AI Colonialism: Is Artificial Intelligence Building a New Digital Empire?

Colonialism once extracted land, minerals, crops and labor from one part of the world while concentrating wealth and decision-making power somewhere else.

Critics of today’s artificial-intelligence economy argue that a strangely familiar pattern may be emerging again.

This time, the resources are different.

They include:

• Human data
• Language
• Cultural knowledge
• Creative work
• Cheap digital labor
• Electricity
• Water
• Minerals
• Computing infrastructure

The resulting AI systems may then be designed, owned and governed by a comparatively small number of corporations and countries, while billions of people whose data, labor and cultures helped make those systems possible receive far less influence over how they operate.

Researchers increasingly describe this imbalance using a provocative term:

AI colonialism.

It does not mean that artificial-intelligence companies are literally recreating nineteenth-century colonial governments or occupying countries with armies. Rather, scholars use the concept to examine whether some of the economic and political relationships surrounding AI reproduce older patterns of extraction, dependency and unequal control. Researchers Shakir Mohamed, Marie-Therese Png and William Isaac have argued that decolonial theory can help identify how power, values and historical inequalities are reproduced through AI systems.

The debate has become increasingly important because AI is no longer simply a software industry.

It is becoming infrastructure.

AI systems are entering education, healthcare, government, banking, employment, policing, media, agriculture, translation and scientific research. If only a small group of companies and countries control the models, chips, cloud infrastructure, datasets and standards behind those systems, technological dependence could become deeply embedded in societies that increasingly rely on them.

The central question is therefore not whether AI itself is “colonialist.”

Algorithms have no empire.

The deeper question is:

Who provides the resources, who performs the work, who owns the infrastructure, who defines the knowledge—and who receives the economic and political power created by AI?

What Does “AI Colonialism” Mean?

There is no single universally accepted definition.

The term overlaps with concepts including:

• Digital colonialism
• Data colonialism
• Algorithmic colonialism
• Technological colonialism
• Coloniality of AI
• Decolonial AI

Media scholars Nick Couldry and Ulises Mejias developed the influential concept of data colonialism, arguing that contemporary digital economies transform human lives, relationships and activities into data that can be extracted and converted into economic value. They explicitly compare this process with historical colonial systems of appropriation, while recognizing that the objects being appropriated have changed.

AI colonialism applies similar reasoning specifically to artificial intelligence.

The argument is that AI may reproduce colonial-style relationships when one group controls the technology while another provides data, labor, natural resources or markets without receiving comparable ownership or decision-making power.

A simplified version looks like this:

Data and labor flow outward → AI systems are built elsewhere → intellectual property and profits accumulate elsewhere → finished technology is sold back to the original communities.

That does not happen in every AI project.

But critics argue that it happens often enough to deserve serious attention.

Why Calling It “Colonialism” Is Controversial

The word carries enormous historical weight.

Historical colonialism involved military conquest, racial hierarchy, political domination, forced labor, dispossession, slavery and direct control of territory.

An AI company operating a cloud service in another country is obviously not identical to the British Empire governing India or European colonial governments ruling African territory.

For this reason, some scholars treat AI colonialism as a conceptual metaphor rather than a literal continuation of classical colonial rule.

Others argue that the comparison is more than metaphorical.

They point out that colonialism was not only about military occupation. It also created economic systems in which peripheral regions supplied raw materials and labor while ownership, manufacturing capability and financial power accumulated in imperial centers.

From that perspective, a modern system in which poorer countries provide data, workers and resources while wealth and technological control accumulate abroad may reproduce important elements of colonial political economy without reproducing colonial government itself.

The strongest use of the term therefore requires precision.

“AI colonialism” should not mean:

Foreign technology exists, therefore colonialism.

It is more useful when describing a specific imbalance involving extraction, dependency and control.

The AI Economy Has a Global Supply Chain

Popular descriptions of artificial intelligence often make AI sound almost immaterial.

A user types a question.

A machine answers.

But behind that simple interaction is a vast physical and human supply chain.

