You know, Herman, I was browsing through some repositories on Hugging Face last night, and I realized I often just click right past the license file without really thinking about what it means for the work I am doing. It is one of those things that feels like legal background noise until you actually want to build something real. It is February eighteenth, twenty twenty-six, and the sheer volume of models and datasets being uploaded every hour is staggering. But behind every green checkmark or download button is a legal framework that most of us just take for granted.
Herman Poppleberry here, and I completely agree. It is the classic developer trap. You spend weeks perfecting the architecture, tuning the hyperparameters, or writing the cleanest code possible, and then at the last second, you just slap a license on it like it is a postage stamp. But those choices actually define the entire future life of your project. In twenty twenty-five, we saw a massive wave of litigation regarding training data and model weights, and it has made everyone a lot more nervous about those little text files. Choosing the wrong one can mean the difference between your project becoming an industry standard or becoming a legal radioactive zone that no company will touch.
It is funny you say that, because today's prompt from Daniel is actually taking us right into that territory. He wants to dig into the art of choosing an open source license. Specifically, he is curious about the differences between things like the MIT license, Creative Commons, and even the more niche options like the unlicense or open data licenses. He is looking for the philosophy behind the choice, not just the legal jargon.
That is a great follow-up to our previous conversation about open source versus open weights. Once you decide to share your work, the license is the set of rules that tells the world how they are allowed to play with it. It is the social contract of the digital age.
And Daniel mentioned something interesting about his own preference. He likes requiring attribution to help build his professional name, but he is otherwise very permissive about commercial use and derivatives. He does not want to create any unnecessary friction for people working downstream. He wants to be the giant whose shoulders people stand on, but he wants them to know whose shoulders they are.
That is a very common and very practical stance. But even within that preference, there are subtle choices to make. If we start with the most common one people see on GitHub, it is usually the MIT license. It is the gold standard for simplicity. It basically says, here is the code, do not sue me if it breaks, and just make sure you keep my name in the credits. It is incredibly short—you can read the whole thing in about a minute.
Right, and that simplicity is why it is everywhere. But I noticed Daniel also asked about instances where someone might want to write their own license or edit an existing template. That sounds like a recipe for a legal headache, doesn't it? Especially now that we have automated scanners checking for license compliance in every major enterprise.
Oh, it absolutely is. Most legal experts will tell you, do not write your own license. The reason we use these standard templates like MIT, Apache two point zero, or the General Public License, is that they have been tested in court. We know what the words mean in a legal sense. When you start editing them or writing your own, you introduce ambiguity. If you change a sentence about commercial use, does that mean you are banning all profit, or just direct sales of the software? A court might see it differently than you intended. Plus, you run into the problem of license proliferation. If everyone has a slightly different license, it becomes impossible for developers to combine different pieces of software because the licenses might contradict each other.
So, unless you are a massive corporation with a team of lawyers who need a very specific proprietary-plus-open hybrid, you should probably stick to the established paths. But what about the Creative Commons licenses? Daniel asked about those specifically, especially the shorthands like NoDerivs or ShareAlike. I see those a lot on datasets or artistic work, but less so on actual software code. Why the split?
That is an important distinction that often trips people up. Creative Commons was really designed for creative works, like images, music, and text, rather than functional software code. For code, you usually want something that handles things like patent grants, which is why the Apache two point zero license is so popular in the corporate world. Apache two point zero is like MIT's more sophisticated older sibling. It still allows for commercial use and modification, but it includes an explicit grant of patent rights from the contributors to the users. This is huge because it prevents someone from contributing code to a project and then suing the users later for patent infringement.
That makes a lot of sense for software. But for the AI models and datasets we see on Hugging Face, Creative Commons is very relevant because those are often viewed more like data or creative content than traditional software.
Exactly. In the AI world, we have this weird duality. The code used to train the model is software, but the resulting weights? Those are often treated as a creative or functional artifact. So, let's break down those Creative Commons shorthands Daniel asked about. If I see a CC-BY license, that is the attribution one Daniel likes, right?
Right. The B-Y stands for attribution. It just means you have to give credit to the original creator. It is the most permissive of the Creative Commons suite. But then it gets more complex. What about CC-BY-SA?
The S-A stands for ShareAlike. This is what we call a copyleft license. It is the spiritual cousin of the G-P-L, or General Public License. It means if you take my work and change it, you have to release your new version under that same license. You cannot take my open work, improve it, and then turn it into a closed, secret product. It forces the "openness" to propagate down the line.
That seems like a big philosophical divide. On one hand, you have the permissive licenses like MIT or CC-BY, where you just want credit and do not care what happens next. On the other hand, you have the ShareAlike or GPL-style licenses where you are trying to ensure the entire lineage of the project stays open forever. It is almost like a viral protection for the commons.
