How important is AI really?
According to BCG, three out of four executives consider AI a strategic priority. Along the same lines, McKinsey reveals that 78% of organizations are already using AI in at least one business function, confirming that it has ceased to be an experiment and has become part of the core operations of companies. In less than a decade, Artificial Intelligence (AI) has become one of the main drivers of global economic transformation. According to the Stanford AI Index Report 2025, private investment in AI in the United States reached $109.1 billion in 2024, nearly 12 times what was invested by China and 24 times that of the United Kingdom.
The economic impact of this technological wave is comparable, in scale, to that of electrification or personal computing. However, the speed and asymmetry in adoption pose a unique challenge: AI not only reconfigures production and employment models, but also the regulatory and tax structures that support them.
Meanwhile, recently, OpenAI announced a strategic partnership with Sur Energy to develop a megadata center in the Patagonian region of Argentina valued at an estimated USD 25 billion, under the project named “Stargate Argentina.” The plan includes a capacity of up to 500 MW of computational power dedicated to AI, and is framed within the regime of incentives for large investments (RIGI). From an economic standpoint, such projects offer a unique opportunity to position the country as a regional AI hub, but also pose challenges: the magnitude of the investment, its financing, and the local capacity to absorb and operate such a facility need to be carefully considered.
A vector for growth and productivity
The International Monetary Fund (IMF, 2024) projects that AI could increase global productivity by 0.3% to 0.6% annually over the next decade, although it warns that the benefits will be unequally distributed: concentrated in countries with high investment in intangible capital and skilled labor.
In Latin America, advancements are slower. The report by the Economic Commission for Latin America and the Caribbean (CEPAL, 2025) estimates that regional spending on AI reached $2.6 billion in 2023, merely 1.5% of the global total, despite the region representing 6.3% of global GDP. Brazil and Mexico lead in adoption, followed by Chile, but the economic impact remains limited: a 1% increase in AI spending is associated with only a 0.036% growth in GDP, a result primarily realized through improved productivity of skilled labor.
The AI Atlas for Latin America and the Caribbean from the United Nations Development Program (UNDP, 2024) confirms that the region faces a “double gap” in terms of AI: in capabilities and governance. On one hand, 80% of private investments in AI are concentrated in just three countries (Brazil, Mexico, and Chile), while half of the countries in the region do not have active policies to promote technology. On the other hand, 70% of public and regulatory institutions lack data infrastructure and sufficient technical talent to adopt or oversee AI systems.
This evidences a structural gap: AI drives efficiency of skilled employment but can widen inequalities where routine or low-skilled tasks prevail. In the region, less than 30% of the adult population has tertiary education (compared to over 50% in advanced economies), which reduces the potential to capture the technological benefits.
The labor potential and limits of transformation
The World Bank (2025) estimates that between 30% and 40% of current jobs in Latin America and the Caribbean could be automated or profoundly transformed by AI, especially in administrative, financial, and logistical tasks. However, it also identifies a significant “potential for new jobs” in data analysis, digital services, and technological maintenance, as long as countries invest in education and retraining.
The UNDP (UNDP, 2024) agrees that AI could be a tool for inclusive development if combined with active innovation policies, institutional strengthening, and state digitalization. However, the region shows an investment in research and development (R&D) of only 0.7% of GDP, compared to 2.7% in OECD countries, which limits technological autonomy and exacerbates dependency on external suppliers.
Meanwhile, a recent study by MIT and Fortune (2024) revealed that only 5% of corporate AI pilot projects manage to generate a measurable impact on productivity, highlighting a paradox: technological investment progresses faster than institutional capacity to integrate it effectively.
These challenges are amplified in economies like Argentina, where the productive structure and institutional environment condition the capacity to adopt new technologies
The Argentine case: regulation that could thwart another historical opportunity
In this global context, Argentina is beginning to debate its own regulatory framework. The bill promoted by the Commission for Science, Technology, and Productive Innovation of the Chamber of Deputies, led by Daniel Gollán, proposes the creation of a Knowledge Management Agency (AGC) with broad powers: to supervise and audit AI systems, require mandatory certifications, impose penalties, and reclassify algorithms.
Although the debate is necessary, the current design of the bill presents significant economic risks. The creation of new regulatory burdens (audits, certifications, registrations, and mandatory contributions equivalent to 5% of the Income Tax for large companies) could significantly increase compliance costs, affecting competitiveness and disincentivizing innovation in startups and tech SMEs.
The proposed legal definition of “AI system,” which encompasses any program that “infers results from the information it receives,” is so broad that it would include everything from accounting software to an e-commerce recommendation engine. A regulation of this scope could generate a contrary effect to the sought: slowing down corporate adoption of AI, particularly in an economy that needs to increase its productivity and export knowledge-based services.
As warned by Fund.ar (2024), the risk in Latin America is not the absence of regulation, but overregulation. A rigid framework could discourage investment and consolidate technological dependence, just when the region should be fostering its own ecosystem.
Discuss a development strategy, not just control
International experience shows that the most successful countries in adopting AI (such as the United States, South Korea, or Israel) prioritized innovation and human capital policies over punitive frameworks. CEPAL proposes advancing a dual agenda: accelerating the accumulation of intangible capital (training, development of sectoral use cases, and adoption in SMEs) while also designing regulatory schemes that are proportional to risk and flexible in the face of technological evolution.
In macroeconomic terms, AI represents a concrete opportunity to increase total factor productivity, reduce transaction costs, and improve public sector efficiency. However, seizing it requires a strategic perspective: invest more in capabilities than in bureaucracy, and understand that the real risk lies not in technology but in not knowing how to use it well.
Artificial Intelligence and national vision: compete or be left behind
The rise of AI redefines value chains, capital flows, and global technological hierarchies. For emerging economies like Argentina, the risk is not being outside the regulation, but outside the productive map. While the world invests to lead the knowledge revolution, overregulation or lack of vision could crystallize our role as peripheral users. Competing demands governing AI with strategy and economic pragmatism: promote investment, protect talent, and create scaling conditions. Otherwise, the digital future may reinforce the gaps that innovation promised to close.
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