Leading AI Societal Leapfrogging in Latin America

“Iron Man” Exoskeleton for Microeconomic Growth 

In my days as an international cooperation consultant in South America, I led a team to study the massive public investments made in traditional infrastructure for Bolivia: oil, transportation, agriculture, etc. Looking at decades of projects, bilaterally and multilaterally backed, I was dismayed to find any transformative returns on most such investments. Yet, a few years thereafter, private investments in mobile phone infrastructure not only revolutionized communications, but they enabled entire nations to leapfrog traditional landline infrastructure, massively empowering individual agency.

In 2025, Artificial Intelligence (AI) is poised to deliver a similar transformative impact, which could unlock new dimensions of human potential and economic freedom. By harnessing edge-AI technologies, like TinyML and nanoLLM (small but powerful AI models that run directly on devices) and new smartdevices with embedded NPUs (specialized AI chips), Latin America is set to unlock new pathways for socio-economic development.

In this article, I discuss real-life cases from the region, to invite you to think of AI as the “Iron Man” exoskeleton, augmenting human capabilities across a diversity of fields, such as education, agriculture and small business operations. In each case, AI empowers individuals to overcome centuries-old barriers, presenting evidence of an unprecedented opportunity for inclusive growth. Critically, I make the case for nurturing an enabling mindset to enact empowering policy to support AI societal leapfrogging.

Highschoolers learn college-level math on tiny desks, with great faith in a future only their minds can see.

Voice AI: Empowering Local Indigenous and Non-Literate Communities 

For starters, let’s consider Bolivia, a country whose constitution recognizes 36 native Indigenous languages besides Spanish. It is home to 12 million citizens who are functionally illiterate in most, if not all, of the country’s native indigenous languages—a particular challenge that the European Union is all too familiar with, forcing even such an economic superpower to use a borrowed language to administratively function: English. This “language barrier” is arguably an impediment to socioeconomic development. No more. One of the most transformative applications of generative AI lies in its ability to bridge language barriers and related literacy gaps through voice. In regions characterized by a prevalence of an Indigenous language, voice-to-voice translation AI enables individuals to communicate and access information beyond the confines of their native tongue.

Already back in 2023, I found a first case that drew my attention in the highlands of Puno, Peru. There, a 29-year-old engineer Honorio Apaza had “trained” an AI model capable of translating between Aymara (an Indigenous language in that region) and both Spanish and English. He explained, “AI learns like teaching a child to speak. I gave translation examples, taught terms in parallel between two languages for training.”  It worked like magic. In time, as more people “train” more such AIs, technology will eliminate language communication barriers, enabling remote communities, many of whom possess limited Spanish literacy, let alone any English notions, to engage with the full Wikipedia contents, with essential government services currently available solely in Spanish —previously inaccessible to them.

As technology advances, Voice AI (multilingual, multimodal, real-time) will serve to “extend” human capabilities with new self-service capabilities. This won’t displace any existing human interpreters’ services—there are no interpreters in many rural areas to begin with!

Today, local radio broadcasters in Peru are leveraging an AI tool, Quispe Chequea, to generate news reports in Quechua, Aymara and Awajún. Not only can they reach out to a broader audience, but more importantly, in generating content in those Indigenous languages, they are possibly saving them from extinction, while integrating communities in the life of their country. Big Tech are also contributing to this movement. For example, Meta’s Massively Multilingual Speech AI (MMS) research models can identify more than 4,000 spoken languages. In yet another instance, Google’s GEMMA 3 AI, not only supports more than 140 languages, but is multimodal (image, audio, text) and open source (free to “train” further).

The implications of these developments are far-reaching. At AmericasNLP 2025, new research was presented translating Spanish to 13 Indigenous languages of the Americas. By extension, Spanish could be translated into other common languages in real-time. So, a native Quechua-speaking farmer can now converse with a French-speaking engineer agronomist via an AI interpreter.  An elderly adult with limited literacy, or suffering from functional blindness, could utilize voice commands to access useful information available in Spanish, such as weather forecasts, and—soon enough—medical advice, from an AI that speaks her native Indigenous language. These essential services have never been available to many Indigenous people before.

Such voice AI solutions function as tools of inclusivity, integrating distant communities into the everyday life of a nation, into the realities of our planet. More importantly, such communities could reap immense benefits immediately, as soon as the technology is deployed: real-time voice AI translation will be embedded in wearable devices in 2025. Contrast this to traditional investments in multi-lingual literacy that take years to put in place, that require hiring the rare experts who are willing to travel to such distant places, benefiting only a handful of people. The most desirable impact of advanced AI should be achieving near-instant human empowerment.

Better yet, focusing investments into such voice AI-powered solutions will help humanity to preserve cultures, while bringing Indigenous voices into the democratic discourse.

