<AI Meets the Todero/>

The Todero Enters the Scene

In Colombia, Venezuela, and parts of Central America, a "todero" is a worker who 'does everything on her own.' Think of the plumber who also writes the quotes, manages the materials, tracks inventory, handles customer complaints, and posts on Instagram. Or the baker who runs the shop, the accounts, the supplier relationships, and the delivery. The todero does not fit neatly into any organisational chart because there is no chart, just a person and a business to keep alive.

Latin American labour market data suggest that this is not an anecdote. According to Atencio-De-Leon et al. (2025), in a matched sample of 101 occupations, the average Peruvian occupation activates 7.1 of nine skill families, compared with 3.6 in the U.S. O*NET benchmark; Peru shows a broader portfolio in 94 of the 101 occupations. And in nearly 6 million job postings from Argentina, Brazil and Uruguay analysed by Carbajal et al. (2025), broad skill demand appears across the occupational hierarchy: from managers to elementary occupations, median breadth remains within a surprisingly narrow range. The todero, in other words, is not a niche occupational category. It is the way work is organised across the occupational spectrum in Latin American labour markets.

This has direct implications for the interpretation of AI exposure indices. Most exposure measures assign scores to occupations on the basis of standardised task descriptions (see, for example, Gmyrek et al., 2025; Pizzinelli et al., 2023; Cazzaniga et al., 2024). Yet the same occupational label may correspond to quite different task bundles across countries. A plumber in Lima may therefore be evaluated using the task profile of a relatively specialised plumber, while the worker in practice may also be quoting, scheduling, searching, writing and managing. In that sense, the index may describe the occupation accurately while still missing a significant part of the job - and therefore part of the surface of work that AI could potentially transform.

“Toderism” as a productivity problem

Before asking which of the todero's daily activities are exposed to AI, it helps to understand why the todero exists in the first place, and what her existence says about stagnant productivity and weak growth.

The classical answer goes back to Adam Smith: the division of labour is limited by the extent of the market. In thin markets, workers absorb more functions because there is not enough demand to sustain specialists in each one. Rosen (1983) formalised the logic for human capital: specialisation is not just a technical property; it is a market outcome. Baumgardner (1988) showed the same logic empirically: in smaller markets, professionals cover broader domains because the market cannot sustain the same degree of division of labour.

The todero, then, is an organisational response to a market with limited depth. Bandiera et al. (2022), in their Jobs of the World project, show that occupational variety - a macro proxy for labour market specialisation - is positively correlated with the level of development. Although these data do not directly measure within-occupation polyfunctionality, they point in the same direction: this kind of polyfunctionality (i.e. toderism) is likely to become more important the further a labour market lies from the level of development and organisational structure embedded in the U.S. benchmark.

That equilibrium division of labour is related, of course, to productivity and growth. A long-standing diagnosis of Latin America's development gap is that low efficiency - not only limited accumulation of capital and skills - accounts for a substantial part of its income shortfall. Delayed technology adoption, misallocation, and organisational inefficiency within firms are among the underlying constraints (see, for example, Araujo et al., 2016). If the way work is organised is part of the productivity problem, then changing the organisation of work can also be part of the solution.

AI Enters the Scene

Here is one of AI's most transformative possibilities for the Global South: AI can supply, from outside the worker, part of the division of labour that the surrounding organisation does not provide.

For a highly specialised worker, AI enters an already differentiated labour market structure. The lawyer may already have research assistants; the analyst may already have data teams and specialised software. For the todero, the dynamic is structurally different. AI is often not competing with a specialist who already exists on the team - because that specialist does not exist. It can supply writing, search, basic analysis, formal communication, planning, or administrative capacity that the thin market never provided.

Consider a baker in a mid-sized Latin American city. In the Peru-U.S. comparison, the difference between occupational labels and actual skill breadth can be dramatic. The technical core - production, quality, the craft of baking - defines the occupation in a standard profile. But the Peruvian baker also activates organisational, cognitive, writing, project-management, customer-service, financial, people-management, and social skills.

And this is where the match becomes especially important. The additional tasks carried by workers in less-specialised labour markets are often precisely the kinds of activities in which AI is already being used. Bick et al. (2026), for example, find substantial adoption in activities such as extracting technical information from documents, preparing reports and analysing data. Large-scale evidence on actual AI use points in the same direction: writing, searching, organising, synthesising and other cognitive and administrative activities feature prominently in observed use (Handa et al., 2025; Iscenko et al., 2026). In other words, the tasks that make the todero's job broader are often the same tasks where AI is already proving useful.

