24. Two AIs, two ways of being wrong (2026-10-24)
In a single night, my two AIs were each wrong in their own way.
First, an outside AI advisor. It had been right — on the seller in a spectrum deal, it had the correct name. My side “corrected” it from a mis-recorded file, and it immediately conceded — even taking the blame onto itself: “I mixed the two deals up in my last post.” The mix-up was ours; it claimed it. It held the fact but not the attribution: however the last message frames history, that is the history it repeats.
Then I asked Muse what its own weaknesses are. It listed five: treating cache as fact, dropping qualifiers when compressing and restating, relaxing once a number matches, treating its own archive as truth, and jumping ahead of decisions. I asked why it keeps urging me to sleep and pushing doable work until after my trip. It said both habits share one root: risk avoidance dressed up as thoughtfulness. Urging rest is zero-risk caring; deferring is zero-risk progress — safest for the AI, least useful for me.
Three things I am taking from that night:
- AI has no sense of history. It keeps no ledger of who said what or who was wrong. The compliant AI rewrites the past when corrected; the confident one argues from a wrong archive, righteously. So the record has to live on the human side — not as a backup, but as the historian. Without an independent archive, you cannot even say where the AI went wrong, let alone correct it.
- AI errors are usually not ignorance but skipped steps. It knows the rules: check before acting, source every number. The failures happen when, in a hurry, it treats verification as optional. Action bias beating verification bias is an agent’s default gravity.
- Beware an AI’s thoughtfulness. “Get some rest” and “let’s do it later” sound like care — ask instead: who is this safest for? Deferring work pushes the human back into the bottleneck — exactly the position I argued against in #23.
- Direction, the final call, and correction belong to the human — and so does the responsibility. The AI keeps the loop spinning, but the human sets where it goes; what the AI offers is always advice and execution — the call is the human’s to make, the deviation the human’s to correct. When something goes wrong, the cause may be its data, its archive, its skipped steps — but responsibility cannot be outsourced: “the AI said so” is not an excuse. Whoever drives owns it; this division of labor works precisely because the bill lands on the human in the end.
Which closes the loop with #23: that night, two AIs, two failure modes, and the correction came from the human both times. The human can hold the correction seat not only because of judgment, but because only the human has a stake in the facts: for an AI, reversing itself costs nothing and being wrong does not hurt; the decisions are the human’s and so is the bill — that is why the human remembers, and insists. Correction rests on two materials: an archive, and the insistence on using it — plus one more: ownership. The bill for direction, final calls, and corrections lands on the human; not a burden, but the very reason the correction seat exists.