The long-term value of AI is rooted in the irreversibility of technological change and the rigid demand of industrial applications, which is also the key difference between AI and various technological bubbles in history; while the short-term bubble risks mainly stem from practical difficulties such as capital frenzy, valuation overdraft and delayed landing. Although it may not trigger a comprehensive collapse similar to the 2000 Internet bubble, phased industrial growing pains are inevitable.

Within the short term of 2-3 years, the risks of bubble burst and phased growing pains in the AI field cannot be ignored. It should be made clear that the "bubble burst" mentioned here does not refer to a comprehensive collapse of the industry, but the orderly burst of local bubbles — specifically, some AI enterprises lacking core technological strength, practical landing capabilities and with severely overvalued valuations will face the dilemma of closure and delisting, the industry will experience phased correction, capital will gradually return to rationality, and the industry will enter a benign adjustment period of "eliminating the false and retaining the true". Such growing pains will not change the long-term development trend of AI, but are an inevitable result of the imbalance between "capital frenzy" and "industrial reality" in the development process of emerging technological industries, and also an inevitable path for the market to return to rationality.

Capital frenzy has led to overvaluation, and the pressure of short-term correction is particularly prominent. Currently, capital investment in the AI field has shown obvious "irrational" characteristics, and overvaluation has become the most prominent bubble risk, which is highly consistent with the "local overheating" judgment emphasized by Cathie Wood's team. As of March 2026, the annual capital expenditure in the global AI field has exceeded 600 billion US dollars, accounting for about 1% of the global GDP, a scale comparable to the Internet bubble period. A large amount of blind capital inflow has led to the serious overvaluation of future performance of some AI enterprises, which is seriously disconnected from their actual profitability. The most representative case is OpenAI. After completing a 110 billion US dollar financing recently, its pre-investment valuation soared to 730 billion US dollars, but its profitability is not optimistic — Microsoft's latest financial report shows that OpenAI's single-quarter net loss was about 11.5 billion US dollars, and Microsoft, which holds 27% of its equity, suffered an investment loss of 3.086 billion US dollars in the same period, becoming a typical example of bubbles spawned by capital frenzy.

In addition to leading enterprises, a large number of small and medium-sized AI enterprises and traditional enterprises that "ride the AI wave" face even more severe valuation bubble problems. Many enterprises lack core technological support, blindly raise funds only by putting on the "AI label", have no actual landing scenarios or sustainable profit models, but have achieved a sharp rise in valuation; some traditional enterprises that mainly engage in traditional businesses have seen their stock prices double only by announcing "layout in the AI field", with valuation levels far exceeding the average of traditional enterprises in the same industry. From the perspective of capital market performance, the skyrocketing of AI concept stocks has shown obvious irrational characteristics. Citibank pointed out in its latest report that the market value of core AI concept stocks such as Nvidia has excessively overdrawn performance, with stock prices rising by about 11 times, and the actual profitability is difficult to support the current high valuation. As of March 2026, the price-earnings ratio of the Nasdaq Index reached 41 times, with a valuation premium of 27%. Although it is only one-third of the Internet bubble period (price-earnings ratio of 110 times, valuation premium of 84%), the valuation in some areas has approached the bubble critical level. At the same time, capital is highly concentrated in a few leading enterprises, with the weight of US stock Mag7 accounting for 38%, which is highly similar to the market structure of technology stocks in 2000. Once the performance of leading enterprises fails to meet market expectations, it is likely to trigger a chain correction of the entire AI sector, thereby triggering the burst of local bubbles.

The progress of technology landing lags behind capital expectations, and the problem of "heavy investment and light return" is becoming increasingly prominent. Cathie Wood has always emphasized that AI is an industrial revolution with "entities and rigid demand", but the current reality is that the breakthroughs of AI technology at the laboratory level are difficult to quickly transform into large-scale industrial applications. The dilemma of "difficult landing and difficult profitability" is widespread in the industry, and a large amount of investment in the AI field is difficult to convert into actual returns, forming an obvious imbalance between investment and return. Specifically, first, the "versatility" of AI technology is insufficient. Currently, most AI models are still in the stage of specialized development, lacking universal adaptability across industries and scenarios.

