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SpaceX's IPO is coming, but what's the word on the street? $135/share, 555M new shares, massive $75B all-primary raise at ~$1.75T. Straight cash to Starship, Starlink dominance & AI compute. The roadshow kicks off this week, SPCX Nasdaq debut June 12. Morningstar wants to slash it in half? I mean... if you want to bet against Elon, be my guest, I surely won't. Polls scream the sentiment: already at 89% for >$1.8T close. The market gets it, this is generational. Source: @zerohedge, @SpaceX, Reuters

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

Coinbase CPO Faryar Shirzad says the CLARITY Act will be "the biggest financial regulatory bill the Congress has done in quite some time," on @MorningsMaria. It's "the first piece of legislation since the 90s that gives the banks new authorization to get into the crypto space."

A Texas family is filing a lawsuit against Open AI, claiming that ChatGPT’s advice led to their 19-year-old son’s accidental overdose and death. Leila Turner-Scott, mother of Sam Nelson, told CBS News’ Jo Ling Kent that she held “Open AI and the creators… the people who bypassed safety guards and took away safety nets” responsible for Sam’s death. In a statement, a spokesperson for OpenAI called Sam’s death “a heartbreaking situation” and said in part: “These interactions took place on an earlier version of ChatGPT that is no longer available. ChatGPT is not a substitute for medical or mental health care, and we have continued to strengthen how it responds in sensitive and acute situations." Watch more of the exclusive interview tomorrow on CBS Mornings.

金融人狂喜，Anthropic 开源了金融全家桶 兄弟们，Anthropic 悄悄在 GitHub 开源了一个金融行业的 AI 工具仓库，我翻了一遍 README 直接惊了。 简单说就是：投行、研究、PE、财富管理、基金运营的常见工作流，他们全给你写好了 AI 代理和技能包。 亮点速览： • Pitch Agent：从 comps 到 LBO 到 PPT，全自动生成 • Model Builder：DCF、三张表直接在 Excel 里搭，不用手敲公式 • Earnings Reviewer：财报出了直接喂进去，自动更新模型 • GL Reconciler：对账找差异不用人肉，效率拉满 • 11 个数据连接器：FactSet、S&P、Morningstar 都接上了，数据源不用愁 使用方式超简单： 可以装 Cowork 插件，也可以用 API 部署，还能在 Excel/PPT/Word 里直接用（微软 365 插件） 技术栈？不存在的 全是 Markdown 和 JSON，零编译。Fork 下来改改提示词就是你自己的，二次开发门槛极低。 Apache 2.0 协议，免费商用，这点很关键。 说实话看完感觉金融民工的春天要来了，以前搭模型写 memo 做 deck 的时间可以省一大半。 当然提醒一句，所有输出都需要专业人士审核，不能直接当投资建议用，工具是工具，决策还得靠自己。 往期GitHub合集： 项目地址放评论区了👇

美股方面有哪些优质信息源？ 美股系列之二，今天来说说关于美股行情应该订阅哪些信息源，要真实、优质，重要的是免费、免费、免费。赶紧点赞、收藏、转发。 新闻类： 彭博社：专业财经媒体，不仅大量的美股行情新闻，连中国政府有什么政策变动，都会第一时间告诉彭博社，而不是官媒。网页版新闻需要付费订阅，但是通过推特、油管可以看免费内容，够用了。 路透社：百年财经大媒体，有很多独家新闻，有自己的数据库，需要付费，但是也有大量可以免费看的新闻，微信公众号路透早报、路透午报是免费订阅的，关键的新闻都可以看得到。 CNBC：CNBC总是被中文散户忽略，其实，上到美国财政部长、美国政府幕僚下到大公司高管，都会定期到CNBC演播室做客，CNBC的币圈新闻也做得挺专业的，油管就可以免费看。 当然，新闻就是涨了报好消息，跌了报坏消息，独立思考，理性读新闻。 政策类： SEC EDGAR数据库：美国证监会官方数据库，搜索任意美股代码即可查看10-K年报、10-Q季报、8-K重大事件公告、13F机构持仓等原始文件。建议每季财报季必看，这是直接的信息。网址： 公司官网投资者关系页面：直接下载最新财报、召开财报电话会议。 财政部资产负债表每日变动：监控美元流动性必须订阅的网站，大财政周期财政部发挥着关键作用，必备。在这个美国财政部官方网站，你还可以看到美国政府的收支情况。网址： 美联储：扩表、缩表，各种流动性工具投放或者更收回了多少流动性，美联储整体的资产负债表规模，各官员关于货币政策的讲话都可以在美联储官网找到。网站： 券商研报、电话会： 海外的券商研报基本需要付费，但是国内券商很多都开通了公众号，刊登一些免费研报。 中金：公众号中金点睛，汇总了中金各研究团队每周写的研报，都是免费的。 中信建投：公众号中信建投研究，有宏观政策、行业研究。 华泰证券：公众号华泰证券海外研究。 散户自己开户的券商APP也会有一些研报内容，有一些免费，可以利用起来 油管、B站很多搬运的券商每周电话会，可以看看。 需要注意的是，同一个券商，不同团队经常观点不一致，找到自己喜欢的分析团队挺重要的。 分析与研究平台的免费部分 Seeking Alpha：大量分析师和投资者文章、财报逐字稿、股息信息。免费浏览，付费解锁更多。社区观点丰富，但要看作者资质。 TipRanks：汇总华尔街分析师评级、成功率和目标价，一目了然。 GuruFocus 和 Morningstar晨星：公司星级评级、公司估值、ETF深度分析。免费版已够用。 刚开始，先看免费的。 美股研究第二弹结束，希望大家都能发财，守财。