In June 2024, China had built or initiated construction on over 250 internet data centers, backed by local governments, state-owned enterprises, and private investors. Despite this explosive growth, the utilization rate of computing resources provided by private servers was reported to be less than 5%
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China’s rapid expansion in artificial intelligence (AI) has led to an oversupply of data centers, many of which remain underutilized. Following the debut of ChatGPT in 2022, the Chinese AI sector experienced a surge in investment, prompting the construction of numerous data centers nationwide. However, this swift development has resulted in an excess of facilities struggling to find clients, with GPU rental prices declining significantly.
In June 2024, China had built or initiated construction on over 250 internet data centers, backed by local governments, state-owned enterprises, and private investors. Despite this explosive growth, the utilization rate of computing resources provided by private servers was reported to be less than 5%, revealing a stark mismatch between supply and demand. The rush to capitalize on the AI boom after ChatGPT’s 2022 debut led to speculative investments, but many facilities now stand half-empty due to limited enterprise adoption.
The rapid rollout of data centers has been plagued by inefficiencies. In 2024 alone, China added at least one million AI chips to its computing capacity. However, these resources were often dispersed across facilities of varying quality, sometimes in regions with minimal demand for high-performance computing. This fragmentation has made it difficult to consolidate GPU power for large-scale AI tasks, further depressing utilization rates.
The environmental impact of China’s data center expansion is equally alarming. Projections indicate that by 2030, these facilities could consume approximately 792 billion gallons of water annually for cooling—equivalent to the total residential water use of South Korea. Energy demands are also soaring, with estimates suggesting data centers will exceed 400 billion kilowatt-hours by 2030, potentially generating over 200 million metric tons of carbon emissions. Such figures underscore the urgent need for sustainable infrastructure planning.
Recognizing these challenges, China’s National Development and Reform Commission introduced a plan in July 2024 to optimize data center distribution and reduce power usage effectiveness (PUE) to below 1.5 by 2025. Tech giants like Alibaba and Tencent are investing in renewable energy to mitigate their carbon footprint. However, these measures come after significant resource waste, highlighting the pitfalls of unchecked expansion.
As Africa accelerates its own digital transformation, China’s experience offers critical lessons. Should the continent prioritize scalable demand over infrastructure, or risk replicating these inefficiencies?
Africa must avoid China’s top-down approach by prioritizing scalable, localized AI solutions over speculative infrastructure. These examples show how aligning AI with immediate needs can drive organic growth.
Kenya has emerged as a hub for practical AI applications. Startups like SuaMia combat counterfeit medicine using machine learning, while Apollo Agriculture helps smallholder farmers optimize crop yields. Platforms like AIBenchmark Africa track adoption trends across the continent.
Nigeria’s fintech boom is fueled by AI-driven innovations. Kudi.ai chatbot streamlines banking services, while FairMoney leverages AI for credit scoring in underserved markets. Startups like Data Science Nigeria are nurturing local expertise to reduce reliance on foreign platforms.
South Africa leads in mining and healthcare AI. DataProphet optimizes manufacturing processes, while Vula Mobile connects rural doctors with specialists via AI-powered diagnostics. Such applications demonstrate how targeted AI deployment can drive sectoral growth and reflect a focus on solving real-world problems rather than speculative infrastructure.
Africa’s energy gaps are well-documented, but the continent also boasts abundant renewable resources. Solar, wind, and geothermal energy could power data centers more sustainably than China’s coal-dependent model. South Africa’s AWS data center, partially solar-powered, and Kenya’s geothermal potential offer blueprints for low-carbon AI infrastructure.
Governments should partner with tech firms to ensure balanced growth. Rwanda’s collaboration with Zipline for AI-powered medical drone deliveries demonstrates how targeted investments yield high impact. Similarly, Egypt’s national AI strategy incentivizes local startups to develop solutions tailored to African challenges, reducing reliance on foreign tech.
To prevent overdependence on imported hardware, Africa must invest in homegrown talent. Initiatives like Data Science Nigeria and DeepLearning.AI’s African courses are equipping developers with AI skills, while the African Union’s Continental AI Strategy emphasizes self-reliance in technology development.
China’s empty data centers reveal the risks of premature infrastructure scaling. For Africa, the key lessons are clear:
By learning from global missteps and leveraging its unique strengths, Africa can harness AI as a tool for inclusive, sustainable development, without repeating China’s costly experiments.
