As policymakers and tech executives debate how to regulate artificial intelligence, one Manhattan Institute scholar is proposing a solution that doesn’t require writing a single new law. Judge Glock, a senior fellow at the think tank, told Breitbart Editor-in-Chief Alex Marlow that centuries-old legal principles already provide a workable framework for holding AI companies accountable when their systems cause harm.
“The old-fashioned law we’ve had … going back hundreds and hundreds of years actually does have a pretty good system to prevent companies from harming third parties,” Glock said during an appearance on “The Alex Marlow Show,” according to a clip published by Breitbart.
Glock’s remarks arrive at a moment when Congress, federal agencies, and state legislatures are scrambling to address concerns about AI safety, bias, and economic disruption. Lawmakers have introduced a flurry of bills aimed at everything from algorithm transparency to mandatory safety testing, while the White House has issued executive orders directing agencies to study the technology’s risks. The debate has largely centered on whether new, AI-specific rules are needed — or whether existing laws can be adapted to the challenge.
A Familiar Framework for a New Technology
Glock’s argument fits into a broader intellectual current among some conservative and libertarian policy analysts who warn that hastily drafted AI regulations could entrench dominant tech companies and stifle competition. Under this view, general liability law — the body of rules governing negligence, product liability, and harm to third parties — offers a flexible, time-tested alternative.
The logic is straightforward: if an AI system causes measurable harm to a person or business, the injured party can seek redress in court under existing doctrines. Companies then have a financial incentive to build safer systems without the government dictating specific design choices. Proponents say this approach avoids the problem of regulators trying to keep pace with a technology that evolves faster than the rulemaking process.
Critics of that position counter that liability lawsuits can take years to resolve and may produce inconsistent outcomes across jurisdictions, leaving victims without timely recourse while allowing companies to externalize risk. They also note that some AI harms — such as the spread of misinformation or subtle discrimination in hiring algorithms — can be difficult to trace to a single responsible party, complicating traditional causation requirements.
The Regulatory Race Heats Up
The interview comes amid intensifying activity on AI policy in Washington and state capitals. Several states have advanced legislation targeting automated decision-making in employment, housing, and lending. Federal regulators have signaled interest in applying existing anti-discrimination and consumer protection statutes to AI-driven tools, even as they acknowledge gaps in their authority. Meanwhile, industry groups have pushed for a national framework that would preempt a patchwork of state rules.
Glock’s comments echo a theme that has surfaced repeatedly in conservative media and policy circles: that the United States already possesses a robust legal infrastructure for dealing with corporate harm, and that the impulse to create new bureaucracies may do more harm than good. By invoking the long history of common-law liability, Glock places AI within a lineage of technological disruptions — from railroads to pharmaceuticals — that were ultimately governed by courts as much as by statutes.
Whether that approach can satisfy public concerns about AI’s rapid deployment remains an open question. Recent surveys show widespread unease about the technology’s impact on jobs, privacy, and safety, and some advocates argue that only dedicated regulation can provide the transparency and accountability that voters demand. Yet the appeal of a solution that doesn’t require expanding the administrative state is likely to resonate with a significant segment of the political right, which has increasingly framed AI oversight as a battleground over free speech and innovation.
Glock, whose work at the Manhattan Institute focuses on housing, urban policy, and economic regulation, did not offer specific legislative proposals during the segment. Instead, he pointed to the durability of existing legal principles as a foundation for addressing novel challenges. The full interview is available through the show’s podcast and radio broadcasts.
For now, the debate over AI’s biggest problem — and its simplest solution — shows no signs of cooling. As Marlow’s audience heard, at least one scholar believes the answer may be hiding in plain sight, in a legal tradition that predates the computer age by centuries.
Source: www.breitbart.com — https://www.breitbart.com/clips/2026/09/20/manhattan-institute-scholar-offers-simple-solution-to-a-i-s-biggest-problem/