To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. ChatGPT, GitHub Copilot, Stable Diffusion, and other generative AI tools that have captured current public attention are the result of significant levels of investment in recent years that have helped advance machine learning and deep learning. This investment undergirds the AI applications embedded in many of the products and services we use every day.
But because AI has permeated our lives incrementally—through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers—its progress was almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.
ChatGPT and its competitors have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.
