Will Cameroon Catch Up as the World Moves Toward Superintelligence?

Is Cameroon Preparing for the Future or Watching It Happen?

The world is experiencing an extraordinary period of technological development.

Artificial intelligence systems are becoming increasingly capable of performing tasks involving programming, scientific analysis, mathematical reasoning, content generation, and complex information processing.

Technology companies are investing heavily in AI research, computing infrastructure, advanced models, and increasingly autonomous systems.

Meanwhile, researchers are exploring the possibility of artificial general intelligence (AGI) and, eventually, artificial superintelligence (ASI).

These developments raise important questions about the future of economies, education, employment, cybersecurity, scientific research, and national competitiveness.

But there is one question I find particularly difficult to ignore.

Where does Cameroon stand in all of this?

While leading technology companies develop increasingly sophisticated AI systems, how prepared are our universities, industries, institutions, businesses, and citizens to participate in the opportunities that may emerge?

Are we building the capabilities required to benefit from artificial intelligence, or are we becoming increasingly dependent on technologies developed elsewhere?

And perhaps most importantly, what would catching up actually mean for a country like Cameroon?

As a Cameroonian engineer interested in artificial intelligence, hardware, and emerging technologies, I believe these questions deserve serious examination.

Not because Cameroon should necessarily compete directly with the world's largest AI laboratories, but because the decisions we make today could influence our technological capabilities for decades.

My concern is not simply whether Cameroon will have access to advanced artificial intelligence. It is whether we will develop the knowledge, infrastructure, institutions, and technical capacity to use it effectively and responsibly.

1. Before We Discuss Catching Up, We Must Understand Where AI Is Heading

Artificial intelligence, artificial general intelligence, and artificial superintelligence are related concepts, but they should not be treated as interchangeable.

Artificial Intelligence (AI)

Artificial intelligence is a broad field concerned with developing computer systems capable of performing tasks associated with human intelligence.

These include recognizing patterns, processing language, analyzing information, making predictions, and supporting decisions.

Many AI applications already operate across industries such as finance, education, healthcare, manufacturing, and software development.

Artificial General Intelligence (AGI)

Artificial general intelligence generally refers to AI capable of performing a broad range of intellectual tasks at a level comparable to humans.

However, AGI does not have one universally accepted definition or evaluation standard.

Different researchers and organizations disagree about the capabilities required before a system should be considered generally intelligent.

Artificial Superintelligence (ASI)

Artificial superintelligence refers to a hypothetical level of machine intelligence that would substantially exceed human cognitive capabilities across a broad range of domains.

Such systems could potentially transform scientific discovery, technological development, economic organization, and many other aspects of society.

However, artificial superintelligence has not been publicly established as an achieved technological milestone.

In June 2026, Google DeepMind published a research paper titled "From AGI to ASI," examining possible pathways from artificial general intelligence toward superintelligence.

The paper explores theoretical possibilities rather than establishing that ASI already exists.

This distinction matters.

We should not build national technology discussions around assumptions presented as facts.

The more defensible position is that AI capabilities are advancing rapidly, while the timing, characteristics, and eventual arrival of AGI or ASI remain uncertain.

For Cameroon, this means we should prepare for increasingly capable AI systems without making national development decisions dependent on speculative timelines.

2. The Global AI Race Is About More Than Intelligent Software

Artificial intelligence is often discussed through the applications people interact with.

Chatbots, image generators, coding assistants, and automated systems have become familiar examples.

However, behind these applications exists an enormous technological ecosystem.

Developing advanced AI requires combinations of specialized talent, high-performance computing, large datasets, research institutions, engineering expertise, energy infrastructure, and substantial financial investment.

According to Stanford University's 2026 AI Index Report, the United States and China remain central to the development of many leading AI models.

The report also highlights the concentration of advanced computing infrastructure and the increasing role of private industry in frontier AI development.

This concentration creates an important distinction between countries that develop foundational AI technologies and countries that primarily access them through external providers.

