On the North/South asymmetry in generative AI as a cultural infrastructure
When a generative AI system is tasked with representing “a city” without further qualification, what it produces is not a generic city. It is a city with identifiable architectural features: glass skyscrapers, squares with ornamental fountains, paved streets with painted lines, and European or North American style street furniture. The “neutrality” of the prompt activates, by default, a specific cultural imaginary. When the same system is asked to represent Jakarta, Lagos, or Lima, it tends to insert Western architectural elements into the result, as if the non-Western were a variation on a background whose nature is Western.
This text examines that operation, its material mechanisms, and its consequences. My thesis is that the North/South asymmetry in generative AI is not a problem of “lack of diversity” in training data—a formulation dominant in current institutional reports which, while descriptively correct, is analytically weak. It is something more structural: the active presence of a specific architecture of cultural power that operates under the language of technical neutrality. And it constitutes, today, the most efficient form of cultural hegemony that human history has produced, precisely because it presents its bias not as bias but as unmarked universality.
This is the fourth text in a series on digital infrastructures that I have been developing in this public notebook. In Mass Technology and Ethical Consumption, I argued that responsibility for digital sustainability is structural, not individual. In When the Frame Paints the Picture, I showed how institutional infrastructure produces artistic experience rather than merely influencing it. In The Price of the Model, I argued that what occurs with cultural labour under generative AI is a displacement of value, not a fragmentation of work. Here, I extend the argument to its geographical and colonial dimension: if generative AI infrastructures actively shape cultural production and experience, the immediate question is who shapes them, from where, on what materials, and with what consequences for the territories excluded from the process.
1. What 'non-neutrality' conceals
The 2025 Report by the UNESCO Independent Expert Group on Artificial Intelligence and Culture opens with a statement that appears sober yet conceals a euphemism: AI algorithms 'are not culturally neutral'. The formula is accurate and, nevertheless, problematic. To state that they are not neutral suggests that they could be if biases were corrected. It suggests that neutrality is the horizon and partiality the deviation. It suggests that the problem is technical, susceptible to adjustment.
The most accurate description is the opposite. Generative AI systems do not fail to be neutral: they are built upon a specific cultural position that operates as neutral because it possesses sufficient power to do so. Neutrality is not a horizon that the system could approach by improving data; it is the political effect produced by the hegemonic position when it presents itself as an absence of position.
This is not an academic nuance. It radically changes the operational question. If the problem is “models are not neutral,” the reasonable response is “let us make models more neutral through more diverse datasets.” If the problem is “models have a specific cultural position that operates as neutral,” the reasonable response is quite different: to question the very operation by which a concrete cultural position is established as the zero point of meaning. The two responses lead to different policies, fund different projects, and produce different results.
It is advisable to keep this conceptual shift in mind before looking at the data.
2. What the data indicate
Empirical evidence regarding cultural asymmetry in generative AI has multiplied over the last two years and converges on an unequivocal pattern. Five findings are particularly relevant.
Composición lingüística del entrenamiento. Hugging Face, desarrollador de Stable Diffusion, ha reconocido que la “gran mayoría” de sus datos de entrenamiento está en inglés. Los modelos abiertos LLaMA 2 y Mistral rinden significativamente peor en idiomas como el igbo nigeriano o el kazajo que en lenguas mayoritarias —no por limitación arquitectónica del modelo, sino por escasez relativa de datos disponibles en esas lenguas dentro del corpus de entrenamiento—. Las lenguas mayoritarias del mundo en términos de hablantes —mandarín, hindi, español, árabe, bengalí— están sistemáticamente subrepresentadas en proporción a su peso demográfico real, y muy por debajo del peso del inglés en los datasets. La distribución no refleja distribución poblacional ni distribución cultural: refleja distribución del contenido digitalizado disponible bajo licencias compatibles con la extracción masiva.
