【一周靓文】Blur空投激发市场热情,比特币风光独好?

火币资讯Pubblicato 2023-02-18Pubblicato ultima volta 2023-02-18

Introduzione

一周靓文,介绍过去一周最值得关注的热点文章,帮助投资者深刻理解市场动态。

一周靓文,介绍过去一周最值得关注的热点文章,帮助投资者深刻理解市场动态。

1、《情人节特辑:Blur空投+发币,惊喜在哪里(The Floor is All Yours, Blur)》

如果看⼀下最近3个⽉的NFT交易量市场流量排名,不难发现Blur的总交易量超过了OpenSea,⼏乎所有的NFT项⽬的地板都挂在Blur上。

究竟是什么原因让Blur如此成功?

它的成功可以持续下去吗?

本⽂会带你详细剖析⼀下Blur的前世今⽣。

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2、《Blur发币 它是如何在短期之内超越Opensea的?未来潜力如何》

Blur是一个针对专业交易员的聚合NFT交易平台,其于2022年10月正式上线。

根据 Dune Analytics 数据,截至 2 月为止,Blur 每天都在整个 NFT 领域的日交易量中名列前茅,在 1 月初和 12 月的大部分时间里,Blur 的交易量也超过了 OpenSea。

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3、《美国CPI数据高于预期,加息之路持续》

伴随 Token 发行预期,Arbitrum 生态近期成了市场关注的最大热点,生态内的诸多项目 Token 均有较大幅度的上涨。当前社区都在讨论什么项目?它们都布局在哪些赛道?

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4、《美国SEC等监管机构前后夹击Paxos,醉翁之意或不在BUSD》

BUSD是否会出现挤兑风险?稳定币又是否应被视为证券?稳定币的监管管辖权究竟该归属哪个部门?

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5、《【研报精选】比特币的过去,现在和未来(一):步步回首》

The Generalist 最近发了一篇文章:主要讲了关于谷歌、ChatGPT 和搜索的未来。其中探讨了几个问题:自去年年底 ChatGPT 推出以来,OpenAI 产品一直被誉为是:搜索的未来,也是潜在的谷歌杀手。那么,谷歌将跟随大英百科全书和黄页一样,逐渐被颠覆吗,还是,ChatGPT 出现,仅仅会带动谷歌的转型?

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6、《【研报精选】比特币的过去,现在和未来(二):中本聪其人》

中本聪的第一个被记录下来的踪迹可以追溯到 2008 年,虽然在许多公开的帖子中,这位匿名人士(也许是匿名人士团体)漫不经心地表示他在 2007 年就已经开始开发叫做 “比特币(Bitcoin)” 的项目了。想象一下,2007 年,这地球上的某个地方,某人已经开始开发比特币了。

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7、《【研报精选】板块轮动不在?这波“小牛市”的背后推动力是?》

方舟投资在报告中指出,加密货币和智能合约在未来十年内,可以分别达到20万亿美元和5万亿美元的市值,2030年,比特币价格将达到100万美元一枚。

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Letture associate

Both Suffer Massive Losses Exceeding $90 Billion, Which Is in Greater Peril: Strategy or Bitmine?

Facing massive paper losses exceeding $90 billion each amidst a sharp market downturn, "Digital Asset Treasury" (DAT) giants Strategy and Bitmine find themselves in a precarious position, but with different underlying risks. Strategy, heavily invested in Bitcoin (BTC), faces significant financial strain. Its strategy relies heavily on debt, including convertible notes and preferred stock (STRC) requiring substantial dividend payments. With its cash reserves dwindling and BTC offering no staking yield for cash flow, Strategy's high leverage makes it vulnerable. A continued price decline could force asset sales to meet obligations, potentially creating a negative feedback loop. Its market value has already fallen sharply. In contrast, Bitmine, an Ethereum (ETH) holder, appears on firmer financial ground. It primarily funds its purchases through equity offerings (like ATM programs), avoiding debt pressure. It also generates income by staking a large portion of its ETH holdings. While not immune to market drops and shareholder dilution concerns, Bitmine maintains more flexibility, recently announcing a new preferred share offering to raise further capital. The core divergence lies in their financing: Bitmine uses equity (investor money), while Strategy uses debt (borrowed money). Consequently, Bitmine currently faces less immediate liquidity pressure than Strategy, which must navigate the dual challenge of servicing debt/dividends and a declining core asset (BTC) price.

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Both Suffer Massive Losses Exceeding $90 Billion, Which Is in Greater Peril: Strategy or Bitmine?

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Where the AI Bubble Really Is: Which Layer of Players Are Naked