Both groups can benefit from artificial intelligence.

However, they may not possess the same influence over technology development, infrastructure, pricing, security arrangements, or access conditions.

For Cameroon, this raises questions about technological dependence.

If our businesses, universities, and institutions increasingly rely on foreign AI systems, what happens when subscription costs increase?

What happens when access conditions change?

Who controls the infrastructure supporting essential applications?

What protections exist for sensitive information?

Can these systems adequately understand our languages and local circumstances?

These questions are not arguments against international technology platforms.

They demonstrate why access to AI should not be confused with complete technological independence.

3. Where Does Cameroon Currently Stand?

Cameroon is not absent from conversations about artificial intelligence.

In July 2025, the country unveiled a proposed National Artificial Intelligence Strategy.

According to information recorded by the OECD AI Policy Observatory in April 2026, the strategy includes ambitions involving governance, digital infrastructure, multilingual AI, edge computing, human capital, innovation, and regional cooperation.

The strategy outlines objectives such as strengthening local research capacity, developing AI skills, encouraging innovation, and supporting applications in priority sectors.

These ambitions are significant.

However, the OECD's April 2026 record classified the strategy as proposed or under development.

That distinction is important because announcing a strategy does not establish that its proposed institutions, programmes, infrastructure, or targets have been implemented.

I believe we should evaluate national AI progress using evidence of implementation rather than policy announcements alone.

For example:

Have proposed AI training programmes begun operating?

How many participants have completed meaningful technical training?

Are research institutions receiving sustained support?

What computing resources are accessible to universities and developers?

Are local AI systems being developed, tested, and deployed?

What measurable improvements have emerged from public-sector AI initiatives?

These are the kinds of questions that can help distinguish ambition from progress.

A national AI strategy can provide direction. But a strategy without implementation capacity, financing, accountability, and measurable outcomes cannot deliver its intended benefits.

4. We Cannot Ignore Cameroon's Digital Infrastructure

Before discussing whether Cameroon can compete in advanced artificial intelligence, we must examine the infrastructure required for widespread technology adoption.

According to International Telecommunication Union estimates, approximately 46.3% of Cameroon's population used the internet in 2024.

World Bank data also estimated electricity access at approximately 72% of the population in 2024.

These figures do not capture every aspect of digital readiness.

Internet usage does not reveal connection quality, affordability, or whether individuals have reliable access to suitable devices.

Similarly, access to electricity does not guarantee uninterrupted power.

Nevertheless, these indicators highlight the importance of foundational infrastructure.

Artificial intelligence cannot become broadly useful if large parts of the population struggle to access the digital systems through which it is delivered.

For a university researcher, unreliable electricity may interrupt computational experiments.

For a small business, costly internet access may influence whether an AI-based service is economically viable.

For a developer building a connected application, network interruptions may affect the system's reliability.

These are engineering constraints as much as they are development challenges.

I believe discussions about advanced AI in Cameroon must therefore remain connected to the practical state of digital infrastructure.

A country cannot sustainably build an advanced digital economy while neglecting the systems that make digital participation possible.

5. The Four Foundations Cameroon Should Prioritize

The World Bank's 2025 Digital Progress and Trends Report identifies four important foundations for AI development and adoption.

These are connectivity, compute, context, and competency.

I believe this provides a useful framework for examining Cameroon's readiness.

A. Connectivity: Can People Reliably Access Digital Technology?

Reliable and affordable connectivity is essential for many AI applications.

For Cameroon, this requires attention to internet access, connection quality, affordability, electricity, and the availability of suitable devices.

However, the objective should not simply be to increase connectivity statistics.

We should also consider whether improved access enables meaningful activities such as education, business development, research, and digital public services.

B. Compute: Can Our Institutions Access the Processing Power They Need?

Artificial intelligence requires computing resources.

For advanced AI development, these may include specialized processors, servers, cloud infrastructure, storage, and high-performance computing systems.

Not every country needs to build a massive AI data centre immediately.

