Regulators should ask AI's purpose, not whether to regulate

Regulators should ask AI's purpose, not whether to regulate.

In a provocative riff on AI policy, a recent op-ed from the Center for Security and Emerging Technology argues the current debate is misframed. The real question, the authors say, is what innovation is for and who benefits from it, not simply whether we should regulate or encourage more development. As Matthias Oschinski and Mina Narayanan put it, the debate isn’t innovation versus regulation, but innovation guided by purpose versus innovation left to default incentives. The call is for governance that shapes outcomes rather than a blunt knee-jerk to restrict or loosen every new capability.

The argument lands at a critical moment. EU policymakers are moving ahead with a risk-based, compliance-heavy approach under the AI Act, while US debates oscillate between guardrails and rapid deployment in the name of American competitiveness. Against that backdrop, the op-ed pushes a shift in emphasis: design rules that anchor AI development to concrete societal aims, rather than treating every new model as a potential crisis. In other words, governance should be outcome-driven, with clear purposes for each use case and a framework that incentivizes alignment with those purposes.

Policy documents show that framing matters. If regulators start from a purpose, for instance ensuring safety, fairness, and transparency in high-stakes applications, regulation becomes a tool to realize those ends rather than a deterrent to innovation. The authors suggest that when incentives are left to default, risk grows that powerful AI systems will optimize for short-term gains or compliance-minimal signals rather than meaningful social benefits. The practical upshot is not a call for laxity, but a plea for governance that attaches real consequences to outcomes, not only to inputs or technology categories.

For industry readers, the piece offers several implications wrapped in a single premise. First, the governance question becomes a product design question. If the purpose is to protect users or ensure trust, product teams should bake accountability and safety into the development lifecycle from day one, not as an afterthought. Second, the incentive problem matters: procurement rules, liability frameworks, and funding criteria can push firms toward purpose-led design or, conversely, toward "check-the-box" compliance that preserves default incentives. Third, standards and benchmarks matter. Defining what counts as acceptable performance against a societal goal provides a tangible target for engineers and regulators alike, reducing the vagueness that often slows compliance or invites regulatory arbitrage. Fourth, there is a risk-management dimension that cannot be ignored. A purpose-driven lens helps regulators differentiate high-risk uses from lower-risk applications, enabling smarter oversight without choking legitimate innovation.

The piece is a reminder that policy can do more than restrict or subsidize. It can steer; it can pause when outcomes drift away from stated aims; and it can reward innovators who deliver measurable public benefits. As debates in Brussels and Washington intensify, the authors urge a shift from a binary debate to a more nuanced one: what should AI effectively achieve, and how can governance ensure it does so? If regulators adopt that frame, the pathway to balanced, responsible innovation becomes less about counting lines of code and more about validating whether the technology serves a defined purpose that society values.

The op-ed is clear about its aim: policy documents show that the debate should be about purpose and governance, not merely the existence of regulation. That reframing, if adopted, could influence how rules are drafted, how risk is measured, and how success is judged in AI deployment.

Sources

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