Here's my thinking:

Cloud:

- You cannot directly execute a remotely-hosted program.

- You cannot run inference on an API-served model.

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Closed-source:

- You can execute a program with the binary. You cannot generate a new binary, but you could try to reverse-engineer it or (painfully) modify its execution.

- You can run inference on a model with the weights. You cannot re-produce a new set of weights from scratch, but you can fine-tune.

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Truly open:

- You can freely modify the source and produce new binaries.

- You can use the original training data and model architecture to independently re-produce the weights (assuming you've got the compute). You can modify the model architecture to get the weights that would've resulted from training the model that way.

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To me these are pretty clear parallels... I don't think the weights provided in a vacuum are in the spirit of open source, historically speaking.

The policy argument is totally separate, of course, and I fully understand why none of the frontier labs are truly open.