Recently, a copyright infringement suit was filed by ANI Media against OpenAI in the Delhi High Court—the first such case against OpenAI outside the United States. OpenAI’s immediate response in the first hearing – informing the court they had already blocklisted ANI’s domains from future training data – might appear as a straightforward compromise. However, this seemingly minor technical decision reveals deeply concerning implications about how opt-out mechanisms could systematically disadvantage the developing world in AI development.
The significance extends far beyond the immediate dispute over copyright. At its core, this is about who gets to shape the architecture that will increasingly mediate our global digital infrastructure. AI systems fundamentally learn to understand and interact with the world through their training data. When major segments of the developing world’s digital content get excluded – whether through active opt-outs or passive inability to effectively participate – we risk creating AI systems that not only reflect but actively amplify existing global inequities.
This piece will examine how the technical architecture of opt-out mechanisms interacts with existing power structures and market dynamics. However, note that by arguing against the opt-out mechanism, I do not imply that publishers do not have a copyright infringement claim against AI companies.
II. The Systemic Impact of Opt-Out Architecture
OpenAI’s response to ANI’s lawsuit reveals several critical dynamics that shape the broader impact of opt-out mechanisms in AI development. The first key insight comes from understanding the technical futility of domain-based blocking as a protective measure. The architecture of the modern internet means that content rarely stays confined to its original domain. News articles propagate across multiple platforms, get archived by various services, and appear in countless derivative works. Consider ANI’s news content: a single story might simultaneously exist on their website, in news aggregators, across social media platforms, in web archives, and in countless other locations. This multiplication of content makes domain blocking more performative than protective.
What makes this particularly problematic is the uneven impact of opt-out requests. Large AI companies, with their extensive infrastructure and resources, are better positioned to navigate these restrictions. They can access similar content through alternative channels, such as partnerships, licensing agreements, or derivative data sources, while still appearing to comply with opt-out requirements. In contrast, smaller players and new entrants—especially those from developing nations—often lack the resources to identify or access equivalent content through alternative pathways. This dynamic effectively entrenches the dominance of established players, creating barriers that disproportionately hinder smaller competitors. This creates what economists recognize as a form of regulatory capture through technical standards—the rules appear neutral but systematically advantage established players.
III. Market Power and Global Inequity
The structural disadvantages created by opt-out mechanisms manifest through multiple channels, compounding existing market dynamics. Early AI developers, predominantly Western companies, leveraged the “wild west” period of AI development, during which unrestricted datasets were readily available. This access allowed them to develop proprietary algorithms, cultivate dense pools of talent, and collect extensive user interaction data. These first-mover advantages have created architectural and operational moats that generate compounding returns, ensuring that even in an environment with reduced access to training data, these companies maintain a significant edge over newer competitors.
This architectural superiority drives a self-reinforcing cycle that is particularly challenging for new entrants to overcome:
Superior models extract greater value from limited training data.
Enhanced performance attracts more users and developers.
Larger user bases generate richer interaction data.
Sophisticated interaction data enables further model improvements.
Improved models continue to attract more users, perpetuating the cycle.
The establishment of opt-out mechanisms as a de facto standard adds another layer of complexity. Participating in modern AI development under such regimes requires significant infrastructure, including:
Content filtering systems to identify and respect opted-out sources.
Compliance monitoring mechanisms across diverse jurisdictions.
Technical systems for verifying content sources and managing data provenance.
Alternative data sourcing infrastructure to replace opted-out data.
As Akshat Agarwal has argued, OpenAI’s opt-out policy, while framed as an ethical gesture, effectively cements its dominance by imposing disproportionate burdens on emerging competitors. Newer AI companies face the dual challenge of building comparable systems with restricted access to training data while contending with market standards set by established players.
The implications are profound. OpenAI’s approach has not only widened the gap between market leaders and new entrants but has also reshaped the trajectory of AI development itself. By normalizing opt-out mechanisms and forging exclusive partnerships for high-quality content, OpenAI has engineered a self-reinforcing system of technical, regulatory, and market advantages. Without targeted regulatory intervention to dismantle these reinforcing feedback loops, the future of AI risks being dominated by a few early movers, stifling both competition and innovation.
For AI initiatives in the developing world, these barriers are particularly burdensome. Established players can absorb compliance costs through existing infrastructure and distribute them across vast user bases, but smaller or resource-constrained initiatives bear a disproportionately higher burden. This creates what is effectively a tax on innovation, disproportionately affecting those least equipped to bear its weight and further entrenching global inequities in AI development.
