The potential — and hype — surrounding machine learning, artificial intelligence, and especially generative AI is everywhere. Some are predicting a full suite of “this changes everything” advances in all industries, for all professions, and for people in their public and private lives. This technology is unmatched at recognizing patterns in data, and its proponents argue it has the potential to be an enormous research laboratory that never stops working, a paradigm-buster that unlocks human creativity, an accelerator for human ingenuity, and a window into reality that is currently beyond reach. Sundar Pichai, of Google, likens it potential to fire and electricity.

I too am genuinely excited, if somewhat more reservedly. Today, AI offers opportunities to improve productivity, which has remained flat for a long time, and to tackle heretofore intractable problems, such as the search for antibiotic-resistant drugs, an understanding of how proteins fold, and finding materials with properties needed to build better batteries. Impressive successes ranging from Amazon’s recommendation engine to Callaway Golf’s design of its next-generation drivers, to PepsiCo’s efforts to manufacture more consistent Cheetos help justify the excitement.

But I also think that progress will take longer and prove far tougher than most expect, especially in commercial settings. As I’ll explain, success with AI demands concerted efforts that extend far beyond the technology. Thus, they demand the full commitment of a company’s most senior leadership.

It is important to note that AI has generated considerable excitement in the past, quelled by AI winters in the mid-1970s and early 1990s. And just three years ago, in 2020, The Economist noted that “Another full-blown winter is unlikely. But an autumnal breeze is picking up.” As one example, self-driving cars benefited from considerable investment, and always seem to be “just around the corner,” but are more probably decades away. Further, during the pandemic, when insights were are a premium, none of the hundreds of AI tools built to catch COVID have passed muster. Indeed, the failure rate of AI projects appears to be north of 80%. Finally, a recent study by Meta (formerly Facebook) researchers under controlled conditions suggests that large language models don’t get the facts right two-thirds of the time.

Still, I’m less concerned about the technology per se and more concerned about the other advances that must accompany AI. History suggests it takes a wide range of related technologies, organizational innovations, and accommodations between the new technologies and society for any new technology to flower. I’ll use electrification and the printing press to illustrate these points, and then explore how they apply to AI.

As the Austrian political economist Joseph Schumpeter pointed out, successful technologies tend to arrive in clusters. With electrification came dynamos, generators, switch gears, and power distribution systems. With the printing press came the technology to make large quantities of cheap paper and ink. And, though not a technology per se, new materials other than the Bible and other classics were needed to fuel the demand for printed materials.

A single missing component can impede the adoption of the new technology. Today, for example, the lack of enough fast-charging stations is slowing the advance of electric cars.

Next, new technologies require new organizational capabilities. While the benefits of electricity and electric motors were easy to see, they required that factories be redesigned. It took 40 years of learning, experimentation, and investment along multiple fronts to fully electrify the factory. Similarly, it took about that long for the publishing industry, which helped match supply, demand, and price, to emerge.

Sooner or later, new technologies and societies must come to accommodate one another. In the beginning, electricity was dangerous — mistakenly touching a live wire could prove fatal! Over time, standard sockets and plugs helped ease that concern. Few people could read when the printing press was invented. However as societies became increasingly literate, the benefits of the printing press grew. Looking at these past clusters can help us understand the moment we’re in now, and what the future of AI might look like. —  Source: Thomas C. Redman.