How serious would an AI bubble be?
The following contribution is from The Atlantic, one of the most prestigious news and general information outlets in the United States.
The author is Rogé Karma, who is part of the editorial team.
The entire US economy is being driven by the promise of productivity gains that seem far from being realized.
If there’s one field where the rise of AI is said to be making humans obsolete—where the dawn of superintelligence is already upon us—it’s programming. This makes the results of a recent study truly surprising.
In the study, published in July, the think tank Model Evaluation & Threat Research randomly assigned a group of experienced software developers to perform coding tasks with or without AI tools.
It was the most rigorous test to date of AI’s real-world performance. Because coding is one of the skills largely mastered by existing models, nearly all participants expected AI to generate huge productivity gains.
In a pre-experiment survey of experts, the median prediction was that AI would speed up developers’ work by nearly 40%.
Afterward, study participants estimated that AI had sped them up by 20%.
But when the METR team analyzed employees’ actual work performance, they found that developers completed tasks 20% slower when using AI than when working without it.
The researchers were stunned. «No one expected that result,» Nate Rush, one of the study’s authors, told me. «We didn’t even consider a potential slowdown.»
No single experiment should be considered the final word. But the METR study is, according to many AI experts, the best we have, and it helps to understand an otherwise paradoxical moment for AI.
On the one hand, the United States is experiencing an extraordinary economic boom driven by AI: the stock market is soaring thanks to the sky-high valuations of AI-related tech giants, and the real economy is being boosted by hundreds of billions of dollars in spending on data centers and other AI infrastructure.
Underpinning all this investment is the belief that AI will make workers vastly more productive, which in turn will boost corporate profits to unimaginable levels.
On the other hand, there is growing evidence that AI is not delivering the expected results in the real world.
The tech giants that invest the most in AI are far from recouping their investments.
Research suggests that companies trying to incorporate AI have seen virtually no impact on their bottom lines. And economists looking for evidence of job losses replaced by AI have found virtually nothing.
None of this means that AI cannot, over time, be as transformative as its biggest proponents claim. But, over time, it could take a long time. This raises the possibility that we are experiencing an AI bubble, in which investor enthusiasm has far outweighed the short-term productivity benefits the technology offers. If this bubble bursts, it could dwarf the dot-com crash, and tech giants and their Silicon Valley backers won’t be the only ones hurt. Almost everyone agrees that programming is the most impressive use case for AI technology today. Prior to its most recent study, METR was known for a March analysis showing that the most advanced systems could handle programming tasks that would take a typical human developer nearly an hour to complete. So how might AI have reduced developer productivity in its experiment?
The answer has to do with the «capacity-reliability gap.»
Although AI systems have learned to perform an impressive array of tasks, they struggle to complete them with the consistency and accuracy demanded by real-life situations.
The results of the March METR study, for example, were based on a 50% success rate, meaning the AI system was only able to complete the task reliably half the time, rendering it virtually useless on its own.
This shortcoming hampers the use of AI in the workplace. Even the most advanced systems make small mistakes or slightly misinterpret instructions, requiring a human to carefully review their work and make necessary changes.
This appears to be what happened during the most recent study.
Developers ended up spending a significant amount of time reviewing and reworking the code the AI systems had produced, often more time than it would have taken them to write it themselves.
One participant later described the process as the «digital equivalent of looking over the shoulder of an overconfident junior developer.»
Since the experiment was conducted, AI coding tools have become more reliable.
The study focused on expert developers, while the greatest productivity gains could come from enhancing or replacing the skills of less experienced workers.
But the METR study could easily be overestimating the productivity benefits associated with AI.
Many cognitive labor tasks are harder to automate than coding, which benefits from massive amounts of training data and clear definitions of success.
«Programming is something that AI systems typically do extremely well,» Tim Fist, director of Emerging Technologies Policy at the Institute for Progress, told me.
«So, if it turns out they’re not even making developers more productive, that could dramatically change the landscape of how AI could impact overall economic growth.»
The gap between capability and reliability could explain why generative AI has so far failed to deliver tangible results for the companies that use it.
When MIT researchers recently analyzed the results of 300 publicly disclosed AI initiatives, they found that 95% of the projects failed to increase profits.
A March report from McKinsey & Company found that 71% of companies reported using generative AI, and more than 80% of them reported that the technology had no «tangible impact» on profits. Given these trends, Gartner, a technology consultancy, recently declared that AI has entered the «disappointment» phase of technological development.
Perhaps AI’s progress is just experiencing a temporary speed bump.
According to Erik Brynjolfsson, an economist at Stanford University, every new technology experiences a «productivity J-curve»: Initially, companies struggle to implement it, causing productivity to drop.
However, over time, they learn to integrate it, and productivity soars.
The classic example is electricity, which became available in the 1880s but didn’t begin generating large productivity gains for businesses until Henry Ford reinvented industrial production in the 1910s. Some experts believe this process will unfold much faster for AI.
«With AI, we’re in the initial, downward phase of the J-curve,» Brynjolfsson told me.
«But by the second half of the 2020s, it will really take off.» Dario Amodei, CEO of Anthropic, has predicted that by 2027, or «not much later,» AI will be «better than humans at almost everything.»
These forecasts assume that AI will continue to improve as rapidly as it has in recent years.
This is not a given. Newer models have been plagued by delays and cancellations, and those released this year have generally shown fewer significant improvements than earlier models, despite being much more expensive to develop.
In a March survey, the Association for the Advancement of Artificial Intelligence asked 475 AI researchers whether current approaches to AI development could produce a system that matches or surpasses human intelligence; more than three-quarters responded that it was «unlikely» or «very unlikely.»
OpenAI’s latest model, GPT-5, was released early last month after nearly three years of work and billions of dollars in investment. (The Atlantic signed a corporate partnership with OpenAI in 2024.) Before its launch, CEO Sam Altman stated that using it would be equivalent to having «a PhD expert in any field» on hand.
In some areas, including programming, GPT-5 represented a significant leap forward. However, based on the most rigorous measurements of AI performance, GPT-5 turned out to be, at best, a modest improvement over previous models.
The prevailing view in the industry is that it’s only a matter of time before companies find the next way to drive AI progress. This could be true, but it’s not guaranteed.
Generative AI wouldn’t be the first tech fad to experience a wave of hype.
What’s distinctive about the current situation is that AI appears to be driving the US economy as a whole.
More than half of the S&P 500’s growth from 2023 onward will come from just seven companies: Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. These companies, collectively known as the Magnificent Seven, are considered especially well-positioned to prosper from the AI revolution.
That prosperity has yet to materialize anywhere except in their stock prices. (The exception is Nvidia, which provides the crucial inputs—advanced chips—that the rest of the Magnificent Seven are buying.)
