I do open source software and have been doing it for many years. I understand open source licenses very well. DeepSeek assigns all rights of the outputs to you, which makes you the legal entity with the ownership of the code:

4.2 Subject to applicable law and our Terms, you have the following rights regarding the Inputs and Outputs of the Services: (1) You retain any rights, title, and interests—if any—in the Inputs you submit; (2) We assign any rights, title, and interests—if any—in the Outputs of the Services to you.

Meanwhile, your confusion when you talk about stealing the code stems from your lack of understanding of how LLMs actually work. People tend to confuse the output looking similar to training data with the mechanism being simple retrieval. There is no database or a lookup table, and there is no copy of any text they were trained on tucked away somewhere inside the weights. What they have is a compressed statistical model of the patterns that exist across trillions of tokens the text was broken down into. When you ask an LLM a question it does not go find the relevant code and regurgitate it. What actually happens is that it generates text token by token based on the probability distribution it learned during training. It's more akin to the way state machines like Markov chains work.

If anything, the way LLMs work is actually much closer to how humans improvise or combine ideas than to how a search engine or database works. When you write a sentence, you don't pull up a specific piece of code you memorized from some repository and copy it. You've absorbed the patterns from countless examples of language over your lifetime and internalized them. Coding is a process of generating novel combinations based on those patterns.

Similarly, the weights of a model encode statistical regularities about word co occurrence, syntactic structures, semantic relationships, and higher level conceptual patterns. When it generates text it is combining those patterns in ways that were never present in the training data. That's how LLMs produce entirely novel sentences that have never been written before, and why they can be prompted to combine ideas from different domains in creative ways.

LLMs can reproduce training data verbatim in some cases, but this happens because of overfitting on rare or repeated sequences. When a specific string appears many times in the training data like a famous quote or a common error message the model learns to assign an extremely high probability to each token in that exact sequence. So when it generates it follows the most likely path to reproduce the original text. It is similar to how a human can memorize a song or a poem by hearing it many times but that does not mean our general language ability works by pulling up stored copies from memory.

We’re talking about the worker, not the bourgeoisie. Their labour was exploited to benefit a corporate entity. And you’re consuming stolen proceeds from the bourgeoisie and defending their contributions.

You can certainly make this argument when it comes to closed corporate models. However, it completely falls apart when applied to open models that aren't owned by any corporation. Open models are available for everyone to use, and you running Qwen on your machine isn't benefiting any corporate entity in the slightest. And the whole stolen goods angle is basically the RIAA anti-piracy argument, you should be deeply ashamed of yourself for parroting it.

You're also confusing tools with the actual capitalist system here which makes your entire argument deeply incoherent. It is simply not possible to avoid using anything produced by exploited labour while living in a capitalist society. And nobody is defending capitalism here.

I would urge you to spend a bit of time trying to actually understand the subject you're attempting to discuss here to form a coherent position on it.

clearly that didn't help you much