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Signs of Cooling in the AI Boom
The investment frenzy in the artificial intelligence sector sparked widespread discussion in 2025, but concerns about an AI bubble intensified this week. An MIT study revealed that 95% of corporate AI application projects failed to generate a return on investment, highlighting the disconnect between technological implementation and commercial value. Meanwhile, the emergence of nearly 500 AI unicorn startups (valued at over $1 billion) worldwide has further fueled fears of a bubble.
In a recent interview, AI "godfather" Sam Altman admitted that AI might be in a bubble, and many investors could face losses, though the technology itself holds significant long-term potential. This candid statement sent shockwaves through the market, with tech stocks declining for four consecutive days at the start of the week, particularly AI-related companies like NVIDIA, whose stock price came under pressure.
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Information Dissemination and Market Reaction
As noted by Marcus Weekly, the spread of information in the market takes time. Although risks in the AI sector have been discussed by industry insiders for weeks—such as Chinese AI company Deepseek launching more efficient models that could reduce demand for NVIDIA chips—this information only recently gained widespread attention, leading to market volatility.
Similar to the dot-com bubble of 2000, many investors in the current AI boom have overly high expectations for short-term returns. In 2000, Barron's explicitly warned about the internet bubble, but the market continued to rise for months before crashing. Similarly, the AI bubble may continue to inflate, but as more negative data emerges, market sentiment could gradually turn cautious.
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Future Outlook
Although AI investments may face short-term adjustments, the technology's long-term potential should not be overlooked. AI holds broad application prospects in fields like healthcare, logistics, and education, but companies need more precise implementation strategies to achieve sustainable commercial value. Investors should be wary of short-term hype and focus on AI companies with clear profit models and practical application scenarios.
