To The Moon

AI names are crashing 10-20% 🔴 Yet, AI investments are accelerating - $Microsoft(MSFT)$ $100B - $Meta Platforms, Inc.(META)$ $115-135B - $Alphabet(GOOGL)$ $175-185B FOLLOW THE MONEY 💸 Here are another 10 companies that will dominate the AI age: 11/ $Meta Platforms, Inc.(META)$ $META controls the attention of the world. - 3.6B Daily Users - 15% ARPU Growth - 22% Revenue Growth - 41% EBIT Margin A few months ago, they announced new smart glasses that could bring a new form factor to the masses. And AI will turbocharge everything! Before AI, social networks relied on manual sharing and simple algorithms to surface posts an

$特斯拉(TSLA)$ 今晚415沒問題

$Adobe(ADBE)$ Filled more

$AAPL$ Apple has seen a large bullish call order. The May 15th expiry 285 call $AAPL 20260515 285.0 CALL$ had 69,000 contracts opened, with a total notional value of approximately $34+ million. Although I believe Apple's performance this year may still be weighed down by high memory prices, this doesn't prevent the market and large investors from perceiving a potential bottom. With earnings this week, selling the 250 put is an option: $AAPL 20260130 250.0 PUT$ . $FXI$ Regarding the large bearish China ETF orders, I discovered someone shared my analysis in a group, causing some panic. However, a massive, deep out-of-the-money bearish order like

$Tesla Motors(TSLA)$ I made an additional investment in Tesla despite recent headwinds. GLJ Research raised its price target to $25.28, citing concerns over Tesla’s core automotive business, which still accounts for over 70% of sales and is labeled “fledgling.” While global deliveries fell 1.1% in 2024, and steeper declines are projected for 2025 and 2026 due to EV tax credit roll-offs, increasing Chinese competition, and European brand erosion, Tesla’s Energy Generation & Storage segment continues to show promising growth at 12.1% YoY with strong margins. This diversification supports my conviction for long-term potential in Tesla.

生成式人工智能的快速演進，正把AI芯片從一個高度專業化的半導體分支，推向全球科技競爭的戰略核心。算力不再只是性能指標，而成為國家產業政策、企業商業模式與地緣供應鏈博弈的交匯點。在這樣的背景下，來自矽谷、深圳與歐洲的AI芯片設計公司，走出了三條看似分歧、實則互補的全球化路徑，而這些差異，正在為2026年的產業佈局埋下伏筆。 美國AI芯片公司的設計思路，始終圍繞「平臺」二字展開。無論是雲端訓練還是推理加速，其核心目標都是打造一種高度通用、可被軟件生態不斷放大的算力底座。這類芯片在架構上追求極高的可編程性，容許不同模型、框架與算法快速迭代，即便在能效與成本上並非最優，仍能憑藉生態優勢建立長期護城河。 中國，尤其是深圳一帶的AI芯片設計公司，則更傾向於「場景先行」。在智慧城市、工業視覺、車載系統與邊緣計算等應用中，客戶更關心的是即時性、能耗與整體方案成本，而非通用性。這使得中國企業在設計時，往往針對特定算法與負載進行深度優化，形成「專用算力」的競爭優勢。這種思路不一定在全球話語權上佔先，但在落地效率與商業化速度上更具現實意義。 歐洲企業的設計邏輯則顯得剋制而工程化。其AI芯片多服務於汽車、工業控制與關鍵基礎設施，對安全性、可靠性與法規合規有極高要求。算力不是唯一追求，長期穩定運行與系統整合能力纔是核心，這也使歐洲AI芯片在性能競賽中保持低調，卻在高門檻市場中站穩腳跟。 客戶結構差異 設計思路的不同，直接塑造了三地企業截然不同的客戶結構。美國AI芯片公司高度依賴少數超大型平臺型客戶，雲服務商與科技巨頭既是最大買家，也是技術路線的重要塑造者。這種結構帶來的是「贏者通吃」的擴張效應，但同時也意味著收入高度集中、供應鏈議價博弈激烈。 中國企業的客戶結構則更為分散。大量來自不同行業的中大型客戶，推動芯片快速迭代與定製化發展。雖然單一訂單規模有限，但風險分散，且更容易複製到新興市場。這種模式讓

$Microsoft(MSFT)$ [Tongue] [Tongue] [Tongue] [Grin] [Grin] [Grin] [Grin] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Glance] [Grin]

$Adobe(ADBE)$