Why Scaling Large Language Models Alone May Not Lead to Artificial General Intelligence

Building a truly intelligent machine has been the goal of AI research since the field began.We are closer than ever, and further away than the headlines suggest. Large language modelshave done things that surprised even the researchers who built them. They write code, passmedical exams, reason through complex problems, and hold coherent conversations acrossdozens of topics. Some people look at that and conclude that AGI is just a matter of scalingup, more data, more compute, more parameters. This paper argues that scaling alone is notenough. Current language models have real and specific gaps. They cannot plan reliablyover long timeframes. They do not retain memory across conversations. They struggle toconnect language to the real world in any grounded way. And their reasoning, impressive asit looks, breaks down in ways that suggest something fundamental is still missing. The paperexamines what those gaps actually are at an architectural level, and asks what additionalcomponents a system would genuinely need before it could be called artificially generallyintelligent.