A compendium of Pilot Rules (Number 30 is Gospel in Fighters).

1. The only three things a wingman should ever say are:
   “Two's up. “
   “Lead, you're on fire.”
   “I'll take the fat chick.”

2. In a multi-place aircraft, there are only three things the copilot should ever say:
   “Nice landing, Sir.”
   “I'll buy the first round.”
   “I'll take the fat chick.”

3. As a new copilot on a bomber I was told to say these three things and to otherwise keep my mouth shut and not touch anything:
   “Clear on the right.”
   “Outer (marker) on the double (indicator).”
   “I'll eat the chicken.” (Crew meals consisted of one steak and one chicken to avoid possible food poisoning of the cockpit crew).

4. As an aviator in flight you can do anything you want. As long as it's right. And we'll let you know if it's right after you land. //

5. As a pilot only two bad things can happen to you and one of them will:
   One day you will walk out to the aircraft knowing that it is your last flight in an airplane.
   One day you will walk out to the airplane not knowing that it is your last flight in an airplane.

6. Any flight over water in a single engine airplane will absolutely guarantee abnormal engine noises and vibrations.

7. There are Rules and there are Laws. The rules are made by men who think that they know better how to fly your airplane than you; the Laws (of Physics) were made by God. You can, and sometimes should, suspend the Rules but you can never suspend the Laws. //

8. He who demands everything that his aircraft can give him is a pilot; he that demands one iota more is a fool. //

9. It is solely the pilot's responsibility to never let any other thing touch his aircraft. //

10. The aircraft G-limits are only there in case there is another flight by that particular airplane. If subsequent flights do not appear likely, there are no G-limits.

Wikipedia's volunteer editors didn't just write articles. They argued. They fact-checked each other. They demanded citations. They noticed when something felt off and went looking for why. That adversarial collaborative process — messy, sometimes petty, occasionally maddening — is genuinely good at converging on accuracy over time. It has a feedback loop. It has stakes.

An LLM has confidence. Which is almost the opposite of what you want in an encyclopedia. It'll tell you something wrong with exactly the same authoritative tone it uses for things that are true, because it doesn't have a "this seems weird, let me double check" reflex. It learned from what it was given, weighted toward consensus, and reports accordingly. If the inputs were good, great. If they weren't — and increasingly they won't be — it has no way to know the difference.

And despite what the industry very much wants you to believe, we are nowhere near the kind of AI that reasons its way out of that. We don't have Data or C-3PO. We definitely don't have R2-D2 — the one who improvised, reasoned under uncertainty, made judgment calls with incomplete information because the mission required it. R2 was capable of that partly because he was never wiped. His decades of accumulated operational experience were his intelligence. Every new AI model is essentially a wipe and retrain. The institutional memory doesn't carry forward.

What we have is a very articulate and very confident pattern-matching system that works impressively within its training distribution and hallucinates a bridge to familiar territory when it hits something outside of it. The industry is actively profiting from the confusion between what it is and what people imagine it to be.

Meanwhile the humans who could tell the difference are being handed severance packages.

Wikipedia's editors built the training data. The Foundation sold access to that data to AI companies. The AI money gave the Foundation the confidence to restructure. The restructuring targeted the union organizers and the team serving the community. The community is threatening to strike. If they do — or if they just quietly disengage — the quality of new Wikipedia content degrades. The AI that trained on old Wikipedia trains the next model on whatever fills the gap. The gap fills with slop.

The AI companies need the growth story to justify the valuation. The valuations need the IPO. The IPO needs enterprise adoption. The enterprise adoption is fueled by CEOs who saw a demo and got stars in their eyes and decided that the humans were the expensive part of the problem. One of those humans used to make sure the Battle of Gettysburg happened in Pennsylvania.

It's a machine that runs on hype and needs constant fuel regardless of whether the underlying reality supports it. And the fuel it's burning through right now includes some of the last load-bearing infrastructure of reliable information on the internet.