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Micron will be a $3,000 stock within a few years and Jensen Huang just spent a week in Korea telling the world exactly why (Save this). Jensen announced four new products at the Korea event and every single one of them has memory at the center of its architecture. Vera Rubin, the next generation AI supercomputer, needs massive quantities of HBM. The new Vera CPU needs large amounts of LPDDR5. RTX Spark, the first major PC reinvention in 40 years according to Jensen, needs a lot of LPDDR5. And Nvidia's new robotics and autonomous driving platforms are being built in deep partnership with the Korean memory and electronics ecosystem. Every single growth vector for Nvidia in 2026 and 2027 runs directly through memory and Micron is the only US based company that manufactures all of it. Here is what the numbers look like right now. Fiscal Q2 2026 revenue came in at $23.86 billion, up 196% year over year, with 75% gross margins and $6.9 billion in free cash flow, a quarterly record. Management guided Q3 revenue to $33.5 billion at roughly 81% gross margins, with EPS of $19.15. These are not the numbers of a cyclical memory company but rather the numbers of a company that has been structurally repriced by the largest demand supercycle in the history of the semiconductor industry. The reason the bull case reaches $3,000 comes down to three things that have never been true at the same time in Micron's history. First, the entire 2026 HBM supply is already sold out under multi-year contracts. CEO Sanjay Mehrotra told analysts that Micron can currently only fulfill 50% to two thirds of key customers' HBM demand at any price. Second, Micron has begun volume shipment of HBM4 12-Hi specifically for Nvidia's Vera Rubin platform, the exact product Jensen was talking about in Korea and has signed its first five year strategic customer agreement, converting what was historically a quarterly negotiation business into something closer to a long-term recurring revenue model. Third, Wolfe Research's bull case model points to $160 billion in calendar year 2027 revenue and $80 in EPS. At even a 20x earnings multiple, modest for a company with this growth profile, that is a $1,600 stock. UBS has already tripled its price target to $1,625. The path to $3,000 requires HBM4 to ramp smoothly, supply constraints to persist into 2027 as Mehrotra says they will, and hyperscaler AI capex to continue growing at its current trajectory, all three of which Jensen Huang just confirmed in Seoul. The HBM total addressable market alone is projected to reach $100 billion by 2028, a forecast Micron itself already pulled forward two years ahead of schedule because demand arrived faster than anyone modeled. Micron trades at roughly 9x forward earnings today. That is cheaper than a grocery chain, for a company growing revenue at 196% year over year, with its entire production sold out, supplying the infrastructure for the most important technology buildout in history. Come join Milk Road Pro for our full breakdown of the Micron bull case how we think about the HBM4 transition timeline, what multi-year customer contracts mean for Micron's valuation multiple expansion, and our entire AI thesis. Link below!

