OpenAI, Microsoft and Google are among companies driving the global artificial intelligence (AI) revolution. But behind many of the technologies powering this transformation is another major player: Nvidia, a company that has become one of the most influential names in the technology industry. The company became the centre of attention recently after its founder and chief executive resigned amid reports that he had an affair with a Chinese spy. In October 2025, Nvidia became the first publicly traded company to reach a market valuation of $5 trillion, reflecting investor confidence in the growing demand for AI technology, according to Reuters. ALSO READ: Jensen Huang resigns as Nvidia CEO Founded in 1993, the company initially focused on developing graphics chips for video games. More than three decades later, it has become a major supplier of computing technology used in AI, scientific research, robotics and other industries. The early years Nvidia was founded by Jensen Huang, Chris Malachowsky and Curtis Priem, with the ambition of bringing three-dimensional graphics to the gaming and multimedia markets. At the time, computers relied largely on central processing units (CPUs) to perform their operations. As video games became more sophisticated, producing realistic images and movements required more powerful computing capabilities. The founders saw an opportunity to develop specialised processors that could handle graphics calculations more efficiently. In 1999, the company introduced the GeForce 256, which it describes as the world's first graphics processing unit (GPU). Unlike a CPU, which handles a wide range of computing tasks, a GPU is designed to perform many calculations simultaneously. This makes it particularly useful for processing graphics and handling the large volumes of data involved in certain scientific and computing tasks. ALSO READ: Nvidia’s DGX Spark puts supercomputing within arm’s reach Although initially developed for gaming, GPUs would later become important tools in artificial intelligence. The shift beyond gaming A major step in that transition came in 2006, when the chipmaker introduced CUDA (Compute Unified Device Architecture), a software platform that allows programmers to use its GPUs for tasks beyond graphics, including scientific research, data analysis and AI. This opened up the technology to researchers and developers who needed powerful computing systems to process large amounts of information and solve complex problems. It also helped the company build a software ecosystem around its chips, encouraging more developers and businesses to adopt them. Norbert Haguma, a Rwandan tech expert, said the company's early investment in GPU computing helped it benefit from the growth of AI. “What made Nvidia so successful is that, well before the rise of generative AI, it bet that GPUs would be used for artificial intelligence instead of CPUs,” Haguma told The New Times. “That bet proved remarkably successful, and the company captured a large part of the AI computing market.” The role of GPUs in AI development Another turning point came in 2012, when AlexNet, a neural network designed to learn patterns from data, won the ImageNet image-recognition competition. The system had been trained using two Nvidia graphics cards, demonstrating how GPUs could help computers learn to recognise images. The achievement accelerated interest in deep learning, a branch of AI that uses networks with multiple layers to identify complex patterns in data. As researchers developed more advanced AI models, the need for computing power increased. In 2017, the company introduced Tensor Core technology in its Volta GPU architecture to accelerate calculations required by AI workloads. These developments helped position the chipmaker to benefit from the next major shift in technology: generative AI. Growing demand for AI chips Demand for Nvidia's technology grew further with the expansion of generative AI, which creates new content such as text, images and audio, particularly after the public release of ChatGPT in November 2022. ALSO READ: Top eight breakthrough gadgets from CES 2025 that will transform daily life Developing and running these systems requires enormous computing resources. This has increased demand for powerful chips and data centres, which house interconnected computers and storage systems used to process and store information. Major technology companies, including Microsoft, Google and Amazon, have invested heavily in AI infrastructure, creating a growing market for the company's products. Haguma said Nvidia's influence extends beyond chip manufacturing to several parts of the AI industry. “When you look at the entire AI stack, from energy all the way to applications, Nvidia is present at every major layer. From electricity that powers the systems to ChatGPT you are using, Nvidia’s technology is embedded at every layer.” The technology is also reaching AI developers in Rwanda. According to AI Skills and Compute Africa, a programme supporting African AI innovators, two Rwandan companies, Pindo and Digital Umuganda, were each allocated access to four Nvidia H200 GPUs for 24 weeks through its Compute Grant and Technical Assistance Programme. The growing demand for AI computing is reflected in the company's financial performance. According to its 2026 annual report, Nvidia generated $215.9 billion in revenue in the financial year ended January 25, a 65 per cent increase from the previous year. Its Data Centre business generated $193.7 billion, accounting for nearly 90 per cent of total revenue. In results released on August 26, the company reported revenue of $96.2 billion for the three months ended July 26, up 106 per cent from the same period a year earlier. What gives Nvidia an edge In its November 2025 report, Competition in Artificial Intelligence Infrastructure, the Organisation for Economic Co-operation and Development (OECD) identified Nvidia's early investment in GPU computing, chip performance and its CUDA software platform as key factors behind its leading position. CUDA has built a strong developer ecosystem, making it costly for some customers to switch to competing products because they may need to adapt their applications to different software. Although Nvidia competes with chipmakers such as AMD and Intel, its established software platform gives it an advantage. The company also operates a fabless manufacturing model, focusing on chip design while relying on external suppliers, including Taiwan Semiconductor Manufacturing Company (TSMC), to manufacture, assemble and test its products. A leadership transition The company's rise has been closely associated with co-founder Huang, who led it for more than three decades. However, on November 12, Nvidia announced that he had resigned as president and CEO, effective immediately. Huang's resignation came days after Nvidia announced an investigation into reports that he had been targeted by a foreign intelligence effort, and he admitted to employees that he had been unfaithful to his wife. The board appointed CFO Colette Kress as interim CEO.