AI depends on:

• Semiconductor fabrication
• Advanced lithography
• GPUs and specialized accelerators
• High-bandwidth memory
• Cloud infrastructure
• Data centers
• Fiber networks
• Submarine cables
• Electricity generation
• Cooling systems
• Training datasets
• Software engineers
• Researchers
• Data annotators
• Content moderators
• Evaluators
• Translators
• Domain experts

The OECD has warned that important parts of the AI infrastructure supply chain are highly concentrated. Its analysis identifies major concentration around advanced lithography, leading-edge semiconductor fabrication, GPUs, high-bandwidth memory, cloud services and electronic-design software.

That concentration matters because whoever controls the infrastructure can influence who is capable of building advanced AI.

The New Divide Is Not Just Internet Access

For decades, policymakers talked about the digital divide.

Some countries had widespread internet access.

Others did not.

AI adds another layer.

A country may have millions of internet users and a thriving software industry while still lacking the computing infrastructure necessary to train advanced models.

The ILO and the UN Secretary-General’s technology office warn that unequal infrastructure, investment, adoption and skills could widen existing economic disparities. Their report highlights electricity, broadband, computing access and digital skills as factors that determine whether countries can actually benefit from AI-driven productivity.

The result is sometimes called the AI divide.

Internet connectivity lets a country consume AI.

Compute capacity helps it create AI.

Those are not the same thing.

Compute Is Becoming a Form of Power

Modern frontier AI requires enormous computing resources.

The ability to buy advanced chips is only the beginning.

Organizations also require:

• Data centers
• Reliable power
• Cooling
• High-speed networking
• Engineering expertise
• Cloud infrastructure
• Capital

The OECD has warned that access to high-end computing may create “haves and have-nots,” not only between countries but even among universities and companies within wealthy nations.

That imbalance is especially significant for poorer countries.

A researcher may understand how to design an excellent machine-learning system but still be unable to train it competitively.

A university may possess brilliant students but lack GPUs.

A startup may develop a useful local-language model but remain dependent on foreign cloud providers for inference.

In that situation, intellectual talent exists locally while infrastructure ownership remains external.

This is one of the strongest arguments behind the AI-colonialism critique.

UNCTAD Warns That AI Development Is Highly Concentrated

The economic stakes are enormous.

UN Trade and Development projected in its 2025 Technology and Innovation Report that the global AI market could reach approximately $4.8 trillion by 2033. At the same time, UNCTAD warned that development and economic benefits are heavily concentrated among a small number of countries and companies.

This creates a fundamental development question.

If AI becomes one of the most valuable industries in the world, will poorer countries participate primarily as:

• Customers?
• Raw-data sources?
• Low-cost labor markets?
• Data-center locations?

Or will they also become:

• Model builders?
• Infrastructure owners?
• Research centers?
• Intellectual-property creators?
• Standards setters?

That distinction may determine whether AI reduces global inequality or reinforces it.

The Invisible Workers Behind “Artificial” Intelligence

One of the most important parts of the AI economy is also one of the least visible.

Human beings.

Machine-learning systems require enormous amounts of human work.

Workers may:

• Label images
• Transcribe audio
• Translate sentences
• Categorize documents
• Compare model answers
• Identify harmful content
• Draw boundaries around objects
• Verify search results
• Evaluate generated text
• Moderate disturbing material

The International Labour Organization emphasizes that AI remains dependent on a large human-in-the-loop workforce and warns that many of these workers perform low-paid, precarious and psychologically demanding work.

AI may appear automated to the final user.

Much of the labor required to create that automation is hidden.

Why So Much AI Data Work Goes to the Global South

Data annotation can be outsourced through international platforms.

Companies seeking large quantities of human labor naturally look for regions where:

• Wages are lower
• Digital skills are available
• Internet connectivity is sufficient
• Large workforces are available
• Workers speak required languages

The ILO explicitly identifies data curation and annotation as an important source of employment in developing countries while calling for decent working conditions throughout the AI value chain.

Researchers studying AI data work in Venezuela, Brazil, Madagascar and France found cross-border production systems in which workers in lower-income countries perform essential tasks supporting AI products whose highest-value activities and ownership often remain elsewhere. The authors argue that these structures can resemble older patterns of global economic dependency.

This is sometimes described as the hidden factory floor of AI.

The factory is digital.

The division of labor is global.

