USDe 的杠杆工具到底是陷阱还是馅饼?

深潮Pubblicato 2025-08-07Pubblicato ultima volta 2025-08-08

正是当前推动 Ethena 的 USDe 市值飙升的机制,日后也将成为导致其崩盘的元凶。

撰文:Duo Nine⚡YCC

编译:Saoirse,Foresight News

一旦空头回归,Ethena 和 Fluid 将严重冲击加密货币市场。

这一点是确定无疑的,我将在下文解释这一切将如何发生。

正是当前推动 Ethena 的 USDe 市值飙升的机制,日后也将成为导致其崩盘的元凶。

我们之前在哪里见过这种情况?

撰写本文时,Ethena 的 USDe 稳定币市值已接近 100 亿美元,成为当前第三大稳定币。这不该让你兴奋,而应让你警惕。因为这种增长大多并非基于坚实基础,更像是空中楼阁。

简而言之:杠杆循环正在催生这个泡沫!

泡沫的形成

只需 10 万美元,你就能通过这套操作构建出总价值 170 万美元的头寸。而且,在 Fluid 协议上,你只需点击「leverage」(杠杆策略)按钮就能一键完成。他们正是通过这种方式夸大总锁仓价值(TVL)。

(注:文中指的是名义资产规模达到 170 万美元,而非实际净资金。是下表中总抵押金额(Total Supplied)的总和。本质是通过杠杆循环放大了负债和抵押品的规模,并非真正「创造」了 170 万价值。)

你拿出 10 万美元兑换成 USDe,并将其作为抵押品存入 Fluid 的稳定币资金池(如 USDT-USDC 池)。然后,你借出 9 万美元 USDT,并将其兑换为 9 万美元 USDe。

将这 9 万美元 USDe 再次存入作为抵押品,借出 8.1 万美元 USDT,并兑换成 USDe。重复上述兑换和借贷流程 20 次。20 轮循环后,你的剩余借贷额度为 1 万美元。恭喜,你就这样创造出了「魔法般的网络货币」。

图表一

借助 Fluid 等协议,如今这种操作变得极其简单。查看 Fluid 的稳定币池就会发现,几乎所有池子都已接近满仓。

例如,USDe-USDT/USDC-USDT 资金池的借贷比例已达 89.2%,而最大抵押率上限为 90%,一旦达到 92% 就会触发清算。

若 USDe 相对 USDT/USDC 脱锚 2% 会发生什么?下文将详细说明。

为何人们争相购买 USDe?

因为它的收益最高!

目前,我们用 10 万美元初始资金构建的 170 万美元头寸,每年可产生 3 万美元收益。扣除借贷成本后(抵押品年化 8% - 债务年化 5%,详见图表一),实际年化收益率仍达 30%

(注:年化收益率 = ( 抵押收益 - 借贷成本 ) / 初始资金,根据图表一 20 次循环产生的总抵押金额和总借贷金额计算,即 APY = (890,581×8% - 790,581×5%) / 100,000,约等于 31.717%)

若 sUSDe 基差交易收益率进一步上升,相同本金的年化收益甚至可能达到 50% 或 100%。听起来是不是很明智?

只有在「音乐停止前离场」才算明智。正如历史所示,天下没有免费的午餐。总有人要为此付出代价,你必须确保自己不是那个买单的人

否则,你的全部本金可能在一夜之间蒸发,尤其是当你把收益重新投入这个循环时。撰写本文时,AAVE 在以太坊网络上的 15 亿美元 USDe 供应已达上限,人们正把这种杠杆操作推向极致。

在泡沫破裂前,USDe 的市值还会不断刷新纪录。当其日增规模达到 10 亿美元左右时,务必彻底离场。顶部已近在眼前,总有人要为这场盛宴买单。

确保那个人不是你!

崩盘的必然性

此刻可以肯定的是,崩盘终将发生。为何如此断言?

因为 USDe 的市值越大,当「音乐停止」时,泡沫破裂的压力就越大。

从本质上讲,当空头回归时,USDe 的市值越高,其脱钩的速度就越快、幅度就越大。仅 2% 的脱钩就足以触发 Fluid 上的大规模清算,进而加剧危机。

⚠️ USDe 脱钩正是这个巨大泡沫的「安全阀」!