I asked Claude to apply a capital cycle analysis to $MU. Here's what it came up with: Net reading: 11 of 14 capital cycle signals are bearish or strongly bearish. The framework reads this as late-cycle, not early/mid-cycle. The two unambiguously bullish signals (equipment lead times, industry concentration) are eroding rather than strengthening. Insights Yielded by Capital Cycle Analysis: 1) "Structural change" rhetoric is itself diagnostic. The capital cycle framework treats coordinated industry-wide CEO claims of regime change as evidence of late-cycle euphoria. The same language was deployed by the same CEOs (Mehrotra at Micron specifically) in 2017–2018 and was wrong. Bayesian base rates argue against accepting the current claims at face value. The previous analysis under-weighted this base-rate evidence. 2) Look at total capital flowing into the supply curve, not just incumbent capex. The structural-change analysis focused on Big Three capex. The capital cycle lens forces aggregation of all capital flowing into memory output: a) Incumbent capex: ~$104B in 2026 across DRAM + NAND; b) CXMT IPO proceeds: ~$4.2B (with state-aligned co-financing many multiples larger); c) YMTC capacity additions (privately financed) d) Substitute technology capital (Cerebras, photonic startups, CXL controller designers) — billions of dollars of equity raised to reduce HBM intensity per dollar of AI compute deployed. When aggregated, total effective supply-side capital formation in 2026 is materially higher than the Big Three capex alone suggests. The supply response is being underestimated. 3) The customer base is doing exactly what late-cycle customers do. Hyperscalers locking in 3–5 year LTAs, pre-ordering 2027 NAND, building strategic inventory — these are not signs of confident long-cycle visibility, they are signs of late-cycle scarcity panic. Historically (DRAM 2017–2018, oil 2008, shipping 2007), customer pre-buying at peak prices is followed by sharp inventory destocking when prices roll over. The structural-change narrative frames LTA penetration as a benefit; the capital cycle frames it as a peak signal. 4) Multiple expansion + earnings expansion = asymmetric downside. The previous analysis flagged the 15x NTM P/E multiple as aggressive (referring to UBS PT raise). The capital cycle framework sharpens this: when both earnings and multiple are at peak, the compound drawdown when either reverts is severe. Memory historically goes from 60% gross margin to negative gross margin and from 10x P/E to <5x P/E. Even a modest reversion to 35% gross margin and 8x P/E from current levels implies a 60–75% equity drawdown for the memory primaries — without any disorderly cycle. 5) Supply lag is real but not unique. The bullish point about EUV/TSV/hybrid bonding lead times is correct but mis-weighted. The capital cycle history of other capital-intensive industries (oil refining, shipbuilding, semiconductor wafer fab) shows that long lead times increase the eventual amplitude of the down-cycle: capital decisions made at peak are not reversible when conditions soften, leading to capacity overhang. Long lead times delay the down-cycle; they do not abolish it. 6) China is the textbook capital-cycle disruptor. In Chancellor's historical case studies (steel, shipbuilding, solar, panels, batteries), state-backed Chinese entrants repeatedly compressed margins of consolidated Western/Korean/Japanese oligopolies once technology gaps narrowed. The U.S. equipment restrictions on China have created the illusion that this dynamic is paused, but the data shows CXMT doubled DRAM share in 18 months and is targeting domestic HBM3. The structural-change analysis appropriately flagged this; the capital cycle framework would weight it heavier as the single most important multi-year risk. 7) Substitute capital formation is its own supply curve. The capital cycle framework treats financing flows into substitutes as a parallel supply expansion. Cerebras' $5.5B IPO, Marvell's $5B Celestial acquisition, the Sandisk/SK hynix HBF JV, and the CXL ecosystem (ALAB, MRVL, MCHP) are collectively financing "HBM intensity reduction." Even if HBM unit demand is met, the value capture per dollar of AI compute is diluted. Capital is flowing in adjacent to the memory primaries to reduce the share of AI spend that ends up in their P&L. 8) The bull case relies disproportionately on demand visibility. The capital cycle warns against demand-anchored theses. The bull case requires AI capex to continue at current levels or accelerate, hyperscaler ROI economics to remain favorable, sovereign AI to scale, and inference workloads not to migrate to non-HBM architectures. Each of these is plausible; the joint probability that all hold through 2028 is materially lower than the headline narrative suggests. 9) Sell-side estimate trajectory is itself a signal. UBS's PT trajectory ($535 → $1,625, a 3x increase in one revision) is historically associated with peak euphoria. Estimate revisions of this magnitude have a poor forward record. The framework would treat the velocity of estimate revisions as a contra-signal. 10) Where the asymmetry sits. The capital cycle framework reframes the risk/reward calculation. Even if the bull thesis is right and earnings hold through 2028, the upside from current levels is modest (multiple expansion has already happened). If the bull thesis is partially wrong — say, 2028 brings 25% peak-to-trough EPS decline rather than 50% — the equity drawdown is still material because multiples will compress simultaneously. The asymmetry is not favourable at current valuations. Bottom line: The structural change thesis was directionally correct but materially overweighted by the original analysis. The capital cycle framework appropriately reweights toward supply-side caution and treats current peak conditions, peak valuations, peak management confidence, and accelerating capital inflows as a coherent set of late-cycle signals. The memory industry has undergone real and beneficial structural change in shape, but the empirical base rate against the "cycle has been abolished" claim is overwhelming. The economic characteristics of memory businesses have improved but have not been transformed into stable, compounding, low-volatility ones — and the next 18–30 months are statistically more likely to mark the end of this up-cycle than a transition to a new regime.

