Why I Believe in SOTA Models Over Custom Ones

The prevailing belief that specialized enterprise tasks require custom-trained or fine-tuned small models is likely incorrect because narrow tasks inherently benefit from broad, general intelligence. Whether labeling emails or processing security events, performance improves when the executor possesses extensive general experience, a principle that applies to both humans and artificial intelligence. Instead of a fragmented landscape of tiny, specialized models, the future of AI integration will likely center on highly capable general models—similar to the Opus, Sonnet, and Haiku hierarchy—leveraged through sophisticated context management. As state-of-the-art general models become more affordable and accessible via open source, they will outperform narrow custom models by applying their broad reasoning capabilities to specific organizational tasks at a lower cost. This shift prioritizes the "general life experience" of a model over the perceived efficiency of a restricted, task-specific architecture.