届时,所有在 Fluid 及其他协议上进行杠杆循环操作的用户都将面临清算。数亿美元的 USDe 将突然涌入公开市场抛售。

随着清算连锁反应的启动,USDe 可能脱钩 5% 甚至更多。无数人将血本无归,这可能引发系统性风险,冲击整个去中心化金融生态系统。局势可能急转直下。

此外,崩盘的导火索也可能是市场需求枯竭:当 USDe 基差交易收益率持续走低(甚至转为负值),直到杠杆循环操作失去盈利空间,即借贷成本高于收益时,危机就会爆发。

这将首先导致 Fluid 上的杠杆用户收到清算通知,若引发连锁清算,USDe 的挂钩机制将彻底崩溃。没人能预测具体时间,但以当前速度发展,这一天必然到来。

唯有那些没有清算点或清算阈值极低的用户才能幸存。当杠杆彻底出清后,USDe 或许能恢复挂钩。

缓解危机的理想情景是 USDe 泡沫缓慢有序地消退,但当前市场环境下这几乎不可能。尤其是对于一个市值超 100 亿美元的泡沫,市场反转往往极为猛烈。

历史总是重演

这次并非例外。这与我经历的每一轮周期如出一辙。Ethena、Fluid 及众多协议正在主动为这场崩盘创造条件。

没人谈论这个问题,因为它不够「性感」。

Ethena 乐见其成 ,USDe 市值飙升意味着收入暴增;Fluid 也乐见其成,总锁仓价值暴涨带来收益增长。但请注意:他们不是买单者,他们是餐厅老板。

这正是加密货币市场周期性波动的根源!

熊市是杠杆出清的周期,牛市则是杠杆催生泡沫的周期。

太阳底下无新事。

个人观点

我从 Ethena 推出初期,就参与其收益挖矿,他们至今的成就令人印象深刻。但我目前不持有任何 USDe,未来也暂无此计划,风险实在太高。

市场上有更好的协议,能在更低风险下提供相当或更高的年化收益。例如 Resolv 的 USR/RLP 或 Hyperliquid 的 HLP 金库。HLP 不支持杠杆循环操作,这恰恰是其优势。

至于 Fluid,他们确实有所创新:让用户能在稳定币上获得更高收益,并将杠杆循环操作简化到人人可用。从其增长规模来看,这一模式无疑成功了。

然而,这两个协议及所有基于 Ethena 或 Fluid 构建的项目,都在共同吹大一个巨大的泡沫。我发出警告,是因为这类场景已见过太多次。

我本身对这些协议并无偏见,它们只是当前泡沫中最大的推手,而追随者正越来越多。

最后想说的是,比特币是加密货币市场的最后流动性来源。这意味着当危机爆发时,比特币将吸收冲击,其价格会承受压力,为人们在自己制造的泡沫中提供缓冲。同时也请关注 Saylor 及其 MSTR 泡沫的动向。

每轮周期都有新玩家,但剧情从未改变。

Letture associate

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit1 h fa

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit1 h fa

Spicy Commentary | Michael Saylor's 'Player Talk'; 60-Year-Old Aunt Liquidated After 'Scamming a Young Man'

**"Spicy Commentary": Three Tales of Crypto's Wild Week** This week's "Spicy Commentary" column highlights three dramatic stories from the cryptocurrency world. First, **MicroStrategy's Michael Saylor** addressed the controversy over his company potentially selling Bitcoin. At the BTC Prague event, he clarified, "I never said the company can't sell Bitcoin. I told *you* never to sell *your* Bitcoin." This "do as I say, not as I do" stance was criticized by netizens as peak linguistic gymnastics, noting a history of him previously stating the company would "never" sell. Second, a **bizarre fraud case** emerged from Beijing. A 60-year-old woman, obsessed with getting rich from crypto but unwilling to risk her own savings, posed online as the 20-something "god-daughter" of a high-ranking official. She catfished a young man, convincing him to give her over 200,000 yuan for fabricated emergencies. She then invested all the stolen money into cryptocurrency with 10x leverage, only to lose everything in a market crash. The woman was sentenced to four years in prison for fraud. Finally, a **sobering trader's tale** surfaced on Reddit. A user posted "Tale of a crypto trader," confessing their net worth had plummeted from a peak of $45 million to roughly $17,200, primarily due to holding meme coins too long. The post, described as a crypto "book of confessions," sparked reactions ranging from sympathy to critique about greed, poor risk management, and the perils of treating meme coins as long-term investments instead of taking profits. The column concludes that this week featured masterful rhetoric, elaborate scams, and extreme financial volatility, stitching together another chapter in crypto's unpredictable theater.

