Access Gaps

Introduction

If AI really can make intelligence more abundant, one of the central questions is whether that abundance will reach most people or remain concentrated among already advantaged groups. Falling model costs and better software matter, but they are only part of the story. Access to AI expertise depends on electricity, internet access, affordable devices, local languages, education, trust, institutional capacity, and political choices about who gets served first.

Access Gaps illustration 1 The danger is that AI becomes “universal” in theory while remaining unequal in practice: highly capable systems for wealthy firms, elite universities, and English-speaking professionals, while poorer regions receive slower, weaker, or less reliable tools. UNESCO has warned that the world risks moving from a digital divide to an “AI divide”, where unequal access to knowledge systems deepens existing inequalities rather than reducing them. [UNESCO]unesco.orgAI literacy and the new Digital DivideUNESCOAI literacy and the new Digital Divide - A Global Call for…6 Aug 2024 — This divide represents the unequal access, benefits, and…

The optimistic AI bloom vision depends not just on creating more intelligence, but on distributing it widely enough that ordinary people can use it to learn, solve problems, improve health, and participate more fully in economic and civic life. That turns questions of access and governance into core parts of the story, not side issues.

Falling inference costs and why they matter

One reason many technologists believe AI expertise could spread broadly is that the cost of running advanced models has fallen rapidly. Training frontier systems still requires enormous capital, but using already-trained models — often called inference — has become dramatically cheaper and more efficient. Stanford’s 2025 AI Index highlights falling inference costs and improving hardware efficiency as major drivers of wider deployment. [hai-production.s3.amazonaws.com]hai-production.s3.amazonaws.comArtificial Intelligence Index Report 2025Generative AI saw particularly strong momentum, attracting $33.9 billion globally in private inv… [Stanford HAI]hai.stanford.eduStanford HAIThe 2025 AI Index Report | Stanford HAIGenerative AI saw particularly strong momentum, attracting $33.9 billion globally in p…

This matters because expensive expertise has historically been scarce. A lawyer, specialist doctor, tutor, or software engineer can only help so many people at once. AI systems can potentially scale parts of that labour at near-zero marginal cost. A student asking for explanations, a nurse checking guidance, or a small business translating documents no longer necessarily requires a dedicated human expert every time.

But lower inference costs alone do not guarantee equal access. Electricity became cheap in many countries long before everyone had reliable power. Internet bandwidth costs fell long before universal broadband access. AI may follow the same pattern: technically abundant, socially uneven.

Several bottlenecks remain stubbornly physical and institutional:

Smartphones and laptops are still expensive for many households.
Reliable internet access remains limited in parts of the world.
Cloud-based AI systems depend on data centres concentrated in a small number of countries.
Many leading models are controlled by a handful of firms with commercial incentives.
Subscription pricing can exclude lower-income users even when “free” versions exist.
Governments and schools often lack the money or expertise to deploy systems well.

The result is that AI capability can advance much faster than AI inclusion.

The divides of income, age, region, and language

The most obvious AI access gap is income. Wealthier individuals and organisations can buy better devices, faster internet, premium subscriptions, specialised tools, and training. They also tend to work in environments where AI adoption is encouraged and supported.

Stanford’s AI Index shows enormous concentration of investment and advanced AI development in a small group of countries, especially the United States and China. [Stanford HAI]hai.stanford.eduStanford HAIThe 2025 AI Index Report | Stanford HAIGenerative AI saw particularly strong momentum, attracting $33.9 billion globally in p… More recent research tracking AI usage across countries also finds that adoption is heavily skewed toward richer economies, even after adjusting for population size. [arXiv]arxiv.org2504.07139] Artificial Intelligence Index Report 2025by N Maslej · 2025 · Cited by 484 — New in this year's report are in-depth analyses… 2arXiv

This creates a compounding effect. Regions already rich in universities, technical talent, venture capital, and digital infrastructure gain access to better tools first, which can further accelerate productivity and innovation. Poorer regions risk falling further behind precisely because AI increasingly amplifies the value of existing knowledge and infrastructure.

Connectivity still limits cognitive abundance

Many discussions of AI assume permanent, high-speed connectivity. In reality, large parts of the world still experience unstable electricity, expensive mobile data, or limited broadband access. UNESCO has repeatedly warned that digital learning systems cannot become genuinely universal while basic infrastructure gaps persist. [UNESCO]unesco.orgspotlights how digital learning can promote equity low resource contextsUNESCOUNESCO spotlights how digital learning can promote…Apr 3, 2025 — UNESCO is leading global efforts to make digital learning acces… [UNESCO]unesco.orgStartling digital divides in distance learning emergeDisparities are particularly acute in low-income countries: in sub-Saharan Africa, 8…

This matters especially in education and healthcare, where AI could theoretically deliver large social gains. A sophisticated tutoring model is far less useful if pupils share a single device or cannot reliably connect to online services. AI-assisted healthcare systems depend on records infrastructure, connectivity, secure data systems, and trained staff.