According to The Wall Street Journal, Alphabet, Amazon, Meta, and Microsoft have seen their free cash flow decline 30% over the past two years.
By one estimate, Meta, Amazon, Microsoft, Google, and Tesla will have invested $560 billion in AI-related capital investments by the end of this year since the beginning of 2024 and will have generated only $35 billion in AI-related revenue.
OpenAI and Anthropic are generating large revenues and growing rapidly, but they are still far from profitable.
Their valuations—roughly $300 billion and $183 billion, respectively, and rising—far exceed their current revenues. (OpenAI projects about $13 billion in revenue this year; Anthropic, between $2 billion and $4 billion.)
Investors are betting heavily that all this spending will soon generate record profits.
However, if that belief collapses, investors could start selling en masse, triggering a dramatic and drastic correction in the market.
During the internet revolution of the 1990s, investors poured money into virtually every company with a «.com» in its name, convinced that the internet was about to revolutionize business.
However, by 2000, it became clear that companies were wasting their money without making much in the way of profits, and investors responded by dumping the most overvalued technology stocks.
From March 2000 to October 2002, the S&P 500 fell nearly 50%. Over time, the internet transformed the economy and gave rise to some of the most profitable companies in human history. But that didn’t stop many investors from going bankrupt.
The dot-com crash was severe, but it didn’t trigger a crisis. The collapse of the AI bubble could be different.
AI-related investments have already surpassed the level reached by telecommunications at the peak of the dot-com boom as a percentage of the economy.
In the first half of this year, business spending on AI contributed more to GDP growth than all consumer spending combined.
Many experts believe that one of the main reasons the US economy has been able to weather tariffs and mass deportations without a recession is that all this AI spending acts, in the words of one economist, as a «massive private-sector stimulus program.»
An AI collapse could lead, overall, to lower spending, fewer jobs, and slower growth, potentially dragging the economy into a recession.
Economist Noah Smith argues that it could even trigger a financial crisis if the unregulated «private credit» lending that finances much of the industry’s expansion goes bust at once.
Rogé Karma: Does the stock market know something we don’t?
If we do end up in an AI bubble, the silver lining would be that fears of a sudden AI-driven job displacement are overblown.
In a recent analysis, economists Sarah Eckhardt and Nathan Goldschlag used five different measures of AI exposure to estimate how the new technology might be affecting a range of labor market indicators and found virtually no effect on any of them.
For example, they note that the unemployment rate for workers least exposed to AI, such as construction workers and fitness trainers, has risen three times faster than that of workers most exposed, such as telemarketers and software developers.
Most, but not all, other studies have reached similar conclusions.
But there is also a more unusual, intermediate possibility. Even if AI tools don’t increase productivity, the hype surrounding them could drive companies to continue expanding their use.
«I hear the same story over and over again from companies,» Daron Acemoglu, an economist at MIT, told me.
«Middle and senior managers are told by their bosses that they need to use AI for X percent of their work to satisfy the board.»
These companies might even lay off workers or slow down their hiring because they’re convinced—like the software developers in the METR study—that AI has made them more productive, even when it hasn’t.
The result would be an increase in unemployment that would not be offset by real improvements in productivity.
Although it seems unlikely, a similar scenario occurred in the not-so-distant past. In his 2021 book, «A World Without Email,» computer scientist Cal Newport points out that, beginning in the 1980s, tools such as computers, email, and online calendars allowed knowledge workers to manage their own communications and schedule their own meetings.
In turn, many companies decided to lay off their secretaries and typists. In a perverse result, the most skilled employees began spending so much time emailing, writing notes, and scheduling meetings that they became far less productive at their actual work, forcing companies to hire more employees to accomplish the same amount of work.
A subsequent study of 20 Fortune 500 companies found that those with computer-induced «staffing imbalances» spent 15% more on salary than necessary.
«Email was one of those technologies that made us feel more productive, but it actually had the opposite effect,» Newport told me.
«I worry that we’re going down the same path with AI.»
On the other hand, if the alternative is a stock market crash that precipitates a recession or financial crisis, that scenario might not be so bad.
This project received support from the William and Flora Hewlett Foundation.
Tech guru Erik Gordon says investors will suffer far more from the rise of AI than from the dot-com crash.
The following contribution is from the prestigious Business Insider website and is written by Theron Mohamed, a correspondent on Business Insider’s Trends team, based in London. His coverage covers finance, investing, wealth, markets, and economics.
Theron joined Business Insider in 2019 as a Markets Insider reporter and rose to correspondent before joining the Trends team in 2024. Previously, he covered technology, media, and telecom stocks for Investors Chronicle magazine and briefly contributed to the Financial Times’ Data team. He interned at the Wall Street Journal in New York, where he primarily wrote for Heard on the Street.
Business professor Erik Gordon commented on Pets.com, which went bankrupt during the dot-com crash: «The loss was minimal compared to what we might see with AI.» The AI boom will fail and dwarf the dot-com crash due to its larger scale, Erik Gordon said.
AI startups like CoreWeave threaten greater losses for investors than companies like Pets.com, he added.
The business professor has previously warned that AI is an «overvaluation bubble of considerable magnitude.»
The fall in one company’s stock shows how the financial consequences of a stalled AI boom will be far greater than those of a dot-com bust, tech guru Erik Gordon told Business Insider.
Gordon, an entrepreneurship professor who researches financial markets and technology at the University of Michigan’s Ross School of Business, has previously called the AI boom an «overvaluation bubble of considerable magnitude.»
Some investors have claimed that tech stocks soaring on AI optimism will collapse like the dot-com companies of the early 2000s, but others, like Kevin O’Leary, have dismissed the comparison.
Gordon compared the market values of Pets.com, the online pet supply store that became what he called the «poster boy» of the dot-com craze, and CoreWeave, an AI infrastructure startup that went public in March.
Shares of Nvidia-backed CoreWeave have fallen 33% in the past two days, wiping approximately $24 billion off its market capitalization.
This shows that «more investors will suffer than suffered in the dot-com crash, and their suffering will be even greater» amid the bursting of the AI bubble, Gordon said.
The fall came after the company’s latest results showed mounting losses and infrastructure limitations.
Pets.com, backed by Amazon and several well-known venture capital firms, reached a market value of $410 million at its peak in February 2000.
However, within the next 12 months, the company filed for bankruptcy and announced the liquidation of its assets, resulting in its shares being delisted.
«If we assume the $410 million was lost, the loss was minimal compared to what we might see in AI,» Gordon said.
