【研报精选】上海升级临近,LSD再次兴起

MirrorPublicado em 2023-03-05Última atualização em 2023-03-03

Resumo

SSV是不可避免的,希望你从中获得一些价值。

随着上海的升级换代即将到来,LSD再次兴起。其中一个项目,是一个捡拾和铲除的游戏引起了我的注意,而且它远不止于此。以下是我为什么相信$SSV能从众多项目中脱颖而出,并拥有巨大的上升空间的原因。

TL;DR

- $SSV提供去中心化的L0质押基础设施

- DVT是ETH路线图中的关键部分;$SSV具有先发优势

- LSD将利用$SSV的技术优势

- 代币有真正的价值积累

- 通过收费/赠款的动态变化可以实现飞轮效应

- 5000万美元的生态基金

目录:

1.$SSV和DVT的解释

2.投资理论与代币经济学

3.$SSV和L2之间的区别

4.估值和结论

$SSV和DVT的解释

$SSV正在开发分布式验证器技术[DVT],其目标是进一步分散ETH的安全层并使其更具弹性。DVT是Vitalik路线图上的一个关键组成部分。甚至ETH基金会本身也委托$SSV来处理这个具有里程碑意义部分。

DVT

用简单的话解释。

- 它允许一个验证器在一个以上的节点或机器上运行

- 由个人/操作者运行的节点可以作为一个单一的验证器一起运行。

- 每个节点持有完整验证器密钥的一个密钥份额[完整的密钥从不在一个地方]

我们得到了关于DVT的入门知识,但是是什么让它改变了游戏规则?为了确定这一点,我们首先要了解单节点验证器的主要问题:它们造成了单点故障。

单节点验证器的缺点:

- 如果电脑/机器死了,验证器就会脱机

- 设置备份节点会带来许多问题,例如节点的错误配置会导致砍价。

- 热键可能被破坏

- 股权和客户中心化

等等

DVT[节点集群]的优势:

- 即使一些节点离线,也不会影响性能,因为验证器仍在运作

- 由于不需要主动-被动节点备份,减少了削权风险

- 降低硬件成本;可以在较少的机器上运行更多的节点

- 需要削减保险更少

- 使得普通人更容易获得ETH押注;可以运行多个节点来验证

简而言之:

DVT提高了弹性,降低了削减风险,并使以太坊进一步去中心化。它还消除了单点故障方面的问题。

投资理论与代币经济学

我们已经有了$SSV的2个令人信服的论据:

- 第一个实现DVT,被定位为ETH的安全层 => 先发优势

- 每一个使用DVT的验证者,ETH网络都会变得更加分散和安全 => 大量的使用案例

Tokenomics。

一个项目可能会取得巨大的成功并被频繁使用,而代币却趋于零,这主要是由于缺乏价值累积/使用案例。通常情况下,根本不需要代币,其也没有真正的效用。

$SSV在这方面的表现如何?该代币主要有两个用途:

1. 治理

SSV持有者可以提交/投票于有助于塑造DAO的提案。这本身就是最低限度的效用,而且治理代币通常表现不佳。幸运的是,第2种情况是相当重要的......

2. $SSV网络上的支付工具。

$SSV是质押者向运营商支付的通用货币,用于代表他们运行节点和验证器。质押者也为使用网络支付$SSV费用;这些费用流入DAO,用于进一步的生态系统发展。

在我看来,第二部分尤其具有相当大的飞轮潜力:

DVT的使用加速了ETH的去中心化 为DAO提供了更多的费用 更多的项目将获得资金,在SSV的基础上建立/使用DVT来建立他们的应用程序 DVT的使用增加 产生更多的费用...

谈到资金问题;下面是几个获得资助并已在$SSV之上构建的高知名度项目,总共有20多个项目,其中有很多是利用DV技术的LSD项目。

有没注意到LSD这个庞然大物也是该集团的一部分?

FDV:MC的比例是有利的,为1.56倍,64%的供应在流通。不像ETH[1x]那么好,但比APT[6x]高得多。但请记住:如果你希望这个比例尽可能地接近1倍,这意味着更少的稀释和通货膨胀。ST FDV是一个模因,LT它绝对重要。

$SSV和L2之间的区别

最近的新闻:

$SSV推出了一个5000万美元的生态系统基金,专门用于推动DVT的发展。"DVT和正在使用该技术构建的各种用例类似于早期的L2实施。"毕竟其是早期采用者。

很难想象一个没有L2的ETH世界;它们大大增加了ETH的可扩展性,而采用率却不断降低。DVT[阅读:$SSV]可能同样重要,因为它不仅提高了安全性,而且还提高了分散性。可谓一举三得。

$SSV估值:

在TradFi中确定价格目标已经很困难了;而加密货币则将其放大了一个数量级。无论如何,这都只是一个fugazi和神奇的互联网资金。尽管如此,我们可以进行一些比较,并将价格目标作为一个单纯的思想实验。

在此之前,让我们回顾一下:

- SSV在Vitalik的ETH路线图中发挥着巨大的作用

- 在DVT中拥有巨大的先发优势

- 受以太坊基金会委托,使ETH更加去中心化、安全并增强抗审查能力

- 成立了一个5000万美元的基金以支持DVT的发展,有20家公司正在建设中

- 有像LDO这样的重量级公司利用它的技术

- 良好的代币机制、价值累积机制和飞轮潜力

- 对于ETH的未来,可能与L2同样重要。

综上所述,$SSV的图表只涨不跌并不奇怪。

"ETH敬畏地看着,不确定$SSV是否能够完成它所承担的巨大任务。去中心化和安全的信念取决于它。"

估值和结论

估值判决:

