Aave Founder: What is the Secret of the DeFi Lending Market?

marsbitPublished on 2026-02-10Last updated on 2026-02-10

Abstract

Chain-based lending, which began as an experimental concept around 2017, has evolved into a market exceeding $100 billion, primarily driven by stablecoin borrowing backed by crypto-native collateral like Ethereum and Bitcoin. This system enables liquidity release, leveraged strategies, and yield arbitrage. The key advantage of on-chain lending lies not in technological novelty but in its elimination of financial inefficiencies, offering lower costs (around 5% for stablecoins) compared to centralized crypto lenders (7-12%) due to open capital aggregation, transparency, and automation. On-chain lending is structurally due to permissionless markets that excel in capital pooling and risk pricing, fostering competition and innovation without intermediaries. This model reduces operational costs, replacing manual processes with code, and benefits both capital providers and borrowers. However, the current limitation is not a lack of capital but a shortage of diverse, borrowable collateral. The future of on-chain lending depends on integrating real-world economic value with crypto-native assets, moving beyond abstract financial strategies to serve broader adoption. Traditional lending remains expensive due to inefficiencies in loan origination, risk assessment, and servicing, where misaligned incentives and manual processes inflate costs. Decentralized finance can disrupt this by automating end-to-end operations, ensuring transparency, and reducing expenses. When on-chain lending ...

Author: Stani.eth

Compiled by: Ken, Chaincatcher

On-chain lending began around 2017 as a fringe experiment related to crypto assets. Today, it has grown into a market exceeding $100 billion, primarily driven by stablecoin lending, largely collateralized by crypto-native assets like Ethereum, Bitcoin, and their derivatives. Borrowers use it to release liquidity through long positions, execute leverage loops, and engage in yield arbitrage. The key is not creativity, but validation. The behavior over the past few years has shown that automated lending based on smart contracts had genuine demand and true product-market fit long before institutions began to take notice.

The crypto market remains volatile. Building a lending system on top of the most dynamic existing assets forces on-chain lending to immediately address risk management, liquidation, and capital efficiency issues, rather than hiding them behind policies or human discretion. Without crypto-native collateral, it would be impossible to see just how powerful fully automated on-chain lending can be. The key is not cryptocurrency as an asset class, but the cost structure transformation brought about by decentralized finance.

Why On-Chain Lending is Cheaper

On-chain lending is cheaper not because it's new technology, but because it eliminates layers of financial waste. Today, borrowers can access stablecoins on-chain at a cost of around 5%, while centralized crypto lending institutions charge interest rates of 7% to 12%, plus fees, service charges, and various surcharges. When conditions favor the borrower, choosing centralized lending is not only not conservative, it's irrational.

This cost advantage does not come from subsidies, but from capital aggregation in an open system.Permissionless markets are structurally superior to closed markets in pooling capital and pricing risk, because transparency, composability, and automation drive competition. Capital moves faster, idle liquidity is penalized, and inefficiencies are exposed in real-time. Innovation spreads immediately.

When new financial primitives like Ethena's USDe or Pendle emerge, they absorb liquidity from the entire ecosystem and expand the use of existing primitives like Aave, all without sales teams, reconciliation processes, or back-office departments. Code replaces management costs. This is not just an incremental improvement; it is a fundamentally different operating model. All cost structure advantages are passed on to capital allocators and, more importantly, to borrowers.

Every major shift in modern history has followed the same pattern. Heavy-asset systems become light-asset systems. Fixed costs become variable costs. Labor becomes software. Centralized scale replaces local duplication. Excess capacity is converted into dynamic utilization. Changes initially look bad. They serve non-core users (e.g., lending for cryptocurrency, not mainstream use cases), compete on price before quality improves, and don't look serious until they scale to a point where incumbents can't cope.

On-chain lending fits this pattern perfectly. Early users were mostly niche cryptocurrency holders. The user experience was poor. Wallets felt alien. Stablecoins didn't touch bank accounts. None of that mattered because the cost was lower, execution was faster, and access was global. As everything else improved, it became more accessible.

