Is the AI bubble about to pop? Sam Altman is prepared either way. (arstechnica.com)

OpenAI CEO Sam Altman recently cautioned that investor enthusiasm around AI might be overheated, likening the current hype to the dot-com bubble of the 1990s. Despite OpenAI’s soaring valuation—from $300 billion to $500 billion in just months—and Altman’s ambitious forecast that ChatGPT could soon serve billions of daily users, he warned that "someone" stands to lose a "phenomenal amount of money." His prediction underscores the tension between extraordinary market optimism and the practical realities of AI adoption at scale. This skepticism is echoed by new MIT research highlighted in Fortune, which reveals that 95 percent of enterprise AI pilots fail to rapidly generate revenue. The study, "GenAI Divide: State of AI in Business 2025," attributes failure primarily to implementation challenges and organizational gaps rather than flaws in AI model quality itself. Purchased AI tools have a significantly higher success rate (67 percent) compared to in-house AI systems, which succeed only about one-third as often. This suggests that corporate IT struggles more with adapting and integrating AI than with the technology’s fundamental capabilities, raising important questions about how businesses navigate AI deployment amid soaring expectations. Together, Altman’s remarks and the MIT research provide a nuanced picture of AI’s promise and pitfalls, emphasizing that sustainable success hinges not just on model performance but on strategic execution and realistic market appraisal.