After the Noise, AI as a Development Issue

By 2025, the digital world had reached a decisive point. It stopped being something we logged onto and became something woven into how we search, learn, work, and decide what to trust. This shift happened fast, and largely without the social, political, or institutional structures needed to manage its consequences.

AI now sits at the center of this acceleration. It is no longer a background tool or a future promise. It actively shapes information flows, economic opportunity, and public belief. The problem is not the speed of AI development itself, but the fact that existing systems of governance, accountability, and public oversight were never designed to keep up with it.

One clear sign of this imbalance is the collapse of visual trust. AI-generated images have moved from crude and obvious to convincingly real. Tools like Google’s Nana Banana Pro can now produce flawless photographs of events that never happened  ¹. When images can no longer function as evidence by default, journalism, human rights documentation, and public debate all become harder to sustain. The resulting uncertainty is not just a media problem. It is a governance problem.

Yet AI is still treated primarily as a commercial product rather than a form of social infrastructure. Innovation is used as a shield to avoid building the rules that should govern it. Its development is driven by competition, market dominance, and profit, not by collective need or long-term public benefit. This reflects a broader neoliberal logic in which technological progress is assumed to be inherently positive, regardless of who controls it or who benefits from it.

This is why AI can no longer be framed as a purely technical transformation. It is a development issue. Its risks and rewards are unevenly distributed, with countries in the Global South facing different vulnerabilities than those in the Global North. The central question is no longer whether AI is good or bad, but who governs it, whose priorities shape it, and whether its growth serves public welfare or private power.

This article argues that the current unregulated AI trajectory cannot hold. It discusses why its risks are unevenly distributed, placing countries in the Global South at a structural disadvantage, and why future models of global development must treat AI governance as a central priority rather than an afterthought.

The Problem We Keep Forgetting to Name

We have built a digital ecosystem that shapes what we see, believe, and do. Platforms can censor content when it serves their interests, yet largely fail to address the harms caused by synthetic media that distorts public perception. As we have already been articulating in The Instagram Algorithm Knows Too Much already pointed out, this is not just a platform issue. It is a structural one  ².

We often frame the problem as platform responsibility. The truth is bigger. When the line between reality and fabrication fades, personal judgment erodes. That erosion affects democratic decision making, childhood development, and the entire information environment people rely on  ³.

This development has now reached a critical point. Influencers pop up on feeds with AI tutorials, get-rich-quick promises, and endless shortcuts to create content, make money, automate workflows, or build entire identities with a few clicks. It feels like everyone is selling a method, a hack, a pathway to instant success powered by the latest tool. Even Meta’s own AI campaigns run through influencers, telling people to join the system and start creating.

Marketing has always been an important part of the digital economy. But the intensity at which algorithms push AI tools, opportunities, and promises of fast wealth feels different. It signals a kind of desperation, as if the platforms want users not only to adopt AI but to absorb it as a way of life. That constant pressure is shaping expectations around creativity, productivity, and identity long before society can understand the consequences.

This intensity is not accidental. It reflects the economic constraints built into the current AI model, where high development costs and fierce competition make rapid adoption a necessity rather than a choice. Platforms depend on scale, data, and user dependency to justify investment and secure market position  ⁴. As a result, AI is promoted not simply as a useful tool, but as something people must integrate into their work and identity to remain productive or relevant. That pressure is reinforced by a broader capitalist system that rewards speed and growth over reflection, making this push feel inevitable unless external constraints intervene  ⁵.

How We Reached This Point

The logic that brought us here is rooted in a neoliberal mindset. The market is left free to innovate. The government steps back.  What began as a policy stance has evolved into a well-financed push against regulation, widely adopted in the United States and now gaining ground across Europe. Businesses are expected to grow, compete, and deliver progress, and the wealth created at the top is supposed to trickle down to society. That is the theory, but in practice, it has barely materialized  ⁶. We can see it in rising inequality, in concentrated corporate power, and in political choices that often feel disconnected from the needs of the people who elected those leaders  ⁷.

