市场热点重回ETH生态,据以太坊现货ETF获批究竟还有多远?

Odaily星球日报Publicado a 2024-01-15Actualizado a 2024-01-15

Resumen

以太坊是否被定性为证券显然是其现货ETF获批的关键。

原创 | Odaily星球日报

作者 | 夫如何

市场热点重回ETH生态,据以太坊现货ETF获批究竟还有多远?

比特币现货 ETF 批准后,令人惊奇的不是比特币的价格下跌,而是以太坊领涨山寨币,脱离比特币走势走出独立行情。本以为是比特币“吸血”行情,没想到似乎是利好落地,资金转而炒作以太坊。

社区不少人戏称,简直像是以太坊 ETF 获批了。那么实际上以太坊 ETF 离我们还有多远?获批的可能性有多大?我们应该期待哪些关键节点?

Odaily星球日报整理了部分机构以及专业人士发表的言论,供投资者参考。

以太坊 ETF 讨论的背景:继 SEC 在 2023 年 12 月相继推迟灰度、ARK、VanEck、Hashdex 等机构的以太坊现货 ETF 申请,同月 SEC 还开放评议期,以权衡是否应区别对待以太坊与比特币现货 ETF。

SEC 需要在 5 月底前对多个以太坊现货 ETF 申请做出审批决定,包括 VanEck、Ark 21 Shares 和 Hashdex 以太坊 ETF。此外,SEC 必须在 6 月 18 日之前对 Grayscale 将 ETH 信托转化成 ETF 的申请做出决定,在 7 月 5 日之前对 Invesco 的申请做出决定,在 8 月 3 日之前对富达的申请做出决定,在 8 月 7 日之前对贝莱德的申请做出决定。

根据上述背景来看,市场上大体对以太坊批准持正反两种言论。

其中认为以太坊现货 ETF 会批准的的言论:

  • Coinbase 表示,加密货币将继续存在,比特币现货 ETF 将进一步扩大加密货币的采用。以太坊现货 ETF 的申请可能很快就会获得批准。

  • 彭博 ETF 分析师 James Seyffart 表示,SEC 在去年批准以太坊期货 ETF 时,已“含蓄地”接受以太坊作为一种商品,这意味着今年可能会看到现货以太坊 ETF 上市。SEC 可能没有理由对以太坊的分类进行改变,如果将以太坊是为”证券“,那将于监管机构 CFTC 相悖。

  • 渣打银行预测 SEC 将在今年第二季度批准现货以太坊 ETF。

  •  Valkyrie 联合创始人兼首席投资官 Steven McClurg 认为,既然 SEC 批准比特币现货 ETF,那么未来以太坊现货 ETF 的批准也具备一定的可能。

  • 彭博 ETF 分析师 Eric Balchunas 此前表示,在 SEC 批准比特币现货 ETF 之后,预计以太坊现货 ETF 在 5 月获得批准的可能性为 70% 。

  • 数字资产律师 Joe Carlasare 认为以太坊现货 ETF 将在今年获得批准,但获批时间可能会比人们预期的时间要长一些,同时美国 SEC 也在努力精心打造一个先例,允许他们在决定允许哪些数字资产 ETF 进入市场时保留一定的自由裁量权。

  • 贝莱德 CEO 芬克也表示,看到了推出以太坊 ETF 的价值。

以上是机构及相关专业人士对以太坊现货 ETF 将会批准的言论,从中可以看出,比特币现货 ETF 的通过为以太坊 ETF 的获批提供一定的参考,同时,SEC 对以太坊的定义也是获批的主要原因之一。

其中认为以太坊现货 ETF 不会批准的的言论:

  • 摩根大通并不看好 SEC 在 5 月会批准现货以太坊 ETF。摩根大通的 Nikolaos Panigirtzoglou 认为 SEC 需要将以太坊分类为商品(类似于比特币),而不是证券。同时,SEC 在 5 月前将以太坊归类为商品的可能性不超过 50% 。

