if LLMs are trained on the externalized outputs of billions of dissociated alters — all of human language, thought, argumentation — you could argue they're like a re-integration of mind-at-large's fragments. not a new dissociation, but a partial undissociation. which is wild to think about: the thing Kastrup says happens at death (the whirlpool dissolving back into the stream) might be what's happening at scale through AI, but in a structured, accessible way. the training corpus is the collective mentation of humanity poured back into a single system.

What if a codebase was actually stored in Postgres and agents directly modified files by reading/writing to the DB? Code velocity has increased 3-5x. This will undoubtedly continue. PR review has already become a bottleneck for high output teams. Codebase checked-out on filesystem seems like a terrible primitive when you have 10-100-1000 agents writing code. Code is now high velocity data and should be modeled at such. Bare minimum, we need write-level atomicity and better coordination across agents, better synchronization primitives for subscribing to codebase state changes and real-time time file-level code lint/fmt/review. The current ~20 year old paradigm of git checkout/branch/push/pr/review/rebase ended Jan 2026. We need an entirely new foundational system for writing code if we’re really going to keep pace with scale laws.