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2025-11-23

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Tigris | 会讲课教授是好老师 @tig88411109
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我来解读一下,现在美股大家都说贵,理由很简单,标普500 的市盈率 22 倍,明显高于历史均值。 但问题是,大部分人连指数是怎么算的都没搞清楚。标普不是“500 家公司的平均”。 它是按市值权重算的。权重越大,对指数的估值拉动越强。 结果就是… 现在前 7 家巨头也就是 Mag7权重市值占市场38%,直接把整个标普的估值抬到 22 倍,看起来“全市场都很贵”。 可如果你用等权重来算,每家公司只占 1/500,估值立刻掉到 16 倍。 这才是真实的市场:大部分公司并不贵,是那几家超级巨头贵。 为什么会这么集中?前十家公司占了整个标普 42% 的市值。历史新高。 原因也不复杂。投资者知道在这轮科技革命里,能活下来、能吃掉全球算力红利的,就是最强的那几家。 市场不是盲目涌进去,而是看得非常清楚:强者恒强,当然随着ai传导,小企业一样也会有春天。
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保存时间:2026-09-27T12:30:38.116Z
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Tigris | 会讲课教授是好老师 @tig88411109
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Investors keep asking for a clean long-term valuation range on $NVDA, so here is the framework that actually matters. If AI becomes an internet-scale platform, it will unlock roughly 20 trillion dollars of new value over the next decade. Semiconductors historically capture 15 to 20 percent of the compute layer, and Nvidia controls the critical layers of that stack: silicon, systems, software, and ecosystem lock-in. Even under conservative share assumptions, that translates into 2 to 3 trillion dollars of incremental enterprise value.This is what underpins the long-term optimistic band near 300 dollars per share. On earnings mechanics alone, the math is straightforward. Next year’s 6 dollars EPS at 40x gives around 240. A more optimistic 50x pushes toward 300. That’s the baseline for an internet-level innovation cycle. But the real story is non-linear. If AI compounds into robotics, autonomous systems, bio-automation, and agentic computing, the total value pool expands to 20–40 trillion. At that point, Nvidia’s advantage is not a higher multiple which is a structural position as the bottleneck supplier of compute. Paradigm shifts do not reward the average firm; they reward the few that become indispensable. This is where the reflexivity kicks in. Human FOMO pulls valuations forward. Markets price future dominance before the cash flow arrives. If investors begin anchoring to 2027–2028 EPS, the extreme upside becomes a function of psychology as much as fundamentals. Of course, every supercycle carries a risk. Two years of over-extrapolation can produce a bubble that wipes out the incremental premium. When the index moves 1 percent and Nvidia moves 3 percent, leverage and algorithms magnify fear and euphoria in both directions. Even under the most conservative industry assumptions, 4.6 dollars EPS in 2026 and 6 dollars in 2027, applying long-term semiconductor multiples of 30–40x still yields a fundamental floor around 180. Everything above that is the blend of structural dominance, macro liquidity, and investor positioning. Markets quote every day. You don’t need to trade every day. This range is a guide to the long-duration technology curve, not a short-term call. Personal research only. I hold $NVDA and may be biased.
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Tigris | 会讲课教授是好老师 @tig88411109
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The memory sector has been the strongest performer this year. Prices are rising almost monthly and Micron is already up 2x. So when $MU sells off right after Google drops a new AI model, people assume the worst: “CFO raised Capex. Is this the start of another overbuild?” That reaction completely misreads where we are in the cycle. Micron is not dealing with a traditional DRAM boom-bust. It’s sitting inside a multi-year, AI-driven supercycle with demand coming from every direction. Model sizes are still compounding. Inference is just getting started. AI agents and on-device intelligence are about to enter consumer platforms. Cloud providers, AI chip companies, and even government HPC programs have already locked 3–5 year budgets. This multi-source, cross-industry demand pattern has never existed in memory history. The old 2017–2018 playbook doesn’t apply. HBM is not a business you can scale by simply “deciding to expand.” The stacking process is brutally hard and capacity takes years to qualify. The industry isn’t expanding recklessly. It’s expanding reluctantly. Micron’s Capex hike is not aggressive expansion. It’s forced expansion. Customers have already pre-booked most of 2026 supply. HBM is structurally undersupplied and will remain so. If Micron doesn’t invest now, the real risk isn’t memory oversupply. It’s a bottleneck across the entire AI supply chain: GPUs underutilized, compute nodes delayed, inference costs stuck too high, and model upgrades slowed for everyone. Capex going up is not a warning sign. It’s confirmation of overwhelming demand. We’re nowhere near the point where oversupply is a realistic concern. The winners of this cycle will be the companies willing to scale within a structurally constrained industry. This is the real signal from Micron’s latest commentary.
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