Baschez argues shared AI factories will outperform personal AI agents

Nathan Baschez argues that the end state of AI work is not individual employees running personal agents like Codex or Claude Code, but shared, specialized "AI factories." He contends factories beat personal agents because they are shared, task-specific, and scrutinized, which creates feedback loops for systematic improvement.

In a 100-person consulting firm comparison, concentrating about 18.3 hours of AI tuning per task on 1–2 tasks gives 5 times deeper learning than spreading 3.7 hours across 5–10 tasks.