2024-08-19
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As the “bubble panic” spreads, will the AI boom repeat the Internet bubble?
Column manager: Li Haidan Hao Boyang
Planning and interviews: Li Haidan and Hao Boyang
Video appearance: Li Haidan
Text by: Li Anqi and Yang Zhe
Editor: Hao Boyang
On August 5, 2024, global stock markets experienced a "Black Monday".
The S&P 500 fell 3.1% and the Nasdaq fell 3.4%.
Among them, technology stocks and chip stocks led the decline. Nvidia fell 6%, Apple fell 4.6%, and Tesla fell 4.2%.
The combined market value of the “Big Seven” evaporated by $1.3 trillion in early trading. Although their share prices have since recovered, their total market value loss for the day was still slightly more than $650 billion.
The "fear index" VIX once soared 181% to 65.73, the highest since the outbreak in March 2020.
In this regard, Sun Lijian, director of the Financial Research Center of Fudan Development Research Institute, said that the decline in U.S. stocks was because they were at a bubble high where it was too cold to be at high altitudes.
The most prominent industry in this bubble is the AI industry, as it has been falling for more than half a month.
(Changes in the NYSE AI Index)
On July 18, the market value of the "Big Seven" in U.S. stocks evaporated by a total of US$1.1 trillion in five days.
A week later, on July 24, the "Big Seven" fell collectively again, causing the U.S. stock market to evaporate more than $750 billion in market value throughout the day. This also caused the S&P 500 and Nasdaq indexes to record their largest single-day declines since the end of 2022.
(The M7 index fell from July 10 to early August. Source: CNBC)
On July 31, the stock price of Nvidia, the biggest winner of this AI boom, plummeted 7.2% in one day, and fell 20% in a total of one month. Including this drop, Nvidia has fallen nearly 30% in July.
Intel, which has been pushing for AIPC, saw its stock price plummet 26% after the market closed after it released its financial report last Thursday, and plans to lay off 15,000 employees.
Outside of the stock market, warnings about the AI bubble continue to emerge.
In March this year, Apollo chief economist Torsten Sløk wrote that the AI bubble is not only worse than in the 1990s, but has also exceeded the peak of the Internet bubble.
Note: Apollo Global Management: an American investment management company.
Sequoia partner David Chan issued warnings last year and this year, saying that AI companies need to generate $600 billion in annual revenue to pay for infrastructure construction, which is impossible at the moment.
The most serious concern about the AI bubble comes from the report "Generative AI: High Costs, Low Benefits" released by Goldman Sachs at the end of June. Many experts interviewed in the report said that people have too high expectations for AI and have invested too much, but its existing and potential benefits are too small. At present, there is a huge risk of AI bubble.
(The three most famous statements about the AI bubble)
Is AI a bubble that is accumulating? If so, has the bubble burst now? What impact will it have in the future? After reading this article, you may find the answer.
To judge a bubble, we need to first understand what a bubble is.
Bubbles often arise from the emergence of new technologies. The market is overly optimistic about the future development of technology, which leads to over-investment and blind following, causing its value to exceed the level that the real economy can bear, followed by a sharp decline and finally bursting like a soap bubble.
Combining several classic papers on economic bubbles, such as Hyman Minsky's "The Financial Instability Hypothesis" and Jordi Galli's "Monetary Policy and Rational Asset Price Bubbles", we have summarized the core conditions for the formation of bubbles.
(Papers and monographs on bubble research)
The main reasons include: economic fundamentals are favorable for investment, the emergence of information gap, and the expansion effect of psychological and behavioral factors. In simple terms, the market has money and investors invest irrationally.
The first is that the market must have money, which means that the market must have sufficient liquidity.The basic economic reality of credit expansion and excess liquidity in a low-interest rate environment can trigger a bubble.
For example, in 2022, we experienced a period known as the "everything bubble". In response to the economic downturn caused by the pandemic, the Federal Reserve implemented near-zero interest rates and quantitative easing (QE) from 2020 to 2021. This move attracted investors to riskier investments and allowed unsustainable business models to develop on the basis of cheap loans. Almost all stock market assets appreciated at a high speed, setting a record in US history. It was not until 2022 that the Federal Reserve raised interest rates again to curb inflation, and the stock market plummeted. In one year, Google fell 40%, and Tesla and Meta's stock prices fell 60%.
The second is irrational investment by investors.New technologies allow investors to obtain fairly high returns through early investments. The monopoly nature of certain tracks themselves makes their potential future returns even higher. Sufficiently high profit margins lead to blind optimism in the market, causing investors to underestimate risks and overestimate returns.
