The Synthetic Parenthesis: How 'LatamGPT' Bridges Two Revolutionary Frameworks
(research diary · Sydney, Australia / 18 July 2025)
Sometimes the most profound insights come from unexpected convergences. As I've been tracking LatamGPT's development (Latin America's ambitious large language model set to launch in September), two theoretical frameworks keep coming to my mind. One is the Gutenberg Parenthesis, which suggests we're closing a 500-year chapter of print-based knowledge. The other is synthetic ethnography, which offers tools for understanding how AI creates new kinds of social situations. Together, they reveal why LatamGPT represents something far more significant than just another regional AI project.
Let me walk you through how I think these ideas converge to show that Latin America is not merely catching up to global AI development, but potentially leading us into an entirely new era of knowledge creation.
The Moment Everything Changed
As reported by Rest of World, Juan Palma asked ChatGPT for directions in Santiago, Chile and got sent the wrong way. He experienced something that perfectly captures this transitional moment. This wasn't just a mapping error; it was a collision between three different ways of knowing. The pre-print way (asking a local), the print way (consulting a fixed map), and what's emerging now: conversing with an AI that synthesises vast amounts of information but might not grasp the lived reality of a specific place.
This simple misdirection illuminates both frameworks I'm working with. From the Gutenberg Parenthesis perspective, Palma wasn't consulting a fixed text but entering a conversation, marking our shift from print's one-to-many broadcast to AI's dynamic dialogue. From the synthetic ethnography lens, he was inhabiting what Knorr Cetina calls a "synthetic situation"—a space where screen-mediated projections create new social realities that don't quite align with the physical world.
Understanding the Double Transformation
To grasp what LatamGPT represents here, we need to understand two parallel transformations happening simultaneously. The first is historical: we're leaving behind 500 years of print dominance. From roughly 1500 to 2000, knowledge was something fixed, finished, and owned. Books had definitive editions. Authority came from publication. Information flowed from centres to peripheries. From Europe (and then North America) to Latin America.
But this was actually an anomaly. Before print, knowledge was fluid, conversational, constantly remixed. We're now entering what scholars call "secondary orality", that is a high-tech return to these older patterns, but amplified by computational power. Knowledge is becoming conversational again, but now our conversation partners include GenAIs.
The second transformation is technological: we're creating synthetic situations where human and artificial intelligence interact to produce new forms of knowledge. When Latam-GPT spearheaded and assembled strategic alliances to build the new model, they weren't just gathering data. They were constructing a "synthetic commons", or a shared space where regional knowledge could be computationally represented and made interactive.
The Art of Technological Translation
Here's where LatamGPT becomes particularly fascinating. It's not trying to replicate ChatGPT with a regional accent. It's engaged in "technological translation", taking the form of a large language model and translating it into a Latin American context in ways that transform both the technology and the context.
This translation operates at multiple levels. Linguistically, it encompasses not only Spanish or Portuguese but also Indigenous languages such as Nahuatl, Quechua, and Mapudungun. But more profoundly, it's translating ways of knowing. When an AI model can move fluidly between Spanish and Quechua, it's creating a synthetic space where these languages can interact computationally in ways they might not in daily life.
The 8 terabytes of regional data being processed aren't just training material; they're the substrate for a new kind of synthetic situation that emerges from Latin American realities rather than being imposed upon them. This is technological translation as a creative act, not mere replication.
Productive Constraints and Regional Advantages
The developers acknowledge that LatamGPT will "lag behind on general questions" compared to ChatGPT or Claude. Here, traditional analysis might view this as a limitation. Still, both theoretical frameworks suggest to me that we should read it as a feature: in the post-print world, universal authority matters less than situated expertise. In synthetic situations, depth of contextual understanding trumps breadth of general knowledge.
By explicitly focusing on regional knowledge, LatamGPT embodies productive constraints. A model that truly understands Chilean or Argentinean Spanish doesn't just recognise that "cachai?" means "¿entiendes?", or that “quilombo” means mess”. It grasps the social contexts, generational markers, and situations where each would be used. This isn't a limitation, it's specialisation that enables deeper, more meaningful interactions.
Moreover, regions that were peripheral during the print era might have advantages in the emerging paradigm. They're less wedded to print's assumptions and institutions. They can build new systems without having to dismantle old ones first. The networked, collaborative way Latam-GPT assembled its partnerships mirrors how knowledge actually flows in the post-parenthetical age, through networks rather than hierarchies.
