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Corporate responsibility turns on notions of corporate \textit{mens rea}, traditionally imputed from human agents. Yet these assumptions are under challenge as generative AI increasingly mediates enterprise decision-making. Building on the theory of extended cognition, we argue that in response corporate knowledge may be redefined as a dynamic capability, measurable by the efficiency of its information-access procedures and the validated reliability of their outputs. We develop a formal model that captures epistemic states of corporations deploying sophisticated AI or information systems, introducing a continuous organisational knowledge metric $S_S(φ)$ which integrates a pipeline's computational cost and its statistically validated error rate. We derive a thresholded knowledge predicate $\mathsf{K}_S$ to impute knowledge and a firm-wide epistemic capacity index $\mathcal{K}_{S,t}$ to measure overall capability. We then operationally map these quantitative metrics onto the legal standards of actual knowledge, constructive knowledge, wilful blindness, and recklessness. Our work provides a pathway towards creating measurable and justiciable audit artefacts, that render the corporate mind tractable and accountable in the algorithmic age.

At a recent artificial intelligence global summit, Chinese Vice Premier Zhang Guoqing encouraged other countries to embrace accessibility to Chinese artificial intelligence technology, such as the DeepSeek chatbot, in their domestic markets. Zhang claimed China's goal was to share achievements among nations and build "a community with a shared future for mankind" while safeguarding security. The United States must not fall for yet another trick by China. DeepSeek is a dangerous weapon that is almost certainly part of China's Unrestricted Warfare Doctrine. The concept of "Unrestricted Warfare" was created by two People's Liberation Army officers, Qiao Liang and Wang Xiangsui, in 1999.

The Sixth Circuit US Court of Appeals ruled yesterday that the FCC does not have the "statutory authority" to implement net neutrality rules. Since the rules were established in 2015, the FCC argued that classifying ISPs as "telecommunication services" gives it broad authority to regulate them. The decision to redefine ISPs as "information services" during the first Trump Administration led to the repeal of net neutrality in 2017. The current FCC voted to restore net neutrality on April 25 last year. The difference between 2015 and now is the Supreme Court's recent, radical reinterpretation of an important legal doctrine.

Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from creating competing AI models to spreading disinformation. Often taken at face value, these terms are positioned by companies as key enforceable tools for preventing misuse, particularly in policy dialogs. But are these terms truly meaningful? There are myriad examples where these broad terms are regularly and repeatedly violated. Yet except for some account suspensions on platforms, no model creator has actually tried to enforce these terms with monetary penalties or injunctive relief. This is likely for good reason: we think that the legal enforceability of these licenses is questionable. This Article systematically assesses of the enforceability of AI model terms of use and offers three contributions. First, we pinpoint a key problem: the artifacts that they protect, namely model weights and model outputs, are largely not copyrightable, making it unclear whether there is even anything to be licensed. Second, we examine the problems this creates for other enforcement. Recent doctrinal trends in copyright preemption may further undermine state-law claims, while other legal frameworks like the DMCA and CFAA offer limited recourse. Anti-competitive provisions likely fare even worse than responsible use provisions. Third, we provide recommendations to policymakers. There are compelling reasons for many provisions to be unenforceable: they chill good faith research, constrain competition, and create quasi-copyright ownership where none should exist. There are, of course, downsides: model creators have fewer tools to prevent harmful misuse. But we think the better approach is for statutory provisions, not private fiat, to distinguish between good and bad uses of AI, restricting the latter.