For example, the Internet bubble burst in 2000. In 1995, a large amount of venture capital poured into Internet-related fields such as e-commerce, telecommunications, and software services, with a return on investment far exceeding that of other industries such as chemicals, energy, and finance. When speculators noticed the rapid growth of stock prices, they bought in anticipation of further increases. In 1999, the amount of investment in Internet-related industries in the United States reached $28.7 billion, nearly 10 times that of 1995.
Can this bubble be compared with previous bubbles?
Xiong Weiming, founder of Huachuang Capital, judged this bubble as follows:“The extent of this wave of bubbles is actually far less than the Internet bubble 20 years ago, or even the cryptocurrency bubble in 2017, or the NFT bubble in 2021. These bubbles are characterized by valuations that far exceed the investment return cycle that can be obtained from actual products and services.If measured in proportion, I think the extent of this bubble is probably only 20% to 30% of the dotcom or NFT bubble. The extent of this bubble is definitely not as large as the previous ones.”
The reason is that both conditions for bubble formation are not sufficient.
The first of the two prerequisites for the bubble mentioned above is that there must be money in the market.
However, the current liquidity of the US financial market is not optimistic, which means that the upper limit of the AI bubble cannot be high.
The financing environment has been relatively poor in the past two years. In order to curb the highest inflation in 40 years caused by monetary easing during the epidemic, the Federal Reserve raised interest rates 11 times from March 2022 to July 2023.
At the same time, the Fed also began a large-scale balance sheet reduction. Starting in June 2022, the Fed will reduce its holdings of Treasury bonds by $60 billion and its holdings of mortgage-backed securities (MBS) by $35 billion each month.
(The speed of interest rate hikes in the United States, the yellow part is the interest rate hikes in 2022-2023)
To sum up, during the AI outbreak, the Federal Reserve is implementing the most aggressive monetary tightening policy since the 1980s.
However, the easing during the bubble period of 2021 brought a huge amount of liquidity, and the aftermath has not yet been eliminated. Xiong Weiming said in comparison:"In the past two years, AI may have reached its peak from the perspective of capitalization. In 2021, the United States issued $6 trillion in half a year, which is the only time in human history. This capital-accelerated effect is unprecedented."
But the market is still not as active as expected.Even though almost all VCs are caught in FOMO, the overall trend of venture capital in the US stock market is still declining. According to Crunchbase data, the total global financing in the first half of this year fell by 5% year-on-year.
Of course, AI startups among them stood firm against the wind, growing 24% year-on-year, and even received the largest quarterly investment of US$24 billion in the second quarter of this year, but the total value is still only 70% of that in 2021.
But VCs are still holding on to their money much tighter than they were in 2021.
According to the data provided by COATUE, although this round of AI investment is lively, VCs have not yet given their all. Private equity firms still have $1 trillion in uninvested funds, which is at an all-time high.
There are two main reasons for this.
First, the exit path is not smooth and VC investments are hesitant.After the last round of the "everything bubble", the number of unicorns soared, from 67 in 2016 to 580 in 2021. But their refinancing rate has been falling sharply. From 2016 to 2022, the proportion of unicorns that received refinancing during the same period dropped from 50% to less than 20%.
What about IPOs? The numbers are even worse, with the number of IPOs basically in the single digit since 2022.
Xiong Weiming also mentioned, "In fact, there were 970 IPOs in the US stock market in 2021, which dropped to 162 in 2022, and only about 44 in the first half of this year. This shows that the contraction of the global capital market is an obvious trend."
In this case, the only exit option left is M&A, which is too narrow.
Another reason is that the current stage of AI development has a high investment threshold, which limits the entry of many VCs.
"The early Internet industry needed to build its own servers and infrastructure, similar to today's AI field," said Xiong. "The cost of running a large model ranged from tens of thousands of dollars to hundreds of millions of dollars. It was in the early stages of building a new infrastructure."
According to Coutue's data, most of the money entering the field of artificial intelligence flows to foundational layer companies, which are the large model companies we are familiar with, such as OpenAI, Anthropic, Gemini, etc.
They then use this money to purchase chips from computing layer companies such as Nvidia to train their own large models.
Therefore, the current position of the AI industry is more like the infrastructure construction period. It is precisely this stage characteristic that makes it difficult for small VCs with insufficient funds to enter the market.
