The Future of the Digital Divide: Who Gets Left Behind?

In rural Fresno County, California, a 13‑year‑old girl named Sofia sits under hot sun, her phone tethered to her father’s car battery to get WiFi, so she can attend her online class. Across the world, millions just like Sofia are caught between possibility and poverty: the online world beckons, but the bridge to cross is shaky, expensive, or altogether missing. As we stand in 2025, the digital divide isn’t a problem for “other regions”, it’s a global fault line shaping futures, incomes, opportunities, and dignity. Let’s explore how the digital inequality landscape is evolving, why some are still offline, and what we must do moving forward.

 

Why Some Communities Still Can’t Get Online in 2025

Despite technological advances, there are key barriers:

1. Infrastructure deficits in rural, remote, or low‑income regions. Rolling out broadband fibers or reliable mobile towers is expensive in sparsely populated areas.
2. Cost of access: Devices (smartphones, computers) and ongoing costs (data plans, electricity) remain prohibitive for many households.
3. Geographical and topographical obstacles: Mountains, islands, deserts complicate laying cable or building towers; power instability adds another layer of challenge.
4. Regulatory or market failures: In some countries the market for connectivity is not well served due to monopolies, lack of incentives for ISPs, or insufficient subsidies.

For example, a 2025 report found that globally around 2.9 billion people (≈ 37% of the world population) still lack access to a telephone, computer, or internet. Women and girls are particularly over‑represented among the offline.

In Bangladesh, internet penetration is about 44.5% in early 2025, meaning over half the population is still offline.

In Ethiopia, only about 21.3% of the population are internet users; rural regions lag even further.

These statistics show that even with fast‑spreading mobile connectivity, major populations are still excluded.

 

How Tech Skills Divide Us More Than Ever

Access to hardware and connectivity is only part of the picture; having the skills to use them is equally crucial.

• Digital literacy means more than being able to open an email. It includes navigating platforms, understanding privacy risks, using financial and health services online, even coding or critical thinking about what one finds online.
• There is a growing gap between younger, educated, urban populations and older, rural, less formally educated people. In many places, even where infrastructure exists, elderly or low‑education groups can be left behind because they don’t know how to use it well, or fear being scammed.
• Employers increasingly expect digital skills. Job postings often assume familiarity with online tools; without that, many are excluded even if the jobs are local.

For instance, in LMICs (low‑ and middle‑income countries), many women entrepreneurs own smartphones, but struggle to use them meaningfully due to lack of skills or irregular internet access and high costs.

 

Is Your Income Deciding Your Digital Future?

Income remains one of the strongest predictors of whether someone gets online, how well, and how often.

• Low‑income households often lack stable electricity, cannot afford modern devices, or have to prioritize food, shelter, health over “luxury” of internet and computers.
• Even when mobile phones are common, data plans may be limited; slow speeds, poor service degrade experience. So poorer users get marginalized even among those “connected.”
• Affordability is frequently cited as a major barrier. According to reports, many people in developing countries skip regular internet access due to cost.

 

Why Women Are Still Locked Out of the Digital Economy

Gender is a major vector of inequality in digital access and usage.

• Globally, more men use the internet than women. In low‑income and lower middle‑income countries, the gap can be large.
• Women entrepreneurs report owning smartphones, but often experience high cost of data, unreliable connectivity, safety and privacy concerns, and online harassment. These limit their ability to engage fully.
• Cultural, educational, legal and social norms can also discourage or prevent women from using technology (e.g. in some places girls are discouraged from STEM education; women may have lower literacy; norms around using public computers or phones).

The economic cost is large: women’s exclusion is estimated to have cost many countries up to USD 1 trillion in GDP in recent years, with projections rising if the gap is not closed.

 

How Biased Algorithms Make Inequality Worse (and What You Can Do About It)

Even for those who are online, digital systems can reinforce inequality.

• Algorithmic bias arises when algorithms are trained on data that reflect historical inequalities or under‑representation of certain groups. These can amplify stereotypes, discriminate in hiring, education, or services. For example, image generation models underrepresent women in male‑dominated fields; facial recognition systems often perform worse on darker skin tones.
• Recommendation systems and search engines may favor popular content, meaning voices from marginalized communities are less visible.

What you can do (as individuals, communities, developers, policy makers):

• Demand transparency: ask how AI models are trained, what data is used.
• Audit algorithms: third‑party reviewers or academic scrutiny.
• Use inclusive design: ensure datasets include diverse populations.
• Regulate for fairness: policies requiring non‑discrimination, penalties if AI causes harm.