Demographic biases in outputs. A systematic analysis of images generated by Midjourney found that only 23% of the people represented are women and only 9% are people of African descent. These figures do not reflect actual population distribution or the demographic distribution of content available online. They reflect the specific composition of the training corpus, which privileges certain types of work, certain aesthetics and certain representations over others. The model does not produce a world: it produces a specific world that is presented as the world.
Estereotipos nacionales. Un estudio publicado en Rest of World sobre tres mil imágenes generadas por Midjourney con prompts adaptados a distintos países documentó patrones inequívocos: “un indio” aparece casi siempre como hombre mayor con barba; “una persona mexicana” frecuentemente como hombre con sombrero; los paisajes urbanos de Nueva Delhi aparecen sistemáticamente “sucios”; la comida de Indonesia se representa casi siempre sobre hojas de plátano. El sistema no representa la complejidad y heterogeneidad de estas culturas: las aplana en estereotipos concretos que circulan en los corpus occidentales sobre esos lugares. La IA generativa no inventa estos estereotipos. Los reproduce, amplifica y normaliza a escala que las representaciones humanas previas nunca habían alcanzado.
Cultural differences between models. A comparative study between GPT, a model from OpenAI, and ERNIE, a model developed by Baidu in China, found that the same questions posed in English and Chinese respectively produce markedly different responses. In English, models tend to emphasise individualistic orientation; in Chinese, collective orientation. This is not a defect of the models: it is the imprint of the corpora upon which they were trained, which reflect the cultural contexts in which those corpora were produced. The operational consequence is significant: generative AI is not a single system with biases; it is multiple systems, each with its own cultural configuration, presenting themselves simultaneously as universal in their respective markets.
Sesgos en representaciones especializadas. Un trabajo publicado en npj Digital Medicine en 2025 documenta que los generadores de imágenes producen retratos de pacientes con enfermedades específicas que sobrerrepresentan personas blancas de peso normal, en proporciones desproporcionadas respecto a la distribución epidemiológica real de esas enfermedades en la población. Esto no es problema cosmético: cuando estos sistemas se incorporan a flujos educativos, materiales clínicos o herramientas de comunicación médica, propagan un imaginario específico de “paciente normal” que tiene consecuencias materiales sobre cómo se reconoce o no se reconoce la enfermedad en cuerpos racializados o con corporalidades distintas de la norma representada.
The five findings are not independent aspects. They form a pattern. The pattern is the systematic operation by which a specific cultural configuration—predominantly Anglo-American, male, white, and urban middle-class—presents itself as the zero point from which everything else is measured. What lies outside that configuration appears as a difference from the norm, not as a cultural configuration on equal footing.
3. Algorithmic hegemony is no accident
It is worth examining why the composition of the corpora is as it is. There are three material reasons that matter, because they change where possible intervention lies.
First. Differential digital availability. Not all cultures, languages, and productions across the world possess the same degree of digitisation. The English language benefits from centuries of textual production digitised by massive projects (Project Gutenberg, Anglo-Saxon university archives, extensively digitised press). The majority languages of the Global South possess corpora that are significantly smaller in proportion to their number of speakers. Minority languages, and especially oral languages without an extensive written tradition, are virtually invisible to the data-scraping processes upon which current models rely. The digitisation of the world is not uniform; it is the product of specific historical investments, of institutions that decided what to archive and what not to, and of legal frameworks that permitted or impeded extraction.
Segunda. Acceso bajo licencia compatible. Aun cuando exista contenido digitalizado, no todo está disponible bajo condiciones que permitan su incorporación a datasets de entrenamiento. La doctrina del “fair use” estadounidense ha funcionado de facto como permiso amplio para extracción masiva de contenidos publicados en internet. Otras jurisdicciones —la Unión Europea, varios países del Sur global— mantienen marcos más restrictivos. El efecto operativo de esta asimetría jurídica es perverso: cuanto más permisivo es el marco jurídico de un país respecto a la extracción, más contenidos de ese país acaban en los corpus de entrenamiento; cuanto más protector, menos. La protección legal de la producción cultural propia se traduce, paradójicamente, en exclusión del horizonte algorítmico.