Foresight News2 h fa

Spicy Commentary | Michael Saylor's 'Player Talk'; 60-Year-Old Aunt Liquidated After 'Scamming a Young Man'

Foresight News2 h fa

Tremble Humans, AI Continues Its Accelerated Sprint

Trembling, Humans: AI Continues Its Accelerated Sprint Yes, AI is still rapidly accelerating. While deep learning seemed to stall quickly in its early years, large models after years of development show no sign of hitting their ceiling. At the Zhiyuan Conference 2026, the focus is on enabling AI to move from the digital world into the physical world. Scaling Law remains effective, continuing to drive advancements in both large language models and multimodal models. The industry is now entering a phase of pursuing World Models, though unresolved technical paths and data issues mean this exploration may take 3-5 more years. Concurrently, breakthroughs in Agents are accelerating AI's real-world application in fields like healthcare and meetings. Making Agents truly useful requires key hardware-software co-design, evident from the strong presence of chip vendors at the conference. We stand at a new historical threshold where AI is becoming a foundational force reshaping the world. The first day of the conference highlighted AI's evolution from "knowing how to chat" to "knowing how to work." Scaling Law persists, World Models are the next key battleground, and Agents are transitioning from usable to好用 (user-friendly). Scaling Law is not ending but diversifying. New models like Anthropic's Fable 5 demonstrate scaling through parameter size, synthetic data, and reinforcement learning. Advancements in AI Coding and Agent deployment are enabling a trend of AI self-evolution, potentially allowing AI to take over digital world iterations. World Models represent the next frontier for large models extending into the physical realm, but no current model is truly impressive at solving real-world problems. Technical consensus is lacking, with debates on data sources (video, simulation, real-world). Different approaches are emerging: language-centric, pixel-centric, 3D-structure-centric, and visual-representation-centric models. Zhiyuan Institute is exploring a fifth path: unified latent space modeling fusing language and visual representations, and introduced its own under-development World Model, Physis-v0.1. On the product side, Agents are key to bringing AI into daily life. Since 2025, the "Year of the Agent," products have become more proactive and capable of complex tasks. Zhiyuan showcased four vertical Agents for cardiac diagnosis, autonomous research, meeting summarization, and protein risk discovery. However, technical challenges remain, particularly in context engineering like memory and orchestration. "Harness" – the engineering framework around an Agent – is crucial for maximizing its capabilities by clarifying intent, designing workflows, and incorporating validation and feedback. In summary, AI's breakneck pace continues on multiple fronts: foundational model scaling, the ambitious pursuit of World Models for physical understanding, and the ongoing refinement of practical Agents. The journey from capable to truly reliable and useful AI systems is well underway.

marsbit2 h fa

Tremble Humans, AI Continues Its Accelerated Sprint

marsbit2 h fa

The Backside of Musk's Trillion-Dollar Fortune: 85% Can't Be Sold

Elon Musk becomes the world's first trillionaire, driven by SpaceX's IPO valuing the company at $1.77 trillion. However, his vast wealth is largely illiquid: he holds over 85% voting control, likely through super-voting shares that are subject to lock-ups and selling restrictions. While his net worth surpasses $1 trillion across SpaceX, Tesla, and private holdings, only a tiny fraction (potentially under 2% annually) could be converted to cash without jeopardizing control and market confidence. SpaceX's IPO also creates paper millionaires for roughly 4,400 employees, but their holdings face lock-up periods, exercise costs, and taxes, delaying and reducing actual cash proceeds. Only 4.2% of total shares are initially available for public trading, making the stock price highly sensitive to limited net buying or selling pressure. A major test will come when lock-ups expire for the remaining 96% of shares. The article contrasts SpaceX's wealth distribution with potential AI IPOs. Anthropic and OpenAI could generate employee wealth pools 20 times larger than SpaceX's in paper value, due to their higher valuations relative to revenue and potentially more distributed ownership. However, sustaining those high price-to-sales multiples post-IPO is uncertain. A key financial puzzle for SpaceX investors is its xAI unit. While it has locked in an estimated $26 billion in annual compute revenue from clients like Anthropic and Google, the unit reported a $6.4 billion loss in 2025. More critically, estimated annual capital expenditures of ~$30.8 billion exceed that revenue. The long-term viability of SpaceX's AI narrative hinges on whether this compute income can eventually cover the unit's massive ongoing investments and losses.

链捕手2 h fa

The Backside of Musk's Trillion-Dollar Fortune: 85% Can't Be Sold

链捕手2 h fa

Trading

Spot
Futures
活动图片