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Copyrightability of LLM-generated code: Can we license “vibe code” into Free Software?

The use of large language models (“LLMs”) has exploded in recent years, including in the generation of source code. But even as their usage gains popularity, these trends bring with them pressing legal questions as well: if code is generated by an LLM, is that code copyrightable? And if so, who owns the copyright to that generated code?

It is important for developers to know how to answer these questions, because copyright is a foundational pillar of the legal framework that supports Free Software. In this article, we aim to help our readers understand how copyright law affects your ability to create Free Software when programmes are made with the help of LLMs, by providing a breakdown of the various legal principles and court decisions regarding copyright and machine generated content.

Copyright and Licensing in Free Software

Copyright is a legal construct that grants a person exclusive rights over a piece of creative work: only the copyright holder is allowed to reproduce a work, give copies of their work to others, and modify the work. Copyright comes about by default; it vests inherently in the original author of the work in the instant that work is created, including in software developers when they write code.

One problem with copyright is that it makes software illegal to share by default. To overcome this, a license is used in order to define the terms under which the copyright holder allows the recipient of the license to use the software. If that license is drafted in such a way as to allow the recipient to enjoy the Four Freedoms, then that license is a Free Software license.

If a piece of software is not copyrightable, that software has no rights reserved and is in the public domain. This also means that no one has the rights to apply any kind of license terms to that software, including copyleft license terms that work to maintain the Four Freedoms in any downstream distributions and derivatives of that software.

Copyright only applies to implementation

The use of a machine or computer to generate works that are traditionally created by human beings does not fit neatly into the traditional understandings of copyright principles described above. This is because copyright traditionally covers an author’s creative implementation and their specific expression of an idea via execution, rather than the ideas themselves. This gets complicated when the implementation and execution is taken over by machines.

Indeed, the underlying assumption in copyright law is that the implementation of an idea in order to produce a creative work to fruition is the difficult part, which is why this implementation is deserving of legal status. As an example, the idea of lions loosely doing Hamlet is not copyrightable, but the implementation of that idea in the form of the film “The Lion King” is. In legal terms, this separation of what is and is not covered by copyright is generally referred to as the “idea-expression dichotomy”.

The idea-expression dichotomy means that copyright has traditionally been understood to exist only for human-created work, and copyright has been similarly understood to be owned only by human beings. Up until recent technological developments, creative implementation has for the most part always had to be executed by humans. The introduction of generative LLMs however disrupts this traditional workflow of idea-expression, as (in the case of wholly LLM-generated work) a human being can now contribute merely the idea, with the implementation and expression of that idea performed by the LLM.

Nevertheless, we can see that copyright law across multiple jurisdictions remains mostly clear: the copyright owner has to be a human being, and the copyright should only extend to human-created work.

Only a natural person can be a copyright owner

This idea that only a human being (or “natural person”) may be considered an author of copyrightable work has been a historical feature of copyright law. Indeed, the US Copyright Office stated back in 1965 that:

“[t]he crucial question appears to be whether the “work” is basically one of human authorship, with the computer merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.”

This understanding has persisted since then, with the US Copyright Office releasing a report in January 2025 reaffirming their 1965 stance, even in light of the advent of AI technologies.

Such understanding can be seen as well in the European Union (“EU”). While there is a current lack of harmonised, specific legislation throughout all member states on the copyrightability of LLM-generated works, there are nonetheless strong indications that copyright does not apply to purely LLM-generated works within the EU, and only a natural person can be considered an author. This was outlined in the results of a policy questionnaire conducted by the Council of the EU in 2024 to explore the relationship between AI and copyright, where a majority of EU member states agreed that current copyright principles within the EU and national legislations already adequately address the copyrightability of LLM-generated work.

Specifically, member states acknowledged that wholly LLM-generated work cannot be copyrightable, but partly LLM-generated work may be so if it can be shown that the human input in the creative process was significant, similar to the stance from the US Copyright Office.

Existing case law at both the Court of Justice of the European Union (“CJEU”) and at the member state level further support this. For example, the CJEU has clarified in a number of cases that, in order for authorship and copyright to be applicable, a creative work has to represent the expression of the intellectual creation of a natural person. In other words, the work has to be able to reflect the author’s personality, in the sense that the author was able to express their creative abilities in the production of the work by making free and creative choices.

At the member state level, the Municipal Court of Prague in the Czech Republic stated in 2023 in the specific context of LLMs that a work of authorship must be the unique result of the creative activity of a natural person. Unless a creator can demonstrate that an LLM-generated image is the result of their unique creative contribution, authorship cannot be claimed.

