US standards for copyrightability require human creativity and model weights likely don’t have the right kind of human creativity in them to be copyrightable in the US. No court to my knowledge has ruled on the question as yet, but that’s the US Copyright Office’s official stance.

By contrast, standards for copyrightability in the UK are a lot weaker than-and so no court has ruled on the issue in the UK yet either, it seems likely a UK court would hold model weights to be copyrightable

So from Google/Meta/etc’s viewpoint, asserting copyright makes sense, since even if the assertion isn’t legally valid in the US, it likely is in the UK - and not just the UK, many other major economies too. Australia, Canada, Ireland, New Zealand tend to follow UK courts on copyright law not US courts. And many EU countries are closer to the UK than the US on this as well, not necessarily because they follow the UK, often because they’ve reached a similar position based on their own legal traditions

Finally: don’t be surprised if Congress steps in and tries to legislate model weights as copyrightable in the US too, or grants them some sui generis form of legal protection which is legally distinct from copyright but similar to it-I can already hear the lobbyist argument, “US AI industry risks falling behind Europe because copyrightability of AI models in the US is legally uncertain and that legal uncertainty is discouraging investment”-I’m sceptical that is actually true, but something doesn’t have to be true for lobbyists to convince Congress that it is

The inference code and model architecture IS open source[0] and there are many other high quality open source implementations of the model (in many cases contributed by Google engineers[1]). To your point: they do not publish the data used to train the model so you can't re-create it from scratch.

[0] https://github.com/google-deepmind/gemma [1] https://github.com/vllm-project/vllm/pull/2964

No. AiCore service internally uses the inference on Tensor (http://go/android-dev/ai/gemini-nano)

> Is there anything at all from anyone I can download that'll run it on Tensor?

No.

> If there isn't, why not? (i.e. this isn't the first on device model release by any stretch, so I can't give benefit of the doubt at this point)

Mostly because 3P support has not been a engineering priority.

I guess there's benefit to running that step without subsampling to the initial 256 tokens per image/frame ( https://ai.google.dev/gemma/docs/gemma-3n/model_card#inputs_... ) to go on from that, https://github.com/antimatter15/reverse-engineering-gemma-3n suggests these are 2048 dimensional tokens, which makes these 60 Hz frame digestion rate produce just under 31.5 Million floats-of-your-choosen-precision per second. At least at the high (768x768) input resolution, this is a bit less than one float per pixel.

I guess maybe with very heavy quantizing to like 4 bit that could beat sufficiently-artifact-free video coding for then streaming the tokenized vision to a (potentially cloud) system that can keep up with the 15360 token/s at (streaming) prefill stage?

Or I could imagine just local on-device visual semantic search by expanding the search query into a bunch of tokens that have some signed desire/want-ness each and where the search tokens get attended to the frame's encoded tokens, activation function'd, scaled (to positive/negative) by the search token's desire score, and then just summed over each frame to get a frame score which can be used for ranking and other such search-related tasks.

(For that last thought, I asked Gemini 2.5 Pro to calculate flops load, and it came out to 1.05 MFLOPS per frame per search token; Reddit suggests the current Pixel's TPU does around 50 TOPS, so if these reasonably match each terminology wise, assuming we're spending about 20% of it's compute on the search/match aspect, it comes out to an unreasonably (-seeming) about 190k tokens the search query could get expanded to. I interpret this result to imply that quality/accuracy issues in the searching/filtering mechanism would hit before encountering throughout issues in this.)