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超狂！世界首富的「每日作息」大公開！ 揭秘這位真實版鋼鐵人的日常安排... #比特幣# #MU# #川普# #AI泡沫# #AIBubble# #台股# #股市# #半導體# #TSMC# #investing# #bitcoin# #加密貨幣# #Bitcoin# #加密货币# #股票# #Crypto# #AI# #美股# #股災# #黃仁勳# #加密貨幣# #台積電# #kevinwarsh# #聯發科# #聯電# #cardano# #eth# #bonniechang# #bonnieblockchain# #邦妮區塊鏈#

When AI bubble gets burnt out Stocks have very little left to give Crypto is still not considered all that safe Not to the masses, and in some ways Rightfully so AI will be taking satoshi era wallets in less than a few years You also have the risk of hacks / governments in crypto Don’t even get me started on Saylor But people don’t want to sit on fiat Because that’s an inflationary scam as we all know So where will people turn to to protect and preserve there wealth? Not real estate. Not crypto. No fiat. No the stock market. My bet is it flows to Gold, Silver, Bronze and Oil. On top of this. We have a real argument we are already in a Cold War / and heading to a World War. Stay safe. Stay ahead of the herd 🐑 - Wynn

🚨 BREAKING: THE GUY WHO PREDICTED CRASH IN 2008, MICHAEL BURRY, SAID: "BEFORE PAYING $1 TRILLION FOR ANTHROPIC, COUNT TO 1 TRILLION AND IN 240,000 RECONSIDER." HE HOLDS $1 BILLION AI SHORT SINCE 2025: $912M IN $PLTR AND $187M IN $NVDA HE KNOWS THE AI BUBBLE WILL COLLAPSE...

🚨 BREAKING: BRIDGEWATER FOUNDER RAY DALIO JUST SAID LIVE: "THE AI BUBBLE WILL BURST EVENTUALLY. THE ONLY QUESTION IS WHEN." THIS GUY PREDICTED CRASH IN 2008 AND MADE $15.8 BILLION HE KNOWS SOMETHING BAD IS COMING...

Former BlackRock fund manager Ed Dowd on the AI bubble "pop" "we're at maximum AI hype right now" "they're [punching] out three IPOs, SpaceX, Anthropic and OpenAI" "a lot of these companies... [are] not going to go away" "[But] OpenAI may go to zero and Anthropic may go to zero, [and] their assets will be bought up for pennies on the dollar" This clip of Dowd (@DowdEdward), a former BlackRock fund manager and co-founder of Phinance Technologies, is taken from an interview with Greg Hunter (@USAWatchdog) posted to Rumble on May 29, 2026. ----------------Partial transcription of clip--------------- "What it means is eventually all this CapEx spending stops because the credit markets and I suspect— we're at maximum AI hype right now because they're trying to punch out three IPOs, SpaceX, Anthropic and OpenAI. And these guys are not making enough money to justify the amount of CapEx they're doing. "The other thing that I think is going to potentially blow up the AI bubble is they don't have enough power to plug in all this CapEx into. "So they're announcing all this CapEx, they're pre-buying equipment and chips but they can't plug it into the power grid. We just don't have enough power to justify all these data center buildouts. The constraining factor is power. "And look, there's a disconnect. I think Wall Street is less focused on the public outrage that's going on that you can see happening all across the country. People are protesting these data centers. College, students are booing commencement speakers that talk about AI. "There seems to be a very, very large anti AI sentiment going on out there which will muck up the works and slow down the data center buildouts politically. "And if you slow down the capex build out, the valuations of all these companies go a lot lower because they rely on you know, exponential growth and when the growth doesn't show up at these valuations they'll pop... "And look a lot of these companies that are doing the AI, Microsoft, Oracle, Google, they're not going to go away. They'll just cut back their CapEx. They won't go bankrupt but their valuations and their earnings will go lower as they write off all these mal investments. So it's not like a lot of companies are going to go bankrupt. I mean, OpenAI may go to zero and Anthropic may go to zero, but their assets will be bought up for pennies on the dollar."