 

Country Table: Digital Access, Demographics & Stats

Here’s a comparative table of some countries to show how digital access interacts with demographics:

Country	% Internet Users (2025)	Rural vs Urban Access Gap	Gender Gap (Women vs Men)	Major Barriers
Bangladesh	~ 44.5% of population online	Rural penetration much lower than urban (urban closer to ~ 70‑80%, rural low)	Female users still less than male (~ few % difference), cost & literacy issues	Poor infrastructure in rural, high cost, low digital skills
Ethiopia	~ 21.3% (28 million people)	Huge rural gaps – many rural areas largely unconnected or poorly connected	Women less likely to be online; literacy and cost major factors	Infrastructure, affordability, electricity, literacy
Low & Middle Income Countries (global, aggregated)	Many LMICs still have < 60‑70% penetration; some much less	Gaps large between urban and rural; remote regions often under‑served	Significant gender gap: women are 15‑25% less likely in many regions to use mobile internet vs men	Cost, social norms, data privacy/safety, regulation
High Income Countries	Often > 90% internet access; for example, many households in Europe	Smaller rural/urban gap, though remote regions still lag in speed or quality	Gender gaps exist, but usually smaller; somewhat more access to education etc.	Sometimes cost, sometimes skills among older populations, sometimes data privacy concerns

 

Policy & Advocacy Focus: What Must Be Done

To create a future where digital access is universal and just, policy interventions are essential. Below are key strategies.

National broadband investment strategies to close rural gaps

• Governments should treat broadband infrastructure as essential public utility, investing in fiber, wireless, satellite connectivity for remote and underserved areas.
• Public‑private partnerships (PPPs) help share cost and risk; subsidies, incentives for ISPs to operate in remote regions.
• Example: Some countries have universal broadband targets for 2030 or legal mandates to ensure minimum broadband speeds.

Digital education programs targeting low‑literacy adults and seniors

• Adult education programs: classes, workshops, community centers where older individuals or those with low formal schooling can learn digital basics.
• Tailored curricula: focus on practical skills (online banking, telehealth, using apps) rather than “tech jargon.”
• Use trusted local institutions (libraries, community centers, NGOs) to build trust and relevance.

Subsidized tech access for low‑income families

• Device subsidies or voucher programs so low‑income households can obtain smartphones, laptops, or tablets.
• Affordable or free WiFi zones in public areas (libraries, community hubs), subsidized data plans.
• Maybe device “loan” programs or shared community devices with safe, reliable access.

Gender‑inclusive policies in tech training and access

• Ensure digital training programs explicitly include women and girls; schedule to accommodate non‑traditional hours, childcare support.
• Outreach, mentorship programs; counter cultural and social barriers.
• Address safety and online harassment, ensure privacy and protection – because if the online environment is hostile, women may self‑restrict use.

AI accountability and transparency regulations to prevent bias

• Regulate AI systems used in public services (health, education, hiring, financial services) with standards for fairness, testing, audit.
• Require transparency: what data was used, how decisions are made, recourse for people who are harmed.
• Encourage or mandate “algorithmic impact assessments” similar to environmental impact assessments.

 

Use Case: Bridging the Digital Divide in Rural Bangladesh

Let’s tell the story of Kumudini, a teacher in a rural village in Bangladesh. In 2022, the local school had intermittent electricity, no broadband; students sometimes traveled to nearby towns to access internet.

• When COVID‑19 struck, online schooling was nearly impossible. Kumudini bought a low‑cost smartphones and tried using mobile internet, but data plans were expensive, speeds slow. Many students had no device or no connectivity at home.
• An NGO, in coordination with local government, initiated a program: solar panels for electricity in the school, subsidized internet via a community tower, free device library, and weekend training workshops for parents and students on digital literacy.
• By 2025, many students were able to attend blended classes, participate in online learning platforms; girls in particular benefited as the school offered safe spaces for them to use tech. Local markets saw small businesses able to reach customers via social media; Kumudini’s students improved performance in math and science as more resources became available.

This case shows how multiple elements, electricity/infrastructure, affordability, skills training, gender‑sensitive design, must act together to make digital access meaningful, not just “being online.”

 

FAQs

Can simply building infrastructure solve the digital divide?
No. Infrastructure is necessary but insufficient. Barriers like cost, skills, gender norms, safety, and local relevance must be addressed too.

Is AI regulation likely to stifle innovation?
Not if done well. Smart regulation can guide fairness and transparency rather than block innovation, helping build trust and more inclusive technologies.

 

Conclusion

In 2025, the digital divide is no longer a distant issue, it’s central to who wins or loses in education, economy, health, democracy. As we’ve seen: some communities still remain cut off; tech skills divide us even when connections exist; income, gender, and biased algorithms all tilt the scales away from equality. But there is a path forward. With intentional policies, broadband investment especially in rural & remote areas; digital literacy efforts for all ages; device and data access for low‑income households; gender‑inclusive programs; and strong oversight of AI, our future can be one of inclusion, not exclusion.

The question is: will we act broadly, wisely, urgently? Because the cost of waiting is paid in lost potential, of individuals, communities, and entire nations.