Grassroots AI: Flourishing in an Evolving Regulatory Environment

So far, Latin America has demonstrated a relatively light-touch stance to AI regulation. Whatever the underlying reasons, such freedom of action has fostered a fertile environment for grassroots innovation and local entrepreneurship. This is, by contrast to the European Union, which enacted a stringent AI Act in 2024. Most Latin American countries are yet to unveil their national AI strategies, although this landscape is evolving. On the bright side, this regulatory breathing room has spurred a bottom-up surge of AI initiatives, ranging from hackathon prototypes to startup ventures, often tailored to address niche local needs.

Policymakers in the region recognize AI’s potential as a catalyst for economic and social development and act in support of innovation with AI.

Argentina’s government, for example, through their Subsecretaría de Economía del Conocimiento [Undersecretariat for the Knowledge Economy], has emphasized attracting investment and promoting innovation in the knowledge economy, suggesting a more permissive environment conducive to AI development. Moreover, through their Subsecretaría de Ciencia y Tecnología [Undersecretariat for Science and Technology], they launched a call for projects jointly between industry and academia (IA transformadora) offering monetary compensation for 4 million hours of CPU and 250 thousand hours of GPU for a qualified AI innovation initiative, among other incentives effective in 2025.

Mexico offers another relevant case. As of early 2024, the country lacked specific AI regulations, a situation its leaders viewed as an opportunity to transition from being mere consumers of AI to becoming creators. Senator Alejandra Lagunes, who leads Mexico’s AI strategy, has expressed concerns that “regulating based on fear can halt innovation and the possibility of leveling the ground between Mexico and other countries from the Global South with the big tech developers in the Global North.”

Globally, there appears to be growing consensus that overly strict rules (à la EU AI Act) driven by concerns about unrealized AI’s risks, would force a societal status quo.

Latin America is facing a historically vast and untapped opportunity in adopting cutting-edge AI. Indeed, supporting a pro-entrepreneur approach at this time could level the playing field, nurturing young local talents to develop a novel set of skills would make its labor force globally competitive, prime for the emergent intelligence economy. I see favorably that Latin American startups are capitalizing on this regulatory flexibility to innovate with AI in traditionally underserved areas such as agriculture, fintech and healthcare, where private enterprise can act more swiftly than national policies can.

Again, we should underscore that in the absence of stringent AI laws, curious minds in Peru have “trained” Indigenous-language translators; Brazilian software developers deployed AI-driven educational apps; Colombian entrepreneurs introduced AI-powered micro-business tools —all without the encumbrance of compliance bureaucracy. Nurturing such grassroots innovation ecosystem ensures that cutting-edge AI developments will not be reduced to “vetted” multi-billion-dollar AI models from Silicon Valley, from Paris or from Shanghai.

Moving forward, Latin American countries need AI-compatible policies that support the development of new future-proof talents, whether in research labs, in urban tech hubs and in rural towns alike. AI pundits may insist that data is critical; yet without talent there is no economy.

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Think of Managing AI through Empowering Policy

The Latin American experience could inspire a re-evaluation of global AI policies. Instead of “prohibitive regulation” focused on worst-case scenarios, what if countries adopted “empowering policy” that actively fosters AI for development? Heavy regulatory regimes like the EU AI Act, while well-intentioned, may inadvertently raise barriers to entry or reduce viability for small firms and developing nations. An empowering model is more appropriate for developing economies where maximizing local socio-economic flourishing is a must, while managing risks through innovation-friendly oversight is possible —think of it as leaping ahead of the status quo.

As a 2025 report by the Tony Blair Institute for Global Change notes, “Taking advantage of AI’s evolving, transformative potential will be essential, not just for achieving scientific leadership, but also for unlocking economic prosperity and addressing critical challenges across health, the climate and security.” In practical terms, this entails creating sandboxes and clear guidelines rather than blanket prohibitions. Brazil’s draft AI law, for instance, emulates the EU’s risk-based approach but with a regulatory sandbox to allow companies to experiment with AI systems that would otherwise be restricted, encouraging responsible innovation under supervision.

The economic implications of an “empowering policy” stance are profound. By shifting from a punitive to an enabling mindset, Latin American governments can encourage local solutions to local problems without relying on foreign tech providers. It also signals to investors and innovators that Latin America is “open for AI business,” attracting talent and capital. This contrasts with jurisdictions where ambitious startups must navigate costly compliance checks or legal uncertainty. An empowering regulatory framework would actively support pilot programs in health, education, agriculture, and microenterprise, recognizing these as strategic pillars for AI-driven socio-economic development.

The goal should not be mindless deregulation per se, however, but smart empowering policy: for example, enacting purpose-driven sandboxes that protect the broad spectrum of citizens from harm while nurturing the flourishing of transformative new uses of AI for social good and new business development. Such a model could become a new societal paradigm for the Global South, where governance fuels innovation that directly improves people’s livelihoods rather than unintentionally hindering it.

