Batnick Sees AI Capex and Yields Creating Top Bear Case

Batnick认为AI资本支出与收益率构成最强看空理由 尽管大盘整体估值依然保持锚定，但高度集中的AI基础设施支出与不断攀升的美债收益率，已构成近年来最强劲的股市回调诱因。 Michael Batnick和Ben Carlson分析了当前的宏观格局，评估了围绕AI过度投资、利率趋势以及Nvidia等超大市值科技股估值集中度的看空论点。他们探讨了AI如何改变投资组合构建和劳动力市场，同时讨论了包括住房不平等和科技暴富在内更广泛的社会经济趋势。两位主持人最后还坦诚分享了个人理财感悟、青少年体育故事以及媒体推荐。 当前市场宏观与微观环境形成了做空者的“绝佳击球机会”（Fat Pitch），高利率、粘性通胀、AI 资本支出泡沫风险以及美联储换届预期共同构成了潜在阻力。 (0:24) 全美目前拥有约4,000座现有数据中心且有近3,000座正在建设中，这种庞大的建设规模引发了对重演1990年代末电信暗光纤及19世纪铁路过度扩张泡沫的担忧。 (5:41) 英伟达（Nvidia）未来12个月的净利润预期从2023年的约150亿美元飙升至2,230亿美元，其在市值加权指数中的庞大体量对估值倍数形成了结构性天花板。 (10:22) 高盛数据显示，被动指数基金与 ETF 合计仅占美股总市值的约16%，而家庭直接持股（以创始人和企业高管为主）占比高达约40%，有力反驳了被动投资正在摧毁市场的论调。 (22:55) Citadel 创始人 Ken Griffin 指出，Agentic AI 智能体系统已将以往需要金融硕士与博士团队耗费数周或数月的复杂研究建模工作，压缩至数小时或数天内完成。 (33:30)

Hosts Michael Batnick and Ben Carlson evaluate a macro environment where market bears face a compelling setup of AI infrastructure overbuilding, persistent inflation, and elevated yields, while underlying corporate earnings growth and disciplined investor behavior continue to push back against calls for an imminent crash. The Bearish Thesis and Infrastructure Risks (0:24) Michael Batnick outlines what he describes as the 'fattest pitch setup for bears,' driven by high interest rates, sticky inflation, leadership transitions at the Federal Reserve, and massive artificial intelligence capital expenditures. Citing Torsten Slok, Batnick notes that the U.S. data center footprint is set to double, with roughly 4,000 existing facilities and nearly 3,000 under construction. This pace draws parallels to historical overbuilds, including 19th-century railroads and the late-1990s fiber-optic bubble detailed in the book Broadbandits. While Ben Carlson questions what would force hyperscalers to reduce capex given current compute demand, Batnick points out that institutional herding often triggers oversupply, similar to legacy media's unprofitable rush into streaming. Additionally, Slok's data indicates that AI infrastructure now accounts for roughly 50% of investment-grade corporate debt issuance and 87% of venture capital funding, creating broader systemic vulnerability if capital expenditures slow down. Market Concentration and Valuation Dynamics (10:22) Addressing concerns of widespread market euphoria, Batnick cites Duality Research showing that outside staples, industrials, and real estate, sector-level earnings per share have expanded while price-to-earnings multiples have contracted. The valuation stretch remains concentrated in mega-cap technology: 20% of the S&P 500 index by market cap trades between 30x and 35x forward earnings, while roughly 30% of index constituents by name trade between 10x and 15x forward P/E, with most broad market stocks trading below 20x. Nvidia serves as a primary example of this structural concentration. Its projected 12-month forward net income expanded from approximately $15 billion in 2023 to $223 billion. At 25x forward earnings, the stock represents 8.5% of the total market, while a hypothetical 40x multiple would imply a $10 trillion valuation, demonstrating how market-cap weighting imposes natural ceilings on mega-cap multiples. Resilient Fundamentals Against Macro Yield Pressures (14:33) Despite the 30-year Treasury yield climbing to 5.1%–5.2%—its highest level since 2007—Carlson argues that financial commentators are overplaying the risk of an impending bond crash. Over the last 50 years, long-term Treasury yields have averaged 6.2%, and the 20+ Year Treasury Bond ETF (TLT) has fallen only roughly 2.5% year-to-date inclusive of yield. Carlson points out that U.S. and Japanese equities have remained near all-time highs despite elevated rates. Underlying fundamental strength is also appearing in smaller companies, with Mike Zaccardi projecting Russell 2000 EPS growth of 40% this year and 38% next year. Furthermore, Carlson debunks the narrative that passive funds are destabilizing markets: passive ETFs and mutual funds represent only 10% and 6% of the total equity market, respectively, while direct household ownership—heavily composed of corporate insiders—accounts for roughly 40%. Behavioral Traps and Real-Economy Divergences (17:37) The hosts emphasize that market timing remains a structural trap for retail and professional investors alike, citing valuation expert Aswath Damodaran's acknowledged misstep in raising cash out of AI bubble fears in late 2025. Batnick and Carlson caution against retail investors building unmanaged AI-driven momentum models, noting that while top quantitative strategies maintain win rates of only 55% to 60%, unassisted investors tend to abandon strategies during unavoidable regime shifts and drawdowns. In the real economy, wide financial divides are developing. Data from the Economic Innovation Group highlights that existing homeowners who locked in sub-3% mortgage rates have housing cost-to-income burdens near 35-year lows, while new buyers face steep penalties. Meanwhile, secondary market access through layered private Special Purpose Vehicles (SPVs) for tech firms poses ownership verification risks, and private credit has seen sudden cooling, exemplified by the Blue Owl Credit Income Fund's retail monthly inflows dropping 95% year-over-year from $500 million to $26 million. While bears hold a fundamentally logical case based on rate pressures and aggressive capex commitments, strong underlying corporate earnings and steady macro absorption of higher yields continue to defer a systemic breakdown.

主持人Michael Batnick与Ben Carlson深入剖析了美股面临的宏观与微观分化：尽管AI资本开支泡沫、高利率与潜在基建过度建设构成了看似强劲的空头逻辑，但指数整体估值并未普遍过热，债券收益率攀升与被动投资也未引发系统性危机。与此同时，AI对资本市场、高技能就业与财富分配的结构性重塑正在全方位展开，房地产和私募信贷领域的断层风险同样值得关注。 空头逻辑、基建过建与估值K型分化 (0:24) Michael Batnick指出当前市场呈现出自近年以来最强劲的看跌催化剂组合，包括AI硬件与存储领域的庞大资本支出泡沫隐患、顽固通胀、居高不下的利率以及美联储主席更迭预期。根据Apollo经济学家Torsten Slok的数据，全美目前拥有约4000座在运营数据中心，另有近3000座处于在建状态，这种基建扩张速度令主持人联想到1990年代末的光纤电信泡沫与19世纪铁路过度铺设的历史。不过Ben Carlson强调，与当年光纤缺乏即时需求不同，当前云计算巨头的算力需求依然旺盛。 在微观估值层面，Duality Research的数据表明，除必需消费品、工业和房地产外，绝大多数行业均呈现盈利扩张伴随市盈率收缩的态势。以Nvidia为例，其远期净利润预期从2023年的约150亿美元暴增至2230亿美元，市盈率压缩源于盈利分母的剧增而非价格便宜；其庞大市值构成了结构性估值上限——在25倍远期市盈率下占全市场8.5%，在35倍时占12%，40倍估值则隐含10万亿美元市值。标普500指数内部呈现典型的K型分布：按市值计算有20%的权重处于30至35倍市盈率的高估区间，但按公司数量计算有30%的股票仅处于10至15倍市盈率，绝大多数成分股估值均低于20倍。 美债收益率脱敏与被动投资泡沫真相 (14:33) 尽管30年期美债收益率攀升至5.1%至5.2%（创2007年金融危机前以来的最高水平），但市场并未发生灾难性崩溃。Ben Carlson指出，美国长期国债收益率的50年均值高达6.2%，且20年期以上国债ETF（TLT）年内仅微跌约2.5%，美日股市依然处于历史高位附近，市场对债务危机论调出现脱敏。此外，Mike Zaccardi引用的数据显示，罗素2000小盘股指数今年和明年的每股收益预计将分别增长40%和38%。 针对市场上所谓被动指数基金摧毁定价机制的论调，高盛关于美股所有权结构的数据给出了有力反驳：被动指数基金与ETF合计占美股总市值的比例极小，甚至低于外国投资者超过16%的持仓；美股最大所有权主体为直接美国家庭持股（约占40%），主要是企业创始人及高管等极少抛售的内部人，ETF和主动共同基金各占10%，被动共同基金仅占6%。主持人同时警告了试图精准择时的心理陷阱，提及估值专家Aswath Damodaran因担忧AI泡沫在2025年末减仓后公开承认错误，以及Michael Burry从2019年唱空被动泡沫、2022年预言危机超越2008年到2023年单字发推“卖出”等屡次落空的择时记录。 AI资本渗透、算力争夺与高技能岗位冲击 (33:30) AI投资超级周期已深度嵌入全球金融市场体系。Torsten Slok的数据显示，当前AI相关项目占据了87%的风险投资资金，以及近50%的投资级公司债发行量，OpenAI亦与Oracle签署了5年期高达3000亿美元的云算力承诺。Om Malik在分析中指出，早期流媒体迫使宽带网络针对“下载”扩建，而AI时代作为关键应用则彻底逆转为依赖“上传”，驱动网络架构进行底层重构。尽管量化投资者正聚焦于相关设备与组件，但光纤网络与传统有线电视运营商等末端基础设施仍属于非共识领域。 技术演进对现实劳动力市场的颠覆正在加速。Citadel创始人Ken Griffin在活动中坦承，其公司原本由金融硕士和博士耗费数周或数月完成的复杂建模与研究任务，如今已被AI代理系统在数小时或数天内压缩完成。尽管当前美国失业率维持在4.5%，且Gavin Baker估算全球仅有约10个基点（0.10%）的人口经常使用高级模型，但高技能智力劳动的替代趋势已十分明显。硬件端，Figure创始人Brett Adcock展示的人形机器人已实现超过48小时不间断自主分拣包裹，分拣速度达每件约3秒，与人类效率完全一致；Ben认为在全球生育率下滑的背景下，劳动力短缺将倒逼人形机器人成为经济运行的必要支撑。 贫富断层、房产分化与私募信贷流动性退潮 (43:09) AI繁荣在硅谷内部引发了极端的分化与心理异化。Anthropic、OpenAI及Nvidia等企业的早期员工（约1万人）因股权增值身价迅速突破2000万美元跨入“后经济状态”，而周边普通科技从业者则直面裁员与生活成本飙升的双重压力。这种突发暴富现象还带来了意义丧失与社会比较的心理危机，创始人因恐惧丧失社会身份而抗拒出售企业，重现了影视行业在斩获巅峰荣誉后的心理虚无状态。 宏观层面，住房和私募市场同样面临断裂带。经济创新集团（EIG）的数据显示，锁定疫情期间3%以下超低房贷利率的存量房业主，其住房成本占收入比重处于近35年来最低水平，为消费提供了充沛支撑；而新购房者则承受着剧烈攀升的利率与房贷负担，形成了悬殊的K型消费阶层。与此同时，私募股权与信贷市场开始显露裂痕：面向SpaceX、OpenAI等独角兽的二级市场多层SPV结构透明度极低，投资者甚至面临无法确权底层资产的法律风险；规模近200亿美元的Blue Owl旗舰信贷基金在5月1日仅录得2600万美元零售净流入，单月腰斩且同比暴跌95%，显示出非流动性信贷产品在零售端的资金流入骤降。 节目总结指出，尽管当前市场面临高估值集中度、基础设施资本开支过剩隐忧及深层财富断层，但整体经济与指数成分股展现出超出悲观预期的韧性。投资者面临的核心挑战并非预测系统性崩盘的时间点，而是在AI重构生产力与K型宏观分化加剧的环境中，防范过度择时操作，并警惕流动性收紧与结构性失衡带来的微观资产风险。

## Background 本期视频由知名财经频道 The Compound 于 2026 年 6 月 13 日在 YouTube 平台发布，为播客栏目《Animal Spirits》的第 465 期节目。节目简介显示，两位主持人迈克尔·巴特尼克（Michael Batnick）与本·卡尔森（Ben Carlson）围绕美股见顶风险、英伟达规模诅咒、市场择时与空头预警陷阱、AI 投资策略与网络重构、实体机器人、暴富人群的不快乐、好莱坞名人的感恩之道等话题展开了深度探讨。 ## TL;DR 两位主持人首先剖析了巨头资本支出狂潮与粘性通胀下空头迎来的“绝佳击球机会”，并对照历史上的电信泡沫警示数据中心扩产与英伟达规模效应的隐忧，同时指出达摩达兰与迈克尔·伯里等专家的翻车印证了市场择时之难。随后，节目深入探讨了 AI 驱动上行流量引发的网络重构、自主智能体对高技能白领工作的猛烈冲击，以及硅谷新贵在缺乏心理缓冲的骤然暴富后陷入的精神虚无。最后，节目梳理了比特币与软件股走势的高度趋同、新老业主成本撕裂对 K 型经济的推力、未上市公司股权 SPV 嵌套的确权陷阱与私人信贷失速，并分享了关于代际消费心理与陪伴女儿足球赛压哨夺冠的生活感悟。 ## Atmosphere & style 整期节目呈现出一种举重若轻、既插科打诨又充满智慧的老友对谈氛围，两人在互相拆台与默契抛梗中完成了极高密度的市场数据复盘与宏观思辨。叙事弧线从开场对牛市狂热与做空叙事的犀利拆解，自然滑向对科技颠覆下技术性失业、财富差距以及富人精神虚无的深层哲学反思，情绪层次丰富饱满；而在探讨通胀阴影、房产割裂与父辈省钱习惯时，又巧妙融入了极具生活质感的自嘲与温情，让听众在开怀大笑之余也能真切感知到复杂经济现实投射在普通人身上的真实重量。 ## Bulletin - [01:50](https://www.youtube.com/watch?v=vj-hZaGuieE&t=110s) 美股空头正迎来绝佳催化窗口，芯片资本支出激增叠加通胀粘性与高利率环境，全美在建数据中心近 3,000 座接近存量翻倍，供给过剩风险或重演当年的电信光纤泡沫。 - [22:54](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1374s) 高盛持仓数据证伪“被动投资泡沫”，直接家庭持股占美股约 40%，而被动共同基金与 ETF 合计仅占 16%，市场并非缺乏基本面锚定。 - [35:48](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2148s) AI 技术引发上行流量网络重构与白领替代焦虑，但加文·贝克指出全球仅 0.1% 人口使用大模型即面临严重算力瓶颈，需求远超供给表明行业周期远未见顶。 - [53:24](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3204s) 私人市场潜藏重大风险，散户通过多层嵌套 SPV 抢购 SpaceX 与 OpenAI 等明星股权面临无法确权危机，同时 Blue Owl 旗舰零售信贷基金月流入同比暴跌 95%。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=vj-hZaGuieE&t=4s) 市场出现脱离均线的超买走势，4% 至 7% 的技术性回调反而有助于消化狂热情绪，是维持长线健康的良药。 - [01:50](https://www.youtube.com/watch?v=vj-hZaGuieE&t=110s) 空头叙事迎来了逻辑丰满的击球机会：存储芯片资本支出泡沫、顽固高通胀与潜在加息预期叠加，构成了强大的估值下修催化剂。 - [03:36](https://www.youtube.com/watch?v=vj-hZaGuieE&t=216s) 这轮牛市呈现“第一层次思维”特征，尽人皆知的宏观利空并未压垮走势，好公司在过去十年里持续打破“好公司不等于好股票”的经典定式。 - [05:41](https://www.youtube.com/watch?v=vj-hZaGuieE&t=341s) 美国现存约 4,000 座数据中心而新建逼近 3,000 座，管理层的羊群效应或导致 AI 算力基础设施步入类似光纤电缆时代的产能过剩周期。 - [10:22](https://www.youtube.com/watch?v=vj-hZaGuieE&t=622s) 英伟达正面临体量过大的“规模诅咒”，若维持 30 至 35 倍远期 PE，其占标普 500 指数权重将达到 10% 至 12%，市值扩张逼近数学极限。 - [12:44](https://www.youtube.com/watch?v=vj-hZaGuieE&t=764s) 美股呈 K 型估值分化，市值端虽有 20% 处于 30 倍以上高 PE，但多达 30% 的公司远期 PE 仅在 10 到 15 倍，非 AI 板块估值依旧低廉。 - [17:37](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1057s) 达摩达兰等顶级学者公开承认市场择时失误，警示投资者不要迷信任何权威专家对 AI 或宏观走势的择时预测。 - [21:31](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1291s) 迈克尔·伯里等知名空头屡屡高喊“狼来了”，但传奇对冲基金经理的宏观言论往往与其真实投资组合风控脱节。 - [25:28](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1528s) 自建量化策略的最大难点不是模型构建，而是散户在面临正常回撤时总会陷入频繁微调（tinkering）的人性弱点。 - [28:03](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1683s) AI 是“反向的 Netflix”，过去流媒体革命重塑了宽带的下行下载能力，而 AI 时代则是由模型推理和海量上行（Upload）数据流驱动的网络重构。 - [34:04](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2044s) 自主智能体（Agentic AI）开始在数天内完成以往数人年的高技能工作量，正对金融投研等高阶白领岗位产生实质替代冲击。 - [37:35](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2255s) 高校毕业典礼对 AI 演讲频发嘘声，社会不满可能催生针对裁员企业的“机器人税”等激进监管政策。 - [39:02](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2342s) Figure 人形机器人实现连续 48 小时自主分拣包裹，实体机器人有望成为对冲全球生育率骤降与劳动力断崖的核心支撑。 - [43:09](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2589s) 硅谷短期暴富引发心理虚无，缺乏奋斗爬坡过程的财富骤增极易让人丧失人生意义感并陷入无止境的攀比焦虑。 - [49:49](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2989s) 2021 年以来比特币与软件股 ETF（IGV）走势高度拟合，所谓数字黄金与抗通胀叙事在实盘中沦为披着外衣的高贝塔科技股。 - [51:33](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3093s) 锁定 3% 房贷利率的老业主住房支出比创 35 年新低，而新买家背负数倍重担，成为驱动美国 K 型经济与消费撕裂的核心推手。 - [53:24](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3204s) 以“俄罗斯套娃”式多层 SPV 转售非上市科技独角兽股权潜藏产权落空陷阱，同时零售私人信贷面临流动性枯竭警讯。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=vj-hZaGuieE&t=4s) 赞助广告与开场：股市不可能每天都涨 节目在 Grayscale 的赞助口播中拉开序幕。作为全球规模领先且深耕加密领域十余年的投资平台，Grayscale 致力于在行业发展早期通过投研和长线视角，帮助投资者理清数字资产与不断演进的加密生态系统。随后，主持人迈克尔·巴特尼克（Michael Batnick）与本·卡尔森（Ben Carlson）正式开启本期《Animal Spirits》。迈克尔调侃道，事实证明股票市场确实“不可能也不可能每天都上涨”。本随即反讽：“怎么，就因为我们刚刚经历了一天的下跌吗？”迈克尔澄清说实际上跌了一天半到两天，但财经媒体已经按捺不住，开始长篇大论寻找下跌的替罪羊。 关于国债收益率与通胀隐忧，迈克尔表示收益率攀升至较长时间以来的高点确实不容忽视，不能当作看不见直接扫到地毯下。本则对此嗤之以鼻，认为市场对利率有一种条件反射式的恐慌：“每次美债收益率逼近 5%，市场就吓得歇斯底里，但随后利率再次回落，什么大事也没发生。”迈克尔笑称但愿“盲目自满的本”是对的，但他强调，标普 500 指数和以纳指为主的 QQQ 目前已经严重脱离了正常的均线走势。在这种高耸的斜率下，市场哪怕出现 4% 甚至 7% 的回调，不仅情理之中，反而是“医生开出的一剂良药”。本也同意，那些暴涨成倍的半导体股票迟早会迎来获利了结盘，投资者总会在赚得盆满钵满后选择先收手落袋。 --- ### [01:50](https://www.youtube.com/watch?v=vj-hZaGuieE&t=110s) 美光获利了结与“空头的绝佳击球机会” 迈克尔分享了一件身边发生的趣事。周中有一位平时完全不炒股、大概也从不听播客的朋友突然发短信，称赞他们在节目里聊美光（Micron Technology）的内容讲得很棒。这位朋友之前买入美光赚了一大笔，股票翻了三倍，但他发信息时却因为刚卖掉手中 10% 的仓位而感到纠结懊恼。迈克尔对此感慨道，如果你持有一只翻了三倍的牛股，只获利了结 10% 还在郁闷卖早了，这本身就是市场情绪极端狂热的真实写照。 紧接着迈克尔抛出一个核心论点：当前可能是空头们多年未遇的“绝佳击球机会”（The Fat Pitch）。虽然这只是他刚冒出来的想法、还没来得及复盘历史走势，但空头如今的叙事逻辑极其完备且极具杀伤力： - 投资者可以轻而易举且极具说服力地论证，市场正处于资本支出（Capex）或存储芯片的巨大泡沫之中； - 宏观层面面临美联储主席更迭预期，通胀顽固粘滞，高利率居高不下，甚至存在进一步加息的可能性； - 这种高估值与宏观紧缩预期的结合，就像《周六夜现场》（SNL）里调侃的烈性鸡尾酒一样，构成了极具威力的做空催化剂。 面对本质疑“难道 AI 概念一出还不足以化解这一切吗”，迈克尔反驳称他并不是说空头预言一定会成真，而是客观上空头逻辑目前拥有“极其丰满的骨肉”供人咀嚼下咽。 ```mermaid flowchart TD accTitle: 空头看空逻辑架构图 accDescr: 展示科技股微观资本支出泡沫与宏观高利率通胀叠加的做空催化路径 subgraph Macro["宏观环境隐忧"] M1["通胀呈现顽固粘性"] M2["美债收益率持续维持高位"] M3["美联储面临换届与潜在加息可能"] end subgraph Micro["科技与芯片基本面"] C1["巨头 Capex 资本支出激增"] C2["存储与半导体估值高涨"] C3["AI 叙事演化为短期投机泡沫"] end subgraph Impact["潜在市场冲击"] R1["第一层次思维遭遇反转"] R2["均值回归式深度回调风险"] end Macro --> Impact Micro --> Impact ``` --- ### [03:36](https://www.youtube.com/watch?v=vj-hZaGuieE&t=216s) 第一层次思维的牛市，霍华德·马克斯的烦恼 本提出了一个深层反思：这轮牛市最诡异的地方在于，它一直是一场彻头彻尾的“第一层次思维”（First-Level Thinking）牛市，根本不需要复杂的“第二层次思维”。市场上的所有风险其实都已经是人尽皆知的明牌——顽固的通胀、地缘冲突、利率攀升、AI 资本狂潮带来的产能过剩，每个人都能随口列出这些风险。但股市却依然沿着最简单粗暴的路径狂奔。 迈克尔回顾道，短短七八个月前市场上根本没人把“AI 泡沫”当成一回事。当时相关股票惨遭抛售，根本没有如今动态市盈率被业绩拉平、甚至暴涨数倍的记忆存储股；当时的普遍叙事是“根本不存在 AI 泡沫”，AI 实际上在摧毁其他一切行业的同时自身估值并未冲天。谈到第一层次思维，迈克尔自嘲道，2014 或 2015 年左右有一位亲戚长期重仓苹果（Apple），对方曾对他说“下一个苹果就是苹果本身”。迈克尔当时心里暗自嘲笑对方缺乏投资见识，但事实证明那位亲戚完全正确。迈克尔打趣说，霍华德·马克斯（Howard Marks）的投资哲学彻底毁了那些自诩聪明的市场参与者，因为大家都笃信“好公司不等于好股票”，结果在过去十年里，所有最顶尖的好公司无一例外都成了最赚钱的大牛股。 不过本指出，当前可能出现一种让所有人都不痛快的折中结局：市场突然来一波 10% 的迅猛跳水，完成一次技术性获利回吐，但并没有发生彻底清洗体系中投机泡沫的熊市出清；仅仅几个月后，指数又重振旗鼓，回到原来的上涨轨道继续狂奔。 --- ### [05:41](https://www.youtube.com/watch?v=vj-hZaGuieE&t=341s) 羊群效应与数据中心暴增：电信泡沫重演？ 谈到究竟是什么力量能够阻挡当前市场前行的列车，两位主持人引用了此前在播客《The Compound and Friends》（TCAF）上与嘉宾凯（Kai）的深度讨论。物理学定律表明运动中的物体会保持运动，除非受到外力阻拦。关于上市公司管理层是否会理性规避风险，两人展开了激烈的探讨，期间还插科打诨探讨起迈克尔最近频繁使用的俚语“混球/蠢蛋”（dumbass）的起源，顺带提及了网飞上线《瘪四与大头蛋》（Beavis and Butt-Head）以及《回到未来》电影里的经典台词。 迈克尔强调，不可否认今天的企业无论在商业模式还是利润率上都远优于几十年前，但凯指出了关键的一点：**这与管理层的智商高低无关，这是纯粹的人类行为学规律**。即使企业高管掌握着海量数据，但在群体狂热中，大家天生有着跟随潮流一起跳下悬崖的羊群效应。好莱坞传统制片厂当年跟风网飞（Netflix）无节制砸钱做流媒体、最终几乎把自己拖垮就是典型例证。 为了印证这种供给过剩的隐患，迈克尔引用了阿波罗全球管理公司（Apollo）首席经济学家托斯滕·斯洛克（Torsten Slok）公布的一组震撼数据： - 目前全美现存的数据中心数量大约为 4,000 座； - 当前处于在建状态的数据中心数量竟接近 3,000 座； - 这意味着美国的数据中心总量在短期内即将近乎翻倍。 迈克尔认为，这种近乎失控的扩产建设，极有可能重演当年电信泡沫破裂时全美过度铺设光纤电缆、导致产能严重过剩的历史。本虽然反驳称这有些在“硬凑风险”，并坚信真正击垮市场的必然是所有人意料之外的黑天鹅，但迈克尔坚称，产能过剩（Oversupply）向来是经济周期中最扎实、最经典的破灭主线，这绝非凭空捏造的假想敌。即便二十年后回过头来写一本关于 AI 泡沫的书，这组暴增的数据中心建设统计也必将成为标志性的注脚。 ### [08:33](https://www.youtube.com/watch?v=vj-hZaGuieE&t=513s) 光纤泡沫的历史回响与当下算力需求的博弈 Michael Batnick 与 Ben Carlson 从数据中心过度建设的风险切入展开讨论。Michael 提到，当前市场上可能有 3000 个原本计划动工的数据中心，最终或许会有一半都建不起来。这与美国铁路狂潮时代的剧本如出一辙：铺设了无数轨道，最终由于供给过剩大量废弃。为了研究这一现象，Michael 还特意买了一本实体书《Broadbandits》（因为没有有声书版本），书中详述了当年光纤电缆过度铺设的历史。大众常把当年的网络泡沫当成互联网泡沫，但从基础设施的角度来看，它本质上是一场电信基建泡沫。 ```mermaid flowchart TD accTitle: 历史基建过剩与当前AI算力对比 accDescr: 对比铁路时代、电信光纤泡沫与当前AI计算在供应、需求及潜在后果方面的异同 subgraph Historical["历史基建泡沫"] H1["过度扩建 (铁路 / 光纤)"] --> H2["终端需求未跟上 (缺少适配应用)"] H2 --> H3["供需脱节 / 泡沫破裂"] end subgraph CurrentAI["当前 AI 算力热潮"] C1["超大规模资本支出 (Hyperscaler CapEx)"] --> C2["真实且井喷的终端需求"] C2 --> C3{"未来 5 年走向"} C3 -->|"乐观假设"| C4["需求平稳消化过剩产能"] C3 -->|"悲观假设"| C5["巨头削减支出引发链条重定价"] end ``` 然而，Ben 提出了一个关键疑问：究竟什么力量能让当下那些坐拥海量资金的超大规模科技巨头（Hyperscalers）停止巨额资本支出？毕竟当年光纤泡沫破裂的核心在于缺乏承接算力的终端电脑与应用场景，需求严重缺位；而如今的市场状况恰恰相反，各方叫苦不迭的都是“计算能力严重不足”。Michael 承认眼下需求旺盛，但强调“两件事可以同时成立”：即使需求未来足以消化 5 年后出现的产能过剩，中间也可能发生周期性回撤。当被问及巨头之间的增长动力是否能像“接力棒交接”一样平稳过渡时，Michael 引用了美剧《发展受阻》（Arrested Development）中 Tobias Fünke 的经典自嘲桥段：“这在别人身上行不通，但对我们也许管用。”（期间 Ben 还打趣了季后赛 7 场得 0 分的 NBA 球员 Tobias Harris 是个水货）。 ### [10:22](https://www.youtube.com/watch?v=vj-hZaGuieE&t=622s) 估值收缩还是盈利见顶：美股的“罗夏墨迹测验” 两人转向 Duality Research 每周发布的行业监测图表。数据显示，除日常消费品（Staples）、工业（Industrials）和房地产（Real Estate）外，标普 500 指数几乎所有板块都在经历“每股收益（EPS）正增长”与“市盈率估值倍数（PE Multiple）收缩”并存的现象。这构成了典型的市场“罗夏墨迹测验”： - **多头视角**：市场根本没有陷入狂热与非理性繁荣，基本面极其扎实，股价随着估值下修反而变得更加便宜，仍有充沛的上行空间。 - **空头视角**：市场极其聪明，估值倍数收缩是因为资金心知肚明目前的暴利只是“一记临时强心针”（one-time shot in the arm），这种高盈利增速根本无法长期维系。 谈及英伟达（Nvidia），Michael 指出该公司正陷入一种由于体量过大导致的“规模诅咒”（Size Problem）。即使英伟达的远期市盈率（Forward PE）处于过去近 10 年区间的低端，那也是完全理性的，因为其未来 12 个月的远期净利润预期已经从 2023 年的约 150 亿美元狂飙到了惊人的 2230 亿美元。Matt 制作的测算模型显示：如果英伟达维持 25 倍远期 PE，它占标普 500 指数的权重已达 8.5%；若扩张到 30 倍，市值占比将达 10%；若升至 35 倍，占比更是高达 12%。如果给它与成长速度相匹配的 40 倍 PE，其实际市值将直逼 10 万亿美元。 ### [12:44](https://www.youtube.com/watch?v=vj-hZaGuieE&t=764s) 广度分化与小盘股的离奇盈利预期 面对“美股指数整体深陷泡沫”的论调，Michael 拿出 Duality Research 绘制的标普 500 远期市盈率分布图予以反驳。