According to the International Data Corp. (IDC), a global provider of market intelligence and analysis, big changes in the U.S. workforce will happen quickly and at a scale you never would have expected. The report, “IDC FutureScape: Worldwide Robotics 2017 Predictions,” outlines several jarring projections: By 2019, a hefty 35 percent of industries in health, commercial goods and utilities will use robotic automation in their operations. Nearly 40 percent of robots currently in use will be connected to a cloud network, increasing their efficiency and ability to access vast quantities of information.

Not only will robots continue to play larger roles in manufacturing and service industries, but they will press forward in white-collar fields as well. Martin Ford talks about the implications of this scenario in his book, Rise of the Robots: Technology and the Threat of a Jobless Future.

He provides an interesting preview of where the growth and already widespread use of robotic technology may take us as a society. In a gripping narrative that explores how technology has impacted various sectors of the economy to date, from manufacturing to retail and medicine, the author sketches out the effects of a greater robotic presence for the U.S. workforce and the larger economy.

A Story of S-Curves: The Explosive Growth of IT

Moore’s Law refers to an observation made by Gordon Moore of Intel Corp. about the speed at which the number of transistors per square inch on an integrated circuit grows every two years. Moore claimed the number doubles every year, a prediction he said would continue until the year 2020. Ford shows how Moore’s Law has been validated by the exponential pace of technological progress.

New technology has consistently followed an S-curved trajectory—exponential gains are followed by a plateau in new developments. This plateau in progress is the result of reaching certain limits in key physical laws. After a certain point, for example, transistors simply can’t get much smaller.

What’s unique about the field of information technology, however, is that one S curve representing steep and significant development often launches another S-curve. For example, Ford predicts that the potential is great to improve computer speeds even more by experimenting with networks of processors or by using carbon-based elements to rework chip design.

Consider that in the early 1980s, the state of computer hardware was such that a particularly difficult computing problem would require roughly 100 years to compute. Today, processing speeds have advanced to the point that a complex computation can be performed in a minute or less, representing a rate of increase in processing speed of no less than 43 million.

Ford argues that information technology is like no other technology in recent modern history. But how does this progress translate to the tools we’re likely to see in the workplace?

What Does an IT-Dependent Society Look Like?

Signs of an IT-driven culture are everywhere. Electronic medical records are pulled up during doctor’s visits, banking processes are computerized, and our daily lives depend on data we provide our personal computers or smartphones. We depend on GPS units that spit out anticipated time of travel given traffic and road conditions. We ask Apple’s “Siri” questions, and she tells us precisely what we want to know within seconds.

Ford calls IT a true “general-purpose technology.” We have grown to depend on computers to perform all types of routine tasks. The increasing numbers of complex algorithms, the ability of computers to find new relationships given set inputs (i.e., machine learning), coupled with access to vast quantities of data available in the cloud means we’re on the cusp of a time when computers can perform truly sophisticated tasks.

Ford introduces the next part of his argument with unsettling predictions: “Computers will cease to be tools that enhance their [human] productivity and instead become viable substitutes” (emphasis added). But how does the replacement of a human workforce by a largely robotic one come to pass?

Unleashing Watson

A pivotal moment in recognizing what computers were capable of happened when “Watson” defeated Jeopardy champions Ken Jennings and Brad Rutter in 2011. Drawing from a vast amount of information collected from everything from reference books to newspaper clippings, web pages and other material, a team of researchers built a computer that could take apart a cryptic clue, analyze the question and, using thousands of algorithms, dip into the stored categories of data to sort and identify the best options. Watson had the ability to rank data and identify which algorithms were likely to produce successful answers, ultimately giving the winning response with nearly 100 percent confidence.

IBM’s creators then went on to apply Watson’s cognitive skills to other fields such as medicine. After Watson was connected to the cloud, accessing reams of medical data in the form of journal articles, case studies and physicians’ notes, Watson could come up with surprisingly accurate medical diagnoses. Given a large-enough dataset, Watson’s creators concluded, identifying correlations between datasets became easy. This is what makes access to Big Data so powerful.

Combined with the revolutionary cognitive computational ability Watson represents, artificial intelligence (AI) moves to an entirely new level. Now Watson is being used to train oncologists to better deliver medical treatments. Ford points to multiple applications of machine learning already at work such as Google translator and autonomous cars.

Because humans can’t make sense of huge amounts of data, often the conclusions and work computers produce exceed in quality that which could be produced by a human. Genetic programming or machine learning combined with access to the vast datasets of the cloud represent a powerful combination.

AI and the Engineer

Computers have composed symphonies, discovered new designs for commercial products, outcompeted humans in challenging contests such as chess and Jeopardy, and made strides in other complex fields. Although Ford concedes that at this point computers are mainly performing routine tasks, the frontline is changing rapidly.

He claims that the white-collar jobs that now are being offshored are the ones that will be fully automated in the relatively near future. Ford foresees robots doing legal analysis and other tasks once performed by executives with years of experience. It’s no secret that many of the world’s most-respected thinkers in the field of AI are alarmed. Elon Musk, for example, has been a vocal advocate of proceeding with caution, not just because of the threat to jobs in the United States, but because of machine-learning technology and the potential to put AI in a position of power relative to the humans that created it.

Could a robot with sophisticated cognitive abilities replace an engineer? I foresee our engineering software automating more of the routine tasks of the civil engineer. After raw data are entered, the software can perform much of the analysis that an engineer typically has done manually, so computers likely will replace the mundane tasks of engineers fresh out of college.

But I see computers and engineers working as a team—not one taking responsibility from the other. I’m skeptical of robots completely automating the work of seasoned engineering professionals. Is it likely that a computer equipped with AI could completely replicate the knowledge-base of a senior engineer? Could a robot equipped with AI arrive at an analysis that intelligently processes the many variables and considerations a complex engineering project usually involves? The question of world conquest by robots aside, I think the work of most engineers is safe for now. But ask me again in six months, and my opinion may have changed.