Podcasts about capex

Andrew, Ben, and Tom discuss Micron's blowout quarter with revenue up 346% to $41.5 billion, 84.9% gross margin, and DRAM/NAND supply now constrained through 2027, the implications of doubling CapEx to $40-50 billion in FY27, Trump's $88 billion supplemental spending request for the Iran war, farmer aid, and Ebola, the canceled signing of the 21st Century ROAD to Housing Act, escalating Senate Republican tensions over Iran, the DOJ's egg price-fixing settlement with Cal-Maine, the narrowing K-shaped economy spending gap, today's PCE inflation print, and rates finally moving as oil drops below $70.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

Brian Szytel recaps a broad market sell-off led by technology and semiconductors, highlighting a nearly 10% drop in South Korea's KOSPI—an index heavily concentrated in Samsung and SK Hynix—attributed to valuation, demand shifts, and DRAM supply issues after a major run-up. He notes similar 5–10% declines in high-flying semiconductor names and emphasizes that despite real AI-driven demand and a rare reversal of decades-long chip price declines due to supply-demand imbalance, valuations still matter. On the economic front, flash PMIs were strong: manufacturing surged to 55.7, the highest in a little over four years, and services also beat expectations, supporting an improving growth backdrop tied partly to data-center CapEx. He addresses concerns about the U.S. dollar losing reserve status, arguing no viable replacement exists, citing dollar dominance in FX (90%) and global reserves (57%) versus the euro (20%). 00:00 Summer Market Check-In 00:31 Global Tech Sell-Off 01:38 Semis Valuation Reality 02:01 AI Chip Demand Shift 02:48 PMI Data Highlights 03:43 Dollar Reserve Status Fears 04:32 What Could Replace Dollar 05:53 Reserve Currency Numbers 06:32 Wrap Up and Q&A Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

The South Korean KOSPI fell 10%, caused in large part from SK Hynix and Samsung. Joe Mazzola says the selling action isn't calling valuations into question, but instead earnings and CapEx expectations as Mag 7 names ramp up the latter figure. He explains what to watch in markets Tuesday ahead of the opening bell. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Daily Snippet vom 23.06.2026 Das eigentliche Signal bei den Mag7 ist für mich das Verhältnis von CapEx zu Cashflow. Diese Firmen investieren riesige Summen in KI Infrastruktur, Datencenter, Chips, Strom und Rechenleistung. Die entscheidende Frage ist: Kommt daraus später genug Cashflow zurück? Meine Einordnung liest du im Blog: https://www.julianhosp.com/de/blog/daily-snippet-23-06-2026 —— ♦️ DEEP DIVE, PORTOFOLIO, STRATEGIEN Inner Circle: https://products.i-unlimited.de/inner-circle —— Folge mir für ehrliche Finanz-Einblicke!   #dailysnippet Abonniere jetzt meinen kostenlosen Newsletter um immer auf den neusten Stand zu sein: https://julianhosp.de/newsletter

欢迎收听雪球出品的财经有深度，雪球，国内领先的集投资交流交易一体的综合财富管理平台，聪明的投资者都在这里。今天分享的内容叫资本折叠：港股的困局与美股的狂欢，来自三体人在地球。今年以来，资本市场呈现出一种撕裂的景象：一端是狂飙突进的美股市场；另一端则是长期承压的港股市场。如果你说港股也有涨的很多的公司，那么它们大概率是呼应了美股的某些概念，或者直接是美股AI巨头的供应商。面对这种分化，市场的主流叙事往往将其归结于微观层面的企业能力的强弱。因为美国拥有最前沿的AI技术和最赚钱的科技巨头，所以美股享受高估值；因为中国正处于经济结构的转型期，所以港股面临基本面的阵痛。然而，如果我们将视角从微观的企业视角拉升至宏观的全球资本循环，就会发现这种解释过于单薄。港股和美股的分化，不只是企业基本面的分化，而是两套货币流动性结构的分化：一个被美元周期压制，一个被美元回流放大。而真正的风险，在于当美元体系自身开始承压时，这种分化可能会进入反转。第一部分：港股的宏观枷锁联系汇率制度的诞生与代价港股的疲软，不仅是部分行业盈利周期的下行，更是其底层架构在外部环境下的系统性承压。这个架构的核心，在于其独一无二的“错配”属性。香港资本市场的基石，是确立于1983年的联系汇率制度。要理解这个制度，就必须回到它诞生时那段历史中去。事实上，港元并非一开始就认准了美元。在1935年之前，香港实行的是银本位制；1935年全球爆发白银危机后，作为英国殖民地的香港，顺理成章地将港元与当时其宗主国的货币——英镑挂钩（16港元兑1英镑）。然而，到了1972年，英国经济深陷泥潭，英镑被迫实行自由浮动，港元也随之失去了稳定的锚。为了避免被剧烈波动的国际汇市拖累，香港在1974年正式宣布港元实行“自由浮动汇率制”。而到了1982至1983年间，中英关于香港前途问题的谈判陷入拉锯。巨大的政治不确定性在香港社会引发了信心危机。在自由浮动机制下，港元汇率从1981年的约5.1港元兑1美元，一路狂泻至1983年9月的接近9.6港元兑1美元。危机的顶峰出现在1983年9月24日，史称“黑色星期六”。这一天，恐慌情绪彻底失控，香港市民不仅疯狂兑换美元，还涌入超市抢购大米、食用油和厕纸。部分银行甚至出现了挤提的苗头，整个香港金融系统濒临全面崩溃的绝境。为了在生死存亡之际挽救金融体系，港英政府别无选择，必须寻找一个最强大的“外部信用”来为港元背书。当时的大英帝国早已日薄西山，英镑不再具备全球避险属性。于是，在1983年10月17日，香港政府颁布了“联系汇率制度”——宣布将港元与美元，以 7.80 : 1 的固定比例进行硬挂钩。这是一次 “断臂求生”：香港以彻底放弃货币主权为代价，买入了一份全球认可的信用担保。从历史的后视镜来看，这一制度无疑有成功的部分。它极大地降低了国际资本进入香港的交易成本，奠定了香港作为国际金融中心的地位。然而，“不可能三角”（资本自由流动、汇率稳定、货币政策独立性）法则下，香港为了获得前两者，彻底放弃了“独立的货币政策”。这意味着，香港没有办法根据本土经济、市场的冷热程度自由决定加息或降息、扩表或缩表。香港的货币环境，只能是被动地跟随美国的货币周期起舞。时来天地皆同力随着过去二十年中国经济的腾飞，大量内地企业赴港上市。时至今日，港股市场中超过七成的市值和交易量均来自中资企业。这就造成了港股市场在全球独一无二的结构性特征：分子端（企业盈利）：由“中国经济基本面”决定。上市公司的营收、利润、扩张速度，高度依赖于中国内地的宏观经济周期、产业政策和消费环境。分母端（估值折现率）：由“海外流动性”决定。由于联系汇率制度，外资在为港股定价时，使用的无风险利率锚是美国国债收益率。同时，港股估值也对流动性极为敏感，容易被亚洲其他市场所影响。在太平盛世，当中美经济周期同频共振时，这种错配并不致命，甚至能形成双重共振的牛市。但在过去几年，当中美周期严重错位时，这个宏观机制便成为了沉重的枷锁。因此，当中国宏观数据走弱，叠加美国加息预期，再叠加亚洲其他地区（日韩）股市被AI叙事带动而暴涨，就给香港股市造成了致命的虹吸效应。比起短期的流动性承压，更长期的问题是，这样的流动性结构，几乎注定了一种牛短熊长的宿命。 如果一个市场对流动性没有主动权，对盈利端也没有主动权，那么只有在各种微观和宏观条件共振的特殊时间窗口，这个市场才能实现溢价。而这样的窗口却往往是短暂的，更多时间里这个市场可能会因为流动性问题长期处在低估状态。更致命的是，这样的宏观属性，也深刻塑造了市场主要参与者的风格，从而进一步加强了市场的整体风格。换句话说，港股不是简单低估，而是长期处在一个没有货币主动权、没有本土长期资金充分定价权的市场结构里。时来天地皆同力，运去英雄不自由。也许这就是目前港股的真实写照。第二部分：美元资本循环和美股AI热潮美元体系的运作机制如果说港股是“有资产、无水”的洼地，那么大洋彼岸的美股，则是“水漫金山”的镜像。理解了港股资金的流出，就能更好地看清美股资金的涌入。美股AI热潮，不仅是一次科技股行情，也是美元体系全球资本循环的重要一环。美元体系的核心运作机制，在于“全球资源定价—美元盈余积累—回流美国资产”。全球资源出口国和制造业顺差国在赚取美元后，这些庞大的美元储蓄必须寻找安全、流动且容量巨大的金融资产进行投资。这就构成了美债和美股的底层买盘。在疫情后的周期中，美国通过几轮极其激进的财政刺激，向全球输出了巨量美元。随后，美联储又开启了极其陡峭的加息周期。高达5%以上的无风险基准利率，将游荡在欧洲、日本、新兴市场的资本吸回美国本土。这就形成了一个巨大的资本“堰塞湖”。如此庞大的回流资金，如果仅仅停留在短期国债里，则无法满足资本长期增值的诉求。因此，面对这样的资本回流，美国资本市场亟需一个故事，一个值得全球资本长期锁定的故事。资本循环借壳生产力革命从宏观角度来看，AI不仅是一项技术，更是填补美元资产“成长性预期”的最佳叙事。美国需要向全球证明：尽管面临通胀、制造业空心化和地缘政治的挑战，但美国掌握着下一代先进生产力，美国的企业拥有最宽阔的护城河，因此全球资本继续以高溢价配置美股是合理的。从微观产业特征来看，AI的基础设施建设（如算力集群、数据中心）具有资本密集型特征。无论是英伟达的GPU芯片，还是微软、谷歌动辄数百亿美元的数据中心投入，都需要巨量的资金支持。这就形成了一种绝妙的宏观与微观的“相向而行”：回流美国的巨量美元流动性需要一个能够吞吐天量资金的庞大资产池；而AI产业的早期基建，恰恰需要大量的资本浇灌。资本与技术，在这一刻达成了完美的相互成就。当理解了上述逻辑，我们才能看懂美股AI热潮中最为核心的“正反馈循环”机制：1. 叙事推高估值：AI的生产力革命叙事，促使全球被动资金和主动管理机构不断买入美股科技巨头。2. 估值创造融资能力：极高的股价和市值，压低了这些科技企业的股权和债务融资成本。3. 融资转化为真实Capex：科技巨头利用廉价的资金，或者通过复杂的表外结构，将海量资金投入到上游硬件和基础设施中。4. Capex验证叙事：上游硬件厂商（如英伟达、光模块企业、电力设备商）因此获得了实打实的爆炸性订单和利润增长。5. 利润再次推高股价：超预期的财报发布后，进一步坐实了AI的确定性，引发新一轮的资金涌入。换句话说，这是一种典型的货币现象借壳科技革命叙事的现象，底层其实是一套资本循环的机制。我们不能否认AI这场生产力革命，也并非全盘否定AI产业链带来的巨大价值，正如我们在中国房地产的扩张时期也无法否认城镇化的大趋势。然而，这不代表这个叙事背后隐藏的资本循环链条是合理的。第三部分：美元盛世的暗礁“新特里芬难题”再回顾在美股AI狂欢的盛宴背后，整个美元资本循环体系似乎正面临着挑战。这种危机并非短期的股市回调，而是触及了“美元信用”这一核心底座。早在上世纪60年代，经济学家罗伯特·特里芬就指出了美元作为全球货币和金本位之间的矛盾。而在金本位崩塌之后，学界进一步提出了新特里芬难题，即美元同时作为美国本国货币和作为全球货币的内生矛盾。为了充当全球贸易的基石，美国必须向全球源源不断地输出美元。这意味着美国必须长期保持巨额的经常账户逆差。在这个体系下，美国确实获得了巨大的金融特权：它可以用自己发行的“金融负债”（美元、美债、美股），去交换全球制造业大国和资源国的“真实财富”（矿产、商品、能源）。然而，随着全球经济总量的增长和贸易的繁荣，世界需要越来越多的美元流动性，美国的对外负债就必须呈指数级膨胀。但致命的悖论在于：美国的国家信用（即其本土的税收能力和经济体量）是有限的。当为了满足全球流动性而累积的美元负债，庞大到超越了美国本土主权信用所能覆盖和兑付的极限时，这套资本循环的底座就会迎来崩塌。这才是当前去美元化暗流涌动的真正逻辑。全球央行系统性地增持黄金，并不是单纯出于对某场地缘冲突的政治表态，而是基于一个关于资产负债表的考量：美国主权信用还能否支撑起如此庞大且仍在加速膨胀的负债端呢？伊朗战争的冲击而就在美国负债水平居高不下，Trump试图通过关税战缓解问题却最终无功而返的时候，当下接近尾声的这场伊朗战争，可能将成为一个深刻的历史转折点。它给宏观格局带来的冲击，可以分为三层：第一层，是战争对全球供应链与通胀的冲击。能源价格的狂飙通过航运运费、化肥及化工原料向下游传导，导致全球通胀预期大幅反弹。第二层，是通胀预期将美联储逼入了“滞胀”与“高息”的货币政策死角。面对战争引发的输入性通胀，美联储陷入了进退维谷的境地。原本市场寄希望于美联储通过降息来缓解债务压力和科技股的高估值风险；但在能源通胀的倒逼下，美联储被迫释放加息预期。第三层，在于那份带有“赔款”性质的停战协议对美元信用的永久性损害。为了换取霍尔木兹海峡的重新开放，当前传闻是，美国不仅解冻了伊朗巨额资产，更承诺提供高达3000亿美元的所谓“战后重建基金”。如果最终事实如此，这标志着美元信用的一大支柱——美国的全球军事能力，将会开始被怀疑。然而，尽管我们目前似乎已经可以看到美元的衰败征兆，但我们也必须承认，当下时点美元和美债作为全球货币和无风险资产锚点，似乎还没有太好的替代品。但是，正如在2007年时没人能预测到金融海啸如何爆发，今天我们只能判断这套系统的脆弱性在加强，但真正的危机从来只会在市场意想不到的地方以意想不到的方式爆发。而一旦这样的改变发生，带来的将是全球资本市场甚至是国际秩序的大重构。第四部分：港股的突围之路治本之策：货币机制的演进在详尽剖析了美元与美股之后，让我们重新把视线拉回港股。面对美联储的周期摆动和潜在的国际金融动荡，港股难道只能永远充当被动挨打的“流动性提款机”吗？要走出泥潭，港股必须在战略上进行根本性的调整。如前文所述，港股困境的根源在于“中国资产 + 美元流动性”的宏观错配。从长期来看，破局的终极方案无疑是逐步改变货币挂钩机制。随着人民币国际化的推进，探索将港元的锚定物从单一美元转向以人民币为基础的一篮子货币，是不可回避的战略命题。但这注定是一条漫长的道路。在人民币实现资本项目完全可兑换之前，冒然动摇联系汇率制度，不仅可能引发剧烈的资本外逃，甚至会危及香港自身的金融稳定。因此，远水解不了近渴。“AH融合”的深意与未来定价权真正的破局迹象，隐藏在近期释放的政策信号中。在2026年的陆家嘴金融论坛上，中国证监会等部门释放了极其明确且深远的信号：不仅进一步完善沪深港通机制，更明确表态支持符合条件的港股上市公司在境内发行上市。同时，与会专家也提出要联合编制覆盖沪深港的综合及科创行业指数。在传统的割裂状态下，同一家优质的中国企业，在A股享受着国内充裕流动性带来的合理估值，在H股却因为海外流动性的干涸而惨遭错杀。通过鼓励港股公司回A，或者A股公司赴港，辅以更加畅通的互联互通机制，监管层实际上是在致力于打通两个水池之间的无形壁垒。这一战略的核心逻辑在于：用人民币流动性，去稀释外资在港股定价权上的垄断地位。当更多的企业实现A+H双重上市，当跨市场套利机制变得足够平滑时，港股的估值体系将被逐步拉回至中国经济本身的内在价值，而非单纯受制于美联储的利率指挥棒。在未来可能爆发的风险面前，建立一个以内需为基本面、以人民币流动性为核心支撑、同时保持高度国际化接口的A+H资本市场，是防范金融风险最有效的防火墙。尽管价值投资倡导研究公司的微观基本面，然而在如今世界的变局中，资产的长期发展，仍受到宏观结构的深刻影响。因此，我不是想夸大宏观因素的影响，只是反对将所有的涨跌问题，都被动地归因为公司的基本面问题，而忽视市场背后的宏观结构问题。上述的关于港股和美股的看法，纯属个人观点，不一定对，也不构成投资建议。但是几乎可以肯定的是，一次范式转移级别的重构，可能越来越近了。