The World Health Organization has stressed that digital health systems in low- and middle-income countries often face structural barriers including unreliable infrastructure, weak governance capacity, workforce shortages, and limited interoperability between systems. [World Health Organization]who.intWorld Health OrganizationDigital healthThe purpose of WHO's Global Strategy on Digital Health is to support countries in strengthening th… [Springer Link]link.springer.comSpringer LinkBarriers to digital health implementation in low- and middle…by QB Olayiwola · 2026 · Cited by 2 — Low- and middle-income…

This creates a paradox. The regions that could benefit most from scalable expertise often have the weakest foundations for deploying it.

Language inequality may become a knowledge inequality

Language is one of the least discussed but most important AI access barriers.

Most frontier AI systems perform best in English and a relatively small set of high-resource languages. Training data, benchmarks, documentation, and safety testing are heavily concentrated around those languages. Users speaking minority or low-resource languages may receive weaker answers, poorer translations, or fewer specialised services.

UNESCO has warned that linguistic inequality in digital systems risks marginalising entire communities and weakening cultural diversity. [UNESCO]unesco.orgUNESCOAI and technologies in educationUNESCO partners with countries and institutions on ideas, research, and evidence-informed digital l…

This problem is larger than convenience. If AI systems increasingly mediate access to education, government services, medicine, law, and economic opportunity, then weaker support for certain languages can become a structural disadvantage.

A rural farmer able to query crop disease information in a local language may gain real economic value from AI. A citizen unable to use government AI systems comfortably in their own language may become more excluded from public services rather than less.

The same issue appears inside wealthy countries. Immigrant communities, older people, and citizens with lower literacy levels often struggle with digital systems designed around highly educated users. AI interfaces may simplify some interactions through speech and translation, but poorly designed systems can also intensify exclusion.

Age and confidence gaps matter too

Access is not only technical. It is psychological and social.

Older adults often adopt new digital tools more slowly, especially when systems appear opaque or unreliable. People with lower digital confidence may avoid AI entirely even when access technically exists. Research on AI adoption patterns increasingly suggests that perceived trust, usefulness, and social legitimacy strongly shape who benefits from these tools. [arXiv]arxiv.org2504.07139] Artificial Intelligence Index Report 2025by N Maslej · 2025 · Cited by 484 — New in this year's report are in-depth analyses…

This matters because AI systems are not passive infrastructure like roads or water pipes. Users must actively engage with them, judge their outputs, and integrate them into work or learning. People with stronger educational backgrounds are often better positioned to spot mistakes, compare sources, and use AI critically.

In that sense, AI can magnify pre-existing educational inequality. A skilled user may become dramatically more productive with AI assistance, while a less confident user may struggle to evaluate misleading or fabricated answers.

Cheap access to a model is therefore not the same thing as meaningful cognitive empowerment.

Access Gaps illustration 2

Public-interest deployment may matter more than consumer apps

One of the biggest open questions in the AI bloom debate is whether AI access will mainly be organised through private consumer markets or through public institutions.

If access depends mostly on personal purchasing power, then inequalities are likely to persist. Wealthier households will simply buy better tools and more capable subscriptions. But if AI systems are integrated into schools, healthcare systems, libraries, welfare services, and public infrastructure, the distribution of benefits could look very different.

Historically, many technologies only became broadly empowering after institutional deployment. Literacy expanded through public education systems. Electricity spread through public infrastructure investment and regulation. Vaccination programmes required organised public-health capacity.

AI may follow a similar path.

Schools and universities

Education is one of the clearest examples where public-interest deployment could matter enormously.

AI tutoring systems may eventually provide personalised explanation, translation, practice, and feedback at scales previously impossible. But unequal implementation could widen educational divides instead of narrowing them. Wealthy schools may gain advanced tutoring systems, teacher support tools, and customised curricula long before underfunded schools do.

UNESCO’s education work increasingly focuses on avoiding exactly this outcome: ensuring AI systems support inclusion rather than creating a new hierarchy between digitally rich and digitally poor learners. [UNESCO]unesco.organd g20 promote linguistic diversity digital ageUNESCOUNESCO and G20 promote Linguistic Diversity in Digital Age21 Nov 2025 — Nearly half of the world's 7,000 languages are now at risk… [UNESCO]unesco.orgArtificial intelligence in educationAIUNESCO AI in Education guides the ethical use of artificial intelligence to enhance learning, teaching, and assessment globally…

The quality of implementation matters as much as the technology itself. Simply giving pupils unrestricted chatbot access is not the same as building well-designed educational systems with teacher oversight, curriculum alignment, and safeguards against misinformation.

