摘要
- 超大型雲端服務供應商追求先發優勢,並認為一旦落後將面臨失去策略優勢的風險,因此人工智慧資本支出預計將持續高築。
- 市場核心主題是供應短缺而非需求疲弱,致勝之道在於快速及大規模交付的能力而非價格競爭。
- 訓練/推理效率提升可能減低運算需求,但同時推高對整體token(詞元)的需求,而硬體差異化則可能限制供應過剩風險,反映隨後的下行週期將較過往溫和。
我最近出席了在台灣舉辦的亞洲科技會議。科技產業的供應鏈高度集中於亞洲,因此是我們投資組合中的重要配置。支撐全球人工智慧建設的半導體、先進封裝、記憶體及電源基建絕大部分在亞洲生產,尤其集中在台灣、韓國及日本。投資人較易忽略的是,亞洲科技產業也是重要且持續成長的股息收益來源,有助投資人以更吸引的途徑,參與相關結構性成長趨勢。除了與我們投資的企業會面外,這次會議也是與駐台灣同事交流觀點與想法的絕佳機會。
人工智慧是否另一個泡沫?
我在此行一開始便思考一個核心問題:為何超大型雲端服務供應商在硬體價格見頂之際仍積極投資人工智慧基建?這股趨勢又將在何時停止?雖然懷疑論者認為這是另一場正在形成的泡沫,但我認為這輪週期與過往不同。
有別於過往科技週期,人工智慧確實存在顛覆市場的可能。大型語言模型實現利潤的前景日益明朗,對token的需求呈爆發式成長,但運算能力根本無法滿足需求。從人工智慧訓練轉向推理的趨勢進一步加劇供求失衡。與訓練不同,推理需求屬持續性質,並隨著使用者採用程度提升而擴大,且無法如訓練流程那樣分階段進行。
上述現象均基於超大型雲端服務供應商爭取「先發優勢」的思維,並認為一旦落後將意味著失去難以重拾的優勢。因此,即使有關投資報酬的爭論不斷,資本開支仍持續上升。
圖1. 美國超大型雲端服務供應商的資本支出投入持續攀升

資料來源︰彭博,2026年4月
產能成為全新致勝關鍵
硬體需求的兩項發展尤其值得關注。首先是專用集成電路(ASIC)晶片的興起。這類專門設計的晶片正逐漸成為圖形處理器(GPU)的可靠替代品。其次是共封裝光學(CPO)技術的出現。這項新技術可將光學元件直接整合至單一封裝內,降低數據中心的耗電量。雖然預計共封裝光學技術難以在2027年前成為主流,但其已影響硬體內容成長的趨勢,並促成嶄新投資理念。
記憶體市場的主要爭論集中在獲利可持續性,因自2024年以來,市場經歷前所未見的上行週期,商品記憶體價格在過去六至九個月大幅上升。記憶體製造商一直依賴長期協議來確保收入穩定,客戶在相關協議中就指定採購量作出初步承諾。新的長期協議的合約約束力提高,並涉及採購價格及數量承諾的協商,部分情況下更包含客戶預付款項。隨著市場上行週期消退,企業的獲利韌性將取決於更有力的合約條款在多大程度上由例外情況演變為產業常態。
過濾市場雜音
行程結束後不久,一間美國大型科技企業發佈一種新型數據壓縮演算法,並聲稱其可將大型語言模型的記憶體需求減少六倍。市場憂慮需求急挫,拖累記憶體股遭大幅拋售。隨後,我們評估了該演算法對整體人工智慧價值鏈的實際影響,包括其對記憶體需求的影響,並持續觀察開發者社群,以了解該技術有否出現可行的廣泛複製跡象。評估結果顯示,該演算法a)僅優化高頻寬記憶體的部分使用效率;b)由於大部分大型語言模型已下調部署精度,實際高頻寬記憶體節省量料將遠低於六倍;以及c)對儲存或系統記憶體需求的影響微乎其微。
因此,我們認為市場高估了相關風險。更廣泛而言,我們的研究結果再次印證一個事實︰這類效率提升往往會刺激對token的需求,而不會導致需求減弱。這一事件也突顯了評估重大消息走向以區分真正風險與市場雜音的重要性,尤其是科技週期迅速演變,初步反應未必全面反映實況。
關鍵風險變數
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