IV. The Hidden Costs of Biased Training
The consequences of opt-out mechanisms extend far beyond market dynamics into the fundamental architecture of AI systems, which can be described as a form of “cognitive colonialism.” Evidence of systematic bias is already emerging in current AI systems, manifesting through both direct performance disparities and more subtle forms of encoded cultural assumptions.
Research indicates that current large language models exhibit significant cultural bias and perform measurably worse when tasked with understanding non-Western contexts. For example, in Traditional Chinese Medicine examinations, Western-developed language models achieved only 35.9% accuracy compared to 78.4% accuracy from Chinese-developed models. Similarly, another study found that AI models portrayed Indian cultural elements from an outsider’s perspective, with traditional celebrations being depicted as more colorful than they actually are, and certain Indian subcultures receiving disproportionate representation over others.
This representational bias operates through multiple reinforcing mechanisms:
Primary Training Bias: Training data predominantly consists of Western contexts, limiting understanding of non-Western perspectives.
Performance Optimization: Superior performance on Western tasks leads to higher adoption in Western markets.
Feedback Amplification: Increased Western adoption generates more interaction data centered on Western contexts.
Architectural Lock-in: System architectures become optimized for Western use cases due to skewed data and priorities.
Implementation Bias: Deployed systems reshape local contexts to align with their operational assumptions.
The opt-out mechanism exacerbates these issues by creating a systematic skew in training data that compounds over time. As publishers from developing regions increasingly opt out—whether intentionally or due to logistical barriers—the training data grows progressively more Western-centric.
A surprising study found that even monolingual Arabic-specific language models trained exclusively on Arabic data exhibited Western bias. This occurred because portions of the pre-training data, despite being in Arabic, frequently discussed Western topics. Interestingly, local news and Twitter data in Arabic were found to have the least Western bias. In contrast, multilingual models exhibited stronger Western bias than unilingual ones due to their reliance on diverse, yet predominantly Western-influenced, datasets.
Addressing these biases through post-training interventions alone is challenging. If regional news organizations, such as ANI, continue to opt out of contributing their data for AI training, frontier models risk becoming increasingly biased toward Western contexts. This would result in AI systems that depict non-Western cultures from an outsider’s perspective, further marginalizing diverse viewpoints.
The implications for global AI development are profound. As these systems mediate our interactions with digital information and shape emerging technologies, their embedded biases reinforce a form of technological determinism that systematically disadvantages non-Western perspectives and needs.
V. Beyond Individual Opt-Outs: Systemic Solutions
The challenge of creating more equitable AI development requires moving beyond the false promise of individual opt-out rights to develop systematic solutions that address underlying power asymmetries. This requires acknowledging a fundamental tension: the need to protect legitimate creator rights while ensuring AI systems develop with sufficiently diverse training data to serve global needs. The current opt-out framework attempts to resolve this tension through individual choice mechanisms, but as the above analysis has shown, this approach systematically favors established players while creating compound disadvantages for developing world participants.
A more effective approach would operate at multiple levels of the system simultaneously:
First, at the technical level, we need mandatory inclusion frameworks that ensure AI training data maintains sufficient diversity:
Required diversity quotients in training datasets, specifically ensuring representation from developing world contexts
Technical standards that measure and enforce cross-cultural performance metrics
Architectural requirements that explicitly optimize for performance across diverse contexts
However, mandatory inclusion alone is insufficient without corresponding economic frameworks. We need compensation mechanisms that fairly value data contributions while accounting for power asymmetries in global markets:
Progressive compensation schemes that provide higher rates for underrepresented sources
Collective licensing frameworks that allow developing world creators to aggregate bargaining power
Investment requirements tied to data usage that support local AI development capacity
The infrastructure layer presents another crucial intervention point:
Direct support for AI research institutions in developing nations
Technology transfer programs that share architectural insights, not just data
Collaborative training approaches that preserve data sovereignty while enabling knowledge sharing
We need new governance models that move beyond the current paradigm of individual property rights in data:
International frameworks for managing collective digital resources
New approaches to data commons that preserve cultural sovereignty
Governance structures that ensure balanced representation in technical standard-setting
VI. Conclusions and Implications
Moving forward requires recognizing that the challenges posed by opt-out mechanisms cannot be addressed through incremental adjustments to current frameworks. Instead, we need new governance models that actively correct for power asymmetries rather than encoding them.
The alternative—allowing current opt-out frameworks to shape the architecture of emerging AI systems—risks encoding current global power relationships into the fundamental infrastructure of our digital future. This would represent not just a missed opportunity for more equitable technological development, but a form of technological colonialism that could persist and amplify for generations to come.
