[Bread-Baking Typewriter] The Structural Innovation Brought by Predictive Machine AI

"So, what benefits do we get if we introduce artificial intelligence (AI)?"

From all sectors of industry such as finance, securities, and automotive to corporate job areas like HR, finance, and sales, AI is mentioned everywhere. Everyone talks about AI. Yet, it is not easy to get a clear answer on how the industry I belong to or my organization will change. There are countless use cases, such as how Silicon Valley big tech companies did it or how financial fintech companies innovated with AI, but the understanding remains vague. The book AI Economics provides a clear answer: "AI is a technology for prediction."

Canadian fintech company Verafin developed a financial fraud detection system using AI. They created a system that predicts financial fraud and authenticates bank customers' identities. Their technical capability, utilizing cutting-edge machine learning models, was outstanding. It became a unicorn company when Nasdaq acquired it for $2.75 billion (about 3.8 trillion KRW). The use of AI is not unique to Verafin. Existing large corporations are also incorporating AI into their services and launching new AI-powered services. However, successful cases are hard to find.

The authors focus on the fact that Verafin integrated ‘prediction through AI’ into the core workflow within the organization. It is more than just showing that ‘prediction generates money’; it is a case demonstrating that for prediction business to be possible, organizational innovation must also accompany it.

During World War I, the British Army introduced tanks. At a time when cavalry was still active, tanks were a game-changing weapon. Britain assigned these tanks to the cavalry units. When Germany began rearming, Britain increased horse feed tenfold. Cavalry officers were provided two horses each, while tank officers received one horse each. The military organization that truly benefited from tanks was Germany, whose forces were seriously disorganized. They did not attach tanks to existing organizations. They believed new technology required new organizations and strategies. In history books, the term ‘Blitzkrieg’ remains a symbol of the German army’s power.

New technology is not introduced vaguely. There are three stages of technology adoption: introduction, application, and system stages. Most technologies penetrate up to the introduction and application stages, and sometimes productivity improvements can be expected at this level. However, true innovation is only possible when ‘organizational innovation’ accompanies it. Real change begins when innovators create new system-stage solutions. Nathan Rosenberg said, "Countless business failures mostly occurred because founders did not understand the interdependence between the part they happened to be fascinated with and the rest of the entire system."

Moreover, new technology always faces resistance from existing organizations and inertia. Even after Thomas Edison invented the light bulb, electricity remained a rare power source well into the 20th century. Edison gained fame in 1897 by demonstrating the light bulb. A few years later, he operated a power plant on Pearl Street in Manhattan, lighting the street with electric lamps. Yet, 20 years later, only 3% of American households used electricity.

The book shows how electricity became the dominant power source surpassing steam and why AI will become the new electricity of the 21st century. The authors previously proposed a draft of AI economics through the book The Prediction Machine, but it only dealt with the first stage of technology adoption, the ‘introduction stage.’ The true innovation of AI was none other than the innovation of the ‘system,’ which was the part they admitted to having missed, and this book starts from that self-criticism.

AI Economics | Ajay Agrawal et al. | Translated by Cheon Hyungseok | Eco Livre | 384 pages | 22,200 KRW