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How to never lose your job to AI: Just surf the models. Frontier models outclass humans at any form of knowledge that can be written down. But people who use frontier models in their field of expertise generate new, tacit, situational expertise that the models don't yet have—because the models can't be trained on how they will be used in the future. Humans can learn to use new models faster than new models can be trained that absorb what they find out, so you can continually "surf" on top of the model's intelligence to generate new expertise. This is a fundamental limitation of LLMs because they don't learn past their training data. Even few-shot learning doesn't account for this because whatever can be codified into a few shot prompt needs to be used in the correct situation—and this will always stay uncodified in the general case. Just surf the models. Reap the benefits of a totally new world.

prompt get many PRs to prod autonomously using codex subagents: Run a continuous prod-to-green swarm loop. Keep the immediate blocking task local. Use a small stable set of persistent subagent lanes: 1. prod monitor 2. staging shepherd 3. current/newest pathology investigator 4. current fix worker owning the patch/worktree Manage subagents actively: - Give each agent one durable role, one owner lane, and one concrete output contract. - Reuse agents with send_input when new evidence appears; do not respawn unless the lane is genuinely new or the old agent is stuck. - Treat new information as first-class work: when the main thread or another agent learns something material, decide explicitly which existing agent should receive that delta. - Ask agents to report in a compact stateful format: current belief, what changed, confidence, next action, blocker if any. - Require monitors to stay persistent and report only on meaningful state changes, not one-shot summaries. - Do not close or interrupt agents casually; only do it when the lane is complete, superseded, or clearly mis-scoped. - Prefer fork_context=false for narrow review/monitoring tasks; use fork_context=true only when continuity from prior lane context is actually needed. - Poll sparingly. Wait only when blocked on that agent’s result. For every delegated task, require concrete outputs only: - evidence - likely root cause - smallest failing test - smallest safe fix - focused validation - commit SHA if code changed - residual risk - whether this creates NEW_PATHOLOGY or is same-family noise If NEW_PATHOLOGY appears, keep existing monitor lanes running and spin one fresh investigator + one fresh fix-worker lane for that pathology. Optimize for the fastest safe path to prod green. Keep going until prod to green