该图表按股票数量和市值占比进行了双重视角拆解： - 按市值计算，处于 30 至 35 倍高估值区间的股票确实占了约 20%。 - 但按公司数量来看，有高达 30% 的成分股远期市盈率仅在 10 到 15 倍之间；整体市场中绝大多数成分股的估值倍数都运行在 20 倍以下。 Ben 直言，这是一个典型的“K 型分化市场”。若 AI 交易彻底崩塌，全市场恐难独善其身，但对于担忧 AI 泡沫的防御型资金而言，指数内部其实存在大量低估值标的可供栖身。紧接着，他们讨论了面向理财顾问的 Exhibit A 平台图表：当前季度的预期盈利犹如火箭般垂直飙升，走势甚至神似疫情时期的极端图表。Michael 警告，如果这些企业盈利被打回原形，大盘随时可能暴跌 40%。 更令他们匪夷所思的是分析师 Mike Zaccardi 披露的罗素 2000（Russell 2000）小盘股数据：小盘股今年的 EPS 预期增速竟然高达 40%，明年仍有 38%。Ben 和 Michael 感到不可思议，追问这究竟是 AI 生产力赋能的真实体现，还是单纯由于 2025 年的低基数效应所致。 ### [15:29](https://www.youtube.com/watch?v=vj-hZaGuieE&t=929s) 美债收益率危机论是“狼来了”的故事？ 节目的最后阶段转向了债券市场的争端。美国 30 年期国债收益率冲至 5.1% 至 5.2% 附近，市场评论家们纷纷高呼这是“全球金融危机以来的最高水平”。然而，Ben 对这种末日论调展现出极大的审美疲劳： - 追踪 20 年期以上美债的 ETF（TLT）即使在利率大涨的背景下，算上票息收益，年内也仅下跌约 2.5%；而去年在收益率剧烈波动中甚至录得了 4% 至 5% 的正收益。 - 从 50 年的历史长河来看，美国长期国债收益率的平均水平是 6.2%，如今的 5.2% 连历史均值都未达到。 Ben 痛斥市场评论家是在反复上演“狼来了”的把戏：每一次收益率微升，财经舆论便大肆渲染债务危机与系统性风险，但现实中股市照样创出历史新高（不论是美股还是收益率飙升的日本股市）。“如果没有危机爆发，就别成天冲着我喊危机要来了！”面对 Ben 近乎情绪化的宣泄，Michael 虽然也对反复兜售的危机叙事感到厌倦，但还是忍不住幽默地提醒道：“伙计，我打心底里希望你这段话以后不会被打脸，毕竟有时候狼是真的会来的。” ### [17:37](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1057s) 达摩达兰的市场择时打脸与“聪明人”预测陷阱 本段讨论从市场择时（Market Timing）展开。Ben Carlson 提到了被誉为“估值院长”的纽约大学金融学教授阿斯瓦斯·达摩达兰（Aswath Damodaran）。去年 11 月感恩节期间，达摩达兰做客同在纽约大学任教的斯科特·加洛韦（Scott Galloway）主持的《Prof G》播客时，罕见地表现出极其悲观的态度。达摩达兰当时表示，自己对当前的 AI 泡沫感到极其担忧，并表示这辈子几乎第一次主动开始提高现金储备，因为一旦 AI 交易破裂，市场上将“无处可藏”。当时加洛韦也惊呼从未听过达摩达兰如此看空。然而就在最近，达摩达兰再次回到该节目更新观点时，坦诚承认自己犯了错：“这就是为什么你永远不应该尝试做市场择时，我错了。” Michael Batnick 和 Ben 对达摩达兰勇于认错表示赞赏，但同时也强调了这给所有投资者的深刻警示：如果连全市场最聪明、写了厚如砖头的估值巨著的顶级学者都无法准确择时，那么普通投资者若能猜中，纯粹是运气使然，绝非靠真正的分析能力。Michael 坦言自己过去极易受到聪明人的影响，尤其是“操着英国口音、打着领结”的看空学者或策略师，简直是他的软肋。如果能赋予普通投资者一种超能力，那就是学会对这些顶级专家的宏观预测直接“打折过滤”。Ben 也回忆起自己在 2008 年金融危机前后的顿悟：入行之初曾自惭形秽，觉得自己永远无法企及华尔街大佬的智慧，直到亲眼见证危机爆发后所有专家全军覆没，才明白谁也无法预测未来。 - **达摩达兰的择时反思**：去年 11 月高呼“AI 交易全面过热、无处藏身”并号召提升现金头寸，半年多后公开坦白判断失误，称“市场择时实在太难”。 - **专家的预测局限**：Michael 举例听 Dwarkesh Patel 的播客探讨群体演化与基因演变，以及 Derek Thompson 播客讨论生育率专家，指出这些学者在各自垂直领域拥有全球顶尖知识，但在预测未来走势时，其实和“抛硬币”并无二致。 --- ### [21:31](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1291s) 迈克尔·伯里的崩盘预警与宏观言论的“言行不一” 话题转向知名空头、《大空头》原型迈克尔·伯里（Michael Burry）。近期媒体再次充斥着伯里的崩盘警告，标题赫然写道“伯里警告股市将重演 2000 年顶峰并爆发崩盘”。伯里断言，无论狂欢再持续一周、一个月、三个月还是一年，最终的结局必然是断崖式下跌，且后果严重到“无论躲在哪里都无法幸免”。Ben 指出，《华尔街日报》的斯宾塞·雅各布（Spencer Jakab）曾将伯里历年来的看空预警绘制在美股走势图上，形成了一幅经典的“喊狼来了”画卷： - **2019 年**：预言被动投资泡沫将引发崩盘，称局面会变得异常难看； - **2022 年**：断言即将到来的危机将比 2008 年次贷危机还要严重； - **2023 年初**：发布了著名的单字推文“Sell（卖出）”，随后市场展开了波澜壮阔的牛市。 Michael 猜测，伯里实际掌管基金的真实业绩可能并没有媒体报道中看起来那么糟糕。正如斯坦利·德鲁肯米勒（Stanley Druckenmiller）、雷·达利奥（Ray Dalio）或保罗·都铎·琼斯（Paul Tudor Jones）一样，很多对冲基金传奇在 CNBC 上发表宏观观点时往往极度悲观，但当你审视他们的实际持仓时，会发现他们的投资组合运作完全独立于那些宏观预测，绝不会让空头口嗨直接侵蚀实际头寸。 --- ### [22:54](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1374s) 被动投资泡沫伪命题与美股真实持仓分布 针对市场上持续发酵的“被动投资泡沫”（Passive Bubble）论调，Ben Carlson 引用了高盛（Goldman Sachs）关于美国股市所有权结构的最新研究数据进行反驳。市场上流传着“被动指数基金占全部基金资产 60%”的惊悚标题，但若放到整个美国股票市场的广阔池子里，实际格局完全不同。 ```mermaid flowchart TD accTitle: US Stock Market Ownership Structure accDescr: Goldman Sachs data showing that households and founders hold the largest share of the US stock market compared to passive funds and ETFs. subgraph CoreHolders["最大持股主体"] HH["直接美国家庭持股: 约40%<br>(包含马斯克、贝索斯等创始人及员工股)"] FI["外国投资者 (Foreign Investors)"] end subgraph FundHolders["基金及投资工具"] PMF["被动共同基金: 约6%"] ETF["ETF: 约10%"] AMF["主动共同基金: 约10%"] end HH -.->|"长期锁定，不轻易抛售"| CoreHolders ``` 很多人抨击被动投资导致买入股票不看基本面，但事实上，全市场占比最大的是直接家庭持股（占比约 40%），这里面包括了埃隆·马斯克（Elon Musk）、杰夫·贝索斯（Jeff Bezos）等科技巨头的创始人，以及大量持有公司限制性股票的普通员工，这部分股份基本上属于长期锁定、极少主动抛售的基本盘。而被动共同基金仅占 6%，ETF 占 10%（规模与主动型共同基金的 10% 相当），甚至被动基金和 ETF 合计占比还不如外国投资者的持仓比例。 随后，Michael 探讨了当前市场结构演变的一个真正痛点：过去华尔街拥有大量股票研究分析师，能深入覆盖中盘股和小型股；如今由于分析师资源收缩，大量小盘股失去了投研关注，导致没有资金去发掘其内在价值，沦为“死钱（dead money）”。不过 Ben 认为 AI 正在颠覆这一现状，随着大语言模型和 AI 投研工具的普及，股票分析师覆盖个股的边界将大幅拓宽，未来不会再有“被市场遗忘的冷门股”，所有上市公司都将被 AI 驱动的研究网络无死角覆盖。 --- ### [25:03](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1503s) 听众来信：用 AI 免费复制机构的动量选股策略？ 节目切入了一封颇具代表性的听众来信。该听众提到，在听到 Ritholtz 财富管理公司推出的全新“Porterhouse SMA”（独立管理账户）动量投资策略后，心中冒出了一个想法：既然 AI 如此强大，是否可以直接向 AI 索取该策略的底层动量选股逻辑，从而省去将资金托付给 Ritholtz 的繁琐流程与管理费，直接“从机器那里免费白嫖顶级专家的专业智慧”？这位听众还将大模型给出的策略回答一并附在邮件中，由此引出了两人关于投资者如何看待 AI 复现策略与财富管理真正价值的讨论。 ### [25:28](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1528s) DIY 投资组合的诱惑与无法停止的“微调” 散户投资平台（如 Public 等）如今已经支持用户一键创建自定义指数：投资者可以自定规则、打包一篮子股票并交给系统自动执行交易。然而 Ben 指出，这种完全消除进入门槛的工具反而会诱发人性的弱点——人们会陷入永无止境的“微调”（tinkering）。一旦某周策略回撤，投资者就会立刻想要修改参数、剔除某些标的；只要按钮就在手边且毫无阻碍，几乎没有人能管住自己的手。Ben 强调，构建一套选股漏斗或量化指标在今天根本不是什么高深科技，最难的部分从来都是“坚持到底的毅力”（stick-to-itiveness）与资产配置管理。毕竟在股票市场中，即使是极其优秀的策略，胜率通常也只有 55% 到 60%，没有任何策略可以一直奏效。 Michael 对此深有同感，并坦承自己天生就是个喜欢瞎折腾的“微调狂人”。他提到最近有人在他们的长期动量策略（Momentum Strategy）创下历史级优异表现后冷嘲热讽，质问这是否已经见顶。Michael 无奈回应称，这个模型早在去年冬天就开始搭建，动量因子本身就具有极强的周期性，“它有光芒万丈的日子，也有烂得一塌糊涂的时期”，动量策略的去杠杆和回撤往往极其痛苦。Ben 补充道，仓位管理（Position Sizing）同样至关重要：如果全仓押注动量策略，遇到逆风期会痛苦万分；但若只将其作为资产组合的补充部分，体验则完全不同。 ```mermaid flowchart TD accTitle: 投资者的微调死循环 accDescr: 展示散户自建策略后因缺乏行为纪律陷入不断微调的心理怪圈 subgraph Build["策略构建门槛归零"] A["自建规则 / 筛选指标"] --> B["一键生成专属指数"] end subgraph Trap["行为陷阱 (Tinkering)"] B --> C["策略遭遇正常回撤"] C --> D["心态失衡：认为模型失效"] D --> E["频繁修改规则 / 剔除股票"] E --> C end subgraph Reality["成熟投资的现实"] F["动量/因子天然具有周期性"] G["长期胜率仅 55% - 60%"] H["核心在于仓位管理与坚持执行"] end ``` ### [28:03](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1683s) AI 是新的 Netflix？上行流量时代的网络变革 谈到 AI 的实际落地，Michael 借用海明威的名句形容自己对 AI 工具的接纳过程是“逐渐发生，而后突然爆发”（gradually, then suddenly）。他坦言一年半前自己完全无法理解那些成天把 AI 挂在嘴边的人究竟用它来干什么，直到最近阅读了投资人 Howard Lindzon 分享的一篇科技专栏作家 Om Malik 的深度技术文章——《AI 是新的 Netflix》（AI is the new Netflix），他的工作流彻底被颠覆。 对于这篇文章，Ben 直言自己读完后根本没搞懂其中的类比逻辑。Michael 解释道，早期的互联网基础设施是完全针对“下载”（Download）设计的；到了 2008 年左右，流媒体视频（以 Netflix 为代表）横空出世，作为杀手级应用彻底挤爆了现有的宽带带宽，迫使全美有线电视和光纤公司不得不全面重建底层网络以迎合下行速率的需求。而 Om Malik 提出的核心论点在于：AI 正在扮演当年的 Netflix 角色，但方向完全相反——AI 的关键不是它下载了多少数据，而在于向模型推理和数据中心发起的海量“上传”（Upload）。AI 是由上行数据流驱动的全新时代颠覆者。 ### [30:36](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1836s) Claude 锐评持仓：共识交易、赢家法则与研报替代 为了验证这套宏大叙事与实盘投资的关联，Michael 借助接入了实时行情数据的工具（通过 YCharts 插件）让 Claude 分析他们旗下的动量策略投资组合（Porterhouse）。Claude 调取实时持仓后给出了极其犀利且直击要害的拆解： - **重仓标的与共识逻辑**：组合中权重最高的三大核心持仓包括 Ciena（CIEN）、Vertiv（VRT）和 Amphenol（APH），深度暴露于 AI 硬件与数据中心基础设施建设（Infra Capex）这一“拥挤的共识主题”。 - **缺乏差异化敞口**：Claude 一针见血地指出，组合对 Om Malik 论文中最具差异化、竞争最不拥挤的“接入层光纤与宽带网络”（如电信巨头 AT&T、Verizon，以及具备网络结构优势的 Comcast 和 Charter）的敞口基本为零。 - **因子属性的客观诊断**：Claude 补充解释，这并非对选股信号的批评，因为电信股此前根本没有足够的动量走势进入策略池；但这一分析直观揭示了该策略表达的是“共识版 AI 主题”，而非冷门的逆向视角。 面对 Claude 的“犀利点评”，Ben 调侃道“幸亏我们没买 AT&T”。Michael 则借此深入探讨了动量投资对抗人性弱点的本质。他坦言自己天生缺乏“长线拿住牛股”的心态，一旦有股票暴涨就总想获利了结。彼得·林奇（Peter Lynch）曾将这种行为经典地比喻为“拔掉鲜花去浇灌杂草”（pulling out your flowers and watering the weeds）；霍华德·马克斯（Howard Marks）也曾指出散户卖出股票只有两个理由：“涨了卖是因为害怕它跌回去，跌了卖是因为害怕它跌得更惨”。系统性的动量模型恰恰用纪律强行让利润奔跑，克服了人性恐惧。 最后，Ben 盛赞 Claude 是目前所有大语言模型中当之无愧的“最佳股市分析师”，其解析标普 500 指数持仓与行业表现的能力远超竞争对手。Michael 进而引用了城堡投资（Citadel）创始人肯·格里芬（Ken Griffin）在周末发表的震撼言论：以往需要金融学硕士和博士团队耗时数周才能完成的投研分析，如今普通人借助 AI 在 10 分钟内就能高质量产出，这正在对传统金融服务业产生颠覆性的冲击。 ### [34:04](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2044s) 智能体AI对高技能白领工作的替代与冲击 迈克尔·巴特尼克（Michael Batnick）与本·卡尔森（Ben Carlson）深入探讨了一段引人深思的行业观察：过去需要人类团队数月甚至数年才能完成的高难度工作，如今正被自主AI智能体（agentic AI）在短短几小时或数天内搞定。这不再是自动化那些可替代的中端白领琐事，而是直接替代了极其高技能的岗位。那位受访者甚至在周五回家时感到深深的沮丧与无力，因为当在自家公司的高墙之内亲眼目睹以往需要数人年（man years）的工作量被压缩至数天或数周时，任何人都无法忽视它对整个人类社会构成的剧烈冲击。 本对这种恐慌与沮丧深表赞同，但他提出了一个核心疑问：“AI究竟是在接管整个工作岗位（jobs），还是仅仅在完成具体任务（tasks）？”迈克尔反驳称，结合他自己周末借助AI完成的工作以及眼前的技术表现来看，这绝对是在替代工作本身，而不仅限于初级职位的零碎任务。面对技术带来的狂喜与恐惧，本打趣地提出了一个经典对比：“到底是指数基金还是AI会让更多分析师失业？是杰克·博格尔（Jack Bogle）砸了饭碗，还是AI会给他们最后一击？”迈克尔则形象地比喻道，这两者叠加就像是“干柴遇烈火”（Fire and gasoline）。 ```mermaid flowchart TD accTitle: AI对职场与资本市场的演进影响 accDescr: 展示从AI智能体取代复杂高技能工作到资本市场全方位渗透的逻辑传导过程。 subgraph Displacement["职场自动化演进"] A["传统数月至数年的人工工作量"] --> B["高技能工作被智能体AI接管"] B --> C["数小时/数天内高效完成"] end subgraph Expansion["技术渗透与市场"] C --> D["极低人口普及率与算力短缺"] D --> E["资本市场全方位绑定"] end ``` ### [35:48](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2148s) 算力短缺与普及率之争：目前连泡沫都谈不上？ 尽管AI的技术飞跃令人瞠目结舌，但本指出当下的失业率依然顽固地保持在 4.5% 的低位，这似乎与技术替代论存在脱节。迈克尔随即指出，将眼下的低失业率视作AI没有颠覆性的证据简直就是个笑话，因为整个革命才刚刚拉开序幕。他重点提到了加文·贝克（Gavin Baker）在索恩投资大会（Sohn Conference）上时长 25 分钟的重磅演讲。贝克在演讲中提出了令人宽慰的反直觉观点：当全社会仅有 10 个基点（即 0.1%）的人口在使用这些前沿大模型时，整个行业就已经陷入了严峻的算力短缺。如果未来普及率上升到 5%，需求将呈现何种爆发态势？ 贝克的核心逻辑在于：只要真实需求远远超出供给上限，这轮行情就绝不能被简单定性为泡沫，而更可能是一轮“更平稳、更持久”（smoother for longer）的长期上行周期。本分享了自己几周前参加“疯狂三月”（March Madness）聚会时的切身经历：当他在聚会上兴致勃勃地谈论自己在工作中如何根据不同任务灵活切换使用 Claude、Gemini 和 ChatGPT 时，同桌的一个人居然茫然地问他：“Gemini 是什么？我从没听说过。”这足以印证普通大众对于AI的认知距离全面渗透还有着巨大的鸿沟，当前的变革仅仅处于孕育期。 - 加文·贝克指出当前全球仅约 0.1%（10 个基点）的人口在真正使用大模型。 - 算力供给瓶颈严重制约当前产能，需求端远未见顶。 - 大众对前沿工具认知极度分化，多数人仍停留在仅知晓基础聊天机器人的阶段。 ### [37:35](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2255s) “把机器人推向反派”：毕业典礼上的嘘声与政治监管风险 随着AI以不可逆的态势席卷各行各业，社会层面的抵触情绪正以前所未有的速度积聚。本提到了阿尔·弗兰肯威茨（Al Frankenwitz）在社交媒体上的一条推文：在近期各大高校的毕业典礼上，只要演讲嘉宾提起AI，全场毕业生就会爆发嘘声。例如前谷歌首席执行官埃里克·施密特（Eric Schmidt）在亚利桑那大学发表演讲时，凡是谈及人工智能的地方均被学生们报以全场狂嘘。在公众眼中，AI正在被塑造成剥夺年轻人未来的“大反派”。 迈克尔和本就此探讨了随之而来的政治后果。公众的反弹极有可能倒逼政客出手干预，甚至出台“机器人税”（tax the robots）等激进法案，哪怕只是为了在选民面前做做样子。两人回忆起 2008 年金融危机后，尽管金融体系重创普通大众，却没有哪怕一个华尔街高管锒铛入狱，这种逃脱惩罚的民怨直接催生了当今社会的极端撕裂与民粹情绪。未来，企业因部署AI而进行大规模裁员是否会遭遇惩罚性税收？继续雇佣人类员工的企业是否会获得税收减免？在科技巨头认为“技术演进不可阻挡”的同时，民间汹涌的厌恶情绪预示着政治风险正成为最大的不可测变量。 ### [39:02](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2342s) 人形机器人长跑与人口老龄化的救赎 除了软件层面的智能体，实体机器人技术也迎来了里程碑式的突破。布雷特·阿德考克（Brett Adcock，人形机器人公司 Figure 创始人）近期对外直播了一场机器人包裹分拣测试。研发团队原本的目标只是完成连续 8 小时的全自主无故障运行，然而机器人在没有人工介入的情况下连续运转超过 48 小时，实现了前所未有的全自主操作记录。该机器人的分拣流程包括识别条形码、抓取包裹、调整条码朝向并面朝下平稳放置在传送带上；人类工人的平均操作速度约为 3 秒一个包裹，而该人形机器人已经达到了与人类并驾齐驱的作业水平。 本指出人形机器人固然引发了抢夺底层劳工饭碗的恐慌，但从人口结构来看它却是一场刚需救赎。结合播客此前讨论过的全球生育率危机，当一个家庭只生育一个孩子时，人口置换率已经完全崩溃，未来必然面临断崖式的人口缩减与劳动力真空。如果没有机器人承接繁重的人工操作与护理服务，整个社会体系和经济运转终将崩溃。本甚至半开玩笑地提出一种构想：如果未来AI与人形机器人彻底实现了乌托邦式的解放，让人类拥有充足的闲暇时间去繁衍后代，引发一场不可思议的全新“婴儿潮”，那将是科技发展最具讽刺意味的完美结局。 - Figure 人形机器人连续自主运行超 48 小时无故障，远超最初 8 小时的测试目标。 - 机器人分拣小包裹并翻转条码的操作效率达人类水平（人类基准约为每件 3 秒）。 - 人口老龄化与生育率骤降倒逼机器人在蓝领服务与工业领域加速补位。 ### [40:52](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2452s) 资本市场的AI绑定狂潮与旧金山的财富鸿沟 在宏观资金层面，阿波罗全球管理公司（Apollo）首席经济学家托斯滕·斯洛克（Torsten Slok）发布的数据证明，AI早已不仅是股票市场的投机热点，而是全面渗透了整个资本市场体系。数据显示，AI相关投资已经占据了近半数的投资级债券（Investment Grade）发行量、高达 87% 的风险投资（VC）资金流向，以及高收益债（High Yield）中迅速扩大的份额。资本市场如今与AI的命运高度捆绑，一旦AI的叙事停摆，整个金融市场的音乐也将戛然而止。 与华尔街日报专栏作家格雷格·伊普（Greg Ip）探讨的“AI放缓可能未尝不是好事”不同，迈克尔与本一致认为现实已不容踩刹车。市场预期曾反复摇摆——起初市场断言技术无法落地，如今又恐慌其运转得过于高效。以 Anthropic 为代表的领军企业营收展现出史无前例的井喷式增长；此前在秋季因萨姆·奥特曼（Sam Altman）言论引发市场对 OpenAI 与甲骨文（Oracle）五年 3000 亿美元合作能否落地的极度悲观，现已被两家公司靓丽的基本面彻底粉碎。 这种疯狂的造富狂潮在旧金山湾区引发了令人不安的极端撕裂。有推文指出，旧金山当下的社会氛围近乎癫狂，财富分配的鸿沟达到了历史极值：过去 5 年间，分布在 Anthropic、OpenAI 和英伟达（Nvidia）等核心公司的约 10,000 名员工，凭借手中暴涨的股票期权迅速达成了超过 2000 万美元的退休级财富自由；而在这一极小圈子之外，即便从事年薪近 50 万美元的高薪白领，也沮丧地意识到自己终其一生都不可能企及这一财富层级，更可怕的是，科技行业的裁员潮仍在残酷推进。 ### [43:09](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2589s) 硅谷暴富者的精神虚无与“登山”的人生哲学 在科技与人工智能浪潮的推动下，硅谷涌现了一批年纪轻轻便实现暴富的新贵，但随之而来的却是深刻的精神危机。本·卡尔森（Ben Carlson）分享了一篇关于当下科技富豪生存状态的观察：某位 Anthropic 的员工刚套现股票就能全款买房，将普通购房者彻底挤出市场；然而，这些已经“上岸”的人却并不快乐。很多人在短短几年内从年薪不足 15 万美元跃升至身家 5000 万美元，没有任何缓冲期和心理适应过程（no ramp），人生瞬间被彻底颠覆。有人选择逃往纽约试图重温真实生活，有人年仅 30 岁便出于争夺虚荣资本（status points）而盲目创立新公司。一位已经实现财务自由的“后经济时代（post-economic）”创始人甚至直言，自己绝不会卖掉公司，因为“现在所有人都想和我交谈，如果把公司卖了，我就只剩下钱了”。这种在外人看来近乎扭曲的病态生活，立刻在社交网络上招致了铺天盖地的嘲讽。 迈克尔·巴特尼克（Michael Batnick）则认为，大众的冷嘲热讽固然合情合理，但若设身处地，置身于那个狂热小圈子中的个体所承受的心理冲击是极其真实且符合人性的。迈克尔回忆起自己的亲身经历，坦言自己人生最幸运的转折正是当年曾跌入谷底——他曾经历过长期失业、职业前景一片灰暗的极度挣扎，随后整整耗费了 15 年的漫长爬坡期才走到今天的位置。正是这种充满挫折、一步一个脚印的艰难攀登，才让他对当下的生活时刻心怀敬畏与感激（"how f***ing lucky I am"）。倘若巨额财富是一夜暴富降临，没有经历过痛苦奋斗以及随之而来的渐进式生活改善，人类的大脑往往会被瞬间击溃。 这种骤然暴富的环境极易滋生严重的不平衡感。本·卡尔森补充道，想象一下如果你比别人晚两年加入 OpenAI，即便你的技术和才华远胜于先来的同事，对方却仅仅因为入职时机正确就比你富有十倍，这种纯粹由运气驱动的差距足以扭曲任何人的心智。好莱坞影星乔治·克鲁尼（George Clooney）与乔·汉姆（Jon Hamm）也曾公开表示，庆幸自己直到三十多岁、历经多年打拼之后才成名，否则过早的巨大名利必然会毁掉自己。迈克尔联想到此前讨论过的电影大师传奇，史蒂文·斯皮尔伯格（Steven Spielberg）、弗朗西斯·福特·科波拉（Francis Ford Coppola）与乔治·卢卡斯（George Lucas）在拍出轰动全球的旷世巨作之后，几乎无一例外陷入了重度抑郁——“站在山顶并无乐趣可言，真正的意义在于攀登的过程”。更有甚者，当年身家已有 6 亿美元的斯皮尔伯格，在与身为亿万富翁的大卫·格芬（David Geffen）合作时，依然会不由自主地产生攀比焦虑，质问自己为何还没达到 10 亿身家。两人总结道，摆脱这种无休止比较与虚无怪圈的唯一解药，就是坚守内心的感恩之心，只与过去的自己对比，而非盲目仰望他人。 - 极端财富跃迁：部分初创公司员工缺乏心理过渡，数年间身家从十几万美元飙升至 5000 万美元，导致人生意义感骤失。 - 攀比与运气扭曲：晚入职两年的优秀员工与早入职者身家可达十倍之差，财富分配机制极度依赖时机而非纯粹的能力或努力。 - 登顶悖论：电影巨头在缔造现象级票房神话后普遍陷入心理低谷，印证了人生价值源于攀登过程而非终点。 ### [47:23](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2843s) 邮局里的通胀恐慌：从魏玛德国到不可磨灭的心理烙印 本·卡尔森分享了自己前不久在办公室附近的邮局寄书时遇到的一则插曲。由于要给读者寄送新书的亲笔签名版，本一周内连去了两三次邮局。接待他的是一位身材极其魁梧健硕、名叫克莱图斯（Cletus）的邮局工作人员。在得知本写的是一本关于投资与金融市场的书籍后，克莱图斯突然严肃地向他发问：“你对当下发生的一切到底怎么看？你了解魏玛德国（Weimar Germany）时期的恶性通货膨胀吗？你听说过人们用手推车推着满车现金上街买面包的历史吗？我总觉得我们现在正不可逆转地滑向那个地步。” 迈克尔调侃本当时应该直接送他一本签名书并叫他“赶紧读读”，本则表示克莱图斯言辞极为敏锐清晰，显然是深陷于某种加密货币圈常见的末日叙事兔子洞之中。本当时决定不当场与其发生争论，而是答应下次再来寄书时会带一本自己的书送给他。这次遭遇让本陷入了深思：回顾近几年的宏观经济，高通胀对大众心理层面的深远冲击是所有人此前严重低估的。尽管过去十几年里整个投资圈都在研究并撰写行为金融学，但恶性物价上涨给民众带来的创伤性心理反应，其剧烈程度依然超乎想象。 本分析指出，通货膨胀正在被普通大众视作摧毁国家根基的罪魁祸首。正如 1930 年代的大萧条孕育出了整整一代行为极端节俭、只知疯狂储蓄而不敢消费的“大萧条婴儿（depression babies）”，本轮由疫情引发的剧烈通胀周期所遗留下的心理阴影，其持续时间与长尾效应（long tail）必然会比主流经济学界和市场预期的要漫长得多。 ### [49:49](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2989s) 比特币只是软件股？与 IGV 高度趋同的走势之谜 迈克尔将话题引向了资产定价领域的一个奇特现象。知名策略师朱利安·埃马纽埃尔（Julian Emanuel）此前在社交媒体上发布了一张对比图，将比特币（Bitcoin）与软件行业 ETF（IGV）自 2021 年以来的走势进行叠合。为了避免图表因压缩 Y 轴而产生视觉误导，迈克尔亲自在 YCharts 中调取数据并进行了堆叠对比，结果显示两者在方向上确实展现出了惊人的一致性。尽管在某些微观时间段内走势有所分化，但拉长至数年周期来看，两条曲线几乎如同复刻一般同步起伏。 针对市场上给比特币赋予的种种宏大叙事——“数字黄金”、“抗通胀对冲”、“去中心化”、“全球流动性载体”、“抗审查性”以及“投资组合多元化工具”，朱利安·埃马纽埃尔打上了一连串问号，并给出了一个充满讽刺意味的结论：“归根结底，比特币不过就是一只披着外衣的软件股。” ```mermaid flowchart TD accTitle: 比特币与软件股相关性演变逻辑 accDescr: 展示比特币从微型资产到宏观资产的变化过程，以及其与软件板块走势趋同并可能在未来发生转变的机制。 subgraph Narratives["市场叙事"] A["宏观叙事假设<br/>数字黄金 / 抗通胀 / 去中心化"] end subgraph MarketReality["市场现实 (2021 至今)"] B["高流动性科技溢价"] C["与软件 ETF (IGV) 走势高度重合"] end subgraph FutureDynamic["未来动态演变"] D["资产体量成熟与定位转换"] E["周期性脱钩并寻求新相关性"] end A --> B B --> C C --> D D --> E ``` 本·卡尔森对此提出了不同的见解，探讨了这种相关性背后的长期机制： - 资产相关性的周期演变：如同近年来股票与债券的相关性出现破裂并转为正相关一样，真正多元化的优质资产往往会在不同宏观周期中转换属性，比特币在未来某一时刻也必然会摆脱与软件股的捆绑，转向与其他资产挂钩。 - 历史体量维度的差异：迈克尔补充指出，在 2017 年之前，比特币的总市值极为微小，根本无法被视作一个正式的独立资产类别，因此拿当时的数据与当下的宏观表现进行类比缺乏可比性。 - 投资者的现实困惑：无论长期逻辑如何推演，面对当前图表展现出的铁证，投资者不得不重新审视自己的持仓组合：持有一篮子加密资产，在本质上是否仅仅等同于重仓了软件科技板块。讨论的最后，本顺势引出了本年代最严峻的财富不平等核心议题——房地产与购房阶层的分化。 ### [51:33](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3093s) 房产新旧业主的鸿沟与 K 型经济 Ben Carlson 和 Michael Batnick 探讨了当下美国房地产市场中既存业主（Existing Homeowners）与新购房者（New Homeowners）之间极端的成本撕裂。数据显示，既存业主在住房上的支出占其收入的比例，已经降至自 1990 年以来约 35 年间的最低水平；许多人在前几年将房贷利率锁在了 3% 左右，使得每月房贷负担极轻。与此形成鲜明反差的是，新购房者的住房成本占收入比却像火箭般飙升，承担着极高的买价与高昂的按揭利率。Ben 直言，这正是全美财富不平等的巨大根源——市场上人们总在困惑“消费狂潮中的钱到底都是从哪儿来的”，答案显而易见：正是那些在低房价、低利率时代锁定住房成本的庞大群体，他们手中拥有极其充裕的可支配收入。 Michael 对此感触颇深，并分享了发生在自己身上的生动对比。他作为一名新购房者，环顾街区时无奈地发现，自己买下这套房子所付出的价格，大概是对街邻居当年购房花费的三倍左右。Michael 开玩笑骂了句“那个混蛋（That MF'er）”，随后坦承自己虽然无权抱怨，但这种现实确实让人感觉很糟糕。如今不少新搬入社区的家庭账面收入或总财富可能比老居民高出数倍，但每月背负的居住开销却极其沉重，形成了诡异的居住割裂。两人一致认同，这种新旧业主的成本鸿沟正是推动当下“K型经济（K-shaped economy）”最强劲的动力之一。 - **住房成本占收入比例**：锁定约 3% 低息房贷的既存业主，其住房成本收入比处于 1990 年以来的 35 年低点。 - **贫富分化驱动力**：低息房贷家庭享有大量沉淀出来的可支配收入，持续支撑着各类消费。 - **社区倒挂怪象**：高收入新买家以老住户数倍的价格入场，实际每月现金流反而承受更高重压。 --- ### [53:24](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3204s) 私募市场警示：SPV“套娃”陷阱与 Blue Owl 信贷失速 Michael 面向投资者发布了一则“公益警示”（Public Service Announcement），直指当下炙手可热的头部非上市科技巨头（如 SpaceX、OpenAI、Anthropic 等）在二级股权交易中潜藏的巨大风险。大量散户和高净值投资者渴望在这些巨头 IPO 之前分一杯羹，各类机构便通过特殊目的载体（SPV, Special Purpose Vehicle）进行份额分销。更荒谬的是，市场上出现了大量“载体套载体”的套娃式架构（Russian doll），底层的实际买家往往根本不知道第一手流动性的源头是谁，且许多标的企业根本没有监管或承认这类私下转让。Michael 警告，这极其可怕——“想象一下你自以为拥有 OpenAI 或 SpaceX 的股份，眼巴巴盼着它上市发财，到头来却发现自己根本没有正规股权”。《华尔街日报》（The Wall Street Journal）对此形容道，这就像在眼睁睁看着房价飞涨时，突然被告知自己手头根本没有房产证（Title）一样绝望。 随后，两人将目光转向另一个承压的另类资产领域——私人信贷（Private Credit）。根据《金融时报》（Financial Times）报道，巨头 Blue Owl 旗下规模近 200 亿美元的旗舰零售信用基金（Blue Owl Credit Income Fund）在 5 月 1 日录得的新增投资额断崖式暴跌，仅有 2600 万美元，不仅较前一个月骤降约 50%，与去年同期相比更是跌去了惊人的 95%。要知道，一年前该基金单月吸金规模接近 5 亿美元。面对这从 5 亿到 2600 万美元的冰火两重天，Michael 与 Ben 不禁感叹：在当下负面头条漫天飞舞的背景下，究竟需要多硬心肠的理财顾问，才敢继续硬着头皮向客户推销这类产品。 ```mermaid flowchart TD accTitle: SPV Multi-Tier Risk Structure accDescr: 示意图展示了终端投资者通过层层嵌套的特殊目的载体购买未上市公司股权所面临的产权脱钩风险。 subgraph Target["未上市明星巨头"] A["SpaceX / OpenAI / Anthropic 等非上市公司"] end subgraph Vehicles["层层嵌套的特殊目的载体 (SPV)"] B["原始持股实体 / 一级 SPV"] C["二级 SPV / 衍生渠道"] D["嵌套 SPV (类似俄罗斯套娃)"] end subgraph Retail["终端投资者"] E["散户与高净值投资人"] end A -. "缺乏监管 / 未经确权".- B B --> C C --> D D --> E ``` - **SPV 结构风险**：多层 SPV 嵌套导致股权穿透困难，终端买家极易面临挂名持股或最终无法确权的风险。 - **Blue Owl 募资断崖**：规模近 200 亿美元的零售信用基金，单月净流入从去年同期的近 5 亿美元暴跌 95% 至仅 2600 万美元。 --- ### [55:14](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3314s) 消费心理学：父辈的肥皂水、20美元的鸡肉排与27美元的沙拉 Ben 抛出了一个来自生活细节的个人财务心理学话题。他周末回到密歇根州北部的特拉弗斯城（Traverse City）探望父母，每次在洗手间洗手时都会被逗乐：父亲为了榨干洗手液瓶底的最后一滴肥皂，总是会往里面灌水兑成稀薄的肥皂水。Ben 解释，父亲从小和四个兄弟姐妹挤在一栋狭小的房子里长大，祖父母经历过大萧条时代，家庭绝非中产，因此身上深深烙下了克勤克俭的印记。哪怕父亲如今早已完全负担得起充足的洗手液，兑水也绝非为了省钱，而是一种深入骨髓的“原则问题（Principle thing）”。 这引发了两人关于“自己在哪些方面依然改不掉抠门习惯”的剖析： Michael 坦承自己的成长心理走向了完全相反的极端。