CoreWeave demonstrates how sudden and significant losses can be for shareholders, Gordon added. The loss in its market capitalization is nearly 60 times larger than Pets.com’s peak capitalization. CoreWeave shares closed Thursday around $100 per share, more than double their trading price of $40.
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Tech investor Ross Gerber says AI is nothing like the dot-com bubble, and that Warren Buffett was wrong to cut Apple’s budget.
«It takes one hype-fueled tech stock to instantly destroy $20 billion in wealth,» Gordon said.
CoreWeave did not immediately respond to a request for comment from Business Insider. CEO Michael Intrator said in a statement accompanying the results that they showed «continued momentum across all dimensions of our business.»
The collapse of the dot-com bubble caused the S&P, including dividends, to fall by about 9% in 2000, 12% in 2001, and 22% in 2002. Dozens of startups filed for bankruptcy, and thousands of tech workers lost their jobs.
Tech giants account for a large portion of the US stock market’s value, and their profits and market dominance have made them mainstays of retirement portfolios and pension funds.
In 2022, Gordon told Business Insider that more people were investing in AI than in dot-com companies 25 years ago. He predicted there would be «more plates of spaghetti» after the AI boom exploded, as those hurt by the crisis would cook at home to save money and cut expenses whenever possible. Speaking to Business Insider last week, O’Leary said the AI boom wasn’t «the same hype as the internet bubble,» because today, productivity can be seen and measured dollar for dollar.
Is the AI bubble about to burst and send the stock market tumbling?
The following contribution is from the prestigious British media outlet The Guardian and is written by
Phillip Inman, senior economics writer for The Guardian.
US tech stocks are falling, but it probably wouldn’t be wise for fund managers to pull back.
There are growing fears of an imminent stock market crash, which will turn from a slump to a steep decline as the euphoric headlines about the wonders of artificial intelligence begin to fade.
US tech stocks have fallen in recent weeks, and a spate of negative numbers is expected to become the norm before the end of the month.
We could be back in the 2000s, and like the bursting of the dot-com bubble, it could be ugly, with investors dumping companies that once looked good in theory but now look like a huge liability.
Federal Reserve Chairman Jerome Powell is one of the policymakers tasked with keeping the wolf away from the door.
On Friday, at the annual meeting of central bank governors in Jackson Hole, Wyoming, Powell attempted to calm the situation.
He stated that the Fed was concerned about rising inflation, while also being willing to help an economy beset by the uncertainty generated by Donald Trump and the global economic slowdown.
The Relief of Falling Interest Rates
With stagflation a real possibility—as the US economy slows and inflation remains high—Powell signaled to the stock markets that interest rates will fall, easing pressure on indebted companies.
The stock markets are in Powell’s sights, even more than usual, now that so many personal pensions in the US are invested directly in publicly traded companies. And, more specifically, in tech stocks that are making huge investments in AI and have yet to generate a single dollar of profit.
A recent report from the Massachusetts Institute of Technology (MIT) revealed that 95% of companies investing in generative AI have yet to see any financial returns.
This revelation came after Sam Altman, director of OpenAI, owner of ChatGPT, warned that some companies’ valuations were «insane.»
Ipek Ozkardeskaya, senior analyst at the currency firm Swissquote, said: «Altman’s comments could have been a wake-up call for investors, causing a sharp drop in the most highly valued companies.»
Earlier this week, the share price of Palantir, a data mining and spyware company with billions of dollars in US government contracts, plummeted nearly 10%.
AI chipmaker Nvidia fell more than 3%, while other AI-linked stocks, such as Arm, Oracle, and AMD, also lost ground.
Most pension funds will invest in these tech companies, along with more established companies like Amazon, Microsoft, Alphabet (Google), and Meta (Facebook).
Should fund managers pull out? It would probably be unwise.
Investment in AI by companies like Google and Meta is enormous, and while the potential of this technology is the subject of much speculation, white-collar professionals are becoming increasingly familiar with its supposed benefits.
Corporate messages urging them to use AI for presentations, report writing, and research are commonplace (accompanied, of course, by false promises that staff cuts are not being considered).
Following the newsletter hype, Microsoft’s Copilot and many other useful AI tools are becoming integrated into office life and are beginning to perform a large number of low-level tasks.
If this trend takes off—and in many sectors of the economy it already has—the tech industry will face a soft landing, even as some of the more unstable and speculative ventures are discarded and fail.
If anything, a recession helps large companies salvage new technological breakthroughs from the rubble at a low price.
Palantir’s price-to-earnings ratio exceeds 500, when most investors would panic at values above 50. Nvidia has a price-to-earnings ratio of 56.
The Palantir/Nvidia ratio could decline as its stock price approaches a reasonable profit outlook, but the companies aren’t going bankrupt, even in the worst stock market storms.
Trump is another major supporter, paving the way for AI to become even more embedded in corporate life.
His support for cryptocurrencies, including his own, and for deregulated social media platforms, also his, are indicative of his sympathies.
AI will likely be detrimental to humanity, given that politicians and regulators are light years ahead of the tech moguls and magnates who back it, many of whom see it as a new way to disempower and dominate workers.
However, as an investor, AI is not going away, whether there is a crisis or not.
OpenAI’s Sam Altman predicts the formation of an AI bubble as spending in the industry increases
The following contribution is from CNBC, the world’s leading network for economic and financial news. It defines itself as follows: Our mission is to help influential and aspiring people make the right decisions to move forward. CNBC International ensures that, wherever you are, you stay up-to-date with the latest economic and financial news. With international headquarters in London and Singapore, we deliver the best global financial information around the clock. From the start of the Asian trading day to the close of trading on Wall Street, our dynamic and impactful business coverage makes CNBC a must-see channel for any high-level business leader.
The author is Dylan Butts, an associate general affairs reporter at CNBC, based in Singapore. He covers everything from business and technology to geopolitics in the Asia-Pacific region.
Key Points
OpenAI CEO Sam Altman has reportedly stated that he believes AI could be in a bubble, comparing market conditions to those of the dot-com boom in the 1990s.
«Are we in a phase where investors in general are overexcited about AI? My opinion is yes.
Is AI the most important thing that’s happened in a long time? My opinion is also yes,» he stated.
Alibaba co-founder Joe Tsai, Ray Dalio of Bridgewater Associates, and Apollo Global Management chief economist Torsten Slok have issued similar warnings.
Sam Altman says OpenAI pushed for a «much warmer» tone for GPT-5
OpenAI CEO Sam Altman believes the artificial intelligence market is in a bubble, according to a report by The Verge published Friday.
«When bubbles emerge, smart people get overly excited about a grain of truth,» Altman told a small group of reporters last week.