So "last year and the year before, a large number of AI companies, especially those in Silicon Valley, made early investments, which seemed active, but 80% of the investments were concentrated in the early stages, and many companies were eliminated in the upgrade of large models. Large companies have obvious advantages in the field of NLP because the cost of each test is too high, which is similar to the development of the Internet 20 years ago. At that time, the Internet cost was high, and fiber optic cables and computer rooms were built. Now the investment cost of AI is also very high. The change in infrastructure from small parameters to large parameters gives large companies a natural advantage. "Xiong Weiming believes that it is the threshold and nature issues that make this wave of AI investment stand out. "This wave of investment, whether in China or the United States, is mainly dominated by large companies. The United States is also dominated by several major companies. Startups are not the mainstream in this wave of innovation, and the mainstream is still large companies."
Therefore, whether from the overall performance of the financial market or from the enthusiasm of VC participation, the hot money currently placed in the AI pool by the investment community is not as much as during the previous bubble period.
There is little money in the market and the investment threshold is high, so who is playing this investment game?
In fact, the core players in this round of AI investment are mainly the leaders of the Internet era, who have a natural tendency to spend money. The most typical of them are the "Seven Giants of US Stocks".
According to a report jointly released by Flow Partners and Dealroom at the end of May this year, the combined market value of the seven largest US stocks accounts for 32% of the S&P 500 index, and their economic profits account for nearly half (45%) of the S&P 500 index.
So much so that in the past year, the Big Seven became the largest investors in AI, participating in 208 venture capital projects in 2023 alone.
In the first half of 2024, the Big Seven invested nearly $25 billion, more than the total of all venture capital in the UK, and most of this money went into the field of artificial intelligence.
Whether it is a large model or a chip company, the Big Seven are always behind it. Even before Musk's X.ai raised $6 billion from non-Big Seven investors, the Big Seven accounted for nearly 70% of all basic model investments.
These giants who are so "heavily invested" in AI, with their left hand investing and their own research with the right hand, personally tell stories to the capital market, and the technology stock prices are continuously pushed up by the promised technological myths.
Today, the average price-to-earnings ratio of the seven giants with a total market value of 16 trillion US dollars has reached 45 times (the average of the S&P 500 is 28 times), and the market value of corresponding investment darlings such as OpenAI, Anthropic and other start-ups is also rising.
Is this a rational market? We need to look at the reasons why the giants are betting.
The attitude of the giants towards AI is almost a gamble. Huang Renxun said in a recent conference call with Nvidia:
“Let me give you an example of why time is really valuable, why the idea of being able to get a data center up and running is so valuable, and why getting training time is so valuable. The reason is,The next company to reach a major milestone will announce a breakthrough A.I.And the second company after that will announce something that’s only 0.3% better than that.So the question you have to ask yourself is, do you want to be the company that repeatedly delivers breakthrough AI, or do you want to be the company that only delivers 0.3% performance improvements?… So that’s why we’re building the Hopper superchip system like crazy right now, because the next major milestone is right around the corner.”
AI is a technology of the times that is visible everywhere. Whoever seizes the first opportunity will control the rules of the next game. For the Big Seven, whether they are in a bubble or not, the decision they make is the same. Because it does not depend on whether you are willing to distinguish whether it is a bubble or an opportunity, but on whether you can survive in this competition.
However, the investment of the giants is not very aggressive compared with the cash flow they generate.
Judging from the financial reports, these companies basically achieved revenue of more than 10 billion US dollars in the last quarter.
Microsoft achieved a profit of $22.04 billion in Q2 of fiscal year 2024. Despite such heavy investment in AI, its net profit margin only dropped from 39.44% in Q3 of fiscal year 2023 to 34.04% in Q2 of fiscal year 2024. Alphabet's profit in Q2 reached $23.6 billion, and Amazon's $13.4 billion.
The Big Seven have healthy profits overall, and they have a lot of cash in their pockets that they can't spend.
Apple's free cash flow has now exceeded $100 billion. Microsoft, Alphabet and Amazon are expected to join the "$100 billion free cash flow club" in the next few years based on their revenue growth rates. Meta's free cash flow this year may exceed $30 billion.
Nvidia and Tesla have slightly less free cash flow, but before the explosion of AI, Nvidia was already able to generate billions of dollars in free cash flow each year. After making a lot of money in the past two years, it should be able to reach the level of tens of billions.
The Big Seven now expects their combined investment in AI to be no more than $50 billion in 2024, which is well within their means in terms of both profits and cash.