Patrick Moorhead and Daniel Newman return from a packed week of travel, covering HPE Discover 2026 and Pure Accelerate hosted by Everpure. They break down the government-forced shutdown of Anthropic's Mythos 5, the Apple-Intel foundry signal, the xAI-Cursor acquisition, and whether enterprise AI spending is actually contracting or simply concentrating. Episode 309 of The Six Five Pod covers the week's events, market moves, and the structural questions that follow. The handpicked topics for this week are: Anthropic Mythos 5 Forced Shutdown: The U.S. government issued a 90-minute compliance window and a worldwide kill switch on Anthropic's Mythos 5 and Claude Fable 5 models, forcing them offline across all geographies. Patrick and Daniel examine what this means beyond the immediate headlines: model access has entered the same geopolitical variable set as semiconductor export controls, and every enterprise CIO now has a new on-premises infrastructure argument on the table. The shutdown also surfaced an unexpected counterpoint from the cybersecurity community, which argued that Mythos 5, operating in a defensive capacity, was itself a protection layer against the use of adversarial models. Anthropic's decision to revoke access globally rather than implement citizenship-based authentication reflected both the 90-minute timeline and the practical impossibility of real-time identity verification at scale. (The Decode) HPE Discover 2026: The Agentic Infrastructure Story: Six Five Media spent multiple days at HPE Discover in Las Vegas, live-streaming coverage that drew more than 30,000 viewers across the event. Patrick and Daniel break down HPE's most complete agentic stack story to date, covering its networking-led compute approach, expanded NVIDIA and Broadcom silicon partnerships, autonomous networking through Marvis, and Juniper's integration into the AMD Helios interconnect as a path into hyperscale deals HPE previously lacked access to. (The Decode) Pure Accelerate 2026 and the Everpure Data Primacy Pitch: At Pure Accelerate, Everpure made its clearest case yet for a data intelligence layer designed to reduce token costs in enterprise AI workflows by operating across any storage vendor, any enterprise application, and without being hard-coded into the underlying array. Patrick and Daniel assess the value proposition and the proof burden separately: the concept is differentiated, particularly against Snowflake and Databricks, in that Everpure does not require its own storage hardware, but the company still needs to demonstrate ROI at scale and earn permission to compete in a market where data platform players have already established category positioning. (The Decode) Apple and Intel: The 18AP Signal and What It Sets Up for 14A: The announcement that Apple will manufacture chips with Intel sent Intel's stock up roughly 10%. The hosts parse what that deal likely looks like in practice: 18AP as a test drive for lower-risk logic-layer parts, with the more consequential milestone being a potential M7 SoC on Intel's 18AP process. The underlying driver is the TSMC capacity constraint, with Samsung logic deals picking up across the industry for the same reason. The real inflection point that Patrick notes is 14A: if Intel's backside power delivery process reaches risk production and scales to iPhone volume by 2028, the strategic weight of the Apple relationship will fully materialize. (The Decode) xAI Acquires Cursor for $60 Billion: Elon Musk's xAI acquired Cursor for $60 billion using equity inflated by SpaceX's IPO run-up, a move Patrick characterizes as buying market position in a category where xAI arrived late, having missed the window on thinking models and tool calling. Cursor brought $4 billion in ARR, 7 million monthly active users, and 50% Fortune 500 penetration into the deal. The open question remains whether xAI can convert that installed base into a durable enterprise AI stack or whether it remains primarily a GPU capacity provider selling at well above neo cloud market rates, with the Google-SpaceX deal drawing additional scrutiny as a related-party transaction preceding the IPO. (The Decode) The Flip: Is Enterprise AI Spending Contracting or Concentrating? Patrick takes the position that enterprise AI is entering a rationing phase, pointing to Accenture's bookings decline, Microsoft cutting developer access to cloud code, Uber blowing through cloud licenses, and the emergence of AI cost management as a venture category as converging proof points. Daniel argues the opposing case: dollar volume is growing even as project counts fall, hyperscaler CapEx guidance continues to accelerate across Microsoft, Google, Amazon, and Meta, and what reads as contraction is the market moving from subsidized pilots to production deployments tied to measurable P&L outcomes. Both agree the hard ROI era is arriving, and the real debate is whether that transition reads as discipline or deceleration on the way in. (The Flip) Fed Chair Kevin Warsh's First Meeting: New Fed Chair Kevin Warsh held rates steady in a unanimous decision but delivered remarks that the market viewed as hawkish, sending the S&P lower and two-year yields up 16 basis points before a partial recovery the following day. Patrick and Daniel note the structural signal beneath the reaction: Warsh is establishing the Fed's independence from political pressure while also signaling an intent to move away from survey-based data that arrives three to six months stale, in favor of more real-time economic inputs. Daniel draws a direct line to the kind of forward-looking data infrastructure that firms like Palantir, Databricks, and Snowflake are positioned to provide at the institutional level. (Bulls and Bears) Iran-Israel-U.S. Developments and Oil Below $80: A Memorandum of Understanding between Iran, Israel, and the U.S. briefly sent oil below $80 and signaled a potential opening of the Strait of Hormuz, though by the time of recording, reports were already emerging that the situation may be reversing. Patrick and Daniel keep it brief: the market has largely looked through the geopolitical noise, rallying through the period of conflict, and the oil price signal matters more to the macro environment than the diplomatic specifics. (Bulls and Bears) Accenture Earnings — The Services Layer Faces the Agentic Reckoning: Accenture beat on earnings but missed on revenue. The company reported a bookings decline of 2%, trimmed its 2026 revenue guide by 3-4%, and saw its worst single-day stock reaction in years. Patrick and Daniel use the result as a structural lens rather than a single-quarter data point: agentic AI and enterprise technology vendors are absorbing exactly the work that large professional services firms have historically owned, and the market is beginning to price that displacement ahead of the labor data catching up. Patrick flags this as the canary in the coal mine for the global services industry broadly. (Bulls and Bears) SpaceX IPO Volatility and Valuation Reality: The SpaceX IPO debuted at $135, surged above $210 on its first day of trading, and finished the week around $181. At its peak, the company briefly surpassed the market capitalizations of both Amazon and Microsoft before pulling back. Patrick and Daniel unpack the gap between the premium investors are assigning to Elon Musk and the company's underlying fundamentals. Despite generating roughly $50 billion in annual revenue, SpaceX remains unprofitable, and upcoming lock-up expirations could introduce meaningful volatility, particularly on the downside. Patrick points to long-term comparisons with Amazon and Tesla, while noting that many retail investors are still near break-even. The discussion explores how much of SpaceX's valuation is based on future potential versus current performance—and how much room remains for investor expectations to reset before fundamentals catch up. (Bulls and Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode  US Government Forces Anthropic to Disable Claude Fable 5 + Mythos 5 Worldwide — First-Ever Federal Shutdown of a Commercial Frontier AI Model; 90-Minute Compliance; EU + UK Sovereign-AI Talks Accelerate https://www.anthropic.com/news/fable-mythos-access  HPE Discover 2026 — Neri Bets the Company on Networking as the AI Control Plane; Juniper Integration Operational; Vultr Standardizes on HPE + NVIDIA https://www.crn.com/news/networking/2026/hpe-ceo-antonio-neri-five-boldest-statements-from-hpe-discover-2026 Everpure - Pure//Accelerate 2026 — First Conference Under New Name; "Data Primacy" Vision; Data Stream Built on NVIDIA AI Data Platform; Data Intelligence GA https://www.prnewswire.com/news-releases/everpure-unveils-data-primacy-architecture-for-the-ai-era-302803097.html  Apple's Chip Supply Chain Realigns in One Week — Intel 18A-P Enters Risk Production June 16; White House Confirms Apple-Intel Foundry Deal June 18 (INTC +9% to Record $135); Cook Says iPhone/Mac/iPad Price Hikes "Unavoidable" on RAM Crunch https://www.investing.com/analysis/appleintel-chip-manufacturing-deal-reshapes-foundry-race-200682398 SpaceX Buys Cursor for $60B All-Stock Four Days After IPO — Largest Developer-Tooling Acquisition Ever; Cursor at $4B ARR / 50%+ Fortune 500; Musk's xAI Loses the Code War, Buys the Winner https://www.cnbc.com/technology/ The Flip Are enterprise AI budgets contracting — is the procurement boom ending and the rationing phase beginning? FOR: Yes — Accenture cut its guide and bookings declined today; Uber blew through AI budget in months; Meta killed its leaderboard. https://www.businesswire.com/news/home/20260618029271/en/Accenture-Reports-Third-Quarter-Fiscal-2026-Results AGAINST: No — AI infrastructure capex is accelerating; enterprise demand is supply-constrained, not budget-constrained. https://ca.investing.com/news/stock-market-news/stifel-raises-jabil-stock-price-target-to-460-on-ai-growth-93CH-4698089 Bulls & Bears MACRO — FOMC Chair Kevin Warsh's Inaugural Meeting: Unanimous Hold at 3.5–3.75%, Statement Stripped of Cutting Bias; Dot Plot Flips to a 2026 HIKE at 3.8% Median; Warsh Refuses Own Dot; Worst Fed Day for a New Chair Since 1994 https://www.cnbc.com/2026/06/17/fed-meeting-today-live-updates.html  MACRO — Oil Cracks Below $80: Brent $78 (3-Month Low), WTI $75; US-Iran 14-Point MoU Signed at Versailles; Strait of Hormuz Reopening; IEA Projects 5.05 Mbpd Supply Glut in 2027 https://finance.yahoo.com/economy/policy/articles/oil-plunge-below-80-already-174253019.html Accenture (ACN) Q3 FY26 ACTUALS — EPS $3.80 Beats $3.70 (+9% YoY); Revenue $18.72B Slight Miss; Bookings DECLINE −2% to $19.3B; FY26 Guide Trimmed to 3–4% Local; Stock −13.3% Open; $9B Cybersecurity Acquisition Push https://www.businesswire.com/news/home/20260618029271/en/Accenture-Reports-Third-Quarter-Fiscal-2026-Results  SpaceX (SPCX) Post-IPO Trading Action — Melt-Up to $225.64 Tuesday Intraday Briefly Surpasses Amazon at $2.85T; Round-Trips to $192 by Wednesday Close on Fed Hawkish Pivot; Morningstar Fair Value $62 (~69% Implied Downside) https://www.cnbc.com/2026/06/15/evercore-isi-says-landmark-spacex-ipo-could-reignite-bull-market-send-sp-500-to-9000.html  

The Iran deal looked like a breakthrough until both sides started spinning it within the hour, but oil kept falling and the dollar stayed bid anyway. Marty and John walk through a week of narrative violations, from WTI dropping into the mid seventies to Fed Chair Warsh's hawkish first FOMC press conference. They dig into why hyperscaler CapEx exploding while free cash flow collapses makes Volcker 2.0 impossible, how housing affordability and debt service are pushing the Fed and Treasury back together, and why frontier AI is now a state secret. They also check in on Bitcoin's quiet grind, with Taiwan's central bank exploring reserves and BlackRock still building products in the background.

Dan Nathan and Guy Adami host a special Risk Reversal episode with guest Danny Moses to discuss the latest Fed meeting under Kevin Warsh, emphasizing peak hawkish messaging, reduced forward guidance (including questioning the dot plot), and the market's feedback loop. They debate surging volatility and extreme AI/semiconductor valuations, highlighting Intel's sharp rally on customer speculation and concerns about narrative-driven pricing, correlation risk, and potential CapEx pullbacks, with Micron's upcoming earnings as a key test. The group also covers gold's pullback, favoring gold miners like AEM, and argues energy could rebound despite recent weakness. They note consumer strain using Kroger's warnings on rising costs and promotional shopping, alongside elevated delinquencies and credit card debt. After the break, Dan speaks with CNBC's Deirdre Bosa about SpaceX's IPO, “vibe investing,” xAI's compute strategy, the Cursor acquisition, AI token-cost pressures, and how export controls may accelerate adoption of Chinese open-source models like DeepSeek. —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Most people look at multifamily real estate and see the benefits: Cash flow. Equity growth. Tax advantages. Financial freedom. What they don't see is everything happening behind the scenes. In this episode, I pull back the curtain on what it's really like to be a general partner operating apartment buildings. I'm currently navigating multiple refinances, property sales, partner negotiations, CapEx issues, lender requirements, insurance concerns, taxes, and acquisition opportunities—all while working multiple jobs and trying to be present for my family. This isn't meant to scare anyone away from real estate investing. It's meant to help you understand the difference between being an active operator and being a passive investor. We discuss: • The reality of being a GP • Why many investors may be better suited as LPs • Managing partner relationships and investor expectations • Refinancing challenges in today's market • CapEx surprises and operational headaches • The emotional and mental demands of ownership • How to decide which role is right for you If you've ever wondered what apartment ownership actually looks like behind the social media posts and success stories, this episode is for you. Connect with me: https://Stan.store/Buybuildings Remember: It only takes a Small Axe to build a lasting empire.

In this Strategy Saturday episode, Charles Carillo looks back at his first multifamily deal — a 3-family property he purchased in 2006 — and shares the costly mistakes he made, what he learned from them, and what he would do differently today. Charles explains why his first property renovation was more work than expected, why not having a clear renovation plan created unnecessary stress, and how better planning could have made the entire deal easier to manage. In this episode, Charles discusses: • Buying a heavy value-add property as a first deal • Why a clear renovation plan matters before closing • The importance of hiring the right contractor • Why every rental property needs a long-term business plan • How delayed CapEx projects can create problems later • Why market timing matters when buying multifamily real estate Buying your first rental property is one of the best learning experiences in real estate investing, but it can also be one of the most challenging. This episode will help new investors avoid common beginner mistakes and approach their first multifamily property with more clarity and preparation. Links Referenced in Episode:  SS116: How to Minimize Risk When Buying Your First Rental Property - https://youtu.be/kp7wbc5HsrM Connect with the Global Investors Show, Charles Carillo and Harborside Partners: ◾ Setup a FREE 30 Minute Strategy Call with Charles: http://ScheduleCharles.com ◾ Learn How To Invest In Real Estate: https://www.SyndicationSuperstars.com/  ◾ FREE Passive Investing Guide: http://www.HSPguide.com ◾ Join Our Weekly Email Newsletter: http://www.HSPsignup.com ◾ Passively Invest in Real Estate: http://www.InvestHSP.com ◾ Global Investors Web Page: http://GlobalInvestorsPodcast.com/

There's an uptick in ship crossings through the Strait of Hormuz, says Charles Schwab's Kevin Gordon, believing it offers a small reprieve for the energy trade. He sees Micron's (MU) earnings and this week's inflation prints as prominent catalysts to move markets. Michelle Gibley sticks with the AI trade by explaining what the MSCI reclassification means for global stocks, including South Korean tech giants Samsung and SK Hynix. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Meta Platforms (META) has a lot to prove when it comes to showing investors it can provide significant ROI with its AI endeavors, says Jason Ware. He talks about the Mag 7 giant's push toward LLM creation and its substantial CapEx headwinds. That said, Jason believes the company is undervalued and sees a $800 target for the stock. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Episode Description:In this episode, we break down a massive week on Wall Street, starting with the historic SpaceX IPO that shattered public market records. Then, we preview Micron's high-stakes fiscal Q3 earnings report as AI-driven memory demand pushes margins to historic levels. Plus, we look at Adobe's quiet rebound, why Celsius (CELH) is starting to regain traction, and the structural roadmap that could launch Marvell (MRVL) on a legitimate path toward a $1 trillion valuation.The Historic Debut: SpaceX formally went public on the Nasdaq at an initial valuation of $1.77 trillion, raising an unprecedented $75 billion in gross proceeds.Market Impact: Trading surged on day one to push its market cap over $2 trillion. We discuss the unique, multi-staged insider lock-up structure and how fast-tracked index rules are shifting the mechanics of passive ETF tracking.Pure Profit Growth: Ahead of its fiscal Q3 earnings call on June 24, Wall Street consensus is targeting a mind-boggling ~1,000% year-over-year increase in adjusted EPS.What to Watch: We break down the absolute dominance of their HBM3E/HBM4 product cycles, massive CapEx scaling across new fabs, and whether the memory cycle is turning into a permanent structural moat.ADBE: Creative Cloud is showing resilient enterprise retention as AI monetization begins to manifest in actual ARR.CELH: After a painful distribution and inventory rightsizing over the past few quarters, the stock is showing technical and fundamental signs of a text-book volume accumulation phase.The Trillion-Dollar Blueprint: Why optical DSPs and custom silicon partnerships position Marvell perfectly to capture the massive capital expenditure tailwinds from hyperscalers building out data centers. Is a $1T valuation realistic, or is it getting ahead of its fundamentals?

