Have AI LLMs Hit a Plateau?
A growing chorus of experts and industry insiders suggests that progress in large language models (LLMs) may be slowing—not due to lack of ambition, but because of hard limits like diminishing returns, rising energy costs, and shrinking ROI. Recent releases of cutting-edge AI systems have shown only marginal improvements over their predecessors, reigniting fears that the industry is approaching a plateau. Constraints such as limited high-quality training data, soaring compute demands, and the flattening impact of brute-force scaling are pointing toward a potential AI tipping point. Industry leaders warn that the cost of AI development is converging with the cost...