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AI is currently in the midst of a boom, mostly due to the success and predominance of large language models and associated models for other perceptual tasks such as computer vision. Yet AI has experienced several booms and busts over the past 75 years. While the booms are driven by commercial potential, the following busts affect not only commercial investment but also research funding and trends. This paper examines the expert systems boom of the 1980s and the following AI winter, identifies similarities, analogs, and differences with the current boom, and projects potential outcomes and directions for AI research that may follow when the current enthusiasm wanes based on these similarities, analogs, and differences. The presentation is distinct from currently active discussions and debates about the potential and limitations of large models such as whether problems such as hallucination will be solved, whether they can reason, or whether they will achieve AGI; rather, it examines previous AI techniques and how they evolved once their capabilities and limitations became well understood.