The Machine Wasn't There Afterward
Being wrong is still one of the best mechanisms by which you become less wrong
We often treat experience as a set of facts. Your local expert has seen more cases and learned more rules. Models on the frontier can now reach far beyond the set of facts any expert will accumulate. They can and will read more than any person, compare thousands of failures, and tell you what a skilled founder, engineer, doctor, lawyer, or teacher might do next in situation X.
But “experience” is much more than a collection of evidence to gather, it is “what happened to us next” after we chose.
Examples: You launch a feature and no one uses it. You hire an impressive candidate and watch the team slow down. You trust the metrics on your dashboard while your business materially weakens. You ship a change that broke your service, and have to explain why to the people who pay for it.
These type of events do more than teach you a lesson. They also change what you believe and impact how much weight you give facts.
If anyone can refute me—show me I’m making a mistake or looking at things from the wrong perspective—I’ll gladly change. — VI. 21
“Talk to customers” is easy to agree with. It means more to you after you spend six months building things you can’t get customers to care about. “Keep systems simple” sounds wise before you create an abstraction that another team spends months trying to remove.
That weighting is a large part of what we call judgement.
Models can tell you many a “lesson”. And we all know they will be wrong and hallucinate. But they will never bear being wrong.
They will not choose the plan, put their name on it and watch results come in. They can describe costs with great precision. But they will not have lost money, trust, customers, or time.
This distinction matters greatly because we now ask AI “to step in” before we conduct many of the acts that once formed our own judgment. A student can easily get a proof before spending an hour wrestling with a false idea. An executive can and will ask for twelve options, pick one, and later forget which claims came from the model and which came from them1.
The work might appear better but the person, as we know, will stay the same.
Every time you let AI answer before you commit to a view, it may improve your choices but it will remove tests that would have improved you. You never exposed your belief, so results cannot correct it. You might receive the answer without truly learning how far your own answer would have been from the truth.
This is AI’s odd bargain.
I think: Before asking AI what to do, you should write down what you believe. Before you ship, state what you expect to happen and what would prove you wrong. Then use the model. Improve the plan to move faster. Then, compare the result with the claim you made before the answer arrived.
This is also what good teaching must protect. A course cannot stop at prompts, rules, or examples. The learner has to make a call on real work, see what follows and reconcile the gap. Otherwise the class may give them better words or better output while leaving judgment untouched.
The same rule should shape companies. Keep decisions tied to expected results. Make the person(s) who said yes look at what happened next. Let AI remove the dull work, the search and maybe even the third draft. But it should not be allowed to remove moments when reality can prove us wrong.