这个投资者日,闪迪的「炸裂数字」震撼市场

marsbitPublished on 2026-08-14Last updated on 2026-08-14

Abstract

在2026年投资者日上,闪迪公布了2028至2030财年(约2027年7月起的三个财年)的激进长期财务目标,引发市场强烈反应,其股价盘中一度大涨近18%。 **核心财务目标**:公司预计在此期间营收将保持**中高双位数增长**,同时非GAAP口径下的毛利率和营业利润率将分别达到约**80%**和**75%**,调整后自由现金流利润率约**50%**。 **战略重心转变**:闪迪强调将不再盲目追求比特出货量,而是根据**盈利能力优化**的需要,主动灵活调整可供销售的比特量。这意味着公司更看重“每比特的利润”,而非单纯的数量增长。通过在新NAND技术节点切换时主动控制晶圆产出,公司旨在维护市场价格和资本效率。 **资本回报承诺**:公司承诺,在完成支持业务增长的必要投资后,将把**100%的超额现金**返还给股东。 **增长信心来源**: 1. **新商业模式**:已与8家客户签署长期协议(NBM),覆盖了2028财年约三分之二的比特出货量,旨在稳定收入、降低行业周期性影响。 2. **AI推理需求**:公司判断,AI从训练转向推理将催生巨大存储需求,预计到2030年,企业数据中心闪存总市场(TAM)将达到**1.2泽字节(ZB)**。闪迪正在推进高带宽闪存(HBF)等技术以抓住此机遇。 总体而言,闪迪向市场传递了明确信号:AI驱动的需求增长,结合公司对供给的主动管理和新的商业模式,有望使其未来数年摆脱传统存储行业的强周期性,实现持续的高增长与高盈利。

原文作者:李丹

原文来源:华尔街见闻

AI 推理需求持续推高存储需求之际,闪迪给出了极为激进的长期财务目标。

美东时间 13 日周四,闪迪在 2026 年投资者日公布长期财务模型,提出一系列 2028 财年至 2030 财年将要达到的财务目标,包括这期间内,公司营收将保持中高双位数增长。闪迪还澄清市场对比特增长的疑虑,提出可供销售比特量将根据盈利优化需要调整,同时承诺,在完成业务投资后,将把 100%的剩余现金返还股东。

消息公布后,市场迅速用股价投票。闪迪(SNDK)周四盘中涨幅一度扩大至17.6%;存储板块同步走强。到收盘,闪迪涨近 14%,SK 海力士和西部数据涨超 7%,希捷科技涨近 5%,美光科技涨逾 4%,

FY2028-30 目标:毛利率 80%、营业利润率 75%

此次投资者日最受市场关注的,无疑是闪迪给出的长期财务模型。

公司预计,在2028 至 2030 财年期间,营收将保持中高双位数增长,增速与比特出货量增长相匹配;与此同时,非 GAAP 口径下,毛利率预计维持在约80%,营业利润率约为75%。

这里需要特别注意财年的时间概念。闪迪的财年并不与自然年重合,公司财年截至最接近 6 月 30 日的周五,通常为 52 周,2026 财年、即上一财年于 2026 年 7 月 3 日结束。2026 年 7 月初已经进入 2027 财年,因此此次公布的 2028 至 2030 财年区间并非自然年 2028 年至 2030 年,而是指从 2027 年 7 月左右开始、连续三个财年的未来区间

在这一财务模型下,闪迪预计运营费用占营收比例约为5%,其他收入及支出不会产生重大影响。即使计入税收、资本开支以及支撑业务增长所需的营运资金,公司仍预计调整后自由现金流利润率能够达到约50%

对于此前高度周期性的 NAND 存储行业而言,这组目标尤其激进。闪迪实际上是在向市场传递一个明确判断:AI 驱动的存储需求增长,有望让公司未来数年的营收增长与盈利能力保持在远高于传统存储周期平均水平的位置。

不盲目追求“比特量”:闪迪将根据盈利能力灵活调节可销售产量

而闪迪此次释放的另一个重要信号,是公司并不准备简单地以扩大比特出货量作为增长目标,而是会主动根据盈利能力调整可供销售的比特量

此前市场曾关注闪迪 FY2027 的比特量增长指引:公司预计输入端比特量增长达到中高双位数,而可供销售的输出比特量增长则可能低于这一水平。投资者日上,管理层进一步澄清,这并不意味着公司缺乏扩大产出的能力。

