A staggering 85% of capital deployed in concentrated-liquidity pools across decentralized exchanges went underutilized during the first half of 2026, according to onchain research conducted by Dune Analytics on behalf of 1inch, the DEX aggregator platform. Nearly a third of that capital — 29.5% — was positioned entirely outside the active trading range, rendering it effectively dormant. The findings raise pointed questions about capital efficiency in DeFi, a space that has long marketed concentrated liquidity as a superior alternative to traditional automated market maker models.
What the Dune Research Actually Shows About DeFi Capital Efficiency
Before diving into the strategic implications, it helps to frame the scale of the problem. Concentrated-liquidity mechanisms, popularized by Uniswap V3 and adopted widely since, allow liquidity providers to allocate capital within specific price bands rather than across an infinite range. In theory, this should dramatically improve capital utilization. In practice, the Dune data tells a different story.
- 85% average underutilization: Across major DEXs, the vast majority of concentrated-liquidity capital contributed no meaningful trading depth during H1 2026.
- 29.5% fully out of range: Close to a third of all liquidity sat in price bands that never intersected with market activity, generating zero fees for providers.
- The research was commissioned by 1inch, which aggregates liquidity across DEXs and has a direct commercial interest in surfacing inefficiencies across venues.
- Structural misalignment persists: Despite years of tooling improvements and active management vaults, liquidity providers still struggle to position capital where it’s actually needed.
- These figures suggest that the capital-efficiency promise of concentrated liquidity remains, at least on paper, largely unrealized at the aggregate level.
Why Liquidity Providers Keep Getting It Wrong
The gap between design intent and real-world outcomes in concentrated liquidity is not new. But the persistence of the problem through mid-2026 is telling. Several forces contribute.
Retail liquidity providers, who make up a significant share of pool participants, often set price ranges once and rarely revisit them. Market conditions shift; their positions don’t. Professional market makers, by contrast, actively rebalance — but they represent a smaller fraction of total liquidity in many pools. The result: a market caught between two frameworks, one passive and one active, with the passive side dragging down aggregate efficiency metrics.
Automated liquidity management protocols (think Arrakis, Gamma Strategies, and similar vault operators) were supposed to solve this. And for some pools, they have. Yet the Dune data implies that adoption of these tools hasn’t scaled enough to move the needle at the ecosystem level. Whether that’s a distribution problem, a trust problem, or simply a fee-structure problem remains an open question.
The Aggregator Angle
It’s worth noting that 1inch commissioned this research. That context matters. As a DEX aggregator, 1inch benefits when liquidity is fragmented or poorly allocated — its routing algorithms find the best execution across venues, and inefficiency on individual DEXs strengthens the case for aggregation. Still, the underlying data from Dune’s onchain analytics platform is publicly verifiable, which lends credibility to the findings even if the framing carries a commercial undertone.
Reframing Capital Efficiency Through an NLP Lens
From a neuro-linguistic programming perspective, the way DeFi protocols communicate capital efficiency shapes how liquidity providers behave. Language constructs like “concentrated liquidity” and “active range” carry implicit assumptions that providers understand dynamic market positioning. But understanding a concept intellectually and acting on it consistently are two very different cognitive processes.
NLP principles suggest that behavioral change requires more than information — it requires reframing. When a protocol tells users they can “earn more by concentrating capital,” the anchoring effect is powerful. Providers anchor to the upside without fully internalizing the management burden. The mental model they form is static: set a range, earn fees. The reality is kinetic: markets move, ranges expire, and unmanaged positions bleed value through impermanent loss and missed fee opportunities.
Effective protocol design could borrow from NLP’s concept of “chunking” — breaking complex decisions into smaller, more manageable steps. Rather than asking users to define a price range from scratch (a high-cognitive-load task), interfaces could present pre-calibrated options based on current volatility, historical range data, and the user’s stated risk tolerance. Some protocols have begun experimenting with this approach, but it’s far from standard.
There’s also the matter of feedback loops. NLP emphasizes that behavior is reinforced or extinguished by the quality and timing of feedback. Most DEX interfaces provide delayed, abstract performance data. A liquidity provider might not realize their position has drifted out of range for days. Faster, clearer signals — push notifications when a position exits the active band, real-time fee accrual dashboards — could meaningfully alter provider behavior over time. The technology exists. The implementation gap is what persists.
What Happens Next for DEX Liquidity Design
The Dune findings arrive at a moment when several Layer 1 and Layer 2 ecosystems are rethinking how liquidity is sourced and managed. Uniswap V4’s hook-based architecture, expected to gain traction through 2026, could enable more sophisticated automated rebalancing at the protocol level. Other projects are exploring hybrid models that blend concentrated and full-range liquidity to cushion against the exact inefficiencies this research highlights.
But protocol-level fixes only go so far if user behavior doesn’t evolve alongside them. And that behavioral evolution (a distinction regulators have been slow to engage with in DeFi governance discussions) depends on better tooling, better defaults, and arguably better incentive alignment between protocols and their liquidity providers.
One number stands out above all others: 29.5% of capital sitting completely outside any active range. That isn’t a rounding error or an edge case. It’s a structural feature of how concentrated liquidity operates when left unmanaged.
Frequently Asked Questions
What is concentrated liquidity in DeFi?
Concentrated liquidity allows providers to allocate capital within specific price ranges on a decentralized exchange, rather than spreading it across all possible prices. The goal is higher capital efficiency and better fee generation — though as the Dune research shows, execution often falls short of that promise.
Why does so much liquidity end up out of range?
Most retail providers set their price ranges when they deposit and don’t actively manage them. When the market moves, their capital drifts outside the active trading zone, earning nothing.
Does this mean concentrated liquidity is a failed model?
Not necessarily. The model works well for active, sophisticated participants. The issue is that passive providers, who make up a large share of total liquidity, aren’t equipped — or incentivized — to manage positions dynamically. The gap between design and behavior is the real problem.
