The danger of AI tool adoption is not subscription cost — it’s process lock-in. When a team builds workflows, automation scripts, documentation conventions, and memory structures around a specific harness, switching is not “learning a new tool.” It requires re-engineering the team’s entire operational architecture.
Migrating from Claude Code to Codex (or vice versa) means discarding accumulated institutional memory: custom skills, artifact formats, verification protocols, and muscle memory built over months.
This makes AI tool selection an architectural commitment, not a procurement decision. Teams should evaluate harnesses the same way they evaluate infrastructure choices, not software subscriptions.
Practical implication for leaders: Do not choose an AI tool based solely on model benchmarks. Evaluate the harness — and understand that the investment compounds in both directions (for you if you stay, against you if you switch).