Advanced Artificial Intelligence (AI), particularly deep learning, is faced with a significant interpretability crisis. This paper frames this crisis using Daniel Dennett’s hierarchy of stances, identifying a dual challenge: design-stance opacity, where the internal mechanisms of black-box architectures are impervious to human understanding, and intentional-stance incoherence, in which systems heavily reliant on statistical pattern matching defy modeling as rational agents. This opacity and incoherence critically hinder progress towards trustworthy and capable Artificial General Intelligence (AGI). This paper proposes a proactive, two-pronged solution to address this problem. Firstly, we advocate for advancing design-stance transparency by championing inherently interpretable hybrid algorithmic paradigms such as neurosymbolic systems, formal theorem proving, and structured knowledge representation. These approaches promote more robust, generalizable, and mechanistically understandable reasoning within AI systems. Secondly, on the basis of this enhanced transparency, we can then meaningfully adopt the intentional stance. This involves applying insights from cognitive science to model these more structured and transparent AI systems as rational agents with emergent cognitive processes. By elevating interpretability from a reactive concern to a proactive design principle, this integrated strategy becomes crucial for understanding current AI capabilities and developing sophisticated, reliable AGI.