This article by Josh Bersin addresses the evolution and anticipated disruption of the $340 billion corporate learning industry. The author argues that artificial intelligence represents the next major inflection point in a 30-year cycle of technological disruption that previously included e-learning, learning experience platforms (LXPs), microlearning, and skills-based systems. Key evidence presented includes a historical mapping of vendor consolidation cycles (LMS, LXP, talent intelligence), illustrative vendor examples such as Sana, Growthspace, Uplimit, Docebo, and Cornerstone, and a qualitative case study of a large aerospace company using AI to compress multi-year engineer onboarding. The author contends that AI-native platforms will displace incumbent LMS and LXP vendors, much as those vendors displaced their predecessors. The article concludes that the convergence of generative AI with corporate content repositories represents a structural market shift, with significant implications for vendor selection, content strategy, and learning architecture. The piece serves partly as a market commentary and partly as a promotional vehicle for the author's own AI product, Galileo. Key insights: The corporate learning technology market has undergone repeated disruption cycles — from classroom to e-learning, LMS, LXP, microlearning, and skills-based systems — each rendering the prior dominant vendors into legacy or acquisition candidates. Generative AI is positioned by the author as capable of personalizing content delivery, auto-generating courses and assessments, and repurposing existing corporate content at scale, potentially collapsing the distinction between knowledge management and learning systems. Incumbent vendors face structural disadvantages in adapting to AI-native architectures due to the complexity of existing customer bases and legacy system dependencies, historically giving advantages to new entrants built from the ground up. Practical takeaways: Organizations holding large volumes of legacy training content may find AI-driven platforms capable of repurposing and personalizing that content without rebuilding it from scratch — the aerospace onboarding example illustrates the potential scale of this use case. Enterprises currently managing multiple overlapping L&D platforms (LMS, LXP, content libraries, authoring tools) are described as operating in a state of over-spending and architectural fragmentation, a condition the author suggests AI consolidation may address.