Jordi Visser is a veteran macro investor with 30+ years of experience and the author of the VisserLabs Substack. In this conversation, we discuss the AI pivot happening with hyperscalers, the rise of open source models, what the Mythos/Fable Five situation means for governments and investors, Kevin Warsh's first Fed press conference, where inflation is actually headed, and why bitcoin is still in a bear market and what needs to change.====================Simple Mining makes Bitcoin mining simple and accessible for everyone. We offer a premium white glove hosting service, helping you maximize the profitability of Bitcoin mining. For more information on Simple Mining or to get started mining Bitcoin, visit https://www.simplemining.io/pomp====================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.====================Looking for a better place to trade? BloFin gives traders access to deep liquidity, advanced futures products for crypto AND TradFi assets, fast execution, and a clean, intuitive interface—all in one platform. To celebrate their partnership with us, they're giving away $100,000 in Deposit & Trade Rewards. Deposit, trade, and earn rewards based on your activity during the campaign.====================0:00 - Intro0:57 - AI pivot & hyperscaler weakness5:47 - Open source models & US vs China AI race7:19 - Token demand, Jevons Paradox & AI adoption trends14:22 - When does the CapEx spending become a problem?17:59 - Open source vs closed AI models — who wins long term?19:25 - Agency & what it means for individuals24:34 - Is AI & energy the only thing holding the market up?28:32 - Kevin Warsh's first Fed press conference31:36 - Inflation outlook & next CPI print37:44 - Why so many Americans feel trapped & real cost of living44:51 - Bitcoin bear market & what needs to change

Is the AI trade a bubble? Imran Khan — founder of Proem Asset Management, former Snap executive, and the banker behind the Alibaba and Mercado Libre IPOs — isn't convinced. Dan Nathan sits down with Imran to pressure-test the bear case, from Nvidia's below-market multiple to the cyclical-vs-secular debate in memory, and to dig into why a big chunk of SpaceX's $2.5T valuation may not be a space story at all. Topics Covered Why hyperscalers underperform during heavy CapEx cycles — and why that's historically the best time to buy Distribution vs. technology: how Gemini won while arguably being the inferior model, and why Grok couldn't Meta's setup — cheap on earnings, not cheap on free cash flow — and the Zuckerberg "big swing" risk Nvidia at a $5T market cap: the $20B debt raise, buybacks, and the customers-are-competitors problem Micron and high-bandwidth memory sold out into 2027, and the cyclical-vs-secular question that decides the stock The "bottleneck trade" everyone's chasing — and why earnings durability is the thing to watch Energy constraints, data center delays, and the long-term demand picture Imran's contrarian case that AI won't create structural unemployment SpaceX's valuation decoded: rocket launch, Starlink, and the xAI cloud ramp What OpenAI and Anthropic coming to market could mean for the AI trade —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

As AI investment keeps growing, our strategists Carolyn Campbell and Vishwas Patkar discuss the many ways tech infrastructure gets financed and the opportunities for investors.Read more insights from Morgan Stanley.----- Transcript -----Carolyn Campbell: Welcome to Thoughts on the Market. I'm Carolyn Campbell, Morgan Stanley's Asset-Backed Securities Strategist. Vishwas Patkar: And I'm Vishwas Patkar, Morgan Stanley's Head of U.S. Corporate Credit Strategy. Carolyn Campbell: Today, how fixed income markets are helping fund the AI build-out. It's Thursday, June 18th, at 10am in New York. Let's get right into it, Vishwas. We've both come on this podcast before to talk about how credit markets are financing the AI build-out. And over the last ten months, I think it's fair to say that things are faster, broader, deeper than we perhaps expected initially. This investment now spans investment-grade corporate bonds, high yield loans, and a range of securitized products. From your seat in corporate credit, why does AI infrastructure matter so much, to investors right now? Vishwas Patkar: This is a big talking point in our client discussions. it's also telling that less than a year ago, we wrote about this topic for the first time, identifying a $1.5 trillion financing gap that credit markets could help bridge. At that time, data center debt was not something that investors were really focused on. Yet less than 12 months forward, this, I think, is the number one theme dominating both your and my market. And why it's important, I would say, is across, three key vectors. First, just the scale. So, if you look at overall AI-related debt issuance so far this year, we're close to $250 billion. For the balance of the year, we expect that number to double, so about $500 billion of total AI debt financing for 2026. Increasingly the second vector, I think, is around the complexity of deals. So initially, while AI financing was dominated by vanilla investment-grade corporate bond deals, we are now seeing that broaden out into project finance style deals in the high-yield market. We have seen an uptick in chip financing across the different credit silos. And that's important for investors, as identifying value across these different options does require deep credit expertise. And third, as this investment cycle rolls along, it's also important to be cognizant of risks that are building. Not just from a very broad top-down sense around the demand for compute. But also, what are some of the nuances in these different structures – whether it is in data center construction or is in chip financing that investors will need to monitor. So, it's across these three themes that we think data center debt financing is gaining importance. Carolyn Campbell: Now, the underlying demand for AI infrastructure is very strong. That doesn't necessarily mean that every bond tied to this theme is automatically going to be attractive. And as you mentioned, [$]500 billion of supply for the year; a large amount of complexity between those structures.How should credit investors think about the various risks within these different structures? Vishwas Patkar: So, in investment grade, the story is a bit simpler. So, we have had unsecured hyperscaler bond issuance. We have had issuance from semiconductor names. And then we've had some, what we call, private style data center deals. But the vast majority still comes from hyperscaler investment grade rated bonds. For this market, our focus is less on fundamentals because fundamentals are very strong. And then hyperscaler are some of the more most creditworthy companies that we've seen in the history of the market. Our emphasis more is on just the quantum of supply. So, year to date, we have had north of [$]100 billion of hyperscaler debt in the dollar market. We've had north of [$]50 billion being issued in other currencies. If you look at the overall investment grade market, supply is up almost 25 percent versus last year. That's consistent with our call for a year of record issuance this year. And increasingly, if you look forward and then map these issuance numbers to our CapEx estimates, where we could very much be on track for another record to be hit next year. So, the issue of the investment grade market is not around the fundamentals of the companies or these deals. It's more about the quantum of supply, which we think eventually will test the demand capacity of this market. And our base case for the investment grade space is similar to 1997-1998, where credit was starting to finance the business cycle, spreads widened modestly, and IG could underperform other risk assets. But over a longer time horizon, spreads still look historically very low. Carolyn Campbell: Now, what about further down the credit spectrum into the non-investment grade portion? What about that part of the issuance spectrum for AI? Vishwas Patkar: Yeah. So, what we're seeing in the sub-investment grade space, especially in high yield, is very different. There, the growth in data center financing has happened around project finance deals for data center construction. In many cases, these have come from crypto miner companies that effectively provide what we call speed to power solutions. We've also had some unsecured issuance from neo clouds, although that's relatively small. But this sector has expanded from effectively zero billion around the fall of last year to about [$]40 billion this year. We expect to see another [$]20 billion of issuance by the end of 2026. And the way they fit into this whole ecosystem is – these project finance deals we think are interesting diversifiers for regular credit investors. They do come with construction risks, especially initially for the first two to three years till the data center is up and running. But on the flip side, you do get a lot of structural enhancements and creditor protections, which is something you don't see in the vast majority of the high yield market. So, I think a key shift in the framework that investors have to do for these deals is focus on asset-level risk, which is again, I think a big divergence from how the vast majority of the credit market trades, which is largely unsecured corporate-level risk that investors have been used to. Carolyn Campbell: All right. You just brought up construction risks. Do you think that's the biggest risk facing the high-yield investors today? Vishwas Patkar: Yes. I think for the high-yield deals in particular, construction risk is the dominant vector that investors are focused on. Because it's important to remember a lot of the debt issuers are first-time borrowers. And they have a limited track record of construction in the past. So, you could see potential delays and things like cost overruns that can affect sentiment on the sector. Or at least on specific bond deals. And this will be especially important to monitor going into the second half of the year, as we have some of the first delivery dates coming up for the deals in the sector that were announced last year. That being said, you know, even though some of the tenants have termination rights, if delays go beyond 180 days, our view is that given the structural power constraints, these termination rights are unlikely to be exercised. So, while construction milestones can affect sentiment and short-term valuations, we would look at any blips as buying opportunities in the space. Alright. So Carolyn, let me throw this back to you. So, construction risk clearly very important for the corporate credit market, especially for high yield investors. Is that something ABS investors or commercial mortgage-backed investors care about? And in what other ways are these asset classes different from corporate credit? Carolyn Campbell: Okay. So first and foremost, the biggest difference is that in securitized products, the assets are stabilized, they're cash flowing, they're online. We don't have that first vector of construction risk in our space. The second biggest difference is while in high yield and IG we've mostly seen – or we've entirely seen single campus, single tenant data centers; in securitization issuance, it's mostly multi-tenant, multi-asset, multi-regional, deals that have come to market. And so, it's a very different risk profile. And as a consequence, investors are focused not just on who is behind this one single lease and what are the termination rates, but what does the landscape look like in general for compute? How does that affect vacancy and churn rates? And then lastly, the issuers themselves are different. You talked about the crypto companies. You get a little bit more of the data center, data center construction. Whereas in securitized products, these are companies that have been around for 5, 10, 20 years. They're accustomed to managing a fleet of assets, dozens if not hundreds of tenants. They've got a little bit more of a track record for the most part, than the types of issuers we're seeing in the credit market. Vishwas Patkar: Your market post-construction, more leverage to the thematic of demand for compute – and how the AI investment cycle is playing out. Versus the corporate credit market, which is largely exposed to construction risks as the data centers get built out. So that's a very important difference.That being said, one theme that ties both our markets are just healthy fundamentals, but at the same time heavy supply. So, I talked about how we see that affecting our view on investment grade. How is that same tension showing up in securitized products? Carolyn Campbell: So exactly as you said, the fundamental story is very strong. We don't see deterioration in performance of the assets either that has happened yet or that we expect to come in the near term. So, it really is a technically driven story. Supply in this space, we're forecasting at around [$]30 billion for year, so smaller in magnitude, but relatively large for the market. That has very elevated supply expectations, and so as a consequence, we've seen spreads back up across the space. We do think that some of the cross-asset comparisons will help keep spreads contained from here. And so, we do see value in securitized credit across the stack for the rest of the year. Vishwas Patkar: All right. So, you brought up the cross-asset comparison. And so, we've discussed the fundamental differences in our market, how much issuance we expect. But, you know, just to end on a commercial note – if we are advising investors on where is the best relative value and what's the framework for comparing opportunities, how do you think about that? Where do we see value across the ecosystem? Carolyn Campbell: I mean, I think this is probably the biggest question that investors that are looking at this space are facing today. And there's... If we're thinking just about the data center backed assets, I think there are two main things. One is the asset itself, where we're focused on things like the geography, the tenant, the interconnectivity, the flexibility of this asset for multiple uses. And then the second is on the structure of the deal itself. How much leverage is being raised against the asset? How cash flowing is it? And then of course, the duration as well. But it's a great question. And because of the complexity of this space, it can be really hard to compare one to the other. Vishwas Patkar: Yeah. And, at the risk of providing a non-answer, I very much think investors are in the process of coming up with a framework because these deals have come very quickly. This is a new sector for most credit investors to analyze. But I think what we can say with a high degree of certainty is this is blurring the lines between corporate credit and securitized credit. So, you know, this opens up more avenues for us to collaborate on this topic going forward. Carolyn Campbell: All right. That's a great place for us to leave it today with that nice cross-collaboration. Vishwas, thank you so much for taking the time to talk. Vishwas Patkar: Great speaking with you, Caroline. Carolyn Campbell: Thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or colleague today.

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

What does it look like when one of the world's most longstanding institutional investors ($120B) decides to go deeper into venture capital? And what happens when one of the most respected LP teams in the business joins forces with them?Thomas Kristensen, who is responsible for the venture capital business at LGT Capital Partners, joins hosts Nick and Beezer for a wide-ranging conversation that doubles as an announcement: the Sapphire Partners team – including Beezer, Laura and Nate – have joined LGT Capital Partners. Thomas explains why the fit made sense.LGT Capital Partners manages over $120 billion on behalf of more than 700 institutional clients and is owned by the Princely Family of Liechtenstein. That ownership brings a long-term perspective, often measured in decades rather than years, and it shapes how Thomas and his team approach venture: with patience, close partnership and a willingness to be both buyer and seller in private markets.Together with Nick and Beezer, Thomas unpacks the firm's recently published white paper on the case for increasing venture allocation, built on three pillars: lifecycle diversification, an innovation hedge against AI-driven displacement and the maturation of secondary markets as a liquidity tool. He also offers a frank assessment of the current market, noting that the pace of deployment feels reminiscent of 2020-2021, and that a valuation dip may be coming regardless of how transformative AI ultimately proves to be.From the endowment model's stress test to the temptation of clinging to a single fund-returner, this is a thoughtful conversation about long-term thinking from an investor who has spent more than two decades refining his approach.Quotes“It's not new that incumbents are challenged by new entrants. I think what's new is the speed at which this is happening. In the age of AI, it feels like there is a risk that many incumbents could be displaced quite quickly. And so including venture capital in your portfolio is a way of hedging against this.”“Are LPs gonna run out of capital? I don't know. Sometimes I wonder if the world is going to run out of capital to fund CapEx for AI right now. We always say, ‘Listen, if you come into venture capital as an asset class, if you're an allocator, you can go in when it's hot, you can go in when it's not. You will only really figure out in hindsight.' The most important thing if you start committing to venture is, make sure that you can commit at a steady pace over a long, long period of time because it's a market you cannot time. There's just no way.”Time Stamps04:13 Meet Thomas Kristensen, Head of Venture Capital at LGT Capital Partners05:52 The Princely Family of Liechtenstein and the Long-Term Mindset08:37 The White Paper: Why LGT Capital Partners Is Increasing Its Venture Allocation09:45 Three Pillars: Lifecycle Diversification, Innovation Hedge, and Secondary Markets12:35 Big News: The Sapphire Partners Team Joins LGT Capital Partners17:35 LP Consolidation: What It Means for GPs23:47 GP Advice: Keep First Things First on LP Alignment32:03 Engineering a More Liquid Private Portfolio37:07 AI Market Heat and Fundraising Pace45:44 Closing Remarks and What's NextLinksConnect with the guest and hosts on LinkedIn!• Thomas Kristensen• Beezer Clarkson• Nick ChirlsLearn more about:• LGT Capital Partners• Asylum Ventures• OpenLP• LGT Venture Capital White Paper

In the third episode of First Principles with Andy Constan, Andy breaks down the changing structure of markets as the IPO window reopens, AI CapEx accelerates, and corporate buybacks shift toward new equity supply. We discuss what the SpaceX IPO says about capital markets, whether AI spending can create disinflationary growth, why the consumer is still holding up, and what could challenge the current market bubble.Follow First Principles on SpotifyFollow First Principles of Apple PodcastsTopics covered:Why IPOs are central to the purpose of public marketsHow Andy evaluates whether the SpaceX IPO workedWhy issuers may want IPOs to trade higher after pricingThe shift from stock buybacks to new equity issuanceWhy AI CapEx is changing the supply and demand for sharesHow hyperscaler spending is being funded through cash, bonds, and stockThe economic test for whether AI investment pays offDisinflationary productivity growth versus labor displacementWhy the current economy is still supported by consumptionThe role of wealth effects and consumer dissavingWhy falling oil prices may not eliminate inflation pressureWhat Andy is watching in Fed policy, tariffs, AI CapEx, and equity issuanceHow Kevin Warsh could approach rates, QT, and the Fed balance sheetTimestamps:00:00 Intro and key themes04:18 How Andy reads the SpaceX IPO08:27 Why underwriters and regulators want IPOs to work13:00 Why issuers may want IPOs to trade higher17:05 From stock buybacks to new equity supply21:06 The 600 to 700 billion dollar shift in share supply26:42 The economic test for AI tokens32:09 Can AI create disinflationary productivity growth?38:10 Is AI CapEx holding up the economy?41:00 Wealth effects, dissaving, and the consumer45:52 Oil prices, war, and inflation49:07 Jalen Brunson, incentives, and long-term value52:00 Fed policy, tariffs, and what matters this summer55:36 Kevin Warsh, QT, and the Fed balance sheet58:42 Closing thoughtsNo information on this podcast should be construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.