«Are we in a phase where investors in general are overexcited about AI? My view is yes. Is AI the most important thing that’s happened in a long time? My view is also yes,» he said.
Altman appeared to compare this dynamic to the infamous dot-com bubble, a stock market crash centered on internet-based companies that generated massive investor enthusiasm in the late 1990s.
Between March 2000 and October 2002, the Nasdaq lost nearly 80% of its value after many of these companies failed to generate revenue or profits.
His comments add to growing concerns among experts and analysts that AI investment is moving too fast.
Alibaba co-founder Joe Tsai, Ray Dalio of Bridgewater Associates, and Apollo Global Management chief economist Torsten Slok have issued similar warnings.
Last month, Slok stated in a report that he believed the current AI bubble was, in fact, larger than the internet bubble, and that the top 10 companies in the S&P 500 were more overvalued than they were in the 1990s.
In an email to CNBC on Monday, Ray Wang, director of semiconductor, supply chain, and emerging technology research at Futurum Group, said that while Altman’s comments had some validity, the risks depend on the company.
“From the perspective of increased investment in AI and semiconductors… I don’t see it as a bubble. Supply chain fundamentals remain strong, and the long-term trajectory of the AI trend supports continued investment,” he said.
Overvaluation Funds
However, he added that there is a growing amount of speculative capital seeking companies with weaker fundamentals and only perceived potential, which could lead to pockets of overvaluation.
Fears of an AI bubble reached a fever pitch earlier this year when Chinese startup DeepSeek launched a competitive reasoning model.
The company claimed that a version of its advanced large language models had been trained for less than $6 million, a fraction of the billions invested by US AI market leaders like OpenAI, though these claims were also met with some skepticism.
Earlier this month, Altman told CNBC that OpenAI’s annual recurring revenue was on track to exceed $20 billion this year, but that despite this, it was still unprofitable.
The release of OpenAI’s latest AI model, GPT-5, earlier this month was also problematic, with some critics complaining about its lack of intuition.
This prompted the company to restore access to legacy GPT-4 models for paying customers.
Following the model’s release, Altman also sounded more cautious about some of the AI industry’s more optimistic predictions.
Speaking to CNBC, when asked if the GPT-5 model brings the world closer to AGI, he stated that he believed the term artificial general intelligence, or «AGI,» was losing relevance.
AGI refers to the concept of a form of artificial intelligence that can perform any intellectual task a human can, something OpenAI has been working toward for years and which Altman previously stated could be achieved in the «reasonably near» future.
However, investor confidence in OpenAI has remained strong this year.
CNBC confirmed on Friday that the company was preparing to sell approximately $6 billion in stock as part of a secondary sale that would value it at approximately $500 billion.
In March, it announced a $40 billion funding round at a $300 billion valuation, by far the largest amount ever raised by a private tech company.
In a Friday article in The Verge, the OpenAI CEO also discussed OpenAI’s expansion into consumer hardware, brain-computer interfaces, and social media.
Altman also stated that he expects OpenAI to invest trillions of dollars in building its data center in the «not-too-distant» future and noted that the company would be interested in buying Chrome if the US government forced Google to… sell it.
Asked if he would be OpenAI’s CEO in a few years, he replied, «Maybe an AI will be that in three years. That’s a long time.»
Is the current AI boom bigger than the dot-com bubble?
The following contribution is from Reuters and written by Jamie McGeever, who has been a financial journalist since 1998, reporting from Brazil, Spain, New York, London, and now back in the United States. His experience and expertise focus on global markets, economics, public policy, and investments. Jamie has held positions in print and television, including reporter, editor, and columnist, and has covered key events and policymakers in several cities around the world.
Wall Street’s concentration in the vibrant technology sector is, by some measures, greater than ever, eclipsing the levels reached during the dot-com bubble of the 1990s.
But does this mean history is destined to repeat itself? The growing concentration in US equities immediately evokes the internet and communications frenzy of the late 1990s.
The tech-heavy Nasdaq peaked in March 2000 before plummeting 65% in the following 12 months. And it didn’t reach its previous high again for 14 years.
It seems unlikely we’ll see a repeat of this today, right? Perhaps.
The market’s reaction function appears to be different than it displayed during the dot-com boom and bust.
Just look at the current rebound following the post-Liberation Day tariff crash in early April—one of the fastest on record—or its rebound during the pandemic.
But for all these differences, there are also some troubling parallels. Investors should keep both factors in mind. TOP 10 CLUB
The most obvious similarity between these two periods is the concentration of technology and related industries in US stock markets.
The technology sector as a whole currently accounts for 34% of the S&P 500’s market capitalization, according to some data, surpassing the previous record of 33% set in March 2000.
Of the top 10 companies by market capitalization today, eight are technology or communications giants. These include the so-called «Magnificent 7»: Apple (AAPL.O), Amazon (AMZN.O), Alphabet (GOOGL.O), Meta (META.O), Microsoft (MSFT.O), Nvidia (NVDA.O), and Tesla (TSLA.O), as well as Berkshire Hathaway (BRKa.N) and JPMorgan (JPM.N).
In contrast, only five of the 10 largest companies in 1999 were technology companies. The other five were General Electric (GE.N), Citi, Exxon (XOM.N), Walmart (WMT.N), and Home Depot (HD.N).
Furthermore, the presence of the top 10 companies in the S&P 500 (SPX) is much larger today than it was back then.
The combined market capitalization of the top 10 is currently nearly $22 trillion, or 40% of the index’s total, significantly higher than the comparable 25% in 1999.
All of this reflects the fact that technology plays a much larger role in the US economy today than it did at the beginning of the millennium.
AI BUBBLE?
By some measures, the current tech boom, driven in part by enthusiasm for artificial intelligence, is more extreme than the tech bubble of the late 1990s.
As Torsten Slok, chief economist at Apollo Global Management, points out, the 12-month forward earnings valuation of the top 10 S&P 500 stocks is higher than it was 25 years ago.
However, it’s worth remembering that the dot-com bubble was characterized by a frenzy of public offerings and a multitude of companies whose shares were valued at triple-digit multiples of their future earnings. Today, this is not the case.
While the S&P technology sector is currently trading at 29.5 times forward earnings, a high by historical standards, this figure is far from the peak of nearly 50 times recorded in 2000. Similarly, the S&P 500 and Nasdaq are currently trading around 22 and 28.5 times forward earnings, compared to dot-com highs of 24.5 and over 70 times, respectively.
$3 TRILLION INVESTMENT HURDLE
With all of the above, a significant and prolonged market correction cannot be ruled out, especially if AI-driven growth does not materialize as quickly as investors expect.
