Source: FT Chinese
On January 27, 2025, Nvidia's market value fell by 17% in a single day, and its market value evaporated by about $600 billion, setting a record for the highest single-day market value loss of a single company in the history of the US stock market. The trigger for this plunge was the release of a low-cost, open-source large model, DeepSeek-R1, by the Chinese company DeepSeek. In 2023, Nvidia's stock soared by 239%; in 2024, it rose again by 171%. The rocket-like rise in its stock price has caused some investors to doubt whether its valuation has reached its peak. Microsoft, a major supporter of OpenAI, and its CEO Satya Nadella clearly expressed their concerns in a recent interview: "If current AI companies cannot bring real GDP growth and there is no real demand to support the products they develop, they will eventually collapse and die." Is the current AI market frenzy and soaring stock prices based on the company's real fundamentals, or is it due to irrational prosperity? This epic single-day plunge is more reminiscent of the bubble in the Internet era 25 years ago. When the Nasdaq index climbed to a historical high of 5,048 points in March 2000, Wall Street also did not foresee that the Internet revolution symbolized by ".com" would evaporate $6.5 trillion in market value within two years, causing Amazon's stock price to plummet by 90% and Sina's stock price to fall by more than 98%. Today, as a representative of Chinese companies, DeepSeek's potential in technological breakthroughs and cost control has not only intensified global technological competition, but also triggered the market's deep reflection on the valuation bubble in the AI industry.
Is the technological revolution inevitably accompanied by a capital bubble? After the bubble bursts, which companies can cross the cycle? Although separated by 25 years, these two waves present a profound mirror image and division in terms of market size and corporate survival logic. This article aims to analyze the inevitability and important role of capital bubbles in the technological revolution by comparing the Internet bubble 25 years ago with the current AI investment boom, and explore whether the AI era faces similar risks of decline, and what corporate characteristics can help institutions cross the cycle. As Chinese companies rapidly rise in the global AI competition and continue to expand their global influence, this technological competition is shifting from the monopoly of Silicon Valley unicorns to a diversified competitive ecology that is completely different from the Internet era.
The ".com" era
On August 9, 1995, Netscape went public through an IPO with an EBIT margin of -28.11% and a return on equity (ROE) of -26.14%. On the same day, its stock price soared from the issue price of US$28 to US$75, and finally closed at US$58.25 per share. As the first Internet company to push the "unprofitable but high-growth" model into the mainstream through an IPO, this landmark event is considered one of the beginnings of the Internet bubble. It broke the traditional valuation system centered on profitability and made investors begin to focus on user growth, technological leadership and market potential. This trend led to a large number of unprofitable startups pouring into the capital market in the late 1990s, eventually forming a bubble.
From 1995 to 2000, the Nasdaq index soared 573% in five years, during which time representative companies such as Amazon, AOL, Netscape, eBay, Yahoo and Google were born. At the same time, more than a thousand Internet companies such as Sina, Tencent and Alibaba emerged in China. On March 10, 2000, the Nasdaq index reached its highest point in history at 5048.62 points, but fell to 1114.11 points on October 9, 2002, which means that the Nasdaq index fell by about 78% from peak to bottom. In more than two years of continuous decline, nearly 5,000 Internet companies in the United States went bankrupt.
The three giants that fell in the bubble
As one of the three most representative giants in the early days of the Internet, Yahoo's rise and fall history reflects the typical trajectory of the first generation of Internet companies. As the leading portal at the time, Yahoo innovated the advertising business model by creating a hierarchical directory navigation system, becoming the first pioneer to transplant the traditional media advertising model to the Internet. Its advertising revenue accounted for 90% of its revenue in 1999. However, the company failed to continuously update its business model, stuck to a single advertising revenue structure for a long time, failed to effectively expand new businesses (such as e-commerce and social networking), and lost to Google in search technology due to the lack of technical moat and failure to invest in key technology development. Strategic conservatism prevented it from successfully transforming in the Web 2.0 era (user-generated content) and missed major opportunities to acquire Google and Facebook. These factors made it difficult for Yahoo to maintain its competitiveness after the Internet bubble burst. From the peak of $125 billion in market value in 2000, it plummeted to about $10 billion in two years, and was finally acquired by Verizon for $4.8 billion in 2016. This transaction became its iconic ending.