Markets are digesting all the hyperscaler spending on the AI buildout, says Arun Sundaram, pointing to Amazon's (AMZN) $200 billion CapEx goal as something for investors to watch. However, the Mag 7 giant's fastest-growing tech businesses are also the most profitable. Arun highlights strengths he sees for the company's growth, including Amazon Leo and ways it serves as a competitor to SpaceX's (SPCX) Starlink. Tom White offers an example options trade for Amazon's stock. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Epic spent five years and over $100M breaking the platforms' 30% tax — then cut V-Bucks by 20% to "pay the bills." If the company that won the fee war still gets squeezed, what does that say about everyone else?The answer isn't about fees. When AI makes content infinite and attention stays finite, the only asset that appreciates is the direct relationship with your players — the one distribution channel that gets cheaper the stronger it gets. And a nearly invisible economy of community-run game servers has been proving its dollar value for fifteen years.I sit down with Liam Wiltshire, GM of Tebex — the merchant-of-record platform behind direct payments for Rockstar, Take-Two, Hytale, and FiveM — to unpack it.In this episode:Why "is 30% dead?" is the wrong questionCreator codes: how trust drives 50–227% more spendThe BNPL and crypto data that surprised even TebexWhy 35% of desktop game purchases happen on a phoneHow Hytale launched off Steam and secured two years of runway from pre-orders aloneThe £20, 16-year-old origin story behind a company that's processed $1.5BRead the full breakdown and subscribe at gamemakers.com.Chapters00:00 — Epic cut V-Bucks: why it's really a margin story03:47 — When content is infinite, what's actually scarce?07:38 — The shadow games industry: Hypixel, FiveM & a $1.5B economy10:13 — The data: creator codes, BNPL & buying on a second screen13:33 — Liam Wiltshire joins: the state of the industry16:35 — Why every player purchase is a "CapEx decision"18:50 — Is the 30% platform fee dead?21:00 — Who really owns the player relationship?23:27 — D2C across mobile, web, PC & console34:59 — Treating the platform as an acquisition channel42:57 — UGC servers & what a "merchant of record" actually does1:00:40 — Creator codes: how trust drives more spend1:19:04 — BNPL & crypto: the numbers that surprised Tebex1:31:20 — Payment optimization & one-click checkout1:40:43 — The £20 origin story & the $29M exit

Jeff Schulze of ClearBridge Investments joins host John Przygocki to discuss US economic resilience amid Middle East energy disruptions. With 11 of 12 of his Recession Risk Dashboard indicators now green, Schulze highlights a strong labor market, durable consumer spending, and a CapEx cycle broadening that extends well beyond AI. He views the economy as mid-cycle, remains constructive on US equities, and sees compelling opportunity in emerging markets.

Patrick Moorhead and Daniel Newman cover Tim Cook's final WWDC as CEO and Apple's Gemini-powered Siri strategy, the $35 billion Apollo and Blackstone deal backing Anthropic's capacity expansion, Intel's packaging wins with Google and NVIDIA, SpaceX's IPO at a $1.77 trillion valuation, Anthropic's Claude Fable 5 and Mythos 5 launch across every major cloud, and earnings reactions from Oracle, Micron, and Adobe. The handpicked topics for this week are: Apple's Siri AI Will Run on Gemini, Closing Out Tim Cook's Final WWDC as CEO: At WWDC, Apple confirmed Siri AI will run on Gemini through a new billion-dollar per year, multi-year deal, while Apple's Foundation Model Cloud Pro runs on NVIDIA GPUs inside Google Cloud. The announcement marks Tim Cook's last WWDC as CEO before John Ternus takes over on September 1. Apple isn't building its own AI cluster or competing on CapEx. They're betting that by owning the consumption layer, backed by access to health data and private messaging through iMessage, Apple will have a moat that compute spending can't replicate. (The Decode) Apollo and Blackstone Close the Largest Private Credit Deal Ever Backing Anthropic's Capacity Expansion: A $35 billion deal, the largest private credit transaction on record, will fund Google TPU capacity tied to Anthropic's compute needs, with Broadcom backstopping senior debt tranches and Google backstopping lease payments. The structure treats compute as a lendable asset class and signals more than 20 gigawatts of demand still being built out through 2028. Circular financing between chipmakers, cloud providers, and AI labs has moved from controversial to standard practice. (The Decode) Intel's Foundry Wins Packaging Work on Google's TPUs, Not a Full Fab Deal: Reports that Intel landed a deal tied to Google and NVIDIA reframe what's actually being handed off. Intel gets the packaging work on over 3 million TPUs, the compute die stays with TSMC, and the I/O die is being negotiated with Samsung at 2nm. INTC rose 12% Monday. The deal represents a low-risk path for Intel to augment, not replace, TSMC, while raising questions about anti-competitive dynamics in the foundry market. (The Decode) SpaceX Becomes an AI Infrastructure Company With a $1.77 Trillion IPO: SpaceX's IPO priced amid oversubscribed demand, with its valuation now reflecting not just Starlink connectivity and launch dominance but a newly material AI business, including AI1 orbital data center tests planned for late 2027 and a $920 million per month Google compute contract running through 2029. A sum-of-the-parts breakdown of the connectivity, launch, and AI segments lands well short of the trading price, with the gap largely explained by confidence in Elon Musk's track record of execution. (The Decode) Anthropic Launches Claude Fable 5 and Mythos 5 Across Every Major Cloud: Anthropic shipped Claude Fable 5 and Mythos 5 with same-day availability across Snowflake, AWS Bedrock, Vertex AI, and Microsoft Foundry, pricing at $10 and $50 per million tokens. The hyperscaler-neutral distribution strategy lands ahead of Anthropic's anticipated IPO. The models represent a real step up in research capability over Opus 4.8, but they come with a significant change. Users no longer have the option to opt out of data sharing with Anthropic, a shift some enterprises, including Microsoft, are already responding to. (The Decode) Is SpaceX a Once-in-a-Generation Entry or the Top of the Market? One side argues SpaceX represents a generational opportunity on par with early Amazon or Netflix, with interplanetary travel and off-world resource extraction as the long-term payoff that justifies looking past current valuation math. The other side argues this is peak euphoria: a company trading at roughly 95 times sales, propped up in part by circular investment from Google into both SpaceX and its AI segment, with a steep drawdown likely before any sustained climb. (The Flip) The Chip and Security Trade Reverses From Broken to Bifurcated: The semiconductor sector posted its biggest single-day gain since 2020, with the SOX up 5% on Monday, June 8, as a prior selloff in names like Broadcom, CrowdStrike, and Palo Alto Networks fully reversed. Intel rose 12%, Marvell 10%, and Corning 7%. The rebound reframes the AI trade narrative from a broad breakdown to a split between winners and laggards within the same sector. (Bulls & Bears) Oracle Posts a Record Quarter, But the Market Focuses on a $50 Billion Funding Plan: Oracle delivered record revenue of $19.2 billion, up 21 %, with EPS of $2.11, beating estimates of $1.89. IaaS grew 93 %, the fastest pace among hyperscalers, and RPO hit $638 billion, up $85 billion quarter over quarter, including $75 billion in AI contracts. FY27 guidance of $90 billion was maintained, and EPS guidance was raised, yet the stock fell 5% after hours amid concerns about Oracle's capital spending plans. Oracle's AI cloud backlog now exceeds those of AWS, Google, and Microsoft, built heavily on commitments from Anthropic and OpenAI. (Bulls & Bears) Micron's Profit Trajectory Puts It in Google's Earnings Tier: Micron is projected to generate nearly as much profit in 2027 as Google, with Q2 revenue of $23.86 billion, up 22 % and beating estimates, and Q3 guidance of $33.5 billion in revenue, $19.15 EPS, and 81 % gross margin. The stock is up 776%, with Wall Street firms, including UBS, raising price targets. The open question is whether memory has broken its historically cyclical pattern given sustained AI demand. (Bulls & Bears) Adobe Beats Across the Board, But the Stock Drops on CEO Departure and Freemium Pivot: Adobe posted record revenue of $6.62 billion, up 13 % and beating consensus of $6.45 billion, with non-GAAP EPS of $5.96, topping estimates of $5.81. AI first ARR tripled year over year to over $500 million, with total ARR reaching $27.1 billion, and FY26 guidance was raised. The stock still fell 5.5 % after hours, driven by the CFO's departure to Marvell and market concern over a strategic shift toward freemium pricing that delays near-term profitability. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Apple WWDC- Apple Caves to Google AND NVIDIA — Siri AI Runs on Gemini ($1B/yr) + Apple Foundation Model Cloud Pro Runs on NVIDIA GPUs in Google Cloud; Tim Cook's Final WWDC as CEO Before John Ternus Succeeds Him Sept 1 https://www.cnbc.com/2026/06/08/apple-wwdc-2026-live-updates.html Google's $35B Infra Deal — Apollo + Blackstone Close the Largest Private Credit Deal Ever; Broadcom Backstops Senior Tranches; Google Backstops Lease Payments https://www.reuters.com/business/apollo-blackstone-back-anthropics-35-billion-capacity-expansion-new-broadcom-tie-2026-06-09/ Intel's Foundry Reportedly Wins Google Packaging (Not Full Fab) — The Information Reframed: 3M+ TPU Packaging by Intel, Compute Die Still TSMC, I/O Die Being Negotiated With Samsung 2nm; INTC +12% Monday; Pat Calls Out TSMC Anti-Competitive Risk https://www.trendforce.com/news/2026/06/09/news-intel-foundry-gains-momentum-as-google-reportedly-orders-3m-tpus-nvidia-evaluates-18a-for-multi-die-gpu-design/ SpaceX Becomes an AI Infrastructure Company — Friday IPO at $1.77T; AI1 Orbital Data Center Tests Late 2027; Google $920M/mo Compute Contract Through 2029 https://finance.yahoo.com/markets/stocks/articles/spacex-poised-history-record-75-100000402.html Anthropic Ships Claude Fable 5 + Mythos 5 — Same-Day Distribution Across Snowflake, AWS Bedrock, Vertex AI, Microsoft Foundry; Hyperscaler-Neutral by Design Ahead of IPO; $10/$50 per M Tokens https://www.anthropic.com/news/claude-fable-5-mythos-5 The Flip FOR: https://www.cnbc.com/2026/06/11/spacex-billionaire-investing.html AGAINST: https://www.nytimes.com/2026/05/20/technology/elon-musk-spacex-ipo.html Bulls & Bears The Chip + Security Tape Recovery — SOX +5% Monday June 8 (Biggest Day Since 2020); AVGO/CRWD/PANW Selloff Reversed; Intel +12%, Marvell +10%, Corning +7%; the AI Trade Pivots From "Broken" to "Bifurcated" https://www.investopedia.com/stock-market-today-dow-jones-s-and-p-500-06082026-11992852 Oracle (ORCL) Q4 FY26 ACTUALS — Record $19.2B Rev (+21%), EPS $2.11 Beat ($1.89); IaaS +93%; RPO HITS $638B (+$85B QoQ, $75B AI Contracts); FY27 $90B Guide Maintained, EPS Guide Raised; Stock −5% AH on Massive Capex Plan https://www.tradingkey.com/analysis/stocks/us-stocks/261959450-oracle-record-q4-2026-earnings-report-cloud-data-center-stock-tradingkey "$MU Will Generate Almost As Much Profit in 2027 as $GOOGL"; Q2 Rev $23.86B (+22% Beat), Q3 Guide $33.50B / $19.15 EPS / 81% GM; MU Stock +776%; UBS Among Wall Street Raising Targets https://247wallst.com/investing/2026/06/11/wall-street-just-put-a-monster-target-on-micron-is-the-stock-still-too-cheap/ Adobe (ADBE) Q2 FY26 ACTUALS — Record $6.62B Rev (+13%) Beats Consensus $6.45B; Non-GAAP EPS $5.96 Beats $5.81; AI-First ARR Triples YoY to $500M+; Total ARR $27.10B; FY26 Guide RAISED; Stock −5.5% AH Despite Beat-and-Raise https://www.businesswire.com/news/home/20260611677110/en/Adobe-Reports-Record-Q2-Results    

¿Sabías que esta semana Elon Musk se ha convertido en el primer trillonario de la historia gracias al debut de SpaceX en bolsa? ¿Y por qué el resto del sector espacial cayó un 10% el mismo día que SpaceX subía un 20%? En este resumen semanal analizamos todo lo que ha movido el mercado esta semana: el debut histórico de SpaceX, la caída del sector espacial, Oracle hundiéndose un 15% pese a buenos resultados, Adobe en mínimos de 5 años y Robinhood reventando expectativas. En este resumen tratamos el debut de SpaceX con una subida del 20% y la prima Musk, por qué el sector espacial cayó mientras SpaceX subía, Elon Musk como primer trillonario de la historia, Oracle cae un 15% por el CAPEX de la IA, Adobe en mínimos de 5 años aunque bate todas las previsiones, Robinhood revienta resultados y análisis de opciones, HIMS aprobada en Reino Unido y la segunda derivada de las cervezas, y cómo el mercado ha pasado de euforia a miedo en una sola semana.Este vídeo es solo contenido educativo e informativo. No es asesoramiento financiero. Suscríbete y activa la campanita para no perderte ningún resumen semanal. Dos cosas que debes saber: 1 - Cada día mandamos un email con una idea, estrategia o reflexión privada para que avances más rápido en tu camino como inversor. El de hoy ya te lo has perdido, si quieres recibir el de mañana, te apuntas en: https://locosdewallstreet.com/7-errores/ 2 - Al apuntarte recibes un video titulado «7 errores fatales (muy habituales) en la selección de oportunidades en bolsa». Me da igual en lo que inviertas, tus años de experiencia o el tamaño de tu cartera. Si inviertes deberías verlo (antes de tomar una decisión de la que poder arrepentirte). Lo recibes al apuntarte en nuestra newsletter aquí: https://locosdewallstreet.com/7-errores/ ══════════════ DISCLAIMER El contenido de este canal de YouTube tiene exclusivamente fines educativos y no constituye asesoramiento financiero ni recomendaciones de inversión. Todos los temas tratados están diseñados para ayudar a los espectadores a entender mejor el mundo de las finanzas, pero las decisiones de inversión deben tomarse de forma personal y bajo la responsabilidad de cada individuo. Invertir en mercados financieros conlleva riesgos significativos debido a su complejidad y volatilidad. Es posible perder parte o la totalidad del capital invertido. Por ello, es fundamental que realices tu propio análisis antes de tomar cualquier decisión y, si lo consideras necesario, consultes con un profesional financiero acreditado. Recomendamos: - Contar con un fondo de emergencia equivalente a al menos tres meses de tus gastos básicos antes de invertir. - Analizar muy detenidamente y con precisión cualquier inversión. - En caso de duda consultes con un asesor financiero certificado por CNMV - Mantenerte alejado de promesas de rentabilidades astronómicas, dinero rápido u otros esquemas engañosos. En Locos de Wall Street, nuestra misión es fomentar una educación financiera sólida, ética y accesible para todos, ayudando a nuestros seguidores a tomar decisiones informadas y responsables. #SpaceX #ElonMusk #ResumenMercado #LocosDeWallStreet #Oracle #Adobe #Robinhood #HIMS #ASTS #InversiónEnBolsa #Invertir2026 #WallStreet #Bolsa #AnálisisBursátil #IPOSpaceX ══════════════