AI has developed inside this same system. Its growth is encouraged as long as it fuels competition and drives investment. The sector is treated as another engine of innovation, not as a force that reshapes behaviour, culture, labour, and public understanding of reality. The dangerous parts are acknowledged, studied, debated, but they are not completely structurally controlled. The market moves first. Governance reacts later, and often only when the consequences become too visible to ignore. In the meantime, millions are put at risk, as seen in the rapid rollout of tools like Grok and ChatGPT in education systems, where untested impacts are treated as acceptable collateral.

As a result, AI sits in a space where the incentives to scale are far stronger than the incentives to safeguard. Copyright, piracy, data ownership, algorithmic accountability, and labour exploitation were already complex before generative AI appeared. Now they are part of a system that evolves faster than the legal and economic frameworks meant to manage it  ⁸.

What Is Happening In India, AI as a Development Issue

The year 2025 also became the year global AI companies turned to India with aggressive expansion strategies. Free premium plans. Partnerships with telecom giants. Year-long access to advanced tools bundled with data packs  ⁹.

On the surface, these moves appear generous. In practice, they are calculated investments. India offers what few other markets can: scale, a young online population, linguistic and cultural diversity, and some of the cheapest data in the world. With more than 900 million internet users, India represents both a massive user base and a long-term bet on future dependency  ¹⁰(IBEF, n.d.).

Other large markets may rival India in size, but not in openness. China’s digital ecosystem is tightly regulated and largely closed to foreign AI firms  ¹¹. India, by contrast, offers an open and competitive digital market with relatively low regulatory barriers  ¹². Bundling AI tools with mobile data plans mirrors the strategy that once brought millions of Indians online through heavily discounted internet access. The logic is to encourage habitual use now, monetise later. Even if only a small fraction of users eventually pay, the volumes involved make the model attractive. More importantly, widespread use generates vast amounts of first-hand behavioural data. In a country as diverse as India, these data streams become invaluable training material for generative AI systems, producing use cases that can later be exported globally.

Consumers gain access. Companies gain data. The trade-off is rarely questioned because it feels convenient. Yet this is where India reveals a broader global pattern. The benefits of AI adoption are unevenly distributed. Those with digital literacy, stable connectivity, and platform awareness are positioned to extract value, while others become passive data sources in systems they barely understand. India already exhibits sharp inequalities in who benefits from technological change, and AI risks deepening this divide.

India has no dedicated AI law yet. The Digital Personal Data Protection Act (DPDP) 2023 is still awaiting implementation rules and does not address algorithmic responsibility  ¹³. This flexibility makes the country attractive for rapid deployment, something that would trigger compliance barriers in the EU with its EU AI Act  ¹⁴.

This does not mean India should shut the door to AI. It means that adoption without parallel growth in governance amplifies existing inequalities. India’s experience shows what is likely to happen elsewhere: AI spreads fastest where regulation is light, data is abundant, and populations are young, while protections lag behind. Seen this way, India is not an outlier, but a preview. It illustrates how unregulated AI can accelerate development for some while leaving others further behind, turning a technological transition into a development challenge.

India’s current AI landscape shows how some countries become playgrounds for new technology, offering scale and data that companies crave. Meanwhile, the Global North moves cautiously, highlighting a growing gap that leaves people in less regulated regions more exposed, something that calls for thoughtful coordination and cooperation.

The difference in regulatory capacity between regions shapes how AI companies operate. The Global North leans toward strict compliance frameworks. The Global South leans toward openness to investment and innovation. Both approaches make sense in their economic context, but they create an uneven global landscape that companies can navigate strategically  ¹⁵.

Why AI Is Now a Development Issue

At this point, it is no longer enough to treat AI as a technology story or a market trend. It has become a development issue. The speed at which AI is expanding, the way it shapes economies, education, labour markets, and governance structures, places it squarely within the terrain of global development. These are not impacts that can be managed through scattered national responses alone; they require structured international attention.

Several efforts are underway to introduce structure into global AI governance. One example is the OECD’s work on mapping AI infrastructure. By assessing the availability of public cloud compute infrastructure across countries, the OECD has taken a step toward making visible how unevenly the material foundations of AI are distributed. The methodology is transparent, cost-effective, and based on publicly available data. Applied fully, it offers policymakers a useful snapshot of domestic AI infrastructure and potential gaps in readiness, though it does not capture private or government-owned compute resources, nor does it directly assess governance or regulatory frameworks  ¹⁶.

