AI Around the World
AI is often discussed as if it were a single global technology that everyone experiences the same way. In reality, where you live shapes almost everything about your relationship to AI — whether you can access it, what language it speaks, how it treats your data, whether your government uses it to watch you, and whether AI development is creating jobs or eliminating them in your local economy.
The United States: Private-Led and Market-Driven
The United States has produced many of the world's most influential AI companies — Google, Microsoft, OpenAI, Anthropic, Meta, and Amazon among them. AI development there is largely driven by private investment and market competition, with the government playing a relatively lighter regulatory role compared to other regions. This approach has produced rapid innovation and a global spread of American AI products. It has also produced concerns about the concentration of power in a few large companies, insufficient safeguards for users, and the export of AI systems that reflect American cultural assumptions into contexts where those assumptions may not fit.
China: State Strategy and Scale
China has made artificial intelligence a central element of its national strategy, committing to become the world's leading AI power by 2030. The Chinese government provides substantial funding for AI research, supports domestic companies in competing with American tech firms, and creates large national datasets — partly through government systems and state-owned enterprises — that fuel AI development. China is a leader in several specific AI applications: facial recognition deployed in public spaces, AI systems used in judicial and criminal justice contexts, and AI-powered manufacturing automation. Chinese companies like Baidu, Alibaba, Tencent, and Huawei are significant global players. Important differences in privacy law and civil liberties between China and democratic countries mean that data collection and surveillance applications acceptable under Chinese law would be prohibited in many other places. This creates different baseline assumptions about what AI systems should and should not be allowed to do.
The AI systems a country builds reflect that country's laws, values, and priorities. A country with strong privacy protections builds different AI than one that prioritizes security surveillance. Neither represents a purely neutral technical choice — both embed political values into technology.
The European Union: Rights-Based Regulation
The European Union has taken a distinctive approach: rather than leading in building AI, it has positioned itself as a leader in governing AI. The EU's General Data Protection Regulation (GDPR) established strong rules about how personal data can be collected and used, affecting AI training worldwide because companies want to serve European users. The EU Artificial Intelligence Act, which entered enforcement in phases starting in 2024 and 2025, classifies AI uses by risk level and prohibits certain applications outright — including real-time public facial recognition in most contexts and AI systems that manipulate people in harmful ways. High-risk applications like AI in hiring, education, and healthcare face strict requirements for transparency and human oversight. This regulatory approach prioritizes individual rights but may slow the deployment of some AI applications compared to less regulated markets.
The Global South: Building, Using, and Being Affected
Countries in Africa, Latin America, South and Southeast Asia, and the Middle East contain the majority of the world's population. Their relationship with AI is complex and varied. Some countries are growing tech hubs: Kenya's Nairobi has a thriving technology startup scene. India produces enormous numbers of software engineers and runs a rapidly expanding AI sector. Brazil has a growing AI research community. These countries are not only passive consumers of AI built elsewhere — they are active builders. At the same time, many populations in lower-income countries are on the receiving end of AI systems they had no role in designing, that were not built with their languages or cultural contexts in mind, and whose benefits may not reach them due to the digital divide. Data labeling work that makes AI possible often flows to workers in these regions, creating low-paid jobs while the profits flow back to wealthier countries.
Most large AI language models were trained predominantly on English-language text. This means they perform far better in English than in most of the world's other languages. A student in rural Nigeria using an AI tutor in Hausa will have a much poorer experience than a student using the same system in English. Language is not a small issue — it is a gateway to all the AI's other capabilities.
Match each region to its most distinctive approach to AI.
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Why does the fact that most large AI models were trained predominantly on English text matter for global equity?
The EU's AI Act classifies AI applications by risk level and bans some entirely. What value does this approach prioritize?
Global AI Perspectives
- Step 1: Choose two countries from different regions — for example, one from the United States or Europe and one from Africa, Southeast Asia, or Latin America.
- Step 2: Research how AI is being used in each country. Look for at least two specific examples in each — government programs, companies, healthcare, education, or agriculture applications.
- Step 3: For each country, identify one way the local context (language, infrastructure, laws, economy) shapes how AI is built or used.
- Step 4: Identify one AI application that exists in one country but would be illegal or impractical in the other. Explain why the difference exists.
- Step 5: Write a paragraph reflecting on whose values and priorities seem to be most reflected in the AI tools that are available globally.