Every major technology transition in modern history has faced the same fundamental tension: the people who build a new system are rarely the same people who decide how it should be governed. The railroad companies did not write the laws that created the Interstate Commerce Commission. The telephone companies did not design the regulatory framework that eventually governed them. And the artificial intelligence industry — currently among the most powerful and least regulated sectors in the global economy — is following exactly the same pattern, with consequences that may prove far more consequential than anything the railroads or telephone companies ever achieved.
This is not a conspiracy. It is a structural feature of how technology development works in market economies. The people who understand a technology best are the ones who built it. The people who understand its risks and societal implications are often not in the room when the key decisions are made. And the political systems that are supposed to provide oversight are consistently several steps behind the technology itself — outpaced by the pace of innovation, under-resourced in technical expertise, and vulnerable to the sophisticated lobbying of an industry with very large financial interests at stake.
In 2026, artificial intelligence has reached a level of capability and deployment that makes this governance gap genuinely dangerous. We are not talking about theoretical risks. We are talking about systems that are making real decisions about people’s access to credit, employment opportunities, healthcare, and information — decisions that are opaque, largely unaccountable, and subject to biases that are poorly understood even by the people who built the systems.
The Scale of the Deployment Problem
Consider just a few of the domains where AI systems are already making consequential decisions. In hiring, algorithmic screening tools are used to filter job applicants before any human sees a resume — systems that have been shown to encode and reproduce gender and racial biases present in their training data, often in ways that are invisible to the people deploying them. In criminal justice, risk assessment algorithms inform sentencing and parole decisions in jurisdictions across the United States, despite ongoing legal challenges to their accuracy and fairness. In financial services, AI-driven credit scoring models determine who gets loans and at what interest rates — decisions that shape economic mobility across generations.
None of these systems emerged from deliberate democratic processes. None of them were subject to regulatory approval before deployment. And in most jurisdictions, including the United States, there is no systematic mechanism for affected individuals to challenge the decisions these systems make, no requirement that the systems be audited for bias or accuracy, and no meaningful transparency about how the decisions were reached.
This is not a novel observation. Civil liberties organizations, academic researchers, and even some technology companies themselves have been raising alarms about the governance gap for years. What has changed is the scale and capability of the systems being deployed, and the degree to which they are becoming indispensable infrastructure rather than optional products. Once an AI system becomes a utility — once its decisions are embedded in processes that citizens cannot opt out of — the governance challenge becomes more urgent, not less.
The Three Failures of the Current Moment
The current state of AI governance can be understood as three simultaneous failures. The first is a failure of process: the people who are making decisions about how AI is developed and deployed are not the same people who bear the consequences of those decisions. The engineers and executives who build AI systems are not the same people who are subject to their decisions. This is the classic problem of unaccountable power, and it is not unique to AI — but the scale and speed of AI capabilities makes it particularly acute in this domain.
“The people who built the system are not the people who are harmed by it. And the political system that is supposed to provide oversight is consistently several steps behind.”
— AI policy researcher, Brookings Institution, 2025
The second failure is a failure of information. Even when there is political will to regulate AI, the technical complexity of modern machine learning systems makes it genuinely difficult for regulators to understand what they are regulating. Model weights are opaque. Training data is proprietary. The behavior of large language models in particular can be unpredictable and context-dependent in ways that make standard regulatory approaches — which typically rely on testing and certification of known properties — difficult to apply.
The third failure is a failure of international coordination. AI development is a global phenomenon, with leading labs in the United States, China, the United Kingdom, and the European Union all pursuing similar capabilities simultaneously. No national regulatory framework can effectively govern technology that is developed, deployed, and accessed across borders. And yet meaningful international coordination on AI governance has proven extraordinarily difficult to achieve, because the geopolitical competition between major powers creates incentives to avoid binding constraints on AI development.
What Accountability Would Look Like
Genuine AI accountability would require at minimum four things. First, mandatory pre-deployment impact assessments for high-stakes AI systems — analogous to environmental impact assessments — that require developers to demonstrate that their systems have been tested for relevant risks, including bias, accuracy, and safety. Second, independent algorithmic auditing with access to model weights and training data, so that external researchers can systematically evaluate how AI systems behave across different demographic groups and use cases. Third, a public registry of high-risk AI deployments that would allow researchers, journalists, and affected communities to track where these systems are operating. Fourth, whistleblower protections for employees who identify safety concerns inside AI labs.
None of these requirements are technically complex or ideologically exotic. They are straightforward accountability mechanisms that we apply routinely to other domains where powerful systems affect human welfare. We do not allow pharmaceutical companies to deploy new drugs without clinical trials and FDA approval. We do not allow airlines to operate new aircraft without safety certification. We should not allow AI systems that make consequential decisions about human lives to be deployed without comparable oversight.
The counterargument — that regulation will stifle innovation — deserves to be taken seriously, not dismissed. But it is also frequently used as a rhetorical stopping point, a way of ending debate rather than contributing to it. The more honest version of the innovation argument is that poorly designed regulation could be harmful. This is true. Which is why the content of regulation matters — not just whether it exists.
The Path Forward
There is no technical solution to the governance problem. Better AI systems will not solve the political challenge of who gets to decide how AI is used and for what purposes. That is a question that has to be answered through democratic processes — through legislation, regulation, and the exercise of political power by citizens who are affected by these systems.
The most important thing right now is to be clear-eyed about what is at stake. AI is not just another technology. It is a system for exercising power — over access to information, over economic opportunity, over political discourse, and ultimately over the shape of the society we live in. The question of who governs AI is not a regulatory technicality. It is the defining governance question of the next decade, and the decisions we make — or fail to make — in the next few years will shape the options available to us for decades after that.
Anna Schmidt is a Senior Opinion Writer for Media Hook, offering sharp commentary on politics, culture, and the ideas that define our times.
