Scaling AI opportunity across the globe: Learnings from GitHub and Andela (opens in new tab)
GitHub and Andela argue that AI opportunity should not depend on geography or employer resources. Their AI Academy trained 3,000 engineers by embedding GitHub Copilot into real production work rather than isolated exercises. The approach improved developers’ ability to understand unfamiliar systems, work with legacy code, and focus more time on higher-value decisions—while preserving human review and accountability. ## Unequal Access to AI Skills - Developers across Africa, South America, and Southeast Asia have substantial technical talent but uneven access to: - Emerging AI tools - Mentorship and structured training - Reliable connectivity and high-performance computing - Affordable cloud services and data - Much existing training assumes constant internet access, well-resourced environments, and localized content. - Contract-based or informal work can leave developers with limited time and financial capacity for reskilling. - Without affordable access and regionally relevant learning communities, AI could deepen existing technology inequalities. ## Learning AI Within Production Work - Mid-career developers generally cannot leave live systems and deadlines to experiment with new tools. - Simply giving teams access to AI does not guarantee adoption; organizations also need: - Clear role and use-case definitions - Training tied to actual responsibilities - Updated review and quality standards - Andela selected developers whose work involved complex production systems and incorporated Copilot into: - IDE workflows - Pull request reviews - Refactoring and maintenance - This made training practical and exposed AI tools to legacy code, architectural complexity, and real production risks. ## Faster Orientation in Unfamiliar Systems - One of the first benefits was not raw code-generation speed but faster understanding of existing systems. - Developers used AI to: - Generate unit tests before changing legacy code - Reveal system behavior and architectural patterns - Draft refactors and clarify control flow - Sketch diagrams of system boundaries - Tests provided safer boundaries for modifying poorly covered legacy code. - AI suggestions still required cleanup and could introduce subtle errors, making disciplined review essential. ## Confidence and Productivity Gains - After several weeks, developers reported: - Faster onboarding - Greater confidence handling ambiguous work - Less time spent on setup and more on business and engineering decisions - Senior engineer Daniel Nascimento estimated that Copilot increased his productivity by about 50%. - The main value was not merely completing tasks faster, but freeing time to understand business needs and focus on meaningful impact. ## Practical Model for AI Adoption - AI training is most effective when it is: - Embedded in everyday development - Based on real systems and responsibilities - Supported by structured guidance - Evaluated through production-quality standards - Organizations should treat AI as a capability developed through practice, not as a standalone certification or experiment. The GitHub–Andela experience suggests that inclusive AI adoption requires more than tool access. Pairing affordable, structured training with real production work can help developers worldwide build confidence, improve productivity, and participate more fully in the AI-driven future.