星球日报 | SBF被判有罪；Paypal收到SEC关于稳定币PYUSD的传票（11.3）

Odaily星球日报Pubblicato 2023-11-03Pubblicato ultima volta 2023-11-03

Introduzione

Matrixport：比特币价格到年底有望涨至5.6万美元。

头条

SBF 被陪审团认定七项罪名指控成立，被判有罪

Sam Bankman-Fried 因 FTX 的崩溃而被判有罪。

经过 15 天的作证和大约四个半小时的审议，陪审团作出裁决，认定他犯有七项欺诈和共谋罪。

Paypal 收到 SEC 关于稳定币 PYUSD 的传票

PayPal Holdings 在周四表示，已于 11 月 1 日收到美国 SEC 执法部门关于其美元稳定币 PYUSD 的传票。PayPal 表示，传票要求提供相关文件，公司正在配合 SEC 就此请求展开合作。

行业要闻

Matrixport：比特币价格到年底有望涨至 5.6 万美元

加密服务提供商 Matrixport 预测，到今年年底，比特币的价格有望上涨至 56, 000 美元。

Matrixport 研究和策略主管 Markus Thielen 在周四发给客户的报告中表示：“如果比特币今年到此时至少上涨 100 ％，那么比特币在年底结束时上涨的可能性为 71 ％，（按照历史规律）其年底平均涨幅达 65 ％。由于比特币价格往往会在 12 月 18 日达到峰值，我们可以把 11 月初到 12 月中旬的六到七周称为比特币的圣诞反弹行情（Santa Claus Rally）。”
Thielen 指出，根据历史数据分析，当比特币到 10 月底上涨至少 50% 时，平均而言，比特币有 78% 的机会在年底进一步上涨。截至年底，比特币在之前的 9 次反弹行情中有 7 次平均上涨 68% 。

MicroStrategy 10 月购入 155 枚 BTC，总持仓 158, 400 枚

MicroStrategy 官方发布了 2023 年第三季度财务报告，显示在十月份以 530 万美元的价格额外购买了 155 枚 BTC。截至 2023 年 10 月 31 日，MicroStrategy 共持有 158, 400 个比特币，总成本为 46.9 亿美元，即每个比特币 29, 586 美元。

MicroStrategy 三季度总收入达 1.295 亿美元，同比增长 3% ，软件许可收入为 4500 万美元，同比增长 16% ，订阅服务收入为 2100 万美元，同比增长 28% 。

香港证监会将发布两份代币化相关通知，其中之一重点关注对发行授权基金的要求

香港证监会行政总裁梁凤仪今日在香港第八届香港金融科技周主论坛上表示，该监管机构计划在今天晚些时候发布两份关于代币化的通知。

她表示，其中一份通知将涉及“识别这项新技术的风险”以及“监管机构在进行尽职调查、转让或首次发行时对中介机构的期望”。
梁凤仪补充说，另一份待定通知将重点关注证监会对发行授权基金的要求，“我们对它的所有期望都将有额外的保障措施。”
她补充称：“这一切都是为了确保资产被安全托管、转移并记录所有权，因为这毕竟是一项新技术。虽然我们支持这个行业，进行试验、创建更多的用例，但我们也看到了与创新技术相关的新风险，特别是在这些代币的转移，所有权和记录保存方面。”

香港持牌交易所 OSL：APP 拟于 11 月上架，不会推出平台币

香港持牌虚拟资产交易所 OSL 首席财务官胡振邦表示，目前已完成 APP 开发工作，正在进行各项安全测试，预计将在本月内上架应用商店。

不过胡振邦表示，料不会推出平台币，而是重点关注人工智能应用，APP 逐步完善后将配合市场技术推出更多服务。

以太坊信标链质押总量突破 2800 万枚 ETH，上海升级后净流入逾 838 万枚 ETH

Dune 数据显示，以太坊信标链质押总量达 28, 017, 509 枚 ETH，质押 ETH 占 ETH 总供应量的 23.31% 。其中，流动性质押协议 Lido 的质押份额达 31.42% 。此外，自上海升级后净流入 8, 382, 248 枚 ETH。

CCData：Coinbase 在 CEX 中安全评级最高，DEX 中仅 Uniswap 获得 AA 评级

根据 CCData 发布的最新交易所基准报告，Coinbase、Bitstamp、Kraken 等七家中心化现货交易所获得最高评级 AA。Coinbase 在中心化交易所基准中脱颖而出，拥有最高的安全评级，取代了 Bitstamp 成为榜首。

在衍生品交易所类别中， 27 个平台中有 8 个获得顶级评级“BB+”；OKX 位居榜首，获得 AA 评级。Bybit 也获得 AA 评级。
在去中心化交易所中，仅 Uniswap 获得 AA 评级，因其卓越的安全性和流动性而受到认可。Curve 获得 A 评级，dYdX 和 GMX 获得 BB 评级。
监管合规度正在上升，根据 VASPnet 的数据， 107 家中心化交易所中有 75 家持有监管许可证。
交易所在 KYC 实践方面的水平正在提升，平均 KYC 评分从六个月前的 2.8 增加到 3.2 （满分为 4）。