AI Can Create Jobs Without Creating Equal Power

Outsourcing is not automatically exploitation.

A data-labeling job may provide useful income.

AI investment can create real employment.

International technology partnerships can transfer knowledge.

The problem emerges when the value chain becomes structurally unequal.

Imagine an AI system worth billions of dollars.

Workers in poorer countries may have helped:

• Annotate its training data
• Moderate its harmful outputs
• Translate its language
• Evaluate its answers

But they may receive only task-level compensation.

They generally do not own:

• The model
• The patents
• The cloud infrastructure
• The customer relationship
• The resulting datasets
• The company shares

The largest economic gains occur elsewhere.

This pattern resembles older commodity economies in which developing regions exported inexpensive raw materials while finished products generated far greater value abroad.

That is why labor is one of the central pillars of the AI-colonialism argument.

Data Is the New Extractive Resource

The comparison becomes even stronger when data enters the picture.

Modern AI systems learn from enormous collections of:

• Text
• Images
• Video
• Speech
• Code
• Scientific documents
• Cultural archives
• Human interactions

These materials contain human knowledge accumulated across generations.

When a community’s books, artwork, conversations or cultural descriptions become training material, the resulting system can extract statistical patterns from them and turn those patterns into commercial capability.

Data-colonialism scholars argue that this process changes human experience into a resource that can be captured and monetized.

The central issue becomes:

Who has the authority to transform knowledge into training data?

Data Extraction Can Be Especially Sensitive for Indigenous Communities

Not all knowledge is intended to be universal.

Some Indigenous communities maintain knowledge through:

• Oral tradition
• Community custodianship
• Ritual
• Restricted cultural practice
• Collective ownership

A model trained on publicly accessible digital material may not understand these distinctions.

The fact that information is technically accessible does not necessarily mean a community considers unrestricted reuse legitimate.

UNESCO’s AI ethics framework calls for the protection of cultural diversity, Indigenous knowledge, endangered languages and participatory approaches to AI development. It also emphasizes national sovereignty in data governance.

The AI-colonialism debate therefore involves more than privacy.

It involves cultural authority.

Language Is a Form of Digital Power

AI systems are dramatically better in some languages than others.

The reason is straightforward.

Languages with enormous amounts of:

• Digitized literature
• News
• Wikipedia content
• Government documents
• Social-media text
• Translations
• Academic publications

provide far richer training material.

Smaller languages may have only a fraction of this digital footprint.

UNESCO reported in 2025 that more than 7,000 languages are spoken globally while only around 1,000 have a meaningful online presence.

That imbalance becomes extremely important in the age of generative AI.

A language poorly represented online may also be poorly represented inside AI.

AI Can Quietly Standardize Culture

Suppose a model has enormous amounts of American and European data but relatively little material from a local community.

When asked about that community, it may generate answers based on:

• Simplified stereotypes
• Tourist descriptions
• Foreign scholarship
• Dominant-language translations
• Highly visible internet content

rather than lived local knowledge.

A 2026 report on the AI-colonialism debate highlighted concerns that mainstream models can flatten cultural differences because much of their training material comes from Western, educated and highly digitized societies. One example discussed how broad descriptions of Indian cuisine can incorrectly reduce extremely diverse regional food traditions to generic ideas such as everything being heavily spicy or aromatic.

The problem appears small.

Its implications are not.

AI increasingly mediates how people learn about the world.

Imagine Asking AI About Your Own Culture

Consider a student asking an AI system:

“What does this tradition mean?”

The model answers confidently.

But its answer is based largely on foreigners writing about the tradition.

The student may never know that.

At scale, this produces something scholars call epistemic colonialism.

Epistemic power concerns who gets to define knowledge.

If one technological system becomes a global mediator of information while relying disproportionately on the perspectives of particular societies, the resulting imbalance is not just technical.

It is cultural.

The Problem Is Not That Western Knowledge Is Bad

A decolonial critique does not require rejecting European or American science, philosophy or technology.

Nor should every piece of knowledge be evaluated primarily according to the nationality of its creator.

The problem arises when one cultural perspective becomes the invisible default.

A global AI system should ideally understand:

• Multiple legal traditions
• Regional histories
• Religious diversity
• Local medicine
• Cultural context
• Linguistic variation
• Different moral frameworks

without treating one society’s assumptions as universal.