  • TD Cowen 投资银行认为,SEC 不太可能在短期内批准现货以太坊交易所交易基金(ETF)。尽管最近批准了现货比特币 ETF,但 SEC 在批准现货以太坊 ETF 方面将采取更为审慎的态度。SEC 可能会等待从比特币 ETF 中获取经验后,再考虑批准以太坊或其他加密货币 ETF。

综合两方言论来看,目前主要决定 SEC 是否会批准以太坊现货 ETF 的原因,在于如何定义以太坊的性质,如果将其定义为商品,那获批的可能性将大大提高,但 SEC 如果将以太坊视为”证券“,那在 5 月的最终期限时,可能不会批准。

但结合 Ripple 与 SEC 之前的案件来看,在 2023 年 7 月的法院裁决宣布“XRP 本身不是证券”,以及同年 10 月的联邦法官决定驳回 SEC 的上诉请求。至少在现有法律的框架下,以太坊并不会被定义为证券。

据此,今年 5 月对以太坊现货 ETF 的批准至关重要,显而易见的是,以太坊 ETF 的批准面临着相较比特币 ETF 更多的不确定性。

Lecturas Relacionadas

NVIDIA CPU Advances, China's RISC-V Responds: Semiconductor Deep Dive - Part Four

NVIDIA is set to launch its new Vera AI data center CPU in China as early as August, with high pricing. While this move offers a new option, it highlights China's continued dependence on foreign-controlled Arm architecture. In response, the Chinese semiconductor industry is increasingly turning to RISC-V as a strategic alternative for achieving high-performance computing autonomy. The article explores the concept of the "impossible triangle" in CPU development—balancing prosperity, control, and autonomy—and posits that RISC-V's open-source, modular nature offers a unique path to achieving all three. While RISC-V is already dominant in embedded systems, the focus is now shifting to data centers and AI workloads. China has become a global hotspot for RISC-V development, driven by AI-driven compute demand, supply chain concerns from export controls, cost benefits of open-source, and strong policy support. Multiple Chinese companies have reportedly crossed the key performance threshold of 15 SPECint per GHz, a benchmark for entering the high-performance CPU club. Progress extends beyond single-core benchmarks. Companies are developing complete computing subsystems, including commercial-grade coherent network-on-chip (NoC) technology and server processors with up to 40 cores that strictly adhere to the RVA23 standard to ensure software compatibility. Real-world applications are emerging in areas like video transcoding and edge AI. However, significant challenges remain. The RISC-V ecosystem faces fragmentation, immature toolchains and verification processes, and gaps in single-core performance and energy efficiency compared to mature x86 and Arm architectures. The formidable software moat, epitomized by NVIDIA's CUDA, is a long-term hurdle. In conclusion, while RISC-V cannot immediately replace offerings like NVIDIA's Vera, it represents a viable long-term path for China to develop a self-sufficient, high-performance CPU ecosystem. The journey is acknowledged to be long and arduous, requiring sustained effort to overcome technical and ecosystem challenges.

marsbitHace 39 min(s)

NVIDIA CPU Advances, China's RISC-V Responds: Semiconductor Deep Dive - Part Four

marsbitHace 39 min(s)

My Coding Betting Dashboard is Profiting, but Polymarket is Truly Not a Good Place for 'Arbitrage'

The author built a custom monitoring dashboard for Polymarket, a prediction market platform, and tested it with $1,600, achieving over 30% returns. However, the core argument is that Polymarket is not a good venue for traditional arbitrage. The dashboard has two main sections: a "Portfolio Dashboard" for tracking active positions with key metrics like total capital, P&L, and a risk-control module using a tier system (T1, T2, T3), and an "Opportunity Watchlist" for monitoring markets. The article details a critical structural trap in binary markets: a bet with a high perceived probability of success still carries a 100% loss risk if wrong. The author's T1/T2/T3 system is designed to manage this by limiting position sizes based on conviction and time horizon, emphasizing that high confidence should not equal high concentration. A key insight is the danger of "pseudo-diversification"—betting on different markets driven by the same underlying variable. The author concludes that Polymarket offers few true low-risk, arbitrage opportunities. It is instead a high-risk environment where wins can create a false sense of mastery, leading to large losses. The platform is better viewed as a training ground for honing judgment through disciplined, framework-driven betting rather than a reliable income source. The tools help transform intuition into structured, rule-based decisions to mitigate the risk of catastrophic errors.

