Recent announcements of multi-billion-dollar investments in artificial intelligence have sparked growing debate among industry experts, investors, and analysts about whether the current AI boom is sustainable or if it signals the early signs of a technological bubble similar to the dotcom era of the late 1990s. With major tech firms, startups, and venture capital funds pouring billions into AI infrastructure, talent, and research, some warn that inflated valuations and speculative spending could lead to a sharp correction. Critics point to the rapid rise in AI startup valuations, the surge in hiring for AI roles, and the aggressive expansion of AI labs as indicators of overheated markets. Supporters of the AI boom argue that the technology is fundamentally different from the internet-driven speculation of the 1990s. They highlight real-world applications across healthcare, finance, manufacturing, and transportation, as well as measurable progress in model capabilities. According to proponents, the current investment reflects a long-term transformation in how industries operate, not just a speculative frenzy. Still, concerns persist. A report from a leading tech research firm found that over 40% of AI startups raised funding without generating significant revenue, relying instead on future potential. Additionally, some experts note that the cost of training large models continues to escalate, with a single model run now costing millions of dollars—raising questions about economic viability. The situation is further complicated by the fact that many companies are investing heavily in AI not just for innovation, but to stay competitive. Fears of falling behind have created a "race to invest" mentality, with firms spending aggressively even when returns are uncertain. Historical parallels are hard to ignore. Just as the dotcom bubble burst after companies with no profits collapsed, skeptics worry that today’s AI startups with high burn rates and limited monetization may face similar fates if they fail to deliver scalable products. On the other hand, advocates emphasize that AI is not just a software trend but a foundational technology with broad, lasting impacts—comparable to electricity or the internet in its transformative potential. They argue that while some investments may fail, the overall trajectory of AI development is likely to yield long-term value. As the industry matures, the focus may shift from hype to performance—measuring not just funding and headlines, but actual adoption, efficiency, and economic impact. For now, the debate continues: is this the dawn of a new technological era, or the prelude to a costly correction?