What is the Future of Artificial Intelligence Regulation?
Reflection on the future of Artificial Intelligence (AI) regulation is conditioned by the fact that this technology is advancing at an extraordinarily rapid pace. Today, we know that we are moving towards AI that is increasingly closer to human intelligence—agentive, contextual, emotional, and culturally shaped.
An AI that will act as an invisible, omnipresent infrastructure, integrated into our daily routines, functioning as a cognitive companion capable of making decisions on our behalf. In this scenario, regulation should not be based on how it works, regulating according to its internal processes, but rather on the consequences it may produce. Therefore, it must become much more dynamic, technical, and continuous.
The Need for Dynamic Regulation
Regulation will have to adapt to a reality where AI is not merely a product, but an infrastructure. Supervision must be permanent, based on real-time data and automated auditing using algorithms capable of monitoring and explaining other algorithms. The requirements of transparency, traceability, natural explainability, and continuous risk assessment must form the basis of the new regulatory framework.
It is important to raise the level of discourse on risks, looking not only at the micro level but also at the macro level: society, culture, politics, democracy, and also the individual as a free agent. At the same time, equal and non-discriminatory access to technology must be provided if we do not want to have first-, second-, or third-class citizens in areas such as agentive AI or neurotechnology.
Global Differences in Regulation
Differences in regulation between countries or regions reflect different views on the role of the state, technology, and fundamental rights. The European Union promotes a more protective framework focused on the protection of individuals and risk management; the United States maintains a sectoral approach, more dependent on private innovation; and China focuses on a strongly centralized model oriented towards control, national security, and productivity.
In any case, all regions share a challenge: to regulate while avoiding interventions that hinder AI deployment.
Why Regulate AI?
Regulating AI is essential because it amplifies human capabilities, makes decisions with real impact, and operates in deeply sensitive areas such as health, employment, education, security, and fundamental rights. AI has enormous transformative potential that requires a framework guaranteeing fairness, transparency, security, respect for privacy, and non-discrimination.
It is not about stifling innovation, but ensuring society can trust that AI is developed within clear ethical and legal boundaries. Furthermore, the move towards agentic AI models increases the need to rethink regulation. New expressions of individual rights and new obligations for developers and operators are required to protect individual autonomy and cognitive integrity associated with combining AI and neurotechnology.
Pros and Cons of Regulating AI
Regulation must aim at effectively protecting individuals, society, and the democratic model. It establishes limits and safeguards that prevent abuse, discrimination, and decisions made without necessary transparency. In a world where AI will be ubiquitous, a robust yet flexible and accountable framework of trust is essential.
On the other hand, regulation must avoid unnecessarily hindering innovation and technological progress. AI will bring advances in health, science, security, and the environment, serving as a foundation for progress. Additionally, regulating fast-evolving technologies is complex and risks distortion. Thus, future regulation must be flexible, based on continuous governance and adaptable mechanisms.
Future vs Current Regulation
Future AI regulation will differ from current frameworks by supervising systems that learn, interact, self-adapt, and communicate. Models based on one-off assessments will give way to continuous supervision, algorithmic auditing, transparency, and traceability of the systems’ life cycle. Regulation will require the use of supervisory AI to explain and evaluate AI, an area we are just beginning to explore.
Furthermore, the internet will change, as will interactions with computers and smartphones, online shopping, and information access. Ethical and semantic interoperability protocols will enable different intelligent agents, platforms, and supervisors to “speak the same language.” Responsibilities throughout the value chain, from model providers to end operators, will also need strengthening. In short, regulation will be lively, technical, dynamic, and deeply integrated into the technology’s functioning.
Challenges Facing AI Regulation
The first challenge is technical: regulating constantly evolving systems requires flexible mechanisms, real-time auditing, continuous risk assessments, and regulatory structures capable of understanding AI’s structural complexity.
The second challenge is institutional: regulators and supervisory authorities will need new capabilities, resources, and tools to oversee an ecosystem dominated by large-scale intelligent agents.
The third challenge is global: avoiding regulatory fragmentation. Incompatible national rules would complicate interoperability between intelligent agents and effective supervision.
Finally, there is a social and political challenge: ensuring new expressions of individual rights—such as disconnection, explainability, or portability—translate into real and effective mechanisms. We must not only mitigate AI’s negative risks but ensure AI helps build a better society, improving the lives of the most disadvantaged and extending technological progress to all corners of society. Future regulation must protect rights while anticipating political, social, cultural, and cognitive impacts of living with ubiquitous AI and promote its most favorable development.
