> We absolutely had a choice (D), in that no one was forced to call it "open source" at all

Technically yes, practically no.

You’re describing a prisoner’s dilemma. The term was available, there was (and remains) genuine ambiguity over what it meant in this context, and there are first-mover advantages in branding. (Exhibit A: how we label charges).

> causing collateral damage outside the AI bubble, and is nothing like Betamax vs. VHS

Standards wars have collateral damage.

> We should probably call them "free to run", if the "it's cheap" connotation of "freeware" needs to be avoided. Or maybe "open architecture"

Language is parsimonious. A neologism will never win when a semantic shift will do.

> Language is parsimonious. A neologism will never win when a semantic shift will do.

Agreed, but I think it's worth lamenting the danger in that. History is certainly full of transitory calamity and harm when semantic shifts detach labels from reality.

I guess we're in any case in "damage is done" territory. The question is more about where to go next. It does appear that the term "open source" isn't working for what these folks are doing (you could even argue whether the "available" term they chose was a strong one to lean on in the first place), so we'll see what direction the next shift takes.

> we're in any case in "damage is done" territory. The question is more about where to go next

Sort of. We can learn from the example. Perfect is the enemy of the good.

The source code is absolutely open which is the traditional meaning of open source. You are wanting to expand this to include data sets, which is fine, but that is the divergence.

Data is code, code is data.

When you can use LLMs to write code with English (or other) language, it's pretty disingenuous to not call the training data source code just because it's not exclusively written in a programming language like Python or C++.

> source code is absolutely open

It’s ambiguously open.

Nonono the code for (pre-)training wasn't released either and is non trivial to replicate. Releasing the weights without the dataset and training code is equivalent of releasing a binary executable and calling it open source. Freeware would be more accurate terminology.

I think I see what you mean. I suppose it is kinda like an opaque binary, nevertheless, you can use it freely since all is under the MIT license right?

Yes even for commercial purposes which is great, but the point of and reason why "open source" became popular is that you can modify the underlying source code of the binary which you can then recompile with your modifications included (as well as selling/publishing your modifications). You can't do that with deepseek or most other LLMs that claim to be open source. The point isn't that this makes it bad, the point is we shouldn't call it open source because we shouldn't loose focus on the goal of a truly open source (or free software) LLM on the same level than chatgpt/o1.

You can modify the weights which is exactly what they do when training initially. You do not even need to do it in exactly the same fashion. You could change things such as the optimizer and it would still work. So in my opinion it is nothing like an opaque binary. It's just data.

We have the weights and the code for inference, in the analogy this is an executable binary. We are missing the code and data for training, that's the "source code".

> that's the "source code"

Then it’s never distributable and any definition of open source requiring it to be is DOA. It’s interesting, as an argument against copyright. But that academic.

it's not academic. Why can't ChatGPT tell me how to make meth? why doesn't deepseek want to talk about tiananmen square? what other things has the model been molested into how it should be? without the full source, we don't know

While I appreciate the argument that the term "open source" is problematic in the context of AI models, I think saying the training data is the "source code" is even worse, because it broadens the definition to be almost meaningless. We never considered data to be source code and realistically for 99.9999% of users the training data is not the preferred way of modifying the model, just because the don't have millions of $ to retrain the full model, they likely don't even have the HDD space to save the training data.

Also I would say arguing that the model weights are just the "binary" is disingenuous, because nobody wants releases that only contain the training data and scripts to train and not the model weights (which would be perfectly fine for open source software if we argue that the weights are just the binaries), because they would be useless to almost everyone, because they don't have the resources to train the model.