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【Joe’s華爾街脈動】專題：AI 的循環融資是否會加劇泡沫化？
處於AI熱潮核心的各大企業，已相互投資了數十億美元。這引發了部分人士質疑AI的循環融資模式是否具有永續性。
Joe 盧, CFA | 2025年10月26日 美東時間
摘要
- 美國巨頭企業之間的循環融資正驅動AI產業的擴張，導致成長指標膨脹，但終端用戶需求卻沒有相應成長。
- 台灣的半導體與資料中心供應鏈雖從此資本中受益，但若美國AI投資放緩，將面臨重大風險。
- 龐大的AI基礎設施支出與日益增長的電力需求，對美國和台灣的能源系統都構成壓力，壓縮了製造商的利潤。
- 台灣對少數幾家美國巨頭企業的出口依賴，造成了高度的集中度風險，即使是強健的企業資產負債表也無法完全緩解。
- 對投資者而言，監控AI資本支出動能、台灣出口訂單及能源成本，是區分投機性過剩與可持續結構性增長的關鍵。
在「循環融資」結構的推動下，AI正驅動著現代史上最大規模的資本週期之一；在此結構中，輝達(Nvidia)、OpenAI、甲骨文(Oracle)、亞馬遜(Amazon)和Google等公司，同時扮演著投資者、供應商和客戶的多重角色。此一動態強化了企業估值，並創造了一個直接衝擊全球晶片製造中心——台灣的資本循環，這為台灣帶來了創紀錄的出口，但也累積了日益增加的風險曝險。
本分析將探討當前AI熱潮與網路泡沫之間的相似與相異之處，評估這些資本流動最終將演變為可持續的增長，或是在自身的槓桿下崩潰，並重點關注其對台灣半導體產業的意涵。
自我融通的人工智慧機器
三家公司定義了當前的循環：輝達(Nvidia)、OpenAI和甲骨文(Oracle)。
輝達(Nvidia)對OpenAI投資高達1,000億美元；OpenAI用這筆錢購買輝達的晶片；而為OpenAI提供3,000億美元雲端服務的甲骨文(Oracle)，則購買數百億美元的輝達GPU以履行該合約。
每家公司都報告了更高的營收和上漲的股價，但相同的資本卻在一個封閉的系統內循環。彭博社(Bloomberg)將輝達(Nvidia)形容為「AI 的央行」，透過擴展流動性來維持此波熱潮。
此結構與1990年代電信熱潮時的「供應商融資」(vendor financing)如出一轍，儘管AI的支出主要由內部現金流而非債務所資助。然而，這些關係使得營收看起來比實際更為強勁，因為資本是在一個封閉的網絡內循環，而非源自終端用戶，從而虛增了整個AI供應鏈的感知需求。此模式類似於戰後日本的「經連會」(keiretsu)或韓國的「財閥」(chaebol)，其交叉持股雖確保了供應穩定，卻也掩蓋了財務風險。
台灣正處於此資本循環的正下方。台積電(TSMC)製造輝達的GPU，日月光(ASE)進行封裝，而緯創(Wistron)、廣達(Quanta)和英業達(Inventec)則組裝甲骨文(Oracle)和亞馬遜(Amazon)所部署的AI伺服器。美國巨頭企業之間的每一份新合約，都為台灣的工廠帶來一波訂單，但同時也將其營收與相同的融資週期綑綁在一起。此循環解釋了為何在AI商業化變現遲滯的情況下，台灣的半導體月出口額仍能達到歷史新高。這也解釋了其脆弱性：如果全球融資緊縮，同一個循環可能會反向解開，在美國股市反映經濟放緩之前，就已拖累台灣的出貨量。
AI週期的脆弱基礎
AI熱潮建立在一個脆弱的基礎上，投資者必須監控其三大核心脆弱性：資本密集度與商業化變現之間的差距、能源的物理性限制，以及供應鏈內部的高度風險集中。
首要的脆弱性是支出與收入之間的不平衡。麥肯錫(McKinsey)預計到2030年，AI基礎設施的投資將達5.2兆美元，但貝恩(Bain)估計，該產業需要2兆美元的年營收才能支撐此等級的投資。OpenAI約130億美元的預估銷售額，突顯了此一鴻溝。將真正的終端用戶變現仍難以實現，只有5%的ChatGPT用戶付費訂閱服務。更關鍵的是，只有不到15%的企業AI試點計畫能轉入全面生產，因為大多數公司尚未實現顯著、可衡量的生產力提升。幾年前預測的、由AI驅動的大規模裁員尚未實現，進一步表明該技術的經濟效益尚未被大規模地實現。對台灣而言，此一差距將直接轉化為訂單的波動性。晶圓製造和組裝依賴於長達數季的能見度，如果美國客戶為了使資本支出與實際營收保持一致而暫停投資，台灣的出貨量將在美國股市反映經濟放緩之前就已下滑。
其次，此資本與營收的不平衡延伸至實體基礎設施，特別是能源。光是OpenAI的「星際之門」(Stargate)專案就需要23千兆瓦(GW)的電力——約等於23座核反應爐的發電量。在台灣，經濟部能源署預計到2030年，工業用電需求將持續增長，主要由晶圓廠和資料中心所驅動。此一壓力勢必導致電價調整，即使全球AI需求依然強勁，上升的電力成本將壓縮製造利潤，並對ESG的目標構成挑戰。
