Stop Asking If AI Should Be Regulated

Stop asking whether AI should be regulated, start asking what it’s for.

Policy makers and tech leaders are in a philosophical echo chamber, says a new op-ed from Georgetown’s Center for Security and Emerging Technology. The piece, published in Newsweek by Matthias Oschinski and Mina Narayanan, argues that the core disagreement in AI policy isn’t innovation versus regulation. It’s innovation guided by purpose versus innovation left to default incentives. In plain terms: governance should aim outcomes, not merely police capabilities.

The authors push back against the familiar binary framing that has dominated headlines and legislative proposals. The real question, they write, is not whether AI should be regulated but how governance can steer AI toward socially beneficial ends. The piece emphasizes that “the real debate is innovation guided by purpose versus innovation left to default incentives.” In other words, rules alone won’t fix misaligned incentives; institutions must align incentives with public goods from the outset. Policy documents show that this distinction matters because it reframes risk from “can this technology exist” to “will this technology be used to serve people fairly and safely.”

What would this look like in practice? The op-ed points toward governance designs that shape outcomes rather than merely tightening constraints. That means more emphasis on governance mechanisms that evaluate the purposes and consequences of AI deployments before and after they go to market. The authors argue for institutions that impose consequences when AI outcomes diverge from stated public aims, rather than relying solely on broad prohibitions or narrow safety checks. It’s a call for accountability that travels beyond expert risk assessments into everyday impact on workers, consumers, and communities.

For policy professionals, the takeaways are concrete in spirit if not in distance. First, governance should tie incentives to actual public benefits. This could involve procurement criteria that favor AI systems demonstrably aligned with health, safety, or equity goals, and funding models that reward responsible deployment over speed to market. Second, governance needs ongoing measurement of outcomes. Impact assessments, post deployment surveillance, and transparent reporting can help ensure that the intended purpose remains the actual use case in practice. Third, regulation must be adaptable. The rapid pace of AI innovation means static rules quickly become obsolete; a framework built around purpose and outcomes can evolve with technology without stifling invention.

Industry observers, too, will note the tension this approach creates. A shift toward purpose oriented governance places heavier emphasis on evaluating real world effects, not just technical compliance. That can improve trust and legitimacy, but it also increases the burden on developers to articulate and prove the societal value of their products. The challenge is balancing this accountability with competitive incentives, ensuring that ambitious, beneficial AI remains attractive to invest in and deploy across diverse markets. And because AI is global, governance designed to steer outcomes must account for cross border dynamics, data flows, and harmonization where possible, while respecting local values.

The op-ed is not a rejection of regulation; it’s a warning against regulation as a substitute for thoughtful governance. It invites policymakers to redefine questions from “What rules do we need?” to “What outcomes do we want, and how do we measure progress toward them?” If framed this way, the AI policy debate could become a conversation about public purpose, risk, and the practical steps to ensure technology serves society.

If there is a practical, implementable takeaway for practitioners right now, it’s this: design regulatory and procurement paths that elevate purpose as a design constraint, not an afterthought. Begin by articulating the public outcomes you want AI to deliver, then build the incentives and compliance checks that keep deployments aligned with those outcomes over time.

Sources

Stop Asking Whether to Regulate AI—Start Asking What It’s For