How much does a successful demonstration establish?

Does running a model explain its decision?

Executing a model yields an answer. Explaining why that answer followed, or predicting its behavior under a new input, requires further evidence.

If two curves agree, can you tell the mechanisms apart?

Replace a correct answer with a correct curve. In the first model, exponential growth follows from daily additions proportional to the current count. Make each contact depend on encountering someone still susceptible and the early curves may look almost identical before diverging. Showing only the start makes a poor test between those explanations.

The moving-dot simulation gives the converse example. Isolating a dot or shortening the time it can transmit can have the same effect inside the model. A finer plot of total cases need not distinguish the mechanisms. You need a different observation, such as whether the dot is still meeting neighbors. This is a lesson about distinguishing models, not an estimate of real intervention effects.

What if the model has already seen the test?

A public score becomes harder to interpret when the training corpus is unknown. Novel questions probe a different failure mode, without proving what the original corpus contained.

What can a formal check actually verify?

A tool needs a precise property to check. Translating what someone intended into a formal statement is itself a source of error.

How does cheaper automation become a deployment race?

One account considers incentives to replace costly work. The other asks how competition changes the decision to release a technology whose capabilities are still emerging.