UNESCO’s Recommendation on the Ethics of Artificial Intelligence specifically calls for cultural and linguistic diversity, inclusion and the participation of marginalized groups throughout the AI life cycle.

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Alignment Raises an Even Deeper Question

Generative AI systems do not simply learn language.

Developers also attempt to shape how they behave.

This is broadly called alignment.

Systems may be trained not to:

• Encourage violence
• Generate discriminatory content
• Facilitate dangerous activity
• Violate privacy

These goals are generally beneficial.

But difficult questions appear when systems must make value judgments.

Whose definitions determine what is:

• Offensive?
• Respectful?
• Politically neutral?
• Morally acceptable?
• Culturally appropriate?

Research on decolonial AI alignment argues that globally deployed systems risk treating culturally specific moral assumptions as universal if excluded communities do not participate meaningfully in alignment decisions.

A global AI should not require one global culture.

Infrastructure Dependence Creates Another Layer

Imagine a government modernizing its education, healthcare and public administration around AI.

It uses foreign:

• Cloud infrastructure
• Foundation models
• APIs
• Cybersecurity services
• Chips

Initially, this may be extremely efficient.

Building everything domestically would be expensive.

But dependence can deepen.

Over time, replacing the technology may become difficult because:

• Government systems depend on proprietary APIs
• Employees are trained on one ecosystem
• Data formats become integrated
• Procurement contracts accumulate
• Local alternatives disappear
• Switching costs rise

This is known as technological lock-in.

If essential national services depend on infrastructure controlled outside the country, AI becomes a question of sovereignty.

Colonialism Through Dependency Rather Than Occupation

This is one of the more sophisticated forms of the argument.

A country does not need to be conquered if essential systems become structurally dependent on another country’s technology.

The dependent state remains politically independent.

But key infrastructure may be controlled externally.

Possible dependencies include:

• Cloud hosting
• Semiconductor imports
• Model APIs
• Software ecosystems
• Cybersecurity tools
• Payment infrastructure

Critics argue that this can produce a form of asymmetric technological power in which richer countries or corporations gain leverage without formal political rule.

Whether that deserves the word colonialism remains debated.

The dependency itself is real.

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AI Colonialism Is Not Only a Western Problem

The discussion is sometimes reduced to:

West versus Global South.

Reality is more complicated.

China is also a major AI power.

Regional corporations can dominate local markets.

Governments in the Global South can deploy AI irresponsibly against their own populations.

Domestic elites can benefit from extractive digital systems.

A company headquartered in a developing country can exploit workers in another developing country.

Colonial patterns concern power relationships, not simply geography.

The more useful question is therefore:

Where does control accumulate relative to the people who provide the resources and experience the consequences?

Environmental Extraction Is Part of the Debate

AI is physical.

Training and operating large models requires:

• Electricity
• Cooling
• Data centers
• Hardware
• Metals
• Semiconductor manufacturing

These systems therefore have environmental footprints.

UNESCO’s global ethics recommendation specifically calls on governments and AI actors to reduce environmental impacts, including carbon emissions and unsustainable exploitation of natural resources.

The colonialism concern appears when environmental costs and economic benefits occur in different places.

A community might supply:

• Energy
• Water
• Minerals
• Land for infrastructure

while most profits accrue to distant corporations.

Again, the key issue is distribution.

Who receives the benefits?

Who absorbs the costs?

AI Colonialism and South Asia

South Asia occupies an especially interesting position.

Countries such as India possess:

• Massive engineering workforces
• Major technology industries
• Large digital populations
• Enormous linguistic diversity
• Outsourcing sectors
• Growing AI ecosystems

India is simultaneously capable of being an AI producer, a major market and a provider of labor to foreign technology companies.

The ILO notes that business-process outsourcing is particularly significant in countries including India and the Philippines and may itself be transformed by generative AI.

This illustrates why the AI-colonialism framework cannot divide the world neatly into colonizers and colonized.

Different layers of the same economy may contain different power relationships.

Bangladesh Faces Similar Strategic Questions

For countries such as Bangladesh, the fundamental AI question is not simply whether citizens can access powerful chatbots.

