The hottest trade in AI may also be the oneCathie Woodis avoiding.
人工智慧領域最熱門的交易，或許也正是Cathie Wood所迴避的那個。
As investors have piled into high-bandwidth memory (HBM) suppliers such asSK hynix Inc.(NASDAQ:SKHY) andMicron Technology, Inc.(NASDAQ:MU) on expectations of years of AI-driven demand, theARK Investfounder believes history points in a different direction. And to explain why, she reached for an example fromTesla, Inc.(NASDAQ:TSLA).
隨著投資者因預期人工智慧將帶來多年的高帶寬內存（HBM）需求，而紛紛湧入SK hynix Inc.（納斯達克：SKHY）和美光科技（納斯達克：MU）等HBM供應商，ARK Invest創始人卻認為歷史指明瞭另一個方向。為瞭解釋原因，她舉了特斯拉（納斯達克：TSLA）的一個例子。
Technology companies have a long history of engineering their way around expensive supply constraints rather than accepting them as permanent, she argued on the Insightful Investor podcast.
她在《Insightful Investor》播客中指出，科技公司歷來傾向於通過工程手段繞過昂貴的供應限制，而非將其視為永久性約束。
"We've seen it many times with Tesla," she said. "If there's a supply chain issue... Cobalt from the Congo, using slave labour and all of that, Elon engineered it out – of the batteries. Or, mostly out. And, so we're seeing engineering out the need for high-bandwidth memory."
「我們在特斯拉身上已經多次看到這種情況，」她說，「如果出現供應鏈問題……比如剛果的鈷礦涉及奴工等問題，Elon就通過工程手段將其從電池中剔除——或者至少基本剔除了。因此，我們如今也看到，工程師們正在設法消除對高帶寬內存的需求。」
That analogy forms the backbone of her investment thesis.
這一類比構成了她投資論點的核心。
Rather than viewing HBM as a permanently scarce resource, Wood sees today's pricing as an incentive for chip designers to rethink AI architectures altogether.
Wood並不將高帶寬內存視為一種永久稀缺的資源，而是認為當前的價格水平正激勵晶片設計者徹底重新思考人工智慧架構。
Why Avoid SK Hynix and Micron?
為何迴避SK hynix和美光科技？
Wood described memory as the "most cyclical" and "most commoditized" segment of the semiconductor industry, arguing that periods of extraordinary pricing rarely persist.
Wood稱記憶體器是半導體行業中「周期性最強」且「最商品化」的細分領域，並認為異常高昂的價格時期很少能持續下去。
"When prices triple or quadruple or go up tenfold, that is not the normal state for technology," she said. "That's actually a negative, but most people think it's a huge positive."
「當價格漲至三倍、四倍甚至十倍時，這並非科技行業的常態，」她說，「這實際上是個負面信號，但大多數人卻認為這是巨大的利多。」
Instead of betting on sustained pricing power for memory manufacturers, Wood believes innovation will gradually reduce dependence on external HBM for AI inference.
Wood認為，與其押注記憶體器製造商能維持持久的定價能力，不如相信創新將逐步降低人工智慧推理對外部高帶寬內存的依賴。
She pointed to companies includingCerebras Systems Inc.(NASDAQ:CBRS) andGroq, whose inference-focused architectures rely heavily on fast on-chip memory and, in certain workloads, can reduce or avoid the need for traditional high-bandwidth memory. According to Wood, "We're seeing engineering out the need for high-bandwidth memory when it comes to inference."
她指出，包括Cerebras Systems Inc.（納斯達克：CBRS）和Groq在內的公司，其專注於推理的架構嚴重依賴快速片上內存，在某些工作負載下可以減少甚至避免對傳統高帶寬內存的需求。據Wood表示：「我們在推理領域看到工程師正在設法擺脫對高帶寬內存的需求。」
That distinction matters because industry observers expect inference—the stage where trained AI models generate responses—to become a much larger computing market than model training.
這一區別至關重要，因為業內觀察人士預計，推理——即訓練好的AI模型生成響應的階段——將成為比模型訓練規模大得多的計算市場。
Cathie Wood Questions AI Memory Cycle, Not the AI Boom
Cathie Wood質疑的是AI內存周期，而非AI熱潮
Wood isn't bearish. Instead, she is drawing a distinction between AI demand and the components that capture the most value from that demand.
Wood並非看空。相反，她是在區分AI需求與從該需求中獲取最多價值的組件。
Extraordinary profits tend to attract capital, new supply and ultimately competitive pressure. But soaring HBM pricing is "an invitation for SK Hynix and Samsung" to expand production, she said.
超常利潤往往會吸引資本、新增供應，並最終引發競爭壓力。但她表示，高帶寬內存（HBM）價格飆升「是在邀請SK Hynix和Samsung擴大產能」。
"When I see a lot of capital flowing very quickly into a very cyclical industry, I basically say, you have it. I'm going to be focused on how to solve that pricing problem," she added.
她補充道：「當我看到大量資本迅速湧入一個高度周期性的行業時，我基本上會說，你們去吧。我會專注於如何解決這個定價問題。」
That is a different investment philosophy from simply chasing the fastest-growing segment of the semiconductor market.
這與單純追逐半導體市場中增長最快細分領域的投資理念有所不同。
Wood's thesis ultimately rests on a familiar principle in technology investing: today's bottleneck often becomes tomorrow's engineering challenge. Whether AI inference architectures meaningfully reduce reliance on HBM remains an open question, particularly as demand for increasingly powerful AI models continues to grow.
Wood的投資論點最終基於科技投資中的一個熟悉原則：今天的瓶頸往往成為明天的工程挑戰。AI推理架構是否能顯著降低對高帶寬內存（HBM）的依賴，仍然是一個懸而未決的問題，尤其是隨著對日益強大的AI模型的需求持續增長。(Benzinga)