小时候他家虽不贫困，但母亲严格的预算限制给他留下了深刻记忆——去小餐馆时被明令禁止点 20 美元的帕尔玛干酪鸡肉排（Chicken Parm）。这种童年压抑反噬成为他成年后的报复性开销，Ben 也打趣自己在 Arby's 总是被迫只能点小号烤牛肉堡（Junior Roast Beef），而内心极度渴望正常分量。Michael 自嘲自己是“天生的过度支出者”，即便在薪水微薄的青年时期也完全不热衷于攒钱。不过，Michael 唯独受不了高价外卖：当妻子 Robin 买一份 27 美元的沙拉时，他依然会忍不住抓狂抱怨“这太荒谬了”。 Ben 则表示自己在汽车上存在不可逾越的心理红线。他开的福特探险者（Ford Explorer）租约即将到期，最近在挑选新 SUV 时遭遇了严重的“标价休克（Sticker Shock）”。Ben 强调自己绝对买得起豪华车，但他骨子里认为车辆的本质就是“从 A 点开到 B 点”的代步工具，原则上绝不愿在豪车上挥霍重金。最后两人谈到，正是因为当代社会像老一辈那样由衷节俭的人越来越少，才使得如今的“FIRE”（财务自由、提早退休）群体的极致节俭显得格外扎眼与不合群。 --- ### [58:11](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3491s) 吐槽日常：Sonos 音量难题与疯狂的青少年俱乐部足球 在节目间隙，Michael 突然向苹果（Apple）发难，抱怨智能家居体验糟糕。他在后院装了音箱，但如果想要调节音量，无法直接使用 iPhone 机身上的实体物理按键，必须点开 Sonos 专属 App 才能滑动调音。Michael 坚信这是苹果对第三方硬件的封杀限制；Ben 则毫不留情地驳斥，指出这完全是 Sonos 自己的锅——Ben 自己也是 Sonos 用户，直言其官方 App 体验极差，自从大约两年前改版后就把许多功能搞崩了，彻底激怒了用户群体。 话题随后切入 Ben 的家庭生活——他对美国狂热的青少年体育运动（Youth Sports）充满了“爱恨交织”的情感。他的两个女儿都参加了青少年足球俱乐部，身边许多朋友经常用看傻子一样的眼神看他，认为让小女孩辗转各地打比赛既折腾又荒唐： - **过早职业化选拔**：孩子们才刚满 7 岁就必须参加选拔赛（Tryouts），被无情地区分为一队（最佳）、二队和三队（垫底），Ben 认为这种过早的分层对低龄孩子极为残酷。 - **特拉弗斯城锦标赛盛况**：这次全家驱车两个半小时奔赴特拉弗斯城参赛，现场竟然整整铺排了 35 块足球场同时开赛。 - **后勤与环境折磨**：整个场地只有一条狭窄的通道进出，停车现场堪称噩梦；比赛期间更充斥着暴雨、雷击延误以及随后的闷热天气。 奔波劳顿让 Ben 一度在心里质问自己“为什么要受这份罪”，直到他 8 岁刚满 9 岁的小女儿 Kate 踏上球场开启首场比赛，事情才迎来了治愈的转折。 ### [59:58](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3598s) 压哨绝杀的喜悦与家长群退群后续 本段对话紧接着 Ben Carlson 讲述女儿的足球比赛。球队原本以 0 比 2 落后，随后顽强将比分扳为 2 比 2 平。球场边只有大约 20 位家长和祖父母在观战，就在全场比赛时间几乎耗尽的瞬间，Ben 的女儿打进了一记压哨绝杀球。足球场上极少见到这种最后一秒定胜负的“压哨绝杀”，他的女儿并非平日里包揽进球的明星球员，但进球哨响、球队最终夺冠的那一刻，女儿脸上绽放出的灿烂笑容让 Ben 瞬间释怀并感叹：“这就是我们当父母陪跑的意义所在。”虽然 Michael Batnick 开玩笑打趣说换作自己还是懒得去，但也承认孩子发自内心的快乐确实胜过一切。 随后 Michael 提到了上一期节目中关于他退出少儿腰旗橄榄球家长群的后续反馈。不少听众发邮件给他支招：“Michael，你把群消息静音不就行了吗？”Michael 无奈地解释称，自己早就把对话静音了，但如果群聊里有非 iPhone（Android）用户，系统就会出现漏洞，消息仍然会不断弹出提示音打扰。面对这种情况，Michael 果断直接退群。这导致其他家长在群里拿他打趣开玩笑，他的妻子 Robin 甚至觉得尴尬丢脸，不过 Ben 却宽慰他，觉得 Michael 这种雷厉风行退群的做派反而挺酷的。 ### [01:01:29](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3689s) 播客与纪录片推荐：哈里森·福特与马丁·肖特的感恩之道 在推荐环节，Ben 顺着上一段关于“感恩”的讨论，分享了他心目中今年最精彩的一期播客节目——Ted Danson 与 Woody Harrelson 共同主持的《Where Everybody Knows Your Name》，本周嘉宾正是好莱坞传奇巨星 Harrison Ford。Ben 评价 Harrison Ford 堪称过去 50 年间最伟大、最重要的演员之一，但他在节目中表现得极其自嘲且低调，反感自吹自擂或大谈特谈电影技艺，反而频爆粗口、随性自然得就像在酒吧里一起喝啤酒的哥们儿。尽管地位崇高，Harrison Ford 却将一切归功于幸运与感恩。Michael 也附和道，当年在好莱坞做木匠、在屋顶上叼着烟赤膊干活的经历，简直就像《好莱坞往事》里的 Brad Pitt 一样酷。 紧接着，Ben 还推荐了 Netflix 上关于喜剧大师 Martin Short 的纪录片（约 1 小时 40 分钟）。结合近期关于 John Candy 和 Steve Martin 的纪录片，这群喜剧天才早年的交集令人动容——包括 Eugene Levy、Martin Short、Dan Aykroyd、John Candy、Catherine O'Hara 以及 Gilda Radner 等人在 20 多岁时聚在一起创造喜剧的历史。Ben 指出，Harrison Ford 和 Martin Short 身上都有着一种难能可贵的共性：在取得巨大成功后，依然能保持踏实接地气的品格与强烈的感恩之心，这也正是他们在名利场中找到幸福的根源。 - **推荐播客**：Ted Danson 与 Woody Harrelson 主持的《Where Everybody Knows Your Name》（嘉宾：Harrison Ford）。 - **推荐纪录片**：Netflix 关于喜剧演员 Martin Short 的生平纪录片，展现了 1970 年代 SCTV 与北美喜剧圈黄金一代的青春与创作历程。 ### [01:03:47](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3827s) 悬疑暗黑剧集推荐与股市杂感收尾 轮到 Michael 推荐时，他带来了一部画风截然相反的影视作品——HBO 播出的六集限定迷你剧《Half Man》（由凭借爆款剧《驯鹿宝贝》一举成名的 Richard Gadd 打造）。Michael 坦言该剧非常阴暗沉重且包含大量暴力与露骨镜头，但剧情张力极强。故事讲述了主角 Niall 的婚礼上，与他关系疏远的兄弟 Ruben 突然现身，随即引发了一场毁灭性的暴力爆发，剧情以此为引子，一路回溯两位主角从 1980 年代直至今日的纠葛人生。尽管题材压抑，Michael 仍给出了强烈推荐，而一向偏爱短小精悍剧集的 Ben 也对这六集的篇幅表现出浓厚兴趣。 节目尾声，两人顺带瞄了一眼当天的金融市场。看到美股再次走低，两人笑称“市场确实该来一场健康的回调了”，但随即又自我调侃道，投资者往往只有在回调没发生时才会轻松地说“市场需要回调”，而一旦真正身处暴跌之中，大家内心的真实独白永远都是“求求赶紧停下来吧，这一点都不好玩”。节目在轻松的调侃与听众来信邮箱（animalspirits@thecompoundnews.com）的致谢中正式结束。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=vj-hZaGuieE&t=4s) 每次美债收益率逼近 5%，市场就吓得歇斯底里，但随后利率再次回落，什么大事也没发生。 — 本·卡尔森 > [03:36](https://www.youtube.com/watch?v=vj-hZaGuieE&t=216s) 下一个苹果就是苹果本身。 — 迈克尔·巴特尼克的亲戚 > [05:41](https://www.youtube.com/watch?v=vj-hZaGuieE&t=341s) 这与管理层的智商高低无关，这是纯粹的人类行为学规律。 — 凯（Kai） > [15:29](https://www.youtube.com/watch?v=vj-hZaGuieE&t=929s) 如果没有危机爆发，就别成天冲着我喊危机要来了！ — 本·卡尔森 > [17:37](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1057s) 这就是为什么你永远不应该尝试做市场择时，我错了。 — 阿斯瓦斯·达摩达兰 > [30:36](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1836s) 拔掉鲜花去浇灌杂草。 — 彼得·林奇 > [30:36](https://www.youtube.com/watch?v=vj-hZaGuieE&t=1836s) 涨了卖是因为害怕它跌回去，跌了卖是因为害怕它跌得更惨。 — 霍华德·马克斯 > [43:09](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2589s) 现在所有人都想和我交谈，如果把公司卖了，我就只剩下钱了。 — 科技初创公司创始人 > [43:09](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2589s) 站在山顶并无乐趣可言，真正的意义在于攀登的过程。 — 迈克尔·巴特尼克 > [49:49](https://www.youtube.com/watch?v=vj-hZaGuieE&t=2989s) 归根结底，比特币不过就是一只披着外衣的软件股。 — 朱利安·埃马纽埃尔 > [01:03:47](https://www.youtube.com/watch?v=vj-hZaGuieE&t=3827s) 求求赶紧停下来吧，这一点都不好玩。 — 迈克尔·巴特尼克 ## Mentioned 迈克尔·巴特尼克 (Michael Batnick), 本·卡尔森 (Ben Carlson), Grayscale, 美光 (Micron Technology), 霍华德·马克斯 (Howard Marks), 阿波罗全球管理公司 (Apollo Global Management), 托斯滕·斯洛克 (Torsten Slok), 英伟达 (Nvidia), 阿斯瓦斯·达摩达兰 (Aswath Damodaran), 斯科特·加洛韦 (Scott Galloway), 迈克尔·伯里 (Michael Burry), 高盛 (Goldman Sachs), 埃隆·马斯克 (Elon Musk), 杰夫·贝索斯 (Jeff Bezos), Ritholtz Wealth Management, Porterhouse SMA, 奥姆·马利克 (Om Malik), Claude, Gemini, ChatGPT, 肯·格里芬 (Ken Griffin), 城堡投资 (Citadel), 加文·贝克 (Gavin Baker), 埃里克·施密特 (Eric Schmidt), 布雷特·阿德考克 (Brett Adcock), Figure, OpenAI, Anthropic, 甲骨文 (Oracle), 朱利安·埃马纽埃尔 (Julian Emanuel), Blue Owl, 哈里森·福特 (Harrison Ford), 马丁·肖特 (Martin Short), 理查德·加德 (Richard Gadd), 《Half Man》, 《Broadbandits》 ## Video description On episode 465 of Animal Spirits, Michael Batnick and Ben Carlson discuss: what can stop the stock market, Nvidia is too big, the boy who cried wolf predictions, market timing reminders, Michael Burry crash calls, AI portfolio strategies, AI is the new Netflix, the robots are coming, rich people who aren't happy, Harrison Ford, Martin Short and more. This episode is sponsored by Grayscale. To learn more, visit https://www.grayscale.com/ ► 00:00 - Grayscale Ad ► 00:34 - Stock Market ► 17:37 - Investor Behavior ► 28:01 - AI ► 47:19 - Crypto ► 51:22 - Real Estate ► 53:21 - Private Markets ► 55:14 - Personal Finance ► 58:08 - Story Time ► 1:01:26 - Recommendations 👕 Check out The Compound shop: https://idontshop.com 🎙️Listen to our podcasts: TCAF: https://podcasts.thecompoundnews.com/show/TCAF/ Animal Spirits: https://podcasts.thecompoundnews.com/show/animalspirits/ Ask The Compound: https://podcasts.thecompoundnews.com/show/ATC/ The Compound Newsletter: https://www.thecompoundnews.com/subscribe Linkedin: https://www.linkedin.com/company/the-compound-media/ Instagram: https://instagram.com/thecompoundnews Twitter: https://twitter.com/thecompoundnews Tik Tok: https://www.tiktok.com/@thecompoundnews Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Ben Carlson are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. See our disclosures here: https://ritholtzwealth.com/podcast-youtube-disclosures/ The Compound Media, Incorporated, an affiliate of Ritholtz Wealth Management, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here https://ritholtzwealth.com/advertising-disclaimers. Grayscale Disclosure: Grayscale is the world’s largest crypto-focused asset manager based on AUM as of 12/31/2025. For other companies in this category, AUM is considered as of most recent public disclosure. AUM is subject to change.  Investing involves risk, including loss of principal. For more information, visit grayscale.com #thecompound #investing #stockmarket [Source](https://www.youtube.com/watch?v=vj-hZaGuieE)

[0:04] 本·卡尔森: 每次美债收益率逼近 5%，市场就吓得歇斯底里，但随后利率再次回落，什么大事也没发生。 [3:36] 迈克尔·巴特尼克的亲戚: 下一个苹果就是苹果本身。 [5:41] 凯（Kai）: 这与管理层的智商高低无关，这是纯粹的人类行为学规律。 [15:29] 本·卡尔森: 如果没有危机爆发，就别成天冲着我喊危机要来了！ [17:37] 阿斯瓦斯·达摩达兰: 这就是为什么你永远不应该尝试做市场择时，我错了。 [30:36] 彼得·林奇: 拔掉鲜花去浇灌杂草。 [30:36] 霍华德·马克斯: 涨了卖是因为害怕它跌回去，跌了卖是因为害怕它跌得更惨。 [43:09] 科技初创公司创始人: 现在所有人都想和我交谈，如果把公司卖了，我就只剩下钱了。 [43:09] 迈克尔·巴特尼克: 站在山顶并无乐趣可言，真正的意义在于攀登的过程。 [49:49] 朱利安·埃马纽埃尔: 归根结底，比特币不过就是一只披着外衣的软件股。 [63:47] 迈克尔·巴特尼克: 求求赶紧停下来吧，这一点都不好玩。

[2:24] Michael Batnick: the next Apple is Apple [14:33] Ben Carlson: Nothing ever happens. Give me a crisis! [17:37] Michael Batnick: somebody with a British accent and a bowtie [17:37] Michael Burry: Sell [28:05] Om Malik: AI is the New Netflix [28:05] Peter Lynch: pulling out your flowers and watering the weeds [33:30] Ken Griffin: It has been really interesting to watch, to be blunt, work that we would usually do with people with Master's and PhDs in finance over the course of weeks or months, being done by AI agents over the course of hours or days. [33:30] Michael Batnick: Fire and gasoline. [43:09] Anonymous Founder: If I sell, I will only have money [47:23] Cletus: What do you think about everything that's going on right now? Are you familiar with Weimar Germany? Hyperinflation? People carrying wheelbarrows full of cash down the street? I kind of think that's where we're heading. [51:26] Michael Batnick: That MF'er. [55:14] Ben Carlson: What's something in your life that you're still cheap on? [64:40] Ben Carlson: we could use a correction

[0:04] Curious about crypto and not sure where [0:06] to start? Start with Grayscale. [0:08] Grayscale is the world's largest [0:10] crypto-focused investment platform and [0:12] has been in crypto for over a decade. [0:14] That's a long time when you consider how [0:15] early we still are in crypto adoption. [0:18] In markets like these, education matters [0:19] more than ever. Grayscale provides [0:21] research, insights, and long-term [0:23] perspective to help investors better [0:24] understand digital assets and the [0:26] evolving crypto ecosystem. Whether [0:28] you're exploring crypto for the first [0:29] time or looking to deepen your [0:30] knowledge, let Grayscale be your guide. [0:35] Welcome to Animal Spirits with Michael [0:36] and Ben. It turns out the stock market [0:38] cannot and will not go up every single [0:40] day. [0:42] >> Why? Because we had one down day. [0:43] >> We had uh [0:45] one and a half. We had two. Um but now [0:48] there are of course headlines that are [0:50] following why stocks are down and I'm [0:52] not dismissing the yield inflation [0:54] stuff. I, you know, I think yields being [0:56] at their highest level ever, not that it [0:57] would Jesus. Not ever. Be at their [0:59] highest level in a quite some time. You [1:01] know, it's worth looking at, not [1:03] sweeping it under the rug. Um [1:05] but [1:05] >> that under the rug. [1:06] >> Okay, fine. [1:06] >> Every I've got plenty to say about this, [1:08] but every time [1:10] rates get to 5%, people flip out, and [1:12] then what happens? Rates fall again, and [1:14] nothing happens. [1:15] >> So, let's let's hope that uh complacent [1:18] Ben is right. [1:19] That uh but I'm just saying like the the [1:22] the S&P and the the the Qs in particular [1:25] are so far [1:27] so so far above any sort of normal trend [1:31] moving average, whatever. [1:33] If we get a 4% pullback, heaven forbid [1:35] even seven, um it might just be what the [1:37] doctor ordered. [1:38] >> Yes, this could This is This is has to [1:40] be one of those eventually we'll get a [1:42] Okay, I'm taking profits here. All those [1:44] semiconductor stocks that are up a [1:45] million percent, [1:47] people will go, "Okay, fine. I'm getting [1:48] out. I'm taking some profits. I made a I [1:49] made so much money." [1:50] >> I So, if I had to buy it, I I I was like [1:53] uh I don't want to [1:54] say it's not sure. It's not really nice, [1:55] but um last what day of the week was it [1:58] when we was texting me? [2:00] I don't know, Tuesday. Maybe Tuesday or [2:01] Wednesday, I can't remember. And this is [2:03] the friend of mine that like I I don't I [2:04] didn't even I we never we don't speak [2:06] stocks. I assume he doesn't listen to [2:07] the podcast. [2:09] Um, and he said I like what you guys are [2:10] saying about Micron. I don't even know [2:11] what show he was talking about cuz we [2:12] was talking about it so much. Uh, he [2:14] sold 10% of Micron at whatever, he [2:17] crushed, he made a ton, you know, [2:18] percentage-wise, I don't know how much I [2:19] know, obviously I'm not asking how much [2:20] money he put in, but he did he did quite [2:22] well in the trade. And he was saying how [2:23] he sold 10% of his Micron he sold 10%. [2:25] And I was like, "Yeah, you know, if [2:27] you're mad that you sold 10% of a stock [2:29] that tripled, I don't know what he [2:31] bought it, like that's, you know, [2:32] that's that's a pretty good good sign. [2:34] Anyway, um, [2:36] I do think I do think that so friend, if [2:40] you're listening, sorry to dunk on you, [2:41] but uh, [2:42] whatever. Um, it's a fair dunk. It's a [2:44] fair dunk. [2:46] I do think that this is [2:49] the fattest pitch setup for bears. [2:53] Now, I just thought of this, so I [2:55] haven't really like gone back and [2:56] checked the tape, that we've had in a [2:58] while. Because you have you could very [3:01] credibly or easily make the case that we [3:04] are in a capex [3:06] memory bubble, whatever. Choose your [3:08] bubble. [3:09] While at the same time we have a new Fed [3:11] chairman coming in, inflation is sticky, [3:14] rates are high, we might actually need [3:15] to raise rates. Like that is that is a [3:18] very plausible potent potable. Remember [3:20] potent potables from Saturday Night [3:22] Live? I don't even remember what those [3:23] were, but that's a potent cocktail. [3:25] >> But wouldn't the wouldn't the [3:28] the [3:29] comeback would just be AI? [3:30] >> Well, yeah, I'm not I'm not suggesting [3:31] that [3:32] I'm not suggesting that that's going to [3:33] play out. I'm just saying there's a lot [3:35] of meat on that bone for bears to chew. [3:36] >> The The weird thing is that this has [3:37] been the first-level thinking bull [3:39] market for so long now. It's not It has [3:40] been second-level thinking. So, could we [3:42] actually for once have a first-level [3:44] thinking bear market as well? Because [3:47] the the risks are so widely known right [3:49] now. Everyone knows what the risks are, [3:51] right? Yeah, inflation is sticky, the [3:52] war, [3:53] interest rates are going up, AI [3:55] potentially just blowing out way too [3:57] much excess, and that's Those are all [3:58] the risks. Everyone knows them. [4:00] >> But, the AI uh [4:03] bubble, 7 months ago, that was not a [4:05] thing because as we all remember, [4:08] everyone wanted no part of the bubble. [4:10] These stocks were getting murdered. [4:12] There was no memory up eight X uh [4:15] earnings. Like we were in a different [4:16] environment, and it happened so fast. [4:19] So, like 8 months ago, nobody was saying [4:21] the AI bubble. It was the anti-bubble. [4:23] It As a matter of fact, the narrative [4:24] that we were saying was there is no AI [4:26] bubble, [4:27] and as a matter of fact, it is actually [4:30] destroying everything else in its wake [4:32] while simultaneously not getting blown [4:33] up itself, blown up to the to the [4:35] upside. [4:36] But, then speaking of like the the first [4:39] uh first-level thinking, I've told this [4:40] on the uh I've said this before on the [4:42] show. [4:43] In 20, [4:45] I don't know, 14, 15, [4:47] uh uh family member of mine had been a [4:49] long-term shareholder of Apple, [4:51] and he said the next Apple is appler [4:52] something like that, and I definitely [4:53] like [4:54] haha, you know? I definitely like [4:55] laughed at him like that and internally. [4:57] Uh yeah, he was right. It's been a [4:59] first-level thinker bull market. Howard [5:01] Marks ruined it for for semi-intelligent [5:05] market participants. Anybody that [5:06] thought that they were like moderately [5:07] clever could could blame Howard Marks [5:10] for ruining it. [5:10] >> company, but that doesn't mean it's a [5:12] great stock. But, guess what? All the [5:13] great companies have been great stocks. [5:15] >> Yeah. [5:16] >> It It is really like the first time in [5:17] history it's lasted [5:18] >> Mhm. [5:18] >> this long. So, yeah, maybe it will be [5:20] just the the obvious risks will be the [5:22] ones that eventually get us. But, the [5:23] thing is the the one the outcome that is [5:25] going to leave no one satisfied is just [5:27] that we have a really quick correction, [5:30] and then this thing just keeps going, [5:31] right? We have a 10% whoosh because like [5:33] we need to pull back a little bit, but [5:35] it's not the system clear everyone wants [5:37] to see, [5:38] and then we just kind of a few months [5:39] later pick back right back up where we [5:40] left off. [5:41] >> We'll talk about uh smoother for longer [5:43] there in the show, but let's start here. [5:45] So, we were talking on TKF [5:48] with Kai who killed it. He was really [5:49] good on the show. [5:51] And we've we've had this conversation