一览稳定币市场现状:USDT稳赢,交易所一致呈现流入趋势

Odaily星球日报Publicado em 2023-11-06Última atualização em 2023-11-06

Resumo

本文从市场份额、交易量和投资角度分析稳定币格局,以及最近加密市场回暖如何影响交易所稳定币的流动。

原文作者:Pedro M. Negron, IntoTheBlock

原文编译:Felix, PANews

在过去的一年里,受监管变化、危机和新兴机遇的影响,稳定币赛道经历了重大变革,每一次变革都在行业中留下了自己的印记。USDT 经历了显著增长,而 USDC 在地区银行业危机后出现萎缩。DAI 最近在链上交易量中占据了中心地位,这要归功于其创新的稳定币策略,即将资金存入短期美国国债,目前这些国债提供了较高 APY。最后,本文还研究了最近加密市场回暖如何影响交易所稳定币的流动。

USDT 的市值最近达到历史新高,巩固了其作为加密市场中采用最广泛的稳定币地位。

一览稳定币市场现状:USDT稳赢,交易所一致呈现流入趋势

来源:IntoTheBlock

稳定币市场的几个主要参与者在过去一年中经历了各种事件。BUSD 曾是市场上第三大稳定币,但由于与美国当局的法律问题,而不得不停运。自该事件发生以来,币安 BUSD 的运营商 PAXOS 只能处理用户提款,导致市值随着提款的进行而逐渐下降。USDC 的外汇储备中有 33 亿美元,被曝存放于破产的硅谷银行(Silicon Valley Bank),这对能否维持与美元挂钩至关重要。

这一系列事件使得本已是稳定币龙头的 USDT 吸引了一波新用户,进一步扩大了市场份额。

一览稳定币市场现状:USDT稳赢,交易所一致呈现流入趋势

来源:IntoTheBlock

随着稳定币赛道接二连三的事件,使得 USDT 背后的实体 Tether 不断发展,USDT 占据了市场的主导地位。USDT 目前的市值为 850 亿美元,占稳定币市场份额的 68% ,巩固了其稳定币头部供应商的地位。

尽管 Tether 占据过半的市场份额,但稳定币赛道仍有进一步增长和创新的潜力。这一趋势尤其值得注意,因为一些稳定币供应商现在为加密用户提供了产生收益的机会。这些稳定币随后被用来购买短期美国国债,目前这些国债的回报率是 2007 年以来最高的。这一机制使稳定币持有者和加密用户能够在不直接参与的情况下进入美国债券市场。

一览稳定币市场现状:USDT稳赢,交易所一致呈现流入趋势

来源:IntoTheBlock

DAI 背后的协议 MakerDAO 在引领这一趋势方面发挥了关键作用。随着 DSR (Dai 储蓄率)的重新推出,MakerDAO 使用户能够将他们的 Dai 资产锁定在智能合约中,从而获得回报。这一购买美国债券的新举措于 2023 年 8 月推出,当时 DSR 利率达到最高水平。此后,DAI 的链上交易量显著增加。显然,交易大户们渴望获得投资回报,因为超过 10 万美元的交易占 DAI 总交易量的 90% 以上。过去两周,DAI 一直保持着稳定币链上交易量的榜首位置。这一成就意义重大,特别是考虑到 DAI 是按市值计算的第三大稳定币。

最后,近期加密货币价格的上涨也清楚地反映在稳定币市场上。

一览稳定币市场现状:USDT稳赢,交易所一致呈现流入趋势

来源:IntoTheBlock

各交易所的流量指标(指进出交易所的资产)一致显示出流入趋势。资金流入通常与用户在交易所抛售资产有关,这也是他们将资产转移到这些交易所的原因。这表明用户目前正在使用之前持有的稳定币来购买加密资产,与最近价格的飙升相吻合。

总之,稳定币市场在过去一年中发生了重大变化,其特点是监管挑战、危机和创新策略。尽管 BUSD 和 USDC 等一些主流稳定币遭受挫折,但 USDT 牢牢占据主导地位,市值达 850 亿美元,占据 68% 的市场份额。然而,稳定币赛道仍然存在增长和创新的空间,通过投资短期美国国债来获得锁定稳定币收益的趋势就是例证。MakerDAO 在开创这一举措方面发挥了重要作用,特别是在重新启动 DSR 方面。最近加密货币价格的飙升也体现在稳定币市场上,特别是用户购买加密资产,导致持续流入交易所。

Leituras Relacionadas

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbitHá 2h

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbitHá 2h

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbitHá 2h

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbitHá 2h

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbitHá 4h

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbitHá 4h

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbitHá 4h

Token Inefficient, Economy Tokenless

marsbitHá 4h

Trading

Spot
Futuros
活动图片