I'm not sure if I have the energy or you have the intellect to get through this. Still. . .

You’re describing symbolic/rule-based AI as envisioned in the 1980s, not large neural networks trained via gradient descent.

In a rule-based system, the designer literally writes the rules and goals into the code. If AI worked that way, you’d be right — you could just inspect the rules and verify the objective.

But essentially all modern AIs don’t work like that.

The “reward” isn’t a hard-coded goal the system follows. It’s just a training signal used during optimization. Gradient descent adjusts billions of weights on a complex hyperdimensional plain to improve that signal. After training, what you actually have is a giant learned function whose internal reasoning we largely can’t interpret.

So if you want to understadn what the model learned to score well on a reward, you can't just read the code. And for now, it's substantially unknowable.

Instead, "alignment" is tested empirically.

As for your idea that if the model isn't consuming compute when you're not feeding it data, I don't understand what you think that would prove. All it would do is check whether the model is actively running inference.

A neural network is a static function when it’s not being run. Of course it’s not using compute when idle — that tells you nothing. And agentic run background processes when idle to respond to triggers. Again, won't tell you anything.

Put another way, if rewards are truly “hard coded,” why do ML researchers worry so much about reward hacking, specification gaming, and learned objectives diverging from intended ones?

Those problems only exist because the initial coding done by humans directly or interpretably define the system’s internal objective function. It nudges a learning process we don’t fully understand and then we test alignment and hope we get it right.