Chief Asia Economist Chetan Ahya joins Head of India Research and Chief India Equity Strategist Ridham Desai to break down India's macro outlook, capital flows and sector opportunities.Read more insights from Morgan Stanley.----- Transcript -----Chetan Ahya: Welcome to Thoughts on the Market. I'm Chetan Ahya, Morgan Stanley's Chief Asia Economist.Ridham Desai: And I'm Ridham Desai, Morgan Stanley's Head of India Research and Chief India Equity Strategist.Chetan Ahya: Today, the biggest takeaways from our India Investment Forum in Mumbai. From the shifting outlook for India's markets and flows to the sectors driving the next phase of corporate earnings and CapEx.It's Friday, June 12th at 7PM in Hong Kong.Ridham Desai: And 4:30PM in Mumbai.Chetan Ahya: Ridham, the Morgan Stanley's India Investment Forum took place in Mumbai last week, and I was there with you. These events are a great opportunity to speak with investors who come across from the globe to attend. Now that we have had a few days to process the conversations, what stood out to you? What was the biggest shift in investor sentiment that you picked on?Ridham Desai: So, Chetan, I think it's been the case of a continuing story about India. Domestic investors look that they are bullish, and foreign investors continue to stay rather cautious on the Indian markets. We could see that in the overall attendance. In contrast, I think domestic investors were looking for the next stock that they wanted to buy. They were seeking opportunities, and there was a lot of interest in meeting companies.Before we get into markets, let me turn back to you from a macro side. India's growth story remains strong, but relative growth appears to be cooling. This is in contrast to markets like Japan, Taiwan, Korea, and the US. How should investors think about India's macro positioning in that context?Chetan Ahya: So, Ridham, when I look at the macro data in India, they're all indicating a meaningful upside in the growth trend. So I'll just cite two key cyclically sensitive macro data points. One is the banking system credit growth, and number two is the auto sales, particularly the passenger vehicle. So bank credit growth is growing as of the last biweekly data point that we got. It's growing at seventeen point seven percent year-on-year, and car sales are growing at twenty-seven percent in the month of May.But as you were mentioning earlier, the relative growth opportunity is a challenge for India and to just share the numbers on the earnings growth for the first quarter that we saw across the region. So we saw Korea's earnings growth at one hundred and seventy percent. We saw Taiwan's earnings growth at forty-eight percent year on year. Japan at thirty-three percent. The US has seen a growth of about twenty-seven percent year on year.So in that context, when India is reporting thirteen percent growth, it's becoming a challenge for investors to look for opportunities in India relative to other markets. Either they are more focused on the other markets than India. So let me come back to you, Ridham. Staying with the investment implications, India projects stable valuations and strong corporate earnings, but its relative growth advantage has narrowed. How should investors reconcile this contradiction?Ridham Desai: If I go back thirty-five years, as long as we have the MSCI index series, and as far as I have been in this industry, this is the lowest relative multiple that India has traded at. And indeed, growth last year was weak. But if you see QOQ, we have started to accelerate. The broad market earnings growth trajectory has shown a doubling in the quarter that ended March over the quarter that ended December.But it underscores the point you made about the relative growth complex. It's clearly not in India's favor. And a lot of the capital in the world is short-term oriented, and it cares for what growth is gonna come in the next quarter or two. And that's the state of the market right now.However, what I would say is that equities is a quintessential long-duration asset class. In the long run, what matters is terminal growth. I don't really think India's terminal growth has moved much. It remains far superior to a lot of other countries around the world. And therefore, I think this does present itself as a great opportunity for a long-term investor while the markets are digesting this relative growth disadvantage that India seems to have over the next, say, three or four quarters.Chetan Ahya: And Ridham, another theme from the forum was policy action to attract capital. Policymakers announced a number of measures right as our conference ended and they aimed to withdraw withholding tax on debt investors, also providing banks with an incentive to take up more dollar borrowing. How central are these measures to sustaining foreign inflows into Indian markets?Ridham Desai: I think the measures taken by policymakers are very important, probably amongst the most important policy actions this year. The removal of taxation on debt investors will make a difference. The provision for hedging to external commercial borrowings as well as to foreign currency deposits will make a difference.It should boost flows into India over the next twelve months. That said, these measures may not help the equity flows because the equity flows, I think, are going to depend on the relative growth situation. Now, there's only that much India can do to lift its growth. It may accelerate to the high teens. So growth elsewhere needs to decelerate for equity investors to return. Or India needs to see the start of a major IPO cycle because in primary issuances, foreigners do come to buy, and that may change the net picture on FBI flows in the equity markets.But as far as the debt markets are concerned, I think the measures taken last week are going to prove to be quite potent, and India should see the benefits accruing over the next few weeks and months.Chetan, from your perspective, how important is the policy backdrop right now in determining whether India can keep attracting long-term global capital despite more competitive returns elsewhere in the short run?Chetan Ahya: So Ridham, I think the key focus for the policymakers had been with these measures to boost short-term capital inflows to stabilize the currency. There has been a balance of payment deficit. So from that perspective, the short-term capital inflow augmentation effort as you mentioned, has been the correct move. But from the long-term perspective, we think that the government needs to boost competitiveness of the Indian manufacturing. Because in the context in which AI could affect India's services exports, there is a need to augment more export receipts from the manufacturing sector. At the same time, if they improve the competitiveness of the manufacturing sector, it will help India to attract more capital inflows from long-term investors for the purpose of FDI.And the good news is that the government is on it. They are taking a number of measures to boost that competitiveness in the manufacturing. But we think that there is more action needed and hopefully in the intention to improve the balance of payment dynamics and exports from manufacturing sector, we will see more actions from the government in the coming months.Ridham Desai: Chetan, you've also written extensively about the structural capital spending cycle in Asia and India. Can you walk us through the key details here, especially in the Indian context?Chetan Ahya: I think the key story that we are observing, it's sort of more or less global, but definitely very clearly seen in Asia, that there seems to be a super cycle for CapEx as well as industrial activity. This CapEx cycle is effectively driven by spending in four key sectors, and that is AI and AI-related digital infrastructure, energy, defense, and industrial onshoring-related CapEx.Now, as far as India is concerned, we are seeing investments in all the four segments that I just mentioned. In fact, it's seeing a significant amount of activity in the space of energy. And, similarly, we are seeing a lot of policy measures, I mentioned earlier, in terms of boosting manufacturing competitiveness.But at the heart of it is government's effort to onshore industrial supply chain. So India's CapEx has also inflected higher. Having said that, the difference between India and, let's say, North Asia, which is Korea, Taiwan, Japan and China, is that they are also a big player in the export market for capital goods when there is global CapEx cycle upswing happening. Nevertheless, India will see the benefit of this CapEx cycle in terms of its own growth push, as well as improvement in productivity.So Ridham, how would you think about the sectoral opportunity within the Indian markets?Ridham Desai: We see a lot of interest in some of these sectors which you mentioned. But actually, I would like to start off with financials. I see the banks in a very sweet spot. Balance sheets are in pristine condition. The interest rate cycle has troughed, which means margins for the banks have also bottomed and credit growth is finally accelerating. If this CapEx cycle unfolds like the way you are describing it, I think financials will stand to gain the most.And interestingly, the valuations are quite good, both on an absolute as well as on a relative basis. Also, of course, investors can go directly into those sectors which are doing this capital spend. Energy to start with, semiconductors, fertilizers, data centers and aerospace.The only thing to note here is that not everywhere are the valuations attractive enough because in some cases the market has recognized the coming growth cycle and has started to price that in. So we have to be careful about the valuations. But I think financials and industrials are clearly great opportunities in the context of this CapEx recovery that India is likely to see in the coming five years.Chetan Ahya: And additionally, the most requested companies at the summit, Ridham, were consumer sector companies. What do you think investors are looking for at this sector over others?Ridham Desai: So, Chetan, I think from a structural perspective, the Indian consumer is quite clearly the best place to be. In fact, I would say that it's the leverage that India enjoys over the rest of the world.The one point five billion people in this country are split across, say, a hundred and fifty cohorts of ten million each, and each of these cohorts have got different consumption opportunities. So depending on what product or service you're offering to your consumers, there's a market in India, and which in nominal terms is growing between ten and fifteen percent.As we know, last year India accounted for something around seventeen or eighteen percent of global GDP growth, which means depending again on what you are selling to your consumer, India could be between ten and hundred percent of your revenue growth. So India's consumer is something that hardly anybody can avoid.So in summary, Chetan, when I look at it from an investment opportunity, financials, industrials, and consumption, not necessarily in that particular order, are probably the best places for investors to look at. However, IT services, I think could be the dark horse. It's a sector right now which is disrupted or potentially disrupted by AI, and there's a lot of confusion there.But I think as the dust settles on this, it may emerge as one of the most interesting areas for investors to look at. So there's a lot of stuff in India happening right now. I think growth is accelerating. Valuations are looking quite interesting. In fact, the best that they've been in many, many years.Trading performance suggests that investors are not positioned at all. And if things start looking up, then India could be a very good market in the coming twelve months.Chetan Ahya: Ridham, thanks for taking the time to talk.Ridham Desai: Great speaking with you, ChetanChetan Ahya: And thanks for listening. If you enjoy our Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or a colleague today.

"You can't be hyperbolic enough" when it comes to AI's impact on the markets and the U.S. economy, says Rebecca Walser, making it difficult to gauge valuations for the industry. She adds that CapEx from hyperscalers clashes with rising energy costs as the U.S.-Iran war lingers on. Rebecca says the way AI is financed needs to change for bulls to have a wider runway. On her stock picks, she likes Reddit's (RDDT) ties to LLMs and robust balance sheet. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

During last week's NYU IHIF, I talked with Ryan Bosch of Arriba Capital because hotel transactions have picked up, but closing deals still takes a lot more creativity than people may realize. Ryan sees a growing performance gap between hotels where owners continually invested in their properties during the last six years and hotels where owners delayed CapEx. Now that gap is showing up in value, refinancing pressure, and whether some owners decide they're better off selling. Deferred CapEx has split hotel performance inside the same markets Owners who continually invested in their properties now have stronger assets Some owners now face refinance pressure and larger cash-in requirements More sellers need a transaction, not another round of market testing Buyers still want "meat on the bone" so they have room for value creation after closing Creative capital stacks and alternative financing help more hotel deals close Thanks to Actabl. Actabl gives you the power to profit. Visit actabl.com. Want the weekly roundup of news, videos, and what you might've missed? Text HOTEL to 66866.

Ben and Tom discuss Oracle's 8% drop on a $40 billion capital raise plan and CapEx ballooning to $90-95 billion against just 6% EPS growth, the ECB hiking rates for the first time since 2023 to 2.25% into a sagging expansion, today's PPI print and 30-year auction, Rick Rieder's case for Fed cuts and fixed-income opportunities, OpenAI weighing drastic token price cuts to fend off Anthropic, and why Google's equity-funded AI buildout raises real questions about ROIC.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

Send us Fan MailWhile everyone's been fixated on the SpaceX IPO, Google quietly pulled off the largest equity offering in history—roughly $85 billion—and basically front-ran the entire market to do it. In this episode of The Skinny on Wall Street, Kristen and Jen break down why a cash-printing machine like Alphabet would raise money at all, and why they did it in the most fascinating way possible: a Berkshire Hathaway private placement at a discount, a common stock offering across Google's quirky three share classes, a $40 billion at-the-market program, and the structure that confuses almost everyone—the mandatory convertible.If you've ever nodded along to "convertible debt" but secretly wondered what the hell stock that converts into stock actually is, this one's for you. Kristen (the First Lady of Valuation herself) walks through exactly how a mandatory convert works—why the number of shares you receive is a moving target tied to the share price, how the conversion math plays out from zero to a 25% premium and beyond, and why Google layered on a capped call to claw back even more upside. Along the way, they get into book-runner drama, IPO fee structures, why Tesla loved these trades, and what it really signals when sophisticated issuers are dumping rich equity, rich volatility, and rich call skew onto a market full of bullish retail buyers.The bigger picture? This is the AI build-out narrative wearing a new outfit. With 100% CapEx deductibility on the table and a talent war driving nine-figure pay packages, the smart money is raising as much as it can, as fast as it can—and using the hype to do it on favorable terms. Tune in for a clear, no-jargon breakdown of one of the most interesting capital markets moves of the year. Want to go deeper? Check out our Investment Banking & Private Equity Fundamentals course taught by Kristen Kelley—20 years of Wall Street knowledge, yours for two years.Shop our Self Paced Courses:Investment Banking & Private Equity Fundamentals HEREFixed Income Sales & Trading HERESubscribe to our Substack: https://substack.com/@thewallstreetskinny

Brian Glenn connects geopolitical tensions and energy-driven inflation to broader market moves before shifting to the AI trade. He highlights strong momentum in names like Micron (MU) and rising hyperscaler spending, while noting mixed results in software, including Adobe (ADBE). He also points to Meta Platforms (META) and others raising capital for AI, drawing comparisons to the shale boom as investors begin to demand clearer return on investment.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

SUMMARY DEL SHOW Rebote amplio tras el anuncio de Washington de que “completó” sus ataques en Irán. NASDAQ lidera, seguido por $SPX y $DOW, con yields bajando un poco en modo alivio. Hoy manda macro. PPI esperado en 0.7% m/m y 6.4% a/a, más jobless claims, en un tape que sigue sensible a energía y titulares. $ORCL se desploma por un plan de CapEx agresivo hacia 2027, el sector espacial se recalienta rumbo al debut de $SPCX, $WMT y Wing escalan entregas por dron, y $BABA con $JD sienten presión regulatoria por el 618.