More recently, the Munich District Court dismissed a copyright claim over three logos after finding that a person who created them with an LLM could not claim them as their own work. In line with the previous reasoning of the CJEU and the Municipal Court of Prague, the decisive factor of copyrightability here is whether or not personal creative work of the natural person is directly reflected in the resulting implementation, despite the automated creative process. Nevertheless, the Munich District Court here did not generally dismiss the idea of LLM-generated or -assisted works to be copyrightable, but merely ruled that the specific LLM outputs at hand were not.

Public Domain and the level of human authorship

The implication of these principles is that wholly LLM-generated outputs are not copyrightable as they have no human author, and will therefore by default be considered to be in the public domain, as affirmed by an EU wide study commissioned by the European Parliament.

Nevertheless, while most jurisdictions agree on the above, this understanding is overly-simplistic. In practical usage, LLM-generated output in many cases only forms just part of the creative work, with human input forming the other part. The laws regarding copyrightability therefore tend to be insufficiently clear and specific on what happens when LLMs are used together with human contributions in the current creation of creative works, including when producing software code.

Outliers for authorship requirements: The UK and Ireland

It is also worth mentioning that not all jurisdictions fully recognise that only the works of natural persons are copyrightable. Outside of the EU in the United Kingdom (“UK”), Section 9(3) of the Copyright, Designs, and Patents Act of 1988 provides that:

“[i]n the case of a literary, dramatic, musical, or artistic work which is computer-generated, the author shall be taken to be the person by whom the arrangements necessary for the creation of the work are undertaken."

This copyright framework results in a situation in the UK in which all creative works must and will have an author, regardless of the lack of human contribution in their creation. That being said, this was formulated before current technological advancements, and the practical application of Section 9(3) to LLM-generated content has not yet been tested and questioned in the courts.

Ireland has a provision similar to the UK in Section 21(f) of their Copyright and Related Rights Act 2000, which hands authorship and copyright ownership to, “in the case of a work which is computer-generated, the person by whom the arrangements necessary for the creation of the work are undertaken”. As an EU member state, this unfortunately puts Ireland at odds with the general EU position requiring human creative contributions for authorship and copyright to be applicable.

Principles of copyrightability in Assisted Works from the USA

Nevertheless, we can take some guidance from existing case law and legislation that currently deal with assisted creations, either by machine or other means. For this, US case law can be useful to conceptualise and establish principles of what constitutes a creative element that is copyrightable in light of emerging technologies.

The copyrightability of photographs, for example, was the subject of considerable debate at the time when cameras were new inventions, as can be seen in a US Supreme Court case from 1884 (“Burrow-Giles”). Here, it was argued that photographs were the products of machines and therefore lacked the requisite human authorship to be copyrightable.

When making their decision, the court considered that taking a photograph requires various choices to be made by the photographer, including selecting and arranging objects to be included in the frame, arranging the subject, and other decisions to evoke the desired expression or emotional impact, before using the machine itself to capture the image. In the court’s opinion, once the choices of the photographer have been made,

“the remainder of the process is merely mechanical, with no place for novelty, invention, or originality. It is simply the manual operation, by the use of these instruments and preparations, of transferring to the plate the visible representation of some existing object, the accuracy of this representation being its highest merit."

The court therefore rejected an argument that photographs lacked human authorship and were the product of a machine, instead ruling that the use of a machine does not negate copyrightability by default. Rather, the work is copyrightable if it contains sufficient human authored expressive elements. Because of the rote and directly mechanical nature of the machine in producing the image, its creative contribution was greatly diminished to the point where almost all creative authorship should be credited to the human who took the photograph.

A Third Circuit US Court of Appeals case from 1991 (“Andrien”) establishes a similar important principle. This case concerned copyright over a compilation of maps that the plaintiff had requested a third party printer rescale and print. The plaintiff argued that he had expressly directed the preparation of the copies in specific detail, so that the compilation only required a simple and mechanical process to achieve its final tangible form. Because the printer did not change the substance of the plaintiff’s original expression, the court ruled that authorship belonged wholly to the plaintiff, as he was:

“the person who translates an idea into an expression that is embodied in a copy by himself or herself, or who authorises another to embody the expression in a copy."

Nevertheless, the court was quick to point out that this definition is subject to limits. Similar to the reasoning in Burrow-Giles, a process that is rote or mechanical in a manner that does not require intellectual modification or highly technical enhancement can be disregarded as a contributing author.