最後，儘管AI龍頭企業現金充裕，但其財務實力卻有著系統性風險。與由債務驅動的網路泡沫不同，微軟(Microsoft)、亞馬遜(Amazon)、Google和輝達(Nvidia)等公司，每年超過2,000億美元的自由現金流，它們自我融通AI擴張的能力延長了此一週期，但也使台灣的供應鏈高度依賴少數關鍵參與者的支出決策。台積電(TSMC)的前五大美國客戶已佔其營收的60%以上。即使其中僅有一家放緩，也可能降低多個製程節點的晶圓廠產能利用率。
歷史表明，此類的過度建設往往為未來的生產力播下種子。1990年代的光纖網路和2010年代的太陽能產能都遵循了相同的模式：初期的泡沫留下了有用的基礎設施。台灣的角色與此類似。在今日的熱情下建立的硬體基礎，將成為AI主流階段的基石。投資的要點並非預測崩盤的時機，而是去預判此一轉變——從投機性的資本支出，轉向商業化變現的部署，其標誌正是生產力的普遍提升，從而證明大規模投資的合理性。
與網路泡沫的差異
然而，一些關鍵的差異將此AI週期與網路泡沫區分開來。2000年的泡沫建立在債務和疲弱的資產負債表之上。今日的AI巨頭——微軟(Microsoft)、Google、亞馬遜(Amazon)和輝達(Nvidia)——現金充裕，合計每年產生2,000億美元的自由現金流，並能從內部為擴張提供資金。回到2000年，像世界通訊(WorldCom)和環球電訊(Global Crossing)等公司大量借貸來建設它們無法填滿的網絡。當信貸市場緊縮時，違約在整個行業中引發了連鎖反應。而當今的企業即使明天削減支出，仍能保持獲利。
另一個區別是資產的有形性。光纖泡沫留下了未被充分利用的電纜；AI熱潮則正在建設充滿可重複使用晶片和電力基礎設施的、全球分佈的資料中心。其利用率很高：資料中心的空置率低於5%，而運算需求仍然供不應求。此一建設雖具侵略性，卻是為了應對可見的使用量——而非純粹的投機。
AI基礎設施正以接近滿載的狀態運行，與2000年代初閒置的光纖網路不同。
至關重要的是，商業化變現已在進行中。雲端巨頭從AI雲端工作負載、廣告優化和生產力工具中賺取實際收入。輝達(Nvidia)的晶片銷售代表著有形的需求，而不僅僅是炒作。瓶頸不在於採用率，而在於電力供應——這與2000年代初空蕩蕩的伺服器機櫃是完全不同的問題。
另一個關鍵的區別在於估值。儘管偏高，但今日的估值錨定在一個2000年時普遍缺乏的、真實盈餘與現金流的基礎上。在1990年代末，像思科(Cisco)和甲骨文(Oracle)等領先的網路股，其預估本益比(forward P/E)超過60倍，許多未獲利的公司則以「眼球數」或「點擊率」等投機性指標進行估值。今日，AI龍頭股的預估本益比約在35倍左右。更重要的是，今日本益比中的「E」(盈餘)，代表著符合一般公認會計原則(GAAP)的龐大利潤。市場並非為從零開始的無限增長定價，而是在一個既有龐大且獲利的基礎上，實現強勁增長而定價。
對台灣而言，此一區別至關重要。在2000年，網路泡沫幾乎未觸及台灣的出口產業。而今日台灣則處於AI價值鏈的核心，台積電(TSMC)、日月光(ASE)、緯創(Wistron)和廣達(Quanta)都扮演著關鍵角色。同樣的的循環，在虛增美國估值的同時，也直接驅動了台灣的出口盈餘。即使泡沫修正，實體的基礎設施——晶圓廠、工具和人才——依然存在。
簡言之，AI可能正處於泡沫之中，但它並非建立在虛無飄渺之上。危險不在於資不抵債，而在於估值過高。當預期重新設定時，即使基礎技術持續進步，股價也可能急遽下跌。網路泡沫以崩盤告終，但它也為我們奠定了數位經濟的基礎。AI很可能正遵循同樣的劇本——只是這一次，台灣的半導體產業是主角之一。
為台灣投資者剖析的觀點
台灣處於投機性金融與實體生產的交匯點。作為AI硬體骨幹的角色，確保了其近期的盈餘動能，卻也放大了週期性風險。近期的好處是實質可見的：更高的工廠利用率和創紀錄的出貨量。中期的風險也同樣清晰：能源壓力、產能過剩，以及對少數全球客戶的依賴。
對成熟的投資者而言，關鍵在於為週期性轉變進行佈局，而非僅僅著眼於長期趨勢。此一框架包含：
因此，對投資者而言，目標是追求清晰度，而非預測。需要監控的關鍵問題是，資本流動何時能變得自我維持，而非自我參照。那個轉折點，即AI需求反映的是真正的生產力提升，而非循環的資金時，將標誌著台灣科技經濟下一個結構性的躍進。
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本電子報僅供參考，不構成任何證券或資產類別的投資建議或買賣推薦。文中所表達的觀點為作者截至發布日期的觀點，如有變動，恕不另行通知。所呈現的資訊乃基於從相信可靠的來源所獲取的數據，但其準確性、完整性和及時性不作保證。過往表現並非未來結果的指標。投資涉及風險，包括可能損失本金。讀者在做出任何投資決策前，應諮詢其財務顧問。作者及相關實體可能持有本文所討論的資產或資產類別的部位。
