A better way of thinking about the AI bubble 

The article discusses the concept of an AI bubble, emphasizing that tech bubbles need not be catastrophic but rather reflect overinvestment where supply outpaces demand. A key challenge in assessing the AI bubble lies in the mismatch between the rapid development of AI software and the slow, complex process of building and powering data centers. Since data centers take years to complete and depend on evolving technologies in energy, semiconductors, and power transmission, predicting future supply needs is difficult. Large-scale investments are already underway, with companies like Oracle, Softbank, and Meta committing hundreds of billions of dollars to AI infrastructure, highlighting the scale of current bets on AI’s growth. Despite this massive investment, demand for AI services remains uncertain. A recent McKinsey survey shows that while most companies use AI in some capacity, few have integrated it extensively or seen significant business impact, indicating many are still cautious about scaling AI adoption. Infrastructure challenges also pose risks: Microsoft CEO Satya Nadella noted that data center space, rather than chip