In recent months, I’ve relied heavily on AI platforms—for coding, for research, for creating content, and even as a support system for my side businesses. These tools are undeniably powerful. Yet one reality has become increasingly clear: access to the most advanced models is expensive.
Consider this: subscriptions to leading AI platforms like OpenAI’s ChatGPT Plus or Anthropic’s Claude Pro cost between $20 and $30 per month, before factoring in additional API usage fees. For professionals in the United States or Europe, this may be manageable. But in many African countries, where average monthly incomes are significantly lower, these costs represent a substantial barrier. For example, a $20 subscription in Nigeria equates to nearly 10% of the median monthly wage. For students, freelancers, or small entrepreneurs, sustained use is simply out of reach.
This should concern us deeply, because it signals a familiar pattern: Africa risks once again being left behind in a technological transformation that will define the next era of knowledge and productivity.
The Accessibility Gap
The United States, China, and Europe are making massive investments in AI infrastructure, research, and talent pipelines. Africa, by contrast, has a limited footprint in the global AI value chain—whether in building models, generating training data, or shaping governance standards.
As Nanjira Sambuli, a Kenyan researcher and digital policy analyst, observed: “Africa cannot afford to be a passive participant in the AI revolution. If we don’t shape these technologies with our realities in mind, they will be shaped for us—and not necessarily in ways that serve us.”
If access remains prohibitively expensive, the continent risks developing a two-tier system of knowledge. On one side, corporations and affluent individuals pay for premium AI—capable of deep analysis, reasoning, and creative problem-solving. On the other, the majority are left with lighter, free versions: tools that can summarise or generate bullet points, but lack depth and precision.
From Google to Generative AI
The contrast with the early days of the internet is striking. Google Search democratized access to information. Anyone with a device and connectivity could retrieve knowledge on almost any subject. Generative AI, however, seems to be evolving in the opposite direction. The most valuable insights are locked behind high paywalls.
If AI becomes the default interface for learning, working, and decision-making, then limiting access to its full capabilities effectively limits access to the future itself. Unless addressed, knowledge risks becoming stratified—where deep, high-quality learning remains confined to a small elite.
Charting an African Path Forward
The situation is not inevitable. Africa has an opportunity to shape its own AI trajectory by focusing on accessibility, affordability, and participation. Three priorities stand out:
The situation is not inevitable. Africa has an opportunity to shape its own AI trajectory by focusing on accessibility, affordability, and participation. Four priorities stand out:
1. Foster open-source AI ecosystems
Projects like Masakhane, a grassroots initiative building natural language processing tools for African languages, show what is possible when communities pool talent and data. Open-source models—lighter, adaptable, and community-driven—can provide a foundation for innovation without prohibitive costs.
2. Build shared infrastructure for affordability
The continent lacks the large-scale compute resources that fuel frontier AI. Regional data centres, cloud-sharing initiatives, and public–private partnerships could help spread the cost of infrastructure. Just as undersea internet cables once transformed Africa’s connectivity, shared AI infrastructure could democratise access to powerful computing.
3. Invest in local data and talent
AI is only as strong as the data it learns from. Governments, companies, and researchers should prioritise building local data ecosystems that reflect Africa’s diversity—linguistic, cultural, agricultural, industrial. Rwanda’s use of AI-powered drones for medical deliveries and South Africa’s AI health diagnostics startups show that with the right data and talent, innovation can thrive.
4. Policymakers as enablers
Regulators and governments must take an active role in shaping the AI landscape. This means creating incentives for affordable AI access, investing in STEM education, supporting open data initiatives, and ensuring equitable licensing agreements with global AI firms. Without deliberate policy choices, Africa risks becoming only a consumer, never a creator.
Why This Matters
If Africa fails to act, the continent risks becoming a passive consumer of AI, dependent on foreign models with high price tags and limited local relevance. But with deliberate action, Africa can do more than catch up—it can lead in shaping inclusive AI that is affordable, culturally grounded, and globally competitive.
The choice is stark: accept the “short side of the stick” once more, or seize the opportunity to ensure AI is not a tool reserved for the few, but a force for empowerment for the many.
Join the Conversation
This is only a starting point. I would love to hear from entrepreneurs, students, policymakers, and researchers across Africa:
- What challenges have you faced in accessing AI tools?
- Are there local initiatives in your country that inspire you?
- How can we collectively push for a more inclusive AI future?
Your insights can help shape a richer, more grounded conversation on Africa’s place in the AI era.
Footnote
- Income vs cost of AI tools in Nigeria — “Why AI Tools Are Expensive in Nigeria (2025)”: In Nigeria, a ₦32,000 digital-subscription cost can represent 10-20% of a worker’s income, showing how even modest fees are a heavy burden. aihub.com.ng
- Average salary context — “The average salary in Nigeria: Detailed comparison” (TimeDoctor): average monthly salary ~ ₦339,000 (~US$775). timedoctor.com
- Open-source African NLP (Masakhane) — Masakhane is a grassroots organisation working to strengthen NLP research in African languages, for Africans by Africans. It develops datasets, open tools, and emphasises African language representation. masakhane.io+2masakhane.io+2
- MakerereNLP (East Africa) — Part of the Masakhane network: open and accessible text and speech datasets for low-resource East African languages (Uganda, Kenya, Tanzania), e.g. Luganda, Runyankore-Rukiga, Swahili. masakhane.io+1
- Case study: Zipline in Rwanda — Use of drones to deliver blood and medical supplies: reduced delivery times, lowered expiry and wastage in rural health facilities. The Reach Alliance+2WIRED+2
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