By Javier Surasky
The rapid expansion of artificial intelligence is already reshaping key areas of social, economic, and political life, making AI governance a central challenge for international institutions, national regulators, companies, and civil society. This post offers a conceptual guide to ten elements that are essential for understanding the global governance of AI: its purposes, life cycle, data foundations, legal environment, institutional architecture, multistakeholder nature, regulatory levels, adaptability, monitoring, and dispute-resolution mechanisms. Together, these pieces help explain why AI governance cannot be reduced to technical regulation, but must be understood as a political, legal, and institutional challenge.
This is not the first time in human history that technology has had disruptive effects on the social order: the invention of the printing press, the steam engine, the use of nuclear energy, or the advent of computers are examples that require no further explanation in a list that we could extend even to stone tools in prehistory.
Each new
technological change has proven more potent than its predecessors, as
it has developed by standing on their shoulders. This helps explain why each
change has generated social fear and adverse reactions: in the early 19th
century, the Luddites, a group consisting mainly of English artisans, organized
to destroy the "new" machines that threatened their work.
Adaptation
to new technologies, due in part to their impact on communications, has
historically occurred over increasingly accelerated periods but, until now, has
required more than one generation.
These
elements place AI on a plane of major technological change: it has a more significant disruptive potential than any previous technology, is occurring at an
unprecedented speed, and generates fears and rejections.
These are
precisely the reasons why it is essential to establish an AI governance
framework: its dual power (it has no inherent purpose so that it can be used
both "for good" and "for evil"), its social extension and
penetration, and the promotion of peace at both national and international
levels.
Of course, these
reasons for establishing an AI governance framework can be split into motives
of equity, social justice, capacities for sustainable development, closing or
preventing gaps between rich and poor states, peace and security, and various
other factors. As we noted, the impacts of AI are already felt in multiple
fields, and the potential for good or evil it brings is unprecedented. While it
creates new risks and opportunities, the biggest change AI brings is enhancing
existing ones.
Thinking
about an AI governance scheme today implies thinking contextually and
holistically, in a framework where uncertainty plays a prominent role that we would be wrong to try to
deny. On the contrary, uncertainty is part of the reality that AI enhances.
We must consider many elements in our attempts to provide a legal framework for
AI. Below, I present here 10 of them that I understand as critical:
10 Critical Elements
1. Debating and agreeing on its purposes is necessary.
All regulation is based on an axiological source (it seeks to
"protect" a value considered positive and/or "confront" a
value deemed negative). The values that underpin AI governance are neither
natural nor exempt from disputes. Therefore, the concept of "AI for
Good" seems useless to me. What is "good"? Instead, if we talk
about "AI for Sustainable Development," we have an internationally
reached agreement on what that means.
2. Governing AI is nothing more, nor less, than governing the stages of its life cycle.
While a simple schematization of
technology life cycles can be summarized in six stages (product definition →
product development → prototype testing → early user adoption → widespread use
→ obsolescence), De
Silva and Alahakoon identify a 19-stage life cycle for AI.
3. An AI governance scheme must necessarily include the regulation of data
Including its production, storage, management, transmission, and use. Not considering this
chapter is equivalent to regulating food consumption without addressing its
production.
4. AI development does not occur in a normative vacuum:
There are binding international norms in
fields such as property rights, trade, humanitarian law, human rights, and the
United Nations Charter that already apply directly to AI.
5. We are not facing an institutional vacuum.
We already have international experience in
creating governance frameworks for disruptive technologies: there are lessons
to be learned and good practices that come from institutions of the United
Nations system such as the International Telecommunication Union (ITU), the
United Nations Office for Outer Space Affairs (UNOOSA), the International
Atomic Energy Agency (IAEA), and the International Civil Aviation Organization
(ICAO), but also from other institutions such as the International Organizationfor Standardization (ISO), the Internet Governance Forum (IGF), the World
Summit on the Information Society (WSIS), and even from spaces such as the
Internet Corporation for Assigned Names and Numbers (ICANN) and the European
Organization for Nuclear Research (CERN).
6. Other more "traditional" organizations can provide indications on how to deal with the characteristics specific to AI regulation.
For example, the
International Labour Organization (ILO) was created in 1919, but its tripartite
nature is extremely attractive when thinking about a governance scheme that
must necessarily be multi-stakeholder (see the next point).
7. AI governance must be a multistakeholder governance.
While states are the ones who carry normative
power, AI development takes place, especially in the private sector, which is
why it must be part of the process. Its priorities and demands must be
counterbalanced, so it is essential to include actors that provide expert
knowledge (academia, think tanks) and those who will feel its final
consequences (civil society). By its nature, it is particularly relevant to
include an institutional channel that allows the needs of future generations
and children to reach the debates.
8. Any framework for AI governance requires work on three levels: national, regional, and global.
By its nature, AI does not recognize geographical
limits, and its regulation requires, at the very least, addressing cross-borderand interoperability issues.
9. Establishing a definitive AI governance when it is in full development is a utopia.
Instead, we should base ourselves on anticipation exercises (with a high degree of uncertainty) to create a regime capable of being nimbly adapted as new developments occur. It is good to remember here that Thomas Friedman told us in his book Thank You for Being Late: An Optimist's Guide to Thriving (2016) that the speed of change in new technologies could surpass the ability of societies and policymakers to adapt to the changes they generate. More specifically, he pointed out that the renewal rate of technological platforms moved within five to seven years while implementing new regulatory measures required between ten and fifteen. As Collingridge’s Dilemma puts it, when a technology is just developing, it is hard, when not impossible, to predict what impacts that technology will have. Consequently, any regulations imposed at early stages are likely to be ill-fitted, but when those impacts have become known, it is often too late to regulate them.
10. AI governance must include a substantial chapter on monitoring and reviewing, compliance, and advances in AI itself:
Including a rapid dispute resolution scheme based on expert work. Without disregarding their (earnest) shortcomings, the Universal Periodic Review of Human Rights conducted by the United Nations Human Rights Council and the WTO dispute settlement panels present exciting avenues that can be adapted to AI.
Although it
may seem difficult to imagine today, reality tells us we need a
"Digital San Francisco moment." When what seems impossible is
indispensable, it is good to remember Arthur Clarke: "The only way of
discovering the limits of the possible is to venture a little way past them
into the impossible" (Profiles of the Future: An Inquiry into the
Limits of the Possible. Harper & Row, 1962).
Update (May 2026)
Since this post was first published, the global AI governance landscape has moved from general debate toward early institutional design.
- In September 2024, UN Member States adopted the Pact for the Future and its annex, the Global Digital Compact, which placed artificial intelligence within a broader agenda of digital cooperation, human rights, sustainable development, and institutional coordination.
- In August 2025, the General Assembly adopted resolution A/RES/79/325, establishing the terms of reference and modalities for the Independent International Scientific Panel on AI and the Global Dialogue on AI Governance.
These developments do not solve the governance puzzle described in this post, but they confirm its central argument: AI governance requires a combination of scientific assessment, multistakeholder participation, institutional coordination, data governance, monitoring, and mechanisms that can adapt to rapid technological change.