a [5:52] bunch. Like, all right, what stops us? [5:55] Right? An object in motion stays in [5:56] motion. There has to be something to [5:57] knock it off its its course. [6:00] And Kai was talking about like [6:02] cuz I I I said like um [6:05] what do we think about the fact that [6:07] companies, management, and listen, I [6:11] know there are a lot of dumbasses out [6:12] there, okay? So, it's very easy to like [6:14] say what about this this this this and [6:16] this. But, management teams have a lot [6:18] more information. [6:19] >> By the way, great You've been using the [6:20] word dumbass a lot lately. For some [6:21] reason, it always it always makes me [6:22] laugh, yeah. I don't know. You did it on [6:24] the show the other day and I laughed. [6:25] >> Okay. [6:26] >> I feel like that's a that's a good [6:27] That's a term we used a lot in the '90s, [6:29] I feel like. So, I'm glad you're [6:30] bringing it back. [6:31] >> Speaking of the '90s, I saw Beavis and [6:32] Butt-Head is going to Netflix. [6:34] >> Oh, really? Okay. [6:35] >> You know, that might be the origin of [6:36] the word dumbass, for all I know. [6:38] >> Possible. [6:40] >> So, let's just [6:41] >> the guy in uh Back to the Future used [6:42] the word dumbass to Marty McFly. [6:44] >> Okay. [6:45] Let's all agree that companies are [6:48] better today than they were in the past. [6:49] And look no further than the profit [6:50] margins. I mean, they just they're [6:51] better businesses, okay? Whether you [6:53] want to ascribe that to management or or [6:55] the products or who cares, it doesn't [6:56] matter. But, the point that Kai made was [6:59] it doesn't matter. It's It's behavioral. [7:01] So, it's not that they're less [7:02] intelligent or more intelligent than [7:04] than, you know, management in years [7:06] past. It's that there is a behavioral [7:07] component to follow what everybody else [7:09] is doing, perhaps right off the cliff. [7:11] Matter of fact, Netflix streaming, great [7:13] example. [7:14] All of these All of these companies that [7:16] were in traditional legacy Hollywood [7:19] media studios, they followed Netflix [7:21] right over the streaming cliff. They [7:22] overspent and it it nearly sunk them. [7:23] So, maybe my theory is uh is bunk. So, [7:26] anyhow, [7:28] from Torsten Slok, America's data center [7:30] is a count is about to nearly double. [7:33] In the US, there are roughly 4,000 [7:35] existing data centers, and there are [7:36] almost 3,000 data centers under [7:38] construction. [7:39] So, the [7:42] perhaps oversupply [7:44] the way the telecom bubble bust maybe [7:46] that's just how this one plays out, too. [7:48] >> I feel like we're reaching here. [7:50] We're [7:51] We're reaching for risks here. [7:52] Everyone's just everyone want [7:54] I feel like everyone wants to [7:56] >> dude. Hold on. Last week you said like [7:58] how do you not sound like a cheerleader? [8:00] >> Yeah. [8:01] >> True. I mean, should we do that every [8:02] episode? Just be like, "This is amazing! [8:05] Holy [ __ ] can you believe these [8:06] earnings?" [8:07] >> My My whole thinking though is that [8:08] whatever whatever knocks us off of our [8:11] current trajectory, it's going to be [8:12] something that we're no one's talking [8:13] about. That's just the way that I [8:15] >> not always true. [8:17] That's not always true. [8:18] >> It's not always true, but I [8:19] >> I dude, I'm We're not reaching here. I'm [8:20] not saying if this, this, and then this, [8:22] and then this, and then this happens. [8:23] I'm just saying oversupply. Like an [8:25] overbuild that. That's not a reach. [8:27] >> What when I write a book about bubbles [8:29] 20 years from now and I talk about the [8:31] AI trade, that'll be a stat that's in [8:33] there, like, "Hey, there was 3,000 data [8:35] centers that were that were set to go [8:36] under construction. Half of them never [8:38] got built." Something like that. That's [8:39] what happened to the railroads, right? [8:40] They had all these lines they were going [8:42] to build, never got built because they [8:44] oversupplied. [8:45] Yeah, I that that's certainly a [8:46] possibility. [8:47] >> I actually bought a book, a physical [8:49] book cuz they don't have it in in audio, [8:51] called Broadbandits. [8:53] About the over the overbuilding of of [8:55] the fiber optic cables. And we think a [8:57] lot about like the dot-com bubble like [8:58] it was an internet bubble. It was really [9:00] the telecom bubble. Like the build out [9:02] of all this stuff. So, um [9:05] I think that's a very plausible ending [9:06] for this. [9:08] Uh Duality research [9:10] >> But but what causes the mega cap [9:13] hyperscalers to pull back on their [9:15] spending? What is the thing that makes [9:16] them stop? [9:17] >> Oh, I don't know. [9:19] >> Because it seems to me like the demand [9:20] is there. So, what's going to cause them [9:21] to all of a sudden be like, "All right, [9:23] fine. We're getting off the train." [9:24] >> That I agree with you, but two things [9:26] can be true. [9:27] There can be enough demand to soak up [9:29] all of the excess supply that is going [9:30] to happen in five years. [9:32] But like I'm you know, I'm not saying [9:33] it's tomorrow. I'm saying [9:34] >> Cuz that's the thing in the fiber optic [9:35] cable, there wasn't enough demand. There [9:37] what We didn't have the computers and [9:38] stuff ready for that yet. It had to come [9:40] down the line. It seems like now we have [9:42] the demand. [9:44] >> The too much demand. All All you hear is [9:46] there's not enough computers. So, that's [9:47] a very simple [9:48] >> I And I ask Kai this. Like, what What is [9:49] a better What is more likely outcome? [9:51] The baton handoff? And he's like, "No, [9:53] that never happens." And I totally [9:55] agree. [9:55] >> It could happen. [9:56] >> I'm just I'm just giving I'm having an [9:58] open mind to that. Like, what if it does [10:00] though? [10:00] >> I'm I'm with you, dude. I'm [10:01] >> I'm doing the Tobias from [10:04] >> The Tobias Harris who couldn't score a [10:06] point in the final seven What a What a [10:09] bum that guy is. [10:09] >> Tobias Funke from [10:11] Arrested Development. [10:13] Didn't work for those people, but it [10:14] might work for us. [10:16] >> I I I think that's a very like likely [10:20] possible outcome as well. All right. So, [10:22] Duality Research every week, they show [10:24] the They show a chart of the the price [10:26] turn of the sectors. [10:28] Then they show the EPS growth, and then [10:29] they show the PE multiple growth. [10:32] And [10:33] what you'll notice is EPS growth [10:35] positive in every sector and multiple [10:38] contraction in every sector besides for [10:41] staples, industrials, and real estate. [10:44] >> It's a good chart. [10:45] >> So, you could look at this from [10:47] you know, this is a war shack test. [10:49] You could say that if you're a bull, you [10:51] could say this is phenomenal. The market [10:53] is getting cheaper. [10:55] Or we still have room to run. There's no [10:57] euphoria. [10:58] >> Yeah. Fundamentals are not detached from [10:59] reality. Fundamentals are literally [11:01] great. [11:01] >> And a bear could very easily say, "Hey, [11:04] dumbass." Sorry, I did it again. [11:06] Um [11:08] the market's not stupid. [11:10] These multiples are contracting because [11:11] the earnings keep going up, but the [11:13] market knows that they're not [11:14] sustainable, so they can't possibly [11:16] >> One One time shot in the arm for [11:18] earnings. Yeah, great. You guys keep [11:19] talking about earnings, but this is not [11:20] going to last. [11:21] >> So, we We spoke about this last week, [11:23] and we used this on TCAF, but I don't [11:25] want to assume that everybody watches [11:26] every single thing that we do. So, these [11:28] charts are too good not to share here. [11:30] We spoke about Nvidia just getting [11:33] bigger and bigger. The num Excuse me, [11:35] the earnings, the market cap, and yet [11:37] the forward PE is is going down. And I [11:40] said that's not cheaper, that's [11:41] rational. [11:43] So, look at the 12-month forward net [11:46] income for the forward PE ratio. Yes, [11:48] the forward PE ratio is on the lower end [11:50] of its range that it's been over the [11:51] last, I don't know, [11:52] nearly 10 years. [11:54] It has to be when you consider that the [11:56] forward 12-month net income has gone [11:59] from [12:00] What What even I It's like I can't even [12:02] see it. It's at 15 billion in 2023 to [12:04] 223 billion dollars today? [12:07] You can't get a premium, it's too big. [12:09] So, Matt showed what [12:12] the market cap of Nvidia would be if it [12:15] were to trade at a at various forward [12:17] PEs. And right now at 25 times, it's 8 [12:20] and 1/2% of the market. [12:22] If it were to take up to 30 times, it [12:24] would be 10%. At 35 times forward [12:27] earnings, that's 12% of the market. [12:29] It's just It's It's It's a size problem. [12:30] It's [12:31] too big. [12:32] >> if a company's growing this fast, why [12:33] isn't it given a 40 times multiple? Like [12:36] it should be, but because then it would [12:37] be a $10 trillion company. [12:39] It's already a $5.5 trillion company or [12:40] whatever. [12:41] >> Correct. Um, all right. I [12:44] for anybody that thinks that we're in a [12:46] stock market bubble, [12:47] not an AI hyper scale, but just a stock [12:50] market bubble, all right? Let's like put [12:52] a pin in the I trade. The S&P 500 is in [12:54] a bubble. Oh, really? [12:56] Duality Research, check this out. [12:59] The distribution of forward PEs. I've [13:00] never seen this before and I love it so [13:02] much. [13:03] The distribution of forward PEs [13:05] by He shows it both by the percentage of [13:08] stocks and by the percentage of market [13:10] cap. [13:11] So true, the percentage of market cap [13:14] that is trading between 30 and 35 times [13:16] earnings, not cheap, [13:18] is 20%. [13:21] But, look at all of the percentage of [13:25] stocks [13:26] that are trading between [13:28] 10 and 15 times, 15 and 20 times, 20 and [13:31] 25 times. So, 30% of the index by name [13:35] is between 10 and 15 times. 30%. [13:38] >> So, the majority of the market trades [13:39] below 20 times earnings. [13:41] >> Just yeah. [13:43] Not by market cap. [13:44] >> Right. But just number of stocks. [13:47] >> Isn't that wild? [13:48] >> It is and if AI trade blows up, it's not [13:50] going to matter. The market will all go [13:52] down. [13:52] >> True. This is the K-shaped stock market. [13:54] >> But this is the kind of thing where you [13:55] said if you were really concerned about [13:57] it, if you were really worried about an [13:59] AI bubble bursting, [14:00] there's plenty of other places to go. [14:02] >> Totally. [14:04] >> Yes. A lot of places. All right. [14:06] Exhibit A chart of the week. Exhibit A [14:08] for advice.com. If you want to try a [14:10] free trial, [14:11] uh Matt and team did one on earnings [14:13] growth and we talked about this. It [14:14] looks like it's a picture [14:15] >> A exhibit A we speak about it all the [14:17] time. It's It's for advisors only. [14:19] >> Yes. Yes. I think we had a few [14:20] individuals try it and like like hey [14:21] yeah, this isn't for me. Like yeah, of [14:22] course. It's for financial advisors. [14:25] But look at the earnings expected [14:26] earnings this quarter just shot off shot [14:29] up like a rocket ship. It doesn't look [14:30] like it's real. It looks like a COVID [14:32] chart. [14:33] So, yeah. A lot of people could be [14:34] saying to your point, [14:36] hey DAs, instead of dumb ass, [14:39] uh this is a one-time boost, a one-time [14:42] shot in the arm. [14:43] Like it the market has already priced [14:45] this in and it's going to move on. [14:46] >> How about this? If these earnings come [14:48] back to where they were, the market will [14:50] fall fall 40%. [14:52] >> How about this though? Mike Zaccardi [14:55] >> Yeah. Yeah. Yeah, it have to Yeah. It [14:56] would go the other way. Mike Zaccardi, [14:58] Russell 2000 is seeing EPS growth 40% [15:03] this year, 38% next year. Uh this is for [15:06] the small caps. [15:07] >> to look inside these numbers. That's [15:08] wild. [15:09] >> Yes, we're going to have to put Charkit [15:10] on this one cuz I've never seen this. [15:11] >> This has to be This has to be earnings [15:13] growth. This has to be AI productivity. [15:16] What else could it possibly be? [15:18] >> Are we really seeing that much [15:19] productivity growth for small caps? May [15:20] It It could just be because [15:22] >> it possibly What could it be? [15:24] >> I don't know because growth was so low [15:26] in 2025. I don't know. [15:27] I honestly have no idea. [15:29] All right. I'm putting You mentioned [15:31] yields at the start. So, yield the [15:32] 30-year Treasury bond is up to like 5.1 [15:34] 5.2%. Everyone keeps saying this is the [15:37] highest yield since the great financial [15:38] crisis. [15:39] And oh boy, this is a this is this is [15:42] something. Just wait. [15:44] Okay. So, here's here's I can't remember [15:45] where I pulled this chart from. [15:46] It shows it's highest since 2007. Okay. [15:50] So, Treasury yields are up a lot. Do you [15:52] know what TLT So, TLT is a 20-plus year [15:56] government bond ETF. Do you know what [15:58] the performance is this year for TLT? [16:01] Inclusive of the yield. What is the [16:02] performance this year? If you had to [16:03] guess cuz rates are up a lot. [16:05] >> Down 3%. [16:06] >> Yeah, it's down 2 and 1/2% or so. Do you [16:08] know what the performance was last year? [16:11] >> It was up. [16:12] >> Yeah, it was up like 4 or 5%. [16:13] >> Yeah. Yeah, but that but that doesn't [16:15] mean anything. [16:16] >> But I'm saying that so [16:17] >> No no nobody is saying the price of your [16:19] bonds is the risk. [16:20] >> But this to me is a boy who cried wolf [16:22] thing. Show me a real impact of this and [16:25] I'll believe you. But every time this [16:26] happens, people freak out and nothing [16:28] happens. So, I'm saying this is a boy [16:30] who cried wolf. [16:30] >> Sometimes the wolf comes. [16:32] >> Do you know what the average Treasury Do [16:34] you know what the average long-term [16:36] Treasury yield is going back 50 years in [16:38] the United States? What's the long-term [16:40] average for the yield? [16:41] >> 6. [16:42] >> 6.2%. Today it's 5.2%. [16:45] >> So, what? [16:47] >> My my That's what I'm saying. So, what? [16:49] Stop telling me this is a crisis when [16:50] nothing ever happens. [16:51] >> I'm not saying this is a crisis. [16:53] >> I'm sick of hearing about government [16:54] debt and yet and Treasury bonds. In [16:56] Japan, they're saying Japan bond yields [16:58] are up so much. Guess what? Stocks are [16:59] at all-time highs. Where are stocks at [17:00] all-time highs in the US? [17:02] Same thing. [17:03] It doesn't matter if yields are higher. [17:05] Until it does. [17:06] Show me a crisis, then. I'm sick of [17:08] hearing people complain about it. Show [17:10] me. [17:11] This is I'm sick of the boy who cried [17:12] wolf stuff. [17:14] Nothing happens. Nothing ever happens. [17:16] Give me a crisis. [17:18] >> Please. I really really really hope this [17:20] doesn't age poorly. [17:23] Uh now listen, I am with [17:24] >> Throw it in my face. [17:25] >> I am sick of hearing it. I am with you. [17:26] I am firmly with you. [17:28] Um [17:30] but we are here to talk about the market [17:32] and this is a potential this is a [17:34] potential risk. [17:35] >> Yeah, and I'm saying no, it's not. [17:36] >> Okay. I hope you're right. [17:37] >> All right. I want to talk about market [17:39] timing. [17:40] So, on the Prof G podcast last November, [17:43] they had asked about the motor mouth. [17:45] And he's one of the smartest If you [17:46] listen to his pod any him on any [17:47] interview, he's one of the smartest [17:49] people alive in the markets probably. [17:50] >> Easily. [17:51] >> He's ridiculously smart. [17:53] I remember I read one of his books his [17:54] like books on valuations like this you [17:56] know like 4 in thick when I first a lot [17:59] of it was over my head but anyway really [18:00] smart guy. So, he was saying listen, I [18:02] am super super worried about an AI [18:04] bubble and I'm raising cash for the [18:05] first