How AI-Ready Special Economic Zones Could Shape AI Development in Latin America
An interview with Dr. Said Saillant
Dr. Said Saillant recently created the concept for an AI-ready special economic zone, with Latin American countries in mind. He argues that this could address problems stemming from AI development being concentrated in a handful of countries.
Saillant is an AI researcher who advises governments and international institutions on AI policy, including work with the United Nations Industrial Development Organization, the OECD, and the United Kingdom. Originally from the Dominican Republic, his work focuses on how AI can advance economies across Latin America and other developing regions.
In our conversation, Saillant expands on how AI-ready special economic zones, or AI-SEZs, would work and why they offer a way for developing economies to establish effective AI regulation through incentives, rather than deregulation.
This interview has been edited for clarity and length. Interviewer: Meenakshi Dalal (MD) Interviewee: Said Saillant (SS)
The Problem
MD: What problem are you trying to solve?
SS:
Right now, AI development is concentrated primarily in the U.S., Europe, and China. This makes most countries consumers of AI with limited pathways to become builders or regulators.
That concentration is a problem because it means that only a small set of countries capture the compounding benefits of creating AI systems, like money, talent, and agenda-setting power. The goal is to identify a repeatable way for more countries to build expertise, test AI systems in the real world, and accumulate governance know-how.
MD: I understand why the current geographical concentration is an issue from a benefit-sharing point of view. But if AI systems are being developed quickly and effectively in the U.S., Europe, and China, why does it matter who builds them?
SS:
It matters for two reasons. First, the current geographical concentration can counterintuitively slow innovation over time. Second, it shapes which ideas get developed in the first place.
The strong institutions and mature regulatory frameworks in advanced economies are valuable. But those rules can be cumbersome and difficult to adapt in a timely fashion, which can hinder innovation by limiting opportunities to experiment with and deploy new AI systems.
Beyond regulatory friction, it also influences which ideas ever get a chance to surface. When frontier AI work is clustered in just a few countries, access to jobs, compute, data, networks, and real-world deployment opportunities also become clustered.
As a result, many promising ideas, especially those rooted in different languages, sectors, and lived realities, never get explored. Over time, that will lead to fewer breakthroughs and AI systems being shaped by the priorities of a narrow slice of humanity.
The Proposed Solution
MD: To address those problems, you’ve proposed something you call an AI-SEZ. What is it and how does it differ from a traditional SEZ?
SS:
What I’m proposing is a geographically defined area with permanent regulatory status for AI-related activities. It would operate within broad guardrails set by national constitutions and international obligations.
Traditional SEZs offer regulatory and infrastructure advantages, but their main incentive to draw in investments has historically been major tax breaks.
An AI-SEZ is different. It’s not a tax holiday for companies. Its primary draw is maximum regulatory autonomy, which allows faster experimentation and deployment of AI systems within clearly defined limits.
MD: Maximum regulatory autonomy in less advanced economies sounds like a loophole for big tech companies to essentially do whatever they want.
SS:
Not at all. Regulatory autonomy doesn’t mean an absence of regulation. It means structuring regulation differently so innovation can move quickly without crossing constitutional, legal, or international red lines.
We know that AI systems evolve faster than traditional regulatory processes can adapt. Rather than rewriting laws every time the technology changes, an AI-SEZ creates a stable environment where experimentation and scaling can happen within limits set by national constitutions, international obligations, and market requirements.
MD: Can you walk me through how maximum regulatory autonomy would be structured in an AI-SEZ? How does it both speed up innovation and expand who shapes AI systems?
SS:
Regulatory autonomy removes the constraints imposed by existing regulatory frameworks. When companies are no longer bound by the rules of their home market, they can step back and think more creatively about how to structure their operations, their technology, and their business models.
That creativity is intentional. Rather than the state prescribing a single model, companies or zone operators would propose how they think activities should be organized within the zone, based on what they believe creates the most value. This creates a space where governments, firms, and local institutions learn together by running pilots, building operational capacity, and developing governance practices that can later be applied more broadly. That means local actors would directly shape how the systems are built, deployed, and governed.
An AI-SEZ is different. It’s not a tax holiday for companies. Its primary draw is maximum regulatory autonomy, which allows faster experimentation and deployment of AI systems within clearly defined limits.
Those proposals would then be reviewed by an oversight body within the country, which would decide whether they align with national priorities, attract the right kind of investment, and advance the issues the country cares about.
In practice, this works because regulatory authority within the zone is centralized. Instead of navigating multiple ministries, companies interact with a single office—a “one-stop shop”—that has already been given the necessary powers. This model is already used in places like Panama and Costa Rica, and it’s a key reason those zones are able to operate faster and more efficiently.
The (Hypothetical) Paraguay Example
MD: You mentioned that right now no AI-SEZs with this kind of regulatory autonomy exist yet. How does a country create one?
SS:
There is no turnkey recipe because every country will have a different process as it navigates politics, its legal system, financing, permitting, available infrastructure, ability to enforce, and so on.
To give a very simplified example, I can walk you through how it might look in a country like Paraguay.
The starting point is a feasibility study. Before creating any zone, the country needs to identify whether there is international demand for a particular kind of regulatory flexibility, given what that country can actually offer.
Paraguay is a useful example because it has very low-cost, 100 percent renewable energy, as well as significant water reserves. Those features make it potentially attractive for energy-intensive parts of the AI value chain, such as data centers that need both power and water.