AI, the new engine of technological development, will require large capital investments, especially in data centers, which could mean that earnings growth and stock prices in the technology sector could slow in the short term.
According to Morgan Stanley, the transformative potential of generative AI will require approximately $2.9 trillion of global data center investment through 2028, comprising $1.6 trillion in hardware such as chips and servers and $1.3 trillion in infrastructure.
This means more than $900 billion will be needed by 2028, they estimate. For context, the combined capital spending of all S&P 500 companies last year was around $950 billion.
Wall Street analysts are familiar with these figures, suggesting that at least a percentage of these enormous sums should be factored into current stock prices and expected earnings. But what if the benefits of AI take longer to materialize? Or what if a startup (remember China’s DeepSeek) dramatically alters the growth expectations of a major index component, such as Nvidia, the $4 trillion chipmaker?
Of course, technology is so fundamental to today’s society and economy that it’s hard to imagine its market presence shrinking too much for too long, as this raises the inevitable question of where investment capital would go.
Therefore, it’s reasonable to wonder whether a current tech crisis would take more than a decade to recover from.
But on the other hand, it’s that kind of thinking that has gotten investors into trouble in the past.
(The views expressed here are those of the author, a Reuters columnist.)
By Jamie McGeever; Editing by Emelia Sithole-Matarise
AI Stock Euphoria: Is Another Dot-Com Crash Like 2000 Looming?
The following contribution is from FoxBusiness and is written by Ted Jenkin, president of Exit Stage Left Advisors and partner at Exit Wealth.
Tech giants generate massive cash flow, unlike the speculative startups that crashed the market in 2000.
Charles Payne: Market pessimists believe AI spending is causing an exaggerated market.
«Making Money» host Charles Payne analyzes the causes of the market’s decline.
If you were investing in the late 1990s, you’ll remember the euphoria of the dot-com boom. Any company with a «.com» at the end of its name could raise millions of dollars in capital and see its stock price double or triple overnight.
Investors believed the internet would change everything, and, to be fair, it eventually did. But between 2000 and 2002, that dream turned into a nightmare when the Nasdaq lost nearly 80% of its value, squandering trillions of dollars in wealth.
Today, with artificial intelligence grabbing headlines and fueling investor enthusiasm, many are wondering if we’re about to experience another dot-com crash.
AI and the Stock Market
AI is perceived as the new internet: a transformative technology that promises to revolutionize industries from healthcare to finance and entertainment. ( / iStock)
Parallels to the Late 1990s
There are some undeniable similarities between the two periods. Back then, internet companies with little more than a business plan and a website were valued at astronomical levels.
Today, AI is perceived as the new internet: a transformative technology that promises to revolutionize industries from healthcare to finance and entertainment.
The narrative is powerful, and capital is pouring in. Recently, Palantir, a fan-favorite stock right now, traded with a PE of 522!
TRUMP’S AI PLAN IS A BULWARBOURN AGAINST THE RISING THREAT FROM CHINA
Another similarity is market concentration. In 1999, Cisco, Intel, Sun Microsystems, and AOL were the poster children for the boom. Today, the so-called «Magnificent 7» (Apple, Microsoft, Alphabet, Amazon, Meta, Tesla, and Nvidia) represent more than 30% of the S&P 500.
To put this in perspective, the S&P 500 is supposed to be a diversified index of leading U.S. companies. But if only a few stocks generate the majority of the returns, this creates real risks if those companies fail. The market capitalization of the top 10 S&P 500 stocks accounts for nearly 40% of the index.
The Differences That Matter
While the echoes of the dot-com era are strong, the differences are even stronger.
Jason Katz, managing director and senior portfolio manager at UBS, discusses artificial intelligence in the US markets and the performance of cryptocurrencies in the stock market. Video
Jason Katz on the Impact of AI on the US Markets: We’ve Barely Scratched the Surface
Jason Katz, managing director and senior portfolio manager at UBS, discusses artificial intelligence in the US markets and the performance of cryptocurrencies in the stock market.
First, valuations are exaggerated, but not as absurd as they were in 1999. Back then, the S&P 500’s forward price-to-earnings (P/E) ratio was over 25, an exorbitant figure for the time. Many internet stocks didn’t generate any profit, rendering traditional valuation metrics meaningless.
WE MUST ACT NOW TO PREVENT AI FROM BECOMING A FAR-LEFT TROJAN HORSE.
Today, the S&P 500’s forward PE is hovering around 21. This is high compared to the long-term average of 15-16, but nowhere near the dot-com level.
Crucially, the tech giants that dominate the current index are highly profitable companies that generate massive cash flow. The only sector where we see these dot-com patterns emerge is in AI stocks. Put the two letters AI next to a stock and it sparks an investor frenzy.
Second, the companies leading the change are not speculative startups with unproven business models. Apple, Microsoft, and Alphabet are multi-billion-dollar companies with solid balance sheets and decades of consistent profitability. Nvidia, the crown jewel of the AI sector, sells real products with extraordinary demand. Unlike Pets.com, Webvan.com, and eToys (remember those?), these companies have sustainable revenue streams and lasting competitive advantages.
Is AI the new dot-com?
AI certainly seems vibrant. Just as investors in the late 1990s believed the internet would transform all businesses, many now believe AI will transform every aspect of the economy.
Some of this optimism is justified. Nancy Prial, co-CEO of Essex Investment Management, reveals the factors driving the improved market breadth in the video «Making Money.»
AI will allow small-cap companies to do more with less, according to one expert.
The internet has changed our way of life today. AI has the potential to boost productivity, reduce costs, and create entirely new industries.
But in the short term, markets almost always overestimate the speed of adoption, and AI companies emerge so quickly that many are destined to fail.
Therein lies the risk: not in whether AI will change the world, but in how quickly investors believe it will happen. History teaches us that transformative technologies often go through hype cycles.
We thought people would no longer be writing checks by now, but still, 50% of Americans wrote at least one check in the last 12 months. There will be winners, but also plenty of losers along the way.
Why isn’t this the year 2000?
Despite the hype, I don’t think we’re headed for a repeat of the dot-com crash. Here’s why:
Earnings capacity: The largest companies in the S&P 500 are cash-generating machines. Apple alone generates more than $100 billion in free cash flow annually. That’s a far cry from the cash-burning dot-coms of the past.
Stronger balance sheets: American companies are healthier today. Many leading companies have low debt levels and huge cash reserves. In 2000, balance sheets were much weaker.
Regulation and maturity: The financial system is more prepared. The lessons of the dot-com bust and the 2008 crisis have led to more cautious capital markets.
Why the AI Boom Won’t Burst Like the Dot-Com Bubble
The following post is from the Finshots portal, which defines itself as: Simple Financial News.Join Finshots and get your daily dose of the latest, most important financial news, in simple language. In less than 3 minutes.
