散户“带头大哥”Serenity vs 新晋股神Leopold:两大顶级猎手如何掘金AI“物理极限”?

marsbitPublicado em 2026-05-27Última atualização em 2026-05-27

Resumo

过去两年,单纯买入英伟达的策略已逐渐失效。如今,两位风格迥异的投资者成为AI投资领域的新风向标。 一位是匿名交易者Serenity,他隐藏在社交媒体背后,通过拆解AI供应链最底层的“卡脖子”环节进行投资。他提出“紫苏叶理论”,专注于寻找那些市值小、技术垄断性强、对产业链不可或缺的隐形厂商,例如在光电共封装(CPO)等领域的关键材料供应商。据称,其投资年内获得了惊人收益。 另一位是年仅24岁的Leopold Aschenbrenner,他曾是OpenAI研究员,后创办规模超百亿美元的对冲基金。他的核心理念是,AI发展的真正瓶颈在于电网、土地、数据中心等物理基础设施,而非算法本身。其投资策略是“基础设施套利”:一方面重仓押注存储硬件、算力云和比特币矿企等物理资源;另一方面,大规模做空半导体板块,认为芯片估值已严重脱离基础设施的实际建设速度。 两人策略的核心共同点是:抛弃软件层叙事,重仓受物理法则约束的硬件和基础设施。他们都认为,谁掌握了物理世界的稀缺资源,谁就掌握了AI时代的“算力买路财”。 然而,两种策略也各有风险。Serenity推荐的微盘股面临流动性低、波动性巨大的风险,且其个人背景未经证实,跟单需谨慎。Leopold的宏观押注则面临市场情绪滞后和巨头股价持续强势的挑战,可能导致其空头头寸承受巨大压力。 他们的走红标志着AI投资逻辑的转变:产业价值正从半导体本身,向更底层的材料、设备、能源和土地等物理瓶颈环节迁移。

作者:Jae,PANews

过去两年,曾经最简单、最暴利的初代多头逻辑是买入英伟达,但这套策略正在失效。当所有人都知道H100供不应求,每一份财报都像复制粘贴一般超预期时,Alpha就消失了。

真正的聪明钱开始穿透软件层与PPT叙事,重新审视AI运转背后的物理根基。今年,两个风格迥异的人,成了AI投资领域最受瞩目的新风向标。

一位隐藏在X平台女性动漫头像背后的的匿名交易者,他自称拒绝过英伟达Offer、发表过Nature论文,靠拆解供应链最底层的元器件,已在年内斩获45倍惊人收益。没有人知道他的真实身份,只知道他叫Serenity;

另一位年仅24岁的OpenAI“弃徒”,从失意研究员华丽转身,创办管理规模已达

百亿美元的对冲基金,以物理约束押注能源、算力基建与存储的重定价。他叫Leopold Aschenbrenner,是硅谷精英中的异类。

一个从微观层面寻找“卡脖子”环节,一个从宏观层面押注“物理瓶颈”的重构。他们的走红,不仅是两种投资策略的碰撞,更是AI时代底层资产重估的一声号角。

Serenity:“紫苏叶”理论掘金隐形黑马

如果长期关注X上的美股社区,近期你几乎不可能绕开一个名为Serenity(@aleabitoreddit)的账号。二次元头像,发帖密集,信息大多是关于半导体材料、光模块衬底、边缘计算板卡的研究内容,很少讨论热门AI应用。

没人知道他的真实身份。他自称有编程和学术背景,是Nature论文作者、RISC-V基金会成员,还曾在2018年拒绝过英伟达向其发出的AI团队主管邀请,彼时英伟达股价仅6美元。

Serenity的成名战,始于2022年初的Reddit著名散户论坛r/wallstreetbets(WSB)。彼时,边缘磷化铟衬底生产商AXTI无人问津,他以账号“AleaBito”发布了一篇深度研究贴,直指其为AI光模块的材料底座。随后,这只冷门微盘股一路从12美元暴涨到70美元,涨幅近6倍。他的精准预测却被平台以“诱导炒作”为由禁言,去年7月他转战X平台,并迅速成长为拥有超40万拥趸的“AI供应链侦探”,成为X上的新晋AI投资圈散户带头大哥,甚至有人根据他的推特制作了投研面板。

相比于涨幅本身,Serenity的研究方式给市场留下了深刻的印象。他将其投资心法,浓缩为自创的“紫苏叶理论”。

他以东京顶级寿司店作喻,食客最趋之若鹜的食材无疑是金枪鱼大腹。然而,整盘寿司的呈现,完全依赖伊豆半岛某些特定小农场供应的紫苏叶:去腥、装饰,缺一不可。一旦这些农场因天气或物流原因而断供,再顶级的金枪鱼也无法上桌,高档寿司店将必不得不歇业。

