{"slug": "copyrightabiliy-of-llm-generated-code-can-we-license-vibe-code-into-free", "title": "Copyrightabiliy of LLM-generated code: Can we license “vibe code” into Free Software?", "summary": "The Free Software Foundation Europe (FSFE) published an analysis concluding that code generated by large language models (LLMs) may lack copyright protection, which could prevent it from being licensed under Free Software terms. The article explains that copyright law traditionally requires human authorship, and that using prompts alone or machine-generated output without significant human modification likely fails to meet that requirement, citing U.S. court decisions and noting exceptions in the UK and Ireland. FSFE recommends that developers document their AI usage and ensure sufficient human involvement to maintain copyrightability and the ability to apply Free Software licenses.", "body_md": "# Copyrightabiliy of LLM-generated code: Can we license “vibe code” into Free Software?\n\nThe 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?\n\nIt 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.\n\nTable of contents\n\n**Copyright and Licensing in Free Software****Copyright only applies to implementation****Only a natural person can be a copyright owner****Public Domain and the level of human authorship****Outliers for authorship requirements: The UK and Ireland****Principles of copyrightability in Assisted Works from the USA****Using prompts alone is insufficient for copyright****Machine modification of original input****Modification and/or rearrangement of generated output****Copyright infringement****Formalized recognition of copyright in assisted work****Copyfraud and concealment of AI usage****Recommendations for AI usage in Free Software contributions**\n\n### Copyright and Licensing in Free Software\n\nCopyright 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.\n\nOne problem with copyright is that it makes software illegal to share\nby default. To overcome this, a license is used in order to define the\nterms under which the copyright holder allows the recipient of the\nlicense to use the software. If that license is drafted in such a way as\nto allow the recipient to enjoy the [Four Freedoms](/freesoftware/#freedoms), then\nthat license is a Free Software license.\n\nIf a piece of software is not copyrightable, that software has no\nrights reserved and is in the public domain. This also means that no one\nhas the rights to apply any kind of license terms to that software,\nincluding [copyleft\nlicense terms](/freesoftware/legal/faq.en.html#copyleft) that work to maintain the Four Freedoms in any\ndownstream distributions and derivatives of that software.\n\n### Copyright only applies to implementation\n\nThe 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.\n\nIndeed, the underlying assumption in copyright law is that the\nimplementation of an idea in order to produce a creative work to\nfruition is the difficult part, which is why this\nimplementation is deserving of legal status. As an example, the idea of\nlions loosely doing Hamlet is not copyrightable, but the implementation\nof that idea in the form of the film “*The Lion King*” is. In\nlegal terms, this separation of what is and is not covered by copyright\nis generally referred to as the “**idea-expression\ndichotomy**”.\n\nThe 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.\n\nNevertheless, we can see that copyright law across multiple jurisdictions remain mostly clear: the copyright owner has to be a human being, and the copyright should only extend to human-created work.\n\n### Only a natural person can be a copyright owner\n\nThis idea that only a human being (or “natural person”) may be\nconsidered an author of copyrightable work has been a historical feature\nof copyright law. Indeed, the US Copyright Office [stated\nback in 1965](https://www.copyright.gov/reports/annual/archive/ar-1965.pdf) that:\n\n“[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.”\n\nThis understanding has persisted since then, with the US Copyright\nOffice [releasing\na report](https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf) in January 2025 reaffirming their 1965 stance, even in\nlight of the advent of AI technologies.\n\nSuch understanding can be seen as well in the European Union (“EU”).\nWhile there is a current lack of harmonised, specific legislation\nthroughout all member states on the copyrightability of LLM-generated\nworks, there are nonetheless strong indications that copyright does not\napply to purely LLM-generated works within the EU, and only a natural\nperson can be considered an author. This was outlined in the results of\na [policy\nquestionnaire](https://data.consilium.europa.eu/doc/document/ST-16710-2024-REV-1/en/pdf) conducted by the Council of the EU in 2024 to explore\nthe relationship between AI and copyright, where a majority of EU member\nstates agreed that current copyright principles within the EU and\nnational legislations already adequately address the copyrightability of\nLLM-generated work.\n\nSpecifically, 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.