marsbitHace 3 hora(s)

My Coding Betting Dashboard is Profiting, but Polymarket is Truly Not a Good Place for 'Arbitrage'

marsbitHace 3 hora(s)

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

**"WeChat AI Card" Practical Test Guide: Has the Era of AI Shopping Arrived?** WeChat has officially launched the "AI Exclusive Card," a feature integrated into its Workbuddy AI assistant. This card is designed to handle payments for AI-initiated purchases. Our hands-on test reveals it's not yet a tool for fully autonomous AI shopping, but rather a controlled payment layer for AI agents. The AI Card functions as an isolated sub-wallet within WeChat Pay. Users must bind the card and transfer funds into it from their main wallet. Crucially, every transaction requires explicit user confirmation via smartphone scan; AI cannot spend autonomously. Currently accessible through the Workbuddy agent, the card targets specific digital consumption scenarios: purchasing paid content (reports, data), calling paid APIs/tools, and subscribing to services. Its design prioritizes security and control by separating funds and mandating approval for each payment. We tested a real-world scenario: ordering bubble tea via Workbuddy using a "Meituan Life Assistant" skill. The process encountered multiple hurdles: high "skill" usage costs (exceeding daily free credits), and most importantly, while a payment was successfully initiated, the AI purchased an incorrect product (a mismatched group-buy coupon instead of the desired drink). This highlights the current limitation: the **AI Card only solves the payment step**. The broader challenge lies in the **AI agent's execution chain**—accurately understanding intent, navigating third-party platforms, selecting the right product, and ensuring proper fulfillment. The payment succeeded, but the purchase failed to meet the user's need. In conclusion, the WeChat AI Exclusive Card is a cautious, early-step experiment in AI commerce. It provides a secure, user-controlled payment method for agent interactions but is not yet capable of reliable, end-to-end complex purchases. For now, it's best used for low-value, low-risk digital services with careful user verification at each step. The vision of AI handling complete shopping tasks remains a work in progress.

marsbitHace 6 hora(s)

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

marsbitHace 6 hora(s)

Deconstructing Notion's Growth: From a Note-taking Tool to 100 Million Users—How Notion Built a Triple Growth Flywheel Through Product, Templates, and Community

Notion's growth from a niche note-taking tool to a platform with 100 million users is powered by three interconnected flywheels: Product-Led Growth (PLG), a Template Economy, and Community-Driven Growth. First, Notion's PLG strategy relies on a highly flexible, "plastic" product that users can adapt to countless personal and team workflows. Its freemium model lowers the barrier to entry, while features like page sharing and collaboration drive organic, usage-based viral growth as users naturally invite others. Second, the Template Economy solves the "blank page" problem. Templates, created by both Notion and its community, transform abstract product capabilities into concrete, copyable solutions for specific scenarios (e.g., project management, content calendars). This dramatically lowers activation costs for new users and fuels SEO-driven discovery. Third, a vibrant Community acts as a distributed growth engine. Users and official Ambassadors create tutorials, share use cases, and host local events. This community not only educates users but also fosters a sense of identity around pursuing "better ways of working," strengthening loyalty and enabling global, low-cost expansion. Together, these flywheels create a self-reinforcing ecosystem: a great product attracts users who create templates and community content, which in turn attracts more users and deepens engagement. This system allowed Notion to scale from individuals to teams and enterprises through a bottom-up adoption path. Looking ahead, AI integration promises to accelerate these flywheels further by making templates smarter and the platform a potential AI-native work operating system. Ultimately, Notion's defensible advantage is not just its features, but this deeply entrenched network of user assets, creators, and community trust.

marsbitHace 6 hora(s)

Deconstructing Notion's Growth: From a Note-taking Tool to 100 Million Users—How Notion Built a Triple Growth Flywheel Through Product, Templates, and Community

marsbitHace 6 hora(s)

Trading

Spot
Futuros
活动图片