Precisely. It is a choice between maximum reach and maximum protection of openness. If you use a permissive license, a big company can take your code, put it inside their secret software, and sell it without ever sharing their improvements. Some people think that is fine because it encourages adoption. Others feel like that is a form of exploitation, so they use something like the General Public License, which requires any derivative work to also be open source. In twenty twenty-four and twenty twenty-five, we saw a lot of debate about this in the context of large language models. If I release a model under a copyleft license, and you fine-tune it, do you have to release your fine-tuned weights? Under ShareAlike, the answer is generally yes.
And then there is the NoDerivs tag Daniel mentioned. N-D stands for No Derivatives. That feels a bit counter-intuitive for the open source world, doesn't it? If I cannot make a derivative, I cannot really build on it.
It is very restrictive. If you label something as NoDerivs, people can share it and use it, but they cannot change it. In the context of AI, that is a massive hurdle. It means you can use a model for inference—basically, you can ask it questions—but you cannot fine-tune it on your own data because a fine-tuned model is technically a derivative work. You would use this if you want to ensure the integrity of the work, like a specific research paper, a medical dataset where you do not want people altering the labels, or a very specific artistic model where the creator wants to maintain a specific "voice."
What about the commercial side? The N-C or Non-Commercial tag. That is a huge point of contention in the AI space right now. I see a lot of models on Hugging Face that say "for research purposes only."
It is huge. CC-BY-NC means you can use it, you can change it, you have to give credit, but you cannot make money from it. The problem is that the definition of commercial use is actually quite blurry. If a researcher at a university uses an N-C-licensed model for a study, but that study is funded by a corporate grant, is that commercial? If a startup uses it for internal testing but not for a customer-facing product, is that commercial? Because of that uncertainty, many companies will flat-out ban their employees from using anything with an N-C tag. It creates a "poison pill" effect where your work might get a lot of hobbyist attention but zero professional adoption.
So, if Daniel wants to be permissive about commercial use, he should definitely avoid that N-C tag. But he also mentioned the unlicense and open data licenses. I have seen the unlicense on some small utility scripts. It is basically the legal equivalent of saying, I do not want any part of this anymore, right?
Pretty much. The unlicense is a way to dedicate your work to the public domain. It is even more permissive than MIT because it waives the requirement for attribution. You are basically saying, this belongs to the world now, do whatever you want, and you do not even have to mention my name. It is great for tiny snippets of code or basic algorithms where you feel like the overhead of a license file is just silly. However, in some countries, you actually cannot legally "waive" your moral rights as an author, so the unlicense includes a very broad fallback license just in case.
But when we get into data, things change. Daniel brought up open data licenses. Why do we need a separate category for that? Why can't I just use MIT for my dataset?
This is where it gets really nerdy and interesting. In many jurisdictions, facts and data cannot be copyrighted. You can copyright the creative expression of data, like a beautiful chart or a well-written summary, but you cannot copyright the number forty-two or a list of daily temperatures. This creates a weird legal vacuum for datasets. If I spend a million dollars scraping and cleaning a dataset, and copyright doesn't apply to the facts themselves, how do I protect my investment?
Wait, so if I spend months scraping and cleaning a massive dataset, I might not actually own the copyright to the data itself? That sounds terrifying for data scientists.
Exactly. That is why organizations like the Open Data Commons created things like the O-D-B-L, the Open Database License, or the C-D-L-A, which is the Community Data License Agreement. Instead of relying on copyright law, which is about creative expression, these licenses often rely on contract law or specific database rights that exist in places like the European Union. They are designed to handle the specific needs of data, like how to handle updates or how to attribute a database that is constantly changing. They also address the "extraction" of data, which is a concept that doesn't really exist in standard software licenses.
That seems like a critical distinction for the AI era. If the model weights are considered a mathematical representation of data rather than creative code, the type of license you choose might be even more important for its legal standing. We have seen the Open Source Initiative, or O-S-I, working hard on the "Open Source AI Definition" over the last couple of years.
Yes, and that definition, which was finalized recently, really emphasizes that for an AI model to be truly "open source," you need to provide the training data, or at least a very detailed description of it, along with the code and the weights. This has led to a lot of "open weights" models being reclassified. They aren't "Open Source" in the capital-O, capital-S sense; they are just "available." This is why you see things like the Llama Community License from Meta. They aren't traditional open source because they have usage restrictions, like the number of monthly active users—if you have more than seven hundred million users, you have to ask for a special license.
Seven hundred million! That is a very specific bar. It is basically the "Are you a tech giant?" test.