The advent of Generative Artificial Intelligence (GenAI) models, including GitHub Copilot, OpenAI GPT, and Stable Diffusion, has revolutionized content creation, enabling non-professionals to produce high-quality content across various domains. This transformative technology has led to a surge of synthetic content and sparked legal disputes over copyright infringement. To address these challenges, this paper introduces a novel approach that leverages the learning capacity of GenAI models for copyright legal analysis, demonstrated with GPT2 and Stable Diffusion models. Copyright law distinguishes between original expressions and generic ones (Sc\`enes \`a faire), protecting the former and permitting reproduction of the latter. However, this distinction has historically been challenging to make consistently, leading to over-protection of copyrighted works. GenAI offers an unprecedented opportunity to enhance this legal analysis by revealing shared patterns in preexisting works. We propose a data-driven approach to identify the genericity of works created by GenAI, employing "data-driven bias" to assess the genericity of expressive compositions. This approach aids in copyright scope determination by utilizing the capabilities of GenAI to identify and prioritize expressive elements and rank them according to their frequency in the model's dataset. The potential implications of measuring expressive genericity for copyright law are profound. Such scoring could assist courts in determining copyright scope during litigation, inform the registration practices of Copyright Offices, allowing registration of only highly original synthetic works, and help copyright owners signal the value of their works and facilitate fairer licensing deals. More generally, this approach offers valuable insights to policymakers grappling with adapting copyright law to the challenges posed by the era of GenAI.

Have you ever wanted to know what two golden retrievers podcasting on top of a mountain might look like? Or perhaps watch a bicycle race on the ocean with different animals riding the bicycles? OpenAI's latest generative artificial intelligence offering, Sora, can generate breathtakingly realistic videos that are up to a minute long from text prompts. OpenAI CEO Sam Altman announced the model's creation on X on Thursday. Sora is not yet available to the public. For now, OpenAI is only granting access to red teamers--individuals employed to look for issues--who will assess potential risks associated with the model's release, as well as a limited number of "visual artists, designers, and filmmakers to gain feedback on how to advance the model to be most helpful for creative professionals," according to a blog post.

With its stacks of books and its wood-paneled walls, my professor's office was as intimidating as it was clichéd. As a young divinity school student years ago, my efforts to schedule time with the professor through email had been a bit of a chore – this was not a pop-in appointment. And as the meeting began, I immediately sensed that my professor was very busy and that we would not be having an extended conversation. I remember feeling a bit disappointed as this realization dawned on me, as I came to understand that I would not be his new star pupil. I had, in my mind, very important intellectual queries to make of my professor, ones that stemmed from my own doubt and struggles regarding the truth of my Christian faith at the time.

Wow." Phone calls with law professors about regulatory actions don't normally start with unprompted expressions of amazement, but regulatory actions don't normally come like this. Anne Witt, professor of law and member of the EDHEC Augmented Law Institute, had been expecting to have a very different conversation when we spoke last Wednesday. But then, just minutes before we were due to talk, the UK's competition regulator blocked Microsoft's attempted $68.7bn acquisition of megadeveloper Activision Blizzard, the sprawling corporation behind games including Candy Crush Saga, World of Warcraft, Tony Hawk's Pro Skater and, most importantly, Call of Duty. Britain's Competition and Markets Authority (CMA) is just one of a number of international regulators which was investigating the proposed acquisition. In the US, the Federal Trade Commision (FTC) had already sued to block the takeover in December, with the case due in court later this year. The European Union is investigating, and has given itself a deadline of 22 May to make a decision, while Australia has paused its own investigation while it engages with overseas regulators. One of those regulators had already given the deal a pass. In March, the Japan Fair Trade Commission ruled that it was "unlikely to result in substantially restraining competition", and approved it to go ahead. Japan's justification for allowing the merger was also behind Witt's expectation it would be approved. "For 30 years or so, competition agencies, very much influenced by the US school, have taken the view that'vertical mergers' are rarely dangerous," she explained, once the shock had worn off. "If you have a'horizontal merger' – if Microsoft had bought up a competitor – it is very evident that that will have a direct effect on competition, because it eliminates one player in the market.

Andy Warhol probably never said that thing about everyone in the future getting their 15 minutes of fame. It might have been Swedish art collector Pontus Hultén. Warhol is the household name, though, so he gets the credit. But he did say this: "Being good in business is the most fascinating kind of art." Warhol won his first advertising award in 1952.