与「Micron」相关的搜索结果

周四晚安，兄弟们——美股复盘：硬科技持续领跑 兄弟们，今天美股三大指数小幅收涨，市场整体稳健。 道指涨0.36%，标普500涨0.02%，纳斯达克涨0.07%。科技与硬科技板块继续展现强势。 亮点个股： 美光科技（Micron）：周三继续冲高逾3%，市值成功超过伯克希尔哈撒韦，成为全球焦点。存储板块强势仍在延续。 台积电（TSMC）：股价涨超2%，再创历史新高。有消息称其3纳米下半年有望涨价15%，明年或再涨10%，先进制程需求旺盛。 商业航天概念大爆发：AstroTech暴涨459.11%，月球资源开发等相关题材持续火热。 市场在稳中向好，硬科技趋势依然强劲。美光、台积电的持续创新高，再次证明AI、数据中心、先进制程的底层需求非常坚实。 接受市场的节奏：小幅震荡中热点轮动很正常。 时间拉长看趋势：短期涨跌是噪音，长期看的是真实需求和公司竞争力。 坚持定投最稳：市场越是活跃或波动，用固定金额、固定时间定投的心态就越重要。不追涨杀跌，心态最放松，长期复利效果最好。 兄弟们，科技趋势仍在，继续保持理性，保持定投即可！ 定投我只用 #okx#

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.

周三晚安，兄弟们——5美股复盘：存储板块大爆发 兄弟们，昨天美股分化中科技与存储领涨。 三大指数涨跌不一：道指小跌0.23%，标普500涨0.61%创历史新高，纳斯达克涨1.19%也再创新高。 最亮眼的是存储板块集体爆发： 美光科技（Micron）大涨19.29%，市值首次突破1万亿美元，成为全球第13家万亿美元公司！过去12个月股价翻了8倍！ 西部数据涨超8%，闪迪涨超7%，希捷涨4.06%。 存储ETF（DRAM）涨14.56%，4月初上市以来已经翻倍。 半导体板块也表现活跃，AMD涨7.78%，联电涨15.70%。 市场总是这样，在分化中轮动。存储和芯片的强势再次证明，底层需求（AI、数据中心）依然强劲。 接受分化：不是所有板块都一起涨，热点轮动很正常。 时间拉长：短期暴涨暴跌都是噪音，真正赚到钱的，还是那些看清长期趋势的人。 坚持定投最稳：市场越是兴奋或波动，越适合固定金额、固定时间买入。不追涨杀跌，心态最放松，长期复利效果最好。 兄弟们，科技趋势仍在，继续保持理性，聚焦优质成长方向。

🔥 SK Hynix 刚刚做了一件市场很多人还没完全意识到意义的事： 它开始“拒绝”Alphabet、Microsoft、Meta 的数百亿美元投资支持。 不是因为缺钱。 而是因为它终于站在了 AI 供应链权力结构的最顶端。 过去几十年，半导体行业大多数时候都是客户主导。 云厂商、手机厂商、PC 厂商拥有订单权。 芯片公司负责拼命扩产、降价、抢客户。 但 AI 时代第一次出现了真正的角色反转： 现在不是科技巨头挑供应商。 而是供应商开始挑客户。 SK Hynix 的担忧其实非常直接。 如果接受 Meta、Microsoft、Alphabet 的资金去建晶圆厂，未来就会形成一种“绑定式供应关系”。 问题在于： 一旦经济周期反转、AI 需求放缓、库存调整出现，大客户可能要求： 优先供货 锁定低价 长期折扣 独家产能 价格底线限制 这会直接破坏 SK Hynix 现在最核心的优势： 超级供应商地位。 而这个地位，来自 AI 推理时代最稀缺的东西： HBM。 现在市场很多人还停留在“GPU 最重要”的阶段。 但真正跑 AI 推理的人越来越清楚： GPU 决定算力上限。 HBM 决定模型真正能否持续推理。 没有 HBM，GPU 只是空转。 尤其进入 Agentic AI 时代后，模型需要： 更长上下文 更大 KV Cache 更多并行推理 更多实时检索 更多持续状态保持 这会直接推高 HBM 的重要性。 而真正能稳定量产高端 HBM 的厂商，全球几乎只剩： SK Hynix Samsung Micron 其中 SK Hynix 又是 NVIDIA HBM 供应链里的核心主导者。 这意味着什么？ 意味着 Meta、Microsoft、Google 这些本来拥有全球最强资本实力的公司，现在开始反过来： 希望帮供应商建厂。 这是 AI 时代非常重要的权力信号。 因为这些科技巨头真正害怕的，不是 GPU 不够。 而是未来 HBM 不够。 报道里有一个细节特别重要： Meta、Microsoft、Alphabet 甚至愿意帮助支付 ASML High-NA EUV 设备费用。 这不是普通设备。 这是下一代先进 DRAM 节点的核心工具。 单台接近 5.5 亿美元。 科技巨头愿意帮忙买设备，本质上是在提前“锁产能”。 因为他们知道： 未来 AI 基础设施真正的瓶颈，可能已经从 GPU 慢慢转向： 先进内存。 而 SK Hynix 最聪明的地方在于： 它没有直接拒绝这些资金。 它只是拒绝“股权式绑定”。 然后把这些科技巨头的焦虑，转化成： 更高预付款 更长期合同 更强价格保护 更长锁单周期 这才是最强势供应商会做的事情。 你会发现，现在 AI 产业链里真正拥有议价权的公司，开始越来越像： “资源垄断者” 而不是传统电子制造商。 这也是为什么我一直认为： AI 的下一阶段核心，不只是 GPU。 而是： HBM DRAM NAND 先进封装 EUV 电力 冷却 高速光互连 这些才是 AI 推理真正的“氧气层”。 而很多人低估了： $MU 可能是整个美国市场里，最重要的 AI Memory 战略资产之一。 因为美国最终一定会希望： 先进 HBM 供应不能完全依赖韩国。 这也是为什么现在越来越多人开始重新理解： $DRAM 本质上不是传统存储逻辑。 而是 AI 推理时代的“认知容量基础设施”。 谁控制 AI 的记忆系统。 谁就控制未来 AI 的一部分上限。 你觉得未来 AI 最大瓶颈会先出现在： GPU、HBM、电力，还是数据中心本身？

Micron is better positioned than both Samsung and SK Hynix in building out the memory-compute platform. - SK Hynix has the best current HBM execution and the tightest Nvidia relationship but it is geographically concentrated, not full-stack, and more exposed to any AI capex slowdown. - Samsung has the most raw resources and broadest technology portfolio but has lost HBM share in the past, has inconsistent execution, and its conglomerate structure creates strategic diffusion. - Micron has the most durable structural positioning for a 3-5 year platform transition: the only full-stack U.S. memory supplier, with SOCAMM2 competition rights, hyperscaler co-design relationships, broadest IP portfolio and the geopolitical moat that neither Korean player can replicate. $MU $DRAM

据 Lookonchain 监测，某用户在 Hyperliquid 做多美光 Micron (MU)，目前浮盈 820 万美元。值得注意的是，该用户已为 MU 设置了 2800 美元和 2900 美元的止盈订单。如果 MU 真的达到该目标价格，该用户将获得超过 5000 万美元的利润。

This guy is long Micron ($MU) on Hyperliquid and is already sitting on $8.2M in unrealized profit! Notably, he has set take-profit orders for $MU at $2,800 and $2,900. If $MU really reaches his target prices, he will make over $50M in profit!