AI Bubble: Where It Really Is and Who's Swimming Naked This analysis dissects the AI industry not as a single entity but as a five-layer pyramid, arguing that bubbles are concentrated in specific tiers, not uniformly distributed. **Key Distinction from the 2000 Dot-com Bubble:** Unlike 2000, where companies had stock prices before revenue, today's leading AI players have massive, contract-backed revenue driving their valuations. Core infrastructure demand is real, with every GPU running at full capacity for paying customers. **The Five-Layer Pyramid & Bubble Assessment:** * **L0 (Fab/Manufacturing) & Top L4 (Leading AI Apps): NO BUBBLE.** Companies like TSMC, NVIDIA, major cloud providers (Microsoft, Google, Meta, Amazon), and top AI labs have real revenues and orders. Supply is tightly constrained by TSMC's disciplined capacity control and physical limits like power/land for data centers, preventing a supply glut. * **L1 (Memory): BATTLEGROUND.** Sky-high HBM margins could signal a new structural cycle or a classic "boom before bust." The oligopoly of three major players may enforce supply discipline, making this a high-stakes bet. * **L2 (Interconnect/Optical Modules): BUBBLE TERRITORY.** Companies like Lumentum and AAOI have seen stock surges (4-10x) far outpacing revenue growth. This hardware segment has lower physical barriers to expansion than fabs, allowing speculation. It mirrors the 2000 bubble's epicenter—optics. * **L3 (Infrastructure/"GPU Landlords"): VULNERABLE.** GPU leasing companies profit from the current compute shortage but own no long-term moat. Their business model relies on a temporary bottleneck that will ease as big tech expands and new tech (e.g., potential space-based data centers) emerges. * **L4 Long Tail (VC-backed Startups): STRONG BUBBLE SIGNALS.** VC funding concentration in AI is twice that of the 1999 peak. Many startups with little revenue use the valuation logic of successful giants to justify their own, creating high risk of a "valuation crunch" when funding dries up. **Critical Risks to Monitor:** 1. **GPU Depreciation & Accounting:** Companies extending the assumed useful life of GPUs artificially boost profits. The true economic life depends on future generational leaps from NVIDIA. 2. **"GPU Credit" & Off-Balance-Sheet Leverage:** Emerging structures where shell companies borrow to buy GPUs and lease them out (with chipmakers sometimes investing) move debt off major balance sheets. This echoes the "vendor financing" of 2000 and the securitization risks of 2008, though currently small-scale. 3. **TSMC Abandoning Caution:** If the primary supply bottleneck (TSMC's conservative capacity planning) breaks, runaway supply could trigger a bust. 4. **Algorithmic Efficiency Breakthrough:** A major leap in software efficiency could drastically reduce the need for raw compute hardware, undermining the investment thesis. **Conclusion:** The AI boom is expensive and has frothy areas, but its core is underpinned by real demand and physical supply constraints. The bubble risk is layered: most present in optical components, GPU leasing, and the long-tail startup ecosystem, while the foundational chip manufacturing and leading application layers remain relatively solid—for now.

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Where the AI Bubble Really Is: Which Layer of Players Are Naked

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Standing in the Light: A Comprehensive Guide to the Optical Module and CPO Supply Chain

"Standing in the Light: Understanding the Optical Module and CPO Industry Chain" This article analyzes the critical role of optical communication technology, specifically optical modules and Co-Packaged Optics (CPO), as the "nervous system" for modern AI data centers. With exponential growth in AI computational demands (e.g., NVIDIA's Vera Rubin architecture), traditional electrical interconnects using copper cables face severe bottlenecks in bandwidth, power consumption, and signal integrity over distance. The core function of an optical module is to act as a "translator," converting electrical signals from chips into optical signals for transmission over fiber (and vice-versa). Key internal components include lasers, modulators, photodetectors, drivers, and DSP chips. The industry is currently transitioning from 800G to 1.6T modules. However, the future lies in CPO. This next-generation technology integrates the optical engine directly with the switch ASIC/XPU on the same package substrate, drastically reducing power consumption (by ~3.5x according to NVIDIA), overcoming bandwidth density limits, and minimizing signal attenuation compared to traditional pluggable modules. Key challenges for CPO include advanced packaging capacity (dominated by TSMC), thermal management, repairability, and standardization. The article details the broader technology landscape, including Near-Packaged Optics (NPO, a pragmatic intermediate step), Linear-drive Pluggable Optics (LPO), Optical I/O (OIO for chip-level integration), and Optical Circuit Switches (OCS). A comprehensive CPO industry chain is mapped, highlighting shifting power dynamics: * **Architecture Definers:** NVIDIA, Broadcom, and Marvell now hold greater influence. * **Advanced Packaging & Manufacturing:** TSMC is central; Fabrinet is a key EMS player. * **Lasers ("The Heart"):** A strategic bottleneck. EML lasers are led by Lumentum and Coherent (both receiving major NVIDIA investments). CW lasers, favored for CPO/silicon photonics, see strong Chinese players like Source Photonics and Sicoya. * **Silicon Photonics Chips:** The mainstream path for CPO engines, with key players like Broadcom, Intel, Marvell, and China's Accelink. * **Fiber Connectivity Components:** A major new, high-growth market created by CPO, including Fiber Array Units (FAU), Polarization-Maintaining Fiber (PMF), and MPO connectors. Companies like Tianfu Communication and US Conec are leaders. * **Fiber & Cable:** Experiencing a super-cycle (e.g., Corning, Yangtze Optical Fiber). * **PCB/Substrates:** Requiring advanced materials (e.g., Shengyi Tech). * **DSP & SerDes:** Functions are integrated into switch ASICs in the CPO era (e.g., Broadcom, Astera Labs). * **Optical Module Makers:** Transitioning from standalone module suppliers to providers of optical engines and NPO/LPO solutions while riding the current pluggable boom (e.g., Zhongji Innolight, Eoptolink). The investment timeline is segmented: Short-term (2026-2027) features the "last feast" for pluggable modules and CPO's initial rollout. Medium-term (2027-2029) will see CPO expand and NPO peak. Long-term (2029-2032+) involves CPO/OIO penetration into intra-rack scaling. In conclusion, optical interconnects are fundamental to AI infrastructure. The competitive landscape sees US firms leading in architecture and high-end chips, TSMC in advanced packaging, and Chinese firms holding strong positions in modules, connectivity components, CW lasers, and fiber/cable. The future belongs to companies that can navigate the technological shift from "selling shovels" (modules) to "building highways" (CPO/OIO infrastructure).

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Standing in the Light: A Comprehensive Guide to the Optical Module and CPO Supply Chain

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