Such investments involve substantial financial commitments, specialized expertise, energy requirements, and long-term maintenance obligations.

Cameroon should examine the economic and technical justification for different approaches.

These could include partnerships with existing cloud providers, shared university computing resources, regional computing facilities, or appropriately sized local infrastructure.

The most suitable option would depend on demand, security requirements, costs, reliability, and institutional capacity.

C. Context: Does AI Understand Our Realities?

Artificial intelligence systems are influenced by the information used to develop and evaluate them.

A system trained primarily on information from other regions may perform poorly when asked to interpret Cameroonian terminology, local administrative procedures, cultural expressions, or less-represented languages.

This is particularly relevant in a country with English and French as official languages and numerous indigenous languages.

Consider an AI system designed to help citizens access public information.

Can it accurately explain relevant Cameroonian administrative requirements?

Can it distinguish outdated information from current regulations?

Can it recognize uncertainty instead of inventing answers?

Can it communicate effectively in the languages used by its intended audience?

These questions should be investigated through controlled testing.

Developing locally relevant datasets and evaluation methods may be more useful than immediately attempting to train a large language model from scratch.

D. Competency: Do We Have the Skills to Build, Evaluate, and Govern AI?

Digital literacy and advanced technical expertise are both necessary, but they serve different purposes.

Not everyone needs to become a machine-learning engineer.

However, people using AI should understand its capabilities, limitations, and risks.

At the same time, Cameroon needs professionals capable of developing software, managing data, securing digital systems, evaluating AI models, and conducting advanced research.

This requires more than teaching people how to write prompts.

It requires sustained education in computer science, mathematics, software engineering, cybersecurity, data science, and related disciplines.

It also requires opportunities for students and researchers to apply their knowledge to real problems.

A country that only teaches people to consume AI applications may expand adoption without developing the technical capabilities needed to shape the technology.

6. Are Cameroonian Universities Preparing Students for the Next AI Era?

Universities have an important role in developing a country's technological capabilities.

They train future engineers, researchers, educators, entrepreneurs, and technical specialists.

However, offering courses related to artificial intelligence does not automatically establish a strong AI research ecosystem.

Research capacity depends on several additional factors.

Do students have access to appropriate computing resources?

Can researchers obtain funding for experiments?

Are laboratories equipped to support meaningful technical work?

Do universities collaborate with industry?

Can students participate in original research?

Are research findings published, independently evaluated, and developed into practical applications?

These questions matter because advanced technical competence develops through sustained practice and research.

I believe Cameroon should strengthen the relationship between university education and real-world engineering.

Students studying artificial intelligence should have opportunities to build systems, evaluate models, examine security vulnerabilities, and investigate locally relevant problems.

For example, a university research group could evaluate how different language models perform on questions involving Cameroonian public information.

Another group could investigate whether smaller AI models can operate effectively under limited computing resources.

Others could develop and test assistive technologies or methods for identifying security weaknesses in AI applications.

Such projects would not necessarily require the financial resources of a global AI laboratory.

However, they could produce meaningful technical knowledge and help develop research expertise.

The objective should be to move beyond familiarity with AI tools toward the ability to investigate, build, and evaluate AI systems independently.

7. What Can Cameroon Learn From Other African Countries?

Cameroon does not need to look exclusively to Europe, the United States, or China when examining approaches to AI development.

Other African countries are also developing national policies and institutional capabilities.

Rwanda published its National AI Policy in 2023.

The policy addresses areas including AI literacy, responsible innovation, data ecosystems, public-sector transformation, and private-sector adoption.

Kenya launched its National Artificial Intelligence Strategy 2025–2030 in March 2025.

Its strategy emphasizes digital infrastructure, data governance, research, innovation, and commercialization.

These examples are useful because they demonstrate how African governments are organizing their AI ambitions.

However, publishing a national strategy is not proof of successful implementation.

Cameroon should examine which programmes have produced measurable outcomes, what resources were required, and whether the underlying institutional conditions are comparable.