\n\nExisting case law at both the Court of Justice of the European Union\n(“CJEU”) and at the member state level further support this. For\nexample, the CJEU has clarified in a number of cases that, in order for\nauthorship and copyright to be applicable, a creative work has to [represent\nthe expression of the intellectual creation of a natural person](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:62008CJ0005). In\nother words, the work has to to be able to reflect the author’s\npersonality, in the sense that the author was able to [express\ntheir creative abilities in the production of the work by making free\nand creative choices](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:62010CJ0145).\n\nAt 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.\n\nMore recently, the [Munich\nDistrict Court dismissed a copyright claim](https://www.gesetze-bayern.de/Content/Document/Y-300-Z-BECKRS-B-2026-N-1513) over three logos after\nfinding that a person who created them with an LLM could not claim them\nas their own work. In line with the previous reasoning of the CJEU and\nthe Municipal Court of Prague, the decisive factor of copyrightability\nhere is whether or not personal creative work of the natural person is\ndirectly reflected in the resulting implementation, despite the\nautomated creative process. Nevertheless, the Munich District Court here\ndid not generally dismiss the idea of LLM-generated or -assisted works\nto be copyrightable, but merely ruled that the specific LLM outputs at\nhand were not.\n\n### Public Domain and the level of human authorship\n\nThe implication of these principles is that wholly LLM-generated\noutput are not copyrightable as they have no human author, and will\ntherefore by default be considered to be in the public domain, as\naffirmed by an [EU\nwide study commissioned by the European Parliament](https://www.europarl.europa.eu/RegData/etudes/STUD/2025/774095/IUST_STU(2025)774095_EN.pdf).\n\nNevertheless, 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.\n\n### Outliers for authorship requirements: The UK and Ireland\n\nIt is also worth mentioning that not all jurisdictions fully recognise\nthat only the work of natural persons are copyrightable.\nOutside of the EU in the United Kingdom (“UK”), Section 9(3) of the [Copyright,\nDesigns, and Patents Act of 1988](https://www.legislation.gov.uk/ukpga/1988/48/data.pdf) provides that:\n\n“[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.\"\n\nThis 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.\n\nIreland has a provision similar to the UK in Section 21(f) of their\n[Copyright\nand Related Rights Act 2000](https://www.irishstatutebook.ie/eli/2000/act/28/enacted/en/html), which hands authorship and copyright\nownership to, “*in the case of a work which is computer-generated,\nthe person by whom the arrangements necessary for the creation of the\nwork are undertaken*”. As an EU member state, this unfortunately\nputs Ireland at odds with the general EU position requiring human\ncreative contributions for authorship and copyright to be\napplicable.\n\n### Principles of copyrightability in Assisted Works from the USA\n\nNevertheless, 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.\n\nThe copyrightability of photographs, for example, was the subject of\nconsiderable debate at the time when cameras were new inventions, as can\nbe seen in a [US Supreme\nCourt case from 1884](https://supreme.justia.com/cases/federal/us/111/53/) (“*Burrow-Giles*”). Here, it was argued\nthat photographs were the products of machines and therefore lacked the\nrequisite human authorship to be copyrightable.\n\nWhen 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,\n\n“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.\"\n\nThe 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.\n\nA [Third\nCircuit US Court of Appeals case from 1991](https://law.justia.com/cases/federal/appellate-courts/F2/927/132/110312/) (“*Andrien*”)\nestablishes a similar important principle. This case concerned copyright\nover a compilation of maps that the plaintiff had requested a third\nparty printer rescale and print. The plaintiff argued that he had\nexpressly directed the preparation of the copies in specific detail, so\nthat the compilation only required a simple and mechanical process to\nachieve its final tangible form. Because the printer did not change the\nsubstance of the plaintiff’s original expression, the court ruled that\nauthorship belonged wholly to the plaintiff, as he was:\n\n“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.\"\n\nNevertheless, the court was quick to point out that this definition\nis subject to limits. Similar to the reasoning in *Burrow-Giles*,\na process that is rote or mechanical in a manner that does not require\nintellectual modification or highly technical enhancement can be be\ndisregarded as a contributing author.\n\nThe US Supreme Court has also considered the degree of creative\ncontribution necessary to qualify for authorship and copyright. In [ Community\nfor Creative Non-Violence v Reid](https://supreme.justia.com/cases/federal/us/490/730/) (“\n\n*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.