Rebuilding AI from the Small: The Path Toward Democratized Intelligence

AI is reshaping Software Engineering, but the tools with the highest capabilities remain difficult for many researchers and organizations to rely on. Some are accessible only through closed interfaces that change unpredictably, hindering reproducibility and long-term scientific progress. Others require hardware far beyond the reach of typical academic labs or industry teams, limiting who can meaningfully participate in this technological shift. Small Language Models offer a practical middle ground: they are open, efficient, and deployable in controlled environments. Yet they continue to lag behind larger models in tasks requiring deep reasoning. This dissertation aims to bridge that gap by studying how the form and structure of prompts influence model performance, and by developing new reasoning frameworks inspired by principles of human cognition. By enhancing the problem-solving abilities of smaller models, this work supports a future in which advanced AI capabilities are accessible, auditable, and usable by the broader SE community.