Exactly. And like we talked about earlier, custom licenses like that create friction. If I am a developer at a mid-sized company, I have to go to my legal department and ask, hey, are we under this specific user threshold? What happens if we get acquired by a company that is over the threshold? If it were just MIT or Apache two point zero, the answer would be a simple yes. Custom licenses are often called "source-available" rather than "open source" because they violate the O-S-I's rule against discriminating against fields of endeavor or specific groups of users.
So, if you are a creator and you want people to actually use your stuff, the best path is usually the one with the least resistance. For Daniel, that sounds like CC-BY for his content and something like MIT or Apache for his code. But Herman, you mentioned Apache two point zero earlier. Why would someone choose that over MIT if they both allow commercial use?
I would lean toward Apache two point zero for anything substantial in the code world. It is very similar to MIT in that it is permissive and requires attribution, but that patent grant is the key. In a world where tech companies are constantly suing each other over patents, that extra paragraph of legal protection is really valuable for anyone building a business on your work. It says, "I am giving you this code, and I am also giving you a license to any patents I have that are necessary to run this code." It is a huge peace-of-mind feature for corporate users.
That is a great point. It is not just about the right to see the code, it is about the right to actually run it without fear of a patent troll—or even the original creator—coming after you later.
Exactly. And to Daniel's point about building a professional name, requiring attribution is not just an ego thing. It is how the ecosystem tracks provenance. In twenty twenty-six, we are dealing with a lot of "model collapse" where AI is trained on AI-generated data. Knowing the provenance—the origin story—of a piece of code or a dataset is vital. If I see a great piece of code that is attributed to Daniel, and then I see another one, I start to recognize his work. It builds trust. In a world of AI-generated everything, knowing that a human expert like Daniel stands behind a specific repository is actually a huge value add. It is a signal of quality.
Let's talk about the ShareAlike aspect again for a second. We mentioned it is a copyleft concept. In the software world, that is the G-P-L. If I use a G-P-L library in my app, I have to open source my entire app. That has always felt a bit aggressive to some people. Is there a middle ground?
There is. It is called the L-G-P-L, or the Lesser General Public License. It was designed specifically for libraries. It says, you can use this library in your closed-source app, and you do not have to open source your app, but if you make changes to the library itself, you have to share those changes back to the community. It is a compromise. It protects the library but allows it to be used in commercial, proprietary software. It was very popular for things like graphics libraries or compression algorithms.
That sounds like a very healthy middle ground for a lot of infrastructure projects. It ensures the commons gets better while still being useful for the private sector. But you mentioned it is becoming less popular?
It is. We are seeing a massive shift toward the more permissive licenses like MIT and Apache. I think that is because the speed of development is so fast now that people would rather have more users and faster feedback than force people to share their modifications. In the AI world, the "moat" isn't usually the code anymore; it is the data and the compute. So companies are more willing to give away the code and the weights to set the industry standard. If everyone uses your model architecture, you win, even if you don't see their specific fine-tuning tweaks.
That makes sense. If you are trying to set a standard, you want to remove every possible barrier to entry. But what about the risks? If I release everything under MIT, and a giant tech company takes it, improves it, and never shares those improvements, haven't I just given away my competitive advantage for free?
That is the risk. But you have to ask yourself, what is your goal? If your goal is to change how the industry works, then having a giant company adopt your method is a huge win, even if they keep their specific optimizations secret. Your name is still on the original invention. But if you are trying to build a business that relies on being the only one with a certain piece of tech, then open source probably isn't the right model for that specific component anyway. You have to be strategic. You open source the "how-to" to build the community, but you keep the "secret sauce" or the proprietary data behind a closed door.
It really does come down to the art of the choice, like Daniel said. You have to align the license with your personal and professional goals. If you want to be a thought leader and have your name everywhere, you go permissive with attribution. If you are a digital activist who wants to ensure software freedom, you go G-P-L.
And if you are just sharing a cool little experiment you did on a weekend, maybe the unlicense is the way to go. But I want to go back to Daniel's question about editing templates. There is one specific type of editing that is actually becoming quite common, and that is adding a Commons Clause or a Responsible AI License, often called a R-A-I-L license.
R-A-I-L? Tell me more about those. I have seen those on some of the newer generative models.
R-A-I-L licenses are a response to the ethical concerns of AI. They are standard licenses—like Apache—but with an added section that restricts how the model can be used. For example, you might be banned from using the model for medical advice, for creating deepfakes without consent, or for surveillance. It is an attempt to bake ethics into the legal code.
That sounds noble, but does it actually work? Who is enforcing that?