What Happens Next

During bear markets, demand falls, yields compress, revealing a more important dynamic. Capital in on-chain lending is always in competition. Liquidity does not stagnate due to quarterly committee decisions or balance sheet assumptions. It is constantly repriced in a transparent environment. Few financial systems are as relentless.

On-chain lending does not lack capital, it lacks collateral available for lending. Most on-chain lending today just recycles the same collateral for the same strategies. This is not a structural limitation, but a temporary one.

Cryptocurrency will continue to generate native assets, productive primitives, and on-chain economic activity, thereby expanding the scope of lending. Ethereum is maturing into a programmable economic resource. Bitcoin is solidifying its role as an economic energy store. Neither is a final state.

If on-chain lending is to reach billions of users, it must absorb real economic value, not just abstract financial concepts. The future lies in combining autonomous crypto-native assets with tokenized real-world rights and obligations, not to replicate traditional finance, but to operate it at an extremely low cost. This will be the catalyst for replacing the backend of old finance with decentralized finance.

What's Wrong with Lending

Lending is expensive today not because capital is scarce. Capital is abundant. Quality capital clears at 5% to 7%. Risk capital clears at 8% to 12%. Borrowers still pay high interest rates because everything surrounding capital is inefficient.

The loan origination process is bloated with customer acquisition costs and lagging credit models. Binary approvals cause quality borrowers to overpay, while subprime borrowers receive subsidies until they default. Servicing remains manual, compliance-heavy, and slow. Incentives are misaligned at every layer. Those who price risk rarely actually bear it. Brokers don't bear default risk. Loan originators sell exposure immediately. Everyone gets paid regardless of the outcome. The flaw in the feedback mechanism is the real cost of the loan.

Lending has not been disrupted because trust trumps user experience, regulation stifles innovation, and losses mask inefficiencies until they explode. When lending systems fail, the consequences are often catastrophic, reinforcing conservatism over progress. As a result, lending still looks like an industrial-era product clumsily grafted onto digital capital markets.

Breaking the Cost Structure

Unless loan origination, risk assessment, servicing, and capital allocation become fully software-native and on-chain, borrowers will continue to overpay, and lenders will continue to rationalize these costs. The solution is not more regulation or marginal UX improvements. It is breaking the cost structure. Automation replaces processes. Transparency replaces discretion. Certainty replaces reconciliation. This is the disruption decentralized finance can bring to lending.

When on-chain lending becomes demonstrably cheaper end-to-end than traditional lending, adoption is not a question, it is inevitable. Aave exists in this context, poised to serve as the foundational capital layer for a new financial backend, serving the entire lending landscape from fintech companies to institutional lenders to consumers.

Lending will become the most empowering financial product, simply because the cost structure of DeFi will allow fast-moving capital to flow into the applications that need it most. Abundant capital will create abundant opportunity.

Related Questions

QWhat is the core reason why on-chain lending is cheaper than traditional lending according to the article?

AOn-chain lending is cheaper not because it is new technology, but because it eliminates layers of financial waste. Its cost advantage comes from capital aggregation in an open system, where transparency, composability, and automation drive competition.

QWhat does the article identify as the main limitation for the growth of on-chain lending?

AThe main limitation is not a lack of capital, but a lack of borrowable collateral. Most current on-chain lending recycles the same collateral for the same strategies, which is a temporary constraint.

QHow does the article describe the fundamental operational difference that DeFi's cost advantage is built upon?

AThe advantage is built on a fundamentally different operating model where code replaces management costs. Automation replaces processes, transparency replaces discretion, and determinism replaces reconciliation, breaking the traditional cost structure.

QWhat future development does the article suggest is necessary for on-chain lending to reach billions of users?

ATo reach billions of users, on-chain lending must absorb real economic value, not just abstract financial concepts, by combining autonomous crypto-native assets with tokenized real-world rights and obligations.

QAccording to the article, why is traditional lending expensive despite capital being abundant?

ATraditional lending is expensive because everything surrounding the capital is inefficient. The processes of loan origination, risk assessment, and servicing are bloated with costs, lagging models, manual work, compliance burdens, and misaligned incentives.

Related Reads

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit3m ago

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit3m ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit10m ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit10m ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit2h ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit2h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit2h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit2h ago

Trading

Spot
Futures
活动图片