A programme that works in Rwanda or Kenya may not automatically produce the same results in Cameroon.

Differences in infrastructure, institutional arrangements, financing, education systems, and economic conditions matter.

At the continental level, the African Union endorsed its Continental AI Strategy in July 2024.

The strategy provides a framework for cooperation, responsible AI development, and strengthening Africa's capacity to participate in emerging technologies.

For Cameroon, regional cooperation could offer opportunities in research, skills development, shared infrastructure, and local-language technologies.

But cooperation should complement domestic capacity rather than become a substitute for it.

8. Must Cameroon Build Its Own Superintelligence to Catch Up?

I do not believe that should be our starting objective.

Developing frontier AI models requires extraordinary resources.

Attempting to replicate the infrastructure and research operations of the world's largest AI laboratories without an established economic or technical justification could divert resources from more immediate priorities.

Cameroon has other opportunities.

We can develop expertise in adapting existing AI systems.

We can build applications addressing local problems.

We can investigate how to improve the reliability of AI systems operating in our environment.

We can develop technical standards for responsible adoption.

We can improve cybersecurity capabilities.

We can strengthen locally relevant datasets and research methods.

These activities may not attract the same attention as announcing a national superintelligence project.

But they could provide a more realistic foundation for meaningful participation in the AI economy.

The key distinction is between developing foundational AI technology, adapting existing technologies, and applying AI to specific problems.

These activities involve different levels of investment, expertise, and infrastructure.

Cameroon should determine where it can create the greatest value rather than assuming technological leadership must begin at the most expensive end of the AI development process.

9. AI Sovereignty: Will Cameroon Control Its Technological Future?

Artificial intelligence introduces important questions about technological sovereignty.

When a country relies heavily on external digital infrastructure, its institutions may become dependent on providers operating under different legal, economic, and technical conditions.

This can affect access, costs, data governance, security, and long-term continuity.

However, sovereignty should not be confused with complete technological isolation.

Building every AI system domestically is neither necessary nor automatically beneficial.

International partnerships, open-source technologies, and cloud services can provide access to capabilities that would otherwise be difficult to develop.

The important questions concern control, resilience, and accountability.

Can an institution move its data to another provider?

Are contractual responsibilities clear?

Can sensitive information be protected?

Does the organization understand where its systems are hosted and how they operate?

What happens when an external provider changes its services or pricing?

Are there alternatives if access becomes restricted?

I believe Cameroon should develop a practical understanding of AI sovereignty.

The objective should be to reduce harmful dependencies while preserving the benefits of international collaboration.

Technological sovereignty should be measured by meaningful capabilities and control, not simply by the number of systems labelled as locally developed.

10. Cybersecurity Must Be Part of Cameroon's AI Readiness

Artificial intelligence introduces new opportunities and new security challenges.

AI systems can be vulnerable to prompt injection, sensitive information disclosure, insecure integrations, malicious manipulation, and other forms of misuse.

Increasingly autonomous AI applications may also interact with external services, databases, or organizational systems.

These capabilities can create additional security risks if appropriate controls are absent.

For Cameroon, this becomes especially important when considering AI adoption in institutions handling sensitive information.

A public-facing AI chatbot that provides general information may present different risks from a system that can access confidential records or modify administrative data.

These systems should not be governed as though they have identical security requirements.

I believe responsible AI adoption should include clear expectations concerning data protection, system evaluation, access controls, incident response, and human oversight.

Cameroon enacted Law No. 2024/017 of December 23, 2024, relating to personal data protection.

This provides an important part of the legal context for AI systems processing personal information.

However, data protection legislation alone does not resolve every challenge associated with AI security and governance.

Institutions must also develop the technical and operational capacity to identify and manage risks.

A country cannot responsibly accelerate AI adoption while treating cybersecurity as an afterthought.

11. What Should Cameroon Prioritize Over the Next Five Years?

I believe Cameroon should focus on a manageable set of priorities rather than attempting to pursue every emerging AI opportunity simultaneously.