比英伟达快 10 倍， $MU 仅用 48 天市值破万亿 5 月 26 日，美光科技（ $MU）股价单日飙升约 19%，市值首次突破 1 万亿美元。 触发因素是 UBS 分析师 Timothy Arcuri 将目标价从 535 美元上调至 1625 美元，是华尔街 46 位覆盖美光的分析师中最高的。这个目标价意味着，以上周五 751 美元的收盘价计算，还有翻倍以上空间。 据道琼斯市场数据，美光从 5000 亿到万亿仅用了 48 个交易日，英伟达走完同一里程碑用了约 490 天，苹果约 1520 天，伯克希尔·哈撒韦约 1580 天。美光的速度是英伟达的 10 倍。 UBS 给出的核心判断是，AI 驱动的长期供应协议（LTA）锁定了产量并部分固定了价格，美光正在从周期性商品股转变为结构性成长股，「没有理由不按类似英伟达的市盈率水平交易」。 按 UBS 预测，美光 2027 至 2029 财年每股盈利将超过 100 美元，即便以盘中高点约 891 美元计算，前瞻市盈率也仅约 8.4 倍，标普 500 整体约 21 倍。 支撑这条曲线的是存储芯片 40 多年来最严重的供需失衡。数据中心预计 2026 年消耗全球 70% 的存储芯片产出，HBM 产能已售罄至 2027 年，DRAM 和 NAND 价格在 2026 年 Q1 暴涨超过 90%。 美光 CEO Sanjay Mehrotra 说：「AI 不仅增加了对存储的需求，它从根本上将存储重新定义为 AI 时代的关键战略资产。」 一年前美光市值约 1070 亿美元，如今翻了近 10 倍。一个月前涨了约 80%，自 3 月底低点以来涨幅达 180%，同期为标普 500 贡献的市值增量几乎与亚马逊相当。 值得注意的是，这轮行情中英伟达是缺席的。费城半导体指数与英伟达股价出现了罕见的大幅分化，存储和设备股接过了 AI 半导体行情的接力棒。美光在标普 500 中仅占约 1.5% 的权重，远低于「七巨头」各自 6% 以上，但 5 月 26 日当天对指数的贡献超过了任何一家七巨头。 美光是全球三大存储芯片厂商中唯一的美国本土企业（另外两家是韩国的 SK 海力士和三星）。预测市场平台 Kalshi 上，关于美国政府是否会在 2026 年入股美光的赌盘，概率已达 40%。

川普在空军一号上发文，表示老黄必须接上： “CNBC错误报道称，伟大的Jensen Huang没有受邀参加这场由全球最杰出的商界男女精英组成、即将前往中国的盛大访问。事实上，Jensen现在就在空军一号上，除非我要求他离开——而这种情况几乎不可能发生——否则CNBC的报道就是错误的，或者像政界常说的那样：假新闻（FAKE NEWS）！ 能够与Jensen、Elon Musk、Tim Cook、Larry Fink、Stephen Schwarzman、Kelly Ortberg、Brian Sikes、Jane Fraser、Larry Culp、David Solomon、Sanjay Mehrotra、Cristiano Amon等众多人士一同前往伟大的中国，是一种荣幸。 在那里，我将请求习近平——一位卓越非凡的领导人——“进一步开放”中国，让这些杰出人才能够施展他们的才华，帮助中华人民共和国迈向更高的发展水平！ 事实上，我保证，当我们几个小时后会面时，这将成为我提出的第一个请求。我从未见过、也从未听说过有什么想法，会比这对我们两个伟大国家更加有利！” ——总统 Donald Trump

特朗普访华高管名单流出 特斯拉 CEO 马斯克 苹果 CEO 库克 通用电气 CEO 卡尔普 波音总裁 Kelly Ortberg Meta 总裁 Dina Powell 贝莱德董事长 Larry Fink 黑石创始人 苏世民 美光科技总裁 Mehrotra 万事达总裁 Michael Visa CEO 麦克伦尼 高通 CEO 艾蒙 嘉吉主席 Brian Sikes 花期银行 CEO 范洁恩 高盛董事长 苏德巍 相干公司 CEO Jim Anderson 因美纳 CEO Jacob Thaysen 此次访华规模，预计超过 2017 年，此外思科的老板也收到了邀请，但因财报冲突，没有随行前往。

Recent claims suggest AI now consumes more than half of all the world’s RAM memory and this is just the beginning. According to Micron Technology’s fiscal Q2 2026 earnings call, CEO Sanjay Mehrotra stated that AI demand is driving data center bit requirements for DRAM and NAND to exceed 50% of the total industry TAM for the first time in calendar 2026. Micron can currently fulfill only about 50-67% of key customers’ requirements.

吉田朱里さんプロデュースカラコン 「メロット」を付けさせて頂きました！ 私が付けたカラーは「Morelady｣です👀💞 ナチュラルなカラーで目に光が入ってうるうるになるところが可愛くてどタイプです♡ #pr# #メロット　# #melotte# #カラコン　# #モアレディ# @melotte_lens ＠_yoshida_akariさん