How do we compare to others in our industry? Where do we have relatively high or low exposure to disruption from machine learning?
The founders of Second Machine Age Technologies are the trusted experts in understanding how digital innovations improve and disrupt businesses. They have conducted path-breaking research, written bestsellers, and shown the world how the industrial era is rapidly giving way to the second machine age.
Erik Brynjolfsson is Director of the MIT Initiative on the Digital Economy, Schussel Family Professor at the MIT Sloan School, and Research Associate at the NBER. He is among the most cited researchers in information systems and economics and his work has been recognized with Ten Best Paper Awards and five patents. At MIT, he teaches popular courses on the Economics of Information and the Analytics Lab. A graduate of Harvard and MIT, he has served on the boards of both Fortune 500 companies and start-ups.
Andrew McAfee is the Co-Founder and Co-Director of the Initiative on the Digital Economy and a Principal Research Scientist at the MIT Sloan School of Management. Throughout his career at MIT and Harvard, he has studied how digital technologies are changing the business world. His book More From Less: How We Finally Learned to Prosper Using Fewer Resources – and What Happens Next was published by Scribner in the fall of 2019. He has advised many companies, governments, and international organizations.
Daniel Rock is a researcher at the Initiative on the Digital Economy and the MIT Sloan School of Management. His research focuses on the economic impact of digital technologies on markets, companies, and the Future of Work. Daniel received his Ph.D. from MIT. Beginning in July 2020, he will be a professor at the Wharton School of the University of Pennsylvania.
McAfee and Brynjolfsson are the only people named to both the Thinkers50 list of the world’s top management thinkers and the Politico 50 group of people transforming American politics.
Their books together include the New York Times bestseller The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies and Machine, Platform, Crowd: Harnessing Our Digital Future.
Our offerings are based on research published in Science, Harvard Business Review, and other top publications. We have extended this work to create proprietary algorithms, datasets, and analytic techniques, all of which we continually refine.
We build upward from the task and skill level to understand which job categories, functions, business units, and geographies are most (and least) likely to be affected by increasingly powerful machine learning technologies.
What Can Machine Learning Do? Workforce Implications
Science, December 2017
“The recent wave of supervised learning systems have already had considerable economic impact. The ultimate scope and scale of further advances in ML may rival or exceed that of earlier general-purpose technologies like the internal combustion engine or electricity. These advances not only increased productivity directly but, more important, triggered waves of complementary innovations in machines, business organization, and even the broader economy. Individuals, businesses, and societies that made the right complementary investments—for instance, in skills, resources, and infrastructure— thrived as a result, whereas others not only failed to participate in the full benefits but in some cases were made worse off.”
The Business of Artificial Intelligence. What it can – and cannot – do for your organization
Harvard Business Review, July 2017
“AI won’t replace managers, but managers who use AI will replace those who don’t.”
“[…] AI is poised to have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination.”
What Can Machines Learn and What Does It Mean for Occupations and the Economy?
AEA Papers and Proceedings, May 2018
“Automation technologies have historically been the key driver of increased industrial productivity. They have also disrupted employment and the wage structure systematically. However, our analysis suggests that ML will affect very different parts of the workforce than earlier waves of automation.”