Silicon Valley has declared a new AI arms race milestone—but who controls the mind of the machine, and at whose expense?
Google’s latest AI model, Gemini 2.5 Pro, is being marketed as a leap forward in machine reasoning. The tech giant claims this new generation of “thinking” models can process tasks step-by-step to produce deeper, more reliable outputs in math, coding, logic, and even multimodal inputs like video and audio.
But behind the impressive performance metrics lies a more urgent question for Africa: As AI grows more powerful, who shapes the values, languages, and cultural assumptions it learns from?
While Google hails Gemini 2.5 Pro as the top performer in benchmark tests—surpassing rival systems from OpenAI, Anthropic, xAI, and DeepSeek—the conversation is still being led and coded in the West. The model reportedly excels in STEM fields and is already accessible via Google AI Studio and Gemini Advanced subscriptions. However, these platforms remain tethered to Western data ecosystems, largely excluding African perspectives, languages, and local knowledge systems.
Google DeepMind’s CEO Demis Hassabis celebrated the model’s position as “No. 1 on LMArena” with a significant margin in ELO scores, but those metrics don’t measure bias, linguistic exclusion, or ethical misuse in African contexts. Nor do they answer critical concerns about who owns the data being used to train these increasingly omnipotent systems.
Google also promises that Gemini’s enhanced reasoning capabilities and upcoming 2-million-token context window will make the model even more capable of handling complex prompts. But complexity, in this case, is framed through a Euro-American epistemology. What does “reasoning” look like when trained mostly on English-language data, Eurocentric logic, and capitalist-coded objectives?
One demonstration featured Gemini programming a video game from a single prompt. Impressive, no doubt. But again, the tech narrative centers convenience and efficiency over cultural sensitivity, local adaptability, and social justice. For many African developers and policymakers, the question isn’t how fast AI can code games—but whether Africans can co-create AI systems rooted in their own values and realities.
This innovation raises concerns about data sovereignty as well. As Gemini integrates deeper into Google’s infrastructure—Android, YouTube, Gmail, and cloud services—how much African data is being scraped, monetized, and used to refine models without public transparency or compensation?
Africa is already a consumer of AI technology, not a co-architect. Yet our voices, languages, and data are feeding these global systems. What happens when those systems, armed with “reasoning,” begin shaping everything from education to public policy across the continent?
Shouldn’t African governments, universities, and technologists be building indigenous AI models, rather than remaining dependent on Western tools cloaked in open-access PR?
Google calls this evolution a step toward “more capable, context-aware agents.” But whose context are they referring to? And are African contexts—diverse, multilingual, historically nuanced—being represented at all?
AfricaFirst.news calls for more than technological marveling. We demand transparency. We demand equity. And most of all, we demand that Africa’s digital future not be dictated by Western labs chasing profit and power.
