Regulatory frameworks and institutional support will drive continued adoption, with over 20 countries in Latin America and the Caribbean presenting national AI policies or roadmaps. DRIVING THE DATACENTERSThe AI revolution is impacting the region's investment landscape in another way. Latin America sits at the intersection of these global megatrends, including artificial intelligence (AI), the transition to a low-carbon economy, and geopolitical fragmentation. The implementation of AI, along with other megatrends, has the potential to create new sources of capital and boost demand for local expertise and resources. For investors, the challenge will be understanding how these trends materialize in each country and sector, managing inherent risks in a rapidly evolving environment. With its wealth of energy and mineral resources, Latin America could play a pivotal role in developing this enabling infrastructure. For example, the International Energy Agency estimates that data centers will represent 1-2% of global copper demand by 2030. AI, in particular, has emerged as the dominant megatrend for global investors, with long-term implications for market growth and spurring massive investments. Corporate spending on AI infrastructure, from computer chips to data centers, is so massive that it's generating a significant macroeconomic impact. While uncertainty remains on whether AI revenues will match this scale of spending, the technology's true potential may lie in how it creates entirely new revenue streams across the economy. The rise of AI is an example of how Latin America's economies could navigate and even benefit from global megatrends. The increase in energy demand due to the expansion of data centers reinforces the need for investment in power systems, grids, and renewable energy infrastructure. INTERSECTING FORCESWhile AI is the dominant global megatrend at the moment, other structural changes continue to shape the direction and create tactical opportunities in Latin America. The Latin American AI market is projected to grow from $4.71 billion in 2024 to $30.20 billion by 2030, a compound annual growth rate of nearly 23%, according to IMARC Group analysis. Across the region, notable AI use cases are emerging in industries like energy, finance, healthcare, and supply chain management, with the potential to drive efficiency improvements and revenue growth. Another consequence of geopolitical shifts is that Mexico has become a primary provider of high-tech goods to the U.S., as the latter seeks to bring semiconductor, electric vehicle, and advanced materials production closer to its territory. Additionally, AI could drive further investment in renewable energy infrastructure in the region, adding to the growth in demand driven by urbanization and electric vehicles. We believe global megatrends will continue to influence markets and investment opportunities in Latin America over a five-year horizon and beyond. While investment in AI is still relatively low in the region, AI adoption by consumers and enterprises is solid. Urbanization is driving significant infrastructure investments in projects in Colombia. These forces must be considered together, as they interact in complex and mutually reinforcing ways. The region also hosts around 60% of the world's lithium resources, a critical element for energy storage in modern devices and data centers. The construction of AI data centers is also exerting growing pressure on global energy resources, a constraint that could limit the technology's positive impact. In this context, active management and local knowledge are essential to identify winners and losers. By embracing megatrends, Latin America has the potential to transform its structural challenges into engines of lasting growth. Beyond the events and policies that make headlines, large structural forces continue to shape the global economy. The transition to a low-carbon economy could offer opportunities for countries like Brazil and Chile to lead in renewable energy. AI is expected to account for 5% of total electricity consumption in Latin America and the Caribbean by 2035, according to the Latin American and Caribbean Energy Organization (OLACDE). As a result, the growth of AI, along with cloud computing and digital transformation, is intensifying the global demand for resources. While much of that capex will be located in the U.S., some Latin American countries are also beginning to attract significant investments in data center infrastructure. Just last year, Microsoft launched a new cloud data center in Chile; Amazon Web Services (AWS) opened its new center in Mexico; and the city of Rio de Janeiro announced plans to build a 1.8-gigawatt AI data center campus in partnership with Alphabet's innovation lab and Brazilian data center company Elea. For example, geopolitical tensions have spurred the development of “sovereign AI” capabilities in countries like Brazil and Chile to reduce reliance on foreign technology. Latin American countries produce nearly half of the world's raw copper, with Chile and Peru holding the largest global reserves of this critical mineral. In the financial sector, innovations like AI-driven credit models are transforming customer interaction and operational efficiency. Geopolitical fragmentation is elevating Mexico's strategic role as a nearshoring destination. Corporate capital expenditure (capex) projections for AI range from $5 to $8 trillion by 2030, according to the BlackRock Investment Institute's “Investment Outlook 2026” report. Capex spending estimates for AI from 2022 to 2040 suggest that AI's contribution to GDP growth could approach that of computers and the internet. Commercial real estate firm Cushman & Wakefield predicts a nearly 60% increase in data center capacity delivered in Latin America by 2030. CAPTURING THE BENEFITSThe implementation of AI could prove to be faster and more widespread than past technological revolutions, including the invention of electricity and the steam engine. Furthermore, the growing relevance of megatrends like AI is reducing the efficiency of overly broad diversification, reinforcing the importance of selective approaches. The training and deployment of large language models depend on massive inputs of energy and materials.
