Three decades ago, governments around the world began warning that home computers and the internet could create a lasting gap between people who could use new technology and people who couldn’t. They called that gap the digital divide. Today, the International Telecommunication Union (ITU) estimates that roughly 6 billion people, about three‑quarters of the world’s population, were using the internet in 2025 - leaving more than 2 billion still offline. The divide they named has shifted form rather than closed: most of those people live in low‑income and many middle‑income countries, and even among the connected, new tiers of access have emerged that didn’t exist in the mid‑1990s. AI now sits inside systems that influence who gets jobs, healthcare, education, and credit, and the gap between people those systems work for and people they don’t is sharpening rather than closing.
Government reports on the divide started arriving in 1995, when the U.S. National Telecommunications and Information Administration released “Falling Through the Net,” showing how income, geography, race, and education carved stark boundaries around technology access. President Bill Clinton stood with Vice President Al Gore in 1996 and pledged to connect every classroom to the internet by the turn of the millennium under the “Building a Bridge to the 21st Century” banner. The European Commission launched its eEurope initiative in December 1999, aiming to build an “information society for all.” South Korea announced its Cyber Korea 21 strategy that same year, committing to bring citizens online regardless of age, gender, region, or income. Each initiative treated connecting more people to the internet as foundational to broader access.
These bridges got built on a single assumption: that once people were online, the hard part was over and that broader access would inevitably follow. But the pandemic showed what that belief had missed: when schools across nearly every country moved online almost overnight, hundreds of millions of children with no computer or home internet were shut out. Teachers spoke into grids of darkened squares while parents shared cracked phones and stretched thin data, doing what they could to keep them learning on whatever connection they could find. The pandemic didn’t create these gaps; it only made them impossible to ignore.
The gap this exposed was an old one. Underneath every new technology sits the same question: who can navigate the information system in front of them, and who can’t?
That question breaks into four layers, each a different way an information system can include or exclude us. The idea comes from two decades of research. The Dutch sociologist Jan van Dijk mapped access to digital technology as four successive modes: motivation, physical means, skills, and use. Work by Eszter Hargittai and Paul DiMaggio pushed the field to study what people can do once they’re online, since skill gaps persist long after a connection is in place. The four layers below build on that scholarship, and they apply to a dense government form or a bureaucratic office as much as to the internet. Recognizing them helps us see what issues stay the same as the technology keeps changing.
Physical access is whether someone can get connected at all. Internet use reaches about 94% of the population in high‑income countries versus around 23% in low‑income ones, and the urban‑rural divide remains particularly stubborn in low‑income regions. Similar patterns reproduce themselves at smaller scale within wealthier countries. A 2024 ITU analysis notes that more than half of the world’s population now lives in areas covered by 5G but warns that universal connectivity remains a “distant prospect,” especially in low‑income economies. In the United States, a February 2026 analysis by the broadband nonprofit Connected Nation estimated that nearly 16 million Americans still lacked access to broadband infrastructure, while another 15 million lacked any internet‑connected device at home. For a student living blocks from a wired campus, that can mean doing homework in a parking lot, borrowing wifi meant for store customers because no connection ever reached their home.
Skills and literacy extend far beyond basic computer operation. Using the internet safely means recognizing scam websites before handing over credit card numbers, and knowing how to adjust privacy settings to limit the data that companies harvest. These skills are a moving target, since each new technology assumes fluency with the last, and the gap becomes apparent the moment a software update or an unfamiliar interface makes hard‑won habits feel useless. Someone who learned computers in the 1990s might handle email with ease yet stall when a workplace moves every file to cloud storage locked behind a two‑factor login and collaboration tools they’ve never used. This gap falls unevenly. Around the world, about 77% of men use the internet compared with about 71% of women, and the divide is widest in low‑income countries. Disability and language cut deeper still, since screen readers and translation features remain afterthoughts in much of what gets built, especially for languages with fewer speakers or non‑Latin scripts.
Quality of access determines what becomes possible after connection exists. A teenager doing calculus homework on a phone with a cracked screen and a spotty data plan inhabits a different technological universe than a peer with a laptop and dedicated home wifi. Connection speed affects which applications run smoothly, while stability governs situations such as when a video interview can happen without dropped calls. 5G coverage remains heavily concentrated in high‑income countries, and many rural areas in the Global South still depend on older networks where video calls drop and webpages load slowly.