Is AI’s Circular Financing Inflating a Bubble?
Companies at the center of the AI boom have been investing billions of dollars in each other. This has led some to question whether AI's circular financing is sustainable.
Joe 盧, CFA | 2025-10-26
Executive Summary
- Circular financing among U.S. hyperscalers drives the AI expansion, inflating growth metrics without corresponding end-user demand.
- Taiwan's semiconductor and data-center supply chains benefit from this capital but are exposed to significant risk if U.S. AI investments slow.
- Massive AI infrastructure spending and rising power demand strain both U.S. and Taiwan energy systems, compressing manufacturer margins.
- Taiwan’s export dependence on a few U.S. hyperscalers creates high concentration risk that strong corporate balance sheets cannot fully mitigate.
- Monitoring AI capex momentum, Taiwan export orders, and energy costs is essential for investors to distinguish speculative excess from sustainable structural growth.
AI is driving one of the largest capital cycles in modern history, fueled by a circular financing structure where firms like Nvidia, OpenAI, Oracle, Amazon, and Google act simultaneously as investor, supplier, and customer. This dynamic reinforces valuations and creates a capital loop that directly impacts Taiwan, the epicenter of global chip manufacturing, translating into record exports but also mounting exposure. This analysis explores the parallels and differences between the current AI boom and the dot-com bubble, assessing whether these capital flows will evolve into sustainable growth or collapse under their own leverage, with a focus on the implications for Taiwan's semiconductor industry.
The Self-Funding Artificial Intelligence Machine
Three firms define the current loop: Nvidia, OpenAI, and Oracle. Nvidia invests up to US$100 billion in OpenAI. OpenAI spends that money to buy Nvidia chips. Oracle, which provides OpenAI with US$300 billion in cloud services, buys tens of billions of dollars of Nvidia GPUs to fulfill that contract.