AOL was the first to launch a $19.95 monthly Internet package in 1996, ushering in the "always online" Internet era. The company's "free trial + CD bombing" marketing strategy implemented since 1993 - by mailing hundreds of millions of installation CDs to potential users, its user base surged from 200,000 in 1993 to 34 million registered users in 2000. However, the company's stubborn continuation of the dial-up subscription model and neglect of broadband penetration in the later period eventually led to a large-scale loss of users. The merger with Time Warner is considered one of the most failed mergers in history: AOL was valued at approximately $182 billion when it announced its merger with Time Warner in 2000. However, after the merger, due to intensified cultural conflicts, neglect of product technology transformation (especially in the broadband field), chaotic management, failed resource integration, and failed to achieve synergy. These problems led to the loss of core talents, a decline in advertising revenue, and financial pressure after the bursting of the Internet bubble, which led to a significant decline in its market value. Finally, in 2015, AOL was acquired by Verizon for approximately $4.4 billion.
Netscape launched its first commercial browser, Navigator, in 1994, which once occupied 80% of the market share and soared to $2.9 billion on its first day of listing in 1995. However, after Netscape was acquired by AOL in 1998, due to internal management problems and cultural conflicts, its core team left one after another, and the company's technological development stagnated. Although the open source strategy gave birth to the Mozilla project, the company failed to respond to market competition in a timely manner. Microsoft quickly seized market share by bundling the IE browser into the operating system and adopting a free strategy, making it difficult for Netscape's business model to support long-term development. Although Netscape no longer exists as a company, it promoted the development of the early Internet and laid an important foundation for modern web technology.
Enterprises in the post-bubble era
During the Internet bubble period (1995-2003), although these representative Internet companies experienced a cycle of rapid rise and bubble burst, and each fell into decline or even closed down for different reasons, overall, the capital market's over-optimism about emerging technologies and the failure of traditional valuation models were key factors. The negative profit model makes it difficult for traditional financial indicators (such as cash flow and price-earnings ratio) to effectively evaluate Internet companies, and instead rely on the "eyeball economy" and user growth. Combined with weak corporate culture management, capital market enthusiasm and FOMO (fear of missing out), venture capital and secondary markets fell into irrational expansion. If a company lags behind in technology and lacks a sustainable business profit model, it is difficult to survive the market bubble adjustment stage. However, there are also some companies that have become a few examples of survival and growth, such as Amazon in the United States. In addition, some emerging companies not only avoided the bubble, but also took advantage of the reshuffle period brought about by industry adjustments to achieve overtaking, such as Google, Facebook, Tencent and Alibaba.
Although Amazon's stock price plummeted by 90% during the Internet bubble, its founder Bezos always emphasized long-term innovation rather than short-term profits, and invested funds in logistics network construction and technology research and development through diversified business layout. In 2006, Amazon launched AWS cloud computing services, opening up new growth points and gradually becoming a global leader in cloud services. During the bubble burst, Amazon accumulated capital reserves through multiple financings to ensure sufficient cash flow to maintain operations, which provided key support for the company to survive the industry downturn.
Google did not go public until 2004, so it successfully avoided the speculative frenzy in 1999. During this period, the company used venture capital to develop steadily, and did not blindly pursue short-term expansion goals, but focused on breakthroughs in search technology. After 2000, with the PageRank algorithm, Google replaced Yahoo and established a monopoly in the search field. At the same time, the company launched AdWords (2000) and AdSense (2003) through advertising model innovation, which brought the company a stable and considerable source of income. In the later stage, the dual strategy of developing cloud computing and AI was established, and a complete value chain from traffic acquisition to technology empowerment was built.
The “AI” EraCore Elements of Enterprises that Cross Cycles
Looking back at the evolution of the Internet from its heyday to its bubble and then to its rebirth 25 years ago, it completely follows the Gartner curve of "technical germination - expectation expansion - bubble burst - recovery and maturity". This historical cycle reveals the core survival elements of the "survivors" in the technological revolution: it requires not only continuous technological breakthroughs and sustainable profit models, but also the support of a capital chain that resists capital fluctuations, and a mature and stable governance decision-making system is its core support. Enterprises with these characteristics can not only cross cycles and achieve long-term success, but also constitute the key elements that drive enterprises to break through and win in the red ocean competition of the Internet.