Third. Geographical location of development. The companies that develop mass-market generative models are located in few countries and cities: San Francisco, Seattle, Beijing, Hangzhou, and certain European enclaves. Their teams for development, evaluation, fine-tuning, and testing operate from these geographies. Decisions regarding what to include, what to exclude, what to label as problematic, and what to adjust through human reinforcement are taken from these contexts. Even where there is an explicit desire to incorporate cultural diversity—as has been the case in several recent initiatives—the horizon from which it is evaluated what is diverse, what is representative, and what is offensive remains culturally localised. Diversity incorporated under Western evaluation remains, ultimately, diversity legitimised by a Western gaze.
These three reasons are not neutral in relation to one another. They reinforce each other. Unequal digitisation produces unequal corpora. Unequal corpora operated from specific geographies produce models with specific biases. Models with specific biases are presented as neutral in global markets. Global markets assimilate these biases as the new zero point. Subsequent corpora, partially fed by the outputs of current models, incorporate this already consolidated zero point. The cycle closes.
Llamar a esto “sesgo” subestima lo que está ocurriendo. “Sesgo” sugiere desviación corregible respecto a un eje neutro. Lo que ocurre con la IA generativa es construcción activa del eje. No es un sistema sesgado: es un sistema que produce, a escala global y a velocidad sin precedentes, un eje cultural específico que después se reconoce a sí mismo como universal.
4. Why it matters: double, not additive, gap
The consequences of this asymmetry operate on two planes that do not add up arithmetically, but rather multiply.
Economic level. UNESCO data document that the majority of the benefits of global cultural trade are concentrated in a few high-income countries. Although countries in the Global South doubled their exports of cultural goods in recent years, they continue to represent little more than 20% in digital cultural services. The projected 24% fall in musical creators' income by 2028 that I noted in The Price of the Model is not distributed uniformly among all creators worldwide. It falls more heavily upon the most vulnerable economies, upon creators with less capacity for collective bargaining, upon cultural sectors with weaker institutional infrastructure. Creators in the South were already at a disadvantage in the global cultural market; generative AI amplifies that disadvantage rather than correcting it.
Plano simbólico. Aquí el desplazamiento es más insidioso. Jill Walker Rettberg, en Issues in Science and Technology (2024), advierte que sin intervenciones específicas la IA generativa tenderá a “reducir la diversidad cultural”, uniformando la expresión humana según patrones predominantemente estadounidenses incorporados en los corpus. Pone el ejemplo del cuento noruego Cardamom Town, narrativa local con tradición específica, que podría quedar sepultado si los chatbots empiezan a regar sus respuestas con narrativas estadounidenses homogeneizadas. Multiplicado a escala global, esto significa la pérdida progresiva de patrimonio narrativo, estético y simbólico no por destrucción material —los archivos no desaparecen— sino por invisibilización algorítmica. Lo que la IA no reproduce, deja de circular. Lo que deja de circular, se atrofia. Lo que se atrofia durante una generación, se vuelve irrecuperable como referencia viva, aunque permanezca como objeto de archivo.
The multiplication between the two planes operates as follows. A creator from the Global South has less visibility in the dominant generative market because the aesthetics of their tradition are not the aesthetics that the model reproduces with fluency. This reduces their economic access to the global circuit. Reduced economic presence reduces their capacity to exert pressure on platforms, all of which are operated from distant jurisdictions. Reduced political pressure reduces the probability that platforms will invest in improving the representation of their tradition. Reduced investment perpetuates the asymmetry in the corpus. And so on.
It is not a symmetrical vicious circle. It is a spiral. Each turn aggravates the asymmetry of the previous one. And it operates within short timeframes: what took decades or centuries for historical cultural processes occurs in months under the current regime.
5. The language of diversity is not enough
El documento de UNESCO al que vengo refiriéndome propone medidas razonables: datos culturales como bien común, plurilingüismo, transparencia en datasets, colaboración internacional. Estas propuestas operan en la dirección correcta. Pero su lenguaje —el lenguaje de la “diversidad cultural” como bien que debe protegerse— tiene una limitación analítica importante que conviene nombrar.
