The “next big thing†lists are a well-worn staple of technology analysts and consultants, typically delivered just before the calendar turns to a new year.
A new report from the McKinsey Global Institute, the research arm of the consulting firm, delivers a twist on the art form, and the difference is more than the timing. The 154-page report not only selects a dozen “disruptive†technologies from a candidate list of 100, but also measures their economic impact.
By 2025, the 12 technologies - led by the mobile Internet, the automation of knowledge work, and the Internet of Things - have the potential to deliver economic value of up to $33 trillion a year worldwide, according to the McKinsey researchers.
That would be a sweeping and disruptive effect indeed, since economists project that by 2025 global economic output will be about $100 trillion.
The McKinsey report does include the estimated value of the social benefits of using a more efficient technology, like time saved. Such benefits - known as “consumer surplus†- are not included in conventional measures of economic output. (An example would be the value of time saved by quickly finding answers to questions by using a search engine. Google economists estimate that saving at up to $65 billion annually.)
The estimated range of the impact of the dozen technologies is also quite wide, from $14 trillion to $33 trillion by 2025. That approach, McKinsey researchers say, takes account of the many uncertainties when projecting possible outcomes more than a decade in the future. Two of the 12 technologies identified in the McKinsey report, for example, are “renewable energy†and “advanced oil and gas exploration and recovery.†Energy prices will have a big effect on the measured impact of those technologies - and energy prices can fluctuate widely. Over the last decade, oil prices ranged from a a low near $23 to a high of about $146.
“We're not in the prediction business, and we're not in the forecasting business,†said Michael Chui, a principal of the McKinsey Global Institute. “We wanted to show potential, and do that with a quantitative perspective.â€
The research institute has done other quantitative technology assessments in recent years. Two years ago, McKinsey published a report on the potential impact of the explosion in the quantity and variety of digital data and the use of artificial-intelligence software to find insights - a combination known as Big Data.
The current report does not include Big Data as a separate technology. Mr. Chui explained that the Big Data tools are coming to be a foundation technology for several of the 12 categories, including automation of knowledge work, advanced robotics, next-generation genomics, and Internet of Things, which involves embedding sensors, smart software and communications capability into machines and other physical objects.
The McKinsey report is a brief for technological optimism. “It weighs in on the side that there's a lot of technology innovation going on and it will have a significant impact,†said Martin Baily, an economist at the Brookings Institution, who was an adviser to McKinsey on the study.
In the economics profession, there is a lively debate on that subject. The case for pessimism have been most forcefully presented recently by Robert J. Gordon, an economist at Northwestern University. Another adviser on the McKinsey study, Erik Brynjolfsson, an economist at Massachusetts Institute of Technology, has been perhaps the most prominent optimist.
The dueling economists even faced off in a debate last month at a TED conference.
Notes on chart sizing, from McKinsey Global Institute: These economic impact estimates are not comprehensive and include potential direct impact of sized applications only. They do not represent gross domestic product or market size (revenue), but rather economic potential, including consumer surplus. The relative sizes of technology categories shown do not constitute a “ranking,†since our sizing is not comprehensive. We do not quantify the split or transfer of surplus among or across companies or consumers, since this would depend on emerging competitive dynamics and business models. Moreover, the estimates are not directly additive, since some applications and/or value drivers are overlapping across technologies. Finally, they are not fully risk- or probability-adjusted.