Importantly, beyond OECD, a patchwork of global and regional frameworks has emerged. UNESCO’s Recommendation on the Ethics of Artificial Intelligence, articulates shared principles around human rights, fairness, accountability, and sustainability  ¹⁷. The Council of Europe’s Framework Convention on Artificial Intelligence goes further by creating a binding legal instrument focused on human rights, democracy, and the rule of law  ¹⁸. Multi-stakeholder initiatives such as the Global Partnership on AI, as well as dialogues convened by the International Telecommunication Union, aim to foster cooperation, knowledge-sharing, and more inclusive approaches to AI development  ^19 20.

Yet the existence of these frameworks has not translated into a coherent global response to the structural challenges AI creates. Most remain normative rather than enforceable, offering ethical guidance without mechanisms to shape how compute, capital, and data are concentrated. Governance approaches diverge sharply across geopolitical lines, reflecting different priorities around innovation, control, and regulation. Within the UN system itself, AI governance is fragmented across agencies, with limited coordination and uneven technical capacity  ²¹. As a result, global efforts often excel at setting principles, but struggle to influence the underlying political economy of AI.

Multiculturalism cooperation is frequently presented as a solution, yet partnerships with Big Tech are constrained by asymmetric power relations  ²². Corporate actors retain control over infrastructure, models, and deployment decisions, while public institutions rely on voluntary commitments and non-binding safeguards. The current available arrangements can promote dialogue, but they rarely address questions of dependency, long-term lock-in, or the unequal distribution of AI capabilities between countries.

This is precisely where multilateral development institutions could play a more meaningful role. By supporting countries that lack resources, coordination capacity, or bargaining power. As with the UN Sustainable Development Goals ²³, their influence would not lie in enforcement, but in agenda-setting. Frameworks shape expectations. Benchmarks define what counts as readiness. Norms signal what responsible development looks like in practice, not just in principle.

If AI is going to shape development trajectories for decades to come, then development institutions cannot remain peripheral to its governance. A balanced international approach could give countries a foundation to grow AI responsibly without stifling innovation, and without leaving nations with fewer resources at a structural disadvantage. If AI is going to shape development pathways, then development institutions and civil society should be part of shaping AI.

Conclusion

AI has become a central force in modern life, yet we still lack the systems needed to guide and contain its influence. Without structured governance, we risk deepening global inequality, weakening democratic processes, and losing our shared sense of what is real.

This piece has discussed how neoliberal systems leave markets unchecked, how India becomes a test bed for global companies, and how the gap between the North and South widens as AI grows without coordination.

This is why AI must now be treated as a development priority. The stakes reach far beyond industry or innovation. They touch identity, justice, political stability, and the everyday ability of people to trust what they see. Building a global framework that keeps AI aligned with human needs is not just necessary – it is urgent. The longer we delay, the harder it becomes to restore clarity in a world where the line between real and artificial is rapidly fading.

