Agent Labs focus on creating AI agents rather than just models, contrasting with the approach of Model Labs. The term "Agent Labs" signifies a shift towards practical applications and business plans that generate revenue through agent-based systems, rather than solely relying on advanced model development. Companies like Cursor, Perplexity, and Cognition exemplify this trend, emphasizing product-first strategies and outcome-based pricing that allows them to charge significantly more than traditional subscription models. This enables higher margins and better growth potential as these agents can effectively replace parts of human labor. A key difference lies in how these labs operate. Model Labs often prioritize control and autonomy for users, aiming for fully autonomous systems. In contrast, Agent Labs focus on speed and user interaction, continuously evolving their frameworks to improve functionality without being tied to specific models. Metrics and evaluations in Agent Labs lean toward practical usage and cost-effectiveness, while Model Labs traditionally emphasize pushing maximum capabilities, often at the expense of efficiency. The article also highlights a notable trend in resource allocation, suggesting that Agent Labs value customer learning and engagement more than Model Labs. Anecdotal evidence shows that Agent Labs attract talent from various backgrounds, including former founders, while Model Labs struggle with retention. Recent shifts in companies like OpenAI indicate a movement towards becoming AI cloud providers, which aligns with the growing emphasis on agent-based solutions. This landscape is evolving, with significant investment from competitors like Anthropic, demonstrating a competitive atmosphere for both Agent and Model Labs in the AI sector.