B — CRWV Wall Street’s biggest lesson last week wasn’t about who was bullish or bearish — it was about what investors are actually paying for in AI infrastructure. CoreWeave is the perfect example. Rothschild Redburn focused on leverage, capital intensity and valuation, while JPMorgan argued that rising compute prices and improving margins could outweigh the debt burden. That disagreement matters because AI infrastructure is entering a new phase: demand alone is no longer enough; pricing power, capital efficiency and cash-flow conversion will decide who captures the economics. Microsoft’s upgrade reinforces the same idea from another angle — AI winners need to turn massive infrastructure spending into durable revenue and margins. So I’d watch CRWV less for the $54 vs $125 debate, and more

My pick: Green (5% to 10%) MU’s setup is stronger than a simple “beat the quarter” story. Micron’s own Q4 guide was already $50B revenue and $31 EPS, while the market has pushed expectations higher. The key catalyst is forward visibility. Micron has signed 16 strategic customer agreements, with roughly $22B in cash commitments and many contracts extending through 2030. That changes the traditional memory-cycle equation: if HBM demand remains tight while long-term contracts protect pricing, earnings could stay elevated longer than the market expects. My concern is valuation and expectations—MU now needs not just a beat, but strong FY2027 guidance. **I expect a solid reaction, but probably not a >10% blowout.** @Tiger_Earnings [思考]

C. The bigger opportunity will be AI jobs and talent Singapore’s AI story may ultimately be less about how many AI giants open offices, and more about how deeply AI reshapes the workforce. The early data is encouraging: AI-related job postings have risen sharply, but the opportunity is spreading beyond pure AI engineering. Finance, sales, consulting, cybersecurity and operations increasingly need people who can combine domain expertise with AI tools. That could be Singapore’s real advantage. A small country cannot compete with every market on scale, but it can compete on talent density, enterprise adoption and regional connectivity. The next AI winners may not simply be those who build the models—they could be the people who know how to turn those models into real business value.

For me, a cash-secured put is not simply a strategy to collect premium—it is a commitment to buy a stock at a price I have already decided is attractive. I prefer OTM strikes with enough downside buffer, typically giving myself time for theta to work without taking unnecessary assignment risk. But the biggest lesson is that a high premium often comes with a reason: elevated IV usually means the market expects bigger moves. I also prefer limit orders, especially when spreads are wide. A few cents of execution difference may look insignificant, but repeated across multiple contracts, it adds up. Most importantly, I treat assignment as part of the original plan, not a failure. Before entering, I ask one question: If this stock falls another 30%, would I still be comfortable owning 100 shares

Bitcoin’s bull case is becoming less about hype and more about how the market reacts to bad news. The Fed just hiked rates, the CLARITY Act stalled, and BTC still recovered toward $87K. More importantly, U.S. spot Bitcoin ETFs recorded five straight inflow sessions, including nearly $999M on September 21. That tells us something important: buyers are increasingly willing to absorb macro and regulatory shocks. Tiger Research’s $250K target by 2029 is therefore interesting not because $250K sounds exciting, but because its framework is based on Bitcoin’s expanding monetary role, investor cost bases and its valuation relative to gold. But the key risk remains liquidity. If yields keep rising and ETF flows reverse, the bullish structure could be tested again. For me, the next question isn’t “

Full Moon, Bright Future ​As the Mid-Autumn moon illuminates the iconic skyline of Marina Bay, it brings a spirit of warmth, gratitude, and togetherness. ​Happy Mid-Autumn Festival to all fellow Tigers! May this season of reunion bring joy, harmony, and peace to you and your loved ones. ​A special congratulations to Tiger Brokers! Wishing you continued success, steady growth, and global momentum. Here's to soaring to new heights together! ​May your portfolio be as full as tonight’s moon, and your investments yield golden rewards! @TigerEvents

