To The Moon

8-SECOND SPOT THE DIFFERENCE 1) Amazon + 6 2) Microsoft + 3 3) Alphabet + 8 4) Meta + 4 5) Starbucks + 6 6) Apple + 2 7) Tesla + 5 8) Nvidia + 7 @TigerEvents [龇牙]

Burry closing his NVDA and PLTR puts doesn’t convince me that the AI bubble thesis is dead. It actually highlights the biggest risk in this trade: timing. The AI fundamentals are still powerful. NVDA’s data-center growth and PLTR’s extraordinary revenue and margin expansion show that this isn’t simply another story stock. But great businesses can still become bad investments when expectations move faster than earnings. That’s why I see Burry’s move as risk management rather than surrender. Short-dated puts need a catalyst, while extreme valuations need near-perfect execution. If AI spending remains strong, bears can bleed slowly through time decay. If growth eventually disappoints, however, the downside could be brutal. For me, the smarter question isn’t “Is Burry right?” It’s whether fut

Answer: B. Stock A has a lower margin requirement. The interesting part is that margin isn’t determined simply by how much you invest. Two stocks worth USD 10,000 can consume very different amounts of buying power because brokers assess factors such as volatility, liquidity, price movements and overall risk. For example, a stock with a 30% margin requirement would tie up USD 3,000, while another with a 50% requirement would tie up USD 5,000—even though the position values are identical. And here’s the part margin traders shouldn’t overlook: margin requirements can change. A stock offering high leverage today may require more margin tomorrow if market conditions deteriorate. So, “up to 4× leverage” should never be interpreted as guaranteed borrowing power. The smarter question is not “How m

I’d choose ② NAND / SSD, but I wouldn’t chase it simply because Kioxia is considering a U.S. listing. The bigger story is valuation. AI data centers are generating enormous volumes of training data, checkpoints, databases and inference workloads, pushing storage from a “commodity memory” narrative toward a potential AI-infrastructure narrative. Kioxia’s U.S. ADS plan could also give global investors a cleaner benchmark against MU and SNDK, potentially attracting more AI-focused capital into the NAND/SSD space. That makes SNDK particularly interesting to me, especially if enterprise SSD demand accelerates while NAND suppliers remain disciplined. But I’d watch pricing carefully: storage remains cyclical, and aggressive capacity expansion could quickly destroy margins. So my bet isn’t simply

[你懂的]  AI’s Next Battle May Not Be GPUs: Meet the Company Selling the Data and “Tests” Behind AI When investors talk about AI, the names usually come quickly: NVIDIA for compute, Broadcom for networking and custom chips, and Microsoft, Amazon and Google for cloud infrastructure and foundation models. But there is another question that is becoming increasingly important: Even if you have all the GPUs in the world, what will AI actually learn from without high-quality data? And as AI moves beyond simple chatbots toward AI agents, reasoning models and enterprise applications, another question emerges: How do we know whether an AI system is actually good? Is its answer correct? Does it hallucinate? Can it complete a complex task as instructed? And in high-stakes industries such as fi

[思考]  $100 Oil Is Back. But Is That Really the Problem? Oil is back above $100 a barrel. At first glance, the trade looks simple: Oil up → Energy stocks up. Oil up → Tech stocks down. But I think that misses the bigger picture. The real question isn't whether oil is above $100. The real question is: Why is it above $100 — and how long can it stay there? That distinction could determine whether this becomes a short-term market shock or the beginning of a much bigger rotation. 🟢 The Winners: Energy Is the Obvious One — But Not the Only One The clearest beneficiary is the energy sector. When crude prices rise, upstream producers can potentially generate much higher cash flow because their production costs don't necessarily rise as quickly as selling prices. That puts companies acros

I’d choose B — Maybe, but I’m cautiously bullish on the bigger picture. The iPhone Duo isn’t simply another hardware upgrade; it could create an entirely new premium category for Apple. At $1,999, Apple doesn’t need mass adoption. Even relatively modest volumes could lift average selling prices, revenue per user and overall iPhone economics. The supply-chain angle may be just as interesting. Foldables require more sophisticated displays, hinges, cooling and structural components, potentially increasing component value per device. But I wouldn’t chase AAPL purely because of the launch. The real test begins now: preorder demand, delivery times, customer reviews and replacement rates. If consumers prove willing to pay nearly $2,000 for a foldable iPhone, Apple could turn today’s niche form f

If I had to pick today, I’d choose C — both, but with a slight edge to META. The bigger opportunity isn’t simply building a smarter chatbot. It’s turning AI into an execution layer between consumer intent and transactions. Muse could eventually search, compare, book, purchase and complete tasks on a user’s behalf. Google, meanwhile, already controls a huge amount of commercial intent through Search and Gemini. META’s biggest advantage is distribution: billions of users already live inside its ecosystem. But I wouldn’t mistake Morgan Stanley’s $30T opportunity estimate for $30T of revenue. The real investment test is adoption → retention → frequency → transactions → monetization. If Muse becomes habitual, META could unlock a powerful new business model. If not, it remains expensive optiona

The biggest opportunity here may not be nuclear itself, but reliable power. AI data centers are creating an electricity demand shock, while grid expansion and permitting simply cannot move at the same speed. That makes dependable 24/7 generation increasingly valuable. Hyperscalers are therefore securing long-term nuclear PPAs, while fuel cells and onsite generation can provide power closer to where demand actually exists. I would separate cash flow from speculation. Established nuclear and power producers offer stronger fundamentals, while SMR and microreactor stocks such as SMR and NNE offer potentially explosive upside—but also significant technology, regulatory, financing and valuation risks. To me, the bigger theme is not “nuclear is back.” It is that AI has turned electricity into st

For me, a US$638 billion backlog is impressive—but a backlog is only a promise until it becomes revenue, cash flow and ultimately free cash flow. Tonight, I would focus on three things: RPO conversion, AI revenue growth, and cash generation. If Oracle can show that major AI contracts are moving into actual revenue faster than expected, while keeping margins under control, the backlog starts to look like a genuine earnings engine rather than a headline number. The bigger question is capex. Oracle is spending heavily to build AI infrastructure before customers fully pay for it. That creates a dangerous gap if financing costs stay high. So I would not buy simply because the backlog is huge. I want evidence that AI demand is converting into cash faster than Oracle is converting cash into data