"We almost went out of business several times."
Usually founders don't talk about their company's near-death experiences. But Jen-Hsun Huang, the boss of Nvidia, has no reason to be coy. His firm, which develops microprocessors and related software, is on a winning streak. In the past quarter its revenue increased by 55 percent, reaching $2.2 billion, and in the past 12 months its share price has almost quadrupled.
A big part of Nvidia's success is because demand is growing quickly for its chips, called graphics processing units (GPUs), which turn personal computers into fast gaming devices. But the GPUs also have new destinations: notably data centers where artificial-intelligence (AI) programs gobble up the vast quantities of computing power that they generate.
Soaring sales of these chips are the clearest sign yet of a secular shift in information technology. The architecture of computing is fragmenting because of the slowing of Moore's law, which until recently guaranteed that the power of computing would double roughly every two years, and because of the rapid rise of cloud computing and AI. The implications for the semiconductor industry and for Intel, its dominant company, are profound.
Things were straightforward when Moore's law, named after Gordon Moore, a founder of Intel, was still in full swing. Whether in PCs or in servers (souped-up computers in data centers), one kind of microprocessor, known as a "central processing unit" (CPU), could deal with most "workloads," as classes of computing tasks are called. Because Intel made the most powerful CPUs, it came to rule not only the market for PC processors (it has a market share of about 80 percent) but the one for servers, where it has an almost complete monopoly. In 2016 it had revenue of nearly $60 billion.
This unipolar world is starting to crumble. Processors are no longer improving quickly enough to be able to handle, for instance, machine learning and other AI applications, which consume more number-crunching power than entire data centers did just a few years ago. Intel's customers, such as Google and Microsoft together with other operators of big data centers, are opting for more and more specialized processors from other companies and are designing their own to boot.
Nvidia's GPUs are one example. They were created to carry out the massive, complex computations required by interactive video games. GPUs have hundreds of specialized "cores" (the "brains" of a processor), all working in parallel, whereas CPUs have only a few powerful ones that tackle computing tasks sequentially. Nvidia's latest processors boast 3,584 cores; Intel's server CPUs have a maximum of 28.
The company's lucky break came in the midst of one of its near-death experiences during the global financial crisis. It discovered that hedge funds and research institutes were using its chips for new purposes, such as calculating complex investment and climate models. It developed a coding language, called CUDA, that helps its customers program its processors for different tasks. When cloud computing, big data and AI gathered momentum a few years ago, Nvidia's chips were just what was needed.
