Mark McElhatten publishes The McElhatten Report on Substack, analyzing the technical foundations of AI-human collaboration and knowledge systems. His research examines how digital scribes and intelligent agents capture and enrich human-AI interactions. The publication specifically investigates AI feedback loops and their role in building contextual understanding between humans and machines. McElhatten's analysis centers on three core areas: semantic retrieval mechanisms, extended context windows, and memory augmentation systems. His work maps the technical architecture enabling AI systems to transform conversational data into structured knowledge bases. These investigations support professionals developing enterprise AI applications and knowledge management frameworks. The McElhatten Report connects theoretical AI concepts to applied knowledge engineering challenges in organizational settings. His technical writing illuminates how AI systems process, store, and retrieve information across extended temporal and contextual spans. The publication documents emerging patterns in human-AI interaction design and their implications for knowledge work.