This piece cuts through the noise of AI panic by offering a pragmatic, "low-regret" roadmap for government intervention that doesn't rely on halting progress. Noah Smith, channeling a report from the Institute for Progress, argues that the most dangerous outcome isn't rapid AI advancement, but rather a political backlash that results in clumsy, broad bans. The article's distinctive value lies in its refusal to choose between innovation and safety, instead proposing twenty-three specific, targeted mechanisms to manage risk while accelerating the diffusion of beneficial technology.
The Case for Pacing Without Paralysis
Smith frames the central dilemma not as a binary choice between speed and safety, but as a strategic necessity to avoid poorly reasoned policy. The authors of the underlying report, Tim Fist and Saif Khan, argue that "despite substantial uncertainty, we believe some preparatory policy action is warranted." This is a crucial pivot. Instead of waiting for a catastrophe to justify regulation, the piece advocates for building the institutional scaffolding now. Smith highlights that without preparation, the preferred strategy of "pacing" AI development—slowing down only when specific risk thresholds are crossed—becomes impossible to implement.
The argument rests on the premise that the government is currently blind to the most critical developments. Smith notes that while AI capabilities are doubling at an alarming rate, "much of the best information remains inside company walls." This opacity leaves the public and policymakers unprepared for the moment automated AI research begins to accelerate itself. The proposed solution is a radical return to scientific transparency, urging a shift away from the current trend of secrecy.
"Disclosing information about the science of AI and general AI capabilities would be a return to the historical norms of open scientific publication in the US AI industry."
This call to action is compelling because it leverages historical precedent. Smith points out that as recently as 2020, OpenAI published detailed training data for GPT-3, and Google released scaling laws in 2022. The erosion of these norms has left a vacuum that the current administration's agencies are ill-equipped to fill. Critics might argue that forcing disclosure could stifle competition or compromise national security, but Smith counters that the public interest in understanding risk management outweighs commercial sensitivity.
Building the State's Capacity to Respond
The most concrete part of the commentary focuses on "state capacity"—the government's ability to actually understand what is happening. Smith argues that "absent a state authority trusted by society at large, managing the pace of rapid AI capability improvement will be impossible." This is a sobering admission that the current federal bureaucracy is outmatched. The piece proposes a specific, well-funded solution: empowering the Center for AI Standards and Innovation (CAISI).
The recommendations are precise. Smith details a proposal to resource CAISI with at least $84 million annually and 184 personnel, giving it the autonomy to "forward deploy its staff into the frontier AI companies." This is a significant departure from the usual arm's-length relationship between regulators and industry. It suggests a model where government experts are embedded directly within the labs, monitoring risk management cultures in real-time.
"Only democratically accountable leaders possess the legitimacy and power to coordinate the kind of mass reallocation toward defense and diffusion that may be needed."
This framing elevates the discussion from technical regulation to democratic governance. It acknowledges that while private companies drive innovation, only the public sector can manage the societal trade-offs. The article draws a parallel to the International Atomic Energy Agency (IAEA), noting that just as the IAEA was created to manage the risks of nuclear energy through verification and transparency, a similar body is needed for AI. However, unlike the IAEA, which was born after the technology was already weaponized, the goal here is to establish these norms before the risks become irreversible.
A counterargument worth considering is whether the US government has the technical talent to effectively evaluate frontier models, even with increased funding. The piece assumes that CAISI can become a center of excellence quickly, but the war for AI talent is fierce, and private sector salaries often dwarf public sector pay. Smith acknowledges the need for "third-party AI evaluators" to bridge this gap, but the reliance on external experts introduces new challenges regarding accountability and consistency.
Five Criteria for Low-Regret Policy
What makes this list of twenty-three recommendations particularly useful for busy readers is the strict framework used to generate them. Smith outlines five criteria that any policy must meet to be considered "low-regret." These include targeting only activities that could lead to serious harm, minimizing slowdowns in the diffusion of existing capabilities, and avoiding the creation of a regulatory apparatus that could be misused.
"If 'pacing' becomes necessary, we think it should consist of two parts: first, specifying thresholds for when automated AI R&D is likely to pose severe risks; and second, if a threshold is exceeded, incentivizing AI companies to reallocate resources away from the most risky research."
This nuanced approach avoids the trap of "pacing" as a blanket slowdown. Instead, it proposes a targeted reallocation of resources. The article suggests that if a risk threshold is breached, the government should incentivize companies to focus on safety and alignment rather than raw capability. This mirrors the logic of the "Tragedy of the Anticommons," where too many overlapping rights can stifle innovation; the proposed policy aims to prevent the opposite tragedy, where unregulated competition leads to a race to the bottom on safety.
The recommendations also address the geopolitical dimension, specifically the competition with China. Smith argues for extending the US AI lead to "buy more time to manage risks and increase US leverage in international negotiations." This is a pragmatic acknowledgment that AI policy cannot be isolated from national strategy. However, the tension between maintaining a lead and fostering open scientific collaboration remains a delicate balance.
Bottom Line
Noah Smith's commentary delivers a sophisticated, actionable blueprint that avoids the fatalism of "doom" and the naivety of "move fast and break things." Its strongest element is the insistence on building state capacity before a crisis forces a reaction, turning the government from a passive observer into an active participant in risk management. The biggest vulnerability lies in the political will required to fund and empower agencies like CAISI to the degree necessary; without that commitment, the detailed recommendations risk becoming a theoretical exercise. Readers should watch for whether the proposed transparency bills, such as the FRONTIER Act, gain traction in Congress, as this will be the first real test of whether the US can operationalize this "low-regret" framework.