David King from Gulf Wind Technology returns to discuss serial uptower blade repairs, passive load shedding, and data-driven testing. Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTube, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us! Welcome to Uptime Spotlight, shining light on wind energy’s brightest innovators. This is the progress powering tomorrow Allen Hall : David, welcome back to the program.  David King: Yeah, I’m so glad to be here. A lot’s happened since the last time I was on, so, uh, this is gonna be great.  Allen Hall : It’s been about a year. Mm-hmm. And last year we were at OM&S in Nashville, and you were talking about root fusion, and this is the insert fix uptower for the blade inserts, right? So we’re having a lot of blade bolt issues, and the inserts are starting to pull out or become loose, and the blades are moving around. A lot of our operators in the States are trying to solve that problem, and they don’t wanna remove the blades and bring anything down tower. They would like to fix it uptower. That’s where your solution came in. How’s that going?  David King: Yeah, so I mean, it, it’s really been a five-year journey for us. I mean, we’ve been doing this- I remember that, yeah … for a [00:01:00] very long time. You know, it started like any process does, with a problem statement. Sure. And we’ve been working through from problem statement, you know, going through process development, going through structural development, going through pilots. Uh, we did a, a huge pilot deployments about three years ago, where those were being monitored. Um, we’re now in a position where we’re in serial deployment, and that’s what’s really exciting. You know, we’re doing about 200 blades a year, uh, of, of serial deployment. We’ve, we’ve done that now, uh, we’re going into our second year of that. Nice. So we’re extremely excited by that. That comes with its own sets of challenges as you scale up. How do you maintain quality? We even touched a little bit on a few of these things last year. Um, but yeah, we’re really excited to be doing that. Uh, we’re trying to keep it, you know, again, process-driven. How do you simplify a process that allows you to scale up appropriately, train people appropriately? A- a- and that’s what we’re really excited about this year, is being able to bring this, uh, so that we’re not, um, you know, basically supply constrained, ’cause there is a lot of demand for this, and still able to maintain a very high level of, of quality as we, [00:02:00] we scale up. Allen Hall : Yeah, and that’s the key to all sort of repairs in the wind industry. You like to do it once and be done with the life of the turbine. Now, so you’re going uptower. You’re drilling some holes up along the blade, injecting those with a resin system, curing it, basically reinforcing what is already there That all makes sense to me. Engineering-wise, that makes sense to me. But a- again, it goes back to the technicians and the training and the deployment of it. Are you starting to train technicians, bring them in, show them how to use the, use the machines and, and get them out in the field so they are ready to go? It, it… ‘Cause it seems like you’re at that threshold now. David King: No, absolutely. So we, we believe in people first, right? Yeah. People at the end of the day make things happen. And so, you know, the best ways to do that is give people the right tools to be successful, and where that comes from is training. That’s a huge part of it. We have a, a certified training program that we run. Uh, it started out as an internal program we were running. It basically has five levels to it. Uh, we’ve now extended that to, uh, enabling, uh, you know, basically [00:03:00] preferred partners to be able to take part in that training, uh, to be able to utilize modular kits, pumps and equipment, to be able to, you know, go out and meet that demand that’s out there, but do so in a way that’s, uh, controlled. Yeah. And so really that comes back to that certified training program. And really, you know, level one is about a lot of your basic safety, procedural base type, uh, you know, making sure people are competent, uh, they’re not gonna get themselves hurt. Right. They’ve got the right personality traits about focus, uh, you know, detail focus and things like that. Yeah. Uh, level two to that program is, is really about, um, basically getting people to a stage in which they can be a, uh, team member. Uh, they’re able to be on a team and contribute to that team in an effective manner, be in the field.  Allen Hall : That’s really important. A lot of-  David King: Absolutely …  Allen Hall : companies miss that aspect of being a team member instead of an individual. Yeah, you have to work with other people. Yeah. It’s, it’s critical.  David King: It’s massively important. Personalities clash. You’ve got to be able to work through that sort of thing. And so that level one to level two is really kind of taking your green horn hat off and putting, “Okay, I, I, I can be on this team and I’m, I’m a, a contributing [00:04:00] member.” And then at level three, that’s your team leads. Those are people that are leading teams. They’re leaders. They’re up and coming. They’ve got a career path, career trajectory. Level four is our mentors. That’s the people that are going out there and that are basically qualified to now actually mentor other people in the field. Allen Hall : Yeah.  David King: And then your level five is train the trainer. How do you grow more trainers so that you’re not constrained on that training factor? And that, that’s kind of how we, we typically run training.  Allen Hall : Uh, and Gulf Wind has the ability to do that. I mean, I’ve been to your facilities, they’re impressive, and that’s one of the limitations for a lot of companies. They don’t have the facilities to train people, and they don’t have the resources you do. That opens up a lot of opportunities. Obviously, you’re in the composite repair business. You have crews out fixing wind turbine blades. Some of the more complex ones is what I hear. I mean, I hear it secondarily, but I assume that’s what’s happening. What are, are the areas that you get called in on to do composite repairs?  David King: We, we really do anything that stops somebody else. Okay. So we wanna be there when there’s a problem where you’re like, “I don’t know where to go next. Uh, this is a big [00:05:00] problem. We’re unsure. Maybe there’s a new technology at play. Maybe it’s, uh, a carbon spar cap. Maybe it’s something, uh…” You know, obviously the root stuff that’s very complicated. Sure. And, uh, it’s just gonna require a little bit more engineering. It’s gonna require a little bit more rigor, and that- that’s where we say, look, we, we can, whether it means testing something, verifying something, training somebody on a process, developing a process- Yeah or just doing something complicated, that’s where we excel.  Allen Hall : Well, that- that’s what I hear from the road is, uh, Gulf Winds here and I think, “Uh-oh. You must have a really serious problem because you’re calling in the experts to do the, the difficult things.” Carbon pultrusions, carbon fabric in, in blades today is such a massive problem because it’s not, it’s not fiberglass. It’s just a lot more to deal with, and some of the loading issues we’re finding and, boy, it’s just all over the place. They need Gulf Winds Technology to, to come on site to give them a hand. Now, a- as part of the growth of the business, and you guys have been growing. Every year I, I see they’re just… it’s just a little bit bigger, a little more [00:06:00] people. I walked on LinkedIn and hiring some engineers and some people to work over the summertime. That’s all great. What’s the structure look like now? How are you trying to organize yourself as a business?  David King: Yeah, so we really break down into three different structures. We have our service division, and that’s, um, putting people out there to solve problems in the field. As simple as it gets, right? It’s like you’ve got a problem, we’ve got the right people with the right solutions, and they’re gonna go deliver, uh, a result. Um, and then we’ve got an engineering division. That’s about developing problems. It also has a lot to do with IP. You know, things like root fusion, that’s a pat- protected technology. Sure. All of our technology, we do a lot of investments in, in, you know, patent protection and IP work, and so that sits inside that engineering division. Uh, it’s how we, we have the smarts of the company kinda sat in there. Uh, it also is what allows us to really get into some of these, uh, kinda juicy problem statements that are a little bit prickly maybe. Uh, and we love getting into those and solving them. Yeah. And then the third and final thing is the composite side of things, and that’s the, the manufacturing. That’s that 30,000 square [00:07:00] foot composite manufacturing facility where we wanna be the best in vacuum infusion. We wanna be the best in prepreg, the best in pultrusions, complex assemblies, and be trying to de- uh, just deliver really high-quality composites to the industry. Allen Hall : Yeah, and you have the equipment to do a lot of testing. And I think a, a lot of operators don’t realize what you have And the knowledge that’s sitting there, when I run into operators across the country that have complicated issues, particularly if they have carbon, I mean, oh my gosh, you, you need to be calling experts here. And if they have issues they haven’t really sussed out, they don’t know, they don’t understand the engineering that went into that blade, they need to be talking to you guys about Why is this blade designed the way it is? How should I approach this? Do I need to be turning my turbines off until I figure out a solution? A lot of times there’s not a lot of resources there because the, the designs are more complex than ever. But on the, on the same hand, I would say they’re not doing a lot of testing of their own materials. [00:08:00] David King: Yeah, and there’s a huge space for that. And which is crazy. Absolutely. Yeah. It’s, it’s, uh, it’s definitely a gap. It is. And we see it as a gap that needs to be filled. Yes. And so that’s where, you know, we, we say you’ve gotta give the engineers the tools to be successful. Sure. And so what are those tools? You know, that could be anything from what does an aerodynamicist need? They might need a metrology scanner. Right. So we do 70 million plus point scans of full blades. We’ve done now a full blade scan and, uh, I think we did it in about an hour, which was a, a new record of how quickly you could get 70 million points on a blade. Wow. And then that allowed- Uptower  Allen Hall : or  David King: downtower? It was downtower. Okay. Okay. It was outside in the field, but it was downtower. Okay. It’s still impressive. So that was a little, little, little bit easier than uptower. Sure. Maybe that’s next. Um- Yeah. But, um, no, and then so what can you do with that? Well, then you can go, uh, really analyze, you know, the performance of that blade. Maybe you can go do something in a wind tunnel with it. So coming back to that toolkit- Yep … an aerodynamicist needs a wind tunnel. We have aerodynamicists, so we have a wind tunnel. Then going on to, like, a structural engineer. What does a structural engineer need? Well, they need their FE tools. They need some good first principle approaches to, to structures. But they also need test equipment. Right. They need to be [00:09:00] able to develop and characterize materials both in static and fatigue. And so we’ve made a lot of investment in those sort of test equipment, uh, so that we can, we can put numbers to things. You know, I think the wind industry needs more data. Less speculation and more data-driven decisions, and the, where that starts is really building up that test base. And we, we believe in this thing called the testing pyramid, and what it is is, like, you’ve gotta characterize the material. That’s where you’re gonna have thousands of samples. Right. That’s your tensile, double lap shear testing, all the basics. Then you do your subcomponents. Add some geometry into that, that- Add some shape. Exactly. Maybe that’s hundreds of samples. And then you’re gonna go on top of that to, like, your full component. And look, we don’t have a blade test stand yet, but- Right … that’s kind of that, that space. And then the final top of that pyramid is go do it in the field, get results- Run it … and then run that back into your design cycles. And I think the more we can do that as an industry, the more successful we’re gonna be as an industry.  Allen Hall : Yeah, and I think a lot of operators don’t think they have to participate in that, and they’re sadly mistaken. And the fact that the industry has grown as fast as it has means [00:10:00] there’s some holes in some of the engineering that maybe they didn’t consider the, the site assessment properly or they didn’t understand some of the manufacturing variability. Now you own this product, you’re gonna have to do some of the homework that maybe the OEM should have done. It’s your site. You own it. And a lot of times I think, uh, as an owner/operator, they don’t realize there’s resources. Like, okay, well maybe do some mechanical testing. Maybe the repairs I had last summer aren’t working out the way that I think. Maybe I need to look at some materials  David King: and see if- And we want you to own your data. Well, that’s exactly it, right? That’s really what it comes down to is like you wanna own the data, know your blades, know your products, whether it’s, you know… I know you’re very, uh, you know, uh, specialized in lighting, really know your stuff. Everybody’s gotta take that same approach. Know your stuff- You need to know it … or go find the experts that know it- Right … and work with them. Yeah.  Allen Hall : Well, at, at this point in the industry’s growth, you realize who’s all percolated towards the top, right? You, you, you see the companies like Goldwind that have the expertise in-house and, and have established themselves as a [00:11:00] knowledge center, as a resource for the US and globally, and there’s only a couple of those spread around the world in that- We as an industry need to be utilizing you more to help us solve problems. Because if I don’t tell Gulf Wind what’s going on, Gulf Wind can’t help come to a solution.  David King: And we find that really, like, just the more you know, you start finding all sorts of new opportunities. Yeah. ‘Cause we almost learn what you don’t know, in a way. You kind of realize that, like, there’s so much more out there. Yeah. And that’s where it gets really exciting. That’s where it’s like you can get these novel solutions, people who take creative approaches. Um, and, and I really think that’s what’s gonna take this industry forward, especially now when, you know, there are some headwinds for wind. And all that means is we’ve gotta get sharper, and we’ve gotta be, uh, more agile. And I think it’s actually almost times like this that create some of the best, uh, behaviors in an industry to, uh, take it forward into the future really.  Allen Hall : Yeah. Wind’s not gonna go anywhere, but it’s being stressed a little bit. And in those stress points, we need to take the time to reflect and to make the industry [00:12:00] stronger. But in order to do that, we need to be relying upon the sources that we have. There are global sources. There are so many resources to touch into. I think you guys are, are doing amazing things. Obviously, being down in your facility, seeing the wind tunnel, just blown away by that. Seeing the mechanical testing, seeing the, the 3D printing of air foils and all that work you’re doing, plus the ability to scan blades, do large scale studies. I remember one was on CMS at the time, thinking, “All right. Somebody’s, somebody’s actually doing the right thing. There’s a study happening so we can understand what’s happening in CMS.” Like, those things need to happen as an industry to grow.  David King: Oh, absolutely. And I know you and I were at WOMA- Yes … quite recently. Yeah. And we heard about that LEP study. Yes. And what a prime example- … of people going out there, getting real life data. Yes. And then, uh, making it accessible so that people can make smart decisions, and again, drive the cost of energy down and make wind successful. It’s, it’s amazing.  Allen Hall : It, uh- Yeah. Yeah, yeah. But the transfer of knowledge is the key, right? And you guys are involved [00:13:00] in looking at some, what LEP will do to improve a blade, but also what leading edge damage will do to erode performance. Those are some of the things that a lot of operators don’t understand. Like, is that blade being in that damaged form even affecting my AEP? It depends on the turbine, I think, a lot of times. But you better be asking the question at least. Talk to somebody who knows.  David King: Yeah. ‘Cause it, it’s really interesting. I mean, you know, I think it so much drives back to that business case for the operator, and they all have their own approaches. And, and really- Yeah you know, most people are repairing LEP when it becomes structural. That’s the- That’s right … that’s the predominant approach. And, you know, I understand that approach very… You know, I, I get it from an operator’s point of view. Um, but yeah, there’s definitely, uh, other things you could do to try and make a, a data-based business decision. Um- Sure.  Allen Hall : Sure. Now, what are some of the cool new things that Gulf Wind is working on, that you haven’t announced to the world yet, but you’d like to announce? I know you’ve been working on things. I’ve seen all the white papers being published. There’s some things- Back behind the scenes, what’s new?  David King: Yeah. I mean, so, you know, you take something like Roof [00:14:00] Fusion, right? Right. Which is a long process to develop. So we, knowing that everything that, uh, you have as an idea is gonna take almost maybe three, four, five years to actually bring to market- Sure … we’re always starting on this constant cycle of development. Right. And so the things- You know  Allen Hall : it’s gonna be five years. David King: Exactly. Yeah. And so, you know, I mean, it’s like the patents on this stuff take three, four, five years to work out. Yeah. And so it- it’s a very important part of the entire process. Yeah. But to, to answer your question, we do have some exciting things both in the aero side, uh, side of the world. Uh, we have been doing a lot of development work around, uh, basically, uh, passive load shedding, so the ability for a turbine, or actually any structure, to be able to react to the wind in a passive manner. Uh, so you don’t need any sort of mechanicals. You don’t need anything, uh, that’s going to break in the field, and the structure itself is able to actually react to the load that’s coming onto it and change its aerodynamic, uh, profile and change its load that it’s experiencing. So you get these… Uh, that’s a very interesting new technology. Yes. Uh, it’s something that we’ve been working on for about three or four years now. It’s now, uh, [00:15:00] getting demonstrated, uh, which we’re very excited about. Uh, we also have some technologies, uh, around new connection types between metal and composites. So this is, uh, something that’s, uh, probably got a lot of, um, application in aerospace, but I think it’s also gonna find its way into wind. And this is just a new way of really trying to fix some of the problematic joints that we’ve been dealing with now for the last few years, but looking forward, not looking backward. Yeah. Right. Sure. Not being retroactive. Right. But how do we do that next generation of roof pushing design, for example? And we’ve got a really exciting method for that, that, uh, is been tested now. We have test results for it, and they look extremely good. Uh, we also are making some major CapEx investments this year into- Sure … new manufacturing equipment. So we have, um, some… I, I would say some, some pretty advanced, um, automation we’re trying to bring to composite manufacturing- Okay … around pre-preg carbon fibers and things like that, which is gonna be very, very exciting I think. Uh, I hope it finds its way into the wind industry. It’ll probably start in other industries. Sure. Maybe kind of this, uh, [00:16:00] subsea, you know, and, uh, and air, uh, space first- Sure … you know, around UAVs, ROVs- Sure … that sort of thing. But I think it’s also gonna have applications in wind, and we’re really, really excited about that. Well,  Allen Hall : that’s good because it, it does seem like wind is downstream of a lot of aerospace things ’cause it does, definitely costs money to develop those, and aerospace is a place where that can happen. However- If you work out all the kinks and you solve all the manufacturing issues, it is directly applicable to wind. David King: And it’s massive volume. The beautiful thing about wind is that the volume, when you get something right and you do it right, you get to deploy technology. Yeah. Yes. You, you get to take it off the shelf- Right … and put it in the world and make it happen, which is, there’s nothing more exciting as an engineer. Allen Hall : Well, I mean, in, in terms of blade manufacturing, how many times have we talked about automating that so we have less things like wrinkles and some ply issues, overlaps, those kind of things where automation would help, but we just haven’t really refined it enough to i- implement it at a large scale in a blade factory. David King: Exactly. And it’s always usually too bespoke, you know? It is. It’s like you solve the problem for the, the 40-meter blade, and now- Right … there’s a [00:17:00] 45-meter blade, and we need all new CapEx. Right. And then it doesn’t, uh, doesn’t scale well.  Allen Hall : That doesn’t scale at all. No. Right. So that’s why they haven’t done it, is because they know the next generation of blade is coming. It’s another 10 meters longer, and that’s not gonna fit in this building, and doesn’t make sense- We’re in trouble … to buy the equipment.  David King: Yeah, exactly.  Allen Hall : Right. So it, it, it’s a- Yeah … it’s a constant evolving industry. Now, I, I had looked at your load shedding patent application or patent. Maybe it came out as a patent. David King: Yep.  Allen Hall : Mm-hmm. Okay. I wanna understand that a little bit since I’m here talking to you now. The load shedding piece was because, uh, you’re in Louisiana, that’s where hurricanes- Come up … every once in a while, if people haven’t read the papers. But the load shedding technology makes sense because now you can deploy wind turbines in places that you otherwise may not do it because of the risk of typhoons, hurricanes, even tornadoes on some level, some odd wind situations. You wanna explain what that technology is? Yeah.  David King: Really what it’s doing is it’s trying to decouple the, uh, turbine’s ability to protect itself from its requirement to maintain power and maintain [00:18:00] control. So if you have something that relies on electrical hydraulics or anything like that- Yeah … it’s gonna be extremely susceptible to failing, uh, when- Yes there’s a grid outage or when you have a battery that fails or, you know, most airplanes require, like, dual redundancy or triple- Triple … triple redundancy because of that very reason, and we just can’t afford to do that in wind. No. And so the innovation then that gets required is you have to have something that’s passive, something where the structure itself has been designed in a way where the laminate is designed in a way where it’s going to not react progressively like a linear fashion as you apply load, right? It keeps bending and bending and bending. Right, right, right. But it’s gonna have quite a sudden reaction to a very particular load case. And so that’s what we’ve been able to do is-  Allen Hall : Okay …  David King: basically construct that laminate in a way where when it, the right load is applied, in this case, that’s the, the hurricane load or the extreme load- Right we can shed that load, uh, completely by the structure simply reacting to the load, and that’s very exciting for wind. It has a lot of other applications ’cause- Sure it does … basically allowing you to hinge composites. We now can- Right … with [00:19:00] composites almost in an origami fashion, hinge them any way we want, which is really, really exciting. Nice. And we’re excited to bring that now to other areas besides just wind and, and wind will be a key one as well.  Allen Hall : Sure it will. Yeah. Wow, okay. That’s cool. I mean, that’s why I follow Gulf Wind Technology on LinkedIn to see all the cool things that are coming out because, uh, if, if you’re thinking about- What’s new, what’s next. There’s probably three or four places, honestly, in the world that I rely upon, DTE being one, Fraunhofer being another, and then Gulf Wind Technology. Like, okay, let’s… So they tram for it here. I… Let’s, let’s see what’s going on this week. That’s amazing. And I, I know that as you guys get more experience out in the field and people will start to recognize the name, it’s just only gonna grow to something even bigger. So that, that’s fantastic. I know you, you spend a lot of time making  David King: this business go. We’re de- definitely very excited about it. Yeah. But with, with growth comes, you know, a, a discipline. Right. You have to be very disciplined. Yes. And so that’s something, you know, we’ve gotta be very focused on. Yeah. That’s where things like that certified training program are important. Yes. It’s where [00:20:00] how we patent things is very important. Yes. How we, uh, you know, kind of set up company structure is very important. So I know we touched on a few of those subjects today. Yeah. But those are really just about trying to be able to maintain quality as we grow. A- and that’s really important to our customers, it’s important to us, and it’s how we maintain the brand. Allen Hall : We gotta get back down to Louisiana. I’m really curious to see what’s happening inside the buildings and see where you’re at, because, uh, I know there’s great things happening there. And I really appreciate the time. Thank you for coming over to Australia. I thought your, your talks and your, your presentation and being on panels in Australia was really insightful to a lot of Australians, because you’re just bringing a different viewpoint into that marketplace. And, and that’s what Gulf Wind does. So I, I appreciate all that effort. And, uh, yeah, we should connect up this summer. Come down and check out what’s going on.  David King: Absolutely. If you’re willing to brave the heat- Oh, no. … you are always welcome. And our aim is that every time you come to that factory, hopefully it’s like a, a whole new world. We wanna surprise you with something new, because, uh, that’s the only way we can demonstrate progress.  Allen Hall : Oh, that’s a deal.  David King: So.  Allen Hall : Okay, great. Well, thank you,  David King: Dave. Great to see [00:21:00] you. Thanks  Allen Hall : for being on the  David King: podcast. Thank you very much.

Get in touch - leave me a messageNo one wants to ship water around the world. That one line says a lot about the next phase of energy storage.In this episode of Climate Confident, I'm joined by Min Tang, Director of International Business at Rongke Power, one of the world's leading vanadium flow battery companies. We get into why long-duration storage is moving from climate tech side-story to core grid infrastructure, and why that matters for decarbonisation, energy transition planning, net zero delivery, emissions reduction, and policy.You'll hear why vanadium flow batteries are not trying to replace lithium-ion batteries, and why that matters. Different problem. Different tool. Min explains how flow batteries can run for more than 20,000 cycles, retain capacity over decades, and support grid-scale black start, the kind of resilience that becomes rather important when grids are asked to absorb more renewables, power more electrification, and stay upright while demand from industry and AI data centres grows.We dig into the economics too: why storage duration changes cost, how electrolyte leasing can cut upfront CapEx, and why local supply chains could become a major strategic advantage. You might be shocked to learn that localisation is baked into this technology because the electrolyte is mostly water. Glamorous? No. Important? Absolutely.

