The (Surprisingly) Simple Economics of Artificial Intelligence
EVERYONE HAS HAD — or will soon have — an AI moment. We have grown accustomed to a media saturated with stories of new technologies that will change our lives. We are so used to the constant drumbeat of technology news that we numbly recite that ‘the only thing immune to change is change itself’. Until we have our AI moment. Then we realize that this technology is different.
Some computer scientists experienced their AI moment in 2012, when a student team from the University of Toronto delivered such an impressive win in the visual object-recognition competition ImageNet that the following year, all top finalists used the then-novel ‘deep learning’ approach to compete. These scientists recognized that object recognition is more than just a game; it actually enables machines to ‘see’.
Some technology CEOs experienced their AI moment when they read the headline in January 2014 that Google had paid more than $600 million to acquire UK-based DeepMind, even though the start-up had generated negligible revenue relative to the purchase price but had demonstrated that its AI had learned — on its own, without being programmed — to play Atari video games with super-human performance.
Some regular citizens experienced their AI moment later that year, when renowned physicist Stephen Hawking emphatically explained, “Everything that civilization has to offer is a product of human intelligence. Success in creating AI would be the biggest event in human history.”
Our own AI moment came in 2012, when a trickle and then a surge in the number of early-stage AI companies employing state-of-the-art machine-learning techniques applied to the Creative Destruction Lab at the Rotman School of Management. The applications spanned industries — drug discovery, customer service, manufacturing, quality assurance, retail, medical devices. The technology was both powerful and general purpose, creating significant value across a wide range of applications.