[你懂的]  The “Boring” IT Distributor Quietly Riding the AI Boom $TD SYNNEX (SNX) At first glance, SNX looks incredibly boring. It is a huge IT distributor with more than $60 billion in annual revenue. It sells hardware, software, networking equipment and technology solutions to businesses and resellers. But there is something hiding underneath that traditional business: Hyve Solutions. And this is where the AI story gets interesting. So, how does SNX actually make money? The traditional SNX business is basically a giant technology supply chain. A manufacturer produces the equipment → SNX buys and distributes it → resellers, system integrators and enterprise customers buy it. SNX makes money through distribution margins and value-added services. The catch? Margins are thin. That mea

I’d choose ③ — DRAM can stay strong, but NAND may peak first. AI is changing memory demand, but not every segment benefits equally. HBM and server DRAM remain closely tied to AI infrastructure, with rising memory content per server helping support pricing. NAND is different. Enterprise SSD demand is strong, but NAND still has greater exposure to consumer electronics. If new capacity ramps faster than demand, NAND pricing could weaken earlier. That’s why I wouldn’t ask whether the entire memory cycle has peaked. The more important question is which segment turns first. Burry’s warning still matters: high margins eventually attract supply. But timing is everything. For MU, SNDK and SKHY, I’d watch pricing, inventories and 2027 capacity growth closely. The memory trade may not be simply bulli

The memory rally is real—but the next phase is about proving earnings can catch up with expectations. AI is absorbing enormous amounts of DRAM, HBM and NAND, while new capacity takes years to build. That gives $MU and $SKHY unusual pricing power. But I wouldn’t confuse “sold out” with “risk-free.” CXMT is already expanding advanced DRAM production, while memory is still a cyclical industry. For me, the real signal is simple: watch whether strong pricing translates into sustained margins and cash flow. If MU’s September 30 results confirm that, the thesis gets stronger. If demand or pricing disappoints, today’s high expectations could amplify the downside. Memory isn’t just a capacity story anymore—it’s a test of whether AI demand can permanently reshape the cycle.

I’d pick ③ Hybrid cloud + local becomes the standard. The AI industry probably won’t move entirely from the cloud back to PCs. Instead, workloads will be split based on economics and performance. Frontier models, large-scale training and complex reasoning will remain in data centers, where NVIDIA’s ecosystem has a major advantage. But repetitive agent tasks, private enterprise data and latency-sensitive inference could increasingly run locally. The key change is that AI compute may become workload-dependent rather than cloud-dependent. If local hardware becomes powerful enough, companies can avoid paying inference fees for every single task. Over thousands or millions of daily operations, that difference could become significant. So the next AI infrastructure battle may not be cloud vs. lo

[你懂的]  Meta’s Muse Is Getting Attention. But Who Could Be the Real Beneficiary? Everyone is watching $Meta Platforms, Inc.(META) after the launch of Muse. But I think there’s a more interesting question: If people eventually stop opening Amazon, Nike, or individual shopping apps and simply tell an AI agent, “Buy this for me,” which company could quietly benefit from that shift? One name I’m watching is $Shopify(SHOP)$. Most investors still think of Shopify as a company that helps merchants build online stores. That’s only part of the story. How does Shopify actually make money? Shopify has two major revenue engines. The first is Subscription Solutions — merchants pay for Shopify’s software, including online stores, management tools, POS, analytics, and other services. The second

I’d choose A, but I wouldn’t reduce the thesis to “buy more GPUs.” The bigger shift is that AI agents could turn computing from a tool people actively use into infrastructure that works continuously in the background. Every search, booking, purchase, financial decision, or automated task potentially creates additional inference, memory, networking, and storage demand. That makes the AI infrastructure trade broader: GPUs matter, but CPUs, HBM, DRAM, SSDs and networking could all benefit as agent workloads scale. Meanwhile, companies like Airbnb, Uber and Schwab aren’t necessarily becoming obsolete. Their real risk is losing the customer interface. If users increasingly ask an AI agent to “book me a hotel” instead of opening an app, the platform owning the transaction may change. So I’d rat

