I’ve been thinking a lot about what it actually means to “learn AI” as a working developer. Not in the sense of building transformer models from scratch, but in the more practical sense of integrating AI tooling into real production systems while deadlines pile up and legacy code waits for no one.
The story of how Andela trained 3,000 engineers on GitHub Copilot across Africa and Latin America isn’t really about the tool itself. It’s about what happens when you stop treating artificial intelligence access as a solved problem and start acknowledging that geography still determines who gets to participate in the AI era.
The Real Barriers Have Nothing to Do With Talent
Here’s what bugs me about most AI training programs. They assume you have reliable internet, access to expensive cloud infrastructure, and the luxury of stepping away from production work to experiment. They assume you’re working in an environment where “just try it out” is a reasonable suggestion.
For developers in regions like Southeast Asia, Africa, and South America, those assumptions fall apart quickly. Unreliable connectivity isn’t a minor inconvenience. Limited access to high-performance compute isn’t just slower development, it’s a barrier to even running modern tooling. The cost of cloud services and data isn’t a line item, it’s a significant portion of income.
And yet somehow the narrative around AI adoption still centers on “skills gaps” rather than access gaps. As if talent is the variable here.
Stephen N’nouka A’ Issah, a React developer from Cameroon working in Rwanda, captured this perfectly when he admitted his initial skepticism about AI tools handling complex production systems. That skepticism wasn’t ignorance. It was pattern recognition from watching tools demo well in controlled environments and fail under real constraints.
Learning Inside Production Work, Not Adjacent to It
What Andela did differently was refuse to treat AI as a separate certification track removed from actual work. Through its AI Academy, training happened inside the systems developers were already maintaining. Not toy projects. Not sanitized examples. Real legacy code with real consequences.
Abraham Omomoh, a learning program manager at Andela, said something that should be obvious but apparently isn’t: “Training has to reflect what developers are actually asked to do at work, not idealized exercises.”
This matters because most mid-career developers can’t afford to step away from production responsibilities to “learn AI.” Systems stay live. Deadlines don’t pause. Reputations are built incrementally over shipped work, not through courses completed on the side.
Daniel Nascimento, a senior engineer in Brazil with 25 years of experience, described working on legacy code “nobody wants to touch” where the real risk isn’t speed but unintended consequences. His first question when approaching unfamiliar systems: “What does this project actually do? What’s the architecture? What are the weaknesses?”
His approach now involves using AI tools to generate unit tests before refactoring. Not to move faster, but to create clearer boundaries for what can be modified without breaking behavior. “Legacy code usually doesn’t have coverage,” he explained. “So I use it to build that coverage first. Then I know what I’m playing with.”
What Actually Changed in Practice
After several weeks of working with Copilot inside production systems, the patterns that emerged weren’t about AI replacing engineering judgment. They were about compressing orientation time and reducing repetitive overhead.
Stephen described using AI to surface intent, architectural patterns, and constraints before making changes to unfamiliar codebases. The work still involved significant cleanup. Suggestions still introduced subtle issues. Disciplined code review remained essential.
Daniel estimated around 50% productivity gains, but emphasized the source: “It’s not just speed. It gives me more time to connect with the business and focus on real impact.” Most of that gain came from offloading boilerplate and context-switching overhead, not from the AI writing production-ready code unsupervised.
For developers who previously lacked structured exposure to AI tooling, the impact went beyond immediate productivity. Certifications strengthened credibility. AI fluency expanded the scope of work they could take on. In regions where job opportunities correlate directly with demonstrable skills on emerging technologies, that access translates to economic mobility.
Koffi Kelvin, an Andela engineer based in Kenya, put it bluntly: “GitHub Copilot is a portal that catapulted my professional trajectory into a literal other dimension.”
Why This Actually Matters for Everyone
Expanding structured access to AI tools in the Global South isn’t charity work or some feel-good diversity initiative. It’s about ensuring the developers building AI-assisted systems reflect the full diversity of global engineering talent.
When training content assumes well-resourced environments, constant connectivity, and financial cushion for experimentation, it creates a selection effect. The people shaping how AI gets integrated into software are disproportionately from regions where those assumptions hold. The problems that get prioritized, the edge cases that get considered, the failure modes that get anticipated all reflect that narrow context.
Sammy Kiogara Mati, an Andela engineer who works on GitHub itself, said something that cuts through all the hype: “GitHub Copilot has expanded my view of what’s possible for global tech talent. AI does not level the playing field on its own. Structured access does.”
That’s the part that keeps getting glossed over in conversations about AI democratization. Access to the tool isn’t enough. Structured learning pathways matter. Mentorship within real production contexts matters. Community-driven ecosystems that can adapt training to local constraints matter.
The AI skills gap is fundamentally an access gap, and where structured access exists, learning compounds quickly.