A Busier Government, Not a Better One

Nick Caputo’s recent essay argues that government bureaucracy is not only likely to survive powerful AI but may in fact grow alongside it. He defines bureaucracy as “society’s system for perceiving, reasoning, deciding, and remembering at scale,” and provides a series of arguments why AI could improve these cognitive functions despite the known pathologies of government agencies — above all that “everything takes too long and costs more than it should.” Yet the processes the public sees are largely formalities, while decisions are mostly shaped elsewhere. So what if the binding constraint on government is less the quality of information it receives and produces, and more the incentives that drive it? What might AI do to realign them?

While I agree with Caputo’s rejection of the fantasy proposed by Séb Krier and others that personal AI agents could feasibly replace shared formal rules with millions (or billions) of bilateral negotiations, I disagree with the essay’s overall conclusion that AI is on a path to making government more effective, provided it is used to improve existing functions. The same tools that upgrade an agency’s capabilities can also be used to undermine the incentives of the people and firms who supply it with information, while leaving the incentives of the officials who make policy untouched.

In economic terms, Caputo has modeled the production function but left the objective functions out of the discussion. The future this portends is a much more active government that fails to become more effective, unless larger institutional changes are put in place along with the new technology.

The use and abuse of rulemaking

To better understand how a technology can improve a government agency’s functions while simultaneously undermining its performance, we need look no further than notice-and-comment rulemaking. The 1946 Administrative Procedure Act is the U.S. federal law that governs how agencies propose and establish regulations. Agencies are required to give interested persons “an opportunity to participate through submission of written data, views, or arguments,” and “after consideration of the relevant matter presented, incorporate in the rule a concise general statement of the rule’s basis and purpose.”

This process is known as “notice-and-comment rulemaking.” Submissions accumulate in the rulemaking docket — a public file that holds a proposed rule along with supporting technical, scientific and economic analyses compiled to justify it, and every comment received by the agency. Any agency that ignores a significant comment risks having its rule struck down by a court as “arbitrary and capricious.”

This process sometimes works well. When the Government Accountability Office surveyed 52 program offices spanning different agencies and policy areas, it found almost all of them reported comments producing at least some substantive change in final rules. In many cases these comments benefited from technical expertise and early timing. Writing a substantive comment used to be expensive, requiring in-depth knowledge and perhaps attorney time, so filing one screened for two things an agency cannot observe directly: whether the commenter has something at stake, and something worth saying.

But this also has downsides. Participation has long been dominated by the biggest regulated firms. Legal scholar Wendy E. Wagner has documented what she calls information capture, in which well-resourced parties flood dockets with technical material, raising the cost of effective participation for everyone else. AI changes who can afford that strategy.

Akerlof’s market for comments

A comment’s polish and technical detail used to be reliable proxies for the sender’s skin in the game and on-the-ground knowledge — informative because they were costly to fake. The risk today is that AI collapses the cost of production for every type of sender,¹ so that an expert-sounding comment will no longer provide useful hints about who sent it or why.

Dockets are already being flooded with cheaply produced comments. An investigation by the New York Attorney General concluded that nearly 18 of the 22 million comments on the FCC’s 2017 net neutrality repeal were fabricated, and that was before generative AI made plausible comments practically free to produce.

Caputo claims that “AI could help make bureaucracy scrutable.” But this assumes the reasoning an agency displays formally is the reasoning it actually used.

When comments become cheap, agencies may categorically discount them, and the informed commenter’s incentive to invest in the process disappears. This is the logic of economist George Akerlof’s market for “lemons”: when buyers can’t tell good products from bad, they discount everything, which drives high-quality sellers out of the market, and the buyers’ skepticism becomes self-fulfilling.

If a docket no longer reveals even the limited information it once did about who is most directly affected and how, agencies will rely more on familiar firms and trade associations. The flood of AI-generated voices becomes background noise, while groups the agency already knows are disproportionately elevated. AI could theoretically help agencies locate affected parties who would never have thought to comment. But the agency must first want to hear from them, and the affected parties must be informed and motivated enough to participate, two factors that AI is not obviously destined to influence.

Who reads the comments anyway?