That is the million-dollar question. Enforcement is incredibly difficult. Plus, once you add those restrictions, the license is no longer "Open Source" according to the O-S-I. They have a very strict definition that says you cannot discriminate against fields of endeavor. If you say "you cannot use this for military purposes," that is a restriction on a field of endeavor. So, you end up in this weird gray area where your code is visible and free for most, but it doesn't carry that official open source seal of approval. It can make big companies nervous because "ethical use" can be subjective and hard to audit.
This really highlights how nuanced this is. It is not just a legal choice; it is a community choice. When you pick a license, you are signaling to other developers what kind of relationship you want to have with them. It is like choosing the rules for a sandbox. Do we all share the toys, or can you take a toy home if you promise to tell everyone I bought it?
That is the best way to put it. A license is a social contract. If I see a G-P-L license, I know I am entering a community where sharing and reciprocity are the highest values. If I see MIT, I know I am in a place that values utility and ease of use above all else. If I see a R-A-I-L license, I know the creator is deeply concerned about the ethical impact of their work.
So, for someone like Daniel, or any of our listeners who are putting their work out there on GitHub or Hugging Face, what is the practical takeaway? How do they actually make the choice without needing a law degree?
I usually suggest a three-step process. First, decide on your primary goal. Is it adoption, protection of the code, or building your brand? If it is brand building, go CC-BY or Apache. Second, look at what everyone else in your specific niche is using. If you are building a new Python library and every other library in that space uses MIT, you should probably use MIT too, because that is what your users expect and are already cleared to use by their legal departments. You don't want to be the one weirdo with a different license that stops everyone from using your tool.
And the third step?
The third step is to use a standard, unmodified license. Do not try to be clever with the wording. Use the tools that GitHub and Hugging Face provide to select a standard license. They use something called S-P-D-X identifiers—it stands for Software Package Data Exchange. It is a standard way of naming licenses so that machines can read them. If you use a standard S-P-D-X tag, automated tools can scan your repo and tell people exactly what they can and cannot do. If a big company's automated compliance tool sees a custom license it doesn't recognize, it will just flag it as a risk, and they won't use your work. You might think you are being helpful by adding a little note to your license, but you might actually be making it invisible to the very people you want to reach.
That is such a practical point. In the age of automated dev-ops, if the machine can't read your license, it might as well not exist.
Exactly. The machine-readability of licenses is a huge part of the modern ecosystem. When you look at a model card on Hugging Face, that little tag that says MIT or Apache two point zero is powered by standardized metadata. If you mess with the template, you break that metadata. You lose the ability to be filtered for in searches. People literally search for "Apache two point zero models" to ensure they can use them commercially. If you aren't in that search result, you don't exist to them.
I also want to touch on the Creative Commons shorthands one more time, because I think they are so useful for people who aren't necessarily coders—maybe they are making datasets of images or text. We talked about B-Y, S-A, N-D, and N-C. You can combine these, right?
You can. You can have a CC-BY-NC-SA license. That would mean you must give attribution, you cannot use it for commercial purposes, and if you change it, you must share it under the same license. It is like a stack of rules. The more letters you add, the more restrictive it becomes. But be careful—some combinations are redundant or contradictory. For example, you can't really have a "ShareAlike" and "NoDerivs" license because if you can't make a derivative, there is nothing to share alike!
It is like a programming language for permissions. If X then Y, else Z.
It really is. And for things like datasets or AI model weights, which occupy that strange middle ground between code and content, Creative Commons is often the most expressive way to tell people what you want. But again, for the actual code that runs the model—the training scripts, the inference engine—stick to the software licenses like Apache or MIT.
This has been a really enlightening look at something that, on the surface, seems so dry. It is actually about the philosophy of sharing and how we build things together. It is about trust.
It is the foundation of the entire open movement. Without these legal frameworks, we would all be working in silos, afraid to share anything because we wouldn't know how to protect ourselves or our work. These licenses are what allow us to stand on the shoulders of giants. They provide the safety net that lets us be generous with our intellectual property.
And I think Daniel's approach is a great example of that. He wants his name on the giant's shoulder, but he wants to make sure as many people as possible can climb up there with him. He is using the license as a tool for reputation and growth.
I think he has the right idea. Focus on the attribution to build your reputation, and let the work itself travel as far and wide as it can. In the long run, the value of being the person who created a widely used tool is worth much more than the few dollars you might make by trying to restrict its use.
Well, I think that covers the landscape of licenses for today. It is definitely something I am going to be more mindful of next time I am starting a new project or even just pulling a library from GitHub. I will actually look for that S-P-D-X tag!
It is worth the five minutes of thought at the beginning of a project to save yourself five months of headache later on. If you are ever in doubt, "choosealicense dot com" is a fantastic resource that walks you through the choice based on your goals.
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This has been My Weird Prompts. Thanks for joining us for episode six hundred and sixty-seven.
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