If a data-center operator is interested in setting up there, they may also be looking for greater flexibility around environmental guardrails. That’s where the reciprocal exchange comes in. Regulatory flexibility would not be offered unconditionally, but in return for clear benefits to the country itself.
Those benefits would be defined through performance metrics. For example, a company might commit to helping build out Paraguay’s electrical grid in phases, and continued autonomy would depend on actually meeting those commitments.
Once demand is established and the terms for the AI-SEZ are clear, the country could then create the zone. The most likely pathways for this are through legislation or a presidential decree. This really depends on the country.
The AI-SEZ would then rely on an oversight committee, or its “one-
Other SEZs Designed Around Speed And Capability
MD: Are there examples of SEZs that operate with a similar logic?
SS:
While AI-SEZs with the regulatory autonomy I’ve described don’t exist yet, several governments have announced SEZs for the AI sector, like Oman, India, and Tajikistan. Public information is a bit limited, but so far none of them appear to be operational. As far as we know, they also don’t commit to the kind of regulatory autonomy that we’ve been discussing in terms of having the authority to set and enforce data rules, keep up with the speed of change, or maintain oversight across political cycles.
There are some special economic zones in other sectors that are prioritizing innovation velocity over tax incentives, and they tend to be among the most successful globally.
Zones in Costa Rica, Panama, and the United Arab Emirates consistently rank among the top performers because they focus on efficient regulation, strong infrastructure, and high-quality services that attract leading tenants.
In Costa Rica, for example, that approach helped attract part of the operations of Databricks, a major AI services firm. And in the UAE, the model has gone a step further. They’ve begun exporting their zone framework, most recently through a partnership with Ghana, to help de-risk AI investment and build regional AI hubs.
Those cases show that when zones are designed around speed, predictability, and capability they can attract serious AI activity and scale beyond their original borders.
Not a Race to the Bottom
MD: AI-SEZs clearly make a country more attractive to investors. But how do they ensure that local populations actually benefit? Isn’t there a risk that countries hungry for investment end up prioritizing tech companies instead?
SS:
That concern is understandable, but regulatory autonomy does not mean competing on weaker standards. The idea is that, if these zones are designed correctly, market forces can actually push standards upward from the start.
Many jurisdictions are starting with no AI-specific statute at all. An AI-SEZ lets a country build rules in real time. The zone can establish a clear baseline for how AI systems are governed, covering issues like data handling, audits, incident reporting, and user redress. Operators within the zone can also run supervised pilots and measure outcomes. What proves effective can be translated into national policy, allowing governments to learn what works before applying it nationwide.
Furthermore, many AI companies want to serve highly regulated markets, such as the EU. To do that, they need to meet strict data-protection and governance requirements wherever they operate. That creates economic pressure to adopt stronger data rules, not weaker ones, even inside an AI-SEZ.
AI-SEZs give countries a practical way to participate in the AI transition without rewriting the entire legal system at once
The same logic applies to talent. In many countries, the absence of AI rules creates a one-way pipeline: ambitious engineers, researchers and founders must leave to level up. That functions as a de facto “race to the bottom” for local capacity. Each wave of departures shrinks the domestic talent base and weakens opportunities for mentorship or entrepreneurship. Over time, that makes the next generation even more likely to leave. AI-SEZs aim to reverse that dynamic by creating opportunities for highly-skilled workers at home.
Finally, autonomy in an AI-SEZ is conditional. Companies receive regulatory flexibility in exchange for meeting performance requirements tied to national priorities, and an oversight body ensures those commitments are enforced over time. The benefits to companies are explicitly linked to longer-term benefits for the local economy.
Why This Matters Now
MD: Why are AI-SEZs relevant now?
SS:
AI ecosystems are forming quickly, and early policy decisions are already shaping where innovation, talent, and investment will be concentrated for decades to come. Countries that wait, risk becoming permanent consumers of AI technologies developed elsewhere.
These zones can build local know-how by creating a focused place to pilot AI systems, train workers, and develop practical governance approaches that can later be applied across the wider economy.
MD: Which countries are AI-SEZs best suited for?
SS:
I’ve mainly focused my thinking on Latin American countries, especially my home country of the Dominican Republic. That’s because we already have the basis for a strong AI-SEZ with a long history of special economic zones, talent base, and strategic geographical location.
That being said, this concept can work across emerging and scaling economies provided a few basic preconditions hold. A country must have: 1) reliable power and connectivity; 2) a basic pool of skilled workers; 3) enough rule-of-law and administrative capacity to enforce commitments.
Fragile states typically miss one or more of these prerequisites because their governments must prioritize security and basic services over tech adoption.
It would be less appropriate for very advanced economies that have mature institutions and dense, economy-wide regulation.
Even where national indicators lag, many developing countries have certain areas that are already relatively strong, like industrial parks, major ports, and universities or research centers. AI-SEZs focus on these sites, bringing together the infrastructure and talent needed to run supervised pilots that generate evidence governments can later use to scale rules nationally.
Ultimately, AI-SEZs give countries a practical way to participate in the AI transition without rewriting the entire legal system at once.
AI Elsewhere is produced by Meenakshi Dalal. For collaboration, story ideas, or related work, you can reach her at info@aielsewhere.com