OpenAI co-founder Elon Musk has sued the company, his fellow co-founders, associated businesses and unidentified others. He claims that, by chasing profits, they're violating OpenAI's status as a non-profit and its foundational contractual agreements to develop AI "for the benefit of humanity." The suit alleges that OpenAI has become a "closed-source de facto subsidiary" of Microsoft, which has invested 13 billion and holds a 49 percent stake. Microsoft uses OpenAI tech to power generative AI tools such as Copilot. According to the filing, under OpenAI's current board, it is allegedly developing and refining an artificial general intelligence (AGI) "to maximize profits for Microsoft, rather than for the benefit of humanity. This was a stark betrayal of the Founding Agreement."

Elon Musk is suing OpenAI and Sam Altman for allegedly abandoning OpenAI's original mission to develop artificial intelligence to benefit humanity. "OpenAI, Inc. has been transformed into a closed-source de facto subsidiary of the largest technology company in the world: Microsoft," Musk's lawyers wrote in the lawsuit, which was filed late on Thursday in San Francisco. "Under its new board, it is not just developing but is refining an AGI [Artificial General Intelligence] to maximize profits for Microsoft, rather than for the benefit of humanity," claims the filing. "On information and belief, GPT-4 is an AGI algorithm." OpenAI, which counts Musk among its cofounders, has a unique corporate structure.

How can firms optimally negotiate bilateral contracts with each other in a financial network? Every firm seeks to maximize the utility it gains from its portfolio of contracts. We focus on mean-variance utilities, where each firm has its own beliefs about the expected returns of the contracts and the covariances between them (Markowitz, J. Finance 7(11), 1952). Instead of revealing these beliefs, a firm may adopt a different negotiating position, seeking better contract terms. We formulate a contract negotiation process by which such strategic behavior leads to a network of contracts. In our formulation, any subset of firms can be strategic. The negotiating positions of these firms can form Nash equilibria, where each firm's position is optimal given the others' positions. We give a polynomial-time algorithm to find the Nash equilibria, if they exist, and certify their nonexistence otherwise. We explore the implications of such equilibria on several model networks. These illustrate that firms' utilities can be sensitive to their negotiating position. We then propose trade deadlines as a mechanism to reduce the need for strategic behavior. At the deadline, each firm can unilaterally cancel some or all of its contracts, for a penalty. In our model networks, we show that trade deadlines can reduce the loss of utility from being honest. We empirically verify our insights using data on international trade between 46 large economies.

Commercial contracts are known to be a valuable source for deriving project-specific requirements. However, contract negotiations mainly occur among the legal counsel of the parties involved. The participation of non-legal stakeholders, including requirement analysts, engineers, and solution architects, whose primary responsibility lies in ensuring the seamless implementation of contractual terms, is often indirect and inadequate. Consequently, a significant number of sentences in contractual clauses, though legally accurate, can appear unfair from an implementation perspective to non-legal stakeholders. This perception poses a problem since requirements indicated in the clauses are obligatory and can involve punitive measures and penalties if not implemented as committed in the contract. Therefore, the identification of potentially unfair clauses in contracts becomes crucial. In this work, we conduct an empirical study to analyze the perspectives of different stakeholders regarding contractual fairness. We then investigate the ability of Pre-trained Language Models (PLMs) to identify unfairness in contractual sentences by comparing chain of thought prompting and semi-supervised fine-tuning approaches. Using BERT-based fine-tuning, we achieved an accuracy of 84% on a dataset consisting of proprietary contracts. It outperformed chain of thought prompting using Vicuna-13B by a margin of 9%.

Evisort, the no-code contract intelligence platform beloved by legal, procurement and sales operation teams worldwide, is entering a new era with the launch of the industry's first generative artificial intelligence (AI) capabilities. Evisort's reimagined AI technology provides transparent, understandable contract recommendations to drive decision-making and contract execution. Evisort's generative AI builds upon its existing AI capabilities by creating entirely new content. The Evisort AI Labs' innovation empowers legal and contracting professionals to use Large Language Models to draft, redline and negotiate contracts automatically -- freeing up time for strategic counseling. Evisort AI Labs' technology can also suggest edits that speed re-negotiations on existing complicated contracts.

We propose a new formalization of legal competences, and in particular for the Hohfeldian categories of power and immunity, through a deontic reinterpretation of dynamic epistemic logic. We argue that this logic explicitly captures the norm-changing character of legal competences while providing a sophisticated reduction of the latter to static normative positions. The logic is completely axiomatizable, and we apply it to a concrete case in German contract law to illustrate that it can capture the distinction between legal ability and legal permissibility.

While I do think it presents an existential threat to some lawyer jobs -- specifically those doing low-skill tasks as part of Biglaw behemoths -- when a company told me several years ago that they would license AI based off the brains of famous attorneys within the decade I went right ahead and laughed. But then we started talking about some of the cool technology Casepoint is bringing to the party and discussed how the system's ability to break down all the data and map out the connections for itself and build a real story of events. Hurt feelings aren't necessarily a fraud claim and keeping the presumption in favor of the four corners of the document can dissuade cases that -- even if they're true -- would be difficult to prove because we can't reliably muster the whole life cycle to sort out in litigation. Would judges eyeing a motion to dismiss start to balk at the risk of missing a fraud when the costs of getting at the whole story in discovery isn't prohibitive? Transactional lawyers might need to think about the well-worn language to avoid an influx of fights if this is the sort of material that a party could easily compile in a dispute.

I've never been one for hyperbolic talk about artificial intelligence. While I do think it presents an existential threat to some lawyer jobs -- specifically those doing low-skill tasks as part of Biglaw behemoths -- when a company told me several years ago that they would license AI based off the brains of famous attorneys within the decade I went right ahead and laughed. The point is, AI is a tool, and a very powerful one, that you're using all the time without even thinking about it. It's why midway through this article, Google is likely showing you an ad for the thing you researched buying last night. So it surprised me the other day when I found myself musing that AI is driving us to the point where long accepted tenets of law might need rethinking.

Picking the right tech vendors for your small or medium-sized business can be hard, especially with the cloud and everything-as-a-service providers giving you access to enterprise-level IT. ZDNet helps SMBs build a technology stack that promotes innovation and enables growth. DocuSign is launching DocuSign Analyzer, an artificial intelligence service, that aims to speed up contract negotiations, save billable legal hours and get better terms. The product, part of DocuSign's Agreement Cloud, uses AI to give legal and procurement teams insights about risks and opportunities. DocuSign Analyzer is also designed to spot errors and anomalies that can hamper deals.

Prominent members of Europe's so-called "Skype Mafia," all co-founders or early employees of the voice-over-Internet conferencing service, are backing Pactum, a startup that uses artificial intelligence to automate business contract negotiations. Founded late last year but only emerged from stealth mode on Wednesday, Pactum uses a chatbot-like interface to conduct contract talks. The bot can offer changes to standard terms, including price, delivery conditions and days to pay, in order to reach a better deal. The company is based in Mountain View, Calif., with engineering offices in Tallinn, Estonia, where Skype's first engineering offices were also located. Among those investing in the small startup are Jaan Tallinn, a Skype co-founder who has become a prominent backer of A.I.-related startups and research groups, Taavet Hinrikus, an early Skype employee who went on to found international payments firm TransferWise, Ott Kaukver, another early Skype employee who is now the chief technology officer at Twilio, and Sten Tamkivi, a general manager at Skype who is now chief product officer at Topia.