant capital idleness.

According to the findings, as much as 85% of concentrated liquidity is left unused in actual trades. This capital mismatch is analyzed as a structural problem arising from liquidity providers' inability to respond quickly to market price fluctuations.

Dune Research pointed out that while the concentrated liquidity model theoretically offers higher efficiency than traditional Constant Product Market Makers (CPMM), its potential is being eroded in the actual operational phase due to poor range management by liquidity providers (LPs). Once the price moves outside the set range, the capital immediately becomes inactive, which acts as a factor hindering the liquidity depth of the entire ecosystem.

The fact that 85% of concentrated liquidity is unused reveals a massive efficiency gap facing the DeFi ecosystem, which directly correlates to a decline in profitability for liquidity providers.

It is estimated that liquidity providers are missing out on approximately $150 million in annual fee revenue due to these inefficiencies. This is a clear opportunity cost resulting from LPs locking up capital in the wrong price ranges rather than active trading zones, and it is cited as a challenge that must be addressed to improve the revenue structure of the DeFi market.

Mechanisms of Concentrated Liquidity and Management Limitations

Concentrated liquidity is a method that maximizes efficiency by concentrating capital only within a specific price range, but maintaining this requires continuous monitoring and rebalancing. Most individual investors tend to 'set and forget,' causing capital to be pushed out of the price range when market prices change rapidly, resulting in lost revenue opportunities.

• Liquidity range deviation and capital inactivation due to market price fluctuations
• High gas costs and operational complexity arising from manual rebalancing processes
• Absence of professional data analysis tools for efficient range setting

In a commentary released on July 16, 2026, ARK Invest predicted that improving the efficiency of these DeFi rails will be a decisive variable for institutional investor adoption. ARK emphasized that traditional financial institutions will eventually choose open DeFi infrastructure over permissioned blockchains, and that technical progress to close the capital efficiency gap must precede this.

Large-scale capital movements to solve these problems have already begun in the market. On June 25, 2026, Spark signaled the beginning of automated liquidity management by migrating $150 million in stablecoin liquidity to Uniswap v4. The strategy is to preemptively address the idle capital issue pointed out by the Dune report by optimizing liquidity ranges in real-time through Uniswap v4's 'Hooks' feature.

Security and Operational Risks Following the Pursuit of Yield Optimization

However, active management and the introduction of complex automation tools to increase capital efficiency are accompanied by new security threats. The $18 million exploit of the Ostium protocol on July 16, 2026, clearly demonstrated the vulnerabilities that complex DeFi designs can cause. Furthermore, the recent $11.7 billion capital outflow caused by the Kelp DAO contagion serves as a warning of the systemic risks hidden behind the yield competition.

In conclusion, the 85% liquidity underutilization rate raised by Dune Research is a hurdle that the DeFi industry must overcome to enter a mature stage. In the future, the DeFi market is expected to move beyond simply depositing capital and become a technical competition ground for constantly redistributing idle capital into active trading ranges through automated optimization tools.