\n\nUpon remanding the case back to the trial courts for further\nconsideration, the [lower\ncourt specified](https://law.justia.com/cases/federal/appellate-courts/F2/846/1485/396942/) that the acts of commissioning the sculpture and\nproviding detailed instructions constituted only ideas, which as\nexplained by the idea-expression dichotomy, is not enough to warrant\nauthorship and copyrightability.\n\nA 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:\n\n- The use of a machine to create a work does not negate\ncopyrightability for the human creator (\n*Burrow-Giles*); - When a person hires someone or uses something to execute their\nestablished expression, the executing party or thing has no claim to\nauthorship if they use a process that does not require intellectual\nmodification or highly technical enhancement of the expression\n(\n*Andrien*); and - Providing detailed instructions for the creation of the work by\nsomeone else constitutes only the provision of non-copyrightable ideas,\nand that person providing instructions has not done enough to warrant\nauthorship (\n*CCNV*).\n\nThere 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.\n\n### Using prompts alone is insufficient for copyright\n\nAdvocates for human authorship of LLM-generated work often argue that\nthe required human creativity for the output exists in prompts,\ncomparing prompting to commissioning a creative work, or providing an\nartist with instructions. This opinion is however not generally\nreflected in case law and the general principles of copyright law, where\nprompts are unable to provide sufficient human control to allow users of\nan AI tool to be recognised as authors of the resulting output. Just\nlike the detailed instructions given by the non-profit to the sculptor\nin *CCNV*, prompts can be seen to merely be instructions that\nconvey ideas, which are not covered by copyright law.\n\nAs a side note, prompts themselves can be copyrightable if they are\nsufficiently creative to pass the [threshold of\noriginality](/news/2025/news-20250515-01.en.html). However, even in situations where specific prompts are\ncopyrightable, they would still be considered a distinct and separate\nentity from the output.\n\nThe US Copyright Office additionally [points\nout key differences](https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf) between prompting an LLM and commissioning\ncreative work:\n\n__Potential for involvement in the creative process__\n\nIn 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.__Lack of control over conversion of ideas into a fixed expression__\n\nIn 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.\n\nThe 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.__Effort required to revise and refine prompts is irrelevant__\n\nPrompting 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.\n\nAffirming 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.\n\nNevertheless, 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.\n\n### Machine modification of original input\n\nThere are several generative tools where inputs are able to be\nsubstantially retained as being part of the output. This allows for\nusers to use such tools to amend, adapt, translate, or otherwise modify\ntheir own human created works. For example, a software developer may use\nsuch a tool to search for bugs or syntax errors in their code, which\nwill be automatically fixed. This is analogous to what happened in\n*Andrien*, where the plaintiff already had implemented his idea,\nresulting in a copyrightable expression with his collection of maps,\nbefore seeking the help of the printer to finalize and refine his\nexpression.\n\nWhen 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.\n\n### Modification and/or rearrangement of generated output\n\nThe 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.\n\nGenerating output with AI tools is also often an initial or\nintermediate step, with human-authored contributions being added to, or\nmodified into, the AI-generated content before it is presented as a\nfinished work. The non-copyrightability of individual elements in an\noverall work does not prevent that work from being copyrightable under\nthe law, if the presence of human creative elements in the entirety of\nthe work passes a certain threshold, as seen in *Burrow-Giles*.\n\nThis principle has been demonstrated by the copyright dispute over a\ncomic book titled “*Zarya of the Dawn*” by writer Kris Kashtanova\nin the USA. Although Kashtanova had initially applied for and received\nformal copyright recognition from the US Copyright Office in 2022, the\nCopyright Office later [partially\nrevoked such recognition](https://www.copyright.gov/docs/zarya-of-the-dawn.pdf), upon their discovery that she had utilised\nthe image generation software Midjourney to produce the images used in\nthe book.\n\nIn 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.\n\nThis case is particularly relevant for copyright over software, as\nsoftware elements produced as output are often used as building blocks\nin the assembly of a larger work. *Zarya of the Dawn *can be\nviewed analogously with the use and/or linking of various functions and\nmodules in a software project: developers often reuse elements that they\ndo not own copyright over, and their use of such elements does not mean\nthat they can claim authorship over them. They nevertheless are entitled\nto general authorship and copyright over the overall program that they\nhave created. Similar to the creative choices made by Kashtanova in the\narrangement and compilation of images in *Zarya of the Dawn*, the\ncompilation, arrangement, and internal structure of a software program\ncan be considered creative enough if it was implemented by a human\ndeveloper.