Chances are you're reading this blog post on a modern computer that's either on your lap or on the palm of your hand. This modern computer has a central processing unit (CPU) and other dedicated chips for specialized tasks such as graphics, audio processing, networking and sensor fusion and others. These dedicated processors can perform their specialized tasks much faster and more efficiently than a general purpose CPU. We've been pairing CPUs with a specialized processor since the early days of computing. The early 8-bit and 16-bit CPUs of the 70s were slow at performing floating-point calculation as they relied on software to emulate floating-point instructions.

CPU speed is no longer growing fast enough to keep up with demanding workloads, highly complex computing, and astronomical data growth, but specialized processors can fill the gap. Today's leading companies need to be data-driven and, more important, able to act on data insights with speed. This takes many forms, from personalizing customer experiences at the point of interaction to processing billions of data points to detect fraudulent activity to pivoting strategies as new data indicates changing environments. There's been no shortage of innovation among data applications over the past decade, helping organizations yield value from growing data volumes. Modern data platforms -- data lakes, data warehouses, data lake houses -- have largely risen to the challenge.

Perhaps in no other technology has there been so many decades of large year-over-year improvements as in computing. It is estimated that a third of all productivity increases in the U.S. since 1974 have come from information technology,a,4 making it one of the largest contributors to national prosperity. The rise of computers is due to technical successes, but also to the economics forces that financed them. Bresnahan and Trajtenberg3 coined the term general purpose technology (GPT) for products, like computers, that have broad technical applicability and where product improvement and market growth could fuel each other for many decades. But, they also predicted that GPTs could run into challenges at the end of their life cycle: as progress slows, other technologies can displace the GPT in particular niches and undermine this economically reinforcing cycle. We are observing such a transition today as improvements in central processing units (CPUs) slow, and so applications move to specialized processors, for example, graphics processing units (GPUs), which can do fewer things than traditional universal processors, but perform those functions better. Many high profile applications are already following this trend, including deep learning (a form of machine learning) and Bitcoin mining. With this background, we can now be more precise about our thesis: "The Decline of Computers as a General Purpose Technology." We do not mean that computers, taken together, will lose technical abilities and thus'forget' how to do some calculations.

The world of high-tech innovation can change the destiny of industries seemingly overnight. Now we are on the cusp of a new grand leap thanks to the democratization of machine learning, a form of artificial intelligence that enables computers to learn without being explicitly programmed. This process of democratization is already underway. Image credit: Shyam's Imagination Library Last month, at the CloudNext conference in San Francisco, Google announced its acquisition of Kaggle, an online community for data scientists and machine-learning competitions. Although the move may seem far removed from Google's core businesses, it speaks to the skyrocketing industry interest in machine learning (ML).