The Current State of LLMs: Riding the Sigmoid Curve

Recently, the sentiment in the AI community has shifted from unbridled optimism to a more realistic outlook due to constraints faced in large language model development. These include data availability, energy and infrastructure costs, economic viability, and an "AI trust crisis." The industry is now embracing a sigmoid curve model of growth instead of exponential, recognizing that after an initial period of rapid progress, advancement will level off as natural limitations are reached. This shift has led to the adoption of foundation models and generative AI entering the "Trough of Disillusionment" phase in Gartner's Hype Cycle for AI. However, this is a necessary step in the maturation of any technology, leading to more reliable and safe implementations. As we move forward, AI technologies are at different stages, with knowledge graphs showing promise. The focus now shifts from "wow" to "how," emphasizing practical implementation and value addition.