These are proposals for consideration, not claims about programmes already approved or funded.

Priority 1: Establish a Measurable National AI Readiness Baseline

Before committing major resources, Cameroon should assess its existing capabilities.

This could involve documenting AI-related education programmes, research infrastructure, available computing resources, institutional capacity, digital skills, and current AI applications.

The assessment should identify what already exists, what is missing, and where investments would produce the greatest practical value.

It should also distinguish available infrastructure from infrastructure that is proposed or under construction.

Priority 2: Strengthen AI Education at Different Levels

AI education should not be limited to introductory demonstrations of popular tools.

Students, teachers, workers, engineers, researchers, and public-sector officials have different learning needs.

A credible national approach would distinguish foundational AI literacy from specialized technical training.

It would also evaluate whether educational programmes produce measurable learning outcomes.

Priority 3: Support Applied AI Research and Technical Experimentation

Universities and research institutions should have opportunities to investigate locally relevant problems.

Initial projects could focus on model reliability, language performance, low-resource computing, accessibility, and AI security.

Shared equipment, supervised research programmes, and collaboration between institutions may help reduce costs.

However, sustained support would require transparent selection processes, realistic budgets, and clear evaluation criteria.

Priority 4: Develop Practical AI Governance Capacity

Before deploying AI in sensitive institutional environments, organizations need procedures for assessing risks.

A phased approach could begin with pilot projects, documented evaluations, procurement requirements, and clear accountability.

High-impact systems should face stronger scrutiny than low-risk applications.

Governance measures must also remain proportionate to the institution's capabilities and the risks involved.

Priority 5: Build Partnerships That Transfer Knowledge

International collaboration can support education, infrastructure, research, and access to technical expertise.

For example, in August 2026, Cameroon's Ministry of Posts and Telecommunications reported discussions with China concerning cooperation in artificial intelligence, training, innovation, and governance.

Such partnerships may provide opportunities.

However, the long-term value depends on what is actually delivered.

Training commitments should be evaluated through completed programmes and demonstrated skills.

Infrastructure partnerships should consider maintenance, ownership, security, and operating costs.

Research collaborations should help strengthen local expertise and institutional capacity.

The objective should be partnerships that leave Cameroon with more than access to external technology.

They should contribute to the ability to understand, develop, maintain, and evaluate technological systems independently.

12. How Would We Know Cameroon Is Making Progress?

If we want to assess whether Cameroon is catching up, we need indicators that measure capability rather than ambition.

I would begin by examining questions such as:

Digital access: Are more people gaining affordable, reliable connectivity and access to suitable devices?

Technical education: Are students and professionals demonstrating stronger AI engineering, cybersecurity, and data skills?

Research: Are institutions producing reproducible AI research and developing functioning technical systems?

Infrastructure: Can researchers and businesses access affordable, dependable computing resources?

Local relevance: Are AI systems being evaluated and adapted for Cameroonian languages, institutions, and user needs?

Governance: Are institutions establishing and enforcing practical safeguards for AI deployment?

Economic contribution: Are businesses achieving measurable improvements from AI adoption, after accounting for implementation costs?

Institutional capacity: Can public organizations maintain and oversee AI systems without relying entirely on external providers?

These indicators would require clear definitions, baseline measurements, reliable data collection, and periodic review.

Not all improvements would be attributable solely to AI policy.

But without appropriate measurements, it would be difficult to distinguish real progress from announcements and public enthusiasm.

13. What Happens If Cameroon Fails to Prepare?

The consequences of insufficient preparation deserve consideration.

Cameroon could become increasingly dependent on external AI systems without developing enough local expertise to evaluate or adapt them.

Businesses might adopt technology without adequately understanding its limitations.

Educational institutions could struggle to keep technical programmes aligned with changing professional requirements.

Public organizations might deploy AI applications without the data quality, security, or oversight required for reliable operation.

Local languages and institutional contexts could remain poorly represented in widely used AI systems.

These are plausible risks, not inevitable outcomes.