Access is valuable.

But long-term technological capability depends on whether a country develops:

• AI researchers
• Local datasets
• Bangla-language technology
• Domestic startups
• University research
• Public-sector expertise
• Computing infrastructure
• Responsible data governance

Without those capabilities, a country may become an enthusiastic AI consumer while remaining dependent on systems designed elsewhere.

That does not automatically constitute colonialism.

But it creates precisely the kind of structural dependency that critics of AI colonialism are warning about.

Why Local Languages Matter So Much

Language technology offers one of AI’s greatest opportunities for developing countries.

Good local-language AI could help:

• Translate government services
• Improve education
• Assist farmers
• Expand healthcare information
• Support people with low English proficiency
• Digitize cultural archives
• Improve accessibility

But developing those systems requires quality data.

That creates another dilemma.

A country may need to digitize enormous amounts of local-language material to prevent linguistic exclusion.

At the same time, communities may reasonably ask who will own the resulting datasets and models.

The solution cannot simply be:

Upload everything and hope someone else builds the technology.

UNESCO recommends AI education and digital resources in local and Indigenous languages and urges greater inclusion of linguistic diversity in AI governance.

Is Open-Source AI the Solution?

Open models can reduce dependency.

They can allow:

• Local fine-tuning
• Independent research
• Language adaptation
• Domestic hosting
• Academic experimentation

But “open” does not automatically solve every power problem.

A model may be publicly downloadable while still requiring expensive hardware.

Training data may remain unknown.

The most capable development infrastructure may still be concentrated elsewhere.

Researchers advocating decolonial AI alignment therefore discuss multiple kinds of openness—including technological openness, openness to society and openness to excluded knowledge systems.

Open weights are useful.

They are not the same thing as technological sovereignty.

What Would Decolonizing AI Actually Mean?

“Decolonizing AI” can sound abstract.

In practical terms, it would mean redistributing participation and power throughout the AI lifecycle.

That could include several changes.

Build local computing infrastructure

Countries need affordable access to high-performance computing.

The ILO and UN technology office recommend investment in data centers, cloud infrastructure and international arrangements that expand access to computing resources.

Invest in local AI research

Universities should not merely teach students to use foreign APIs.

They should develop the capacity to conduct independent AI research.

Protect data sovereignty

Countries need laws establishing how locally generated data can be collected, transferred, commercialized and reused.

UNESCO explicitly recognizes national sovereignty in data governance while requiring respect for international human-rights standards.

Give communities meaningful control

Indigenous and minority communities should participate when technology uses their languages, knowledge or cultural material.

Participation should happen before systems are deployed—not merely after harm occurs.

Improve conditions for data workers

AI supply chains should provide:

• Fair compensation
• Safe working conditions
• Psychological support
• Predictable contracts
• Worker rights
• Transparency

The ILO calls for decent work throughout the AI value chain, including data annotation and curation.

Build local-language AI

Models should understand the languages people actually use.

This requires long-term investment rather than treating low-resource languages as optional additions.

Diversify governance

People affected by AI need representation in:

• Dataset design
• Safety evaluation
• Model alignment
• Regulation
• Standards development

AI should not be governed entirely from a handful of technology capitals.

Global South Countries Also Need to Cooperate

Building independent frontier AI infrastructure is expensive.

Many smaller economies will never replicate every element of the United States or China’s AI ecosystem individually.

Regional collaboration may therefore be essential.

Countries can potentially share:

• Compute infrastructure
• Research networks
• Language datasets
• Safety evaluation capacity
• Training programs
• Regulatory expertise

Recent UN discussions on bridging the AI divide have emphasized regional cooperation, affordable models, diverse language data and increased access to compute for developing economies.

AI sovereignty does not necessarily mean technological isolation.

It can mean negotiating partnerships from a stronger position.

Does AI Colonialism Mean We Should Reject Foreign AI?

No.

That conclusion would be economically damaging and technologically unrealistic.

Foreign AI systems can provide enormous benefits.

Developing countries can use them to improve:

• Productivity
• Education
• Translation
• Healthcare
• Public administration
• Software development
• Research

The goal should not be to build digital walls.

It should be to avoid permanent dependency.

