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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In an essay in The Atlantic titled “In Defense of Flock,” the Manhattan Institute’s Charles Fain Lehman rightly recognizes a foundational principle of criminology: The certainty of apprehension serves as a potent crime deterrent. Yet, in framing automated surveillance as a technological panacea, Lehman ignores the critical trade-off at the heart of a free society. As Benjamin Franklin famously warned: “Those who would give up essential liberty to purchase a little temporary safety deserve neither liberty nor safety.”

To test Lehman’s thesis, one need only apply his logic to his own doorstep. Imagine if the local police department installed a high-resolution pole camera directed squarely at his front door. By his own logic, Lehman should be elated: The camera would deter prospective burglars and, should an intruder ignore it, capture their every movement in crisp detail to ensure swift apprehension.

Every government benefit has a recipient, but it also has a payer. Politics tends to encourage us to focus on the first and forget the second.

Many hardships are visible, immediate, and emotionally compelling. They provide a rationale for Congress to pass a law, for an agency to distribute money, and for elected officials to congratulate themselves on their compassion. But almost no one asks the question that the pioneering American sociologist and political economist William Graham Sumner believed mattered most: Who provided the assistance and at what cost?

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Cleveland police officers Bailey Gannon and Jennifer Kilnapp responded to a 911 report of a disturbed man with a firearm. Entering a boarding house, they were told by the caller that the man was in a bathroom on the second floor. Gannon opened the bathroom door and, upon seeing the man, fled and blindly fired. One of Gannon’s shots tore through Kilnapp’s arm and lodged in her back, causing lasting physical and psychological damage that ended her police career.

Kilnapp sued Gannon, alleging excessive force. The district court denied Gannon’s motion for judgment on the pleadings based on qualified immunity and a Sixth Circuit panel affirmed. However, a later Sixth Circuit panel vacated and remanded the district court’s denial of summary judgment to Gannon based on qualified immunity. The court held that Gannon seized Kilnapp when he “fired his weapon in circumstances that objectively manifested an intent to restrain” the man in the bathroom. However, the court described this holding as only “largely established” by Supreme Court decisions from 1989 and 2007—but not “crystallized” until a 2021 decision issued after Gannon shot Kilnapp. The court thus held that Gannon was entitled to qualified immunity.

Cato filed an amicus brief asking the Supreme Court to review and reverse this decision. A legal principle can always be “crystallized” further—but the law was clearly established that shooting someone other than the target is a seizure. The mere contingency of a later decision recognizing that a plaintiff like Kilnapp could seek relief did not make Gannon’s decision to blindly open fire reasonable.

JD Vance’s book Communion devotes a chapter to arguing that policymakers have spent decades chasing GDP at the expense of his version of the greater good. I wrote earlier this week to show what Vance gets wrong about the country’s economic history—namely, that policymakers have never been the GDP-maximizers he makes them out to be. But even granting that premise, the chapter misfires in a deeper way.

Its greatest mistake isn’t to misunderstand GDP or even to misrepresent what policymakers aim for. It’s to conflate GDP statistics with a motive. Lots of things we do every day grow GDP, yet few of us wake up in the morning with GDP maximization in mind. GDP is a by-product of much human action and decisionmaking—decisions that are for the most part voluntary. Reading the chapter carefully, it becomes clear that Vance’s problem isn’t really with GDP per se but with people who make choices that contribute to higher GDP, but who don’t chime with his own view of what’s best.

Firms respond to minimum wage hikes in multiple ways. 

One study considers a 2023 California bill that raised the minimum wage for fast-food workers from $16 to $20 per hour. 

The researchers estimated 

a 4.9–5.1 percent increase in fast-food prices and a 2.1–2.2 percent increase in full-service prices.… Additionally, [the] findings suggest that price increases reduced consumers’ demand for fast food by 3.9–4.1 percent and for full-service meals by 1.7–1.8 percent.

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.