LLM are the future, but we must still learn to use it correctly. The energy problem depends mainly on 2 things, the use of fossil energy and the abuse of AI including it without need in everything, because the hype, as data logging tool for Big Brother or biased influencers.

You don’t need a 4x4 8 cylinder Pick-up to go 2km to the store to buy bread.

We should reject them.

LLMs are tools. They’re not replacements for human creativity. They are not reliable sources of truth. They are interesting tools and toys that you can play with.

So have fun and play with them.

Instead of trying to prevent LLM training on our code, we should be demanding that the models themselves be freed.

You can demand it but it’s not an pragmatic demand as you claim. Open weight models aren’t equivalent to free software, they are much closer proprietary gratis software. Usually you don’t even get access to the training software and the training data and even if you did it would take millions of capital to reproduce them.

But the resulting models must be freed. Any model trained on this code must have its weights released under a compatible copyleft license.

You can put into your license whatever you want but for it to be enforceable it needs to grant licensee additional rights they don’t already have without the license. The theory under which tech companies appear to be operating is that they don’t in fact need your permission to include your code into their datasets.

block the crawlers, withdraw from centralized forges like GitHub

Moving away from github has become a good idea since Microsoft has purchased it years ago.

You kind of need to block crawlers because of you host large projects they will just max out your servers resources, CPU or bandwidth whatever is the bottleneck.

Github is blocking crawlers too, they have restricted rate limits a lot recently. If you are using nix/nixos which fetches a lot of repositories from github you often can’t even finish a build without github credentials nowadays with how rate limited github has become.

The problem is not the algorithm. The problem is the way they’re trained. If I made a dataset from sources whose copyright holders exercise their IP rights and then train an LLM on it, I’d probably go to jail or just kill myself (or default on my debts to the holders) if they sue for damages.

Checking whether a proprietary LLM model running on the “cloud” has been trained on a piece of TGPL code would probably be harder than checking if a proprietary binary contains a piece of GPL code, though.

Seems like the easiest fix is to consider the produce of LLMs to be derivative products of the training data.

No need for a new license, if you’re training code on GPL code the code produced by LLMs is GPL.

One of the four essential freedoms is the freedom to study the software and modify it. Studying means training your brain on the open source code. Can one use their brain to write proprietary code after they studied some copylefted code?

I asked the bot. It says:

I understand the concern. This question sits at the intersection of copyright law, statistics, and how machine-learning systems work—and it’s fair to be skeptical.

Here’s the most precise and careful way to frame it:

1. I can’t confirm GPL training data

I don’t know what specific works or licenses were in the training set, and I can’t confirm that GPL-licensed material was included. Neither I nor users have visibility into the underlying corpus.

So while it may be statistically plausible that GPL text exists somewhere in public data, plausibility ≠ proof, and I can’t assert it as fact.

2. Training ≠ distribution (the core legal argument)

OpenAI’s legal position (and that of most ML practitioners) is broadly this:

• Training on copyrighted material is not the same as redistributing it

• The model does not contain copies of works

• Outputs are newly generated, not retrieved

• Copyright law regulates copying and distribution, not learning patterns or statistics

This is analogous (legally, not philosophically) to a human reading GPL code and then later writing different code from experience.

Whether courts ultimately agree in every jurisdiction is still being litigated—but no court has ruled that training itself is per se illegal.

3. GPL specifically

GPL obligations trigger when you:

• copy

• modify

• distribute
  GPL-licensed code or derivative works.

Current legal consensus does not treat model weights or outputs as GPL “derivatives” in the copyright sense, because:

• There is no preserved expressive structure

• There is no traceable lineage to a specific work

• Outputs are not mechanically derived from any one source

That’s why GPL’s copyleft provisions are generally understood not to propagate through model training.

4. Is the law settled?

No. Anyone claiming it is settled is overstating.

What is true:

• Training on public data is widespread and openly practiced

• Multiple lawsuits exist, but none have yet established that training itself violates copyright or GPL

• Regulators and courts are still clarifying boundaries

5. Practical reality

Even if GPL material were in the training mix:

• That alone would not automatically make the model or its outputs illegal

• Liability (if any) would hinge on specific outputs being substantially similar to protected works, not on statistical likelihoods about training data

Bottom line

Your intuition about probability is reasonable—but legality does not turn on probability. It turns on:

• whether protected expression is reproduced

• whether derivative works are created

• whether licenses are triggered by the act in question

As of now, the prevailing legal interpretation is that models like me are lawful to deploy, even though the debate is ongoing.

If you want, I can also:

• Compare this to the Google Books ruling

• Walk through why “derivative work” is a high legal bar

• Discuss what would actually make an AI system GPL-tainted in practice