A healthy AI ecosystem can combine:

• International technology
• Domestic innovation
• Open-source models
• Local infrastructure
• Strong governance
• Fair competition

The danger is not international cooperation.

The danger is a relationship in which one side can only consume what the other controls.

AI Can Also Help Reverse Historical Inequality

The AI-colonialism framework is a warning, not a prediction.

AI could also make sophisticated capabilities cheaper and more accessible.

A small company in a developing country can now use tools that previously required enormous research organizations.

Students can access translation and tutoring.

Doctors can potentially gain decision-support tools.

Local developers can build products using powerful foundation models.

Governments can digitize difficult administrative processes.

AI could therefore reduce certain forms of technological inequality.

The final outcome depends heavily on ownership, access, governance and investment.

The Risk of Turning “AI Colonialism” Into a Buzzword

There is another danger.

If every inequality involving technology is called colonialism, the concept loses analytical value.

For example:

• A foreign app being popular is not automatically colonialism.
• Buying cloud services from another country is not automatically colonialism.
• Using English-language software is not automatically colonialism.
• International outsourcing is not automatically colonialism.

The stronger case involves structural asymmetry:

One side repeatedly supplies resources, data or labor while another systematically captures ownership, decision-making power and economic value.

That is the pattern worth investigating.

Seven Signs an AI Relationship May Be Becoming Extractive

A useful way to evaluate the problem is to ask seven questions.

1. Who owns the data?

Were communities meaningfully involved in deciding how their data could be used?

2. Who performs the labor?

Are workers compensated fairly and given adequate protections?

3. Who owns the model?

Can the country modify and operate the technology independently?

4. Who controls the infrastructure?

Would essential services stop functioning if one foreign provider withdrew?

5. Whose language and culture dominate?

Does the AI understand local reality, or simply translate foreign assumptions?

6. Who makes the rules?

Are affected communities represented in governance and safety decisions?

7. Where does the profit go?

Does significant economic value remain within the society contributing data, labor and resources?

The more consistently these answers point in one direction, the stronger the colonialism analogy becomes.

AI Colonialism Is Ultimately About Power

The technical details of artificial intelligence change quickly.

Models improve.

Chips become faster.

Companies rise and fall.

But the underlying political question is much older.

Who controls productive capability?

Colonial economies were powerful partly because they controlled the systems connecting:

• Resources
• Labor
• Manufacturing
• Trade
• Capital

The AI economy contains its own chain:

Data → labor → compute → models → platforms → economic power

If every stage becomes concentrated among the same small group of actors, societies outside that group may become technologically dependent even while using remarkably advanced tools.

That is the scenario critics are warning about.

The Most Important Difference From Historical Colonialism

There is also a reason for optimism.

Countries today possess something colonial subjects often did not:

political sovereignty.

Governments can:

• Regulate data
• Invest in universities
• Build infrastructure
• Create competition policy
• Support local startups
• Negotiate technology partnerships
• Cooperate regionally
• Protect workers
• Require transparency

UNESCO’s AI ethics framework specifically encourages states to build capacity while avoiding the exploitation of countries that lack infrastructure, education or legal resources.

AI colonialism is therefore not inevitable.

Policy choices matter.

The Real Battle Is Over Whether Countries Become Consumers or Creators

Every major technological revolution produces consumers.

The more important economic question is who becomes a producer.

Countries that merely purchase AI products may gain productivity.

Countries that build:

• Models
• Chips
• Infrastructure
• Research institutions
• Startups
• Intellectual property

can capture a much larger share of the value.

This is why developing countries should measure AI readiness by more than chatbot adoption.

A million people using AI does not necessarily mean the country possesses an AI industry.

Consumption is not sovereignty.

The Most Accurate Verdict on AI Colonialism

AI colonialism is a serious analytical framework, but it should not be treated as a literal claim that modern technology companies are identical to historical colonial empires.

The concept becomes useful when it identifies recurring patterns of:

• Data extraction
• Cheap outsourced labor
• Infrastructure dependency
• Concentrated ownership
• Cultural dominance
• Linguistic exclusion
• Environmental burden
• Unequal rule-making

Evidence for several of these inequalities is substantial.