FTX 相关事件

SBF 律师完成结案陈词，坚持为 SBF 做无罪辩护

SBF 代理律师 Mark S. Cohen 于当地时间周三晚间于法庭上完成结案陈词，寻求法庭及陪审团认定 SBF 在经营 FTX 和 Alameda Research 期间始终“善意”行事，因此不能判其犯有欺诈罪。

关于去年 11 月导致 FTX 破产的事件和决定，以及 Alameda 挪用了交易所数十亿美元客户资金的披露，Cohen 提供了他所谓的“另类历史”，而不是检察官的说法。“ Sam（SBF）竭尽全力在新市场创办和经营两项价值数十亿美元的企业，”Cohen 在向陪审员发表充满感情的结案陈词时说道，“有些决定结果很好，有些决定结果很糟。”
其还表示，是“现实世界的沟通不畅”、“错误”和“延误”对 FTX 以及相关公司造成了破坏，而不是故意欺诈。

FTX/Alameda 将 4600 万美元的 9 项资产转移至 Coinbase 等三家交易所

据 Spot On Chain 监测，过去 7 小时内，FTX/Alameda 将价值 4600 万美元的资产转移至 Kraken、币安和 Coinbase。

自 10 月 26 日以来，FTX/Alameda 已转出价值约 1.7 亿美元的 30 项资产。

项目要闻

Memeland 联创：正在开发 SocialFi，社区可自行赋能 MEME 代币

NFT 项目 Memeland 联创 Ray Chan 在 Twitter Space 表示，Memeland 是社区型公司，以社区发展为重心，正努力开发 SocialFi，已推出 3 个系列的 NFT 产品。

Ray Chan 表示，Memecoin（MEME）代币没有任何承诺与路线图，团队在发行代币时关注的是公平机制，社区可以自行赋能。Memeland 可能会推出元宇宙产品。

数据分析基础设施 Dune 推出 DuneAI

Web3数据分析基础设施 Dune 宣布推出 DuneAI，用户可以使用其自然语言引擎以任何语言提出问题，无需了解 SQL 即可获得加密数据见解。

USDT 总市值突破 850 亿美元，创历史新高

CoinGecko 数据显示，USDT 总市值突破 850 亿美元，创历史新高。

LSD 稳定币协议 Prisma Finance 宣布治理代币 PRISMA 已上线以太坊主网

据官方消息，LSD 稳定币协议 Prisma Finance 正式宣布治理代币 PRISMA 已上线以太坊主网，并公布代币合约地址。

CoinGecko 数据显示，PRISMA 暂报 3.27 美元。

DeFi 借贷协议 Kinza Finance 推出空投积分系统，以确定 KZA TGE 后空投分配

据官方消息，DeFi 借贷协议 Kinza Finance 推出空投积分系统，可根据用户在 Kinza 主网上存入流动性的金额和时长，提供空投积分实时反馈，以确定用户在 KZA TGE 之后符合条件的空投分配数额。

Aragon 宣布解散，将向 ANT 持有者提供 8.6 万枚 ETH 用于兑换 ANT

Aragon 宣布解散，将向 ANT 持有者提供 8.6 万枚 ETH 用于兑换 ANT，兑换价为 0.0025376 ETH / ANT。剩余资金将用于产品开发。

Aragon 协会表示，由于法律限制，特别是代币投机和市场操纵引发的监管风险，该决定无法提交公众投票，但考虑了 Aragon 治理论坛的意见。

人物*声音

资管公司 AllianceBernstein：现货比特币 ETF 若获批，将吸引比特币流通供应量的 10%

全球资产管理公司 AllianceBernstein 预计，到 2025 年，比特币的价格将达到 15 万美元。该公司专门研究数字资产的高级分析师 Gautam Chhugani 在一份报告中写道，这一预测是由美国 SEC 将很快批准现货比特币 ETF 的乐观情绪推动的。

此外，该公司预测，现货比特币 ETF 的批准将使比特币流通供应量的 10% 进入 ETF。

Letture associate

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.

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

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Zcash Bug Could Have Minted Unlimited ZEC Undetected

A critical vulnerability in Zcash's Orchard shielded pool, discovered by researcher Taylor Hornby on May 29, 2026, could have allowed an attacker to create an unlimited amount of undetectable counterfeit ZEC. The flaw, involving an under-constrained element in the Orchard circuit, existed from the pool's 2022 activation until an emergency fix was deployed by June 2, 2026. Hornby identified the bug using AI-assisted auditing tools and confirmed its exploitability in a test environment. Due to Orchard's privacy features, which hide transaction amounts and history, there is no cryptographic way to prove whether the vulnerability was exploited before the fix. While Shielded Labs assesses prior exploitation as unlikely, this uncertainty has sparked a debate on proving supply integrity in privacy-preserving systems. In response, Shielded Labs and other developers are exploring a network upgrade, potentially involving a new shielded pool and formal verification of the circuit rules to prevent future vulnerabilities and allow verification of the ZEC supply's integrity. ZEC's price fell nearly 45% following the disclosure.

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Zcash Bug Could Have Minted Unlimited ZEC Undetected

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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.

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

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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.

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Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

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Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

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