Ethereum Takes The Lead In DeFi Lending Revenue, Leaving Rivals Behind – See How

bitcoinistPublished on 2025-12-19Last updated on 2025-12-19

Abstract

Despite facing selling pressure, Ethereum has solidified its dominance in DeFi lending revenue, capturing 80-90% of all activity and fees. Data from Leon Waidmann shows Ethereum remains the core settlement layer for DeFi, significantly outpacing rivals like Base and Arbitrum. Aave drives over 50% of lending funds and 60% of active loans on Ethereum, generating $885 million in fees in 2025 alone. While Layer 2s improve user experience, the mainnet continues to be reinforced as the trusted hub for liquidity and credit markets. Additionally, active Ethereum addresses are nearing all-time highs, with 2.4 million weekly users, signaling strong retail and institutional engagement.

Ethereum’s price may be hampered by selling pressure, but the leading network continues to experience heavy utilization from developers and users. After robust interaction from the participants, the blockchain giant emerged once again as the leader in Decentralized Finance (DeFi) lending.

DeFi Lending Still Pays Best On The Ethereum Network

A recent report has underscored Ethereum’s growing dominance within the blockchain sector. The network is solidifying its position as the financial foundation for decentralized finance lending, and the data is starting to present a convincing picture.

A look at the data shared by Leon Waidmann, a market expert and the head of research at On-Chain Foundation, shows that ETH is now the revenue center of DeFi lending. This implies that most of the revenue flowed through the ETH ecosystem, outpacing other major chains like Base, Plasma, and Arbitrum.

From borrowing fees to interest paid by active users, the ETH network continues to be the key settlement layer where value is persistently created. ETH is at the center of the revenue outlines the network’s usage in addition to its ongoing dominance as the fundamental infrastructure driving DeFi’s most lucrative lending activity.

Source: Chart from Leon Waidmann on X

As seen on the chart, Ethereum mainnet steadily secured over 80% to 90% of all DeFi lending revenue and activity, reinforcing its increasing role in the financial landscape. Interestingly, this share has remained a dominant force even with the vigorous expansion of the Layer 2 and alt-Layer 1 chains.

Data shows that usage may be fragmented, but fees do not. Meanwhile, at the protocol layer, Waidmann highlighted that concentration is quite stronger. Amid this rising DeFi revenue lending, Aave is the core revenue engine on the Ethereum mainnet, attracting more than 50% of the total lending funds.

This part of the network was also responsible for over 60% of all active loans on ETH. In the end, the project generated approximately $885 million in fees in 2025 alone, reflecting the significant usage of the network.

While Ethereum mainnet secures balance sheets and profits, layer 2s are optimizing execution and User Experience (UX). Waidmann noted that where confidence and liquidity are greatest, DeFi credit markets converge. “Ethereum Mainnet is not being disrupted, but is being reinforced,” the expert added.

Active ETH Addresses Targeting Its Peak

Another instance of robust engagement across the Ethereum network is a spike in active wallet addresses. Joseph Young, a crypto enthusiast, previously highlighted that the active users on the network are drawing close to its all-time high. Such a rise in active addresses suggests a resurgence of interest and conviction among larger and retail investors.

At the time of the post, about 2.4 million wallet addresses were actively interacting with the network every week. This is an indication that tokenization, stablecoins, and privacy infrastructure are all converging on Ethereum. Currently, Young stated ETH is dominating the big three metas, while expressing his conviction in the network’s prospects.

ETH trading at $2,954 on the 1D chart | Source: ETHUSDT on Tradingview.com

Trending Cryptos

Related Questions

QAccording to the article, which blockchain network is the leader in DeFi lending revenue?

AEthereum is the leader in DeFi lending revenue, securing over 80% to 90% of all activity.

QWhat is the name of the expert and head of research at On-Chain Foundation cited in the report?

AThe expert cited is Leon Waidmann, the head of research at On-Chain Foundation.

QWhich protocol is highlighted as the core revenue engine on the Ethereum mainnet for DeFi lending?

AAave is the core revenue engine on the Ethereum mainnet, attracting more than 50% of the total lending funds.

QWhat does the significant increase in active Ethereum wallet addresses suggest, according to the article?

AThe spike in active wallet addresses suggests a resurgence of interest and conviction among both larger and retail investors.

QWhat role do Layer 2 networks play in relation to the Ethereum mainnet, as described in the article?

ALayer 2 networks are optimizing execution and User Experience (UX), while the Ethereum Mainnet secures balance sheets and profits, thereby being reinforced rather than disrupted.