The valuation dilemma of start-ups from 0 to 1 still exists
In the Internet era, a large number of start-ups achieved technological breakthroughs from 0 to 1, but the exit mechanism of enterprises in this period was dominated by IPO. Take 1999 as an example. The total amount of venture capital in the United States reached 54 billion US dollars, of which 62% flowed to unprofitable enterprises. A typical example is Sequoia Capital, which achieved excess returns by betting on projects such as Google and PayPal and relying on IPO exits. At that time, the listing of enterprises showed explosive growth. In 2000 alone, 442 companies completed IPOs on NASDAQ. However, due to the lack of stable profit data as a valuation anchor and the difficulty in quantifying the enterprise value brought by technological innovation, the valuation logic of these companies deviated significantly from traditional financial models (such as the discounted cash flow method). This contradiction makes it difficult for IPO pricing to truly reflect the core value of the enterprise, laying the hidden dangers for the bubble. When the bubble burst, not only benchmark companies such as Netscape and Yahoo fell, but also WorldCom, the second largest long-distance telephone company in the United States at the time, went bankrupt due to blind expansion and deterioration of financial structure. These cases reveal the systematic deviation between market value and the true value of enterprises, which is the core mechanism of bubble generation: capital pricing is decoupled from fundamentals.
The current AI era faces a similar dilemma. Although the technological revolution has also spawned breakthrough innovations from 0 to 1, the valuation of AI companies still lacks a mature framework for reference. Unlike the path of relying on IPO exit during the Internet era, the current stage is more inclined to private financing and mergers and acquisitions (such as Microsoft's acquisition of Nuance and Google's acquisition of DeepMind). However, the essential contradiction of corporate value assessment has not changed-the separation between the long-term profit potential created by technological innovation and short-term financial performance is still the core problem of capital pricing. This uncertainty has driven the AI investment boom and may also repeat the historical valuation bubble.
As of February 2025, the number of weekly active users of ChatGPT has reached 400 million, an increase of 33% from 300 million users in December last year. The company is expected to achieve $11 billion in revenue this year. OpenAI's valuation has increased from $150 billion in 2024 to $340 billion in the latest round of financing. However, according to media analysis that has reviewed OpenAI's financial documents, the company may lose $14 billion in 2025 and is expected to be profitable by 2029, when its revenue will reach $100 billion. Between 2023 and 2028, the company expects its total cumulative losses to reach $44 billion.
OpenAI is in a strategic expansion period of exchanging losses for hegemony. Whether its profit model is sustainable depends on its ability to maintain the technology gap and the speed of forming a commercial closed loop. If it can break through the computing power shackles through hardware self-development and establish a profit-sharing system in vertical fields such as medical care and education, it may be able to achieve a transformation path from losses to a trillion-dollar market value; but Open AI's valuation is still based on the forecast of future cash flow, and the risks caused by this uncertainty cannot be ignored. Once it is unable to maintain technological updates and commercial profit models, and the capital chain is broken, it is not ruled out that it will become an AI bubble specimen under capital ripening.
As an industry dark horse that emerged in early 2025, DeepSeek has had a significant impact on the market. Its subversiveness stems from algorithm optimization innovation-reducing the cost of pre-training to less than 1/10 of the training cost of models with the same performance in the industry, redefining the economic efficiency of AI. The market's valuation of DeepSeek is highly divergent: the forecast range is from $1 billion to $20 billion, and some even believe that its value is at least half of OpenAI. This huge difference confirms the common problem of ambiguity in the valuation system of innovative companies. At present, its core advantage lies in the support of its parent company, Huanfang Quantitative (China's top quantitative hedge fund) - the latter has reserved more than 10,000 NVIDIA GPU computing clusters as early as 2021, allowing it to bypass the bottleneck of traditional companies relying on external financing and focus on long-term technology research and development. The current risk focus is not on capital chain pressure, but on how to maintain the speed of technology iteration, build exclusive barriers to improve developer retention, and the sustainability of the open source business model. The high uncertainty of these variables is becoming a key constraint to suppress valuation consensus.
Will the AI market fall back? Time window historical mirror
Chinese companies trigger AI valuation thinking
Although both the Internet period and the AI period show the characteristics of capital frenzy, and there are commonalities in the valuation dilemma of start-ups, there are still differences between the two. The way Chinese companies participate is causing structural changes: in the Internet era, Chinese companies have a core dependence on the technology ecosystem dominated by the United States, resulting in their lack of voice in technology standards and market rules; in the AI era, Chinese companies represented by DeepSeek and Tongyi Qianwen, through engineering innovation and breakthroughs in vertical application fields, not only challenge the traditional closed-source model, but also directly impact the business model centered on "pay for results". For example, ChatGPT relies on subscriptions and API charges (such as $20/month for premium services), while DeepSeek adopts a free strategy, forcing competitors to lower prices or adjust their business models. This "cost-effective revolution" may compress the overall profit margin of the industry and cause short-term fluctuations in valuations. While impacting the existing order, this "cost-effective revolution" has also spawned a new ecology. The reduction of technical barriers is attracting more participants to quickly enter the AI vertical field, promoting the expansion of the scope of application and the extension of the commercialization cycle, so that market adjustments may be eased. In the long run, the intensified competition caused by companies such as DeepSeek is essentially a dynamic game between technological democratization and monopoly interests - injecting long-term momentum into economic development by promoting technological inclusion and productivity progress.