据 Lynx Equity Strategies 分析师 KC Rajkumar 对管理层表态的解读,闪迪 CEO David Goeckeler 明确表示,长期输入比特量增长目标为中高双位数,但可供销售的比特量将根据优化盈利能力的需要灵活调整;在某些时期,实际输出比特量增长甚至可能超过中高双位数。

这意味着,闪迪更看重的是“每一比特能赚多少钱”,而不是单纯追求“卖出多少比特”。

尤其是在 NAND 技术节点切换过程中,公司还会有选择地削减晶圆产出,以避免新技术带来的比特密度提升过快、进而导致市场供应过剩。

Rajkumar 指出,闪迪每次 NAND 技术节点切换平均能够带来约54%的比特增长。因此,如果公司完全释放技术升级带来的新增产能,很容易重新制造供过于求的局面。通过在节点切换期间主动减少晶圆产出,闪迪可以控制进入市场的比特量,从而更好地维护价格、利润率和资本效率。

这套思路也解释了为什么闪迪敢于给出约 80%的长期毛利率目标:技术进步带来的比特密度提升,不一定全部转化为供给增长,公司可以主动“踩刹车”,将部分技术红利转化为盈利能力。

“赚到的钱”更多返还股东:超额现金 100%回购或分红

除了营收和利润率目标,闪迪还给出了非常明确的资本回报承诺。

闪迪首席财务官(CFO)Luis Visoso 表示,在完成支持业务增长所需的投资之后,公司预计将把100%的超额现金返还给股东

这意味着,闪迪未来的资本配置框架将围绕三条主线展开:首先投资于支持增长的业务和技术,其次保持强劲的自由现金流创造能力,最后将剩余现金尽可能返还给股东。

而约50%的调整后自由现金流利润率,意味着如果长期财务模型能够兑现,闪迪未来产生现金的能力将非常强,这也是市场愿意给予其更高估值的重要原因之一。

八大客户签长期协议,已覆盖 FY2028 约三分之二比特出货量

闪迪之所以对上述长期财务模型表现出较强信心,一个重要原因在于公司正在改变传统 NAND 行业的商业模式。

公司披露,目前已经与8 家客户签署新商业模式(NBM)协议。这些协议包括承诺采购量、具有约束力的合同框架、最低财务保障以及结构化定价机制,可以加强客户需求与公司产能规划之间的匹配,并降低传统存储行业周期性波动的影响。

更重要的是,这些协议覆盖的规模已经相当可观:目前签署的 NBM 协议约覆盖FY2027 比特出货量的 50%,以及FY2028 比特出货量的约三分之二

闪迪认为,这种模式能够带来更加可预测的营收、更高的现金流可见度以及更加持久的盈利增长。

换句话说,闪迪不仅在押注 AI 带来的存储需求增长,也在试图通过长期协议把传统 NAND 业务较强的周期属性部分转化为更加稳定、可预测的收入和现金流。

AI 推理催生更大存储市场,2030 年企业级闪存 TAM 料达 1.2ZB

闪迪对未来三年高增长的另一大支撑,是 AI 从训练向推理扩张后,对存储基础设施提出的新需求。

闪迪估计,闪存市场可能会从历史上每年 600 亿美元的收入基础增长到 2026 年的 3000 亿美元以上,并在 2027 年接近 5000 亿美元。

公司表示,AI 推理工作负载正在推动 Token 使用量快速增长,KV Cache 正在重塑数据中心的内存层级。随着 AI 推理规模扩大,AI 数据中心将变得更加依赖存储,闪迪预计,到2030 年企业数据中心闪存的总可用市场(TAM)将达到 1.2 泽字节(ZB)

在技术层面,闪迪正在推进基于 CMOS 直接键合阵列(CBA)的二维扩展战略,以便更加灵活地开发满足不同市场需求的定制产品,同时提高资本效率。

公司最新的 BiCS9 QLC 技术就是这一战略的首个案例。该技术将 BiCS8 阵列与基于 BiCS10 的 CMOS 晶圆结合;与此同时,新的 BiCS10 QLC 节点较 BiCS8 实现60%的比特密度提升