The diversity framework presupposes that the issue is additive: that the problem is the lack of presence of non-dominant cultural traditions in the systems, and that the solution lies in adding those traditions to the existing corpus. This produces quota-style policies: x% of work in minority languages, y% of diverse representations, z% of content from the Global South. Such policies are better than their absence. But they do not address the central problem.
El problema central no es que falten contenidos diversos. Es que la arquitectura entera del sistema —desde la elección de qué datos extraer hasta cómo se evalúan los outputs, pasando por qué constituye “buena calidad” en un modelo— está construida desde una posición específica. Añadir contenidos diversos al corpus sin cuestionar la posición desde la cual se evalúan esos contenidos produce “diversidad asimilada”: tradiciones no dominantes incorporadas al sistema bajo los criterios de evaluación del sistema mismo. Es inclusión sin transformación de la regla de inclusión.
Para ilustrar el problema: incluir poesía en quechua en los corpus de entrenamiento de un modelo grande mejora cuantitativamente la representación lingüística. Pero si los criterios mediante los cuales el modelo aprende a generar “buena poesía” están entrenados sobre tradición occidental —escansión silábica, rima, métrica, géneros canónicos de la poesía europea—, el modelo aprenderá a generar lo que parece poesía en quechua según los criterios estéticos del castellano o del inglés. La diversidad de la lengua se incorpora; la diversidad del régimen estético no. El resultado es asimilación tecnificada: poesía quechua aparente, evaluada desde fuera de la tradición que la nombra.
Resolving this requires something other than increasing diversity. It requires questioning the architecture of evaluation itself. And that is conceptual and political work, not technical.
6. What they are doing, and where their limits lie
There are genuine initiatives attempting to address the problem from non-Western positions. It is appropriate to mention them with honesty: neither to idealise them nor to minimise them.
Masakhane is a distributed research community across Africa working on natural language processing for African languages. It brings together researchers in South Africa, Nigeria, Kenya, Ghana, Senegal, and other countries, develops datasets in languages such as Yoruba, Igbo, Swahili, and Amharic, and publishes work in international academic venues. It is one of the most robust initiatives from the Global South in this field.
Te Hiku Media in Aotearoa-New Zealand has developed speech recognition systems for te reo Māori under explicit principles of indigenous data sovereignty. Its governance model—where data belongs to the Māori community and cannot be used without their consent—is an international benchmark for how AI can be developed while respecting cultural power structures distinct from hegemonic ones.
Ai4D and Lelapa AI are African initiatives working on AI infrastructure conceived from and for African contexts, with international public and local private funding.
Latin American hubs. Various efforts in Argentina, Brazil, Mexico, Colombia, and Chile are working on the evaluation of linguistic biases in American Spanish and Brazilian Portuguese within large models, as well as the development of corpora for Latin American indigenous languages.
These initiatives are important and deserve to be recognised. However, it is also honest to acknowledge their structural limitations. They all operate with small budgets compared to large AI corporations, with partial dependence on funding originating in the North, with computational infrastructure rented from the very companies whose hegemony they seek to counterbalance, and with publication in academic venues whose evaluation criteria are located on the same cultural axis as the problem itself. These are real material conditions that cannot be resolved through mere voluntarism.
Serious intervention in the North/South asymmetry problem in generative AI requires something that these initiatives, however robust they may be, cannot achieve alone: changing the political economy of global computational infrastructure. That is to say, changing who controls large-scale computing resources, who defines technical standards, who funds research, and who captures the rent. Without that fundamental transformation, initiatives from the South operate as a rearguard action: necessary and valuable, but structurally insufficient to reverse the asymmetry.
7. The consolidation of 2026: power is concentrated while diversity is debated
While the public conversation regarding AI and culture has advanced in the language of diversity and representation, the actual market has consolidated its architecture in a direction that is worth examining with precision. Four data points from 2026 are particularly revealing.
