Most of the AI conversation happens in English, from Silicon Valley, about Silicon Valley problems. If you only follow the mainstream tech press, you'd think AI adoption is a uniform global phenomenon — a wave washing over every industry at the same pace. Having spent time between Morocco, Canada, and talking with people working in tech across Europe and the Middle East, I can say it's not that simple at all.
AI adoption is deeply shaped by local context — by infrastructure, regulation, language, labor markets, and cultural attitudes toward technology. And the differences are more interesting than the similarities.
Infrastructure Shapes Everything
In North America, the conversation around AI assumes reliable internet, cloud infrastructure, and access to compute. These are background conditions that nobody even mentions. But in much of the world, these aren't givens. In parts of North Africa and Sub-Saharan Africa, connectivity is still inconsistent enough that cloud-dependent AI solutions are impractical for many use cases.
This doesn't mean AI isn't happening in these regions — it just looks different. Mobile-first solutions that work offline or on low bandwidth. Lightweight models that run on devices rather than in the cloud. Applications designed around SMS and voice rather than web interfaces. The innovation is real; it just doesn't match the template from San Francisco.
I find this fascinating because it challenges the assumption that AI adoption follows a single path. The path depends on the starting point, and different starting points lead to genuinely different solutions — some of which might be better suited for the majority of the world's population than the GPU-intensive, cloud-dependent approaches that dominate Western tech discourse.
Regulation Creates Different Incentives
The EU's AI Act is creating a regulatory framework that doesn't exist in the US or most of Asia. This shapes what companies build and how they build it. European AI companies are investing heavily in explainability, fairness auditing, and risk assessment — not because they're more ethical, but because the regulatory environment requires it.
Meanwhile, countries like the UAE, Saudi Arabia, and Singapore are positioning themselves as AI-friendly regulatory environments, explicitly trying to attract AI companies and talent. Morocco's own digital strategy is somewhere in between — aspirational on AI adoption but still working out the regulatory details.
What's interesting from a student's perspective is how these different regulatory environments create natural experiments. We'll be able to see, over the next decade, whether heavy regulation slows innovation or whether it builds more trustworthy systems that ultimately see wider adoption. The answer probably isn't one or the other — it probably depends on the sector and the application.
Language Is an Underrated Factor
Most AI models are trained primarily on English data. This creates a real capability gap for non-English languages — and not just in obvious ways like chatbot quality. It affects everything from sentiment analysis accuracy to speech recognition to the quality of AI-assisted search.
Arabic is a particularly interesting case. It has dozens of dialects that differ significantly from Modern Standard Arabic, and most NLP work focuses on MSA because that's where the data is. A chatbot trained on MSA might work fine for formal contexts but be nearly useless for customer service in Morocco, where people speak Darija, or in Egypt, where the dialect is different again.
I've been following the work of researchers and startups trying to close this gap — building datasets for underrepresented languages, fine-tuning models for specific dialects, creating tools that work for the 80% of the world that doesn't speak English natively. It feels like one of the most impactful areas of AI work that gets the least attention in mainstream tech media.
Labor Market Differences Matter
In high-wage economies, AI automation is primarily framed as a labor cost reduction. Automate customer service because agents are expensive. Automate data entry because labor costs are high. The economic case is straightforward.
In lower-wage economies, the calculus is different. When labor is cheaper, the ROI on automation is lower, and the social cost of displacement is potentially higher because safety nets are thinner. This doesn't mean these countries shouldn't adopt AI — but it means the use cases that make sense are different. AI for agricultural yield optimization, for healthcare diagnostics in underserved areas, for educational access — these applications create value without primarily displacing workers.
What strikes me about all of this is how poorly the "one-size-fits-all" AI strategy works at the international level. The most interesting AI work over the next decade might not come from the biggest labs or the most well-funded startups. It might come from teams who deeply understand a local context and build AI solutions that actually fit it. That's the kind of work I find most compelling — not AI for its own sake, but AI shaped by the real constraints and needs of a specific place.