OpenAI Just Cheapened Repetitive Judgment

Morning. Damian built an AI clone to take the first shift again. Still one of his best delegation moves. DayLift Signal. AI-curated. Five minutes.

Repetitive AI judgment just got CHEAP. Not smart-agent cheap… operationally cheap. I read through this morning's stack, and most of it was model noise — this is the one update that can change how you price real work.

OpenAI's Decisions A P I is now generally available at ten cents per million input tokens, built for bounded question sets — up to roughly four hundred decisions in one pass with GPT-six Luna. That sounds technical. The business verdict is simpler… narrow decision work just moved a lot closer to utility pricing. Classification, triage, scoring, basic evaluation — the boring judgment layer — is where this lands first.

Team leads and managers — this is for the workflows you already own: ticket triage, lead scoring, inbox sorting, intake checks, content review queues. Work that happens every day, at volume, with rules. Owners and decision-makers — this should change how you approve AI spend. Test the narrow decision before you fund the shiny agent. If the cheap layer handles eighty percent of the cases, your whole budget math changes. Individual operators and solo professionals — worth knowing, but this is not mainly your story today unless you handle repeated client intake or high-volume content decisions. You're still paying premium-model prices for work that should have become cheap background infrastructure by now. Smart move: pick one repeated judgment task this week… benchmark it against your current human process, then keep a human approval step for anything consequential.

Here is the lever. This one's for team leads and managers first — owners should ask to see the sheet. Build a one-page scorecard in Notion or Google Sheets with five fields: weekly volume, minutes saved per case, error cost, integration effort, and data sensitivity. Then sample twenty anonymized cases. Test the workflow in ChatGPT or Claude first to prove the task is even stable, then estimate the production version against OpenAI's Decisions A P I at ten cents per million input tokens. Track accuracy, review time, and exception rate. Keep customer, employee, financial, and confidential data out of consumer tools unless you have the right business or A P I agreement.

Here is my honest take… most teams do NOT need another agent right now. What they need is to batch up cheap, boring decisions and stop treating every tiny judgment call like a strategy problem. The real win is not that AI can think harder. It is that routine judgment can start costing almost nothing when you organize the work correctly.

This is the trap I keep seeing. A model release drops, somebody opens accounts, rewrites prompts, books a demo… and last week's pilot still has no baseline, no owner, no stop rule. Of course it feels like progress — there are tabs open everywhere. But testing tools is not the same as improving work. Better pattern: keep a short AI backlog ranked by hours saved, quality upside, risk, and switching cost. A release only earns a test when it can beat the current workflow on a defined benchmark.

So here is the question. Which repetitive decision in your work should become cheap infrastructure this week — and what evidence would make you scale it, or kill it?