When I began my career as a forecaster over two decades ago, it was a given that the core of futures research lay beyond the reach of traditional quantitative forecasting and it's mathematical tools. This meant that futures researchers would not enjoy the full labor-saving benefits of number-crunching computers, but at least it guaranteed job security. Economists and financial analysts might one day wake up to discover that their computer tools were stealing their jobs, but futurists would not see machines muscling their way into the world of qualitative forecasting anytime soon.
I was mistaken. I now believe that in the not too distant future, the best forecasters will not be people, but machines: ever more capable "prediction engines" probing ever deeper into stochastic spaces. Indicators of this trend are everywhere from the rise of quantitative analysis in the financial sector, to the emergence of computer-based horizon scanning systems in use by governments around the world, and of course the relentless advance of computer systems along the upward-sweeping curve of Moore's Law.
We already have human-computer hybrids at work in the discovery/forecasting space, from Amazon's Mechanical Turk, to the myriad online prediction markets. In time, we will recognize that these systems are an intermediate step towards prediction engines in much the same way that human "computers" who once performed the mathematical calculations on complex projects were replaced by general-purpose electronic digital computers.
The eventual appearance of prediction engines will also be enabled by the steady uploading of reality into cyberspace, from the growth of web-based social activities to the steady accretion of sensor data sucked up by an exponentially growing number of devices observing and increasingly, manipulating the physical world. The result is an unimaginably vast corpus of raw material, grist for the prediction engines as they sift and sort and peer ahead. These prediction engines won't ever exhibit perfect foresight, but as they and the underlying data they work on co-evolve, it is a sure bet that they will do far better then mere humans.