Material Grounding in Virtual Spaces
The placement of LatamGPT's infrastructure at the University of Tarapacá, powered by solar energy in the drought-stricken north, reveals another crucial insight. Both frameworks remind us that even the most virtual technologies have material substrates. The Atacama Desert, long a site of mineral extraction, is becoming a site of knowledge construction. This isn't just making do with available resources. It's building infrastructure suited to a world where knowledge creation must be sustainable and adapted to local conditions.
New Literacies for New Situations
As we close the Gutenberg Parenthesis and enter an age of synthetic situations, the skills needed for meaningful participation are changing dramatically. In the print era, literacy meant reading comprehension. Now we need conversational comprehension, or the ability to prompt, probe, and synthesise through dialogue with AI.
This actually raises the bar for engagement. You can't passively receive knowledge from LatamGPT the way you might from a textbook. You need to be an active participant in knowledge creation, asking the right questions, pushing for connections, and evaluating responses. Latin American educational systems that recognise this shift early (teaching students to be skilled AI conversationalists rather than passive consumers) will have significant advantages.
The synthetic ethnography framework adds another layer: users need to understand they're inhabiting synthetic situations where the boundaries between human and machine knowledge blur. When a Colombian entrepreneur uses LatamGPT in the future to develop business strategies, she's not just retrieving information. They are co-creating knowledge in a synthetic space that combines her experiential understanding with the model's pattern recognition across thousands of regional business cases.
Beyond Competition to Creation
Perhaps most importantly, both frameworks help us move beyond tired narratives of technological competition. In a world of conversational AI and synthetic situations, what matters isn't who built the biggest model or processed the most parameters. What matters is who creates the most useful syntheses, enables the most productive conversations, and generates the most innovative combinations of knowledge.
LatamGPT's focus on regional depth over universal breadth, its inclusion of Indigenous languages, and its sustainable infrastructure choices aren't compromises or limitations. They're design decisions for a different kind of technological future, one that values situated understanding over abstract universality.
When LatamGPT launches in September, it won't just be answering questions about Latin America. It will generate new possibilities, creating unexpected connections between Colombian biodiversity research and Chilean renewable energy innovations, as well as between Mexican urban planning and Brazilian social technologies. These are thematic syntheses that might never emerge from models trained primarily on North Atlantic data.
The Questions That Matter
As I continue tracking LatamGPT's development through both theoretical lenses, certain questions become crucial. Not "How does it compare to ChatGPT?" but "What new kinds of conversations does it enable?" Not "Is it as accurate?" but "Does it understand context in ways that matter locally?"
Will LatamGPT help preserve and revitalise local knowledge systems by making them conversationally accessible to new generations? Will it enable forms of regional integration where insights flow as easily across borders as capital once did? Will it create new economic possibilities by enabling innovations that emerge from uniquely Latin American syntheses?
These questions can only be answered by watching how people actually use, adapt, and transform this technology. That's why I see LatamGPT as a perfect case study in both technological translation and post-parenthetical knowledge creation. It's a living experiment in what happens when a region adopts a global technological form and makes it resonate with local realities.
Building the Future We're Theorising
What excites me most about LatamGPT is that it's not waiting for the future; it's actively building it. While scholars theorise about post-print knowledge and synthetic situations, Latin American researchers and engineers are creating concrete instantiations of these ideas. They're not just studying how AI changes knowledge; they're shaping what those changes look like.
The convergence of the Gutenberg Parenthesis and synthetic ethnography frameworks reveals LatamGPT as something more than a technological project. It's a paradigm shift in action, a demonstration that the future of knowledge isn't predetermined by those who dominated the print era. In an era of conversational AI and synthetic environments, new possibilities emerge for those willing to imagine and develop innovative approaches.
As we stand at this intersection of theoretical insight and practical construction, LatamGPT reminds us that the most profound transformations often come not from the centres of old power but from the margins ready to become new centres of possibility. The parentheses are closing, new synthetic realities are emerging, and Latin America isn't just participating in this transformation; it's (finally, maybe) helping to define what comes next.
Currently researching the convergence of synthetic ethnography, localised impacts of GenAI and post-print knowledge systems from Sydney, Australia. If you're working on regional AI projects or thinking about knowledge in the age of conversational machines, I'd love to hear from you: luishernando.lozanoparedes@uts.edu.au