time basically in my life. [18:07] And Galloway was like man cuz they work [18:10] together at NYU. He's like I've never [18:11] heard you sound this bearish before. [18:13] This is I've I've never heard you like [18:14] this. [18:15] It's crazy. And he said yeah, I'm I'm [18:16] telling people they should maybe raise [18:18] cash because this is and I've never done [18:19] this before. So, he was on the show [18:21] again this past week [18:23] for an update and he's like you know [18:24] what? [18:25] >> kidding? [18:25] >> He No, he was like you know what? This [18:27] is why you don't time the market. I was [18:28] wrong. [18:29] >> Uh [18:30] >> And he he he he you know, he owned up to [18:32] it and he said [18:33] >> Why when when when did he say that? When [18:34] did he say raise cash? [18:35] >> So, this is last November like last [18:37] Thanksgiving. I remember listening to it [18:39] when I was at Disney. [18:40] And I was like man, this is a really [18:41] smart person. He sounds uber bearish. [18:43] He's like listen, this whole AI trade [18:44] everything is the AI trade and when it [18:46] blows up there's nowhere to hide. I'm [18:48] raising cash. [18:49] He said I've never done this before. And [18:50] then he said you know what? [18:52] Timing the market is hard. I was wrong. [18:55] And it's I just I want to remind people [18:57] how hard it is to time this thing how [18:59] long this thing has been going on and [19:00] how no one is going to get it perfectly. [19:02] And if they do, they're they're lucky. [19:03] They're pulling it out of their ass. [19:05] It's not like a real analysis. You can't [19:07] time these things. [19:08] >> I'm not as susceptible to market [19:11] opinions from smart people as I was in [19:13] the past. I would I used to be very [19:15] easily influenced, especially if [19:16] somebody had a British accent and was [19:17] bearish. [19:18] >> The bowtie. Bowtie gets you every time. [19:20] >> That was my kryptonite. Um [19:24] if I can if I could like give [19:26] investors, general inves- like people [19:28] listening, one [19:30] ability, it would be to listen to these [19:32] smart people [19:33] and just completely discount it. [19:35] >> Yes. [19:35] >> Just say, "Okay, these are intelligent [19:36] people. They can't see the future. [19:38] Intelligent people can't see the [19:40] future." It's so hard. It's so hard [19:42] because these Demotoren whoever you [19:44] listen to, they know 400 times as much [19:47] as you do. [19:48] >> Yes. [19:49] >> And so you think that they're experts [19:50] and therefore they can predict. [19:53] It just is not the case. [19:55] >> I learned this early, too. When I first [19:56] was in meetings, when I first joined the [19:58] industry, I would I I would listen to [20:00] these people talk about the markets and [20:01] I'm like, "Oh my gosh, I'm never going [20:03] to be as smart as these people." And [20:04] then I saw what happened in the great [20:05] financial crisis [20:07] in the years afterwards and I thought, [20:08] "Oh, none of these people knew what was [20:09] going to happen. They've all been [20:11] wrong." And that was like a real wake-up [20:12] call for me, like, "You're right. Like, [20:14] don't [20:15] take some analysis and tidbits from [20:16] them, but they cannot predict the [20:17] future. They can't." [20:18] >> Yeah. And I'm also not discounting [20:20] experts. [20:21] >> No. [20:21] >> Right? Like Like it's not just like [20:22] Demotoren legitimately, these some of [20:24] these people that you listen to are [20:26] brilliant and know so much Okay, so I [20:30] was listening to uh [20:31] uh Dwarkesh I I was listening to a [20:33] podcast [20:34] yesterday or the day before. It ended on [20:37] Spotify and it automatically started [20:38] playing the new one, right? So it was [20:40] Dwarkesh Patel and uh he had like um [20:44] he had somebody on that studies uh how [20:48] uh my god, I can't even I can't even [20:50] describe what they were talking about. [20:51] How um your your genes change over like [20:54] millennium, right? Like natural [20:55] selection, how that all works. [20:57] And these guys were speaking a different [20:59] language. And that guy Dwarkesh could go [21:00] so wide. It's amazing how many different [21:03] things that guy could talk about. [21:05] Anyway, I I don't even know why I was [21:07] listening to do frankly. I didn't [21:08] understand a single thing they were [21:08] saying. It was just sort of background [21:10] noise. [21:11] Um so this guy who was talking about [21:13] like how things change, he can't predict [21:15] the future either. [21:16] >> Right. [21:18] >> And did you hear the fertility guy with [21:19] Derek Thompson? [21:20] >> Yes. [21:21] >> Like and that guy can't predict the [21:22] future. So those are legitimate experts. [21:25] They know more about their field than [21:26] anybody on the planet. [21:29] And yet they're they're tossing they're [21:31] tossing a coin. [21:31] >> And the reason is because they're human. [21:33] So Michael Burry [21:34] Uh there was another headline, "Michael [21:36] Burry Warns of Stock Crash as Tech Jump [21:37] Echoes 2000 Peak." [21:39] He says history tells us that even if [21:40] the party goes on for the week, month, 3 [21:42] months, or year, the resolution will be [21:44] much lower prices. [21:45] >> Why do you think he's doing this? [21:47] >> air so extreme the consequences will be [21:49] unavoidable no matter where one hides. [21:51] Kind of sounding similar. Uh Spencer [21:53] Jacob at the Wall Street Journal did [21:54] this thing where he he plotted Burry's [21:56] calls on the on the line of the market, [21:59] right? Remember 2019 he was calling for [22:00] a passive bubble. [22:02] Uh he said in 2019 he said this will be [22:04] ugly. In 2022 he said this will be worse [22:06] than 2008. Remember he had the the sell [22:08] tweet in 2023. [22:10] I just like to remind people of these [22:11] not to like shove it down someone's [22:13] throat. Like haha you were wrong. But [22:14] it's it's to your point you can't listen [22:17] to every one of these predictions. [22:18] >> I would love to see what [22:19] >> you insane. [22:20] >> I would love to see what his returns [22:21] look like. [22:23] I'm guessing [22:23] >> I bet they've been okay. [22:24] >> I'm guessing they've been not even [22:26] Like if you if you lined up the the [22:28] things that we see about him in the [22:29] newspaper versus performance, you would [22:31] say something is not tracking here. [22:33] Because there's no way that he's lost [22:35] 20% a year for I mean obviously, right? [22:37] >> But it's like that with all those [22:38] people. With Druckenmiller and Dalio and [22:41] PTJ, you hear their what they say on [22:43] CNBC and you go, "Oh my gosh, these guys [22:44] are so bearish. They're predicting [22:45] another crash." Then you look at what [22:47] they hold and it's not like that at all. [22:49] They're making macro predictions, but [22:50] they don't allow that to seep into their [22:52] portfolio. That's the thing. [22:53] >> Right. [22:54] >> All right. Um speaking of a passive [22:56] bubble, go good one from Goldman Sachs [22:57] that I love is ownership of the US [22:59] equity market. [23:00] So I saw someone put it post a thing the [23:02] other day saying passive index funds now [23:04] make up 60% of all fund assets. It's [23:07] like, "Holy crap." Now, look at for the [23:09] total stock market. [23:11] >> Right. [23:11] >> Passive mutual funds make up 6%. ETFs [23:14] make up 10%, which is the same as active [23:15] mutual funds at 10%. Um passive mutual [23:19] funds and ETFs make up less than foreign [23:21] investors. [23:22] So, it's it's a tiny piece of the [23:24] overall pie. [23:24] >> But, what if foreign investors are [23:26] holding ETFs [23:27] and index funds? [23:29] >> I don't think it works like that. It [23:31] doesn't work like that. They'd be they'd [23:32] be included in that. [23:33] >> Uh your point is taken. It's it's it's [23:36] it's simultaneously obviously a gigantic [23:38] part of the market, I mean, obviously, [23:39] right? It's like it's many many many [23:40] many trillions, but it's also there's [23:43] other things going on inside of the [23:44] stock market. Stock market index [23:45] >> Here's here's So, people keep saying [23:47] uh people have no decisions, there's no [23:49] fundamentals involved with buying this [23:51] stock. The the biggest piece of the pie [23:53] is direct household ownership. And guess [23:54] who owns those shares? Elon Musk, Jeff [23:57] Bezos, the start founders of these [23:59] companies. Are they ever selling them? [24:01] And that's 40% of the market. That's [24:02] where most of that money is. It's people [24:04] who work at these companies and own [24:06] shares in the stock. [24:07] They're not selling, either. [24:09] >> Here's I what I think one of the things [24:11] that is very legitimate about the [24:14] changing structure of how the market is [24:16] held and traded. [24:19] Back in the day, there was a lot of [24:20] analysts, a lot more analysts than there [24:21] are today, covering a bunch of different [24:23] stocks. [24:24] Like mid-cap stocks, small-cap stocks [24:26] used to have a lot of analyst coverage. [24:29] And they just don't anymore. [24:31] And if nobody discovers these names, it [24:32] is harder for them to trade on [24:33] fundamentals, [24:34] which is why I think a lot of the active [24:36] managers have had such a hard time. It's [24:37] like, there could be value in these [24:38] stocks, but they're just dead money [24:39] because nobody's there to recognize the [24:40] value. [24:41] >> But, all of these companies are going to [24:43] be covered now because of AI. There's [24:45] not going to be any more [24:46] stones left unturned. Think about how [24:48] much easier it'll be for an equity [24:49] analyst to to have a wider cast a wider [24:52] net in terms of the companies that they [24:53] follow. [24:54] >> Yeah, good point. [24:55] >> Right? There's you no going to be no [24:57] more uh no one pays attention to these [24:58] companies anymore. That's not that's a [24:59] thing of the past. Equity research [25:01] departments are going to cover every [25:02] stock they can. [25:03] >> All right, so speaking of AI, let's get [25:04] to this email that we got. [25:06] >> Okay. [25:07] As you discuss your new Porterhouse SMA [25:09] strategy, I wondered to myself, could I [25:11] ask AI to tell me about their momentum [25:13] strategy so I could skip all that pesky [25:14] having to send Ritholtz my money and [25:16] just get the expertise free from the [25:18] machine. And then this guy sent us like [25:19] whatever AI spit out [25:21] and [25:23] I wanted to talk about this cuz this is [25:25] going to be a thing where you can go to [25:26] you'll be able to go to AI and I'm sure [25:27] there's I think there's already, you [25:28] know, public has something like this [25:29] already. I'm going to create my own [25:30] index [25:32] and I'm going to hit a button and it's [25:33] going to say trade it for me. And I can [25:35] create my own rules and I want this [25:37] basket of stocks and I want it to do [25:38] this and I want it to change this. [25:41] Do it for me. That is definitely going [25:43] to be a thing. [25:44] >> Oh, yeah. [25:46] >> But I think what's going to happen is [25:48] you're going to create one of these [25:49] systems and then you're going to tinker. [25:51] And then you're going to change this and [25:53] you're going to change that. Wait, that [25:55] didn't work. Let's change this. Well, [25:56] that didn't work. Take that out. And [25:58] you're never going to be able to stop [25:59] tinkering because it'll be there. [26:01] There's no barriers to entry [26:03] and sometimes having strategies with [26:06] relatively simple rules and our strategy [26:08] actually is not that simple. It's a lot [26:10] that goes into it. But the hard part is [26:12] just sticking with something. That's the [26:14] thing. [26:15] And I I think AI is going to just the [26:18] barriers to entry being knocked down [26:20] it's going to be impossible for people [26:21] to stop tinkering with stuff like this. [26:24] >> Depends on the personality, totally. For [26:25] me, absolutely. I'm a tinkerer. You know [26:27] that. [26:28] >> So, what do you think about these people [26:29] who want to just all right, I'm just [26:30] going to replicate what you do. I'll [26:31] take I'll I'll I'll get 75% of the way [26:33] there and I'll I'll try to do it myself. [26:36] More power to you. [26:37] >> Um [26:38] Yeah, sure. Why not? Experiment. [26:41] Yeah, go for it. [26:42] >> But I I that [26:43] you can outsource trading systems and [26:46] stock screens and all this stuff, but if [26:48] you don't understand how a strategy [26:49] works and why it still [26:51] >> that's the hard part. It's like the [26:52] portfolio construction. It's not [26:53] necessarily [26:55] building a screen is not rocket science. [26:57] I mean there's you know, not to discount [26:59] how much work went into it. [27:00] But it's the portfolio management and [27:02] it's the and it's the decision-making [27:03] and the behavior. And can you stick you [27:04] know, the stick-to-itiveness. [27:06] >> And like understanding guess what? This [27:08] strategy is not going to work at some [27:09] point. No no strategy works all the [27:10] time. There's going to be [27:13] a good win rate in the stock market is [27:15] maybe 55 or 60%. [27:17] >> Oh yeah. So we we had somebody busting [27:19] our chops about long-term momentum [27:20] strategy um [27:21] after one of after like an all-time [27:23] performance run for momentum. [27:25] Is this the top? And he was just busting [27:27] balls. Like and I cuz I respond I said, [27:28] we started building this in the winter. [27:30] Like I I wish that this didn't happen. [27:32] >> Right. [27:33] >> Um because momentum like anything else [27:35] is seasonal, right? It has its days in [27:37] the sun and then it has its periods [27:38] where it sucks [ __ ] And there's no [27:39] doubt about it that momentum unwinds are [27:41] really painful. [27:42] That's the nature of the beast. [27:44] >> Yeah, so and you also have to figure out [27:45] the position sizing of well, how much of [27:47] my portfolio is this going to be? [27:48] Right? If you put all your portfolio in [27:50] it, you're going to it's going to be [27:51] probably kind of painful. If you put a [27:53] piece of your portfolio in it and you [27:54] have complements to it, that's a [27:56] different thing. And that's you're [27:57] right, it's the portfolio management [27:58] side of things. Anyone can create any [27:59] screen they want these days. That's [28:01] easy. [28:02] Fair question, though. [28:03] >> So I want to stick with a I want to [28:05] stick we're going to stick with this. I [28:06] want to stick with some of the uh AI [28:08] stuff because [28:12] I think a couple of months ago like me [28:13] and the the my personal usage of AI has [28:16] happened [28:17] um [28:20] gradually than suddenly and I was trying [28:21] to think of a different phrase because [28:22] it's so cliché, but it is true. So like [28:24] maybe like a year and a half ago I was [28:25] thinking like, how exactly are you how [28:27] how exactly are you using AI? And I [28:30] don't mean you Ben Carlson, I just mean [28:31] like people that are talking about it so [28:32] much, how are they using it? [28:34] So I want to give an example of how I [28:35] used it over the weekend. [28:38] Howard Lindzon shared an article um AI [28:42] is the new Netflix. [28:43] And [28:45] it was fairly technical. [28:48] And I'm not a technical person, [28:50] especially when it comes to technology. [28:52] >> I'm not going to lie. I read this piece. [28:53] I still don't quite get the analogy. [28:54] >> I did. [28:55] >> Okay, I don't get it. [28:56] >> So, if I understand it correctly, the [29:00] internet was built, and Duncan can jump [29:02] in here if this is a Duncan thing. The [29:05] internet was built for download. [29:07] It was like and and when streaming came [29:08] along and it really disrupted the [29:10] broadband [29:11] um [29:12] uh availability, like I think it things [29:14] sort of buckled. We didn't have enough [29:15] infrastructure. So, we were built to [29:17] receive information. [29:20] AI, we are pushing it out. This is