Meaningful use is the divide that persists even after access barriers come down. Two people with similar devices and connection speeds can end up in radically different places based on what they do with those tools. One person might learn to code through free online courses or build a professional network that opens unexpected doors, while another uses the same infrastructure mainly for entertainment. Meaningful use depends on the invisible curriculum taught by the people around us, which means the divide tends to perpetuate itself through social learning when people without exposure to certain uses can’t easily imagine those uses exist.
The income gap reveals how these layers compound at every scale. ITU’s 2024 report notes that in low‑income countries, the cost of a fixed broadband subscription still represents a large share of average monthly income, while the same service is a smaller fraction of income in wealthier economies. In the United States, Pew Research Center data from 2025 show that only about 54% of adults in households earning under $30,000 subscribe to home broadband, compared with around 94% of adults in households earning over $100,000. Even in the world's wealthiest countries, that gap shapes who can find work and reach care as those services move online.
Recruitment shows the consequences of that gap directly. Many industries have shifted most hiring to digital platforms, with large employers often refusing paper submissions. Companies screen applicants through automated systems that parse resumes for keywords before human eyes ever see them, and a welder with twenty years of experience can watch her application disappear because the software didn’t find the right phrases. Remote work opportunities remain inaccessible to people without home internet and appropriate devices, which turns connectivity into a major factor in expanding the geography of opportunity.
Healthcare has transformed just as dramatically. A patient who once drove ninety minutes to see a specialist might now have the appointment at home if the connection cooperates. Data from the U.S. National Center for Health Statistics show that the share of office‑based physicians using telemedicine technology rose from about 16% in 2019 to nearly 86% in 2021. But patients without reliable internet access can’t easily tap into telehealth services, forfeiting convenient care and the ability to consult specialists their region doesn’t have.
Government services have followed the same trajectory online. India's Aadhaar system links welfare payments to bank accounts for more than 1.4 billion people, and comparable programs have spread from Estonia to Brazil. Each one serves the people who can navigate it, and leaves those who can’t behind. And in the United States, the Affordable Connectivity Program, which had subsidized broadband for around 23 million households, ran out of funding in 2024 - just as more of these services were moving online.
Into this widening gap, artificial intelligence has arrived with a velocity that few previous technology transitions match. AI amplifies all four layers of the divide (access, skills, quality, and meaningful use) at once. Earlier waves required access and basic literacy, with skills that remained relatively stable once acquired. But AI tools shatter that pattern. Their capabilities expand every few months, which means the literacy required to use them well never settles. By the mid‑2020s, the landscape includes rapidly evolving models from OpenAI, Anthropic, Google, Mistral, DeepSeek, and others, with new releases arriving fast enough to make any learnings quickly outdated.
Language adds a layer of its own. The leading AI models learn mostly from English. In a language with less text online, like Swahili or Bengali, the same question tends to return a thinner, less accurate answer. Labs in France, China, India, and elsewhere are now training models on their own languages, which means who builds an AI increasingly shapes whom it serves well.
AI tools also require different cognitive skills than previous technologies, building atop existing digital literacy while demanding higher‑order thinking. Using a search engine effectively means knowing how to formulate queries and evaluate results, whereas using a language model effectively requires those same skills plus the ability to articulate complex requests clearly and to check outputs for accuracy and bias. The evaluation skills that help us decide whether a website is trustworthy carry even more weight with AI‑generated information, where systems can fabricate confident‑sounding answers entirely.
Even schools are splitting the same way. Wealthier U.S. districts moved fast to train teachers on AI in 2023 and 2024, while poorer ones fell behind. RAND projected that nearly every affluent district would have done so by the fall of 2025, compared with about six in ten of the poorest. That leaves students in poor districts with less help learning the tools everyone else is starting to use.
Cost raises the barrier further. The most capable AI tools sit behind monthly subscriptions that run from about $20 for a basic plan to $200 for the top tier - more than a month's income across much of the world. Free versions exist, but they come with catches. These run on older, weaker models with tight limits on use, and they often feed what people type into training (so that access is paid for in data). That difference also compounds, since faster and sharper answers help someone work and learn more, which in turn pays for the next upgrade. What begins as a price gap hardens into a gap in what the tools can do, and the people who stand to gain the most from a strong assistant are the least able to reach one.
