EigenLayer 的经济模型失败了嘛?

链捕手Pubblicato 2024-08-16Pubblicato ultima volta 2024-08-16

原标题:《Eigenlayer's economic model is broken》

作者:Zach Rynes | CLG

编译:Peisen,BlockBeats

 

编者按:

在研究了 Eigenlayer 节点运营商和用户之间的僵局、AVS 的经济负担以及预言机面临的技术挑战后,@ChainLinkGod 指出,Eigenlayer 经济模型的实际运作暴露出了一系列深层次的问题,其并未提供真正的解决方案来启动新的去中心化基础设施协议。

Eigenlayer 的经济模型出现了问题

该协议并未提供真正的解决方案来启动新的去中心化基础设施协议。

启动问题是经典的「鸡和蛋」问题,具体如下:

(1) 节点运营商不会加入并保护网络,除非这样做对他们有利可图。

(2) 用户不会付费使用网络,除非已经有一组节点运营商在保护网络。

因此,存在一个僵局,其中供应和需求的存在相互依赖。

这一僵局通过发行新代币来解决,通过代币通货膨胀补贴供应方,以确保节点加入是有利可图的——即使在网络本身尚未盈利之前。

然后,如果网络提供了有价值的服务,并且需求方的采用增加,用户费用的增长最终会取代补贴,使网络变得净盈利。

在 Eigenlayer 上启动的协议(AVS)仍然需要以完全相同的方式进行启动,但 Eigenlayer 的特性使得问题更加严重:

(1) AVS 放弃了代币的效用,因为其原生发行的代币不再是唯一的质押 / 担保资产,取而代之的是质押的 ETH/EIGEN。

(2) 由于 AVS 在起步阶段并不盈利,它们必须通过自身代币供应的通货膨胀来支付质押的 ETH/EIGEN——参与者对该 AVS 代币缺乏一致性,可能会出售以积累更多的 ETH/EIGEN。

(3) 对于任何成功的 AVS,它们将需要将收入让渡给 ETH/EIGEN 质押者,从而对协议造成净流失,因为收入流出其生态系统。

这种安排对资金充足或位置良好的项目没有意义,这些项目不需要削弱其代币的效用和价值来吸引资本和验证者。

任何成功并生成收入的 AVS 很可能会脱离 Eigenlayer,以保留更多的自身收入,并为其原生代币累积更多价值,就像许多 dApp 成为自己的 L2/L3/appChain 以捕获更多费用 /MEV 一样。

协议只有在以下情况下才会想要成为并保持 AVS:(1)其成本通过 EIGEN 代币通货膨胀得到补贴,(2)基于再质押炒作获得 VC 融资,或(3)通过类似于失败的 L1 转型为 L2 的叙事转变获得利益。

除了经济学方面,成为 AVS 并不意味着用户能够获得更高质量的服务或更优的安全保障。

特别是对于预言机,我们可以看到三大主要挑战:

(1) DevOps:节点运营商是否是知名的可靠实体,能够管理高性能且抗干扰的基础设施?其基础设施能否扩展到数千个数据源,并在极端区块链网络拥堵和对抗性 P2P 网络条件下保持低延迟?运营商能否及时识别和解决问题?

(2) 数据质量:运营商是否仅从具有严格准确性 / 可用性保证的高质量数据提供者处汇总数据?数据汇总方法是否能在极端市场波动期间反映资产的体积 / 流动性加权市场价格?网络参与者能否及时识别和解决数据提供问题?

(3) 代码质量:链上和链下代码是否抗操控和漏洞?是否有足够的第三方审计 / 评审,如果出现漏洞,问题能多快被识别和解决?

Eigenlayer 并未提供任何解决方案,因此,即使一个预言机 AVS 拥有大量质押的 ETH/EIGEN,这也无法保证该预言机的可靠性、准确性或性能。

迄今为止,预言机或桥尚未遭遇任何经济攻击,因为质押的担保物只是额外的安全层(协议可以更有效地自我提供的)。

Eigenlayer 转型并向 AVS 代币作为再质押资产的支持,实际上是承认 Eigen 的核心经济模型存在问题,从未合理,他们自己也在尝试找到对其 120 亿美元担保资产的收益。

在可预见的未来,Eigenlayer 仍将是 ETH 质押者的补贴收益池。

Letture associate

South Korean Institutions' Crypto Race: Dual Explosion of Stablecoins and RWA

**Summary: South Korea's Institutional Crypto Race: Stablecoins and RWA Take Off** South Korea is undergoing a structural shift in its crypto ecosystem, moving beyond its historical role as a major retail trading hub. Major financial institutions and internet platforms are now building institutional-grade blockchain infrastructure, with stablecoins and Real-World Asset (RWA) tokenization as the primary drivers. The push for a regulated Korean won stablecoin market is a major policy and corporate focus. This is driven partly by an estimated $115 billion outflow into dollar stablecoins like USDC, threatening the domestic financial system. Banks (e.g., KB Financial, Hana), payment giants (e.g., Shinhan Card, BC Card), and internet super-apps (KakaoPay, NAVER Pay) are all conducting pilots. The goal is to anchor future digital finance to the Korean won and local regulations. In RWA, South Korea is advancing rapidly within regulatory sandboxes, focusing on unique domestic assets beyond typical global templates like US Treasuries. Projects involve tokenizing ships (with Hyundai Heavy Industries), defense supply chain assets, and K-pop intellectual property, alongside more conventional assets. A legal framework is set for 2027, and platforms like NXT are preparing for regulated trading. Key opportunities for crypto-native projects lie in providing the underlying technology these traditional institutions lack: global distribution channels for tokenized assets, cross-chain liquidity solutions, and enabling infrastructure tools (e.g., for asset packaging and management). Partnerships, such as Solana with Shinhan Card or LayerZero with the Korea Gold Exchange, exemplify this proactive approach. Crucially, user access is being shaped by consumer platforms. NAVER's planned acquisition of Upbit's operator Dunamu and Kakao's development of a unified wallet aim to seamlessly integrate crypto with everyday payments for tens of millions of users. The race is now about which protocols and projects will become the foundational standards as regulation solidifies and institutional adoption accelerates.

