Nikhil Pradhan (Independent) has posted “Intellectual Property Strategies for AI-Enabled Drug Development” (Bringing Medicines to Life: How Intellectual Property Enables Innovation in the Life Sciences (eds. Jonathan M. Barnett and Bowman Heiden, Cambridge University Press, forthcoming 2026)) on SSRN. Here is the abstract:

Conventional biopharma IP strategy, focused on tangible drug assets, faces disruption on several fronts. AI-driven drug discovery technologies continue to improve and bring candidates into trials, if not yet to full FDA approval. Greater awareness of the high cost and failure rate of traditionally developed drugs is also highlighting the potential of AI technologies to bring drugs to market faster and with lower cost. The impending patent cliff for several blockbuster drugs will also lead firms to reevaluate efforts allocated to asset-focused patent protection. In addition, more stringent disclosure requirements for AI technologies used in drug development may shift the line on the tradeoff between patent and trade secret protection.

This chapter will outline these disruptions as well as the current AI drug development landscape, including identifying trends on how AI-focused firms are currently allocating resources to assets, specific targets or modalities, and/or underlying AI technologies. In view of this landscape and other disruptions in the biopharma market, the chapter will outline actionable IP strategies for players across the landscape including academic institutions, early-stage companies, and large pharmaceutical enterprises. Specific considerations for executing on IP strategies and other approaches for establishing exclusivity around new technologies and business models will be evaluated, including guidance on the patent vs. trade secret decision and tactics to strengthen patent applications for examination and litigation success, enabling stakeholders to adapt and thrive in this evolving landscape.

Øystein Baste (U Oslo) et al. have posted “Open Justice Data in Europe: A Patchwork” on SSRN. Here is the abstract:

The publication of court judgments is essential to upholding rule of law and democratic norms as well as facilitating legal research, and new legal technologies. However, many European states struggled to transition to online publication at scale. In this article we address three questions: what are the obligations of states to publish judgments; which states are making progress; and what are the challenges and solutions in ensuring greater publicity? We examine the overarching duties in the ECHR and EU law and the relevant legal requirements and practice in 12 national jurisdictions and two regional courts. Our findings show tremendous variation in duties and practice, and identify barriers to progress (legal, organisational, and budgetary) but also promising innovative solutions in certain jurisdictions. Ultimately, while this publication diversity provides a form of experimental governance, it would be timely to move towards common standards and approaches.

Tomaž Lajovic (Splato) has posted “Foundation Models as Infringers: Should Large-Scale AI Training Trigger Collective Licensing Obligations under EU Law?” on SSRN. Here is the abstract:

The essay addresses the copyright implications of foundation models (FMs), particularly large language models, under EU law. It examines how training datasets, memorisation, and output generation implicate copyright and database rights, and assesses whether collective licensing regimes or exemptions with remuneration rights could balance the interests of AI developers and rightsholders.

Hannah Kuker (U Miami) has posted “When Opt-Outs Fail Us: Charting a More Effective Course for Attribution & Monetization on AI Platforms” on SSRN. Here is the abstract:

At present, improvements to AI image-generating technology have been forestalled at the crossroads of the very debate through which intellectual property law was born: the balance between the protection of individual creator rights and the progression of science and the useful arts. These interests at odds have been reflected in recent legal turmoil inundating the court system between artists and AI developers. In the interim, the legislature and tech industry alike have been advocating for an “opt-out,” or “notice and consent,” approach to assembling training datasets. Yet, notice and consent frameworks have been historically ineffective, with complexity and opaque information flows creating a false appearance of user autonomy. We face this illusion of control should we adopt an opt-out approach to AI training dataset permissions. Because opt-outs are locationspecific, they ignore downstream copying, which is misleading for artists who believe if they have opted-out once, they have done so successfully across the board. AI companies are primed to manipulate this environment, exploiting artists’ inability to effectively opt-out, all under the guise of compliance. At the same time, we must recognize the profound impact AI can have on the arts-a potential that falls flat without rich, diverse, and high-quality training data. There exists a need for an alternative that respects the interests of both parties, or better yet encourages positive relationships between them. This Essay offers that solution. It calls for the regulation of data provenance recording practices by AI developers to facilitate mechanisms for attribution and monetization without sacrificing AI functionality. This Essay’s proposal avoids the pitfalls of opt-out schemas to preserve the key promises of intellectual property law.

Clark D. Asay (Brigham Young U J. Reuben Clark Law) has posted “Artificial Creators” (2 George Washington Journal of Law and Technology (forthcoming 2026)) on SSRN. Here is the abstract:

Artificial intelligence systems cannot be inventors or authors under current U.S. law. On that point, the U.S. Patent and Trademark Office and the U.S. Copyright Office agree. Yet beyond that, the two regimes sharply diverge. The USPTO has adopted a more flexible approach to AI-assisted invention, permitting extensive AI involvement so long as a human being can be said to have conceived of the claimed invention. The Copyright Office, by contrast, has taken a far more restrictive stance, effectively denying registration to works whose expressive elements are generated by AI—even where humans engage in detailed, iterative prompting and exercise some amount of creative direction.

