Can Governments Still Govern AI?

— When Technological Speed Outruns State Capacity

2026 Election Issues Series · Part X

In modern states, governance has never been merely about setting policy goals or expressing political intent. At its core, governing means translating abstract principles into rules that are enforceable, sustainable, and predictable in practice. Whether in financial regulation, food safety, or public health, effective governance ultimately depends on a single condition: the ability of institutions to keep pace with changing realities.

The rapid expansion of artificial intelligence now poses an unprecedented challenge to that condition. The central question is not whether governments recognize the importance of AI, but whether, as technological development increasingly outpaces institutional adaptation, states still possess governing capacity itself.

AI as a Stress Test for Existing Governance Models

At first glance, AI appears to be a new regulatory object. Institutionally, however, it functions more as a comprehensive stress test of existing governance models.

Traditional regulatory systems tend to rest on relatively stable assumptions: identifiable risk pathways, clearly assignable responsibility, and a degree of predictability in technological change. AI systematically undermines all three.

Machine-learning models evolve rapidly, applications are highly fragmented across sectors, decision-making processes are often opaque even to their designers, and responsibility becomes diffused among developers, platforms, deployers, and users. Governance frameworks built around linear sequences—ex ante definition, ongoing supervision, ex post accountability—struggle to function under such conditions.

As a result, AI increasingly occupies a gray zone in which institutional response lags persistently behind technological reality.

The Core Governance Dilemma: Expertise, Authority, Responsibility

Public debate often assumes that governance failures stem from insufficient political will. In the case of AI, this explanation is incomplete.

Legislative bodies rarely possess mechanisms for continuously updating technical expertise. Administrative agencies face structural disadvantages in competing with technology firms for skilled personnel. Regulatory processes rely heavily on industry input, and technical assessments are frequently outsourced.

The consequence is structural rather than incidental: governments formally retain responsibility for governance while substantively relying on regulated actors to define risks, explain systems, and frame regulatory choices.

When those subject to regulation also become the primary interpreters of regulatory reality, governance capacity is weakened at a structural level—not merely through enforcement gaps, but through epistemic dependence.

When Governance Becomes Reactive, Capacity Is Already Compromised

A pattern that is increasingly visible, yet rarely stated explicitly, is the shift toward reactive governance in the AI domain.

Institutional intervention typically occurs only after systems have been widely deployed, social harm has become apparent, and public controversy has intensified. This does not necessarily reflect negligence. Rather, it reflects the compression of institutional time horizons under accelerating technological development.

When governance operates primarily through post hoc responses, public confidence in the state’s ability to shape outcomes erodes. Authority becomes associated with damage control rather than anticipatory rule-setting.

The True Divide: Can States Still Institutionalize Technology?

The central dividing line in AI governance is not between regulation and deregulation. It lies in whether states retain the capacity to transform complex technologies into governable institutional objects.

This capacity requires more than legislation. It depends on a constellation of institutional capabilities: sustained technical understanding, independent risk assessment, enforceable accountability pathways, and effective coordination across bureaucratic domains.

Absent these capacities, governance risks degenerating into a sequence of statements, pilot programs, and ad hoc interventions—symbolic activity substituting for institutional control.

AI Governance as a Test of State Capacity

Artificial intelligence will not wait for institutions to become ready. It will continue to expand according to its own technological logic.

The fundamental risk, therefore, is not whether governments want to govern AI, but whether, under conditions of accelerating complexity, they still possess the capacity to translate rules into reality.

If AI is reshaping economies and societies, then whether governments can still govern it will determine not the existence of the technology itself, but the manner in which social order adapts—or fails to adapt—to its advance.

By Voice in Between