Foresight News7 min fa

South Korean Institutions' Crypto Race: Dual Explosion of Stablecoins and RWA

Foresight News7 min fa

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

**How to Detect AI-Generated Videos: A Survey on Dynamic, Traceable, and Explainable Detection Systems** With rapid advances in AI video generation (e.g., Sora, Veo), creating highly realistic, multi-minute videos is now possible, widening the gap with detection research. Current AI video detection, often limited to unreliable binary classifications, is insufficient. This survey, accepted at ACL 2026, reframes the goal as **"factual fidelity verification"**—checking if a video's content (who, when, where, what) aligns with the real world perceptually and cognitively. It categorizes AI-generated videos into three paradigms: **Local Manipulation Videos (LMV**, e.g., face swaps), **Audio-Visual Editing (AVE**, e.g., lip-syncing), and **Generative Video Synthesis (GVS**, fully synthetic videos like Sora's). Detection challenges evolve from visual artifacts in LMV to multi-modal inconsistencies in AVE and higher-level world knowledge violations in GVS. The core proposal is a **Vision-Language Dual-View framework** with four hierarchical layers: 1. **Layer 1 (Intrinsic Visual Cues):** Analyzes low-level signal statistics, noise patterns, and physiological signals. 2. **Layer 2 (Spatiotemporal Consistency):** Checks for temporal coherence in object motion and scene dynamics. 3. **Layer 3 (Cross-Modal Consistency):** Verifies alignment between video, audio, and text within the video. 4. **Layer 4 (Language-Guided World-Level Reasoning):** Uses external knowledge, facts, and physical laws to judge semantic plausibility and factual correctness. The survey traces a shift in detection focus from lower layers (1 & 2) toward higher, language-involved layers (3 & 4). It also reviews evolving evaluation metrics and datasets tailored for each video paradigm. The conclusion advocates for a **dynamic, evidence-first detection system** that moves beyond simple classification. Future trustworthy detection requires combining visual evidence (from CV) with semantic reasoning and explanation (from NLP & multimodal AI), ultimately creating traceable and explainable judgments about a video's adherence to real-world constraints.

marsbit43 min fa

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

marsbit43 min fa

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

The article explores the surprising trend where AI's first major impact on crypto has been in security auditing, not in areas like trading or analytics. It details how AI-powered tools are dramatically lowering the barrier to finding smart contract vulnerabilities, enabling attackers to scan thousands of contracts and execute exploits within minutes. This has rendered traditional, manually-produced audit reports with their month-long validity periods increasingly obsolete, creating a critical "structural crack" in the old security model. Cases like Drift Protocol and KelpDAO show that even extensively audited protocols can be hacked through social engineering, operational flaws, or infrastructure misconfigurations beyond pure code review. Attackers are also using AI to find and exploit vulnerabilities in years-old, deployed contracts. Notably, OpenZeppelin's co-founder has expressed a grim view that "all DeFi is insecure" due to AI's asymmetric advantage. In response, the audit industry is undergoing a fundamental shift. While there's a short-term spike in defensive re-audits, the long-term business model is changing. Firms are developing AI-assisted systems and moving from one-time report deliveries towards embedded, continuous services like real-time monitoring and formal verification. Examples include AI tools uncovering critical, previously missed vulnerabilities in heavily audited protocols like Curve Finance and Zcash. The conclusion is that security must become a continuous investment, not a one-time checkbox, and audit firms must rapidly evolve their tools and service models to survive.

marsbit49 min fa

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

marsbit49 min fa

Never expected that the first tangible application of AI x Crypto is in security auditing

Unexpectedly, the initial major application of AI in the Crypto sphere has turned out to be security auditing. In 2026, DeFi has faced significant security challenges, with 121 hacking incidents resulting in approximately $942 million in losses. While AI was expected to first impact areas like quantitative trading, its initial breakthrough has instead transformed security auditing by drastically lowering the cost and skill barrier for finding smart contract vulnerabilities. The traditional audit model is facing obsolescence. Advanced AI models, such as Claude Mythos, enable attackers to scan thousands of contracts and identify vulnerability patterns at scale, compressing the time from discovery to execution to mere minutes. This renders the month-long validity of traditional audit reports ineffective. Notably, attacks now frequently target well-audited, established protocols by exploiting business logic flaws, operational security weaknesses, and even years-old historical contracts, demonstrating that old audit reports offer zero protection. This pressure is forcing a fundamental shift in the industry. In the short term, a wave of defensive re-auditing is occurring, driven by projects seeking to meet new AI-era security standards and regulatory requirements. In the long run, audit firms' business models are diverging. The one-time report delivery model is declining in value, as evidenced by platforms like Code4rena shutting down. Leading firms are now pivoting towards AI-powered defense, integrating continuous monitoring, real-time on-chain risk detection, and embedding security directly into the development phase, as seen with tools like OpenZeppelin's Skills system. Ultimately, the era of "audit once, secure forever" is over. Security must become a continuous, embedded infrastructure investment for projects. For audit companies, survival depends on proactively transforming from traditional service providers into platforms offering AI-native, ongoing security solutions.

链捕手57 min fa

Never expected that the first tangible application of AI x Crypto is in security auditing

链捕手57 min fa

Trading

Spot
活动图片