隐私赛道爆发,一文详解a16z连续领投两轮的隐私区块链Seismic

marsbitPubblicato 2025-11-16Pubblicato ultima volta 2025-11-17

随着 ZEC 等隐私币近期迎来价格上涨行情,隐私板块重新成为市场关注焦点。a16z crypto 近日在社交媒体上发文强调,「随着加密货币触达更多主流用户,隐私需求比以往任何时候都更加紧迫」。

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在这一背景下,隐私区块链项目 Seismic 宣布完成新一轮 1000 万美元融资,这已是 a16z crypto 今年内第二次领投该项目。项目已完成两轮融资,总额达 1700 万美元。2025 年 3 月,其宣布获得 700 万美元种子轮融资,由 a16z crypto 领投, Polychain、1kx 等参投;11 月 12 日,再次完成 1000 万美元融资,a16z crypto 继续领投,新增 Amber Group 等投资者,资金主要用于技术研发与生态拓展。

项目定位与技术特色

Seismic 是一个专为金融科技场景设计的隐私保护区块链。该项目基于 EVM 构建,开发者可以使用熟悉的 Solidity 语言进行开发,只需通过额外的语法控制隐私设置。这种设计降低了开发者的学习成本,同时提供了必要的隐私保护能力。

该项目的核心技术特色体现在三个方面。通过协议层加密和安全硬件支持,Seismic 实现了默认的隐私保护设置。项目采用可信执行环境(TEE)技术,而非零知识证明方案,所有节点必须在 TEE 中运行以确保数据安全。系统新增了屏蔽数据类型,包括屏蔽整数、布尔值和地址,使开发者能够方便地处理隐私数据。

此外,Seismic Reth 作为其原生执行客户端,进一步强化了隐私能力:支持私有存储、加密交易,并集成了 6 个新增预编译合约(如 AES-GCM 加密、ECDH 密钥交换),方便开发者快速调用隐私功能。该客户端已于 2024 年 10 月开源,以降低开发者接入门槛。

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Seismic 由 Lyron Co Ting Keh 创立,创始人 Lyron 拥有斯坦福大学计算机科学与数学学士学位,曾创办过教育科技企业,开发过 Ruby on Rails 培训平台,并参与过 Zcash、Aztec 等隐私项目的工具开发。

核心工程师 Peter He 此前主导过 Ethereum 应用专用区块链框架的开发,擅长分布式系统与隐私技术,目前负责 Seismic 的技术落地。业务开发负责人 Terence 则专注于推动 fintech 合作,致力于将 Seismic 的隐私能力输出至更多金融场景。团队虽未全员公开,但从公开履历看,成员多来自 Wintermute、Google X、Apple R&D 等机构。

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今年 9 月,基于 Seismic 构建的首个应用 Brookwell 正式亮相。这款由创业者 Ravi 和 Rohan 开发的金融科技产品,旨在让用户在使用传统支付渠道的同时获得 DeFi 收益。

Brookwell 选择在 Seismic 上构建,主要是看中其隐私保护能力。该应用处理包括房租支付、工资发放等深度个人金融交易,这些敏感数据不适合在公有链上公开可见。通过 Seismic 的隐私保护技术,Brookwell 能够在保护用户隐私的前提下,实现加密货币与传统金融系统的连接。目前项目官网已开放 Waitlist 申请。

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Robinhood is launching its own Layer 2 blockchain (Robinhood Chain) and "tokenized stocks," but these are not actual equity shares. The tokens are legally structured as debt securities or derivatives, offering economic exposure to a reference stock without granting voting rights or direct ownership. This move represents Robinhood's strategy to expand from a traditional brokerage into a "financial super app," building a user-friendly, programmable financial interface on top of complex, legally compliant, and jurisdiction-specific backend structures. The company's existing business remains strong, driven by options, event contracts, and stock trading. The new blockchain and tokenization efforts are an ambitious layer of infrastructure built atop this core, aiming to make financial products more portable and globally accessible via crypto rails. Key components include the Robinhood Wallet, Bitstamp acquisition (for institutional reach), the Lighter perpetual contracts platform, and Robinhood Earn (DeFi yield). The central challenge is the "brokerage chain paradox": maintaining a simple, intuitive user experience while the underlying assets are highly structured, regulated, and legally distinct from direct ownership. The success of this strategy depends on users, developers, and regulators accepting this model. If the complexity is misunderstood or deemed misleading, it could create product liability issues and stall expansion. The initiative is a significant infrastructure play, but its long-term viability hinges on navigating this fundamental tension between simplicity and legal reality.

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Zhipu, Afraid of Becoming the Next MiniMax

Title: Zhipu, Fearing to Become the Next MiniMax In July 2026, amid the success of its coding-focused AI, Zhipu's founder, Tang Jie, issued an internal letter titled "The Giant Wave Has Come." It notably avoided celebrating recent triumphs, such as Zhipu's trillion-HKD market cap and booming MaaS revenue driven by its GLM-5.2 model in coding applications. Instead, the letter pivoted the narrative to future-oriented concepts like Long Horizon Task, Autonomous Agents, Self-Evolving systems, and AGI. This strategic shift in messaging followed the sharp devaluation of its competitor, MiniMax. After its lock-up period expired, MiniMax's stock plummeted as the market began evaluating it with traditional SaaS metrics like ARR and user growth, rather than as a frontier AI pioneer. Seeing this, Tang Jie aimed to preempt a similar revaluation of Zhipu. He fears that if the market starts viewing Zhipu primarily as a profitable "AI coding company," its valuation would become anchored to conventional financial metrics, losing the premium associated with AGI potential. Therefore, the letter reframed Zhipu's mission. While acknowledging that coding was the current commercial driver, Tang positioned Zhipu on the "infrastructure path," akin to OpenAI and Anthropic. The new focus is on developing agents capable of complex, long-term planning and autonomous operation—moving from assisting individuals (OPC: One Person Company) to automating entire organizations (NPC: No People Company). This "Touch High" plan explicitly prioritizes long-term AGI research over short-term monetization. The article frames this as a critical divergence in China's AI landscape: the "commercialization path" (exemplified by MiniMax) versus the "infrastructure path" (chosen by Zhipu). The former risks being judged harshly by internet-era metrics once growth slows, while the latter risks failing if technological breakthroughs stall. Tang Jie's letter is thus a calculated move to secure Zhipu's identity as an AGI contender, buying time before the inevitable market demand for commercial proof. The core question remains: can Zhipu's "mo gao" (reach high) plan achieve genuine technological leaps fast enough to outpace the market's diminishing patience for stories over substance?

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