{
  "id": 18469,
  "title": "AI Is Hyper-Scaling Digital Inequality",
  "url": "https://urgent.news/2026/07/29/ai-is-hyper-scaling-digital-inequality",
  "topic": "ai",
  "section": "AI",
  "published": "2026-07-29T11:00:04.000Z",
  "source": {
    "name": "IEEE Spectrum",
    "slug": "ieee-spectrum",
    "url": "https://spectrum.ieee.org/ai-digital-divide"
  },
  "original_language": "en",
  "account": "Artificial intelligence is rapidly becoming integral to everyday infrastructure, with applications in email composition, software programming, job application filtering, recommendation systems, education, healthcare, finance, and public administration. While industry leaders advocate for \"AI for everyone\" and governments draft national AI strategies, decades of working on digital inclusion and literacy projects have revealed a persistent pattern: new transformative technologies are introduced on landscapes already divided by connectivity, skills, and institutional capacity. The current AI wave exacerbates these existing fractures. Nonetheless, some nations are exploring ways to engage in AI development without participating in the dominant frontier-model race led by the United States and China. Recent examples from South Africa and Indonesia demonstrate both the potentials and challenges of this approach. Stakeholders must consider not only access to AI but also the opportunity to build local innovation ecosystems, enhance public-sector capacity, and ensure that their languages, cultures, and societal values are represented in AI systems. AI compute is increasingly concentrated in a handful of countries, with the United States alone hosting over 5,000 data centers, nearly ten times more than any other nation. This concentration of compute translates into dependency, as U.S. exports of cloud computing and data-storage services accounted for about 87 percent of global cloud exports in 2023. Consequently, AI development is not only technologically but also commercially and geopolitically outsourced, leaving most countries unable to control computational engines powering global systems. Systems trained, standardized, and governed within limited institutional and linguistic contexts may struggle to serve a diverse global public effectively. Digital skills remain deeply stratified, with only around 40 percent of adults in OECD countries possessing advanced computational and AI-related competences. These competencies are concentrated among highly educated professionals and technology-intensive sectors. While governments accelerate AI integration into education, particularly at higher levels, opportunities for AI literacy in primary and lower secondary education and ethical training for educators remain uneven. Those with strong educational backgrounds, advanced digital skills, and stable connectivity are most capable of leveraging AI as a tool to augment their abilities. The situation is further complicated by AI-related training being strongly linked to educational attainment. OECD data indicate that 36 percent of tertiary-educated individuals engaged in AI-related training in the past year, compared to just 18 percent of those with upper-secondary education. The consequences of this divide are far-reaching, with those on the wrong side of the divide more likely to perceive AI as an opaque system acting upon them, such as in algorithmic welfare systems or AI-assisted hiring tools, rather than a technology they can actively influence or shape. The concentration of AI development power is largely in the hands of a small group of industry actors and technologically advanced states. Consequently, most countries find themselves in a perpetual catch-up position, adapting imported models, standards, and \"trustworthy AI\" frameworks to their contexts, often lacking local capacity to assess trade-offs or propose alternatives. In Indonesia and South Africa, communities generate substantial data yet have limited influence over AI system design, governance, and deployment. Their languages receive limited representation in training datasets, their institutions are under-resourced in regulatory forums, and their experiences are rarely reflected in benchmark datasets. For many global South countries, participation in AI typically involves adapting imported systems rather than shaping them. In South Africa, the Department of Communications and Digital Technologies released a draft national AI policy in April 2026, proposing new oversight institutions. However, the department withdrew the draft after discovering AI-generated hallucinations in its academic citations, highlighting the gap between AI governance ambition and the institutional capacity needed to implement it. Meanwhile, Indonesia showcases deliberate public-sector agency in AI implementation, with the National Research and Innovation Agency (BRIN) focusing on practical tools aimed at underserved communities rather than pursuing frontier capabilities.",
  "summary": "Artificial intelligence is rapidly becoming part of everyday infrastructure–in some places. It helps write emails and software code, filters job applications, powers recommendation systems, and is increasingly being integrated into education, health care, finance, and public administration. Industry leaders talk about “AI for everyone,” while governments rush to publish national AI strategies and…",
  "key_points": [
    "AI integration exacerbates existing digital inequality fractures.",
    "U.S. hosts 5,000+ data centers, controlling 87% of global cloud exports.",
    "Indonesia and South Africa generate data but lack influence over AI systems."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}