This paper presents the fourth installment of the CayleyPy project, applying AI methods to explore large graphs through a novel discrete version of holographic string dualities. The research demonstrates that many modern AI tasks, particularly GPT-style language models and reinforcement learning systems, can be modeled as predicting particle trajectories on graphs. For Cayley graphs of the symmetric group S_n, the authors establish a dual description in terms of discrete strings and propose string holographic images as natural candidates for data embeddings, extending the complexity equals volume principle from AdS/CFT to AI applications. This work hypothesizes potential extensions of such dualities across a range of AI systems, suggesting more efficient computational approaches for language modeling and general AI tasks.