The ILO documents the hidden importance of data workers and the unequal infrastructure needed to benefit from AI. The OECD identifies concentrated AI infrastructure markets. UNCTAD warns that AI’s economic gains risk becoming concentrated among a small number of countries and firms. UNESCO is calling for data sovereignty, cultural inclusion, local languages and participation by marginalized communities.

The important question is not whether we agree with the hashtag #AIColonialism.

The important question is whether the AI revolution creates a world where billions of people become permanent tenants of a technological system owned somewhere else.

If data, language, human labor and natural resources flow outward while ownership and power accumulate inward, the colonial analogy will become increasingly difficult to dismiss.

But another future remains possible.

Countries can build local expertise.

Workers can receive a fair share of AI’s value.

Communities can control their data.

Languages can be preserved rather than flattened.

Infrastructure can be distributed.

International partnerships can transfer capability instead of merely selling dependency.

Artificial intelligence does not arrive with a predetermined political system.

People will build that system around it.

And perhaps the defining question of the AI age will not be whether machines become more intelligent than humans.

It will be whether humanity allows one of its most powerful technologies to reproduce some of its oldest inequalities.

Frequently Asked Questions About AI Colonialism

What is AI colonialism?

AI colonialism is a critical framework describing situations where powerful countries or corporations control AI infrastructure and economic value while extracting data, labor, knowledge or resources from less powerful communities.

Is AI colonialism an established scientific fact?

It is a theoretical and political framework rather than a single experimentally measurable phenomenon. A substantial academic literature uses colonial and decolonial theory to analyze AI power structures.

Is AI colonialism the same as historical colonialism?

No. Historical colonialism involved territorial conquest, political rule and often extreme violence. AI colonialism is generally used to describe analogous patterns of extraction and dependency in digital economies.

What is data colonialism?

Data colonialism is the idea that modern economic systems increasingly appropriate human activity by converting it into data that can be collected and monetized. The concept was developed prominently by Nick Couldry and Ulises Mejias.

How does AI extract data?

AI models can learn from digitized text, images, audio, video and other information. Questions arise when communities have little influence over how their information is incorporated into commercial systems.

Why is AI labor considered part of colonialism?

AI depends on data annotators, moderators and other human workers, many of whom work through outsourced labor markets. Critics argue that low wages combined with concentrated ownership of the resulting AI products can reproduce unequal global labor relationships.

Are AI systems really trained by humans?

Yes. Human workers perform tasks including data annotation, evaluation, transcription and content moderation that are essential to many AI development processes.

Where do AI data workers live?

Data work is distributed globally, with significant activity in developing countries. Research has examined workers in countries including Venezuela, Brazil and Madagascar, while ILO reporting discusses AI-related digital labor across the Global South.

What is the AI divide?

The AI divide describes inequalities in countries’ ability to access, build and benefit from AI because of differences in infrastructure, computing resources, skills and investment.

Why is computing power important?

Advanced AI requires large amounts of specialized computation. Without affordable access to GPUs, cloud infrastructure and data centers, researchers and companies may be unable to compete.

Is AI infrastructure concentrated?

Yes. OECD research identifies significant concentration across important parts of the AI infrastructure supply chain, including advanced chips, GPUs and cloud services.

How large could the AI market become?

UNCTAD projected that the global AI market could reach approximately $4.8 trillion by 2033 while warning that the benefits risk becoming highly concentrated.

What is linguistic colonialism in AI?

It refers to situations where dominant languages and cultures shape AI systems disproportionately, potentially marginalizing smaller linguistic communities.

How many languages are represented online?

UNESCO reported that more than 7,000 languages are spoken worldwide while only around 1,000 have an online presence.

Why does this matter for AI?

AI models generally perform better when high-quality digital training material exists. Languages with less online content may therefore receive weaker AI support.

Can AI erase culture?

AI cannot literally erase a culture by itself, but systems that repeatedly simplify, misrepresent or exclude minority cultures can contribute to cultural homogenization.

What is epistemic colonialism?

It concerns unequal power over defining what counts as legitimate knowledge. In AI, it can occur when systems largely reflect the perspectives of dominant societies while presenting those perspectives as universal.

Is AI colonialism only about Western companies?