🚨 BREAKING: THE GUY WHO PREDICTED THE 2008 CRASH, MICHAEL BURRY, JUST SAID: "MUSK AND NVIDIA DEAL IS BUILT ON FAKE NUMBERS. BILLIONS WORTH OF GPUS DISAPPEARE." HE ALSO HOLDS $1 BILLION AI SHORT: $912M IN $PLTR AND $187M IN $NVDA HE KNOWS AI BUBBLE WILL COLLAPSE...

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.

这下设计师真要失业了，只需一句话就用 @dappOS_com的新产品 @xBubble_ai 做出了堪比资深设计师的海报作品。 重点是我并非一个会写AI提示词的用户，相反我只是小白，xBubble 这种专注于成果的AI也太好用了吧。 之前用 Midjourney，如果 Prompt 写的不好，出图就很抽象，需要一次次的调整，花了很多时间最终还是不尽人意。 这不是我一个人的问题，而是当前AI领域存在的痛点： 会写 Prompt 的用户 vs 不会写的用户：前者的图片精准可控，后者的输出飘忽不定。 对于不会写提示词的人来说，用AI很难得到自己想要的成果。 xBubble 则是用创新的 Low-prompt AI 解决了这个最大的应用痛点，它的底层是两个核心系统： Bubble Engine：负责在后台"学习"怎么用 AI。对于特定任务，它会自动测试哪些模型和工具组合效果最好，生成最优的执行方案。 Bubble Pilot：负责在运行时"使用"AI。它读懂你的简短请求，识别任务类型，然后把任务分发给最合适的执行路径，无论是现成的 SOP，还是更复杂的项目工作区。 简单来说，Bubble Engine 负责学习怎么用 AI，Bubble Pilot 负责替你用 AI，你只需要设定目标。 其次就是 xBubble 的两种运行环境，太懂不同用户想要的是什么了。 Bubble Computer：端到端项目工作空间，当 Pilot 检测到多步骤任务（比如既要出图又要写文案），自动路由至此，一次性交付完整成果，全程无需用户管理中间步骤。 Bubble Personal：本地环境模式，可以安全操作用户本机文件、浏览器、应用与日程；需要安装或系统级变更的操作在云端容器执行并销毁，本机只执行明确授权的动作。 总之，AI 图像模型的能力每个月都在进步，但绝大多数普通用户完全跟不上。 xBubble 的核心产品理念很简单：让 AI 主动去学习和使用 AI，让用户只需要设定目标即可，这就是我为什么推荐大家要尝试一下xBubble。 入口：

玩了一上午这个 @dappOS_com 的新产品xBubble，产品本身主打的low promt，意思就是不要过多的去调试，AI自己会跟AI学习，试了一下感觉在做视频和图片方面确实还不错。 做的动作以及结果 简单聊天问答：回答问题逻辑比较清晰 简单文字让做个视频：视频质量、流畅度和成品都基本符合要求 有细节的文字让做短剧：跟我在抖音刷到的那种差不多，重要的是消耗积分也不多，当然，我只生成了个15s的视频 让给一个20u做空山寨币的策略：只给了逻辑，没给具体币种，但是直接给了一段监控机器人的核心代码，所有的逻辑及判断条件全部写好了。 把我的山寨币模型发给他让推币：更上面一样，不推币，只给了逻辑 让制定云南旅游计划：计划比较细节，避坑指南都有 让他做个指定类型的网页：现在好像没有单独的vibe coding功能，所以只给了我一串代码让我自己去运行 随便截了张图让生成主图和banner图：作图方面确实没得说，很标准的电商图 总结一下： 基础的处理都很强了，特别是在视频和图片处理方面，越玩越有意思，但是可能因为我没有什么商业场景，其实一些做电商的和需要做ppt这些办公处理的用会感觉更强，我这个臭炒币的第一时间就是去让分析币 看了下官方文档，xBubble的底层逻辑是 Bubble Pilot  =　AI 替用户使用 AI Bubble Engine  = AI 学习 AI 他会自己内部生成一套固定的sop，用户的问题来了，他会自动匹配去用哪一套 dappos在拿到polychain、红杉、yzilabs如此豪华的投资的情况下，不着急忙着tge，而是不断从市场转型中打磨产品，拓展用户，并且在web2有一定用户，还在积极努力的找新方向，也算是熊市builder了 这是我用dappos嘲讽会所的demo，太可爱了。