Recent breakthroughs in Large Language Model (LLM) development have rekindled hopes for broadly capable artificial intelligence. Yet, these models still exhibit notable limitations – particularly in deductive reasoning and efficient skill acquisition. In contrast, neuro-symbolic approaches, which integrate sub-symbolic pattern extraction with explicit logical structures, offer more robust generalization across diverse tasks. We argue that additional factors – such as modular transparency, flexible representations, and targeted prior knowledge – are crucial to further enhance this generalization. Our analysis of both historical and contemporary AI methods suggests that a multi-component neuro-symbolic implementation strategy is necessary for efficient general intelligence. This position is reinforced by the latest performance gains on the ARC-AGI benchmark and by concrete case studies demonstrating how neuro-symbolic designs address gaps left by purely neural or purely symbolic systems.