The US Supreme Court has also considered the degree of creative contribution necessary to qualify for authorship and copyright. In Community for Creative Non-Violence v Reid (“CCNV”), a non-profit organisation commissioned a sculpture, giving the sculptor detailed instructions on what it should look like. Both parties eventually got into a dispute over who should be considered the legal author of the resulting sculpture. In awarding authorship to the sculptor, the court reiterated that the author of a copyrighted work is the person who translates an idea into a fixed, tangible expression.

Upon remanding the case back to the trial courts for further consideration, the lower court specified that the acts of commissioning the sculpture and providing detailed instructions constituted only ideas, which as explained by the idea-expression dichotomy, is not enough to warrant authorship and copyrightability.

A general trend we see across these cases is that a distinction needs to be made between using machinery as a tool to assist in the creation of works, and using it as a stand-in for human creativity. From these three cases, we can see some legal principles relating to copyright that are useful for an analysis of the use of LLMs:

1. The use of a machine to create a work does not negate copyrightability for the human creator (Burrow-Giles);
2. When a person hires someone or uses something to execute their established expression, the executing party or thing has no claim to authorship if they use a process that does not require intellectual modification or highly technical enhancement of the expression (Andrien); and
3. Providing detailed instructions for the creation of the work by someone else constitutes only the provision of non-copyrightable ideas, and that person providing instructions has not done enough to warrant authorship (CCNV).

There are certain features in the workflow of using LLMs that raise the question of whether or not they count as human contribution to a level that warrants authorship and copyright. Using these established principles can therefore help us better understand the copyrightability of an AI-generated work.

Using prompts alone is insufficient for copyright

Advocates for human authorship of LLM-generated work often argue that the required human creativity for the output exists in prompts, comparing prompting to commissioning a creative work, or providing an artist with instructions. This opinion is however not generally reflected in case law and the general principles of copyright law, where prompts are unable to provide sufficient human control to allow users of an AI tool to be recognised as authors of the resulting output. Just like the detailed instructions given by the non-profit to the sculptor in CCNV, prompts can be seen to merely be instructions that convey ideas, which are not covered by copyright law.

As a side note, prompts themselves can be copyrightable if they are sufficiently creative to pass the threshold of originality. However, even in situations where specific prompts are copyrightable, they would still be considered a distinct and separate entity from the output.

The US Copyright Office additionally points out key differences between prompting an LLM and commissioning creative work:

1. Potential for involvement in the creative process
   In the case of human to human commissioned works, the hiring party is able to oversee, direct, and/or understand the contributions and process of the commissioned artist. This allows for a commissioning party to potentially be considered a joint author in some circumstances. With AI tools, the prompter receives the completed output after the generative process has been completed, and plays no role in the creative process of the output after the prompt is received.
2. Lack of control over conversion of ideas into a fixed expression
   In general, prompts do not adequately determine the expressive elements produced, or control how the system translates them into a specific output. In other words, while prompts reflect a user’s idea, the user does not control the specific way in which that idea is expressed.
   The gaps between prompts and the resulting outputs demonstrate that the user lacks control over the conversion of idea into expression, and that the system is largely responsible for the expressive elements in the output. Additionally, where no instructions were given, including for elements in the work that are necessary but may have been overlooked by the prompter, the system works to fill in the gaps. Indeed, the fact that identical prompts may on separate occasions generate different outputs further indicates a lack of human control.
3. Effort required to revise and refine prompts is irrelevant
   Prompting can often take repeated revisions and refinements (aka “prompt engineering”) in order to get a desirable outcome for the user. However, such effort is not relevant as copyright extends only to original authorship, regardless of the effort or work put in. Moreover, inputting a revised prompt does not currently seem to be materially different from inputting a single prompt. This can be viewed as simply “re-rolling the dice”, which causes the system to select more outputs for the user, but does not represent the required degree of control over the creation of the output that is required for authorship.

Affirming this principle, the aforementioned case in the Municipal Court of Prague also stated that simply writing a prompt to wholly generate a work cannot generally amount to authorship under copyright law.

Nevertheless, the principles regarding prompts may evolve in the future depending on how law and policy view prompting and outputs. For example, in the aforementioned copyright claim in the Munich District Court, the court considered that the progressive attempts of the user during the prompting process may lead to output that reflects their personality, giving more weight to the idea of output authorship.

