科技產業的發展歷史，往往呈現出一種反覆循環的節奏：在技術突破初現時，市場先進入近乎狂熱的想像階段，資本大量湧入，推高估值與期待；然而當現實獲利追不上願景，幻滅與修正隨之而來。從2000年的網路.com泡沫、2010年前後依賴政策補貼的綠能投資熱潮，到近年部分生技公司的「本夢比」現象，都遵循著類似的劇本——技術前景被過度放大，資金配置失衡，最終引發市場重新定價與財務損失。當前的AI浪潮，也自然被放在這條歷史軌跡之中檢視，人們開始思考：人工智慧究竟是下一次產業革命，還是下一個泡沫前夕。

首先必須理解的是，AI的發展確實帶有強烈的資本密集特性，這點與過去的能源轉型周期高度相似。大型科技公司正以前所未見的速度投入資本支出，建立資料中心、購買高階GPU與擴充雲端基礎設施。對企業而言，這是一場不能缺席的軍備競賽，因為一旦在算力與模型能力上落後，就可能永久失去平台主導權。然而，龐大的投資同時也帶來「獲利焦慮」。若AI應用——例如企業助理、生成式工具或SaaS自動化服務——無法在可預期時間內轉化為穩定收入，市場對未來成長的信心便可能迅速動搖。歷史上，多數泡沫並非因技術不存在，而是因資本投入速度遠快於商業模式成熟速度，導致估值提前透支未來數年的成果。

將AI與生技產業相比，可以更清楚看見兩者風險結構的差異。生技公司的核心問題在於極端的不確定性與漫長週期，一項新藥往往需要十年以上研發時間，且最終可能在臨床試驗的最後階段失敗，使先前投入幾乎完全歸零。市場因此容易出現以「故事」支撐股價的現象。然而AI的情況並不完全相同。現階段的人工智慧工具已在多個產業中產生可觀的實際效益，例如程式碼生成提升開發效率、文件審核自動化降低人力成本，以及客服系統的即時回應能力提升。這意味著AI並非純粹依賴未來承諾，而是已開始形成早期現金流與生產力增幅，雖然規模尚不足以完全支撐當前所有估值，但至少具備現實落地的基礎。

真正值得關注的，是AI是否會經歷典型的「泡沫化曲線」。依照科技史的經驗，一項顛覆性技術通常會先進入期待被極度放大的階段，市場相信它能迅速解決幾乎所有問題，資本因而推升估值至高點。隨後，當企業發現導入成本高於預期、效率提升不如宣傳速度快，或商業模式尚未完全成熟時，市場情緒會急速反轉，進入所謂的「幻滅谷底」。在這個階段，大量依賴敘事而缺乏核心競爭力的公司將被淘汰，股價大幅修正，投資熱度降溫。

然而，歷史同樣顯示，泡沫的破裂往往不是技術的終點，而是產業真正成熟的起點。2000年網路泡沫雖然摧毀了無數公司，卻留下Amazon與Google這類最終成為全球基礎設施的企業。泡沫清洗掉的是過度槓桿與缺乏商業模式的參與者，而非技術本身。以同樣邏輯來看，AI很可能也會經歷估值修正甚至產業震盪，但真正掌握演算法、算力生態與應用場景的企業，反而可能在泡沫過後建立更穩固的壟斷優勢。

因此，AI 是否會泡沫化，答案很可能是「部分會」。市場過高的短期期待與資本過度集中，使修正幾乎難以避免；但AI是否會像某些產業熱潮般消失，答案則幾乎可以確定是否定的。更合理的推測是，人工智慧正處於一場長期技術革命的早期階段，而當前的資本狂熱，只是通往下一代數位基礎設施之前必經的震盪期。

The history of technological development often follows a recurring cycle: when a breakthrough first appears, the market enters a phase of near-euphoric expectations, capital floods in, and valuations surge. However, when real profitability fails to keep pace with those visions, disillusionment and correction inevitably follow. From the dot-com bubble of 2000, to the subsidy-driven renewable energy boom around 2010, and more recently the valuation-driven hype surrounding parts of the biotech sector, each period has followed a similar script — technological promise becomes overstated, capital allocation becomes distorted, and markets eventually reprice risk after significant financial losses. Today’s AI boom is increasingly being examined within this same historical framework, raising the question of whether artificial intelligence represents a genuine industrial revolution or merely the prelude to another bubble.

The first key point is that AI development is undeniably capital-intensive, a characteristic that closely resembles previous energy transition cycles. Major technology companies are investing at unprecedented speed, building data centers, purchasing advanced GPUs, and expanding cloud infrastructure. For these firms, participation is not optional; falling behind in computing power or model capability could mean permanently losing platform leadership. Yet this enormous investment also creates what might be called “profitability anxiety.” If AI applications — such as enterprise assistants, generative tools, or SaaS automation services — fail to translate into sustainable revenue within a reasonable timeframe, investor confidence may weaken rapidly. Historically, bubbles rarely form because a technology lacks value; rather, they emerge when capital deployment advances much faster than the maturation of viable business models, causing valuations to price in years of future success prematurely.

A comparison with the biotechnology industry further clarifies the difference in risk structure. Biotech companies face extreme uncertainty and long development cycles: a single drug may require more than a decade of research only to fail in late-stage clinical trials, wiping out most invested value. As a result, stock prices in the sector are often supported more by narrative than by realized earnings. AI, however, differs in an important way. Current AI tools are already generating measurable productivity gains across industries — improving software development efficiency through code generation, reducing labor costs via automated document review, and enhancing customer service responsiveness. This indicates that AI is not purely dependent on future promises; it is already producing early economic value and operational benefits. While these gains may not yet justify every current valuation, they provide a tangible commercial foundation absent in many speculative biotech cases.

The central issue, therefore, is whether AI will follow the classic “bubble curve.” Historically, transformative technologies first enter a phase of inflated expectations, during which markets believe the innovation will rapidly solve nearly all productivity challenges. Capital inflows then push valuations to extreme levels. Eventually, companies discover that implementation costs are higher than expected, efficiency improvements arrive more gradually than advertised, or business models remain immature. Market sentiment then reverses sharply, leading to what analysts often describe as a “trough of disillusionment.” During this stage, companies built primarily on hype rather than competitive advantage are eliminated, share prices correct significantly, and investment enthusiasm cools.

History also shows, however, that the bursting of a bubble rarely marks the end of a technology; instead, it often signals the beginning of true industry maturity. The dot-com crash destroyed countless firms but ultimately left behind companies such as Amazon and Google, which later became foundational pillars of the global digital economy. What bubbles remove are excessive leverage and unsustainable business models — not the underlying technological shift itself. By the same logic, AI will likely experience valuation corrections and industry turbulence, yet companies possessing core technologies, computing ecosystems, and scalable applications may emerge even stronger afterward.

Therefore, the most balanced conclusion is that AI will likely experience partial bubble dynamics. Excessive short-term expectations and concentrated capital investment make some level of correction almost inevitable. However, it is highly unlikely that AI will disappear like a passing trend. A more realistic interpretation is that artificial intelligence is still in the early phase of a long technological revolution, and today’s capital enthusiasm represents a volatile but necessary stage on the path toward becoming the next generation of global digital infrastructure.