References

Thanks to Zoshua Colah from Unsplash

1. Google & Raisinghani, N. (2025). Introducing Nano Banana Pro. The Keyword. https://blog.google/technology/ai/nano-banana-pro/?utm_source
2. Ananthu, A. (2025, October 21). https://digital-peace.org/the-instagram-algorithm-knows-too-much. Digital Peace. https://digital-peace.org/the-instagram-algorithm-knows-too-much
3. Li, S. (n.d.). The Social Harms of AI-Generated Fake News: Addressing Deepfake and AI Political Manipulation. https://www.researchgate.net/publication/389217747_The_Social_Harms_of_AI-Generated_Fake_News_Addressing_Deepfake_and_AI_Political_Manipulation
4. Bertin, M. (n.d.). An economic perspective on data and platform market power. EconStor. Retrieved December 15, 2025, from https://www.econstor.eu/bitstream/10419/266519/1/jrc122896.pdf
5. Witt, U. (2022 September). Innovative Capitalism Needs Institutional Co-Evolution. Journal of Open Innovation: Technology, Market, and Complexity. https://www.sciencedirect.com/science/article/pii/S2199853122007326
6. Machan, T., Cobb, J., Raico, R., & Vallier, K. (2021, June 9). Neoliberalism (Stanford Encyclopedia of Philosophy). Stanford Encyclopedia of Philosophy. Retrieved December 15, 2025, from https://plato.stanford.edu/entries/neoliberalism/
7. Qureshi, Z. (2023, May 16). Rising inequality: A major issue of our time. BROOKINGS. https://www.brookings.edu/articles/rising-inequality-a-major-issue-of-our-time/?utm
8. Greenstein, S. (2022). Preserving the rule of law in the era of artificial intelligence (AI). Artif Intell Law, 30, 291–323.
9. BBC. (2025, November 7). ChatGPT, Gemini: Why OpenAI, Google and Perplexity are offering free AI in India? BBC. https://www.bbc.com/news/articles/c14pr0enjr6o
10. IBEF. (2025, January 17). India’s internet users to exceed 900 million in 2025, driven by Indic languages. IBEF. https://www.ibef.org/news/india-s-internet-users-to-exceed-900-million-in-2025-driven-by-indic-languages?
11. MERICS. (2025, July 22). China’s drive toward self-reliance in artificial intelligence: from chips to large language models. MERICS. https://merics.org/en/report/chinas-drive-toward-self-reliance-artificial-intelligence-chips-large-language-models?
12. International Trade Administration. (2024, September 18). India – Digital Economy. International Trade Administration. https://www.trade.gov/country-commercial-guides/india-digital-economy
13. The Gazette of India. (2023, August 11). THE DIGITAL PERSONAL DATA PROTECTION ACT, 2023 (NO. 22 OF 2023) An Act to provide for the processing of digital personal data in. Ministry of Electronics and Information Technology. https://www.meity.gov.in/static/uploads/2024/06/2bf1f0e9f04e6fb4f8fef35e82c42aa5.pdf
14. EU. (2025, February 19). EU AI Act: first regulation on artificial intelligence | Topics. European Parliament. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence
15. Yu, D., Rosenfeld, H., & Gupta, A. (2023, January 16). The ‘AI divide’ between the Global North and Global South. The World Economic Forum.https://www.weforum.org/stories/2023/01/davos23-ai-divide-global-north-global-south/
16. OECD, Lehdonvirta, V., Wu, B., Caira, C., Russo, L., & Hawkins, Z. (2025, October 29). The geography of AI compute: Mapping what is available and where. OECD.AI. https://oecd.ai/en/wonk/the-geopgraphy-of-ai-compute-mapping-what-is-available-and-where
17. UNESCO. (n.d.). Ethics of Artificial Intelligence. UNESCO. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics?
18. COE. (n.d.). The Framework Convention on Artificial Intelligence – Artificial Intelligence. The Council of Europe.https://www.coe.int/en/web/artificial-intelligence/the-framework-convention-on-artificial-intelligence
19. OECD. (n.d.). Global Partnership on Artificial Intelligence. OECD. https://www.oecd.org/en/about/programmes/global-partnership-on-artificial-intelligence.html
20. ITU. (n.d.). International Telecommunication Union (ITU). AI For Good. https://aiforgood.itu.int/about-us/un-ai-actions/itu/
21. UN IAWG-AI. (2024). United Nations System White Paper on AI Governance: An analysis of the UN system’s institutional models, functions, and existing international normative frameworks applicable to AI governance. https://unsceb.org/sites/default/files/2024-04/United%20Nations%20System%20White%20Paper%20on%20AI%20Governance.pdf
22. Bandama, R. (2025, November 28). How Big Tech’s Monopoly of AI Threatens Fair Competition. TRENDS Research & Advisory. https://trendsresearch.org/insight/how-big-techs-monopoly-of-ai-threatens-fair-competition/?srsltid=AfmBOooyqzWKOFp4r_LpQMFxeAHVv0V1cU0Apx9URZTjGV_3muzx3spE
23. United Nations. (2025, August 12). The Sustainable Development Goals – United Nations Sustainable Development. The United Nations. https://www.un.org/sustainabledevelopment/development-goals/