Authored by the team.
In today’s Finshots, we tell you how the AI boom could be different from the dot-com bubble.
The Story
In 2022, ChatGPT sparked a gold rush in the AI world, sparking dreams of a future where artificial intelligence would revolutionize everything.
Tech giants jumped on the bandwagon, investing billions in chips, data centers, and everything AI-related.
The hype reached fever pitch, and in June of this year, Nvidia, the chipmaker powering much of this AI revolution, surpassed the $3 trillion market capitalization mark.
Its stock soared ninefold from 2022, even briefly surpassing Microsoft and Apple to become the world’s most valuable company.
But then, reality hit.
Just days after reaching its 2024 peak, Nvidia lost a staggering $1 trillion in valuation, roughly 30% of its total value. And it wasn’t just Nvidia. All the AI heavyweights—Microsoft, Apple, Tesla, Amazon, Meta, Alphabet—began to plummet, losing hundreds of billions of dollars in stock market value.
Panic set in. Rumors of the AI bubble bursting began to circulate, and many feared a repeat of the infamous dot-com crash.
But is that really what’s happening? Is the AI bubble about to burst?
And if it does, will it collapse in the same catastrophic way as the dot-com bubble?
To answer this question, let’s first understand how stock market bubbles form and ultimately disappear.
Economic bubbles occur when the price of an asset—whether real estate, stocks, or something else—rises far beyond its true value.
When investors see an industry gaining momentum, they start investing in it, which drives up prices.
This creates a bandwagon effect: more people join in, convinced that prices will continue to rise.
But eventually, they realize they’ve paid too much for something that isn’t worth it.
So they start selling, and the same herd mentality that drove prices up now brings them crashing down. Not only do stocks collapse, but so do the industries that support them.
It’s a lot like the dot-com bubble, right? Well, yes and no, because there’s a subtle but important difference.
During the dot-com bubble, company stocks soared thanks to pure hype, long before real business models existed.
Take the e-commerce companies of that time, for example. They sold everything online: toys, clothing, even groceries. They invested heavily in warehouses, supply chains, and even offered free delivery to attract customers. They believed this would, over time, generate huge revenues and profits.
But the internet was just beginning to take off, and there weren’t enough people connected yet to sustain these businesses. Despite huge investments in marketing, customers didn’t arrive in the numbers expected, and profits were nonexistent.
Many of these companies, such as Webvan, a grocery delivery service that went public in 1999, had to file for bankruptcy.
Webvan’s stock doubled on its first day of trading, giving the company a valuation of $6 billion, despite generating less than $10 in revenue per customer and spending more than $27 processing each order.
Despite having loyal customers and excellent service, Webvan accumulated more than $1 billion in losses and shut down in 2001, eventually being acquired by Amazon.
And if you haven’t noticed the difference yet, during the dot-com bubble, many companies were overwhelmed by unrealistic expectations and unproven business models. They grew rapidly and created innovations, but they didn’t generate profits.
But with AI, the story isn’t the same.
The stocks that are falling now belong to tech giants like Microsoft, Apple, Tesla, Amazon, Meta, Alphabet, and Nvidia—long-established companies that have invested billions in AI.
Furthermore, the demand for professionals who have completed an artificial intelligence course is skyrocketing, further cementing AI’s position as a key driver of the future economy. Companies like Meta are increasingly seeking people with specialized skills, such as those who have completed a master’s degree in business analytics.
So, despite these positive aspects, why are they plummeting? You might ask.
Well, the reasons are more practical.
First, investors expect the US Federal Reserve (Fed) to cut interest rates. You see, US inflation has been hovering around 3%, much lower than the 9% target in 2022 and closer to the Federal Reserve’s 2% target. With inflation under control, investors believe rate cuts are likely.
Lower interest rates make borrowing cheaper for smaller companies, allowing them to expand and profit more quickly. As a result, investors are shifting their money away from Big Tech and into smaller companies. And since these AI giants are the big players, their stocks are taking a hit.
This move also makes sense because, let’s face it, AI is a money-grubber and isn’t going to generate huge returns anytime soon. It’s energy-intensive and requires massive computing power, both of which come at a high cost.
To put that in perspective, it costs OpenAI close to $1 million just to run ChatGPT per day. But the revenue doesn’t match that.
A Sequoia analysis suggests that for the entire AI industry to be sustainable, it needs to generate at least $600 billion in annual revenue. But OpenAI, the largest company in the sector, only generates about $3 billion a year. The others aren’t even close.
Yet these companies are doubling down and plan to invest another trillion dollars in AI in the coming years.
Even if profits arrive, they’ll likely be a decade away
when more people find AI products beyond ChatGPT that are actually worth investing in.
Essentially, AI companies will need to deliver significant value over the years for consumers to open their wallets, just as happened with the internet.
Remember how email revolutionized traditional postal services or how digital news platforms began to replace physical newspapers, reducing distribution costs? Social media and microblogging sites decentralized the web and turned it into a money-making machine. These tech giants understood this after the dot-com crash, and AI could follow a similar path.
And that’s making investors impatient.
No wonder they’re pulling back, realizing it’s not worth inflating these companies’ valuations just yet.
But that doesn’t mean AI companies will go away like many did during the dot-com crash. Once they figure out how to monetize AI, the hype and money will return.
And that’s when investors will come back in.
So yes, is the AI bubble about to burst? Maybe not yet, and definitely not in a dot-com-style way. But if it ever does, trust us, you won’t miss it.
Until then…
Will the AI market experience a repeat of the dot-com bubble of 2000?
The following contribution is from the HKU Business School website and is written by Maurice Tse, who earned a PhD in Finance from Michigan State University. In 1988, while still a doctoral candidate, he received the University-wide Teaching Excellence Award as a graduate course instructor. Maurice joined the Indiana University School of Business in 1989. In 1991, he also earned the professional title of Associate of the Society of Actuaries (ASA). Maurice’s teaching has primarily focused on teaching students how to get rich slowly but surely, rather than quickly.
The artificial intelligence (AI) arms race, triggered by the rise of ChatGPT two years ago, has intensified.
Cutting-edge technology is poised to drive irrevocable change across all industries. Amid heavy investment in AI by national governments and corporations, the Japanese group SoftBank announced its plan to invest $500 million in OpenAI late last month.
In a research report published in July 2024, Jim Covello, director of equity research at Goldman Sachs, argues that an AI investment bubble is forming.
While it is unlikely to burst soon, the performance of all related products launched to date has been disappointing.
For example, insufficient cost-saving measures have been implemented for AI coding and customer support, and there are times when AI’s search capabilities are suboptimal.