Each company reports higher revenue and rising stock prices, but the same capital circulates within a closed system. Bloomberg describes Nvidia as “the central bank of AI,” extending liquidity to keep the boom going.
This structure mirrors the vendor financing of the 1990s telecom boom, though AI spending is largely funded by internal cash flow rather than debt. Still, these relationships make revenue appear stronger than it is, as capital circulates within a closed network instead of originating from end users, inflating perceived demand across the AI supply chain. This pattern resembles post-war Japan’s keiretsu or Korea’s chaebol, where cross-ownership ensured supply stability but also obscured financial risk.
Taiwan sits directly under this capital loop. TSMC fabricates Nvidia’s GPUs, ASE packages them, and Wistron, Quanta, and Inventec assemble the AI servers that Oracle and Amazon deploy. Each new contract between U.S. hyperscalers sends a wave of orders through Taiwan’s factories, but also ties their revenue to the same set of financing cycles. This loop explains why Taiwan’s monthly semiconductor exports reached record highs even as AI monetization lagged. It also explains the vulnerability: if global financing tightens, the same loop can unwind, pulling Taiwan’s shipments down before U.S. equities reflect the slowdown.
The AI Cycle's Fragile Foundation
The AI boom is built on a fragile foundation, with three core vulnerabilities that investors must monitor: the gap between capital intensity and monetization, the physical constraint of energy, and the high concentration of risk within the supply chain.
The primary vulnerability is the imbalance between spending and income. McKinsey projects a $5.2 trillion in AI infrastructure investment by 2030, yet Bain estimates the sector needs $2 trillion in annual revenue to justify that level. OpenAI’s estimated US$13 billion in sales highlights this chasm. Real end-user monetization remains elusive. Only five percent of ChatGPT users pay for subscriptions. More critically, fewer than 15% of corporate AI pilots transition to full production because most companies have yet to realize significant, measurable productivity gains. The widespread, AI-driven layoffs predicted just a few years ago have not materialized, further indicating that the technology’s economic benefits are not yet being captured at scale. For Taiwan, this gap translates directly into order volatility. Fabrication and assembly rely on multi-quarter visibility, and if U.S. customers pause to align capex with real revenue, Taiwan’s shipments will decline before the U.S. stock market prices in the slowdown.
Second, this capital-revenue imbalance extends to physical infrastructure, particularly energy. OpenAI’s “Stargate” project alone targets 23 gigawatts of power capacity—roughly the output of 23 nuclear reactors. In Taiwan, the Bureau of Energy projects a 70% growth in industrial electricity demand by 2030, largely driven by fabs and data centers. This strain necessitates tariff adjustments, and rising power costs will compress fabrication margins and challenge ESG mandates, even if global AI demand remains strong.
Finally, while today’s AI leaders are cash-rich, their financial strength concentrates systemic risk. Unlike the debt-fueled dot-com boom, firms like Microsoft, Amazon, Google, and Nvidia generate more than US$200 billion in annual free cash flow. Their capacity to self-fund the AI expansion extends the cycle but also makes Taiwan’s supply chain highly dependent on the spending decisions of a few key players. TSMC’s top five U.S. customers already represent over 60% of its revenue. A slowdown from even one of them could reduce fab utilization across multiple process nodes.
History shows that such overbuilding often seeds future productivity. The fiber-optic networks of the 1990’s and the solar capacity of the 2010’s followed the same pattern: initial bubbles that left behind useful infrastructure. Taiwan’s role is similar. The hardware base built under today’s exuberance will become the foundation for AI’s mainstream phase. The investment takeaway is not to time a collapse but to anticipate the transition—from speculative capex to monetized deployment, marked by the widespread evidence of productivity gains that justify the massive investment.
Differences From the Dot-Com Bubble
However, critical differences distinguish this AI cycle from the dot-com bubble. The 2000 bubble was built on debt and weak balance sheets. Today’s AI giants—Microsoft, Google, Amazon, and Nvidia—are flush with cash, generating a combined US$200 billion in annual free cash flow, and can fund expansion internally. Back in 2000, companies like WorldCom and Global Crossing borrowed heavily to build networks they couldn’t fill. When credit markets tightened, defaults cascaded across the industry. Today’s players could cut spending tomorrow and remain profitable.
