Nigeria Charts Course for AI Independence Amid Global Tech Race

The global competition for artificial intelligence is intensifying, and Nigeria is determined not to be left behind. At the recent GITEX Nigeria summit in Abuja, government officials signaled a clear shift toward digital sovereignty as they outlined plans to build domestic AI capabilities.

NITDA Director-General Kashifu Inuwa Abdullahi warned that relying on foreign technology infrastructure creates vulnerabilities. “We missed the 4th industrial revolution; we mustn’t miss the AI revolution,” he stated, emphasizing that AI should be treated as essential national infrastructure.

The call for independence extends beyond mere adoption—Nigeria seeks to control the algorithms shaping its economy, security, and public services. As Inuwa noted, “Whosoever controls the AI can hold us hostage.” The government views data as the critical foundation for this effort, arguing that AI bias stems from foreign training datasets.

“AI is not biased; it’s the training data that’s biased,” Inuwa explained. “If we want AI that will serve us, we need to train it with our data.” This principle underpins several policy initiatives already underway.

The National Digital Cloud Policy aims to attract $750 million in private investment over 24 months, while Galaxy Backbone (GBB) is expanding its role as the digital backbone for government agencies. GBB operates Tier III and Tier IV certified data centers alongside a sovereign cloud platform—all operating under a presidential mandate designating digital infrastructure as Critical National Infrastructure.

Complementing these efforts is Project BRIDGE, which will deploy 90,000 kilometers of fiber-optic cable nationwide to connect underserved communities. On the human capital front, the Three Million Technical Talents (3MTT) initiative has already drawn over 1.8 million applications for training in AI and related fields.

While physical infrastructure and talent are essential pieces, officials acknowledge that building indigenous foundational models remains a key challenge—moving beyond simply using foreign APIs to create genuinely homegrown solutions.