SSV是在ETH基金会本身的祝福下,远远超过了LSD的基础设施游戏。以一个领先的L2[OP]和一个顶级LSD[RPL]之间的中位数计算,结果是:每个SSV为111.20美元[还有155%的上涨空间],但仅仅是理论。

代币

我摘录了SSV WP的一段话作为本文章的结尾:

SSV是不可避免的。谢谢你的阅读--希望你从中获得一些价值。

Leituras Relacionadas

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges

The stock price of Marvell Technology surged 32.5% on June 2nd, driven by NVIDIA CEO Jensen Huang highlighting its custom ASICs and optical interconnects as core to AI data center architecture. This event brought attention to the Chinese semiconductor family behind Marvell: the Dai siblings. The story centers on three siblings, all UC Berkeley graduates, whose three-decade entrepreneurial journey aligns with major semiconductor industry shifts. In 1995, youngest sister Dai Wei Li co-founded Marvell with her husband Sehat Sutardja and his brother, focusing on storage controllers. Eldest brother Dai Wei Min founded EDA company Ultima, later sold to Cadence, and later founded VeriSilicon (芯原) in China, becoming a leading semiconductor IP provider. Second brother Dai Wei Jin co-founded EDA firm Silicon Perspective (sold to Cadence) and GPU IP company Vivante, later acquired by VeriSilicon. The combined "Dai-Sutardja" family network extends beyond Marvell. Their ventures and investments form a comprehensive ecosystem for the post-Moore's Law, chiplet era. Key holdings include: Dream Big Semiconductor (AI SuperNICs, acquired by Arm), Alphawave (high-speed SerDes IP, acquired by Qualcomm), and Silicon Box (a chiplet advanced packaging foundry). VeriSilicon itself thrives on the AI ASIC and IP boom in China. Collectively, the family's AI infrastructure-related portfolio is estimated at over $22 billion. Their strategy represents a distinct path: building critical components for open standards and key manufacturing capacity in the chiplet era, rather than pursuing standalone AI chip dominance. While this path may not create the next NVIDIA, it has enabled repeated successful exits and sustained influence within the global semiconductor industry.

marsbitHá 36m

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges

marsbitHá 36m

Microsoft is Afraid of Being Marginalized by AI Giants

Microsoft, once the defining force of the PC era, now faces a familiar challenge in the AI age: the risk of being relegated to a profitable but invisible infrastructure provider. This anxiety was laid bare at Build 2026, where CEO Satya Nadella unveiled a major strategic pivot. The catalyst was a quiet April agreement that dissolved Microsoft's exclusive licensing and cloud-hosting deal with OpenAI, its once-vital partner. This erased Microsoft's key AI moat. With OpenAI and Anthropic defining AI applications and gaining enterprise traction—even within Microsoft's own ranks—Nadella had to answer: without exclusivity, what is Microsoft's role? The answer was a suite of seven in-house AI models, a developer-focused AI workstation (Surface RTX Spark Dev Box), and, most crucially, the Agent 365 platform for enterprise AI governance. The models, notably targeting Anthropic's strengths in coding and enterprise, signal a defensive move. However, the broader strategy is to make the models themselves less decisive. Financially, Microsoft's AI revenue is strong, driven largely by Azure running others' models. Yet its user-facing products like Copilot show weak penetration and engagement. Microsoft earns infrastructure money but lacks direct user mindshare. Nadella's core fear is being "hollowed out." As OpenAI and Anthropic prepare for IPOs and gain financial independence, they may build their own infrastructure, threatening Azure's lucrative AI revenue stream. Microsoft's window is to entrench itself deeper: not as the model creator, but as the indispensable platform for securely deploying, managing, and governing all AI models within the enterprise through Agent 365. Build 2026 revealed Microsoft's bet: in the AI era, the ultimate power lies not in any single model, but in the enterprise "operating system" that controls them. Nadella is determined to ensure Microsoft is the driver of this new era, not just a passenger.

marsbitHá 50m

Microsoft is Afraid of Being Marginalized by AI Giants

marsbitHá 50m

CPU, Quietly Returning to the Center of the AI Computing Power Stage

Over the past three years, AI computing power narratives have been dominated by GPUs. However, starting in 2026, this story began to shift. While training large models remains GPU-intensive, the rapid growth of inference and AI agent workloads, which require high levels of task orchestration, concurrency, and data flow management, has highlighted a renewed critical role for CPUs. These are tasks GPUs are not designed to handle. Intel's recent launch of the Xeon 6+ processor, built on its Intel 18A process and featuring up to 288 efficiency cores (E-cores), exemplifies this strategic pivot. It is positioned not as a mere companion to GPUs but as the essential "control plane" for AI infrastructure, optimized for high-density, energy-efficient, and high-throughput workloads characteristic of AI agents and inference. This "CPU resurgence" is not about CPUs outperforming GPUs in raw computation. It reflects a systemic bottleneck: as AI scales from training single models to deploying countless intelligent agents, the demand for coordination and data handling surges. Major cloud providers are also developing their own high-density ARM-based server CPUs for similar workloads. However, Intel's success with this strategy faces significant challenges. Competition includes NVIDIA's integrated CPU-GPU solutions, the expanding adoption of cloud vendors' in-house ARM CPUs, and the crucial market test of Intel's 18A manufacturing process against rivals like TSMC's N2. In conclusion, CPUs are indeed reclaiming a central, though redefined, role in AI compute—managing the complex orchestration that enables massive-scale AI deployment. While the trend is clear, which company will ultimately lead this CPU resurgence remains an open question to be decided in the data centers of 2027 and beyond.

marsbitHá 1h

CPU, Quietly Returning to the Center of the AI Computing Power Stage

marsbitHá 1h

Trading

Spot
Futuros
活动图片