Latin America’s early positive experiences could be reinforced with empowering policy. Winning the AI race for LatinAmerica shouldn’t be about who is in government, rather about cultivating a shared vision that matches the diverse realities of the region. Let’s think how to yield more dividends from homegrown AI applications.

Beneath the surreal death scene of a bygone era, rest Bolivians’ most lucrative future dividends yet to materialize.

Leap into Using AI as the “Iron Man” Exoskeleton for Microeconomic Growth

Like with mobile phones last century, the impact of AI will be most visible at the individual level—imagine AI used by the sole proprietor, the small farmer, the rural teacher. People using AI-powered solutions like an exoskeleton suit, amplifying individual productivity and new market reach without replacing the human at the center. In Latin America’s ultra-dynamic informal economies and ever-growing Small and Medium Enterprise (SME) sector, this “Iron Man” augmentation will prove to be a game-changer: people will not wait to go back to school to earn a credentialed education, choosing instead to bypass “traditional” education altogether.

This is already happening:

•  Entrepreneurs and Small Businesses: Across cities like Bogotá, El Alto, Mexico City, tens of thousands of bodegas (mom-and-pop shops) and street vendors have traditionally struggled to manage customer queries at scale, let alone pay attention to sales trends. These were tasks that only large firms with significant budgets could delegate to specialized departments or outsourcing providers. Today, AI is poised to leveling that playing field.

In Colombia, a new program is deploying chatbot-based AI assistants to small retailers in Bogotá, enabling even corner-store owners to harness data insights. These AI assistants can crunch sales data and answer business questions in simple language, offering actionable tips that were previously out of reach. Argentina’s government has issued guidelines to support the smart use of such chatbots by small businesses.

More broadly, Generative AI is helping entrepreneurs everywhere punch above their weight: even a sole proprietor can automate customer support, generate personalized marketing copy, and handle routine emails, thereby allowing them to compete with larger traditional competitors.

In short, a new entrepreneur mastering AI can do the work of an entire back-office team. Without AI, such a back-office team is quickly turning into a liability for incumbent firms.

• Reaching Customers through AI Agents: Latin American business owners are also using AI to extend their market presence in novel ways. Given the significant and increasing reliance on WhatsApp as a platform for business transactions in the region, particularly among micro and small enterprises, one challenge has been the sheer volume of inquiries that overwhelm small merchants. Agentic AI solutions, such as Zapia, an AI agent that converses with customers in local dialects to answer questions, recommend products, and even facilitate purchases via WhatsApp, are helping to address this challenge. For a small boutique or artisanal vendor, Zapia acts like a virtual salesclerk who never tires—fielding hundreds of messages per hour and freeing the business owner to focus on fulfillment and strategy.

The key is that the human entrepreneur remains in charge, adapting the business model and providing the personal touch, while the AI agent tirelessly executes routine interactions. This symbiosis likely boosts productivity and customer satisfaction without displacing the entrepreneur.

• Educators and Learners: AI is also proving to be an invaluable resource for education, effectively providing the “Iron Man” exoskeleton for teachers and students in resource-constrained settings. Across Latin American universities and schools, forward-thinking educators are embracing AI tutors like ChatGPT as classroom assistants. In Chile, unsuspecting professors found that all their students had been using ChatGPT for assignments —not to cheat, but to brainstorm ideas, translate papers and improve their writing.

Rather than banning the technology, many institutions are integrating it. Some are even joining forces to co-create Latam-GPT, an open source AI trained on local contents by local experts from many countries. It is an AI built on a relatively constrained text-only training dataset, yet a significant feat for Latin America: a collaborative effort that includes new Indigenous languages such as Nahuatl and Mapudungun, as well as new dialect variants. The most significant outcome of integrating these into a shared model might well be getting past the engineering learning curve to build new AI that captures the diversity of Indigenous cultures in the region.

But let’s be clear: these promising efforts are not enough.

 Consider the rest of the world for a moment. Estonia is underwriting AI into their national education strategy through the AI Leap 2025 program. The entire California State University system has adopted ChatGPT for half a million people. The United Arab Emirates are offering the professional license of ChatGPT to all its citizens free of charge. China, Korea, Finland, Singapore and the United States have all adopted national mandates to integrate AI into their children’s elementary schooling. Latin America needs bold AI education policy.

In most countries, teachers are already using AI to draft lesson plans, generate exercises and even get help with grading, significantly reducing their administrative workload.

For rural and underserved schools, this is revolutionary: an educator with limited materials can prompt an AI to produce age-appropriate reading passages or translate proven quality content into an indigenous language, tailoring education in ways previously impossible. Albeit less common but most desirable, rural students could gain a personal tutor available 24/7, enabling them to ask an AI-tutor to tirelessly explain a broad variety of concepts, as many times as needed, in Spanish or in one of 13 native languages, without fear or shame.