No. Colonial-style digital power relationships can emerge between any actors when one side controls resources and decision-making while another becomes dependent.

Is China part of the AI colonialism debate?

Yes. China is one of the world’s major AI powers. The broader debate concerns concentration of technological power regardless of whether it occurs in the United States, China or elsewhere.

Is India vulnerable to AI colonialism?

India is both a major technology producer and a large AI market, while also participating heavily in global outsourcing. This makes its position more complex than simply labeling it either a dependent or dominant AI economy.

Could Bangladesh become dependent on foreign AI?

Yes, particularly if essential AI services rely entirely on foreign models, cloud providers and datasets. International technology use is not inherently problematic, but long-term dependence without domestic capability can create strategic vulnerabilities.

What is data sovereignty?

Data sovereignty refers broadly to the ability of states or communities to establish rules governing data generated within their jurisdictions or by their members.

Does UNESCO support data sovereignty?

UNESCO’s Recommendation on the Ethics of AI states that national sovereignty should be respected in data governance while remaining consistent with international law and human rights.

What is decolonial AI?

Decolonial AI is an approach that examines historical power structures in AI development and attempts to include marginalized knowledge, communities and perspectives in technology design and governance.

Can open-source AI prevent technological colonialism?

It can reduce some forms of dependency by allowing greater local control and customization. However, compute costs, infrastructure concentration and unavailable datasets mean open-source access alone does not eliminate inequality.

What should developing countries do?

Priorities include local AI education, compute infrastructure, data governance, domestic research, regional cooperation, local-language models and policies supporting AI workers.

Why should governments invest in compute?

Without computing infrastructure, researchers and startups may remain dependent on foreign providers even when they possess the talent to develop AI systems.

Should every country build its own frontier AI model?

Probably not. Training frontier models is extremely expensive. Regional cooperation, smaller specialized models, open-source systems and shared compute may be more realistic for many countries.

Can regional cooperation reduce AI dependency?

Yes. Shared research networks, computing infrastructure and language resources can help smaller economies gain capabilities they could not afford individually.

Is foreign cloud computing bad?

No. Foreign cloud platforms can provide valuable, efficient infrastructure. The strategic issue is whether governments and industries retain alternatives and bargaining power rather than becoming irreversibly locked into one external provider.

Is outsourcing AI work exploitation?

Not automatically. Outsourcing can create valuable jobs. The issue is whether workers receive fair wages, protections and meaningful participation in the economic value they help create.

What does the ILO recommend for AI data workers?

The ILO calls for decent work and protection of fundamental labor rights throughout the AI value chain, including data curation and annotation.

Does AI have an environmental cost?

Yes. AI infrastructure consumes energy and resources, and UNESCO has called for environmental impacts to be assessed and reduced throughout the AI lifecycle.

Can AI colonialism affect education?

Yes. If educational systems rely heavily on foreign AI that poorly represents local history, language or culture, students may receive information shaped disproportionately by external perspectives.

Can AI colonialism affect government?

Potentially. Governments that build essential public services entirely around foreign proprietary AI may create long-term technological dependencies.

Does using ChatGPT or another foreign AI make someone colonized?

No. Individual use of an AI tool is not what the concept means. AI colonialism describes broader structural relationships involving ownership, extraction, dependency and power.

Is the term AI colonialism exaggerated?

Sometimes it can be. Using the word for every technological inequality weakens its usefulness. It is strongest when applied to identifiable patterns of resource extraction and unequal control.

What is the alternative to AI colonialism?

The alternative is a more distributed AI ecosystem in which countries and communities have meaningful control over infrastructure, data, languages, labor standards and governance.

What does UNESCO recommend?

UNESCO emphasizes human rights, diversity, inclusion, data governance, environmental sustainability, local languages, public participation and stronger technological capacity in developing economies.

Will AI inevitably widen inequality?

No. AI could also reduce barriers to education, research, software development and access to expertise. Outcomes will depend heavily on policy, infrastructure and how economic benefits are distributed.

Is AI colonialism really about technology?

Only partly.

At its core, the debate is about power.

The algorithms matter.

But the deeper issues are who owns them, who supplies their resources, whose knowledge they represent and who gets to decide what kind of AI-powered world everyone else will live in.