Despite the huge amounts invested by large tech companies, the apparent profitability of these new products is meager. While generative AI can perform computer programming, its constant errors force users to make corrections from time to time. Therefore, its ability to solve complex problems is questionable.
Tracing the Origin of Market Bubbles
When speculators inflate the valuations of technology companies, a tech bubble can emerge, triggering drastic market adjustments.
This could cause massive losses for investors and even exert widespread influence on the economy.
Investors, policymakers, and industry stakeholders are encouraged to understand the psychological, economic, and structural factors that lead to a market bubble.
Looking at the collapse of the tech bubble in the late 1990s, although the business models of many dot-com companies were unproven, capital continued to flow.
While AI was not yet the focus of investor attention, similar speculation already existed among companies developing AI technology at the time.
The bursting of the dot-com bubble led to the bankruptcy of these companies one after another, and the NASDAQ Composite Index plummeted after reaching its peak, leading to bankruptcies and colossal losses.
The resurgence of AI technology between 2015 and 2018 ushered in a new wave of investment, with startups attracting billions of US dollars in investments.
Companies that incorporated AI into their often overvalued business models lacked clear profit strategies, even if they managed to generate a technologically revolutionary impact.
Add to this the emergence of cryptocurrencies and blockchain technology, and 2017 witnessed the brief rise and fall of cryptocurrencies, a clear example of the high volatility and risks associated with digital assets.
The COVID-19 pandemic accelerated technology adoption, leading to the overvaluation of software-as-a-service (SaaS) companies and radical market adjustments.
Suppose generative AI could revolutionize every industry in the next five to ten years.
However, if their development remains static, these tech companies, despite their ability to raise massive funding, will not contribute to boosting productivity or corporate profits.
An asset bubble formed over time is destined to burst. While leading AI companies like Meta, Google, and Microsoft have a clear path to profitability, AI startups in private markets may be valued at near-bubble levels.
This naturally raises a key question on Wall Street: when will these companies be able to generate profits from AI?
Ultimately, investors’ concerns boil down to: Is it all really worth it?
Rational Market Assessment
As Covello points out in the research report, most technological transformations in history, particularly revolutionary ones, have involved replacing high-cost solutions with low-cost ones. Investment in AI infrastructure development, expected to reach $1 trillion in the coming years, is by no means cost-effective, as existing technologies will be replaced at exorbitant costs.
Given AI investments amounting to hundreds of billions of US dollars, if these investments fail to improve productivity and profitability, all stocks that have soared due to inflated AI projections will inevitably suffer a drastic decline.
While generative AI is still in its infancy, its adoption by businesses is also in its infancy.
Key suppliers such as Taiwan Semiconductor Manufacturing Company can see potential for huge profits, but they need to maintain sustainable development across all aspects of the value chain to achieve significant revenue and impact for businesses.
An article in the July issue of The Economist cites US statistical data as evidence of investor concern about an AI bubble. While the use of chatbots, such as ChatGPT, has become quite common, the AI adoption rate by businesses is very low. According to the survey results published in the article, less than 5% of the companies surveyed have used AI in the past two weeks, while less than 7% intend to use it in the next six months.
This demonstrates that the use of AI in the business sector is minimal.
A study by the Adecco Group reveals that, among more than 2,000 senior executives from nine countries on four continents,
up to 57% distrust the AI skills and knowledge of their company’s management.
As for companies that benefit from AI, such as Walmart, they do not see a significant increase in their stock price.
Companies that truly benefit from the technology are likely those that focus on supply, like Nvidia, rather than those that focus on demand.
Despite the flourishing development of AI in recent years, no country has experienced significant productivity growth, including developed countries like the United States.
Clarifying Investment Psychology
In fact, investor psychology can also lead to a stock market bubble.
For example, the fear of missing out (FOMO) can trigger irrational buying behavior and drive up stock prices.
Massive media coverage contributes to fueling the hype surrounding emerging technologies.
Low interest rates and an environment of inflows of capital will stimulate risky investing, causing a sudden surge in tech stock prices.
While technological advancements can attract capital, realizing benefits takes time.
Numerous psychological factors can distort the decision-making process, paving the way for tech bubbles.
By addressing market fluctuations, understanding these adverse factors can help investors better understand their own biases, enabling them to make rational investments.
In addition to making poor decisions due to FOMO, investors may also overestimate their ability to predict market trends, making them more prone to risk-taking and thus contributing to the gradual development of bubbles.
Furthermore, this bias can convince them to remain invested, believing that prices will continue to rise, ignoring even objective warning signs.
Blinded by their belief in high and infinite growth based on past prices and trends, they are unable to recognize when a speculative bubble is forming.
As Meta predicted: «We do not expect our generative AI products to be a significant revenue driver in 2024. But we do expect them to open up new revenue opportunities over time that will allow us to generate a solid return on our investment.»
However, long accustomed to the practice of quarterly sales and earnings, many investors may underestimate the long-term impact of generative AI and overestimate its short-term potential.
Gil Luria, an analyst at D.A. Davidson said, “If you invest now and get returns in 10 or 15 years, that’s a risky investment. It’s not an investment in a publicly traded company. With publicly traded companies, we expect a return on investment in much shorter time frames. This is a concern because we don’t see the types of applications or application-derived revenue we would need to justify these investments right now.”
After leading the market in an upward trend during the second quarter of this year, the largest AI company dragged the market down in the third quarter.
As a result, many investors have opted for large-cap tech stocks and opted for value stocks.
The figure shows that the Morningstar Global Artificial Intelligence Index plummeted from a market high on July 16, 2024, to a new low on August 5, 2024, representing a drop of 18.56%, twice that of the Morningstar US Market Index, which was 9.21%.
Despite having recovered some of their lost ground, AI stocks have been dragging down the market’s returns in recent months.
Since July 16, the 12 stocks in the Morningstar US Market Index that dragged down the market’s returns are technology stocks closely associated with AI.
Past experience with the formation and bursting of technology bubbles demonstrates the cyclical nature of technology investing.
While initial overinvestment could lead to overvaluation, once the market has adjusted to reality, significant stock price adjustments will occur.
In light of the continued development of AI and its integration into various industries, understanding the law of market cycles is clearly a top priority for investors and policymakers.
AI Frenzy: Are We in a Dot-Com Bubble Again?
The following contribution is from the NUS (National University of Singapore) portal and was written by the team.
Abstract
The recent AI boom has propelled markets to new highs, led by companies like NVIDIA.
However, following the plunge in AI stock prices in July and August, many are wondering if an AI bubble is forming.