Clinics and healthcare systems

Healthcare shows both the promise and the risks of AI deployment.

In theory, AI could help address shortages in medical expertise through triage support, translation, record summarisation, diagnostics assistance, and administrative automation. WHO continues to emphasise that workforce shortages are especially severe in lower-income countries. [World Health Organization]who.intWorld Health OrganizationDigital healthThe purpose of WHO's Global Strategy on Digital Health is to support countries in strengthening th…

But healthcare also demonstrates how badly unequal or poorly governed AI deployment can fail.

Recent reporting on Kenya’s AI-linked health reforms showed how opaque algorithmic systems can end up harming poorer citizens when implemented without transparency, accountability, or adequate safeguards. [The Guardian]theguardian.comIntended to replace the outdated national insurance scheme and extend affordable care to the informal sector, the reform uses a machine-l… The issue was not simply “bad technology”. It reflected deeper problems involving governance, incentives, institutional capacity, and weak public oversight.

This is likely to become a recurring theme in AI deployment more broadly. Systems intended to widen access can instead reinforce exclusion if they are built around flawed assumptions, poor data, or cost-cutting priorities.

Libraries, local government, and civic infrastructure

Some of the most important AI access decisions may happen outside headline technology companies altogether.

Public libraries, municipal governments, universities, and community organisations could become critical intermediaries. They can provide trusted access points, training, translation, verification, and human support for people who might otherwise struggle to use AI systems effectively.

That institutional layer matters because abundant intelligence is not merely a technical condition. It is also a social capability. People need ways to verify information, contest decisions, learn safely, and combine AI assistance with human judgement.

Without those supporting institutions, AI can become another system that rewards already advantaged users while leaving others overwhelmed or excluded.

Access Gaps illustration 3

Why openness alone will not solve the problem

Open-source and open-weight AI models are often presented as the answer to unequal access. There is some truth in this. Freely available models can reduce dependence on a few large firms and allow local adaptation for different languages, cultures, and use cases.

Open models may prove especially important for lower-income countries, universities, researchers, and public-interest organisations that cannot afford expensive commercial systems.

But openness is not enough on its own.

Running powerful models still requires hardware, electricity, technical expertise, maintenance, cybersecurity, and organisational capacity. Even open systems can end up concentrated among institutions with sufficient computing resources and skilled personnel.

There is also a risk that countries without strong domestic AI ecosystems become permanently dependent on external infrastructure providers. If advanced AI services are controlled abroad, then local governments may have limited influence over pricing, privacy, censorship, reliability, or strategic priorities.

This is one reason debates about compute infrastructure, semiconductor supply chains, and national AI capacity increasingly matter for development policy, not just industrial competition.

The deeper question behind AI access gaps

The central issue is not whether AI systems will exist almost everywhere. They probably will.

The harder question is whether people will gain meaningful agency from them.

A future where billions of people occasionally use basic chatbots is not the same as a future where high-quality cognitive assistance genuinely expands human capability across society. The latter requires reliability, education, trust, institutional support, linguistic inclusion, and broad economic access.

The optimistic version of AI abundance imagines something historically unusual: expertise becoming widely available rather than remaining concentrated among elites. That could improve education, healthcare, scientific participation, entrepreneurship, and democratic participation on a very large scale.

But history gives reasons for caution. Many transformative technologies spread unevenly at first, often reinforcing existing hierarchies before broader access arrived. AI may do the same unless governments, schools, public institutions, and civil society actively shape how the technology is distributed.

The bloom case therefore depends not only on building more capable systems, but on whether societies choose to treat access to cognitive tools as a broadly shared public good rather than merely a commercial product for those already best positioned to benefit.

Endnotes

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Additional References

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Amazon book picks

AI Access Gaps

Introduction

Falling inference costs and why they matter

The divides of income, age, region, and language

Connectivity still limits cognitive abundance

Language inequality may become a knowledge inequality

Age and confidence gaps matter too

Public-interest deployment may matter more than consumer apps

Schools and universities

Clinics and healthcare systems

Libraries, local government, and civic infrastructure

Why openness alone will not solve the problem

The deeper question behind AI access gaps

Endnotes

Additional References

Further Reading

The Oxford Handbook of AI Governance

Foundational Principles of AI Governance and Policy

Digital Divide

Third Digital Divide

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