Just ahead of Oracle's (ORCL) earnings after Wednesday's close, Steven Dickens says the company's backlog will be the biggest metric to watch, along with how it converts it all into revenue. How Oracle builds out AI infrastructure and balancing it with future CapEx are also paramount to Steven, though he doesn't see it as a concern. He calls Oracle the "fourth hyperscaler" and sees the company competing against existing giants in Alphabet (GOOGL), Microsoft (MSFT), and Amazon (AMZN). ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Jacob Sonenshine breaks down how Oracle's (ORCL) $50 billion CapEx plan, including AI chips and data centers, supports infrastructure stocks. He notes limited expectations for spending cuts and warns that share issuance from Alphabet (GOOGL), Super Micro (SMCI), and Meta Platforms (META) weighs on sentiment. He advises waiting for downside momentum to slow before entering names like Microsoft (MSFT) or the broader S&P 500 (SPX).======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Stocks give up earlier gains, with tech the biggest laggard once again. The homebuilder KBW says can defend margins in the K-shaped housing market.  Plus, Fast Money trader Tim Seymour on the differences he's seeing in today's sell-off versus last week's.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mandeep Singh, global head of Technology Research at Bloomberg Intelligence, joins the podcast to provide an update on the growing impact of artificial intelligence and other cutting-edge innovations.----------------------------------------------------------------------------------------------Subscribe Here to the ROI Podcast & other First Trust Market News Website:  First Trust PortfoliosConnect with us on LinkedIn: First Trust LinkedInFollow us on X: First Trust on XSubscribe to the First Trust YouTube ChannelSubscribe to the ROI Podcast YouTube Channel

In today's Cloud Wars Minute, I analyze how a trillion dollars in cloud backlog is driving innovation beyond technology and into corporate finance. Highlights 00:03 — In the Cloud Wars, all sorts of crazy things are going on with the technology, what customers are doing with it, but also in how this whole remarkable time is being funded. I want to talk a little bit today about how Google Cloud and Oracle are choosing to fund this unprecedented market demand and why new possibilities require new ways of doing things. 01:25 — In Oracle's most recent quarter, it reported that its RPO, or Remaining Performance Obligation, similar to backlog, is over $550 billion. For Google Cloud, it had an amazing jump as well in its most recent quarter, ended March 31, $462 billion in backlog, almost double what it had been a year before that. So there's amazing demand, these two companies totaling a trillion dollars. 02:09 — Six months ago, Oracle reached out and said, “No, no, we're going to go to some outside funding, some borrowing, to do that.” But the market reacted with a panic. “Oh my God, nobody's ever done this.” And, you know, "What if they can't pay it back?” So there was a lot of skepticism about Oracle's plan six months ago. 02:58 — Now, a week ago, we see Alphabet step up and say, “Hey, we're going to do some equity financing. We're going to take $10 billion from Warren Buffett and some other places. We need this money. We think it's the best way to pursue funding our own data center expansions, our own CapEx needs, which will be somewhere between $185 and $190 billion.” Oracle's will probably be around $75 billion. 04:37 — Oracle and Google Cloud have risen to the top of the Cloud Wars Top 10 because they brought innovation at levels in technology and go-to-market, how they think about customers, deployment models, and so forth, that have really set the new standard for what's happening in the AI cloud business now. Seeking outside funding to meet this demand shows another way to do it. Visit Cloud Wars for more.

Guy Adami and Dan Nathan break down a strange Friday tape: a strong jobs report that sent stocks lower as the market prices out rate cuts — and even flirts with hikes. They dig into the Broadcom-led selloff in semis, Anthropic's call to slow down AI development and what it could mean for the CapEx trade, and Bitcoin getting cut in half at ~$60K alongside the unraveling of the crypto treasury-company trade. Then Guy unloads on the SpaceX IPO and Jamie Dimon's endorsement of the deal, asking whether someone just rang the bell at the top. In the second half, Dan sits down with Jim Brooks, CEO of Team Rubicon, on his path from Navy SEAL to the CIA to the C-suite — and what grit, culture, and leadership look like when you're leading a force of 200,000 volunteers. They close on defense tech, drones, and the future of the space economy. Show Notes Anthropic Urges Global Pause in AI Development, Flags ‘Self-Improvement' Risk (WSJ) Goldman Sachs expects SpaceX's AI revenue to increase 100-fold by 2030 (FT) Morgan Stanley Sees SpaceX's Revenue Reaching $3.4 Trillion in 2040 (WSJ) Elon Musk's near-daily online posts about race are turning off some fans (Washington Post) Musk Leaves Investors Starstruck at Dimon's SpaceX Extravaganza (Bloomberg) —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Microsoft Build 2026 announced an end-to-end agentic AI stack. COMPUTEX Taipei confirmed heterogeneous AI infrastructure across ARM, Marvell, Intel, Qualcomm, and NVIDIA. Alphabet raised $80 billion. Cisco Live repositioned the network as the AI platform. Patrick Moorhead and Daniel Newman break it all down alongside earnings from Broadcom, HPE, Palo Alto Networks, and CrowdStrike, plus the token cost conversation, the edge AI push, and what Palantir and Oracle are saying about proprietary data as the real AI moat. The handpicked topics for this week are: Microsoft Build 2026 Announced an End-to-End Agentic AI Stack: Microsoft shipped MAI-Thinking-1, its first homegrown thinking model, alongside Scout, Microsoft IQ, Project Solara, and a Majorana 2 quantum update targeting a 2029 commercial timeline with claims of a 1,000x reliability gain. Pat describes MAI-Thinking-1 as likely better than Sonnet 4.6 in blind testing and delivering close to GPT 5.5 quality at a far lower cost. Scout is Microsoft's first autopilot agent, anchoring the M365 Agent Suite with Office Pilot Agent Mode and Agent 365. Microsoft IQ serves as the context layer, integrating M365, business data, boundary IQ, and web IQ with GitHub Copilot, Foundry, and Copilot Studio. Project Solara is a new Android-based platform built for agent-first devices across transportation, retail, and hospital settings. Microsoft also added 83 Unix commands to the Windows stack. Dan frames Microsoft's real play as distribution, not frontier model development, noting that the open model ecosystem being pulled into the platform will matter more to CFOs managing token costs at scale. (The Decode) The AI Stack Goes Multi-Silicon — COMPUTEX Taipei 2026 Confirms Heterogeneous AI Infrastructure: ARM's AGI CPU is in production with Google moving its TPU head node to ARM, and adding Oracle and ByteDance as new customers. ARM also introduced a new switch, the TT100, and put the 51T CPO switch on stage. Marvell received a trillion-dollar company endorsement from Jensen Huang, adding $90 billion in market cap on the comment alone. Intel announced disaggregated inference details and Xeon 6+ Clearwater Forest, its first 18A data center processor. Vista Equity and Cambium Capital announced a NeoCloud called Vector Core Compute, with Xeon 6 handling orchestration, Salmonova RUs handling decode, and Blackwell GPUs handling pre-fill. Qualcomm's Cristiano Amon announced the Dragonfly data center brand with Snapdragon C details coming at their June investor day. The WSTS raised the 2026 semiconductor TAM forecast by 90% to $1.51 trillion, with Pat noting the market could hit a trillion dollars if memory is excluded entirely. (The Decode) NVIDIA RTX Spark and the Edge AI Push: NVIDIA coordinated with ARM and Microsoft around the RTX Spark at COMPUTEX, with the shared message being that the future of Windows is here. Signal65's Ryan Shrout asked Jensen directly why NVIDIA wants to be in the PC business, given low margins and diminishing returns. Dan frames the answer in the context of devices increasingly becoming mobile data centers, capable of running models at much greater efficiency than cloud delivery. The edge AI conversation is also directly tied to token cost economics: as intelligence delivery moves closer to the device, the cost per token drops significantly. The jury is still out on whether NVIDIA will meaningfully disrupt the PC market, but its influence over OEMs like Lenovo and Dell that depend on it for data center gives it real leverage over SKUs. (The Decode) Token Economics and Frontier Model Cost Pressure: Dan and Pat discuss a substantive shift in how enterprises are thinking about AI consumption costs. Dan argues that "token maxing," the practice of defaulting to the most powerful frontier model for every task, has now effectively peaked, as bills have come due at scale. Companies paying for tokens in volume are starting to question whether they can afford the prices that frontier models actually cost to deliver. Pat pushes back, saying the dynamic is still present, but both analysts agree that the market is moving toward a model where token selection is matched to the job, with Microsoft's MOE approach and thinking models positioned to help CFOs manage that economics story. (The Decode) Continuum Goes Public at Highest Valuation for an AI Platform: Dan notes that Continuum, the Honeywell-spawned quantum company, went public this week at what he calls the highest valuation for an AI platform to date. He flags that IonQ will likely contest that characterization. The broader context is Microsoft entering the quantum conversation with Majorana 2 at Build, a name that has largely been absent from the quantum race, while IBM has received most of the attention. (The Decode) AI CapEx Has Outgrown Cash Flow — Alphabet's $80 Billion Equity Raise: On June 1, Alphabet announced an $80 billion equity capital raise, upsized to $85 billion, structured as $40 billion ATM, $30 billion underwritten, and a $10 billion private placement with Berkshire Hathaway anchoring. Pat frames the questions over CapEx returns as entirely dependent on whether you are an AI boomer or a doomer: if the payback comes, the raise is the right move. If it does not, the math doesn't close. Dan argues the investment is existential, drawing parallels to how infrastructure-first companies have always spent ahead of monetization, and notes that Google's equity is being used as a capital engine that may be more efficient than the debt markets right now. Both analysts flag the downstream implications for Broadcom, MediaTek, and Marvell given the TPU connection. (The Decode) The Network Becomes the AI Platform: Cisco Live 2026: Cisco launched Silicon One P200, the Secure AI Factory with NVIDIA and Spectrum X, AgenticOps, MCP-native automation, Cisco IQ, LiveProtect, and folded Astrix Security and Galileo into Splunk under one control plane. Pat identifies Cisco Cloud Control as the biggest announcement of the entire show, pulling together Catalyst, Meraki, Nexus, Firewall, and WebEx under agentic ops that run natively through MCP, with code running directly on smart switches that have x86 processors. Pat also credits Cisco for establishing Silicon One as a credible chip alternative for hyperscalers capable of taking on Tomahawk and Jericho. Dan frames the long-term opportunity as campus and branch enablement when industrial AI and robotics deployments accelerate, arguing that the numerator of AI's economic impact has barely started, as edge deployment spending has not yet begun. (The Decode) The Flip: Did Microsoft Build 2026 Effectively End the OpenAI Partnership? Pat argues the divorce decree has been filed. MAI-Thinking-1 was built with zero distillation from third-party models offering clean enterprise data lineage, with Maia 200 in production plus Anthropic chip supply, which signals vendor hedging. OpenAI is going all-in on AWS, which means you cannot be married to two people, and the full Build stack covering model, OS containment via MXC, agents via Scout and Agent 365, and context via Microsoft IQ removes every architectural dependency on OpenAI. Dan counters that Microsoft is hedging rather than leaving and predicts the partnership will run through the decade. Enterprise Copilot customers are explicitly showing in data that they demand GPT 5.5, internal benchmarks have not been independently validated, and Microsoft stands to make meaningful money from the OpenAI IPO. (The Flip) Broadcom Q2 FY26 Earnings: Broadcom posted revenue of $22.19 billion, a narrow miss depending on which consensus data set is used, with EPS of $2.44 beating estimates and AI semis at $10.8 billion. Hock Tan declined to raise the $100 billion full-year AI chip target, and the stock dropped 13% in premarket trading. Q3 guide came in at $29.4 billion. Pat calls the miss a timing issue driven by Google's multi-sourcing across Marvell, MediaTek, and Broadcom rather than a fundamental problem. Dan flags that Hock Tan opened the earnings call by accidentally reading from the 2025 print, calling it "not the best moment." Sell-side re-ratings held in the 500s across Jefferies, Mizuho, and Deutsche Bank despite the drop, with Futurum Equities having it at 600. (Bulls and Bears) Hewlett Packard Enterprise Q2 FY26 Earnings: HPE delivered revenue of $10.68 billion, up 40% year over year, and EPS of $0.79, up 100%. Juniper integration and AI servers both outperformed, and all FY26 guides were raised. The stock jumped 19% after hours before settling into a roughly 15% gain, with HPE up 68% over the last month. Pat frames HPE as a value play rather than a volume play, methodically targeting enterprise and sovereign cloud deals where it can maintain profitability, rather than competing for massive NeoCloud volume. Antonio Neri was clear on the call that the profitability pull-forward is a one-shot deal. Pat and Dan will both be at HPE Discover the week after next to interview Neri and the C-suite. (Bulls and Bears) Palo Alto Networks Q3 FY26 Earnings: Palo Alto posted revenue of $3.0 billion, up 31% year over year, beating the $2.94 billion estimate, with non-GAAP EPS of $0.85, beating the $0.79 to $0.81 range. NGS ARR reached $8.1 billion, up 60% year over year, including $1.6 billion from CyberArk and Chronosphere. RPO hit $18.4 billion, up 36%. Both FY26 revenue and EPS guides were raised. Adjusted FCF margin came in at 38.5% TTM, up 430 basis points. The stock jumped 11% immediately after hours, then drifted lower. Pat points to 2,200 platformized customers and 120% net retention as the most important metrics. Dan notes the SaaSpocalypse thesis continues to be wrong. (Bulls and Bears) CrowdStrike Q1 FY27 Earnings and the Proprietary Data Moat Argument: CrowdStrike posted revenue of $1.39 billion with EPS of $1.10 and ARR of $5.51 billion. Net new ARR of $255.8 million set a Q1 record, up 32% year over year. FY27 net new ARR guide was raised by $52 million to a $1.29 billion midpoint, and FY27 revenue was raised to $5.915 to $5.959 billion. A 4-for-1 stock split was announced effective July 2nd. The stock dropped 11% despite the beat after a 64% year-to-date run into earnings. Dan uses the results to make a broader argument against the software disruption thesis, referencing Palantir CEO Alex Karp daring customers to build without him using Anthropic or OpenAI, and Larry Ellison's argument that the real AI value unlock sits in proprietary enterprise data that is not accessible to frontier models. Enterprises with governed, secure, proprietary data will continue to need platforms like CrowdStrike regardless of what frontier models can do. (Bulls and Bears) Six Five Summit is coming. Salesforce CEO Mark Benioff will kick off the event. Register and stay current at sixfivemedia.com/summit. Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode.   The Decode Microsoft Declares Independence — Build 2026 Ships an End-to-End Agentic AI Stack (MAI-Thinking-1 + Scout + Microsoft IQ + Project Solara + Majorana 2) https://www.theverge.com/tech/941738/microsoft-build-2026-biggest-announcements The AI Stack Goes Multi-Silicon — Computex 2026 Confirms a Heterogeneous AI Infrastructure (ARM + Marvell + Intel ASIC + Qualcomm + RTX Spark); WSTS Raises 2026 Semi TAM Forecast 90% to $1.51T https://www.tomshardware.com/tag/computex AI Capex Has Outgrown Cash Flow — Alphabet's $80B Equity Raise Is the Largest in U.S. Corporate History; Berkshire Anchors $10B https://abc.xyz/investor/news/news-details/2026/Alphabet-Announces-Proposed-80-Billion-Equity-Capital-Raise-to-Expand-AI-Infrastructure-and-Compute-2026-b0myAMewCa/default.aspx The Network Becomes the AI Platform — Cisco Live 2026 Launches Silicon One P200, Secure AI Factory (with NVIDIA), AgenticOps, Astrix Security + Galileo https://www.cisco.com/site/us/en/about/whats-new/index.html The Flip Did Microsoft Build 2026 Effectively End the OpenAI Partnership? MAI-Thinking-1 Beats Sonnet 4.6 in Blind Testing, Microsoft Claims GPT-5.5 Parity at 10x Cost Efficiency — Will MS Quietly Wind Down OpenAI Exclusivity by FY28, or Is OpenAI Still the Frontier Anchor Microsoft Needs?   FOR:  MAI-Thinking-1 beating Sonnet 4.6 in blind preference + GPT-5.5 parity at 10x cost efficiency is a frontier-model independence proof point https://www.latent.space/p/ainews-microsoft-build-mai-thinking Build 2026: Accumulating Evidence of Microsoft's AI Independence — EDN (June 4) — https://www.edn.com/build-2026-accumulating-evidence-of-microsofts-ai-independence/ Maia 200 in production + Anthropic-Maia chip talks signal Microsoft is hedging its inference vendor stack https://blogs.microsoft.com/blog/2026/01/26/maia-200-the-ai-accelerator-built-for-inference/ Microsoft canceled Anthropic's internal software licenses + pivoted to chip-supply pursuit — customer-not-competitor positioning https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   AGAINST:  Enterprise Copilot customers explicitly demand GPT-5.5 — internal benchmarks don't replace the brand https://learn.microsoft.com/en-us/microsoft-365/copilot/release-notes?tabs=all MAI-Thinking-1 benchmarks haven't been third-party verified — Microsoft is the only source https://www.latent.space/p/ainews-microsoft-build-mai-thinking The MS-OpenAI partnership is contractual through 2030+ — unwinding it is impractical and expensive https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/ Microsoft's actual strategic risk is OpenAI leaving, not MS leaving — Anthropic + OpenAI IPOs make OpenAI exit risk the real concern https://www.anthropic.com/news/confidential-draft-s1-sec Bulls & Bears Broadcom (AVGO) Q2 FY26 ACTUALS — Rev $22.19B (Narrow Miss) + EPS $2.44 (Beat); AI Semis $10.8B; Hock Tan Refuses to Raise the $100B Full-Year AI Chip Target — Stock −13% Premarket; Q3 Guide $29.4B https://www.cnbc.com/2026/06/03/broadcom-avgo-earnings-report-q2-2026.html Hewlett Packard Enterprise (HPE) Q2 FY26 ACTUALS — Blowout: Rev $10.68B (+40%), EPS $0.79 (+100%); Juniper Integration + AI Servers Both Outperform; FY26 Guides All Raised; Stock +19% AH https://www.businesswire.com/news/home/20260601866494/en/HPE-Reports-Fiscal-2026-Second-Quarter-Results Palo Alto Networks (PANW) Q3 FY26 ACTUALS — Beat-and-Raise: Rev $3.0B (+31% YoY, Beat $2.94B), Non-GAAP EPS $0.85 (Beat $0.79-0.81); NGS ARR $8.1B (+60% YoY, $1.6B from CyberArk + Chronosphere); RPO $18.4B (+36%); FY26 Revenue + EPS Guides BOTH RAISED; Adj FCF Margin 38.5% TTM (+430 bps); Stock +11% Immediate AH, Then Drifted Lower https://www.paloaltonetworks.com/company/press/2026/palo-alto-networks-reports-fiscal-third-quarter-2026-financial-results CrowdStrike narrowly beats estimates on AI tailwinds, but stock falls 9% — CNBC (June 3) — https://www.cnbc.com/2026/06/03/crowdstrike-crwd-q1-2027-earnings.html  

