Dharma AI builds on the 2026 paper by Goldfeder, Wyder, LeCun and Shwartz-Ziv to show why AI systems with narrow focus consistently outperform general-purpose ones. Drawing on the “no free lunch” theorem (Wolpert & Macready, 1997), they point out that any gain on one distribution of tasks costs performance on others. With finite compute, data, and development time, a model tuned for a specific domain will allocate its limited resources more effectively than a model trying to cover everything. In practice, you see this in breakthroughs like protein-folding predictors engineered for that single problem, rather than in catch-all architectures. They broaden the argument with examples from biology and markets. Evolution favors specialists because traits optimized for one niche give an edge, while generalists never dominate any environment. Competitive markets do the same: firms that meet a clear performance bar in a tight domain survive, while unfocused players fall behind. Inside machine learning, negative transfer shows up when multi-task training drags down accuracy on individual tasks, confirming that shared capacity can create conflict. Even large “general” models use mixture-of-experts routing to activate specialized subnetworks, essentially embedding narrow expertise inside a broader system.