On 31 March 2026, OpenAI closed a funding round of 122 billion dollars, presenting it as leverage for computational infrastructure, distribution, and deployment services. On 11 May, it launched OpenAI Deployment Company, a specific division to place implementation teams within enterprise clients, and declared that it had surpassed one million enterprise users of its products. Anthropic allocated 100 million dollars in March 2026 to its partner network. On 9 March, Microsoft announced the general availability of its enterprise agent platform; in April, Google pushed its Gemini Enterprise Agent Platform. The signal is not merely one of investment: it is one of consolidating control over the deployment, governance, and operation layer of systems on a global scale.
Europe has simultaneously landed its regulatory and industrial capacity agenda. The European Commission announced in April 2026 that 19 AI factories are already operating on the continent, with gigafactories in preparation. The AI Act begins its enforcement on 2 August 2026; the obligations regarding general-purpose models entered into force a year earlier. It is a genuine regulatory will, without precedent in other jurisdictions at this scale.
The important point here, for the argument of this text, is what these data reveal in their geographical overlap. The concentration of power in deployment, distribution, and operation at scale is occurring in the same poles that already dominated the corpus: the United States first, China second, and Europe in an ambivalent position—possessing strong regulatory capacity, intermediate industrial capacity, and high infrastructural dependence. The Global South does not feature in these consolidation figures, neither as a headquarters, nor as a massive deployment actor, nor as a governance agent. The initiatives mentioned in the previous section—Masakhane, Te Hiku Media, Lelapa AI—operate on a different plane of magnitude.
La consecuencia es que mientras los marcos internacionales discuten cómo asegurar la “diversidad cultural” en los sistemas de IA generativa, el poder material para definir qué se considera buena IA, qué se considera despliegue responsable, qué se considera gobernanza efectiva, se está concentrando en muy pocos actores geográficamente localizados. La diversidad se discute; la infraestructura se construye. Y la infraestructura se construye más rápido y con más recursos que la discusión.
This is not an apparent contradiction: it is the concrete mechanism by which hegemony is reproduced in digital systems. Not through direct political imposition, but through speed of deployment, scale of investment, and the capture of the operational bottleneck, while the conversation regarding alternatives advances at a different cadence, with different budgets, and from different geographies.
8. The operational question: who, from where, about what
I reformulate the question posed by the title of this text: who trains the world. If the North/South asymmetry in generative AI is not an accident correctable through technical adjustments, but an active architecture of cultural power, then the operational question breaks down into three concrete questions that any serious public policy on this matter should answer.
First. Who decides what enters the training corpora. Today the answer is: the technical teams of the companies developing the models, without a generalised obligation for transparency regarding the criteria applied or the consent of the communities whose cultural productions are incorporated. This is unsustainable. A responsible generative AI infrastructure would require: mandatory transparency regarding the geographical, linguistic, and cultural composition of the corpora; effective mechanisms for consent from the communities whose productions are incorporated; and independent audits regarding cultural representation in the outputs.
Segunda. Desde dónde se evalúan los outputs. Hoy, los procesos de “refinamiento mediante feedback humano” —que ajustan los modelos después del entrenamiento inicial para hacerlos más útiles, seguros y aceptables— operan principalmente desde geografías localizadas, con personal contratado en contextos específicos, según criterios definidos por equipos que no son culturalmente representativos del público global del modelo. Esto debería cambiar: los procesos de evaluación deberían incluir, de forma estructural y no decorativa, agentes culturales situados en las geografías cuya representación está en juego, con capacidad real de modificar criterios y no solo de aprobar resultados.
Third. Regarding the materials utilised and the distribution of their yield. This connects directly to the argument presented in The Price of the Model. If training corpora include cultural production from the Global South—and they do, albeit in a fragmentary manner—the legal and economic frameworks must ensure that the resulting yield returns, in significant part, to the communities whose productions enabled the model to be trained. Today, this occurs almost not at all. Extraction is free; exploitation, private.