Machine modification of original input

There are several generative tools where inputs are able to be substantially retained as being part of the output. This allows for users to use such tools to amend, adapt, translate, or otherwise modify their own human created works. For example, a software developer may use such a tool to search for bugs or syntax errors in their code, which will be automatically fixed. This is analogous to what happened in Andrien, where the plaintiff already had implemented his idea, resulting in a copyrightable expression with his collection of maps, before seeking the help of the printer to finalize and refine his expression.

When a person A inputs their own human-created, copyrightable content directly into an LLM, modified outputs of that input may still be credited to A and be copyrightable, provided that the modified output retains enough features of the original that it retains identifiable and perceptible features of its original human authorship. A’s own creative expression identifiable in the modified output may be copyrightable, in a similar manner that copyright law currently extends to derivative works. Especially in the case of source code, human created inputs tend to result in a limited range of what the modified output can look like when run through LLMs for amendments or modifications, allowing the output to retain the characteristics of its human authorship.

Modification and/or rearrangement of generated output

The non-copyrightability of individual elements in an overall work does not prevent that work from being copyrightable under the law. This is particularly relevant for copyright over software, as software elements produced as output by LLMs are often used as building blocks in the assembly of a larger work.

Generating output with AI tools is also often an initial or intermediate step, with human-authored contributions being added to, or modified into, the AI-generated content before it is presented as a finished work. The non-copyrightability of individual elements in an overall work does not prevent that work from being copyrightable under the law, if the presence of human creative elements in the entirety of the work passes a certain threshold, as seen in Burrow-Giles.

This principle has been demonstrated by the copyright dispute over a comic book titled “Zarya of the Dawn” by writer Kris Kashtanova in the USA. Although Kashtanova had initially applied for and received formal copyright recognition from the US Copyright Office in 2022, the Copyright Office later partially revoked such recognition, upon their discovery that she had utilised the image generation software Midjourney to produce the images used in the book.

In their analysis, the Copyright Office reiterated that the images that were wholly produced by Midjourney were non-copyrightable. Nevertheless, they found that the text in the book was written entirely by Kashtanova herself, and was therefore copyrightable due to it being a product of human authorship. In addition to the text, the Copyright Office also found that the manner in which Kashtanova had chosen to select and arrange the non-copyrightable images together with her text was sufficiently creative, and reflected her human authorship. Accordingly, copyright was considered to cover Kashtanova’s authorship of the overall text and compilation of the book, but not to each of the individual generated images that make up the book.

This case is particularly relevant for copyright over software, as software elements produced as output are often used as building blocks in the assembly of a larger work. Zarya of the Dawn can be viewed analogously with the use and/or linking of various functions and modules in a software project: developers often reuse elements that they do not own copyright over, and their use of such elements does not mean that they can claim authorship over them. They nevertheless are entitled to general authorship and copyright over the overall program that they have created. Similar to the creative choices made by Kashtanova in the arrangement and compilation of images in Zarya of the Dawn, the compilation, arrangement, and internal structure of a software program can be considered creative enough if it was implemented by a human developer.

In practical terms, this means that developers should structure their programs independently if they wish to claim authorship over such programs and license them as Free Software. They should not be using LLMs to produce a complete program for them, but rather only use such tools to support them in the implementation of their ideas.

Copyright infringement

However, legal problems can also arise when the tool reproduces something that is already copyrighted, or at least closely enough to be legally actionable. This can be a concern with tools that have been trained on datasets that include copyrighted materials, and the likelihood of it happening shifts depending on a number of factors, including the ways in which the tool has been prompted, as well as the parameters used for the tool’s training model.

In such situations, the reproduction of copyrighted material (as well as its distribution or subsequent publication) constitutes copyright infringement. Generally in most jurisdictions, copyright infringement is a strict liability act: this means that the intention to commit copyright infringement is not necessary to establish whether or not the infringer is at fault. In other words, “I did not know that the LLM copied/reproduced this work” is not a valid defence against copyright infringement.

Liability for copyright infringement, if discovered, generally falls on the party who distributes it, which would be the user and not the creator of the LLM in question. Nevertheless, the traditional understanding of copyright infringement is also being challenged by the rise of AI technologies. For example, in the ongoing case of Author’s Guild et al v OpenAI, the US courts are still evaluating arguments about whether the production of copyrighted works in outputs should be considered “derivative works”, or merely replications. Until we have more legal certainty, it is worthwhile to keep these considerations in mind when using the various LLMs available to the public.