Related Reads

Li Fei-Fei's Latest Long-Form Article: When Video Generation, Robotics, and NVIDIA All Call Themselves World Models, We Need a Taxonomy

In a new article, Dr. Fei-Fei Li addresses the widespread and often inconsistent use of the term "world model" in AI. She proposes a clear, functional taxonomy rooted in the classic Partially Observable Markov Decision Process (POMDP) loop (agent → action → state → observation → agent). According to this framework, current systems called "world models" are different projections of this loop, categorized by their primary output: 1. **Renderers**: Output observations (pixels). Their goal is visual fidelity for human consumption (e.g., video generation models like Sora). They are the most commercially mature but are limited by a focus on appearance over physical accuracy. 2. **Simulators**: Output states (geometric, physical, dynamic representations). They provide a structurally accurate world for both human professionals (e.g., architects) and computational agents (e.g., robots for training). Li argues simulators are the crucial, underappreciated bridge, as they can underpin both rendering and planning. 3. **Planners**: Output actions. Given an observation and a goal, they decide what an agent should do next (e.g., robotic action models). This area is highly promising but remains the least mature for real-world deployment. Li highlights a key trend: the boundaries between these three categories are beginning to blur, as they all rely on a shared underlying understanding of geometry, physics, and dynamics. The logical endpoint is a unified world foundation model capable of switching between rendering, simulation, and planning based on downstream needs. This convergence, she concludes, is central to advancing spatial intelligence—enabling machines not just to talk about the world, but to truly understand, imagine, and interact with it.

marsbit1h ago

Li Fei-Fei's Latest Long-Form Article: When Video Generation, Robotics, and NVIDIA All Call Themselves World Models, We Need a Taxonomy

marsbit1h ago

Forbes Feature: Stablecoin Cross-Border Payments Are Faster, But Not Yet Cheaper

A Forbes feature delves into the state of stablecoin-based cross-border payments, noting rapid growth but a key shortfall: while faster and more accessible, they are not yet cheaper. At a recent industry conference in Mexico City, optimism about technology, regulation, and volume was tempered by discussions with practitioners. The core issue is liquidity. Traditional FX brokers charge 60-70 basis points, and stablecoins promise to slash this to 2-5 basis points. However, this theoretical cost advantage cannot be realized until deep liquidity pools are established at scale, requiring significant institutional capital inflow. A major adoption barrier is trust. Businesses often rely on long-standing relationships with traditional brokers, valuing reliability over marginal cost savings. This shift will be gradual. Furthermore, successful companies in the space are not positioning themselves as replacements for legacy systems like SWIFT, but as complements. They leverage stablecoins for speed while using traditional rails for their standardization and reliability in ensuring accurate payment details—a critical factor for supplier payments to avoid customs issues. Companies like Caliza, experiencing high monthly growth, exemplify this hybrid approach. The industry anticipates consolidation, as long-term viability will depend on securing the essential trifecta: proper licensing, robust fiat on/off-ramps, and deep liquidity. Without these, firms risk being mere intermediaries rather than building sustainable businesses.

marsbit1h ago

Forbes Feature: Stablecoin Cross-Border Payments Are Faster, But Not Yet Cheaper

marsbit1h ago

Li Feifei's Latest Article: When Video Generation, Robotics, and NVIDIA All Claim to Have 'World Models,' We Need a Taxonomy

"World Model" has become a widely used yet ambiguous term in AI. Drawing from the classic POMDP framework (agent → action → state → observation), this article proposes a functional taxonomy to clarify the concept. It identifies three distinct types, categorized by their output in the perception-action loop: 1. **Renderers**: Output visual observations (pixels). These models, like advanced video generators, prioritize visual fidelity but often lack underlying physical accuracy. 2. **Simulators**: Output the state of the world (geometry, physics, dynamics). They provide a structurally accurate representation for professionals (e.g., architects) and serve as training environments for robots and AI agents. 3. **Planners**: Output actions. Given an observation and a goal, they determine what an agent should do next, closing the perception-action loop (e.g., vision-language-action models). While renderers are currently the most commercially mature and planners are the most aspirational, the article argues that **simulators are the crucial, underappreciated hub**. By working at the level of geometry and physics, a simulator can project upwards to create visuals for humans and downwards to predict action consequences for agents. The future lies in the convergence of these three functions. Emerging research and products, like World Labs' Marble model which outputs both visual splats and physical collision meshes, are beginning to blur these boundaries. The logical endpoint is a unified world foundation model capable of rendering, simulating, and planning based on a shared understanding of spatial and temporal structures—ultimately enabling machines to understand, imagine, and interact with the physical world.

链捕手2h ago

Li Feifei's Latest Article: When Video Generation, Robotics, and NVIDIA All Claim to Have 'World Models,' We Need a Taxonomy

链捕手2h ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片