简单来说,最昂贵的是金枪鱼,不可或缺的却是紫苏叶。

映射到AI供应链,紫苏叶就是那些市值微小、流动性稀薄,却在关特定造环节拥有绝对技术垄断的隐形厂商。

相比常规的财报数据堆砌,Serenity的研究方法论是深潜到产业链最底层:啃材料学论文、掌握物理规律、绘制供应链图谱,甚至将研究草稿输入多个AI进行对抗性测试,只为锁定每一个“不可替代”的瓶颈点(chokepoint)。

过去2年,Serenity将主要精力聚焦在光电共封装技术(CPO)。他认为,随着AI集群规模扩大,传统的铜线连接和插拔式光模块将撞上功耗和速率的物理墙,而把光学器件与硅芯片封装在同一基板上的CPO会是产业的必经之路。

围绕这一判断,他连续发掘并向市场推荐了三个具有爆发力的卡脖子标的,分别是Sivers、Raspberry Pi和Soitec。

Serenity依然在继续深入供应链的最底层,他还发掘出了生产半导体级高纯度磷等前驱体材料的日本化学公司NCI,将“瓶颈点”推进到了分子级材料层面。

Leopold:2亿做到百亿,主打基础设施套利策略

与隐匿在互联网深处的民间猎手Serenity不同,Leopold Aschenbrenner是站在聚光灯下、手握百亿资本的硅谷天才。

他的履历堪称“精英范本”。19 岁以哥伦比亚大学第一名毕业,先后任职 FTX Future Fund、OpenAI Superalignment团队。2024年4月,Leopold却因疑似信息泄露被OpenAI解雇。

这一变故促成了他向投资界的转型。2024年6月,他发表了一份长达165页的行业宣言《情境意识:未来十年》。其中,Leopold大胆预言AGI将在2027年左右实现,而超智能将于2030年降临。而实现这一切的真正瓶颈,并不在算法与模型,而是电网、土地、数据中心和高带宽存储等物理资源。

基于这一极具前瞻性的理论,他创办了对冲基金Situational Awareness LP。Nat Friedman、Daniel Gross、Stripe创始人Collison兄弟等硅谷大佬们纷纷慷慨解囊,2.25亿美元种子轮资金迅速就位。

而Leopold所在的圈层,也令人关注。他的未婚妻Avital Balwit曾任职于牛津大学未来人类研究所(FHI),长期研究变革性人工智能相关议题,随后加入Anthropic,担任CEO Dario Amodei的幕僚长。FTX曾是Anthropic早期最重要的资方之一。在FTX崩盘前,Leopold与Avital也都曾在旗下慈善机构FTX Future Fund担任核心成员。

这样的关系网,为Leopold后续的研究框架与投资布局提供了独特的信息流、认知视角和资源或许这也是其最大、也是极难复制的Alpha。

5月18日,Situational Awareness LP提交了一季度13F持仓报告,Leopold的基金管理规模已超百亿美元。这份文件首次向市场披露了其高度集中的存储股票多头仓位,以及针对整个半导体与芯片制造板块、总额接近85亿美元的庞大看跌期权(Put Option)组合。

从投资组合布局来看,Leopold采用的是基础设施套利策略。一方面,他大举买入内存硬件制造商闪迪SanDisk以及专业算力云CoreWeave,牢牢卡位物理存储的硬壁垒。

另一方面,他将数十亿美元投入到针对英伟达(NVDA)、台积电(TSM)、博通(AVGO)、阿斯麦(ASML)及半导体ETF(SMH)的看跌期权中,几乎做空了整个半导体板块。

在他看来,目前,芯片板块的估值已严重脱离电网、数据中心等物理基础设施的实际建设速度。AI算力集群的落地,离不开稳定的电力、充足的土地以及成熟的散热系统,而这些物理基建的建设周期长达3-5年,远慢于芯片的出货节奏。短期来看,芯片巨头们的高增长难以为继,估值有可能面临回撤,看跌期权则将捕获板块下行的做空收益。

加密企业也同样在Leopold的投资版图中,他把约10亿美元的多头仓位重注到比特币矿企上,大举买入IREN、Core Scientific、Riot、CleanSpark等标的。在他眼中,比特币矿企是AI算力中心的折价替代品,被市场严重低估。

弃软件、重实体,AI算力“买路财”暗藏杀机

尽管Serenity和Leopold的“工具箱”不同,但他们的AI投资内核却高度相似:抛弃缺乏物理壁垒的软件层,重仓受到物理法则约束的硬件

无论是Serenity眼中的外置CW激光器光源和高纯度磷,还是Leopold眼中的变电站与土地,都揭示了一点:不管AI在模型层如何创新,谁掌握了物理世界的稀缺资源,谁就拥有了在AI时代向科技巨头征收“算力买路财”的权力。