If this is a life-and-death battle for the next era, are they keeping these profits and cash for retirement?
Precisely because the giants can afford the risk, it cannot be said to be irrational.
When giants with ample cash flow invest in AI, they are also investors. At this time, the health of the giants' own valuations has become an important indicator for judging the AI bubble. After all, only by stabilizing themselves can there be a steady stream of cash flow to support a virtuous cycle.
Here's a chart that uses the "Rule of X" to assess the market capitalization of the Big Seven relative to their revenue growth and profit margins.
In simple terms, the sloping line in the graph represents the theoretical fair value. If a company's point is above the sloping line, it means that its market value is overvalued relative to its revenue, and if it is below the line, it is undervalued.
usIt can be seen that the market capitalization of Amazon, Tesla, Alphabet (Google's parent company), and Meta (formerly Facebook) below the diagonal line is underestimated relative to expected revenue. In other words, the possibility of a bubble in the stock prices of these companies is low because their market capitalization does not show signs of over-inflation.
Microsoft and Apple, which are listed on the slash line, have a slight premium, but they rank first and second in market capitalization respectively. One is the largest investor behind OpenAI, and the other is a company that can easily establish a deep cooperative relationship with OpenAI. Needless to say, their strength is naturally beyond words.
Even Nvidia, which is considered to be the most suspected of a bubble, will have a market value of $1.3 billion by the first quarter of 2024.Over the past six quarters, the stock price has risen by 744% and profits have risen by 330%. This can be said to be the bubble with the most fundamental support.
Hedge fund COATUE also made an estimate.likeTaking Cisco, which had the most obvious growth during the Internet bubble period, as an example, its five-year average P/E ratio was 37 times, but it was as high as 132 times during the bubble period.
The same calculation method applies to Nvidia, whose average P/E ratio over the past five years was 40 times, but today it is 68 times, far from the level during the Cisco bubble period.
(Source: COATUE, this version is the modified Nvidia P/E data)
As an emerging overlord, Nvidia's price-to-earnings ratio is only above average even in the semiconductor industry.
(Nvidia's P/E ratio among all semiconductor companies' P/E ratios: red)
Therefore, the giants have healthy earnings, sufficient cash, and reasonable valuations. Their and the stock market's risk resistance is completely different from that during the Internet bubble.
"Recently, the market value of seven large companies evaporated by $1 trillion in one day. Although this scale is huge, its impact is much smaller than the fluctuation of the same market value 20 years ago." Xiong Weiming analyzed the recent stock market fluctuations. "20 years ago, during the Internet bubble, the decline in market value was distributed among many small companies, and each company fell from $100 to $2. This decline had a huge impact on the market. Now, the adjustment of market value is mainly concentrated in a few large companies. Therefore, even if the market value of these companies fluctuates greatly, the impact on the overall capital market is relatively small. This is why I don’t think the correction in the AI market will cause a huge shock to the capital market like the Internet bubble in 2000."
So the market is far from being dangerously irrational.
But another rational premise is that investment needs to have corresponding returns.
The giants are willing to participate in the arms race. When money is not an issue, the question of return on investment needs to be further responded to. This is also the core of the report released by Goldman Sachs and the 600 billion question of Sequoia Capital.
The current stage of AI is more like infrastructure. The payback period of infrastructure is different from that of short-term investment, which is basically five years. Even the payback period of data centers is generally around 4.5 years.
Xiong Weiming believes that "it may take 5 to 10 years for AI to be commercialized. Looking back at the development of the Internet, the initial business models such as advertising and search engines also took a long time to cultivate. Therefore, we need to be patient and have space for the commercialization of AI."
Since this is an investment with a long payback period, when can we earn back the money invested in AI?
Couteue did the math for us. AI is expected to cost $1.2 trillion in infrastructure construction by 2030, which is roughly 25 million GPUs plus related expenses. This seems huge, but it only accounts for 18% of global IT spending.
Based on a 25% ROI, or an expected profit of $600 billion plus $1.2 trillion, AI investment must generate $1.8 trillion in revenue by 2030 to break even.
This can be achieved in two ways,One is to reduce costs.If AI can reduce the total global technician wages by 5% or the wages of all workers by 3%, it can achieve a benefit of 1.8 trillion.Another is to increase revenue.If AI can bring about a 2% increase in global GDP and boost the revenue of all listed companies by 3%, then AI companies only need to earn half of the profits, which will reach 1.8 trillion.
So the question is, can AI reduce costs and increase efficiency?
