The AI bubble represents a phase of intense enthusiasm and investment in artificial intelligence, drawing parallels to the dot-com bubble of the late 1990s. As companies rush to adopt AI technologies, advancements such as machine learning and natural language processing are reshaping industries across the board. Major players like Google, Microsoft, and OpenAI are at the forefront, pioneering innovations that promise to enhance productivity and efficiency. However, this rush has raised concerns about sustainability and valuation, echoing the rapid rise and fall witnessed during the dot-com era. Experts like Bill Gates have remarked on the similarities between the current AI hype and past tech bubbles, suggesting that the frenzy may not be entirely grounded in reality, and cautioning investors about potential pitfalls [3]. The implications of the AI bubble extend beyond financial markets; they are also reshaping the job landscape. Recent statistics indicate that the tech sector is experiencing significant layoffs, with job cuts in October reaching their highest level in over 22 years, largely attributed to AI integration and restructuring efforts. As companies streamline operations, the demand for traditional roles may diminish, leading to a shift in required skills and job functions within the industry [5]. Consequently, the future of work in an AI-driven economy remains uncertain, raising questions about job security and the nature of employment in the years ahead. In light of these trends, it becomes essential to draw lessons from the past, guiding both investors and tech enthusiasts in navigating the complexities of this evolving landscape.