\n\nIn 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.\n\n### Copyright infringement\n\nHowever, 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.\n\nIn 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.\n\nLiability for copyright infringement, if discovered, generally falls\non the party who distributes it, which would be the user and not the\ncreator of the LLM in question. Nevertheless, the traditional\nunderstanding of copyright infringement is also being challenged by the\nrise of AI technologies. For example, in the ongoing case of\n*Author’s Guild et al v OpenAI*, the US courts are still\nevaluating arguments about whether the production of copyrighted works\nin outputs should be considered “derivative works”, or merely\nreplications. Until we have more legal certainty, it is worthwhile to\nkeep these considerations in mind when using the various LLMs available\nto the public.\n\nHaving said that, there are also efforts to produce LLMs trained on\n“safe” datasets. For example, the datasets used in [GPT-NL Public Corpus](https://gpt-nl.nl/) LLMs from the\nNetherlands have been curated for proper compliance, using public and\npermissively licensed content to train the GPT-NL model, in an attempt\nto establish a model that mitigates the risk of copyright infringement.\n\n### Formalized recognition of copyright in assisted works\n\nAs 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.\n\nFor example, in Italy, a [new\nlaw touching on AI technologies](https://www.normattiva.it/uri-res/N2Ls?urn:nir:stato:legge:2025;132) entered into force in October 2025.\nSpecifically, it functions to include the adjective “human” to the\ndefinition of intellectual works in Article 1 of the [Italian\nCopyright Act of 1941](https://www.normattiva.it/uri-res/N2Ls?urn:nir:stato:legge:1941-04-22;633!vig=1996-01-01), as well as clarifying that copyright applies\nto works of human ingenuity created with the aid of LLMs, provided that\nsuch work can also be established to be the result of the human author’s\nown intellectual work.\n\nIn Ukraine, legislation also now provides an alternative framework\nfor LLM-generated and -assisted work, in the form of what is known as a\n“*sui generis* right”. In legal terms, a *sui generis\n*right refers to a type of right that extends to things that are so\nunique that they cannot fit into traditional forms of categorisation,\nand therefore need to be in their own one-of-a-kind classifications.\n\nIn cases of unique output generated by a computer program, Article\n33(2) of the [Ukrainian Law 2811-IX\non Copyright and Related Rights](https://www.wipo.int/wipolex/en/text/587392) grants *sui generis* rights\nto the authors of the computer program, their heirs, persons to whom the\nauthors or their heirs transferred economic rights to the computer\nprogram, or the lawful users of the computer program. Traditional\ncopyright principles will nevertheless still apply to any parts of the\nwork that was created by humans.\n\nIn 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.\n\n### Copyfraud and concealment of AI usage\n\nAs 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.\n\nBecause 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.\n\nIndeed, 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.\n\n### Recommendations for AI usage in Free Software contributions\n\nWith these issues in mind, the recently published *Recommendations\nWhen Using LLM-Backed Generative AI Systems for FOSS\nContributions** *by the SFC outlines some of the legal\ndifficulties for copyright and licensing that generative systems have\ncreated for software developers who wish to write, maintain, or\ncontribute to Free Software projects.\n\nNotably 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:\n\n“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.\"\n\nIndeed, 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.\n\nAdditionally, 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.", "url": "https://wpnews.pro/news/copyrightabiliy-of-llm-generated-code-can-we-license-vibe-code-into-free", "canonical_source": "https://fsfe.org/news/2026/news-20260825-01.en.html", "published_at": "2026-08-24 23:00:00+00:00", "updated_at": "2026-08-25 09:44:51.847750+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-policy", "ai-ethics", "generative-ai", "large-language-models"], "entities": ["Free Software Foundation Europe", "FSFE", "United States", "United Kingdom", "Ireland"], "alternates": {"html": "https://wpnews.pro/news/copyrightabiliy-of-llm-generated-code-can-we-license-vibe-code-into-free", "markdown": "https://wpnews.pro/news/copyrightabiliy-of-llm-generated-code-can-we-license-vibe-code-into-free.md", "text": "https://wpnews.pro/news/copyrightabiliy-of-llm-generated-code-can-we-license-vibe-code-into-free.txt", "jsonld": "https://wpnews.pro/news/copyrightabiliy-of-llm-generated-code-can-we-license-vibe-code-into-free.jsonld"}}