In a recent letter to the CEOs of artificial intelligence (AI) companies Anthropic, OpenAI, and Google, Senator Bernie Sanders (I‑VT) called for a pause on AI development. In the letter, Sanders argues that the recent loss-of-control incidents that occurred while testing OpenAI’s, Anthropic’s, and Meta’s models, and the announcement by a Stanford University research team of the synthesis of new viruses with the aid of AI, justify the pause. At first glance, these examples could build a compelling argument for a pause. But a closer look reveals that, in reality, they are hardly a reason to pause AI development and, in some cases, should even be a source of encouragement for rapid AI growth.

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The textbook for AI Policy 101 does not exist. There is no AI Governance for Dummies. Recent incidents involving internally deployed AI models escaping the testing environments crafted by their developers sound like the stuff of science fiction. Yet cybersecurity, biological, surveillance, and loss-of-control risks from frontier AI models are very much real and very much something that existing government institutions are poorly equipped to evaluate.

Complexity, however, is not an excuse for inaction. Regulation of the securities sector through the Financial Industry Regulatory Authority, or FINRA, provides one example of Congress developing a regulatory framework that moves at the pace of the underlying industry, incorporates industry expertise, and retains federal oversight. It is therefore unsurprising that labs and lawmakers have rallied behind the idea of a FINRA for AI.

On August 4, Axios reported that the highly anticipated update of the White House framework for voluntary testing of frontier AI models will not be released to the public. In other words, and as others have put it, the White House is essentially putting the AI testing regime in a black box. This approach contravenes the rule of law, risks becoming as prescriptive as a licensing regime, and, most importantly, fails to fulfill its most basic objective: to build trust in the population.

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Automated pricing tools are becoming increasingly ubiquitous in the modern economy as many businesses automate their pricing strategies to ensure their prices reflect market realities. Media attention, regulatory proposals, and a timely congressional hearing have largely focused on “surveillance pricing” and what we discussed as individualized dynamic pricing (IDP) in a previous blog. Despite fears of price gouging, these pricing methods have real efficiency benefits and are particularly effective at reducing waste.

However, there are other forms of automated tools in our day-to-day lives that do not necessarily rely on individuals’ data, as IDP does. This piece will focus on algorithmic pricing, understood as the general practice of automating the price-setting process, and the allegations that it might enable collusion amongst competitors. In the context of general algorithm-powered pricing, this alleged “collusion” typically refers to the use of automated tools to analyze market data to coordinate competitors’ prices. With a few notable exceptions, this is typical competitive behavior. For firms to compete on price, they must be able to track their competitors. Algorithmic pricing reduces the costs associated with monitoring and responding to market conditions. Taken alone, it is just using new technology to make an old process more efficient.

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This blog was updated on July 31, 2026.

When Appellant Shakira Leslie declined an offer of water seven hours after being arrested, jail officers insisted that “it would be some time before she had another opportunity to drink.” She took a sip from the disposable cup, then officers told her to “keep drinking.” They pulled her DNA from that cup, developed a profile from the sample, and entered it into New York City’s “Suspect Index.”

Leslie is far from the only person on it. For more than a decade, New York City has been amassing the DNA samples of New Yorkers without their consent or any court involvement as a routine matter, specifically for inclusion in its “Suspect Index,” a rogue database not authorized by law. Defendants regularly include samples from people who, as in this case, have been expressly excluded as suspects in a crime, suspects who have never been charged with a crime, arrestees who have never been convicted of a crime, and exonerated or acquitted individuals.

The proper delineation of authority between the federal government and the states has often been the subject of controversy in the debate over AI policy. Proponents of both approaches reference the constitutional clauses that support their claim. The reality is that this issue, as with many things in AI, is not a neat list of discrete policy matters. Instead, there is a clear mix of AI policy issues that require national attention—those that are the exclusive domain of the states and the issues that can be taken on by both the federal government and the states (or a collection of states). So how might a court or policymaker identify what level of governance is appropriate for different AI issues?

My paper, “The Obstruction Doctrine,” connects the jurisprudential dots that jointly establish a framework for evaluating whether a state’s exercise of power aligns with the letter and spirit of the Constitution. The first prong prevents states from inhibiting access to national markets. The second prong forecloses states from advancing laws that hinder the federal government’s ability to respond to issues that require national attention. And the third prong blocks states from interfering with national initiatives, such as extensive infrastructure projects. 

On Wednesday, July 22, the House Energy and Commerce Committee will hold a hearing on multiple consumer protection bills, including the App Store Freedom Act (ASFA). Supporters of ASFA claim that this bill will promote competition in the app store market by forcing developers of an electronic device’s operating system to allow the installation of third-party app stores, to allow the use of third-party payment processors, and a myriad of other restrictions. Ironically, the bill’s provisions would make the app store market less competitive by reducing covered companies’ ability to engage in competitive practices that make their products more appealing to potential customers. It would also have negative spillover effects in the hardware market by restricting hardware developers from offering closed-system products to customers and prescribing a business model as a whole.

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