Their severity will depend on how technology evolves and how Cameroon responds.

It is also important to avoid assuming that AI will eliminate every traditional industry or occupation.

AI's economic effects will vary across tasks, sectors, businesses, and institutional environments.

Preparing responsibly requires examining evidence rather than treating the most dramatic predictions as established facts.

14. Can Cameroon Still Catch Up?

I believe the answer depends on what we mean by catching up.

If catching up means immediately matching the frontier AI research budgets and computing infrastructure of the United States or China, that is not a realistic near-term objective.

If it means developing the capacity to adopt, adapt, evaluate, secure, and build useful AI systems, the question becomes considerably more practical.

Cameroon does not need to lead every technological breakthrough to create meaningful value from artificial intelligence.

It does, however, need institutions capable of understanding technological developments and making informed decisions about their adoption.

It needs engineers who can build and evaluate systems.

It needs educators who can develop meaningful AI competencies.

It needs researchers who can generate credible evidence.

It needs businesses capable of identifying useful applications and measuring their results.

And it needs governance mechanisms that support innovation while protecting the people affected by technology.

These capabilities will not emerge automatically because advanced AI tools become more accessible.

They require deliberate investment, experimentation, institutional coordination, and sustained attention.

Final Thoughts: We Cannot Prepare for Tomorrow by Ignoring Today's Foundations

Artificial intelligence is advancing, and the possibility of increasingly capable systems raises questions about the future of technological development.

However, I believe Cameroon's immediate challenge is not to predict the exact date when artificial superintelligence might emerge.

Our challenge is to strengthen the foundations that allow us to participate meaningfully in the technologies already transforming the world.

We need reliable infrastructure.

We need stronger research capabilities.

We need practical AI education.

We need cybersecurity and responsible governance.

And we need technological innovation that responds to the actual needs of our people.

I do not believe Cameroon should measure its progress solely by how many AI tools its citizens use or how many ambitious technology initiatives are announced.

We should also examine what our institutions can build, what our researchers can investigate, what our engineers can maintain, and what measurable value our technological investments produce.

The real danger is not simply that the world may reach superintelligence before Cameroon develops comparable technology.

The greater danger is that we may enter an increasingly AI-driven future without developing enough capacity to shape how these technologies affect our own economy, institutions, and society.

Catching up does not necessarily mean becoming another Silicon Valley.

It means developing the capabilities to make informed technological choices, create meaningful solutions, and participate in the future from a position of growing competence rather than permanent dependence.

And that work should begin long before artificial superintelligence becomes a reality.

Research and Further Reading

1. Google DeepMind (2026). From AGI to ASI. Research publication, June 12, 2026.
2. Stanford Institute for Human-Centered Artificial Intelligence (2026). The 2026 AI Index Report.
3. World Bank (2025). Digital Progress and Trends Report: Strengthening AI Foundations.
4. World Bank (2026). World Development Report 2026: The Promise of Artificial Intelligence.
5. OECD AI Policy Observatory (2026). Cameroon's National Artificial Intelligence Strategy (SNIA). Policy Navigator.
6. African Union (2024). Continental Artificial Intelligence Strategy.
7. International Telecommunication Union (2024). Individuals Using the Internet: Cameroon. ITU DataHub.
8. World Bank (2024). Access to Electricity (% of Population): Cameroon.
9. Ministry of Posts and Telecommunications, Cameroon (2026). Intelligence artificielle: Coopération renforcée entre le Cameroun et la Chine. August 12, 2026.
10. Presidency of the Republic of Cameroon (2024). Law No. 2024/017 of December 23, 2024, Relating to Personal Data Protection.
11. Ministry of ICT and Innovation, Rwanda (2023). National Artificial Intelligence Policy.
12. Ministry of Information, Communications and the Digital Economy, Kenya (2025). Kenya National Artificial Intelligence Strategy 2025–2030.

Written by Ealeonore Mafor — Engineer, AI Educator, and Technology Professional.