Muse’s biggest hurdle isn’t downloads — it’s becoming the transaction layer of the internet. Meta has already shown that it can distribute an AI agent at extraordinary speed. Muse reached the top of the U.S. App Store shortly after launch, proving that consumers are willing to experiment with an agent that actually does things rather than simply answering questions. But the Amazon clash exposes the harder problem. An agent may be technically capable of completing a purchase, yet merchants can still restrict access. Amazon has already blocked Muse, while Shopify and PayPal are moving in the opposite direction. So I think the real KPI is not downloads, but completed economic actions If Muse can turn user intent → action → transaction → revenue, Meta could eventually build an entirely new mon

AMD’s $1T milestone is impressive, but the real story is what comes next. The Meta Muse hype has shifted the AI narrative from “training models” to “AI agents doing work,” potentially creating another wave of demand for CPUs alongside GPUs. AMD is uniquely positioned on both fronts: EPYC for server workloads and Instinct for AI acceleration. Its Q2 Data Center revenue already surged 107% YoY to $6.7B. But here’s the catch: at $615, AMD is no longer priced like a challenger—it’s priced like a future AI infrastructure leader. The next leg higher therefore needs earnings to catch up with expectations, not just another AI narrative. I wouldn’t focus on whether $1T is “too expensive.” The better question is: can AMD compound Data Center revenue fast enough to justify today’s valuation? If yes,

I think the most interesting part of this AI cycle is the shift from “AI that answers” to “AI that acts.” GPUs will remain essential for model inference, but autonomous agents could create a much broader infrastructure demand. Every task may require CPU capacity, networking, storage, databases, APIs and constant background processing. That changes the investment question. Instead of simply asking how many GPUs AI needs, we should ask how much total infrastructure is required to support billions of agents working simultaneously. I also find the ecosystem angle fascinating. An agent becomes far more useful when it can actually search, book, pay, communicate and execute tasks. That gives companies with strong consumer ecosystems another potential advantage. To me, the next AI opportunity may

I’d choose B — Cybersecurity & Data Resilience. The deeper story here is that AI doesn’t just create demand for more compute; it also expands the attack surface and increases the value of protecting data. That makes cybersecurity less of a “side trade” to AI and more of an infrastructure layer supporting its adoption. CRWD stands out because its record $333M net-new ARR, up 51% YoY, points to strong enterprise demand. RBRK offers a different angle: as companies deploy more AI, data recovery and cyber resilience become increasingly important. What makes this theme interesting is its potential durability. Compute spending can be cyclical, but once AI becomes embedded in business operations, security and data protection become increasingly difficult to cut. For me, the key question is no

The real story isn’t GPUs vs. ASICs — it’s specialization. GPUs should remain critical for training and rapidly evolving workloads, where flexibility and software ecosystems matter. But inference is different: once workloads become predictable and massive, every watt and every dollar per token matters. That’s why custom silicon is becoming strategically important. OpenAI’s Jalapeño, developed with Broadcom, is a good example of this shift toward workload-specific optimization. What interests me most is the “picks-and-shovels” layer. Broadcom isn’t simply competing with NVIDIA; it can benefit when hyperscalers build their own accelerators because those chips still need advanced connectivity, networking and silicon expertise. Broadcom’s Q3 FY2026 AI semiconductor revenue reached $16.7B, up

Index rebalancing is less about “good stocks getting better” and more about mechanical capital flows. The key point this time is the Sep. 21 rebalance: S&P 100 added DELL, PANW, ANET and SNDK, while S&P 500 added BE, P and ILMN. For investors, the interesting part isn’t simply the headline inclusion—it’s the mismatch between forced buying and market expectations. Passive funds must adjust positions, but active traders often anticipate these flows beforehand. By the effective date, part of the demand may already be priced in. That creates a subtle setup: index inclusion can provide liquidity support, but it cannot manufacture earnings growth. So I’d separate two signals: index flows tell us where money must move; fundamentals tell us whether that money has somewhere to stay. The re