like [29:22] an upload thing. [29:24] So, I think this article is about like [29:25] how the system is going to evolve. So, [29:27] anyway, [29:28] I asked Claude uh to break it down for [29:32] me. And here's what Claude said. [29:34] Back in 2008, video streaming was the [29:36] thing that made people care about [29:37] download speed. It ate bandwidth, drove [29:39] everyone to upgrade their internet, and [29:41] forced the cable fiber companies to [29:42] rebuild their networks around it. Om [29:44] Malik, he's he's the the author. Uh and [29:46] also the guy that wrote uh Broadband. [29:47] That's that's where I found the book. Om [29:49] Malik's argument is that AI is now [29:51] playing that same role, except it's [29:53] flipping the direction. [29:54] AI is the killer app of the next era, [29:56] not because of what it downloads, but [29:58] because of what it uploads. So, I never [30:00] thought about it that way, and I said, [30:02] "Hmm, [30:03] let me ask Claude [30:05] how our Port- Porter House portfolio is [30:08] positioned for this potential [30:10] disruption, this potential theme. Now, [30:12] obviously, this is not investment [30:14] advice, okay? Duh. [30:16] So, [30:17] uh [30:18] so, we have we have Porter House and [30:20] Wide Charts as a plugin, and so, it [30:22] pulled it directly, and it said, "Pulled [30:24] the live book. Porter House is heavily [30:25] exposed to this theme, [30:27] but through a specific slice of it, and [30:29] not the slice [30:31] not the slice Om Malik actually [30:33] emphasizes." So, I said, "Hmm, okay." [30:36] So, it showed me uh [30:39] the holding, the ticker, the weight, and [30:40] where it sits in the thesis. So, Ciena, [30:42] Vertiv, and Amphenol, three of the [30:43] bigger names in the portfolio. And it [30:45] described what's going on here. [30:47] And then it said to me, [30:49] "You have these You have essentially [30:51] zero exposure to the part of Ohm's [30:54] thesis that's actually differentiated." [30:57] Uh Claude said his sharpest, least [31:00] crowded point was the access layer [31:02] structural split fiber, which is Verizon [31:05] and AT&T. Uh as well as structurally [31:07] advantaged cable, Comcast and Charter. [31:10] Um Porter holds none of these. No [31:13] telecom, no cable. So, the portfolio is [31:16] loaded on the crowded AI infra capex [31:19] reading of the thesis. By the way, [31:21] yeah, is Claude taking shots at our [31:23] portfolio? Momentum by definition, [31:25] especially when it's working, is [31:26] >> guess what? I'm glad I don't own AT&T. [31:29] >> Um yeah, when momentum works, it is [31:31] crowded by definition, okay? So, that's [31:32] that's not like that. [31:33] >> Yeah, cuz these aren't momentum names [31:34] yet. Maybe they will if this thesis [31:35] plays out. [31:36] >> Okay, so but but but here's what Claude [31:37] said. Let me finish. [31:39] Um [31:40] Uh da da da da [31:41] So, the portfolio is loaded on the [31:43] crowded AI infra capex reading of the [31:45] thesis and has no position in the [31:47] genuinely contrarian last mile angle. [31:50] That's not a criticism of the signal. [31:52] Telecoms haven't had the momentum to [31:54] qualify. [31:55] But it tells you the factor is [31:56] expressing the consensus version of the [31:58] theme, not the differentiated one. Not [32:00] that you asked, but it is important, at [32:02] least for me personally. One of the [32:03] reasons why I love this love the [32:04] strategy so much, not to make this sound [32:06] too much like a commercial, but I can't [32:08] ride winners. I've said this to you guys [32:10] a million times. I don't have the [32:12] ability to do it. It's not in my [32:14] personality. I know how hard stocks uh I [32:17] know how many stocks are terrible. And [32:19] so, when I have a winner, I I take it. [32:22] And Peter Lynch famously said, "Selling [32:24] a winner [32:25] and adding to a loser." Now, I don't do [32:26] that. I don't add to losers. But selling [32:28] a winner and adding to losers, which is [32:29] like the natural thing to do, is like [32:32] pulling out your flowers and watering [32:33] the weeds. [32:35] And having a systematic strategy that [32:37] doesn't do that, for me personally, [32:39] is hugely beneficial. [32:40] >> I'm the same way. I could not hold a [32:42] stock for 500%. I I sent you the quote [32:44] yesterday. Howard Marks was like, [32:45] "There's two reasons you you sell a [32:47] stock. Because they go up and because [32:48] they go because they go down. You sell [32:50] when they're up because you worry [32:51] that they're going to go back down. And [32:53] you sell the the down because you worry [32:54] they're going to go down even further." [32:55] >> Exactly. I mean, that's [32:56] >> Cuz momentum is it's a totally [32:58] behavioral strategy, and that's why [32:59] there's not a lot of money in these [33:01] strategies to begin with. There's It's [33:03] way easier to understand a value [33:04] strategy. [33:05] I'm buying a dollar for 50 cents or 60 [33:07] cents. That's easy to understand. [33:09] Momentum is not easy to understand like [33:10] that. Um I do want to say Claude, by [33:13] far, is the best stock market analyst [33:15] there is for these LLMs, and it's not [33:17] even close. So, I take I'll take the [33:19] year-to-date holdings for the S&P 500, [33:21] and I will upload them to Claude and [33:23] say, "What's going on here?" And it'll [33:24] give me a sector breakdown, a stock [33:26] breakdown. It's It's actually pretty [33:28] unbelievable. [33:29] >> So, he's kind of [33:30] So, this brings us to what Ken Griffin [33:32] said over the weekend. Like the the work [33:33] that I just described to you that I over [33:35] the weekend, which took me all of, I [33:37] don't know, like I was start to finish [33:39] between reading the article and and [33:40] getting this in 10 minutes, [33:42] that's a job. That might be multiple [33:45] people's jobs. So, Ken Griffin was [33:47] talking about this [33:49] um [33:49] how this is going to impact financial [33:50] services, and I want to play this and [33:52] get your reaction, Ben. [33:53] >> It has been really interesting to watch, [33:56] to be blunt, work that we would usually [33:59] do with people with master's and PhDs in [34:01] finance over the course of weeks or [34:04] months, being done by AI agents over the [34:07] course of hours or days. [34:11] So, these are not These are not mid-tier [34:14] white-collar jobs. These are like [34:16] extraordinarily high-skilled jobs [34:19] being [34:21] I'm I'm going to pick a word. Being [34:22] automated by [34:24] agentic AI. And I I got to tell you, I [34:27] went home one Friday, actually [34:30] fairly depressed by this [34:33] because you could just see how this was [34:35] going to have such a dramatic impact on [34:39] society. [34:41] Like and and when you witness it in your [34:43] own four walls, when you see work that [34:44] used to be man years of work being done [34:47] in days or weeks, [34:50] it's like wow, like that's the first [34:52] time I've seen real impact in our four [34:55] walls. [34:57] >> Thoughts? I did listen to this. [35:00] I agree with him. It's it's kind of [35:02] scary and depressing in a ways. The [35:03] biggest question I have is it is AI [35:06] doing jobs or is it doing tasks? [35:08] And I think that's a big distinction. [35:10] >> Dude, that's that's [35:12] some that's a job. [35:14] What I just did over the weekend and [35:15] what he is describing, at this point [35:17] that's not that's not like entry-level [35:19] jobs. I mean, I mean, you know, maybe [35:20] the stuff that I was doing was, but [35:23] So it is it is certainly part scary and [35:25] depressing. I mean, it really is. [35:27] And of course, it's also super exciting. [35:29] >> So what will put more analysts out of [35:31] work, AI or index funds? Did Jack Bogle [35:33] do it or is AI going to do it? Because I [35:35] could you could make the case that index [35:36] funds had a bigger impact. [35:38] >> Fire and gasoline. [35:39] >> Okay. [35:41] Yes, I I agree. [35:43] There is a lot of scary stuff with this. [35:45] And [35:47] yet, the unemployment rate is still 4 [35:48] and 1/2%. [35:50] >> Dude, we're so early. So So to that [35:52] point [35:53] >> Uh [35:54] Hold on. Hold on. No, no, no, no, no, [35:55] no, no. What's the time, Sam? 2027 [35:57] before you can't see that anymore? [35:58] >> So um Gavin Baker [36:02] did a [36:04] I don't know. He He was at the Sohn [36:05] conference talking about this, and it's [36:08] 25 minutes, and I highly highly highly [36:10] recommend it. [36:11] >> I didn't listen to these yet, but I [36:12] heard a bunch of people say it's yeah, [36:13] worth listening to. [36:13] >> He He makes you feel way better about [36:16] the bubble, about like [36:17] He makes Whatever. Just just listen to [36:19] it. Um but Gavin said [36:23] 10 basis points of the population are [36:25] using these models. [36:28] And there is a shortage of compute. [36:31] What happens when it's 5%? [36:35] >> So he So he was saying like that it [36:37] can't be a bubble if the demand [36:38] outstrips the supply. Is that what he [36:40] >> He He Yeah, he's basically saying like [36:41] this was his case for smoother for [36:43] longer. But [36:45] dude, it is so early. [36:47] >> I was at a party a couple weeks ago for [36:49] like a March Madness party and we were [36:50] talking about, you know, how much do you [36:51] actually use AI in your jobs? And I was [36:53] talking about how I switch between [36:55] Claude and Gemini and Chat GPT depending [36:57] on what I'm doing. And this one guy was [36:59] like, "What's Gemini?" [37:00] I've never heard that before. [37:01] >> So don't don't don't [37:03] >> that's most people though. That's most [37:04] people. [37:05] >> don't tell me about the unemployment [37:06] rate today. And I'm not predicting 10% [37:08] anything like that. But to look at the [37:09] unemployment rate today and say AI is [37:11] not disruptive is a joke and you're [37:13] better than that. [37:13] >> Do you you [37:15] If you looked at the numbers of what's [37:17] happening and this is still going on [37:18] after it's been out for [37:20] three or four years now, wouldn't you be [37:21] surprised that the unemployment is still [37:23] as low as it is given every given all [37:24] the leaps forward that we've had? [37:26] >> Yes, but it it [37:28] it it was invented [37:30] three or four years ago. [37:33] It has only just begun then. [37:35] >> Here's the Here's the one thing that I [37:36] think is impossible to predict in all [37:37] this. So Al Frankenwitz put this tweet [37:40] out. This is incredible. AI is getting [37:42] booed out of the stadium at any [37:43] commencement address it's mentioned. So [37:44] Eric Schmidt did a commencement speech [37:46] at Arizona and anytime he mentioned AI, [37:49] the students booed. [37:50] >> Oh, I love it. [37:51] >> AI is a villain. [37:54] And so the question is [37:56] what are the political ramifications? [37:58] And like are companies going to get [38:01] penalized if they [38:02] >> We have to tax the robots. [38:04] >> That Yes, that's [38:05] >> If If If for nothing else to make us [38:06] feel like we're doing something. [38:08] >> Yes. [38:09] >> Because I don't know that anything can [38:10] stop this can slow this down. [38:11] >> Because I think if we you just look at [38:13] what happened sentiment-wise after 2008 [38:15] and none of those bankers went to jail, [38:17] which is still beyond imagination from I [38:19] don't know how that how no one went to [38:20] jail. [38:21] >> You could draw a You could draw a direct [38:22] line from that the lack of uh [38:25] ramifications to where we are today. [38:27] >> Definitely. So, my question is like are [38:30] politicians actually going to do [38:32] something about it? Will companies be [38:33] penalized if they have a mass layoff [38:34] because of AI? Will there be [38:36] And will you say, "Hey, your company [38:38] gets a lower tax rate if you keep hiring [38:41] people and don't lay them off because of [38:43] AI?" Like I don't You don't You can't [38:45] See, cuz some people just like, "You [38:46] know what? There's no use fighting it. [38:47] It's inevitable." And I get that, but I [38:50] also think you can't predict what the [38:52] political ramifications of this are [38:53] going to be cuz people are people hate [38:55] this so much. [38:57] >> Yeah. [38:58] So, that that is a clear, you know, I [38:59] know we keep talking about this. That is [39:01] a scary part of it. All right, uh [39:02] potentially exciting part of it, [39:04] although maybe scary, too. So, Brett [39:06] Adcock, the founder of a humanoid robots [39:09] company called um [39:11] Oh my god. [39:14] What is the name of this company? [39:15] Gosh dang it. Ah, it doesn't matter. Um [39:17] >> iRobot. [39:18] >> No. [39:20] So, they [39:23] have been [39:25] live-streaming robots sorting packages. [39:29] They said, "Our original goal was an [39:31] 8-hour run. [39:32] We wanted to run non-stop and fully [39:34] autonomous. Since then, we made a [39:35] decision to keep the party going. We're [39:37] now over 48 hours of non-stop autonomous [39:39] operation without a failure to perform [39:40] the use case. This is uncharted [39:42] territory." [39:44] So, uh the task is small package [39:47] sorting. The robot detects the barcode, [39:49] picks up the package, and reorients its [39:50] barcode face down onto the conveyor. [39:52] Humans average around 3 seconds per [39:53] package. The robot is now around human [39:55] parity. Uh I I think it's still running. [39:58] Um [39:59] man oh man. [40:00] >> So, this is one of those things that I [40:02] think this is a double-edged sword of [40:04] boy, this is scary, robots taking our [40:05] jobs, but I also think I think we need [40:08] robots in this country. So, you talked [40:09] about Derek's podcast about the [40:10] fertility crisis, right? That we're not [40:13] We're not having We're not replacing [40:15] enough If you If you A couple has one [40:17] child, there's the replacement is not [40:19] there for humans. So, like eventually [40:21] population is going to decline. [40:23] We have We're going to need robots to [40:24] help take care of people in services and [40:27] we're we we have to have them. [40:29] >> Well, otherwise this whole thing falls [40:30] apart. [40:31] >> Looks like they're coming. [40:32] Uh we were talking about [40:34] >> Hang on. Is a baby Would a baby boom be [40:36] the most surprising thing in the next 20 [40:38] years if we had a baby boom? Would that [40:39] be the most surprising thing that could [40:40] possibly happen? [40:41] >> Imagine AI and robots make the world [40:44] like uh this nirvana state and everybody [40:46] just has free time to procreate and [40:48] replenish the population. Hilarious. [40:51] Well done, Ben. [40:52] Um all right, a couple of weeks ago we [40:54] were talking about uh there was an [40:55] article in the journal. Was it Greg Ip [40:57] who we like? But like he was making the [40:59] I think I think he was making the [41:00] contrarian case that an AI slowdown [41:01] would actually be fine. [41:03] No, it wouldn't. Um [41:05] So, Torsten Slok [41:07] uh showed AI is penetrating every corner [41:10] of financial markets. What began as an [41:12] equity market phenomenon has become a [41:13] capital markets wide transformation. AI [41:16] now accounts for nearly half of all [41:17] investment grade issuance, 87% of VC [41:19] funding and a growing share of high [41:21] yield, underscoring how deeply the AI [41:23] investment cycle has penetrated every [41:25] corner of finance. If this stops, the [41:27] music stops. [41:27] >> Can I just say though that we have moved [41:28] the goal posts a lot on this because at [41:30] first it was no, this is never going to [41:32] work. And now it's like oh, now it's [41:33] working too well. [41:34] >> Yeah. [41:34] >> So, I think we've moved the goal posts [41:36] in some ways. So, this is better than [41:37] the alternative of we spend all this [41:38] money and nothing happened. [41:40] >> The [41:41] the uh the top line numbers that are [41:44] being reported at Anthropic [41:47] are like nothing we've ever ever seen [41:48] before and and [41:51] in when was the mag seven in the AI [41:53] trade really in the [ __ ] It was when [41:54] Sam Altman, one of the podcasts, was [41:55] that the fall? I can't even remember. It [41:56] was It was over the fall? [41:58] >> Sounded all right. [41:59] >> And all these names got hit and Oracle [42:01] was at the center because like wait a [42:02] minute. Um I don't think [42:06] OpenAI's five-year $300 billion [42:08] commitment to Oracle is going to come to [42:09] fruition. [42:11] And now that has turned dramatically, [42:14] both Oracle shares and the entire [42:16] narrative, because the fundamentals are [42:18] proving every doubter wrong. [42:20] >> So, [42:22] I can't living in like San Francisco in [42:24] that area has to be so trippy because [42:26] everyone who talks about this stuff is [42:28] talking about the world changing at the [42:30] fastest pace in history. If you hear the [42:32] the tech bros go on podcast and talk [42:33] about this stuff, [42:35] they're you know, we talk about AI and [42:36] what it's going to do. They talk about [42:38] it like it's the the you know, Nirvana [42:40] is already here. But, it's also the [42:42] wealth piece. So, this Dideon Twitter [42:44] post, he said, "The vibes in SF feel [42:46] feel pretty frenetic right now. Uh the [42:48] divide in outcomes is worse than I've [42:49] ever seen." So, he said, "Over the last [42:51] 5 years, a group of say 10,000 people at [42:53] Anthropic, OpenAI, uh Nvidia [42:57] have hit retirement wealth of above $20 [42:58] million based on their shares in the [43:00] company. Um everyone outside of that [43:02] group feels like they can work their [43:03] well-paying jobs less than 500,000 for [43:05] their whole life and never get there. Uh [43:07] worse yet, layoffs are in full swing, [43:08] but this is funny. He he goes to all [43:09] these different people and why they're [43:11] miserable because of this wealth. And [43:12] one of them is like, "Good luck [43:14] competing with someone from Anthropic [43:16] who just cashed out their shares to buy [43:17] a house." [43:18] Right? But, he said, "The rich aren't [43:19] particularly happy either. [43:21] Uh no one is shedding tears for them, [43:23] but those who have made it experience [43:24] profound lack of purpose. Some have gone [43:26] from less than $150,000 to $50 million [43:29] in a few years with no ramp. It flips [43:30] your life upside down. [43:32] Um [43:33] he said, "Some of them escape to New [43:35] York to live life. Others start [43:36] companies just because to win status [43:38] points they never imagined by age 30." [43:39] He said. Um I I once asked a [43:41] post-economic founder friend why they [43:43] didn't just sell the company, and they [43:44] said, "Then do what? Right now, everyone [43:46] wants to talk to me. If I sell, I will [43:47] only have money." [43:49] It That just It has to be a dystopian [43:52] weird kind of place to live, is all I'm [43:54] saying. [43:55] >> Uh there's a lot of people dunking on [43:56] this, and rightfully so. [43:59] I I understand the dunks completely. [44:00] That That's [44:01] >> He's He's He's totally on point. He's [44:02] totally right. [44:03] >> So, on from the outside looking in, that [44:05] whole pocket of life is bizarre world. [44:08] There is more to life than this, right? [44:12] Okay, but if you are inside of that life [44:14] and that is your life, [44:16] all of the human emotions that he's [44:18] describing are human. [44:20] You need [44:22] like one of the things that I find so [44:25] incredibly satisfying and lucky about my [44:28] path is that I hit rock bottom. [44:31] I was unemployed for a long time and I [44:34] had no career prospects. [44:36] And uh things were going incredibly bad [44:39] for me. [44:40] And I had a 15-year [44:43] ramp [44:44] to get to the point where I am today, [44:47] which I never thought I would get to, [44:49] and it has been I still cannot believe [44:51] the journey that I've been on and how [44:53] [ __ ] lucky I am. And I think about it [44:56] every second of every day for the most [44:57] part. [44:58] >> I wanted to talk about this today. [44:59] >> happened if that happened to me [45:00] overnight, [45:02] >> Yeah. [45:02] >> it would totally screw [45:04] without the struggle and without like [45:06] the gradual improvements and now I can [45:08] do this and now I can do that and now I [45:10] can do this and and do this for my [45:12] family and that. If that just happens [45:14] overnight, it breaks your brain and then [45:17] and then living in society with your [45:18] friends and your neighbors, when [45:20] everything is so public, you become like [45:23] oh [ __ ] that guy. [45:24] >> Think about if you joined OpenAI two [45:26] years later and you're more talented [45:28] than someone else who joined two years [45:29] before you, but they're 10 times richer [45:31] than you just because they happened to [45:33] join the company before you. [45:34] Is it [45:35] >> Cuz that's that's that that scrambles [45:36] anybody's emotional [45:37] >> Is it because they're smarter than you [45:38] or they're better than you or they're [45:39] harder working than you? Maybe not. They [45:40] might have just got lucky and timed it [45:42] right. And so Clooney and Jon Hamm have [45:44] talked about how they are happy that [45:46] they found fame in their like later 30s [45:49] and they struggled for a while before to [45:50] get there because they're like if I [45:51] would have got it earlier, I would have [45:52] been so screwed up. And sometimes [45:54] waiting is so I agree the perspective [45:56] thing [45:57] I just think it's uh [45:59] but I mean again, imagine trying to [46:01] compete for a house in that area when [46:03] these people who just got all these [46:04] shares go, "I don't care. I'm buying [46:05] whatever house I can." [46:07] I don't care if I pay more. I got this [46:08] got $10 million. Ah. [46:11] >> A couple of weeks ago we were talking [46:12] about the three kings book or not three [46:13] kings, whatever it's called. The with [46:15] with with with Coppola and Spielberg and [46:18] George Lucas. And they each said when [46:20] they made their monster monster hits [46:21] they were depressed afterwards because [46:22] >> Yes. That was the thing I pulled out of [46:24] the Spielberg thing that was like, "Oh [46:25] my gosh." [46:26] >> Being on top of the mountain is not fun. [46:28] It's climbing the mountain. [46:31] And that's like the whole purpose of [46:32] life. [46:32] >> But he also talked about how [46:34] Spielberg started working with David [46:35] Geffen and David Geffen was a [46:36] billionaire. And Spielberg at that time [46:38] was only worth $600 million. And he's [46:40] like looking up at This is the guy who's [46:42] created the best some of the best movies [46:43] in history. And he's like, "Why don't I [46:45] have a billion dollars?" [46:47] It's It's It's a natural human emotion. [46:50] >> So you can't you just it just [46:53] If you the listener if anybody was in [46:55] these shoes these people are all human [46:56] beings. This is just what happens. [46:59] >> Yes. I I I agree with you. [47:00] >> I'm very happy. I think I think [47:01] everybody reading this is like, thank [47:03] [ __ ] god that I'm not that's not my [47:05] life cuz that sounds miserable. [47:07] >> But you're right. The I I do think the [47:08] one thing is the the way that you break [47:11] that cycle is just gratitude. Like I'm [47:13] better off than I was before. I don't [47:14] care if I'm that better off than that [47:15] person. I'm better off than I was than I [47:17] I ever thought possible. [47:19] >> Yeah. Five, seven, 10 years ago. It is [47:21] but yeah, that that is the way. [47:22] >> All right. I want to talk about a [47:23] discussion I had at the post office. So [47:26] >> What? [47:27] >> about inflation. [47:28] So I've been go I go to the post office [47:30] right next to my office essentially. And [47:33] I I'm sending out books to people. I'm [47:34] sending out signed copies of my book. [47:36] And I went there two or three times in [47:37] one week cuz I had this big batch of [47:38] books. And the guy at the post is is [47:40] this really large guy like muscular like [47:43] built, you know, kind of guy. His name [47:44] is Cletus. Okay? So I get to talking a [47:46] little bit. He goes, [47:47] "Why do you keep sending out so many [47:49] books, man?" Cuz you have to tell them [47:50] what are you sending out? It's a book. [47:51] He said, "Are you an author or [47:52] something?" I said, "Yeah, I am an [47:53] author. I wrote a book." He said, "Well, [47:53] what do you write in a book about?" I [47:54] said, "Well, it's about investing in [47:55] markets and" He said, "Let me ask you [47:57] something." [47:58] >> You still You still like Micron here? [48:00] >> He goes, "What do you think about [48:01] everything that's going on right now?" [48:03] And I was like, [48:05] "What do you mean? Go Keep going." And [48:07] he's like, "Just He's like, "You Are you [48:09] familiar with Weimar Germany? [48:11] Hyperinflation? [48:13] People um carrying [48:16] wheelbarrows full of cash down the [48:17] street?" [48:18] He's like, [48:19] "I kind of think that's where we're [48:20] heading." [48:21] I said, [48:22] "Okay." [48:23] >> You should have [48:24] You should have handed him a a signed [48:26] copy of your book and said, "Read this." [48:27] >> What I told him is, "All right, man. [48:29] Listen, I don't want to like debate you [48:30] on this." And I He He This is a very [48:32] articulate guy. [48:33] I think he went down kind of a crypto [48:35] rabbit hole on this stuff. And I said, [48:37] "I'll tell you what. Next time I come in [48:39] with the next Backer Butch books, I'm [48:40] going to give you a copy and I want you [48:42] to read it and let me know what you [48:42] think." [48:45] But this This was a [48:47] Here's my thinking on this. [48:48] I think having sky-high inflation this [48:51] decade was one of the most surprising [48:53] psychological outcomes. I I would I [48:55] totally underestimated what it would do [48:56] to people. [48:58] And I think for him, it was all about [48:59] inflation and how he's like, "Inflation [49:01] is basically ruining the country." And [49:04] I think I think [49:06] Again, [49:07] all we all we we've all been reading and [49:09] writing about behavioral psychology for [49:12] 10 years now or something. I was I [49:14] underappreciated [49:16] the psychological impact of inflation on [49:18] people. [49:20] >> It was a dizzy. [49:22] >> Yes. And And I I think that's going to [49:25] Remember we talked about like what the [49:26] What is the long-lasting impact of the [49:28] COVID pandemic going to be? Cuz the [49:30] Great Depression caused all these [49:32] depression babies who wouldn't save or [49:33] or save too much and wouldn't spend. [49:35] They're frugal or whatever. [49:36] Um [49:37] the outcome of the [49:39] COVID pandemic is whatever however long [49:41] the sentiment from high inflation lasts. [49:43] I think it's going to be a have a longer [49:44] tail than most people realize. [49:47] >> I agree. [49:49] Uh [49:52] All right, this this is interesting. [49:54] Julian Emanuel tweeted a chart of [49:57] Bitcoin and software going back to 2021. [50:01] And some of these charts are hard to [50:03] eyeball when you squeeze the Y axis and [50:05] make them different. [50:07] So I I put them into Y charts and [50:10] um I stacked the charts and yeah, they [50:12] they do look directionally the same. [50:13] Not the same at all points in time, but [50:15] just eyeballing this they look like they [50:17] move together, no? [50:18] >> It's pretty wild that it goes that far [50:20] back. [50:21] That's all That's a pretty long time for [50:22] the correlation, right? [50:23] >> Yeah, I mean it's it's it's you know, [50:25] again, if you zoom in on different [50:26] periods of time [50:27] maybe they diverge a little bit, but [50:29] zoom out and they sure look like the [50:31] same chart. All right, anyway. [50:33] He says, so it's Bitcoin and software [50:34] IGV. [50:35] Digital gold, monetary hedge, [50:37] decentralization, global liquidity, all [50:39] with question marks. [50:41] Sensor resist censorship resistance, [50:44] portfolio diversification. [50:46] Turns out Bitcoin was just a software [50:47] stock. [50:48] >> I wonder what it would look like [50:50] in the 2017 to 2021 era though. [50:53] Cuz one of the things that people talk [50:55] about with bonds and stocks, people are [50:56] saying now that bonds are broken because [50:58] they're more correlated with stocks. But [51:01] I think in a in a diversified asset, you [51:03] want something that's going to change [51:04] its stripes and sometimes it's [51:06] correlated with this asset and sometimes [51:07] it's not. So eventually Bitcoin won't be [51:09] correlated with software stocks anymore. [51:10] It'll be correlated with something else. [51:11] >> You would think. Um but but in 2017 [51:14] Bitcoin was not an asset class. Like it [51:15] was What was the market cap? It was you [51:17] know, it was it was tiny. [51:18] >> That's right. [51:18] >> Um anyway, not to say that this will [51:20] forever and always be the thing, but I [51:21] was just like, huh, huh, is it really [51:23] is it really that simple that I really [51:24] own that much software? [51:26] >> All right, I want to talk about the [51:26] biggest inequality wealth inequality [51:28] this decade, what I think it is. [51:30] Uh I can't remember where I pulled this [51:30] from, but it's the Economic Innovation [51:32] Group. They look at the new homeowner [51:33] penalty. So it's existing homeowners, [51:36] the proportion of their income that they [51:37] spend on housing costs versus people who [51:39] are now buying new homeowners. [51:41] And existing homeowners are basically [51:44] the lowest they've been since 1990. [51:46] >> What is this What is it [51:47] >> Housing costs as a percentage of income. [51:49] >> Got it. Okay. [51:50] >> So, if you owned a home, you refinanced [51:53] into 3% mortgage, your housing costs as [51:55] a percentage of your income are about as [51:56] low as they've been [51:58] over the last, you know, [52:00] how many years has it been? It's been 12 [52:01] years since 1990, right? [52:03] 15 years? No. [52:04] Over the last, whatever, 35 years. But [52:06] new homeowners is has just shot up like [52:09] a rocket and paying way more. This is a [52:11] big source of wealth inequality. And we [52:14] keep asking, "Where's all the money [52:16] coming from? Where's all the money [52:17] coming from that's going into all this [52:18] stuff?" Guess who has more disposable [52:20] income? People who owned a home with a [52:23] with a way low lower price [52:25] and a low mortgage rate. Think about how [52:27] much more disposable income those [52:28] households have. And that's a big [52:30] proportion of the country. [52:33] >> So, this is this is what we were just [52:35] talking about earlier with gratitude and [52:36] whatever? [52:38] I had a thought, and this is not [52:39] something that I spent a lot of time [52:40] thinking about, but I'm a new homeowner. [52:42] And my friend who lives across the [52:44] street, I probably paid, I don't know, [52:46] three times what for this house what he [52:48] paid for his house whenever he bought [52:50] it. [52:51] >> Right. [52:51] >> "That MF'er." All right. This is, you [52:54] know, but [52:55] >> That's right. You're You're in no You're [52:57] in the new homeowner thing. [52:58] >> Yeah, I'm in no position to complain, [52:59] and I'm not. But, [53:01] it does suck. [53:03] >> Yeah. [53:04] >> It And it is a huge source of like the [53:06] K-shaped economy. Is the new homeowner [53:08] versus the existing homeowner. Huge. [53:10] >> Yes. You're right. Yes. [53:12] Yeah, cuz there there's people now who [53:14] are moving into neighborhoods who are [53:15] like five times richer than other people [53:17] who live there. [53:19] But they their housing costs are way [53:20] higher. [53:21] >> Yeah, it's weird. [53:22] >> Right? It's definitely weird. It's a [53:23] bizarre thing. [53:24] >> All right, I want to give a public [53:25] service service announcement. I I'm sure [53:27] we've spoken about this at some point [53:28] over the over the course of the show. [53:30] There was a um large demand for these [53:36] basically publicly traded companies that [53:37] were privately held. [53:39] So, SpaceX, [53:42] OpenAI, Anthropic, like all of these [53:43] giant