Daron Acemoglu, a professor at MIT, pointed out in a Goldman Sachs report that the economic benefits that generative AI can generate in the short term are very limited. Although he does not deny the potential of AI technology, he still asserts that within 10 years, AI can only affect 4.6% of all work tasks, and bring very little GDP growth, only 0.9%.
Such doubts are not without reason. Looking back at the history of technology, we will find that it takes a long time for a new technology to enter the market and penetrate into the lives of ordinary people.
For example, the suitcases we often use actually took shape as early as 1887, but it was not until 1972 that the patented design of installing wheels on the suitcase appeared, and it was not until 1991 that the most common roller suitcases were available.
Even a simple invention like a suitcase took 100 years from its design to widespread use before finding the correct "way to open" it, not to mention artificial intelligence technology, which has complex principles and is still a black box.
But is AI really as ineffective as Acemoglu claims? To find out, we looked at Acemoglu’s own papers and two studies he cited.
It turns out that Acemoglu's argument is difficult to stand up to scrutiny.
His argument cites data from two studies, using the futureThe proportion of tasks that may be affected by AI (20%) × the proportion of tasks that will actually use AI (23%) = the proportion of tasks that will be affected by AI in the future (4.6%), and concluded that AI has little benefit. And based on this, we calculated the ultimate impact of AI on GDP.
However, Acemoglu used the most pessimistic predictions about the development of AI in his paper. First, he believed that there would be no software that could effectively integrate large language models in the market in the next ten years. Second, he believed that the cost of using AI would not decrease in the short term.
For the first point,existAcemoglu cited the paper, the authors clearly pointed out that if GPTs were used, about 15% of all worker tasks in the United States could be completed significantly faster while maintaining the same quality. However, when integrating software and tools built on LLMs, this proportion increased to 47% to 56% of all tasks.
But Acemoglu only used the value of 15% for calculation.
However, almost all technology giants are currently trying to integrate AI into their own software. Software like Microsoft's Copilit and Adobe's Firefly are being continuously updated, and it is not uncommon to integrate LLMs.
As early as the launch of GPT-4o, OpenAI revealed the idea of developing a system-level application dominated by a large language model. This was confirmed again in OpenAI's two acquisitions this year. Combined with the current development of Agent, we have reason to expect to see LLMOS in the near future.
If it is true as Acemoglu said, that in the next ten years, no software that can effectively integrate large language models will appear on the market and be widely used, then it is not unfair to say that AI is a bubble, but this is obviously not true at the moment.
Regarding the second point,AcemogluThe judgment on the cost and penetration rate of AI is also not accurate enough.The cited paper states that American companies will choose not to automate most tasks that can be AI-enabled, and that only 23% of workers in visual tasks have wages that are attractive for automation. But the cited article makes it clear that the current slow adoption of AI will accelerate if costs fall rapidly or it is deployed through AI-as-a-service platforms that are larger than a single enterprise.
What's more, the reduction in AI costs is already a more obvious trend at present.
The founding CEO of the Allen Institute for Artificial Intelligence in the United States said in an exclusive interview with the "Daily Economic News" that Moore's Law in the chip era is still applicable in the AI era, and the cost of AI training and reasoning may drop by half every 18 months.
(Image source: Madrona Venture Group official website)
Taking ChatGPT as an example, Altman said in an interview at the beginning of the year:
“GPT-3 is our longest-running and most optimized model. In the more than three years since its launch,We have reduced its cost by 40 times…As for GPT-3.5,We believe we have reduced its cost by nearly 10 times...We have the steepest cost-reduction curve of any technology I know of.”
Judging from the actual market price, two years ago, GPT 3.5 cost 0.06 USD per thousand tokens. Now, Gemini Flash only costs 0.05 USD per million tokens. In just two years, the cost of AI has been reduced by 100 times, and the capability has been improved by 10 times.
In addition, a research report released by McKinsey in May showed that the global AI adoption rate will increase significantly in 2024, and the usage rate of generative AI will double compared to last year, which shows that more and more organizations and individuals are beginning to use AI.
In a survey by JPMorgan Chase, more than 55% of companies expect to use AI for production by 2025. The proportion of AI infiltrating companies is much higher than 23%.
so,Acemoglu's judgment can only be described as unrealistically pessimistic.
Of course, in addition to refuting Acemoglu's point of view, we have more evidence to prove the long-term and even short-term value of AI.