Trade policy is once again in the news with the announcement of new tariffs. Our Head of Public Policy Research Ariana Salvatore digs into why tariffs may not be a disruptive factor for markets this time.Read more insights from Morgan Stanley.----- Transcript -----Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Head of Public Policy Research for Morgan Stanley. Today, I'll be talking about how investors should be digesting the latest tariff headlines and what they could mean for the broader economic and market outlook. It's Friday, June 5th at 10am in New York. Tariffs are back in focus as the U.S. administration has proposed new levies following Section 301 investigations into more than 60 of our trading partners. At the same time, USMCA negotiations appear to have begun in earnest, with recent headlines focused on autos, including the possibility of raising regional content requirements for vehicles and auto parts. Now, at first glance, these developments sound like a meaningful escalation in trade policy. But we think these headlines are best understood as a continuation of the existing tariff regime rather than a new and more disruptive phase. Let's start with Section 301. Listeners may recall that the administration replaced the IEEPA tariffs with Section 122 following the Supreme Court's decision back in February. However, that was done under a temporary authority that expires in the end of July. It's been our view that as we approach that deadline, the administration would seek to replace the existing regime under a new authority. The conclusion of the Section 301 investigations is really a step in that direction; or said differently, a continuation of existing policy. We see the administration preserving the current tariff regime come July, but without a larger inflation or growth shock. The second issue is the USMCA. Raising regional content rules may be part of the negotiation now, and those changes could create sector-level friction. Similarly, we think it's possible we see escalation ahead of the July deadline as all three countries work to improve the existing trade deal. Now that being said, we're still constructive on the longer-term trade alignment between the U.S., Mexico, and Canada, and we see structural and procedural constraints that are going to limit the downside risk to something like a potential withdrawal from the agreement. We still expect the USMCA carve-out to remain in place even for Section 301 goods on a range of trading partners. That's because we think the administration sees value in maintaining supply chain integration within North America across a number of sectors. In general, we actually think the recent pattern on tariffs has been toward less, not more, trade pressure at the margin. Recent months have come with several carve-outs, exemptions, and delays on broad-based and sectoral tariffs. That suggests that the administration is still sensitive to the downstream cost impact of tariffs, and of course, affordability matters politically heading into the midterm elections in November. That view also fits with our broader U.S. economics outlook. Our economists continue to see a relatively benign macro backdrop. Growth is expected to remain trend-like, with consumer spending slowing but not collapsing, and strong AI-led CapEx offsetting some of the drag from higher energy prices and policy uncertainty. On inflation, tariffs remain part of the story, but much of the pass-through appears to be already in the data. That pairs with a more constructive outlook for equity markets as well, as our strategists there see a strong earnings story supported by things like positive operating leverage, AI adoption, improving pricing power, and a broadening out in earnings growth. So, the key message for investors is this: tariff policy is still noisy, and it will remain a source of headline risk. But in our base case, the administration is moving toward a more durable version of the current tariff regime, not a materially more disruptive or restrictive one. Section 301 replaces Section 122, the USMCA carve-out stays in place, and selective exemptions continue where the affordability or supply chain costs are too high. Thanks for listening. As a reminder, if you enjoy Thoughts on the Market, please take a moment to rate and review us wherever you listen, and share the podcast with a friend or colleague today.

This week on Autonomy Signals, Grayson Brulte and Rob Grant discuss Uber's OEM-agnostic robotaxi strategy in Europe, FedEx Freight CEO's declaration that autonomous trucks are ready for prime time, and the AUKUS alliance accelerating undersea autonomy.At GTC Taipei, Uber, Autobrains, and NVIDIA announced a strategic collaboration to launch a robotaxi program in Munich, pending regulatory approval, built on Autobrains' agentic AI and the NVIDIA DRIVE Hyperion Level 4 platform. With no German OEM attached and Stellantis the likely production partner, the move extends Uber's asset-light playbook of contributing its demand network while pushing vehicle CapEx off its balance sheet and onto its partners.On June 1st, FedEx Freight began trading as an independent standalone company, and CEO John Smith stated that its autonomous tractor-trailers can run yard to interstate to facility with 99.9% autonomy. By framing the primary barrier to commercialization as regulatory rather than technical, Mr. Smith flipped the industry narrative from can we build it to will we be allowed to use it.Then there is AUKUS, where Australia, the United States, and the United Kingdom formally initiated a trilateral project to develop unmanned undersea vehicles with an aggressive 2027 delivery target. The UUVs are designed for reconnaissance, strike, anti-submarine warfare, and protection of critical infrastructure like undersea cables, signaling that autonomy is no longer just a commercial endeavor but a core pillar of national security, though trilateral interoperability and contested deep-sea environments pose real execution risk.Episode Chapters00:00 Signal 1: Uber's European Robotaxi Strategy33:19 Signal 2: AUKUS Accelerates Unmanned Undersea Autonomy56:16 Signal 3: FedEx Freight CEO Flips the Script01:09:26 AUTNMY AIAutonomy Signals is presented by KPMG.--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary market intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Subscribe today: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Given that the Iran war-driven surge in oil prices doubled Q1 E&P profits, a surge in capex to capture fatter cash flows seemed like a sure thing, but that would have been a losing bet. Today, we review oil and gas producers' investment and production guidance and analyze potential future strategy.

The world is shifting from asset light and infinitely scalable to asset heavy. Across the globe, trillions of dollars are going into assets. Capital expenditure is on the rise. What is driving this? What sectors are we seeing it in? What are the challenges and bottlenecks companies face in delivering on CapEx programs? And how do new technologies and approaches to talent overcome this? Our guest is Erikans Kok, Senior Partner at McKinsey where he leads their Capital Excellence Practice globally. For related content and to find out more about HC Group, a search firm dedicated to the energy & commodities sector, visit https://www.hcgroup.global

Andrew, Ben, and Tom discuss Google's $80 billion equity raise to fund AI infrastructure CapEx through mandatory convertible preferred stock, Class A and C common stock, and an at-the-market offering, Berkshire Hathaway taking a $10 billion stake at a discount, the administrative shift to corporate cash for employee RSU tax obligations, the broader AI cash crunch with $80 billion of SpaceX stock and Anthropic's IPO filing hitting the market, and which hyperscaler MSFT, ORCL, META, or AMZN could be next to raise.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

June is here so guess what? It's officially Hot AI Summer.

Marty sits down with John Tinsman to discuss the astronomical ROI of AI data centers, why hyperscaler CapEx has made the US economy rate-inelastic, and the brewing agricultural input crisis. John on X: https://x.com/JohnTinsman AOT: https://aotetf.com/ STACK SATS hat: https://tftcmerch.io/ Our newsletter: https://www.tftc.io/bitcoin-brief/ TFTC Elite (Ad-free & Discord): https://www.tftc.io/#/portal/signup/ Discord: https://discord.gg/yHGkvYxdqT Opportunity Cost Extension: https://www.opportunitycost.app/ Shoutout to our sponsors: Bitkey https://bitkey.world/ Aven https://www.aven.com/bitcoin CrowdHealth https://www.joincrowdhealth.com/tftc Unchained https://unchained.com/tftc/ Salt of the Earth: https://drinksote.com/tftc Join the TFTC Movement: Main YT Channel https://www.youtube.com/c/TFTC21/videos Clips YT Channel https://www.youtube.com/channel/UCUQcW3jxfQfEUS8kqR5pJtQ Website https://tftc.io/ Newsletter tftc.io/bitcoin-brief/ Twitter https://twitter.com/tftc21 Instagram https://www.instagram.com/tftc.io/ Nostr https://primal.net/tftc Follow Marty Bent: Twitter https://twitter.com/martybent Nostr https://primal.net/martybent Newsletter https://tftc.io/martys-bent/ Podcast https://www.tftc.io/tag/podcasts/

Dan Nathan hosts Dan Niles of Niles Investment Management on the Risk Reversal podcast to discuss macro conditions, AI-driven market leadership, and lessons from prior tech cycles. Niles compares the current AI build-out to 1997–1998's internet infrastructure boom, arguing recent macro scares (tariffs, Iran/oil) created buying opportunities and that a bubble can persist, with further gains likely before a potential 30–50% drawdown next year. He cites a January 30 “agentic AI” step-change increasing token/compute demand, supporting strong CapEx and earnings growth, and notes Nvidia's growth versus valuation relative to past leaders like Cisco. They debate rising yields, inflation measures, and expectations for a rate-cutting Fed chair (Kevin Warsh). The conversation covers Intel's potential benefit from agentic shifts, corporate AI cost pressures, likely disruption to software/IT services and knowledge work, Micron's HBM-driven surge and cyclicality risks, and how major IPOs like SpaceX, OpenAI, and Anthropic could reshape flows and create new short opportunities. —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media

In this deal segment episode, Axel sits back down with Phil MacArthur to break down one of Phil's most recent acquisitions: a 20-unit portfolio deal across four buildings in New Hampshire, picked up on the MLS after months of sitting on cash from prior refinances. The conversation gets into the real nuances of buying from long-term mom-and-pop owners: the informal nature of their leases, the difficulty of getting estoppels, and why small-deal variance is just part of the game when you're playing in the 5 to 30 unit space. Phil and Axel also share a candid back-and-forth on tenant retention — and why tenants know the rental market far better than most landlords give them credit for.This episode is essential listening for any investor buying smaller multifamily deals direct from mom-and-pop owners — and who wants a clear-eyed picture of what the due diligence process actually looks like when the seller isn't exactly playing by the book.Join us as we dive into:How Phil found this 20-unit, four-building deal on the MLS after sitting on cash from four prior refinances for six months.Why the appraiser — from a large Boston institution — applied a 5% loss-to-lease penalty on four vacant units and capped the bank's lending at $3M (65–70% LTV)How Phil bridged the $300,000 financing gap with a short-term hard money lender to get the deal closedThe business plan: light CapEx on roofs and exterior, and bumping rents from an in-place average of $1,600 toward a market rate of ~$1,950 — already achieved on newly leased unitsWhy almost none of the existing tenants left — and why that was better than expected given the previous owner's warningsWhy tenants know the rental market better than investors give them credit for — and why that works in your favor when your rents are modestly below marketThe exit plan: refinance out the hard money, stabilize the rent roll, and target a cash-out refi within 12–24 months to recover 75%+ of invested capitalConnect with Phil:Connect with him on LinkedinFollow Windrift Real Estate on InstagramLearn more about Windrift Real Estate, LLCListen to the Previous Episode with Phil: Ep119 - Living in an Expensive Market and Investing out of State + Quickly Building a Personally Owned Portfolio of 70+ Units via Spotify or AppleAre you looking to invest in real estate, but don't want to deal with the hassle of finding great deals, signing on debt, and managing tenants? Aligned Real Estate Partners provides investment opportunities to passive investors looking for the returns, stability, and tax benefits multifamily real estate offers, but without the work - join our investor club to be notified of future investment opportunities.Connect with Axel:Follow him on InstagramConnect with him on LinkedinSubscribe to our YouTube channelLearn more about Aligned Real Estate Partners

Our Chief Asia Economist Chetan Ahya looks at why spending not only on AI, but also on energy and defense, could drive Asia's strongest industrial cycle in decades.Read more insights from Morgan Stanley.----- Transcript -----Welcome to Thoughts on the Market. I'm Chetan Ahya, Morgan Stanley's Chief Asia Economist. Today – why Asia is headed toward its strongest industrial cycle since the mid-2000s. It's Tuesday, May 26th, at 2pm in Hong Kong. The market narrative in Asia has been narrowly – almost exclusively – focused on artificial intelligence. But AI is just one aspect of a much broader shift across the region. We think Asia is entering an industrial supercycle. And this is being driven by a sustained rise in capital expenditures across AI, energy, defense and [the] broader industrial sector. The numbers behind this are substantial. We forecast Asia's total investment could rise from about $11 trillion today to $16 trillion by 2030. So this implies a 7 percent annual growth rate over the next five years, which is triple the pace of the past two years, making it quite significant. And for the high growth sector such as AI, energy, defense and broader industrial sector we expect capex to grow at an even faster runrate of about 16 percent a year. Now let's talk about the drivers. No doubt, the first big driver behind this momentum is AI. Asia needs to invest more in AI infrastructure. At the same time, Asian chipmakers and memory producers are lifting capex to meet demand of U.S. hyperscalers for building data centres. The second driver is energy. Asia needs to invest in the energy sector for three reasons – for powering AI, energy transition and energy security. The power demand for AI compute is growing exponentially. On top of that, economies are having to shift towards renewables, and that needs more investment in grids, storage, and power generation equipment. Moreover, the recent geopolitical tensions have made energy security a bigger policy priority, especially for Asia which is dependent on imported energy. The third driver is defense. Now, even before the recent escalation in the Middle East, defense budgets across Asia were moving higher. This year, China has planned their defense spending to grow at a pace faster than its GDP growth. Meanwhile, India has raised budgetary allocations for defense capex by 18 percent this year. At the same time, Japan, Korea, and Taiwan are aiming to lift their combined defense spending from about 1.7 percent of GDP to 3 percent. The fourth driver is broader industrial sector investment. Every economy in the region is working to secure their supply chains and focused more on onshoring of critical inputs for their domestic production. So what does this mean for Asia? The region stands to reap the benefits of a rise in capex [spending] twice over. First, the increase in Asia's capex will fuel its industrial cycle. Second, you have to consider [that] Asia is the world's production house. And as rest of the world is increasing capex investment in the areas I identified earlier, Asia benefits from feeding this global demand. Already, the evidence of a strong industrial cycle is visible. We prefer to look at capital goods imports as a proxy for capex. And that has been growing at an impressive rate of 27 percent on a year-over-year basis in dollar terms. Industrial production [growth] is nearing a four-year high. And non-tech exports, which are important from industrial production perspective, have staged a strong recovery since the fourth quarter of last year. So which Asian economies will benefit? As such, all of them. But China, Japan, Korea, and Taiwan are the biggest beneficiaries because they are meeting both domestic and export demands. On the other hand, India's industrial sector benefits primarily from its own domestic capex cycle. The pickup in Asia's industrial production is pushing industrial commodities prices higher, helping Australia and Indonesia, the two biggest commodity exporters in the region. This next chapter of Asia's growth story will filter through – from capex to jobs and income growth, and then through to the consumer. That's why this is not just an AI story. It will become a broader economic recovery across the region. Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.