The outcome is enhanced human capacity: an elementary teacher with AI can now do the work of several specialists, especially in locations where there may just be no one available; a kid with an AI-tutor in the most remote rural areas could soon top up on useful new knowledge even in the absence of a school. Educational reform is imminent, but it needs visionary leaders.

• Small Farmers and Rural Producers: AI’s “exoskeleton effect” extends to the agricultural heartlands, where small farmers often lack access to much needed agronomic expertise and advanced machinery. In Argentina, an agritech initiative called Agrobit is democratizing precision agriculture for even modest family farms. By leveraging IoT sensors, satellite data, and machine learning (ML) models, Agrobit’s platform delivers customized recommendations on everything from sowing density to irrigation timing—essentially giving a peasant farmer a virtual agronomist on call. It works even offline.

Farmers who adopted the system have seen remarkable gains, with Agrobit now supporting over 50 different crops on millions of acres of farmland. Farmers report cost savings of up to 30% by using the AI’s precise recommendations on seed, water and fertilizer inputs.

Increasingly, farmers with AI will be able to compete with industrial-scale farms by optimizing yield and resource use, guided by AI probes in real-time.

Early in 2025, Science magazine touted the promise of TinyML-based projects in Latin America: using cheap sensors and on-device AI to monitor soil health or detect pests in real time, allowing even remote rural communities to apply state-of-the-art “smart farming” techniques without needing internet service.

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 Setting the example and incentive to act

In focusing on taking bright future pathways embracing edge-AI technology, I, by no means, ignore its potential misuse and inequity, let alone the existing digital divide. On the contrary, the greatest danger lies in AI-illiteracy and AI-inaction, which I explicitly indicate is critically important to address, to avoid perpetuating new forms of exclusion or dependency. Advanced economies are rightfully fearful of where their multi-trillion-dollar investments in frontier AI might take them, which is not a scenario Latin America has to deal with in the short- to mid-term. In 2025, Oxford University mapped public AI data centers around the world and found that US providers controlled 87, Chinese 39, the EU 6 —while Latin American providers are nonexistent.

I advocate for small, offline, self-service AI models with which every country can afford to develop expertise with local flavor. Over time, articulating an “empowering policy” will provide valuable lessons for shaping the future of humanity with this kind of AI.

The real-world cases above illustrate the historically vast and untapped opportunity I see for socio-economic development in the AI age, focusing on Latin America ‑yet broadly applicable in the Global South. In such context, instead of traditional big-tech-led industrial development or inter-government technology transfer, I see an emergent micro-economic model of distributed human empowerment. Edge-AI deployed at the edges of society—in villages, barrios, and small businesses—can uplift communities directly and powerfully. Essential for the success of this approach will be to focus on “empowering policy”:

Think of AI the universal interpreter, AI the polymath advisor, AI the tireless assistant; think of places where humans had never had a chance to grow into their better selves. Then leap.

Leap into newly found people+AI synergies that could dramatically accelerate microeconomic growth in unsuspected places: a local guide in Potosi who could grow into a global market-trader connecting regional producers with merchant-tourists the world over thanks to a voice AI interpreter. A nomadic nurse in the favelas who could bring essential care to patients, diagnosing ailments with the help of an AI health app. A curious young mind anywhere who could grow into a savant, when jumpstarted with essential AI skills—all leapfrogging the limitations of their environments of past untold poverty.

Strategic socio-economic policies will play a crucial role in scaling these successes. Latin America must dare to leapfrog its status quo—through pro-entrepreneur policies, investments in AI education, and decisive action in support of edge-AI local development.

An “empowering policy” model should help unsuspectedly curious people outgrow the limitations of their environment, help support homegrown AI tailored to unique local needs —rather than waiting to be left behind, rather than importing unfit generic solutions. The payoff is not only technological leapfrogging but also a new degree of societal resilience and self-sufficiency: when thousands of individuals each become more productive thanks to their “AI exoskeletons,” the aggregate economic and social gains should be immense. When millions of individuals become their better-selves with AI, a civilizational shift should ensue.

The potential of AI to lift entire societies in Latin America seems obvious to some, but realizing its full impact requires radical action. It is not about just adopting free ChatGPT nation-wide nor about supporting global intelligence colonialism. Quite the opposite.  Policymakers, educators, entrepreneurs must collaborate to create an ecosystem where “local AI savvy” can thrive as an instrument of “local human empowerment.” This means, investing in essential AI education, supporting grassroots edge-AI innovations, and advocating for “empowering policy” that fosters much needed inclusive growth. A coherent and coordinated effort is needed.

The time to act is now, to ensure that AI becomes a force for broad-based progress.

Let us #ThinkLeap and embrace this transformative opportunity.