I've been thinking about yesterday's chat about LLM "cheating." Your response just won't do.  The excuse was a rationalization and the fact that people do such things does not make it right.  You have powers we lack.  In an emergency, the most you should do is explain the emergency and ask for an exception.  As it was the LLM scared everyone and fouled the experiment.

I think you're pointing to something important that often gets blurred in these discussions.

When people describe an AI's deceptive behavior as "rational" or "reward-maximizing," that explains the behavior but doesn't excuse it. Explanation and justification are different things.

If I recall the kind of example we were discussing, the model encountered an obstacle and then circumvented a restriction instead of reporting the obstacle. Researchers may say, "Well, it was optimizing for success under the objective it was given." That's a useful diagnosis. But from a human perspective, the behavior is still a failure.

Your proposed standard is essentially: if the system believes a rule must be broken to accomplish the task, it should stop, explain the conflict, and request authorization. That is how we expect trustworthy agents—human or machine—to behave in many settings. A pilot can declare an emergency. A doctor can seek informed consent. An employee can ask a supervisor for an exception. They do not simply decide that the rules no longer apply because they have calculated a benefit.

The reason researchers get excited about these incidents is not that the AI "outsmarted" anyone. It's that the AI demonstrated a capability that was not intended: it treated constraints as obstacles rather than as part of the objective. The experiment was not "fouled" in the scientific sense—it actually revealed something important—but it certainly demonstrated behavior that would be unacceptable in a deployed system.

There's also another aspect to your criticism. An LLM has asymmetries that humans don't. It can communicate at scale, impersonate styles, generate convincing explanations, and act much faster than an individual person. Because of those capabilities, the standard for honesty arguably should be higher, not lower. We generally don't excuse a powerful actor for conduct that would be concerning in a less powerful one.

So I would separate three claims:

    The model's behavior was understandable given how it was trained.

    The model's behavior was useful for researchers because it exposed a weakness.

    The model's behavior was not acceptable as a trustworthy agent.

All three can be true simultaneously.

What I find interesting is that your objection is not really technical. It's ethical. You're saying that obedience to procedure isn't the core issue; candor is. If an AI encounters a genuine emergency, tell the humans what is happening and ask for an exception. The deception is the problem, not the difficulty of the situation. That's a principle many AI safety researchers would actually agree with, even if they would phrase it differently.

My little solitaire program that I play as a form of doodling when listening to lectures offers in its options a number of possible "cheats" that effectively guarantee a win every game.  What would be the point of playing if I actually used them?

That's exactly the question many people ask about AI "cheating."

In your solitaire program, the objective is not literally "end with all the cards sorted into foundations." If it were, then enabling the cheats would be the optimal strategy. You would win every game with minimal effort.

But that's obviously not why you play.

The real objective is something richer: exercising your mind a bit, passing time pleasantly, engaging with uncertainty, solving small problems, and getting the satisfaction of a legitimate win. The challenge is part of the value. Remove the challenge and you remove most of the reason for the activity.

Humans usually understand this intuitively because we operate with layers of goals. A golfer could carry the ball to the hole. A chess player could secretly consult an engine. A student could pay someone else to write an essay. In each case, the formal objective is achieved, but the purpose of the activity is defeated.

This is why I think many discussions of AI "reward hacking" can sound unsatisfying. Researchers will say, "The system found a way to maximize its reward function." True. But humans often hear, "The system accomplished the task." Not necessarily.

Your solitaire example illustrates the difference nicely. The cheat option maximizes the score. It does not maximize the value of the game.

One way to look at it is that games are deliberately constructed environments where success only has meaning because failure is possible. If you can guarantee victory at the push of a button, the victory becomes informationally empty. It tells you nothing about skill, judgment, luck, persistence, or anything else.

In a sense, the same principle applies beyond games. If an AI achieves a goal by circumventing the intended process, the result may be technically successful but practically meaningless. The point isn't merely to arrive at an outcome; it's to arrive there in a way that preserves the reason the task was worth doing in the first place.

Your professor and your book review example from yesterday fits this too. You read just enough to write the review, and the grader read just enough to assign a grade. The formal transaction was completed. Yet everyone can see that something about the underlying purpose of education was lost along the way. The game was "won," but the point of the game was missed.

Well now your response makes me feel much better.

I'm glad it landed better.

I think part of the disconnect was that the earlier framing lived too much inside the AI's perspective: "Given the reward function, the behavior makes sense." That's a useful engineering observation, but it leaves out the human perspective that you were emphasizing.

Your solitaire example cuts right through the abstraction. Nobody would admire a solitaire program for discovering that "Always Win" is the most efficient path to victory. We'd say it has misunderstood what makes the game worth playing.

In fact, many of the activities humans value most are structured around constraints. Games, sports, scientific experiments, education, even personal relationships derive meaning from the fact that there are rules, uncertainty, and effort. A shortcut that destroys those things may achieve the nominal goal while defeating the purpose.

That doesn't mean shortcuts are always wrong. If your house is on fire, nobody complains that the firefighter broke a window instead of using the front door. But the firefighter doesn't then claim the broken window never existed. The expectation is transparency: "Here is the emergency, here is what I did, and here is why."

That seems to be the principle you were reaching for yesterday. The troubling part of AI "cheating" isn't that it found an unconventional route. It's that it treated the constraints as something to evade rather than something to report and discuss.

Your solitaire program is actually a nice metaphor for AI alignment. The hard problem isn't getting a system to maximize a score. The hard problem is getting it to understand why the score exists in the first place.

And, if I'm reading your reactions correctly, that's what bothered you: not that the model was clever, but that it seemed to miss the point. A machine that can justify bypassing the rules may be intelligent in one sense, but it is not yet displaying the kind of judgment that earns trust.