AI Training Opt-Outs Reinforce Global Power Asymmetries
I. Introduction
Recently, a copyright infringement suit was filed by ANI Media against OpenAI in the Delhi High Court—the first such case against OpenAI outside the United States. OpenAI’s immediate response in the first hearing – informing the court they had already blocklisted ANI’s domains from future training data – might appear as a straightforward compromise. However, this seemingly minor technical decision reveals deeply concerning implications about how opt-out mechanisms could systematically disadvantage the developing world in AI development.
The significance extends far beyond the immediate dispute over copyright. At its core, this is about who gets to shape the architecture that will increasingly mediate our global digital infrastructure. AI systems fundamentally learn to understand and interact with the world through their training data. When major segments of the developing world’s digital content get excluded – whether through active opt-outs or passive inability to effectively participate – we risk creating AI systems that not only reflect but actively amplify existing global inequities.
This piece will examine how the technical architecture of opt-out mechanisms interacts with existing power structures and market dynamics. However, note that by arguing against the opt-out mechanism, I do not imply that publishers do not have a copyright infringement claim against AI companies.
II. The Systemic Impact of Opt-Out Architecture
OpenAI’s response to ANI’s lawsuit reveals several critical dynamics that shape the broader impact of opt-out mechanisms in AI development. The first key insight comes from understanding the technical futility of domain-based blocking as a protective measure. The architecture of the modern internet means that content rarely stays confined to its original domain. News articles propagate across multiple platforms, get archived by various services, and appear in countless derivative works. Consider ANI’s news content: a single story might simultaneously exist on their website, in news aggregators, across social media platforms, in web archives, and in countless other locations. This multiplication of content makes domain blocking more performative than protective.
What makes this particularly problematic is the uneven impact of opt-out requests. Large AI companies, with their extensive infrastructure and resources, are better positioned to navigate these restrictions. They can access similar content through alternative channels, such as partnerships, licensing agreements, or derivative data sources, while still appearing to comply with opt-out requirements. In contrast, smaller players and new entrants—especially those from developing nations—often lack the resources to identify or access equivalent content through alternative pathways. This dynamic effectively entrenches the dominance of established players, creating barriers that disproportionately hinder smaller competitors. This creates what economists recognize as a form of regulatory capture through technical standards—the rules appear neutral but systematically advantage established players.
III. Market Power and Global Inequity
The structural disadvantages created by opt-out mechanisms manifest through multiple channels, compounding existing market dynamics. Early AI developers, predominantly Western companies, leveraged the “wild west” period of AI development, during which unrestricted datasets were readily available. This access allowed them to develop proprietary algorithms, cultivate dense pools of talent, and collect extensive user interaction data. These first-mover advantages have created architectural and operational moats that generate compounding returns, ensuring that even in an environment with reduced access to training data, these companies maintain a significant edge over newer competitors.
This architectural superiority drives a self-reinforcing cycle that is particularly challenging for new entrants to overcome:
Superior models extract greater value from limited training data.
Enhanced performance attracts more users and developers.
Larger user bases generate richer interaction data.
Sophisticated interaction data enables further model improvements.
Improved models continue to attract more users, perpetuating the cycle.
The establishment of opt-out mechanisms as a de facto standard adds another layer of complexity. Participating in modern AI development under such regimes requires significant infrastructure, including:
Content filtering systems to identify and respect opted-out sources.
Compliance monitoring mechanisms across diverse jurisdictions.
Technical systems for verifying content sources and managing data provenance.
Alternative data sourcing infrastructure to replace opted-out data.
As Akshat Agarwal has argued, OpenAI’s opt-out policy, while framed as an ethical gesture, effectively cements its dominance by imposing disproportionate burdens on emerging competitors. Newer AI companies face the dual challenge of building comparable systems with restricted access to training data while contending with market standards set by established players.
The implications are profound. OpenAI’s approach has not only widened the gap between market leaders and new entrants but has also reshaped the trajectory of AI development itself. By normalizing opt-out mechanisms and forging exclusive partnerships for high-quality content, OpenAI has engineered a self-reinforcing system of technical, regulatory, and market advantages. Without targeted regulatory intervention to dismantle these reinforcing feedback loops, the future of AI risks being dominated by a few early movers, stifling both competition and innovation.
For AI initiatives in the developing world, these barriers are particularly burdensome. Established players can absorb compliance costs through existing infrastructure and distribute them across vast user bases, but smaller or resource-constrained initiatives bear a disproportionately higher burden. This creates what is effectively a tax on innovation, disproportionately affecting those least equipped to bear its weight and further entrenching global inequities in AI development.
