Category:Artificial Intelligence
AI Data Centers and Rising Electric Bills
Electric bills are rising in many places, and the rapid expansion of AI data centers is adding new pressure to the power system. The big issue is how the electric grid pays for the infrastructure needed to serve rapidly growing electricity demand tied to AI. Serving that demand can require costly upgrades to the electric grid as well as securing additional electricity supply. When those costs are recovered through broadly applied rates instead of being assigned to the large new loads that triggered them, residential customers can see higher bills. State commissions and federal regulators influence these outcomes through tariffs, cost-allocation rules, and market design. As AI electricity use accelerates, questions of fairness and reliability have moved to the forefront of energy regulation.
Trump’s Executive Order Signals Federal Disruption for New State AI Laws
In the absence of comprehensive federal artificial intelligence (AI) legislation, states have moved aggressively to regulate AI. Beginning January 1, 2026, several major state AI laws imposed new safety and accountability obligations on AI developers. Just weeks before those laws took effect, President Trump’s Administration issued an executive order signaling a shift toward federal deregulation and preemption. The result is a looming conflict between state enforcement and federal resistance that is likely to continue to define the United States AI regulation in 2026.
Synthetic Media, Real Harm: Regulating AI-Generated Deepfakes
Carolyn Nsimpasi
Associate Editor
Loyola University Chicago School of Law, JD 2026
The rapid advancement of artificial intelligence (AI) has enabled the creation of highly realistic synthetic media, commonly known as deepfakes. These AI-generated images, videos, and audio recordings can convincingly replicate real people, making it increasingly difficult to distinguish truth from fabrication. While deepfake technology has legitimate uses in entertainment, education, and accessibility, its growing misuse presents significant social, political, and ethical risks. As a result, the regulation of AI-generated deepfakes has become an urgent necessity.
To Compete Or Not to Compete: A Legal Question
Today, federal and state antitrust laws are as important as ever. However, modern courts struggle to apply the traditional interpretation and application of antitrust law to modern technology and related anti-competitive practices. This is particularly true in the realm of emerging technologies, where algorithms, automation, and artificial intelligence increasingly dominate. As a result, regulators face a host of unique challenges in an increasingly interconnected, data driven, and automated era. From business to finance, healthcare to housing, the importance of anti-competition law cannot be easily understated.
Work Related: AI Governance and Regulation in the Employment Law Context
Today, an explosion in Artificial Intelligence (AI) development is taking the U.S. and global economic systems by storm. Companies like Nvidia (the first company to reach an approximately 5 trillion valuation), Microsoft, Alphabet (Google), and Open AI (formerly a non-profit which still cites the common good as a core tenant of its charter) have kicked off what is widely understood to be an AI “Arms Race.” Investors- from venture capitalists to private equity behemoths- continue to pour billions of dollars into AI technology companies and associated ventures. As AI companies move from beta testing to widespread adoption and integration, debates on AI transparency, accountability, and regulation have risen to the forefront. As a result of this monumental shift and ongoing uncertainty, the necessity of properly understanding (and regulating) AI and automation technology is now more pressing than ever before. Further, the need for strong regulatory oversight, a broad regulatory consensus and clear guidance, a baseline code of ethics (at minimum), as well as strong federal and state regulation- has become one of the most important issues of our time.
America’s Fractured Approach to AI Regulation
Federal efforts to promote artificial intelligence (“AI”) innovation by avoiding comprehensive regulation has prompted state legislatures to fill the regulatory void, creating a fractured regulatory landscape. This threatens the very innovation AI was meant to create in a global race towards general AI. Today’s AI systems are examples of Artificial Narrow Intelligence, trained to perform specific tasks but are unable to operate outside their defined parameters. In contrast, Artificial General Intelligence, or Strong AI, is a theoretical form of AI capable of apply prior knowledge and skills to new contexts, enabling it to learn and perform any intellectual task a human can without additional human training of the underlying models. This pursuit has driven unprecedented investment, technology corporations have poured billions of dollars into AI capital expenditures with this number only continuing to rise. Compliance teams are left scrambling to manage an increasingly complex regulatory environment that is evolving faster than legal departments and regulators can effectively manage.
Will AI Make Trade Secrets No Longer Secret?
Most companies own valuable trade secrets, such as the recipe for Coca-Cola or Google’s algorithm. But can a company that develops AI have trade secrets? The Uniform Trade Secrets Act defines a trade secret as “information, including a formula, pattern, compilation, program, device, method, technique, or process,” that derives economic value and is the subject of efforts to maintain its secrecy. The protection of trade secrets is essential for companies to maintain their competitive edge and drive economic growth. As such, they are instrumental in both corporate governance and compliance. Companies already deal with the risks of employees using generative artificial intelligence (AI) and exposing trade secrets; however, recent AI regulations in Europe and the United States have further increased risks relating to trade secrets.
The Regulatory Framework of Our Data Privacy Legislation is Changing Amidst the Rise of Artificial Intelligence
With the rapid evolution of artificial intelligence (AI) comes unprecedented opportunities and legitimate challenges, especially in the realm of data privacy. The rising capabilities of AI systems to process, analyze, and use massive amounts of personal data has generated amplified regulatory scrutiny across the globe. Governments and regulatory bodies are wrestling with how to balance innovation and economic growth propelled by AI against the need to protect individuals’ privacy, ensure transparency, and safeguard data from misuse.
Generative AI- The Next Frontier in Fighting Financial Crime
Artificial intelligence (AI) is the latest tool in a financial institution’s arsenal to restrict the flow of money being channeled to fund illegal activities worldwide. As criminals get more innovative and sophisticated in using the latest technology to evade detection of their financial crimes, financial institutions must follow suit and utilize similar technology to root out these crimes or risk facing regulatory sanctions. Money laundering generally refers to financial transactions in which criminals, including terrorist organizations, attempt to disguise the proceeds of their illicit activities by making the funds appear to have come from a legitimate source. However, this is not a new phenomenon. Congress passed the Bank Secrecy Act (BSA) in 1970 to ensure financial institutions follow a set of guidelines known as KYC (Know Your Customer/Client) to detect and prevent money laundering through their systems.
Regulating AI Used to Predict and Prevent Sports Injuries: A Crucial Need
The use of Artificial Intelligence (AI) in sports could revolutionize the way athletic injuries are predicted and managed. Notably, a variety of AI companies have developed software that forecasts potential injuries, possibly prolonging athletes’ careers. This technology analyzes data about the biomechanics of players, their frequency of play or training, and past injuries to identify patterns, find potential causes, and predict future injuries. There is immense value in preventing injuries in athletics for players, teams, and fans alike. It would therefore make sense that everyone is eager to utilize the findings of AI, but without regulation this could cause more harm than good.