While this could be a temporary market reaction, the hype surrounding AI could resemble a bubble, albeit less severe than the dot-com bubble.
AI stocks appear to be overvalued, and the industry needs more revenue to sustain itself, as investors may overestimate the value of rapidly advancing semiconductor chips.
Read on for an analysis of the current situation, along with a comparison to the dot-com bubble of the early 2000s.
Introduction
The presence of Artificial Intelligence (AI) is infiltrating numerous aspects of our lives, from code debugging for college students to intelligent medical diagnosis in hospitals.
Riding the AI wave, tech companies began experiencing exponential increases in their valuations and stock prices, propelling markets to new heights.
Notably, the Magnificent Seven (Alphabet, Amazon, Apple, Meta Platforms, Microsoft, NVIDIA, and Tesla) recorded gains ranging from 50% to 240% in 2023 and accounted for more than 60% of the S&P 500’s growth through 2024.
However, the prices of several AI stocks began to fall in July and August, along with the broader stock market. Are these AI stocks leading the market toward a bubble that will eventually burst, similar to the dot-com bubble of 2000?
A stock market bubble occurs when stock prices rise so rapidly that they far exceed a company’s intrinsic value or earnings.
Overly optimistic stock valuations, waves of bullish optimism and fear of missing out (FOMO) among investors, and a disconnect between stock market growth and economic growth are some of the key characteristics of bubbles.
Expansionary monetary policies, such as quantitative easing and low interest rates, changing economic and social needs, and technological innovations can all contribute to the formation of a bubble.
When a bubble bursts, stock prices begin to fall, leading to panic selling and sometimes even a stock market crash.
This chart illustrates the typical evolution of asset prices throughout a bubble, largely following the market bubble mechanism mentioned above.
With NVIDIA and Microsoft stocks as proxies for AI stocks, asset price trends appeared to be in a boom-to-euphoria phase by mid-2023.
The AI Boom Could Be a Bubble
It’s been almost two years since the launch of Chat GPT, and we’ve seen enormous potential and benefits stemming from generative AI.
Tech stock prices have skyrocketed since then: the stock price of NVIDIA, the AI chip provider, soared from $16 per share on Chat GPT’s launch date to an impressive high of $131, reaching a record valuation of over $3 trillion.
Shortly after reaching its 2024 all-time high, NVIDIA lost 30% of its valuation ($1 trillion!), and its stock price plummeted 20%.
Similarly, the Magnificent 7’s stock price began to fall starting in July 2024 in response to disappointing second-quarter trading results: Microsoft fell 14%, Amazon 17%, and Tesla 23%.
This raises concerns that AI investments would involve rising costs with modest returns—a far cry from the promised fairy tale of incredible profits.
David Cahn, a partner at Sequoia Capital, noted that the AI industry needed to generate $600 billion in annual revenue to sustain itself.
This figure was only $200 billion in September 2023. With Open AI accounting for the lion’s share of revenue, at $3.4 billion, the revenue gap between the company and other companies remains considerable.
AI companies would definitely need to deliver products with significant value to justify their valuations, not simply software with GPT add-ons.
The rapid pace of technological advancement in the semiconductor industry means that older chips quickly lose their value as manufacturers like NVIDIA continue to produce higher-quality, next-generation chips, such as the B100.
As a result, investors could be overestimating the value of current chips in the near future.
It’s possible that we’ve been overly pessimistic about the AI hardware industry, unless we’ve reached a peak in semiconductor technology or increased demand for AI chips offsets this depreciation effect.
The AI bubble won’t be as severe as the dot-com bubble.
All of these factors appear to replicate the boom and bust of the dot-com era.
During the honeymoon phase of the dot-com bubble, valuations of internet-based companies like Pets.com soared, and the NASDAQ Composite Index rose from under 500 in 1995 to a peak of 5000 in 2000.
When the Federal Reserve began raising interest rates to curb inflation in early 2000, it made borrowing more expensive and reduced investment capital.
The bubble burst: Investors began a wave of selling in dot-com stocks, which wiped out nearly all of the NASDAQ’s gains, and most of these stocks went bust in 2001; some, like Pets.com and eToys.com, went bankrupt.
As overvalued as the Magnificent Seven may seem, their valuations are actually much lower than those of internet stocks during the dot-com boom.
The price-to-earnings ratio (current share price divided by expected earnings per share) of the NASDAQ 100 was 60.1x in March 2000, but only 26.4x in November 2023.
This doesn’t mean that all AI companies have viable business plans: some startups, such as Character.ai and Humane Inc., have raised substantial funding despite lacking a product.
During the dot-com era, many venture-backed companies had shaky fundamentals but were unfortunate in that there were no established companies with a track record of profitability leading the market.
With the Magnificent Seven as pillars of the AI market, the industry is at least supported by stronger fundamentals, suggesting that the current enthusiasm for AI might not be as sentiment-driven as the dot-com hype.
Today’s investors are also more cautious and better informed about the downsides of our current technology
such as hallucinations (generating misleading results), security risks, and job losses, to name just a few.
With greater transparency and information flow, the investor base likely won’t be as sentiment-driven as it was in the dot-com era.
Conclusion
Even the enthusiasm for AI can feel like a bubble, though not as extreme as the dot-com bubble. Black swan events, such as economic and political risks, can bring surprises.
Following a disappointing US jobs report in August, many economists warn of an increased likelihood of a recession ahead.
Goldman Sachs estimates a 12-month recession risk of between 15% and 25%. This leads to altered business perceptions, which could dampen investments in the AI sector. Furthermore, rising geopolitical tensions could trigger an arms race in the AI sector or a ban on certain AI products, which also increases market uncertainty.
As individual investors, the best strategy for navigating an ever-changing market is to remain cautious, stay abreast of new technological trends and economic developments, and avoid making excessively risky investments driven by fear of missing out (FOMO).
Note
The launch of DeepSeek, a generative AI model developed at a fraction of the cost of Chat GPT and other large language models, caused Magnificent 7’s stock price to plummet in late January. This offset a record $593 billion single-day loss in NVIDIA’s market value on January 27.
However, this wasn’t an indication of the bursting of the AI bubble, but rather an immediate market reaction. In fact, NVIDIA’s stock price has largely recovered in the following weeks, driven by better-than-expected earnings.
However, this DeepSeek event reflects the narrative from last August, which suggested that we may have been overly optimistic about AI. This could change the market perception that AI development is an expensive undertaking, a belief that has driven a massive influx of investment into the sector.
The reality is that much of this investment may be based on inflated expectations. If AI could be developed at a fraction of the cost, the high valuations of stocks in this sector may not be sustainable. Market optimism breeds overvaluations that, over the long term, may not be sustained.