HBF 押注 AI 推理,存储板块集体大涨

闪迪还在推进面向 AI 推理的新型高带宽闪存(HBF)技术。公司称,HBF 正在成为应对 AI 推理时代存储需求的重要技术方案,目前相关产业生态也正在形成。

在市场看来,这意味着闪迪的 AI 存储逻辑已经不再局限于“AI 数据中心需要更多 SSD”,而是进一步延伸至 AI 推理架构本身对更高性能、更低功耗和更高存储密度的需求。

这一预期也迅速传导至整个存储板块。闪迪自身的涨势更为突出:在公布长期财务目标后,其股价盘中一度涨近 18%。今年以来,闪迪股价累计涨幅已经超过 530%。

不过,闪迪同时强调,前述长期财务目标属于前瞻性指标,建立在一系列估计和假设基础上,实际结果仍可能受到需求、平均售价、竞争、技术迭代、供应链以及存储行业周期等因素影响。

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Related Questions

Q闪迪在投资者日公布的2028至2030财年长期财务目标中,预计的毛利率和营业利润率分别是多少?

A在2028至2030财年期间,闪迪预计非GAAP口径下的毛利率将维持在约80%,营业利润率约为75%。

Q根据文章,闪迪提出了怎样的资本回报承诺?

A闪迪承诺,在完成支持业务增长所需的投资之后,公司将把100%的超额现金返还给股东,主要通过回购或分红的方式。

Q闪迪如何看待并管理其比特出货量的增长?

A闪迪并不单纯追求比特出货量的增长,而是会根据盈利优化的需要,灵活调整可供销售的比特量。这意味着公司更看重‘每一比特能赚多少钱’,而非‘卖出多少比特’,并会在NAND技术节点切换期间主动控制晶圆产出以避免市场供应过剩。

Q闪迪提到的‘新商业模式(NBM)协议’有什么特点?目前已覆盖多少出货量?

A新商业模式(NBM)协议包括承诺采购量、具有约束力的合同框架、最低财务保障以及结构化定价机制。目前,闪迪已与8家客户签署此类协议,这些协议约覆盖2027财年比特出货量的50%,以及2028财年比特出货量的约三分之二。

Q闪迪预测AI推理的发展将如何影响企业级闪存市场?

A闪迪预测,AI推理工作负载将推动存储需求快速增长。随着AI推理规模扩大,数据中心将更加依赖存储。公司预计,到2030年,企业数据中心闪存的总可用市场(TAM)将达到1.2泽字节(ZB)。

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Below is a suggested timeline mapping significant events in the evolution of SPERO,$$s$: Conceptualisation and Ideation Phase: The initial ideas forming the basis of SPERO,$$s$ were conceived, aligning closely with the principles of decentralisation and community focus within the blockchain industry. Launch of Project Whitepaper: Following the conceptual phase, a comprehensive whitepaper detailing the vision, goals, and technological infrastructure of SPERO,$$s$ was released to garner community interest and feedback. Community Building and Early Engagements: Active outreach efforts were made to build a community of early adopters and potential investors, facilitating discussions around the project’s goals and garnering support. Token Generation Event: SPERO,$$s$ conducted a token generation event (TGE) to distribute its native tokens to early supporters and establish initial liquidity within the ecosystem. Launch of Initial dApp: The first decentralised application (dApp) associated with SPERO,$$s$ went live, allowing users to engage with the platform's core functionalities. Ongoing Development and Partnerships: Continuous updates and enhancements to the project's offerings, including strategic partnerships with other players in the blockchain space, have shaped SPERO,$$s$ into a competitive and evolving player in the crypto market. Conclusion SPERO,$$s$ stands as a testament to the potential of web3 and cryptocurrency to revolutionise financial systems and empower individuals. With a commitment to decentralised governance, community engagement, and innovatively designed functionalities, it paves the way toward a more inclusive financial landscape. As with any investment in the rapidly evolving crypto space, potential investors and users are encouraged to research thoroughly and engage thoughtfully with the ongoing developments within SPERO,$$s$. The project showcases the innovative spirit of the crypto industry, inviting further exploration into its myriad possibilities. While the journey of SPERO,$$s$ is still unfolding, its foundational principles may indeed influence the future of how we interact with technology, finance, and each other in interconnected digital ecosystems.

429 Total ViewsPublished 2024.12.17Updated 2024.12.17

What is $S$

What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

1.1k Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

Discussions

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