Joseph Briggs, an economist at Goldman Sachs, said:“While there is considerable uncertainty about the potential of generative AI, its ability to generate content indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a significant advance with potentially large macroeconomic implications.”
This impact comes first from a significant increase in production efficiency.
McKinsey estimates that GenAI can automate 70% of repetitive work, generating $2.6 trillion to $4.4 trillion in revenue each year, while increasing the impact of all AI by 15% to 40%.
In this light, compared with the optimistic estimate that it can contribute $2.6 trillion to $4.4 trillion in economic growth each year, is Sequoia's proposed $600 billion annual investment in AI infrastructure still a bubble?
Second, the scale effect caused by technology may have a disruptive impact beyond productivity.
There is no doubt that the Internet is the industry that has created the most wealth in the past 20 years. The last wave of Internet technology brought about e-commerce, platform economy, mobile social networking... It is a technological vehicle that connects the world as a whole. However, it was originally invented for national defense communications. At that time, few people would have thought that the Internet could so profoundly shape our current economic behavior and lifestyle.
Although it is difficult to define the far-reaching impact of AI at present, humans are always accustomed to overestimating their own judgment and underestimating the influence of technology.
"The Internet solves the problem of interconnection, moving the original offline data online and realizing digital twins. For example, dishes that could not be found on the Internet more than a decade ago, such as Beijing-style shredded pork with soy sauce, are now given an "IP address" for each item, just like the transition from IPv4 to IPv6. Every person and every item has a unique identifier. AI does not solve the problem of connection, but reorganizes the means of production and improves productivity." Xiong Weiming said, "AI can play a greater role in the world of digital twins and replace human processing capabilities. It is not just through connection, but through intelligent judgment and automated operation. For example, after air conditioners and refrigerators are connected to the Internet, they still need to set parameters manually, but with AI, these devices can make autonomous judgments and perform operations, such as automatically turning on the air conditioner when the temperature exceeds 28 degrees. This is the role of AI in the economic ecology, which is different from the role of the Internet. In fact, many industries need AI more than the Internet."
Therefore, our conclusion is: AI has a bubble, but this bubble is limited and does not deviate from its true value. Existing bubble talk is overly pessimistic.
Moreover, even if we follow the pessimistic view, after the explosive period of AI application, it may trigger a huge bubble like the one in 2000. If the fundamentals change, those small start-ups that are not profitable but have AI-related business ideas will receive a large amount of capital injection from venture capital and large-scale IPOs. Perhaps history will really repeat itself.
“If an application similar to Hotmail in 1999 emerges in the future and triggers the next wave of valuation growth, this is indeed possible. However, I think AI is more like a manifestation of hardware capabilities and a software application of computing power rather than a simple product design improvement. Therefore, AI valuation may not grow as quickly and significantly as the Internet.”
So, we are still in 1995.
"In 1995, the first wave of applications such as Yahoo were just beginning to emerge, similar to the technology we see now for generating images and videos. At that time, people were amazed and curious about the way Internet content was organized and searched. At that time, even a modem was a high-tech product, and products like Cisco's were only available to large companies."
From an infrastructure perspective, the situation is similar. Back then, companies needed to go to a telecom business office to get email services, which was expensive and required shared use. Today, only large companies can afford AI technology.”
Finally, we need to ask whether it is reasonable to use the current ROI to measure a technological advancement of infrastructure nature? Or, is the consequence of the bubble burst necessarily bad?
The current AI does face the problems of high investment and difficult application, but if we look further and turn to those infrastructure periods in history that are known as bubbles, we will find different things.
Before the Internet bubble burst, telecommunications companies raised $1.6 trillion on Wall Street and issued $600 billion in bonds, building 80.2 million miles of fiber optic cables, accounting for 76% of the total basic digital wiring in U.S. history, laying the foundation for the maturity of the Internet.
Looking further back, the British railway bubble in the 1840s and the railways built as a result laid the foundation for Britain's highly industrialized revolution. The mileage of railway plans approved during the economic bubble accounted for 90% of the total mileage of the British railway system.
When we talk about the dot-com bubble, we are not referring to the internet technology as a bubble, but to a specific business model, mainly e-commerce, that was hyped up by overexcited speculative investment. Similarly, the South China Sea bubble was not the bursting of a maritime trade bubble, but just a specific monopoly.
Artificial intelligence means more, and the wheel of history will not stop because of the bursting of bubbles. Supporters of artificial intelligence are always quick to point out that AI is the new Internet - a fundamental new technology architecture - if this is true, it will not turn into a bubble.
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