names. [53:45] And the way that investors would access [53:47] them was with something called a special [53:49] purpose vehicle. [53:50] And there were special purpose vehicles [53:53] inside of special purpose vehicles. It [53:55] It was like a Russian doll. [53:56] Where you don't really know who the [53:58] primary source of liquidity was [54:01] because the companies have in some [54:03] cases, in many cases, no oversight into [54:05] who's selling what shares to who. [54:07] So, imagine you thought that you were in [54:09] a shareholder in in OpenAI. And you're [54:12] like or SpaceX and you're like licking [54:13] your chops to find out that it goes [54:15] public. [54:16] And you don't even own the stock. [54:18] Can you imagine the nightmare fuel? So, [54:20] anyway, [54:21] for people that are thinking or working [54:23] with an advisor or asking somebody, [54:24] "Hey, can you get me in?" [54:26] Just be very, very, very careful. [54:27] >> Well, this story from the Wall Street [54:28] Journal says people are freaking out. [54:30] It's like realizing you don't have the [54:31] title to your home as a market rips [54:33] higher. [54:34] >> Yeah. [54:35] Scary. [54:36] Scary, scary. [54:37] Uh this is also scary. Blue Owl has seen [54:40] inflows at this from the FT. Blue Owl [54:42] has seen inflows at its flagship credit [54:44] investment fund for retail investors all [54:46] but dry up. Uh the group's near $20 [54:48] billion Blue Owl Credit Income Fund [54:50] reported just $26 million [54:52] in new investments on May 1st, about a [54:54] 50% decline from the prior month and a [54:57] 95% decrease from this time a year ago. [55:00] So, at this time last year, the fund [55:01] attracted nearly $500 million a month. [55:04] From $500 million a month to what did it [55:06] say? 26? [55:08] Woo. [55:09] >> Can you imagine being an advisor trying [55:10] to talk your clients into Blue Owl right [55:12] now, though, with all the headlines? [55:13] >> And [55:14] Yeah, that's that's got to be a special [55:16] group of advisors. [55:17] >> I I got a personal I got a personal [55:18] finance question for you. [55:19] >> Go ahead. [55:20] >> So, I was home in Traverse City this [55:21] weekend for a soccer tournament. That's [55:23] where my parents live in northern [55:24] Michigan. I'll more on that later. Uh [55:27] and it gets me every time. I go in the [55:28] bathroom and I wash my hands and I go to [55:30] the soap dispenser [55:31] and it's soapy water that comes out. And [55:34] my father, [55:35] when it when the soap gets to the end [55:37] and there's a little soap left, to keep [55:38] it going, he fills it with water. [55:40] Okay? [55:41] My dad did not grow up with a lot. He [55:43] grew up in a tiny house with four other [55:44] siblings. I look at the house now and I [55:45] go, "I How did they have five kids in [55:47] there?" Like, he did not come from a not [55:50] even, you know, I wouldn't even call it [55:51] middle class back then, right? So, he he [55:53] came from not a lot. He has those few [55:54] frugal That frugal mindset still is with [55:56] him. [55:57] And some of these things I my dad, [55:59] "You can afford this stuff. Why don't he [56:01] It's for him it's not like a money [56:02] thing. It's a principle thing." [56:04] >> Yeah. [56:05] >> So, I was thinking, like, what's [56:06] something in your life that you're still [56:08] cuz you can't spend a Some things you [56:09] just out of principle you go, "No, I'm [56:10] going to be cheap on this." [56:12] So, what are you still cheap on? Cuz I [56:13] was thinking about this. I'm like, [56:15] >> What does Rob say I'm cheap on? I want [56:17] So, I went the other way. [56:18] I grew up uh I'm not even going to [56:20] pretend that there was poverty cuz it [56:21] definitely wasn't. But, I had I had [56:23] restrictions, like, I remember my mother [56:25] saying that I couldn't order like [56:26] chicken parm at the diner and that like [56:28] sort of screwed my brain a little bit. I [56:29] was like, "Why not? Why can't I just get [56:30] chicken parm?" It was like it was cuz [56:32] it's $20. [56:33] >> me when we we'd go to Arby's, I'd have [56:34] to get the junior roast beef. But, I [56:35] wanted the regular roast beef. [56:37] >> Um so, I went the other way. Like, my [56:40] limitations in terms of what I could buy [56:42] as a child or what my parents could [56:43] afford caused me to like overspend. [56:46] So, I am like the opposite of frugal. I [56:48] don't I And [56:50] it's not cuz I make good money now. [56:51] Like, even when I made a lot less, I [56:52] just didn't care about saving. I So, I [56:54] was I was always a natural overspender. [56:56] But, I'm sure there are things that I am [56:58] cheap about. Um [57:00] >> Like, for for me, for instance, I'm [57:01] looking my lease is up on my Explorer. I [57:03] drive a Ford Explorer. I'm looking at [57:04] new SUVs now and I I'm looking at the [57:07] prices and big-time sticker shock for [57:09] me. I I think I will never be able to [57:11] buy a luxury vehicle. [57:13] I can afford it. I don't think I ever I [57:15] will never [57:16] principally, I will never because I'm an [57:17] I'm an A-to-B guy as far as vehicles go. [57:20] I I think I will never be like one of [57:22] those people that could spend a ton of [57:24] money on a car. [57:25] I just my my [57:27] internally won't allow me to do it. [57:28] >> Oh, here here here. Yeah, no, I Like it. [57:31] The sticker shock is is something. For [57:33] me, I would say it's probably takeout [57:34] food. [57:35] Now, I still I've gotten over it, but [57:37] like it still pisses me off. I still [57:39] give Robin a hard time when she gets a [57:40] salad for $27. I'm like, are you [57:43] kidding me? [57:44] And she'll always say like, why is this [57:45] a thing? I'm like, cuz it's ridiculous. [57:46] It's a salad. [57:47] >> But I think this is one of the reasons [57:48] that FIRE people get so much flak from [57:50] from everyone else because that thing [57:51] with my dad having like that, you know, [57:53] growing up his parents were depression [57:55] era people. [57:56] Like [57:57] it stands out more if you're like a FIRE [57:59] person now because more people are were [58:01] cheap like that in the past, cheap [58:02] frugal whatever you want to say, and now [58:04] it you stand out if you're that way cuz [58:05] not many people are anymore. [58:08] >> Yeah. [58:09] >> It's not as prevalent as it once was. [58:10] Anyway. [58:11] >> Um all right, Ben. I got I have another [58:13] bone to pick with Apple if you could [58:14] believe it or not. [58:15] >> All right. [58:17] >> I have a I have a speakers in my [58:20] backyard. [58:22] I have to go into the Sonos app to [58:24] change the volume. [58:26] I can't [58:27] I can't use the volume on my phone to [58:28] control this the the speaker the speaker [58:30] volume. Is that some [ __ ] [58:32] >> That's a Sonos problem, not an Apple [58:33] problem. Don't blame Apple for that. [58:35] >> Apple blocked it. Why Wait, tell me why. [58:38] >> Cuz the Sonos app is awful. I have Sonos [58:40] as well. It's terrible. It's a Sonos [58:43] problem, not an Apple problem. [58:44] >> I'm pretty sure that Apple will not [58:45] allow you to do it. [58:47] >> I don't know. I think this the Sonos app [58:49] is just real They they made a change [58:50] like 2 years ago and it pissed everyone [58:52] off cuz it stopped working. [58:54] >> All right, I'm a I'm I'm a new to Sonos, [58:57] so [58:59] >> Okay. [58:59] I have a love-hate relationship with [59:02] youth sports. I've talked about it here [59:03] before. The whole like I have some [59:05] friends who we have both my daughters in [59:06] club soccer and they travel a little bit [59:08] and my friends [59:09] like talk about it like I'm an idiot. [59:10] Like I can't believe anyone would ever [59:11] do that. Like at like age seven these [59:14] kids have to start doing tryouts, which [59:15] seems like not even fair. Like why do we [59:17] have to have and like you're on the best [59:18] team and you're on the second best team [59:20] and you're on the third best team, which [59:21] is actually the worst it like [59:22] and then you you have to travel to these [59:24] places and there's games all weekends [59:26] and like we went to we went to a [59:28] tournament in Traverse City this [59:29] weekend. There was 35 soccer fields all [59:32] going on at once. [59:33] >> 35? [59:34] >> 35 fields. There's one road that goes [59:37] into this place to park. So the parking [59:39] was an absolute nightmare. We're dealing [59:41] with lightning delays and [59:45] uh rain and you know, it then it got hot [59:48] and it's like and we you know, we [59:49] traveled 2 and 1/2 hours to get there [59:50] and it's like why do why do we do this? [59:53] And then my little princess Kate, she's [59:55] 8 years old just turned 9. [59:57] And the very first game was really [59:58] close. We were down two to nothing then [59:59] we scored and tied two to two. And you [60:01] know, the only people watching these [60:02] games are of course parents and [60:03] grandparents. So there's 20 people 20 [60:04] people on the sideline. [60:06] And my daughter kicked in the winning [60:07] goal basically as time expired. It [60:09] doesn't happen in soccer. She's not the [60:10] star player, you know, she's she's not [60:12] the one that like scores all the goals. [60:14] And uh I saw the smile on her face and [60:16] that was like, oh, this is why we do it. [60:18] This is when it clicks like, oh, the [60:20] look on her face like I don't care if [60:22] they win or lose. I don't care if they [60:23] score or they don't score. I just care [60:24] that they try hard. But when they're [60:26] happy, that's why you do it because [60:27] they're so happy. It was like the [60:28] craziest like she scored and then they [60:30] blew the whistle like that's game. And [60:31] it was it was like usually you don't [60:32] have like a buzzer beater in soccer, you [60:33] know. [60:34] And uh then they went on and won the [60:35] championship for their thing. And like [60:37] the smile on her face was it like, oh, [60:39] this is why we do this. [60:40] >> I'm guessing. [60:41] >> Cuz it makes your kid smile. [60:42] >> I still I still wouldn't go. I'm just [60:43] kidding. That's great. [60:45] >> Anyway. [60:46] >> That's great. [60:47] >> All right, recommendation. [60:48] >> way, I got a few emails. The reason So [60:49] the reason I had a few people email me [60:51] what about me leaving the parents the [60:54] the flight football chat. [60:56] They said, Michael, just mute your [60:57] phone. I said, hey, were you not [60:58] listening? [60:59] I muted the conversation. [61:02] Apparently, if there are non-iPhone [61:04] users in the group chat, they sneak [61:08] through and ding you. [61:08] >> They're Android people. So did you leave [61:10] the Did you leave the chat? [61:11] >> Of course I left the chat. [61:12] >> Okay. [61:14] Did your Did Robin give you flak about [61:16] it? Like, hey, I saw you left the chat. [61:18] >> No, no, no. Well, people were [61:19] people were making fun of me in the [61:20] chat. [61:21] >> Ah. [61:22] >> So, Robin was like, "I'm so [61:23] embarrassed." [61:25] >> Uh that's I I think you look come out [61:27] looking cool on that though. Okay, all [61:29] right. Recommendations. [61:30] Uh speaking of like the whole gratitude [61:32] thing, I've got two I I I think I [61:34] listened to the best podcast of the year [61:35] this week. [61:36] And I hear many people talk about it, [61:37] but Ted Danson and Woody Harrelson have [61:39] a podcast called [61:40] >> What? [61:41] >> Yes, isn't that crazy? So, Woody [61:43] Harrelson barely comes on, but Ted [61:44] Danson interviews celebrities. And so, [61:46] there's a podcast this week with Ted [61:48] Danson, Woody Harrelson, and Harrison [61:50] Ford. [61:50] >> Oh man. [61:51] >> Oh my god, it was awesome. [61:53] >> What's the name of the show? [61:55] >> Where everybody knows your name, I [61:56] think. Everybody knows or something like [61:57] that. [61:58] >> to I'm going to find it right now. [61:59] >> I don't know that I've maybe he's been [62:00] on it before. I don't think I've ever [62:01] heard Harrison Ford on a podcast before. [62:04] And as a guy who's quite possibly the [62:07] biggest actor of the past arguably the [62:09] past 50 years, [62:10] maybe one of the most important of the [62:12] past 50 years, [62:13] he you've seen him on [62:15] interviews before. He's He's very [62:16] self-deprecating. He hates talking about [62:18] himself. He hates talking about movies. [62:20] Um but he was with two other, you know, [62:22] really big actors talking about the [62:25] craft. And I think Harrison Ford might [62:27] be the coolest guy alive. [62:28] >> I can't wait to listen to it. [62:29] >> It is so And like him, he's he he drops [62:32] F bombs a lot, which is kind of funny. [62:34] He sounds like a guy that you just love [62:35] to have a beer with, but his whole thing [62:37] is he hates talking about himself. He [62:38] could have the biggest ego in the world, [62:39] but he talks about how grateful he is [62:41] for like and how lucky he was. [62:42] >> Well, he was lucky. He was He was He was [62:44] Brad Pitt in Once Upon a Time in [62:46] Hollywood. [62:47] >> Yes. He was a carpenter. [62:48] >> He was the guy building celebrities' [62:50] houses on the roof, probably smoking a [62:52] cigarette, looking all cool with a shirt [62:55] off. [62:55] >> Yes, but [62:56] >> of the coolest guys ever. [62:57] >> Ea- Yes, easily. And he So, he But he [62:59] has this He has this aura of gratitude. [63:00] Like, that's how you find happiness with [63:02] success is you have gratitude. So, the [63:04] other one I'm watching is Marty Marty [63:06] Life Is Short on Netflix. So, it's a [63:07] Martin Short documentary. [63:09] So, I've now seen the John Candy one [63:10] recently. Steve Martin had a great [63:12] documentary. And all these kind of [63:13] intersect because they knew each other. [63:15] And the Martin Short one on Netflix is [63:16] not that long, it's like an hour and 40 [63:18] minutes. And same thing. [63:20] He He is a very grateful guy for what he [63:22] has and all the success he has. And he [63:24] he seems like well-grounded and it's [63:26] great. But just the old stories of [63:29] Eugene Levy and Martin Short and Dan [63:32] Aykroyd and John Candy and Catherine [63:33] O'Hara and Gilda Radner, all these [63:36] people coming together at the same time [63:37] in their 20s to create comedy. It's uh [63:40] anyway, really like I've I've read his [63:42] book before and I just really like his [63:43] outlook on life. [63:45] Both of those guys. [63:47] >> Um all right, I was [63:49] hesitant to recommend the show cuz it is [63:51] extraordinarily dark. [63:53] All right? It is not an uplifting show. [63:55] Um and it's quite graphic. It's called [63:57] Half Man, it's on HBO, it's a [63:58] mini-series. [63:59] >> But you think I would like it? [64:01] >> I'm pretty sure you would like it. [64:02] >> Okay. [64:03] >> Um [64:04] it's by this guy Richard Gadd who did [64:06] Baby Reindeer, which was a phenomenon. I [64:07] didn't see that show. [64:09] >> Uh never heard of it. [64:10] >> Here's a description. When Nile's [64:12] estranged brother Reuben shows up at his [64:14] wedding, [64:15] it leads to an explosion of violence [64:17] that catapults us back to their lives [64:20] from the '80s to the present day. [64:22] And uh it is intense. It's It's [64:25] something. [64:26] >> Okay. But it's a mini-series, six [64:27] episodes? [64:28] >> Six episodes. [64:28] >> You can sign me up for that. [64:29] >> It's a It's a strong It's a strong wreck [64:31] for me. But uh yeah, it's graphic, very [64:34] graphic. [64:35] >> Okay. [64:37] >> Okay. Um [64:40] All right. [64:40] >> Stock market's Stock market's down [64:42] again. [64:44] >> Good. [64:44] >> We We could use a correction. [64:46] >> Yeah. [64:46] >> I always say this, we could though. [64:48] >> Well, I don't say we could use a [64:49] correction when we're in one. [64:52] >> That's true. [64:53] >> Um when when we're in a correction, I [64:55] say, "Please stop. This isn't fun." All [64:57] right, animal spirits at the [64:58] compoundnews.com. Thank you for the [64:59] emails. Thank you for listening. We'll [65:00] see you next time. [65:10] >> Oh. [65:10] Oh, yeah.