There is nothing in Caputo’s essay to suggest that AI will transform the core incentives driving regulatory agencies. In most cases, much of the substance in notice-and-comment rulemaking is worked out through politics and interest-group bargaining long before a proposal is ever published, turning the comments process into a kind of post facto legal insurance.²

To give an example, we can return to net neutrality. The FCC adopted rules barring internet providers from throttling or blocking traffic in 2015, repealed them in 2017, then restored them in 2024, drawing millions of comments in the process — including the fabricated ones described above. Yet the outcome tracked the party controlling the White House more than anything in the docket (until the U.S. Court of Appeals for the Sixth Circuit struck down the restored rules in early 2025).

A similar bias can be observed in agency findings. When the Obama administration tightened fuel economy standards in 2012, the government’s analysis found large net benefits to doing so. When the Trump administration proposed rolling the same standards back six years later, the new government’s analysis found large net benefits to doing the opposite. Who is right? When the expected impact of a policy flips with the administration, the analysis is following the decision instead of driving it. Many rules are issued without sufficient quantitative assessment. Even when agencies produce a relatively complete analysis, it is often to justify a choice that has already been made.³

In his essay Caputo claims that “AI could help make bureaucracy scrutable.” But this assumes the reasoning an agency displays formally is the reasoning it actually used, and there is little evidence that the administrative record has ever worked that way. Political pressure and bargaining shape a rule long before an agency writes formal justifications for it. For example, in one study of the EPA’s air toxic emission standards, industry representatives contacted the agency 84 times per rule on average in the pre-proposal stage.

Since a model that drafts flawless, reasoned responses makes the public-facing rationale cheaper to produce, and the true one easier to conceal, the likely result is a kind of scrutability theater. Polished arguments quickly become effortless, while the real decision-making happens off the record.

The reforms with the best chance of turning AI’s capability gains into better government are those which harness the incentives agencies already respond to, like the fear of having rules overturned.

A major rule can take years to complete, and few of the people who write one are eager to see it undone. An agency whose objective is legal survival will use AI accordingly. Federal pilots for comment analysis hint at where this could end up: AI-generated comments answered by AI-generated responses. This pattern could occur across government. AI lowers the cost not only of producing comments, but also rules, guidance, permits, and enforcement actions. Agencies could deploy agents to monitor submissions and enforce penalties around the clock, but there is nothing to stop the firms they regulate from launching petitions and appeals with equal fury. All that activity, yet the political and legal forces that actually drive rulemaking remain undisturbed.

An awareness that AI-created material has saturated rulemaking does not guarantee that the process will be overhauled. In nearly a dozen jurisdictions where AI-generated submissions have already flooded government processes, officials have responded with measures like blocking suspicious traffic or dismissing submissions in bulk. They are coping rather than rethinking the procedures themselves. Broken processes persist in Washington as well. Congress has approved a spending package on schedule only four times since the modern budget process took effect in the 1970s. It hasn’t happened once since 1996. Yet the process limps on through the use of stopgaps rather than getting fixed.

Given the difficulty of passing major reforms, it is to be expected that a broken comment process would simply continue, even in an AGI world. But if reform does become politically possible, a number of options are available.

Reform should focus on judicial review and early engagement

The reforms with the best chance of turning AI’s capability gains into better government are those which harness the incentives agencies already respond to, like the fear of having rules overturned. Most agencies are not required to weigh the costs and benefits of their rules in any way a court will enforce. The analytical requirements that do exist usually come from executive orders rather than statutes. To start, Congress could codify analytical standards and subject them to judicial review. Doing so would flip the agency’s relationship to public input.

Today a rule survives review if the agency can show it responded to comments. This turns the docket into a liability to be managed where responses are boxes that must be checked. If a rule’s survival instead depended on the quality of the analysis behind it, the fear of losing in court would mean agencies start treating the docket as a way to get the best evidence they can on the record.

