How Productivity Advances – Econlib

• Every upward trend, every dip in expenses, and every extra ounce of efficiency squeezed from a collection of inputs is a testament to intentional action—an orchestra of workers, engineers, factory managers, and line supervisors meticulously refining products, reconfiguring factories, and exploring innovative methods.
• —Brian Potter, The Origins of Efficiency (304)

Economists typically assess productivity improvements in broad strokes, often glossing over their origins and simply attributing them to “technological change.”

In his insightful work, The Origins of Efficiency, Brian Potter adopts a grassroots perspective on productivity enhancements. He meticulously details the myriad strategies firms employ to reduce production costs, bolstered by historical examples that illuminate his analysis.

Potter dissects production as a process of converting inputs into outputs, emphasizing that the efficiency of this transformation hinges on five critical factors: the transformation method, production rate, input costs, buffer size (work in process), and output variability.

Take, for instance, a bakery: the transformation method encompasses the recipe for crafting a loaf of bread, the production rate specifies how many loaves are produced each hour, and input costs account for ingredients like flour, yeast, sugar, and labor. Additionally, work in process refers to loaves that are shaped but not yet baked, while variations in rising times can lead to inconsistencies in the final product.

Potter highlights that new transformation methods often follow an S-curve trajectory of improvement. Initially, a novel technique may exhibit limited performance, making progress sluggish due to numerous obstacles that render it impractical. However, through both theoretical insight and practical experimentation, many challenges are overcome, leading to the emergence of a dominant design that invites further innovation. This results in rapid advancements until the technology reaches a plateau where only marginal improvements are left to pursue.

• “An S-curve pattern means that early on, a new technology often performs significantly worse than an established technology along the most important measures of performance, even if its theoretical performance ceiling is much higher.” (50)

Mechanization stands out as a powerful means to reduce costs. Potter illustrates this with the example of glass bulbs for electric lights being produced by machines rather than skilled artisans. Yet, he points out that humans possess a unique ability to adapt to varying conditions and handle softer, more diverse materials.

• “Successful mechanization has thus historically required reducing or otherwise limiting the amount of information processing that must be performed and the environmental variation that must be considered.” (70)

A fascinating source of efficiency lies in the elimination of redundant steps within the production process. For instance, raising the height of a conveyor belt on an assembly line can reduce the need for workers to bend and lift, making the process smoother and less strenuous.

Contemporary commentators often employ ironic quotation marks around terms like “scientific management” or “Taylorism,” suggesting that time-and-motion studies functioned as tools of oppression against workers. However, Potter reveals that these studies were primarily aimed at uncovering ways to enhance manufacturing processes. The conveyor belt height adjustment exemplifies a scientific management strategy that benefits both employees and employers alike.

As I delved into The Origins of Efficiency, I observed numerous parallels with the latest advancements in artificial intelligence. The debut of ChatGPT, for instance, has drawn substantial investment and innovation towards similar neural network models utilizing the “transformer” algorithm, indicating that we are currently navigating the steep ascent of the S-curve of improvement.

While conventional machines often lack flexibility and adaptability, AI presents a potential solution to these limitations. Self-driving vehicles serve as a pertinent example of this capability.

The realm of robotics stands on the brink of transformation through AI. In hospitals, for instance, a nurse or phlebotomist is currently required to initiate an intravenous drip for patients. However, with advancements in AI, it is conceivable that robots could take on this responsibility. Similarly, construction tasks that rely heavily on nuanced knowledge and experience could also become accessible to AI-equipped robots in the future.

There’s significant potential for AI to streamline the processes involved in delivering goods and services. For example, corporations might no longer need to create overly complex menus on their websites; instead, users could rely on AI interfaces to efficiently locate the information they require.

However, a crucial lesson drawn from Potter’s analysis is that the application of promising technologies tends to progress at a sluggish pace.

• “Fixing one problem with a nascent technology tends to simply reveal more problems, so significant time and effort might be invested without any noticeable increase in performance.” (40)

As of now, early adopters of AI might be experiencing some of these growing pains.

The Origins of Efficiency is a dense work that resists simple summarization. Its array of valuable concepts and carefully selected examples create a richness that is best appreciated by engaging with the text in its entirety.