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Chris Hohn: "Most companies don't have pricing power. They can only price, if they're lucky, at inflation. But there is a special group of super companies that can price above inflation." "Real pricing power above inflation can be very valuable. If you can price 1% above inflation and you have a 20% profit margin, your profits will grow 5% faster than revenue." (h/t @NicolaiTang1)

✨第一弾情報解禁✨ "朗読×ACT"theater 『アイディール・セミナー/Final session-Replay-』 2026年7月25日(土)~7月27日(月) #アイディールセミナー# ￣￣￣￣￣￣￣￣￣￣￣￣￣￣￣￣ 大切な人を守るために。 ただ全てを、排除するだけ。 ￣￣￣￣￣￣￣￣￣￣￣￣￣￣￣￣ 『アイディール・セミナー』Replay session "最終章"!! 朗読×ACTの新感覚バトルエンターテイメント!! Final session 待望の再演決定!! 《公演詳細》 《セミナー参加者》 ◇READING 小林愛香 緒方佑奈 鈴原希実 坂倉花 山岸理子 山下七海 田中有紀 高尾奏音／湊みや 大渕野々花／鳴海まい 七瀬つむぎ 真野美月／阿部寿世 福嶋晴菜／原田彩楓 堺萌香／石田みなみ 集貝はな／花耶 青山菜花 星野晴海 (THE SUPER FRUIT) 山中真尋／やみえん 正木郁／草野太一 〈特別出演〉 秋本帆華／鈴木萌花 ◆ACTOR 石原美沙紀(おとな小学生) 赤坂麻凪／石井萌々果／篠原望 小越春花／髙橋彩香／佐藤美波／大澤萌々 大橋篤／井川勇樹／加藤光大 〈おとな小学生〉 花塚廉太郎／三島京華／星野美夢 ◆ENSEMBLE 橋爪亮磨／松谷海里 桃原華恋／北村夏未／倉橋伶奈／八神日胡 他, 《創始者》 加藤光大 《劇場》 シアター1010 ========== ◆出演者アカウント 小林愛香 @Aikyan_ 緒方佑奈 @ogata_yuna_ 鈴原希実 @NozomiSuzuhara 坂倉花 @Sakakura_Sakura 山岸理子 @yamagishi_riko_ 山下七海 @773_paradise 田中有紀 @yuki_t0626 高尾奏音 @Kanon_Takao 湊みや @miya_minato1216 大渕野々花 @tiny_WildFlor 鳴海まい @mai_narumin 七瀬つむぎ @tsumugi_nanase 真野美月 @Mano_Miduki_ 阿部寿世 @hisayoabe 福嶋晴菜 @haruna_fukushim 原田彩楓 @sayaka_harada_ 堺萌香 @hkt48_moeka 石田みなみ @ishida_minami 集貝はな @HN_TMGI_ 花耶 @KAYA_official_ 青山菜花 @aoyamananoha910 星野晴海 (THE SUPER FRUIT) @harumi_supafuru 山中真尋 @1222_yama やみえん @Yamien_san 正木郁 @ksfa79 草野太一 @taichi_1104 〈特別出演〉 秋本帆華 @a_honoka_mg 鈴木萌花 @amefura_moeka ◆ACTOR 石原美沙紀(おとな小学生) @misaki_milk 赤坂麻凪 @mana_aotk1130 石井萌々果 @ishii_momoka17 篠原望 @nozomi_920 小越春花 @haruka_ogoe626 髙橋彩香 @sayaka__team8 佐藤美波 @sato_minami16th 大澤萌々 @osawa_momo 大橋篤 @ohashi_atsushi 井川勇樹 @yuki__ikawa 加藤光大 @ko_daiiiiii 〈おとな小学生〉 花塚廉太郎 @hanaren0908 三島京華 @mishima_kyoka 星野美夢 @miyu_hoshino_ ◆ENSEMBLE 橋爪亮磨 @ryoma_hashizume 松谷海里 桃原華恋 @momo_karen_ 北村夏未 @natkit_official 倉橋伶奈 @kurahashi_rena 八神日胡 @NICO_O219

说几个可以抄作业的大佬 胜率比较高的 子时 @silverfang88 老师，币圈的消息非常早期，到最近我才后知后觉。子时老师最近提到的美股也起飞了，他提到的标的都得重点研究。 川沐@xiaomustock 老师不亏是专业交易员，今年美股封神了，他提到过的标的真的得重视，今年美股最少5倍收益➕ Nico @tychozzz 老师对美股投研很深，油管频道有公开持仓收益，可以跟着Nico坚持定投。 链研社 @lianyanshe 老师，去年开始就一直在买美股，美股投研很深入，要是我去年就听了他的话就好了。 Rocky @Rocky_Bitcoin 老师，也是专业美股交易员，香港大会期间，他跟我讲光互联，我听得一知半解，到后面才意识到，太牛了。当时还跟我提到了消费板块、运动板块。 最后欢迎大家补充…

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.