Rules based on weak economic or scientific evidence should be vulnerable in court. There is some precedent for this. In 2011, the D.C. Circuit vacated an SEC rule because its economic analysis was inadequate. The SEC then issued new guidance on economic analysis, and by one study’s measure, the average quality of the agency’s analyses nearly doubled afterward. The Supreme Court has also ruled that an agency acted unreasonably when it deemed cost irrelevant to a regulatory decision. But both cases turned on analytical requirements written into those agencies’ own statutes, duties most agencies don’t have. The executive orders that mandate analysis across the executive branch explicitly state that they don’t create legal rights, so an agency’s economic analysis can easily be thin or wrong without legal consequence.

AI-relevant reforms should also reach earlier into the rulemaking process where influence actually operates. Congress could require agencies to issue advance notice when they plan on making changes to major rules — a step that is optional today and frequently skipped. In one study tracking commenter influence across the life cycle of Transportation Department rules, the authors found that early commenters played an outsized role in setting the agenda. The advance-notice stage, in particular, positioned them to shape the content of future rules and sometimes thwart unwanted ones.

Early in the process, agencies should invite the public to submit data, economic evidence, and competing regulatory proposals — work AI now makes far cheaper to produce. Commenters already compete with one another, but they do this on advocacy terms. An open call for rival proposals and evidence would focus that competition on the quality of analysis instead. Once a rule has been in place for a while, agencies should be required to look back and determine whether it worked. They might even invite the public, again with AI support, to do the looking for them.

Projected costs and claimed benefits become testable once a rule has been enforced. This is markedly different from Caputo’s belief that AI will make the government scrutable. Scrutability requires agencies to explain themselves, but explanations can be manufactured. Retrospective review asks whether the world actually turned out the way the agency predicted, which is a question the agency can’t choose an answer to arbitrarily.

Sunset clauses in the age of AI

Sunset provisions mean rules expire automatically unless a legislature or agency actively renews them, and they supply both the occasion and the consequence for retrospective review. Repealing a rule today requires a rulemaking of its own, which makes removal expensive. A rule that automatically expires unless renewed will be expected to face its own record at each renewal. If it doesn’t pass muster, it lapses. Sunset provisions are not a new idea and have been used to great effect already,⁴ but AI makes it practical to apply them widely, because each renewal decision is better informed.

Consider a hypothetical workplace safety rule, originally projected to prevent a thousand injuries a year, which has an eight-year sunset clause. As renewal approaches, the agency is expected to put evidence on the record showing that the rule performed roughly as promised. Outside parties are encouraged to file rival assessments. If the promised benefits did not materialize, renewal fails and the rule lapses by default. If the agency renews anyway on the strength of faulty analysis, the renewal itself can be challenged in court. AI can assist at every stage: finding the data, running the comparisons, and evaluating the rival submissions.

Caputo is right that scrutability is not only something agencies supply. As he argues in the paper his essay builds on, AI also lowers the cost for the public, courts, and legislatures to investigate agency actions on their own. Oversight of that kind addresses part of the incentive problem, since agencies seek to avoid public embarrassment. But outside scrutiny changes agency behavior most when it is attached to consequences, which is what judicial review and sunset provisions supply.

Behind the ceremony

The diffusion of AI all but guarantees a more active government. Whether we get a more effective one, however, is a separate question, one which hinges on the transformation of government incentives along with the change in capabilities.

None of what I have suggested is bulletproof. Generalist human judges can be imperfect referees of economic evidence. Sunset provisions can decay into rubber stamps, and agencies that come to fear their dockets may retreat towards other subregulatory activities that are much harder to oversee.

One could argue that nothing stops economic analysis from becoming a machine-generated theater of its own. But at least an analysis makes claims that can be checked against the world. A court cannot tell whether an agency seriously engaged with a comment, but it can tell whether the data behind an estimate exist or whether a forecast came true.

More than three decades ago, legal scholar and former EPA general counsel E. Donald Elliott observed that “notice-and-comment rulemaking is to public participation as Japanese Kabuki theater is to human passions — a highly stylized process for displaying in a formal way the essence of something which in real life takes place in other venues.” In the near term at least, notice-and-comment and similar sclerotic processes are likely to survive AI diffusion. If they do, their persistence will point to a broader problem, which is that institutions tend to evolve much more slowly than the capabilities they govern.