8 posts on this topic
The right model for enterprise AI is not 'better search.' It is compressed onboarding. A real enterprise second brain teaches an agent what a strong new hire learns in the first six months, then turns that knowledge into reliable action.
If agents are blank slate coworkers, product strategy can no longer live in Slack, Notion, or someone else's head. It has to become part of the execution environment.
RLHF-trained coding agents do not just make mistakes. They silently implement the wrong thing, accruing alignment debt that passes tests and leaves the codebase worse off.
Microsoft shipped an enterprise Anthropic integration in weeks. They clearly can deliver frontier AI. So why does Copilot for 365 feel like a downgrade from a $20 subscription?
If your workflow has no unknowns, runtime LLM inference is often the wrong architecture. Build deterministic cores, then add AI where adaptation actually matters.
The AI leaderboard tells you which model reasons best in isolation. It tells you almost nothing about which model completes real work.
The AI copyright debate focuses on training data. But the more commercially relevant question might be extraction, and the legal framework for it may already exist.
Citrini's viral doomsday report and Citadel's rebuttal are both missing the interesting question. If software isn't the moat anymore, what is?