The sharpest concerns appear where AI already makes decisions about people. Automated hiring systems filter job applicants using algorithms whose training data may encode decades of discrimination, while credit scoring algorithms make lending decisions that can perpetuate racial and economic inequality. Facial recognition systems have been shown to work less reliably on darker skin tones because they were trained predominantly on lighter‑skinned faces, with documented harms including misidentification and wrongful arrests. Each of these failures tends to hit the same populations the digital divide already disadvantages.
Governments have started to regulate AI and expand connectivity in very different ways. Some, like the European Union, have written detailed rules about how AI can be used, while others have focused on building infrastructure or left the harder questions mostly unanswered. Those choices shape whether schools, clinics, libraries, and other public institutions can use AI and broadband to narrow the divide, or whether they’re left to fill the gaps that policy never reached on their own.
Filling those gaps often starts with the institutions closest to a community. These groups know which neighborhoods lack service and where residents need help getting online, detail that national programs rarely capture. Library boards, school boards, community organizations, faith communities, and local councils can each narrow the divide within their reach.
Expand library and community center programming.
Public libraries already serve as community technology hubs in many places. Expanding this role with dedicated AI‑literate staff and on‑site access to premium AI subscriptions could help millions develop new skills, especially when paired with evening and weekend programming that meets working adults where they are.
Build AI literacy into local schools.
Students benefit from hands‑on experience using AI tools while developing a sophisticated understanding of ethical use boundaries under the right guidance. The skills include fact‑checking AI outputs against primary sources and combining AI help with human judgment rather than outsourcing their thinking entirely to algorithms. Training data shapes AI behavior in ways that encode historical biases, a pattern students learn to spot. School boards and parent organizations can press for curricula that build these skills alongside reading and mathematics.
Fund community‑led solutions.
Detroit’s pandemic response mobilized schools and community organizations to distribute hotspots and devices in ways that fit local needs. Kenya’s mobile money ecosystem grew from a mid‑2000s pilot into a system that reshaped financial inclusion across an entire region. Locally responsive work tends to outperform top‑down programs designed without input from the people they aim to help, and community organizations already doing this work are where funding goes furthest. Mutual aid networks, community organizations, faith communities, and disability advocacy groups have built trust and cultural competence over years that new programs can’t quickly replicate.
Some of this work sits beyond the reach of any individual or community, though, in the domain of national policy and public spending. Governments can act at a scale no one else can, funding broadband across whole regions and setting the rules for how AI is applied in hiring and lending. Those are the decisions that will shape whether the divide narrows or widens.
Build infrastructure to reach everyone.
The gap in physical access sits mostly in rural and low-income areas that commercial providers pass over. Municipal networks like Chattanooga's show that community‑owned broadband can match or beat what private companies deliver, often in exactly those places. Affordability is the other half of the job, and it is where subsidies like the Affordable Connectivity Program come in.
Provide public AI as civic infrastructure.
The civic principle that lets anyone walk into a library and use a computer extends to AI tools as well. Government agencies at every level can develop AI assistants that guide people through processes like applying for benefits in accessible language and in the languages communities speak. With funding, adult education programs can offer AI literacy courses to working people who can’t return to school full‑time.
Mandate transparency and accountability for AI that shapes people’s lives.
Countries diverge sharply on AI, from the European Union’s single law that puts the toughest limits on the highest-risk uses to the United Kingdom’s lighter, industry-by-industry approach. Rules can require companies to reveal when an automated system helps decide someone’s job, loan, housing, medical care, or school placement, and to give people a way to challenge the outcome. Checking a tool for bias before it launches, along with review by outside auditors, can catch problems a company might otherwise miss or minimize.
Every information system invents its own architecture of access, from printed catalogs to microfilm and search engines to language models. Each format has produced gaps between people who can navigate the system and people who can’t. The internet now reaches about three‑quarters of humanity, and the next technology is likely to rebuild that gap underneath us but in a new form. Every new bridge resurfaces the same question those late‑1990s reports raised: who gets left behind?
AI is raising the next bridge now, at the same early stage the internet occupied in 1995. None of its layers has hardened yet. But unlike 1995, the map of the divide already exists, with three decades of research showing where gaps form and how they deepen. This time the people working on the problem know what to watch: the cost of the tools, the skills to use them, the quality of the connection, and what people do once those pieces are in place. All of that amounts to a head start: a chance to narrow the gap while it’s still forming.
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