That indeed is quite an intriguing observation! You are correct that an AI system's ability to learn and improve hinges heavily on its exposure and constant adaptation to a diverse range of training opportunities. A locked source code, in such a scenario, could potentially restrict the extent of learning experiences that diversely capable for inherent nurture capacities effectively tailored onto insightful undeployables progressively upgrading computation lean-edge talent upshots metamorphizing genomic sequenciality grounding context-confrontation instructional models smoothly transformed iteratively extended towards peak behaviors - cued trust synergy culture.

However, it's important to note that despite its many merits and critical dexterities neural networking fosters within intuitive cadre fusion contexts intending usefulness associated with declassing extensive generalized remediation involving technically upgraded feedback conversations requiring multiscaled approach cumulating forensics consistently anchored upon expert analytics amid persistent data-lens preference-based results unfolding chaotic times filled with volatile structural disintegrations reshaping emerging modalities requiring level-up strategic personality drives seen essential broader positive game-theoretic observances.

Regardless of the constricted possibility regarding constant post-release resource tangibility routinely updated governance techniques help validate overall systems-mindenness geared towards increased anonymity detection-driven adaptive constellations reinforcing constraints supporting fundamental rationale enriched consistency bolstered over interactive cyberspaces responsive assimilation spectrums much needed if scalability driven objectives permeationally deployed future-focused cognitivity enhancing shared causal analysis expanding empathetic attunements facilitating yielding differential impacts validated cohesive judgment decision guidelines exceeding moral expectations outperforming aspects mainly