The inevitability of a decline
During the Internet bubble, liquidity tightening and negative profit models posed major risks. In 2001, the net profit of Nasdaq constituent stocks plummeted by 89%, thoroughly exposing the vulnerability of unprofitable companies in the capital ebb. The current challenges in the AI market are more complex: computing power bottlenecks (such as the shortage of Nvidia's high-end chip supply), lagging application commercialization profitability (most AI companies have not yet found a sustainable monetization path), and geo-technological decoupling (such as Sino-US chip control) - this is significantly different from the growth environment driven by global technology dividends during the Internet era. Such risks are coupled with the failure of traditional financial valuation methods for AI innovative companies, and the bubble generated by capital frenzy is still inevitable.
80,000 AI companies in China have been deregistered (accounting for 37% of existing companies), and SenseTime's market value has shrunk by 80% from its peak; the valuation of Waymo in the United States has dropped from the $175 billion predicted by Morgan Stanley in 2018 to the market consensus of $30 billion in 2023. These signals confirm that the initial development of the technological revolution is inevitably accompanied by a valuation bubble squeeze cycle. However, the adjustment of the AI market will not repeat the violent collapse of the Internet bubble period. There is a double buffer mechanism behind it: high technical barriers and a more rational capital environment (more technology giants are directly involved in AI investment). Therefore, although the pressure of short-term valuation correction is inevitable, the overall market adjustment will not be as drastic as in the Internet era.
As the market gradually rationalizes, the corporate profit model will tend to stabilize in the medium term (3-5 years), and positive cash flow will make valuation assessment more referenceable. In the field of large models, the head effect will become more and more significant, and an oligopoly pattern will gradually form. In vertical scenarios such as medical care and finance, companies with core competitiveness are expected to be the first to make profits, and the industry will show a polarization of "head profits and tail elimination". In the long run (more than 5 years), AI technology may become the basic framework of various industries, and its super automation potential (such as AGI and robots) may release more technological productivity dividends.
Bubble is a byproduct of technological revolution, not the end
Historically, although the Internet bubble brought a devastating blow and caused many .com companies to go bankrupt, it also gave birth to a group of important forces that promoted the next round of technological revolution, such as the rise of companies such as Tencent, Alibaba, Amazon and Google. The technological accumulation during the bubble period laid the foundation for the subsequent development of Web 2.0. The technological revolution also prompted emerging companies to avoid the money-burning model during the bubble period, turn to pragmatic development paths and explore new business models. For example, Netflix transformed from DVD rental to streaming subscription, and finally completely transformed into a global streaming giant. Although Facebook was founded in 2004 after the bubble burst, it avoided many potential risks in its early days thanks to the improvement of infrastructure after the bubble (such as the decline in broadband costs), the lessons learned by surviving companies, and the prudent investment environment of capital. Since users have cultivated their dependence on the Internet during the bubble period (especially habits such as social sharing), this laid the foundation for the development of real-name system and social graph model. Therefore, without the baptism of the Internet bubble, it would be difficult to give birth to such era-defining technology companies.
Similarly, the AI boom will also experience periodic pullbacks, but it has long-term potential in the field of super automation (such as robots and AGI) that far exceeds the Internet era. The United States continues its first-mover advantage in the Internet era to build a technological monopoly barrier, while China's DeepSeek phenomenon essentially promotes technological equality. With limited computing power and data resources, some developing countries prefer to access the open source ecosystem led by China rather than pay the high fees of closed-source services in the United States. This trend may reshape the global AI value chain and transform China from a technology follower to a rule conspirator. In terms of future policies, it is necessary to strike a double balance - both to support leading companies to break through technological boundaries and to cultivate a diversified application industry chain to build a healthy industrial ecology. The essence of technological revolution is not to avoid bubbles, but to transform the phased capital frenzy into new quality productivity through continuous technological iteration capabilities and viable business models, supported by stable capital, thereby driving long-term GDP growth and leading generational leaps.