This does not mean that AI will necessarily be adopted at scale in Latin America. Country-level usage data already reveal large and persistent gaps in AI diffusion across economies, even if different sources disagree on whether those gaps are currently widening or beginning to narrow (see, for example, Microsoft AI Economy Institute, 2026; Massenkoff et al., 2026; OpenAI, 2026). The distance between potential exposure and effective use is large, and closing it requires a broad set of complementarities - from infrastructure and connectivity to appropriate technologies, worker skills and organisational capabilities - which we return to below. It tells us this: if workers in less-specialised labour markets perform broader portfolios of tasks than conventional occupational profiles imply, then the surface of work that AI can potentially transform may also be broader than our standard maps suggest.

AI, Labour Specialisation, and Productivity

Return to the Peruvian baker. Hand some of those peripheral functions to a reliable AI assistant and she does not become a different kind of worker. She may become a more productive baker - and perhaps run a more capable business. Gans and Goldfarb's (2026) focus mechanism shows how automating one task can release scarce attention for the remaining bottlenecks; Althoff and Reichardt's (2026) simplification mechanism shows how technology can lower the skill requirement of activities that previously required specialist support. For a todero, both can operate at once: AI can remove low-value overhead while making capabilities such as market research, product development, formal communication or basic analysis easier to access. The result could be more time for the craft and the customer relationship, but also better products, more professional business practices, and access to customers and markets that were previously out of reach.

The channel is direct: AI can enable more effective specialisation, which in turn can raise productivity. Not only at the frontier, but throughout the occupational distribution.

This is not the conventional story of a technology invented in rich countries slowly diffusing to poorer ones. It is a story about using AI to reorganise work where occupational variety is limited and organisational depth is scarce. The potential gains on this margin may therefore be larger where the organisational gap is larger. That is precisely why the mechanism may matter beyond Latin America, for any Global South economy with thin markets, small productive units, and limited specialisation.

So what? New lens, new design spaces

If this mechanism is right, the familiar automation-versus-augmentation distinction is too coarse. The opportunity is not to protect existing jobs exactly as they are, but to automate the right surrounding tasks so that workers can specialise more effectively in what remains.

In this sense, AI opens a window to design a better future of work. To seize that opportunity, we need to bring AI closer to workers through inclusive technology: mobile-first interfaces, voice and image interaction, local language and domain vocabulary, and simple ways to verify outputs and retain control. But we also need to bring workers' capabilities closer to AI. Workers need opportunities to identify what can be delegated, formulate useful requests, evaluate results, and reintegrate AI into real workflows. This calls for situated AI training: learning AI not through generic prompting exercises ("ChatGPT for dummies"), but inside the actual task bundles of an occupation. At Sur Futuro, this is the direction we are beginning to explore: training organised around real occupations, real tasks and real problems, with AI as a tool embedded in work rather than as a skill taught in isolation.

That window also requires a new lens on the labour market. Imagine comparable public data serving as O*NET-like satellites outside the United States! Better statistics matter, but they are not enough. We need to go out and see who does what, when, where, and how tasks unfold over time. The goal is simple: stop inferring work only from occupational labels and go observe how people actually work. Work diaries can reconstruct the sequence of tasks people actually perform; experience-sampling methods can capture activities, interruptions and surrounding functions in real time; and AI itself can make it possible to collect, transcribe, classify and analyse this kind of granular information at a scale that was previously prohibitively costly. And when the question is about causality or policy design, we need more field experiments, including randomized controlled trials (RCTs), like the one we conducted with Cruces et al. (2026). Better maps, richer observation and more experimentation can help us discover which combinations of human and AI capabilities may create value in a labour market full of toderos.

And that takes us back to Greenmarket Square. The workers who initially looked peripheral to the AI story were not necessarily peripheral at all — the map was missing part of their work. A market trader in Cape Town, a baker in Lima, a Sri Lankan plumber running a tiny business: through a standard occupational lens, they appear only weakly exposed. But once we look inside how their work is actually organised, the picture changes.

Perhaps the Global South is not a bystander in the AI transformation after all. Perhaps our maps were simply not built to see where that transformation could happen. If AI can supply some of the specialised capabilities that thin markets and small organisations do not provide, its transformative reach may extend much further across the occupational spectrum than we have assumed. For economies that have struggled for decades to close productivity gaps, that would make AI more than a story about jobs: it could open a new channel for much-needed catch-up growth.