IV. The Hidden Costs of Biased Training
The consequences of opt-out mechanisms extend far beyond market dynamics into the fundamental architecture of AI systems, which can be described as a form of “cognitive colonialism.” Evidence of systematic bias is already emerging in current AI systems, manifesting through both direct performance disparities and more subtle forms of encoded cultural assumptions.
Research indicates that current large language models exhibit significant cultural bias and perform measurably worse when tasked with understanding non-Western contexts. For example, in Traditional Chinese Medicine examinations, Western-developed language models achieved only 35.9% accuracy compared to 78.4% accuracy from Chinese-developed models. Similarly, another study found that AI models portrayed Indian cultural elements from an outsider’s perspective, with traditional celebrations being depicted as more colorful than they actually are, and certain Indian subcultures receiving disproportionate representation over others.
This representational bias operates through multiple reinforcing mechanisms:
Primary Training Bias: Training data predominantly consists of Western contexts, limiting understanding of non-Western perspectives.
Performance Optimization: Superior performance on Western tasks leads to higher adoption in Western markets.
Feedback Amplification: Increased Western adoption generates more interaction data centered on Western contexts.
Architectural Lock-in: System architectures become optimized for Western use cases due to skewed data and priorities.
Implementation Bias: Deployed systems reshape local contexts to align with their operational assumptions.
The opt-out mechanism exacerbates these issues by creating a systematic skew in training data that compounds over time. As publishers from developing regions increasingly opt out—whether intentionally or due to logistical barriers—the training data grows progressively more Western-centric.
A surprising study found that even monolingual Arabic-specific language models trained exclusively on Arabic data exhibited Western bias. This occurred because portions of the pre-training data, despite being in Arabic, frequently discussed Western topics. Interestingly, local news and Twitter data in Arabic were found to have the least Western bias. In contrast, multilingual models exhibited stronger Western bias than unilingual ones due to their reliance on diverse, yet predominantly Western-influenced, datasets.
Addressing these biases through post-training interventions alone is challenging. If regional news organizations, such as ANI, continue to opt out of contributing their data for AI training, frontier models risk becoming increasingly biased toward Western contexts. This would result in AI systems that depict non-Western cultures from an outsider’s perspective, further marginalizing diverse viewpoints.
The implications for global AI development are profound. As these systems mediate our interactions with digital information and shape emerging technologies, their embedded biases reinforce a form of technological determinism that systematically disadvantages non-Western perspectives and needs.
V. Beyond Individual Opt-Outs: Systemic Solutions
The challenge of creating more equitable AI development requires moving beyond the false promise of individual opt-out rights to develop systematic solutions that address underlying power asymmetries. This requires acknowledging a fundamental tension: the need to protect legitimate creator rights while ensuring AI systems develop with sufficiently diverse training data to serve global needs. The current opt-out framework attempts to resolve this tension through individual choice mechanisms, but as the above analysis has shown, this approach systematically favors established players while creating compound disadvantages for developing world participants.
A more effective approach would operate at multiple levels of the system simultaneously:
First, at the technical level, we need mandatory inclusion frameworks that ensure AI training data maintains sufficient diversity:
Required diversity quotients in training datasets, specifically ensuring representation from developing world contexts
Technical standards that measure and enforce cross-cultural performance metrics
Architectural requirements that explicitly optimize for performance across diverse contexts
However, mandatory inclusion alone is insufficient without corresponding economic frameworks. We need compensation mechanisms that fairly value data contributions while accounting for power asymmetries in global markets:
Progressive compensation schemes that provide higher rates for underrepresented sources
Collective licensing frameworks that allow developing world creators to aggregate bargaining power
Investment requirements tied to data usage that support local AI development capacity
The infrastructure layer presents another crucial intervention point:
Direct support for AI research institutions in developing nations
Technology transfer programs that share architectural insights, not just data
Collaborative training approaches that preserve data sovereignty while enabling knowledge sharing
We need new governance models that move beyond the current paradigm of individual property rights in data:
International frameworks for managing collective digital resources
New approaches to data commons that preserve cultural sovereignty
Governance structures that ensure balanced representation in technical standard-setting
VI. Conclusions and Implications
Moving forward requires recognizing that the challenges posed by opt-out mechanisms cannot be addressed through incremental adjustments to current frameworks. Instead, we need new governance models that actively correct for power asymmetries rather than encoding them.
The alternative—allowing current opt-out frameworks to shape the architecture of emerging AI systems—risks encoding current global power relationships into the fundamental infrastructure of our digital future. This would represent not just a missed opportunity for more equitable technological development, but a form of technological colonialism that could persist and amplify for generations to come.