The Between Times.

The journey of technological adoption typically follows a sequence of discovery, initial excitement, a period of steady progress, and finally, widespread acceptance. Standing on the threshold of the AI age, the unfolding narrative seems to mirror that of electricity.

Electricity's story began with Thomas Edison unveiling the electric light bulb in 1879, followed by the illumination of Pearl Street Station in Manhattan. Yet, two decades later, only 3% of US households had electricity. Fast forward another twenty years, and half the population was connected to this groundbreaking resource. During this transitional period, electricity evolved from a mere novelty to an essential commodity.

Similarly, we find ourselves in "The Between Times" for AI. Present-day AI applications vary from straightforward "point solutions" that replace older machine-generated predictive analytics to complex adaptations necessitating substantial redesigns of products, services, and organizational structures.

The shift from viewing AI as just a tool for improved prediction to using it as a catalyst for transforming entire systems and processes is reminiscent of the change in attitude towards electricity—from a method of "saving fuel costs" to a driver for "vastly more productive factory design". This change in perspective led to a wave of innovations in factory and workflow designs, paving the way for economic transformation.

The true advantage of AI is not merely in its predictive power but in its ability to separate prediction from the rest of the decision-making process, much like how electricity separated energy consumption from its source. By assigning the prediction task to machines, AI promotes system-level innovation, enhancing decision-making—a foundational element for operational and strategic evolution in organizations.

This comparison also highlights similar challenges. The early stages of electricity adoption witnessed basic replacements like light bulbs for candles and electric motors for steam engines. In the same vein, the initial wave of AI adoption focuses on point solutions, improving existing predictive analytics. However, the full potential of AI, akin to electricity, will be realized when its broader capabilities are harnessed to reshape processes and business models.

An interesting aspect to consider is the economic shift caused by more affordable prediction through AI. This shift paints a picture where the worth of human prediction decreases, while the value of data and judgment rises. This dynamic between prediction and judgment lays the groundwork for a new age of decision-making, with AI at its core.

In summary, the adoption and transformative path of AI, when viewed alongside electricity's historical trajectory, highlights a period of exploration, experimentation, and eventual industry-wide metamorphosis. As we navigate these "Between Times", it's evident that while the buzz around AI suggests a radical shift, history suggests a more evolutionary progression, echoing the sentiment that history doesn't repeat itself, but it often rhymes.

'Power and Prediction' from Ajay Agrawal , Joshua Gans and Avi Goldfarb is an inspiring read. Highly recommended!