Another distinction is the tangibility of assets. The fiber-optic bubble left behind underused cables; the AI boom is building globally distributed data centers filled with reusable chips and power infrastructure. Utilization is high: data-center vacancy rates are below 5%, and compute demand still exceeds supply. The buildout, while aggressive, is responding to visible usage—not pure speculation.
AI infrastructure is running near full capacity, unlike the idle fiber-optic networks of the early 2000s
Crucially, monetization is already underway. Hyperscalers earn real income from AI cloud workloads, advertising optimization, and productivity tools. Nvidia’s chip sales represent tangible demand, not just hype. The bottleneck is not adoption but power supply—an entirely different problem from the empty server racks of the early 2000s.
A key distinction also lies in valuation metrics. While elevated, today’s valuations are anchored to a foundation of real earnings and cash flow that was largely absent in 2000. In the late 1990s, leading internet stocks like Cisco and Oracle traded at forward P/E ratios exceeding 60x, with many unprofitable firms valued on speculative metrics like "eyeballs" or "clicks." Today, the AI leaders trade closer to 35x forward earnings. More importantly, the "E" in today's P/E represents substantial, GAAP-compliant profits. The market is not pricing in infinite growth from zero; it is pricing in strong growth from an already massive and profitable base.
For Taiwan, this distinction matters. In 2000, the internet bubble barely touched the island’s export sector. Today, Taiwan sits at the core of the AI value chain, with TSMC, ASE, Wistron, and Quanta all playing critical roles. The same feedback loop that inflates U.S. valuations directly drives Taiwan’s export earnings. Even if the bubble corrects, the physical infrastructure—fabs, tools, and talent—remains.
In short, AI may be in a bubble, but it is not built on vapor. The danger is not insolvency—it is overvaluation. When expectations reset, stock prices could fall sharply even as the underlying technology keeps advancing. The internet bubble ended in a crash, but it also gave us the foundation for the digital economy. AI is likely following the same script—only this time, Taiwan’s semiconductor industry is one of the main characters.
The Investor Lens for Taiwan
Taiwan sits at the intersection of speculative finance and real production. The island’s role as the hardware backbone of AI ensures near-term earnings momentum but magnifies cyclical risk. The near-term benefit is tangible: higher factory utilization and record shipments. The medium-term risk is also clear: energy strain, overcapacity, and dependency on a few global customers.
For sophisticated investors, the key is to position for cyclical shifts rather than just the secular trend. A framework for this includes:
The AI cycle’s circular financing has lifted Taiwan’s exports and valuations but also tied them tightly to the spending behavior of a few U.S. giants. As these financing loops inevitably tighten, Taiwan will feel the turn first—through export orders, power demand, and valuation shifts.
The objective for investors, therefore, is clarity, not prediction. The key question to monitor is when capital flows become self-sustaining rather than self-referential. That turning point, where AI demand reflects genuine productivity gains instead of recycled funding, will mark the next structural advance for Taiwan’s technology economy.
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This newsletter is provided for informational purposes only and does not constitute investment advice or a recommendation to buy or sell any security or asset class. The views expressed are those of the author as of the date of publication and are subject to change without notice. Information presented is based on data obtained from sources believed to be reliable, but its accuracy, completeness, and timeliness are not guaranteed. Past performance is not indicative of future results. Investing involves risks, including the possible loss of principal. Readers should consult with their own financial advisors before making any investment decisions. The author and associated entities may hold positions in the assets or asset classes discussed herein.
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關於《Joe’s 華爾街脈動》
鉅亨網特別邀請到擁有逾 22 年美國投資圈資歷、CFA 認證的機構操盤人 Joseph Lu 擔任專欄主筆。
Joe 為台裔美國人，曾管理超過百億美元規模的基金資產，並為總資產高達數千億美元的多家頂級金融機構提供資產配置優化建議。
Joe 目前帶領著由美國頂尖大學教授與博士組成的精英團隊，透過獨家開發的 "趨勢脈動 TrendFolios® 指標"，為台灣投資人深度解析全球市場脈動，提供美股市場第一手專業觀點，協助投資人掌握先機。