不过,世界上没有完美的策略。他们的策略都将在不同维度面临考验。

对Serenity而言,其最大软肋在于微盘股的“流动性深渊”。当他向X上的40万拥泵推荐市值仅数亿美元的微盘股时,少量的散户资金涌入就足以推高股价。然而,这种“狂欢”是建立在低流动性基础上的。一旦大盘流动性收紧,或者被推荐企业在技术验证中遭遇挫折,这些微盘股的价格将断崖式下跌,高位涌入的散户或将血本无归。

另外,Serenity的供应链研究在技术细节上虽然透彻,但其身份、背景、历史业绩均未经过考证。投资者不宜将其奉为“股神”进行盲目的全盘复制,盲目跟单,风险较高。微盘股的“卡脖子”策略虽然极具爆发力,但其背后极高的资本开支、稀薄的利润以及潜在的客户流失风险,都决定了这一策略只适合作为资产配置中的“高贝塔催化剂”,并辅以大盘蓝筹股进行风险对冲,执行严格的仓位管理。

对Leopold而言,他最大的敌人是宏观博弈的“时间差”。物理基建大幅慢于算力需求,在因果关系上完全成立,也是客观事实。然而,资本市场通常存在非理性的情绪和更长的滞后效应,这可能会让芯片巨头的高估值持续更久。当面临英伟达等巨头超预期的强劲财报和股价轧空时,他的巨额看空期权将承受巨大的账面亏损。

某种程度上,Serenity和Leopold代表着新阶段的AI投资逻辑。AI 产业的价值捕获,正在从半导体本身,走向芯片背后的材料、设备、电力和土地。

随着模型规模和算力需求持续增长,围绕着AI产业中具有稀缺性、技术壁垒和供给条件的关键环节,或许将在未来得到市场更多关注。

Perguntas relacionadas

QSerenity和Leopold这两位AI投资领域的新风向标,他们的核心投资策略分别是什么?

ASerenity的策略是自创的“紫苏叶理论”,专注于挖掘AI供应链中市值微小、流动性稀薄,但在特定制造环节(如半导体材料、光模块衬底)拥有绝对技术垄断的“隐形黑马”公司。Leopold则采用“基础设施套利策略”,从宏观物理约束(如电网、土地、数据中心)出发,一方面大举买入存储硬件等物理基础设施,另一方面通过巨额看跌期权做空他认为估值过高的整个半导体板块。

Q文章中提到的“紫苏叶理论”具体指的是什么?请用比喻和实例说明。

A“紫苏叶理论”以顶级寿司店作比喻:食客追逐的金枪鱼大腹虽然昂贵,但整盘寿司的呈现却依赖于特定小农场供应的、不起眼却不可或缺的紫苏叶。映射到AI供应链,紫苏叶就是那些市值小、但技术垄断关键环节的公司。例如,Serenity早期成功挖掘的边缘磷化铟衬底生产商AXTI,以及他后来关注的CPO(光电共封装)技术相关公司Sivers、Raspberry Pi和Soitec,都属于这类“不可或缺”的瓶颈环节供应商。

Q根据文章,Leopold Aschenbrenner在对冲基金的一季度持仓中,采取了怎样看似矛盾的多空布局?

ALeopold采取了高度集中且看似矛盾的多空布局。一方面,他大举买入内存硬件制造商闪迪(SanDisk)和专业算力云CoreWeave等,做多物理存储和算力基础设施。另一方面,他投入了近85亿美元的巨额资金,买入针对英伟达(NVDA)、台积电(TSM)、博通(AVGO)、阿斯麦(ASML)及半导体ETF(SMH)的看跌期权,实质上做空了整个半导体板块。他认为芯片估值已严重脱离物理基建的实际建设速度。

QSerenity和Leopold的投资策略各自面临的主要风险是什么?

ASerenity策略的主要风险在于微盘股的“流动性深渊”。他推荐的超小市值股票,少量资金涌入即可推高股价,但流动性差,一旦市场转向或公司基本面出问题,价格容易断崖式下跌,跟风散户风险极高。此外,其匿名身份和未经考证的背景也增加了盲目跟单的风险。Leopold策略的主要风险是宏观博弈的“时间差”。尽管物理基建慢于算力需求是事实,但资本市场情绪和非理性可能让芯片巨头的高估值持续更久,若其股价继续上涨,Leopold的巨额看空期权将面临巨大亏损压力。

Q文章认为,Serenity和Leopold的走红标志着AI投资逻辑正在发生怎样的根本性转变?

A他们的走红标志着AI产业的价值捕获和投资逻辑,正在从聚焦半导体芯片本身(如英伟达),转向更深层、更受物理法则约束的硬件与基础设施。即从“软件层与PPT叙事”转向“AI运转背后的物理根基”。无论是Serenity寻找的分子级材料,还是Leopold押注的电网、土地和存储,都表明:在AI时代,谁掌握了物理世界的稀缺资源和关键瓶颈环节,谁就拥有了真正的定价权和“征收算力买路财”的权力。

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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.

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

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