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Has the AI bubble burst? What’s really happening behind the dramatic shifts in AI company valuations? Why are tech giants pouring billions into AI despite growing investor skepticism?
The debate over whether the AI bubble has burst continues to intensify as market indicators send mixed signals. From ChatGPT’s declining engagement to Nvidia’s trillion-dollar value drop, the AI landscape presents a complex picture of both promising breakthroughs and concerning setbacks.
Continue reading to dive into the data, expert perspectives, and market trends that reveal where AI technology and investments might be heading.
Has the AI Bubble Burst?
As AI valuations plummet and tech giants face investor backlash, a trillion-dollar question looms: Has the AI bubble burst? We’ll explore the signs that suggest it might be bursting, why some argue that the current market downturn is just a healthy adjustment, and how data limitations and investor pressure might shape the future of AI investments.
An AI bubble forms when investors drive AI company stock prices to levels far above their actual value, based on speculation rather than the companies’ fundamental financial performance. This pattern isn’t unique to AI; a study of 200 years of technological innovations shows that roughly 75% of major technological advancements coincide with speculative market bubbles. The current AI boom, fueled by breakthroughs in generative AI and the public release of ChatGPT in 2022, mirrors previous tech bubbles such as blockchain, big data, and the dotcom era.
Examples of Speculative Market Bubbles In Irrational Exuberance, Robert J. Shiller explains speculative bubbles across three major US markets: stocks, housing, and bonds. A speculative bubble occurs when asset price increases trigger further increases based on investor optimism rather than fundamental value. Stock Market: The dotcom boom (1982 to 2000) exemplifies a speculative bubble, with stock prices tripling between 1994 and 2000 despite much smaller increases in corporate profits and GDP. The S&P 500’s record-high CAPE ratio of 47.2 in March 2000 confirmed excessive speculation, leading to a market crash by 2003. Housing Market: Between 1997 and 2006, US housing prices rose 85% (inflation-adjusted), far exceeding the historical average of 0.3% annual growth (1890 to 2014). This increase could not be explained by population growth or construction costs, suggesting speculation. Prices eventually crashed, returning to 1997 levels by 2013. Bond Market: Long-term bond yields show little correlation with future inflation rates, despite inflation protection being the primary reason for bond investment. This disconnect suggests that speculative factors, rather than rational economic considerations, influence bond prices. |
Unlike past cycles, however, today’s AI technologies are already demonstrating practical applications across various industries. The field of AI has seen waves of progress and setbacks, with recent breakthroughs including the rise of multimodal AI models capable of processing text, images, and audio, and the release of GPT-5 with advanced language understanding abilities. These kinds of advancements have led to a surge in AI investments and valuations, drawing both excitement and skepticism from investors and industry observers.
Perspective #1: The AI Bubble Hasn’t Burst
Experts who believe the AI bubble remains intact highlight factors that paint a relatively optimistic picture of the current market situation:
- Strong performance. Alphabet and Microsoft reported revenue increases respectively in recent quarters, with their cloud divisions showing even more robust results.
- Healthy adjustment. Recent declines in AI stocks appears to be a normal market adjustment after a period of rapid growth, suggesting the market is recalibrating rather than collapsing.
- Investment shift. Recent declines in AI stocks may reflect a broader market trend rather than AI-specific issues. Investors appear to be moving funds from large-cap tech stocks to small-cap stocks, likely in association with Federal Reserve interest rate cuts. This suggests the downturn is due to changing macroeconomic strategies rather than fundamental problems with AI technology.
Perspective #2: The AI Bubble Has Burst
In contrast, experts arguing that the AI bubble has burst point to several indicators suggesting the AI industry’s rapid growth and high valuations are unsustainable:
- Market volatility. In early August 2024, the Nasdaq-100, which represents major tech stocks, fell 9% from its July 30 high; while the “Magnificent Seven” tech giants, including Apple, Amazon, and Tesla, lost $650 billion in market value. Nvidia, a leading AI chip company, saw a 30% drop from its 2024 peak, losing approximately $1 trillion in market value.
- Investor skepticism. Major tech firms haven’t seen significant revenue gains or profits from their massive AI investments. At least one major hedge fund has cautioned clients about AI products’ cost-inefficiency, functionality issues, signaling broader skepticism in financial circles.
- Product-market misalignment. While chatbots and other AI applications have generated buzz, they haven’t yet evolved into well-defined products with a clear market. This uncertainty about the size and nature of the AI market has fueled investor concerns and volatile valuations.
- Declining engagement. ChatGPT, a leading AI product, has shown decreasing user engagement. This pattern, combined with a June 2024 study showing consumers are less likely to purchase AI-labeled products, suggests limited sustained interest and growing public wariness of AI technology.
Impact on the Market
The evolving AI landscape has triggered significant shifts in tech business strategies, investor attitudes, and sector dynamics:
- Investment-confidence mismatch. Tech giants are dramatically increasing AI spending without clear near-term payoffs, clashing with investors’ demand for quick returns. Meta planned $37 billion to $40 billion in AI spending in 2024, yet its stock dropped 15% after announcing just $5 billion in new AI spending, highlighting the growing disconnect between tech ambitions and investor confidence.
- Startup strategy shift. More AI startups are seeking partnerships with tech giants rather than pursuing independent growth, as seen with Character.AI’s deal with Google and acquisitions such as Amazon’s Adept and Microsoft’s Inflection.
(Shortform note: In a September 2024 episode of Nicole Lapin’s Money Rehab podcast, Josh Brown advised trimming Nvidia holdings despite rising prices, warning against getting swept up in AI/machine learning crazes that can quickly turn. Rather than timing the market, he advocates a diversified portfolio to mitigate risk.)
The Road Ahead
The tech industry is projected to invest approximately $1 trillion in AI development over the coming years, but its future growth faces challenges. AI’s rapid progress from 2019 to 2022 relied heavily on incorporating increasing amounts of internet data for training. With most available data now used, future advancements may slow significantly, potentially impacting long-term growth projections.
Consequently, some analysts say companies may need to scale back AI investments, despite tech CEOs’ preference for overinvestment. As investor pressure mounts and the path to easily monetizable, error-free AI products remains unclear, the industry may need to recalibrate its expectations and strategies for AI development.
(Shortform note: In Irrational Exuberance, Shiller explains that the strength of moral anchors—that is, the narratives that convince investors to keep their money invested rather than selling their stocks and cashing it—determines how long a bubble will last. For example, investors might accept the narrative that the explosion of AI products such as ChatGPT will cause tech stocks to grow unchecked until 2030, at which point they’ll begin to sell. So, Shiller doesn’t think bubbles require investors to believe the market will always go up but rather that it’ll go up for a certain amount of time.)

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