This Essay explores the reasons for that divergence and questions whether it is justified. While copyright’s idea–expression dichotomy and independent creation requirement may appear to provide some justification for copyright law’s more restrictive approach, those doctrines do not compel the Copyright Office’s denial of copyright registration in AI-assisted works. Indeed, copyright law has long accommodated technologically mediated creativity—from photography to film—by focusing on human control and creative contribution rather than the mechanics of execution.

Drawing on patent law’s conception requirement, as well as copyright doctrines governing joint authorship and derivative works, this Essay argues that copyrightability standards should move more in patent law’s direction. Where a human meaningfully conceives of and directs the realization of a work—even if AI performs substantial expressive tasks—copyright law should recognize authorship at least to the extent of the human’s creative contribution. Failing to do so risks undermining copyright’s incentive structure and distorting the future development of creative industries in an era where AI assistance is increasingly ubiquitous.

Jonathan Barnett (USC Gould Law) has posted “The Free Content Illusion” (Journal of Intellectual Property Law (2026)) on SSRN. Here is the abstract:

Peer-to-peer file sharing in the early 2000s destabilized traditional content markets and associated business models that rely on preserving control over the use of creative assets.  Academics and other commentators widely argued that robust forms of intellectual property rights had been rendered largely obsolete in a digital environment of low production and distribution costs. Reflecting this view, courts expanded the fair use doctrine and generously applied safe harbors under the Digital Millenium Copyright Act, which largely immunized platforms against liability for user infringement and consistently favored content aggregators over originators.  The subsequent evolution of digital markets nonetheless shows that exclusivity protections remain critical to sustaining an independently viable content economy that does not rely on philanthropic or governmental patronage.  Streaming services in audio, video, and literary media restored revenue flows to content originators through contractual and technological complements to copyright protection, while content segments (notably, the news industry) that failed to deploy such mechanisms struggled economically.  Contrary to prevailing views, meaningful property rights and other exclusivity protections remain essential for sustaining the production, financing, and development of creative assets in digital environments and, together with technological and contractual complements, are likely to retain this role in supporting a robust flow of original content for the artificial intelligence ecosystem.

Frank Fagan (South Texas College Law Houston) has posted “When Fair Use Fails: Contingent Licensing for AI Training” (forthcoming, Foundation for American Innovation, 2025) on SSRN. Here is the abstract:

As content producers increasingly gate material in response to AI-driven substitution-despite no changes to fair use law-there is growing risk that socially valuable inputs may disappear from the generative AI training ecosystem. This paper proposes a narrowly tailored, contingent licensing scheme to preserve access to high-value content when market failures prevent voluntary licensing. The scheme activates only when three conditions are met: (1) the content is demonstrably valuable for training; (2) the producer is economically marginal-that is, likely to restrict or withdraw access absent compensation; and (3) voluntary licensing has failed due to high transaction costs or bargaining asymmetries. While the proposal is focused on economically marginal creators at risk of exit, it allows for future extension to inframarginal producers if systemic gating emerges (defined here as a sustained, measurable reduction in access to critical content, whether by a majority of producers or by a small set whose gating materially degrades model performance). Drawing on the model of compulsory music licensing, the fallback mechanism operates only when necessary and always includes an opt-out, offering a light-touch intervention to sustain open access without undermining innovation or core publication incentives. In this way, the proposal aims to preserve innovation conditions when asymmetric withdrawal risks distorting competition and locking in advantages for firms with early licensing deals or deep proprietary libraries. Stronger measures that compel content creators to license their works, and without an opt-out, are considered but tentatively rejected as inefficient and likely to distort functioning markets.

Maria De Lourdes Haynes (American U Dubai) has posted “Governing at a Distance: The EU AI Act and GDPR as Pillars of Global Privacy and Corporate Governance” on SSRN. Here is the abstract:

The European Artificial Intelligence Act (AI Act) constitutes a landmark regulatory framework governing artificial intelligence technologies, with core principles grounded in transparency, accountability, and risk mitigation. While designed to foster innovation and safeguard fundamental rights, the Act poses considerable implementation challenges. Organisations must navigate complex compliance obligations imposed to various actors across the value chain. These requirements entail rigorous reporting, auditing, monitoring and governance mechanisms, placing increased demands on corporate governance structures.A defining feature of the AI Act is its extraterritorial scope, mirroring the reach of the General Data Protection Regulation (GDPR). The AI Act applies not only to entities established within the European Union but also to non-EU businesses operating or placing AI products on the EU Market. Its extensive provision, covering authorised representatives and specific duties for actors across the AI value chain, are expected to incentivise non-EU jurisdictions and corporations to align their AI development and deployment practices with EU standards. Non-compliance may lead to hefty fines and exposure to reputational damage along with an erosion of consumer trust.AI Act is poised to emerge as a global benchmark for AI regulation. Board-level governance bodies must reconcile innovation and business objectives with regulatory imperatives, address liability risks, and embed AI literacy into strategic management and decision-making. As the regulatory framework evolves, it reinforces the necessity of integrating multidisciplinary legal, ethical, and strategic considerations into managerial and corporate governance frameworks to navigate this dynamic environment effectively and mitigate emerging risks.

Eduardo Alonso (City U London) and Nicola Lucchi (Universitat Pompeu Fabra Law) have posted “AI And Copyright “Hallucinations”: Does the Text and Data Mining Exception Really Support Generative AI Training?” (European Intellectual Property Review, 2025, volume 47, issue 9, pp. 515-526) on SSRN. Here is the abstract:

This article critically challenges the widespread – and, it is argued, conceptually flawed – assumption that arts 3 and 4 of the CDSM Directive provide a lawful basis for training generative AI systems on copyright-protected content. The article describes this misinterpretation as a form of legal “hallucination”, underscoring its disconnect from the Directive’s textual, technical, and normative foundations. Designed to enable automated analytical extraction for scientific or informational purposes, the TDM exceptions do not encompass the large-scale reproduction, internalisation, and expressive re-use of works characteristic of GenAI training. Article 3 is limited to non-commercial research; Art.4’s opt-out mechanism, based on non-standardised signals, exacerbates uncertainty without ensuring transparency or fair compensation. This misclassification not only undermines core copyright incentives but also distorts the scope of EU exceptions, placing the framework in tension with the three-step test and international norms. The article argues that applying TDM rules to GenAI training introduces structural imbalances, both doctrinal and distributive, that risk entrenching platform asymmetries, weakening authorial agency, and threatening cultural diversity. Rather than relying on strained legal interpretations, a forward-looking response requires bespoke legal reforms that preserve normative coherence while addressing the specific challenges posed by synthetic content creation.

Arthur H. Neill (New Media Rights) et al. have posted “A Framework for Applying Copyright Law to the Training of Textual Generative Artificial Intelligence” (32 Texas Intellectual Property Law Journal 225 (2024)) on SSRN. Here is the abstract:

The rise in the popularity of consumer-facing generative artificial intelligence (“GenAI”) has created considerable confusion and consternation among some copyright owners. Copyright owners argue that GenAI’s ability to automatically generate works is made possible by large-scale direct infringement by OpenAI, Microsoft, and other major GenAI developers. This article explores the application of copyright law to the training of OpenAI’s ChatGPT, specifically focusing on the legal issues surrounding the unauthorized use of copyrighted textual works in the GenAI training process.

The large language models (“LLMs”) that drive ChatGPT and similar GenAI can summarize written works, generate movie scripts, write poetry, and compose stories nearly instantaneously. LLMs can only function in this way due to the use of vast, diverse training datasets comprised of billions of websites and expansive repositories of books. These datasets are processed to derive the functionality and syntax of language, allowing the LLMs to generate new works.

This article discusses the recent lawsuits launched by high-profile authors and copyright owners against OpenAI and Microsoft, claiming direct, vicarious, and derivative infringement. Authors such as George RR Martin, Sarah Silverman, Christopher Golden, and professional organizations such as the Authors Guild contended their works were infringed upon to turn OpenAI into an $80 billion company.

In considering the merits of these lawsuits, we discuss the curation and content of training datasets used in the known iterations of ChatGPT, and characterize the protectability of the different works the datasets included. We then explore whether the transitory nature of OpenAI’s training process uses acceptable, non-infringing copies and how that undermines claims of direct infringement.

The article then looks at the applicability of current fair use precedent to textual GenAI and the various types of works used in training datasets. To do so, we apply settled caselaw and leading decisions to discuss OpenAI’s use of copyrighted works regarding purpose and character, nature of the original work, the amount and substantiality of the works used, and the impact on the market value of the works by ChatGPT. We pay special attention to other innovative technologies that rely on a fair use defense to draw analogies and comparisons to GenAI.

Finally, this article considers the policy and legislation of other countries and their approach to ChatGPT and copyright. In doing so, policy considerations are taken into account to argue the necessity of a finding of fair use to maintain international competitiveness and to prevent an erosion of fair use in other sectors outside of GenAI. The article concludes that there is substantial support for arguments that GenAI training involves only transitory, non-actionable copying, and is also permissable under fair use.