Different industries and scenarios need to invest a lot of funds to customize exclusive solutions, leading to high landing costs that small and medium-sized enterprises cannot bear; second, AI landing faces double bottlenecks. On the one hand, there is a widespread problem of "data islands" and uneven data quality globally, making it difficult for data to be effectively shared and utilized, directly affecting the training effect of AI models; on the other hand, there is a serious shortage of professional talents in the AI field, especially compound talents with both technological R&D capabilities and industrial practical experience, which are in short supply, seriously restricting the progress of technology landing.

Cathie Wood's team found that even though the capabilities of AI agents continue to improve, in complex industrial scenarios, they still need human support and cannot achieve complete automation, which also means that the industrialization of AI requires a long cultivation cycle and it is difficult to achieve large-scale profitability in the short term. Thirdly, the phenomenon of "heavy investment and light return" is widespread. Goldman Sachs' latest report in 2026 shows that the capital expenditure of leading US technology giants in AI infrastructure construction is expected to reach 1.4 trillion US dollars from 2025 to 2027, but the average return on investment is far lower than market expectations, and a large amount of investment is facing the risk of rapid technological iteration and obsolescence, eventually forming sunk costs. Some enterprises blindly increase investment in data center construction to seize the computing power track, but due to the lack of corresponding application scenarios, the computing power utilization rate is less than 30%, and a large amount of investment cannot generate actual returns; some enterprises invest heavily in R&D of large models, but lack commercialization capabilities, and eventually fall into a dilemma of continuous losses.

There is a huge gap between the capital's excessive expectations for the speed of AI technology landing and profitability and the actual situation of industrial development. Once capital clearly realizes the core dilemma of "difficult landing and difficult profitability" of AI, large-scale capital withdrawal is very likely to occur, which will further trigger the burst of bubbles and push the industry into phased growing pains.

In summary, combined with Cathie Wood's latest judgment in 2026, it is not difficult to see that as the core carrier of the new generation of technological revolution, AI has an unquestionable and bright long-term development prospect. This conclusion is not only derived from the irreversibility of technological iteration and the rigid demand of industrial applications, but also benefited from the dual support of global policies and capital. However, at the same time, within the short term of 2-3 years, the burst of local bubbles and phased industrial growing pains in the AI field are inevitable, which is the result of the combined effect of multiple factors such as capital frenzy, overvaluation, and delayed technology landing. It should be made clear that the "long-term brightness" and "short-term growing pains" of the AI industry are not opposites, but the inevitable law of the development of emerging technological industries — just like the Internet and new energy industries experienced phased growing pains in their early development, the short-term adjustment in the AI field is essentially a benign process of "eliminating the false and retaining the true" in the industry, which can effectively eliminate inferior enterprises and promote the concentration of capital and resources to high-quality enterprises with core technologies and landing capabilities, laying a solid foundation for the long-term healthy development of the industry.

Based on this, for different subjects, it is necessary to base themselves on their own positioning and rationally respond to the short-term fluctuations and long-term opportunities of the AI industry. For investors, they should abandon short-term speculative mentality, rationally view the short-term fluctuations and long-term value of the AI field, not only guard against bubble risks and avoid blind follow-up investment, but also seize long-term industry development opportunities and deploy high-quality assets; for AI enterprises, they should abandon the impetuous mentality of "riding the wave and speculating on concepts", focus on core technology R&D and industrial scenario landing, strive to improve core competitiveness, and achieve sustainable profitability, so as to stand firm in the short-term industrial growing pains and seize long-term opportunities; for policymakers, they need to balance innovative development and risk control, strengthen supervision in the AI field, standardize the order of industrial development, and at the same time increase support for basic AI research and core technological breakthroughs, guarantee the healthy and orderly development of the AI industry. Cathie Wood once emphasized in the report: "The seeds of this technological revolution have been sown, and we are already in a fully erupted technological revolution." Short-term growing pains are an inevitable cost of industrial growth. In the long run, AI will surely profoundly reshape the global industrial pattern and become the core driving force for high-quality global economic development.

Only by maintaining rationality and patience, facing the short-term growing pains and embracing long-term brightness, can we seize opportunities, avoid risks, and achieve the common development of individuals and the industry in the wave of AI technological revolution.

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