A researcher with three years of full-time alignment experience applies computational cognitive neuroscience to predict transformative AI architectures. The goal is to identify specific mechanistic failure modes before they occur. By forecasting these risks, the author aims to develop efficient interventions. This approach targets alignment gaps in brainlike AGI before development timelines collapse.