Having said that, there are also efforts to produce LLMs trained on “safe” datasets. For example, the datasets used in GPT-NL Public Corpus LLMs from the Netherlands have been curated for proper compliance, using public and permissively licensed content to train the GPT-NL model, in an attempt to establish a model that mitigates the risk of copyright infringement.

Formalized recognition of copyright in assisted works

As we can now see, the general principles of copyright establish that LLM-generated output is not copyrightable, which therefore places such work in the public domain. However, if there exists a certain amount of human-authored creative content together with the generated output, the overall work can be considered to be LLM-assisted or -modified, and be considered deserving of copyright under the law. Some jurisdictions are now taking steps to codify such recognition of copyrightability into their national laws.

For example, in Italy, a new law touching on AI technologies entered into force in October 2025. Specifically, it functions to include the adjective “human” to the definition of intellectual works in Article 1 of the Italian Copyright Act of 1941, as well as clarifying that copyright applies to works of human ingenuity created with the aid of LLMs, provided that such work can also be established to be the result of the human author’s own intellectual work.

In Ukraine, legislation also now provides an alternative framework for LLM-generated and -assisted work, in the form of what is known as a “sui generis right”. In legal terms, a sui generis right refers to a type of right that extends to things that are so unique that they cannot fit into traditional forms of categorisation, and therefore need to be in their own one-of-a-kind classifications.

In cases of unique output generated by a computer program, Article 33(2) of the Ukrainian Law 2811-IX on Copyright and Related Rights grants sui generis rights to the authors of the computer program, their heirs, persons to whom the authors or their heirs transferred economic rights to the computer program, or the lawful users of the computer program. Traditional copyright principles will nevertheless still apply to any parts of the work that was created by humans.

In other words, Ukrainian law now additionally allows developers or proprietary owners of LLM systems and their users to enjoy rights traditionally held by copyright holders, thereby giving them control and licensing power over such generated content, as well as the ability to restrict unauthorised use. The Ukrainian courts have thus far not yet dealt with the nuances of the application of this law, and such rights have also not been accepted by the majority of member states of the EU.

Copyfraud and concealment of AI usage

As things currently stand, the existing copyright rules across all jurisdictions require a great amount of effort for developers to determine the copyrightability of their work when they use AI-generated outputs. Additionally, it is currently next to impossible to accurately determine whether a particular line of code is AI-generated, or whether it is human written. This creates a situation where the awareness of whether or not there is AI-generated code in a particular repository depends almost entirely on the goodwill of its contributors, and how forthcoming they are.

Because of these factors, some developers might feel encouraged or pressured to conceal their own uses of AI, in order to avoid complications in how they license their work, as well as how they present notices for accurate copyright and licensing information within their project repositories. It is nevertheless still important to keep in mind that even if doing so may be easier in the short term, it can cause serious legal problems downstream. Even if AI-generated content is not covered by copyright, such content may still infringe on existing copyrights, in instances when the output resembles or duplicates copyrighted training data.

Indeed, as noted by the Software Freedom Conservancy (“SFC”), some Free Software projects' leaders have taken a zero-tolerance approach to AI-generated contributions to their projects, in order to simplify the increasingly burdensome responsibilities that maintainers have to shoulder to analyse incoming contributions for their legal requirements in light of generative AI.

Recommendations for AI usage in Free Software contributions

With these issues in mind, the recently published Recommendations When Using LLM-Backed Generative AI Systems for FOSS Contributions by the SFC outlines some of the legal difficulties for copyright and licensing that generative systems have created for software developers who wish to write, maintain, or contribute to Free Software projects.

Notably for the purposes of this article, the SFC recommends the full recording and disclosure of how and when an AI tool was used to assist in the creation of a contribution. As stated by the SFC:

“FOSS project leaders cannot make good decisions about LLM-gen-AI policy if they cannot survey which contributions were assisted, and how much they are assisted. Part of the contribution process should (at least) include a disclosure of what LLM-gen-AI system was used, its version (as these system change over time), and a brief description of how the system assisted the contributor. This information should be included in a machine-readable format in commit logs."

Indeed, such disclosure can be an important foundational step to allow for the accurate assessment of the copyrightability of code that has been assisted or generated by AI tools, in order to assess their licensability into Free Software. Open and clear disclosure is a helpful step for the Free Software community to maintain a healthy licensing ecosystem, which is currently threatened by the legal uncertainties that come with the advent of generative AI.

Additionally, it is worthwhile for developers to document in some capacity the extent of human work that they have done in their software projects, whether it be the writing of code, the selection and arrangement of components within the project, or the extent of human modification of machine generated content.

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