Estas tres líneas de intervención no son novedosas. Distintas instituciones, desde UNESCO hasta organizaciones de la sociedad civil del Sur global, las han venido articulando con mayor o menor precisión. Lo que falta no es diagnóstico. Lo que falta es voluntad política internacional para imponerlas, y esa voluntad solo emergerá si los costes simbólicos y políticos de no hacerlo se vuelven más altos que los costes económicos de hacerlo. Mientras la conversación pública sobre IA siga reducida a debate sobre “sesgos” y “diversidad” sin tocar la cuestión estructural de quién posee y controla la infraestructura, esos costes simbólicos no aumentarán lo suficiente.
9. Conclusion
Three conclusions follow from the analysis.
The first is descriptive. The North/South asymmetry in generative AI is not a problem of a lack of diversity in the data, nor an accident correctable through technical adjustments. It is the active operation by which a specific cultural configuration — predominantly Anglo-American — is constituted as the zero point of meaning in systems that present themselves as universal. The industrial consolidation of 2026 — massive investment rounds, enterprise agent platforms, concentration of deployment in very few actors — reinforces this material operation rather than counteracting it. Hegemony does not operate under the name of hegemony: it operates under the name of neutrality. This is historically new in terms of scale and speed, although conceptually it continues the logic of the colonial cultural order under new media.
La segunda es metodológica. El lenguaje institucional de la “diversidad cultural” que dominan los marcos UNESCO y las políticas internacionales actuales es necesario pero insuficiente. Presupone que el problema es aditivo —faltan contenidos diversos— cuando en realidad es estructural —la arquitectura entera del sistema está construida desde una posición específica que se reproduce a sí misma incluso cuando incorpora contenidos diversos—. Sin desplazar el marco conceptual del problema, las políticas resultantes producirán inclusión asimilada, no transformación.
The third is ethical. Responsibility for the cultural asymmetry in generative AI does not lie with the creator in the Global South who chooses to use Western tools due to an absence of alternatives, nor with the community whose production is assimilated without consent, nor with the state in the South that lacks sufficient computational infrastructure to build its own alternatives. It lies with the actors who design, finance, and operate these infrastructures from specific geographies with the genuine capacity to choose how to do so. And, secondarily, it lies with the international political order that permits these actors to operate without effective obligations of reciprocity, consent, or redistribution.
Whoever trains the world, today, trains it in the image and likeness of very few places. And the trained world tends, by the very mechanics of the system, to recognise itself in that image as if it were its own. Recognising this is not nostalgia or identity-based resistance. It is the prerequisite for any international cultural policy on generative AI to have meaning. Whilst public discourse remains trapped in the language of correctable biases and additive diversity, the decisions that truly move the needle—who owns the infrastructure, who captures the yield, who defines the evaluation criteria, who decides what is incorporated and what is excluded—will continue to be made far from the territories whose culture is being reorganised.
On open conversation
This text concludes, for now, a series of four pieces on digital infrastructures that I have been developing in this public notebook: sustainability and ethical consumption, institutional validation and artistic experience, the displacement of value in cultural work, and now the geographical distribution of cultural power in the era of generative AI. All four share the same underlying thesis: in contemporary digital systems, the decisive factor is not individual conduct but infrastructural design, and ethical responsibility for its consequences is distributed proportionally to the power of those who design, finance, and operate these systems.
If anyone wishes to intervene from the perspectives of postcolonial studies, the political economy of culture, AI studies, the Global South projects mentioned here, or any other relevant perspective, this notebook remains open.
Sources
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Esteban Ruiz, J. A. When the Frame Paints the Picture. Public notebook at juanesteban.art, 2026.
Esteban Ruiz, J. A. The Price of the Model. Public notebook at juanesteban.art, 2026.
Series · Infrastructures
This piece forms part of a six-part series examining the influence of digital and cultural systems upon art. The overarching thesis posits that within contemporary systems, the decisive factor is not individual conduct but rather infrastructural design. The series commences with the capture mechanisms of these systems and concludes with their exclusionary practices.